├── 173 ├── 173.json ├── ColistinMeropenem_Interaction_original_simulated.mod ├── Command.txt ├── DDMODEL00000173.rdf ├── Executable_ColistinMeropenem_Interaction.mod ├── Output_real_ColistinMeropenem_interaction.lst ├── Output_simulated_ColistinMeropenem_Interaction.lst └── Simulated_ColistinMeropenem_Interaction.csv ├── 186 ├── 186.json ├── Command.txt ├── DDMODEL00000186.rdf ├── Executable_Myelosuppression.mdl ├── Executable_Myelosuppression.xml ├── Model_Accommodations.txt ├── Neutrophils time profile.tiff ├── Neutrophils_VPC.tiff ├── Output_simulated_SEE_MONOLIX.txt ├── Output_simulated_SEE_NONMEM.lst └── Simulated_WBC_pacl_ddmore.csv ├── 192 ├── 192.json ├── Command.txt ├── DDMODEL00000192.rdf ├── Executable_PKPD.mlxtran ├── Model_Accommodations.txt ├── Output_simulated_PKPD.txt ├── Simulated_PKPD.txt └── pkpd_model.txt ├── 194 ├── 194.json ├── Command.txt ├── DDMODEL00000194.rdf ├── Executable_likert_pain_count.mod ├── Output_real_likert_pain_count.lst ├── Output_simulated_likert_pain_count.lst └── Simulated_likert_pain_count.csv ├── 195 ├── 195.json ├── Command.txt ├── DDMODEL00000195.rdf ├── Executable_sibrotuzumab.mdl ├── Executable_sibrotuzumab.xml ├── Model_Accommodations.txt ├── Output_simulated_nca_simulation.1.lst └── Simulated_sibrotuzumab.csv ├── 197 ├── 197.json ├── Command.txt ├── DDMODEL00000197.rdf ├── Executable_Biomarker_GIST.mod ├── Output_real_Biomarker_GIST.lst ├── Output_simulated_Biomarker_GIST.lst └── Simulated_Biomarker_GIST.csv ├── 198 ├── 198.json ├── Command.txt ├── DDMODEL00000198.rdf ├── Executable_TGI_GIST.mod ├── Output_real_TGI_GIST.lst ├── Output_simulated_TGI_GIST.lst └── Simulated_TGI_GIST.csv ├── 212 ├── 212.json ├── Command.txt ├── DDMODEL00000212.rdf ├── Executable_tamoxifen.mdl ├── Executable_tamoxifen.xml ├── Model_Accommodations.txt ├── Output_real_tamoxifen.pdf ├── Output_simulated_tamoxifen.lst ├── Output_simulated_tamoxifen_SO.xml └── Simulated_tamoxifen.csv ├── 213 ├── 213.json ├── Command.txt ├── DDMODEL00000213.rdf ├── Executable_Meropenem_MDL.mdl ├── Executable_Meropenem_MDL.xml ├── Model_Accommodations.txt ├── Output_simulated_Meropenem.SO.xml ├── Output_simulated_Meropenem.lst └── Simulated_DatasetMeropenem.csv ├── 214 ├── 214.json ├── Command.txt ├── DDMODEL00000214.rdf ├── Executable_HFSmodel.mod ├── Model_Accommodations.txt ├── Output_real_HFSmodel.lst ├── Output_simulated_HFSmodel.lst └── Simulated_GHFS_HFSmodel.csv ├── 215 ├── 215.json ├── Command.txt ├── DDMODEL00000215.rdf ├── Executable_Pimasertib_AeDropout.mod ├── Model_Accommodations.txt ├── Output_real_Pimasertib_AeDropout.lst ├── Output_simulated_Pimasertib_AeDropout.lst └── Simulated_Pimasertib_AeDropout.csv ├── 217 ├── 217.json ├── Command.txt ├── DDMODEL00000217.rdf ├── Executable_SLD.mod ├── Output_real_SLD.lst ├── Output_simulated_SLD.lst └── Simulated_SLD.csv ├── 218 ├── 218.json ├── Command.txt ├── DDMODEL00000218.rdf ├── Executable_OS.mod ├── Output_real_OS.lst ├── Output_simulated_OS.lst └── Simulated_OS.csv ├── 219 ├── 219.json ├── Command.txt ├── Executable_BDQ_M2_PK_plus_WT_ALB_in_MDR-TB_patients.mod ├── Output_real_BDQ_M2_PK_plus_WT_ALB_in_MDR-TB_patients.lst ├── Output_simulated_executable_BDQ_M2_PK_plus_WT_ALB_in_MDR-TB_patients.lst └── Simulated_data_BDQ_M2_PK_plus_WT_ALB_in_MDR-TB_patients.csv ├── 220 ├── 220.json ├── Command.txt ├── DDMODEL00000220.rdf ├── Executable_run32150.mod ├── Finmod_7_3.lst ├── Output_real_run32150.lst ├── Output_simulated_Executable_run32150.lst └── Simulated_data.csv ├── 221 ├── 221.json ├── Command.txt ├── DDMODEL00000221.rdf ├── Executable_SLD_SUV_OS_GIST.mod ├── Output_real_SLD_SUV_OS_GIST.lst ├── Output_simulated_SLD_SUV_OS_GIST.lst └── Simulated_SLD_SUV_OS_GIST.csv ├── 222 ├── 222.json ├── Command.txt ├── DDMODEL00000222.rdf ├── Executable_Fatigue_GIST.mod ├── Output_real_Fatigue_GIST.lst_Fatigue_PSP_2014 ├── Output_simulated_Fatigue_GIST.lst └── Simulated_Fatigue_GIST.txt ├── 223 ├── 223.json ├── Command.txt ├── DDMODEL00000223.rdf ├── Executable_Novakovic_2016_multiplesclerosis_cladribine_irt.mod ├── Output_real_Novakovic_2016_multiplesclerosis_cladribine_irt.lst ├── Output_simulated_Novakovic_2016_multiplesclerosis_cladribine_irt.lst └── Simulated_Novakovic_2016_multiplesclerosis_cladribine_irt.csv ├── 224 ├── 224.json ├── Command.txt ├── DDMODEL00000224.rdf ├── Executable_myelosuppression_dailyANC.mod ├── Model_Accommodations.txt ├── Output_simulated_Executable_myelosuppression_dailyANC.lst └── Simulated_myelosuppression_dailyANC.csv ├── 225 ├── 225.json ├── Command_target.txt ├── DDMODEL00000225.rdf ├── HO_Bacteria_PKPD _script.r ├── Model_Accommodations.txt ├── cipro202.csv ├── cipro202_VPC.mdl ├── cipro378.csv ├── cipro378_VPC.mdl ├── cipro_simulated.csv ├── executeable_cipro.xml ├── executeable_cipro_pkpd.mod └── output_simulated.lst ├── 227 ├── 227.json ├── Command.txt ├── DDMODEL00000227.rdf ├── Executable_glucoseKinetics.mlxtran ├── Executable_glucoseKinetics.txt ├── Executable_glucoseKinetics_algorithms.xmlx ├── Long_technical_model_description_glucoseKinetics.txt ├── Model_Accommodations.txt ├── Output_real_glucoseKinetics.txt ├── Output_simulated_glucoseKinetics.txt ├── Simulated_glucoseKinetics.csv ├── glucoseKinetics.mdl ├── glucoseKinetics.xml ├── glucoseKineticsPLOT.pdf ├── original_model.txt └── original_project.mlxtran ├── 228 ├── 228.json ├── Command.txt ├── DDMODEL00000228.rdf ├── Executable_run126h.mod ├── Output_real_run126c.lst ├── Output_simulated_run126h.lst └── Simulated_ddmoremockdata2.txt ├── 229 ├── 229.json ├── DDMODEL00000229.rdf ├── GO_PK_model.mdl ├── GO_PK_model.xml └── Model_Accommodations.txt ├── 230 ├── 230.json ├── DDMODEL00000230.rdf ├── IL_21_PK_mode.xml ├── IL_21_PK_model.mdl └── Model_Accommodations.txt ├── 231 ├── 231.json ├── DDMODEL00000231.rdf ├── Sunitinib_MPD6_model.mdl └── Sunitinib_MPD6_model.xml ├── 233 ├── 233.json ├── Command.txt ├── DDMODEL00000233.rdf ├── Executable_opg.R ├── Fc_opg_uNTx_PKPD.mdl ├── Fc_opg_uNTx_PKPD.xml ├── Input_real_opg.R ├── Input_real_opg.pdf ├── Model_Accommodations.txt ├── Output_simulated_opg.pdf ├── Simulated_opg.txt ├── Vignette_opg.R ├── Vignette_opg.pdf ├── opg.cpp └── opgpost.RDS ├── 237 ├── 237.json ├── Command.txt ├── DDMODEL00000237.rdf ├── Executable_APAP.model ├── FigOutput.png ├── Forward_APAP1.in ├── Model_Accommodations.txt ├── Output_real_APAP.txt ├── RawData.R └── Real_APAP_data.csv ├── 238 ├── 238.json ├── Command.txt ├── DDMODEL00000238.rdf ├── Executable_run35b_ddm2.mod ├── Output_real_run35b.lst ├── Output_simulated_run35b_ddm2.lst ├── Readme_ddmore.txt └── Simulated_simdataDDM.csv ├── 239 ├── 239.json ├── Command.txt ├── DDMODEL00000239.rdf ├── Executable_P241.ctl ├── Output_real_P241.res ├── Output_simulated_P241.res ├── Simulate_P241.ctl └── Simulated_P241.csv ├── 240 ├── 240.json ├── Command.txt ├── DDMODEL00000240.rdf ├── Executable_MTP.mod ├── Model_Accomodations.txt ├── Output_real_MTP.lst ├── Output_simulated_MTP.lst └── Simulated_Mtb-H37Rv_In-vitro-NATG.csv ├── 243 ├── 243.json ├── BAST_PTTE_modelling.pdf ├── BAST_surv_functions.R ├── Command.txt ├── DDMODEL00000243.rdf ├── Executable_runCOMPEV1_101.mod ├── Executable_runCOMPEV2_005.mod ├── Executable_runEV1_201.mod ├── Executable_runEV2_105.ctl ├── Executable_runEV2_105.mod ├── Model_Accommodations.txt ├── Output_simulated_runCOMPEV1_101.res ├── Output_simulated_runCOMPEV2_005.res ├── Output_simulated_runEV1_201.res ├── Output_simulated_runEV2_105.res ├── Simulated_event_data.csv ├── VPC_EV1_1.png ├── VPC_EV1_1_dis.png └── VPCs.R ├── 244 ├── 244.json ├── Command.txt ├── DDMODEL00000244.rdf ├── Executable_Rif_PK.mod ├── Output_real_Rif_PK.lst ├── Output_simulated_Rif_PK.lst └── Simulated_Rif_PK_data.csv ├── 245 ├── 245.json ├── Command.txt ├── DDMODEL00000245.rdf ├── Executable_run111.mod ├── Output_real_run111.lst ├── Output_simulated_Executable_run111.lst └── Simulated_comb2.dta ├── 247 ├── 247.json ├── Command.txt ├── DDMODEL00000247.rdf ├── Executable_OriginalModelCode.mod ├── Output_real_OriginalModelCode.lst ├── Output_simulated_OriginalModelCode.lst └── Simulated_MidaCriticallyIll.csv ├── 248 ├── 248.json ├── Command.txt ├── DDMODEL00000248.rdf ├── Executable_OriginalModelCode.mod ├── Output_real_run4.lst ├── Output_simulated_OriginalModel Code.lst └── Simulated_PaediatricMorphinePK.csv ├── 249 ├── 249.json ├── Command.txt ├── DDMODEL00000249.rdf ├── Executable_OriginalModelCode.mod ├── Output_real_OriginalModelCode.lst ├── Output_simulated_OriginalModelCode.lst └── Simulated_MidaCriticallyIll.csv ├── 250 ├── 250.json ├── Command.txt ├── DDMODEL00000250.rdf ├── Executable_AccessWeightModelCode.mod ├── Executable_FinalModelCode.mod ├── Output_real_AccessWeightModelCode.lst ├── Output_real_FinalModelCode.lst ├── Output_simulated_AccessWeightModelCode.lst ├── Output_simulated_FinalModelCode.lst └── Simulated_DatafileMidaObesity.csv ├── 256 ├── 256.json ├── Command.txt ├── DDMODEL00000256.rdf ├── Executable_OriginalModelCode.mod ├── Output_real_run522.lst ├── Output_simulated_OriginalModelCode.lst └── Simulated_PhenobarbitalNewbornsPK.csv ├── 259 ├── 259.json ├── Command.txt ├── DDMODEL00000259.rdf ├── Executable_MTP-GPDI.mod ├── Model_Accomodations.txt ├── Output_real_MTP-GPDI.lst ├── Output_simulated_MTP-GPDI.lst └── Simulated_Mtb-B1585_In-vitro-NATG-RIF-INH-EMB.csv ├── 261 ├── 261.json ├── Command.txt ├── DDMODEL00000261.rdf ├── Executable_simulated_KPD_CTC.count_PSA.mod ├── Output_real_COV_KPD_CTC.count_PSA.lst ├── Output_real_SAEM_KPD_CTC.count_PSA.lst ├── Output_simulated_KPD_CTC.count_PSA.lst └── Simulated_KPD_CTC.count_PSA.csv ├── 262 ├── 262.json ├── Command.txt ├── DDMODEL00000262.rdf ├── Executable_simulated_CPHPC_dataset.ctl ├── Output_real_CPHPC.lst ├── Output_simulated_CPHPC_dataset.lst └── Simulated_CPHPC_dataset.csv ├── 267 ├── 267.json ├── Command.txt ├── DDMODEL00000267.rdf ├── Executable_OriginalModelCode.mod ├── Output_real_OriginalModelCode.lst ├── Output_simulated_OriginalModelCode.lst └── Simulated_APAP_YoungWomen.csv ├── 268 ├── 20120910- DDMORE-WP1.3. Specificationdocument_remoxipride ECMdL.doc ├── 268.json ├── Command.txt ├── DDMODEL00000268.rdf ├── Executable_PK_rats.txt ├── Output_real_PK_rats.lst ├── Output_simulated_PK_rats.lst └── Simulated_PK_rats.csv ├── 269 ├── 269.json ├── Command.txt ├── DDMODEL00000269.rdf ├── Executable_ModelI1_Morphine.mod ├── Executable_ModelII_MM3G.mod ├── Output_real_ModelII_MM3G.lst ├── Output_real_ModelI_Morphine.lst ├── Output_simulated_ModelII_MM3G.lst ├── Output_simulated_ModelI_Morphine.lst ├── Simulated_DataModel1_Morphine.csv └── Simulated_DataModel2_MM3G.csv ├── 271 ├── 271.json ├── Command.txt ├── DDMODEL00000271.rdf ├── Executable_ParacetamolInNewborns.mod ├── Model_Accommodations.txt ├── Output_real_ParacetamolInNewborns.lst ├── Output_simulated_ParacetamolInNewborns.lst └── Simulated_ParacetamolPKnewborns.csv ├── 273 ├── 273.json ├── Command.txt ├── DDMODEL00000273.rdf ├── Executable_Simulated_Dupilumab.ctl ├── Output_simulated_Dupilumab.lst └── Simulated_Dupilumab.CSV ├── 274 ├── 274.json ├── Command.txt ├── DDMODEL00000274.rdf ├── Executable_Terranova_2017_oncology_TGI_HM.ctl ├── Model_Accomodations.txt ├── Output_simulated_Terranova_2017.pdf ├── Simulated_DEB_TGI_data.csv ├── Terranova_2017_oncology_TGI.ctl ├── Terranova_2017_oncology_TGI.mdl └── Terranova_2017_oncology_TGI.xml ├── 280 ├── 280.json ├── Command.txt ├── DDMODEL00000280.rdf ├── Executable_real_TB_Rifampicin_PK_Wilkins_2008.mod ├── Executable_simulated_TB_Rifampicin_PK_Wilkins_2008.mod ├── Output_real_TB_Rifampicin_PK_Wilkins_2008 ├── Output_simulated_TB_Rifampicin_PK_Wilkins_2008 ├── Simulated_TB_Rifampicin_PK_Wilkins_2008.csv ├── TB_Rifampicin_PK_Wilkins_2008_real.lst ├── TB_Rifampicin_PK_Wilkins_2008_simulated.coi ├── TB_Rifampicin_PK_Wilkins_2008_simulated.cor ├── TB_Rifampicin_PK_Wilkins_2008_simulated.cov ├── TB_Rifampicin_PK_Wilkins_2008_simulated.ext ├── TB_Rifampicin_PK_Wilkins_2008_simulated.lst ├── TB_Rifampicin_PK_Wilkins_2008_simulated.phi ├── TB_Rifampicin_PK_Wilkins_2008_simulated.shk ├── TB_Rifampicin_PK_Wilkins_2008_simulated.shm ├── TB_Rifampicin_PK_Wilkins_2008_simulated.xml ├── catab.simulated_TB_Rifampicin_PK_Wilkins_2008 ├── cotab.simulated_TB_Rifampicin_PK_Wilkins_2008 ├── patab.simulated_TB_Rifampicin_PK_Wilkins_2008 └── sdtab.simulated_TB_Rifampicin_PK_Wilkins_2008 ├── 281 ├── 281.json ├── Command.txt ├── DDMODEL00000281.rdf ├── Executable_ddmore_final_run249.ctl ├── Model_Accommodations.txt ├── Output_real_data_original_final_run249.res ├── Output_simulated_ddmore_final_run249.res └── Simulated_Lid_B04_ddmore.csv ├── 284 ├── 284.json ├── Command.txt ├── DDMODEL00000284.rdf ├── Executable_Simulated_IMNIVO_PPK.CTL ├── NIVO-PPKFinalModel-CPT.CTL ├── Output_real_Nivo-PPK.lst ├── Output_simulated_SIMNIVO_PPK.lst └── Simulated_pkdata1_dataset.csv ├── 285 ├── 285.json ├── Command.txt ├── DDMODEL00000285.rdf ├── Executable_Laouenant_2015_CPTPSP_hb_RBV ├── Output_real_Laouenant_2015_CPTPSP_hb_RBV ├── Output_simulated_Laouenant_2015_CPTPSP_hb_RBV └── Simulated_Laouenant_2015_CPTPSP_hb_RBV.txt ├── 290 ├── 290.json ├── Command.txt ├── DDMODEL00000290.rdf ├── Executable_simulated_CPathAD.mod ├── Output_real_CPathAD.lst ├── Output_simulated_CPathAD.lst └── Simulated_data_CPathAD.csv ├── 294 ├── 294.json ├── Command.txt ├── DDMODEL00000294.rdf ├── Executable_Paracetamol_Zebrafish_345dpf.mod ├── Output_real_Paracetamol_Zebrafish_345dpf.lst └── Real_Paracetamol_Zebrafish_345dpf.csv ├── 295 ├── 295.json ├── Command.txt ├── DDMODEL00000295.rdf ├── Executable_CMS_colistin_PK_CRRT.mod ├── Model_Accommodations.txt ├── Output_real_CMS_colistin_PK_CRRT.lst ├── Output_simulated_CMS_colistin_PK_CRRT - Copie.lst └── Simulated_Data_CMS_colistin_PK_CRRT.csv ├── 297 ├── 297.json ├── Command.txt ├── DDMODEL00000297.rdf ├── Executable_run1.mod ├── Output_simulated_run1.lst └── Simulated_run1.csv ├── 298 ├── 298.json ├── Command.txt ├── DDMODEL00000298.rdf ├── Executable_sultiame_nonlinear_PK.mod ├── Model_Accomodations.txt ├── Output_real_sultiame_nonlinear_PK.lst ├── Output_simulated_sultiame_nonlinear_PK.lst └── Simulated_data_PK_sultiame.csv ├── 299 ├── 299.json ├── Command.txt ├── DDMODEL00000299.rdf ├── Description_files.txt ├── Executable_Misspecification Example using LMEM V3.sas ├── Executable_Misspecification Example using Nonlinear MEM V3.sas ├── Misspecification Example Using LMEM V3.htm ├── Misspecification Example Using LMEM V3.log ├── Misspecification Example Using Nonlinear MEM V3.htm ├── Misspecification Example Using Nonlinear MEM V3.log ├── Output_simulated_Misspecification Example Using LMEM V3.lst ├── Output_simulated_Misspecification Example Using Nonlinear MEM V3.lst ├── ReadMe-Case Study of a Misspecified Model V5.docx ├── Simulated_Dataset generator.sas ├── Simulated_concqt.sas7bdat └── Simulated_dataset generator output.log ├── 301 ├── 301.json ├── Command.txt ├── DDMODEL00000301.rdf ├── Executable_merop_PK_run3.mod ├── Model_Accomodations.text ├── Output_real_merop_PK_run3.lst ├── Output_simulated_merop_PK_run3.lst └── Simulated_dataset.csv ├── .gitignore ├── index.js ├── missingModels.txt ├── nmoutputs ├── Finmod_7_3.lst ├── Output_real_AccessWeightModelCode.lst ├── Output_real_BDQ_M2_PK_plus_WT_ALB_in_MDR-TB_patients.lst ├── Output_real_Biomarker_GIST.lst ├── Output_real_CMS_colistin_PK_CRRT.lst ├── Output_real_COV_KPD_CTC.count_PSA.lst ├── Output_real_CPHPC.lst ├── Output_real_CPathAD.lst ├── Output_real_ColistinMeropenem_interaction.lst ├── Output_real_FinalModelCode.lst ├── Output_real_HFSmodel.lst ├── Output_real_MTP-GPDI.lst ├── Output_real_MTP.lst ├── Output_real_ModelII_MM3G.lst ├── Output_real_ModelI_Morphine.lst ├── Output_real_Nivo-PPK.lst ├── Output_real_Novakovic_2016_multiplesclerosis_cladribine_irt.lst ├── Output_real_OS.lst ├── Output_real_OriginalModelCode.lst ├── Output_real_PK_rats.lst ├── Output_real_ParacetamolInNewborns.lst ├── Output_real_Paracetamol_Zebrafish_345dpf.lst ├── Output_real_Pimasertib_AeDropout.lst ├── Output_real_Rif_PK.lst ├── Output_real_SAEM_KPD_CTC.count_PSA.lst ├── Output_real_SLD.lst ├── Output_real_SLD_SUV_OS_GIST.lst ├── Output_real_TGI_GIST.lst ├── Output_real_likert_pain_count.lst ├── Output_real_merop_PK_run3.lst ├── Output_real_run111.lst ├── Output_real_run126c.lst ├── Output_real_run32150.lst ├── Output_real_run35b.lst ├── Output_real_run4.lst ├── Output_real_run522.lst ├── Output_real_sultiame_nonlinear_PK.lst ├── Output_simulated_AccessWeightModelCode.lst ├── Output_simulated_Biomarker_GIST.lst ├── Output_simulated_CMS_colistin_PK_CRRT - Copie.lst ├── Output_simulated_CPHPC_dataset.lst ├── Output_simulated_CPathAD.lst ├── Output_simulated_ColistinMeropenem_Interaction.lst ├── Output_simulated_Dupilumab.lst ├── Output_simulated_Executable_myelosuppression_dailyANC.lst ├── Output_simulated_Executable_run111.lst ├── Output_simulated_Executable_run32150.lst ├── Output_simulated_Fatigue_GIST.lst ├── Output_simulated_FinalModelCode.lst ├── Output_simulated_HFSmodel.lst ├── Output_simulated_KPD_CTC.count_PSA.lst ├── Output_simulated_MTP-GPDI.lst ├── Output_simulated_MTP.lst ├── Output_simulated_Meropenem.lst ├── Output_simulated_Misspecification Example Using LMEM V3.lst ├── Output_simulated_Misspecification Example Using Nonlinear MEM V3.lst ├── Output_simulated_ModelII_MM3G.lst ├── Output_simulated_ModelI_Morphine.lst ├── Output_simulated_Novakovic_2016_multiplesclerosis_cladribine_irt.lst ├── Output_simulated_OS.lst ├── Output_simulated_OriginalModel Code.lst ├── Output_simulated_OriginalModelCode.lst ├── Output_simulated_PK_rats.lst ├── Output_simulated_ParacetamolInNewborns.lst ├── Output_simulated_Pimasertib_AeDropout.lst ├── Output_simulated_Rif_PK.lst ├── Output_simulated_SEE_NONMEM.lst ├── Output_simulated_SIMNIVO_PPK.lst ├── Output_simulated_SLD.lst ├── Output_simulated_SLD_SUV_OS_GIST.lst ├── Output_simulated_TGI_GIST.lst ├── Output_simulated_executable_BDQ_M2_PK_plus_WT_ALB_in_MDR-TB_patients.lst ├── Output_simulated_likert_pain_count.lst ├── Output_simulated_merop_PK_run3.lst ├── Output_simulated_nca_simulation.1.lst ├── Output_simulated_run1.lst ├── Output_simulated_run126h.lst ├── Output_simulated_run35b_ddm2.lst ├── Output_simulated_sultiame_nonlinear_PK.lst ├── Output_simulated_tamoxifen.lst ├── TB_Rifampicin_PK_Wilkins_2008_real.lst ├── TB_Rifampicin_PK_Wilkins_2008_simulated.lst └── output_simulated.lst ├── package-lock.json └── package.json /.gitignore: -------------------------------------------------------------------------------- 1 | node_modules/ 2 | .DS_Store 3 | 4 | -------------------------------------------------------------------------------- /173/173.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_ColistinMeropenem_Interaction.mod", 4 | "Command.txt", 5 | "Output_real_ColistinMeropenem_interaction.lst", 6 | "Simulated_ColistinMeropenem_Interaction.csv", 7 | "DDMODEL00000173.rdf", 8 | "ColistinMeropenem_Interaction_original_simulated.mod", 9 | "Output_simulated_ColistinMeropenem_Interaction.lst" 10 | ], 11 | "version": 12 12 | } -------------------------------------------------------------------------------- /173/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version (if any) = 4.5.2 3 | ###### Original tool version= 7.3 4 | ###### Input data= ColistinMeropenem_Interaction_simulated.csv.csv 5 | ###### Executable model= ColistinMeropenem_Interaction_original.mod 6 | ###### Output= ColistinMeropenem_Interaction_original_real.lst 7 | ColistinMeropenem_Interaction_original_simulated.lst 8 | ###### Script used as execution command for NONMEM via PsN: ############# 9 | 10 | execute ColistinMeropenem_Interaction_original.mod -------------------------------------------------------------------------------- /186/186.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_Myelosuppression.xml", 4 | "Output_simulated_SEE_NONMEM.lst", 5 | "Neutrophils_VPC.tiff", 6 | "DDMODEL00000186.rdf", 7 | "Simulated_WBC_pacl_ddmore.csv", 8 | "Executable_Myelosuppression.mdl", 9 | "Neutrophils time profile.tiff", 10 | "Model_Accommodations.txt", 11 | "Output_simulated_SEE_MONOLIX.txt", 12 | "Command.txt" 13 | ], 14 | "version": 11 15 | } -------------------------------------------------------------------------------- /186/DDMODEL00000186.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | chemotherapy-induced myelosuppression through drug-specific parameters and system-related parameters, common to all drugs 11 | The model consists of one compartment representing drug-sensitive cells in the bone marrow, three compartments representing the maturation of the white blood cells and one compartment for the circulating cells, where the measurements had been done. A feedback mechanism triggered by the number of circulting cells was implemented over the proliferation rate constant. The drug effect was described by Emax-model. 12 | 13 | Yes 14 | No 15 | The model is the same, some accomodations on the dataset were needed 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /186/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | # Model represented 2 | 6 different drugs (Docetaxel, Paclitaxel, Etoposide, DMDC, CPT-11, Vinflunine) 3 | were used to developed the myelosuppression model for neutrophils and leukocites 4 | as described in the publication. The model available at the repository illustrates 5 | the myelosuppresion model for the case of leukocites and paclitaxel chemotherapy 6 | 7 | # Dataset 8 | In order to support model estimation in Monolix and NONMEM 7.3, the following 9 | changes to the dataset were performed. No change to the structure of the model 10 | was done. Thses changes produced only a minor impact on the OBJ function 11 | evaluated as MAXEVAL 0) 12 | 1. EVID column is not supported therefore rows with EVID==2 were removed and 13 | EVID==4 was changed to EVID==1 and the column was used as MDV 14 | 2. Administration of dummy doses to initialise compartments is not supported. 15 | Those rows were removed and compartments initialise using the initial 16 | conditions and note availability (note that model was developed with NONMEM v6, 17 | but is no longer needed since NONMEM 7) 18 | 19 | -------------------------------------------------------------------------------- /186/Neutrophils time profile.tiff: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/186/Neutrophils time profile.tiff -------------------------------------------------------------------------------- /186/Neutrophils_VPC.tiff: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/186/Neutrophils_VPC.tiff -------------------------------------------------------------------------------- /186/Output_simulated_SEE_MONOLIX.txt: -------------------------------------------------------------------------------- 1 | ****************************************************************** 2 | * Executable_Mielosupression_project.mlxtran 3 | * May 19, 2016 at 17:27:19 4 | * Monolix version: 4.3.2 5 | ****************************************************************** 6 | 7 | Estimation of the population parameters 8 | 9 | parameter s.e. (lin) r.s.e.(%) 10 | GAMMA_pop : 0.553 0.007 1 11 | SLOPU_pop : 20.2 1.9 9 12 | CIRC0_pop : 5.88 0.33 6 13 | MTT_pop : 140 2 1 14 | 15 | omega2_GAMMA : 0 - - 16 | omega2_SLOPU : 0.212 0.071 33 17 | omega2_CIRC0 : 0.122 0.029 24 18 | omega2_MTT : 0.00508 0.0016 31 19 | 20 | b : 0.339 0.012 3 21 | 22 | ______________________________________________ 23 | correlation matrix of the estimates(linearization) 24 | 25 | GAMMA_pop 1 26 | SLOPU_pop -0.14 1 27 | CIRC0_pop -0.06 0.01 1 28 | MTT_pop 0.28 -0.06 -0.01 1 29 | 30 | Eigenvalues (min, max, max/min): 0.7 1.4 1.9 31 | 32 | omega2_SLOPU 1 33 | omega2_CIRC0 -0.01 1 34 | omega2_MTT -0.01 -0 1 35 | b -0.07 -0.03 -0.06 1 36 | 37 | Eigenvalues (min, max, max/min): 0.9 1.1 1.2 38 | 39 | 40 | Population parameters and Fisher Information Matrix estimation... 41 | 42 | Elapsed time is 243 seconds. 43 | CPU time is 714 seconds. 44 | ______________________________________________________________ 45 | 46 | Log-likelihood Estimation by linearization 47 | 48 | -2 x log-likelihood: 2362.41 49 | Akaike Information Criteria (AIC): 2378.41 50 | Bayesian Information Criteria (BIC): 2392.87 51 | ______________________________________________________________ 52 | -------------------------------------------------------------------------------- /192/192.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_PKPD.mlxtran", 4 | "Output_simulated_PKPD.txt", 5 | "Command.txt", 6 | "DDMODEL00000192.rdf", 7 | "pkpd_model.txt", 8 | "Simulated_PKPD.txt", 9 | "Model_Accommodations.txt" 10 | ], 11 | "version": 44 12 | } -------------------------------------------------------------------------------- /192/Command.txt: -------------------------------------------------------------------------------- 1 | ##### Prepared and run by Giulia Lestini 2 | ##### Scenario= 4 3 | ##### MONOLIX version=4.3.0 4 | ##### Input data= simRich1.txt 5 | ##### Executable model=pkpd_model.txt 6 | ##### Output=pop_parameters1.txt 7 | 8 | ###### Project to be executed in MONOLIX: ########## 9 | ###### project_Step1.mat -------------------------------------------------------------------------------- /192/DDMODEL00000192.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | The PK is modelled by a one compartment first order absorption model. The inhibition of TGFbeta signalling by the treatment is represented by a turnover model, that is a simplification of the semi-mechanistic model developed by Bueno et al. 9 | Yes 10 | 11 | No 12 | 13 | 14 | 15 | PKPD model developed for a small molecule TGFbeta inhibitor. 16 | No discrepancy 17 | 18 | 19 | -------------------------------------------------------------------------------- /192/Executable_PKPD.mlxtran: -------------------------------------------------------------------------------- 1 | ; this script is generated automatically 2 | 3 | DESCRIPTION: 4 | Executable_PKPD.mlxtran 5 | 6 | DATA: 7 | path = "%MLXPROJECT%/", 8 | file ="Simulated_PKPD.txt", 9 | headers = {ID,TIME,Y,EVID,MDV,AMT,YTYPE}, 10 | columnDelimiter = "\t" 11 | 12 | INDIVIDUAL: 13 | C50 = {distribution=logNormal, iiv=yes}, 14 | Cl = {distribution=logNormal, iiv=yes}, 15 | V = {distribution=logNormal, iiv=yes}, 16 | ka = {distribution=logNormal, iiv=no}, 17 | kout = {distribution=logNormal, iiv=yes} 18 | 19 | STRUCTURAL_MODEL: 20 | file = "mlxt:pkpd_model", 21 | path = "%MLXPROJECT%", 22 | output = {Cc,E} 23 | 24 | 25 | OBSERVATIONS: 26 | y1 = {type=continuous, prediction=Cc, error=proportional}, 27 | y2 = {type=continuous, prediction=E, error=constant} 28 | 29 | TASKS: 30 | ; settings 31 | globalSettings={ 32 | withVariance=no, 33 | settingsAlgorithms="%MLXPROJECT%/Executable_PKPD_algorithms.xmlx", 34 | resultFolder="%MLXPROJECT%/Output_simulated_PKPD"}, 35 | ; workflow 36 | estimatePopulationParameters( 37 | initialValues={ 38 | pop_C50 = 0.3, 39 | pop_Cl = 40, 40 | pop_V = 100, 41 | pop_ka = 2, 42 | pop_kout = 2, 43 | b_y1 = 0.2, 44 | a_y2 = 0.2, 45 | omega_C50 = 0.7, 46 | omega_Cl = 0.7, 47 | omega_V = 0.7, 48 | omega_kout = 0.7 49 | } ), 50 | 51 | -------------------------------------------------------------------------------- /192/Model_Accommodations.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/192/Model_Accommodations.txt -------------------------------------------------------------------------------- /192/Output_simulated_PKPD.txt: -------------------------------------------------------------------------------- 1 | ****************************************************************** 2 | * Executable_PKPD.mlxtran 3 | * May 31, 2016 at 09:50:54 4 | * Monolix version: 4.3.0 5 | ****************************************************************** 6 | 7 | Estimation of the population parameters 8 | 9 | parameter 10 | ka : 1.97 11 | V : 95.1 12 | Cl : 9.91 13 | kout : 0.269 14 | C50 : 0.307 15 | 16 | omega_ka : 0 17 | omega_V : 0.744 18 | omega_Cl : 0.819 19 | omega_kout : 0.706 20 | omega_C50 : 0.822 21 | 22 | b_1 : 0.199 23 | a_2 : 0.196 24 | 25 | 26 | Population parameters estimation... 27 | 28 | Elapsed time is 120 seconds. 29 | CPU time is 594 seconds. 30 | -------------------------------------------------------------------------------- /192/pkpd_model.txt: -------------------------------------------------------------------------------- 1 | DESCRIPTION: 2 | PK PD 3 | 4 | INPUT: 5 | parameter = {ka, V, Cl, kout, C50} 6 | 7 | EQUATION: 8 | Cc = pkmodel(ka, V, Cl) 9 | E_0 = 0 10 | Imax=1 11 | ddt_E = kout*Imax*Cc/(Cc+C50) - kout*E 12 | 13 | OUTPUT: 14 | output = {Cc,E} -------------------------------------------------------------------------------- /194/194.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_likert_pain_count.mod", 4 | "Output_simulated_likert_pain_count.lst", 5 | "Output_real_likert_pain_count.lst", 6 | "Command.txt", 7 | "Simulated_likert_pain_count.csv", 8 | "DDMODEL00000194.rdf" 9 | ], 10 | "version": 14 11 | } -------------------------------------------------------------------------------- /194/Command.txt: -------------------------------------------------------------------------------- 1 | ###################### if the executable model is written in original language ##### 2 | ###################### please specify the block below 3 | ###### Scenario = NONMEM on linux cluster 4 | ###### PsNversion (if any) = 4.5.3 5 | ###### Original tool version = NONMEM version 7.3 6 | ###### Input data = likert_pain_count_simulated.csv 7 | ###### Executable model = likert_pain_count_original.mod 8 | ###### Output = likert_pain_count_output_original.lst 9 | ###################### end block 10 | 11 | 12 | ###### Script used as execution command for NONMEM via PsN: ############# 13 | 14 | execute likert_pain_count_original.mod -------------------------------------------------------------------------------- /195/195.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_sibrotuzumab.xml", 4 | "DDMODEL00000195.rdf", 5 | "Model_Accommodations.txt", 6 | "Output_simulated_nca_simulation.1.lst", 7 | "Command.txt", 8 | "Executable_sibrotuzumab.mdl", 9 | "Simulated_sibrotuzumab.csv" 10 | ], 11 | "version": 5 12 | } -------------------------------------------------------------------------------- /195/Command.txt: -------------------------------------------------------------------------------- 1 | #' ========================================================================================= 2 | #' Command for execution of Sibrotuzumab model 3 | #' ----------------------------------------------------------------------------------- 4 | 5 | #' Initialisation 6 | #' ========================= 7 | #' Clear workspace 8 | rm(list=ls(all=F)) 9 | 10 | #' Set working directory 11 | setwd(file.path(Sys.getenv("MDLIDE_WORKSPACE_HOME"), "Repository", "Sibrotuzumab")) 12 | 13 | #' List files available in working directory 14 | list.files() 15 | 16 | #' Set name of placebo .mdl file and dataset for future tasks 17 | mdl <- "Executable_sibrotuzumab.mdl" 18 | datafile <- "Simulated_subrotuzumab.csv" 19 | 20 | 21 | newdat <- sim.PsN(mdl, samples = 20, seed = 123456) #simulate data from the model 22 | 23 | 24 | #' ESTIMATE model parameters using either Monolix or NONMEM 25 | #' ------------------------- 26 | nm <- estimate(mdl, target="NONMEM", subfolder="NONMEM") 27 | 28 | 29 | -------------------------------------------------------------------------------- /195/DDMODEL00000195.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | Two-compartment PK model with combined linear and saturable elimination. Inter-individual variability on linear and nonlinear clearance and volumes of distribution. Inter-occasion variability on bioavailability after repeated IV dosing (also accounts for uncertainty in actual dose level). Body weight is used as a covariate on linear and nonlinear clearance and volumes of distribution. 9 | 10 | Contary to the original publication, inter-occasion variability on bioavailability was not implemented. 11 | To understand PK and variability in cancer patients of a new monoclonal antibody 12 | 13 | 14 | No 15 | 16 | No 17 | 18 | 19 | -------------------------------------------------------------------------------- /195/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | # Changes to the model: 2 | 3 | Inter-occasion variability on bioavailability was removed. 4 | 5 | In the current version of the DDMoRe framework, encoding bioavailability is only supported with 6 | 1) depot compartments (explicit definition in the ODE system) or 7 | 2) through PK macros (only standard compartment components). 8 | 9 | However, in the associated publication, the model uses bioavailability after IV dosing, amongst others to account for uncertainty in dose level (==> 1 is not possible). 10 | Also, it uses a combined linear-nonlinear elimination model (==> 2 is not possible). 11 | 12 | -------------------------------------------------------------------------------- /197/197.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_Biomarker_GIST.mod", 4 | "Output_real_Biomarker_GIST.lst", 5 | "Command.txt", 6 | "Simulated_Biomarker_GIST.csv", 7 | "Output_simulated_Biomarker_GIST.lst", 8 | "DDMODEL00000197.rdf" 9 | ], 10 | "version": 5 11 | } -------------------------------------------------------------------------------- /197/Command.txt: -------------------------------------------------------------------------------- 1 | ### please fill in commented lines 2 | # this file is mandatory for submission scenarios 1, 2 and 4. 3 | ###################### if the executable model is written in original language ##### 4 | ###################### please specify the block below 5 | ###### Scenario = 4 6 | ###### PsN version = 4.5.9 7 | ###### Original tool version = 7.3 8 | ###### Input data = Simulated_Biomarker_GIST.csv 9 | ###### Executable model = Executable_Biomarker_GIST.mod 10 | ###### Output = Output_simulated_Biomarker_GIST.lst 11 | ###################### end block 12 | 13 | ###### Script used as execution command for NONMEM via PsN: ############# 14 | execute Executable_Biomarker_GIST.mod 15 | -------------------------------------------------------------------------------- /197/DDMODEL00000197.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | No 9 | Yes 10 | 11 | PD model (predicting plasma concentrations) of biomarkers during sunitinib treatment of imatinib-resistant GIST; 12 | 13 | 14 | 15 | Final PD model (predicting plasma concentrations) of biomarkers during sunitinib treatment of imatinib-resistant GIST. 4 biomarkers: VEGF, sVEGFR-2, sVEGFR-3 and sKIT and 4 compartments, 1 for each biomarker, initiated to estimated baselines at t=0. Log-transformed plasma concentrations (DV) and effect on dA/dt for each biomarker as indirect response models. Sigmoid Imax for VEGF/sVEGFR-2 (hill factor) and Imax for sVEGFR-3/sKIT. Inhibition of Kout for VEGF and Kin for sVEGFR-2, sVEGFR-3 and sKIT. Lin. disease progression model for VEGF and sKIT (otherwise baseline only). No covariates. IIV on baseline, MRT (1/Kout), IC50 and disease progression slope. Residual error: Additive (on log scale) on VEGF, sVEGFR-3 and sKIT, and Additive + proportional (on log scale) on sVEGFR-2. Estimation method: FOCE with INTERACTION (+ COVARIANCE STEP). 16 | 17 | 18 | 19 | 20 | 21 | -------------------------------------------------------------------------------- /198/198.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_TGI_GIST.mod", 4 | "DDMODEL00000198.rdf", 5 | "Simulated_TGI_GIST.csv", 6 | "Output_real_TGI_GIST.lst", 7 | "Command.txt", 8 | "Output_simulated_TGI_GIST.lst" 9 | ], 10 | "version": 6 11 | } -------------------------------------------------------------------------------- /198/Command.txt: -------------------------------------------------------------------------------- 1 | ### please fill in commented lines 2 | # this file is mandatory for submission scenarios 1, 2 and 4. 3 | ###################### if the executable model is written in original language ##### 4 | ###################### please specify the block below 5 | ###### Scenario = 4 6 | ###### PsNversion (if any) = 4.5.9 7 | ###### Original tool version= NM7.3 8 | ###### Input data= Simulated_TGI_GIST.csv 9 | ###### Executable model= Executable_TGI_GIST.mod 10 | ###### Output= Output_simulated_TGI_GIST.lst 11 | ###################### end block 12 | 13 | ###### Script used as execution command for NONMEM via PsN: ############# 14 | 15 | execute Executable_TGI_GIST_GIST.mod 16 | -------------------------------------------------------------------------------- /198/DDMODEL00000198.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | Tumor growth inhibition model; PD model predicting tumour size during sunitinib treatment of imatinib-resistant GIST 8 | 9 | 10 | Yes 11 | 12 | 13 | 14 | No 15 | 16 | 17 | Final PD model (predicting tumour size) of drug and biomarkers during sunitinib treatment of imatinib-resistant GIST. 2 biomarkers from BM included: sVEGFR-3 and sKIT. 4 compartments initiated to BM posthoc baselines at t=0. 1st/2nd compartment: sKIT timecourse with and without drug effect (placebo). 3rd compartment: sVEGFR-3 timecourse. 4th compartment: tumour size timecourse. Normal scale tumour measurements (DV). Tumour timecourse is a growth inhibition model. Linear inhibition effect by AUC (+) and sKIT/sVEGFR-3 (-). Sunitinib AUC=DOSE/CL. Baseline tumour size covariate; residual variability as measure error. Tumour regrowth/resistance with exponential time-dependent function. Covariate: Baseline tumour size observation. IIV on rate constants: growth rate, drug effect and sKIT effect. Residual error (shared variance estimate): Proportional on DV and Proportional on baseline tumour size observation. Estimation method: FOCE with INTERACTION (NO COVARIANCE STEP). 18 | 19 | 20 | -------------------------------------------------------------------------------- /212/212.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_tamoxifen.xml", 4 | "Output_simulated_tamoxifen.lst", 5 | "Command.txt", 6 | "Model_Accommodations.txt", 7 | "Output_simulated_tamoxifen_SO.xml", 8 | "Executable_tamoxifen.mdl", 9 | "Output_real_tamoxifen.pdf", 10 | "Simulated_tamoxifen.csv", 11 | "DDMODEL00000212.rdf" 12 | ], 13 | "version": 8 14 | } -------------------------------------------------------------------------------- /212/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 1 2 | ###### MDL version = 3 | ###### IO product = 4 | ###### Input data = Simulated_tamoxifen.csv 5 | ###### Executable model = Executable_tamoxifen.mdl 6 | ###### Output = Output_tamoxifen_SO.xml 7 | 8 | #Initialisation 9 | rm(list=ls(all=F)) #clean your workspace first 10 | 11 | #Set working directory 12 | myfolder <- "tamoxifen" # name of your project folder 13 | setwd(file.path(Sys.getenv("MDLIDE_WORKSPACE_HOME"),myfolder)) 14 | 15 | #Set name of .mdl file and dataset for future tasks 16 | mymodel <- "Executable_tamoxifen" 17 | datafile <- "Simulated_tamoxifen.csv" 18 | mdlfile <- paste0(mymodel,".mdl") 19 | 20 | #ESTIMATE model parameters using NONMEM 21 | nm <- estimate(mdlfile, target="NONMEM", subfolder="NONMEM") 22 | 23 | # Print the estimated parameters 24 | parameters_nm <- getPopulationParameters(nm, what="estimates")$MLE 25 | print(nm) 26 | 27 | -------------------------------------------------------------------------------- /212/DDMODEL00000212.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | Joint parent-metabolite (tamoxifen and endoxifen) PK model with one compartment each and an additonal hypothetical liver compartment to account for the fraction of tamoxifen directly metabolised to endoxifen. CYP2D6 and CYP3A4/5 phenotypes (dextromethorphan model-based individual CL values) have been implemented as power functions and centred around the population median on the formation of endoxifen (CL23). 8 | No 9 | To better understand the highly variable pharmacokinetics of tamoxifen and its major metabolite endoxifen in breast cancer patients considering CYP2D6 and CYP3A4/5 phenotypes. 10 | 11 | 12 | No 13 | 14 | a) EVID and L2 item (as in dataset and used in original model) not us for the estimation task utilising DDMoRe framework; b) Residual error for parent (tamoxifen) and metabolite (endoxifen) estimated without considering correlations and as standard deviations (thus as structural parameters in NONMEM as THETAs); c) lag time handled differently by ddmore products than by NONMEM. These differences led to minor differences in estimates (esp. tlag). 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /212/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | #Differences: Uploaded model vs. related publication referred to. 2 | 3 | ###### Model differences and reasons 4 | a) EVID and L2 item (as in dataset and used in original model) 5 | not us for estimations utilising DDMoRe framework; 6 | b) Residual error for parent (tamoxifen) and metabolite (endoxifen) 7 | estimated without considering correlations and as standard deviations 8 | (structural parameters in NONMEM as THETAs); 9 | c) lag time handled differently by ddmore convertors than by NONMEM. 10 | These differences led to minor differences in estimates (esp. tlag). 11 | 12 | 13 | -------------------------------------------------------------------------------- /212/Output_real_tamoxifen.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/212/Output_real_tamoxifen.pdf -------------------------------------------------------------------------------- /213/213.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_Meropenem_MDL.xml", 4 | "Executable_Meropenem_MDL.mdl", 5 | "Model_Accommodations.txt", 6 | "Simulated_DatasetMeropenem.csv", 7 | "Output_simulated_Meropenem.lst", 8 | "Command.txt", 9 | "DDMODEL00000213.rdf", 10 | "Output_simulated_Meropenem.SO.xml" 11 | ], 12 | "version": 12 13 | } -------------------------------------------------------------------------------- /213/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 1 2 | ###### MDL version = 3 | ###### IO product = 4 | ###### Input data = Simulated_DatasetMeropenem.csv 5 | ###### Executable model = Executable_Meropenem_MDL.mdl 6 | ###### Output = Output_simulated_Meropenem.SO.xml 7 | 8 | #Initialisation 9 | rm(list=ls(all=F)) #clean your workspace first 10 | 11 | #Set working directory 12 | myfolder <- "Lisa_Submission" # name of your project folder 13 | setwd(file.path(Sys.getenv("MDLIDE_WORKSPACE_HOME"),myfolder)) 14 | 15 | #Set name of .mdl file and dataset for future tasks 16 | mymodel <- "Executable_Meropenem_MDL" 17 | datafile <- "Simulated_DatasetMeropenem.csv" 18 | mdlfile <- paste0(mymodel,".mdl") 19 | 20 | #ESTIMATE model parameters using Monolix 21 | nm <- estimate(mdlfile, target="MONOLIX", subfolder="Monolix") 22 | 23 | -------------------------------------------------------------------------------- /213/DDMODEL00000213.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | To understand PK and variability of meropenem in adult patients 9 | No 10 | Covariate effect of creatinine clearance on CL was fixed to original value from publication since no information on the distribution of cretainine clearance was given in the original publication 11 | No 12 | 13 | 14 | Two compartment model with linear elimination; 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /213/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | ###### Model ######## 2 | 3 | - Original model was used as described in publication (Li et al); 4 | - Covariate effect of creatinine clearance (CLCR) on CL was fixed to original value from publication (0.62), since no information on the distribution of CLCR was given in the pulication 5 | 6 | 7 | ###### Simulated Dataset ######## 8 | 9 | - Software used for simulation: simulx 10 | - Number of simulated patients: 79 (=original patient number) 11 | - Dosing regime: 6 dosing groups (as mentioned in original publication) 12 | Group 1: 14 patients: 500 mg --> 1000 mg/h, 13 | Group 2: 13 patients: 1000 mg --> 2000 mg/h, 14 | Group 3: 13 patients: 2000 mg --> 3000 mg/h, 15 | Group 4: 13 patients: 500 mg --> 166.7 mg/h, 16 | Group 5: 13 patients: 1000 mg --> 333.3 mg/h 17 | Group 6: 13 patients: 2000 mg --> 666.7 mg/h 18 | - Creatinine clearance in patients: all median CLCR of 83 mL/min, since no information was given on the distribution of CLCR in original population 19 | - Age in patients: assuming a log normal distribution of age, using information from original publication (median=35.0 yrs, sd= 18.2 yrs) 20 | - Body weight in patients: assuming a log normal distribution of body weight, using information from original publication (median=70.0 kg, sd=16.1 kg) 21 | 22 | -------------------------------------------------------------------------------- /214/214.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_HFSmodel.mod", 4 | "Output_real_HFSmodel.lst", 5 | "Model_Accommodations.txt", 6 | "DDMODEL00000214.rdf", 7 | "Command.txt", 8 | "Output_simulated_HFSmodel.lst", 9 | "Simulated_GHFS_HFSmodel.csv" 10 | ], 11 | "version": 4 12 | } -------------------------------------------------------------------------------- /214/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Prepared and run by Maria Luisa Sardu 2 | ##### Scenario= 4 3 | ##### PsN version=4.4.8 4 | ##### Original tool version=nm_7.3.0 5 | ##### Input data= Original data not shared 6 | ##### Executable model= Executable_HFSmodel.mod 7 | ##### Output=Output_simulated_HFSmodel.lst 8 | 9 | ###### Script used as execution command for NONMEM via PsN: ########## 10 | execute Executable_HFSmodel.mod 11 | 12 | -------------------------------------------------------------------------------- /214/DDMODEL00000214.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Yes 13 | No 14 | Longitudinal model to predict Hand and Foot Syndrome grades dynamics in patients with solid tumors receiving capecitabine in two phase III studies. HFS grades are ordered categorical data from 0 no toxicity to 3 maximum toxicity.The model describes the risk of developing HFS using proportional odds model including a sigmoidal maximum effect driven by capecitabine exposure. As HFS grades are not independent from one point to the other, a Markov model was used to define transition probabilities. To describe Capecitabine exposure the (kinetic-pharmacodynamic) K-PD approach was used. Only baseline-calculated serum creatinine clearance (CRCL) was included as model covariate. 15 | Predict Hand and Foot Syndrome dynamics in cancer patients receiving capecitabine from 2 phase III studies. 16 | 17 | 18 | -------------------------------------------------------------------------------- /214/Executable_HFSmodel.mod: -------------------------------------------------------------------------------- 1 | 2 | 3 | $PROB Logit model with KPD approach EMAX model covariate CRCL 4 | $INPUT C ID WEEK=TIME MDV EVID GHFS=DV AMT ADDL II AGE SEX WT HT SERUM SERCORR WTCOR SEXCOEFF BSA CLCR 5 | $DATA ../Datasets/Simulated_HFSmodel.csv IGNORE=C 6 | $SUBROUTINES ADVAN1 TRANS1 7 | 8 | $PK 9 | TCLCR=THETA(12) 10 | TVK=THETA(1) 11 | 12 | K=TVK*EXP(ETA(1)) 13 | 14 | 15 | IF (TIME.EQ.0) BCLCR=CLCR 16 | IIV=ETA(2)+(BCLCR-75.5)*TCLCR 17 | 18 | B00=THETA(2) 19 | B10=THETA(3) 20 | B20=THETA(4) 21 | EMAX0=THETA(5) 22 | ED50=THETA(6) 23 | 24 | B01=THETA(7) 25 | B11=THETA(8) 26 | B21=THETA(9) 27 | 28 | EMAX1=THETA(10) 29 | EMAX2=THETA(11) 30 | 31 | IF (TIME.EQ.0) SWM1=0 32 | 33 | 34 | $ERROR 35 | CALLFL=0 36 | IPRED=F 37 | EMAX=EMAX0 38 | IF(SWM1.EQ.1) EMAX=EMAX1 39 | IF(SWM1.EQ.2) EMAX=EMAX2 40 | EFF=EMAX*(F*K)/((F*K)+(ED50)) 41 | A0=0 42 | A1=0 43 | SPREC=SWM1 44 | IF (SWM1.EQ.0) THEN 45 | A0=B00 46 | A1=A0+B01 47 | ENDIF 48 | IF (SWM1.EQ.1) THEN 49 | A0=B10 50 | A1=A0+B11 51 | ENDIF 52 | IF (SWM1.EQ.2) THEN 53 | A0=B20 54 | A1=A0+B21 55 | ENDIF 56 | A0=A0-EFF+IIV 57 | A1=A1-EFF+IIV 58 | 59 | SWM1=GHFS 60 | 61 | PC0=EXP(A0)/(1+EXP(A0)) 62 | PC1=EXP(A1)/(1+EXP(A1)) 63 | PC2=1 64 | 65 | 66 | P0=PC0 67 | P1=PC1-PC0 68 | P2=PC2-PC1 69 | 70 | 71 | Y=-1 72 | IF (DV.LT.0.5) Y=P0 73 | IF (DV.GE.0.5.AND.DV.LT.1.5) Y=P1 74 | IF (DV.GE.1.5.AND.DV.LT.2.5) Y=P2 75 | 76 | $THETA 77 | (0,0.159) ; 1 TVK 78 | (4.62) ; 2 B00 79 | (0.683) ; 3 B10 80 | (1.99) ; 4 B20 81 | (3.8) ; 5 EMAX0 82 | (0,13000) ; 6 ED50 83 | (0,0.602) ; 7* B01 84 | (0,5.24) ; 8* B11 85 | (0,0.322) ; 9 B21 86 | (0,6.3) ;10 EMAX1 87 | (0,9.9) ;11 EMAX2 88 | (0,0.00552) ;12 TCLCR 89 | 90 | 91 | $OMEGA BLOCK(2) 92 | 0.468 93 | 0.402 0.8 94 | 95 | $EST METHOD=1 MAXEVALS=9999 PRINT=5 LIKE LAPLACE SIGDIGITS=1 SLOW 96 | NOABORT 97 | $TABLE ID TIME AMT MDV SPREC IPRED K TVK TCLCR 98 | P0 P1 P2 A0 A1 PC0 PC1 PC2 ETA1 ETA2 EMAX ED50 EFF 99 | ONEHEADER NOPRINT FILE=ddmore159.tab 100 | $COV SLOW UNCONDITIONAL MATRIX=S 101 | 102 | -------------------------------------------------------------------------------- /214/Model_Accommodations.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/214/Model_Accommodations.txt -------------------------------------------------------------------------------- /215/215.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_Pimasertib_AeDropout.mod", 4 | "Output_simulated_Pimasertib_AeDropout.lst", 5 | "DDMODEL00000215.rdf", 6 | "Simulated_Pimasertib_AeDropout.csv", 7 | "Command.txt", 8 | "Output_real_Pimasertib_AeDropout.lst", 9 | "Model_Accommodations.txt" 10 | ], 11 | "version": 6 12 | } -------------------------------------------------------------------------------- /215/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Prepared and run by Maria Luisa Sardu 2 | ##### Scenario= 4 3 | ##### PsN version=4.4.8 4 | ##### Original tool version=nm_7.3.0 5 | ##### Input data= Original data not shared 6 | ##### Executable model= Executable_Pimasertib_AeDropout.mod 7 | ##### Output=Output_simulated_PimasertibAeDropout.lst 8 | 9 | ###### Script used as execution command for NONMEM via PsN: ########## 10 | Execute Executable_Pimasertib_AeDropout.mod 11 | 12 | -------------------------------------------------------------------------------- /215/Model_Accommodations.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/215/Model_Accommodations.txt -------------------------------------------------------------------------------- /217/217.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_SLD.mod", 4 | "Output_simulated_SLD.lst", 5 | "Output_real_SLD.lst", 6 | "DDMODEL00000217.rdf", 7 | "Simulated_SLD.csv", 8 | "Command.txt" 9 | ], 10 | "version": 13 11 | } -------------------------------------------------------------------------------- /217/Command.txt: -------------------------------------------------------------------------------- 1 | ### please fill in commented lines 2 | # this file is mandatory for submission scenarios 1, 2 and 4. 3 | ###################### if the executable model is written in original language ##### 4 | ###################### please specify the block below 5 | ###### Scenario = 4 6 | ###### PsNversion (if any) = PsN-4.2.0 7 | ###### Original tool version= NM7.3 8 | ###### Input data= Simulated_SLD.csv 9 | ###### Executable model= Executable_SLD.mod 10 | ###### Output= Output_simulated_SLD.lst 11 | ###################### end block 12 | 13 | ###### Script used as execution command for NONMEM via PsN: ############# 14 | 15 | execute Executable_SLD.mod 16 | -------------------------------------------------------------------------------- /217/DDMODEL00000217.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | Tumour size dynamics model for ovarian cancer patients 9 | 10 | 11 | 12 | 13 | 14 | Yes 15 | No 16 | 17 | 18 | -------------------------------------------------------------------------------- /217/Executable_SLD.mod: -------------------------------------------------------------------------------- 1 | ; Claret-like model for describing CTS. No resistance to drug treatment. Independent additive effect of the two drugs. M3 method for BLQ. Additive error 2 | 3 | $SIZES LIM6=2000 4 | $PROBLEM Model for SLD(t) 5 | 6 | $INPUT CCOM,ID, 7 | TIME, ;in day 8 | DV, ;SLD in mm 9 | BQL, ;the LLOQ is 5 mm 10 | CB, ;exposure to carboplatin (per-cycle average AUC) 11 | G, ;exposure to gemcitabine (per-cycle average AUC) 12 | EVID, 13 | FLG, ;FLG=2 for SLD data, FLG=1 for exposure-related entries 14 | CMT ;CMT=1 for SLD data 15 | 16 | $DATA Simulated_SLD.csv IGNORE=C 17 | 18 | $SUBROUTINE ADVAN6 TOL=3 19 | 20 | $MODEL 21 | ;Tumour 22 | COMP = (TUMOUR, DEFOBS) 23 | 24 | $PK 25 | KG = THETA(1)*EXP(ETA(1)) ;tumour growth rate constant (1/day) 26 | KD0 = THETA(2)*EXP(ETA(2)) ;carboplatin related death rate constant (1/day/AUC0) 27 | KD1 = THETA(3)*EXP(ETA(2)) ;gemcitabine related death rate constant (1/day/AUC1) 28 | IBASE = THETA(4)*EXP(ETA(3)) ;baseline SLD (m) 29 | FADD = THETA(5) ;SD of additive error (mm) 30 | 31 | ; ==== SLD baseline ==== 32 | A_0(1) = IBASE*1000 33 | 34 | ; Backward interpolation of exposure-related data 35 | IF(NEWIND.NE.2) OCB=CB 36 | IF(NEWIND.NE.2) OG=G 37 | E0 = OCB 38 | E1 = OG 39 | OCB=CB 40 | OG=G 41 | 42 | $DES 43 | ; Model for dSLD(t) 44 | DADT(1) = KG/1000 * A(1) - (KD0/1000 * E0 + KD1/100 * E1) * A(1) 45 | 46 | $ERROR 47 | LLOQ = 5 ;5 mm is LLOQ 48 | IPRED = A(1) 49 | 50 | W = FADD ;SD of additive unexpained variability 51 | 52 | ; Probability of SLD 6 | 7 | 8 | 9 | 10 | Yes 11 | Oncology, specifically Phase II/1b to Phase III drug development phases. The developed modelling framework links change in tumour size during treatment to survival probability in metastatic ovarian cancer. In addition the appearance of new lesions and their relationship to survival and disease characteristics has been captured. 12 | 13 | No 14 | None apart from using simulated patient data 15 | 16 | 17 | 18 | -------------------------------------------------------------------------------- /219/219.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_BDQ_M2_PK_plus_WT_ALB_in_MDR-TB_patients.mod", 4 | "Output_real_BDQ_M2_PK_plus_WT_ALB_in_MDR-TB_patients.lst", 5 | "Simulated_data_BDQ_M2_PK_plus_WT_ALB_in_MDR-TB_patients.csv", 6 | "Output_simulated_executable_BDQ_M2_PK_plus_WT_ALB_in_MDR-TB_patients.lst", 7 | "Command.txt" 8 | ], 9 | "version": 5 10 | } -------------------------------------------------------------------------------- /219/Command.txt: -------------------------------------------------------------------------------- 1 | ###################### 2 | ###### Scenario = 4 3 | ###### PsNversion (if any) = 4.6.9 4 | ###### Original tool version= NM version 7.3 5 | ###### Input data= Simulated_data_BDQ_M2_PK_plus_WT_ALB_in_MDR-TB_patients.csv 6 | ###### Executable model= Executable_BDQ_M2_PK_plus_WT_ALB_in_MDR-TB_patients.mod 7 | ###### Output= Output_simulated_executable_BDQ_M2_PK_plus_WT_ALB_in_MDR-TB_patients.lst 8 | ###################### 9 | 10 | 11 | ###### Script used as execution command for NONMEM via PsN: ############# 12 | 13 | execute Executable_BDQ_M2_PK_plus_WT_ALB_in_MDR-TB_patients.mod -------------------------------------------------------------------------------- /220/220.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_run32150.mod", 4 | "Output_real_run32150.lst", 5 | "Finmod_7_3.lst", 6 | "Command.txt", 7 | "DDMODEL00000220.rdf", 8 | "Output_simulated_Executable_run32150.lst", 9 | "Simulated_data.csv" 10 | ], 11 | "version": 10 12 | } -------------------------------------------------------------------------------- /220/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = scenario 4 2 | ###### PsN version= 3.2.10 3 | ###### Original tool version = NONMEM version 6.2 4 | ###### Input data = Simulated_data.csv 5 | ###### Executable model = Executable_run32150.mod 6 | ###### Output = Output_simulated_original_run32150.lst 7 | ###### Output = Output_real_original_32150.lst 8 | ###### Output = Finmod_7_3.lst; the model re-run with real data in NONMEM version 7.3 9 | 10 | 11 | ###### Script used as execution command: 12 | 13 | execute Executable_run32150.mod 14 | -------------------------------------------------------------------------------- /221/221.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_SLD_SUV_OS_GIST.mod", 4 | "DDMODEL00000221.rdf", 5 | "Output_real_SLD_SUV_OS_GIST.lst", 6 | "Command.txt", 7 | "Simulated_SLD_SUV_OS_GIST.csv", 8 | "Output_simulated_SLD_SUV_OS_GIST.lst" 9 | ], 10 | "version": 9 11 | } -------------------------------------------------------------------------------- /221/Command.txt: -------------------------------------------------------------------------------- 1 | ### please fill in commented lines 2 | # this file is mandatory for submission scenarios 1, 2 and 4. 3 | ###################### if the executable model is written in original language ##### 4 | ###################### please specify the block below 5 | ###### Scenario = 4 6 | ###### PsN version = 4.6.9 7 | ###### Original tool version = 7.3 8 | ###### Input data = Simulated_SLD_SUV_OS_GIST.csv 9 | ###### Executable model = Executable_SLD_SUV_OS_GIST.mod 10 | ###### Output = Output_simulated_SLD_SUV_OS_GIST.lst 11 | ###################### end block 12 | 13 | ###### Script used as execution command for NONMEM via PsN: ############# 14 | execute Executable_SLD_SUV_OS_GIST.mod 15 | -------------------------------------------------------------------------------- /221/DDMODEL00000221.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | A tumor growth inhibition model describes the time-course of SLD. An indirect response model in which sunitinib stimulates tumor loss of SUVmax response best described the longitudinal SUVmax data, as assessed on FDG-PET scans. Inter-lesion variability in SUVmax is implemented similarly to inter-occasion variability. No disease progression was identified for SUVmax. A time-to-event model with a constant baseline hazard driven by the relative change in SUVmax from baseline for the lesion that responds the most was used to describe overall survival data. 9 | Changes in FDG-PET standardized uptake values reflecting tumor metabolic activity was suggested as a predictor for long-term clinical outcome in GIST patients treated with anti-angiogenic drugs such as sunitinib. The model characterized the inter-individual and inter-lesion variability in SUVmax response and the predictive ability of SUVmax-related metrics on overall survival. 10 | 11 | No 12 | 13 | 14 | 15 | 16 | None 17 | Yes 18 | 19 | 20 | -------------------------------------------------------------------------------- /222/222.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_Fatigue_GIST.mod", 4 | "DDMODEL00000222.rdf", 5 | "Simulated_Fatigue_GIST.txt", 6 | "Output_real_Fatigue_GIST.lst_Fatigue_PSP_2014", 7 | "Command.txt", 8 | "Output_simulated_Fatigue_GIST.lst" 9 | ], 10 | "version": 9 11 | } -------------------------------------------------------------------------------- /222/Command.txt: -------------------------------------------------------------------------------- 1 | ### please fill in commented lines 2 | # this file is mandatory for submission scenarios 1, 2 and 4. 3 | ###################### if the executable model is written in original language ##### 4 | ###################### please specify the block below 5 | ###### Scenario = 4 6 | ###### PsN version = 4.6.9 7 | ###### Original tool version = 7.3 8 | ###### Input data = Simulated_Fatigue_GIST.txt 9 | ###### Executable model = Executable_Fatigue_GIST.mod 10 | ###### Output = Output_simulated_Fatigue_GIST.lst 11 | ###################### end block 12 | 13 | ###### Script used as execution command for NONMEM via PsN: ############# 14 | execute Executable_Fatigue_GIST.mod 15 | -------------------------------------------------------------------------------- /222/DDMODEL00000222.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | Proportional odds models with a first-order Markov model for Fatigue in sunitinib treated GIST patients 8 | 9 | 10 | 11 | 12 | Yes 13 | 14 | 15 | No 16 | None 17 | Adverse effects may be useful alternative early indicators of pharmacodynamic activity as they could be more practical to use in the clinical setting 18 | 19 | 20 | 21 | -------------------------------------------------------------------------------- /223/223.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_Novakovic_2016_multiplesclerosis_cladribine_irt.mod", 4 | "DDMODEL00000223.rdf", 5 | "Output_real_Novakovic_2016_multiplesclerosis_cladribine_irt.lst", 6 | "Output_simulated_Novakovic_2016_multiplesclerosis_cladribine_irt.lst", 7 | "Command.txt", 8 | "Simulated_Novakovic_2016_multiplesclerosis_cladribine_irt.csv" 9 | ], 10 | "version": 7 11 | } -------------------------------------------------------------------------------- /223/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version = 4.6.9 3 | ###### Original tool version = nm_7.3.0_g 4 | ###### Input data = Simulated_Novakovic_2016_multiplesclerosis_cladribine_irt.csv (original data not shared) 5 | ###### Executable model = Executable_Novakovic_2016_multiplesclerosis_cladribine_irt.mod 6 | ###### Output = Output_simulated_Novakovic_2016_multiplesclerosis_cladribine_irt.lst 7 | 8 | execute Executable_Novakovic_2016_multiplesclerosis_cladribine_irt.mod -------------------------------------------------------------------------------- /224/224.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_myelosuppression_dailyANC.mod", 4 | "Output_simulated_Executable_myelosuppression_dailyANC.lst", 5 | "DDMODEL00000224.rdf", 6 | "Simulated_myelosuppression_dailyANC.csv", 7 | "Model_Accommodations.txt", 8 | "Command.txt" 9 | ], 10 | "version": 16 11 | } -------------------------------------------------------------------------------- /224/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version = 4.6.9 3 | ###### Original tool version = NONMEM 7.3 4 | ###### Input data = Simulated_myelosuppression_dailyANC.csv 5 | ###### Executable model = Executable_myelosuppression_dailyANC.mod 6 | ###### Output = Executable_myelosuppression_dailyANC.lst 7 | 8 | execute Executable_myelosuppression_dailyANC.mod -------------------------------------------------------------------------------- /224/DDMODEL00000224.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | No variability related to the gamma parameter 11 | This model was used to simulate daily measurements of ANC, which was subsequently used to predict the ANC with varying amount of data available for making the predictions. Also the precision of predicting time to baseline recovery, time to nadir and the nadir ANC value, was evaluated 12 | 13 | No 14 | 15 | No 16 | 17 | 18 | -------------------------------------------------------------------------------- /224/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | ###### Model differences = 1) The OMEGA related to the gamma paramater was set to 0 here (estimated to 0.0216452 in the original publication) 2 | 2) Code for extraction of nadir, time to recovery to baseline and time to different neutropenic grades is included 3 | 3) Only docetaxel is used 4 | 5 | ###### Reasons = Used to simulate for a different purpose -------------------------------------------------------------------------------- /225/225.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "executeable_cipro.xml", 4 | "Model_Accommodations.txt", 5 | "Command_target.txt", 6 | "cipro_simulated.csv", 7 | "executeable_cipro_pkpd.mod", 8 | "cipro378.csv", 9 | "cipro202.csv", 10 | "DDMODEL00000225.rdf", 11 | "cipro378_VPC.mdl", 12 | "HO_Bacteria_PKPD _script.r", 13 | "cipro202_VPC.mdl", 14 | "output_simulated.lst" 15 | ], 16 | "version": 11 17 | } -------------------------------------------------------------------------------- /225/Command_target.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/225/Command_target.txt -------------------------------------------------------------------------------- /225/DDMODEL00000225.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | Yes 8 | 9 | PKPD model for ciprofloxacin 10 | M3, B2, MTIME and L2 not included in mdl file. 11 | 12 | 13 | PKPD model for ciprofloxacin 14 | 15 | 16 | No 17 | 18 | 19 | -------------------------------------------------------------------------------- /225/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | Model_Accommodations 2 | This file has to be uploaded when the model is not implemented as in the original publication. 3 | ###### Model differences = L2, MTIME, B2 and M3 not included 4 | 5 | 6 | ###### Reasons = not supported in MDL 7 | 8 | -------------------------------------------------------------------------------- /227/227.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "glucoseKinetics.xml", 4 | "Executable_glucoseKinetics.mlxtran", 5 | "Command.txt", 6 | "glucoseKinetics.mdl", 7 | "original_model.txt", 8 | "glucoseKineticsPLOT.pdf", 9 | "Simulated_glucoseKinetics.csv", 10 | "Output_simulated_glucoseKinetics.txt", 11 | "Model_Accommodations.txt", 12 | "Long_technical_model_description_glucoseKinetics.txt", 13 | "Executable_glucoseKinetics_algorithms.xmlx", 14 | "Executable_glucoseKinetics.txt", 15 | "Output_real_glucoseKinetics.txt", 16 | "original_project.mlxtran", 17 | "DDMODEL00000227.rdf" 18 | ], 19 | "version": 15 20 | } -------------------------------------------------------------------------------- /227/Executable_glucoseKinetics.mlxtran: -------------------------------------------------------------------------------- 1 | ; this script is generated automatically 2 | 3 | DESCRIPTION: 4 | Executable_glucoseKinetics.mlxtran 5 | 6 | DATA: 7 | path = "%MLXPROJECT%/", 8 | file ="Simulated_glucoseKinetics.csv", 9 | headers = {TIME,Y,MDV,DOSE,RATE,ID,X,X,DPT,X,X,X,X}, 10 | columnDelimiter = "," 11 | 12 | INDIVIDUAL: 13 | Emax = {distribution=logNormal, iiv=yes}, 14 | F = {distribution=logNormal, iiv=no}, 15 | KmG = {distribution=logNormal, iiv=yes}, 16 | KmI = {distribution=logNormal, iiv=yes}, 17 | V = {distribution=logNormal, iiv=yes}, 18 | Vmax0 = {distribution=logNormal, iiv=no}, 19 | flambda2 = {distribution=logitNormal, iiv=yes}, 20 | flambda3 = {distribution=logitNormal, iiv=yes}, 21 | fw2 = {distribution=logitNormal, iiv=yes}, 22 | gamma = {distribution=logNormal, iiv=yes}, 23 | t12G = {distribution=logNormal, iiv=no}, 24 | t12I = {distribution=logNormal, iiv=yes}, 25 | w1 = {distribution=logitNormal, iiv=yes} 26 | 27 | CORRELATION: 28 | correlationIIV = {gamma,KmI} 29 | 30 | STRUCTURAL_MODEL: 31 | file = "mlxt:Executable_glucoseKinetics", 32 | path = "%MLXPROJECT%", 33 | output = {G} 34 | 35 | 36 | OBSERVATIONS: 37 | Y = {type=continuous, prediction=G, error=constant} 38 | 39 | TASKS: 40 | ; settings 41 | globalSettings={ 42 | withVariance=yes, 43 | settingsAlgorithms="%MLXPROJECT%/Executable_glucoseKinetics_algorithms.xmlx", 44 | resultFolder="%MLXPROJECT%/Executable_glucoseKinetics"}, 45 | ; workflow 46 | estimatePopulationParameters( 47 | initialValues={ 48 | pop_Emax = 4812, 49 | pop_F = 2688 [method=FIXED], 50 | pop_KmG = 3.88, 51 | pop_KmI = 784, 52 | pop_V = 12648, 53 | pop_Vmax0 = 338, 54 | pop_flambda2 = 0.154, 55 | pop_flambda3 = 0.0582, 56 | pop_fw2 = 0.901, 57 | pop_gamma = 1.62, 58 | pop_t12G = 0.7 [method=FIXED], 59 | pop_t12I = 15.9, 60 | pop_w1 = 0.609, 61 | a_Y = 0.014, 62 | omega2_Emax = 0.112, 63 | omega2_KmG = 0.219, 64 | omega2_KmI = 0.263, 65 | omega2_V = 0.0557, 66 | omega2_flambda2 = 0, 67 | omega2_flambda3 = 0.179, 68 | omega2_fw2 = 0, 69 | omega2_gamma = 0.111, 70 | omega2_t12I = 0.151, 71 | omega2_w1 = 0.773 72 | } ), 73 | estimateIndividualParameters( method={conditionalMode} ), 74 | displayGraphics(), 75 | 76 | -------------------------------------------------------------------------------- /227/Executable_glucoseKinetics.txt: -------------------------------------------------------------------------------- 1 | INPUT: 2 | parameter={flambda2, V, gamma, F, flambda3, Vmax0, KmG, KmI, t12G, t12I, Emax, w1, fw2} 3 | regressor={INS,GLU,TOBS,T1,GLU1,INS1} 4 | 5 | PK: 6 | depot(adm=1, target=X1) 7 | depot(adm=2, target=X) 8 | depot(adm=3, target=Z1) 9 | depot(adm=4, target=Z) 10 | depot(adm=5, target=xHL1, p=1/F) 11 | depot(adm=6, target=xHL2, p=1/F) 12 | 13 | EQUATION: 14 | VHL = 700 15 | deltaHL = 15 16 | delta = 10 17 | w2 = ((1)-(w1))*(fw2) 18 | w3 = ((1)-(w1))-(w2) 19 | lambda1 = ((((((((w1)*(flambda2))*(flambda3))+((w2)*(flambda3)))+(w3))/((flambda2)*(flambda3)))*(delta))*(F))/(((delta)*((V)-(VHL)))-(F)) 20 | lambda2 = (lambda1)*(flambda2) 21 | lambda3 = (lambda2)*(flambda3) 22 | c1 = ((deltaHL)*(F))/(((deltaHL)*(VHL))-((2)*(F))) 23 | c2 = ((-(deltaHL))*(F))/(((deltaHL)*(VHL))-(F)) 24 | I = ((((t)-(T1))/((TOBS)-(T1)))*((INS)-(INS1)))+(INS1) 25 | GL = ((((t)-(T1))/((TOBS)-(T1)))*((GLU)-(GLU1)))+(GLU1) 26 | t0 = 0 27 | X1_0 = 0 28 | X_0 = 0 29 | Z1_0 = 0 30 | Z_0 = 0 31 | Vmax = (Vmax0)+(((Emax)*((Z)^(gamma)))/(((KmI)^(gamma))+((Z)^(gamma)))) 32 | cl = (Vmax)/((KmG)+(X)) 33 | E = (cl)/(F) 34 | xHL1_0 = 0 35 | xHL2_0 = 0 36 | G = ((c1)*(xHL1))-((c1)*(xHL2)) 37 | xPER1_0 = 0 38 | xPER2_0 = 0 39 | xPER3_0 = 0 40 | xPER4_0 = 0 41 | Gv = (delta)*(xPER4) 42 | ddt_X1 = (((GL)-(X1))*(log(2)))/(t12G) 43 | ddt_X = (((X1)-(X))*(log(2)))/(t12G) 44 | ddt_Z1 = (((I)-(Z1))*(log(2)))/(t12I) 45 | ddt_Z = (((Z1)-(Z))*(log(2)))/(t12I) 46 | ddt_xHL1 = ((c2)*(xHL1))+(Gv) 47 | ddt_xHL2 = ((-(deltaHL))*(xHL2))+(Gv) 48 | ddt_xPER1 = ((-(lambda1))*(xPER1))+(((w1)*((1)-(E)))*(G)) 49 | ddt_xPER2 = ((-(lambda2))*(xPER2))+(((w2)*((1)-(E)))*(G)) 50 | ddt_xPER3 = ((-(lambda3))*(xPER3))+(((w3)*((1)-(E)))*(G)) 51 | ddt_xPER4 = ((((lambda1)*(xPER1))+((lambda2)*(xPER2)))+((lambda3)*(xPER3)))-((delta)*(xPER4)) 52 | 53 | OUTPUT: 54 | output={G} 55 | -------------------------------------------------------------------------------- /227/Long_technical_model_description_glucoseKinetics.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/227/Long_technical_model_description_glucoseKinetics.txt -------------------------------------------------------------------------------- /227/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | The model in MDL/PharmML language differs from the one used in the related publication in two aspects: 1) it does not consider that the subject undergoing a paired test is unique; 2) it is used as a simulation model, as performing parameter estimation on the provided dataset ("Simulated_glucoseKinetics.csv") would be meaningless, because of the used model inputs. -------------------------------------------------------------------------------- /227/Output_real_glucoseKinetics.txt: -------------------------------------------------------------------------------- 1 | parameter s.e._sa r.s.e._sa pvalues_sa 2 | KM_pop 3.87734 0.3297 8.5 NaN 3 | VMAX0_pop 337.94204 19.18579 5.68 NaN 4 | ALP_pop 4812.08053 269.26479 5.6 NaN 5 | K1_pop 1.62258 0.07151 4.41 NaN 6 | KM1_pop 784.44216 54.45508 6.94 NaN 7 | TAUI_pop 15.94767 0.75883 4.76 NaN 8 | TAUGn_pop 0.59 0 0 NaN 9 | Vt1_pop 9.44524 0.02424 0.26 NaN 10 | fPEX31_pop -2.78482 0.09637 3.46 NaN 11 | fPEX21_pop -1.70593 0.19564 11.47 NaN 12 | PWG11_pop 0.44405 NaN NaN NaN 13 | fPWG21_pop 2.20819 0.08325 3.77 NaN 14 | Vt2_pop 0 0 0 NaN 15 | fPEX32_pop 0 0 0 NaN 16 | PWG12_pop 0 0 0 NaN 17 | fPEX22_pop 0 0 0 NaN 18 | fPWG22_pop 0 0 0 NaN 19 | F_pop 0.99192 0.09095 9.17 NaN 20 | omega2_KM 0.21922 0.06246 28.49 NaN 21 | omega2_VMAX0 0 0 0 NaN 22 | omega2_ALP 0.11244 0.02929 26.05 NaN 23 | omega2_K1 0.11138 0.02309 20.74 NaN 24 | omega2_KM1 0.26252 0.04696 17.89 NaN 25 | omega2_TAUI 0.15112 0.03536 23.4 NaN 26 | omega2_TAUGn 0 0 0 NaN 27 | omega2_Vt1 0 0 0 NaN 28 | omega2_fPEX31 0 0 0 NaN 29 | omega2_fPEX21 0 0 0 NaN 30 | omega2_PWG11 0 0 0 NaN 31 | omega2_fPWG21 0 0 0 NaN 32 | omega2_Vt2 0.05569 0.00953 17.11 NaN 33 | omega2_fPEX32 0.179 0.06227 34.79 NaN 34 | omega2_PWG12 0.77256 0.23144 29.96 NaN 35 | omega2_fPEX22 0 0 0 NaN 36 | omega2_fPWG22 0 0 0 NaN 37 | omega2_F 0.06969 0.02995 42.97 NaN 38 | " corr(K1,KM1)" -0.43889 0.10489 23.9 NaN 39 | a_1 0.02622 0.00121 4.61 NaN 40 | a_2 0.00736 0.00082 11.09 NaN 41 | a_3 0.005 0 0 NaN 42 | a_4 0.00571 0.00091 15.89 NaN 43 | a_5 0.01158 0.00196 16.89 NaN 44 | a_6 0.004 0 0 NaN 45 | a_7 0.00479 0.0011 22.92 NaN 46 | a_8 0.00893 0.00091 10.18 NaN 47 | a_9 0.00425 0.0002 4.64 NaN 48 | a_10 0.00966 0.0005 5.21 NaN 49 | a_11 0.01397 0.00062 4.44 NaN 50 | a_12 0.01203 0.00039 3.27 NaN 51 | a_13 0.01166 0.00083 7.1 NaN 52 | a_14 0.02511 0.00176 7.02 NaN 53 | a_15 0.00329 0.0004 12.07 NaN 54 | -------------------------------------------------------------------------------- /227/glucoseKineticsPLOT.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/227/glucoseKineticsPLOT.pdf -------------------------------------------------------------------------------- /228/228.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_run126h.mod", 4 | "Output_simulated_run126h.lst", 5 | "Simulated_ddmoremockdata2.txt", 6 | "DDMODEL00000228.rdf", 7 | "Output_real_run126c.lst", 8 | "Command.txt" 9 | ], 10 | "version": 16 11 | } -------------------------------------------------------------------------------- /228/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version = 4.6.9 3 | ###### Original tool version = NONMEM 7.3 4 | ###### Input data = Simulated_ddmoremockdata2.txt 5 | ###### Executable model = Executable_run126h.mod 6 | ###### Output = Output_simulated_run126h 7 | 8 | execute run126h.mod 9 | -------------------------------------------------------------------------------- /228/DDMODEL00000228.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Gastric emptying together with glucose absorption (rate and extent) affects glucose exposure after oral glucose tolerance tests. 13 | Yes 14 | No 15 | 16 | 17 | -------------------------------------------------------------------------------- /228/Executable_run126h.mod: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/228/Executable_run126h.mod -------------------------------------------------------------------------------- /228/Output_real_run126c.lst: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/228/Output_real_run126c.lst -------------------------------------------------------------------------------- /228/Output_simulated_run126h.lst: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/228/Output_simulated_run126h.lst -------------------------------------------------------------------------------- /229/229.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "GO_PK_model.xml", 4 | "Model_Accommodations.txt", 5 | "GO_PK_model.mdl", 6 | "DDMODEL00000229.rdf" 7 | ], 8 | "version": 5 9 | } -------------------------------------------------------------------------------- /229/DDMODEL00000229.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | No 11 | 12 | The model is a mechanism-based model for oncotherapy by a conjugated mAb drug, Gemtuzumab ozogamicin (GO; commercial name Mylotarg), for patients with acute myeloid leukemia (AML). The model illustrates the interactions of the drug, GO, with leukemic blasts expressing a receptor, the cell surface antigen CD33, to which the mAb component of the drug (i.e. Gemtuzumab) binds. The drug-antigen complex is then internalized, allowing for the toxic component (ozogamicin) to induce cell lysis. The system consists of ordinary differential equations describing the dynamics of the drug, receptor, and drug-receptor complex, as well as the drug pharmacokinetics. 13 | 14 | 15 | 16 | The PK model was extended to include one peripheral compartment. The main reason is that the model was re-evaluated on the same data, using mixed-effects approach in Monolix. 17 | 18 | No 19 | 20 | 21 | -------------------------------------------------------------------------------- /229/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | ###### Model differences = The PK model was extended to include one peripheral compartment, in addition to the central compartment. The model parameters were re-evaluated using the same data as in the original publication. 2 | 3 | 4 | ###### Reasons = When comparing the parameter values estimated in Monolix to their values estimated in the original study by Jager et al. PLoS One 2011, some slight differences were observed. Furthermore, the original work estimated the parameters in a step-wise fashion, while here our approach was to estimate all the parameters simultaneously. In cosequence, we found that a two-compartment PK model provides better description of the data, judging by statistical criteria (AIC, BIC). -------------------------------------------------------------------------------- /230/230.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "IL_21_PK_mode.xml", 4 | "Model_Accommodations.txt", 5 | "DDMODEL00000230.rdf", 6 | "IL_21_PK_model.mdl" 7 | ], 8 | "version": 4 9 | } -------------------------------------------------------------------------------- /230/DDMODEL00000230.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | This is PK model of IL-21 in mice, encompassing three different, modes of drug administration. 13 | 14 | 15 | 16 | No 17 | No 18 | The model was simplified by removing one of the compartments and the parameters were estimated using Monolix 19 | 20 | 21 | 22 | -------------------------------------------------------------------------------- /230/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | ###### Model differences = The PK model was simplified. 2 | 3 | 4 | ###### Reasons = The original work estimated the parameters by a step-wise fitting to minimize square error. Here we estimated all the parameters simultaneously, using mixed-effects methodology and compared several different models. In cosequence, we found that the present model has best on performance and robustness (i.e. low Akaike information criterion, low condition number, and minimal residual errors). -------------------------------------------------------------------------------- /231/231.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Sunitinib_MPD6_model.xml", 4 | "Sunitinib_MPD6_model.mdl", 5 | "DDMODEL00000231.rdf" 6 | ], 7 | "version": 3 8 | } -------------------------------------------------------------------------------- /231/DDMODEL00000231.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | This is a semi-mechanistic PK/PD model for sunitinib therapy in non-small cell lung cancer patients. It was developed and validated using clinical trail data provided by the drug developer. 14 | 15 | 16 | 17 | Yes 18 | 19 | 20 | -------------------------------------------------------------------------------- /233/233.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Fc_opg_uNTx_PKPD.xml", 4 | "Output_simulated_opg.pdf", 5 | "Fc_opg_uNTx_PKPD.mdl", 6 | "Input_real_opg.pdf", 7 | "DDMODEL00000233.rdf", 8 | "Vignette_opg.R", 9 | "Input_real_opg.R", 10 | "Executable_opg.R", 11 | "Command.txt", 12 | "opgpost.RDS", 13 | "Model_Accommodations.txt", 14 | "opg.cpp", 15 | "Vignette_opg.pdf", 16 | "Simulated_opg.txt" 17 | ], 18 | "version": 52 19 | } -------------------------------------------------------------------------------- /233/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Prepared and run by Kyle Baron 2 | ##### Scenario= 4 3 | ##### mrgsolve version=mrgsolve_0.7.6.9028 4 | ##### Input data=Real_opg.pdf 5 | ##### Output=Output_simulated_opg.pdf 6 | ######## end block 7 | 8 | library(mrgsolve) 9 | mod <- mread("opg") 10 | 11 | -------------------------------------------------------------------------------- /233/DDMODEL00000233.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | No 11 | 12 | Yes 13 | None that I know of. 14 | 15 | 16 | Early dose selection. 17 | 18 | 19 | -------------------------------------------------------------------------------- /233/Executable_opg.R: -------------------------------------------------------------------------------- 1 | # 2 | # Please see Vignette.R and Vignette.pdf 3 | # Model source code in opg.cpp 4 | # -------------------------------------------------------------------------------- /233/Input_real_opg.R: -------------------------------------------------------------------------------- 1 | ##' --- 2 | ##' title: "" 3 | ##' date: "" 4 | ##' author: "" 5 | ##' output: pdf_document 6 | ##' --- 7 | 8 | 9 | #+ message=FALSE 10 | .libPaths("~/Rlibs/lib") 11 | library(mrgsolve) 12 | library(ggplot2) 13 | library(dplyr) 14 | library(metrumrg) 15 | library(parallel) 16 | library(knitr) 17 | opts_chunk$set(comment='.',echo=FALSE, message=FALSE) 18 | 19 | mod <- mread("opg") 20 | om <- omat(mod) 21 | sg <- smat(mod) 22 | 23 | mod %<>% mrgsolve:::collapse_omega() %>% mrgsolve:::collapse_sigma() 24 | 25 | est <- c(TVCL=168,TVVC=2800,TVP1=443,TVP2=269,TVQ1=15.5, 26 | TVQ2=3.02,TVKA=0.0131,TVVMAX=13300,TVKM=6.74,TVFSC=0.0719) 27 | 28 | rse <- c(3,2,16,14,16,13,4,13,11,0) 29 | var <- (rse*est/100)^2 30 | 31 | estt <- c(TVKSYN=0.864,TVKDEG=0.0204,TVIC50=5.38) 32 | rsee <- c(8,6,21) 33 | varr <- (rsee*estt/100)^2 34 | 35 | 36 | theta <- c(est,estt) 37 | Sigma <- diag(c(var,varr)) 38 | omega <- as.matrix(omat(mod)) 39 | sigma <- as.matrix(smat(mod)) 40 | dimnames(omega) <- list(NULL,NULL) 41 | dimnames(sigma) <- list(NULL,NULL) 42 | 43 | #+ echo=TRUE, comment='.' 44 | 45 | ##' # Fixed effect estimates: 46 | #+ echo=FALSE 47 | data_frame(Name = names(theta), 48 | Estimate = theta, 49 | variance = diag(Sigma)) %>% as.data.frame 50 | 51 | ##' # Between subject variability 52 | om 53 | 54 | ##' # Residual error 55 | sg 56 | 57 | ##' # Simulation of posterior 58 | #+ echo=TRUE 59 | simpost <- function(n) { 60 | post <- metrumrg::simpar(n, 61 | theta, 62 | covar=Sigma, 63 | omega=omega, 64 | sigma=sigma, 65 | odf=100,sdf=1000) 66 | post <- post %>% as.data.frame 67 | nam <- names(post) 68 | nam <- sub(".", "", nam, fixed=TRUE) 69 | thetas <- which(grepl("TH",nam)) 70 | nam[thetas] <- names(theta) 71 | nam <- sub("OM", "OMEGA",nam,fixed=TRUE) 72 | nam <- sub("SG", "SIGMA",nam,fixed=TRUE) 73 | names(post) <- nam 74 | post 75 | } 76 | 77 | #+ echo=TRUE 78 | set.seed(111) 79 | simpost(10) 80 | 81 | #+ echo=TRUE 82 | if(FALSE) { 83 | set.seed(22222) 84 | saveRDS(simpost(1000), file="opgpost.RDS") 85 | } 86 | 87 | ##' # Session Info 88 | sessionInfo() 89 | 90 | -------------------------------------------------------------------------------- /233/Input_real_opg.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/233/Input_real_opg.pdf -------------------------------------------------------------------------------- /233/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | ## Full reference to publication 2 | ## Zierhut ML, Gastonguay MR, Martin SW, Vicini P, Bekker PJ, Holloway D, Leese 3 | ## PT, Peterson MC. Population PK-PD model for Fc-osteoprotegerin in healthy 4 | ## postmenopausal women. J Pharmacokinet Pharmacodyn. 2008 Aug;35(4):379-99. 5 | ## doi: 10.1007/s10928-008-9093-5. PubMed PMID: 18633695. 6 | ###### Scenario = 4 7 | ###### Original publication PubMed ID: 18633695 8 | ###### There are no model differences 9 | 10 | -------------------------------------------------------------------------------- /233/Output_simulated_opg.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/233/Output_simulated_opg.pdf -------------------------------------------------------------------------------- /233/Vignette_opg.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/233/Vignette_opg.pdf -------------------------------------------------------------------------------- /233/opgpost.RDS: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/233/opgpost.RDS -------------------------------------------------------------------------------- /237/237.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_APAP.model", 4 | "Command.txt", 5 | "Model_Accommodations.txt", 6 | "FigOutput.png", 7 | "Output_real_APAP.txt", 8 | "Forward_APAP1.in", 9 | "Real_APAP_data.csv", 10 | "DDMODEL00000237.rdf", 11 | "RawData.R" 12 | ], 13 | "version": 46 14 | } -------------------------------------------------------------------------------- /237/Command.txt: -------------------------------------------------------------------------------- 1 | ##### Scenario = 4 2 | ##### MCSim version = 5.4.0 3 | ##### Input data = Forward_APAP1.in 4 | ##### Executable model = APAP.model 5 | ##### Output = Output_real_APAP.txt 6 | ######## end block 7 | 8 | ###### Script used as execution command for MCsim ########## 9 | $ makemcsim APAP.model 10 | $ ./mcsim.APAP Forward_APAP1.in 11 | 12 | 13 | # Command in R 14 | 15 | Time<-c(0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0) 16 | CPL_APAP_mcgL <- c(9.2797, 9.4681, 9.2923, 9.0719, 8.7246, 8.3994, 8.1881, 7.892, 7.3309, 6.9294, 6.5891) 17 | CPL_AG_mcgL <- c(8.3935, 9.3632, 9.8072, 9.982, 10.0493, 9.926, 9.7817, 9.552, 9.1014, 8.6473, 8.2504) 18 | CPL_AS_mcgL <- c(8.317, 8.7968, 8.9727, 8.887, 8.7968, 8.539, 8.4138, 8.1712, 7.6798, 7.2098, 6.8372) 19 | CPL_APAP_mcgL <- exp(CPL_APAP_mcgL); CPL_AG_mcgL <- exp(CPL_AG_mcgL); CPL_AS_mcgL <- exp(CPL_AS_mcgL) 20 | APAP_data <- data.frame(Time, CPL_APAP_mcgL, CPL_AG_mcgL, CPL_AS_mcgL) 21 | 22 | write.csv(APAP_data, file = "Real_APAP_data.csv") 23 | 24 | APAP_forward1 <- read.delim("Output_real_APAP.txt", skip = 2) 25 | 26 | png(file="Out.png",width=2000,height=800,res=200) 27 | par(mfrow=c(1,3)) 28 | for (i in 2:4) { 29 | plot(APAP_forward1$Time, APAP_forward1[,i], xlab = "Time (hr)", ylab = "", 30 | main = names(APAP_forward1)[i], las = 1, col = "red", lwd = 2, 31 | type = "l") 32 | points(APAP_data$Time, APAP_data[,i], pch=3) 33 | } 34 | dev.off() 35 | 36 | 37 | -------------------------------------------------------------------------------- /237/DDMODEL00000237.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Yes 13 | No 14 | Predicting of ADME of APAP and its conjugated metabolites in humans 15 | 16 | 17 | -------------------------------------------------------------------------------- /237/FigOutput.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/237/FigOutput.png -------------------------------------------------------------------------------- /237/Forward_APAP1.in: -------------------------------------------------------------------------------- 1 | OutputFile("./Output_simulated_APAP.out"); 2 | 3 | 4 | # Mean parameters 5 | lnUGT_VmaxC = 8.2277; 6 | lnCYP_Km = 4.8122; 7 | lnkPAPS_syn = 7.9251; 8 | lnCLC_AS = -2.0404; 9 | lnUGT_Km = 8.2749; 10 | lnTp = -2.0559; 11 | lnSULT_VmaxC = 5.9494; 12 | lnSULT_Ki = 5.9984; 13 | lnKm_AS = 9.7220; 14 | lnCYP_VmaxC = 0.0918; 15 | lnSULT_Km_paps = -1.1493; 16 | lnUGT_Km_GA = -1.4898; 17 | lnCLC_APAP = -4.6564; 18 | lnVmax_AG = 10.9996; 19 | lnCLC_AG = -1.9876; 20 | lnTg = -1.1567; 21 | lnVmax_AS = 13.6788; 22 | lnUGT_Ki = 10.7505; 23 | lnSULT_Km_apap = 6.7507; 24 | lnKm_AG = 9.6067; 25 | lnkGA_syn = 9.0430; 26 | 27 | Simulation { # 1: 28 | # Example with mg dose 29 | #===================== 30 | mgkg_flag = 0; 31 | 32 | OralExp_APAP = NDoses(2, 1 0, 0 0.75); 33 | OralDur_APAP = 0.75; 34 | OralDose_APAP_mg = 1000.0; 35 | lnOralDose_APAP_mg = 6.907755; 36 | 37 | IVExp_APAP = 0.; 38 | IVDose_APAP_mg = 0.; 39 | lnIVDose_APAP_mg = 0.; 40 | 41 | PrintStep(CPL_APAP_mcgL, 0, 14., 0.1); 42 | PrintStep(CPL_AG_mcgL, 0, 14., 0.1); 43 | PrintStep(CPL_AS_mcgL, 0, 14., 0.1); 44 | } 45 | -------------------------------------------------------------------------------- /237/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | ## Full reference to publication 2 | ## Zurlinden TJ, Reisfeld B 3 | ## Physiologically based modeling of the pharmacokinetics of acetaminophen and 4 | ## its major metabolites in humans using a Bayesian population approach. 2016 5 | ## Jun;41(3):267-80. 6 | ###### Scenario = 4 7 | ###### Original publication PubMed ID: 25636597 8 | ###### There are no model differences -------------------------------------------------------------------------------- /237/RawData.R: -------------------------------------------------------------------------------- 1 | # Experimental data from Jansen et al.(2004) J Pharm Biomed Anal 34:585-593 2 | 3 | Time<-c(0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0) 4 | CPL_APAP_mcgL <- c(9.2797, 9.4681, 9.2923, 9.0719, 8.7246, 8.3994, 8.1881, 7.892, 7.3309, 6.9294, 6.5891) 5 | CPL_AG_mcgL <- c(8.3935, 9.3632, 9.8072, 9.982, 10.0493, 9.926, 9.7817, 9.552, 9.1014, 8.6473, 8.2504) 6 | CPL_AS_mcgL <- c(8.317, 8.7968, 8.9727, 8.887, 8.7968, 8.539, 8.4138, 8.1712, 7.6798, 7.2098, 6.8372) 7 | CPL_APAP_mcgL <- exp(CPL_APAP_mcgL); CPL_AG_mcgL <- exp(CPL_AG_mcgL); CPL_AS_mcgL <- exp(CPL_AS_mcgL) 8 | APAP_cal1.1 <- data.frame(Time, CPL_APAP_mcgL, CPL_AG_mcgL, CPL_AS_mcgL) 9 | 10 | write.csv(data, file = "Real_APAP_data.csv") -------------------------------------------------------------------------------- /237/Real_APAP_data.csv: -------------------------------------------------------------------------------- 1 | "","Time","CPL_APAP_mcgL","CPL_AG_mcgL","CPL_AS_mcgL" 2 | "1",0.5,10718.2159692886,4418.25455490923,4092.86298301003 3 | "2",1,12940.2773323416,11651.6141145296,6613.04835658425 4 | "3",1.5,10854.1198871802,18164.0564498428,7884.86201846086 5 | "4",2,8707.15168746052,21633.5363842321,7237.27475888943 6 | "5",3,6152.41520519763,23139.583466448,6613.04835658425 7 | "6",4,4444.39930796318,20455.3552990003,5110.23157410655 8 | "7",5,3597.87977659991,17706.7287201038,4508.86167312117 9 | "8",6,2675.79015296837,14072.8121689708,3537.58652140466 10 | "9",8,1526.75523355034,8967.83889355122,2164.18689118262 11 | "10",10,1021.88066724697,5694.75013233772,1352.62171597682 12 | "11",12,727.126161778766,3829.15717801938,931.876224932784 13 | -------------------------------------------------------------------------------- /238/238.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_run35b_ddm2.mod", 4 | "Readme_ddmore.txt", 5 | "Simulated_simdataDDM.csv", 6 | "DDMODEL00000238.rdf", 7 | "Output_simulated_run35b_ddm2.lst", 8 | "Output_real_run35b.lst", 9 | "Command.txt" 10 | ], 11 | "version": 18 12 | } -------------------------------------------------------------------------------- /238/Command.txt: -------------------------------------------------------------------------------- 1 | nmfe73 run35b_ddm2.mod run35b_ddm2.lst -------------------------------------------------------------------------------- /238/DDMODEL00000238.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | No 8 | 9 | A population PK model for gentamicin TDM in neonates and infants 10 | A 3-compartment PK model with coding for time-varying covariates, and additive and proportional residual error model 11 | 12 | 13 | 14 | Yes 15 | 16 | / 17 | 18 | 19 | -------------------------------------------------------------------------------- /238/Readme_ddmore.txt: -------------------------------------------------------------------------------- 1 | Description of files: 2 | - run35b.lst = final model .lst file 3 | - command = command used for running the model 4 | - simdataDDM = simulated data using the median values for covariates 5 | - run35b_ddm2.mod = model file using simdataDDM 6 | - run35b_ddm2.lst = output file using simdataDDM -------------------------------------------------------------------------------- /239/239.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_P241.ctl", 4 | "Simulated_P241.csv", 5 | "Simulate_P241.ctl", 6 | "DDMODEL00000239.rdf", 7 | "Command.txt", 8 | "Output_real_P241.res", 9 | "Output_simulated_P241.res" 10 | ], 11 | "version": 12 12 | } -------------------------------------------------------------------------------- /239/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### Original tool version = NONMEM 7.3.0 3 | ###### Input data = Simulated_P241.csv 4 | ###### Executable model = Executable_P241.ctl 5 | ###### Output = Output_simulated_P241.res 6 | 7 | nmfe73 Executable_P241.ctl Output_simulated_P241.res -------------------------------------------------------------------------------- /240/240.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_MTP.mod", 4 | "Command.txt", 5 | "Output_simulated_MTP.lst", 6 | "Output_real_MTP.lst", 7 | "DDMODEL00000240.rdf", 8 | "Model_Accomodations.txt", 9 | "Simulated_Mtb-H37Rv_In-vitro-NATG.csv" 10 | ], 11 | "version": 12 12 | } -------------------------------------------------------------------------------- /240/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Prepared and run by Oskar Clewe 2 | ##### Scenario= 4 3 | ##### PsN version= 4.5.3 4 | ##### Original tool version=nm_7.3.0_g 5 | ##### Input data= Original data not shared 6 | ##### Executable model=Executable_MTP.mod 7 | ##### Output=Output_real_MTP.lst 8 | ######## end block 9 | 10 | ###### Script used as execution command for NONMEM via PsN: ########## 11 | execute Executable_MTP.mod 12 | -------------------------------------------------------------------------------- /240/DDMODEL00000240.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | Mycobacterium tuberculosis can exist in different states in vitro, which can be denoted as fast multiplying, slow multiplying and non-multiplying. Characterizing the natural growth of M. tuberculosis could provide a framework for accurate characterization of drug effects on the different bacterial states. 9 | No 10 | Yes 11 | 12 | 13 | 14 | 15 | 16 | 17 | -------------------------------------------------------------------------------- /240/Model_Accomodations.txt: -------------------------------------------------------------------------------- 1 | ## Full reference to publication 2 | ## Clewe O, Aulin L, Hu Y, Coates AR, Simonsson US. 3 | ## A multistate tuberculosis pharmacometric model: a framework for studying anti- 4 | ## tubercular drug effects in vitro. 5 | ## J Antimicrob Chemother. 2016 Apr;71(4):964-74. doi: 10.1093/jac/dkv416. Epub 2015 ## Dec 24. 6 | ###### Scenario = 4 7 | ###### Original publication PubMed ID: 26702921 8 | ###### Model appears in publication with exposure response of rifampicin 9 | -------------------------------------------------------------------------------- /240/Simulated_Mtb-H37Rv_In-vitro-NATG.csv: -------------------------------------------------------------------------------- 1 | TIME,ID,NDV,DV,EVID,MDV,AMT 2 | 0,1,9775,9.19,0,0,0 3 | 4,1,153430,11.9,0,0,0 4 | 7,1,4417126,15.3,0,0,0 5 | 14,1,88986924,18.3,0,0,0 6 | 35,1,148337404,18.8,0,0,0 7 | 45,1,95343812,18.4,0,0,0 8 | 60,1,37132382,17.4,0,0,0 9 | 70,1,17348211,16.7,0,0,0 10 | 80,1,7826296,15.9,0,0,0 11 | 120,1,1749778,14.4,0,0,0 12 | 200,1,1646232,14.3,0,0,0 -------------------------------------------------------------------------------- /243/243.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_runCOMPEV1_101.mod", 4 | "Executable_runCOMPEV2_005.mod", 5 | "Executable_runEV2_105.ctl", 6 | "BAST_PTTE_modelling.pdf", 7 | "Model_Accommodations.txt", 8 | "BAST_surv_functions.R", 9 | "Command.txt", 10 | "VPC_EV1_1.png", 11 | "VPC_EV1_1_dis.png", 12 | "DDMODEL00000243.rdf", 13 | "Executable_runEV1_201.mod", 14 | "Output_simulated_runEV1_201.res", 15 | "Output_simulated_runEV2_105.res", 16 | "Output_simulated_runCOMPEV1_101.res", 17 | "Executable_runEV2_105.mod", 18 | "VPCs.R", 19 | "Simulated_event_data.csv", 20 | "Output_simulated_runCOMPEV2_005.res" 21 | ], 22 | "version": 13 23 | } -------------------------------------------------------------------------------- /243/BAST_PTTE_modelling.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/243/BAST_PTTE_modelling.pdf -------------------------------------------------------------------------------- /243/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Prepared and run by Jon Moss 2 | ##### Scenario= 4 3 | ##### NONMEM version=7.3 4 | ##### Input data= Simulated_event_data.csv 5 | ##### Executable models= 6 | ##### Executable_runCOMPEV1_101.mod 7 | ##### Executable_runCOMPEV2_005.mod 8 | ##### Executable_runEV1_201.mod 9 | ##### Executable_runEV2_105.mod 10 | ##### Output= 11 | ##### Output_simulated_runCOMPEV1_101.res 12 | ##### Output_simulated_runCOMPEV2_005.res 13 | ##### Output_simulated_runEV1_201.res 14 | ##### Output_simulated_runEV2_105.res 15 | ######## end block 16 | 17 | ###### Scripts used as execution command for NONMEM ########## 18 | nmfe73.bat Executable_runCOMPEV1_101.mod Output_simulated_runCOMPEV1_101.res 19 | nmfe73.bat Executable_runCOMPEV2_005.mod Output_simulated_runCOMPEV1_005.res 20 | nmfe73.bat Executable_runEV1_201.mod Output_simulated_runEV1_201.res 21 | nmfe73.bat Executable_runEV2_105.mod Output_simulated_runEV2_105.res -------------------------------------------------------------------------------- /243/DDMODEL00000243.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | No 13 | No 14 | The Guiding documents and the submitted library of survival models facilitates the simulation of clinical trial outcomes with competing events 15 | Parametric time-to-event modelling and simulation of competing events 16 | 17 | 18 | -------------------------------------------------------------------------------- /243/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | ###### Model differences = there is not yet a publication to go along with the model 2 | 3 | 4 | ###### Reasons = We are seeking feedback from the DDMoRe community -------------------------------------------------------------------------------- /243/VPC_EV1_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/243/VPC_EV1_1.png -------------------------------------------------------------------------------- /243/VPC_EV1_1_dis.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/243/VPC_EV1_1_dis.png -------------------------------------------------------------------------------- /244/244.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_Rif_PK.mod", 4 | "Output_simulated_Rif_PK.lst", 5 | "Output_real_Rif_PK.lst", 6 | "Simulated_Rif_PK_data.csv", 7 | "DDMODEL00000244.rdf", 8 | "Command.txt" 9 | ], 10 | "version": 3 11 | } -------------------------------------------------------------------------------- /244/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Prepared and run by Robin Svensson 2 | ##### Scenario= 4 3 | ##### PsN version=4.6.8 4 | ##### Original tool version=nm_7.3.0_g 5 | ##### Input data= Original data not shared 6 | ##### Executable model=Executable_Rif_PK.mod 7 | ##### Output=Output_real_Rif_PK.lst 8 | ######## end block 9 | 10 | ###### Script used as execution command for NONMEM via PsN: ########## 11 | execute Executable_Rif_PK.mod -------------------------------------------------------------------------------- /244/DDMODEL00000244.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | The pharmacokinetics of high dose rifampicin was described using one-compartment disposition kinetics. The absorption was described using the transit absorption compartment model. The data included several non-linearities. Firstly, an enzyme tunr-over model was included to take into account the autoinduction for rifampicin. A Michaelis-Menten relationship was included for clearance in addition to a dose-dependency in the bioavailability where the bioavailability increased at higher doses. 8 | 9 | No difference 10 | Mechanistic Understanding, Dose & Schedule Selection and Label Recommendation, Variability Sources in PK and PD (CYP, Renal, Biomarkers) 11 | 12 | No 13 | Yes 14 | 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /245/245.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_run111.mod", 4 | "Output_real_run111.lst", 5 | "DDMODEL00000245.rdf", 6 | "Command.txt", 7 | "Output_simulated_Executable_run111.lst", 8 | "Simulated_comb2.dta" 9 | ], 10 | "version": 6 11 | } -------------------------------------------------------------------------------- /245/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version = 4.7.0 to run the Executable model 3 | ###### Original tool version 1 = NONMEM version 7.3 to run the Executable model 4 | ###### Original tool version 2 = NONMEM version 5.1 to run the original model 5 | ###### Input data = Simulated_comb2.dta 6 | ###### Executable model = Executable_run111.mod 7 | ###### Output1 = Output_simulated_Executable_run111.lst 8 | ###### Output2 = Output_real_run111.lst 9 | ###### Script used as execution command for NONMEM via PsN: 10 | execute Executable_run111.mod 11 | 12 | -------------------------------------------------------------------------------- /245/Executable_run111.mod: -------------------------------------------------------------------------------- 1 | ;Rerun to estimate parameters at CLCR 70 and WT 75 2 | ;;1. Aim with run 3 | ;; Based on run93 4 | ;; Adding BLOCK(2) 5 | ;;2. Based on run31 from NXY-0004 6 | ;;3. Structural model 7 | ;; Two-compartment model 8 | ;;4. Covariate model 9 | ;; CLCR on CL (nonlinear) 10 | ;; WT on V1 11 | ;;5. Interindividual model 12 | ;; Exponential IIV for CL, V1, 13 | ;; and correlation between CL and V1 14 | ;;6. Residual variability 15 | ;; Proportional (but add w logtr data) 16 | ;;7. Estimation 17 | ;; FOCE 18 | ;;8. Other 19 | ;; Simulated_comb2.dta 20 | $PROB NXY-059 Analysis, SA-NXY-0004 and 0003 21 | $INPUT ID OID=DROP CENT=DROP PNO=DROP TARG DAT2=DROP 22 | TIME AMT DUR=DROP RATE ODV DV FU FLAG=DROP 23 | SEX AGE WT HT BMI RACE SCR CLCR FLA2 STUD 24 | $DATA /home/pm_common/Model_database/DDMoRe_Prep/Jonsson-S-2005_NXY059/Data/Simulated_comb2.dta IGNORE=@ 25 | $SUBROUTINES ADVAN3 TRANS4 26 | $PK 27 | CREA = CLCR 28 | IF(CLCR.EQ.-99) CREA = 61.48 29 | IF(CLCR.EQ.-99.AND.ID.EQ.10) CREA = 34.55 30 | IF(CLCR.EQ.-99.AND.ID.EQ.21) CREA = 124.22 31 | IF(CREA.LE.40) CLCLCR = 0 32 | IF(CREA.GT.40) CLCLCR = THETA(6)*(CREA-40) 33 | 34 | IF(WT.EQ.-99) THEN 35 | V1WT = 0 36 | ELSE 37 | V1WT = THETA(7)*(WT-76.00) 38 | ENDIF 39 | 40 | CLCOV=1+CLCLCR 41 | V1COV=1+V1WT 42 | 43 | TVCL = THETA(5)*CLCOV 44 | TVV1 = THETA(2)*V1COV 45 | TVQ = THETA(3) 46 | TVV2 = THETA(4) 47 | 48 | CL = TVCL*EXP(ETA(1)) 49 | V1 = TVV1*EXP(ETA(2)) 50 | Q = TVQ 51 | V2 = TVV2 52 | 53 | S1 = V1 54 | 55 | $ERROR 56 | NPRE = F+0.000001 57 | IPRED = LOG(F+0.000001) 58 | IRES = DV-IPRED 59 | W = THETA(1) 60 | IWRES = IRES/W 61 | Y = IPRED+EPS(1)*W 62 | 63 | 64 | $THETA (0,0.1650) ;1 prop error 65 | $THETA (0,7.8500) ;2 V1 66 | $THETA (0,13.100) ;3 Q 67 | $THETA (0,7.2000) ;4 V2 68 | $THETA (0,2.8800) ;5 TVCL 69 | $THETA (-0.00972,0.0192,0.0505) ;6 CLCR on CLR 70 | $THETA (-0.020,0.0184,0.027) ;7 WT on V1 71 | 72 | $OMEGA BLOCK(2) 0.0536 0.02 0.160 ;IIV CL, V1 73 | 74 | $SIGMA 1 FIX 75 | $EST PRINT=2 METHOD=1 SIG=3 MSFO=msfb111 NOABORT MAX=0 76 | ;;COV 77 | ;;$TAB ID TIME TARG STUD ODV NPRE IPRED IWRES ONEHEADER NOPRINT FILE=sdtab111 78 | ;;$TAB ID V1 ETA(1) CL ETA(2) TVCL TVV1 ONEHEADER NOPRINT FILE=patab111 79 | ;;$TAB ID FU AGE WT HT BMI SCR CLCR ONEHEADER NOPRINT FILE=cotab111 80 | ;;$TAB ID SEX RACE ONEHEADER NOPRINT FILE=catab111 81 | -------------------------------------------------------------------------------- /247/247.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_OriginalModelCode.mod", 4 | "Output_real_OriginalModelCode.lst", 5 | "Output_simulated_OriginalModelCode.lst", 6 | "DDMODEL00000247.rdf", 7 | "Command.txt", 8 | "Simulated_MidaCriticallyIll.csv" 9 | ], 10 | "version": 10 11 | } -------------------------------------------------------------------------------- /247/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version = 4.4.0 3 | ###### Original tool version = NM73 4 | ###### Input data = Simulated_MidaCriticallyIllChildren.csv 5 | ###### Executable model = Executable_OriginalModelCode.mod 6 | ###### Output = Executable_OriginalModelCode.lst 7 | 8 | -------------------------------------------------------------------------------- /247/DDMODEL00000247.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | Item response theory (IRT) was used to quantify the effect of morphine on pain around endotrachial succtioning in newborns that are mechanically ventilated. 10 | 11 | 12 | Yes 13 | 14 | No 15 | 16 | 17 | -------------------------------------------------------------------------------- /247/Executable_OriginalModelCode.mod: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/247/Executable_OriginalModelCode.mod -------------------------------------------------------------------------------- /248/248.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_OriginalModelCode.mod", 4 | "Simulated_PaediatricMorphinePK.csv", 5 | "Output_real_run4.lst", 6 | "DDMODEL00000248.rdf", 7 | "Output_simulated_OriginalModel Code.lst", 8 | "Command.txt" 9 | ], 10 | "version": 10 11 | } -------------------------------------------------------------------------------- /248/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version = 4.4.0 3 | ###### Original tool version = NM73 4 | ###### Input data = Simulated_PaediatricMorphinePK.csv 5 | ###### Executable model = Executable_OriginalModelCode.mod 6 | ###### Output = Output_Simulated_OriginalModelCode.lst 7 | 8 | -------------------------------------------------------------------------------- /248/DDMODEL00000248.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Yes 13 | No 14 | Population PK model for morphine in postoperative newborns (including preterm newborns), infants and toddlers younger than three years of age. 15 | 16 | 17 | -------------------------------------------------------------------------------- /249/249.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_OriginalModelCode.mod", 4 | "Output_simulated_OriginalModelCode.lst", 5 | "Output_real_OriginalModelCode.lst", 6 | "Simulated_MidaCriticallyIll.csv", 7 | "Command.txt", 8 | "DDMODEL00000249.rdf" 9 | ], 10 | "version": 10 11 | } -------------------------------------------------------------------------------- /249/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version = 4.4.0 3 | ###### Original tool version = NM73 4 | ###### Input data = Simulated_MidaCriticallyIllChildren.csv 5 | ###### Executable model = Executable_OriginalModelCode.mod 6 | ###### Output = Executable_OriginalModelCode.lst 7 | 8 | -------------------------------------------------------------------------------- /249/DDMODEL00000249.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Yes 13 | No 14 | Midazolam PK in critically ill pediatric patients, using inflammation (quantified as CRP concentrations) and number of organs failing are most important covariates 15 | 16 | 17 | -------------------------------------------------------------------------------- /250/250.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_FinalModelCode.mod", 4 | "Command.txt", 5 | "Simulated_DatafileMidaObesity.csv", 6 | "DDMODEL00000250.rdf", 7 | "Executable_AccessWeightModelCode.mod", 8 | "Output_real_AccessWeightModelCode.lst", 9 | "Output_simulated_FinalModelCode.lst", 10 | "Output_real_FinalModelCode.lst", 11 | "Output_simulated_AccessWeightModelCode.lst" 12 | ], 13 | "version": 10 14 | } -------------------------------------------------------------------------------- /250/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version = 4.4.0 3 | ###### Original tool version = NM73 4 | ###### Input data = SimulatedDatafileMidaObesity.csv 5 | ###### Executable model = Executable_FinalModelCode.mod 6 | ###### Output = Output_Real_FinalModelCode.lst 7 | 8 | -------------------------------------------------------------------------------- /250/DDMODEL00000250.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Yes 13 | No 14 | The clearance of cytochrome P450 (CYP) 3A substrates is believed to be reduced in children, in patients with inflammation and obese patients. This study investigates the influence of combinations of these variables by quantifying the clearance of midazolam in obese adolescents and morbidly obese adults. 15 | 16 | 17 | -------------------------------------------------------------------------------- /250/Executable_AccessWeightModelCode.mod: -------------------------------------------------------------------------------- 1 | $PROBLEM Mida PK in obese adolescents and adults with access weight quantification 2 | $INPUT ID ;<=30 = adults, >30 = adolescent 3 | TIME ;in min 4 | AMT ;in microgram 5 | RATE ;in microgram / min 6 | DV ;in microgram / L 7 | MDV 8 | CMT 9 | TBW ;total bodyweight in kg 10 | WTAL ;weight for age and length in kg 11 | WTAC ;access weight 12 | 13 | $DATA FinalDatasetMidaObeseAccessWeight.csv IGNORE=@ 14 | $SUBROUTINES ADVAN6 TOL=5 15 | 16 | $MODEL 17 | COMP=(PODOSE) 18 | COMP=(CENTRAL) 19 | COMP=(PERIP) 20 | COMP=(TRANSIT1) 21 | COMP=(TRANSIT2) 22 | COMP=(TRANSIT3) 23 | COMP=(TRANSIT4) 24 | COMP=(TRANSIT5) 25 | 26 | $PK 27 | IF(ID.LE.30) TVCL=THETA(1) 28 | IF(ID.GT.30) TVCL=(THETA(1)*(WTAL/70)**THETA(7))+(THETA(8)*WTAC) 29 | CL=TVCL*EXP(ETA(1)) ;clearance L/min 30 | F1=THETA(2)*EXP(ETA(2)) ;bioavailability 31 | V2=THETA(3)*EXP(ETA(5)) ;central volume L 32 | Q=THETA(4)*EXP(ETA(3)) ;intercompartmental CL L/min 33 | IF(ID.LE.30) TVV3=THETA(5)*(TBW/141.8)**THETA(9) 34 | IF(ID.GT.30) TVV3=THETA(5) 35 | V3= TVV3*EXP(ETA(6)) ;peripheral volume L 36 | KA= THETA(6)*EXP(ETA(4)) ;absoprtion rate min-1 37 | KTR= KA ;transit rate min-1 38 | 39 | S2=V2 40 | S3=V3 41 | 42 | K14=KA 43 | K45=KTR 44 | K56=KTR 45 | K67=KTR 46 | K78=KTR 47 | K82=KTR 48 | K20=CL/V2 49 | K23=Q/V2 50 | K32=Q/V3 51 | 52 | $DES 53 | DADT(1)= -K14*A(1) 54 | DADT(2)= KTR*A(8) -K23*A(2) +K32*A(3) -K20*A(2) 55 | DADT(3)= K23*A(2) -K32*A(3) 56 | DADT(4)= K14*A(1) -KTR*A(4) 57 | DADT(5)= KTR*A(4) -KTR*A(5) 58 | DADT(6)= KTR*A(5) -KTR*A(6) 59 | DADT(7)= KTR*A(6) -KTR*A(7) 60 | DADT(8)= KTR*A(7) -KTR*A(8) 61 | 62 | $ERROR 63 | IPRED=F 64 | Y=F*(1+ERR(1)) ;proportional error model 65 | 66 | IRES=DV-IPRED 67 | DEL=0 68 | IF(IPRED.EQ.0)DEL=1 69 | IWRES=(1-DEL)*IRES/(IPRED+DEL) 70 | 71 | $THETA 72 | (0, 0.447) ;CL ADULTS (L/min) 73 | (0, 0.56) ;F1 74 | (0, 53.7) ;V2 (L) 75 | (0, 1.15) ;Q (L/min) 76 | (0, 168) ;V3 (L) 77 | (0, 0.114) ;KA 78 | (0.75) FIX ;POW CL 79 | (0, 0.00698) ;overweight 80 | (0, 3.22) ;TBW POW V3 ADULTS 81 | 82 | $OMEGA 83 | 0.0497 ;CL 84 | 0.143 ;F1 85 | 0.175 ;Q 86 | 0.226 ;KA 87 | 0.259 ;V2 88 | 0.146 ;V3 89 | 90 | $SIGMA 91 | 0.0892 ;porportional 92 | 93 | $EST SIGDIG=3 MAXEVAL=9999 PRINT=5 NOABORT METHOD=1 INTERACTION POSTHOC 94 | $COV 95 | $TABLE ID TIME IPRED IWRES CWRES CMT NOPRINT ONEHEADER NOAPPEND FILE=sdtab143 96 | 97 | -------------------------------------------------------------------------------- /250/Executable_FinalModelCode.mod: -------------------------------------------------------------------------------- 1 | $PROBLEM Mida PK in obese adolescents and adults 2 | $INPUT ID ;<=30 = adults, >30 = adolescent 3 | TIME ;in min 4 | AMT ;in microgram 5 | RATE ;in microgram / min 6 | DV ;in microgram / L 7 | MDV 8 | CMT 9 | TBW ;total bodyweight in kg 10 | WTAL ;weight for age and length in kg 11 | WTAC ;access weight 12 | $DATA SimulatedDatafileMidaObesity.csv IGNORE=@ 13 | $SUBROUTINES ADVAN6 TOL=5 14 | 15 | $MODEL 16 | COMP=(PODOSE) 17 | COMP=(CENTRAL) 18 | COMP=(PERIP) 19 | COMP=(TRANSIT1) 20 | COMP=(TRANSIT2) 21 | COMP=(TRANSIT3) 22 | COMP=(TRANSIT4) 23 | COMP=(TRANSIT5) 24 | 25 | $PK 26 | IF(ID.LE.30) TVCL=THETA(1) 27 | IF(ID.GT.30) TVCL=THETA(7)*(TBW/104.7)**THETA(8) 28 | CL=TVCL*EXP(ETA(1)) ;clearance L/min 29 | F1=THETA(2)*EXP(ETA(2)) ;bioavailability 30 | V2=THETA(3)*EXP(ETA(5)) ;central volume L 31 | Q=THETA(4)*EXP(ETA(3)) ;intercompartmental CL L/min 32 | IF(ID.LE.30) TVV3=THETA(5)*(TBW/141.8)**THETA(9) 33 | IF(ID.GT.30) TVV3=THETA(5) 34 | V3= TVV3*EXP(ETA(6)) ;peripheral volume L 35 | KA= THETA(6)*EXP(ETA(4)) ;absoprtion rate min-1 36 | KTR= KA ;transit rate min-1 37 | 38 | S2=V2 39 | S3=V3 40 | 41 | K14=KA 42 | K45=KTR 43 | K56=KTR 44 | K67=KTR 45 | K78=KTR 46 | K82=KTR 47 | K20=CL/V2 48 | K23=Q/V2 49 | K32=Q/V3 50 | 51 | $DES 52 | DADT(1)= -K14*A(1) 53 | DADT(2)= KTR*A(8) -K23*A(2) +K32*A(3) -K20*A(2) 54 | DADT(3)= K23*A(2) -K32*A(3) 55 | DADT(4)= K14*A(1) -KTR*A(4) 56 | DADT(5)= KTR*A(4) -KTR*A(5) 57 | DADT(6)= KTR*A(5) -KTR*A(6) 58 | DADT(7)= KTR*A(6) -KTR*A(7) 59 | DADT(8)= KTR*A(7) -KTR*A(8) 60 | 61 | $ERROR 62 | IPRED=F 63 | Y=F*(1+ERR(1)) ;proportional error model 64 | 65 | IRES=DV-IPRED 66 | DEL=0 67 | IF(IPRED.EQ.0)DEL=1 68 | IWRES=(1-DEL)*IRES/(IPRED+DEL) 69 | 70 | $THETA 71 | (0, 0.442) ;CL adults 72 | (0, 0.562) ;F1 73 | (0, 55.2) ;V2 (L) 74 | (0, 1.14) ;Q (L/min) 75 | (0, 172) ;V3 (L) 76 | (0, 0.115) ;KA 77 | (0, 0.71) ;CL adolsecents 78 | (0, 1.2) ;tbw pow CL adolescents 79 | (0, 3.28) ;tbw POW V3 adults 80 | 81 | $OMEGA 82 | 0.0433 ;CL 83 | 0.143 ;F1 84 | 0.165 ;Q 85 | 0.219 ;KA 86 | 0.294 ;V2 87 | 0.164 ;V3 88 | 89 | $SIGMA 90 | 0.0888 ;proportional 91 | 92 | $EST SIGDIG=3 MAXEVAL=9999 PRINT=5 NOABORT METHOD=1 INTERACTION POSTHOC 93 | $COV 94 | $TABLE ID TIME IPRED IWRES CWRES MDV CMT NOPRINT ONEHEADER NOAPPEND FILE=sdtab00134 -------------------------------------------------------------------------------- /256/256.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_OriginalModelCode.mod", 4 | "Simulated_PhenobarbitalNewbornsPK.csv", 5 | "Command.txt", 6 | "Output_simulated_OriginalModelCode.lst", 7 | "Output_real_run522.lst", 8 | "DDMODEL00000256.rdf" 9 | ], 10 | "version": 4 11 | } -------------------------------------------------------------------------------- /256/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version = 4.4.0 3 | ###### Original tool version = NM73 4 | ###### Input data = Simulated_PhenobarbitalNewbornsPK.csv 5 | ###### Executable model = Executable_OriginalModelCode.mod 6 | ###### Output = Output_real_run522.lst 7 | 8 | -------------------------------------------------------------------------------- /256/DDMODEL00000256.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Yes 13 | No 14 | The PK of phenobarbital was quantified in preterm and term newborns, to optimize drug dosing 15 | 16 | 17 | -------------------------------------------------------------------------------- /256/Executable_OriginalModelCode.mod: -------------------------------------------------------------------------------- 1 | $PROBLEM Phenobarbital PK in newborns 2 | $INPUT ID 3 | DOSE=AMT ;in mg 4 | CONC=DV ;in mg/L 5 | WEIGHT ;in kg 6 | CMT ;1 oral depot, 2 central 7 | TIME ;in hours 8 | RATE ;in mg/h 9 | BWEIGHT ;birthweight in kg 10 | AGE ;postnatal age in days 11 | MDV 12 | $DATA SimulatedPhenobarbitalNewbornsPK.csv IGNORE=@ 13 | $SUBROUTINE ADVAN2 TRANS2 14 | $PK 15 | 16 | ;;; VWEIGHT-DEFINITION START 17 | VWEIGHT = ( 1 + THETA(6)*(WEIGHT - 2.70)) 18 | ;;; VWEIGHT-DEFINITION END 19 | 20 | ;;; V-RELATION START 21 | VCOV=VWEIGHT 22 | ;;; V-RELATION END 23 | 24 | 25 | ;;; CLBW-DEFINITION START 26 | CLBW = ( 1 + THETA(5)*(BWEIGHT - 2.59)) 27 | ;;; CLBW-DEFINITION END 28 | 29 | ;;; CLAGE-DEFINITION START 30 | CLAGE = ( 1 + THETA(4)*(AGE - 4.50)) 31 | ;;; CLAGE-DEFINITION END 32 | 33 | ;;; CL-RELATION START 34 | CLCOV=CLAGE*CLBW 35 | ;;; CL-RELATION END 36 | 37 | 38 | TVCL = THETA(1) * CLCOV 39 | TVV = THETA(2) * VCOV 40 | CL = TVCL*EXP(ETA(1)) 41 | V = TVV*EXP(ETA(2)) 42 | F1 = THETA(8) 43 | KA = THETA(7) 44 | S2 = V 45 | K = CL/V 46 | 47 | $ERROR 48 | IPRED = F 49 | W = SQRT(THETA(3) * IPRED**2) 50 | IWRES = (DV-IPRED) / W 51 | Y= IPRED + EPS(1) * W 52 | 53 | $THETA 54 | (0, 0.00909) ;CL 55 | (0, 2.38) ;V 56 | (0, 0.0258) ; prop error 57 | (-0.01, 0.0533,0.2) ; CLAGE 58 | (-0.205, 0.369,0.5) ; CLBW 59 | (-0.555, 0.309,0.444) ; VWEIGHT 60 | (50) FIX ; KA 61 | (0, 0.594,1) ; F 62 | 63 | $OMEGA 0.0898 64 | $OMEGA 0.0504 65 | 66 | $SIGMA 1 FIX 67 | 68 | $ESTIMATION METHOD=1 INTERACTION MAXEVALS=9990 POSTHOC 69 | $COVARIANCE 70 | $TABLE ID TIME PRED IPRED RES WRES IWRES CWRES K KA CL V ETA1 ETA2 71 | ONEHEADER NOPRINT NOAPPEND FILE=sdtab524 72 | 73 | -------------------------------------------------------------------------------- /259/259.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_MTP-GPDI.mod", 4 | "DDMODEL00000259.rdf", 5 | "Model_Accomodations.txt", 6 | "Output_real_MTP-GPDI.lst", 7 | "Output_simulated_MTP-GPDI.lst", 8 | "Command.txt", 9 | "Simulated_Mtb-B1585_In-vitro-NATG-RIF-INH-EMB.csv" 10 | ], 11 | "version": 11 12 | } -------------------------------------------------------------------------------- /259/Command.txt: -------------------------------------------------------------------------------- 1 | /opt/local64/PsN/bin/execute Executable_MTP-GPDI.mod -dir=Executable_MTP-GPDI 2 | -------------------------------------------------------------------------------- /259/DDMODEL00000259.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Yes 13 | No 14 | In vitro exposure response and PD interaction identification 15 | 16 | 17 | -------------------------------------------------------------------------------- /259/Model_Accomodations.txt: -------------------------------------------------------------------------------- 1 | ## Full reference to publication 2 | ## Clewe O, Wicha SG, de Vogel CP, de Steenwinkel JEM, Simonsson US. 3 | ## A model-informed pre-clinical approach for prediction of clinical pharmacodynamic 4 | ## interactions of anti-tuberculosis drug combinations 5 | ## J Antimicrob Chemother. 2017 Accepted 6 | ###### Scenario = 4 7 | ###### Original publication PubMed ID: 8 | -------------------------------------------------------------------------------- /261/261.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_simulated_KPD_CTC.count_PSA.mod", 4 | "Output_real_SAEM_KPD_CTC.count_PSA.lst", 5 | "Command.txt", 6 | "Simulated_KPD_CTC.count_PSA.csv", 7 | "DDMODEL00000261.rdf", 8 | "Output_real_COV_KPD_CTC.count_PSA.lst", 9 | "Output_simulated_KPD_CTC.count_PSA.lst" 10 | ], 11 | "version": 3 12 | } -------------------------------------------------------------------------------- /261/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Prepared and run by Melanie Wilbaux 2 | ##### PsN version = 4.7.0 3 | ##### Original tool version = nm_7.3.0 4 | ##### Input data = Original data not shared 5 | ##### Simulated data: Simulated_KPD_CTC.count_PSA.csv 6 | ##### Executable model = Executable_simulated_KPD_CTC.count_PSA.mod 7 | ##### Output = Output_simulated_KPD_CTC.count_PSA.lst 8 | ######## end block 9 | 10 | ###### Script used as execution command for NONMEM via PsN: ########## 11 | execute Executable_simulated_KPD_CTC.count_PSA.mod 12 | 13 | 14 | ###### Nonmem outputs from the original model: ########## 15 | # 2 Nonmem outputs: (i) Parameter estimations using SAEM: Output_real_SAEM_KPD_CTC.count_PSA.lst 16 | # (ii) Covariance and uncertainty estimation ($COV): Output_real_COV_KPD_CTC.count_PSA.lst -------------------------------------------------------------------------------- /261/DDMODEL00000261.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Yes 13 | No 14 | Assessment of treatment efficacy in metastatic castrationresistant prostate cancer (mCRPC) is limited by the frequent development of nonmeasurable bone metastases. The count of circulating tumor cells (CTCs) is emerging as a promising surrogate marker, which could replace the widely used prostate-specific antigen (PSA). CTC kinetic monitoring during treatment could be used to predict treatment efficacy in patients with mCRPC. However, relationships between the kinetics of CTCs and PSA have never been assessed. We built a semimechanistic population model of CTC and PSA kinetics during treatment. 15 | 16 | 17 | -------------------------------------------------------------------------------- /261/Output_real_COV_KPD_CTC.count_PSA.lst: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/261/Output_real_COV_KPD_CTC.count_PSA.lst -------------------------------------------------------------------------------- /261/Output_real_SAEM_KPD_CTC.count_PSA.lst: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/261/Output_real_SAEM_KPD_CTC.count_PSA.lst -------------------------------------------------------------------------------- /262/262.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_simulated_CPHPC_dataset.ctl", 4 | "Output_real_CPHPC.lst", 5 | "Output_simulated_CPHPC_dataset.lst", 6 | "Simulated_CPHPC_dataset.csv", 7 | "Command.txt", 8 | "DDMODEL00000262.rdf" 9 | ], 10 | "version": 3 11 | } -------------------------------------------------------------------------------- /262/Command.txt: -------------------------------------------------------------------------------- 1 | 2 | #example of text file for the CPHPC TMDD model published by T Sahota, CPT Pharmacometrics Syst Pharmacol. 2015 Feb; 4(2): e15. 3 | ######################################################################## 4 | ######################################################################## 5 | #### Original tool version NM 7.3 6 | #### Input data: Simulated_CPHPC_dataset.csv 7 | #### Executable model: Executable_simulated_CPHPC_dataset.ctl 8 | #### Output: Output_simulated_CPHPC_dataset.lst 9 | #### Script used as execution command for NONMEM via PsN 10 | execute 5_run1.mod 11 | -------------------------------------------------------------------------------- /262/DDMODEL00000262.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | The drug (R)-1-[6-[(R)-2-carboxy-pyrrolidin-1-yl]-6-oxo-hexanoyl]pyrrolidine-2-carboxylic acid (CPHPC, GSK2315698, Ro 63-8695) has been shown to deplete serum amyloid P (SAP) in plasma in studies in healthy volunteers (CPH113776) and patients with amyloidosis (CPH114527). In this modelling work, we have characterized the exposure-response relationship between CPHPC and plasma SAP concentration, both in healthy volunteers and in patients with systemic amyloidosis, and used the results to develop a robust pharmacokinetic-pharmacodynamic (PK-PD) model that predicts suitable dosing regimens for CPHPC in individual subjects. 9 | 10 | 11 | 12 | 13 | None 14 | No 15 | 16 | Yes 17 | 18 | 19 | -------------------------------------------------------------------------------- /262/Output_simulated_CPHPC_dataset.lst: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/262/Output_simulated_CPHPC_dataset.lst -------------------------------------------------------------------------------- /267/267.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_OriginalModelCode.mod", 4 | "Command.txt", 5 | "Output_real_OriginalModelCode.lst", 6 | "DDMODEL00000267.rdf", 7 | "Simulated_APAP_YoungWomen.csv", 8 | "Output_simulated_OriginalModelCode.lst" 9 | ], 10 | "version": 10 11 | } -------------------------------------------------------------------------------- /267/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version = 4.4.0 3 | ###### Original tool version = NM73 4 | ###### Input data = Simulated_APAP_YoungWomen.csv 5 | ###### Executable model = Executable_OriginalModelCode.mod 6 | ###### Output = Output_Simulated_OriginalModelCode.lst 7 | 8 | -------------------------------------------------------------------------------- /267/DDMODEL00000267.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Yes 13 | No 14 | This model quantifies the pharmacokinetics of paracetamol and its glucuronide and sulphate metabolites in young women. The impact of pregnancy, time post partum and use of oral contraceptives was investigated. 15 | 16 | 17 | -------------------------------------------------------------------------------- /268/20120910- DDMORE-WP1.3. Specificationdocument_remoxipride ECMdL.doc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/268/20120910- DDMORE-WP1.3. Specificationdocument_remoxipride ECMdL.doc -------------------------------------------------------------------------------- /268/268.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_PK_rats.txt", 4 | "Output_simulated_PK_rats.lst", 5 | "Output_real_PK_rats.lst", 6 | "Command.txt", 7 | "DDMODEL00000268.rdf", 8 | "Simulated_PK_rats.csv", 9 | "20120910- DDMORE-WP1.3. Specificationdocument_remoxipride ECMdL.doc" 10 | ], 11 | "version": 7 12 | } -------------------------------------------------------------------------------- /268/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsNversion (if any) = 4.6 3 | ###### Original tool version= NONMEM version 7.3 4 | ###### Input data= Simulated_PK_rats 5 | ###### Executable model= Executable_PK_rats.txt 6 | ###### Output= Output_simulated_PK_rats.lst 7 | 8 | 9 | ###### Script used as execution command for NONMEM via PsN: ############# 10 | execute Executable_PK_rats.txt 11 | -------------------------------------------------------------------------------- /269/269.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_ModelI1_Morphine.mod", 4 | "Output_real_ModelII_MM3G.lst", 5 | "Output_simulated_ModelI_Morphine.lst", 6 | "Executable_ModelII_MM3G.mod", 7 | "Simulated_DataModel1_Morphine.csv", 8 | "Output_simulated_ModelII_MM3G.lst", 9 | "Command.txt", 10 | "DDMODEL00000269.rdf", 11 | "Output_real_ModelI_Morphine.lst", 12 | "Simulated_DataModel2_MM3G.csv" 13 | ], 14 | "version": 10 15 | } -------------------------------------------------------------------------------- /269/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version = 4.4.0 3 | ###### Original tool version = NM73 4 | ###### Input data = Simulated_DataModel1_Morphine.csv 5 | ###### Executable model = Executable_ModelI_Morphine.mod 6 | ###### Output = Output_simulated_ModelI_Morphine.lst 7 | 8 | -------------------------------------------------------------------------------- /269/DDMODEL00000269.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Yes 13 | No 14 | Morphine PK in paediatric population using bodyweight-dependent exponent (BDE) model 15 | 16 | 17 | -------------------------------------------------------------------------------- /271/271.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_ParacetamolInNewborns.mod", 4 | "Simulated_ParacetamolPKnewborns.csv", 5 | "Output_simulated_ParacetamolInNewborns.lst", 6 | "DDMODEL00000271.rdf", 7 | "Model_Accommodations.txt", 8 | "Command.txt", 9 | "Output_real_ParacetamolInNewborns.lst" 10 | ], 11 | "version": 9 12 | } -------------------------------------------------------------------------------- /271/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version = 4.4.0 3 | ###### Original tool version = NM73 4 | ###### Input data = Simulated_ParacetamolPKnewborns.csv 5 | ###### Executable model = Executable_ParacetamolInNewborns.mod 6 | ###### Output = Output_simulated_ParacetamolInNewborns.lst 7 | 8 | -------------------------------------------------------------------------------- /271/DDMODEL00000271.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | The publication mentiones additive errors for the recovered amounts in urine, while these were in fact proportional. The uploaded code has the correct error model sturcture 13 | No 14 | No 15 | The model describes the PK of paracetamol and its sulphate and glucuronide metabolite in plasma and urine for term and preterm newborns. 16 | 17 | 18 | -------------------------------------------------------------------------------- /271/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | The publication mentiones additive errors for the recovered amounts in urine, while these were in fact proportional. 2 | The uploaded code has the correct error model sturcture -------------------------------------------------------------------------------- /273/273.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_Simulated_Dupilumab.ctl", 4 | "Command.txt", 5 | "DDMODEL00000273.rdf", 6 | "Simulated_Dupilumab.CSV", 7 | "Output_simulated_Dupilumab.lst" 8 | ], 9 | "version": 8 10 | } -------------------------------------------------------------------------------- /273/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### Wings for Nonmem = 4.4.0 3 | ###### Original tool version = NM73 4 | ###### Input data = Simulated_Duplimab.csv 5 | ###### Executable model = nmgo/Executable_Simulated_Dupulimab 6 | ###### Output = Output_simulated_Dupulimab.lst 7 | 8 | 9 | 10 | -------------------------------------------------------------------------------- /273/DDMODEL00000273.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | No 10 | 11 | The PK models was based on human data can be used to predict concentrations of functional dupilumab in future human studies, support regulatory responses, compare PK parameters of dupilumab to PK parameters of other monoclonal antibodies, support dose selection, test for weight, gender and disease as covariates, conduct allometric scaling, and develop a population PD model. 12 | 13 | 14 | Yes 15 | 16 | 17 | -------------------------------------------------------------------------------- /274/274.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Terranova_2017_oncology_TGI.xml", 4 | "Terranova_2017_oncology_TGI.ctl", 5 | "Model_Accomodations.txt", 6 | "Executable_Terranova_2017_oncology_TGI_HM.ctl", 7 | "DDMODEL00000274.rdf", 8 | "Command.txt", 9 | "Terranova_2017_oncology_TGI.mdl", 10 | "Simulated_DEB_TGI_data.csv", 11 | "Output_simulated_Terranova_2017.pdf" 12 | ], 13 | "version": 5 14 | } -------------------------------------------------------------------------------- /274/Model_Accomodations.txt: -------------------------------------------------------------------------------- 1 | Is the model implemented as in the original publication? 2 | Yes, even if among the drugs considered in the paper, only PACLITAXEL has been used -------------------------------------------------------------------------------- /274/Output_simulated_Terranova_2017.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/274/Output_simulated_Terranova_2017.pdf -------------------------------------------------------------------------------- /274/Simulated_DEB_TGI_data.csv: -------------------------------------------------------------------------------- 1 | ID,TIME,DV,DVID,AMT,EVID,CMT 2 | 1,0,.,1,.,0,. 3 | 1,0,.,2,.,0,. 4 | 1,8,.,1,3e+07,1,1 5 | 1,8,0.2041,2,.,0,. 6 | 1,8,22.4209,1,.,0,. 7 | 1,9,0.4059,2,.,0,. 8 | 1,9,21.3441,1,.,0,. 9 | 1,10,0.4499,2,.,0,. 10 | 1,10,20.5501,1,.,0,. 11 | 1,11,0.4097,2,.,0,. 12 | 1,11,20.7153,1,.,0,. 13 | 1,12,.,1,3e+07,1,1 14 | 1,12,0.4707,2,.,0,. 15 | 1,12,20.1543,1,.,0,. 16 | 1,13,0.4247,2,.,0,. 17 | 1,13,19.3253,1,.,0,. 18 | 1,14,0.4621,2,.,0,. 19 | 1,14,18.9129,1,.,0,. 20 | 1,15,0.5139,2,.,0,. 21 | 1,15,19.1111,1,.,0,. 22 | 1,16,.,1,3e+07,1,1 23 | 1,16,0.5928,2,.,0,. 24 | 1,16,19.5322,1,.,0,. 25 | 1,17,0.7459,2,.,0,. 26 | 1,17,18.6291,1,.,0,. 27 | 1,18,0.8213,2,.,0,. 28 | 1,18,18.6787,1,.,0,. 29 | 1,19,0.9453,2,.,0,. 30 | 1,19,17.6797,1,.,0,. 31 | 1,20,1.0855,2,.,0,. 32 | 1,20,18.0395,1,.,0,. 33 | 1,22,1.4833,2,.,0,. 34 | 1,22,17.7667,1,.,0,. 35 | 1,24,18.2298,1,.,0,. 36 | 1,24,2.2703,2,.,0,. 37 | 1,27,18.3924,1,.,0,. 38 | 1,27,3.2326,2,.,0,. 39 | 2,0,.,1,.,0,. 40 | 2,0,.,2,.,0,. 41 | 2,8,0.2031,2,.,0,. 42 | 2,8,22.0938,1,.,0,. 43 | 2,9,0.5086,2,.,0,. 44 | 2,9,22.1164,1,.,0,. 45 | 2,10,0.7596,2,.,0,. 46 | 2,10,21.8654,1,.,0,. 47 | 2,11,1.0716,2,.,0,. 48 | 2,11,21.6784,1,.,0,. 49 | 2,12,1.5361,2,.,0,. 50 | 2,12,20.9639,1,.,0,. 51 | 2,13,1.9591,2,.,0,. 52 | 2,13,21.0409,1,.,0,. 53 | 2,14,2.4343,2,.,0,. 54 | 2,14,20.8158,1,.,0,. 55 | 2,15,20.4351,1,.,0,. 56 | 2,15,3.1899,2,.,0,. 57 | 2,16,20.1315,1,.,0,. 58 | 2,16,3.7435,2,.,0,. 59 | 2,17,19.7076,1,.,0,. 60 | 2,17,4.4174,2,.,0,. 61 | -------------------------------------------------------------------------------- /280/280.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_real_TB_Rifampicin_PK_Wilkins_2008.mod", 4 | "Simulated_TB_Rifampicin_PK_Wilkins_2008.csv", 5 | "catab.simulated_TB_Rifampicin_PK_Wilkins_2008", 6 | "TB_Rifampicin_PK_Wilkins_2008_simulated.phi", 7 | "Output_real_TB_Rifampicin_PK_Wilkins_2008", 8 | "cotab.simulated_TB_Rifampicin_PK_Wilkins_2008", 9 | "Command.txt", 10 | "TB_Rifampicin_PK_Wilkins_2008_simulated.lst", 11 | "TB_Rifampicin_PK_Wilkins_2008_simulated.shm", 12 | "TB_Rifampicin_PK_Wilkins_2008_simulated.xml", 13 | "TB_Rifampicin_PK_Wilkins_2008_real.lst", 14 | "TB_Rifampicin_PK_Wilkins_2008_simulated.shk", 15 | "TB_Rifampicin_PK_Wilkins_2008_simulated.cor", 16 | "Output_simulated_TB_Rifampicin_PK_Wilkins_2008", 17 | "Executable_simulated_TB_Rifampicin_PK_Wilkins_2008.mod", 18 | "sdtab.simulated_TB_Rifampicin_PK_Wilkins_2008", 19 | "TB_Rifampicin_PK_Wilkins_2008_simulated.cov", 20 | "TB_Rifampicin_PK_Wilkins_2008_simulated.ext", 21 | "TB_Rifampicin_PK_Wilkins_2008_simulated.coi", 22 | "DDMODEL00000280.rdf", 23 | "patab.simulated_TB_Rifampicin_PK_Wilkins_2008" 24 | ], 25 | "version": 3 26 | } -------------------------------------------------------------------------------- /280/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version = 4.6.0 3 | ###### Original tool version = 7.3.0 4 | ###### Input data = Simulated_TB_Rifampicin_PK_Wilkins_2008.csv 5 | ###### Executable model = Executable_simulated_TB_Rifampicin_PK_Wilkins_2008.mod 6 | ###### Output = Output_simulated_TB_Rifampicin_PK_Wilkins_2008 7 | 8 | execute Executable_simulated_TB_Rifampicin_PK_Wilkins_2008.mod -min_retries=5 9 | -------------------------------------------------------------------------------- /280/DDMODEL00000280.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | Population pharmacokinetics of rifampin in tuberculosis patients 8 | Yes 9 | 10 | No 11 | 12 | None 13 | One-compartment PK model with first-order elimination and complex absorption, implemented using a multiple-dose transit model. IIV on CL, V, mean transit time and number of transit compartments. IOV on CL and mean transit time. Combined additive and proportional residual error. 14 | 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /280/Output_real_TB_Rifampicin_PK_Wilkins_2008: -------------------------------------------------------------------------------- 1 | Parameter name Parameter estimate (unit) Relative standard error (%) 2 | [as in the model] [or Standard error; as given in publication] 3 | 4 | CL/F 19.2 (L/h) 1.29% 5 | V/F 53.2 (L) 1.16% 6 | ka 1.15 (/h) 3.91% 7 | MTT 0.424 (h) 3.82% 8 | n 7.13 8.42% 9 | SDC on MTT 1.04 8.78% 10 | SDC on CL 0.236 9.63% 11 | 12 | IIV on CL/F 0.279 (variance) 5.97% 13 | covariance CL-V 0.217 (covariance) 7.45% 14 | IIV on V/F 0.188 (variance) 10.9% 15 | IIV on ka 0.439 (variance) 11.0% 16 | IIV on MTT 0.361 (variance) 7.87% 17 | IIV on n 2.44 (variance) 13.7% 18 | IOV on CL 0.0508 (variance) 10.9% 19 | IOV on MTT 0.461 (variance) 11.3% 20 | 21 | Additive residual error 0.0508 (mg/L) 5.41% 22 | Proportional residual error 0.222 (proportion) 2.89% 23 | -------------------------------------------------------------------------------- /280/Output_simulated_TB_Rifampicin_PK_Wilkins_2008: -------------------------------------------------------------------------------- 1 | Parameter name Parameter estimate (unit) Relative standard error (%) 2 | [as in the model] [or Standard error; as given in publication] 3 | 4 | CL/F 21.9 (L/h) 3.44% 5 | V/F 59.2 (L) 3.22% 6 | ka 1.00 (/h) 6.82% 7 | MTT 0.455 (h) 5.31% 8 | n 1.98 13.4% 9 | SDC on MTT 0.701 15.0% 10 | SDC on CL 0.174 20.8% 11 | 12 | IIV on CL/F 0.233 (variance) 9.27% 13 | covariance CL-V 0.203 (covariance) 9.73% 14 | IIV on V/F 0.182 (variance) 11.1% 15 | IIV on ka 0.412 (variance) 15.2% 16 | IIV on MTT 0.215 (variance) 21.2% 17 | IIV on n 0.967 (variance) 20.4% 18 | IOV on CL 0.0490 (variance) 8.15% 19 | IOV on MTT 0.422 (variance) 7.41% 20 | 21 | Additive residual error 0.0792 (mg/L) 7.71% 22 | Proportional residual error 0.237 (proportion) 2.61% 23 | -------------------------------------------------------------------------------- /280/TB_Rifampicin_PK_Wilkins_2008_simulated.shk: -------------------------------------------------------------------------------- 1 | TABLE NO. 1: First Order Conditional Estimation with Interaction: Problem=1 Subproblem=0 Superproblem1=0 Iteration1=0 Superproblem2=0 Iteration2=0 2 | TYPE SUBPOP ETA(1) ETA(2) ETA(3) ETA(4) ETA(5) ETA(6) ETA(7) ETA(8) ETA(9) ETA(10) ETA(11) ETA(12) ETA(13) ETA(14) ETA(15) ETA(16) ETA(17) 3 | 1 1 -3.98846E-02 -4.10351E-02 1.71610E-01 1.35214E-01 5.26918E-03 1.42824E-02 1.25317E-02 5.42385E-03 4.00342E-03 7.39156E-04 5.12870E-02 2.64835E-02 3.68036E-02 6.54723E-02 3.67596E-02 4.82770E-02 3.04045E-01 4 | 2 1 2.92451E-02 2.56770E-02 3.23682E-02 1.94480E-02 8.61347E-03 9.49508E-03 9.32174E-03 8.41200E-03 7.99103E-03 8.35570E-03 2.28809E-02 2.38664E-02 2.56204E-02 2.41248E-02 2.13255E-02 2.18257E-02 3.50939E-02 5 | 3 1 1.72628E-01 1.10015E-01 1.14887E-07 3.60908E-12 5.40712E-01 1.32534E-01 1.78835E-01 5.19072E-01 6.16379E-01 9.29510E-01 2.49950E-02 2.67146E-01 1.50862E-01 6.64970E-03 8.47548E-02 2.69714E-02 4.62447E-18 6 | 4 1 4.01424E+00 4.57081E+00 2.01005E+01 3.36056E+01 3.83451E+01 3.20346E+01 3.32753E+01 3.97873E+01 4.28005E+01 4.01902E+01 4.42110E+01 4.18081E+01 3.75314E+01 4.11780E+01 4.80033E+01 4.67837E+01 4.34640E+01 7 | 5 1 3.44190E+01 0.00000E+00 0.00000E+00 0.00000E+00 0.00000E+00 0.00000E+00 0.00000E+00 0.00000E+00 0.00000E+00 0.00000E+00 0.00000E+00 0.00000E+00 0.00000E+00 0.00000E+00 0.00000E+00 0.00000E+00 0.00000E+00 8 | 6 1 3.89127E+00 4.70260E+00 2.25468E+01 3.52403E+01 3.74758E+01 3.43688E+01 3.56786E+01 3.83925E+01 3.51787E+01 3.77336E+01 4.18077E+01 4.01318E+01 3.99214E+01 4.27271E+01 4.08871E+01 4.25750E+01 4.91960E+01 9 | 7 1 2.50000E+02 2.50000E+02 2.50000E+02 2.50000E+02 2.50000E+02 2.50000E+02 2.50000E+02 2.50000E+02 2.50000E+02 2.50000E+02 2.50000E+02 2.50000E+02 2.50000E+02 2.50000E+02 2.50000E+02 2.50000E+02 2.50000E+02 10 | -------------------------------------------------------------------------------- /281/281.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_ddmore_final_run249.ctl", 4 | "Model_Accommodations.txt", 5 | "DDMODEL00000281.rdf", 6 | "Command.txt", 7 | "Output_simulated_ddmore_final_run249.res", 8 | "Output_real_data_original_final_run249.res", 9 | "Simulated_Lid_B04_ddmore.csv" 10 | ], 11 | "version": 3 12 | } -------------------------------------------------------------------------------- /281/Command.txt: -------------------------------------------------------------------------------- 1 | nmfe73clear.bat Executable_ddmore_final_run249.ctl Output_simulated_ddmore_final_run249.res -------------------------------------------------------------------------------- /281/DDMODEL00000281.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Yes 13 | No 14 | Model for parent drug and three metabolites 15 | Population PK model 16 | 17 | 18 | -------------------------------------------------------------------------------- /281/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | The uploaded model and the model in the publication used as reference do not differ. -------------------------------------------------------------------------------- /284/284.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_Simulated_IMNIVO_PPK.CTL", 4 | "Output_simulated_SIMNIVO_PPK.lst", 5 | "Command.txt", 6 | "NIVO-PPKFinalModel-CPT.CTL", 7 | "Simulated_pkdata1_dataset.csv", 8 | "Output_real_Nivo-PPK.lst", 9 | "DDMODEL00000284.rdf" 10 | ], 11 | "version": 11 12 | } -------------------------------------------------------------------------------- /284/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### Wings for Nonmem = 4.4.0 3 | ###### Original tool version = NM73 4 | ###### Input data = Simulated_pkdata1.csv 5 | ###### Executable model = nmgo/Executable_Simulated_Imnivo_ppk.ctl 6 | ###### Output = Output_simulated_Simnivo_ppk.lst 7 | 8 | 9 | NMGO Executable_Simulated_IMNIVO_PPK 10 | -------------------------------------------------------------------------------- /284/DDMODEL00000284.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | No 8 | 9 | This analysis assessed the clinical relevance of demographic and pathophysiological covariates affecting PK of nivolumab. The model also explored the PK of nivolumab across tumor types and was used to determine individual exposures in patients to support exposureâ?? response analyses for target populations. This analysis serves as an example for characterizing time-varying clearance for monoclonal antibodies. 10 | None 11 | Yes 12 | Nivolumab pharmacokinetics is linear with a time-varying clearance. A full covariate model was developed to assess covariate effects on pharmacokinetic parameters. Nivolumab clearance and volume of distribution increase with body weight. The final model included the effects of baseline performance status (PS), baseline body weight, and baseline estimated glomerular filtration rate (eGFR), sex, and race on clearance, and effects of baseline body weight and sex on volume of distribution in the central compartment. Sex, PS, baseline eGFR, age, race, baseline lactate dehydrogenase, mild hepatic impairment, tumor type, tumor burden, and programmed death ligand-1 expression had a significant but not clinically relevant (<20%) effect on nivolumab clearance. 13 | 14 | 15 | 16 | 17 | 18 | 19 | -------------------------------------------------------------------------------- /285/285.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_Laouenant_2015_CPTPSP_hb_RBV", 4 | "Simulated_Laouenant_2015_CPTPSP_hb_RBV.txt", 5 | "DDMODEL00000285.rdf", 6 | "Output_real_Laouenant_2015_CPTPSP_hb_RBV", 7 | "Command.txt", 8 | "Output_simulated_Laouenant_2015_CPTPSP_hb_RBV" 9 | ], 10 | "version": 6 11 | } -------------------------------------------------------------------------------- /285/Command.txt: -------------------------------------------------------------------------------- 1 | use the monolix 2016R1 interface -------------------------------------------------------------------------------- /285/DDMODEL00000285.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | Empirical Bayes estimates of individual values of ribavirin PK parameters (exponential model of trough concentrations at steady state) are used as regressors to link the concentration of ribavirin with the inhibition of hemoglobin synthesis (turnover model) 13 | Yes 14 | No 15 | 16 | 17 | -------------------------------------------------------------------------------- /285/Executable_Laouenant_2015_CPTPSP_hb_RBV: -------------------------------------------------------------------------------- 1 | DESCRIPTION: Model for hb-RBV Modcupic 2 | 3 | INPUT: 4 | 5 | parameter = {hb0,Kout,EC50} 6 | regressor = {css_mode,k_mode} 7 | 8 | EQUATION: 9 | 10 | if t <= 0 11 | riba = 0 12 | else 13 | riba = css_mode*(1-exp(-k_mode*t)) 14 | end 15 | 16 | hb_0 = hb0 17 | 18 | ddt_hb = hb0*Kout*(1-(riba/(riba+EC50)))-Kout*hb 19 | 20 | OUTPUT: 21 | output = hb 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | -------------------------------------------------------------------------------- /285/Output_real_Laouenant_2015_CPTPSP_hb_RBV: -------------------------------------------------------------------------------- 1 | ****************************************************************** 2 | * hb.mlxtran 3 | * November 06, 2013 at 16:52:44 4 | ****************************************************************** 5 | 6 | Estimation of the population parameters 7 | 8 | parameter s.e. (lin) r.s.e.(%) 9 | hb0 : 14.3 0.36 3 10 | Kout : 0.124 0.033 27 11 | EC50 : 8.28e+003 8.4e+002 10 12 | 13 | omega_hb0 : 0.0853 0.019 22 14 | omega_Kout : 0.383 0.45 118 15 | omega_EC50 : 0.301 0.087 29 16 | 17 | a : 0.737 0.073 10 18 | 19 | ______________________________________________ 20 | correlation matrix of the estimates(linearization) 21 | 22 | hb0 1 23 | Kout 0.16 1 24 | EC50 -0.21 0.11 1 25 | 26 | Eigenvalues (min, max, max/min): 0.67 1.2 1.8 27 | 28 | omega_hb0 1 29 | omega_Kout -0.02 1 30 | omega_EC50 -0.05 0.03 1 31 | a -0.05 -0.33 -0.15 1 32 | 33 | Eigenvalues (min, max, max/min): 0.64 1.4 2.2 34 | 35 | 36 | Population parameters and Fisher Information Matrix estimation... 37 | 38 | Elapsed time is 22 seconds. 39 | CPU time is 28.2 seconds. 40 | ______________________________________________________________ 41 | 42 | Log-likelihood Estimation by linearization 43 | 44 | -2 x log-likelihood: 252.93 45 | Akaike Information Criteria (AIC): 266.93 46 | Bayesian Information Criteria (BIC): 271.88 47 | ______________________________________________________________ 48 | 49 | -------------------------------------------------------------------------------- /285/Output_simulated_Laouenant_2015_CPTPSP_hb_RBV: -------------------------------------------------------------------------------- 1 | ****************************************************************** 2 | * project.mat 3 | * April 24, 2018 at 14:59:20 4 | * Monolix version: 4.4.0 5 | ****************************************************************** 6 | 7 | Estimation of the population parameters 8 | 9 | parameter s.e. (lin) r.s.e.(%) 10 | hb0_pop : 14.2 0.31 2 11 | Kout_pop : 0.258 0.11 43 12 | EC50_pop : 9.37e+003 1.1e+003 11 13 | 14 | omega_hb0 : 0.0606 0.017 29 15 | omega_Kout : 0.538 0.69 129 16 | omega_EC50 : 0.301 0.097 32 17 | 18 | a : 0.868 0.082 9 19 | 20 | ______________________________________________ 21 | correlation matrix of the estimates(linearization) 22 | 23 | hb0_pop 1 24 | Kout_pop 0.2 1 25 | EC50_pop -0.4 -0.03 1 26 | 27 | Eigenvalues (min, max, max/min): 0.57 1.5 2.6 28 | 29 | omega_hb0 1 30 | omega_Kout -0.02 1 31 | omega_EC50 -0.14 0.04 1 32 | a -0.09 -0.27 -0.14 1 33 | 34 | Eigenvalues (min, max, max/min): 0.66 1.3 2 35 | 36 | 37 | Population parameters and Fisher Information Matrix estimation... 38 | 39 | Elapsed time is 13.5 seconds. 40 | CPU time is 31.2 seconds. 41 | -------------------------------------------------------------------------------- /290/290.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_simulated_CPathAD.mod", 4 | "Simulated_data_CPathAD.csv", 5 | "Command.txt", 6 | "Output_real_CPathAD.lst", 7 | "DDMODEL00000290.rdf", 8 | "Output_simulated_CPathAD.lst" 9 | ], 10 | "version": 5 11 | } -------------------------------------------------------------------------------- /290/Command.txt: -------------------------------------------------------------------------------- 1 | # Example on a text file with executable original code, here example is NONMEM 2 | ommented lines 3 | # this file is mandatory for submission scenario 1 and 2 and 4. 4 | ###################### if the executable model is written in original language ##### 5 | ###################### please specify the block below 6 | ###### Scenario = 4 7 | ###### PsNversion (if any) = 4.7.0 8 | ###### Original tool version= NM 7.4 9 | ###### Input data= Simulated_data_CPathAD.csv 10 | ###### Executable model= Executable_simulated_CPathAD.mod 11 | ###### Output= Output_simulated_CPathAD.lst 12 | ###################### end block 13 | ###### Script used as execution command for NONMEM via PsN: ############# 14 | C:\Perl64\bin/execute -parafile=pirana_auto_mpi.pnm -nodes=3 Executable_simulated_CPathAD.mod 15 | -------------------------------------------------------------------------------- /290/DDMODEL00000290.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | No 10 | Yes 11 | 12 | See publication at PMID 25168488 13 | An updated Alzheimer's disease progression model: incorporating non-linearity, beta regression, and a third-level random effect in NONMEM. For access to the real individual clinical trial data go to https://codr.c-path.org/ 14 | 15 | 16 | 17 | 18 | 19 | 20 | -------------------------------------------------------------------------------- /294/294.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_Paracetamol_Zebrafish_345dpf.mod", 4 | "Command.txt", 5 | "DDMODEL00000294.rdf", 6 | "Output_real_Paracetamol_Zebrafish_345dpf.lst", 7 | "Real_Paracetamol_Zebrafish_345dpf.csv" 8 | ], 9 | "version": 14 10 | } -------------------------------------------------------------------------------- /294/Command.txt: -------------------------------------------------------------------------------- 1 | execute Executable_Paracetamol_Zebrafish_345dpf.mod -------------------------------------------------------------------------------- /294/DDMODEL00000294.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | No 9 | Yes 10 | 11 | Impact of age on absorption and elimination of paracetamol by zebrafish larvae 12 | 13 | 14 | Zebrafish larvae of 3, 4, and 5 dpf have been treated with 1 mM paracetamol in surrounding medium for designated times after which internal exposure is quantified in homogenates (destructive sampling). Model identifies age as covariate on absorption (discrete, changed between 3 and 4 dpf) and on elimination (power relation, 17.5% increased elimination per day post fertilisation). Error model is combination of additive and proportional. 15 | 16 | 17 | -------------------------------------------------------------------------------- /294/Executable_Paracetamol_Zebrafish_345dpf.mod: -------------------------------------------------------------------------------- 1 | ;; x1. Author: R.C. van Wijk (r.c.van.wijk@lacdr.leidenuniv.nl) 2 | ;; 3. Label: Base and covariate model 3 | ;; 4. Dataset: Zebrafish larvae exposed to 1 mM paracetamol at 3, 4, or 5 dpf 4 | ;; Description: Paracetamol PK model of 3-4-5 dpf zebrafish larvae, AGE as covariate on KA between 3 and 4dpf, and on K25 for all ages, prop and additive error 5 | $PROBLEM PK 6 | $INPUT ID TIME AMT DV EVID MDV CMT BQL AGE 7 | $DATA Real_Paracetamol_Zebrafish_345dpf.csv IGNORE=@ IGNORE=(BQL.EQ.1) 8 | 9 | ; units 10 | ; TIME = min 11 | ; DV = pmole / larva 12 | ; CL = central volume / min (V = fixed) 13 | ; V = total larval volume 14 | ; kA = pmole / min 15 | 16 | $SUBROUTINE ADVAN13 TOL=9 17 | $MODEL COMP ; CMT 1 dosing compartment 18 | COMP ; CMT 2 central paracetamol in larva 19 | $PK 20 | TVK12 = THETA(2) ;0-order absorption 21 | IF(AGE.GT.3) TVK12 = THETA(2) * (1 + THETA(3)) ;age-dependent K12 absorption 22 | TVK25 = THETA(1) * EXP(ETA(1)) ;1-order elimination 23 | 24 | K12 = TVK12 25 | K25 = TVK25 * ((1 + THETA(4)) ** (AGE - 3)) ;age-dependent K25 rate of elimination 26 | 27 | ;base parameters 28 | K25_BASE = THETA(1) 29 | K12_BASE = THETA(2) 30 | ;covariate parameters 31 | K12_COVAGE = THETA(3) 32 | K25_COVAGE = THETA(4) 33 | 34 | $DES 35 | DADT(1) = 0 ;constant infusion 36 | DADT(2) = K12 * A(1) - K25 * A(2) 37 | 38 | $ERROR 39 | IPRED = F 40 | Y = IPRED * (1 + EPS(1)) + EPS(2) ; prop and add error 41 | IRES = DV - IPRED 42 | 43 | $THETA (0,0.0192529) ; K25 44 | $THETA (0,0.289485) ; K12 45 | $THETA (0,1.06385) ; AGE_K12 46 | $THETA (0,0.174529) ; AGE_K25 47 | $OMEGA 0 FIX ; IIV K25, undistinguishable from residual variability due to destructive sampling 48 | $SIGMA 0.10906 ; prop error 49 | $SIGMA 0.0084383 ; add error 50 | $ESTIMATION METHOD=1 MAXEVAL=2000 NOABORT PRINT=5 SIG=3 POSTHOC 51 | $COVARIANCE PRINT=E 52 | $TABLE ID TIME DV IPRED PRED CWRES NOAPPEND NOPRINT ONEHEADER 53 | FILE=sdtab001 54 | $TABLE ID K25 K12 K12_COVAGE K25_COVAGE K12_BASE K25_BASE AGE 55 | NOPRINT NOAPPEND ONEHEADER FILE=patab001 56 | 57 | -------------------------------------------------------------------------------- /295/295.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_CMS_colistin_PK_CRRT.mod", 4 | "Simulated_Data_CMS_colistin_PK_CRRT.csv", 5 | "Command.txt", 6 | "Output_simulated_CMS_colistin_PK_CRRT - Copie.lst", 7 | "DDMODEL00000295.rdf", 8 | "Model_Accommodations.txt", 9 | "Output_real_CMS_colistin_PK_CRRT.lst" 10 | ], 11 | "version": 12 12 | } -------------------------------------------------------------------------------- /295/Command.txt: -------------------------------------------------------------------------------- 1 | execute Executable_CMS_colistin_PK.mod Output_real_CMS_colistin_PK.lst -------------------------------------------------------------------------------- /295/Model_Accommodations.txt: -------------------------------------------------------------------------------- 1 | There are no model differences with the publication referenced -------------------------------------------------------------------------------- /297/297.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_run1.mod", 4 | "Output_simulated_run1.lst", 5 | "Simulated_run1.csv", 6 | "DDMODEL00000297.rdf", 7 | "Command.txt" 8 | ], 9 | "version": 33 10 | } -------------------------------------------------------------------------------- /297/Command.txt: -------------------------------------------------------------------------------- 1 | C:\Perl64\bin/execute Executable_run1.mod 2 | -------------------------------------------------------------------------------- /297/DDMODEL00000297.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | 9 | 10 | 11 | Yes 12 | No 13 | Acquiring a malaria infection during pregnancy has been associated with adverse health outcomes for both the mother and the foetus. The aim of this model was to describe the pharmacokinetic properties of artesunate and its metabolite dihydroartemisinin in non-pregnant and pregnant women 14 | None 15 | 16 | A detailed explanation of the model can be found in the published manuscript, Population pharmacokinetics of artesunate and dihydroartemisinin in pregnant and non-pregnant women with uncomplicated Plasmodium falciparum malaria in Burkina Faso (published at Wellcome Open Research) 17 | 18 | 19 | -------------------------------------------------------------------------------- /297/Simulated_run1.csv: -------------------------------------------------------------------------------- 1 | #ID,DATE,TIME,DV,ODV,OODV,WT,EVID,MDV,OMDV,AMT,FLAG,CMT,CMT2,BQL,PREG ,PARA,PARL,SORT,HB,AST,ALT,BIL,EGA 2 | 1,10-07-08,14:03,.,.,0.569170565,51.5,0,1,0,0,1,2,1,1,1,810,6.697034248,1,9.1,20,20.3,0.45,5 3 | 1,10-07-08,14:03,.,.,0.97543622,51.5,0,1,0,0,2,3,2,1,1,810,6.697034248,2,9.1,20,20.3,0.45,5 4 | 1,10-07-08,14:20,.,.,.,51.5,1,1,1,520264.2943,0,1,1,0,1,810,6.697034248,3,9.1,20,20.3,0.45,5 5 | 1,10-07-08,14:36,29,3.36729583,3.36729583,51.5,0,0,0,0,1,2,1,0,1,810,6.697034248,4,9.1,20,20.3,0.45,5 6 | 1,10-07-08,14:36,75,4.317488114,4.317488114,51.5,0,0,0,0,2,3,2,0,1,810,6.697034248,5,9.1,20,20.3,0.45,5 7 | 1,10-07-08,14:51,75,4.317488114,4.317488114,51.5,0,0,0,0,1,2,1,0,1,810,6.697034248,6,9.1,20,20.3,0.45,5 8 | 1,10-07-08,14:51,97,4.574710979,4.574710979,51.5,0,0,0,0,2,3,2,0,1,810,6.697034248,7,9.1,20,20.3,0.45,5 9 | 1,10-07-08,15:20,30,3.401197382,3.401197382,51.5,0,0,0,0,1,2,1,0,1,810,6.697034248,8,9.1,20,20.3,0.45,5 10 | 1,10-07-08,15:20,9,2.197224577,2.197224577,51.5,0,0,0,0,2,3,2,0,1,810,6.697034248,9,9.1,20,20.3,0.45,5 11 | 1,10-07-08,16:22,13,2.564949357,2.564949357,51.5,0,0,0,0,1,2,1,0,1,810,6.697034248,10,9.1,20,20.3,0.45,5 12 | 1,10-07-08,16:22,30,3.401197382,3.401197382,51.5,0,0,0,0,2,3,2,0,1,810,6.697034248,11,9.1,20,20.3,0.45,5 13 | 1,10-07-08,17:23,23,3.135494216,3.135494216,51.5,0,0,0,0,1,2,1,0,1,810,6.697034248,12,9.1,20,20.3,0.45,5 14 | 1,10-07-08,17:23,99,4.59511985,4.59511985,51.5,0,0,0,0,2,3,2,0,1,810,6.697034248,13,9.1,20,20.3,0.45,5 15 | 1,10-07-08,18:21,.,.,0.569170565,51.5,0,1,0,0,1,2,1,1,1,810,6.697034248,14,9.1,20,20.3,0.45,5 16 | 1,10-07-08,18:21,30,3.401197382,3.401197382,51.5,0,0,0,0,2,3,2,0,1,810,6.697034248,15,9.1,20,20.3,0.45,5 17 | 1,10-07-08,19:22,.,.,0.569170565,51.5,0,1,0,0,1,2,1,1,1,810,6.697034248,16,9.1,20,20.3,0.45,5 18 | 1,10-07-08,19:22,27,3.295836866,3.295836866,51.5,0,0,0,0,2,3,2,0,1,810,6.697034248,17,9.1,20,20.3,0.45,5 19 | 1,10-07-08,20:21,.,.,0.569170565,51.5,0,1,0,0,1,2,1,1,1,810,6.697034248,18,9.1,20,20.3,0.45,5 20 | 1,10-07-08,20:21,75,4.317488114,4.317488114,51.5,0,0,0,0,2,3,2,0,1,810,6.697034248,19,9.1,20,20.3,0.45,5 21 | 1,10-07-08,22:20,.,.,0.569170565,51.5,0,1,0,0,1,2,1,1,1,810,6.697034248,20,9.1,20,20.3,0.45,5 22 | 1,10-07-08,22:20,93,4.532599493,4.532599493,51.5,0,0,0,0,2,3,2,0,1,810,6.697034248,21,9.1,20,20.3,0.45,5 23 | 1,10-08-08,0:21,.,.,0.569170565,51.5,0,1,0,0,1,2,1,1,1,810,6.697034248,22,9.1,20,20.3,0.45,5 24 | 1,10-08-08,0:21,.,.,0.97543622,51.5,0,1,0,0,2,3,2,1,1,810,6.697034248,23,9.1,20,20.3,0.45,5 25 | 1,10-08-08,2:20,.,.,0.569170565,51.5,0,1,0,0,1,2,1,1,1,810,6.697034248,24,9.1,20,20.3,0.45,5 26 | 1,10-08-08,2:20,.,.,0.97543622,51.5,0,1,0,0,2,3,2,1,1,810,6.697034248,25,9.1,20,20.3,0.45,5 27 | 1,10-14-08,14:20,.,.,.,51.5,2,1,1,0,0,1,1,0,1,810,6.697034248,3,9.1,20,20.3,0.45,5 28 | -------------------------------------------------------------------------------- /298/298.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_sultiame_nonlinear_PK.mod", 4 | "Output_real_sultiame_nonlinear_PK.lst", 5 | "Output_simulated_sultiame_nonlinear_PK.lst", 6 | "Command.txt", 7 | "DDMODEL00000298.rdf", 8 | "Simulated_data_PK_sultiame.csv", 9 | "Model_Accomodations.txt" 10 | ], 11 | "version": 16 12 | } -------------------------------------------------------------------------------- /298/Command.txt: -------------------------------------------------------------------------------- 1 | ###### Scenario = 4 2 | ###### PsN version = 3 | ###### Original tool version = 4 | ###### Input data = 5 | ###### Executable model = 6 | ###### Output = 7 | 8 | execute Executable_sultiame_nonlinear_PK.mod -------------------------------------------------------------------------------- /298/DDMODEL00000298.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | There are no model differences with the publication that is under review 8 | 9 | Non-linear distribution in erythrocytes 10 | 11 | 12 | No 13 | Theoretical model and differential equations will be available in Figure 1 of the publication (under submission) 14 | 15 | 16 | Yes 17 | 18 | 19 | -------------------------------------------------------------------------------- /298/Model_Accomodations.txt: -------------------------------------------------------------------------------- 1 | There are no model differences with the publication that is under submission. -------------------------------------------------------------------------------- /299/299.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_Misspecification Example using Nonlinear MEM V3.sas", 4 | "Simulated_dataset generator output.log", 5 | "Simulated_Dataset generator.sas", 6 | "Misspecification Example Using LMEM V3.log", 7 | "Command.txt", 8 | "DDMODEL00000299.rdf", 9 | "Misspecification Example Using Nonlinear MEM V3.log", 10 | "ReadMe-Case Study of a Misspecified Model V5.docx", 11 | "Simulated_concqt.sas7bdat", 12 | "Description_files.txt", 13 | "Misspecification Example Using Nonlinear MEM V3.htm", 14 | "Misspecification Example Using LMEM V3.htm", 15 | "Output_simulated_Misspecification Example Using LMEM V3.lst", 16 | "Executable_Misspecification Example using LMEM V3.sas", 17 | "Output_simulated_Misspecification Example Using Nonlinear MEM V3.lst" 18 | ], 19 | "version": 3 20 | } -------------------------------------------------------------------------------- /299/Command.txt: -------------------------------------------------------------------------------- 1 | # for this Model entry there is no command line as the SAS code was directly executed in SAS 2 | -------------------------------------------------------------------------------- /299/Description_files.txt: -------------------------------------------------------------------------------- 1 | # This file explains what is in the model entry: 2 | 3 | - Simulated_concqt.sas7dbat: simulated data generated with Simulated_Dataset generator.sas 4 | 5 | - Executable_Misspecification Example using LMEM V3: linear mixed effect model with mispecification 6 | 7 | - Executable_Misspecification Example using Nonlinear MEM V3 : non linear model without mispecification 8 | 9 | - ReadMe: describe the analysis 10 | 11 | 12 | -------------------------------------------------------------------------------- /299/Misspecification Example Using LMEM V3.htm: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | Sorry, that page doesn’t exist! 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 |
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33 | 34 | 35 | 36 | -------------------------------------------------------------------------------- /299/ReadMe-Case Study of a Misspecified Model V5.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/299/ReadMe-Case Study of a Misspecified Model V5.docx -------------------------------------------------------------------------------- /299/Simulated_concqt.sas7bdat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/299/Simulated_concqt.sas7bdat -------------------------------------------------------------------------------- /301/301.json: -------------------------------------------------------------------------------- 1 | { 2 | "files": [ 3 | "Executable_merop_PK_run3.mod", 4 | "Output_simulated_merop_PK_run3.lst", 5 | "Model_Accomodations.text", 6 | "Simulated_dataset.csv", 7 | "DDMODEL00000301.rdf", 8 | "Command.txt", 9 | "Output_real_merop_PK_run3.lst" 10 | ], 11 | "version": 52 12 | } -------------------------------------------------------------------------------- /301/Command.txt: -------------------------------------------------------------------------------- 1 | /usr/local/bin/execute run3.mod -dir=run3.dir1 -retries=300 2 | -------------------------------------------------------------------------------- /301/DDMODEL00000301.rdf: -------------------------------------------------------------------------------- 1 | 6 | 7 | 8 | Population pharmacokinetic model for meropenem in patients with severe pneumonia. 9 | No 10 | 11 | 12 | 13 | 14 | 15 | 16 | -------------------------------------------------------------------------------- /301/Model_Accomodations.text: -------------------------------------------------------------------------------- 1 | ## Model related to: 2 | ## An input-output approach for model-informed personalized drug dosing for antibiotics: 3 | ## Application to meropenem in adult patients with severe pneumonia. British Journal of Pharmacology (submitted - July 2019) 4 | ## Authors: Pauline Thémans, Joseph J. Winkin, Flora T. Musuamba 5 | ###### Scenario = 4 6 | ###### 7 | ###### There are no model differences 8 | -------------------------------------------------------------------------------- /missingModels.txt: -------------------------------------------------------------------------------- 1 | 155 2 | 156 3 | 157 4 | 158 5 | 159 6 | 160 7 | 161 8 | 162 9 | 163 10 | 164 11 | 165 12 | 166 13 | 167 14 | 168 15 | 169 16 | 170 17 | 171 18 | 172 19 | 174 20 | 175 21 | 176 22 | 177 23 | 178 24 | 179 25 | 180 26 | 181 27 | 182 28 | 183 29 | 184 30 | 185 31 | 187 32 | 188 33 | 189 34 | 190 35 | 191 36 | 193 37 | 196 38 | 199 39 | 200 40 | 201 41 | 202 42 | 203 43 | 204 44 | 205 45 | 206 46 | 207 47 | 208 48 | 209 49 | 210 50 | 211 51 | 216 52 | 226 53 | 232 54 | 234 55 | 235 56 | 236 57 | 241 58 | 242 59 | 246 60 | 251 61 | 252 62 | 253 63 | 254 64 | 255 65 | 257 66 | 258 67 | 260 68 | 263 69 | 264 70 | 265 71 | 266 72 | 270 73 | 272 74 | 275 75 | 276 76 | 277 77 | 278 78 | 279 79 | 282 80 | 283 81 | 286 82 | 287 83 | 288 84 | 289 85 | 291 86 | 292 87 | 293 88 | 296 89 | 300 90 | -------------------------------------------------------------------------------- /nmoutputs/Output_real_COV_KPD_CTC.count_PSA.lst: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/nmoutputs/Output_real_COV_KPD_CTC.count_PSA.lst -------------------------------------------------------------------------------- /nmoutputs/Output_real_SAEM_KPD_CTC.count_PSA.lst: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/nmoutputs/Output_real_SAEM_KPD_CTC.count_PSA.lst -------------------------------------------------------------------------------- /nmoutputs/Output_real_run126c.lst: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/nmoutputs/Output_real_run126c.lst -------------------------------------------------------------------------------- /nmoutputs/Output_simulated_CPHPC_dataset.lst: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/nmoutputs/Output_simulated_CPHPC_dataset.lst -------------------------------------------------------------------------------- /nmoutputs/Output_simulated_run126h.lst: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dpastoor/ddmore_scraping/2e2da3f1cbcae4c1f1e9262d1f833f71a1d7e2b4/nmoutputs/Output_simulated_run126h.lst -------------------------------------------------------------------------------- /package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "ddmore", 3 | "version": "1.0.0", 4 | "description": "", 5 | "main": "index.js", 6 | "scripts": { 7 | "test": "echo \"Error: no test specified\" && exit 1" 8 | }, 9 | "author": "", 10 | "license": "ISC", 11 | "dependencies": { 12 | "left-pad": "^1.3.0", 13 | "lodash": "^4.17.15", 14 | "nightmare": "^3.0.2" 15 | } 16 | } 17 | --------------------------------------------------------------------------------