Open Systems Pharmacology Community—An Open Access, Open Source, Open Science Approach to Modeling and Simulation in Pharmaceutical Sciences

Lippert, Joerg; Burghaus, Rolf; Edginton, Andrea N.; Frechen, Sebastian; Karlsson, Mats O.; Kovar, Andreas; Lehr, Thorsten; Milligan, Peter; Nock, Valerie; Ramusovic, Sergej; Riggs, Matthew; Schaller, Stephan; Schlender, Jan; Schmidt, Stephan; Sevestre, Michael; Sjögren, Erik; Solodenko, Juri; Staab, Alexander; Teutonico, Donato

doi:10.1002/psp4.12473

Cited by 78 publications

(72 citation statements)

References 6 publications

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“…If ageing was to be taken into account, species-specific changes in physiology had to be additionally considered (Schlender et al 2016). Our concepts largely rely on the full and unbiased accessibility of the underlying model structure which is increasingly advocated for in systems life sciences (Lippert et al 2019;Wolstencroft et al 2017Wolstencroft et al , 2015. In that sense, transparency of the basic model equations and parameters is a mandatory pre-requisite of our approach.…”

Section: Discussionmentioning

confidence: 99%

A workflow to build PBTK models for novel species

et al. 2020

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Physiology-based pharmacokinetic and toxicokinetic (PBPK/TK) models allow us to simulate the concentration of xenobiotica in the plasma and different tissues of an organism. PBPK/TK models are therefore routinely used in many fields of life sciences to simulate the physiological concentration of exogenous compounds in plasma and tissues. The application of PBTK models in ecotoxicology, however, is currently hampered by the limited availability of models for focal species. Here, we present a best practice workflow that describes how to build PBTK models for novel species. To this end, we extrapolated eight previously established rabbit models for several drugs to six additional mammalian species (human, beagle, rat, monkey, mouse, and minipig). We used established PBTK models for these species to account for the species-specific physiology. The parameter sensitivity in the resulting 56 PBTK models was systematically assessed to rank the relevance of the parameters on overall model performance. Interestingly, more than 80% of the 609 considered model parameters showed a negligible sensitivity throughout all models. Only approximately 5% of all parameters had a high sensitivity in at least one of the PBTK models. This approach allowed us to rank the relevance of the various parameters on overall model performance. We used this information to formulate a best practice guideline for the efficient development of PBTK models for novel animal species. We believe that the workflow proposed in this study will significantly support the development of PBTK models for new animal species in the future.

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Section: Discussionmentioning

confidence: 99%

A workflow to build PBTK models for novel species

et al. 2020

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“…Mechanistic whole-body PBPK models of metformin and cimetidine have been carefully developed and evaluated to integrate the current pharmacokinetic knowledge on these drugs and to describe the impact of the SLC22A2 808G>T polymorphism, the cimetidine-metformin DDI, and the pathophysiological changes during renal impairment on the exposure of metformin. Both models will be released opensource (https ://www.open-syste ms-pharm acolo gy.org) [49], to support metformin therapy, OCT2/MATE DDI studies during drug development, and to be used as input for pharmacodynamic glucose-homeostasis models [50,51] and other PBPK/pharmacodynamic analyses. The presented analysis has generated insights into the pharmacokinetics during renal impairment, indicating that the kidneys of patients with severe renal disease might be able to adapt to uremia/hyperuricemia by induction of OCT2 and MATE1, as has been shown for hyperuricemic rats [40].…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Drug–Drug–Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals

et al. 2020

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Background Metformin is a widely prescribed antidiabetic BCS Class III drug (low permeability) that depends on active transport for its absorption and disposition. It is recommended by the US Food and Drug Administration as a clinical substrate of organic cation transporter 2/multidrug and toxin extrusion protein for drug-drug interaction studies. Cimetidine is a potent organic cation transporter 2/multidrug and toxin extrusion protein inhibitor. Objective The objective of this study was to provide mechanistic whole-body physiologically based pharmacokinetic models of metformin and cimetidine, built and evaluated to describe the metformin-SLC22A2 808G>T drug-gene interaction, the cimetidine-metformin drug-drug interaction, and the impact of renal impairment on metformin exposure. Methods Physiologically based pharmacokinetic models were developed in PK-Sim ® (version 8.0). Thirty-nine clinical studies (dosing range 0.001-2550 mg), providing metformin plasma and urine data, positron emission tomography measurements of tissue concentrations, studies in organic cation transporter 2 polymorphic volunteers, drug-drug interaction studies with cimetidine, and data from patients in different stages of chronic kidney disease, were used to develop the metformin model. Twenty-seven clinical studies (dosing range 100-800 mg), reporting cimetidine plasma and urine concentrations, were used for the cimetidine model development. Results The established physiologically based pharmacokinetic models adequately describe the available clinical data, including the investigated drug-gene interaction, drug-drug interaction, and drug-drug-gene interaction studies, as well as the metformin exposure during renal impairment. All modeled drug-drug interaction area under the curve and maximum concentration ratios are within 1.5-fold of the observed ratios. The clinical data of renally impaired patients shows the expected increase in metformin exposure with declining kidney function, but also indicates counter-regulatory mechanisms in severe renal disease; these mechanisms were implemented into the model based on findings in preclinical species. Conclusions Whole-body physiologically based pharmacokinetic models of metformin and cimetidine were built and qualified for the prediction of metformin pharmacokinetics during drug-gene interaction, drug-drug interaction, and different stages of renal disease. The model files will be freely available in the Open Systems Pharmacology model repository. Current guidelines for metformin treatment of renally impaired patients should be reviewed to avoid overdosing in CKD3 and to allow metformin therapy of CKD4 patients.

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“…The model evaluation is transparently documented in the Supplementary Materials , showing the model performance for all 66 analyzed trimethoprim studies as well as for all DDI and DDGI studies utilized for model evaluation. The model will be shared with the research and drug development community via the Open Systems Pharmacology repository ( ) [ 86 ], for the investigation of new DDI scenarios with MATE1, OCT1, OCT2, and CYP2C8 victim drugs.…”

Section: Discussionmentioning

confidence: 99%

A Physiologically-Based Pharmacokinetic Model of Trimethoprim for MATE1, OCT1, OCT2, and CYP2C8 Drug–Drug–Gene Interaction Predictions

2020

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Trimethoprim is a frequently-prescribed antibiotic and therefore likely to be co-administered with other medications, but it is also a potent inhibitor of multidrug and toxin extrusion protein (MATE) and a weak inhibitor of cytochrome P450 (CYP) 2C8. The aim of this work was to develop a physiologically-based pharmacokinetic (PBPK) model of trimethoprim to investigate and predict its drug–drug interactions (DDIs). The model was developed in PK-Sim®, using a large number of clinical studies (66 plasma concentration–time profiles with 36 corresponding fractions excreted in urine) to describe the trimethoprim pharmacokinetics over the entire published dosing range (40 to 960 mg). The key features of the model include intestinal efflux via P-glycoprotein (P-gp), metabolism by CYP3A4, an unspecific hepatic clearance process, and a renal clearance consisting of glomerular filtration and tubular secretion. The DDI performance of this new model was demonstrated by prediction of DDIs and drug–drug–gene interactions (DDGIs) of trimethoprim with metformin, repaglinide, pioglitazone, and rifampicin, with all predicted DDI and DDGI AUClast and Cmax ratios within 1.5-fold of the clinically-observed values. The model will be freely available in the Open Systems Pharmacology model repository, to support DDI studies during drug development.

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Open Systems Pharmacology Community—An Open Access, Open Source, Open Science Approach to Modeling and Simulation in Pharmaceutical Sciences

Cited by 78 publications

References 6 publications

A workflow to build PBTK models for novel species

A workflow to build PBTK models for novel species

A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Drug–Drug–Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals

A Physiologically-Based Pharmacokinetic Model of Trimethoprim for MATE1, OCT1, OCT2, and CYP2C8 Drug–Drug–Gene Interaction Predictions

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