Simcyp Simulator - a comprehensive platform and database for mechanistic modelling and simulation of drug absorption, tissue distribution, metabolism, transport and elimination in healthy and disease populations using in vitro knowledge
Jamei M, Feng F, Abduljalil K
Simcyp Ltd
Simcyp Simulator - a comprehensive platform and database for mechanistic modelling and simulation of drug absorption, tissue distribution, metabolism, transport and elimination in healthy and disease populations using in vitro knowledge
Simcyp is a University of Sheffield spin-out company that develops algorithms along with population and drug databases for modelling and simulation (M&S) of the absorption, disposition and pharmacological effects of drugs in patients and specific subgroups of patients across different age ranges.
The Simcyp Population-based ADME Simulator is a particularly powerful tool for carrying out virtual clinical trials for recognition of covariates of PK/PD and optimising early in man studies. Similar capabilities have been developed for preclinical species, namely rat and dog. The platform and its database are licensed to Simcyp's Consortium member clients for use in drug discovery and development. The Consortium guides scientific development at Simcyp, ensuring that the platform and databases continue to meet, and in many cases exceed, industry needs. Simcyp maintains strong academic links and our science team conducts internationally recognised cutting-edge research and development which accelerates decision making in drug discovery and development for member pharmaceutical companies. The Simcyp science team:
- provides a user friendly simulator that integrates genetic information on drug metabolising enzymes into PBPK models for the prediction of pharmacokinetics (PK) and pharmacodynamics (PD) of drugs in diverse patient populations with relevant demographic and physiological characteristics,
- offers consultancy and advice on a broad spectrum of DMPK issues (including optimal study design for metabolic drug-drug interactions, data interpretation, prediction of in vivo ADME from in vitro studies, dose selection for different age groups (particularly neonates and young children), assessing the likely effects of renal impairment, cirrhosis and ethnic variations on ADME, etc)
- delivers an educational program consisting of hands-on workshops and courses covering the concepts and applications of in vitro - in vivo extrapolation (IVIVE) to predict drug clearance, drug-drug interactions, gut absorption handling metabolism/transport interplay, and covariates that determine drug disposition (see http://www.simcyp.com/ProductServices/Workshops/)
Currently, 13 of the top 15 pharmaceutical companies worldwide have access to Simcyp expertise through Consortium membership. Members include Actelion, Allergan, AstraZeneca, Daiichi-Sankyo, Dainippon Sumitomo, Eisai, Eli Lilly, Johnson & Johnson PRD, Lundbeck, Novartis Pharma, Nycomed, Otsuka, Pfizer, sanofi-aventis, Servier, Takeda, UCB Pharma among others. Value is added to decision-making processes by collaboration with regulatory bodies (the FDA, MPA, NAM) and academic centres of excellence worldwide, also within the framework of the Consortium.
In the demonstration session we provide an overview of the capabilities of the Simcyp Simulator to predict drug absorption from gut, lung and skin, enterohepatic recirculation, clearance and metabolic drug-drug interactions, transport in the gut and liver, transport drug-drug interactions and PBPK modelling from in vitro and physiochemical information in diverse populations including paediatric, obese, cirrhosis and renally impaired.
The recently developed parameter estimation (PE) module within the Simcyp Simulator is also presented. This module bridges typical ‘bottom-up' PBPK approaches and common pharmacometric analyses of clinical data to accelerate model building and covariate recognition in drug development. It allows Simcyp models, including PBPK, drug-drug interaction, ADAM and gut and liver transporters, to be fitted to observed clinical data (e.g. concentration-time profiles) for the purpose of estimating unknown/uncertain drug or physiological parameters. Further, it provides a platform for scientists to optimally use information accumulated during drug discovery and development in combination with knowledge on systems biology of healthy and disease populations.
In addition to classical optimisation algorithms, users may select genetic algorithms or hybrid methods which enhance the performance of the PE module for individual fitting of observed data. For population fitting, maximum likelihood (ML) and maximum a posteriori (MAP) algorithms using the Monte Carlo expectation maximisation approach can be employed.
Some details of the scientific background to Simcyp's approaches can be found in our recent publications:
- Rowland Yeo K et al. Physiologically-based mechanistic modelling to predict complex drug-drug interactions involving simultaneous competitive and time-dependent enzyme inhibition by parent compound and its metabolite in both liver and gut-the effect of diltiazem on the time-course of exposure to triazolam. European Journal of Pharmaceutical Sciences 39(5), 298-309, 2010.
- Johnson TN et al. A Semi-Mechanistic Model to Predict the Effects of Liver Cirrhosis on Drug Clearance. Clinical Pharmacokinetics 49(3), 189-206, 2010.
- Johnson TN et al. Assessing the efficiency of mixed effects modelling in quantifying metabolism based drug-drug interactions: using in vitro data as an aid to assess study power Pharmaceutical Statistics, 8(3), 186-202, 2009.
- Jamei M et al. Population-based mechanistic prediction of oral drug absorption, The AAPS Journal, 11(2), 225-237, 2009.
- Jamei M et al. A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: a tale of ‘Bottom-Up' vs ‘Top-Down' recognition of covariates, Drug Metabolism & Pharmacokinetics, 24(1), 53-75, 2009.
- Jamei M et al. The Simcyp® Population-Based ADME Simulator, Expert Opinion On Drug Metabolism and Toxicology, 5(2), 211-223, 2009.
- Rostami-Hodjegan A and Tucker GT. Simulation and prediction of in vivo metabolic drug clearance from in vitro data. Nature Reviews 6(2), 140-149, 2007