Model-based Drug Development

This unit provides further training on the use of mechanistic pharmacokinetic models, which use mathematical descriptions of physiological processes to predict the fate of drug molecules within the human body. Completion of the introductory module is an essential prerequisite. The unit addresses two key aspects of IVIVE:

1. developing mathematical representation of key chemical and physiological processes that affect drug molecules in the body
2. defining the relationships that link these processes. Implementation of this approach will be described in the following areas:

• prediction of drug-drug and drug-disease interactions in special populations such as neonates, children, patients with kidney or liver impairment
• use of IVIVE to assess propagation of genetic differences in drug metabolizing enzymes in different populations
• special considerations for biologics.

The unit aims to:

• provide examples and promote reflection on the use of in vitro assays to characterise ADME properties of new drug entities
• provide experience in critical assessment of recent uses of physiologically based pharmacokinetic models, identifying the valuable information provided by such models and the limitations associated with these methods
• give examples to show how physiologically based models have advanced the process of drug development, and identify advances that have occurred recently or are expected in the near future.

The unit is delivered over a 6-week period and will include:

• lectures
• workshops, entailing guided sequences of analysis with interactive discussion with tutor
• seminars presenting examples of PBPK in drug development
• directed reading
• formative-assessed calculation-based coursework (pass/fail)
• summative-assessed calculation-based coursework.

Students should be able to: 

• discuss examples of how in vitro- in vivo scaling methods are currently being used to guide drug development
• describe how physiological modelling provides insight into drug-development problems such as drug-drug interactions and other drug/system interactions
• describe the specific challenges and modelling approaches that arise in coping with special patient populations and special classes of drug such as biologics.

Students should be able to: 

• critically analyse observations on plasma drug concentration-time profiles and characterise them quantitatively for the purpose of making inferences between different drugs, different patients, different conditions etc
• identify the reasons for differences in the time-courses of drug effect and plasma drug concentration
• make informed predictions of the behaviour of drugs in body with respect to plasma drug concentration-time profile
• apply basic biostatistical concepts and interpret statistical information arising from clinical trials.
• critically assess applications of physiologically based modelling and make judgements about acceptable use of such models.

Students should be able to:

• perform calculations using fundamental pharmacokinetic equations
• use specialised computer software Simcyp and Matlab along with more general data analysis/presentation software to simulate and explore the effects of covariates on drug and metabolite concentrations in the body.

Students should be able to:

• extracting key points from scientific literature
• effective written communication.

Lecture notes cite key research articles and reviews, which include:

• Wienkers, L.C. and T.G. Heath (2005) Predicting in vivo drug interactions from in vitro drug discovery data. Nat Rev Drug Discov 4:825-33.
• Houston, J.B. and A. Galetin (2008) Methods for predicting in vivo pharmacokinetics using data from in vitro assays. Curr Drug Metab. 9:940-51.
• Rostami-Hodjegan, A. (2012) Physiologically Based Pharmacokinetics Joined With In Vitro-In Vivo Extrapolation of ADME: A Marriage Under the Arch of Systems Pharmacology. Clin Pharmacol Ther 92:50-61.
• Jamei, M., S. Marciniak, K. Feng, A. Barnett, G. Tucker, and A. Rostami-Hodjegan (2009) The Simcyp population-based ADME simulator. Expert Opin Drug Metab Toxicol. 5:211-23.
• Rowland, M., C. Peck, and G. Tucker (2011) Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol 51:45-73.
• Zhao, P., M. Rowland, and S.M. Huang (2012) Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions. Clin Pharmacol Ther 92:17-20.
• Tsamandouras, N., A. Rostami-Hodjegan, and L. Aarons (2013) Combining the "bottom-up" and "top-down" approaches in pharmacokinetic modelling: Fitting PBPK models to observed clinical data. Br J Clin Pharmacol.