Model-based Drug Development

This unit provides a focussed introduction to the use of mechanistic pharmacokinetic models, which use mathematical descriptions of physiological processes to predict the fate of drug molecules within the human body. Therefore, 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, and 2) defining the relationships that link these processes. Implementation of this approach will be described in the following areas:

• prediction of drug-drug interactions by extrapolating from in vitro laboratory tests before commencing clinical studies
• prediction of oral absorption; guiding the design of oral drug formulations.

The unit aims to:

• provide background information on in vitro assays used to characterise ADME properties of new drug entities
• indicate the mathematical framework (physiologically-based pharmacokinetics) that is capable of integrating in vitro information with knowledge of the human body to predict pharmacokinetic behaviour
• explain the advantages of physiologically-based mechanistic models and give examples to show how such models have advanced the process of drug development.

Introduction to physiologically-based pharmacokinetics (PBPK) and systems pharmacology

• Modelling of in vitro experiments
• Metabolic assays
• Permeability
• MDDI assay data
• Transporter/transporter-metabolic DDI assay.

Quantitative prediction and IVIVE of metabolic clearance

• Physiology of hepatic and extrahepatic metabolism
• In vitro-in vivo extrapolation (IVIVE) of metabolism
• PBPK modelling and simulation (M&S)
• IVIVE in drug development.

Predicting absorption

• Principles of drug absorption
• The evolution of PBPK absorption models
• PBPK M&S of oral formulation effects
• PBPK M&S in drug and formulation development
• PBPK M&S of food effects
• Dermal absorption; pulmonary absorption.

Drug distribution and binding

• Principles of drug distribution
• Models for predicting volume of distribution
• Whole-body PBPK M&S
• Drug binding and local tissue concentrations.

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: 

• describe the details of in vitro systems used to characterise drug absorption, distribution, metabolism and excretion
• describe the relationships (scaling) between data obtained from in vitro systems and in vivo observations on ADME.

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.

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.