UNIVERSITY OF MALTA
LIFE SCIENCE RESEARCH SEMINARS
Web: http://www.um.edu.mt/events/scisem/
Email: scisem@um.edu.mt
Abstract form
Title: Systems pharmacology: bridging bottom-up and top-down
paradigms
Presenter: Dr Masoud Jamei
Contact address: The Blades Enterprise Centre, John Street, Sheffield, S2 4SU
Tel: +44-(0)114 292 2327
Fax: +44-(0)114 292 2333
Email: m.jamei@simcyp.com
Presentation date: 25 May 2011
Abstract
Systems pharmacology can be defined as the quantitative analysis of the dynamic
interactions between several components of a biological system with the goal of
understanding the behaviour of the system as a whole, as opposed to the behaviour
of its separate components. In this context, three elements, namely characteristics
of the system (i.e. the attributes of the human body for each subject), the
characteristics of the drug and the conditions of the clinical study, which define the
outcome of clinical studies and covariates affecting the observations, are all
integrated together (Jamei et al. 2009).
The system or populations datasets are generally independent of any specific drug
or trial design but contain all the elements for the compound and trial design
datasets to interact with. These include, but are not restricted to: data on
enzymes/transporters and their abundances, including genotypes, rates of synthesis
and degradation, intestinal and stomach motility, intestinal surface area and fluid
dynamics, circulating levels of plasma proteins and red blood cells, organ size
composition and organ blood flow.
Systems pharmacology involves integrating building blocks of the system, in vitro- in
vivo extrapolation algorithms, and the relevant covariates; therefore it is a “bottomup” approach. However, the traditional “top-down” paradigm deals with clinical data
analysis where model building and covariates recognition starts from observed data
which are usually spars.
The presentation focuses on the contrasts between these two methods and
highlights how the strengths of the two techniques can be combined. This bridging
helps with rationalising covariate model building and the design of studies.
Reference
Jamei M, Dickinson GL and Rostami-Hodjegan A (2009) A Framework for Assessing Interindividual
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 'TopDown' Recognition of Covariates. Drug Metab Pharmacokinet 24:53-75.