Session 1: PBTK/TD modelling
Cyprotex Discovery Ltd.
I received my B.Sc. degree in biomedical engineering from Jordan University of Science and Technology in 2005. I then moved to the UK to pursue my postgradu-
ate studies, where I obtained my M.Sc. degree in advanced biomedical engineering from the University
of Warwick in 2006, attaining a first class degree with distinction. In September 2006, I worked for four
months as a Lecturer in the Faculty of Engineering at Al-Ahliyya Amman University where I taught biome-
dical engineering modules and supervised senior year graduation projects. I was awarded the University of Warwick Vice Chancellor’s Scholarship to work towards my Ph.D. degree in biomedical systems modelling, which I earned in 2011. My doctoral thesis looked into mathematically modelling the kinetics and dynamics of the anti-cancer agent topotecan.
I am currently working as a Mathematical Modeller at Cyprotex Discovery Limited, a preclinical discovery and development company, on developing novel in silico models for drug pharmacokinetics/toxicokinetics to improve the predictive power of Cyprotex software.
My major research interests include mathematical modelling and simulation of biomedical, biological and pharmacokinetic/toxicokinetic processes, structural identifiability, system identification/parameter estima-
tion and Monte Carlo simulation/optimisation.
ABSTRACT CONTENT / DETAILS:
Physiologically-based toxicokinetic/toxicodynamic (PBTK/TD) modelling is a mathematical modelling approach which aims at integrating a priori knowledge of physiological processes with other known/observed information to mimic the fates and effects of compounds in the bodies of humans, preclinical species and/or other organisms.
Recently, PBTK/TD models have been widely used by regulatory agencies and consortia including the FDA (Food and Drug Administration) and ITC (International Transporter Consortium).
Understanding the toxicokinetics (TK) and toxicodynamics (TD) of xenobiotics is vital in the pharmaceutical, agrochemical, chemical, cosmetic and other industries; these industries generate numerous compounds to which living organisms are exposed, and that can have a significant influence on their health. In the pharmaceutical industry it is well documented that toxicity is a leading cause of late-stage attrition during the development process, and of post-approval withdrawals.
The associated costs and difficulties with developing new chemicals with acceptably low toxicity is becoming challenging. Therefore, the traditional approach for risk assessment is moving towards in silico predictive toxicology techniques that provide fast and cost effective replacements of (or supplements to) in vivo experiments to identify toxic effects at the different stages of the R&D process.
To investigate the fates and effects of typical drugs and other chemicals, a novel PBTK/TD model for the in vivo TK of compounds has been developed. This model predicts the distribution of chemical substances in the body. The model has been optimised using xenobiotic plasma concentration data following intravenous and oral dosing. Before estimating the unknown model parameters from the experimental, data it is essential to determine parameter uniqueness (or otherwise) from the imposed output structure.
This is formally performed as a structural identifiability analysis, which demonstrates that all of the unknown model parameters are uniquely determined by the output structure corresponding to the experiment. The model has been used to study a number of examples relating to in vivo toxicity, such as cytochrome P450 induction, steatosis and acute lethality.
Such a coupled TK/TD model, has the potential to fulfil several roles in novel compound discovery, including: identification of compounds that are likely to have unacceptable in vivo toxicity; ranking compounds on expected toxicity; optimising the design of pharmaceutical dosing regimes to minimise side effects, whilst maintaining desired therapeutic efficacy.