Modelling in Pharmacology and In-vitro to In-vivo Extrapolation in Oncology/Cardiac Safety

Hitesh Mistry and Leon Aarons (University of Manchester)

Simon Building - Theatre D,

Modelling in Pharmacology
Part 1 (Lean Aarons) (~15 min)
An introduction to pharmacokinetics and pharmacodynamics will be given to set the scene for the modelling talk of Hitesh.  I will describe the processes of absorption, distribution and elimination of drugs which control the drug exposure in the body.  The relationship between drug exposure, principally blood concentration, and pharmacological response will be described with emphasis on factors which cause interpatient variability and requires individualized dosage adjustment.  Finally a brief description of clinical trials which give rise to the data we model will be given.
In-vitro to in-vivo extrapolation in Oncology/Cardiac Safety
Part 2 (Hitesh Mistry) (~25-35 min)
A brief introduction on the different experimental systems that are used in taking a drug in Oncology from discovery into man will be given. I will then focus on one particular translational step, in-vitro to animal efficacy. Both in-vitro and animal experiments consist of measuring a drugs effect on both pathway activation and cancer cell death.  The effect on cancer cell death is measured using cell-count within the in-vitro system and tumour volume within the animal experiment – thus the translational problem involves moving from 2D to 3D. I will show how a mathematical biology model of tumour growth can be used to assist in this translational exercise, via an industrial example, which can reduce the number of animal experiments required when screening a set of compounds.
The second example focusses on correlating in-vitro ion-channel screening data to human cardiac toxicity risk. Here I will focus on comparing the correlative power of biophysical models of the heart versus simpler mathematical biology models across a range of data-sets.  The results will highlight the pitfalls of using over parameterised and structurally uncertain models for quantitative prediction.
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