The paper "The Apoe-/- Mouse PhysioLab® Platform: A Validated Physiologically-based Mathematical Model of Atherosclerotic Plaque Progression in the Apoe-/- Mouse" by Jason Chan and colleagues [
The Chan et al. model, based upon the Apoe-/- mouse, includes elements of cholesterol and macrophage trafficking, inflammation, oxidative stress, endothelial function, and thrombosis. It has the ability to predict relationships between biomarker data, pharmacodynamic effects and clinical outcomes. The model is the outcome of a collaboration between Entelos, an in silico modelling and simulation company, and Philip Morris, a tobacco company. A primary motive for developing the model appears to have been a desire to explore the relationship between smoking (and smoking cessation) and heart disease. However, the scope of the model is broad enough to enable it to be used to model the effects of other lifestyle factors, including diet, and of drug treatment. As an example of drug effects, the model is used to predict effects of ezetimibe (which blocks cholesterol absorption from the intestine) on atherosclerotic progression. By publishing the model in Biodiscovery, the authors have agreed to make it available, free of charge, to all researchers.
In the past, drug developers have regarded computational models of complex biological systems with great scepticism. There has been an impression that the systems involved are so complex that any attempt to describe them mathematically must involve simplifying assumptions that were likely to undermine the dynamics of the system being modelled. There was pessimism about the ability to validate such large models (the Chan et al. model contains 94 ordinary differential equations, 524 algebraic equations, and 3,508 parameters). Yet these same drug developers have for eighty years relied heavily upon pharmacokinetic (PK) models in making drug development decisions, and PK models make equally sweeping simplifications. Why the difference? Two reasons: the first is that PK is a generic technology. We can use the same analytical methods to measure plasma levels of an antihypertensive as we use for an antidepressant, whereas the biological or pharmacodynamic (PD) endpoints that we use in disease modelling are different for every therapeutic area and for every drug class. Secondly, the mathematics of disease modelling and PD modelling is more complex. Both these factors still present barriers to wider use of disease modelling, but the barriers are yielding to advances in technology. Development of prognostic and pharmacodynamic biomarkers (and to an increasing extent, whole-body imaging techniques) is making it easier to collect the data required to validate complex models. In a recent review [
In recent years, computational disease models have been published covering a wide range of therapeutic areas. In HIV disease, we are presented with a complex interactive system where the immune system attacks the virus, and the virus attacks the immune system. Predicting PD effects of antiviral drugs requires a model that captures these complexities. Because of the very high mutation rates of retroviruses, in the early days of anti-retroviral drug development there was pessimism about whether sustained responses could be achieved in the face of acquired drug resistance. A disease model predicted, correctly, that with the use of multi-drug combinations, and with sufficient treatment intensity, disease progression could be arrested for many years [
These disease modelling approaches, formerly the province of theoreticians, are beginning to interest the wider drug development community. Quintiles, a major clinical research organization, recently published an extensive report on modelling and simulation practice throughout the drug development process [
- Chan JR, Vuillaume G, Bever C, Lebrun S, Lietz M, Steffen Y, et al. The Apoe-/- Mouse PhysioLab® Platform: A Validated Physiologically-based Mathematical Model of Atherosclerotic Plaque Progression in the Apoe-/- Mouse. Biodiscovery 2012; 3:2.
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