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Through the collation of extensive demographic, physiological, genomic and in vitro biochemical data, Simcyp has built algorithms allowing you to conduct simulations in representative virtual populations.
Through the guidance of our Consortium Members, Simcyp has overcome one of the main limitations of in vitro – in vivo extrapolation – that the projections are usually only made to mean data collected in studies in healthy, young, Caucasian, male individuals. Other approaches provide inadequate estimates of inter-subject variability based solely on simple demographic parameters. Failure to account for other known covariates, such as genetic differences in enzyme composition, limits the ability to address the extremes of risk.
"Interpretation … should focus not only on mean effect but also the observed and theoretically conceivable extremes" – Krayenbühl et al, EJCP 1999
Our algorithms take account of the following characteristics of the target population:
- Demographics (gender, age, body size, ethnicity etc.)
- Societal factors (environmental effects, dietary habits etc.)
- Genetic differences (in enzymes etc.)
An added benefit of simulating physiological variability is that by using Simcyp you will be able to predict drug disposition in relevant individuals with realistic variability.
Rapid advances in the application of human in vitro systems, coupled with increased understanding of the role of the functional genetics of drug-metabolising enzymes, transporters and receptors, means that the ability to predict pharmacokinetic outcomes in ‘real-world’ patient populations is becoming increasing valuable, if not essential. To achieve this, our algorithms incorporate known variability in demographic and biological components within relevant disease populations.
Accounting for physiological variability is crucial if the characteristics of individuals who are predisposed to the extremes of drug exposure are to be identified prior to clinical studies. This information, when linked to drug-specific physicochemical and in vitro data on absorption and disposition in Simcyp algorithms, allows prediction of drug behaviour in the 'virtual patient population', as opposed to a 'virtual reference man'.