Modeling in the pharmaceutical industry
Clinical development success rates
Every pharmaceutical/biotech company targets the success of its compound as a drug for the disease treatment and a product on the market.
The Likelihood of Approval (LOA) from the Phase I across the considered nosologies is close to 10% and there is a strong need to increase the efficiency in the research and development (R&D).
Mathematical modeling in R&D
Modeling & simulation (M&S) methods are established quantitative tools, which are useful in supporting R&D, regulatory approval, and marketing of novel therapeutics. Applications of M&S help to design efficient studies and interpret their results in context of all available data and knowledge to enable effective decision-making during the R&D process.
There are two main sets of modeling approaches:
the population-based models which are well-established within the pharmaceutical industry today and fall under the discipline of clinical pharmacometrics (PMX);
the systems dynamics models which encompass a range of models of pathophysiology amenable to pharmacological intervention, signalling pathways in biology, and substance distribution in the body (today known as physiologically-based pharmacokinetic models), which today may be collectively referred to as the quantitative systems pharmacology models (QSP).
M&S Decisions historically works on the convergence – or rather selected integration – of PMX and QSP approaches into ‘middle-out’ drug-disease modelling platforms, which retain selected mechanistic characteristics while remaining parsimonious, fit-for-purpose, and able to address variability, uncertainty aspects, as well as the testing of covariates.
To develop Drug-Disease modeling platforms to be used as quantitative companion tools in support of decision-making across the R&D continuum;
To apply such platforms of quantitative knowledge integration towards projects to address recurring questions on compound selection, dose & dose scheduling, therapeutic window, biomarker selection, patient populations, and selection, combination therapies, study design optimization.