Abstract
The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.
Highlights
Drug discovery and development is a challenging, lengthy, and costly process
The MID3 framework should be applied in the development of new TB drug regimens and is necessary for the reliable prediction of the optimal selection of novel TB drug combination therapies based on pre-clinical information, and subsequent decisions on which combinations to evaluate in clinical trials in order to confirm their efficacy and safety
Model-informeddrug drugdiscovery discoveryand and development is given by a quantitative framework for prediction and extrapolation, aimed atand improving the quality, efficiency and cost-effectiveness
Summary
Drug discovery and development is a challenging, lengthy, and costly process. The costs of a novel drug reaching the market can be as much as 2–3 billion dollars [1]. The MID3 framework should be applied in the development of new TB drug regimens and is necessary for the reliable prediction of the optimal selection of novel TB drug combination therapies based on pre-clinical information, and subsequent decisions on which combinations to evaluate in clinical trials in order to confirm their efficacy and safety. MID3,towith modeling and simulation as key to beinform applied throughout the with respect to clinical trial design and the selection of drugs and doses to be carried forward from pre-clinical drug development phases in order to optimize and inform decision making the preclinical andtrial intodesign clinical trial. Different earlier defined exposure-response relationship pre-clinical data phase) is confirmed important drug development decision steps using are subject to(learning learn-and-confirm cycles, for (confirming phase). EBA clinical studies where the earlier defined exposure–response relationship using pre-clinical data (learning phase) is confirmed (confirming phase)
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