In Vitro Methods to Measure Drug Metabolism and Drug Interactions in Drug Discovery and Development

2020 ◽  
Vol 10 (7) ◽  
pp. 2376 ◽  
Author(s):  
Rob C. van Wijk ◽  
Rami Ayoun Alsoud ◽  
Hans Lennernäs ◽  
Ulrika S. H. Simonsson

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.


2013 ◽  
Vol 94 (1) ◽  
pp. 95-112 ◽  
Author(s):  
K L R Brouwer ◽  
D Keppler ◽  
K A Hoffmaster ◽  
D A J Bow ◽  
Y Cheng ◽  
...  

2012 ◽  
Vol 65 (1-2) ◽  
pp. 45-49
Author(s):  
Bozana Nikolic ◽  
Miroslav Savic

Introduction. Since drug interactions may result in serious adverse effects or failure of therapy, it is of huge importance that health professionals base their decisions about drug prescription, dispensing and administration on reliable research evidence, taking into account the hierarchy of data sources for evaluation. Clinical Significance of Potential Interactions - Information Sources. The sources of data regarding drug interactions are numerous, beginning with various drug reference books. However, they are far from uniformity in the way of choosing and presenting putative clinically relevant interactions. Clinical Significance of Potential Interactions - Interpretation of Information. The difficulties in interpretation of drug interactions are illustrated through the analysis of a published example involving assessment made by two different groups of health professionals. Systematic Evaluation of Drug-Drug Interaction. The potential for interactions is mainly investigated before marketing a drug. Generally, the in vitro, followed by in vivo studies are to be performed. The major metabolic pathways involved in the metabolism of a new molecular entity, as well as the potential of induction of human enzymes involved in drug metabolism are to be examined. In the field of interaction research it is possible to make use of the population pharmacokinetic studies as well as of the pharmacodynamic assessment, and also the postregistration monitoring of the reported adverse reactions and other literature data. Conclusion. In vitro and in vivo drug metabolism and transport studies should be conducted to elucidate the mechanisms and potential for drug-drug interactions. The assessment of their clinical significance should be based on well-defined and validated exposure-response data.


2018 ◽  
Vol 39 (1) ◽  
pp. 4-15 ◽  
Author(s):  
Adedamola Olayanju ◽  
Lauren Jones ◽  
Karin Greco ◽  
Christopher E. Goldring ◽  
Tahera Ansari

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