Inhibition of Drug-Metabolizing Enzymes and Drug–Drug Interactions in Drug Discovery and Development

Author(s):  
R. Scott Obach
2019 ◽  
Vol 63 (4) ◽  
Author(s):  
Kelly Bleasby ◽  
Kerry L. Fillgrove ◽  
Robert Houle ◽  
Bing Lu ◽  
Jairam Palamanda ◽  
...  

ABSTRACT Doravirine is a novel nonnucleoside reverse transcriptase inhibitor for the treatment of human immunodeficiency virus type 1 infection. In vitro studies were conducted to assess the potential for drug interactions with doravirine via major drug-metabolizing enzymes and transporters. Kinetic studies confirmed that cytochrome P450 3A (CYP3A) plays a major role in the metabolism of doravirine, with ∼20-fold-higher catalytic efficiency for CYP3A4 versus CYP3A5. Doravirine was not a substrate of breast cancer resistance protein (BCRP) and likely not a substrate of organic anion transporting polypeptide 1B1 (OATP1B1) or OATP1B3. Doravirine was not a reversible inhibitor of major CYP enzymes (CYP1A2, -2B6, -2C8, -2C9, -2C19, -2D6, and -3A4) or of UGT1A1, nor was it a time-dependent inhibitor of CYP3A4. No induction of CYP1A2 or -2B6 was observed in cultured human hepatocytes; small increases in CYP3A4 mRNA (≤20%) were reported at doravirine concentrations of ≥10 μM but with no corresponding increase in enzyme activity. In vitro transport studies indicated a low potential for interactions with substrates of BCRP, P-glycoprotein, OATP1B1 and OATP1B3, the bile salt extrusion pump (BSEP), organic anion transporter 1 (OAT1) and OAT3, organic cation transporter 2 (OCT2), and multidrug and toxin extrusion 1 (MATE1) and MATE2K proteins. In summary, these in vitro findings indicate that CYP3A4 and CYP3A5 mediate the metabolism of doravirine, although with different catalytic efficiencies. Clinical trials reported elsewhere confirm that doravirine is subject to drug-drug interactions (DDIs) via CYP3A inhibitors and inducers, but they support the notion that DDIs (either direction) are unlikely via other major drug-metabolizing enzymes and transporters.


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.


2019 ◽  
Vol 108 (1) ◽  
pp. 87-101 ◽  
Author(s):  
Stephanie Dodd ◽  
Sivacharan Kollipara ◽  
Manuel Sanchez-Felix ◽  
Hyungchul Kim ◽  
Qingshuo Meng ◽  
...  

Science ◽  
1999 ◽  
Vol 286 (5439) ◽  
pp. 487-491 ◽  
Author(s):  
William E. Evans ◽  
Mary V. Relling

Genetic polymorphisms in drug-metabolizing enzymes, transporters, receptors, and other drug targets have been linked to interindividual differences in the efficacy and toxicity of many medications. Pharmacogenomic studies are rapidly elucidating the inherited nature of these differences in drug disposition and effects, thereby enhancing drug discovery and providing a stronger scientific basis for optimizing drug therapy on the basis of each patient's genetic constitution.


2015 ◽  
Vol 2015 ◽  
pp. 1-26 ◽  
Author(s):  
Charu Sharma ◽  
Bassem Sadek ◽  
Sameer N. Goyal ◽  
Satyesh Sinha ◽  
Mohammad Amjad Kamal ◽  
...  

The cannabinoid molecules are derived fromCannabis sativaplant which acts on the cannabinoid receptors types 1 and 2 (CB1and CB2) which have been explored as potential therapeutic targets for drug discovery and development. Currently, there are numerous cannabinoid based synthetic drugs used in clinical practice like the popular ones such as nabilone, dronabinol, and Δ9-tetrahydrocannabinol mediates its action through CB1/CB2receptors. However, these synthetic basedCannabisderived compounds are known to exert adverse psychiatric effect and have also been exploited for drug abuse. This encourages us to find out an alternative and safe drug with the least psychiatric adverse effects. In recent years, many phytocannabinoids have been isolated from plants other thanCannabis. Several studies have shown that these phytocannabinoids show affinity, potency, selectivity, and efficacy towards cannabinoid receptors and inhibit endocannabinoid metabolizing enzymes, thus reducing hyperactivity of endocannabinoid systems. Also, these naturally derived molecules possess the least adverse effects opposed to the synthetically derived cannabinoids. Therefore, the plant based cannabinoid molecules proved to be promising and emerging therapeutic alternative. The present review provides an overview of therapeutic potential of ligands and plants modulating cannabinoid receptors that may be of interest to pharmaceutical industry in search of new and safer drug discovery and development for future therapeutics.


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