Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

Pharmaceuticals

KyleR. Beck¹  TylerC. Beck²  MennyM. Benjamin²  Jordan Morningstar³  RussellA. Norris³ 

[1] College of Pharmacy, The Ohio State University, 217 Lloyd M Parks Hall, 500 West 12th Ave., Columbus, OH 43210, USA;Drug Discovery & Biomedical Sciences, Medical University of South Carolina, 280 Calhoun Street, QF204, Charleston, SC 29424-2303, USA;Regenerative Medicine & Cell Biology, Medical University of South Carolina, 171 Ashley Avenue, 604F CRI, Charleston, SC 29424-2303, USA;

关键词: CYP3A4;    CYP2D6;    CYP2C19;    CYP2C9;    CYP1A2;    cheminformatics;   

DOI  :  10.3390/ph14050472

来源: DOAJ

【 摘 要 】

Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug–drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.

【 授权许可】

Unknown