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

Abstract

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.

Folate-Targeted Curcumin-Loaded Niosomes for Site-Specific Delivery in Breast Cancer Treatment: In Silico and In Vitro Study

“…Among the 57 CYP isoforms, 5 CYPs (CYPs 3A4, 2D6, 2C19, 2C9, and 1A2) metabolize approximately 80% of clinical drugs. The metabolism part indicated that Cur is a noninhibitor and nonsubstrate of CYP1A2, CYP2D6, CYP2C19, and CYP3A4; however, it is a CYP2D6 substrate and can inhibit its function [ 69 , 70 , 71 ]. Notably, Cur–Cur interactions caused by CYP2D6 inhibition remain long after passing through the liver and even long after drug discontinuation.…”

New Imidazole-Based N-Phenylbenzamide Derivatives as Potential Anticancer Agents: Key Computational Insights

“…The good cytotoxic potential of the new derivatives 4a–j encouraged us to undertake their ADME and drug-likeness predictions. The metabolism of foreign compounds is carried out by the cytochrome P450 (CYP) family, and the inhibition of these enzymes leads to significant drug–drug interactions ( Beck et al, 2021 ). All the new derivatives 4a–j showed a similar profile by inhibiting four isoforms (CYP1A2, CYP2C9, CYP2D6, and CYP3A2) and no inhibition of the CYP2C19 isoform ( Table 3 ).…”

Application of Pharmacokinetic Prediction Platforms in the Design of Optimized Anti-Cancer Drugs

“…pkCSM was used to make predictions on AMES toxicity of advanced synthetic intermediates and gastrointestinal absorption (GIA), CNS permeability (CNSP), volume of distribution (VDSS), and clearance (CL) of hits modified with novel side chains. SuperCypsPred, a highly accurate prediction platform (accuracy: 0.930), was used to make binary predictions on CYP inhibition [7]. Pred-hERG is a highly accurate tool (accuracy: 0.900) used to make predictions on hERG inhibition [16,17].…”

“…Pharmacokinetic issues are the most common reason for drug failure, often due to unexpected toxicity. The use of artificial intelligence (AI) is actively revolutionizing drug discovery, allowing for the discovery of safer, more selective drug candidates with lower associated costs and reduced time to market [7][8][9]. AI provides an affordable means of predicting toxicity at the early stages of drug discovery, enabling the selection of compounds with devoid of PK deficits.…”