The review, authored by Shivam Kumar Vyas, Md Imran Ahmed, and Vaibhav A. Dixit at the National Institute of Pharmaceutical Education and Research in Guwahati, India, takes on the question embedded in its title: are cytochrome P450 inhibitors friends or foes? The authors’ answer is deliberately nuanced. Inhibition of specific CYP450 isoforms has emerged as a powerful therapeutic strategy in cancer and infectious disease, while unintended inhibition of drug-metabolizing isoforms remains a leading cause of clinically significant interactions. The challenge, they conclude, is not whether CYP450 inhibition is good or bad, but whether scientists can reliably predict which outcome a given inhibitor will produce.

The therapeutic case for inhibition is striking. CYP19A1, also known as aromatase, catalyzes the conversion of androgens to estrogens, and its blockade by drugs such as letrozole and anastrozole starves hormone-dependent breast cancer cells of the estrogen they need to proliferate. CYP17A1 plays a critical role in androgen biosynthesis, and its inhibition by abiraterone suppresses androgen production in prostate cancer. In the endocrine arena, inhibitors of CYP11B1 and CYP11B2, which drive corticosteroid and aldosterone synthesis, have been explored for Cushing’s syndrome and hypertension. Perhaps most famously, fungal CYP51 catalyzes a key demethylation step in ergosterol biosynthesis, and azole antifungals such as ketoconazole, itraconazole, and fluconazole exploit this dependency to collapse fungal cell membranes. Pathogen CYP450 enzymes have even been pursued as targets in tuberculosis and leishmaniasis.

The same chemistry, however, can turn hostile. Ketoconazole’s inhibition of CYP3A4 dramatically raises plasma levels of drugs like midazolam and simvastatin, producing excessive sedation or, in the case of statins, rhabdomyolysis risk. Fluconazole’s inhibition of CYP2C9 can elevate warfarin concentrations and precipitate bleeding complications. Prodrugs present a subtler trap: clopidogrel requires CYP2C19-mediated activation, and co-administration with inhibitors such as omeprazole blunts the formation of its active metabolite, diminishing antiplatelet efficacy and raising cardiovascular risk. Whether inhibition helps or harms, the authors argue, depends on isoform selectivity, the mechanism of inhibition, and — crucially — what metabolism actually does to the drug in question.

To bring order to a field crowded with overlapping terminology, the review proposes a unified classification. CYP450 inhibitors are sorted into two major mechanistic categories: reversible inhibitors and mechanism-based, or time-dependent, inhibitors. Reversible inhibitors, which are typically competitive and display Type I spectroscopic signatures, occupy the active site without undergoing oxidation. Mechanism-based inhibitors are subdivided into quasi-irreversible and irreversible types. Quasi-irreversible inhibitors coordinate the heme iron through a heterocyclic nitrogen or form metabolite-intermediate complexes, as seen with miconazole, paroxetine, and tadalafil. Irreversible inhibitors, by contrast, are bioactivated by the enzyme itself into reactive metabolites that covalently modify the heme or protein residues — a process the authors prefer to describe without the popular but imprecise label of suicide inhibition.

A key analytical contribution is the authors’ reassessment of kinetic behavior using a modified Michaelis-Menten parameter, β = Km/Ki, which captures the balance between a molecule’s role as a substrate and as an inhibitor of the same enzyme. When log β exceeds 1, non-productive inhibitory binding dominates and the enzyme effectively self-inhibits; when log β falls below 1, productive metabolism prevails. Applying this framework, phenacetin at CYP1A2 shows a log β of roughly 2.64, marking it as strongly inhibitor-like, while dextromethorphan at CYP2D6, with a log β near 0.19, behaves predominantly as a substrate. For mechanism-based inhibitors, the authors introduce a partitioning ratio, kinact/koff, derived from measurable equilibrium constants rather than hard-to-obtain rate constants, to quantify the relative contributions of reversible and irreversible binding. Miconazole’s ratio of 14 signals rapid irreversible binding, whereas tadalafil’s near-zero ratio indicates balanced contributions from both modes.

Spectroscopy provides an independent lens on these mechanisms. The resting ferric enzyme absorbs near 417–420 nanometers in its low-spin state. Type I ligands, which are substrate-like and displace the axial water ligand, shift the heme iron to a high-spin configuration and produce a characteristic difference spectrum with a peak near 385–390 nanometers and a trough near 418–425 nanometers. Type II ligands, typically nitrogen heterocycles such as imidazoles and triazoles, coordinate directly to the ferric heme iron, stabilize the low-spin state, and generate a red-shifted signature with a crest between 425 and 435 nanometers. Yet the authors caution that spectroscopic class and kinetic mechanism do not always align: ketoconazole, though classically Type II, exhibits mixed inhibition kinetics at CYP3A4 because the enzyme’s large, flexible active site can accommodate multiple ligands simultaneously.

On the regulatory side, the review walks through the FDA’s in vitro guidance for evaluating CYP450 substrates, inhibitors, and inducers, and proposes a streamlined workflow that integrates reversible and time-dependent inhibition testing into a single protocol. Reaction phenotyping with recombinant enzymes identifies which isoforms handle a compound; if a single enzyme accounts for more than 25 percent of metabolism, the compound is flagged as a sensitive substrate warranting further study. Inhibition screening at 10 to 30 micromolar, followed by IC50 determination and a preincubation-based IC50 shift assay, distinguishes reversible from time-dependent inhibitors. For reversible inhibitors, an R1 value below 1.02 clears the compound from further DDI concern; for time-dependent inhibitors, an R2 value below 1.25 serves the same gatekeeping function. The authors estimate their consolidated workflow could save four experimental steps compared with running the FDA’s separate protocols in parallel.

Perhaps the most practically useful contribution is a pair of decision criteria for predicting whether a given inhibitor will behave as a friend or a foe. Isoform selectivity, expressed as the difference in pIC50 between the therapeutic target and the nearest drug-metabolizing off-target, emerges as a strong predictor: a gap greater than one log unit — roughly tenfold selectivity — generally correlates with a favorable clinical profile. Letrozole, with a ΔpIC50 of 2.44, and ketoconazole, at 2.63, fit this pattern, while abiraterone, voriconazole, fluconazole, and itraconazole, with gaps at or below 0.75, are associated with significant clinical interactions. The second criterion concerns the direction of metabolism’s effect on drug action: when metabolism activates a prodrug or clears an active drug, inhibition tends to be harmful, as with ticagrelor plus ketoconazole or clopidogrel plus omeprazole; when metabolism inactivates the parent drug, inhibition can be harnessed as a pharmacokinetic booster, as ritonavir and cobicistat do for darunavir, atazanavir, lopinavir, and nirmatrelvir. Ritonavir itself illustrates the context-dependence, acting as a friend for some partners and a foe for others, including posaconazole and venetoclax.

The review closes with a survey of computational models developed over the past five years to predict CYP450 inhibition, ranging from random-forest classifiers like CYPlebrity to graph convolutional networks, deep neural networks such as CYPi-DNN, and multimodal architectures like the Multimodal Encoding Network, which combines molecular fingerprints, graphs, and protein sequences to achieve a reported 93.7 percent accuracy and 98.5 percent AUC. Yet the authors identify a conspicuous gap: no existing model directly predicts isoform selectivity, the very property their analysis shows is most predictive of clinical friend-or-foe behavior. Combining independently trained isoform-specific models, they note, is not a valid substitute because such combinations require their own validation. Until that gap is closed, the review’s central message stands: CYP450 inhibition is neither inherently friend nor foe, and the tools to tell the difference — kinetic, spectroscopic, and now computational — are finally converging on a coherent framework for making that call rationally.

Subject of Research: Mechanisms, classification, and therapeutic roles of cytochrome P450 enzyme inhibitors in drug metabolism and drug-drug interactions

Article Title: Cytochrome P450 inhibitors: friends or foes?

Article References: Vyas, S. K., Ahmed, M. I., & Dixit, V. A. (2026). Cytochrome P450 inhibitors: friends or foes?. Frontiers in Chemistry, 14, Article 1988344. https://doi.org/10.3389/fchem.2026.1988344

Image Credits: AI Generated

DOI: 10.3389/fchem.2026.1988344

Keywords: cytochrome P450, drug metabolism, drug-drug interactions, enzyme inhibition, pharmacokinetics, mechanism-based inhibition, CYP3A4, azole antifungals, machine learning, drug discovery, Type I and Type II ligands, pharmacokinetic boosting