Cytochrome P450 inhibitors: friends or foes?
- 1Department of Medicinal Chemistry, Department of Pharmaceuticals, Ministry of Chemicals and Fertilisers, National Institute of Pharmaceutical Education and Research, Guwahati (NIPER Guwahati), Govt. of India, Guwahati, Assam, India.
Abstract
Cytochrome P450 (CYP450) enzymes play a central role in the metabolism of xenobiotics and endogenous compounds, influencing drug pharmacokinetics, efficacy, and safety. Inhibition of these enzymes has traditionally been viewed as a major liability in drug discovery due to its association with drug-drug interactions (DDIs), toxicity, and metabolic instability. However, inhibition of specific CYP450 isoforms has emerged as a powerful therapeutic strategy in multiple disease areas, including cancer and infectious diseases. This review presents a comprehensive analysis of data and regulatory guidance on CYP450 inhibitors to gain insights into whether CYP450 inhibition is a useful property ("a friend") or a liability ("a foe") for drug discovery scientists. Review of the literature shows that the design of novel CYP450 inhibitors requires an understanding of mechanisms of inhibition, whereas the clinical outcomes are mainly determined by isoform selectivity and effects of metabolism on drug action including reactive metabolite formation. The kinetic behaviour of representative inhibitor drugs is reassessed using variations in Michaels-Menten parameters β = K m/K i, and partitioning factor (k inact/k off). A modified classification of CYP450 inhibitors is proposed which consolidates the often-confusing classifications (mechanistic vs. spectroscopic) reported in the literature and FDA guidelines. The beneficial role of CYP450 inhibition in treatment and pharmacokinetic boosting is also discussed. Finally, advances utilising deep neural networks (DeepCYP) and machine learning methods (CYPlebrity, ML-augmented docking) in the last 5 years in inhibitor predictions are reviewed. This review provides a fresh perspective on classifying CYP450 inhibitors, existing guidelines for evaluation of in-vitro drug-drug interactions, and factors influencing clinical outcomes of CYP450 inhibition. This is expected to help researchers correctly identify CYP450 inhibitors as Friends or Foes in various discovery and clinical settings.
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BibTeX
@article{vyas2026cytochrome,
title = {{Cytochrome P450 inhibitors: friends or foes?}},
author = {Shivam Kumar Vyas and Md Imran Ahmed and Vaibhav A Dixit},
journal = {Frontiers in chemistry},
year = {2026},
month = oct,
doi = {10.3389/fchem.2026.1988344},
url = {https://europepmc.org/article/MED/42948148},
note = {CC BY},
}
RIS
TY - JOUR
TI - Cytochrome P450 inhibitors: friends or foes?
AU - Shivam Kumar Vyas
AU - Md Imran Ahmed
AU - Vaibhav A Dixit
PY - 2026
DA - 2026/10/13
JO - Frontiers in chemistry
DO - 10.3389/fchem.2026.1988344
UR - https://europepmc.org/article/MED/42948148
AB - Cytochrome P450 (CYP450) enzymes play a central role in the metabolism of xenobiotics and endogenous compounds, influencing drug pharmacokinetics, efficacy, and safety. Inhibition of these enzymes has traditionally been viewed as a major liability in drug discovery due to its association with drug-drug interactions (DDIs), toxicity, and metabolic instability. However, inhibition of specific CYP450 isoforms has emerged as a powerful therapeutic strategy in multiple disease areas, including cancer and infectious diseases. This review presents a comprehensive analysis of data and regulatory guidance on CYP450 inhibitors to gain insights into whether CYP450 inhibition is a useful property ("a friend") or a liability ("a foe") for drug discovery scientists. Review of the literature shows that the design of novel CYP450 inhibitors requires an understanding of mechanisms of inhibition, whereas the clinical outcomes are mainly determined by isoform selectivity and effects of metabolism on drug action including reactive metabolite formation. The kinetic behaviour of representative inhibitor drugs is reassessed using variations in Michaels-Menten parameters β = K m/K i, and partitioning factor (k inact/k off). A modified classification of CYP450 inhibitors is proposed which consolidates the often-confusing classifications (mechanistic vs. spectroscopic) reported in the literature and FDA guidelines. The beneficial role of CYP450 inhibition in treatment and pharmacokinetic boosting is also discussed. Finally, advances utilising deep neural networks (DeepCYP) and machine learning methods (CYPlebrity, ML-augmented docking) in the last 5 years in inhibitor predictions are reviewed. This review provides a fresh perspective on classifying CYP450 inhibitors, existing guidelines for evaluation of in-vitro drug-drug interactions, and factors influencing clinical outcomes of CYP450 inhibition. This is expected to help researchers correctly identify CYP450 inhibitors as Friends or Foes in various discovery and clinical settings.
SN - 2296-2646
ER -