Cytochrome P450 3A4 (CYP3A4) is one of the most crucial enzymes in human drug metabolism and is responsible for metabolizing more than 75 % of drugs commonly used in the clinic [1,2]. It plays a central role in the disposition of many therapeutic agents, particularly anticancer drugs [[3], [4], [5]]. Consequently, the catalytic activity of CYP3A4 is a primary determinant of drug efficacy and safety. This is of particular concern for cancer patients, who are often at significant risk of drug-drug interactions (DDIs) due to polypharmacy involving CYP3A4-metabolized regimens [6]. Neratinib, a potent tyrosine kinase inhibitor approved for the extended adjuvant treatment of HER2-positive early-stage breast cancer, exemplifies this susceptibility [7]. For instance, co-administration with rifampin, a potent hepatic enzyme inducer, decreases the area under curve (AUC) and Cmax of neratinib by 76 % and 71 %, respectively [8]. In contrast, co-administration of ketoconazole, a hepatic enzyme inhibitor, increased neratinib's AUC(0–∞) and Cmax by 3.2-fold and 2.1-fold, respectively, compared to neratinib administered alone [9]. While classical CYP3A4 inhibitors (e.g., proton pump inhibitors) are well-characterized, the continuous development of novel therapeutic compounds necessitates the systematic screening of potential inhibitors and the elucidation of their mechanisms of action [[10], [11], [12]].

Beyond chemical inhibitors, the activity of CYP3A4 is also significantly affected by genetic polymorphisms, including single-nucleotide polymorphisms (SNPs), which can lead to reduce or even completely abolished enzymatic function [[13], [14], [15], [16]]. To date, 49 allelic variants of CYP3A4 have been identified, some of which (e.g., CYP3A4∗15, ∗18, ∗19) show distinct and exhibiting specific distribution patterns [[17], [18], [19], [20]]. Previous studies have reported that the metabolic capacity of certain variants for substrates like osimertinib can decreases to 25.68 %–48.25 % of observed in the wild-type enzyme, underscoring the significant role of genotype in contributing to interindividual variability in therapeutic outcomes [21,22].

In the present study, we employed an integrative approach combining computational simulations with experimental validation to investigate these interactions. Molecular docking was utilized to screen potential CYP3A4 inhibitors, after which their impact on neratinib metabolism were assessed using both in-vitro incubation systems and in-vivo rat models. To further characterize the inhibition modalities and mechanisms, we combined binding pocket analysis with enzyme kinetic assessments. Furthermore, we systematically evaluated how 10 specific CYP3A4 genetic variants influence neratinib metabolic kinetics and alter susceptibility to inhibitors. These findings provide significant pharmacogenetic insights aimed at optimizing neratinib therapy and advancing personalized treatment strategies.