Pharmacogenomics Using Quantum Chemical Analysis: Predicting CYP2D6-Mediated Analgesic Metabolism in Oral Cancer Patients

Oral cancer surgery, including glossectomy and mandibulectomy, tends to cause severe post-operative pain. This process significantly impairs recovery and nutritional intake, affecting quality of life. Recently, opioid analgesics such as codeine, tramadol, and oxycodone are commonly prescribed. However, their efficacy and safety depend heavily on cytochrome P450 2D6 (CYP2D6) activity. CYP2D6 exhibits clinically relevant genetic polymorphisms, resulting in four distinct phenotypes: poor metabolizers (PM, 7–10% of Caucasians), intermediate metabolizers (IM), normal metabolizers (NM, 60–80%), and ultra-rapid metabolizers (UM, 1–30% depending on population). PM patients experience therapeutic failure due to inadequate conversion of prodrugs to active metabolites, while UM patients face life-threatening toxicity from accelerated drug accumulation. Current pharmacogenomic approaches are retrospective and observational, lacking prospective predictive tools.

This research develops a computational framework integrating density functional theory (DFT)-derived quantum chemical descriptors and molecular dynamics (MD) simulations to predict CYP2D6-mediated analgesic metabolism. Thereby, this method can facilitate the genotype-guided analgesic selection process for oral cancer patients.

In this paper, analgesic molecules were selected for analysis, and DFT analysis was performed to predict the levels of oxidative metabolism in patients. In summary, the framework used in this paper represents the integration of DFT descriptors with pharmacogenomics for post-operative pain management. Thereby, this study could offer a scalable approach for personalized analgesia in oral cancer surgery.