PHARMACOGENOMIC MACHINE LEARNING MODELS FOR PREDICTING INDIVIDUALIZED OPIOID AND ANESTHETIC DRUG RESPONSE IN CANCER SURGERY PATIENTS

Abstract

Surgery patients with cancer can have very different responses to opioid and anesthetic medications because of variations in genetics, chronic medical conditions, previous cancer treatments, organ function, and physiologic shifts during surgery. However, traditional dosing methods, which are primarily age and body weight based or based on general clinical guidelines, are likely to miss this individual variability, thereby raising the possibility of sub-optimal analgesia, excessive sedation, delayed recovery, and opioid related side effects. This paper looks at pharmacogenomic machine learning models for predicting individualized opioid and anesthetic drug responses in cancer surgery patients. Machine learning models can detect complex patterns of drug-responses that are hard to discern using traditional statistics by incorporating genetic markers, pharmacokinetic and pharmacodynamic parameters, electronic health record data, intraoperative monitoring signals and clinical risk factors. The study emphasizes the promise of neural-pharmacokinetic/pharmacodynamic modeling, reinforcement learning, interpretable artificial intelligence, and model-informed precision dosing for aiding real-time decision making in the perioperative setting. These techniques can help stabilize the anesthetic, increase the effectiveness of the pain control, minimize toxicity, and promote recovery after surgery. But to make it to the clinic requires big and varied sets of data, external validation, transparent model interpretation, standardized reporting, ethical governance and incorporation into current clinical workflows. In conclusion, pharmacogenomic machine learning holds significant potential for advancing precision anesthesia in oncological surgery, potentially leading to more individualized, safer, and evidence-based perioperative care.