AI in Pharmacogenomics

Pharmacogenomics studies how inherited DNA differences change drug response. A small set of genes accounts for most results that can be acted on today. CYP2D6, CYP2C19 and CYP2C9 encode liver enzymes that process many drugs. VKORC1 affects warfarin sensitivity. SLCO1B1 affects how statins such as simvastatin reach the liver. TPMT and NUDT15 guide thiopurine dosing, DPYD guides fluoropyrimidine chemotherapy, and certain HLA-B variants predict severe immune reactions to drugs such as abacavir and carbamazepine. Interpretation runs in steps. The lab identifies variants, groups them into named 'star alleles' (catalogued by PharmVar) and assigns the pair of alleles a person carries, called a diplotype. That diplotype is then translated into a phenotype, such as poor, intermediate, normal, rapid or ultrarapid metabolizer. The Clinical Pharmacogenetics Implementation Consortium (CPIC) publishes peer-reviewed guidelines that turn phenotypes into prescribing actions. CPIC guidelines assume a test result already exists. They explain what to do with it, not whether to order the test. The software layer does most of the day-to-day work. Rules engines in electronic health records check a new order against stored genotypes and alert the prescriber or pharmacist. Machine learning is used in narrower ways: predicting whether rare, uncharacterized variants reduce function, calling difficult genes from sequencing data, and research models that combine genetics with clinical factors. Pharmacists do more than read alerts. They check for phenoconversion, where a drug the patient already takes changes how an enzyme works. For example, strong CYP2D6 inhibitors such as paroxetine or fluoxetine can make a genetic normal metabolizer behave like a poor metabolizer. A common misconception is that a pharmacogenomic panel tells you which antidepressant will work. At best it shows how some drugs will be processed, which is only one factor in whether a drug helps.