Thiopurine methyltransferase (TPMT) and Nudix hydrolase 15 (NUDT15) are key enzymes that catabolize thiopurines. Decreased or no‐function alleles in TPMT and NUDT15 are associated with reduced or no enzyme activity and predictive of pronounced adverse effects, including severe myelosuppression, that may occur among individuals treated with standard doses of thiopurines. Genetic variants in these genes are present in all world populations; however, their frequency varies by ancestry. In this updated guideline, we provide recommendations for adjusting starting doses of mercaptopurine, thioguanine, and azathioprine based on TPMT and NUDT15 genotypes, including for individuals with variants in both genes (updates on www.clinpgx.org ).

Pharmacogenomics (PGx) has emerged as a powerful tool to personalize drug selection and dosing based on a patient’s genetic profile. However, there are a range of challenges that impede uptake in current clinical practice. For example, clinicians often express frustration with commercially available PGx panel tests, which fail to consistently include all key actionable PGx genes (according to the Clinical Pharmacogenetics Implementation Consortium (CPIC), Food and Drug Administration (FDA) PGx guidelines, or The Dutch Pharmacogenetics Working Group (DPWG) guidelines) and instead are too long with clinically unimportant information (unvalidated genotypes). Additionally, the lack of EMR integration, clinician education and awareness of the benefits of PGx impedes uptake. This paper examines key challenges identified in clinical practice and proposes future directions, focusing on limiting PGx reports to essential data, providing point-of-prescription alerts, and establishing reimbursement pathways that encourage adoption. Future directions include leveraging large language models, integrating point-of-prescription alerts and phenoconversion calculators into the electronic medical record, increasing the genomic diversity of PGx study populations, and streamlining coverage by payers.

Pharmacovigilance is a critical component of medication safety. Despite rigorous evaluation of new drugs during clinical trials, some adverse effects might only be identified once pharmaceuticals are used by a larger population for a longer duration. Adverse drug reactions cause negative healthcare outcomes and in severe cases, may lead to hospital admissions, delayed hospital discharges, or deaths. Adverse event reports submitted to pharmacovigilance programs by healthcare professionals and consumers are a key source of information regarding previously unrecognized detrimental effects. Pharmacogenetic markers that indicate how particular genes impact an individual’s response to medication can help explain some idiosyncratic adverse reactions. Incorporating pharmacogenomic guidance in prescribing is proven to decrease the incidence of adverse reactions and improve clinical outcomes. However, this information is not yet routinely included in incident reports. In this era of precision medicine, when prescribing can be tailored to the individual, pharmacogenomic test results yield valuable data that can enhance both individual and population health. Furthermore, advanced artificial intelligence (AI) and machine learning (ML) methods facilitate analysis of complex genetic data, revealing insights not previously available. This white paper outlines current pharmacovigilance and pharmacogenomic practices and recommends that pharmacovigilance programs include pharmacogenomics as a crucial data point in their investigations.