Insights into pharmacogenetics, drug-gene interactions, and drug-drug-gene interactions.
Laura E RussellKatrina G ClawKaja M AagaardSarah M GlassKuheli DasguptaF Leah NezAlex HaimbaughBenjamin J MaldonatoJaydeep YadavPublished in: Drug metabolism reviews (2024)
This review explores genetic contributors to drug interactions, known as drug-gene and drug-drug-gene interactions (DGI and DDGI, respectively). This article is part of a mini-review issue led by the International Society for the Study of Xenobiotics (ISSX) New Investigators Group. Pharmacogenetics (PGx) is the study of the impact of genetic variation on pharmacokinetics (PK), pharmacodynamics (PD), and adverse drug reactions. Genetic variation in pharmacogenes, including drug metabolizing enzymes and drug transporters, is common and can increase the risk of adverse drug events or contribute to reduced efficacy. In this review, we summarize clinically actionable genetic variants, and touch on methodologies such as genotyping patient DNA to identify genetic variation in targeted genes, and deep mutational scanning as a high-throughput in vitro approach to study the impact of genetic variation on protein function and/or expression in vitro . We highlight the utility of physiologically based pharmacokinetic (PBPK) models to integrate genetic and chemical inhibitor and inducer data for more accurate human PK simulations. Additionally, we analyze the limitations of historical ethnic descriptors in pharmacogenomics research. Altogether, the work herein underscores the importance of identifying and understanding complex DGI and DDGIs with the intention to provide better treatment outcomes for patients. We also highlight current barriers to wide-scale implementation of PGx-guided dosing as standard or care in clinical settings.
Keyphrases
- adverse drug
- genome wide
- high throughput
- electronic health record
- healthcare
- drug induced
- primary care
- genome wide identification
- end stage renal disease
- gene expression
- machine learning
- dna methylation
- quality improvement
- deep learning
- palliative care
- newly diagnosed
- drug delivery
- cell free
- pain management
- small molecule
- protein protein
- health insurance
- big data
- patient reported
- circulating tumor