Pharmacogenes that demonstrate high association evidence according to CPIC, DPWG, and PharmGKB

Unveiling the heritability of selected unexplored pharmacogenetic markers in the Saudi population

Background

Pharmacogenomic (PGx) variants can significantly impact drug response, but limited data exists on their prevalence in Middle Eastern populations. This study aimed to investigate the inheritance of certain markers in candidate pharmacogenes among healthy Saudis.

Methods

DNA samples from 95 unrelated healthy Saudi participants were genotyped using the Affymetrix Axiom Precision Medicine Diversity Array. Thirty-eight variants in 15 pharmacogenes were analyzed based on their clinical relevance and lack of previous reporting in Saudi populations.

Results

Twenty-six of the 37 tested markers were undetected in the cohort. The selected variants in six genes [DPYD (rs1801268), CACNA1S (rs772226819), EGFR (rs121434568), RYR1 (rs193922816), CYP2B6 (rs3826711), and MT-RNR1 (rs267606617, rs267606618, rs267606619)] were found to be non-existing among Saudis. In contrast, 11 variants and alleles in nine pharmacogenes were detected at varying frequencies. Notable findings included high frequencies of variants in ATIC [rs4673993, minor allele frequency (MAF) = 0.71)] and SLC19A1 (rs1051266, MAF = 0.48) affecting methotrexate efficacy. Three alleles were identified in CYP3A4, including a common (CYP3A4 rs2242480) and two rare alleles (*3 and *22). Another three markers [rs16969968 in CHRNA5, rs11881222 in IFNL3 (IL28B), and SLCO1B1*14] were found to be highly distributed among the participants (MAF = 0.35, 0.30, and 0.14, respectively). Conversely, three rare markers: CYP2A6*2, NAT2*14, and rs115545701 in CFTR, were identified at low-frequency levels (MAF = 0.021, 0.011, 0.005, respectively). Statistically significant differences in allele frequencies were observed for eight variants between Saudi and African populations, five variants compared to East Asians, and two variants compared to Europeans.

Conclusion

This study provides novel insights into the distribution of clinically relevant PGx variants in the Saudi population. The findings have implications for personalizing treatments for various conditions, including rheumatoid arthritis, cystic fibrosis, and hepatitis C. These data contribute to the development of population-specific PGx testing panels and treatment guidelines.

The Role of Candidate Polymorphisms in Drug Transporter Genes on High-Dose Methotrexate in the Consolidation Phase of the AIEOP-BFM ALL 2009 Protocol

High-dose methotrexate (HD-MTX) infusions are commonly used to consolidate remission in children with acute lymphoblastic leukemia (ALL). We investigate the potential role of candidate polymorphisms in SLCO1B1 (rs4149056 and rs2306283), ABCB1 (rs1045642), ABCC2 (rs717620), ABCC3 (rs9895420), and ABCC4 (rs7317112) drug transporters genes on HD-MTX pharmacokinetics and patients' outcome (meant both as relapse and drug-related toxicities) in an Italian cohort of 204 ALL pediatric patients treated according to the AIEOP-BFM ALL 2009 protocol. TaqMan SNP genotyping assays determined patient's genotypes. Measurements of HD-MTX plasma concentration were available for 814 HD-MTX courses in 204 patients; MTX clearance was estimated by a two-compartmental linear pharmacokinetic model with first-order elimination and a Bayesian approach, via ADAPT. Independent contributions of age and ABCC4 SNP rs7317112 (A>G, intronic) on MTX clearance were detected in a multivariate analysis (p = 1.57 × 10-8 and p = 2.06 × 10-5, respectively), suggesting a delayed elimination of the drug in older patients and an accelerated one in carriers of the variant GG genotype. After multiple corrections, the association between ABCC2 SNP rs717620 (-24 C>T) and severe hematological toxicity was found (p < 0.005). Moreover, SLCO1B1 SNP rs4149056 (c.521T>C, p.V174A) affected patients' outcomes: carriers of the variant C allele presented a reduced risk of relapse compared to wild-type TT (hazard risk: 0.27, 95% confidence interval [CI]: 0.08-0.90, p = 0.037). Taken together, these data highlighted the importance of variants in drug transporters genes on HD-MTX disposition in the AIEOP-BFM ALL 2009 protocol consolidation phase, and their putative role as predictive markers of outcome.

PGxQA: A Resource for Evaluating LLM Performance for Pharmacogenomic QA Tasks

Pharmacogenetics represents one of the most promising areas of precision medicine, with several guidelines for genetics-guided treatment ready for clinical use. Despite this, implementation has been slow, with few health systems incorporating the technology into their standard of care. One major barrier to uptake is the lack of education and awareness of pharmacogenetics among clinicians and patients. The introduction of large language models (LLMs) like GPT-4 has raised the possibility of medical chatbots that deliver timely information to clinicians, patients, and researchers with a simple interface. Although state-of-the-art LLMs have shown impressive performance at advanced tasks like medical licensing exams, in practice they still often provide false information, which is particularly hazardous in a clinical context. To quantify the extent of this issue, we developed a series of automated and expert-scored tests to evaluate the performance of chatbots in answering pharmacogenetics questions from the perspective of clinicians, patients, and researchers. We applied this benchmark to state-of-the-art LLMs and found that newer models like GPT-4o greatly outperform their predecessors, but still fall short of the standards required for clinical use. Our benchmark will be a valuable public resource for subsequent developments in this space as we work towards better clinical AI for pharmacogenetics.

Pharmacogenomic profiling of the South Korean population: Insights and implications for personalized medicine

Adverse drug reactions (ADRs) pose substantial public health issues, necessitating population-specific characterization due to variations in pharmacogenes. This study delineates the pharmacogenomic (PGx) landscape of the South Korean (SKR) population, focusing on 21 core pharmacogenes. Whole genome sequencing (WGS) was conducted on 396 individuals, including 99 healthy volunteers, 95 patients with chronic diseases, 81 with colon cancer, 81 with breast cancer, and 40 with gastric cancer, to identify genotype-specific drug dosing recommendations. Our detailed analysis, utilizing high-throughput genotyping (HTG) of CYP2D6 and comparative data from the 1,000 Genomes Project (1 KG) and the US National Marrow Donor Program (NMDP), revealed significant pharmacogenetic diversity in core pharmacogenes such as CYP2B6, CYP2C19, CYP4F2, NUDT15, and CYP2D6. Notably, intermediate metabolizer frequencies for CYP2B6 in SKR (3.28%) were comparable to Europeans (5.77%) and East Asians (5.36%) but significantly differed from other global populations (p < 0.01). For CYP2C19, 48.74% of SKR individuals were classified as intermediate metabolizers, with the *35 allele (2.02%) being unique to SKR, the allele not observed in other East Asian populations. Additionally, the high-risk *3 allele in CYP4F2 was significantly more frequent in SKR (34.72%) than in other East Asian populations (p < 0.01). NUDT15 poor metabolizers were found in 0.76% of SKR, aligning closely with other East Asians (1.59%), while TPMT poor metabolizers were predominantly observed in Europeans and Africans, with one case in SKR. We identified significant allele frequency differences in CYP2D6 variants rs1065852 and rs1135840. Among the 72 drugs analyzed, 93.43% (n = 370) of patients required dosage adjustments for at least one drug, with an average of 4.5 drugs per patient. Moreover, 31.31% (n = 124) required adjustments for more than five drugs. These findings reveal the substantial pharmacogenetic diversity of the SKR population within the global population, emphasizing the urgency of integrating population-specific PGx data into clinical practice to ensure safe and effective drug therapies. This comprehensive PGx profiling in SKR not only advances personalized medicine but also holds the potential to significantly improve healthcare outcomes on a broader scale.

Will Precision Medicine Meet Digital Health? A Systematic Review of Pharmacogenomics Clinical Decision Support Systems Used in Clinical Practice

Digital health, an emerging scientific domain, attracts increasing attention as artificial intelligence and relevant software proliferate. Pharmacogenomics (PGx) is a core component of precision/personalized medicine driven by the overarching motto "the right drug, for the right patient, at the right dose, and the right time." PGx takes into consideration patients' genomic variations influencing drug efficacy and side effects. Despite its potentials for individually tailored therapeutics and improved clinical outcomes, adoption of PGx in clinical practice remains slow. We suggest that e-health tools such as clinical decision support systems (CDSSs) can help accelerate the PGx, precision/personalized medicine, and digital health emergence in everyday clinical practice worldwide. Herein, we present a systematic review that examines and maps the PGx-CDSSs used in clinical practice, including their salient features in both technical and clinical dimensions. Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines and research of the literature, 29 relevant journal articles were included in total, and 19 PGx-CDSSs were identified. In addition, we observed 10 technical components developed mostly as part of research initiatives, 7 of which could potentially facilitate future PGx-CDSSs implementation worldwide. Most of these initiatives are deployed in the United States, indicating a noticeable lack of, and the veritable need for, similar efforts globally, including Europe.

Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment

Precision medicine is transforming psychiatric treatment by tailoring personalized healthcare interventions based on clinical, genetic, environmental, and lifestyle factors to optimize medication management. This study investigates how artificial intelligence (AI) and machine learning (ML) can address key challenges in integrating pharmacogenomics (PGx) into psychiatric care. In this integration, AI analyzes vast genomic datasets to identify genetic markers linked to psychiatric conditions. AI-driven models integrating genomic, clinical, and demographic data demonstrated high accuracy in predicting treatment outcomes for major depressive disorder and bipolar disorder. This study also examines the pressing challenges and provides strategic directions for integrating AI and ML in genomic psychiatry, highlighting the importance of ethical considerations and the need for personalized treatment. Effective implementation of AI-driven clinical decision support systems within electronic health records is crucial for translating PGx into routine psychiatric care. Future research should focus on developing enhanced AI-driven predictive models, privacy-preserving data exchange, and robust informatics systems to optimize patient outcomes and advance precision medicine in psychiatry.

Clinical utilities and end-user experience of pharmacogenomics: 39 mo of clinical implementation experience in an Australian hospital setting

BACKGROUND

Pharmacogenomics (PG) testing is under-utilised in Australia. Our research provides Australia-specific data on the perspectives of patients who have had PG testing and those of the clinicians involved in their care, with the aim to inform wider adoption of PG into routine clinical practice.

AIM

To investigate the frequency of actionable drug gene interactions and assess the perceived utility of PG among patients and clinicians.

METHODS

We conducted a retrospective audit of PG undertaken by 100 patients at an Australian public hospital genetics service from 2018 to 2021. Via electronic surveys we compared and contrasted the experience, understanding and usage of results between these patients and their clinicians.

RESULTS

Of 100 patients who had PG, 84% were taking prescription medications, of which 67% were taking medications with actionable drug-gene interactions. Twenty-five out of 81 invited patients and 17 out of 89 invited clinicians completed the surveys. Sixty-eight percent of patients understood their PG results and 48% had medications changed following testing. Paired patient-clinician surveys showed patient-perceived utility and experience was positive, contrasting their clinicians’ hesitancy on PG adoption who identified insufficient education/training, lack of clinical support, test turnaround time and cost as barriers to adoption.

CONCLUSION

Our dichotomous findings between the perspectives of our patient and clinician cohorts suggest the uptake of PG is likely to be driven by patients and clinicians need to be prepared to provide information and guidance to their patients.

Influencia de la farmacogenética en la diversidad de respuesta a las estatinas asociada a las reacciones adversas

Introducción

Las estatinas son unos de los medicamentos más prescritos en los países desarrollados por ser el tratamiento de elección para reducir los niveles de colesterol ayudando así a prevenir la enfermedad cardiovascular. Sin embargo, un gran número de pacientes sufre reacciones adversas, en especial miotoxicidad. Entre los factores que influyen en la diversidad de respuesta, la farmacogenética puede jugar un papel relevante especialmente en la prevención de los efectos adversos asociados a estos medicamentos.

Contenido

Revisión de los conocimientos actuales sobre la influencia de la farmacogenética en la aparición y prevención de las reacciones adversas asociadas a estatinas, así como del beneficio clínico del test farmacogenético anticipado.

Resumen

Variaciones genéticas en SLCO1B1 (rs4149056) para todas las estatinas; en ABCG2 (rs2231142) para rosuvastatina; o en CYP2C9 (rs1799853 y rs1057910) para fluvastatina están asociadas a un incremento de las reacciones adversas de tipo muscular y a una baja adherencia al tratamiento. Además, diversos fármacos inhibidores de estos transportadores y enzimas de biotransformación incrementan la exposición sistémica de las estatinas favoreciendo la aparición de las reacciones adversas.

Perspectiva

La implementación clínica del análisis anticipado de este panel de farmacogenética evitaría en gran parte la aparición de reacciones adversas. Además, la estandarización en la identificación de los efectos adversos, en la metodología e interpretación del genotipo, permitirá obtener resultados más concluyentes sobre la asociación entre las variantes genéticas del SLCO1B1, ABCG y CYP2C9 y la aparición de reacciones adversas y establecer recomendaciones para alcanzar tratamientos más personalizados para cada estatina.

Influence of pharmacogenetics on the diversity of response to statins associated with adverse drug reactions

Background

Statins are one of the most prescribed medications in developed countries as the treatment of choice for reducing cholesterol and preventing cardiovascular diseases. However, a large proportion of patients experience adverse drug reactions, especially myotoxicity. Among the factors that influence the diversity of response, pharmacogenetics emerges as a relevant factor of influence in inter-individual differences in response to statins and can be useful in the prevention of adverse drug effects.

Content

A systematic review was performed of current knowledge of the influence of pharmacogenetics on the occurrence and prevention of statin-associated adverse reactions and clinical benefits of preemptive pharmacogenetics testing.

Summary

Genetic variants SLCO1B1 (rs4149056) for all statins; ABCG2 (rs2231142) for rosuvastatin; or CYP2C9 (rs1799853 and rs1057910) for fluvastatin are associated with an increase in muscle-related adverse effects and poor treatment adherence. Besides, various inhibitors of these transporters and biotransformation enzymes increase the systemic exposure of statins, thereby favoring the occurrence of adverse drug reactions.

Outlook

The clinical preemptive testing of this pharmacogenetic panel would largely prevent the incidence of adverse drug reactions. Standardized methods should be used for the identification of adverse effects and the performance and interpretation of genotyping test results. Standardization would allow to obtain more conclusive results about the association between SLCO1B1, ABCG and CYP2C9 variants and the occurrence of adverse drug reactions. As a result, more personalized recommendations could be established for each statin.