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

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.

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.

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.