Exploring the potential impact of collaborative medicines optimisation involving pharmacogenomic testing and general practice pharmacists in primary care in Ireland
Bibliographic record
Abstract
Introduction People are living longer, but not necessarily in better health. The ageing population coupled with the rise in chronic conditions has led to many older people being prescribed polypharmacy, which may not always be appropriate. Medicines optimisation, a patient-centred process, strives for the best clinical outcomes by ensuring the safe and effective use of medicines through medication reviews. However, this necessitates greater collaboration among healthcare professional to individualise care, monitor outcomes closely, review medications more frequently and support patients when needed. Current medical practice often applies the same dose to all individuals based on population studies, overlooking individual differences. Precision medicine, in contrast, seeks to customise pharmacotherapy to individual patients, acknowledging variability in drug response. The study of genetics has been widely applied in precision medicine, and one of the emerging applications is pharmacogenomic-informed pharmacotherapy, which tailors drug selection and dosing based on a patient's genetic profile. Methods A systematic review was conducted to determine the effectiveness of pharmacogenomic interventions in improving outcomes derived from consensus-based core outcome sets in adult patients with multimorbidity and prescribed polypharmacy in all healthcare settings to inform the implementation of pharmacogenomic-guided therapy in clinical practice. A cross-sectional questionnaire study was performed to identify the potential opportunities and challenges of implementing pharmacogenomic testing in Ireland based on the previously unexplored views of those with multimorbidity and polypharmacy, those with a single chronic disease, and those without existing medical conditions. A retrospective study was undertaken using data from the STOP-HF cohort (comprised of Irish patients over the age of 40, with one or more risk factors for developing heart failure) to investigate the potential impact of pharmacogenomic testing. Finally, a qualitative semi-structured interview study was caried out with stakeholders involved in a medicines optimisation service, developed as part of the project to integrate a pharmacist into general practice, to establish their views of the service and the incorporation of pharmacogenomics. Results The results of the systematic review indicated that once the scope extends beyond single drug-gene interactions, there is limited available evidence. Nevertheless, pharmaco-genomic testing was shown to be an effective intervention for improving outcomes in this vulnerable patient cohort, reducing the incidence of adverse drug reactions, reducing healthcare utilisation and costs, and improving clinical decision-making. The questionnaire study provided a comprehensive account of the views of the Irish public regarding pharmacogenomic testing. It was shown that respondents with a chronic disease were more than twice as likely to value pharmacogenomic service availability than those without existing medical conditions. In the retrospective longitudinal study of patients with cardiovascular risk factors, multimorbidity, and polypharmacy, a high level of exposure to medications with potential for drug-gene interactions was observed. Statins accounted for the majority of these interactions; patients receiving a statin were genotyped for SLCO1B1 and ABCG2 no function alleles. A significant association between poor statin transporter function phenotype and progression of heart failure was found, highlighting an opportunity for routine genotyping to reduce the risk of heart failure. The qualitative interview study determined stakeholders' perceptions towards pharmacists working in general practice to provide a telepharmacy collaborative medicines optimisation service in Irish primary care. Barriers and facilitators to the service and considerations around the incorporation of pharmacogenomics into the service were identified. This study demonstrated the feasibility and acceptability of a general practice pharmacist working remotely. Conclusions The culmination of research presented in this thesis provides a comprehensive exploration of the impact pharmacogenomic interventions could have for patients with multimorbidity and prescribed polypharmacy in the context of medicines optimisation. Notably, prior to this research, no published studies had examined the implementation of pharmacogenomic testing in Ireland. Furthermore, the development of a telepharmacy collaborative medicines optimisation service involving a general practice pharmacist in primary care constituted a pioneering and substantial contribution to practice-based research. Rather than attempting to address all aspects of pharmacogenomic testing in a single research project, this thesis identifies opportunities for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".