Pre-emptive Pharmacogenomics
Conditions
Brief summary
In collaboration with Raffles Medical Group, we will be recruiting 500 patients and following them for the next 3-12 months to see whether pharmacogenomics information provided in the Raffles' Electronic Health Records (EHR) will be used by physicians to personalize patients' prescriptions.
Detailed description
Pre-emptive genotyping provides relevant genomic data to physicians to facilitate prescribing and to facilitate checking of prescriptions by pharmacists to ensure drug safety and efficacy. This essential information should be incorporated into electronic healthcare systems and should be readily available. The effectiveness of pre-emptive genotyping to reduce adverse drug reactions (ADRs) is unknown in Singapore. Hence, this study is designed to evaluate whether it is feasible to implement large scale pre-emptive genotyping program at a hospital in Singapore and aim to integrate genomic medicine into clinical practice to improve drug safety and efficacy. This study involves the testing of feasibility of pharmacogenomic genotyping in hospitals whereby our pharmacogenomics panel tests for 5 genes (CYP2D6, CYP2C9, CYP2C19, SLCO1B1 and HLA-B\*58:01) which influences patient's response to more than 165 medications. Reports will be generated for all drugs that have been reported to be in CPIC Level A/B of association with the genes/haplotypes. The patients who are given these tests for free are recommended due to having experienced at least one of the diseases in our list or is at a risk of developing them.
Interventions
DIAGNOSTIC_TESTPharmacogenomics Testing
To test if pharmacogenomics information (produced from testing) included by us in the Raffles' Electronic Health Records (EHR) will be used by physicians to personalize patients' prescriptions.
Sponsors
Nalagenetics Pte Ltd
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
21 Years to 65 Years
Healthy volunteers
No
Inclusion criteria
* Patients who experienced at least one of the following diseases, or is at risk of developing them: 1. Diabetes Mellitus 2. Hypertension 3. Hyperlipidaemia 4. Ischaemic Heart Disease 5. Stroke 6. Osteoarthritis 7. Rheumatoid Arthritis 8. Gout 9. Anxiety 10. Major Depression
Exclusion criteria
* Below ages 21 and above ages 65
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Turnaround time of genotype result, compared to expected | 6 months | — |
| Overall satisfaction of patient | 0 months | Via survey upon recruitment |
| Overall satisfaction of site principal investigators | 6 months | Via survey done at month 6 of the study |
| Overall satisfaction of prescribing physicians | 12 months | Via survey done at month 12 of the study |
| Prevalence of clinically actionable genotypes | 12 months | The number of patients receiving recommendations that includes a certain follow up action which includes monitoring, change of dosing or change of prescription. |
| Recommendation acceptance rate | 12 months | Defined by: (1) Number of physicians who are interested to consider pharmacogenomics information to guide prescription, measured by click-through rate of Pharmacogenomics Access Button; (2) Number of changes made to the medications post-PGx testing; (3) Data obtained from satisfaction survey for prescribing physicians |
Countries
Singapore
Outcome results
None listed