Pharmacist assessment of drug-gene interactions and drug-induced phenoconversion in major depressive disorder: a case report

Toro-Pagán, Nicole Marie Del; Matos, Adriana; Bardolia, Chandni; Michaud, Véronique; Turgeon, Jacques; Amin, Nishita Shah

doi:10.1186/s12888-021-03659-4

Cited by 4 publications

(2 citation statements)

References 42 publications

(42 reference statements)

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“…As a parallel to DDIs, gene‐drug interactions (GDIs) may cause similar problems. A GDI is an interaction between a variant (actionable) genotype and a drug that may be affected in terms of altered clinical response due to changes in pharmacokinetics or pharmacodynamics 22–24 …”

Section: Introductionmentioning

confidence: 99%

“…A GDI is an interaction between a variant (actionable) genotype and a drug that may be affected in terms of altered clinical response due to changes in pharmacokinetics or pharmacodynamics. [22][23][24] The increasing knowledge of the impact of GDIs on treatment outcomes opens the way for using pharmacogenetic testing as a tool when optimizing drug regimens for chronically ill, multimorbid patients. 6,25 By including results from pharmacogenetic testing, the medication review can reveal serious DRPs among patients with polypharmacy.…”

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Implementation of pharmacogenetic testing in medication reviews in a hospital setting

Hjemås¹,

Bøvre²,

Bjerknes

et al. 2023

Brit J Clinical Pharma

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AimTo investigate whether it is feasible to perform pharmacogenetic testing and implement the test results as part of medication reviews during hospitalization of multimorbid patients.MethodsPatients with ≥2 chronic conditions and ≥5 regular drugs with at least one potential gene‐drug interaction (GDI) were included from one geriatric and one cardiology ward for pharmacogenetic testing. After inclusion by the study pharmacist, blood samples were collected and shipped to the laboratory for analysis. For patients still hospitalized at the time when the pharmacogenetic test results were available, the information was used in medication reviews. Recommendations from the pharmacist on actionable GDIs were communicated to the hospital physicians, who subsequently decided on potential immediate changes or forwarded suggestions in referrals to general practitioners.ResultsThe pharmacogenetic test results were available for medication review in 18 of the 46 patients (39.1%), where median length of hospital stay was 4.7 days (1.6‐18.3). The pharmacist recommended medication changes for 21 of 49 detected GDIs (42.9%). The hospital physicians accepted 19 (90.5%) of the recommendations. The most commonly detected GDIs involved metoprolol (CYP2D6 genotype), clopidogrel (CYP2C19 genotype) and atorvastatin (CYP3A4/5 and SLCOB1B1 genotype).ConclusionsThe study shows that implementation of pharmacogenetic testing for medication review of hospitalized patients has the potential to improve drug treatment before being transferred to primary care. However, the logistics workflow needs to be further optimized, as test results were available during hospitalization for less than half of the patients included in the study.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Implementation of pharmacogenetic testing in medication reviews in a hospital setting

Hjemås¹,

Bøvre²,

Bjerknes

et al. 2023

Brit J Clinical Pharma

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show abstract

Antidepressants/quinidine

2023

Reactions Weekly

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Effect of pharmacogenomics testing guiding on clinical outcomes in major depressive disorder: a systematic review and meta-analysis of RCT

Wang

Zhang

et al. 2023

BMC Psychiatry

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Background Pharmacogenomic testing guided treatment have been developed to guide drug selection or conversion in major depressive disorder patients. Whether patients benefit from pharmacogenetic testing remains unclear. We aim to evaluates the effect of pharmacogenomic testing guiding on clinical outcomes of major depressive disorder. Methods Pubmed, Embase, and Cochrane Library of Clinical Trials were searched from inception until August 2022. Key terms included pharmacogenomic and antidepressive. Odds ratios (RR) with 95% confidence intervals (95%CIs) were calculated using fixed-effects model for low or moderate heterogeneity or random-effects model for high heterogeneity. Results Eleven studies (5347 patients) were included. Compared with usual group, pharmacogenomic testing guided group was associated with an increased response rate at week 8 (OR 1.32, 95%CI 1.15–1.53, 8 studies, 4328 participants) and week 12 (OR 1.36, 95%CI 1.15–1.62, 4 studies, 2814 participants). Similarly, guided group was associated with an increased rate of remission at week 8 (OR 1.58, 95%CI 1.31–1.92, 8 studies, 3971 participants) and week 12 (OR 2.23, 95%CI 1.23–4.04, 5 studies, 2664 participants). However, no significant differences were found between the two groups in response rate at week 4 (OR 1.12, 95%CI 0.89–1.41, 2 studies, 2261 participants) and week 24 (OR 1.16, 95%CI 0.96–1.41, 2 studies, 2252 participants), and remission rate at week 4 (OR 1.26, 95%CI 0.93–1.72, 2 studies, 2261 participants) and week 24 (OR 1.06, 95%CI 0.83–1.34, 2 studies, 2252 participants). Medication congruence in 30 days was significantly reduced in the pharmacogenomic guided group compared with the usual care group (OR 2.07, 95%CI 1.69–2.54, 3 studies, 2862 participants). We found significant differences between subgroups of target population in response and remission rate. Conclusion Patients with major depressive disorder may benefit from pharmacogenomic testing guided treatment by achieving target response and remission rates more quickly.

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Pharmacist assessment of drug-gene interactions and drug-induced phenoconversion in major depressive disorder: a case report

Cited by 4 publications

References 42 publications

Implementation of pharmacogenetic testing in medication reviews in a hospital setting

Implementation of pharmacogenetic testing in medication reviews in a hospital setting

Antidepressants/quinidine

Effect of pharmacogenomics testing guiding on clinical outcomes in major depressive disorder: a systematic review and meta-analysis of RCT

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