Dataset Information

Pharmacogenetic information in Swiss drug labels - a systematic analysis.

ABSTRACT: Implementation of pharmacogenetics (PGx) and individualization of drug therapy is supposed to obviate adverse drug reactions or therapy failure. Health care professionals (HCPs) use drug labels (DLs) as reliable information about drugs. We analyzed the Swiss DLs to give an overview on the currently available PGx instructions. We screened 4306 DLs applying natural language processing focusing on drug metabolism (pharmacokinetics) and we assigned PGx levels following the classification system of PharmGKB. From 5979 hits, 2564 were classified as PGx-relevant affecting 167 substances. 55% (n = 93) were classified as "actionable PGx". Frequently, PGx information appeared in the pharmacokinetics section and in DLs of the anatomic group "nervous system". Unstandardized wording, appearance of PGx information in different sections and unclear instructions challenge HCPs to identify and interpret PGx information and translate it into practice. HCPs need harmonization and standardization of PGx information in DLs to personalize drug therapies and tailor pharmaceutical care.

SUBMITTER: Jeiziner C

PROVIDER: S-EPMC8292148 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Pharmacogenetic information in Swiss drug labels - a systematic analysis.

Jeiziner C C Suter K K Wernli U U Barbarino J M JM Gong L L Whirl-Carrillo M M Klein T E TE Szucs T D TD Hersberger K E KE Meyer Zu Schwabedissen H E HE

The pharmacogenomics journal 20201017 4

Implementation of pharmacogenetics (PGx) and individualization of drug therapy is supposed to obviate adverse drug reactions or therapy failure. Health care professionals (HCPs) use drug labels (DLs) as reliable information about drugs. We analyzed the Swiss DLs to give an overview on the currently available PGx instructions. We screened 4306 DLs applying natural language processing focusing on drug metabolism (pharmacokinetics) and we assigned PGx levels following the classification system of P ...[more]

PMID: 33070160

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Project description:Although weight gain is a serious but variable adverse effect of antipsychotics that has genetic underpinnings, a comprehensive meta-analysis of pharmacogenetics of antipsychotic-related weight gain is missing. In this review, random effects meta-analyses were conducted for dominant and recessive models on associations of specific single nucleotide polymorphisms (SNP) with prospectively assessed antipsychotic-related weight or body mass index (BMI) changes (primary outcome), or categorical increases in weight or BMI (?7%; secondary outcome). Published studies, identified via systematic database search (last search: December 31, 2014), plus 3 additional cohorts, including 222 antipsychotic-naïve youth, and 81 and 141 first-episode schizophrenia adults, each with patient-level data at 3 or 4 months treatment, were meta-analyzed. Altogether, 72 articles reporting on 46 non-duplicated samples (n = 6700, mean follow-up = 25.1wk) with 38 SNPs from 20 genes/genomic regions were meta-analyzed (for each meta-analysis, studies = 2-20, n = 81-2082). Eleven SNPs from 8 genes were significantly associated with weight or BMI change, and 4 SNPs from 2 genes were significantly associated with categorical weight or BMI increase. Combined, 13 SNPs from 9 genes (Adrenoceptor Alpha-2A [ADRA2A], Adrenoceptor Beta 3 [ADRB3], Brain-Derived Neurotrophic Factor [BDNF], Dopamine Receptor D2 [DRD2], Guanine Nucleotide Binding Protein [GNB3], 5-Hydroxytryptamine (Serotonin) Receptor 2C [HTR2C], Insulin-induced gene 2 [INSIG2], Melanocortin-4 Receptor [MC4R], and Synaptosomal-associated protein, 25kDa [SNAP25]) were significantly associated with antipsychotic-related weight gain (P-values < .05-.001). SNPs in ADRA2A, DRD2, HTR2C, and MC4R had the largest effect sizes (Hedges' g's = 0.30-0.80, ORs = 1.47-1.96). Less prior antipsychotic exposure (pediatric or first episode patients) and short follow-up (1-2 mo) were associated with larger effect sizes. Individual antipsychotics did not significantly moderate effect sizes. In conclusion, antipsychotic-related weight gain is polygenic and associated with specific genetic variants, especially in genes coding for antipsychotic pharmacodynamic targets.

Dataset Information

Pharmacogenetic information in Swiss drug labels - a systematic analysis.

Publications

Pharmacogenetic information in Swiss drug labels - a systematic analysis.

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