UNASSIGNED: Precise estimation of a patient\'s drug metabolism capacity is important for antiseizure dose personalization.
UNASSIGNED: To quantify the differences in plasma concentrations for antiseizure drugs associated with variants of genes encoding drug metabolizing enzymes.
UNASSIGNED: PubMed, Clinicaltrialsregister.eu, ClinicalTrials.gov, International Clinical Trials Registry Platform, and CENTRAL databases were screened for studies from January 1, 1990, to September 30, 2023, without language restrictions.
UNASSIGNED: Two reviewers performed independent study screening and assessed the following inclusion criteria: appropriate genotyping was performed, genotype-based categorization into subgroups was possible, and each subgroup contained at least 3 participants.
UNASSIGNED: The Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines were followed for data extraction and subsequent quality, validity, and risk-of-bias assessments. The results from the included studies were pooled with random-effect meta-analysis.
UNASSIGNED: Plasma concentrations of antiseizure drugs were quantified with the dose-normalized area under the concentration-time curve, the dose-normalized steady state concentration, or the concentrations after a single dose at standardized dose and sampling time. The ratio of the means was calculated by dividing the mean drug plasma concentrations of carriers and noncarriers of the pharmacogenetic variant.
UNASSIGNED: Data from 98 studies involving 12 543 adult participants treated with phenytoin, valproate, lamotrigine, or carbamazepine were analyzed. Studies were mainly conducted within East Asian (69 studies) or White or European (15 studies) cohorts. Significant increases of plasma concentrations compared with the reference subgroup were observed for phenytoin, by 46% (95% CI, 33%-61%) in CYP2C9 intermediate metabolizers, 20% (95% CI, 17%-30%) in CYP2C19 intermediate metabolizers, and 39% (95% CI, 24%-56%) in CYP2C19 poor metabolizers; for valproate, by 12% (95% CI, 4%-20%) in CYP2C9 intermediate metabolizers, 12% (95% CI, 2%-24%) in CYP2C19 intermediate metabolizers, and 20% (95% CI, 2%-41%) in CYP2C19 poor metabolizers; and for carbamazepine, by 12% (95% CI, 3%-22%) in CYP3A5 poor metabolizers.
UNASSIGNED: This systematic review and meta-analysis found that CYP2C9 and CYP2C19 genotypes encoding low enzymatic capacity were associated with a clinically relevant increase in phenytoin plasma concentrations, several pharmacogenetic variants were associated with statistically significant but only marginally clinically relevant changes in valproate and carbamazepine plasma concentrations, and numerous pharmacogenetic variants were not associated with statistically significant differences in plasma concentrations of antiseizure drugs.

In the past two decades, significant progress has been made in the development of polygenic scores (PGSs). One specific application of PGSs is the development and potential use of pharmacogenomic- scores (PGx-scores) to identify patients who can benefit from a specific medication or are likely to experience side effects. This systematic review comprehensively evaluates published PGx-score studies in psychiatry and provides insights into their potential clinical use and avenues for future development. A systematic literature search was conducted across PubMed, EMBASE, and Web of Science databases until 22 August 2023. This review included fifty-three primary studies, of which the majority (69.8%) were conducted using samples of European ancestry. We found that over 90% of PGx-scores in psychiatry have been developed based on psychiatric and medical diagnoses or trait variants, rather than pharmacogenomic variants. Among these PGx-scores, the polygenic score for schizophrenia (PGSSCZ) has been most extensively studied in relation to its impact on treatment outcomes (32 publications). Twenty (62.5%) of these studies suggest that individuals with higher PGSSCZ have negative outcomes from psychotropic treatment - poorer treatment response, higher rates of treatment resistance, more antipsychotic-induced side effects, or more psychiatric hospitalizations, while the remaining studies did not find significant associations. Although PGx-scores alone accounted for at best 5.6% of the variance in treatment outcomes (in schizophrenia treatment resistance), together with clinical variables they explained up to 13.7% (in bipolar lithium response), suggesting that clinical translation might be achieved by including PGx-scores in multivariable models. In conclusion, our literature review found that there are still very few studies developing PGx-scores using pharmacogenomic variants. Research with larger and diverse populations is required to develop clinically relevant PGx-scores, using biology-informed and multi-phenotypic polygenic scoring approaches, as well as by integrating clinical variables with these scores to facilitate their translation to psychiatric practice.

OBJECTIVE: The aim of this article was to perform a narrative review of how pharmacogenetics and pharmacogenomics is being applied in the clinics, especially in Spain.
METHODS: Publications and websites of major interest have been reviewed.
RESULTS: Pharmacogenes and variants used in several hospitals, available methodologies, and the implementation process are discussed.

OBJECTIVE: The aim of this article is to perform a narrative review of how pharmacogenetics and pharmacogenomics is being applied in the clinic, especially in Spain.
METHODS: Publications and websites of major interest have been reviewed.
RESULTS: Pharmacogenes and variants used in several hospitals, available methodologies, and the implementation process are discussed.

Pharmacogenomics has become integral to personalised medicine in breast cancer, utilising genetic insights to customize treatment strategies and enhance patient outcomes. Understanding how genetic variations influence drug metabolism, response, and toxicity is crucial for guiding treatment selection and dosing regimens. Genetic polymorphisms in drug-metabolizing enzymes and transporters significantly impact pharmacokinetic variability, influencing the efficacy and safety of chemotherapy agents and targeted therapies. Biomarkers associated with the hormone receptor status of breast cancer and mutations serve as key determinants of treatment response, aiding in the selection of therapies. Despite substantial progress in understanding the pharmacogenomic landscape of breast cancer, efforts to identify novel genetic markers and refine treatment optimisation strategies are required. Genome-wide association studies and advanced sequencing technologies hold promise for uncovering genetic determinants of drug response variability and elucidating complex pharmacogenomic interactions. The future of pharmacogenomics in breast cancer lies in real-time treatment monitoring, the discovery of additional predictive markers, and the seamless integration of pharmacogenomic data into clinical decision-making processes. However, translating pharmacogenomic discoveries into routine clinical practice requires collaborative efforts among stakeholders to address implementation challenges and ensure equitable access to genetic testing. By embracing pharmacogenomics, clinicians can tailor treatment approaches to individual patients, maximizing therapeutic benefits while minimizing adverse effects. This review discusses the integration of pharmacogenomics in breast cancer treatment, highlighting the significance of understanding genetic influences on treatment response and toxicity, and the potential of advanced technologies in refining treatment strategies.

Numerous factors, such as genetics, environmental factors, and illness determinants, might contribute to an unpleasant pharmaceutical response. In an effort to increase efficacy and safety, as well as to gain a better understanding of drug disposition and clinical consequences, researchers in the two quickly emerging fields of pharmacogenetics (which focuses on single genes) and pharmacogenomics (which focuses on many genes) have studied the genetic personalization of drug response. This is due to the fact that a large number of pharmacological responses seem to be genetically based, and the relationship between medication response and genotype may be important for diagnosis. We now have a better understanding of the genetic basis of individual medication responses because to research on pharmaceuticals and genes. Pharmacogenomics aims to improve patient outcomes by developing personalized medicine by using the diversity of the human genome and how it affects medication response. Translational in nature, pharmacogenomics research encompasses everything from the discovery of genotype-phenotype associations to clinical investigations that might show therapeutic relevance. Though the conversion of pharmacogenomics research findings into clinical practice has been sluggish, advances in the field offer considerable potential for future therapeutic applications in specific people.

UNASSIGNED: Pharmacogenomics research is currently revolutionizing treatment optimization by discovering molecular markers. Medicines are the cornerstone of treatment for both acute and chronic diseases. Pharmacogenomics associated treatment response varies from 20% to 95%, resulting in from lack of efficacy to serious toxicity. Pharmacogenomics has emerged as a useful tool for therapy optimization and plays a bigger role in clinical care going forward. However, in Africa, in particular in Ethiopia, such studies are scanty and not generalizing. Therefore, the objective of this review was to outline such studies, generating comprehensive evidence and identify studied variants\' association with treatment responses in Ethiopian patients.
UNASSIGNED: The Joanna Briggs Institute\'s updated 2020 methodological guidelines for conducting and guidance for scoping reviews were used. We meticulously adhered to the systemic review reporting items checklist and scoping review meta-analyses extension.
UNASSIGNED: Two hundred twenty-nine possibly relevant studies were searched. These include: 64, 54, 21, 48 and 42 from PubMed, Scopus, Google Scholar, EMBASE, and manual search, respectively. Seventy-seven duplicate studies were removed. Thirty-nine papers were rejected with justification, whereas 58 studies were qualified for full-text screening. Finally 19 studies were examined. The primary pharmacogene that was found to have a significant influence on the pharmacokinetics of efavirenz was CYP2B6. Drug-induced liver injury has frequently identified toxicity among studied medications.
UNASSIGNED: Pharmacogenomics studies in Ethiopian populations are less abundant. The studies conducted focused on infectious diseases, specifically on HAART commonly efavirenz and backbone first-line anti-tuberculosis drugs. There is a high need for further pharmacogenomics research to verify the discrepancies among the studies and for guiding precision medicine. Systematic review and meta-analysis are also recommended for pooled effects of different parameters in pharmacogenomics studies.

Next-generation sequencing (NGS) significantly enhances precision medicine (PM) by offering personalized approaches to diagnosis, treatment, and prevention of unmet medical needs. Little is known about the current situation of PM in Asia. Thus, we aimed to conduct an overview of the progress and gaps in PM in Asia and enrich it with in-depth insight into the possibilities of future PM in Thailand. This scoping review focused on Asian countries starting with non-cancer studies, including rare and undiagnosed diseases (RUDs), non-communicable diseases (NCDs), infectious diseases (IDs), and pharmacogenomics, with a focus on NGS. Subsequent in-depth interviews with experts in Thailand were performed, and a thematic analysis served as the main qualitative methodology. Out of 2898 searched articles, 387 studies were included after the review. Although most of the studies focused on cancer, 89 (23.0%) studies were related to RUDs (17.1%), NCDs (2.8%), IDs (1.8%), and pharmacogenomics (1.3%). Apart from medicine and related sciences, the studies were mostly composed of PM (61.8%), followed by genetics medicine and bioinformatics. Interestingly, 28% of articles were conducted exclusively within the fields of medicine and related sciences, emphasizing interdisciplinary integration. The experts emphasized the need for sustainability-driven political will, nurturing collaboration, reinforcing computational infrastructure, and expanding the bioinformatic workforce. In Asia, developments of NGS have made remarkable progress in PM. Thailand has extended PM beyond cancer and focused on clinical implementation. We summarized the PM challenges, including equity and efficiency targeting, guided research funding, sufficient sample size, integrated collaboration, computational infrastructure, and sufficient trained human resources.

For the same age, sex, and dosage, there can be significant variation in fertility outcomes in childhood cancer survivors. Genetics may explain this variation. This study aims to: (i) review the genetic contributions to infertility, (ii) search for pharmacogenomic studies looking at interactions of cancer treatment, genetic predisposition and fertility-related outcomes. Systematic searches in MEDLINE Ovid, Embase Classic+Embase, and PubMed were conducted using the following selection criteria: (i) pediatric, adolescent, and young adult cancer survivors, below 25 years old at the time of diagnosis, (ii) fertility outcome measures after cancer therapy, (iii) genetic considerations. Studies were excluded if they were (i) conducted in animal models, (ii) were not published in English, (iii) editorial letters, (iv) theses. Articles were screened in Covidence by at least two independent reviewers, followed by data extraction and a risk of bias assessment using the Quality in Prognostic Studies tool. Eight articles were reviewed with a total of 29 genes. Outcome measures included sperm concentration, azoospermia, AMH levels, assessment of premature menopause, ever being pregnant or siring a pregnancy. Three studies included replication cohorts, which attempted replication of SNP findings for NPY2R, BRSK1, FANCI, CYP2C19, CYP3A4, and CYP2B6. Six studies were rated with a high risk of bias. Differing methods may explain a lack of replication, and small cohorts may have contributed to few significant findings. Larger, prospective longitudinal studies with an unbiased genome-wide focus will be important to replicate significant results, which can be applied clinically.

Genetic biomarkers could potentially lower the risk of treatment failure in chronic inflammatory diseases (CID) like psoriasis, psoriatic arthritis (PsA), rheumatoid arthritis (RA), and inflammatory bowel disease (IBD). We performed a systematic review and meta-analysis assessing the association between single nucleotide polymorphisms (SNPs) and response to biologics. Odds ratio (OR) with 95% confidence interval (CI) meta-analyses were performed. In total, 185 studies examining 62,774 individuals were included. For the diseases combined, the minor allele of MYD88 (rs7744) was associated with good response to TNFi (OR: 1.24 [1.02-1.51], 6 studies, 3158 patients with psoriasis or RA) and the minor alleles of NLRP3 (rs4612666) (OR: 0.71 [0.58-0.87], 5 studies, 3819 patients with RA or IBD), TNF-308 (rs1800629) (OR: 0.71 [0.55-0.92], 25 studies, 4341 patients with psoriasis, RA, or IBD), FCGR3A (rs396991) (OR: 0.77 [0.65-0.93], 18 studies, 2562 patients with psoriasis, PsA, RA, or IBD), and TNF-238 (rs361525) (OR: 0.57 [0.34-0.96]), 7 studies, 818 patients with psoriasis, RA, or IBD) were associated with poor response to TNFi together or infliximab alone. Genetic variants in TNFα, NLRP3, MYD88, and FcRγ genes are associated with response to TNFi across several inflammatory diseases. Most other genetic variants associated with response were observed in a few studies, and further validation is needed.