Pharmacogenomics of Drug Metabolizing Enzymes and Transporters: Relevance to Precision Medicine

Exploring CYP2D6 polymorphisms and angiotensin receptor blocker response in the Bai hypertensive population

OBJECTIVE

The CYP2D6 enzyme is crucial for the metabolism and disposition of a variety of drugs. This study was conducted to examine the relationship between CYP2D6 gene polymorphisms and the response to angiotensin receptor blocker (ARB)-based treatment in patients of Chinese Bai ethnicity with hypertension.

METHODS

Seventy-two hypertensive adults from the Chinese Bai ethnic group, exhibiting systolic blood pressure (SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg, were recruited. Targeted regional sequencing was utilized to genotype single nucleotide polymorphisms in the CYP2D6 gene, aiming to assess their frequency and to evaluate their influence on the therapeutic efficacy of ARB medications.

RESULTS

Our research identified nine significant CYP2D6 polymorphisms associated with the efficacy of ARB treatment in the Bai hypertensive cohort. Specifically, patients possessing certain mutant genotype at rs111564371 exhibited substantially greater reductions in SBP and DBP, with P-values of 0.021 and 0.016, respectively, compared to those carrying the wild genotype. Additionally, these mutant genotype at rs111564371 and rs112568578 were linked to approximately 20% higher overall efficacy rates and a 10% increased achievement rate relative to the wild genotype.

CONCLUSION

Our research with the Bai hypertensive group shows that certain CYP2D6 polymorphisms significantly influence ARB treatment outcomes. Mutations at rs111564371 led to better blood pressure control (P-values: 0.021 for SBP, 0.016 for DBP), improving ARB efficacy by appromixately 20% and increasing treatment goal achievement by 10% over the wild-type genotype.

STATEMENTS

Our investigation into CYP2D6 polymorphisms within the Bai hypertensive cohort marks a substantial advancement towards personalized healthcare, underscoring the pivotal influence of genetic constitution on the effectiveness of ARB therapy.

Pharmacokinetics of Ubrogepant in Healthy Japanese and White Adults

Ubrogepant is a calcitonin gene-related peptide receptor antagonist indicated for the acute treatment of migraine with or without aura in adults. The objectives of this study were to evaluate (1) single-dose pharmacokinetics (PK) and dose proportionality of ubrogepant in Japanese participants, (2) the safety and tolerability of ubrogepant in healthy Japanese and White participants, and (3) to compare the PK of ubrogepant in Japanese versus White participants. A total of 48 participants were enrolled into 4 cohorts (N = 12 [9 active + 3 placebo] per cohort). A single dose was administered on Day 1 following an overnight fast to assess the PK of ubrogepant at 3 dose levels and test dose proportionality between 25 and 100 mg in Japanese participants. White participants were randomly assigned to ubrogepant (100 mg) or placebo. Dose proportionality was observed in the dose range of 25-100 mg in Japanese participants. Systemic exposure was 20% lower in Japanese participants as compared with White participants, but this difference is unlikely to be clinically relevant. Single doses of ubrogepant (25-100 mg) had a safety profile similar to placebo, and no differences in the safety profile of ubrogepant 100 mg were observed between Japanese versus White participants.

PBPK modeling to predict the pharmacokinetics of venlafaxine and its active metabolite in different CYP2D6 genotypes and drug-drug interactions with clarithromycin and paroxetine

Venlafaxine, a serotonin-norepinephrine reuptake inhibitor (SNRI), is indicated for the treatment of major depressive disorder, social anxiety disorder, generalized anxiety disorder, and panic disorder. Venlafaxine is metabolized to the active metabolite desvenlafaxine mainly by CYP2D6. Genetic polymorphism of CYP2D6 and coadministration with other medications can significantly affect the pharmacokinetics and/or pharmacodynamics of venlafaxine and its active metabolite. This study aimed to establish the PBPK models of venlafaxine and its active metabolite related to CYP2D6 genetic polymorphism and to predict drug-drug interactions (DDIs) with clarithromycin and paroxetine in different CYP2D6 genotypes. Clinical pharmacogenomic data for venlafaxine and desvenlafaxine were collected to build the PBPK model. Physicochemical and absorption, distribution, metabolism, and excretion (ADME) characteristics of respective compounds were obtained from previously reported data, predicted by the PK-Sim® software, or optimized to capture the plasma concentration-time profiles. Model evaluation was performed by comparing the predicted pharmacokinetic parameters and plasma concentration-time profiles to the observed data. Predicted plasma concentration-time profiles of venlafaxine and its active metabolite were visually similar to the observed profiles and all predicted AUC and Cmax values for respective compounds were included in the twofold error range of observed values in non-genotyped populations and different CYP2D6 genotypes. When clarithromycin or clarithromycin plus paroxetine was concomitantly administered, predicted plasma concentration-time profiles of venlafaxine properly captured the observed profiles in two different CYP2D6 genotypes and all predicted DDI ratios for AUC and Cmax were included within the acceptance range. Consequently, the present model successfully captured the pharmacokinetic alterations of venlafaxine and its active metabolite according to CYP2D6 genetic polymorphism as well as the DDIs between venlafaxine and two CYP inhibitors. The present model can be used to predict the pharmacokinetics of venlafaxine and its active metabolite considering different races, ages, coadministered drugs, and CYP2D6 activity of individuals and it can contribute to individualized pharmacotherapy of venlafaxine.

Comprehensive Analysis of Drug Utilization Patterns, Gender Disparities, Lifestyle Influences, and Genetic Factors: Insights from Elderly Cohort Using g-Nomic® Software

Polypharmacy is a global healthcare concern, especially among the elderly, leading to drug interactions and adverse reactions, which are significant causes of death in developed nations. However, the integration of pharmacogenetics can help mitigate these risks. In this study, the data from 483 patients, primarily elderly and polymedicated, were analyzed using Eugenomic®'s personalized prescription software, g-Nomic®. The most prescribed drug classes included antihypertensives, platelet aggregation inhibitors, cholesterol-lowering drugs, and gastroprotective medications. Drug-lifestyle interactions primarily involved inhibitions but also included inductions. Interactions were analyzed considering gender. Significant genetic variants identified in the study encompassed ABCB1, SLCO1B1, CYP2C19, CYP2C9, CYP2D6, CYP3A4, ABCG2, NAT2, SLC22A1, and G6PD. To prevent adverse reactions and enhance medication effectiveness, it is strongly recommended to consider pharmacogenetics testing. This approach shows great promise in optimizing medication regimens and ultimately improving patient outcomes.

Genetic variations in CYP2A6, CYP2E1, GSTM1, GSTT1 genes and the risk of Nasopharyngeal carcinoma in North African population

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a multifactorial malignancy associated with both genetic and environmental factors. Polymorphic deletions of the phase I and phase II genes involved in the detoxification of potential carcinogens may be a risk factor for nasopharyngeal carcinoma. In this study, we investigated the relationship between CYP2E1 (rs3813867), CYP2A6, GSTM1(rs1183423000) and GSTT1(rs1601993659) gene variations and NPC risk in North African countries with the highest incidence of NPC (Morocco, Algeria and Tunisia). and the evaluation of the potential use of these variants as potential biomarkers for NPC management.

METHODS

A total of 600 NPC cases and 545 controls frequency-matched on ethnicity, sex, age and childhood household type, were recruited from three North African countries (Morocco, Algeria and Tunisia) and analysed. Genotyping of CYP2A6 and CYP2E1(rs3813867) was performed by polymerase chain reaction restriction (PCR)-fragment length polymorphism (RFLP) analysis and the GSTM1 (rs1183423000) and GSTT1(rs1601993659) genetic variations were evaluated using the PCR technique.

RESULTS

The genotype distributions of CYP2E1(rs3813867), CYP2A6, GSTM1(rs1183423000) and GSTT1(rs1601993659) genotypes did not differ significantly among NPC cases and controls (p > 0.05). Furthermore, our data did not reveal any association with smoking and the studied variants, even when the samples were stratified by the duration period of smoking.

CONCLUSION

In this large studied North African population, our findings suggest that the functional CYP2E1, CYP2A6, GSTM1 and GSTT1 variations did not influence NPC susceptibility.

Factors Influencing Mortality in Children with Central Nervous System Tumors: A Cohort Study on Clinical Characteristics and Genetic Markers

Multidrug resistance (MDR) commonly leads to cancer treatment failure because cancer cells often expel chemotherapeutic drugs using ATP-binding cassette (ABC) transporters, which reduce drug levels within the cells. This study investigated the clinical characteristics and single nucleotide variant (SNV) in ABCB1, ABCC1, ABCC2, ABCC4, and ABCG2, and their association with mortality in pediatric patients with central nervous system tumors (CNST). Using TaqMan probes, a real-time polymerase chain reaction genotyped 15 SNPs in 111 samples. Patients were followed up until death or the last follow-up day using the Cox proportional hazards model. An association was found between the rs1045642 (ABCB1) in the recessive model (HR = 2.433, 95% CI 1.098-5.392, p = 0.029), and the ICE scheme in the codominant model (HR = 9.810, 95% CI 2.74-35.06, p ≤ 0.001), dominant model (HR = 6.807, 95% CI 2.87-16.103, p ≤ 0.001), and recessive model (HR = 6.903, 95% CI 2.915-16.544, p = 0.038) significantly increased mortality in this cohort of patients. An association was also observed between the variant rs3114020 (ABCG2) and mortality in the codominant model (HR = 5.35, 95% CI 1.83-15.39, p = 0.002) and the dominant model (HR = 4.421, 95% CI 1.747-11.185, p = 0.002). A significant association between the ICE treatment schedule and increased mortality risk in the codominant model (HR = 6.351, 95% CI 1.831-22.02, p = 0.004, HR = 9.571, 95% CI 2.856-32.07, p ≤ 0.001), dominant model (HR = 6.592, 95% CI 2.669-16.280, p ≤ 0.001), and recessive model (HR = 5.798, 95% CI 2.411-13.940, p ≤ 0.001). The genetic variants rs3114020 in the ABCG2 gene and rs1045642 in the ABCB1 gene and the ICE chemotherapy schedule were associated with an increased mortality risk in this cohort of pediatric patients with CNST.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

A host-directed oxadiazole compound potentiates antituberculosis treatment via zinc poisoning in human macrophages and in a mouse model of infection

Antituberculosis drugs, mostly developed over 60 years ago, combined with a poorly effective vaccine, have failed to eradicate tuberculosis. More worryingly, multiresistant strains of Mycobacterium tuberculosis (MTB) are constantly emerging. Innovative strategies are thus urgently needed to improve tuberculosis treatment. Recently, host-directed therapy has emerged as a promising strategy to be used in adjunct with existing or future antibiotics, by improving innate immunity or limiting immunopathology. Here, using high-content imaging, we identified novel 1,2,4-oxadiazole-based compounds, which allow human macrophages to control MTB replication. Genome-wide gene expression analysis revealed that these molecules induced zinc remobilization inside cells, resulting in bacterial zinc intoxication. More importantly, we also demonstrated that, upon treatment with these novel compounds, MTB became even more sensitive to antituberculosis drugs, in vitro and in vivo, in a mouse model of tuberculosis. Manipulation of heavy metal homeostasis holds thus great promise to be exploited to develop host-directed therapeutic interventions.

Identification of novel genetic variations in ABCB6 and GRN genes associated with HIV-associated lipodystrophy

Protease inhibitors (PIs) are associated with an incidence of lipodystrophy among people living with HIV(PLHIV). Lipodystrophiesare characterised by the loss of adipose tissue. Evidence suggests that a patient's lipodystrophy phenotype is influenced by genetic mutation, age, gender, and environmental and genetic factors, such as single-nucleotide variants (SNVs). Pathogenic variants are considered to cause a more significant loss of adipose tissue compared to non-pathogenic. Lipid metabolising enzymes and transporter genes have a role in regulating lipoprotein metabolism and have been associated with lipodystrophy in HIV-infected patients (LDHIV). The long-term effect of the lipodystrophy syndrome is related to cardiovascular diseases (CVDs). Hence, we determined the SNVs of lipid metabolising enzymes and transporter genes in a total of 48 patient samples, of which 24 were with and 24 were without HIV-associated lipodystrophy (HIVLD) using next-generation sequencing. A panel of lipid metabolism, transport and elimination genes were sequenced. Three novel heterozygous non-synonymous variants at exon 8 (c.C1400A:p.S467Y, c.G1385A:p.G462E, and c.T1339C:p.S447P) in the ABCB6 gene were identified in patients with lipodystrophy. One homozygous non-synonymous SNV (exon5:c.T358C:p.S120P) in the GRN gene was identified in patients with lipodystrophy. One novelstop-gain SNV (exon5:c.C373T:p.Q125X) was found in the GRN gene among patients without lipodystrophy. Patients without lipodystrophy had one homozygous non-synonymous SNV (exon9:c.G1462T:p.G488C) in the ABCB6 gene. Our findings suggest that novel heterozygous non-synonymous variants in the ABCB6 gene may contribute to defective protein production, potentially intensifying the severity of lipodystrophy. Additionally, identifying a stop-gain SNV in the GRN gene among patients without lipodystrophy implies a potential role in the development of HIVLD.

A New Cloud-Native Tool for Pharmacogenetic Analysis

BACKGROUND

The advancement of next-generation sequencing (NGS) technologies provides opportunities for large-scale Pharmacogenetic (PGx) studies and pre-emptive PGx testing to cover a wide range of genotypes present in diverse populations. However, NGS-based PGx testing is limited by the lack of comprehensive computational tools to support genetic data analysis and clinical decisions.

METHODS

Bioinformatics utilities specialized for human genomics and the latest cloud-based technologies were used to develop a bioinformatics pipeline for analyzing the genomic sequence data and reporting PGx genotypes. A database was created and integrated in the pipeline for filtering the actionable PGx variants and clinical interpretations. Strict quality verification procedures were conducted on variant calls with the whole genome sequencing (WGS) dataset of the 1000 Genomes Project (G1K). The accuracy of PGx allele identification was validated using the WGS dataset of the Pharmacogenetics Reference Materials from the Centers for Disease Control and Prevention (CDC).

RESULTS

The newly created bioinformatics pipeline, Pgxtools, can analyze genomic sequence data, identify actionable variants in 13 PGx relevant genes, and generate reports annotated with specific interpretations and recommendations based on clinical practice guidelines. Verified with two independent methods, we have found that Pgxtools consistently identifies variants more accurately than the results in the G1K dataset on GRCh37 and GRCh38.

CONCLUSIONS

Pgxtools provides an integrated workflow for large-scale genomic data analysis and PGx clinical decision support. Implemented with cloud-native technologies, it is highly portable in a wide variety of environments from a single laptop to High-Performance Computing (HPC) clusters and cloud platforms for different production scales and requirements.

Rare variants in pharmacogenes influence clozapine metabolism in individuals with schizophrenia

Clozapine is the only licensed medication for treatment-resistant schizophrenia (TRS). Few predictors for variation in response to clozapine have been identified, but clozapine metabolism is known to influence therapeutic response and adverse side effects. Here, we expand on genome-wide studies of clozapine metabolism, previously focused on common genetic variation, by analysing whole-exome sequencing data from 2062 individuals with schizophrenia taking clozapine in the UK. We investigated whether rare genomic variation in genes and gene sets involved in the clozapine metabolism pathway influences plasma concentrations of clozapine metabolites, assessed through the longitudinal analysis of 6585 pharmacokinetic assays. We observed a statistically significant association between the burden of rare damaging coding variants (MAF ≤ 1 %) in gene sets broadly related to drug pharmacokinetics and lower clozapine (β = -0.054, SE = 0.019, P-value = 0.005) concentrations in plasma. We estimate that the effects in clozapine plasma concentrations of a single damaging allele in this gene set are akin to reducing the clozapine dose by about 35 mg/day. The gene-based analysis identified rare variants in CYP1A2, which encodes the enzyme responsible for converting clozapine to norclozapine, as having the strongest effects of any gene on clozapine metabolism (β = 0.324, SE = 0.124, P = 0.009). Our findings support the hypothesis that rare genetic variants in known drug-metabolising enzymes and transporters can markedly influence clozapine plasma concentrations; these results suggest that pharmacogenomic efforts trying to predict clozapine metabolism and personalise drug therapy could benefit from the inclusion of rare damaging variants in pharmacogenes beyond those already identified and catalogued as PGx star alleles.

The Capacity of Drug-Metabolising Enzymes in Modulating the Therapeutic Efficacy of Drugs to Treat Rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is a rare soft tissue sarcoma (STS) that predominantly affects children and teenagers. It is the most common STS in children (40%) and accounts for 5-8% of total childhood malignancies. Apart from surgery and radiotherapy in eligible patients, standard chemotherapy is the only therapeutic option clinically available for RMS patients. While survival rates for this childhood cancer have considerably improved over the last few decades for low-risk and intermediate-risk cases, the mortality rate remains exceptionally high in high-risk RMS patients with recurrent and/or metastatic disease. The intensification of chemotherapeutic protocols in advanced-stage RMS has historically induced aggravated toxicity with only very modest therapeutic gain. In this review, we critically analyse what has been achieved so far in RMS therapy and provide insight into how a diverse group of drug-metabolising enzymes (DMEs) possess the capacity to modify the clinical efficacy of chemotherapy. We provide suggestions for new therapeutic strategies that exploit the presence of DMEs for prodrug activation, targeted chemotherapy that does not rely on DMEs, and RMS-molecular-subtype-targeted therapies that have the potential to enter clinical evaluation.

The impact of SLCO1B1 rs4149056 on LDL-C target achievement after lipid lowering therapy optimization in men and women with familial hypercholesterolemia

Background and aims

FH women are less likely to receive intensive statin treatment and to obtain a 50% reduction of LDL-C from baseline compared to men with FH. SLCO1B1 rs4149056 might influence statin therapy compliance and thus LDL-C target achievement. Our aim was to evaluate the impact of SLCO1B1 rs4149056 on LDL-C target achievement after lipid lowering therapy (LLT) optimization in men and women with FH.

Methods

This was a retrospective observational study involving 412 FH subjects with a probable or defined clinical diagnosis of FH who had had genetic analysis from June 2016 to September 2022. Biochemical analysis was obtained from all subjects at baseline and at the last follow-up after LLT optimization.

Results

After LLT optimization the percentage of FH subjects on high-intensity statins decreased from the M/SLCO1B1- group to the W/SLCO1B1+ group and the same was found in LDL-C target distribution (for both p for trend < 0.01). The prevalence of SASE fear increased from the M/SLCO1B1- group to the W/SLCO1B1+ group and the same was observed in reported myalgia distribution (for both p for trend < 0.01). Logistic regression analysis showed that the W/SCLO1B1-, M/SCLO1B1+ and W/SCLO1B1+ groups were inversely associated with LDL-C target achievement (p for trend < 0.001) and the W/SCLO1B1+ group exhibited the strongest association.

Conclusion

A low prevalence of FH women with SLCO1B1 rs4149056 were on high intensity statins and they rarely achieved LDL-C target. The genotype effect of SLCO1B1 rs4149056 could be more pronounced in FH women than men.

Pharmacogenomics and non-genetic factors affecting drug response in autism spectrum disorder in Thai and other populations: current evidence and future implications

Autism spectrum disorder (ASD) may affect family and social life profoundly. Although there is no selective pharmacotherapy for ASD, the Food and Drug Administration (FDA) has recommended risperidone/aripiprazole to treat the associated symptoms of ASD, such as agitation/irritability. Strong associations of some pharmacokinetic/pharmacodynamic gene variants, e.g., CYP2D6 and DRD2, with risperidone-induced hyperprolactinemia have been found in children with ASD, but such strong genetic associations have not been found directly for aripiprazole in ASD. In addition to pharmacogenomic (PGx) factors, drug-drug interactions (DDIs) and possibly cumulative effects of DDIs and PGx may affect the safety or effectiveness of risperidone/aripiprazole, which should be assessed in future clinical studies in children with ASD. Reimbursement, knowledge, and education of healthcare professionals are the key obstacles preventing the successful implementation of ASD pharmacogenomics into routine clinical practice. The preparation of national and international PGx-based dosing guidelines for risperidone/aripiprazole based on robust evidence may advance precision medicine for ASD.

Pharmacogenetics in Child and Adolescent Psychiatry: Background and Evidence-Based Clinical Applications

The efficacy and tolerability of psychotropic medications can vary significantly among children and adolescents, and some of this variability relates to pharmacogenetic factors. Pharmacogenetics (PGx) in child and adolescent psychiatry can potentially improve treatment outcomes and minimize adverse drug reactions. This article reviews key pharmacokinetic and pharmacodynamic genes and principles of pharmacogenetic testing and discusses the evidence base for clinical decision-making concerning PGx testing. This article reviews current guidelines from the United States Food and Drug Administration (FDA), the Clinical Pharmacogenetics Implementation Consortium (CPIC), and the Dutch Pharmacogenetics Working Group (DPWG) and explores potential future directions. This review discusses key clinical considerations for clinicians prescribing psychotropic medications in children and adolescents, focusing on antidepressants, antipsychotics, stimulants, norepinephrine reuptake inhibitors, and alpha-2 agonists. Finally, this review synthesizes the practical use of pharmacogenetic testing and clinical decision support systems.

Relevance of pharmacogenetics and pharmacogenomics in veterinary clinical practice: A review

The recent advances in high-throughput next-generation sequencing technologies have heralded the arrival of the Big Data era. As a result, the use of pharmacogenetics in drug discovery and individualized drug therapy has transformed the field of precision medicine. This paradigm shift in drug development programs has effectively reshaped the old drug development practices, which were primarily concerned with the physiological status of patients for drug development. Pharmacogenomics bridges the gap between pharmacodynamics and pharmacokinetics, advancing current diagnostic and treatment strategies and enabling personalized and targeted drug therapy. The primary goals of pharmacogenetic studies are to improve drug efficacy and minimize toxicities, to identify novel drug targets, to estimate drug dosage for personalized medicine, and to incorporate it as a routine diagnostic for disease susceptibility. Although pharmacogenetics has numerous applications in individualized drug therapy and drug development, it is in its infancy in veterinary medicine. The objective of this review is to present an overview of historical landmarks, current developments in various animal species, challenges and future perspectives of genomics in drug development and dosage optimization for individualized medicine in veterinary subjects.

Orally Administered Drugs and Their Complicated Relationship with Our Gastrointestinal Tract

Orally administered compounds represent the great majority of all pharmaceutical compounds produced for human use and are the most popular among patients since they are practical and easy to self-administer. Following ingestion, orally administered drugs begin a "perilous" journey down the gastrointestinal tract and their bioavailability is modulated by numerous factors. The gastrointestinal (GI) tract anatomy can modulate drug bioavailability and accounts for interpatient drug response heterogeneity. Furthermore, host genetics is a contributor to drug bioavailability modulation. Importantly, a component of the GI tract that has been gaining notoriety with regard to drug treatment interactions is the gut microbiota, which shares a two-way interaction with pharmaceutical compounds in that they can be influenced by and are able to influence administered drugs. Overall, orally administered drugs are a patient-friendly treatment option. However, during their journey down the GI tract, there are numerous host factors that can modulate drug bioavailability in a patient-specific manner.

Addressing Ancestry and Sex Bias in Pharmacogenomics

The association of an individual's genetic makeup with their response to drugs is referred to as pharmacogenomics. By understanding the relationship between genetic variants and drug efficacy or toxicity, we are able to optimize pharmacological therapy according to an individual's genotype. Pharmacogenomics research has historically suffered from bias and underrepresentation of people from certain ancestry groups and of the female sex. These biases can arise from factors such as drugs and indications studied, selection of study participants, and methods used to collect and analyze data. To examine the representation of biogeographical populations in pharmacogenomic data sets, we describe individuals involved in gene-drug response studies from PharmGKB, a leading repository of drug-gene annotations, and showcaseCYP2D6, a gene that metabolizes approximately 25% of all prescribed drugs. We also show how the historical underrepresentation of females in clinical trials has led to significantly more adverse drug reactions in females than in males.

Towards pharmacogenomics-guided tuberculosis (TB) therapy: N-acetyltransferase-2 genotypes among TB-infected Kenyans of mixed ethnicity

BACKGROUND

Though persons of African descent have one of the widest genetic variability, genetic polymorphisms of drug-metabolising enzymes such as N-Acetyltransferase-2 (NAT2) are understudied. This study aimed to identify prevalent NAT2 single nucleotide polymorphisms (SNPs) and infer their potential effects on enzyme function among Kenyan volunteers with tuberculosis (TB) infection. Genotypic distribution at each SNP and non-random association of alleles were evaluated by testing for Hardy-Weinberg Equilibrium (HWE) and Linkage Disequilibrium (LD).

METHODS

We isolated genomic DNA from cryopreserved Peripheral Blood Mononuclear Cells of 79 volunteers. We amplified the protein-coding region of the NAT2 gene by polymerase chain reaction (PCR) and sequenced PCR products using the Sanger sequencing method. Sequencing reads were mapped and aligned to the NAT2 reference using the Geneious software (Auckland, New Zealand). Statistical analyses were performed using RStudio version 4.3.2 (2023.09.1 + 494).

RESULTS

The most frequent haplotype was the wild type NAT2*4 (37%). Five genetic variants: 282C > T (NAT2*13), 341 T > C (NAT2*5), 803A > G (NAT2*12), 590G > A (NAT2*6) and 481C > T (NAT2*11) were observed with allele frequencies of 29%, 18%, 6%, 6%, and 4% respectively. According to the bimodal distribution of acetylation activity, the predicted phenotype was 76% rapid (mainly consisting of the wildtype NAT2*4 and the NAT2*13A variant). A higher proportion of rapid acetylators were female, 72% vs 28% male (p = 0.022, odds ratio [OR] 3.48, 95% confidence interval [CI] 1.21 to 10.48). All variants were in HWE. NAT2 341 T > C was in strong complete LD with the 590G > A variant (D' = 1.0, r2 = - 0.39) but not complete LD with the 282C > T variant (D' = 0.94, r2 = - 0.54).

CONCLUSION

The rapid acetylation haplotypes predominated. Despite the LD observed, none of the SNPs could be termed tag SNP. This study adds to the genetic characterisation data of African populations at NAT2, which may be useful for developing relevant pharmacogenomic tools for TB therapy. To support optimised, pharmacogenomics-guided TB therapy, we recommend genotype-phenotype studies, including studies designed to explore gender-associated differences.

The diversity and clinical implications of genetic variants influencing clopidogrel bioactivation and response in the Emirati population

BACKGROUND

Clopidogrel is a widely prescribed prodrug that requires activation via specific pharmacogenes to exert its anti-platelet function. Genetic variations in the genes encoding its transporter, metabolizing enzymes, and target receptor lead to variability in its activation and platelet inhibition and, consequently, its efficacy. This variability increases the risk of secondary cardiovascular events, and therefore, some variations have been utilized as genetic biomarkers when prescribing clopidogrel.

METHODS

Our study examined clopidogrel-related genes (CYP2C19, ABCB1, PON1, and P2Y12R) in a cohort of 298 healthy Emiratis individuals. The study used whole exome sequencing (WES) data to comprehensively analyze pertinent variations of these genes, including their minor allele frequencies, haplotype distribution, and their resulting phenotypes.

RESULTS

Our data shows that approximately 37% (n = 119) of the cohort are likely to benefit from the use of alternative anti-platelet drugs due to their classification as intermediate or poor CYP2C19 metabolizers. Additionally, more than 50% of the studied cohort exhibited variants in ABCB1, PON1, and P2YR12 genes, potentially influencing clopidogrel's transport, enzymatic clearance, and receptor performance.

CONCLUSIONS

Recognizing these alleles and genotype frequencies may explain the clinical differences in medication response across different ethnicities and predict adverse events. Our findings underscore the need to consider genetic variations in prescribing clopidogrel, with potential implications for implementing personalized anti-platelet therapy among Emiratis based on their genetic profiles.

Maternal separation influences hepatic drug-metabolizing CYP450 gene expression without pathological changes in adult mice

OBJECTIVES

The principal motive of this study is to explore the influence maternal separation (MS) exhibits on the mRNA expression of major drug metabolizing-cyp450s in parallel with the assessment of pathological changes that can be induced by MS in the livers of experimental mice.

METHODS

Eighteen Balb/c mouse pups, comprising of both males and females, were separated from their mothers after birth. Following a six-week period during when the pups became adults, the mice were sacrificed and their livers were isolated for analysis of weight, pathohistological alterations, and the mRNA expression of drug metabolizing cyp450 genes: cyp1a1, cyp3a11, cyp2d9, and cyp2c29.

RESULTS

The study demonstrated that MS markedly downregulated (p<0.05) the mRNA expression of all tested drug-metabolizing cyp450s in livers of female and male mice. Furthermore, the mRNA levels of major drug-metabolizing cyp450s were notably lower (p<0.05) in livers of female MS mice as compared with male MS mice. It was found that values of the total body weight and liver weight of MS mice did not vary significantly (p>0.05) from those of the control groups. Additionally, histological examination revealed that the hepatic tissue of MS mice was normal, similar to that of the control mice.

CONCLUSIONS

In summary, MS downregulates the gene expression of major hepatic drug-metabolizing cyp450s without inducing pathological alterations in the livers of mice. These findings provide an explanation for the heterogeneity in pharmacokinetics and drug response of patients with early life stress.

Managing Diabetic Complications with Alternative Therapeutic Strategies

Diabetes is a chronic metabolic disease affecting millions worldwide. It is characterized by a lack of insulin production or impaired insulin function, leading to elevated blood glucose levels. Conventional treatment methods for diabetes management typically include lifestyle changes and medications. However, alternative therapies have gained attention in recent years, including traditional medicine containing bioactive compounds, supplements like vitamin D and Omega-3 fatty acids, aromatherapy, and homeopathy. Diabetic complications are common in patients with uncontrolled diabetes and can lead to serious health problems, including diabetic retinopathy, impaired wound healing, kidney disease, nerve damage, and cardiovascular disease. Alternative remedies, such as traditional medicine containing bioactive compounds, supplements, and aromatherapy, have been studied for their potential benefits in managing these complications. Traditional medicines like bitter melon, cinnamon, and fenugreek have been shown to have anti-diabetic effects due to their bioactive compounds. Similarly, supplements like vitamin D and Omega-3 fatty acids have been found to improve glycemic control in patients with diabetes. Aromatherapy, which involves the use of essential oils, has also been explored for its potential benefits in diabetes management. Homeopathy, which uses highly diluted substances to stimulate the body's natural healing abilities, has been used to treat diabetes-related symptoms like neuropathy and wounds. Personalized care is essential in natural diabetes management because each person's body and health needs are unique. A holistic approach that addresses the individual's physical, emotional, and spiritual well-being is essential. As research in this field continues to expand, a more comprehensive understanding of diabetes management will lead to improved outcomes for those living with this condition.

Update and Application of a Deep Learning Model for the Prediction of Interactions between Drugs Used by Patients with Multiple Sclerosis

Patients with multiple sclerosis (MS) often take multiple drugs at the same time to modify the course of disease, alleviate neurological symptoms and manage co-existing conditions. A major consequence for a patient taking different medications is a higher risk of treatment failure and side effects. This is because a drug may alter the pharmacokinetic and/or pharmacodynamic properties of another drug, which is referred to as drug-drug interaction (DDI). We aimed to predict interactions of drugs that are used by patients with MS based on a deep neural network (DNN) using structural information as input. We further aimed to identify potential drug-food interactions (DFIs), which can affect drug efficacy and patient safety as well. We used DeepDDI, a multi-label classification model of specific DDI types, to predict changes in pharmacological effects and/or the risk of adverse drug events when two or more drugs are taken together. The original model with ~34 million trainable parameters was updated using >1 million DDIs recorded in the DrugBank database. Structure data of food components were obtained from the FooDB database. The medication plans of patients with MS (n = 627) were then searched for pairwise interactions between drug and food compounds. The updated DeepDDI model achieved accuracies of 92.2% and 92.1% on the validation and testing sets, respectively. The patients with MS used 312 different small molecule drugs as prescription or over-the-counter medications. In the medication plans, we identified 3748 DDIs in DrugBank and 13,365 DDIs using DeepDDI. At least one DDI was found for most patients (n = 509 or 81.2% based on the DNN model). The predictions revealed that many patients would be at increased risk of bleeding and bradycardic complications due to a potential DDI if they were to start a disease-modifying therapy with cladribine (n = 242 or 38.6%) and fingolimod (n = 279 or 44.5%), respectively. We also obtained numerous potential interactions for Bruton's tyrosine kinase inhibitors that are in clinical development for MS, such as evobrutinib (n = 434 DDIs). Food sources most often related to DFIs were corn (n = 5456 DFIs) and cow's milk (n = 4243 DFIs). We demonstrate that deep learning techniques can exploit chemical structure similarity to accurately predict DDIs and DFIs in patients with MS. Our study specifies drug pairs that potentially interact, suggests mechanisms causing adverse drug effects, informs about whether interacting drugs can be replaced with alternative drugs to avoid critical DDIs and provides dietary recommendations for MS patients who are taking certain drugs.

Effects of intermittent fasting on the histology and mRNA expression of major drug-metabolizing cyp450s in the liver of diabetic mice

Introduction:There is a variation in drug response among patients who practice intermittent fasting. Alteration in the expression of drug-metabolizing enzymes (DMEs) can affect the pharmacokinetics and drug response.Aims: This research aimed to determine the effect of intermittent fasting on the mRNA expression of major drug-metabolizing cyp450s in the liver of diabetic mice.Methods: Thirty-two male Balb/c mice were divided into four groups; control, nonfasting diabetic, non-diabetic fasting, and diabetic fasting mice. Insulin-dependent diabetes was induced in mice by a single high-dose (250 mg/kg) streptozocin. Mice of non-diabetic and diabetic fasting groups were subjected to 10-day intermittent fasting for 17 hours daily. Then, the mRNA expression of mouse phase I DMEs cyp1a1, cyp2c29, cyp2d9, and cyp3a11 was analyzed using real-time polymerase chain reaction. In addition, the liver of mice in all groups was examined for pathohistological alterations.Results: Diabetes downregulated the mRNA expression of hepatic drug-metabolizing cyp450s in diabetic mice, while intermittent fasting significantly (P < 0.05) increased it. Also, cyp2d9 and cyp3a11 were upregulated in the liver of diabetic fasting mice. These alterations in the gene expression were correlated with the pathohistological alterations, where livers of diabetic mice showed dilatation in the blood sinusoids and inflammatory cells leukocyte infiltrations. Whereas livers of diabetic fasting mice showed almost comparable histological findings to control mice.Conclusions: Intermittent fasting can protect the liver against diabetes-induced hepatotoxicity and the down-regulation of DME genes in the diabetic liver. These results can explain, at least partly, the inter-individual variation in the drug response during practicing fasting.

Impact of Pharmacogenomics in Clinical Practice

Polymorphisms of genes encoding drug metabolizing enzymes and transporters can significantly modify pharmacokinetics, and this can be associated with significant differences in drug efficacy, safety, and tolerability. Moreover, genetic variants of some components of the immune system can explain clinically relevant drug-related adverse events. However, the implementation of drug dose individualization based on pharmacogenomics remains scarce. In this narrative review, the impact of genetic variations on the disposition, safety, and tolerability of the most commonly prescribed drugs is reported. Moreover, reasons for poor implementation of pharmacogenomics in everyday clinical settings are discussed. The literature analysis showed that knowledge of how genetic variations can modify the effectiveness, safety, and tolerability of a drug can lead to the adjustment of usually recommended drug dosages, improve effectiveness, and reduce drug-related adverse events. Despite some efforts to introduce pharmacogenomics in clinical practice, presently very few centers routinely use genetic tests as a guide for drug prescription. The education of health care professionals seems critical to keep pace with the rapidly evolving field of pharmacogenomics. Moreover, multimodal algorithms that incorporate both clinical and genetic factors in drug prescribing could significantly help in this regard. Obviously, further studies which definitively establish which genetic variations play a role in conditioning drug effectiveness and safety are needed. Many problems must be solved, but the advantages for human health fully justify all the efforts.

Highly expressed long non-coding RNA SNHG14 activated MSU-induced inflammatory response in acute gout arthritis through targeting miR-223-3p

AIM

According to reports, long non-coding RNAs (lncRNAs) are involved in the regulation of many inflammatory diseases. Here, our main purpose was to ascertain the expression data of lncRNA SNHG14 in acute gouty arthritis (AGA) and to explore its possible mechanism in the regulation of AGA.

METHOD

Reverse transcription quantitative polymerase chain reaction technology was supplied to detect the lncRNA SNHG14 expression. A receiver operating characteristics curve was drawn to estimate the accuracy of lncRNA SNHG14 in AGA diagnosis. An in vitro AGA cell model was constructed by inducing THP-1 cells with monosodium urate (MSU). The concentrations of inflammatory factors such as interleukin-1β, interleukin-6, and tumor necrosis factor-α were measured by enzyme-linked immunosorbent assay. The luciferase reporter gene was used to verify the relationship between lncRNA SNHG14 and miR-223-3p.

RESULTS

In clinical analysis, the levels of serum lncRNA SNHG14 in AGA patients were significantly higher than those in the control group. Abnormally elevated lncRNA SNHG14 has high sensitivity and specificity for AGA diagnosis. In in vitro cell experiments, silencing lncRNA SNHG14 inhibited the inflammatory response of THP-1 cells stimulated by MSU, and the luciferase reporter gene proved that lncRNA SNHG14 could bind to miR-223-3p. In addition, the level of miR-223-3p declined in AGA patients and the AGA cell model. Overexpression of miR-223-3p is helpful to alleviate an MSU-induced inflammatory response.

CONCLUSION

In the AGA cell model, lncRNA SNHG14, as an miR-223-3p sponge, induces a cellular inflammatory response by controlling the level of miR-223-3p, so aggravating the disease progress of AGA.

Influence of CYP3A5 and ABCB1 polymorphisms on the pharmacokinetics of vincristine in adult patients receiving CHOP therapy

PURPOSE

This study aims to clarify the impact of CYP3A5 and ABCB1 polymorphisms on the pharmacokinetics of vincristine (VCR) in adult patients receiving CHOP therapy.

METHODS

Plasma samples were collected immediately after the end of VCR administration and at 1.5, 2.5, 3.5, 5.5, 9.5, 13.5, and 25.5 h after the start of administration. Areas under the plasma concentration-time curves of VCR in the elimination phase (AUC1.5-25.5) were calculated using the linear trapezoidal rule. Half-lives of VCR during the early phase (1.5-5.5 h) and terminal phase (5.5-25.5 h; t1/2γ) were determined according to the log-linear regression of the concentration-time data for at least 3 sampling points.

RESULTS

A total of 41 adult patients were enrolled in this study. The median t1/2γ and AUC1.5-25.5 were significantly longer and higher in CYP3A5 non-expressers (CYP3A5*3/*3) than in CYP3A5 expressers (CYP3A5*1/*1 or *1/*3) (21.3 vs 13.8 h, P = 0.005 and 35.5 vs 30.0 ng･h/mL, P = 0.006, respectively). Conversely, there were no significant differences in pharmacokinetic parameters among the ABCB1 c.1236C>T, c.2677G>A/T, c.3435C>T genotype groups. A stepwise selection multiple linear regression analysis showed that the dose of VCR administered and CYP3A5 non-expresser status were independent factors influencing the AUC1.5-25.5 (partial R2 = 0.212, P = 0.002 and partial R2 = 0.143, P = 0.010, respectively).

CONCLUSION

The CYP3A5*3 polymorphism was found to be an indicator for predicting exposure to VCR in adult patients receiving CHOP therapy. This information may be useful for the individualization of VCR dosages.

Genetic influences on epilepsy outcomes: A whole-exome sequencing and health care records data linkage study

OBJECTIVE

This study was undertaken to develop a novel pathway linking genetic data with routinely collected data for people with epilepsy, and to analyze the influence of rare, deleterious genetic variants on epilepsy outcomes.

METHODS

We linked whole-exome sequencing (WES) data with routinely collected primary and secondary care data and natural language processing (NLP)-derived seizure frequency information for people with epilepsy within the Secure Anonymised Information Linkage Databank. The study participants were adults who had consented to participate in the Swansea Neurology Biobank, Wales, between 2016 and 2018. DNA sequencing was carried out as part of the Epi25 collaboration. For each individual, we calculated the total number and cumulative burden of rare and predicted deleterious genetic variants and the total of rare and deleterious variants in epilepsy and drug metabolism genes. We compared these measures with the following outcomes: (1) no unscheduled hospital admissions versus unscheduled admissions for epilepsy, (2) antiseizure medication (ASM) monotherapy versus polytherapy, and (3) at least 1 year of seizure freedom versus <1 year of seizure freedom.

RESULTS

We linked genetic data for 107 individuals with epilepsy (52% female) to electronic health records. Twenty-six percent had unscheduled hospital admissions, and 70% were prescribed ASM polytherapy. Seizure frequency information was linked for 100 individuals, and 10 were seizure-free. There was no significant difference between the outcome groups in terms of the exome-wide and gene-based burden of rare and deleterious genetic variants.

SIGNIFICANCE

We successfully uploaded, annotated, and linked genetic sequence data and NLP-derived seizure frequency data to anonymized health care records in this proof-of-concept study. We did not detect a genetic influence on real-world epilepsy outcomes, but our study was limited by a small sample size. Future studies will require larger (WES) data to establish genetic variant contribution to epilepsy outcomes.

Drug-microbiota interactions: an emerging priority for precision medicine

Individual variability in drug response (IVDR) can be a major cause of adverse drug reactions (ADRs) and prolonged therapy, resulting in a substantial health and economic burden. Despite extensive research in pharmacogenomics regarding the impact of individual genetic background on pharmacokinetics (PK) and pharmacodynamics (PD), genetic diversity explains only a limited proportion of IVDR. The role of gut microbiota, also known as the second genome, and its metabolites in modulating therapeutic outcomes in human diseases have been highlighted by recent studies. Consequently, the burgeoning field of pharmacomicrobiomics aims to explore the correlation between microbiota variation and IVDR or ADRs. This review presents an up-to-date overview of the intricate interactions between gut microbiota and classical therapeutic agents for human systemic diseases, including cancer, cardiovascular diseases (CVDs), endocrine diseases, and others. We summarise how microbiota, directly and indirectly, modify the absorption, distribution, metabolism, and excretion (ADME) of drugs. Conversely, drugs can also modulate the composition and function of gut microbiota, leading to changes in microbial metabolism and immune response. We also discuss the practical challenges, strategies, and opportunities in this field, emphasizing the critical need to develop an innovative approach to multi-omics, integrate various data types, including human and microbiota genomic data, as well as translate lab data into clinical practice. To sum up, pharmacomicrobiomics represents a promising avenue to address IVDR and improve patient outcomes, and further research in this field is imperative to unlock its full potential for precision medicine.

Genetical Signature-An Example of a Personalized Skin Aging Investigation with Possible Implementation in Clinical Practice

We conducted a research study to create the groundwork for personalized solutions within a skin aging segment. This test utilizes genetic and general laboratory data to predict individual susceptibility to weak skin characteristics, leveraging the research on genetic polymorphisms related to skin functional properties. A cross-sectional study was conducted in a collaboration between the Private Clinic Medicina Practica Laboratory (Vilnius, Lithuania) and the Public Institution Lithuanian University of Health Sciences (Kaunas, Lithuania). A total of 370 participants agreed to participate in the project. The median age of the respondents was 40, with a range of 19 to 74 years. After the literature search, we selected 15 polymorphisms of the genes related to skin aging, which were subsequently categorized in terms of different skin functions: SOD2 (rs4880), GPX1 (rs1050450), NQO1 (rs1800566), CAT (rs1001179), TYR (rs1126809), SLC45A2 (rs26722), SLC45A2 (rs16891982), MMP1 (rs1799750), ELN (rs7787362), COL1A1 (rs1800012), AHR (rs2066853), IL6 (rs1800795), IL1Beta (rs1143634), TNF-α (rs1800629), and AQP3 (rs17553719). RT genotyping, blood count, and immunochemistry results were analyzed using statistical methods. The obtained results show significant associations between genotyping models and routine blood screens. These findings demonstrate the personalized medicine approach for the aging segment and further add to the growing literature. Further investigation is warranted to fully understand the complex interplay between genetic factors, environmental influences, and skin aging.

Influence of Metabolic, Transporter, and Pathogenic Genes on Pharmacogenetics and DNA Methylation in Neurological Disorders

Pharmacogenetics and DNA methylation influence therapeutic outcomes and provide insights into potential therapeutic targets for brain-related disorders. To understand the effect of genetic polymorphisms on drug response and disease risk, we analyzed the relationship between global DNA methylation, drug-metabolizing enzymes, transport genes, and pathogenic gene phenotypes in serum samples from two groups of patients: Group A, which showed increased 5-methylcytosine (5mC) levels during clinical follow-up, and Group B, which exhibited no discernible change in 5mC levels. We identified specific SNPs in several metabolizing genes, including CYP1A2, CYP2C9, CYP4F2, GSTP1, and NAT2, that were associated with differential drug responses. Specific SNPs in CYP had a significant impact on enzyme activity, leading to changes in phenotypic distribution between the two patient groups. Group B, which contained a lower frequency of normal metabolizers and a higher frequency of ultra-rapid metabolizers compared to patients in Group A, did not show an improvement in 5mC levels during follow-up. Furthermore, there were significant differences in phenotype distribution between patient Groups A and B for several SNPs associated with transporter genes (ABCB1, ABCC2, SLC2A9, SLC39A8, and SLCO1B1) and pathogenic genes (APOE, NBEA, and PTGS2). These findings appear to suggest that the interplay between pharmacogenomics and DNA methylation has important implications for improving treatment outcomes in patients with brain-related disorders.

The impact of ABCB1, CYP3A4/5 and ABCG2 gene polymorphisms on rivaroxaban trough concentrations and bleeding events in patients with non-valvular atrial fibrillation

BACKGROUND

The influence of genetic factors on the pharmacokinetics and clinical outcomes of rivaroxaban in patients with non-valvular atrial fibrillation (NVAF) is poorly understood. This study aimed to explore the effects of CYP3A4/5, ABCB1, and ABCG2 gene polymorphisms on the trough concentrations and the bleeding risk of rivaroxaban in NVAF patients.

PATIENTS AND METHODS

This study is a prospective multicenter study. The patient's blood samples were collected to detect the steady-state trough concentrations of rivaroxaban and gene polymorphisms. We visited the patients regularly at month 1, 3, 6, and 12 to record bleeding events and medications.

RESULTS

A total of 95 patients were enrolled in this study, and 9 gene loci were detected. For the dose-adjusted trough concentration ratio (C_trough/D) of rivaroxaban, the homozygous mutant type was significantly lower than wild type at ABCB1 rs4148738 locus (TT vs. CC, P = 0.033), and the mutant type was significantly lower than the wild type at ABCB1 rs4728709 locus (AA + GA vs. GG, P = 0.008). ABCB1 (rs1045642, rs1128503), CYP3A4 (rs2242480, rs4646437), CYP3A5 (rs776746), and ABCG2 (rs2231137, rs2231142) gene polymorphisms had no significant effect on the C_trough/D of rivaroxaban. For the bleeding events, we found that there were no significant differences among genotypes of all gene loci.

CONCLUSION

This study found for the first time that ABCB1 rs4148738 and rs4728709 gene polymorphisms had a significant impact on the C_trough/D of rivaroxaban in NVAF patients. CYP3A4/5, ABCB1, and ABCG2 gene polymorphisms were not associated with the bleeding risk of rivaroxaban.

Selective control of parasitic nematodes using bioactivated nematicides

Parasitic nematodes are a major threat to global food security, particularly as the world amasses 10 billion people amid limited arable land^1-4. Most traditional nematicides have been banned owing to poor nematode selectivity, leaving farmers with inadequate means of pest control^4-12. Here we use the model nematode Caenorhabditis elegans to identify a family of selective imidazothiazole nematicides, called selectivins, that undergo cytochrome-p450-mediated bioactivation in nematodes. At low parts-per-million concentrations, selectivins perform comparably well with commercial nematicides to control root infection by Meloidogyne incognita, a highly destructive plant-parasitic nematode. Tests against numerous phylogenetically diverse non-target systems demonstrate that selectivins are more nematode-selective than most marketed nematicides. Selectivins are first-in-class bioactivated nematode controls that provide efficacy and nematode selectivity.

Effects of aqueous extract from Baiyedancong-Oolong tea on cytochrome P450 enzymes activities, P-gp and OATs transport abilities and transcription levels in mice

Introduction

Recent studies have been conducted on its influence on drug metabolism and its potential mechanisms, among which the most studies have been focused on CYP3A enzymes.

Methods

In this study, Baiyedancong Oolong tea (BOT) was processed by freeze- and hot air-drying techniques separately to obtain the aqueous extracts of freeze-and hot-dried BOT (FBOT and HBOT, respectively). High and low doses of FBOT (1463.7 and 292.74 mg/kg/d, respectively) and HBOT (1454.46 mg/kg/d, 290.89, respectively) were administered to mice for 7 days.

Results

Aqueous extracts from BOT simultaneously improved liver CYP3A, CYP2E1, and CYP2C37 activities and weakened the transport ability of P-gp and OATs in a dose-dependent manner, thus affecting multiple links of oral drug metabolism in liver, intestinal absorption and metabolism, and renal excretion. Moreover, aqueous extracts from BOT significantly increased the mRNA expressions of liver CYP3A11 and CYP2C37 as well as intestinal CYP3A11. Decreased transcription levels of MDR1 encoding P-gp in small intestine and renal OAT1 and OAT3, which was in the same direction as the regulation of the above enzyme activities and transport capacities. Besides, the transcription level of liver CYP2E1 was weakened, which was inconsistent with its corresponding enzyme activity, suggesting that the increased CYP2E1 activity may be caused by other mechanisms.

Conclusion

Daily consumption or high dose administration of BOT and its related products may affect drug absorption, metabolisms, and excretion.

Impact of KIF6 Trp719Arg gene variant on Coronary Artery Disease Development.. [DOI: 10.21203/rs.3.rs-2705882/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Coronary artery disease (CAD) is a multifactorial disease resulting from the interaction of genetic varia-tion and environmental traditional risk factors (TRFs), including diabetes mellitus, smoking, dyslipidemia, and hypertension. KIF6 Trp719Arg (rs20455; A>G) is an interesting gene variant reported as one of the most important risk factors for CAD in different populations. The study enrolled 150 participants belong-ing to the National Heart Institute (NHI) catheterization unit in Egypt, who were grouped into three main study groups regarding the presence of different TRFs. Biochemical investigations and clinical data were assessed and recorded. Analysis for KIF6 Trp719Arg polymorphism (rs20455; A>G) was performed for all participants using the TaqMan genotyping real-time PCR assay (rs20455). The study demonstrated that diabetes mellitus, hypertension, dyslipidemia, and smoking were highly statistically significant among CAD with TRF and non-CAD with TRF patients with p-values of 0.009*, 0.003*, 0.046*, and 0.001**, re-spectively. The family history of premature CAD represents a high percentage of CAD without TRF pa-tients compared to the other groups with a statistical difference of p-value= 0.004*. A high prevalence of AG+GG genotypes among the different groups was obtained, representing 66.0% of CAD with TRF, 76.0% of CAD without TRF, and 60% of non-CAD with TRF patients. The present study elucidated the impact of KIF6 Trp719Arg as a dependent risk factor for CAD, as it could have a significant role in CAD develop-ment when it interacts with one or more of the other traditional risk factors.

Considerations for Embedding Inclusive Research Principles in the Design and Execution of Clinical Trials

There is a growing recognition that the clinical research enterprise has a diversity problem, given that many clinical trials recruit historically marginalized individuals or patients reflective of real-world data at a rate that is far below the incidence and prevalence of the disease for which the investigational therapy or device is targeting. This lack of diversity in clinical research participation can obscure the safety and efficacy of drug therapies and limits our collective ability to develop effective treatments for all patients, leading to even wider health disparities. This review article provides an in-depth analysis of the impact of this bias on public health, along with a description of some of the barriers that prevent historically marginalized populations from participating in clinical research. Some practical solutions that can be employed to increase diversity in clinical trial participation are also discussed, including the crucial role clinical trial sponsors, research organizations, patients, and caregivers need to play in supporting the industry to achieve this ambitious but necessary goal.

Human Cytochrome P450 1, 2, 3 Families as Pharmacogenes with Emphases on Their Antimalarial and Antituberculosis Drugs and Prevalent African Alleles

Precision medicine gives individuals tailored medical treatment, with the genotype determining the therapeutic strategy, the appropriate dosage, and the likelihood of benefit or toxicity. Cytochrome P450 (CYP) enzyme families 1, 2, and 3 play a pivotal role in eliminating most drugs. Factors that affect CYP function and expression have a major impact on treatment outcomes. Therefore, polymorphisms of these enzymes result in alleles with diverse enzymatic activity and drug metabolism phenotypes. Africa has the highest CYP genetic diversity and also the highest burden of malaria and tuberculosis, and this review presents current general information on CYP enzymes together with variation data concerning antimalarial and antituberculosis drugs, while focusing on the first three CYP families. Afrocentric alleles such as CYP2A6*17, CYP2A6*23, CYP2A6*25, CYP2A6*28, CYP2B6*6, CYP2B6*18, CYP2C8*2, CYP2C9*5, CYP2C9*8, CYP2C9*9, CYP2C19*9, CYP2C19*13, CYP2C19*15, CYP2D6*2, CYP2D6*17, CYP2D6*29, and CYP3A4*15 are implicated in diverse metabolic phenotypes of different antimalarials such as artesunate, mefloquine, quinine, primaquine, and chloroquine. Moreover, CYP3A4, CYP1A1, CYP2C8, CYP2C18, CYP2C19, CYP2J2, and CYP1B1 are implicated in the metabolism of some second-line antituberculosis drugs such as bedaquiline and linezolid. Drug-drug interactions, induction/inhibition, and enzyme polymorphisms that influence the metabolism of antituberculosis, antimalarial, and other drugs, are explored. Moreover, a mapping of Afrocentric missense mutations to CYP structures and a documentation of their known effects provided structural insights, as understanding the mechanism of action of these enzymes and how the different alleles influence enzyme function is invaluable to the advancement of precision medicine.

Regulation of Human Endogenous Metabolites by Drug Transporters and Drug Metabolizing Enzymes: An Analysis of Targeted SNP-Metabolite Associations

Drug transporters and drug-metabolizing enzymes are primarily known for their role in the absorption, distribution, metabolism, and excretion (ADME) of small molecule drugs, but they also play a key role in handling endogenous metabolites. Recent cross-tissue co-expression network analyses have revealed a "Remote Sensing and Signaling Network" of multispecific, oligo-specific, and monospecific transporters and enzymes involved in endogenous metabolism. This includes many proteins from families involved in ADME (e.g., SLC22, SLCO, ABCC, CYP, UGT). Focusing on the gut-liver-kidney axis, we identified the endogenous metabolites potentially regulated by this network of ~1000 proteins by associating SNPs in these genes with the circulating levels of thousands of small, polar, bioactive metabolites, including free fatty acids, eicosanoids, bile acids, and other signaling metabolites that act in part via G-protein coupled receptors (GPCRs), nuclear receptors, and kinases. We identified 77 genomic loci associated with 7236 unique metabolites. This included metabolites that were associated with multiple, distinct loci, indicating coordinated regulation between multiple genes (including drug transporters and drug-metabolizing enzymes) of specific metabolites. We analyzed existing pharmacogenomic data and noted SNPs implicated in endogenous metabolite handling (e.g., rs4149056 in SLCO1B1) also affecting drug ADME. The overall results support the existence of close relationships, via interactions with signaling metabolites, between drug transporters and drug-metabolizing enzymes that are part of the Remote Sensing and Signaling Network, and with GPCRs and nuclear receptors. These analyses highlight the potential for drug-metabolite interactions at the interfaces of the Remote Sensing and Signaling Network and the ADME protein network.

Pharmacogenetics of Anticancer Drugs: Clinical Response and Toxicity

Cancer is the most challenging disease for medical professionals to treat. The factors underlying the complicated situation include anticancer drug-associated toxicity, non-specific response, low therapeutic window, variable treatment outcomes, development of drug resistance, treatment complications, and cancer recurrence. The remarkable advancement in biomedical sciences and genetics, over the past few decades, however, is changing the dire situation. The discovery of gene polymorphism, gene expression, biomarkers, particular molecular targets and pathways, and drug-metabolizing enzymes have paved the way for the development and provision of targeted and individualized anticancer treatment. Pharmacogenetics is the study of genetic factors having the potential to affect clinical responses and pharmacokinetic and pharmacodynamic behaviors of drugs. This chapter emphasizes pharmacogenetics of anticancer drugs and its applications in improving treatment outcomes, selectivity, toxicity of the drugs, and discovering and developing personalized anticancer drugs and genetic methods for prediction of drug response and toxicity.

Predictive genomic tools in disease stratification and targeted prevention: a recent update in personalized therapy advancements

In the current era of medical revolution, genomic testing has guided the healthcare fraternity to develop predictive, preventive, and personalized medicine. Predictive screening involves sequencing a whole genome to comprehensively deliver patient care via enhanced diagnostic sensitivity and specific therapeutic targeting. The best example is the application of whole-exome sequencing when identifying aberrant fetuses with healthy karyotypes and chromosomal microarray analysis in complicated pregnancies. To fit into today's clinical practice needs, experimental system biology like genomic technologies, and system biology viz., the use of artificial intelligence and machine learning is required to be attuned to the development of preventive and personalized medicine. As diagnostic techniques are advancing, the selection of medical intervention can gradually be influenced by a person's genetic composition or the cellular profiling of the affected tissue. Clinical genetic practitioners can learn a lot about several conditions from their distinct facial traits. Current research indicates that in terms of diagnosing syndromes, facial analysis techniques are on par with those of qualified therapists. Employing deep learning and computer vision techniques, the face image assessment software DeepGestalt measures resemblances to numerous of disorders. Biomarkers are essential for diagnostic, prognostic, and selection systems for developing personalized medicine viz. DNA from chromosome 21 is counted in prenatal blood as part of the Down's syndrome biomarker screening. This review is based on a detailed analysis of the scientific literature via a vigilant approach to highlight the applicability of predictive diagnostics for the development of preventive, targeted, personalized medicine for clinical application in the framework of predictive, preventive, and personalized medicine (PPPM/3 PM). Additionally, targeted prevention has also been elaborated in terms of gene-environment interactions and next-generation DNA sequencing. The application of 3 PM has been highlighted by an in-depth analysis of cancer and cardiovascular diseases. The real-time challenges of genome sequencing and personalized medicine have also been discussed.

CYP2C9 Polymorphism Influence in PK/PD Model of Naproxen and 6-O-Desmethylnaproxen in Oral Fluid

Polymorphisms in CYP2C9 can significantly interfere with the pharmacokinetic (PK) and pharmacodynamic (PD) parameters of nonsteroidal anti-inflammatory drugs (NSAIDs), including naproxen. The present research aimed to study the PK/PD parameters of naproxen and its metabolite, 6-O-desmethylnaproxen, associated with allelic variations of CYP2C9. In our study, a rapid, selective, and sensitive Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) method was developed and validated for the determination of naproxen and its main metabolite, 6-O-desmethylnaproxen, in oral fluid. Naproxen and its main metabolite were separated using a Shim-Pack XR-ODS 75L × 2.0 column and C18 pre-column at 40 °C using a mixture of methanol and 10 mM ammonium acetate (70:30, v/v), with an injection flow of 0.3 mL/min. The total analytical run time was 3 min. The volunteers, previously genotyped for CYP2C9 (16 ancestral—CYP2C9 *1 and 12 with the presence of polymorphism—CYP2C9 *2 or *3), had their oral fluids collected sequentially before and after taking a naproxen tablet (500 mg) at the following times: 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 5, 6 8, 11, 24, 48, 72 and 96 h. Significant differences in the PK parameters (* p < 0.05) of naproxen in the oral fluid were: Vd/F (L): 98.86 (55.58−322.07) and 380.22 (261.84−1097.99); Kel (1/h): 0.84 (0.69−1.34) and 1.86 (1.09−4.06), in ancestral and mutated CYP2C9 *2 and/or *3, respectively. For 6-O-desmethylnaproxen, no PK parameters were significantly different between groups. The analysis of prostaglandin E2 (PGE2) proved to be effective and sensitive for PD parameters analysis and showed higher levels in the mutated group (p < 0.05). Both naproxen and its main metabolite, 6-O-desmethylnaproxen, and PGE2 in oral fluid can be effectively quantified using LC-MS/MS after a 500 mg oral dose of naproxen. Our method proved to be effective and sensitive to determine the lower limit of quantification of naproxen and its metabolite, 6-O-desmethylnaproxen, in oral fluid (2.4 ng/mL). All validation data, such as accuracy, precision, and repeatability intra- and inter-assay, were less than 15%. Allelic variations of CYP2C9 may be considered relevant in the PK of naproxen and its main metabolite, 6-O-desmethylnaproxen.

Pharmacogenetics of Drug Metabolism: The Role of Gene Polymorphism in the Regulation of Doxorubicin Safety and Efficacy

Simple Summary

The effectiveness and safety of the anti-cancer agent doxorubicin (anthracycline group medicine) depend on the metabolism and retention of the drug in the human organism. Polymorphism of cytochrome p450 (CYP)-encoding genes and detoxifying enzymes such as CYP3A4 and CYP2D6 were found responsible for variations in the doxorubicin metabolism. Transmembrane transporters such as p-glycoproteins were reported to be involved in cancer tissue retention of doxorubicin. ATP-binding cassette (ABC) family members, including ABCB1 transporters (also known as Multi-Drug Resistance 1 (MDR1)) proteins, were determined to pump out doxorubicin from breast cancer cells, therefore reducing the drug effectiveness. This study critically discusses the latest data about the role of CYP3A4, CYP2D6, and ABCB1 gene polymorphism in the regulation of doxorubicin’s effects in breast cancer patients. The assessment of genetic differences in the expression of doxorubicin metabolizing and transporting enzymes should be explored for the development of personalized medical treatment of breast cancer patients.

Abstract

Breast cancer (BC) is the prevailing malignancy and major cause of cancer-related death in females. Doxorubicin is a part of BC neoadjuvant and adjuvant chemotherapy regimens. The administration of anthracycline derivates, such as doxorubicin, may cause several side effects, including hematological disfunction, gastrointestinal toxicity, hepatotoxicity, nephrotoxicity, and cardiotoxicity. Cardiotoxicity is a major adverse reaction to anthracyclines, and it may vary depending on individual differences in doxorubicin pharmacokinetics. Determination of specific polymorphisms of genes that can alter doxorubicin metabolism was shown to reduce the risk of adverse reactions and improve the safety and efficacy of doxorubicin. Genes which encode cytochrome P450 enzymes (CYP3A4 and CYP2D6), p-glycoproteins (ATP-binding cassette (ABC) family members such as Multi-Drug Resistance 1 (MDR1) protein), and other detoxifying enzymes were shown to control the metabolism and pharmacokinetics of doxorubicin. The effectiveness of doxorubicin is defined by the polymorphism of cytochrome p450 and p-glycoprotein-encoding genes. This study critically discusses the latest data about the role of gene polymorphisms in the regulation of doxorubicin’s anti-BC effects. The correlation of genetic differences with the efficacy and safety of doxorubicin may provide insights for the development of personalized medical treatment for BC patients.

Drug metabolism and drug transport of the 100 most prescribed oral drugs

Safe and effective use of drugs requires an understanding of metabolism and transport. We identified the 100 most prescribed drugs in six countries and conducted a literature search on in vitro data to assess contribution of Phase I and II enzymes and drug transporters to metabolism and transport. Eighty-nine of the 100 drugs undergo drug metabolism or are known substrates for drug transporters. Phase I enzymes are involved in metabolism of 67 drugs, while Phase II enzymes mediate metabolism of 18 drugs. CYP3A4/5 is the most important Phase I enzyme involved in metabolism of 43 drugs followed by CYP2D6 (23 drugs), CYP2C9 (23 drugs), CYP2C19 (22 drugs), CYP1A2 (14 drugs) and CYP2C8 (11 drugs). More than half of the drugs (54 drugs) are known substrates for drug transporters. P-glycoprotein (P-gp) is known to be involved in transport of 30 drugs, while breast cancer resistance protein (BCRP) facilitates transport of 11 drugs. A considerable proportion of drugs are subject to a combination of Phase I metabolism, Phase II metabolism and/or drug transport. We conclude that the majority of the most frequently prescribed drugs depend on drug metabolism or drug transport. Thus, understanding variability of drug metabolism and transport remains a priority.

Role of Genetic Polymorphisms in Drug-Metabolizing Enzyme-Mediated Toxicity and Pharmacokinetic Resistance to Anti-Cancer Agents: A Review on the Pharmacogenomics Aspect

The inter-individual differences in cancer susceptibility are somehow correlated with the genetic differences that are caused by the polymorphisms. These genetic variations in drug-metabolizing enzymes/drug-inactivating enzymes may negatively or positively affect the pharmacokinetic profile of chemotherapeutic agents that eventually lead to pharmacokinetic resistance and toxicity against anti-cancer drugs. For instance, the CYP1B1*3 allele is associated with CYP1B1 overexpression and consequent resistance to a variety of taxanes and platins, while 496T>G is associated with lower levels of dihydropyrimidine dehydrogenase, which results in severe toxicities related to 5-fluorouracil. In this context, a pharmacogenomics approach can be applied to ascertain the role of the genetic make-up in a person's response to any drug. This approach collectively utilizes pharmacology and genomics to develop effective and safe medications that are devoid of resistance problems. In addition, recently reported genomics studies revealed the impact of many single nucleotide polymorphisms in tumors. These studies emphasized the importance of single nucleotide polymorphisms in drug-metabolizing enzymes on the effect of anti-tumor drugs. In this review, we discuss the pharmacogenomics aspect of polymorphisms in detail to provide an insight into the genetic manipulations in drug-metabolizing enzymes that are responsible for pharmacokinetic resistance or toxicity against well-known anti-cancer drugs. Special emphasis is placed on different deleterious single nucleotide polymorphisms and their effect on pharmacokinetic resistance. The information provided in this report may be beneficial to researchers, especially those who are working in the field of biotechnology and human genetics, in rationally manipulating the genetic information of patients with cancer who are undergoing chemotherapy to avoid the problem of pharmacokinetic resistance/toxicity associated with drug-metabolizing enzymes.

Association between gene polymorphisms in the cyclophosphamide metabolism pathway with complications after haploidentical hematopoietic stem cell transplantation

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a curative treatment for patients with hematologic malignances. Haploidentical HSCT (Haplo-HSCT) is an alternative option for patients who do not have an HLA-matched donor. The use of post-transplantation high dose cyclophosphamide (PT-Cy) is commonly employed for graft-versus-host disease (GVHD) prophylaxis in haplo-HSCT. Cyclophosphamide (Cy) is an alkylating agent with antineoplastic and immunosuppressive activity, whose bioactivation requires the activity of polymorphic enzymes in the liver to produce phosphoramide mustard, which is a DNA alkylating agent. To identify polymorphisms in the genes of Cy metabolism and correlate them with post-HSCT complications [GVHD, sinusoidal obstruction syndrome (SOS), hemorrhagic cystitis (HC) and transplant-related mortality (TRM)], we designed a custom next-generation sequencing panel with Cy metabolism enzymes. We analyzed 182 patients treated with haplo-HSCT with PT-Cy from 2007 to 2019, detecting 40 variants in 11 Cy metabolism genes. Polymorphisms in CYP2B6, a major enzyme involved in Cy activation, were associated with decreased activity of this enzyme and a higher risk of Graf-versus-host disease (GVHD). Variants in other activation enzymes (CYP2A6, CYP2C8, CYP2C9, CYP2C19) lead to decreased enzyme activity and were associated with GVHD. Polymorphisms in detoxification genes such as glutathione S-transferases decreased the ability to detoxify cyclophosphamide metabolites due to lower enzyme activity, which leads to increased amounts of toxic metabolites and the development of III-IV acute GVHD. GSMT1*0 a single nucleotide polymorphism previously recognized as a risk factor for SOS was associated with a higher risk of SOS. We conclude that polymorphisms of genes involved in the metabolism of cyclophosphamide in our series are associated with severe grades of GVHD and toxicities (SOS and TRM) after haplo-HSCT and could be used to improve the clinical management of transplanted patients.

Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis

BACKGROUND AND OBJECTIVES

The prediction of multiple drug efficacies using machine learning prediction techniques based on clinical and molecular attributes of tumors is a new approach in the field of precision medicine of oncology. The selection of suitable and effective therapeutic drugs among the potential drugs is performed computationally considering the tumor features. In this study, we developed and validated machine learning models to predict the efficacy of five anti-cancer drugs according to the clinical and molecular attributes of 30 oral squamous cell carcinoma (OSCC) cohorts. This sounds a bit odd - consider: Ranking of the drugs was achieved using their apoptotic priming.

METHODS

We developed multiple drug efficacy prediction models based on three types of tumor characteristics by applying machine learning methods, including multi-target regression (MTR) and support vector regression (SVR). The prediction accuracy of existing machine learning methods was enhanced by introducing novel pre-processing techniques to develop Enhanced MTR (E_MTR), Enhanced Log-based MTR (EL_MTR), Enhanced Multi-target SVR (EM_SVR), and Enhanced Log-based Multi-target SVR (ELM_SVR). As a unique capability, ELM_SVR and EL_MTR rank the drugs based on their predicted efficacy. All the drug efficacy prediction models were built using OSCC real samples and theoretical samples. The best model was selected was based on dataset size and evaluation metrics, such as error terms, residuals and parameter tuning, and cross-validated (CV) using 30 real samples and 340 theoretical samples.

RESULTS

When 30 real tumor samples were used for the train-test and CV methods, MTR models predicted the efficacy with less error than SVR models. Comparatively, using 340 theoretical samples for the train-test and CV methods, though MTR improved the performance, SVR predicted the efficacy with zero error. We found that, for small samples, the proposed MTR provided a 0.01 difference between actual apoptotic priming and predicted priming of five drugs. For large samples, the predicted values by the proposed SVR had a difference of 0.00001. The error terms (Actual vs. Predicted) also reveal that the enhanced log model is suitable when MTR is applied. Meanwhile, the enhanced model is suitable for SVR learning for multiple drug efficacy prediction. It was found that the predicted ranks of the drugs based on the multi-targeted efficacy prediction exactly match the actual rankings.

CONCLUSION

We developed efficient statistical and machine learning models using MTR and SVR analysis for anticancer drug efficacy, which will be useful in the field of precision medicine to choose the most suitable drugs in personalized manner. The performance results of the proposed enhanced ranking techniques are described as follows: i) EL_MTR is the best to predict multiple anticancer drug efficacies and improve the accuracy of ranking drugs, irrespective of sample size; and ii) ELM_SVR performs better than other MTR models with a large sample size and precise ranking process.

Epigenetics in drug disposition & drug therapy: symposium report of the 24th North American meeting of the International Society for the Study of Xenobiotics (ISSX)

The 24th North American International Society for the Study of Xenobiotics (ISSX) meeting, held virtually from September 13 to 17, 2021, embraced the theme of "Broadening Our Horizons." This reinforces a key mission of ISSX: striving to share innovative science related to drug discovery and development. Session speakers and the ISSX New Investigators Group, which supports the scientific and professional development of student and early career ISSX members, elected to highlight the scientific content presented during the captivating session titled, "Epigenetics in Drug Disposition & Drug Therapy." The impact genetic variation has on drug response is well established; however, this session underscored the importance of investigating the role of epigenetics in drug disposition and drug discovery. Session speakers, Drs. Ning, McClay, and Lazarus, detailed mechanisms by which epigenetic players including long non-coding RNA (lncRNAs), microRNA (miRNAs), DNA methylation, and histone acetylation can alter the expression of genes involved in pharmacokinetics, pharmacodynamics, and toxicity. Dr. Ning detailed current knowledge about miRNAs and lncRNAs and the mechanisms by which they can affect the expression of drug metabolizing enzymes (DMEs) and nuclear receptors. Dr. Lazarus discussed the potential role of miRNAs on UDP-glucuronosyltransferase (UGT) expression and activity. Dr. McClay provided evidence that aging alters methylation and acetylation of DMEs in the liver, affecting gene expression and activity. These topics, compiled by the symposium organizers, presenters, and the ISSX New Investigators Group, are herein discussed, along with exciting future perspectives for epigenetics in drug disposition and drug discovery research.

Human Gut Microbiota and Drug Metabolism

The efficacy of drugs widely varies in individuals, and the gut microbiota plays an important role in this variability. The commensal microbiota living in the human gut encodes several enzymes that chemically modify systemic and orally administered drugs, and such modifications can lead to activation, inactivation, toxification, altered stability, poor bioavailability, and rapid excretion. Our knowledge of the role of the human gut microbiome in therapeutic outcomes continues to evolve. Recent studies suggest the existence of complex interactions between microbial functions and therapeutic drugs across the human body. Therapeutic drugs or xenobiotics can influence the composition of the gut microbiome and the microbial encoded functions. Both these deviations can alter the chemical transformations of the drugs and hence treatment outcomes. In this review, we provide an overview of (i) the genetic ecology of microbially encoded functions linked with xenobiotic degradation; (ii) the effect of drugs on the composition and function of the gut microbiome; and (iii) the importance of the gut microbiota in drug metabolism.

CYP2A6 and GABRA2 Gene Polymorphisms are Associated With Dexmedetomidine Drug Response

Background: Dexmedetomidine is a commonly used clinical sedative; however, the drug response varies among individuals. Thus, the purpose of this study was to explore the association between dexmedetomidine response and gene polymorphisms related to drug-metabolizing enzymes and drug response (CYP2A6, UGT2B10, UGT1A4, ADRA2A, ADRA2B, ADRA2C, GABRA1, GABRB2, and GLRA1).

Methods: This study was a prospective cohort study. A total of 194 female patients aged 18–60 years, American Society of Anesthesiologists (ASA) score I-II, who underwent laparoscopy at the Third Xiangya Hospital of Central South University, were included. The sedative effect was assessed every 2 min using the Ramsay score, and the patient’s heart rate decrease within 20 min was recorded. Peripheral blood was collected from each participant to identify genetic variants in the candidate genes of metabolic and drug effects using the Sequenom MassARRAY^® platform. Furthermore, additional peripheral blood samples were collected from the first 99 participants at multiple time points after dexmedetomidine infusion to perform dexmedetomidine pharmacokinetic analysis by Phoenix^® WinNonlin 7.0 software.

Results: Carriers of the minor allele (C) of CYP2A6 rs28399433 had lower metabolic enzyme efficiency and higher plasma concentrations of dexmedetomidine. In addition, the participants were divided into dexmedetomidine sensitive or dexmedetomidine tolerant groups based on whether they had a Ramsay score of at least four within 20 min, and CYP2A6 rs28399433 was identified to have a significant influence on the dexmedetomidine sedation sensitivity by logistic regression with Plink software [p = 0.003, OR (95% CI): 0.27 (0.11–0.65)]. C allele carriers were more sensitive to the sedative effects of dexmedetomidine than A allele carriers. GABRA2 rs279847 polymorphism was significantly associated with the degree of the heart rate decrease. In particular, individuals with the GG genotype had a 4-fold higher risk of heart rate abnormality than carriers of the T allele (OR = 4.32, 95% CI: 1.96–9.50, p = 0.00027).

Conclusion:CYP2A6 rs28399433 polymorphism affects the metabolic rate of dexmedetomidine and is associated with susceptibility to the sedative effects of dexmedetomidine; GABRA2 rs279847 polymorphism is significantly associated with the degree of the heart rate decrease.