Characterisation of CYP2C8, CYP2C9 and CYP2C19 polymorphisms in a Ghanaian population

<p>Abstract</p> <p>Background</p> <p>Genetic influences on drug efficacy and tolerability are now widely known. Pharmacogenetics has thus become an expanding field with great potential for improving drug efficacy and reducing toxicity. Many pharmacologically-relevant polymorphisms do show variability among different populations. Knowledge of allelic frequency distribution within specified populations can be useful in explaining therapeutic failures, identifying potential risk groups for adverse drug reactions (ADRs) and optimising doses for therapeutic efficacy. We sought to determine the prevalence of clinically relevant Cytochrome P450 (<it>CYP) 2C8</it>, <it>CYP2C9</it>, and <it>CYP2C19 </it>variants in Ghanaians. We compared the data with other ethnic groups and further investigated intra country differences within the Ghanaian population to determine its value to pharmacogenetics studies.</p> <p>Methods</p> <p>RFLP assays were used to genotype <it>CYP2C8 </it>(<it>*2</it>, <it>*3</it>, <it>*4</it>) variant alleles in 204 unrelated Ghanaians. <it>CYP2C9*2 </it>and <it>CYP2C19 </it>(<it>*2 </it>and <it>*3</it>) variants were determined by single-tube tetra-primer assays while <it>CYP2C9 </it>(<it>*3, *4, *5 </it>and <it>*11</it>) variants were assessed by direct sequencing.</p> <p>Results</p> <p>Allelic frequencies were obtained for <it>CYP2C8*2 </it>(17%), <it>CYP2C8*3 </it>(0%), <it>CYP2C8*4 </it>(0%), <it>CYP2C9*2 </it>(0%), <it>CYP2C9*3 </it>(0%), <it>CYP2C9*4 </it>(0%), <it>CYP2C9</it>*5 (0%), <it>CYP2C9*11 </it>(2%), <it>CYP2C19*2 </it>(6%) and <it>CYP2C19*3 </it>(0%).</p> <p>Conclusion</p> <p>Allele frequency distributions for <it>CYP2C8</it>, <it>CYP2C9 </it>and <it>CYP2C19 </it>among the Ghanaian population are comparable to other African ethnic groups but significantly differ from Caucasian and Asian populations. Variant allele frequencies for <it>CYP2C9 </it>and <it>CYP2C19 </it>are reported for the first time among indigenous Ghanaian population.</p

HIV Prevention and Care Among Black Cisgender Sexual Minority Men and Transgender Women: Protocol for an HIV Status–Neutral Cohort Study Using an Observational-Implementation Hybrid Approach

Background: Black cisgender gay, bisexual, and other sexual minority men (SMM) and transgender women (TW) continue to be heavily affected by HIV. Further research is needed to better understand HIV prevention and care outcomes in this population. In particular, there is a need for research examining the impact of substance use and sleep health on HIV prevention and treatment outcomes among Black SMM and TW. Objective: This paper outlines the study methods being used in the recently launched follow-up study to the Neighborhoods and Networks (N2) study, which we refer to as N2 Part 2 (N2P2). N2P2 aims to address this gap in the literature, build off the findings of the original N2 study, and identify socioenvironmental determinants of health, including whether neighborhood and network factors mediate and moderate these relationships. Methods: Building on the N2 cohort study in Chicago from 2018 to 2022, N2P2 used a prospective longitudinal cohort design and an observational-implementation hybrid approach. With sustained high levels of community engagement, we aim to recruit a new sample of 600 Black SMM and TW participants residing in the Chicago metropolitan statistical area. Participants are asked to participate in 3 study visits across an 18-month study period (1 visit every 9 months). Four different forms of data are collected per wave: (1) an in-person survey, (2) biological specimen collection, (3) a daily remote ecological momentary assessment for 14 days after each study visit, and (4) data from electronic health records. These forms of data collection continue to assess neighborhood and network factors and specifically explore substance use, sleep, immune function, obesity, and the implementation of potential interventions that address relevant constructs (eg, alcohol use and pre-exposure prophylaxis adherence). Results: The N2P2 study was funded in August 2021 by the National Institute of Drug Abuse (R01DA054553 and R21DA053156) and National Heart, Lung, and Blood Institute (R01HL160325). This study was launched in November 2022. Recruitment and enrollment for the first wave of data collection are currently ongoing. Conclusions: The N2P2 study is applying innovative methods to comprehensively explore the impacts of substance use and sleep health on HIV-related outcomes among an HIV status-neutral cohort of Black SMM and TW in Chicago. This study is applying an observational-implementation hybrid design to help us achieve findings that support rapid translation, a critical priority among populations such as Black SMM and TW that experience long-standing inequities with regard to HIV and other health-related outcomes. N2P2 will directly build off the findings that have resulted from the original N2 study among Black SMM and TW in Chicago. These findings provide a better understanding of multilevel (eg, individual, network, and neighborhood) factors that contribute to HIV-related outcomes and viral suppression among Black SMM and TW. International registered report identifier (irrid): DERR1-10.2196/48548.</p

Identification of Stage-Specific Breast Markers using Quantitative Proteomics

YesMatched healthy and diseased tissues from breast cancer patients were analyzed by quantitative proteomics. By comparing proteomic profiles of fibroadenoma (benign tumors, three patients), DCIS (noninvasive cancer, three patients), and invasive ductal carcinoma (four patients), we identified protein alterations that correlated with breast cancer progression. Three 8-plex iTRAQ experiments generated an average of 826 protein identifications, of which 402 were common. After excluding those originating from blood, 59 proteins were significantly changed in tumor compared with normal tissues, with the majority associated with invasive carcinomas. Bioinformatics analysis identified relationships between proteins in this subset including roles in redox regulation, lipid transport, protein folding, and proteasomal degradation, with a substantial number increased in expression due to Myc oncogene activation. Three target proteins, cofilin-1 and p23 (increased in invasive carcinoma) and membrane copper amine oxidase 3 (decreased in invasive carcinoma), were subjected to further validation. All three were observed in phenotype-specific breast cancer cell lines, normal (nontransformed) breast cell lines, and primary breast epithelial cells by Western blotting, but only cofilin-1 and p23 were detected by multiple reaction monitoring mass spectrometry analysis. All three proteins were detected by both analytical approaches in matched tissue biopsies emulating the response observed with proteomics analysis. Tissue microarray analysis (361 patients) indicated cofilin-1 staining positively correlating with tumor grade and p23 staining with ER positive status; both therefore merit further investigation as potential biomarkers.Cyprus Research Promotion Foundation, Yorkshire Cancer Researc

Predicting Tacrolimus Concentrations in Children Receiving a Heart Transplant Using a Population Pharmacokinetic Model

Objective Immunosuppressant therapy plays a pivotal role in transplant success and longevity. Tacrolimus, a primary immunosuppressive agent, is well known to exhibit significant pharmacological interpatient and intrapatient variability. This variability necessitates the collection of serial trough concentrations to ensure that the drug remains within therapeutic range. The objective of this study was to build a population pharmacokinetic (PK) model and use it to determine the minimum number of trough samples needed to guide the prediction of an individual’s future concentrations. Design, setting and patients Retrospective data from 48 children who received tacrolimus as inpatients at Primary Children’s Hospital in Salt Lake City, Utah were included in the study. Data were collected within the first 6 weeks after heart transplant. Outcome measures Data analysis used population PK modelling techniques in NONMEM. Predictive ability of the model was determined using median prediction error (MPE, a measure of bias) and median absolute prediction error (MAPE, a measure of accuracy). Of the 48 children in the study, 30 were used in the model building dataset, and 18 in the model validation dataset. Results Concentrations ranged between 1.5 and 37.7 µg/L across all collected data, with only 40% of those concentrations falling within the targeted concentration range (12 to 16 µg/L). The final population PK model contained the impact of age (on volume), creatinine clearance (on elimination rate) and fluconazole use (on elimination rate) as covariates. Our analysis demonstrated that as few as three concentrations could be used to predict future concentrations, with negligible bias (MPE (95% CI)=0.10% (−2.9% to 3.7%)) and good accuracy (MAPE (95% CI)=24.1% (19.7% to 27.7%)). Conclusions The use of PK in dose guidance has the potential to provide significant benefits to clinical care, including dose optimisation during the early stages of therapy, and the potential to limit the need for frequent drug monitoring

State-of-the-Art Review on Physiologically Based Pharmacokinetic Modeling in Pediatric Drug Development

Physiologically based pharmacokinetic modeling and simulation is an important tool for predicting the pharmacokinetics, pharmacodynamics, and safety of drugs in pediatrics. Physiologically based pharmacokinetic modeling is applied in pediatric drug development for first-time-in-pediatric dose selection, simulation-based trial design, correlation with target organ toxicities, risk assessment by investigating possible drug–drug interactions, real-time assessment of pharmacokinetic–safety relationships, and assessment of non-systemic biodistribution targets. This review summarizes the details of a physiologically based pharmacokinetic modeling approach in pediatric drug research, emphasizing reports on pediatric physiologically based pharmacokinetic models of individual drugs. We also compare and contrast the strategies employed by various researchers in pediatric physiologically based pharmacokinetic modeling and provide a comprehensive overview of physiologically based pharmacokinetic modeling strategies and approaches in pediatrics. We discuss the impact of physiologically based pharmacokinetic models on regulatory reviews and product labels in the field of pediatric pharmacotherapy. Additionally, we examine in detail the current limitations and future directions of physiologically based pharmacokinetic modeling in pediatrics with regard to the ability to predict plasma concentrations and pharmacokinetic parameters. Despite the skepticism and concern in the pediatric community about the reliability of physiologically based pharmacokinetic models, there is substantial evidence that pediatric physiologically based pharmacokinetic models have been used successfully to predict differences in pharmacokinetics between adults and children for several drugs. It is obvious that the use of physiologically based pharmacokinetic modeling to support various stages of pediatric drug development is highly attractive and will rapidly increase, provided the robustness and reliability of these techniques are well established

State-of-the-Art Review on Physiologically Based Pharmacokinetic Modeling in Pediatric Drug Development

Physiologically based pharmacokinetic modeling and simulation is an important tool for predicting the pharmacokinetics, pharmacodynamics, and safety of drugs in pediatrics. Physiologically based pharmacokinetic modeling is applied in pediatric drug development for first-time-in-pediatric dose selection, simulation-based trial design, correlation with target organ toxicities, risk assessment by investigating possible drug–drug interactions, real-time assessment of pharmacokinetic–safety relationships, and assessment of non-systemic biodistribution targets. This review summarizes the details of a physiologically based pharmacokinetic modeling approach in pediatric drug research, emphasizing reports on pediatric physiologically based pharmacokinetic models of individual drugs. We also compare and contrast the strategies employed by various researchers in pediatric physiologically based pharmacokinetic modeling and provide a comprehensive overview of physiologically based pharmacokinetic modeling strategies and approaches in pediatrics. We discuss the impact of physiologically based pharmacokinetic models on regulatory reviews and product labels in the field of pediatric pharmacotherapy. Additionally, we examine in detail the current limitations and future directions of physiologically based pharmacokinetic modeling in pediatrics with regard to the ability to predict plasma concentrations and pharmacokinetic parameters. Despite the skepticism and concern in the pediatric community about the reliability of physiologically based pharmacokinetic models, there is substantial evidence that pediatric physiologically based pharmacokinetic models have been used successfully to predict differences in pharmacokinetics between adults and children for several drugs. It is obvious that the use of physiologically based pharmacokinetic modeling to support various stages of pediatric drug development is highly attractive and will rapidly increase, provided the robustness and reliability of these techniques are well established