Quantifying Sex-and-Age-Related Differences in Antiretroviral Exposure and Adverse Effects in the MACS/WIHS Combined Cohort Study

摘要 拟议工作的长期目标是提供基于证据的建议，以尽量减少 代谢不良事件，特别是体重增加，在不同人群的艾滋病毒感染者（PLWH）， 识别和量化可能增加患者亚组风险的药物暴露变异性。我们将 考虑药物暴露的各种修改因素，包括人口统计学特征， 实验室值、人体测量学、虚弱表型和药物基因组学。我们的重点是 整合酶链转移抑制剂（INSTI）度鲁特韦（DTG）和比替拉韦（BIC），以及替诺福韦 艾拉酚胺（TAF）。MWCCS包括在III期临床试验中代表性不足的各种PLWH，因此， 研究人员开发的药物的群体药代动力学模型。在AIM 1中，我们将招募不同的PLWH 从四个MWCCS中心收集药代动力学数据，并进一步完善影响 DTG、BIC和TAF药代动力学。将测量血浆和外周血中的药物浓度 血液单核细胞（仅TAF），在美国艾滋病研究中心临床药理学和分析 化学实验室（CFAR CPAC）。非线性混合效应模型将用于制定一个全面的 每种目标药物的PK模型。使用这些模型，然后，在AIM 2中，我们将回顾性地测量药物 储存库标本中的浓度（使用与AIM 1相同的方法和实验室，并预测药物 男性和女性从DTG、BIC和/或TAF开始的清除率。这些药物清除率，预计 每名参与者多次就诊，然后将其作为体重增加、腰臀比增加的预测因素进行分析。 胰岛素抵抗增加（通过HOMA-IR测量） 兴趣，与药物暴露的多种措施。假设是那些接触药物较多的艾滋病毒携带者 (via更慢的药物清除）将更有可能经历这些药物的代谢不良事件。后 完成后，我们希望有一个基于模型的风险估计的基础，进一步发展 前瞻性验证

ABSTRACT The long-term objective of the proposed work is to provide evidenced-based recommendations for minimizing metabolic adverse events, particularly weight gain, in a diverse population of people living with HIV (PLWH) by identifying and quantifying variability in drug exposure that may increase risk in patient subgroups. We will consider a broad array of modifying factors of drug exposure, including demographic characteristics, prior laboratory values, body anthropometrics, frailty phenotype, and pharmacogenomics. Our focus is on the integrase strand transfer inhibitors (INSTIs) dolutegravir (DTG) and bictegravir (BIC), as well as tenofovir alafenamide (TAF). The MWCCS includes diverse PLWH underrepresented in Phase III clinical trials and thus, in the sponsor-developed population pharmacokinetic models of the drug. In AIM 1, we will enroll diverse PLWH from four MWCCS sites to collect pharmacokinetic data and further refine the knowledge of factors that influence DTG, BIC, and TAF pharmacokinetics. Drug concentrations will be measured in blood plasma and peripheral blood mononuclear cells (TAF only) in the UNC Center for AIDS Research Clinical Pharmacology and Analytical Chemistry Laboratory (CFAR CPAC). Nonlinear mixed effects models will be used to develop a comprehensive PK model for each drug of interest. Using these models, then, in AIM 2, we will retrospectively measure drug concentrations in repository specimens (using the same methods and laboratory as AIM 1, and predict drug clearance in men and women from initiation of DTG, BIC, and/or TAF. These drug clearances, predicted for multiple visits per participant, will then be analyzed as the predictors of body weight gain, increased waist to hip ratio, and increased insulin resistance (as measured by HOMA-IR) over the course of treatment on the drug of interest, with multiple measures of drug exposure. The hypothesis is that those PLWH with higher drug exposure (via slower drug clearance) will be more likely to experience the metabolic adverse events of these drugs. Upon completion, we expect to have the underpinnings of a model-based risk estimator developed for further prospective validation.