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混合随机框架预测 HIV 预防效果:不同多替拉韦预防方案的实例。

Hybrid stochastic framework predicts efficacy of prophylaxis against HIV: An example with different dolutegravir prophylaxis schemes.

机构信息

Department of Mathematics & Computer Science, Freie Universität Berlin, Berlin, Germany.

Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom.

出版信息

PLoS Comput Biol. 2018 Jun 14;14(6):e1006155. doi: 10.1371/journal.pcbi.1006155. eCollection 2018 Jun.

Abstract

To achieve the 90-90-90 goals set by UNAIDS, the number of new HIV infections needs to decrease to approximately 500,000 by 2020. One of the 'five pillars' to achieve this goal is pre-exposure prophylaxis (PrEP). Truvada (emtricitabine-tenofovir) is currently the only medication approved for PrEP. Despite its advantages, Truvada is costly and requires individuals to adhere to the once-daily regimen. To improve PrEP, many next-generation regimen, including long-acting formulations, are currently investigated. However, pre-clinical testing may not guide candidate selection, since it often fails to translate into clinical efficacy. On the other hand, quantifying prophylactic efficacy in the clinic is ethically problematic and requires to conduct long (years) and large (N>1000 individuals) trials, precluding systematic evaluation of candidates and deployment strategies. To prioritize- and help design PrEP regimen, tools are urgently needed that integrate pharmacological-, viral- and host factors determining prophylactic efficacy. Integrating the aforementioned factors, we developed an efficient and exact stochastic simulation approach to predict prophylactic efficacy, as an example for dolutegravir (DTG). Combining the population pharmacokinetics of DTG with the stochastic framework, we predicted that plasma concentrations of 145.18 and 722.23nM prevent 50- and 90% sexual transmissions respectively. We then predicted the reduction in HIV infection when DTG was used in PrEP, PrEP 'on demand' and post-exposure prophylaxis (PEP) before/after virus exposure. Once daily PrEP with 50mg oral DTG prevented 99-100% infections, and 85% of infections when 50% of dosing events were missed. PrEP 'on demand' prevented 79-84% infections and PEP >80% when initiated within 6 hours after virus exposure and continued for as long as possible. While the simulation framework can easily be adapted to other PrEP candidates, our simulations indicated that oral 50mg DTG is non-inferior to Truvada. Moreover, the predicted 90% preventive concentrations can guide release kinetics of currently developed DTG nano-formulations.

摘要

为实现联合国艾滋病规划署(UNAIDS)设定的 90-90-90 目标,到 2020 年,新感染 HIV 的人数需减少至约 50 万。实现这一目标的“五大支柱”之一是暴露前预防(PrEP)。特鲁瓦达(恩曲他滨替诺福韦)是目前唯一获批用于 PrEP 的药物。尽管特鲁瓦达具有优势,但它价格昂贵,且需要使用者坚持每日用药。为改善 PrEP,目前正在研究许多新一代方案,包括长效制剂。然而,临床前测试可能无法指导候选药物的选择,因为它往往无法转化为临床疗效。另一方面,在临床上量化预防效果在伦理上存在问题,需要进行长期(数年)和大规模(N>1000 人)的试验,从而排除对候选药物和部署策略的系统评估。为了确定优先顺序并帮助设计 PrEP 方案,我们迫切需要能够整合决定预防效果的药理学、病毒学和宿主因素的工具。我们整合了上述因素,开发了一种高效准确的随机模拟方法来预测预防效果,并用多拉韦林(DTG)进行了示例。我们将 DTG 的群体药代动力学与随机框架相结合,预测血浆浓度达到 145.18 和 722.23nM 时,分别可预防 50%和 90%的性传播。然后,我们预测了 DTG 用于 PrEP、按需 PrEP 和暴露后预防(PEP)在病毒暴露前后减少 HIV 感染的效果。每天一次服用 50mg 口服 DTG 可预防 99-100%的感染,当错过 50%的用药事件时,可预防 85%的感染。按需 PrEP 可预防 79-84%的感染,当在病毒暴露后 6 小时内开始并尽可能长时间持续使用时,PEP 可预防 80%以上的感染。虽然模拟框架可以轻松适用于其他 PrEP 候选药物,但我们的模拟结果表明,口服 50mg DTG 与特鲁瓦达相当。此外,预测的 90%预防浓度可以指导目前正在开发的 DTG 纳米制剂的释放动力学。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78b7/6001963/5db4e731e937/pcbi.1006155.g001.jpg

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