Zazzi Maurizio, Cozzi-Lepri Alessandro, Prosperi Mattia C F
Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, US.
Curr HIV Res. 2016;14(2):101-9. doi: 10.2174/1570162x13666151029102254.
Resistance to antiretroviral drugs is a complex and evolving area with relevant implications in the treatment of human immunodeficiency virus (HIV) infection. Several rules, algorithms and full-fledged computer programs have been developed to assist the HIV specialist in the choice of the best patient-tailored therapy.
Experts' rules and statistical/machine learning algorithms for interpreting HIV drug resistance, along with their program implementations, were retrieved from PubMed and other on-line resources to be critically reviewed in terms of technical approach, performance, usability, update, and evolution (i.e. inclusion of novel drugs or expansion to other viral agents).
Several drug resistance prediction algorithms for the nucleotide/nucleoside/non-nucleoside reverse transcriptase, protease and integrase inhibitors as well as coreceptor antagonists are currently available, routinely used, and have been validated thoroughly in independent studies. Computer tools that combine single-drug genotypic/phenotypic resistance interpretation and optimize combination antiretroviral therapy have been also developed and implemented as web applications. Most of the systems have been updated timely to incorporate new drugs and few have recently been expanded to meet a similar need in the Hepatitis C area. Prototype systems aiming at predicting virological response from both virus and patient indicators have been recently developed but they are not yet being routinely used.
Computing HIV genotype to predict drug susceptibility in vitro or response to combination antiretroviral therapy in vivo is a continuous and productive research field, translating into successful treatment decision support tools, an essential component of the management of HIV patients.
对抗逆转录病毒药物的耐药性是一个复杂且不断发展的领域，对人类免疫缺陷病毒（HIV）感染的治疗具有重要意义。已经开发了多种规则、算法和成熟的计算机程序，以协助HIV专家选择最适合患者的治疗方案。
从PubMed和其他在线资源中检索用于解释HIV耐药性的专家规则以及统计/机器学习算法及其程序实现，以便从技术方法、性能、可用性、更新和发展（即纳入新药或扩展到其他病毒制剂）方面进行严格审查。
目前有几种针对核苷酸/核苷/非核苷逆转录酶、蛋白酶和整合酶抑制剂以及共受体拮抗剂的耐药性预测算法可供常规使用，并且已经在独立研究中得到充分验证。还开发了结合单药基因型/表型耐药性解释并优化联合抗逆转录病毒治疗的计算机工具，并作为网络应用程序实施。大多数系统已及时更新以纳入新药，最近很少有系统扩展以满足丙型肝炎领域的类似需求。最近开发了旨在根据病毒和患者指标预测病毒学反应的原型系统，但尚未常规使用。
计算HIV基因型以预测体外药物敏感性或体内对抗逆转录病毒联合治疗的反应是一个持续且富有成效的研究领域，转化为成功的治疗决策支持工具，这是HIV患者管理的重要组成部分。
