Preparing for a world with artemisinin resistance: using individual-based epidemiological modeling to minimize the long-term detrimental effects of antimalarial drug resistance

项目摘要/摘要 恶性疟原虫是感染人类的五种疟疾寄生虫中最致命的一种。每年， 有2亿多个恶性疟疾症状病例和40多万人死亡--其中大部分 这种情况发生在五岁以下的非洲儿童身上。产生影响的两大干预措施 在过去20年里，减少疟疾流行的关键是蚊子控制和抗疟疾药物的使用。这个 这一努力中最重要的一类药物是青蒿素衍生物，自2005年以来一直是 仅作为青蒿素联合疗法(ACTs)使用，以减少出现和 青蒿素耐药基因的传播。尽管作出了这些努力，青蒿素抗药性确实出现了，我们正在 现在面临着这些耐药基因可能传播到非洲的危险前景，那里的大多数 世界上出现了疟疾病例。 在这项提案中，我们将介绍和评估一些耐药性管理策略，这些策略包括 旨在防止、延迟和减缓恶性疟原虫耐药基因的传播。首先，我们将 在我们现有的基于个体(基于代理)的模拟中取得一些技术进步，我们已经 用于模拟恶性疟原虫在人类种群中的进化和流行病学。我们将添加显式网格- 基于空间结构，使模型更加逼真。此外，我们还将添加一个基因-表型图 和临床参数化药效学/药动子模型，以使模型的药物- 阻力成分在当前数据允许的范围内是真实的。第二，我们将评估如何最好地 管理人口层面的新抗疟疾疗法的引入，这些疗法将于本世纪20年代推出。 这些战略的目的是将新疗法和新疗法的长期耐药性风险降至最低 目前使用的ACTs，以便从长远来看最大限度地减少治疗失败的数量。最后，我们会 对柬埔寨、赞比亚和布基纳法索的国家情景进行参数化，以提供具体的国家一级建议 在低、中、高传播率的疟疾环境中，如何最好地预防耐药性或将其降至最低 电流扩散。由于柬埔寨是当前青蒿素耐药性浪潮的震中，柬埔寨- 将使用特定的模型来提供关于如何遏制和消除当前流通的青蒿素的建议- 恶性疟原虫耐药基因分型的研究。

Project Summary/Abstract Plasmodium falciparum is the deadliest of the five species of malaria parasites that infect humans. Annually, there are over 200 million symptomatic cases of falciparum malaria and over 400,000 deaths – the majority of which occur in African children under the age of five. The two major interventions that have had an effect on reducing malaria prevalence over the past twenty years are mosquito control and use of antimalarial drugs. The most important class of drugs in this effort has been the artemisinin derivatives, which since 2005 have been deployed as artemisinin combination therapies (ACTs) only, in order to reduce the probability of emergence and spread of artemisinin-resistant genotypes. Despite these efforts, artemisinin resistance did emerge, and we are now facing the dangerous prospect that these drug-resistant genotypes may spread to Africa, where most of the world’s malaria cases occur. In this proposal, we will introduce and evaluate a number of drug-resistance management strategies that are intended to prevent, delay, and slow down the spread of drug-resistant genotypes of P. falciparum. First, we will make a number of technical advances in our existing individual-based (agent-based) simulation that we already use to model the evolution and epidemiology of P. falciparum in human populations. We will add explicit grid- based spatial structure to make the model more realistic. Additionally, we will add a genotype-phenotype map and clinically-parameterized pharmacodynamic/pharmacokinetic sub-models to make the model’s drug- resistance component as realistic as current data allow. Second, we will evaluate strategies for how best to manage the population-level introduction of novel antimalarial therapies that will become available in the 2020s. The strategies will be aimed at minimizing the long-term risk of drug resistance to both the novel therapies and to currently used ACTs, in order to minimize the number of treatment failures in the long run. Finally, we will parameterize country scenarios for Cambodia, Zambia, and Burkina Faso to provide specific country-level advice in low, medium, and high transmission malaria settings on how best to preëmpt drug resistance or minimize its current spread. As Cambodia is the epicenter of the current wave of artemisinin resistance, the Cambodia- specific model will be used to provide advice on how to contain and eliminate currently circulating artemisinin- resistant genotypes of P. falciparum.