Applying optimization algorithms to tuberculosis antibiotic treatment regimens.
Applying optimization algorithms to tuberculosis antibiotic treatment regimens.
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DOI：
10.1007/s12195-017-0507-6
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发表时间：
2017-12
影响因子：
2.8
通讯作者：
Linderman JJ
中科院分区：
文献类型：
--
作者：
Cicchese JM;Pienaar E;Kirschner DE;Linderman JJ
结核病是最常见的传染病之一，需要在至少6个月内服用多种抗生素进行治疗。这种长期治疗往往导致患者依从性差，从而可能导致耐多药结核病的出现。迫切需要新的抗生素治疗策略。正在开发或重新利用新的抗生素来治疗结核病，但由于有许多潜在的抗生素、剂量大小和潜在的时间表，新疗法的方案设计空间太大，无法进行详尽的搜索。在这里，我们提出了一种方法，将基于agent的多尺度模型捕获结核病肉芽肿形成与数学优化算法相结合，以确定最佳的结核病治疗方案。我们定义了两种不同的单抗生素治疗，以比较两种优化算法预测最佳治疗方案的效率和准确性：遗传算法（GA）和通过径向基函数（RBF）网络的代理辅助优化。我们还说明了使用RBF网络来优化双抗生素治疗。我们发现，虽然GAs可以更准确地定位最佳治疗方案，但RBF网络为结核病治疗优化提供了更实用的策略，模拟次数更少，并成功估计了最佳的双抗生素治疗方案。我们的研究结果表明，代理辅助优化可以从更大的抗生素，剂量和时间表中找到最佳的结核病治疗方案，并且可以应用于使用系统生物学方法解决其他研究领域的优化问题。我们的发现对治疗结核病等需要长期治疗的疾病或任何需要多种药物的疾病具有重要意义。
Tuberculosis (TB), one of the most common infectious diseases, requires treatment with multiple antibiotics taken over at least 6 months. This long treatment often results in poor patient-adherence, which can lead to the emergence of multi-drug resistant TB. New antibiotic treatment strategies are sorely needed. New antibiotics are being developed or repurposed to treat TB, but as there are numerous potential antibiotics, dosing sizes and potential schedules, the regimen design space for new treatments is too large to search exhaustively. Here we propose a method that combines an agent-based multi-scale model capturing TB granuloma formation with algorithms for mathematical optimization to identify optimal TB treatment regimens. We define two different single-antibiotic treatments to compare the efficiency and accuracy in predicting optimal treatment regimens of two optimization algorithms: genetic algorithms (GA) and surrogate-assisted optimization through radial basis function (RBF) networks. We also illustrate the use of RBF networks to optimize double-antibiotic treatments. We found that while GAs can locate optimal treatment regimens more accurately, RBF networks provide a more practical strategy to TB treatment optimization with fewer simulations, and successfully estimated optimal double-antibiotic treatment regimens. Our results indicate surrogate-assisted optimization can locate optimal TB treatment regimens from a larger set of antibiotics, doses and schedules, and could be applied to solve optimization problems in other areas of research using systems biology approaches. Our findings have important implications for the treatment of diseases like TB that have lengthy protocols or for any disease that requires multiple drugs.
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DOI：
10.1073/pnas.1121497109
发表时间：
2012-08-28
影响因子：
11.1
作者：
Lin, Philana Ling;Dartois, Veronique;Flynn, JoAnne L.
通讯作者：
Flynn, JoAnne L.
影响因子：
3.5
作者：
An, G.;Fitzpatrick, B. G.;Christley, S.;Federico, P.;Kanarek, A.;Neilan, R. Miller;Oremland, M.;Salinas, R.;Laubenbacher, R.;Lenhart, S.
通讯作者：
Lenhart, S.
DOI：
10.1039/c4ib00295d
发表时间：
2015-05
期刊：
Integrative biology : quantitative biosciences from nano to macro
影响因子：
--
作者：
Linderman JJ;Cilfone NA;Pienaar E;Gong C;Kirschner DE
通讯作者：
Kirschner DE
DOI：
10.1002/wsbm.1222
发表时间：
2013-07
影响因子：
7.9
作者：
Hunt, C. Anthony;Kennedy, Ryan C.;Kim, Sean H. J.;Ropella, Glen E. P.
通讯作者：
Ropella, Glen E. P.
影响因子：
4.4
作者：
Fallahi-Sichani, Mohammad;El-Kebir, Mohammed;Linderman, Jennifer J.
通讯作者：
Linderman, Jennifer J.