Prediction of ultra-high-order antibiotic combinations based on pairwise interactions
Prediction of ultra-high-order antibiotic combinations based on pairwise interactions
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DOI：
10.1371/journal.pcbi.1006774
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发表时间：
2019-01-01
影响因子：
4.3
通讯作者：
Alon, Uri
中科院分区：
文献类型：
--
作者：
Katzir, Itay;Cokol, Murat;Alon, Uri
联合用药是一种很有前途的方法，可以在低剂量下实现高效率并克服耐药性。当药物不能在耐受剂量下达到效果时，如癌症和结核病，联合用药特别有用。然而，有效药物组合的发现面临着组合爆炸的挑战，其中可能的组合数量随着药物数量和剂量呈指数增长。最近的一项进展被称为剂量模型，它使用一个数学公式来克服组合爆炸，将问题简化为可行的二次问题：使用几种剂量的药物对数据，剂量模型准确地预测了三种或四种药物在所有剂量下的组合效果。剂量模型尚未在超过四种药物的高阶组合中进行测试。为了解决这个问题，我们测量了多达10种抗生素对大肠杆菌生长的影响，以及多达5种结核病药物对结核分枝杆菌生长的影响。我们发现剂量模型准确地预测了这些高阶组合的效果，包括强协同和拮抗的情况。这项研究支持了药物对之间的相互作用携带了很大程度上决定高阶相互作用的关键信息的观点。因此，对药物相互作用的系统研究是在高维空间中优先考虑药物方案的一个有说服力的策略。
Drug combinations are a promising approach to achieve high efficacy at low doses and to overcome resistance. Drug combinations are especially useful when drugs cannot achieve effectiveness at tolerable doses, as occurs in cancer and tuberculosis (TB). However, discovery of effective drug combinations faces the challenge of combinatorial explosion, in which the number of possible combinations increases exponentially with the number of drugs and doses. A recent advance, called the dose model, uses a mathematical formula to overcome combinatorial explosion by reducing the problem to a feasible quadratic one: using data on drug pairs at a few doses, the dose model accurately predicts the effect of combinations of three and four drugs at all doses. The dose model has not yet been tested on higher-order combinations beyond four drugs. To address this, we measured the effect of combinations of up to ten antibiotics on E. coli growth, and of up to five tuberculosis (TB) drugs on the growth of M. tuberculosis. We find that the dose model accurately predicts the effect of these higher-order combinations, including cases of strong synergy and antagonism. This study supports the view that the interactions between drug pairs carries key information that largely determines higher-order interactions. Therefore, systematic study of pairwise drug interactions is a compelling strategy to prioritize drug regimens in high-dimensional spaces.