Predicting evolutionary dynamics of multi-drug resistance

迫切需要开发新的方法来阻止抗菌素耐药性的演变和传播。联合疗法（将多种药物作为单一处方给予）在预防耐药性和优化针对特定感染的治疗方面都很有希望。然而，最近的实验工作表明，在天然微生物种群所经历的条件下（例如，药物浓度的暂时变化和突变率的升高），联合疗法可以选择多药耐药。为了确定联合治疗是否是一种可行的策略，我们需要预测模型，说明在存在抗生素的现实条件下（包括感染和农业），药物联合在多大程度上预防了耐药性。需要采用建模和实验相结合的方法，因为测试少数几种药物是一项相当大的后勤挑战——5种抗生素以10种剂量筛选需要近1000万次生长分析，超出了高通量技术的限制。然而，模型需要考虑微生物在暂时变化的抗生素水平下生长的基本生物学，这需要实验测量。模型预测将通过实验将细菌群体暴露于最佳和最差的已确定组合中，以观察多药耐药性是否演变，从而得到验证。这项工作对于建立联合疗法作为抗生素耐药性危机的可行解决方案至关重要。

There is an urgent need to develop novel approaches to halt the evolution and spread of antimicrobial resistance. Combination therapies (multiple drugs given as a single prescription) are promising, both for preventing resistance and for optimising treatments for specific infections. However, recent experimental work has shown that combination therapies can select for multi-drug resistance in conditions experienced by natural microbial populations (e.g. temporally-varying drug concentrations and elevated mutation rates).To establish whether combination therapies are a viable strategy, we need predictive models for how well drug combinations prevent resistance under real-world conditions where antibiotics are present (including infection and agriculture). A combined modelling and experimental approach is required because testing more than a handful of drugs is a considerable logistical challenge-5 antibiotics screened at 10 doses requires nearly 10 million growth assays, beyond the limits of high-throughput technologies. However, models need to account for the basic biology of microbial growth under temporally-varying antibiotic levels, which requires experimental measurement. Model predictions will be validated by experimentally exposing bacterial populations to the best and worst identified combinations to see if multi-drug resistance evolves. This work is crucial for establishing combination therapies as a viable solution to the antibiotic resistance crisis.