[论文解读] Rational Design of Antibiotic Treatment Plans

Portia Mira, Kristina Crona+4 more|arXiv (Cornell University)|Jun 6, 2014

Evolution and Genetic Dynamics参考文献 18被引用 3

一句话总结

本研究提出了一套数据驱动的数学框架，以合理设计逆转TEM-1 β-内酰胺酶抗生素耐药性的治疗方案。通过建模16种基因型与15种抗生素之间的适应度景观，该研究识别出能最大化恢复至野生型、药物敏感基因型概率的最优治疗序列——在相关概率模型下最高可达1.0的概率，在循环治疗方案中可达0.7。

ABSTRACT

The development of reliable methods for restoring susceptibility after antibiotic resistance arises has proven elusive. A greater understanding of the relationship between antibiotic administration and the evolution of resistance is key to overcoming this challenge. Here we present a data-driven mathematical approach for developing antibiotic treatment plans that can reverse the evolution of antibiotic resistance determinants. We have generated adaptive landscapes for 16 genotypes of the TEM beta-lactamase that vary from the wild type genotype TEM-1 through all combinations of four amino acid substitutions. We determined the growth rate of each genotype when treated with each of 15 beta-lactam antibiotics. By using growth rates as a measure of fitness, we computed the probability of each amino acid substitution in each beta-lactam treatment using two different models named the Correlated Probability Model (CPM) and the Equal Probability Model (EPM). We then performed an exhaustive search through the 15 treatments for substitution paths leading from each of the 16 genotypes back to the wild type TEM-1. We identified those treatment paths that returned the highest probabilities of selecting for reversions of amino acid substitutions and returning TEM to the wild type state. For the CPM model, the optimized probabilities ranged between 0.6 and 1.0. For the EPM model, the optimized probabilities ranged between 0.38 and 1.0. For cyclical CPM treatment plans in which the starting and ending genotype was the wild type, the probabilities were between 0.62 and 0.7. Overall this study shows that there is promise for reversing the evolution of resistance through antibiotic treatment plans.

研究动机与目标

• 解决耐药性演化后恢复抗生素敏感性的可靠方法缺乏的问题。
• 理解在TEM-1 β-内酰胺酶中抗生素给药与耐药性发展之间的进化动态。
• 设计能最大化恢复至野生型、敏感基因型概率的治疗方案。
• 评估不同概率模型在预测耐药性逆转路径方面的有效性。

提出的方法

• 为16种TEM-1 β-内酰胺酶基因型构建了适应度景观，涵盖四种氨基酸突变的所有组合。
• 测量了每种基因型在15种不同β-内酰胺类抗生素下的生长速率（作为适应度的代理指标）。
• 应用两种概率模型——相关概率模型（CPM）和等概率模型（EPM）——以估算每种抗生素下的突变概率。
• 对全部15种抗生素治疗方案进行穷举搜索，以识别从耐药基因型恢复至野生型的最优逆转路径。
• 评估循环治疗方案，其中起始和结束基因型均为野生型，以最大化逆转概率。
• 使用基于适应度的模型，计算每条治疗路径中选择逆转突变的概率。

实验结果

研究问题

• RQ1 哪些抗生素治疗序列能最大化从耐药TEM-1基因型恢复至野生型、药物敏感状态的概率？
• RQ2 不同概率模型（CPM与EPM）如何影响耐药性逆转预测可能性？
• RQ3 哪些治疗序列在多种耐药基因型中实现最高的逆转概率？
• RQ4 循环治疗方案能否提高恢复至野生型基因型的可能性？
• RQ5 通过优化治疗方案，可实现的逆转概率的定量范围是什么？

主要发现

• 在相关概率模型（CPM）下，优化治疗路径的逆转概率范围为0.6至1.0。
• 在等概率模型（EPM）下，优化路径的逆转概率范围为0.38至1.0。
• 在循环CPM治疗方案中（起始和结束均为野生型），逆转概率范围为0.62至0.7。
• 本研究证明，通过合理且序列特定的抗生素调度，耐药性逆转是可行的。
• 当治疗序列基于适应度景观和概率建模系统性选择时，实现了最高的逆转概率。
• 结果表明，此类治疗方案具有强大的临床转化潜力，可用于应对抗生素耐药性。