Abstract

Antibiotics are the major tool for treating bacterial infections. Rising antibiotic resistance, however, calls for a better use of antibiotics. While classical recommendations favor long and aggressive treatments, more recent clinical trials advocate for moderate regimens. In this debate, two axes of ‘aggression’ have typically been conflated: treatment intensity (dose) and treatment duration. The third dimension of treatment timing along each individual’s infection course has rarely been addressed. By using a generic mathematical model of bacterial infection controlled by immune response, we examine how the relative effectiveness of antibiotic treatment varies with its timing, duration and antibiotic kill rate. We show that short or long treatments may both be beneficial depending on treatment onset, the target criterion for success and on antibiotic efficacy. This results from the dynamic trade-off between immune response build-up and resistance risk in acute, self-limiting infections, and uncertainty relating symptoms to infection variables. We show that in our model early optimal treatments tend to be ‘short and strong’, while late optimal treatments tend to be ‘mild and long’. This suggests a shift in the aggression axis depending on the timing of treatment. We find that any specific optimal treatment schedule may perform more poorly if evaluated by other criteria, or under different host-specific conditions. Our results suggest that major advances in antibiotic stewardship must come from a deeper empirical understanding of bacterial infection processes in individual hosts. To guide rational therapy, mathematical models need to be constrained by data, including a better quantification of personal disease trajectory in humans.Lay summary: Bacterial infections are becoming more difficult to treat worldwide because bacteria are becoming resistant to the antibiotics used. Addressing this problem requires a better understanding of how treatment along with other host factors impact antibiotic resistance. Until recently, most theoretical research has focused on the importance of antibiotic dosing on antibiotic resistance, however, duration and timing of treatment remain less explored. Here, we use a mathematical model of a generic bacterial infection to study three aspects of treatment: treatment dose/efficacy (defined by the antibiotic kill rate), duration, and timing, and their impact on several infection endpoints. We show that short and long treatment success strongly depends on when treatment begins (defined by the symptom threshold), the target criterion to optimize, and on antibiotic efficacy. We find that if administered early in an infection, “strong and short” therapy performs better, while if treatment begins at higher bacterial densities, a “mild and long” course of antibiotics is favored. In the model host immune defenses are key in preventing relapses, controlling antibiotic resistant bacteria and increasing the effectiveness of moderate intervention. In order to improve rational treatments of human infections, we call for a better quantification of individual disease trajectories in bacteria-immunity space.

中文翻译：

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治疗时机改变了短期和长期抗生素治疗相对于感染的益处

 抽象的

抗生素是治疗细菌感染的主要工具。然而，不断上升的抗生素耐药性要求更好地使用抗生素。虽然经典建议倾向于长期和积极的治疗，但最近的临床试验主张温和的治疗方案。在这场争论中，“攻击性”的两个轴通常被混为一谈：治疗强度（剂量）和治疗持续时间。每个人感染过程中的治疗时机的第三个维度很少得到解决。通过使用由免疫反应控制的细菌感染的通用数学模型，我们研究了抗生素治疗的相对有效性如何随其时间、持续时间和抗生素杀灭率的变化而变化。我们表明，短期或长期治疗可能都是有益的，具体取决于治疗开始、成功的目标标准和抗生素疗效。这是由于急性自限性感染中免疫反应的建立和耐药风险之间的动态权衡以及症状与感染变量相关的不确定性造成的。我们表明，在我们的模型中，早期最佳治疗往往是“短而强”，而后期最佳治疗往往是“温和而长”。这表明攻击轴的变化取决于治疗的时间。我们发现，如果按照其他标准或在不同的宿主特定条件下进行评估，任何特定的最佳治疗方案都可能表现更差。我们的结果表明，抗生素管理方面的重大进展必须来自对个体宿主细菌感染过程的更深入的经验理解。为了指导理性治疗，数学模型需要受到数据的约束，包括更好地量化人类的个人疾病轨迹。摘要：由于细菌对所使用的抗生素产生耐药性，细菌感染在世界范围内变得越来越难以治疗。解决这个问题需要更好地了解治疗与其他宿主因素如何影响抗生素耐药性。直到最近，大多数理论研究都集中在抗生素剂量对抗生素耐药性的重要性上，然而，治疗的持续时间和时机仍然较少探讨。在这里，我们使用一般细菌感染的数学模型来研究治疗的三个方面：治疗剂量/功效（由抗生素杀灭率定义）、持续时间和时机，以及它们对几个感染终点的影响。我们表明，短期和长期治疗的成功很大程度上取决于治疗开始的时间（由症状阈值定义）、优化的目标标准以及抗生素疗效。我们发现，如果在感染早期进行治疗，“强而短”的治疗效果会更好，而如果治疗从较高的细菌密度开始，则“温和而长”的抗生素疗程会受到青睐。在该模型中，宿主免疫防御对于预防复发、控制抗生素耐药细菌和提高适度干预的有效性至关重要。为了改进人类感染的合理治疗，我们呼吁更好地量化细菌免疫空间中的个体疾病轨迹。