Antibiotic resistance is one of the major challenges we face in modern times. Antibiotic use, especially their overuse, is the single most important driver of antibiotic resistance. Efforts have been made to reduce unnecessary drug prescriptions, but limited work is devoted to optimising dosage regimes when they are prescribed. The design of antibiotic treatments can be formulated as an optimisation problem where candidate solutions are encoded as vectors of dosages per day. The formulation naturally gives rise to competing objectives, as we want to maximise the treatment effectiveness while minimising the total drug use, the treatment duration and the concentration of antibiotic experienced by the patient. This article combines a recent mathematical model of bacterial growth including both susceptible and resistant bacteria, with a multi-objective evolutionary algorithm in order to automatically design successful antibiotic treatments. We consider alternative formulations combining relevant objectives and constraints. Our approach obtains shorter treatments, with improved success rates and smaller amounts of drug than the standard practice of administering daily fixed doses. These new treatments consistently involve a higher initial dose followed by lower tapered doses.

抗生素耐药性是我们现代面临的主要挑战之一。抗生素的使用，尤其是其过度使用，是抗生素耐药性的最重要的单一驱动因素。已经做出努力以减少不必要的药物处方，但是在开处方时致力于优化剂量方案的工作有限。抗生素治疗的设计可以被表述为一个优化问题，其中候选溶液被编码为每天剂量的载体。该制剂自然会引起相互竞争的目标，因为我们希望最大程度地提高治疗效果，同时最大程度地减少总药物使用量，治疗持续时间和患者所经历的抗生素浓度。本文结合了细菌生长的最新数学模型，包括易感细菌和抗性细菌，使用多目标进化算法来自动设计成功的抗生素治疗方法。我们考虑结合相关目标和约束条件的替代方案。与每天固定剂量的标准做法相比，我们的方法可获得更短的治疗，更高的成功率和更少的药物用量。这些新疗法始终涉及较高的初始剂量，然后降低锥形剂量。