LEAPS-MPS: Optimal Design of Therapeutic Phage Cocktails: a Data-Driven Mathematical Approach

抗菌素耐药性被描述为21世纪对人类健康的最大威胁之一。在人、动物、植物和环境中发现的抗药性细菌迅速出现并在世界各地传播。噬菌体，或简称噬菌体，是一种进化来感染和杀死细菌的病毒。在抗生素广泛使用之前，噬菌体疗法在20世纪20年代和30年代成功地应用于各种感染的治疗。作为一种有百年历史的感染药物，噬菌体疗法目前被视为一种潜在的抗生素替代品，并正在被广泛地重新开发用于治疗多药耐药感染。存在着数百万个噬菌体，每个噬菌体都有不同的特性，因此不可能通过实验测试它们对个别临床病原体的有效性。数学模型可以帮助识别表明噬菌体是一种有前途的治疗候选药物的特征。该项目将导致噬菌体鸡尾酒和抗生素疗法的最佳组合的开发，并进一步降低由于抗菌素耐药性而导致的人-动物-植物-生态系统界面的健康风险。该项目将吸引具有不同研究背景和兴趣的本科生，并支持伍斯特学院STEM中代表性不足的学生。学生将受益于接受全年跨学科培训，将真正的生命科学问题转化为标准的数学问题。该项目还将推进课程和项目的开发，这将改善该机构的研究环境，并进一步在伍斯特大学建立一个持续的、以学生为中心的跨学科数学生物学研究项目。该项目将探索单、双同时和双顺序给药策略对三个假单胞菌噬菌体的治疗效果，方法是使用非线性常微分方程来模拟多个噬菌体与细菌之间依赖密度的相互作用。与以前专注于噬菌体治疗中噬菌体杀死方面的研究不同，这个项目将同时包括噬菌体杀死和细菌噬菌体耐药性的进化。了解两者对于未来发展噬菌体鸡尾酒和抗生素疗法都很重要。研究人员将考虑一种比通常使用的线性相互作用更现实的混合，因为吸附速度与细菌生长有关，研究随机建模方法来捕获抗噬菌体细菌的出现，并使用分歧和敏感性分析来分析模型的结构，以指导进一步的模型修改。该模型框架在临床研究中有许多推论和探索性的用途，例如识别与噬菌体特性相对应的最敏感的模型参数，用于噬菌体的选择以及探索不同的治疗方案。特定物种和特定噬菌体的建模研究可以用作预测分析工具，以帮助设计未来的临床研究并提高对实验数据的理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Antimicrobial resistance has been described as one of the biggest threats to human health in the twenty-first century. Antibiotic-resistant bacteria, found in people, animals, plants, and the environment, have rapidly emerged and spread throughout the world. Bacteriophages, or phages for short, are viruses that have evolved to infect and kill bacteria. Before the widespread use of antibiotics, phage therapy was successfully applied for the treatment of a variety of infections in the 1920s and 1930s. As a century-old infection remedy, phage therapy is currently viewed as a potential antibiotic alternative and is being widely redeveloped to treat multidrug-resistance infections. There are millions of phages in existence, each with different properties, it is therefore impossible to experimentally test them for efficacy against individual clinical pathogens. Mathematical models can help identify characteristics that would suggest that a phage is a promising therapeutic candidate. This project will lead to the development of an optimal combination phage cocktail and antibiotic therapy and further reduce the health risks at the human-animal-plant-ecosystem interface caused by antimicrobial resistance. The project will engage undergraduate students with different research backgrounds and interests and support underrepresented students in STEM at The College of Wooster. Students will benefit from receiving year-round interdisciplinary training in formulating genuine life science questions into standard mathematical problems. The project will additionally advance curricular and program development, which will enhance the institution's research environment and further establish a sustained, student-focused, and interdisciplinary research program in mathematical biology at Wooster. The project will explore the treatment efficacies of single, double simultaneous, and double sequential administration strategies with three Pseudomonas phages by using nonlinear ordinary differential equations to model the density-dependent interactions between multiple-phage and bacteria. Unlike previous studies that focused on the phage-killing aspect of phage therapy, this project will incorporate both phage-killing and the evolution of bacterial phage resistance. Understanding both is important for the future development of a combination phage cocktail and antibiotic therapy. The investigator will consider a more biologically realistic mixing than the commonly used linear interaction, as adsorption rate is linked to bacterial growth, investigate the stochastic modeling approach to capture the emergence of phage-resistant bacteria, and analyze the structure of the model using bifurcation and sensitivity analyses to inform further modeling modifications. The modeling framework has many inferential and exploratory uses for clinical investigation such as identifying the most sensitive model parameters, corresponding to phage characteristics, for phage selection as well as exploring different treatment regimens. The species-specific and phage-specific modeling studies can be used as predictive analytics tools to assist in the design of future clinical studies and to improve the understanding of experimental data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.