Computational Social Science, Frankfurt School of Finance and Management, 60322, Frankfurt am Main, Germany.
Department of Computational Medicine, UCLA, Los Angeles, 90095-1766, USA.
Bull Math Biol. 2022 Apr 22;84(6):59. doi: 10.1007/s11538-022-01013-7.
The rapid rise of antibiotic resistance is a serious threat to global public health. The situation is exacerbated by the "antibiotics dilemma": Developing narrow-spectrum antibiotics against resistant bacteria is most beneficial for society, but least attractive for companies, since their usage and sales volumes are more limited than for broad-spectrum drugs. After developing a general mathematical framework for the study of antibiotic resistance dynamics with an arbitrary number of antibiotics, we identify efficient treatment protocols. Then, we introduce a market-based refunding scheme that incentivizes pharmaceutical companies to develop new antibiotics against resistant bacteria and, in particular, narrow-spectrum antibiotics that target specific bacterial strains. We illustrate how such a refunding scheme can solve the antibiotics dilemma and cope with various sources of uncertainty that impede antibiotic R &D. Finally, connecting our refunding approach to the recently established Antimicrobial Resistance (AMR) Action Fund, we discuss how our proposed incentivization scheme could be financed.
抗生素耐药性的迅速上升是对全球公共卫生的严重威胁。这种情况因“抗生素困境”而加剧：开发针对耐药菌的窄谱抗生素对社会最有利，但对公司的吸引力最小，因为它们的使用量和销售量比广谱药物更有限。在为研究具有任意数量抗生素的抗生素耐药性动态建立了通用的数学框架之后，我们确定了有效的治疗方案。然后，我们引入了一种基于市场的退款计划，该计划激励制药公司针对耐药菌开发新的抗生素，特别是针对特定细菌菌株的窄谱抗生素。我们说明了这种退款计划如何解决抗生素困境，并应对阻碍抗生素研发的各种不确定性来源。最后，将我们的退款方法与最近成立的抗菌药物耐药性（AMR）行动基金联系起来，我们讨论了如何为我们提出的激励计划提供资金。
