Modelling to inform next-generation medical interventions for malaria prevention and treatment

Abstract

Global progress against malaria has stagnated and novel medical interventions to prevent malaria are needed to fill gaps in existing tools and improve protection against infection and disease. Candidate selection for next-generation interventions should be supported by the best available evidence. Target product profiles and preferred product characteristics play a key role in setting selection criteria requirements and early endorsement by health authorities. While clinical evidence and expert opinion often inform product development decisions, integrating modelling evidence early and iteratively into this process provides an opportunity to link product characteristics with expected public health outcomes. Population models of malaria transmission can provide a better understanding of which, and at what magnitude, key intervention characteristics drive public health impact, and provide quantitative evidence to support selection of use-cases, transmission settings, and deployment strategies. We describe how modelling evidence can guide and accelerate development of new malaria vaccines, monoclonal antibodies, and chemoprevention.

Design and selection of drug properties to increase the public health impact of next-generation seasonal malaria chemoprevention: a modelling study

“…This modelling study was grounded in engagement with chemoprevention drug and guideline developers as part of the June, 2021 convening Malaria Prevention: Shaping Next-Gen Medical Interventions. 19 Consultations included representatives from 24 organisations globally, including international health organisations, funding organisations, product developers, and research institutes within malaria-endemic countries. Experts identified the need to quantify the impact of trade-offs between intervention characteristics (ie, efficacy, duration of protection, coverage, and dosing) on public health outcomes, and then set preferred ranges around these characteristics towards achieving health targets for next-generation SMC.…”