AI and silkworm biomanufacturing: a new paradigm for malaria therapeutics access

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Malaria remains one of the most devastating infectious diseases globally, causing an estimated 282 million cases and 610,000 deaths in 2024, predominantly among children in sub-Saharan Africa. While the WHO approval of the RTS,S/AS01 and R21/Matrix-M vaccines represents a historic milestone, these first-generation tools exhibit partial and waning efficacy and face severe manufacturing and deployment bottlenecks. Current biological production systems rely on highly centralized, capital-intensive cell culture facilities predominantly located in the Global North, perpetuating a structural geography of dependence for low- and middle-income countries (LMICs). The convergence of artificial intelligence (AI) and novel whole-insect biomanufacturing platforms offers a transformative solution to these scientific and structural challenges. AI is revolutionizing malaria therapeutics through reverse vaccinology, structural antigen design, and the optimization of potent, broadly neutralizing monoclonal antibodies (mAbs). However, translating these in silico designs into physical vaccines and therapeutics requires overcoming critical production bottlenecks. This perspective paper examines the Bombyx mori (silkworm) baculovirus expression vector system (BEVS) as a highly scalable, decentralizable platform uniquely suited for LMICs. We highlight the silkworm platform’s capacity to produce both complex multi-epitope vaccine antigens and functional IgG monoclonal antibodies at a fraction of the cost of mammalian cell culture. By proposing a farm-to-GMP supply chain model, in which African silkworm farms supply healthy larvae to centralized GMP facilities for baculovirus infection and protein purification, we demonstrate how the integration of AI and the silkworm platform can directly support the Partnerships for African Vaccine Manufacturing (PAVM) framework, providing a sustainable pathway to achieving the African Union’s 2040 local manufacturing targets.

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