In Frontiers in medicine
The impact of pre-existing immunity on the efficacy of artemisinin combination therapy is largely unknown. We performed in-depth profiling of serological responses in a therapeutic efficacy study [comparing artesunate-mefloquine (ASMQ) and artemether-lumefantrine (AL)] using a proteomic microarray. Responses to over 200 Plasmodium antigens were significantly associated with ASMQ treatment outcome but not AL. We used machine learning to develop predictive models of treatment outcome based on the immunoprofile data. The models predict treatment outcome for ASMQ with high (72-85%) accuracy, but could not predict treatment outcome for AL. This divergent treatment outcome suggests that humoral immunity may synergize with the longer mefloquine half-life to provide a prophylactic effect at 28-42 days post-treatment, which was further supported by simulated pharmacokinetic profiling. Our computational approach and modeling revealed the synergistic effect of pre-existing immunity in patients with drug combination that has an extended efficacy on providing long term treatment efficacy of ASMQ.
Andagalu Ben, Lu Pinyi, Onyango Irene, Bergmann-Leitner Elke, Wasuna Ruth, Odhiambo Geoffrey, Chebon-Bore Lorna J, Ingasia Luicer A, Juma Dennis W, Opot Benjamin, Cheruiyot Agnes, Yeda Redemptah, Okudo Charles, Okoth Raphael, Chemwor Gladys, Campo Joseph, Wallqvist Anders, Akala Hoseah M, Ochiel Daniel, Ogutu Bernhards, Chaudhury Sidhartha, Kamau Edwin
2022
artemether-lumefantrine, artemisinin combination therapy, artesunate-mefloquine, computational analysis, immunoprofiling, machine learning, malaria, modeling
