4.6 Article

Age-dependent antibody profiles to plasmodium antigens are differentially associated with two artemisinin combination therapy outcomes in high transmission setting

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FRONTIERS IN MEDICINE
卷 9, 期 -, 页码 -

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FRONTIERS MEDIA SA
DOI: 10.3389/fmed.2022.991807

关键词

artemisinin combination therapy; machine learning; modeling; immunoprofiling; malaria; artesunate-mefloquine; artemether-lumefantrine; computational analysis

资金

  1. Armed Forces Health Surveillance Branch (AFHSB)
  2. Global Emerging Infections Surveillance (GEIS) Section
  3. [P0126_13_KY]

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The study found that pre-existing immunity affects the efficacy of artemisinin combination therapy (ASMQ), but not artemether-lumefantrine (AL). Machine learning models predicted the treatment outcomes of ASMQ, indicating that immune responses may interact with drug half-life to provide long-term efficacy of treatment.
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.

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