The global implementation of Artemisinin (ART)-based combination therapies (ACTs) has significantly reduced disease burden and is recognized by the World Health Organization as the first line treatment against the malaria parasite, Plasmodium falciparum, in all endemic regions. However, recent reports of ART resistance raise extreme alarms for the well-being of this last line of defense. With the looming prospect of the failure of traditional ACTs, the urgent challenge is to expand concepts and strategies to bolster the effectiveness and longevity of ART against multi-drug resistant malaria strains. Historically, this process of finding partners has been ad hoc, relying on a narrow existing array of compounds known to be individually effective against parasites. Rational and precise drug combinations are extraordinarily valuable for improving efficacy, minimizing off-target effects, decreasing the rate of resistance emergence in human pathogens. Traditional empirical approaches encounter significant challenges and new genomic/systems biology offers predictive power to optimally identify targets and focused empirical testing. We propose to formalize a conceptual framework that utilizes ART transcriptional responses of 30 genotypically diverse global isolates and integrates the resulting ART response networks with transcriptional responses to 30 diverse drugs across 3 isolates to predict optimal drug synergies for ART. i) The approach integrates genomic datasets with growth phenotypes from knockout lines and flux-balance analysis (FBA) to predict ART compromised gene interactions that potentially enhance susceptibility to secondary drugs. ii) The approach extends datasets generated in this proposal to the Dialogue for Reverse Engineering Assessments and Methods (DREAM), an established open innovation platform for systems biology, to engage an international community of data analysts in developing novel methods for predicting drug synergy at scale that is not attainable by conventional methods. The proposed project has the potential to advance the search for ART partner drugs while at the same time contributing to novel methods for linking genomic datasets to clinically relevant phenotypes.
Drug resistance threatens efforts to control pathogens. For malaria parasites, there are new reports of failure of the drug Artemisinin (ART), the last line of defense against multi-drug resistant strains. In the era of genomics, huge and diverse datasets are accumulating, but the computational and analytical tools to mine these data are lacking. We propose to construct regulatory networks of gene interactions to predict and test optimal drug partners for ART, and to crowd source the analysis of these data to the global systems biology community.