In vitro validation of chemo-transcriptomic fingerprints for the classification of the mode of action of antimalarial drugs

Abstract

Malaria is an infectious disease brought on by Plasmodium parasites. Resistance development to current treatment measures is a significant challenge to the progress made in eradicating malaria. As a result, developing new drugs with novel targets and modes of action (MoA) is of utmost importance to address this resistance. van Heerden et al. used classification-based machine learning to develop a rationally selected model capable of stratifying compounds into different MoA groups with a 77 % accuracy. This model used chemo-transcriptomic fingerprints to indicate that the variant expression of only 50 transcripts was sufficient to classify different compounds with similar MoA into the same subsets quickly and specifically. This study used real-time, quantitative PCR and the 2-△△Cq relative quantification method to investigate the in vitro expression levels of the biomarkers that van Heerden et al. identified as responsive to compound treatment within parasite populations treated with antimalarial compounds. Five control compounds, each with a known MoA was used to established that the qPCR amplification of the biomarkers was sufficient to distinguish between the compounds and that compounds with specific targets clustered separately from those with non-specific targets. Four clinical candidates with dissimilar MoAs not previously evaluated in the machine learning model solidified that the biomarkers could create distinctive chemo-transcriptomic fingerprints for each compound’s MoA. Lastly, a clinical candidate that shares a target with a control compound was introduced, proving that compounds with overlapping biological activity showed similarities in their chemo-transcriptomic fingerprints. This data indicated that the biomarkers identified using machine learning could be predictive biomarkers for compound MoA classification. The limited number of biomarkers, as well as the established qPCR-based platform parameters in this study, provides a rapid and scalable means to determine a compound’s MoA, therefore greatly benefiting antimalarial drug discovery by allowing drug candidates to be evaluated for unfavourable MoAs and ensuring that the MoA remains unchanged during lead optimisation.