Abstract
Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved drugs with novel mechanisms of action to avoid cross-resistance. Current cell-based drug screens are, however, restricted to binary live/dead readouts with no provision for mechanism of action prediction. Machine learning methods are increasingly being used to improve information extraction from imaging data. These methods, however, work poorly with heterogeneous cellular phenotypes and generally require time-consuming human-led training. We have developed a semi-supervised machine learning approach, combining human- and machine-labeled training data from mixed human malaria parasite cultures. Designed for high-throughput and high-resolution screening, our semi-supervised approach is robust to natural parasite morphological heterogeneity and correctly orders parasite developmental stages. Our approach also reproducibly detects and clusters drug-induced morphological outliers by mechanism of action, demonstrating the potential power of machine learning for accelerating cell-based drug discovery.
Highlights
Cell-based screens have substantially advanced our ability to find new drugs [1]
When we use the median angle variation of the model as the threshold for examples that are too close to call, we get performance that is representative of the human expert average. These results demonstrate that our semi- supervised model successfully identified and segregated asynchronous parasites and infected RBCs from images that contain >90% uninfected RBCs (i.e.,
Driven by the need to accelerate novel antimalarial drug discovery with defined mechanism of action (MoA) from phenotypic screens, we applied Machine learning (ML) to images of asynchronous P. falciparum cultures. This semi-supervised ensemble model could identify effective drugs and cluster them according to MoA, based on life cycle stage and morphological outliers
Summary
Most screens are unable to predict the mechanism of action (MoA) of identified hits, necessitating years of follow-up after discovery. Limitations in finding new targets are becoming especially important in the face of rising antimicrobial resistance across bacterial and parasitic infections. This rise in resistance is driving increasing demand for screens that can intuitively find new antimicrobials with novel MoAs. Demand for innovation in drug discovery is exemplified in efforts on targeting Plasmodium falciparum, the parasite that causes malaria. While there is a healthy pipeline of developmental antimalarials, many target common processes [5] and may fail quickly because of prevalent cross-resistance. Solutions are urgently sought for the rapid identification of new drugs that have a novel MoA at the time of discovery
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