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• W2894559996 abstract "The parasite Plasmodium falciparum is the most lethal species of Plasmodium to cause serious malaria infection in humans, and with resistance developing rapidly novel treatment modalities are currently being sought, one of which being combinations of existing compounds. The discovery of combinations of antimalarial drugs that act synergistically with one another is hence of great importance; however an exhaustive experimental screen of large drug space in a pairwise manner is not an option. In this study we apply our machine learning approach, Combination Synergy Estimation (CoSynE), which can predict novel synergistic drug interactions using only prior experimental combination screening data and knowledge of compound molecular structures, to a dataset of 1,540 antimalarial drug combinations in which 22.2% were synergistic. Cross validation of our model showed that synergistic CoSynE predictions are enriched 2.74x compared to random selection when both compounds in a predicted combination are known from other combinations among the training data, 2.36x when only one compound is known from the training data, and 1.5x for entirely novel combinations. We prospectively validated our model by making predictions for 185 combinations of 23 entirely novel compounds. CoSynE predicted 20 combinations to be synergistic, which was experimentally validated for 9 of them (45%), corresponding to an enrichment of 1.76x compared to random selection from this prospective data set. Such enrichment corresponds to a 43% reduction in experimental effort. Interestingly, we found that pairwise screening of the compounds CoSynE individually predicted to be synergistic would result in an enrichment of 1.36x compared to random selection, indicating that synergy among compound combinations is not a random event. The nine novel and correctly predicted synergistic compound combinations mainly (where sufficient bioactivity information is available) consist of efflux or transporter inhibitors (such as hydroxyzine), combined with compounds exhibiting antimalarial activity alone (such as sorafenib, apicidin, or dihydroergotamine). However, not all compound synergies could be rationalized easily in this way. Overall, this study highlights the potential for predictive modelling to expedite the discovery of novel drug combinations in fight against antimalarial resistance, while the underlying approach is also generally applicable." @default.
• W2894559996 created "2018-10-12" @default.
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• W2894559996 date "2018-10-02" @default.
• W2894559996 modified "2023-10-18" @default.
• W2894559996 title "Using Machine Learning to Predict Synergistic Antimalarial Compound Combinations With Novel Structures" @default.
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• W2894559996 doi "https://doi.org/10.3389/fphar.2018.01096" @default.
• W2894559996 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6176478" @default.
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