Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool

Purpose Antimalarial drug resistance is a global public health problem that leads to treatment failure. Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation of a freely available computational tool, Machine Le...

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Bibliographic Details
Published in:Acta parasitologica 2024-03, Vol.69 (1), p.415-425
Main Authors: Roche-Lima, Abiel, Rosado-Quiñones, Angélica M., Feliu-Maldonado, Roberto A., Figueroa-Gispert, María Del Mar, Díaz-Rivera, Jennifer, Díaz-González, Roberto G., Carrasquillo-Carrion, Kelvin, Nieves, Brenda G., Colón-Lorenzo, Emilee E., Serrano, Adelfa E.
Format: Article
Language:English
Subjects:
Algorithms
Animal Systematics/Taxonomy/Biogeography
Antimalarial agents
Antimalarials - pharmacology
Antiparasitic agents
Biomedical and Life Sciences
Biomedicine
Chemical fingerprinting
Drug Combinations
Drug development
Drug resistance
Drug Synergism
Drug Therapy, Combination
Drugs
Ecology
Humans
Learning algorithms
Machine Learning
Malaria
Malaria, Falciparum - drug therapy
Malaria, Falciparum - parasitology
Medical Microbiology
Microbiology
Original Paper
Parasitology
Plasmodium falciparum
Plasmodium falciparum - drug effects
Prediction models
Public health
Regression analysis
Software
Support vector machines
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Summary:Purpose Antimalarial drug resistance is a global public health problem that leads to treatment failure. Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation of a freely available computational tool, Machine Learning Synergy Predictor (MLSyPred©), to predict potential synergy in antimalarial drug combinations. Methods The MLSyPred© synergy prediction method extracts molecular fingerprints from the drugs’ biochemical structures to use as features and also cleans and prepares the raw data. Five machine learning algorithms (Logistic Regression, Random Forest, Support vector machine, Ada Boost, and Gradient Boost) were implemented to build prediction models. Implementation and application of the MLSyPred© tool were tested using datasets from 1540 combinations of 79 drugs and compounds biologically evaluated in pairs for three strains of Plasmodium falciparum (3D7, HB3, and Dd2). Results The best prediction models were obtained using Logistic Regression for antimalarials with the strains Dd2 and HB3 (0.81 and 0.70 AUC, respectively) and Random Forest for antimalarials with 3D7 (0.69 AUC). The MLSyPred© tool yielded 45% precision for synergistically predicted antimalarial drug combinations that were annotated and biologically validated, thus confirming the functionality and applicability of the tool. Conclusion  The MLSyPred© tool is freely available and represents a promising strategy for discovering potential synergistic drug combinations for further development as novel antimalarial therapies.
ISSN:1230-2821
1896-1851
1896-1851
DOI:10.1007/s11686-023-00765-z