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1.
Acta Parasitol ; 69(1): 415-425, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-38165555

RESUMO

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.


Assuntos
Antimaláricos , Combinação de Medicamentos , Sinergismo Farmacológico , Aprendizado de Máquina , Plasmodium falciparum , Antimaláricos/farmacologia , Plasmodium falciparum/efeitos dos fármacos , Humanos , Quimioterapia Combinada , Malária Falciparum/tratamento farmacológico , Malária Falciparum/parasitologia
2.
Artigo em Inglês | MEDLINE | ID: mdl-38993286

RESUMO

Humans are supra-organisms co-evolved with microbial communities (Prokaryotic and Eukaryotic), named the microbiome. These microbiomes supply essential ecosystem services that play critical roles in human health. A loss of indigenous microbes through modern lifestyles leads to microbial extinctions, associated with many diseases and epidemics. This narrative review conforms a complete guide to the human holobiont-comprising the host and all its symbiont populations- summarizes the latest and most significant research findings in human microbiome. It pretends to be a comprehensive resource in the field, describing all human body niches and their dominant microbial taxa while discussing common perturbations on microbial homeostasis, impacts of urbanization and restoration and humanitarian efforts to preserve good microbes from extinction.

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