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 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.

Protocols for Endovascular Stroke Treatment Diminish the Weekend Effect Through Improvements in Off-Hours Care.

Introduction: The weekend effect is a well-recognized phenomenon in which patient outcomes worsen for acute strokes presenting outside routine business hours. This is attributed to non-uniform availability of services throughout the week and evenings and, though described for intravenous thrombolysis candidates, is poorly understood for endovascular stroke care. We evaluated the impact of institutional protocols on the weekend effect, and the speed and outcome of endovascular therapy as a function of time of presentation. Method: This study assesses a prospective observational cohort of 129 consecutive patients. Patients were grouped based on the time of presentation during regular work hours (Monday through Friday, 07:00-19:00 h) vs. off-hours (overnight 19:00-07:00 h and weekends) and assessed for treatment latency and outcome. Results: Treatment latencies did not depend on the time of presentation. The door to imaging interval was comparable during regular and off-hours (median time 21 vs. 19 min, respectively, p < 0.50). Imaging to groin puncture was comparable (71 vs. 71 min, p < 1.0), as were angiographic and functional outcomes. Additionally, treatment intervals decreased with increased protocol experience; door-to-puncture interval significantly decreased from the first to the fourth quarters of the study period (115 vs. 94 min, respectively, p < 0.006), with the effect primarily seen during off-hours with a 28% reduction in median door-to-puncture times. Conclusions: Institutional protocols help diminish the weekend effect in endovascular stroke treatment. This is driven largely by improvement in off-hours performance, with protocol adherence leading to further decreases in treatment intervals over time.