Novel hydrazone compounds with broad-spectrum antiplasmodial activity and synergistic interactions with antimalarial drugs.

The development of novel antiplasmodial compounds with broad-spectrum activity against different stages of Plasmodium parasites is crucial to prevent malaria disease and parasite transmission. This study evaluated the antiplasmodial activity of seven novel hydrazone compounds (referred to as CB compounds: CB-27, CB-41, CB-50, CB-53, CB-58, CB-59, and CB-61) against multiple stages of Plasmodium parasites. All CB compounds inhibited blood stage proliferation of drug-resistant or sensitive strains of Plasmodium falciparum in the low micromolar to nanomolar range. Interestingly, CB-41 exhibited prophylactic activity against hypnozoites and liver schizonts in Plasmodium cynomolgi, a primate model for Plasmodium vivax. Four CB compounds (CB-27, CB-41, CB-53, and CB-61) inhibited P. falciparum oocyst formation in mosquitoes, and five CB compounds (CB-27, CB-41, CB-53, CB-58, and CB-61) hindered the in vitro development of Plasmodium berghei ookinetes. The CB compounds did not inhibit the activation of P. berghei female and male gametocytes in vitro. Isobologram assays demonstrated synergistic interactions between CB-61 and the FDA-approved antimalarial drugs, clindamycin and halofantrine. Testing of six CB compounds showed no inhibition of Plasmodium glutathione S-transferase as a putative target and no cytotoxicity in HepG2 liver cells. CB compounds are promising candidates for further development as antimalarial drugs against multidrug-resistant parasites, which could also prevent malaria transmission.

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.

Hereditary Angioedema: An Updated Experience with Patients with Angioedema in Puerto Rico.

OBJECTIVE: This was a study of patients with hereditary angioedema (HAE) and their responses to new therapies, measured in terms of HAE attack rates, the number of hospitalizations and emergency room (ER) visits, and the impact of HAE on their quality of life (QOL). METHODS: Patients that came at a private practice with recurrent angioedema without urticaria from 2013 through 2016. All HAE (types I & II) patients received rescue treatment and prophylaxis for those who had 2 or more attacks per month. RESULTS: Of 48 patients, 22 (45.8%) patients with HAE (I or II) were identified. 45.5% of those HAE patients were on prophylaxis and 77.3% were on rescue therapy. Treatment effects were reported as percentages of the HAE patients in each attack/month category: Before treatment, 41.2% of the patients had 0 to 1 attack; after treatment, 84.2%. Similarly, 23.5% had 2 to 3 attacks before treatment, fell to 17.6%, after treatment. Finally, 35.3% experienced more than 3 attacks prior to treatment; and none after treatment. The number of ER visits in 6 months decreased from 64 (3.8 per patient) to 7 (0.4 per patient), and hospitalizations in 6 months decreased from 35 (2.1 per patient) to 7 (0.4 per patient) after treatment. The diagnosis delay averaged 4.3 years; patients diagnosed on or before 2012 averaged 8.6 years; patients diagnosed after 2012 averaged 0.4 years. CONCLUSION: HAE patients showed improved treatment responses as documented by decreased diagnostic delay, attack rates, ER visits and hospitalizations and improved QOL in treated patients.