Glucose-6-Phosphate Dehydrogenase (G6PD) deficiency overlaps with malaria endemicity although it predisposes carriers to hemolysis. This fact supports the protection hypothesis against malaria. The aim of this systematic review is to assess the presence and the extent of protective association between G6PD deficiency and malaria. Thirteen databases were searched for papers reporting any G6PD alteration in malaria patients. Twenty-eight of the included 30 studies were eligible for the meta-analysis. Results showed absence of negative association between G6PD deficiency and uncomplicated falciparum malaria (odds ratio (OR), 0.77; 95% confidence interval (CI), 0.59–1.02; p = 0.07). However, this negative association happened in Africa (OR, 0.59; 95% CI, 0.40–0.86; p = 0.007) but not in Asia (OR, 1.24; 95% CI, 0.96–1.61; p = 0.10), and in the heterozygotes (OR, 0.70; 95% CI, 0.57–0.87; p = 0.001) but not the homo/hemizygous (OR, 0.70; 95% CI, 0.46–1.07; p = 0.10). There was no association between G6PD deficiency and total severe malaria (OR, 0.82; 95% CI, 0.61–1.11; p = 0.20). Similarly, there was no association with other malaria species. G6PD deficiency can potentially protect against uncomplicated malaria in African countries, but not severe malaria. Interestingly, this protection was mainly in heterozygous, being x-linked thus related to gender.

It is essential to continue the search for novel antimalarial drugs due to the current spread of resistance against artemisinin by Plasmodium falciparum parasites. In this study, we developed in silico models to predict hemozoin inhibitors as a potential first-step screening for novel antimalarials. An in vitro colorimetric highthroughput screening assay of hemozoin formation was used to identify hemozoin inhibitors from 9,600 structurally diverse compounds. The physicochemical properties of positive hits and randomly selected compounds were extracted from the ChemSpider database; they were used for developing prediction models to predict hemozoin inhibitors using two different approaches, i.e., traditional multivariate logistic regression and Bayesian model averaging. Our results showed that a total of 224 positive-hit compounds exhibited the ability to inhibit hemozoin formation, with 50% inhibitory concentrations (IC 50 s) ranging from 3.1 M to 199.5 M. The best model according to traditional multivariate logistic regression included the three variables octanol-water partition coefficient, number of hydrogen bond donors, and number of atoms of hydrogen, while the best model according to Bayesian model averaging included the three variables octanol-water partition coefficient, number of hydrogen bond donors, and index of refraction. Both models had a good discriminatory power, with area under the curve values of 0.736 and 0.781 for the traditional multivariate model and Bayesian model averaging, respectively. In conclusion, the prediction models can be a new, useful, and cost-effective approach for the first screen of hemozoin inhibition-based antimalarial drug discovery.