High-Throughput Screening and Prediction Model Building for Novel Hemozoin Inhibitors Using Physicochemical Properties.

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 high-throughput 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 (IC50s) ranging from 3.1 µM to 199.5 µM. 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.

Participants' understanding of informed consent in clinical trials over three decades: systematic review and meta-analysis.

OBJECTIVE: To estimate the proportion of participants in clinical trials who understand different components of informed consent. METHODS: Relevant studies were identified by a systematic review of PubMed, Scopus and Google Scholar and by manually reviewing reference lists for publications up to October 2013. A meta-analysis of study results was performed using a random-effects model to take account of heterogeneity. FINDINGS: The analysis included 103 studies evaluating 135 cohorts of participants. The pooled proportion of participants who understood components of informed consent was 75.8% for freedom to withdraw at any time, 74.7% for the nature of study, 74.7% for the voluntary nature of participation, 74.0% for potential benefits, 69.6% for the study's purpose, 67.0% for potential risks and side-effects, 66.2% for confidentiality, 64.1% for the availability of alternative treatment if withdrawn, 62.9% for knowing that treatments were being compared, 53.3% for placebo and 52.1% for randomization. Most participants, 62.4%, had no therapeutic misconceptions and 54.9% could name at least one risk. Subgroup and meta-regression analyses identified covariates, such as age, educational level, critical illness, the study phase and location, that significantly affected understanding and indicated that the proportion of participants who understood informed consent had not increased over 30 years. CONCLUSION: The proportion of participants in clinical trials who understood different components of informed consent varied from 52.1% to 75.8%. Investigators could do more to help participants achieve a complete understanding.