Exploration of scaffolds from natural products with antiplasmodial activities, currently registered antimalarial drugs and public malarial screen data

In light of current resistance to antimalarial drugs, there is a need to discover new classes of antimalarial agents with unique mechanisms of action. Identification of unique scaffolds from natural products with in vitro antiplasmodial activities may be the starting point for such new classes of antimalarial agents. We therefore conducted scaffold diversity and comparison analysis of natural products with in vitro antiplasmodial activities (NAA), currently registered antimalarial drugs (CRAD) and malaria screen data from Medicine for Malaria Ventures (MMV). The scaffold diversity analyses on the three datasets were performed using scaffold counts and cumulative scaffold frequency plots. Scaffolds from the NAA were compared to those from CRAD and MMV. A Scaffold Tree was also generated for each of the datasets and the scaffold diversity of NAA was found to be higher than that of MMV. Among the NAA compounds, we identified unique scaffolds that were not contained in any of the other compound datasets. These scaffolds from NAA also possess desirable drug-like properties making them ideal starting points for antimalarial drug design considerations. The Scaffold Tree showed the preponderance of ring systems in NAA and identified virtual scaffolds, which may be potential bioactive compounds

Prioritization of anti-malarial hits from nature: Chemo-informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti-malarial drugs

A large number of natural products have shown in vitro antiplasmodial activities. Early identification and prioritization of these natural products with potential for novel mechanism of action, desirable pharmacokinetics and likelihood for development into drugs is advantageous. Chemo-informatic profiling of these natural products were conducted and compared to currently registered anti-malarial drugs (CRAD). Natural products with in vitro antiplasmodial activities (NAA) were compiled from various sources. These natural products were sub-divided into four groups based on inhibitory concentration (IC50). Key molecular descriptors and physicochemical properties were computed for these compounds and analysis of variance used to assess statistical significance amongst the sets of compounds. Molecular similarity analysis, estimation of drug-likeness, in silico pharmacokinetic profiling, and exploration of structure–activity landscape were also carried out on these sets of compounds

Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach

In view of the vast number of natural products with potential antiplasmodial bioactivity and cost of conducting antiplasmodial bioactivity assays, it may be judicious to learn from previous antiplasmodial bioassays and predict bioactivity of these natural products before experimental bioassays. This study set out to harness antimalarial bioactivity data of natural products to build accurate predictive models, utilizing classical machine learning approaches, which can find potential antimalarial hits from new sets of natural products. Classical machine learning approaches were used to build four classifier models (Naïve Bayesian, Voted Perceptron, Random Forest and Sequence Minimization Optimization of Support Vector Machines) from bioactivity data of natural products with in-vitro antiplasmodial activity (NAA) using a combination of the molecular descriptors and two-dimensional molecular fingerprints of the compounds. Models were evaluated with an independent test dataset. Possible chemical features associated with reported antimalarial activities of the compounds were also extracted. From the results, Random Forest (accuracy 82.81%, Kappa statistics 0.65 and Area under Receiver Operating Characteristics curve 0.91) and Sequential Minimization Optimization (accuracy 85.93%, Kappa statistics 0.72 and Area under Receiver Operating Characteristics curve 0.86) showed good predictive performance for the NAA dataset. The amine chemical group (specifically alkyl amines and basic nitrogen) was confirmed to be essential for antimalarial activity in active NAA dataset. This study built and evaluated classifier models that were used to predict the antiplasmodial bioactivity class (active or inactive) of a set of natural products from interBioScreen chemical library

Impact of Climate Change on International Health Security: An Intersection of Complexity, Interdependence, and Urgency

Climate change (CC) can be defined as a long-term shift in global, continental, and/or local climate patterns. Although many equate CC to the rise in global temperatures, the issue is much more complicated and involves a large number of interconnected factors. Among some of the less discussed considerations of CC are its effects on a broad range of public health issues, including the emergence of novel infectious diseases, the encroachment of infectious disease vectors into previously unaffected geographic distributions, and crop failures resulting in threats of malnutrition and mass migration. This chapter will be devoted to key issues related to CC in the context of international health security (IHS)

Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014

The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported

Clinical Impact of Ceftriaxone Resistance in Escherichia coli Bloodstream Infections: A Multicenter Prospective Cohort Study

BACKGROUND: Ceftriaxone-resistant (CRO-R) Escherichia coli bloodstream infections (BSIs) are common. METHODS: This is a prospective cohort of patients with E coli BSI at 14 United States hospitals between November 2020 and April 2021. For each patient with a CRO-R E coli BSI enrolled, the next consecutive patient with a ceftriaxone-susceptible (CRO-S) E coli BSI was included. Primary outcome was desirability of outcome ranking (DOOR) at day 30, with 50% probability of worse outcomes in the CRO-R group as the null hypothesis. Inverse probability weighting (IPW) was used to reduce confounding. RESULTS: Notable differences between patients infected with CRO-R and CRO-S E coli BSI included the proportion with Pitt bacteremia score ≥4 (23% vs 15%, P = .079) and the median time to active antibiotic therapy (12 hours [interquartile range {IQR}, 1-35 hours] vs 1 hour [IQR, 0-6 hours]; P \u3c .001). Unadjusted DOOR analyses indicated a 58% probability (95% confidence interval [CI], 52%-63%) for a worse clinical outcome in CRO-R versus CRO-S BSI. In the IPW-adjusted cohort, no difference was observed (54% [95% CI, 47%-61%]). Secondary outcomes included unadjusted and adjusted differences in the proportion of 30-day mortality between CRO-R and CRO-S BSIs (-5.3% [95% CI, -10.3% to -.4%] and -1.8 [95% CI, -6.7% to 3.2%], respectively), postculture median length of stay (8 days [IQR, 5-13 days] vs 6 days [IQR, 4-9 days]; P \u3c .001), and incident admission to a long-term care facility (22% vs 12%, P = .045). CONCLUSIONS: Patients with CRO-R E coli BSI generally have poorer outcomes compared to patients infected with CRO-S E coli BSI, even after adjusting for important confounders