Investigating antimicrobial resistance genes in Kenya, Uganda and Tanzania cattle using metagenomics

Pollution by antimicrobials and antibiotic resistance genes in East Africa: Occurrence, sources, and potential environmental implications

The escalating burden of antimicrobial pollution in East Africa poses severe threats to public health, environmental integrity, and economic stability. Environmental compartments such as soil and water serve as reservoirs for these pollutants such as antimicrobials and antibiotic resistance genes, creating selective pressure that accelerates the emergence of antimicrobial resistance (AMR). These dynamic fosters the proliferation of multidrug-resistant pathogens, or "superbugs," complicating infection management and amplifying health risks in a region already challenged by inadequate healthcare and sanitation infrastructure. Furthermore, pollution by antimicrobials and antibiotic resistance genes critically disrupts ecological processes, such as nutrient cycling and organic matter degradation, diminishing soil fertility, water quality, and agricultural productivity, thereby threatening food security and overall ecological health. Current surveillance efforts, including the Global Antimicrobial Resistance and Use Surveillance System (GLASS) and the East Africa Public Health Laboratory Networking Project (EAPHLNP), have made strides in tracking AMR trends and guiding policy decisions. However, these efforts remain insufficient to address the growing crisis. This study highlights the urgent need for integrated strategies, including stringent antibiotic usage regulations, advanced wastewater treatment technologies, and comprehensive environmental surveillance. Therefore, there is a need to address the intersections of health, agriculture, and environment, to mitigate AMR and its far-reaching consequences to ensure public health safety and sustainability.

Targeting aldose reductase using natural African compounds as promising agents for managing diabetic complications

Background

Diabetes remains a leading cause of morbidity and mortality due to various complications induced by hyperglycemia. Inhibiting Aldose Reductase (AR), an enzyme that converts glucose to sorbitol, has been studied to prevent long-term diabetic consequences. Unfortunately, drugs targeting AR have demonstrated toxicity, adverse reactions, and a lack of specificity. This study aims to explore African indigenous compounds with high specificity as potential AR inhibitors for pharmacological intervention.

Methodology

A total of 7,344 compounds from the AfroDB, EANPDB, and NANPDB databases were obtained and pre-filtered using the Lipinski rule of five to generate a compound library for virtual screening against the Aldose Reductase. The top 20 compounds with the highest binding affinity were selected. Subsequently, in silico analyses such as protein-ligand interaction, physicochemical and pharmacokinetic profiling (ADMET), and molecular dynamics simulation coupled with binding free energy calculations were performed to identify lead compounds with high binding affinity and low toxicity.

Results

Five natural compounds, namely, (+)-pipoxide, Zinc000095485961, Naamidine A, (-)-pipoxide, and 1,6-di-o-p-hydroxybenzoyl-beta-d-glucopyranoside, were identified as potential inhibitors of aldose reductase. Molecular docking results showed that these compounds exhibited binding energies ranging from -12.3 to -10.7 kcal/mol, which were better than the standard inhibitors (zopolrestat, epalrestat, IDD594, tolrestat, and sorbinil) used in this study. The ADMET and protein-ligand interaction results revealed that these compounds interacted with key inhibiting residues through hydrogen and hydrophobic interactions and demonstrated favorable pharmacological and low toxicity profiles. Prediction of biological activity highlighted Zinc000095485961 and 1,6-di-o-p-hydroxybenzoyl-beta-d-glucopyranoside as having significant inhibitory activity against aldose reductase. Molecular dynamics simulations and MM-PBSA analysis confirmed that the compounds bound to AR exhibited high stability and less conformational change to the AR-inhibitor complex.

Conclusion

This study highlighted the potential inhibitory activity of 5 compounds that belong to the African region: (+)-Pipoxide, Zinc000095485961, Naamidine A, (-)-Pipoxide, and 1,6-di-o-p-hydroxybenzoyl-beta-d-glucopyranoside. These molecules inhibiting the aldose reductase, the key enzyme of the polyol pathway, can be developed as therapeutic agents to manage diabetic complications. However, we recommend in vitro and in vivo studies to confirm our findings.

Machine learning and molecular docking prediction of potential inhibitors against dengue virus

Introduction

Dengue Fever continues to pose a global threat due to the widespread distribution of its vector mosquitoes, Aedes aegypti and Aedes albopictus. While the WHO-approved vaccine, Dengvaxia, and antiviral treatments like Balapiravir and Celgosivir are available, challenges such as drug resistance, reduced efficacy, and high treatment costs persist. This study aims to identify novel potential inhibitors of the Dengue virus (DENV) using an integrative drug discovery approach encompassing machine learning and molecular docking techniques.

Method

Utilizing a dataset of 21,250 bioactive compounds from PubChem (AID: 651640), alongside a total of 1,444 descriptors generated using PaDEL, we trained various models such as Support Vector Machine, Random Forest, k-nearest neighbors, Logistic Regression, and Gaussian Naïve Bayes. The top-performing model was used to predict active compounds, followed by molecular docking performed using AutoDock Vina. The detailed interactions, toxicity, stability, and conformational changes of selected compounds were assessed through protein-ligand interaction studies, molecular dynamics (MD) simulations, and binding free energy calculations.

Results

We implemented a robust three-dataset splitting strategy, employing the Logistic Regression algorithm, which achieved an accuracy of 94%. The model successfully predicted 18 known DENV inhibitors, with 11 identified as active, paving the way for further exploration of 2683 new compounds from the ZINC and EANPDB databases. Subsequent molecular docking studies were performed on the NS2B/NS3 protease, an enzyme essential in viral replication. ZINC95485940, ZINC38628344, 2',4'-dihydroxychalcone and ZINC14441502 demonstrated a high binding affinity of -8.1, -8.5, -8.6, and -8.0 kcal/mol, respectively, exhibiting stable interactions with His51, Ser135, Leu128, Pro132, Ser131, Tyr161, and Asp75 within the active site, which are critical residues involved in inhibition. Molecular dynamics simulations coupled with MMPBSA further elucidated the stability, making it a promising candidate for drug development.

Conclusion

Overall, this integrative approach, combining machine learning, molecular docking, and dynamics simulations, highlights the strength and utility of computational tools in drug discovery. It suggests a promising pathway for the rapid identification and development of novel antiviral drugs against DENV. These in silico findings provide a strong foundation for future experimental validations and in-vitro studies aimed at fighting DENV.