Pharmacoinformatics and molecular dynamics simulation approach to identify anti-diarrheal potentials of Centella asiatica (L.) Urb. against Vibrio cholerae.

Vibrio cholerae, the etiological agent of cholera, causes dehydration and severe diarrhea with the production of cholera toxin. Due to the acquired antibiotic resistance, V. cholerae has drawn attention to the establishment of novel medications to counteract the virulence and viability of the pathogen. Centella asiatica is a medicinal herb native to Bangladesh that has a wide range of medicinal and ethnobotanical applications including anti-bacterial properties. In the present investigation, a total of 25 bioactive phytochemicals of C. asiatica have been screened virtually through molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analyses, and molecular dynamics simulation. Our results revealed four lead compounds as Viridiflorol (-8.7 Kcal/mol), Luteolin (-8.1 Kcal/mol), Quercetin (-8.0 Kcal/mol) and, Geranyl acetate (-7.1 Kcal/mol) against V. cholerae Toxin co-regulated pilus virulence regulatory protein (ToxT). All the lead compounds have been found to possess favorable pharmacokinetic, pharmacodynamics, and molecular dynamics properties. Toxicity analysis revealed satisfactory results with no major side effects. Molecular dynamics simulation was performed for 100 ns that revealed noteworthy conformational stability and structural compactness for all the lead compounds, especially for Quercetin. Target class prediction unveiled enzymes in most of the cases and some experimental and investigational drugs were found as structurally similar analogs of the lead compounds. These findings could aid in the development of novel therapeutics targeting Cholera disease and we strongly recommend in vitro trials of our experimental findings.Communicated by Ramaswamy H. Sarma.

An investigation to study the effects of Tai Chi on human gait dynamics using classical machine learning.

Tai Chi has been proven effective in preventing falls in older adults, improving the joint function of knee osteoarthritis patients, and improving the balance of stroke survivors. However, the effect of Tai Chi on human gait dynamics is still less understood. Studies conducted in this domain only relied on statistical and clinical measurements on the time-series gait data. In recent years machine learning has proven its ability in recognizing complex patterns from time-series data. In this research work, we have evaluated the performance of several machine learning algorithms in classifying the walking gait of Tai Chi masters (people expert on Tai Chi) from the normal subjects. The study is designed in a longitudinal manner where the Tai Chi naive subjects received 6 months of Tai Chi training and the data was recorded during the initial and follow-up sessions. A total of 57 subjects participated in the experiment among which 27 were Tai Chi masters. We have introduced a gender, BMI-based scaling of the features to mitigate their effects from the gait parameters. A hybrid feature ranking technique has also been proposed for selecting the best features for classification. The research reports 88.17% accuracy and 93.10% ROC AUC values from subject-wise 5-fold cross-validation for the Tai Chi masters' vs normal subjects' walking gait classification for the "Single-task" walking scenarios. We have also got fairly good accuracy for the "Dual-task" walking scenarios (82.62% accuracy and 84.11% ROC AUC values). The results indicate that Tai Chi clearly has an effect on the walking gait dynamics. The findings and methodology of this study could provide preliminary guidance for applying machine learning-based approaches to similar gait kinematics analyses.

Vision of a near future: Bridging the human health-environment divide. Toward an integrated strategy to understand mechanisms across species for chemical safety assessment.

There is a growing recognition that application of mechanistic approaches to understand cross-species shared molecular targets and pathway conservation in the context of hazard characterization, provide significant opportunities in risk assessment (RA) for both human health and environmental safety. Specifically, it has been recognized that a more comprehensive and reliable understanding of similarities and differences in biological pathways across a variety of species will better enable cross-species extrapolation of potential adverse toxicological effects. Ultimately, this would also advance the generation and use of mechanistic data for both human health and environmental RA. A workshop brought together representatives from industry, academia and government to discuss how to improve the use of existing data, and to generate new NAMs data to derive better mechanistic understanding between humans and environmentally-relevant species, ultimately resulting in holistic chemical safety decisions. Thanks to a thorough dialogue among all participants, key challenges, current gaps and research needs were identified, and potential solutions proposed. This discussion highlighted the common objective to progress toward more predictive, mechanistically based, data-driven and animal-free chemical safety assessments. Overall, the participants recognized that there is no single approach which would provide all the answers for bridging the gap between mechanism-based human health and environmental RA, but acknowledged we now have the incentive, tools and data availability to address this concept, maximizing the potential for improvements in both human health and environmental RA.

Using nuclear receptor activity to stratify hepatocarcinogens.

BACKGROUND: Nuclear receptors (NR) are a superfamily of ligand-activated transcription factors that control a range of cellular processes. Persistent stimulation of some NR is a non-genotoxic mechanism of rodent liver cancer with unclear relevance to humans. Here we report on a systematic analysis of new in vitro human NR activity data on 309 environmental chemicals in relationship to their liver cancer-related chronic outcomes in rodents. RESULTS: The effects of 309 environmental chemicals on human constitutive androstane receptors (CAR/NR1I3), pregnane X receptor (PXR/NR1I2), aryl hydrocarbon receptor (AhR), peroxisome proliferator-activated receptors (PPAR/NR1C), liver X receptors (LXR/NR1H), retinoic X receptors (RXR/NR2B) and steroid receptors (SR/NR3) were determined using in vitro data. Hepatic histopathology, observed in rodents after two years of chronic treatment for 171 of the 309 chemicals, was summarized by a cancer lesion progression grade. Chemicals that caused proliferative liver lesions in both rat and mouse were generally more active for the human receptors, relative to the compounds that only affected one rodent species, and these changes were significant for PPAR (p0.001), PXR (p0.01) and CAR (p0.05). Though most chemicals exhibited receptor promiscuity, multivariate analysis clustered them into relatively few NR activity combinations. The human NR activity pattern of chemicals weakly associated with the severity of rodent liver cancer lesion progression (p0.05). CONCLUSIONS: The rodent carcinogens had higher in vitro potency for human NR relative to non-carcinogens. Structurally diverse chemicals with similar NR promiscuity patterns weakly associated with the severity of rodent liver cancer progression. While these results do not prove the role of NR activation in human liver cancer, they do have implications for nuclear receptor chemical biology and provide insights into putative toxicity pathways. More importantly, these findings suggest the utility of in vitro assays for stratifying environmental contaminants based on a combination of human bioactivity and rodent toxicity.