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Background: Mental health issues are common among college students, and structured services have been proven to enhance outcomes. Despite increased enrolment for higher education in India, college mental health services remain sparse. JEEVANI is the first structured state-wide college mental health service in India. This paper describes its framework and provides an overview of its functioning during the initial five months. Methods: In 2019–2020, the Directorate of Collegiate Education, Government of Kerala, implemented the program in 66 colleges catering to approximately 60,000 students. Qualified counsellors were trained to identify issues and provide interventions and early referrals, using a stepped-care approach. They conducted awareness programs to promote mental health and reduce stigma. Results: The services were accessed by 2,315 students during the assessment period (October 2019 to February 2020). The beneficiaries were predominantly females (54.1%). Over a third (38.8%) belonged to the lower socio-economic strata, and 2.5% had significant vulnerabilities. Although no syndromal diagnoses were made, anxiety and depression were the most common presentations. The counsellors provided 3,758 individual sessions and facilitated support for students who reported academic difficulties or interpersonal issues. Severe mental illness was detected in 54 students, and referrals for specialist mental health input were initiated in 68. Conclusion: Describing the framework, sharing the technical material, and providing an overview of its functioning from a resource-constrained setting in India may encourage higher education institutions in low- and middle-income countries to consider similar projects.

Background: Mental health issues are common among college students, and structured services have been proven to enhance outcomes. Despite increased enrolment for higher education in India, college mental health services remain sparse. JEEVANI is the first structured state-wide college mental health service in India. This paper describes its framework and provides an overview of its functioning during the initial five months. Methods: In 2019–2020, the Directorate of Collegiate Education, Government of Kerala, implemented the program in 66 colleges catering to approximately 60,000 students. Qualified counsellors were trained to identify issues and provide interventions and early referrals, using a stepped-care approach. They conducted awareness programs to promote mental health and reduce stigma. Results: The services were accessed by 2,315 students during the assessment period (October 2019 to February 2020). The beneficiaries were predominantly females (54.1%). Over a third (38.8%) belonged to the lower socio-economic strata, and 2.5% had significant vulnerabilities. Although no syndromal diagnoses were made, anxiety and depression were the most common presentations. The counsellors provided 3,758 individual sessions and facilitated support for students who reported academic difficulties or interpersonal issues. Severe mental illness was detected in 54 students, and referrals for specialist mental health input were initiated in 68. Conclusion: Describing the framework, sharing the technical material, and providing an overview of its functioning from a resource-constrained setting in India may encourage higher education institutions in low- and middle-income countries to consider similar projects. [ABSTRACT FROM AUTHOR]

Structural cardiotoxicity (SCT) presents a high-impact risk that is poorly tolerated in drug discovery unless significant benefit is anticipated. Therefore, we aimed to improve the mechanistic understanding of SCT. First, we combined machine learning methods with a modified calcium transient assay in human-induced pluripotent stem cell-derived cardiomyocytes to identify nine parameters that could predict SCT. Next, we applied transcriptomic profiling to human cardiac microtissues exposed to structural and non-structural cardiotoxins. Fifty-two genes expressed across the three main cell types in the heart (cardiomyocytes, endothelial cells, and fibroblasts) were prioritised in differential expression and network clustering analyses and could be linked to known mechanisms of SCT. This transcriptomic fingerprint may prove useful for generating strategies to mitigate SCT risk in early drug discovery., (© 2024. Crown.)

During HIV infection of CD4+ T cells, ubiquitin pathways are essential to viral replication and host innate immune response; however, the role of specific E3 ubiquitin ligases is not well understood. Proteomics analyses identified 116 single-subunit E3 ubiquitin ligases expressed in activated primary human CD4+ T cells. Using a CRISPR-based arrayed spreading infectivity assay, we systematically knocked out 116 E3s from activated primary CD4+ T cells and infected them with NL4-3 GFP reporter HIV-1. We found 10 E3s significantly positively or negatively affected HIV infection in activated primary CD4+ T cells, including UHRF1 (pro-viral) and TRAF2 (anti-viral). Furthermore, deletion of either TRAF2 or UHRF1 in three JLat models of latency spontaneously increased HIV transcription. To verify this effect, we developed a CRISPR-compatible resting primary human CD4+ T cell model of latency. Using this system, we found that deletion of TRAF2 or UHRF1 initiated latency reactivation and increased virus production from primary human resting CD4+ T cells, suggesting these two E3s represent promising targets for future HIV latency reversal strategies., Importance: HIV, the virus that causes AIDS, heavily relies on the machinery of human cells to infect and replicate. Our study focuses on the host cell's ubiquitination system which is crucial for numerous cellular processes. Many pathogens, including HIV, exploit this system to enhance their own replication and survival. E3 proteins are part of the ubiquitination pathway that are useful drug targets for host-directed therapies. We interrogated the 116 E3s found in human immune cells known as CD4+ T cells, since these are the target cells infected by HIV. Using CRISPR, a gene-editing tool, we individually removed each of these enzymes and observed the impact on HIV infection in human CD4+ T cells isolated from healthy donors. We discovered that 10 of the E3 enzymes had a significant effect on HIV infection. Two of them, TRAF2 and UHRF1, modulated HIV activity within the cells and triggered an increased release of HIV from previously dormant or "latent" cells in a new primary T cell assay. This finding could guide strategies to perturb hidden HIV reservoirs, a major hurdle to curing HIV. Our study offers insights into HIV-host interactions, identifies new factors that influence HIV infection in immune cells, and introduces a novel methodology for studying HIV infection and latency in human immune cells.

Herein, the interaction of an antidiabetic drug, metformin hydrochloride (MHCl), and a cationic surfactant, cetylpyridinium bromide (CPB) is investigated in an aqueous medium. The critical micellar concentration (CMC) of CPB is estimated through conductivity experiments and found to be reduced on adding MHCl and further decreased in the presence of NaCl. The reduced CMC is attributed to the solubilization of MHCl by CPB through micellization and the micellization is found to be thermodynamically spontaneous that experiences an augmentation in the presence of NaCl. This is identified from the negative value of standard free energy ( Δ G 0 m ). The higher negative value of Δ G 0 m (-55.41 kJ mol -1 ) for CPB + MHCl + NaCl than CPB (-37.89 kJ mol -1 ) and CPB + MHCl (-34.08 kJ mol -1 ) is suggestive of the above phenomenon. The positive values of Δ S 0 m in all three cases confirm that the micellization is entropy driven. The binding of MHCl on CPB is quantified by estimating binding constant using the Benesi-Hildebrand (B-H) plot through UV-visible spectral methods. The binding constant values were calculated to be 2.70 M -1 for CPB + MHCl + NaCl compared to 1.258 M -1 for CPB + MHCl predicting a favoring of micellization in the presence of NaCl which is higher than that in the presence of co-solvents. The molecular interaction of MHCl and CPB is justified using FT-IR and NMR techniques. The surface properties of drug surfactant interactions are assessed using SEM techniques. The point of interaction between the drug and surfactant is visualized through the molecular docking approach. The results suggest that CPB would be an effective solubilizer for developing MHCl drug formulations.Communicated by Ramaswamy H. Sarma.

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge" in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms and were trained on a relatively small data set of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility data sets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge data sets, with the best model, a graph convolutional neural network, resulting in an RMSE of 0.86 log units. Critical analysis of the models reveals systematic differences between the performance of models using certain feature sets and training data sets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modeling complex chemical spaces from sparse training data sets.

The present investigation focuses on the role of surfactant and its charge on the interaction of flavonoid Quercetin (QCT) and Bovine serum albumin (BSA). QCT is known to undergo autoxidation in many chemical environments which has different characteristics compared to its non-oxidised structure. In this experiment, two ionic surfactants used. They are anionic surfactant, Sodium dodecyl sulfate (SDS) and Cationic surfactants Cetyl pyridinium bromide (CPB). The characterizations employed are conductivity, FT-IR, UV-visible spectroscopy, Dynamic light scattering (DLS) and Zeta potential measurements. The critical micellar concentration (CMC) as well as the counter-ion binding constant (β) have been calculated by making use of specific conductance values, in aqueous medium at 300 K. Various thermodynamic parameters, ΔG 0 m, standard free energy of micellization, ΔH 0 m , standard enthalpy of micellization and ΔS 0 m , standard entropy of micellization are calculated. The negative value of ΔG 0 m in all systems is indicative of spontaneous binding occurring in both QCT + BSA + SDS (-23.35 kJ mol -1 ) and QCT + BSA + CPB (-27.18 kJ mol -1 ). The higher negative value infers the latter is a more stable system with greater spontaneity. The UV-visible spectroscopy study points at stronger binding of QCT and BSA in presence of surfactants and also there is stronger binding of CPB in ternary mixture with higher binding constant compared to SDS ternary mixture. Which is evident from the binding constant calculated from Benesi-Hildebrand plot (QCT + BSA + SDS, 244.46 M -1 ; QCT + BSA + CPB, 336.53 M -1 ). Further, the structural alterations occurring in the above systems has been observed by FT-IR spectroscopy. The DLS and Zeta potential measurements also support the above finding.Communicated by Ramaswamy H. Sarma.

Optimization of compound metabolic stability is a highly topical issue in pharmaceutical research. Accordingly, application of predictive in silico models can potentially reduce the number of design-make-test-analyze iterations and consequently speed up the progression of novel candidate molecules. Herein, we have investigated the question if multiple in vitro clearance endpoints could be accurately predicted from image-based molecular representations. Thus, compound measurements for four commonly investigated clearance endpoints were curated from AstraZeneca internal sources, providing a sound basis for building multi-task convolutional neural network models. Application of several increasingly challenging data splitting strategies confirmed that convolutional neural network models were successful at capturing implicit chemical relationships contained in training and test data, similar to what is commonly observed for structural fingerprints. Furthermore, model benchmarking against state-of-the-art machine learning methods, including deep neural networks and graph convolutional neural networks, trained with structure- and graph-based representations, respectively, revealed on par or increased accuracy of convolutional neural networks with clear benefit of multi-task learning across all clearance endpoints. Our findings indicate that image-based molecular representations can be applied to predict multiple clearance endpoints, suggesting a potential follow-up to investigate model interpretability from molecular images., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. A total of 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology., Competing Interests: AM, JB, PT, BS, EB, LC No competing interests declared, LT is a full-time employee and shareholder of AstraZeneca, VS is a full-time employee of AstraZeneca, RA is CEO of Astridbio Technologies Ltd, LP is a scientific advisor of Astridbio Technologies Ltd, AV Reviewing editor, eLife, JS reports funding from GSK and Sanofi and fees from Travere Therapeutics and Astex, (© 2022, Montagud et al.)

Ethnopharmacological Relevance: The ethnopharmacological significance of the fruits of Syzygium paniculatum Gaertn (Magenta Cherry) is widely recognized in the Indian traditional medicine system to treat various disorders, such as diabetes, hyperlipidaemia, hypertension, and cardiovascular problems., Aim of the Study: This research work investigated the supplementation of the aqueous extract of S. paniculatum fruit (AESPF) on liver function; the molecular effects on the expression of the protein of insulin receptor (IR) and insulin receptor substrate 1 (IRS-1) in high-fat diet-induced hepatic insulin resistance in the rat model., Materials and Methods: High-fat diet was used to induce obesity in albino Wistar for 120 days. Biochemical, enzymatic, and histopathological analysis, as well as analysis of hepatic insulin resistance proteins and expression of IRS-1, were performed., Results: The supplementation of AESPF with a dose of 100 mg/kg bw significantly reduced bodyweight, blood sugar, insulin, lipid profiles, and liver enzymes. Hepatic insulin resistance was improved with a reduced level of IR and IRS-1 to protein levels. HFD alters the sensitivity of hepatocytes to insulin due to the down-regulation of insulin receptor proteins., Conclusions: The fruits of S. paniculatum possess biological activities to alleviate all risky effects by regulating hepatic lipogenesis activity that can be used in the progress of medication for HFD-induced hepatic insulin resistance and metabolic disorders., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The potential to predict solvation free energies (SFEs) in any solvent using a machine learning (ML) model based on thermodynamic output, extracted exclusively from 3D-RISM simulations in water is investigated. The models on multiple solvents take into account both the solute and solvent description and offer the possibility to predict SFEs of any solute in any solvent with root mean squared errors less than 1 kcal/mol. Validations that involve exclusion of fractions or clusters of the solutes or solvents exemplify the model's capability to predict SFEs of novel solutes and solvents with diverse chemical profiles. In addition to being predictive, our models can identify the solute and solvent features that influence SFE predictions. Furthermore, using 3D-RISM hydration thermodynamic output to predict SFEs in any organic solvent reduces the need to run 3D-RISM simulations in all these solvents. Altogether, our multisolvent models for SFE predictions that take advantage of the solvation effects are expected to have an impact in the property prediction space.

In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using a multimodal attention-based convolutional encoder. Our model is based on the three key pillars of drug sensitivity: compounds' structure in the form of a SMILES sequence, gene expression profiles of tumors, and prior knowledge on intracellular interactions from protein-protein interaction networks. We demonstrate that our multiscale convolutional attention-based encoder significantly outperforms a baseline model trained on Morgan fingerprints and a selection of encoders based on SMILES, as well as the previously reported state-of-the-art for multimodal drug sensitivity prediction ( R 2 = 0.86 and RMSE = 0.89). Moreover, the explainability of our approach is demonstrated by a thorough analysis of the attention weights. We show that the attended genes significantly enrich apoptotic processes and that the drug attention is strongly correlated with a standard chemical structure similarity index. Finally, we report a case study of two receptor tyrosine kinase (RTK) inhibitors acting on a leukemia cell line, showcasing the ability of the model to focus on informative genes and submolecular regions of the two compounds. The demonstrated generalizability and the interpretability of our model testify to its potential for in silico prediction of anticancer compound efficacy on unseen cancer cells, positioning it as a valid solution for the development of personalized therapies as well as for the evaluation of candidate compounds in de novo drug design.

Transcriptional perturbation signatures are valuable data sources for functional genomics. Linking perturbation signatures to screenings opens the possibility to model cellular phenotypes from expression data and to identify efficacious drugs. We linked perturbation transcriptomics data from the LINCS-L1000 project with cell viability information upon genetic (Achilles project) and chemical (CTRP screen) perturbations yielding more than 90 000 signature-viability pairs. An integrated analysis showed that the cell viability signature is a major factor underlying perturbation signatures. The signature is linked to transcription factors regulating cell death, proliferation and division time. We used the cell viability-signature relationship to predict viability from transcriptomics signatures, and identified and validated compounds that induce cell death in tumor cell lines. We showed that cellular toxicity can lead to unexpected similarity of signatures, confounding mechanism of action discovery. Consensus compound signatures predicted cell-specific drug sensitivity, even if the signature is not measured in the same cell line, and outperformed conventional drug-specific features. Our results can help in understanding mechanisms behind cell death and removing confounding factors of transcriptomic perturbation screens. To interactively browse our results and predict cell viability in new gene expression samples, we developed CEVIChE (CEll VIability Calculator from gene Expression; https://saezlab.shinyapps.io/ceviche/)., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The high similarity between certain sub-pockets of serine proteases may lead to low selectivity of protease inhibitors. Therefore the application of proteochemometrics (PCM), which quantifies the relationship between protein/ligand descriptors and affinity for multiple ligands and targets simultaneously, is useful to understand and improve the selectivity profiles of potential inhibitors. In this study, protein field-based PCM that uses knowledge-based and WaterMap derived fields to describe proteins in combination with 2D (RDKit and MOE fingerprints) and 3D (4 point pharmacophoric fingerprints and GRIND) ligand descriptors was used to model the bioactivities of 24 homologous serine proteases and 5863 inhibitors in an integrated fashion. Of the multiple field-based PCM models generated based on different ligand descriptors, RDKit fingerprints showed the best performance in terms of external prediction with R test 2 of 0.72 and RMSEP of 0.81. Further, visual interpretation of the models highlights sub-pocket specific regions that influence affinity and selectivity of serine protease inhibitors.

Achieving selectivity for small organic molecules toward biological targets is a main focus of drug discovery but has been proven difficult, for example, for kinases because of the high similarity of their ATP binding pockets. To support the design of more selective inhibitors with fewer side effects or with altered target profiles for improved efficacy, we developed a method combining ligand- and receptor-based information. Conventional QSAR models enable one to study the interactions of multiple ligands toward a single protein target, but in order to understand the interactions between multiple ligands and multiple proteins, we have used proteochemometrics, a multivariate statistics method that aims to combine and correlate both ligand and protein descriptions with affinity to receptors. The superimposed binding sites of 50 unique kinases were described by molecular interaction fields derived from knowledge-based potentials and Schrödinger's WaterMap software. Eighty ligands were described by Mold(2), Open Babel, and Volsurf descriptors. Partial least-squares regression including cross-terms, which describe the selectivity, was used for model building. This combination of methods allows interpretation and easy visualization of the models within the context of ligand binding pockets, which can be translated readily into the design of novel inhibitors.