Prediction reliability of QSAR models: an overview of various validation tools.

The reliability of any quantitative structure-activity relationship (QSAR) model depends on multiple aspects such as the accuracy of the input dataset, selection of significant descriptors, the appropriate splitting process of the dataset, statistical tools used, and most notably on the measures of validation. Validation, the most crucial step in QSAR model development, confirms the reliability of the developed QSAR models and the acceptability of each step in the model development. The present review deals with various validation tools that involve multiple techniques that improve the model quality and robustness. The double cross-validation tool helps in building improved quality models using different combinations of the same training set in an inner cross-validation loop. This exhaustive method is also integrated for small datasets (< 40 compounds) in another tool, namely the small dataset modeler tool. The main aim of QSAR researchers is to improve prediction quality by lowering the prediction errors for the query compounds. 'Intelligent' selection of multiple models and consensus predictions integrated in the intelligent consensus predictor tool were found to be more externally predictive than individual models. Furthermore, another tool called Prediction Reliability Indicator was explained to understand the quality of predictions for a true external set. This tool uses a composite scoring technique to identify query compounds as 'good' or 'moderate' or 'bad' predictions. We have also discussed a quantitative read-across tool which predicts a chemical response based on the similarity with structural analogues. The discussed tools are freely available from https://dtclab.webs.com/software-tools or http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/ and https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home (for read-across).

Computational modeling of PET imaging agents for vesicular acetylcholine transporter (VAChT) protein binding affinity: application of 2D-QSAR modeling and molecular docking techniques.

The neurotransmitter acetylcholine (ACh) plays a ubiquitous role in cognitive functions including learning and memory with widespread innervation in the cortex, subcortical structures, and the cerebellum. Cholinergic receptors, transporters, or enzymes associated with many neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD), are potential imaging targets. In the present study, we have developed 2D-quantitative structure-activity relationship (2D-QSAR) models for 19 positron emission tomography (PET) imaging agents targeted against presynaptic vesicular acetylcholine transporter (VAChT). VAChT assists in the transport of ACh into the presynaptic storage vesicles, and it becomes one of the main targets for the diagnosis of various neurodegenerative diseases. In our work, we aimed to understand the important structural features of the PET imaging agents required for their binding with VAChT. This was done by feature selection using a Genetic Algorithm followed by the Best Subset Selection method and developing a Partial Least Squares- based 2D-QSAR model using the best feature combination. The developed QSAR model showed significant statistical performance and reliability. Using the features selected in the 2D-QSAR analysis, we have also performed similarity-based chemical read-across predictions and obtained encouraging external validation statistics. Further, we have also performed molecular docking analysis to understand the molecular interactions occurring between the PET imaging agents and the VAChT receptor. The molecular docking results were correlated with the QSAR features for a better understanding of the molecular interactions. This research serves to fulfill the experimental data gap, highlighting the applicability of computational methods in the PET imaging agents' binding affinity prediction. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-023-00146-4.

In silico modeling for quick prediction of inhibitory activity against 3CLpro enzyme in SARS CoV diseases.

As of 2 September 2020, the 2019 novel coronavirus or SARS CoV-2 has been responsible for more than 2,56,02,665 infections and 8,52,768 deaths worldwide. There has been an urgent need of newer drug discovery to tackle the situation. Severe acute respiratory syndrome-associated coronavirus 3C-like protease (or 3CLpro) is a potential target as anti-SARS agents as it plays a vital role in the viral life cycle. This study aims at developing a quantitative structure-activity relationship (QSAR) model against a group of 3CLpro inhibitors to study their structural requirements for their inhibitory activity. Further, molecular docking studies were carried out which helped in the justification of the QSAR findings. Moreover, molecular dynamics simulation study was performed for selected compounds to check the stability of interactions as suggested by the docking analysis. The current QSAR model was further used in the prediction and screening of large databases within a short time.Communicated by Ramaswamy H. Sarma.

QSAR modelling of inhalation toxicity of diverse volatile organic molecules using no observed adverse effect concentration (NOAEC) as the endpoint.

Nowadays, air pollution due to urbanization and reduction of forestry is emerging as a serious threat to humans and the environment. According to the World Health Organization, respiratory diseases are the third most mortality factor in the world. Chemical research organizations and industries are producing a large number of new chemical compounds continuously. Although toxicity testing of those chemicals on animals is costly, resource and time consuming, these data cannot be properly extrapolated to humans and other animals, and also these raise ethical issues. In this background, we have developed Quantitative Structure-Activity Relationship (QSAR) models using the No Observed Adverse Effect Concentration (NOAEC) as the endpoint to assess inhalation toxicity of diverse organic chemicals, commonly used and exposed by us in our daily life. No Observed Adverse Effect Concentration (NOAEC) can be used for long term toxicity studies towards the human inhalation risk assessment, as recommended by Organization for Economic Co-operation and Development (OECD) in guidance document 39. A particular QSAR model may not be equally effective for prediction of all query compounds from a given set of compounds; therefore, we have developed multiple models, which are robust, sound and well predictive from the statistical point of view to forecast the NOAEC values for the new untested compounds. Subsequently the validated individual models were employed to generate consensus models, in order to improve the quality of predictions and to reduce prediction errors. We have investigated some crucial structural features from these models which may regulate inhalation toxicity for newly produced molecules. Thus, our developed models may help in toxicity assessment towards reducing the health hazards for new chemicals.

Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across.

Endocrine Disruptor Chemicals are synthetic or natural molecules in the environment that promote adverse modifications of endogenous hormone regulation in humans and/or in animals. In the present research, we have applied two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling to analyze the structural features of these chemicals responsible for binding to the androgen receptors (logRBA) in rats. We have collected the receptor binding data from the EDKB database (https://www.fda.gov/science-research/endocrine-disruptor-knowledge-base/accessing-edkb-database) and then employed the DTC-QSAR tool, available from https://dtclab.webs.com/software-tools, for dataset division, feature selection, and model development. The final partial least squares model was evaluated using various stringent validation criteria. From the model, we interpreted that hydrophobicity, steroidal nucleus, bulkiness and a hydrogen bond donor at an appropriate position contribute to the receptor binding affinity, while presence of electron rich features like aromaticity and polar groups decrease the receptor binding affinity. Additionally we have also performed chemical Read-Across predictions using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home, and the results for the external validation metrics were found to be better than the QSAR-derived predictions. The best quality of external predictions emerged from the q-RASAR approach which combines both read-across and QSAR. To explore the essential features responsible for the receptor binding, pharmacophore mapping, molecular docking along with molecular dynamics simulation were also performed, and the results are in accordance with the QSAR/q-RASAR findings.

Repurposing FDA approved drugs as possible anti-SARS-CoV-2 medications using ligand-based computational approaches: sum of ranking difference-based model selection.

The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prevailing, with more than 440 million infections and over 5.9 million deaths documented so far since the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The non-availability of treatment further aggravates the scenario, thereby demanding the exploration of pre-existing FDA-approved drugs for their effectiveness against COVID-19. The current research aims to identify potential anti-SARS-CoV-2 drugs using a computational approach and repurpose them if possible. In the present study, we have collected a set of 44 FDA-approved drugs of different classes from a previously published literature with their potential antiviral activity against COVID-19. We have employed both regression- and classification-based quantitative structure-activity relationship (QSAR) modeling to identify critical chemical features essential for anticoronaviral activity. Multiple models with the consensus algorithm were employed for the regression-based approach to improve the predictions. Additionally, we have employed a machine learning-based read-across approach using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home and linear discriminant analysis for the efficient prediction of potential drug candidate for COVID-19. Finally, the quantitative prediction ability of different modeling approaches was compared using the sum of ranking differences (SRD). Furthermore, we have predicted a true external set of 98 pharmaceuticals using the developed models for their probable anti-COVID activity and their prediction reliability was checked employing the "Prediction Reliability Indicator" tool available from https://dtclab.webs.com/software-tools. Though the present study does not target any protein of viral interaction, the modeling approaches developed can be helpful for identifying or screening potential anti-coronaviral drug candidates. Supplementary information: The online version contains supplementary material available at 10.1007/s11224-022-01975-3.

In silico modelling of acute toxicity of 1, 2, 4-triazole antifungal agents towards zebrafish (Danio rerio) embryos: Application of the Small Dataset Modeller tool.

Nowadays, there is a widespread use of triazole antifungal agents to kill broad classes of fungi in farming lands and to protect herbs, fruits and grains. These agents further deposit into the aquatic systems causing toxicity to the living aquatic creatures, which can then affect human beings. Considering this issue, risk assessment of these toxic chemicals is a very essential task. Due to the inadequate experimental data on acute toxicity of antifungal agents containing the 1, 2, 4-triazole ring, higher testing costs along with the regulatory restrictions and the international regulations to lessen animal testing emphasize on in silico techniques such as quantitative structure-activity relationship (QSAR) studies. The application of QSAR modelling has created an easier avenue to predict activity/property/toxicity of newly synthesized compounds. In the present study, we have used 23 antifungal agents containing the 1, 2, 4-triazole ring to develop 2D-QSAR models and explored their structural attributes crucial for acute toxicity towards embryonic phase of zebrafish (Danio rerio). Here, we have employed simple 2D descriptors to develop the QSAR models. The models were evolved by executing the Small Dataset Modeller tool (https://dtclab.webs.com/software-tools), and the validation of the models was achieved by employing different precise validation principles. The statistical validation metrics confirm that built models are robust, useful and well predictive to forecast the acute toxicity of new compounds.

A Multi-layered Variable Selection Strategy for QSAR Modeling of Butyrylcholinesterase Inhibitors.

BACKGROUND: Alzheimer's disease (AD), a neurological disorder, is the most common cause of senile dementia. Butyrylcholinesterase (BuChE) enzyme plays a vital role in regulating the brain acetylcholine (ACh) neurotransmitter, but in the case of Alzheimer's disease (AD), BuChE activity gradually increases in patients with a decrease in the acetylcholine (ACh) concentration via hydrolysis. ACh plays an essential role in regulating learning and memory as the cortex originates from the basal forebrain, and thus, is involved in memory consolidation in these sites. METHODS: In this work, we have developed a partial least squares (PLS)-regression based two dimensional quantitative structure-activity relationship (2D-QSAR) model using 1130 diverse chemical classes of compounds with defined activity against the BuChE enzyme. Keeping in mind the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have tried to select significant descriptors from the large initial pool of descriptors using multi-layered variable selection strategy using stepwise regression followed by genetic algorithm (GA) followed by again stepwise regression technique and at the end best subset selection prior to development of final model thus reducing noise in the input. Partial least squares (PLS) regression technique was employed for the development of the final model while model validation was performed using various stringent validation criteria. RESULTS: The results obtained from the QSAR model suggested that the quality of the model is acceptable in terms of both internal (R2= 0.664, Q2= 0.650) and external (R2 Pred= 0.657) validation parameters. The QSAR studies were analyzed, and the structural features (hydrophobic, ring aromatic and hydrogen bond acceptor/donor) responsible for enhancement of the activity were identified. The developed model further suggests that the presence of hydrophobic features like long carbon chain would increase the BuChE inhibitory activity and presence of amino group and hydrazine fragment promoting the hydrogen bond interactions would be important for increasing the inhibitory activity against BuChE enzyme. CONCLUSION: Furthermore, molecular docking studies have been carried out to understand the molecular interactions between the ligand and receptor, and the results are then correlated with the structural features obtained from the QSAR models. The information obtained from the QSAR models are well corroborated with the results of the docking study.

Excess of glucocorticoid induces cardiac dysfunction via activating angiotensin II pathway.

BACKGROUND: Glucocorticoid is widely used as an anti-inflammatory drug in various diseases however excess of it often causes cardiovascular complications. The present study was undertaken to understand the molecular mechanism of glucocorticoid-induced cardiac dysfunction. METHODS: Rats were treated daily with synthetic glucocorticoid, dexamethasone with or without mifepristone or losartan up to 15 days. Hemodynamic parameters were measured by PV-loop method using Millar's instrument. Cardiac remodelling, fibrosis and oxidative stress were monitored after 15 days. RESULTS: The systolic blood pressure was increased whereas the heart beat and cardiac output (n=6) were decreased by dexamethasone. Dexamethasone caused increase in the heart weight to body weight ratio (P<0.001, n=20), increased level of mRNA of atrial natriuretic peptide and an increased deposition of collagens in the extracellular matrix of the left ventricle which were inhibited by both mifepristone and losartan. The rate of oxygen consumption was decreased in association with increased levels of hypoxia inducible factor 1alpha, lipid peroxidation (P<0.01, n=3) and superoxide dismutase activity (P<0.01, n=3) in dexamethasone treated rat heart. All these changes were reversed by mifepristone and losartan. CONCLUSIONS: The excess of glucocorticoid induces cardiac remodelling and pathophysiolgical changes of the myocardium via angiotensin II signalling pathway.

Chemometric modeling of larvicidal activity of plant derived compounds against zika virus vector Aedes aegypti: application of ETA indices.

Dengue, zika and chikungunya have severe public health concerns in several countries. Human modification of the natural environment continues to create habitats in which mosquitoes, vectors of a wide variety of human and animal pathogens, thrive, which can bring about an enormous negative impact on public health if not controlled properly. Quantitative structure-activity relationship (QSAR) modeling has been applied in this work with the aim of exploring features contributing to promising larvicidal properties against the vector Aedes aegypti (Diptera: Culicidae). A dataset of 61 plant derived compounds reported in previous literature was used in this present study. A genetic algorithm (GA) was used for QSAR model development employing the "Double Cross Validation" (DCV) tool available at http://teqip.jdvu.ac.in/QSAR_Tools/. The DCV tool removes any bias in descriptor selection from a fixed composition of a training set and often provides an optimum solution in terms of predictivity. Simple topological descriptors, the "Extended Topochemical Atom" (ETA) indices developed by the present authors' group, were used for model development. These descriptors do not require pretreatment of molecular structures by conformational analysis or energy minimization before model development, thus saving computational time and resources. They also avoid ambiguities with respect to the existence of compounds in various conformational states leading to the loss of predictive capability in QSAR models. A number of models were generated from GA, and further, the descriptors appearing in the best model obtained from GA were subjected to partial least squares (PLS) regression to obtain the final robust model. The developed model was validated extensively using different validation metrics to check the reliability and predictivity of the model for enhancing confidence in QSAR predictions. Based on the insights obtained from the PLS model, we can conclude that the presence of hydrogen bond acceptor atoms, the presence of multiple bonds as well as sufficient lipophilicity and a limited polar surface area play crucial roles in regulating the activity of the compounds.

Excess of glucocorticoid induces myocardial remodeling and alteration of calcium signaling in cardiomyocytes.

Ventricular dysfunction is one of the important side effects of the anti-inflammatory agent, glucocorticoid (GC). The present study was undertaken to examine whether abnormal calcium signaling is responsible for cardiac dysfunction due to an excess of GC hormone. The synthetic GC drug, dexamethasone (DEX), significantly (P<0.001, n=20) increased heart weight to body weight ratio, left ventricular remodeling, and fibrosis. The microarray analysis showed altered expression of several genes encoding calcium cycling/ion channel proteins in DEX-treated rat heart. The altered expression of some of the genes was validated by real-time PCR and western blotting analyses. The expression of the L-type calcium channels and calsequestrin was increased, whereas sarcoendoplasmic reticulum calcium transport ATPase 2a (SERCA2a) and junctin mRNAs were significantly reduced in DEX-treated rat left ventricular tissues. In neonatal rat ventricular cardiomyocytes, DEX also increased the level of mRNAs of atrial- and brain natriuretic peptides, L-type calcium channels, and calsequestrin after 24 h of treatment, which were mostly restored by mifepristone. The caffeine-induced calcium release was prolonged by DEX compared to the sharp release in control cardiomyocytes. Taken together, these data show that impaired calcium kinetics may be responsible for cardiac malfunction by DEX. The results are important in understanding the pathophysiology of the heart in patients treated with excess GC.

Clustering of cardiometabolic risk factors in Asian Indian women: Santiniketan women study.

OBJECTIVE: The aim of the present cross-sectional study was to investigate the clustering of cardiometabolic risk factors in premenopausal and postmenopausal Asian Indian women. METHODS: A total of 214 healthy women (25-65 y) from the Bolpur-Santiniketan area, West Bengal, India, took part in the study. The women were categorized into two groups, namely, premenopausal (n = 161) and postmenopausal (n = 53). Anthropometric measures, namely, minimum waist circumference (WC) and the sum of four (biceps + triceps + subscapular + suprailiac) skinfolds (SF4), were measured accordingly. Intra-abdominal visceral fat (IVF) was also measured. Left arm systolic (SBP) and diastolic (DBP) blood pressure was taken in participants. Metabolic profiles, namely, total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), fasting plasma glucose (FPG), insulin, testosterone, and estrogen, were measured accordingly. RESULTS: The four factors identified in premenopausal women were factor 1: WC, SF4, IVF, TC, and TG; factor 2: HDL, SBP, DBP, and insulin; factor 3: TC, TG, LDL, and testosterone; and factor 4: FPG, testosterone, and estrogen. These four factors cumulatively explained 72.97% of the total phenotypic variation. In postmenopausal women, the four factors identified were factor 1: TC, TG, HDL, LDL, and DBP; factor 2: FPG, SBP, and DBP; factor 3: WC, SF4, and IVF; and factor 4: FPG, insulin, testosterone, and estrogen. These four factors together explained 90.71% of the total phenotypic variation in cardiometabolic risk factors. CONCLUSIONS: No common underlying physiological variables in premenopausal and postmenopausal women indicate that a single risk axis for clustering of cardiometabolic phenotypes is highly unlikely.

Stimulation of salmon calcitonin on secretion of 17beta-estradiol by the ovarian follicles of common carp, Cyprinus carpio.

The effects of salmon calcitonin (sCT) on the secretion of 17beta-estradiol (E(2)) were examined in female common carp, Cyprinus carpio. Vitellogenic stage fish adapted to high-Ca water were i.p. injected with vehicle, sCT, human chorionic gonadotropin (hCG), or hCG plus sCT. To determine whether ovarian follicles are equipped with CT receptors, a CT binding assay was conducted. In the in vitro experiments, vitellogenic follicles were incubated with stimulators and inhibitors. Administration of sCT increased the basal and hCG-stimulated E(2) release in vivo and in vitro. Binding characteristics of [(125)I]sCT to plasma membrane preparation of carp ovarian follicles showed saturability with high-affinity (K(d)=48.48 pmol/l and B(max)=1.2 pmol/mg protein). To clarify the mechanism of E(2) production by sCT, in vitro effect of sCT and hCG on aromatase activity (conversion of testosterone to E(2)) and cytochrome P450 aromatase (P450arom) gene expression in carp ovarian follicles were investigated. Salmon CT-stimulated both aromatase activity and P450arom gene expression in ovarian follicles of carp. sCT-stimulated E(2) release by the ovarian follicles in vitro was augmented in the presence of dibutyryl cAMP. Inhibitor of protein kinase A (PKA), SQ 22536 inhibited sCT-stimulated steroid production in a dose-dependent manner. Specific inhibitor of protein kinase C (PKC), NPC-15437 dihydrochloride had no inhibitory effects on sCT-induced E(2) release. The present study indicates that sCT binds specifically to carp ovary and stimulates E(2) production by increasing the activity of cytochrome P450 aromatase and P450arom gene expression. The results further suggest that stimulatory action of sCT on E(2) production is mediated through cAMP pathway.