Preliminary in silico investigation of cox 2 selective inhibitors

We report herein an attempt to generate QSAR models for a large number of structurally diverse compounds (1078 compounds) whose affinities for cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2) were experimentally determined. Initially, individual QSAR models for COX-1 (M1) and COX-2 (M2) for biological activity were developed. A selectivity QSAR model, M3 was then developed using as dependent variable Y the differences in pIC50 values between COX-1 and COX-2. The statistical results for all three models showed a satisfactory to good statistical parameters where the values for squared correlation coefficient (coefficient of determination) for the training set are: M1: 0.872, M2: 0.797 respectively M3: 0.739. The predicted values of affinity in the case of all three models selected M1, M2 and respectively M3, are very good 84.88%, 91.12%, 79.59% which lead to very small diffrences between observed and predicted biological activity/selectivity (less than 0.5 logarithimic units)

Preliminary study of the blood brain barrier penetration of some organic compounds and drugs

Partial Least Squares (PLS) regression of blood–brain permeation data (logBB) including 348 diverse organic compounds and drugs was built using 903 Dragon descriptors. The prediction performance of the obtained PLS model is acceptable: the squared correlation coefficient (cumulative sum of squares of all the Y's explained by all extracted components) R 2 Y(CUM) = 0.822, the crossvalidated correlation coefficient (cumulative fraction of the total variation of the Y's that can be predicted by all the extracted components) Q 2 Y(CUM) = 0.640, the number of independent variables, X=487, for a dataset of 342 compounds (six compounds was outliers). The Y-randomization test demonstrated the absence of chance correlation which is confirmed by the lower values of regression line intercepts for R2 X(CUM) (0.307) and Q2 (CUM) (-0.320). The descriptors such as polar surface area (N,O and N,O,S,P polar contributions), octanol-water partition coefficient (Ghose-Crippen and Moriguchi), hydrophilic factor, complementary information content index and the number of H-bond donor atoms showed the largest Variables Importance in the Projection (VIP) values and can influence the logBB. The values of logBB predicted by our model display lower differences against experimental values of 342 compounds than logBB values predicted by QikProp

Preliminary investigation of common GSK3, PPARγ AND DPP IV chemical space

Cross-target biochemical experiments demonstrated that some molecules display an ample spectrum of biological activities which are therapeutically effective. In this regard we investigated the chemical space of the following targets GSK3, DPP IV and PPAR gamma since the DPP IV inhibitors, and PPAR gamma agonists are used to treat diabetes miellitus of type 2. Nevertheless, GSK-3 inhibitors have shown therapeutic potential for insulin resistant type-2 diabetes, the drug market does not register yet an inhibitor of GSK-2 for therapeutical use. The ChEMBL homo sapiens assay data for GSK-3, DPP IV and PPAR gamma were assembled into are database including 7599 compounds. GSK-3 assay comprise 2497 compounds, from which 1889 are unique divided into 428 chemotypes. DPP IV register 3482 compounds and 3026 were unique sharing 510 chemotypes. PPAR gamma incldes 1620 agonists from which 1333 are unique partitioned into 264 chemotypes. The chemical space of GSK3, DPP IV and PPAR gamma share 12 chemotypes, GSK3 and DPP IV share 30 chemotypes, DPP IV and PPAR gamma share 13 chemotypes, whereas GSK3 and PPAR gamma share 17 chemotypes. The 12 chemotypes active on all three proteins were superposed to develop a common pharmacophore which will be further used to identify novel chemotyes with potential biological activity

Partial least squares model of moulting accelerating compounds with insecticide activity against lepidopteran species

In this study the insecticidal activity of a series of 33 dibenzoylhydrazinederivatives, expressed as the pEC50activity measured in vitro, based on an ecdysone-dependent reporter assay using cell lines derived from one lepidopteran species (the cotton leafworm Spodoptera littoralis), was correlated with structural descriptors using the partial least squares (PLS) approach. The data set was energy pre-optimized by molecular mechanics calculations using the MMFF94s force field. Several 0D, 1D, 2D and 3D descriptors were calculated for the minimum energy conformers. A two-components PLS model was obtained with acceptable statistical quality (R2X(Cum) = 0.705, R2Y(cum) = 0.821 and Q2 (Cum) = 0.793) for modeling the insecticidal activity. The model goodness of fit tested with the Y-randomization test indicated a stable model. Specific dibenzoylhydrazine structural features supplying information about topological distances and descriptors sensitive to any conformational change influence the insecticidal activity

In Silico Ligand-Based Methods Targeting Porcupine Receptor Inhibitors with Potential Anticancer Effect

Porcupine is a protein belonging to the O-acyltransferase family, involved in the catalyzing of palmitoylation of wingless-related integration (WNT) proteins. WNT signaling has significant roles in many physiological functions, e.g., hematopoiesis, homeostasis, neurogenesis, and apoptosis. Anomalous WNT signaling has been observed to be related to tumor generation, and metabolic and neurodegenerative disorders. Therefore, compounds that inhibit this pathway are of great interest for the development of therapeutic approaches. For a better understanding of the common traits of such compounds, we have undertaken an in silico study in order to develop a valid ligand-based pharmacophore model based on a series of porcupine inhibitors. The best pharmacophore hypothesis found after the 3D QSAR validation process is represented by the following features: one hydrogen bond donor (D), three rings (R) and one hydrophobic centroid (H). The 3D-QSAR model obtained using the DRRRH hypothesis shows statistically significant parameters: correlation coefficients for the training set: R2 of 0.90, and a predictive correlation coefficient for the test set, Q2 of 0.86. The assessment of the pharmacophore model was also done and provided very reliable metrics values (Receiver Operating Characteristic—ROC of 1; Robust Initial Enhancement—RIE of 17.97). Thereby, we obtained valuable results which can be further used in the virtual screening process for the discovery of new active compounds with potential anticancer activity

Natural Compounds as DPP-4 Inhibitors: 3D-Similarity Search, ADME Toxicity, and Molecular Docking Approaches

Type 2 diabetes mellitus is one of the most common diseases of the 21st century, caused by a sedentary lifestyle, poor diet, high blood pressure, family history, and obesity. To date, there are no known complete cures for type 2 diabetes. To identify bioactive natural products (NPs) to manage type 2 diabetes, the NPs from the ZINC15 database (ZINC-NPs DB) were screened using a 3D shape similarity search, molecular docking approaches, and ADMETox approaches. Frequently, in silico studies result in asymmetric structures as “hit” molecules. Therefore, the asymmetrical FDA-approved diabetes drugs linagliptin (8-[(3R)-3-aminopiperidin-1-yl]-7-but-2-ynyl-3-methyl-1-[(4-methylquinazolin-2-yl)methyl]purine-2,6-dione), sitagliptin ((3R)-3-amino-1-[3-(trifluoromethyl)-6,8-dihydro-5H-[1,2,4]triazolo[4,3-a]pyrazin-7-yl]-4-(2,4,5-trifluorophenyl)butan-1-one), and alogliptin (2-[[6-[(3R)-3-aminopiperidin-1-yl]-3-methyl-2,4-dioxopyrimidin-1-yl]methyl]benzonitrile) were used as queries to virtually screen the ZINC-NPs DB and detect novel potential dipeptidyl peptidase-4 (DPP-4) inhibitors. The most promising NPs, characterized by the best sets of similarity and ADMETox features, were used during the molecular docking stage. The results highlight that 11 asymmetrical NPs out of 224,205 NPs are potential DPP-4 candidates from natural sources and deserve consideration for further in vitro/in vivo tests

Small Molecules of Natural Origin as Potential Anti-HIV Agents: A Computational Approach

The human immunodeficiency virus type 1 (HIV-1), one of the leading causes of infectious death globally, generates severe damages to people’s immune systems and makes them susceptible to serious diseases. To date, there are no drugs that completely remove HIV from the body. This paper focuses on screening 224,205 natural compounds of ZINC15 NPs subset to identify those with bioactivity similar to non-nucleoside reverse transcriptase inhibitors (NNRTIs) as promising candidates to treat HIV-1. To reach the goal, an in silico approach involving 3D-similarity search, ADMETox, HIV protein-inhibitor prediction, docking, and MM-GBSA free-binding energies was trained. The FDA-approved HIV drugs, efavirenz, etravirine, rilpivirine, and doravirine, were used as queries. The prioritized compounds were subjected to ADMETox, docking, and MM-GBSA studies against HIV-1 reverse transcriptase (RT). Lys101, Tyr181, Tyr188, Trp229, and Tyr318 residues and free-binding energies have proved that ligands can stably bind to HIV-1 RT. Three natural products (ZINC37538901, ZINC38321654, and ZINC67912677) containing oxan and oxolan rings with hydroxyl substituents and one (ZINC2103242) having 3,6,7,8-tetrahydro-2H-pyrido[1,2-a]pyrazine-1,4-dione core exhibited comparable profiles to etravirine and doravirine, with ZINC2103242 being the most promising anti-HIV candidate in terms of drug metabolism and safety profile. These findings may open new avenues to guide the rational design of novel HIV-1 NNRTIs

In Silico Study of Some Natural Flavonoids as Potential Agents against COVID-19: Preliminary Results

Flavonoids, widely distributed in fruits, vegetables, and medicinal herbs, are compounds with multiple biological benefits to human health from anti-inflammatory, antioxidant, anticancer, antibacterial to antiviral activity. Coronavirus disease 2019 (COVID-19), a serious concern in the world today, is a respiratory tract disease involving moderate to severe symptoms of pneumonia, with a major incidence in older people and patients having chronic diseases. This emergency health situation led us to evaluate the possible use of natural products to prevent respiratory diseases. The present study aims to report the potential of four natural flavonoids, known to have anti-inflammatory and antiviral activity, as anti-SARS-CoV-2 through their binding on the 6YNQ protein receptor. Molecular docking study with the FRED program was chosen as an appropriate tool to analyze the interaction of natural flavonoids, quercetin, luteolin, galangin, and naringenin, with the SARS-CoV-2 main protease and to rank the conformations through a scoring function to predict their binding affinity. Overall, our preliminary results indicate the potential of the titled natural flavonoids to fight the new coronavirus, COVID-19