Comparative molecular similarity indices analysis (CoMSIA) studies of 1,2-naphthoquinone derivatives as PTP1B inhibitors

3D-QSAR, ADME-Tox, and molecular docking of semisynthetic triterpene derivatives as antibacterial and insecticide agents

In the present work, 27 triterpene derivatives have been subjected to 3D-QSAR, ADME-Tox, and molecular docking for their insecticidal activity. The selected derivatives are previously semi-synthesized based on compounds obtained from Euphorbia resinifera and Euphorbia officinarum latex. The in silico studies were used to predict and to evaluate the antibacterial and insecticidal properties of the 3D structure of triterpene derivatives. The 3D-QSAR models are developed using CoMFA and CoMSIA techniques, and they have showed excellent statistical results (R² = 0.99; Q² = 0.672; R²_pred = 0.91 for CoMFA and R² = 0.97; Q² = 0.61; R²_pred = 0.94 for CoMSIA). The results indicate that the built models are able to describe the relationship between the structure of triterpene derivatives and the pLD₅₀ bioactivity. Based on contour maps obtained from CoMFA and CoMSIA models, 38 new molecules are designed and their pLD₅₀ activities are predicted. The drug-like and ADME-Tox properties of the molecule designed are examined and led to the selection of four molecules (55, 56, 59, 64) as promising antibacterial and insecticidal agents. Compounds 55, 56, 59, and 64 are able to inhibit the MurE (PDB code: 1E8C) and EcR (PDB code: 1R20) proteins involved in the process of antibacterial and insecticidal activities. This hypothesis is confirmed by the implementation of a molecular docking test. This test predicted the most important referential interactions that occur between the structure of triterpene derivatives and the targeted receptors. Among the four docked molecules, three molecules (55, 56, and 59) showed greater stability than the reference molecule 16 inside the MurE and EcR receptors pocket. Therefore, the structure of the three new triterpene derivatives can be adopted as reference for the synthesis of antibacterial drugs and also in the development of insecticides.

Multiple machine learning based descriptive and predictive workflow for the identification of potential PTP1B inhibitors

In insulin and leptin signaling pathway, Protein-Tyrosine Phosphatase 1B (PTP1B) plays a crucial controlling role as a negative regulator, which makes it an attractive therapeutic target for both Type-2 Diabetes (T2D) and obesity. In this work, we have generated classification models by using the inhibition data set of known PTP1B inhibitors to identify new inhibitors of PTP1B utilizing multiple machine learning techniques like naïve Bayesian, random forest, support vector machine and k-nearest neighbors, along with structural fingerprints and selected molecular descriptors. Several models from each algorithm have been constructed and optimized, with the different combination of molecular descriptors and structural fingerprints. For the training and test sets, most of the predictive models showed more than 90% of overall prediction accuracies. The best model was obtained with support vector machine approach and has Matthews Correlation Coefficient of 0.82 for the external test set, which was further employed for the virtual screening of Maybridge small compound database. Five compounds were subsequently selected for experimental assay. Out of these two compounds were found to inhibit PTP1B with significant inhibitory activity in in-vitro inhibition assay. The structural fragments which are important for PTP1B inhibition were identified by naïve Bayesian method and can be further exploited to design new molecules around the identified scaffolds. The descriptive and predictive modeling strategy applied in this study is capable of identifying PTP1B inhibitors from the large compound libraries.

QSAR studies in the discovery of novel type-II diabetic therapies

INTRODUCTION

Type-II diabetes mellitus (T2DM) is a complex chronic disease that represents a major therapeutic challenge. Despite extensive efforts in T2DM drug development, therapies remain unsatisfactory. Currently, there are many novel and important antidiabetic drug targets under investigation by many research groups worldwide. One of the main challenges to develop effective orally active hypoglycemic agents is off-target effects. Computational tools have impacted drug discovery at many levels. One of the earliest methods is quantitative structure-activity relationship (QSAR) studies. QSAR strategies help medicinal chemists understand the relationship between hypoglycemic activity and molecular properties. Hence, QSAR may hold promise in guiding the synthesis of specifically designed novel ligands that demonstrate high potency and target selectivity.

AREAS COVERED

This review aims to provide an overview of the QSAR strategies used to model antidiabetic agents. In particular, this review focuses on drug targets that raised recent scientific interest and/or led to successful antidiabetic agents in the market. Special emphasis has been made on studies that led to the identification of novel antidiabetic scaffolds.

EXPERT OPINION

Computer-aided molecular design and discovery techniques like QSAR have a great potential in designing leads against complex diseases such as T2DM. Combined with other in silico techniques, QSAR can provide more useful and rational insights to facilitate the discovery of novel compounds. However, since T2DM is a complex disease that includes several faulty biological targets, multi-target QSAR studies are recommended in the future to achieve efficient antidiabetic therapies.

Identification of small molecule inhibitors of PTPσ through an integrative virtual and biochemical approach

PTPσ is a dual-domain receptor type protein tyrosine phosphatase (PTP) with physiologically important functions which render this enzyme an attractive biological target. Specifically, loss of PTPσ has been shown to elicit a number of cellular phenotypes including enhanced nerve regeneration following spinal cord injury (SCI), chemoresistance in cultured cancer cells, and hyperactive autophagy, a process critical to cell survival and the clearance of pathological aggregates in neurodegenerative diseases. Owing to these functions, modulation of PTPσ may provide therapeutic value in a variety of contexts. Furthermore, a small molecule inhibitor would provide utility in discerning the cellular functions and substrates of PTPσ. To develop such molecules, we combined in silico modeling with in vitro phosphatase assays to identify compounds which effectively inhibit the enzymatic activity of PTPσ. Importantly, we observed that PTPσ inhibition was frequently mediated by oxidative species generated by compounds in solution, and we further optimized screening conditions to eliminate this effect. We identified a compound that inhibits PTPσ with an IC₅₀ of 10 µM in a manner that is primarily oxidation-independent. This compound favorably binds the D1 active site of PTPσ in silico, suggesting it functions as a competitive inhibitor. This compound will serve as a scaffold structure for future studies designed to build selectivity for PTPσ over related PTPs.

CoMFA based de novo design of pyridazine analogs as PTP1B inhibitors

PTP1B plays an important role as a negative regulator in insulin and leptin signaling pathways. Potent and orally active PTP1B inhibitors can act as potential agents for the treatment of Type 2 diabetes and obesity. CoMFA (Comparative Molecular Field Analysis) and de novo ligand design using LeapFrog (LF) studies were performed on pyridazine analogs, reported to be selective and non-competitive inhibitors of PTP1B. A robust model was developed which produced statistically significant results with cross-validated and conventional correlation coefficients of 0.619 and 0.990, respectively. Further, the robustness of the model was verified by bootstrapping analysis. LeapFrog (LF) program is a de novo drug discovery tool, which uses CoMFA maps to generate hypothetical cavity and ligands. As the crystal structure of PTP1B-pyridazine complex is not yet known, the contours of CoMFA model was used to serve as a pharmacophoric model to generate hypothetical cavity for LeapFrog calculations. Ligands were optimized using this concept.

Discovery of new potent human protein tyrosine phosphatase inhibitors via pharmacophore and QSAR analysis followed by in silico screening

A pharmacophoric model was developed for human protein tyrosine phosphatase 1B (h-PTP 1B) inhibitors utilizing the HipHop-REFINE module of CATALYST software. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of physicochemical descriptors and pharmacophore hypothesis that yield consistent QSAR equation of good predictive potential (r = 0.87,F-statistic = 69.13,r(BS)2 = 0.76,r(LOO)2 = 0.68). The validity of the QSAR equation and the associated pharmacophoric hypothesis was experimentally established by the identification of five new h-PTP 1B inhibitors retrieved from the National Cancer Institute (NCI) database.