Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies

© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio

3-Dimensional quantitative structure-activity relationship and molecular docking studies of tetrasubstituted pyrazole derivatives as inhibitors of cyclooxygenase-2

Background:Design and development of new drugs is simplified and made more cost-effective because of the advances in the concepts of Quantitative Structure-Activity Relationship (QSAR) studies. A methodology of QSAR studies is one of the approaches to the rational drug design.Methods:3-Dimensional QSAR studies were performed on a series of tetrasubstituted pyrazole derivatives by using Scigress Explorer software suite. Docking studies of these compounds were also performed to understand the interactions with amino acid residues of COX-2 protein.Results:The multiple linear regression analysis was used to correlate the physicochemical descriptors with the COX-2 inhibitory activity of 24 training set of compounds and the best QSAR model was developed. The best model was validated using leave-one-out method and found to be statistically significant, with coefficient of determination (r2) of 0.835. This model was further used to predict the COX-2 inhibitory activity of 10 test set of compounds. Docking analysis revealed that most of the compounds formed H-bond interactions with amino acid residues of COX-2 protein (PDB ID: 1CX2). Predicted pIC50 value of one of the test compounds was 7.048 and it showed H-bond interactions with His90 & Tyr355 residues.Conclusion:The present study shall help in rational drug design and synthesis of new selective COX-2 inhibitors with predetermined affinity and activity and provides valuable information for the understanding of interactions between COX-2 and the novel tetrasubstituted pyrazole derivative compounds.

How the parts organize in the whole : a top-downview of molecular descriptors and properties for QSARand drug design

Sometimes the complexity of a system, or the properties derived from it, do depend neither on the individual characteristics of the components of the system nor on the nature of the physical forces that hold them together. In such cases the properties derived from the 'organization' of the system given by the connectivity of its elements can be determinant for explaining the structure of such systems. Here we explore the necessity of accounting for these structural characteristics in the molecular descriptors. We show that graph theory is the most appropriate mathematical theory to account for such molecular features. We review a method (TOPS-MODE) that is able to transform simple molecular descriptors, such as logP, polar surface area, molar refraction, charges, etc., into series of descriptors that account for the distribution of these characteristics (hydrophobicity, polarity, steric effects, etc) across the molecule. We explain the mathematical and physical principles of the TOPS-MODE method and develop three examples covering the description and interpretation of skin sensitisation of chemicals, chromosome aberration produced by organic molecules and drug binding to human serum albumin

How Can Network-Pharmacology Contribute to Antiepileptic Drug Development?

Network-pharmacology is a field of pharmacology emerging from the observation that most clinical drugs have multiple targets, contrasting with the previously dominant magic bullet paradigm which proposed the search of exquisitely selective drugs. What is more, drug targets are often involved in multiple diseases and frequently present co-expression patterns. Therefore, useful therapeutic information can be drawn from network representations of drug targets. Here, we discuss potential applications of drug-target networks in the field of antiepileptic drug development.Fil: Di Ianni, Mauricio Emiliano. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Talevi, Alan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentin

QSAR Modeling of COX-2 Inhibitory Activity of Thiazinan, Benzthiazinan and Benzdiazinan Derivatives

COX-2 inhibitory activities of some thiazinan, benzdiazinan and benzthiazinan derivatives were modeled by quantitative structure–activity relationship (QSAR) with stepwise-multiple linear regression (SW-MLR) method. The built model was robust and predictive with correlation coefficient (R2) of 0.840 for training and 0.522 for test groups. The quality of the model was evaluated by leave-one out (LOO) cross validation and LOO correlation coefficient (Q2) was 0.639. We also investigated a leverage approach for defining of applicability domain of model. According to QSAR model results, COX-2 inhibitory activity of selected data set had correlation with VE3_Dzm (Logarithmic coefficient sum of the last eigenvector from Barysz matrix weighted by mass), GATS6c (geometrical structure of the considered molecules in a complex way) and GATS5i (Geary autocorrelation of lag 5 weighted by ionization potential) descriptors which derived from their structures

3D QSAR AND DOCKING STUDY OF INDOLE DERIVATIVES AS SELECTIVE COX-2 INHIBITORS

Objective: Non-steroidal anti-inflammatory agents (NSAIDs) continue to be one of the most widely used groups of therapeutic agents. QSAR (quantitative structure-activity relationship) approach is a very useful and widespread technique for drug design. 3D QSAR facilitates evaluation of three-dimensional molecular fields around molecules and generates a relationship of these fields' values with the activity. Methods: 3D QSAR study was performed on selected twenty-four compounds from synthesized indole derivatives using the stepwise variable selection k-nearest neighbor (kNN) molecular field analysis approach for indicating the contribution of the steric and electronic field for activity. The docking study was performed to further confirm the binding affinity of synthesized molecules (ligands) to COX-2 enzyme as well as to study binding nature. Results: Statistically significant model was generated using VLife Molecular Design Suite 3.5 software with cross-validated correlation coefficient q2 of 0.9461 and high predictive correlation coefficient (Pred_r2) of 0.8782 indicating that the model is robust. The results of docking study suggest that the synthesized compounds have a comparable binding affinity with the COX-2 enzyme. Conclusion: The present study may prove to be helpful in the development and optimization of existing indole derivatives as anti-inflammatory agents with selective COX-2 inhibition