Structure-based simulated scanning of rheumatoid arthritis inhibitors: 2D-QSAR, 3D-QSAR, docking, molecular dynamics simulation, and lipophilicity indices calculation

Abstract

Rheumatoid arthritis (RA) is an autoimmune condition in the world, affecting about 1% of the population. It is characterized by a cartilage attack unique to the tissue in the peripheral joints. Different types of drugs are used to treat the disease are aspirin, ibuprofen, naproxen and celecoxib, but their use is less effective due to increased drug resistance and the side effects of these drugs. The need for new anti-rheumatoid arthritis drugs with improved activities and a new mechanism of action along diverse mechanisms is therefore necessary. In this study, 30 compounds from PubChem database with accession number 435,024 were carefully chosen as innovative RA inhibitors to battle autoimmune deficiency and were laid open to quantitative structure-activity relationship (QSAR), comparative molecular field analysis (CoMFA), molecular docking, molecular dynamic simulations, ADMET, Frontier Molecular Orbitals, and golden triangle studies. In the QSAR study, multiple linear regression was used for descriptor selection. The results showed that QSAR (Ntraining = 18; R2 = 0.796; R2adjusted = 0.7523; F = 18.209; Q2 = 0.67999) take good stability and predictability. In the CoMFA fractional factorial design (FFD) and CoMFA smart region definition (SRD) study, based on the Open3DALIGN-based arrangement used, results have shown that the CoMFA (FFD) model with reliable predictive ability was chosen as the final model to perform modeling study. The CoMFA (FFD) model, which shows comparatively good execution, was applied to search the necessary structural areas where the adjustment was essential to design a novel compound with bettered bioactivity. The molecular docking study showed that compound 4 has the least binding energy of -8.0 kcal/mol using PyRx software and compound 17 has the least binding energy of -8.55 kcal/mol using AutoDock4.2 software, respectively. Based on docking results on compounds 4 and 17 have good predictive behavior and relatively good ADMET exposure. MD simulation analysis of 1000 ps shows that one hydrogen bond with the residues was formed by compound 17 (Lys11) and binds distantly to the protein. Compound 4 has a smaller energy gap (-6.5 eV) have great penetrating power, optimized clearance and oral absorption compared to compound 17. These studies offer valuable insights into the discovery and design of RA inhibitors in a modern age.