Physicochemical features and structural analysis of xanthine oxidase as a potential therapeutic target to prevent gout

Discovery of putative inhibitors of human Pkd1 enzyme: Molecular docking, dynamics and simulation, QSAR, and MM/GBSA

Polycystic kidney disease is the most prevalent hereditary kidney disease globally and is mainly linked to the overexpression of a gene called PKD1. To date, there is no effective treatment available for polycystic kidney disease, and the practicing treatments only provide symptomatic relief. Discovery of the compounds targeting the PKD1 gene by inhibiting its expression under the disease condition could be crucial for effective drug development. In this study, a molecular docking and molecular dynamic simulation, QSAR, and MM/GBSA-based approaches were used to determine the putative inhibitors of the Pkd1 enzyme from a library of 1379 compounds. Initially, fourteen compounds were selected based on their binding affinities with the Pkd1 enzyme using MOE and AutoDock tools. The selected drugs were further investigated to explore their properties as drug candidates and the stability of their complex formation with the Pkd1 enzyme. Based on the physicochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, and toxicity profiling, two compounds including olsalazine and diosmetin were selected for the downstream analysis as they demonstrated the best drug-likeness properties and highest binding affinity with Pkd1 in the docking experiment. Molecular dynamic simulation using Gromacs further confirmed the stability of olsalazine and diosmetin complexes with Pkd1 and establishing interaction through strong bonding with specific residues of protein. High biological activity and binding free energies of two complexes calculated using 3D QSAR and Schrodinger module, respectively further validated our results. Therefore, the molecular docking and dynamics simulation-based in-silico approach used in this study revealed olsalazine and diosmetin as potential drug candidates to combat polycystic kidney disease by targeting Pkd1 enzyme.

Comparative molecular docking analysis for analyzing the inhibitory effect of Anakinra and Ustekinumab against IL17F

Interleukin 17 F is a member of IL-17 cytokine family with a 50% structural homology to IL-17A and plays a significant role either alone or in combination with IL-17A towards inflammation in Rheumatoid arthritis (RA). A growing number of drugs targeting IL-17 pathway are being tested against population specific disease markers. The major objective of this research was to investigate the anti-inflammatory effect of Anakinra (an IL-1 R1 inhibitor) and Ustekinumab (an IL-12 and IL-23 inhibitor) by targeting IL17F. The three dimensional structures of IL17F was taken from PDB while structures of drugs were taken from PubChem database. Docking was performed using MOE and Schrodinger ligand docking software and binding energies, including s-score using London-dG fitness function and glide score using glide internal energy function, between drug and targets were compared. Furthermore, Protein-Drug complex were subjected to 150 ns Molecular Dynamics (MD) Simulations using Schrodinger's Desmond Module. Docking and MD simulation results suggest anakinra as a more potent IL17F inhibitor and forming a more structurally stable complex.Communicated by Ramaswamy H. Sarma.

Molecular Modeling Guided Drug Designing for the Therapeutic Treatment of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a systemic inflammatory disorder that can cause destructive joint disease, significant disability, and increased mortality. RA is the most frequent of all chronic inflammatory joint diseases, and its prevalence frequency in Pakistan is 1.6 per thousand people. Different cytokines and receptors were involved in the triggering of RA, including interleukin-6 (ILR-6), major histocompatibility complex (MHC) antigen human leukocyte (HLA-DR) receptor, and CD20. Several studies illustrated RA as an inherent immune response and triggered due to the “shared epitope.” Therefore, the involvement of all these receptors (IL-6, HLA-DR, and CD20) leads to the neurological, ocular, respiratory, cardiac, skin, and hematological manifestations that have been considered a potential therapeutic target for drug design. Various herbal, natural, and synthetic source inhibitors of interleukin-6 (IL-6), human leukocyte (HLA-DR), and CD20 were studied and reported previously. Reported inhibitors are compared to elucidate the best inhibitor for clinical trials, leading to the orally active drug. In this study, a computer-aided drug designing approach disclosed the potential inhibitors for all receptors based on their distinct binding affinity. Moreover, drug suitability was carried out using Lipinski’s rule by considering the adsorption, distribution, metabolism, and excretion (ADME) of ligands. Results elucidated “calycosin 7-O-glucoside” and “angeliferulate” as putative ligands for IL-6 and HLA-DR, respectively. However, the pharmacokinetic properties (ADMET) revealed angeliferulate as an effete ligand for the biological system compared to calycosin 7-O-glucoside. Based on docking, drug toxicity profiling or pharmacokinetics, and MD simulation stability, this study highlights orally active therapeutic inhibitors to inhibit the activity of pivotal receptors (IL6, HLA-DR, and CD20) of RA in humans. After clinical trials, the resultant inhibitors could be potential therapeutic agents in the drug development against RA.

Discovery and characterization of dual inhibitors of human Vanin-1 and Vanin-2 enzymes through molecular docking and dynamic simulation-based approach

The vanins are ectoenzymes with pantetheinase activity and are involved in recycling pantothenic acid (vitamin B5) from pantetheine. Elevated levels of vanin have been linked with the development and severity of several diseases, including steatosis, diabetes, skin diseases, cancer, inflammatory diseases etc. Therefore, vanins have previously been used as a potential drug target to combat related diseases. In this study, we used a molecular docking and molecular dynamic simulation-based approach to screen dual inhibitors of hVnn1, and hVnn2 from a library of 120 chemical candidates. Molecular docking of drug candidates with hVnn1, and hVnn2 using GOLD and MOE revealed that the chemical compound "methotrexate (CID: 126941)" has the highest binding affinity against both the target enzymes which was further validated through molecular dynamic simulation. Toxicity profiling of drug candidates evaluated using Lipinski's rule of five and Molsoft tool, and AdmetSar 2.0 confirms the drug suitability of methotrexate, therefore, suggesting its use as a potential therapeutic agent to inhibit the activity of vainin enzyme in related disease conditions.

Generating and screening de novo compounds against given targets using ultrafast deep learning models as core components

Deep learning is an artificial intelligence technique in which models express geometric transformations over multiple levels. This method has shown great promise in various fields, including drug development. The availability of public structure databases prompted the researchers to use generative artificial intelligence models to narrow down their search of the chemical space, a novel approach to chemogenomics and de novo drug development. In this study, we developed a strategy that combined an accelerated LSTM_Chem (long short-term memory for de novo compounds generation), dense fully convolutional neural network (DFCNN), and docking to generate a large number of de novo small molecular chemical compounds for given targets. To demonstrate its efficacy and applicability, six important targets that account for various human disorders were used as test examples. Moreover, using the M protease as a proof-of-concept example, we find that iteratively training with previously selected candidates can significantly increase the chance of obtaining novel compounds with higher and higher predicted binding affinities. In addition, we also check the potential benefit of obtaining reliable final de novo compounds with the help of MD simulation and metadynamics simulation. The generation of de novo compounds and the discovery of binders against various targets proposed here would be a practical and effective approach. Assessing the efficacy of these top de novo compounds with biochemical studies is promising to promote related drug development.

Recent developments in predicting CYP-independent metabolism

As lead optimization efforts have successfully reduced metabolic liabilities due to cytochrome P450 (CYP)-mediated metabolism, there has been an increase in the frequency of involvement of non-CYP enzymes in the metabolism of investigational compounds. Although there have been numerous notable advancements in the characterization of non-CYP enzymes with respect to their localization, reaction mechanisms, species differences and identification of typical substrates, accurate prediction of non-CYP-mediated clearance, with a particular emphasis with the difficulties in accounting for any extrahepatic contributions, remains a challenge. The current manuscript comprehensively summarizes the recent advancements in the prediction of drug metabolism and the in vitro to in vitro extrapolation of clearance for substrates of non-CYP drug metabolizing enzymes.