In silico screening of a series of 1,6-disubstituted 1H-pyrazolo[3,4-d]pyrimidines as potential selective inhibitors of the Janus kinase 3.

Rheumatoid arthritis is a common chronic disabling inflammatory disease that is characterized by inflammation of the synovial membrane and leads to discomfort. In the current study, twenty-seven 1,6-disubstituted 1H-pyrazolo[3,4-d]pyrimidines were tested as potential selective inhibitors of the tyrosine-protein kinase JAK3 using a number of molecular modeling methods. The activity of the screened derivatives was statistically quantified using multiple linear regression and artificial neural networks. To assess the quality, robustness, and predictability of the generated models, the leave-one-out cross-validation method was applied with favorable results (Q2 = 0.75) and Y-randomization. In addition, the evaluation of the predictive ability of the established model was confirmed by means of an external validation using a composite test set and an applicability domain approach. The covalent docking indicated that the tested 1H-pyrazolo[3,4-d]pyrimidines containing the acrylic aldehyde moiety had irreversible interaction with the residue Cys909 in the active sites of the tyrosine-protein kinase JAK3 by Michael addition. The molecular dynamics for three selected derivatives (compounds 9, 12, and 18) were used to verify the covalent docking by determining the stability of hydrogen bonding interactions with active sites, which are needed to stop tyrosine-protein kinase JAK3. The results obtained showed that the tested compounds containing acrylic aldehyde moiety had favorable binding free energies, indicating a strong affinity for the JAK3 enzyme. Overall, this current study suggests that the tested compounds containing the acrylic aldehyde moiety have the potential to act as anti-JAK3 inhibitors. They could be explored further to be used as treatment options for rheumatoid arthritis.Communicated by Ramaswamy H. Sarma.

[1]  M. Eslami,et al.  Interleukin-35 and Interleukin-37 anti-inflammatory effect on inflammatory bowel disease: Application of non-coding RNAs in IBD therapy. , 2023, International immunopharmacology.

[2]  Tao Jiang,et al.  A Novel Aldisine Derivative Exhibits Potential Antitumor Effects by Targeting JAK/STAT3 Signaling , 2023, Marine drugs.

[3]  S. Almahmoud,et al.  Docking and Selectivity Studies of Covalently Bound Janus Kinase 3 Inhibitors , 2023, International journal of molecular sciences.

[4]  D. Bandyopadhyay,et al.  Small Molecule EGFR Inhibitors as Anti-Cancer Agents: Discovery, Mechanisms of Action, and Opportunities , 2023, International journal of molecular sciences.

[5]  D. Harkati,et al.  A computational study of potent series of selective estrogen receptor degraders for breast cancer therapy , 2022, Journal of biomolecular structure & dynamics.

[6]  H. Pan,et al.  Exosomes as biomarkers and therapeutic delivery for autoimmune diseases: Opportunities and challenges. , 2022, Autoimmunity reviews.

[7]  J. England,et al.  Fedratinib: a pharmacotherapeutic option for JAK-inhibitor naïve and exposed patients with myelofibrosis , 2022, Expert opinion on pharmacotherapy.

[8]  Yu Zhou,et al.  A highly selective JAK3 inhibitor is developed for treating rheumatoid arthritis by suppressing γc cytokine–related JAK-STAT signal , 2022, Science advances.

[9]  M. Horwitz,et al.  Age-associated B cells in autoimmune diseases , 2022, Cellular and Molecular Life Sciences.

[10]  M. Gehringer,et al.  Never Gonna Give You Up - Current Developments in Covalent Protein Kinase Inhibitors. , 2022, Chimia.

[11]  Hadjer Khelfaoui,et al.  In Silico Pesticide Discovery for New Anti-Tobacco Mosaic Virus Agents: Reactivity, Molecular Docking, and Molecular Dynamics Simulations , 2022, Applied Sciences.

[12]  D. Harkati,et al.  QSAR modeling, docking, ADME and reactivity of indazole derivatives as antagonizes of estrogen receptor alpha (ER-α) positive in breast cancer , 2020 .

[13]  Hadjer Khelfaoui,et al.  Molecular docking, molecular dynamics simulations and reactivity, studies on approved drugs library targeting ACE2 and SARS-CoV-2 binding with ACE2 , 2020, Journal of biomolecular structure & dynamics.

[14]  Chetti Prabhakar,et al.  In-silico strategies for probing chloroquine based inhibitors against SARS-CoV-2 , 2020, Journal of biomolecular structure & dynamics.

[15]  Asrin Bahmani,et al.  Introducing a pyrazolopyrimidine as a multi-tyrosine kinase inhibitor, using multi-QSAR and docking methods , 2020, Molecular Diversity.

[16]  Lei Shu,et al.  Novel 1H-pyrazolo[3,4-d]pyrimidin-6-amino derivatives as potent selective Janus kinase 3 (JAK3) inhibitors. Evaluation of their improved effect for the treatment of rheumatoid arthritis. , 2020, Bioorganic chemistry.

[17]  M. Elhallaoui,et al.  Molecular docking and QSAR studies for modeling the antimalarial activity of hybrids 4-anilinoquinoline-triazines derivatives with the wild-type and mutant receptor pf-DHFR , 2019, Heliyon.

[18]  Hanine Hadni,et al.  QSAR and Molecular docking studies of 4-anilinoquinoline-triazine hybrids as pf-DHFR inhibitors , 2019, Mediterranean Journal of Chemistry.

[19]  Adriano D Andricopulo,et al.  ADMET modeling approaches in drug discovery. , 2019, Drug discovery today.

[20]  J. Smolen,et al.  Diagnosis and Management of Rheumatoid Arthritis: A Review , 2018, JAMA.

[21]  David Ramírez,et al.  Is It Reliable to Take the Molecular Docking Top Scoring Position as the Best Solution without Considering Available Structural Data? , 2018, Molecules.

[22]  Lijuan Chen,et al.  Design, synthesis, and SAR study of highly potent, selective, irreversible covalent JAK3 inhibitors , 2018, Molecular Diversity.

[23]  T. Ohba,et al.  Carbon materials with controlled edge structures , 2017 .

[24]  C. Gabay,et al.  Rheumatoid arthritis: from basic findings and clinical manifestations to future therapies , 2017, Seminars in Immunopathology.

[25]  D. Schwartz,et al.  JAK–STAT Signaling as a Target for Inflammatory and Autoimmune Diseases: Current and Future Prospects , 2017, Drugs.

[26]  Olivier Michielin,et al.  SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules , 2017, Scientific Reports.

[27]  S. Chander,et al.  Rational design, synthesis, anti-HIV-1 RT and antimicrobial activity of novel 3-(6-methoxy-3,4-dihydroquinolin-1(2H)-yl)-1-(piperazin-1-yl)propan-1-one derivatives. , 2016, Bioorganic chemistry.

[28]  David S. Goodsell,et al.  The RCSB PDB “Molecule of the Month”: Inspiring a Molecular View of Biology , 2015, PLoS biology.

[29]  Douglas E. V. Pires,et al.  pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures , 2015, Journal of medicinal chemistry.

[30]  J. O’Shea,et al.  Jakinibs: a new class of kinase inhibitors in cancer and autoimmune disease. , 2012, Current opinion in pharmacology.

[31]  Roberto Todeschini,et al.  Comparison of Different Approaches to Define the Applicability Domain of QSAR Models , 2012, Molecules.

[32]  K. Cousins,et al.  Computer review of ChemDraw Ultra 12.0. , 2011, Journal of the American Chemical Society.

[33]  Abdul-Rahman Allouche,et al.  Gabedit—A graphical user interface for computational chemistry softwares , 2011, J. Comput. Chem..

[34]  Alexander Tropsha,et al.  Best Practices for QSAR Model Development, Validation, and Exploitation , 2010, Molecular informatics.

[35]  Davide Ballabio,et al.  Evaluation of model predictive ability by external validation techniques , 2010 .

[36]  J. Rouvinen,et al.  Molecular dynamics studies on the thermostability of family 11 xylanases. , 2007, Protein engineering, design & selection : PEDS.

[37]  Gerta Rücker,et al.  y-Randomization and Its Variants in QSPR/QSAR , 2007, J. Chem. Inf. Model..

[38]  Paola Gramatica,et al.  Principles of QSAR models validation: internal and external , 2007 .

[39]  Timothy E. Long,et al.  Michael addition reactions in macromolecular design for emerging technologies , 2006 .

[40]  Laxmikant V. Kalé,et al.  Scalable molecular dynamics with NAMD , 2005, J. Comput. Chem..

[41]  Scott D. Kahn,et al.  Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships , 2005, Alternatives to laboratory animals : ATLA.

[42]  Stephen R. Johnson,et al.  Molecular properties that influence the oral bioavailability of drug candidates. , 2002, Journal of medicinal chemistry.

[43]  L. Nilsson,et al.  Structure and Dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K , 2001 .

[44]  P. Kollman,et al.  Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. , 2000, Accounts of chemical research.

[45]  C. Weyand,et al.  Cell-cell interactions in synovitis: Interactions between T cells and B cells in rheumatoid arthritis , 2000, Arthritis Research & Therapy.

[46]  A. Rappé,et al.  Application of a Universal Force Field to Organic Molecules , 1992 .

[47]  T. Johnsson,et al.  A procedure for stepwise regression analysis , 1992 .

[48]  T. A. Andrea,et al.  Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. , 1991, Journal of medicinal chemistry.

[49]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.