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Abstract

Existing study involves effort to forecast absorption, distribution, metabolism, excretion, toxicity and polypharmacological profile of 4-((1-(3-nitrophenyl)-5-oxo-3-phenyl-4,5-dihydro-1H-pyrazol-4-yl)methyl)benzoic acid (NPOPPBA), a 3CLpro non-peptidic inhibitors with the aid of by means of in silico methods. In the beginning, PASS online computational software’s utilized to investigate pharmacological action of NPOPPBA. Followed by, Swiss ADME online tool utilized to estimate of physical parameters, chemical properties, log P, solubility, absorption, distribution, metabolism, excretion, drug like property and medicinal chemistry. Lastly, XUNDRUG eMolTox online tool utilized to forecast toxicity. End result of PASS online prediction tool confirmed that NPOPPBA may be used as Fusarinine-C ornithinesterase inhibitor, which may be beneficial in most cancers treatment; Swiss ADME end outcome confirmed molecule may orally absorbable but not able to pass lipophilic membrane of brain and hence will not able to show undesirable effect centrally. Observations of bioavailability study shows NPOPPBA may be taken into consideration as a drug like because it shows all parameters falls inside red location of graph. The log P become observed about 3.7 signifying NPOPPBA may absorb on oral administration, solubility in water was found to be poor demonstrating need of attempts to enhance it in formulation development. This molecule can also additionally inhibits CYP2C19 which performs an essential function in metabolism of drugs live omeprazole, which are utilized to cure of gastrointestinal disorder and need to take precaution in the course of use of proton pump inhibitors. It is also CYP2C9 inhibitor therefore due care need to be taken for drugs undergoing phase I metabolism. XUNDRUG online resource outcomes confirmed hepatic and nephron toxicity possibility of NPOPPBA. Here from this existing analysis, it may be confirmed that the beneficial absorption, distribution, metabolism, excretion, drug like property and easy in synthesis of current molecule recommended that NPOPPBA may be an amazing medicinal agent in upcoming COVID-19 treatment.

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COVID-19 Drug likeness 3CLpro non-peptidic inhibitors.

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Udugade, S., Tare, H., Udugade, B., Wakale, V., & Pulate, C. (2022). in silico Analysis of 4-((1-(3-Nitrophenyl)-5-oxo-3-phenyl-4,5-dihydro-1H-pyrazol-4-yl)methyl)benzoic Acid: An Emerging 3-CLpro Non-peptidic Inhibitors for COVID-19. Asian Journal of Organic & Medicinal Chemistry, 7(1), 137–142. https://doi.org/10.14233/ajomc.2022.AJOMC-P376
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References

  1. D. Tian, Y. Liu, C. Liang, L. Xin, X. Xie, D. Zhang, M. Wan, H. Li, X. Fu, H. Liu and W. Cao, An Update Review of Emerging Small-Molecule Therapeutic Options for COVID-19, Biomed. Pharmacother., 137, 111313 (2021); https://doi.org/10.1016/j.biopha.2021.111313
  2. C.Y. Jia, J.Y. Li, G.F. Hao and G.F. Yang, A Drug-likeness Toolbox Facilitates ADMET Study in Drug Discovery, Drug Discov. Today, 25, 248 (2020); https://doi.org/10.1016/j.drudis.2019.10.014
  3. R. Ramajayam, K.-P. Tan, H.-G. Liu and P.-H. Liang, Synthesis and Evaluation of Pyrazolone Compounds as SARS-Coronavirus 3C-Like Lrotease Inhibitors, Bioorg. Med. Chem., 18, 7849 (2010); https://doi.org/10.1016/j.bmc.2010.09.050
  4. J. Jacobs, V. Grum-Tokars, Y. Zhou, M. Turlington, S.A. Saldanha, P. Chase, A. Eggler, E.S. Dawson, Y.M. Baez-Santos, S. Tomar, A.M. Mielech, S.C. Baker, C.W. Lindsley, P. Hodder, A. Mesecar and S.R. Stauffer, Discovery, Synthesis, And Structure-Based Optimization of a Series of N-(tert-Butyl)-2-(N-arylamido)-2-(pyridin-3-yl) Acetamides (ML188) as Potent Noncovalent Small Molecule Inhibitors of the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) 3CL Protease, J. Med. Chem., 56, 534 (2013); https://doi.org/10.1021/jm301580n
  5. P. Wei, K. Fan, H. Chen, L. Ma, C. Huang, L. Tan, D. Xi, C. Li, Y. Liu, A. Cao and L. Lai, The N-Terminal Octapeptide Acts as a Dimerization Inhibitor of SARS Coronavirus 3C-like Proteinase, Biochem. Biophys. Res. Commun., 339, 865 (2006); https://doi.org/10.1016/j.bbrc.2005.11.102
  6. H.J. Thibaut, A.M. De Palma and J. Neyts, Combating Enterovirus Replication: State-of-the-Art On Antiviral Research, Biochem. Pharmacol., 83, 185 (2012); https://doi.org/10.1016/j.bcp.2011.08.016
  7. M. Turlington, A. Chun, S. Tomar, A. Eggler, V. Grum-Tokars, J. Jacobs, J.S. Daniels, E. Dawson, A. Saldanha, P. Chase, Y.M. Baez-Santos, C.W. Lindsley, P. Hodder, A.D. Mesecar and S.R. Stauffer, Discovery of N-(Benzo[1,2,3]triazol-1-yl)-N-(benzyl)acetamido)phenyl) Carboxamides as Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) 3CLpro Inhibitors: Identification of ML300 and Noncovalent Nanomolar Inhibitors with an Induced-Fit Binding, Bioorg. Med. Chem. Lett., 23, 6172 (2013); https://doi.org/10.1016/j.bmcl.2013.08.112
  8. A. Daina, O. Michielin and V. Zoete, SwissADME: A Free Web Tool to Evaluate Pharmacokinetics, Drug-likeness and Medicinal Chemistry Friendliness of Small Molecules, Sci. Rep., 7, 42717 (2017); https://doi.org/10.1038/srep42717
  9. P. Ertl, B. Rohde and P. Selzer, Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties, J. Med. Chem., 43, 3714 (2000); https://doi.org/10.1021/jm000942e
  10. T. Cheng, Y. Zhao, X. Li, F. Lin, Y. Xu, X. Zhang, Y. Li, R. Wang and L. Lai, Computation of Octanol-Water Partition Coefficients by Guiding an Additive Model with Knowledge, J. Chem. Inf. Model., 47, 2140 (2007); https://doi.org/10.1021/ci700257y
  11. S.A. Wildman and G.M. Crippen, Prediction of Physicochemical Parameters by Atomic Contributions, J. Chem. Inf. Comput. Sci., 39, 868 (1999); https://doi.org/10.1021/ci990307l
  12. I. Moriguchi, S. Hirono, Q. Liu, I. Nakagome and Y. Matsushita, Simple Method of Calculating Octanol/Water Partition Coefficient, Chem. Pharm. Bull. (Tokyo), 40, 127 (1992); https://doi.org/10.1248/cpb.40.127
  13. Silicos-it.be.s3-website-eu-west-1.amazonaws.com, (2019). Silicos-it | Filter-it™. [online] Available at: http://silicos-it.be.s3-website-eu-west- 1.amazonaws.com/software/filter-it/1.0.2/filter- it.html [Accessed 20 Feb. 2022].
  14. J.S. Delaney, ESOL: Estimating Aqueous Solubility Directly from Molecular Structure, J. Chem. Inf. Comput. Sci., 44, 1000 (2004); https://doi.org/10.1021/ci034243x
  15. J. Ali, P. Camilleri, M.B. Brown, A.J. Hutt and S.B. Kirton, In Silico Prediction of Aqueous Solubility Using Simple QSPR Models: The Importance of Phenol and Phenol-like Moieties, J. Chem. Inf. Model., 52, 2950 (2012); https://doi.org/10.1021/ci300447c
  16. A. Daina and V. Zoete, A BOILED-Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules, ChemMedChem, 11, 1117 (2016); https://doi.org/10.1002/cmdc.201600182
  17. T. Eitrich, A. Kless, C. Druska, W. Meyer and J. Grotendorst, Classification of Highly Unbalanced CYP450 Data of Drugs Using Cost Sensitive Machine Learning Techniques, J. Chem. Inf. Model., 47, 92 (2007); https://doi.org/10.1021/ci6002619
  18. R.O. Potts and R.H. Guy, Predicting Skin Permeability, Pharm. Res., 9, 663 (1992); https://doi.org/10.1023/A:1015810312465
  19. C.A. Lipinski, F. Lombardo, B.W. Dominy and P.J. Feeney, Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings, Adv. Drug Deliv. Rev., 23, 3 (1997); https://doi.org/10.1016/S0169-409X(96)00423-1
  20. A.K. Ghose, V.N. Viswanadhan and J.J. Wendoloski, A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases, J. Comb. Chem., 1, 55 (1999); https://doi.org/10.1021/cc9800071
  21. D.F. Veber, S.R. Johnson, H.Y. Cheng, B.R. Smith, K.W. Ward and K.D. Kopple, Molecular Properties that Influence the Oral Bioavailability of Drug Candidates, J. Med. Chem., 45, 2615 (2002); https://doi.org/10.1021/jm020017n
  22. W.J. Egan, K.M. Merz and J.J. Baldwin, Prediction of Drug Absorption Using Multivariate Statistics, J. Med. Chem., 43, 3867 (2000); https://doi.org/10.1021/jm000292e
  23. Y.C. Martin, A Bioavailability Score, J. Med. Chem., 48, 3164 (2005); https://doi.org/10.1021/jm0492002
  24. J.B. Baell and G.A. Holloway, New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays, J. Med. Chem., 53, 2719 (2010); https://doi.org/10.1021/jm901137j
  25. R. Brenk, A. Schipani, D. James, A. Krasowski, I.H. Gilbert, J. Frearson and P.G. Wyatt, Lessons Learnt from Assembling Screening Libraries for Drug Discovery for Neglected Diseases, ChemMedChem, 3, 435 (2008); https://doi.org/10.1002/cmdc.200700139
  26. S.J. Teague, A.M. Davis, P.D. Leeson and T. Oprea, The Design of Leadlike Combinatorial Libraries, Angew. Chem. Int. Ed., 38, 3743 (1999); https://doi.org/10.1002/(SICI)1521-3773(19991216)38:24<3743:: AID-ANIE3743>3.0.CO;2-U
  27. P. Ertl and A. Schuffenhauer, Estimation of Synthetic Accessibility Score of Drug-like Molecules Based on Molecular Complexity and Fragment Contributions, J. Cheminform., 1, 8 (2009); https://doi.org/10.1186/1758-2946-1-8
  28. A. Daina, O. Michielin and V. Zoete, Swiss Target Prediction: Updated Data and New Features for Efficient Prediction of Protein Targets of Small Molecules, Nucleic Acids Res., 47(W1), W357 (2019); https://doi.org/10.1093/nar/gkz382
  29. Xundrug.cn, (2018). eMolTox. [online] Available at: http://xundrug.cn/moltox [Accessed 20 Feb. 2022].
  30. Pharmaexpert.ru, (2018). [online] Available at: http://www.pharmaexpert.ru/passonline/predict.php [Accessed 20 Feb. 2022].