Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope


Дәйексөз келтіру

Толық мәтін

Аннотация

:The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of \"force fields\" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.

Авторлар туралы

Smita Singh

Department of Pharmaceutics, SRM Modinagar College of Pharmacy

Email: info@benthamscience.net

Pranjal Singh

Department of Pharmaceutics, SRM Modinagar College of Pharmacy, SRM Institute of Science and Technology

Хат алмасуға жауапты Автор.
Email: info@benthamscience.net

Kapil Sachan

KIET Group of Institutions,, KIET School of Pharmacy,

Email: info@benthamscience.net

Mukesh Kumar

IIMT College of Medical Sciences, IIMT University,

Email: info@benthamscience.net

Poonam Bhardwaj

, NKBR College of Pharmacy and Research Center

Email: info@benthamscience.net

Әдебиет тізімі

  1. Shaker, B.; Ahmad, S.; Lee, J.; Jung, C.; Na, D. In silico methods and tools for drug discovery. Comput. Biol. Med., 2021, 137, 104851. doi: 10.1016/j.compbiomed.2021.104851 PMID: 34520990
  2. Tibbitts, J.; Canter, D.; Graff, R.; Smith, A.; Khawli, L.A. Key factors influencing ADME properties of therapeutic proteins: A need for ADME characterization in drug discovery and development. MAbs, 2016, 8(2), 229-245. doi: 10.1080/19420862.2015.1115937 PMID: 26636901
  3. Grisoni, F.; Huisman, B.J.H.; Button, A.L.; Moret, M.; Atz, K.; Merk, D.; Schneider, G. Combining generative artificial intelligence and on-chip synthesis for de novo drug design. Sci. Adv., 2021, 7(24), eabg3338. doi: 10.1126/sciadv.abg3338 PMID: 34117066
  4. Armbruster, D.A.; Overcash, D.R.; Reyes, J. Clinical chemistry laboratory automation in the 21st century - amat victoria curam (victory loves careful preparation). Clin. Biochem. Rev., 2014, 35(3), 143-153. PMID: 25336760
  5. Szymański, P.; Markowicz, M.; Mikiciuk-Olasik, E. Adaptation of high-throughput screening in drug discovery-toxicological screening tests. Int. J. Mol. Sci., 2011, 13(1), 427-452. doi: 10.3390/ijms13010427 PMID: 22312262
  6. Kapetanovic, I.M. Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach. Chem. Biol. Interact., 2008, 171(2), 165-176. doi: 10.1016/j.cbi.2006.12.006 PMID: 17229415
  7. Baig, M.H.; Ahmad, K.; Rabbani, G.; Danishuddin, M.; Choi, I. Computer aided drug design and its application to the development of potential drugs for neurodegenerative disorders. Curr. Neuropharmacol., 2018, 16(6), 740-748. doi: 10.2174/1570159X15666171016163510 PMID: 29046156
  8. Ching, T.; Himmelstein, D.S.; Beaulieu-Jones, B.K.; Kalinin, A.A.; Do, B.T.; Way, G.P.; Ferrero, E.; Agapow, P.M.; Zietz, M.; Hoffman, M.M.; Xie, W.; Rosen, G.L.; Lengerich, B.J.; Israeli, J.; Lanchantin, J.; Woloszynek, S.; Carpenter, A.E.; Shrikumar, A.; Xu, J.; Cofer, E.M.; Lavender, C.A.; Turaga, S.C.; Alexandari, A.M.; Lu, Z.; Harris, D.J.; DeCaprio, D.; Qi, Y.; Kundaje, A.; Peng, Y.; Wiley, L.K.; Segler, M.H.S.; Boca, S.M.; Swamidass, S.J.; Huang, A.; Gitter, A.; Greene, C.S. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface, 2018, 15(141), 20170387. doi: 10.1098/rsif.2017.0387 PMID: 29618526
  9. Katiyar, C.; Kanjilal, S.; Gupta, A.; Katiyar, S. Drug discovery from plant sources: An integrated approach. Ayu, 2012, 33(1), 10-19. doi: 10.4103/0974-8520.100295 PMID: 23049178
  10. Zhao, L.; Ciallella, H.L.; Aleksunes, L.M.; Zhu, H. Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov. Today, 2020, 25(9), 1624-1638. doi: 10.1016/j.drudis.2020.07.005 PMID: 32663517
  11. Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W., Jr Computational methods in drug discovery. Pharmacol. Rev., 2014, 66(1), 334-395. doi: 10.1124/pr.112.007336 PMID: 24381236
  12. Hughes, J.P.; Rees, S.; Kalindjian, S.B.; Philpott, K.L. Principles of early drug discovery. Br. J. Pharmacol., 2011, 162(6), 1239-1249. doi: 10.1111/j.1476-5381.2010.01127.x PMID: 21091654
  13. Grant, L.L.; Sit, C.S. De novo molecular drug design benchmarking. RSC Medicinal Chemistry, 2021, 12(8), 1273-1280. doi: 10.1039/D1MD00074H PMID: 34458735
  14. Paul, S.M.; Mytelka, D.S.; Dunwiddie, C.T.; Persinger, C.C.; Munos, B.H.; Lindborg, S.R.; Schacht, A.L. How to improve R&D productivity: The pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov., 2010, 9(3), 203-214. doi: 10.1038/nrd3078 PMID: 20168317
  15. Myers, S.; Baker, A. Drug discovery—an operating model for a new era. Nat. Biotechnol., 2001, 19(8), 727-730. doi: 10.1038/90765 PMID: 11479559
  16. Zhu, T.; Cao, S.; Su, P.C.; Patel, R.; Shah, D.; Chokshi, H.B.; Szukala, R.; Johnson, M.E.; Hevener, K.E. Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis. J. Med. Chem., 2013, 56(17), 6560-6572. doi: 10.1021/jm301916b PMID: 23688234
  17. Kennedy, T. Managing the drug discovery/development interface. Drug Discov. Today, 1997, 2(10), 436-444. doi: 10.1016/S1359-6446(97)01099-4
  18. Venkatesh, S.; Lipper, R.A. Role of the development scientist in compound lead selection and optimization. J. Pharm. Sci., 2000, 89(2), 145-154. doi: 10.1002/(SICI)1520-6017(200002)89:23.0.CO;2-6 PMID: 10688744
  19. Noori, H.R.; Spanagel, R. In Silico Pharmacology: Drug Design and Discovery’s Gate to the Future; Springer, 2013.
  20. Jorgensen, W.L. The many roles of computation in drug discovery. Science, 2004, 303(5665), 1813-1818. doi: 10.1126/science.1096361 PMID: 15031495
  21. Na, D. User Guides for Biologists to Learn Computational Methods; Springer, 2020. doi: 10.1007/s12275-020-9723-1
  22. Wadood, A.; Ahmed, N.; Shah, L.; Ahmad, A.; Hassan, H.; Shams, S. In-silico drug design: An approach which revolutionarised the drug discovery process. Drug Des. Devel. Ther., 2013, 1, 3.
  23. Norinder, U.; Bergström, C.A.S. Prediction of ADMET properties. ChemMedChem, 2006, 1(9), 920-937. doi: 10.1002/cmdc.200600155 PMID: 16952133
  24. Shaker, B.; Yu, M.S.; Lee, J.; Lee, Y.; Jung, C.; Na, D. User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation. J. Microbiol., 2020, 58(3), 235-244. doi: 10.1007/s12275-020-9563-z PMID: 32108318
  25. Martin, Y.C.; Kofron, J.L.; Traphagen, L.M. Do structurally similar molecules have similar biological activity? J. Med. Chem., 2002, 45(19), 4350-4358. doi: 10.1021/jm020155c PMID: 12213076
  26. Prada-Gracia, D.; Huerta-Y’epez, S.; Moreno-Vargas, L.M. Application of computational methods for the discovery, design, and optimization of cancer drugs, Bol. M’ed. Hosp. Infan. M’ex., 2016, 73, 411-423.
  27. Hansch, C.; Maloney, P.P.; Fujita, T.; Muir, R.M. Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients. Nature, 1962, 194(4824), 178-180. doi: 10.1038/194178b0
  28. Leo, A.; Hoekman, D. Exploring QSAR; American Chemical Society, 1995.
  29. Xie, L.; Evangelidis, T.; Xie, L.; Bourne, P.E. Drug discovery using chemical systems biology: weak inhibition of multiple kinases may contribute to the anti-cancer effect of nelfinavir. PLOS Comput. Biol., 2011, 7(4), e1002037. doi: 10.1371/journal.pcbi.1002037 PMID: 21552547
  30. Verma, J.; Khedkar, V.; Coutinho, E. 3D-QSAR in drug design--a review. Curr. Top. Med. Chem., 2010, 10(1), 95-115. doi: 10.2174/156802610790232260 PMID: 19929826
  31. Kothandan, G. A review about the importance of protonation of ionizable molecules on the predictability of CoMFA. J. Chosun Nat. Sci., 2011, 4, 99-102.
  32. Kearsley, S.K.; Smith, G.M. An alternative method for the alignment of molecular structures: Maximizing electrostatic and steric overlap. Tetrahedron Comput. Methodol., 1990, 3(6), 615-633. doi: 10.1016/0898-5529(90)90162-2
  33. Madhavan, T. A review of 3D-QSAR in drug design. Int. J. Chosun Univ., 2012, 5(1), 1-5. doi: 10.13160/ricns.2012.5.1.001
  34. Rognan, D. Structure-based approaches to target fishing and ligand profiling. Mol. Inform., 2010, 29(3), 176-187. doi: 10.1002/minf.200900081 PMID: 27462761
  35. Kaya, S.; Tüzün, B.; Kaya, C.; Obot, I.B. Determination of corrosion inhibition effects of amino acids: Quantum chemical and molecular dynamic simulation study. J. Taiwan Inst. Chem. Eng., 2016, 58, 528-535. doi: 10.1016/j.jtice.2015.06.009
  36. Batool, M.; Ahmad, B.; Choi, S. A structure-based drug discovery paradigm. Int. J. Mol. Sci., 2019, 20(11), 2783. doi: 10.3390/ijms20112783 PMID: 31174387
  37. Supuran, C.T. Advances in structure-based drug discovery of carbonic anhydrase inhibitors. Expert Opin. Drug Discov., 2017, 12(1), 61-88. doi: 10.1080/17460441.2017.1253677 PMID: 27783541
  38. Hardy, L.W.; Abraham, D.J.; Safo, M.K. Structure-based drug design, Burger Med; Chem. Drug Discov, 2003, pp. 417-469.
  39. Craig, J.C.; Duncan, I.B.; Hockley, D.; Grief, C.; Roberts, N.A.; Mills, J.S. Antiviral properties of Ro 31-8959, an inhibitor of human immunodeficiency virus (HIV) proteinase. Antiviral Res., 1991, 16(4), 295-305. doi: 10.1016/0166-3542(91)90045-S PMID: 1810306
  40. Kim, E.E.; Baker, C.T.; Dwyer, M.D.; Murcko, M.A.; Rao, B.G.; Tung, R.D.; Navia, M.A. Crystal structure of HIV-1 protease in complex with VX-478, a potent and orally bioavailable inhibitor of the enzyme. J. Am. Chem. Soc., 1995, 117(3), 1181-1182. doi: 10.1021/ja00108a056
  41. McLeod, G.A.; Davies, H.T.O.; Munnoch, N.; Bannister, J.; Macrae, W. Postoperative pain relief using thoracic epidural analgesia: Outstanding success and disappointing failures. Anaesthesia, 2001, 56(1), 75-81. doi: 10.1046/j.1365-2044.2001.01763-7.x PMID: 11167441
  42. Clark, D.E. What has computer-aided molecular design ever done for drug discovery? Expert Opin. Drug Discov., 2006, 1(2), 103-110. doi: 10.1517/17460441.1.2.103 PMID: 23495794
  43. Anderson, A.C. The process of structure-based drug design. Chem. Biol., 2003, 10(9), 787-797. doi: 10.1016/j.chembiol.2003.09.002 PMID: 14522049
  44. Kitchen, D.B.; Decornez, H.; Furr, J.R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discov., 2004, 3(11), 935-949. doi: 10.1038/nrd1549 PMID: 15520816
  45. Combs, A.P. Structure-based drug design of new leads for phosphatase research. IDrugs, 2007, 10(2), 112-115. PMID: 17285463
  46. Coumar, M.S.; Leou, J.S.; Shukla, P.; Wu, J.S.; Dixit, A.K.; Lin, W.H.; Chang, C.Y.; Lien, T.W.; Tan, U.K.; Chen, C.H.; Hsu, J.T.A.; Chao, Y.S.; Wu, S.Y.; Hsieh, H.P. Structure-based drug design of novel Aurora kinase A inhibitors: Structural basis for potency and specificity. J. Med. Chem., 2009, 52(4), 1050-1062. doi: 10.1021/jm801270e PMID: 19140666
  47. Gohlke, H.; Klebe, G. Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors, Angew, 41st ed; Chem. Inter, 2002, pp. 2644-2676.
  48. Halperin, I.; Ma, B.; Wolfson, H.; Nussinov, R. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins, 2002, 47(4), 409-443. doi: 10.1002/prot.10115 PMID: 12001221
  49. Waugh, D.F. Protein-protein interactions. Adv. Protein Chem., 1954, 9, 325-437. doi: 10.1016/S0065-3233(08)60210-7 PMID: 13217921
  50. Karim, M.; Islam, M.N.; Jewel, G.N.A. in silico Identification of Potential Drug Targets by Subtractive Genome Analysis of Enterococcus Faecium DO BioRxiv, 2020.
  51. Hansson, T.; Oostenbrink, C.; van Gunsteren, W. Molecular dynamics simulations. Curr. Opin. Struct. Biol., 2002, 12(2), 190-196. doi: 10.1016/S0959-440X(02)00308-1 PMID: 11959496
  52. McCammon, J.A.; Gelin, B.R.; Karplus, M. Dynamics of folded proteins. Nature, 1977, 267(5612), 585-590. doi: 10.1038/267585a0 PMID: 301613
  53. Grant, B.J.; Lukman, S.; Hocker, H.J.; Sayyah, J.; Brown, J.H.; McCammon, J.A.; Gorfe, A.A. Novel allosteric sites on Ras for lead generation. PLoS One, 2011, 6(10), e25711. doi: 10.1371/journal.pone.0025711 PMID: 22046245
  54. Nair, P.C.; Malde, A.K.; Drinkwater, N.; Mark, A.E. Missing fragments: Detecting cooperative binding in fragment-based drug design. ACS Med. Chem. Lett., 2012, 3(4), 322-326. doi: 10.1021/ml300015u PMID: 24900472
  55. Gardiner, S.J.; Begg, E.J. Pharmacogenetics, drug-metabolizing enzymes, and clinical practice. Pharmacol. Rev., 2006, 58(3), 521-590. doi: 10.1124/pr.58.3.6 PMID: 16968950
  56. Pollastri, M.P. Overview on the rule of five. Curr. Protoc. Pharmacol., 2010, 9.12
  57. Viana, N.A.M.; das Chagas, P.A.F.; Filgueiras, L.A.; de Carvalho, M.O.A.; Cunha, R.L.O.R.; Rodezno, S.V.A.; Maia, F.A.L.M.; de Amorim, C.F.A.; Braz, D.C.; Mendes, A.N. preADMET analysis and clinical aspects of dogs treated with the Organotellurium compound RF07: A possible control for canine visceral leishmaniasis? Environ. Toxicol. Pharmacol., 2020, 80, 103470. doi: 10.1016/j.etap.2020.103470 PMID: 32814174
  58. Tetko, I.V.; Tanchuk, V.Y. Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program. J. Chem. Inf. Comput. Sci., 2002, 42(5), 1136-1145. doi: 10.1021/ci025515j PMID: 12377001
  59. Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 2017, 7(1), 42717. doi: 10.1038/srep42717 PMID: 28256516
  60. Dhanda, S.K.; Singla, D.; Mondal, A.K.; Raghava, G.P.S. DrugMint: A webserver for predicting and designing of drug-like molecules. Biol. Direct, 2013, 8(1), 28. doi: 10.1186/1745-6150-8-28 PMID: 24188205
  61. Schyman, P.; Liu, R.; Desai, V.; Wallqvist, A. vNN web server for ADMET predictions. Front. Pharmacol., 2017, 8, 889. doi: 10.3389/fphar.2017.00889 PMID: 29255418
  62. Liu, R.; Tawa, G.; Wallqvist, A. Locally weighted learning methods for predicting dose-dependent toxicity with application to the human maximum recommended daily dose. Chem. Res. Toxicol., 2012, 25(10), 2216-2226. doi: 10.1021/tx300279f PMID: 22963722
  63. Karim, A.; Mishra, A.; Newton, M.A.H.; Sattar, A. Efficient toxicity prediction via simple features using shallow neural networks and decision trees. ACS Omega, 2019, 4(1), 1874-1888. doi: 10.1021/acsomega.8b03173
  64. Su, J.; Zhang, H. A Fast Decision Tree Learning Algorithm; AAAI, 2006, pp. 500-505.
  65. Yu, M.S.; Lee, J.; Lee, Y.; Na, D. 2-D chemical structure image-based in silico model to predict agonist activity for androgen receptor. BMC Bioinformatics, 2020, 21(S5), 245. doi: 10.1186/s12859-020-03588-1 PMID: 33106158
  66. Li, Y.; Hao, Z.; Lei, H. Survey of convolutional neural network. Jisuanji Yingyong, 2016, 36, 2508-2515.
  67. Shaker, B.; Yu, M.S.; Song, J.S.; Ahn, S.; Ryu, J.Y.; Oh, K.S.; Na, D.; Light, B.B.B. LightBBB: Computational prediction model of blood–brain-barrier penetration based on LightGBM. Bioinformatics, 2021, 37(8), 1135-1139. doi: 10.1093/bioinformatics/btaa918 PMID: 33112379
  68. Ke, G.; Meng, Q.; Finley, T.; Wang, T.; Chen, W.; Ma, W.; Ye, Q.; Liu, T-Y. Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst., 2017, 3146-3154.
  69. Lee, H.M.; Yu, M.S.; Kazmi, S.R.; Oh, S.Y.; Rhee, K.H.; Bae, M.A.; Lee, B.H.; Shin, D.S.; Oh, K.S.; Ceong, H.; Lee, D.; Na, D. Computational determination of hERG-related cardiotoxicity of drug candidates. BMC Bioinformatics, 2019, 20(S10), 250. doi: 10.1186/s12859-019-2814-5 PMID: 31138104
  70. Santos, L.A.; Prandi, I.G.; Ramalho, T.C. Could quantum mechanical properties be reflected on classical molecular dynamics? the case of halogenated organic compounds of biological interest. Front Chem., 2019, 7, 848. doi: 10.3389/fchem.2019.00848 PMID: 31921771
  71. Gagic, Z.; Ruzic, D.; Djokovic, N.; Djikic, T.; Nikolic, K. In silico methods for design of kinase inhibitors as anticancer drugs. Front Chem., 2020, 7, 873. doi: 10.3389/fchem.2019.00873 PMID: 31970149
  72. de Souza Neto, L.R.; Moreira-Filho, J.T.; Neves, B.J.; Maidana, R.L.B.R.; Guimarães, A.C.R.; Furnham, N.; Andrade, C.H.; Silva, F.P., Jr In silico strategies to support fragment-to-lead optimization in drug discovery. Front Chem., 2020, 8, 93. doi: 10.3389/fchem.2020.00093 PMID: 32133344
  73. Maia, E.H.B.; Assis, L.C.; de Oliveira, T.A.; da Silva, A.M.; Taranto, A.G. Structure-based virtual screening: From classical to artificial intelligence. Front Chem., 2020, 8, 343. doi: 10.3389/fchem.2020.00343 PMID: 32411671
  74. Thafar, M.; Raies, A. Comparison study of computational prediction tools for drug-target binding affinities. Front Chem., 2019, 7, 782.
  75. Reddy, R.; Mutyala, R.; Aparoy, P.; Reddanna, P.; Reddy, M. Computer aided drug design approaches to develop cyclooxygenase based novel anti-inflammatory and anti-cancer drugs. Curr. Pharm. Des., 2007, 13(34), 3505-3517. doi: 10.2174/138161207782794275 PMID: 18220787
  76. Cordeiro, M.N.; Speck-Planche, A. Computer-aided drug design, synthesis and evaluation of new anti-cancer drugs. Curr. Top. Med. Chem., 2012, 12(24), 2703-2704. doi: 10.2174/1568026611212240001 PMID: 23368097
  77. Semighini, E.P.; Resende, J.A.; de Andrade, P.; Morais, P.A.B.; Carvalho, I.; Taft, C.A.; Silva, C.H.T.P. Using computer-aided drug design and medicinal chemistry strategies in the fight against diabetes. J. Biomol. Struct. Dyn., 2011, 28(5), 787-796. doi: 10.1080/07391102.2011.10508606 PMID: 21294589
  78. Balamurugan, R.; Stalin, A.; Ignacimuthu, S. Molecular docking of γ-sitosterol with some targets related to diabetes. Eur. J. Med. Chem., 2012, 47(1), 38-43. doi: 10.1016/j.ejmech.2011.10.007 PMID: 22078765
  79. Krohn, A.; Redshaw, S.; Ritchie, J.C.; Graves, B.J.; Hatada, M.H. Novel binding mode of highly potent HIV-proteinase inhibitors incorporating the (R)-hydroxyethylamine isostere. J. Med. Chem., 1991, 34(11), 3340-3342. doi: 10.1021/jm00115a028 PMID: 1956054
  80. Chen, Z.; Li, Y.; Chen, E.; Hall, D.L.; Darke, P.L.; Culberson, C.; Shafer, J.A.; Kuo, L.C. Crystal structure at 1.9-A resolution of human immunodeficiency virus (HIV) II protease complexed with L-735,524, an orally bioavailable inhibitor of the HIV proteases. J. Biol. Chem., 1994, 269(42), 26344-26348. doi: 10.1016/S0021-9258(18)47199-2 PMID: 7929352
  81. Sham, H.L.; Kempf, D.J.; Molla, A.; Marsh, K.C.; Kumar, G.N.; Chen, C.M.; Kati, W.; Stewart, K.; Lal, R.; Hsu, A.; Betebenner, D.; Korneyeva, M.; Vasavanonda, S.; McDonald, E.; Saldivar, A.; Wideburg, N.; Chen, X.; Niu, P.; Park, C.; Jayanti, V.; Grabowski, B.; Granneman, G.R.; Sun, E.; Japour, A.J.; Leonard, J.M.; Plattner, J.J.; Norbeck, D.W. ABT-378, a highly potent inhibitor of the human immunodeficiency virus protease. Antimicrob. Agents Chemother., 1998, 42(12), 3218-3224. doi: 10.1128/AAC.42.12.3218 PMID: 9835517
  82. Doyon, L.; Tremblay, S.; Bourgon, L.; Wardrop, E.; Cordingley, M.G. Selection and characterization of HIV-1 showing reduced susceptibility to the non-peptidic protease inhibitor tipranavir. Antiviral Res., 2005, 68(1), 27-35. doi: 10.1016/j.antiviral.2005.07.003 PMID: 16122817
  83. Njogu, P.M.; Guantai, E.M.; Pavadai, E.; Chibale, K. Computer-aided drug discovery approaches against the tropical infectious diseases malaria, tuberculosis, trypanosomiasis, and leishmaniasis. ACS Infect. Dis., 2016, 2(1), 8-31. doi: 10.1021/acsinfecdis.5b00093 PMID: 27622945
  84. Honegr, J.; Malinak, D.; Dolezal, R.; Soukup, O.; Benkova, M.; Hroch, L.; Benek, O.; Janockova, J.; Kuca, K.; Prymula, R. Rational design of novel TLR4 ligands by in silico screening and their functional and structural characterization in vitro. Eur. J. Med. Chem., 2018, 146, 38-46. doi: 10.1016/j.ejmech.2017.12.074 PMID: 29407964
  85. Duan, H.; Liu, X.; Zhuo, W.; Meng, J.; Gu, J.; Sun, X.; Zuo, K.; Luo, Q.; Luo, Y.; Tang, D.; Shi, H.; Cao, S.; Hu, J. 3D-QSAR and molecular recognition of Klebsiella pneumoniae NDM-1 inhibitors. Mol. Simul., 2019, 45(9), 694-705. doi: 10.1080/08927022.2019.1579327
  86. Annapoorani, A.; Umamageswaran, V.; Parameswari, R.; Pandian, S.K.; Ravi, A.V. Computational discovery of putative quorum sensing inhibitors against LasR and RhlR receptor proteins of Pseudomonas aeruginosa. J. Comput. Aided Mol. Des., 2012, 26(9), 1067-1077. doi: 10.1007/s10822-012-9599-1 PMID: 22986632
  87. Ahmad, S.; Raza, S.; Abbasi, S.W.; Azam, S.S. Identification of natural inhibitors against Acinetobacter baumannii d-alanine-d-alanine ligase enzyme: A multi-spectrum in silico approach. J. Mol. Liq., 2018, 262, 460-475. doi: 10.1016/j.molliq.2018.04.124
  88. Skariyachan, S.; Narayan, N.S.; Aggimath, T.S.; Nagaraj, S.; Reddy, M.S.; Narayanappa, R. Molecular modeling on streptolysin-O of multidrug resistant Streptococcus pyogenes and computer aided screening and in vitro assay for novel herbal inhibitors. Curr. Comput. Aided Drug Des., 2014, 10(1), 59-74.
  89. Xiong, M.; Guo, Z.; Han, B.; Chen, M. Combating multidrug resistance in bacterial infection by targeting functional proteome with natural products. Nat. Prod. Res., 2015, 29(17), 1624-1629. doi: 10.1080/14786419.2014.991926 PMID: 25518752
  90. Ondetti, M.A.; Rubin, B.; Cushman, D.W. Design of specific inhibitors of angiotensin-converting enzyme: new class of orally active antihypertensive agents. Science, 1977, 196(4288), 441-444. doi: 10.1126/science.191908 PMID: 191908
  91. Brimblecombe, R.; Duncan, W.; Durant, G.; Ganellin, C.; Parsons, M.; Black, J. Proceedings: The pharmacology of cimetidine, a new histamine H2-receptor antagonist. Br. J. Pharmacol., 1975, 53, 435.
  92. Baldwin, J.J.; Ponticello, G.S.; Anderson, P.S.; Christy, M.E.; Murcko, M.A.; Randall, W.C.; Schwam, H.; Sugrue, M.F.; Gautheron, P.; Gautheron, P. Thienothiopyran-2-sulfonamides: Novel topically active carbonic anhydrase inhibitors for the treatment of glaucoma. J. Med. Chem., 1989, 32(12), 2510-2513. doi: 10.1021/jm00132a003 PMID: 2585439
  93. Buchdunger, E.; Zimmermann, J.; Mett, H.; Meyer, T.; Müller, M.; Druker, B.J.; Lydon, N.B. Inhibition of the Abl protein-tyrosine kinase in vitro and in vivo by a 2-phenylaminopyrimidine derivative. Cancer Res., 1996, 56(1), 100-104. PMID: 8548747
  94. Li, W.; Escarpe, P.A.; Eisenberg, E.J.; Cundy, K.C.; Sweet, C.; Jakeman, K.J.; Merson, J.; Lew, W.; Williams, M.; Zhang, L.; Kim, C.U.; Bischofberger, N.; Chen, M.S.; Mendel, D.B. Identification of GS 4104 as an orally bioavailable prodrug of the influenza virus neuraminidase inhibitor GS 4071. Antimicrob. Agents Chemother., 1998, 42(3), 647-653. doi: 10.1128/AAC.42.3.647 PMID: 9517946
  95. von Itzstein, M.; Wu, W.Y.; Kok, G.B.; Pegg, M.S.; Dyason, J.C.; Jin, B.; Van Phan, T.; Smythe, M.L.; White, H.F.; Oliver, S.W.; Colman, P.M.; Varghese, J.N.; Ryan, D.M.; Woods, J.M.; Bethell, R.C.; Hotham, V.J.; Cameron, J.M.; Penn, C.R. Rational design of potent sialidase-based inhibitors of influenza virus replication. Nature, 1993, 363(6428), 418-423. doi: 10.1038/363418a0 PMID: 8502295
  96. Wlodawer, A. Rational approach to AIDS drug design through structural biology. Annu. Rev. Med., 2002, 53(1), 595-614. doi: 10.1146/annurev.med.53.052901.131947 PMID: 11818491
  97. Falcoz, C.; Jenkins, J.M.; Bye, C.; Hardman, T.C.; Kenney, K.B.; Studenberg, S.; Fuder, H.; Prince, W.T. Pharmacokinetics of GW433908, a prodrug of amprenavir, in healthy male volunteers. J. Clin. Pharmacol., 2002, 42(8), 887-898. doi: 10.1177/009127002401102803 PMID: 12162471
  98. Pollack, V.A.; Savage, D.M.; Baker, D.A.; Tsaparikos, K.E.; Sloan, D.E.; Moyer, J.D.; Barbacci, E.G.; Pustilnik, L.R.; Smolarek, T.A.; Davis, J.A.; Vaidya, M.P.; Arnold, L.D.; Doty, J.L.; Iwata, K.K.; Morin, M.J. Inhibition of epidermal growth factor receptor-associated tyrosine phosphorylation in human carcinomas with CP-358,774: dynamics of receptor inhibition in situ and antitumor effects in athymic mice. J. Pharmacol. Exp. Ther., 1999, 291(2), 739-748. PMID: 10525095
  99. Heim, M.; Sharifi, M.; Hilger, R.A.; Scheulen, M.E.; Seeber, S.; Strumberg, D. Antitumor effect and potentiation or reduction in cytotoxic drug activity in human colon carcinoma cells by the Raf kinase inhibitor (RKI) BAY 43-9006. Int. J. Clin. Pharmacol. Ther., 2003, 41(12), 616-617. doi: 10.5414/CPP41616 PMID: 14692718
  100. Koh, Y.; Nakata, H.; Maeda, K.; Ogata, H.; Bilcer, G.; Devasamudram, T.; Kincaid, J.F.; Boross, P.; Wang, Y.F.; Tie, Y.; Volarath, P.; Gaddis, L.; Harrison, R.W.; Weber, I.T.; Ghosh, A.K.; Mitsuya, H. Novel bis-tetrahydrofuranylurethane-containing nonpeptidic protease inhibitor (PI) UIC-94017 (TMC114) with potent activity against multi-PI-resistant human immunodeficiency virus in vitro. Antimicrob. Agents Chemother., 2003, 47(10), 3123-3129. doi: 10.1128/AAC.47.10.3123-3129.2003 PMID: 14506019
  101. Xia, W.; Liu, L.H.; Ho, P.; Spector, N.L. Truncated ErbB2 receptor (p95ErbB2) is regulated by heregulin through heterodimer formation with ErbB3 yet remains sensitive to the dual EGFR/ErbB2 kinase inhibitor GW572016. Oncogene, 2004, 23(3), 646-653. doi: 10.1038/sj.onc.1207166 PMID: 14737100
  102. Jarman, M.; Barrie, S.E.; Llera, J.M. The 16,17-double bond is needed for irreversible inhibition of human cytochrome p45017α by abiraterone (17-(3-pyridyl)androsta-5, 16-dien-3β-ol) and related steroidal inhibitors. J. Med. Chem., 1998, 41(27), 5375-5381. doi: 10.1021/jm981017j PMID: 9876107
  103. Rodig, S.J.; Shapiro, G.I. Crizotinib, a small-molecule dual inhibitor of the c-Met and ALK receptor tyrosine kinases. Curr. Opin. Investig. Drugs, 2010, 11(12), 1477-1490. PMID: 21154129
  104. Syed, Y.Y. Ribociclib: First global approval. Drugs, 2017, 77(7), 799-807. doi: 10.1007/s40265-017-0742-0 PMID: 28417244
  105. Gajdosik, Z. Larotrectinib sulfate. Drugs Future, 2017, 42, 275-280.
  106. Al-Salama, Z.T. Apalutamide: A review in non-metastatic castration-resistant prostate cancer. Drugs, 2019, 79(14), 1591-1598. doi: 10.1007/s40265-019-01194-x PMID: 31489589
  107. Bryson, H.M.; Sorkin, E.M. Cladribine. Drugs, 1993, 46(5), 872-894. doi: 10.2165/00003495-199346050-00007 PMID: 7507037
  108. Markham, A. Erdafitinib: First global approval. Drugs, 2019, 79(9), 1017-1021. doi: 10.1007/s40265-019-01142-9 PMID: 31161538
  109. Syed, Y.Y. Zanubrutinib: First approval. Drugs, 2020, 80(1), 91-97. doi: 10.1007/s40265-019-01188-9 PMID: 31429063
  110. Syed, Y.Y. Selinexor: First global approval. Drugs, 2019, 79(13), 1485-1494. doi: 10.1007/s40265-019-01188-9 PMID: 31429063
  111. Robinson, B.S.; Riccardi, K.A.; Gong, Y.; Guo, Q.; Stock, D.A.; Blair, W.S.; Terry, B.J.; Deminie, C.A.; Djang, F.; Colonno, R.J.; Lin, P. BMS-232632, a highly potent human immunodeficiency virus protease inhibitor that can be used in combination with other available antiretroviral agents. Antimicrob. Agents Chemother., 2000, 44(8), 2093-2099. doi: 10.1128/AAC.44.8.2093-2099.2000 PMID: 10898681
  112. Kempf, D.J.; Marsh, K.C.; Denissen, J.F.; McDonald, E.; Vasavanonda, S.; Flentge, C.A.; Green, B.E.; Fino, L.; Park, C.H.; Kong, X.P. ABT-538 is a potent inhibitor of human immunodeficiency virus protease and has high oral bioavailability in humans. Proc. Natl. Acad. Sci. USA, 1995, 92(7), 2484-2488. doi: 10.1073/pnas.92.7.2484 PMID: 7708670

Қосымша файлдар

Қосымша файлдар
Әрекет
1. JATS XML
Жүктеу

© Bentham Science Publishers, 2024