Approaches for Drug Design and Discovery

Karyn Elizabeth, Eri Amalia

Abstract

Drug discovery in general requires high costs and especially a very long time, which is around 11-16 years. This is because drug development must go through a complete series of research processes to obtain comprehensive data. However, in line with the community's need for the availability of quality drugs, having good efficacy and safety, the development of drug development technology using a computing system is carried out. This is in line with the development of science and collaboration between various disciplines. Approaches that can be used for computational drug discovery include Structure-Based Drug Design and Ligand Based Drug Design which are proven to accelerate and increase the possibility of finding new drugs. This article aims to provide an overview of several approaches to drug discovery development, especially the benefits of computational. The data were collected from 28 primary published journals and 28 supporting literatures. This article discusses the two computational methods, especially from the application aspect which is expected to be useful in the field of drug discovery and development to be more efficient in terms of time and cost. The traditional approach to new drug development takes about 11-16 years but using computational methods can shorten the drug discovery stage to 9-13 years.

 

Keywords: Drug Discovery, Ligand Based CADD, Structure-Based CADD


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Pruss, R.M. Phenotypic Screening Strategies for Neurodegenerative Disease: A Pathway to Discover Novel Drug Candidates and Potential Disease Targets or Mechanism. CNS &Neurological Disorders – Drug Targets. 2010; 9:693-700.

Matthews, H., Hanison, J., Nirmalan, N. “Omics” – Informed Drug and Biomarker Discovery: Opportunities, Challenges, and Future Perspectives. Proteomes. 2016;4:28.

Nofiarny, D. Pengenalan Farmakovigilans: Apa dan Mengapa Diperlukan?. MEDICINUS. 2016;29(1).

Berdigaliyev, N., Aljofan, M. An Overview of Drug Discovery and Development. Future Med. Chem. 2020.

Drews, J. Drug Discovery : A Historical Perspective. Science. 2000;287:1960-1964.

Shaffer, C.L. Defining Neuropharmacokinetic Parameters in CNS Drug Discovery to Determine Cross-Species Pharmacologic Exposure-Response Relationships. Annu. Rep. Med. Chem. 2010;45:55-70.

Lindsay, M.A. Target Discovery. Nature Reviews Drug Discovery. 2003;2:831-838.

Terstappen, G., Schlupen, C., Raggiaschi, R., Gaviraghi, G. Target Deconvolution Strategies in Drug Discovery. Nature Review Drug Discovery. 2007;6(11):891-903.

Rao, V.S., Srinivas, K. Modern Drug Discovery Process : An In Silico Approach. Journal of Bioinformatics and Sequence Analysis. 2011;2(5):89-94.

Lee, J., Bogyo, M. Target Deconvolution Techniques in Modern Phenotypic Profiling. Curr. Opin. Chem. Biol. 2013;17(1):118-26.

Lee, Y.H., Song, G.G. Vascular Endothelial Growth Factor Gene Polymorphisms and Psoriasis Susceptibility : A Meta-Analysis. 2015;14(4):14396-40.

Bleicher, K.H., Bohm, H.J., Muller, K., Alanine, A.I. Hit and Lead Generation : Beyond High-Throughput Screening. Nat. Rev. Drug Discov. 2003;2(5):369-378.

John, G.H., Martyn, N.B., Bristol-Myers, S. High Throughput Screening for Lead Discovery. Wiley Press;2002.

Suresh, K.P., Siddharth, M., Rao, V., Vadlamudi, S.V. Current Approaches in Drug Discovery. Pharma Times. 2006;38(8).

Patidar, A.K., Selvam, G., Jeyakandan, M., Mobiya, A.K., Bagherwal, A., Sanadya, G. et al. Lead Discovery and Lead Optimization : A Useful Strategy in Molecular Modification of Lead Compound in Analog Design. International Journal of Drug Design and Discovery. 2011;2(2):458-463.

Huber, W. A New Strategy for Improved Secondary Screening and Lead Optimization Using High-Resolution SPR Characterization of Compound-Target Interactions. J Mol. Recogn. 2005;18:273-281.

Barile, F.A. Principles of Toxicological Testing. USA: CRC Press;2008.

Faqi, A.S. A Comprehensive Guide to Toxicology in Preclinical Drug Development. Waltham: Elsevier; 2013.

Fitzpatrick, S. The Clinical Trial Protocol. Buckinghamshire: Institute of Clinical Research; 2005.

FDA. New Drug Approval Reports [diunduh 13 Juni 2022]. Tersedia dari: http://www.fda.gov/cder/rdmt/default.htm.

FDA. The FDA and The Drug Development Process : How The FDA Insures That Drugs are Safe and Effective. FDA;2002.

Blaney, J. A Very Short History of Structure-Based Design : How Did We Get Here and Where Do We Need to Go?. J. Comput. Aided Mol. Des. 2012;26:13-14.

Mandal, S., Moudgil, M.N., Mandal, S.K. Rational Drug Design. Eur. J. Pharmacol. 2009;625:90-100.

Urwyler, S. Allosteric Modulation of Family C G-Protein-Coupled Receptors : From Molecular Insights to Therapeutic Perspectives. Pharmacol. Rev. 2011;63:59-126.

Lionta, E., Spyrou, G., Vassilatis, D.K., Cournia, Z. Structure-Based Virtual Screening for Drug Discovery : Principles, Applications and Recent Advances. Curr. Top. Med. Chem. 2014;14:1923-1938.

Fang, Y. Ligand-Receptor Interaction Platforms and Their Applications for Drug Discovery. Expert Opin. Drug Discov. 2012;7:969-988.

Batool, M., Choi, S. Identification of Druggable Genome in Staphylococcus aureus Multidrug Resistant Strain. 2017;270-273.

Kalyaanamoorthy, S., Chen, Y.P. Structure-Based Drug Design to Augment Hit Discovery. Drug Discov. Today. 2011;16:831-839.

Kar, S., Roy, K. How Far Can Virtual Screening Take Us in Drug Discovery?. Expert Opin. Drug Discov. 2013;8(3):245-261.

Gangwal, R.P., Damre, M.V., Das, N.R., Dhoke, G.V., Bhadauriya, A., Varikoti, R.A. et al. Structure Based Virtual Screening to Identify Selective Phosphodiesterase 4B Inhubutors. J. Mol. Graph. Model. 2015;57:89-98.

Scior, T., Bender, A., Tresadern, G., Medina-Franco, J.L., Martinez-Mayorga, K., Langer, T. et al. Recognizing Pitfalls in Virtual Screening : A Critical Review. J. Chem. Inf. Model. 2012;52:867-881.

Jain, A.N., Nicholls, A. Recommendations for Evaluation of Computational Methods. J. Comput. Aided Mol. Des. 2008;22:133-139.

Akdemir, A., Rucktooa, P., Jongejan, A., van Elk, R., Bertrand, S., Sixma, T.K. et al. Acethylcholine Binding Protein (AChBP) as Template for Hierarchical In Silico Screening Procedures to Identify Structurally Novel Ligands for The Nicotinic Receptors. Bioorganic Med. Chem. 2011;19:6107-6119.

Prada-Gracia, D., Huerta-Yepez, S., Moreno-Vargas, L.M. Application of Computational Methods for Anticancer Drug Discovery, Design, and Optimization. Bol. Med. Hosp. Infan. T Mex. 2016; 73:411-423.

Meng, X.Y., Zhang, H.X., Mezei, M., Cui, M. Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery. Curr. Comput. Aided Drug Des. 2011;7:146-157.

Huang, S.Y., Zou, X. Advances and Challenges in Protein-Ligand Docking. Int. J. Mol. Sci. 2010;11:3016-3034.

Sousa, S.F., Fernandes, P.A., Ramos, M.J. Protein-Ligand Docking: Current Status and Future Challenges. Proteins. 2006;65:15-26.

Yang, S.Y. Pharmacophore Modeling and Applications in Drug Discovery: Challenges and Recent Advances. Drug Discov Today. 2010;15:444-450.

Wolber, G., Langer, T. LigandScout: 3-D Pharmacophores Derived From Protein-Bound Ligands and Their Use as Virtual Screening Filters. J Chem Inf Model. 2005;45:160-169.

Lin, J.H. Accommodating Protein Flexibility for Structure-Based Drug Design. Curr. Top. Med. Chem. 2011;11:171-178.

Salsbury, F.R., Jr. Molecular Dynamics Simulations of Protein Dynamics and Their Relevance to Drug Discovery. Curr. Opin. Pharmacol. 2010;10:738-744.

Harvey, M.J., de Fabritiis, G. High-Throughput Molecular Dynamics: The Powerful New Tool for Drug Discovery. Drug Discov. Today. 2012;17:1059-1062.

Alonso, H., Bliznyuk, A.A., Gready, J.E. Combining Docking and Molecular Dynamic Simulations in Drug Design. Med. Res. Rev. 2006;26:531-568.

Durrant, J.D., McCammon, J.A. Molecular Dynamics Simulations and Drug Discovery. BMC Biol. 2011;9.

Hartenfeller, M., Schneider, G. De Novo Drug Design. Methods Mol. Biol. 2011;672:299-323.

Schneider, G., Fechner, U. Computer-Based De Novo Design of Drug-Like Molecules. Nat. Rev. Drug Discov. 2005;4:649-663.

Vinkers, H.M., de Jonge, M.R., Daeyaert, F.F., Heeres, J., Koymans, L.M., van Lenthe, J.H. et al. Synopsis: Synthesize and Optimize System In Silico. J Med Chem. 2003;46:2765-2773.

Tintori, C., Manetti, F., Botta, M. Pharmacophoric Models and 3D QSAR Studies of The Adenosine Receptor Ligands. Curr. Top. Med. Chem. 2010;10:1019-1035.

Vogt, M., Bajorath, J. Predicting The Performance of Fingerprint Similarity Searching. Methods Mol. Biol. 2011;672:159-173.

Langer, T., Hoffmann, R.D. Virtual Screening: An Effective Tool for Lead Structure Discovery? Curr. Pharm. Des. 2001;7:509-527.

Scior, T., Medina-Franco, J.L., Do, Q.T., Martinex-Mayorga, K., Yunes Rojas, J.A., Bernard, P. How to Recognize and Workaround Pitfalls in QSAR Studies: A Critical Review. Curr. Med. Chem. 2009;16:4297-4313.

Mason, J.S., Good, A.C., Martin, E.J. 3-D Pharmacophores in Drug Discovery. Curr. Pharm. Des. 2001;7:567-597.

Patel, Y., Gillet, V.J., Bravi, G., Leach, A. A Comparison of The Pharmacophore Identification Programs: Catalyst, DISCO and GASP. J. Comp. Aided Mol. Des. 2002;16:653-681.

Zhang, S. Computer-Aided Drug Discovery and Development. Methods Mol. Biol. 2011;716:23-38.

Goldman, B.B., Wipke, W.T. Quadratic Shape Descriptors. I. Rapid Superimposition of Dissimilar Molecules Using Geometrically Invariant Surface Descriptors. J. Chem. Inf. Comput. Sci. 2000;40:644-658.

Nikolova, N., Jaworska, J. Approaches to Measure Chemical Similarity. QSAR Comb. Sci. 2003;22:1006-1026.

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