Abstract
Drug discovery (DD) has an unknown history since the origin of mankind with the process of the trial-and-error method. Modern-day DD primarily straddles three main periods, i.e., the nineteenth century, based on DD by chance by the medicinal chemists; the twentieth century, which spans the exploration and reporting of new drug structures; and finally, the twenty-first century, in which all known structures in conjunction with novel techniques, viz., molecular modeling, combinatorial chemistry, and automated high-throughput screening, led to huge advances in DD. In the start, the scientists examine the natural products themselves to find the exact effects. The isolation of active phytochemicals started in the early nineteenth century, while the advancement in chemical and biological sciences has led the modern DD and development. Moreover, recombinant DNA technology revolutionized the development of potential drugs with higher accuracy and precision. In addition, the onset of the “omics” (proteomics, genomics, metabolomics, etc.) era has boosted the increase in biopharmaceutical drugs approved by the FDA/EMEA for clinical uses. Currently, digital and disruptive technologies such as network pharmacology and molecular docking studies are changing the scenario of DD and development by producing more efficient, personalized drugs with little or no harm at all.
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References
Ahn, N. G., & Wang, A. H. (2008). Proteomics and genomics: Perspectives on drug and target discovery. Current Opinion in Chemical Biology, 12(1), 1.
Baker, M. (2013). Fragment-based lead discovery grows up: With multiple drug candidates in the clinic that originated from fragment-based lead discovery, the approach of starting small has become big. Nature Reviews Drug Discovery, 12(1), 5–8.
Barar, F. (2000). Essentials of pharmacotherapeutics. S. Chand Publishing.
Beckmann, N., et al. (2007). In vivo mouse imaging and spectroscopy in drug discovery. NMR in Biomedicine, 20(3), 154–185.
Bernhofer, M., et al. (2016). TMSEG: Novel prediction of transmembrane helices. Proteins: Structure, Function, and Bioinformatics, 84(11), 1706–1716.
Bevan, P., Ryder, H., & Shaw, I. (1995). Identifying small-molecule lead compounds: The screening approach to drug discovery. Trends in Biotechnology, 13(3), 115–121.
Birkholtz, L., et al. (2008). Exploring functional genomics for drug target and therapeutics discovery in plasmodia. Acta Tropica, 105(2), 113–123.
Boike, L., Henning, N. J., & Nomura, D. K. (2022). Advances in covalent drug discovery. Nature Reviews Drug Discovery. https://doi.org/10.1038/s41573-022-00542-z
Bollag, G., et al. (2012). Vemurafenib: The first drug approved for BRAF-mutant cancer. Nature Reviews Drug Discovery, 11(11), 873–886.
Broglia, R., Levy, Y., & Tiana, G. (2008). HIV-1 protease folding and the design of drugs which do not create resistance. Current Opinion in Structural Biology, 18(1), 60–66.
Brooks, B. R., et al. (2009). CHARMM: The biomolecular simulation program. Journal of Computational Chemistry, 30(10), 1545–1614.
Brown, D. K., & Bishop, Ö. T. (2017). The role of structural bioinformatics in drug discovery via computational SNP analysis–a proposed protocol for analyzing variation at the protein level. Global Heart, 12(2), 151.
Bunin, B. A., & Ellman, J. A. (1992). A general and expedient method for the solid-phase synthesis of 1, 4-benzodiazepine derivatives. Journal of the American Chemical Society, 114(27), 10997–10998.
Casali, N., et al. (2014). Evolution and transmission of drug-resistant tuberculosis in a Russian population. Nature Genetics, 46(3), 279–286.
Cavagnaro, J. (2008). Implementation of ICH S6 and the case-by-case approach, in preclinical safety evaluation of biopharmaceuticals: A science-based approach to facilitating clinical trials (pp. 45–65). Wiley.
Cavasotto, C. N., & Phatak, S. S. (2009). Homology modeling in drug discovery: Current trends and applications. Drug Discovery Today, 14(13–14), 676–683.
Chan, W. K., et al. (2015). GLASS: A comprehensive database for experimentally validated GPCR-ligand associations. Bioinformatics, 31(18), 3035–3042.
Chelliah, V., et al. (2004). Distinguishing structural and functional restraints in evolution in order to identify interaction sites. Journal of Molecular Biology, 342(5), 1487–1504.
Chen, X., Jorgenson, E., & Cheung, S. T. (2009). New tools for functional genomic analysis. Drug Discovery Today, 14(15–16), 754–760.
Cheung, G., & Sundram, F. (2017). Understanding the progression from physical illness to suicidal behavior: A case study based on a newly developed conceptual model. Clinical Gerontologist, 40(2), 124–129.
Chin, Y.-W., et al. (2006). Drug discovery from natural sources. The AAPS Journal, 8(2), E239–E253.
Chou, K.-C. (2015). Impacts of bioinformatics to medicinal chemistry. Medicinal Chemistry, 11(3), 218–234.
Congreve, M., Murray, C. W., & Blundell, T. L. (2005). Keynote review: Structural biology and drug discovery. Drug Discovery Today, 10(13), 895–907.
Cragg, G. M., & Newman, D. J. (2005). Biodiversity: A continuing source of novel drug leads. Pure and Applied Chemistry, 77(1), 7–24.
Curatolo, W. (1998). Physical chemical properties of oral drug candidates in the discovery and exploratory development settings. Pharmaceutical Science & Technology Today, 1(9), 387–393.
Dara, S., et al. (2021). Machine learning in drug discovery: A review. Artificial Intelligence Review, 1–53. https://doi.org/10.1007/s10462-021-10058-4
De Cesco, S., et al. (2017). Covalent inhibitors design and discovery. European Journal of Medicinal Chemistry, 138, 96–114.
Di, L., & Kerns, E. (2015). Drug-like properties: concepts, structure design and methods from ADME to toxicity optimization. Academic.
Dias, M. H., et al. (2016). Proteomics and drug discovery in cancer. Drug Discovery Today, 21(2), 264–277.
Ding, H., et al. (2014). Similarity-based machine learning methods for predicting drug–target interactions: A brief review. Briefings in Bioinformatics, 15(5), 734–747.
Drews, J. (2000). Drug discovery: A historical perspective. Science, 287(5460), 1960–1964.
Durrant, J. D., & McCammon, J. A. (2011). Molecular dynamics simulations and drug discovery. BMC Biology, 9(1), 1–9.
Ekins, S., et al. (2015). Machine learning models and pathway genome data base for Trypanosoma cruzi drug discovery. PLoS Neglected Tropical Diseases, 9(6), e0003878.
Emilien, G., et al. (2000). Impact of genomics on drug discovery and clinical medicine. QJM: An International Journal of Medicine, 93(7), 391–423.
Evensen, E., et al. (2007). Ligand design by a combinatorial approach based on modeling and experiment: Application to HLA-DR4. Journal of Computer-Aided Molecular Design, 21(7), 395–418.
Fodor, S. P., et al. (1991). Light-directed, spatially addressable parallel chemical synthesis. Science, 251(4995), 767–773.
Frankel, A., Millward, S. W., & Roberts, R. W. (2003). Encodamers: Unnatural peptide oligomers encoded in RNA. Chemistry & Biology, 10(11), 1043–1050.
Freyer, M. W., & Lewis, E. A. (2008). Isothermal titration calorimetry: Experimental design, data analysis, and probing macromolecule/ligand binding and kinetic interactions. Methods in Cell Biology, 84, 79–113.
Ganesan, A., Coote, M. L., & Barakat, K. (2017). Molecular dynamics-driven drug discovery: Leaping forward with confidence. Drug Discovery Today, 22(2), 249–269.
Garnett, M. J., et al. (2012). Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature, 483(7391), 570–575.
Gershell, L. J., & Atkins, J. H. (2003). A brief history of novel drug discovery technologies. Nature Reviews Drug Discovery, 2(4), 321–327.
Geysen, H. M., Meloen, R. H., & Barteling, S. J. (1984). Use of peptide synthesis to probe viral antigens for epitopes to a resolution of a single amino acid. Proceedings of the National Academy of Sciences, 81(13), 3998–4002.
Gfeller, D., et al. (2014). SwissTargetPrediction: A web server for target prediction of bioactive small molecules. Nucleic Acids Research, 42(W1), W32–W38.
Ghosh, A. K., et al. (2019). Covalent inhibition in drug discovery. ChemMedChem, 14(9), 889–906.
Gilbert, J., Henske, P., & Singh, A. (2003). Rebuilding big pharma’s business model. In Vivo-New York Then Norwalk, 21(10), 73–80.
Goga, A., & Stoffel, M. (2022). Therapeutic RNA-silencing oligonucleotides in metabolic diseases. Nature Reviews Drug Discovery, 21(6), 417–439.
Gohlke, B.-O., et al. (2016). CancerResource—Updated database of cancer-relevant proteins, mutations and interacting drugs. Nucleic Acids Research, 44(D1), D932–D937.
Grosdidier, A., Zoete, V., & Michielin, O. (2011). SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Research, 39(suppl_2), W270–W277.
Guideline, I. H. T. (2006). Impurities in new drug products. Q3B (R2), Current Step, 4, 1–5.
Gupta, P., & Lee, K. H. (2007). Genomics and proteomics in process development: Opportunities and challenges. Trends in Biotechnology, 25(7), 324–330.
Heal, J., et al. (2012). Inhibition of HIV-1 protease: The rigidity perspective. Bioinformatics, 28(3), 350–357.
Hecker, N., et al. (2012). SuperTarget goes quantitative: Update on drug–target interactions. Nucleic Acids Research, 40(D1), D1113–D1117.
Heinis, C., et al. (2009). Phage-encoded combinatorial chemical libraries based on bicyclic peptides. Nature Chemical Biology, 5(7), 502–507.
Holdgate, G., et al. (2013). Biophysical methods in drug discovery from small molecule to pharmaceutical. In Protein-ligand interactions (pp. 327–355). Springer.
Houghten, R. A., et al. (1991). Generation and use of synthetic peptide combinatorial libraries for basic research and drug discovery. Nature, 354(6348), 84–86.
Huber, L. A. (2003). Is proteomics heading in the wrong direction? Nature Reviews Molecular Cell Biology, 4(1), 74–80.
Huber, W., & Mueller, F. (2006). Biomolecular interaction analysis in drug discovery using surface plasmon resonance technology. Current Pharmaceutical Design, 12(31), 3999–4021.
Hudson, T. J., Anderson, W., Aretz, A., Barker, A. D., Grimmond, S. M., Pearson, J. V., Cloonan, N., Gardiner, B. A., Waddell, N. J., Wilson, P. J., & Wainwright, B. J. (2010). International network of cancer genome projects. Nature, 464(7291), 993–998.
Hughes, J. P., et al. (2011). Principles of early drug discovery. British Journal of Pharmacology, 162(6), 1239–1249.
ICH, E. (2011). Impurities: Guidelines for residual solvents Q3C (R5). ICH.
Iskar, M., et al. (2012). Drug discovery in the age of systems biology: The rise of computational approaches for data integration. Current Opinion in Biotechnology, 23(4), 609–616.
Josephson, K., Hartman, M. C., & Szostak, J. W. (2005). Ribosomal synthesis of unnatural peptides. Journal of the American Chemical Society, 127(33), 11727–11735.
Kapetanovic, I. (2008). Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach. Chemico-Biological Interactions, 171(2), 165–176.
Katara, P., et al. (2011). In silico prediction of drug targets in Vibrio cholerae. Protoplasma, 248(4), 799–804.
Katayama, H., & Oda, Y. (2007). Chemical proteomics for drug discovery based on compound-immobilized affinity chromatography. Journal of Chromatography B, 855(1), 21–27.
Kennedy, J. P., et al. (2008). Application of combinatorial chemistry science on modern drug discovery. Journal of Combinatorial Chemistry, 10(3), 345–354.
Kesik-Brodacka, M. (2018). Progress in biopharmaceutical development. Biotechnology and Applied Biochemistry, 65(3), 306–322.
Kim, S. I., et al. (2004). Neuroproteomics: Expression profiling of the brain’s proteomes in health and disease. Neurochemical Research, 29(6), 1317–1331.
Kinnings, S. L., et al. (2010). The mycobacterium tuberculosis drugome and its polypharmacological implications. PLoS Computational Biology, 6(11), e1000976.
Klabunde, T., et al. (1994). The amino acid sequence of the red kidney bean Fe (III)-Zn (II) purple acid phosphatase: Determination of the amino acid sequence by a combination of matrix-assisted laser desorption/ionization mass spectrometry and automated Edman sequencing. European Journal of Biochemistry, 226(2), 369–375.
Kodadek, T. (2011). The rise, fall and reinvention of combinatorial chemistry. Chemical Communications, 47(35), 9757–9763.
Kola, I., & Landis, J. (2004). Can the pharmaceutical industry reduce attrition rates? Nature Reviews Drug Discovery, 3(8), 711–716.
Kopec, K. K., Bozyczko-Coyne, D., & Williams, M. (2005). Target identification and validation in drug discovery: The role of proteomics. Biochemical Pharmacology, 69(8), 1133–1139.
Kumar, A., & Purohit, R. (2014). Use of long term molecular dynamics simulation in predicting cancer associated SNPs. PLoS Computational Biology, 10(4), e1003318.
Kumar, R. D., et al. (2013). Prioritizing potentially druggable mutations with dGene: An annotation tool for cancer genome sequencing data. PLoS One, 8(6), e67980.
Lam, K. S., et al. (1991). A new type of synthetic peptide library for identifying ligand-binding activity. Nature, 354(6348), 82–84.
Li, S., et al. (2016a). A rapid python-based methodology for target-focused combinatorial library design. Combinatorial Chemistry & High Throughput Screening, 19(1), 25–35.
Li, J.-F., et al. (2016b). Sensitive sentinel mutation screening reveals differential underestimation of transmitted HIV drug resistance among demographic groups. AIDS, 30(9), 1439–1445.
Lindahl, E., & Elofsson, A. (2000). Identification of related proteins on family, superfamily and fold level. Journal of Molecular Biology, 295(3), 613–625.
Lipi, F., et al. (2016). In vitro evolution of chemically-modified nucleic acid aptamers: Pros and cons, and comprehensive selection strategies. RNA Biology, 13(12), 1232–1245.
Lipinski, C. A., et al. (2012). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 64, 4–17.
Liu, R., et al. (2017a). Tumor-targeting peptides from combinatorial libraries. Advanced Drug Delivery Reviews, 110, 13–37.
Liu, R., Li, X., & Lam, K. S. (2017b). Combinatorial chemistry in drug discovery. Current Opinion in Chemical Biology, 38, 117–126.
Loh, M., & Soong, R. (2011). Challenges and pitfalls in the introduction of pharmacogenetics for cancer. Annals of the Academy of Medicine, Singapore, 40(8), 369–374.
Lombardino, J. G., & Lowe, J. A. (2004). The role of the medicinal chemist in drug discovery—Then and now. Nature Reviews Drug Discovery, 3(10), 853–862.
Mishra, B. B., & Tiwari, V. K. (2011). Natural products: An evolving role in future drug discovery. European Journal of Medicinal Chemistry, 46(10), 4769–4807.
Murakami, H., et al. (2006). A highly flexible tRNA acylation method for non-natural polypeptide synthesis. Nature Methods, 3(5), 357–359.
Musyoka, T. M., et al. (2016). Structure based docking and molecular dynamic studies of plasmodial cysteine proteases against a South African natural compound and its analogs. Scientific Reports, 6(1), 1–12.
Nicolaou, C. A., & Kannas, C. C. (2011). Molecular library design using multi-objective optimization methods. In Chemical library design (pp. 53–69). Springer.
Niu, B., et al. (2016). Protein-structure-guided discovery of functional mutations across 19 cancer types. Nature Genetics, 48(8), 827–837.
Niwayama, S. (2006). Proteomics in medicinal chemistry. Mini Reviews in Medicinal Chemistry, 6(2), 241–246.
Nutan, P., & Patel, D. (2012). Drug discovery. Journal of Antivirals and Antiretrovirals, 2(4), 63-68. https://doi.org/10.4172/jaa.1000025
O’Hara, P. J., et al. (1993). The ligand-binding domain in metabotropic glutamate receptors is related to bacterial periplasmic binding proteins. Neuron, 11(1), 41–52.
Ohlstein, E. H., Ruffolo, R. R., Jr., & Elliott, J. D. (2000). Drug discovery in the next millennium. Annual Review of Pharmacology and Toxicology, 40(1), 177–191.
Ortega, S. S., Cara, L. C. L., & Salvador, M. K. (2012). In silico pharmacology for a multidisciplinary drug discovery process. Drug Metabolism and Drug Interactions, 27(4), 199–207.
Overington, J., et al. (1990). Tertiary structural constraints on protein evolutionary diversity: Templates, key residues and structure prediction. Proceedings of the Royal Society of London, Series B: Biological Sciences, 241(1301), 132–145.
Overington, J., et al. (1992). Environment-specific amino acid substitution tables: Tertiary templates and prediction of protein folds. Protein Science, 1(2), 216–226.
Overington, J. P., Al-Lazikani, B., & Hopkins, A. L. (2006). How many drug targets are there? Nature Reviews Drug Discovery, 5(12), 993–996.
Payne, D. J., et al. (2007). Drugs for bad bugs: Confronting the challenges of antibacterial discovery. Nature Reviews Drug Discovery, 6(1), 29–40.
Pearl, L. H., & Taylor, W. R. (1987). A structural model for the retroviral proteases. Nature, 329(6137), 351–354.
Phoebe Chen, Y.-P., & Chen, F. (2008). Identifying targets for drug discovery using bioinformatics. Expert Opinion on Therapeutic Targets, 12(4), 383–389.
Pina, A. S., Hussain, A., & Roque, A. C. A. (2010). An historical overview of drug discovery. In Ligand-macromolecular interactions in drug discovery (pp. 3–12). Springer.
Poduri, R. (2021). Historical perspective of drug discovery and development. In Drug discovery and development (pp. 1–10). Springer.
Prakash, N., & Devangi, P. (2010). Drug discovery. Journal of Antivirals and Antiretrovirals, 2(4), 063–068.
Prieto-Martínez, F. D., et al. (2019). Computational drug design methods—Current and future perspectives. In In silico drug design (pp. 19–44). Elsevier.
Pudipeddi, M., et al. (2019). Integrated drug product development: From lead candidate selection to life-cycle management. In Drug discovery and development (pp. 223–261). CRC Press.
Rasheed, A., & Farhat, R. (2013). Combinatorial chemistry: A review. International Journal of Pharmaceutical Sciences and Research, 4(7), 2502.
Ratti, E., & Trist, D. (2001). Continuing evolution of the drug discovery process in the pharmaceutical industry. Pure and Applied Chemistry, 73(1), 67–75.
Rey-Ladino, J., et al. (2011). Natural products and the search for novel vaccine adjuvants. Vaccine, 29(38), 6464–6471.
Rice, D. W., & Eisenberg, D. (1997). A 3D-1D substitution matrix for protein fold recognition that includes predicted secondary structure of the sequence. Journal of Molecular Biology, 267(4), 1026–1038.
Rose, S., & Stevens, A. (2003). Computational design strategies for combinatorial libraries. Current Opinion in Chemical Biology, 7(3), 331–339.
Scapin, G. (2006). Structural biology and drug discovery. Current Pharmaceutical Design, 12(17), 2087–2097.
Schneider, G., & Schüller, A. (2010). Adaptive combinatorial design of focused compound libraries. In Ligand-macromolecular interactions in drug discovery (pp. 135–147). Springer.
Seneci, P., et al. (2014). The effects of combinatorial chemistry and technologies on drug discovery and biotechnology: A mini review. Nova Biotechnologica et Chimica. https://doi.org/10.1515/nbec-2015-0001
Shepherd, C. A., Hopkins, A. L., & Navratilova, I. (2014). Fragment screening by SPR and advanced application to GPCRs. Progress in Biophysics and Molecular Biology, 116(2–3), 113–123.
Sim, S., Kacevska, M., & Ingelman-Sundberg, M. (2013). Pharmacogenomics of drug-metabolizing enzymes: A recent update on clinical implications and endogenous effects. The Pharmacogenomics Journal, 13(1), 1–11.
Singh, J., et al. (2011). The resurgence of covalent drugs. Nature Reviews Drug Discovery, 10(4), 307–317.
Sleno, L., & Emili, A. (2008). Proteomic methods for drug target discovery. Current Opinion in Chemical Biology, 12(1), 46–54.
Smith, G. P. (1985). Filamentous fusion phage: Novel expression vectors that display cloned antigens on the virion surface. Science, 228(4705), 1315–1317.
Southan, C. (2004). Has the yo-yo stopped? An assessment of human protein-coding gene number. Proteomics, 4(6), 1712–1726.
Stefanovich, V. (1980). The role of biochemistry in drug research. Current Medical Research and Opinion, 6(7), 488–499.
Subramanyam, M., et al. (2008). Selection of relevant species. In Preclinical safety evaluation of biopharmaceuticals (pp. 181–205). Wiely.
Sutanto, F., Konstantinidou, M., & Dömling, A. (2020). Covalent inhibitors: A rational approach to drug discovery. RSC Medicinal Chemistry, 11(8), 876–884.
Taboureau, O., et al. (2012). Established and emerging trends in computational drug discovery in the structural genomics era. Chemistry & Biology, 19(1), 29–41.
Thomas, G. (2011). Medicinal chemistry: An introduction. Wiley.
Tuley, A., & Fast, W. (2018). The taxonomy of covalent inhibitors. Biochemistry, 57(24), 3326–3337.
Van Voorhis, W. C., et al. (2009). The role of medical structural genomics in discovering new drugs for infectious diseases. PLoS Computational Biology, 5(10), e1000530.
Veenstra, T. D. (2006). Proteomic approaches in drug discovery. Drug Discovery Today: Technologies, 3(4), 433–440.
Velázquez-Campoy, A., et al. (2004). Isothermal titration calorimetry. Current Protocols in Cell Biology, 23(1), 17.8.1–17.8.24.
Wasinger, V. C., et al. (1995). Progress with gene-product mapping of the Mollicutes: Mycoplasma genitalium. Electrophoresis, 16(1), 1090–1094.
Whittaker, P. A. (2003). What is the relevance of bioinformatics to pharmacology? Trends in Pharmacological Sciences, 24(8), 434–439.
Wirth, M., et al. (2013). SwissBioisostere: A database of molecular replacements for ligand design. Nucleic Acids Research, 41(D1), D1137–D1143.
Wlodawer, A., & Erickson, J. W. (1993). Structure-based inhibitors of HIV-1 protease. Annual Review of Biochemistry, 62(1), 543–585.
Wlodawer, A., & Vondrasek, J. (1998). Inhibitors of HIV-1 protease: A major success of structure-assisted drug design. Annual Review of Biophysics and Biomolecular Structure, 27(1), 249–284.
Yang, J. O., et al. (2011). VnD: A structure-centric database of disease-related SNPs and drugs. Nucleic Acids Research, 39(suppl_1), D939–D944.
Yarbrough, G. G., et al. (1993). Screening microbial metabolites for new drugs-theoretical and practical issues. The Journal of Antibiotics, 46(4), 535–544.
Zhang, M.-Q., & Wilkinson, B. (2007). Drug discovery beyond the ‘rule-of-five’. Current Opinion in Biotechnology, 18(6), 478–488.
Zhang, T., et al. (2019). Recent advances in selective and irreversible covalent ligand development and validation. Cell Chemical Biology, 26(11), 1486–1500.
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We acknowledge the useful suggestions from Dr. Chunpeng (Craig) Wan, Jiangxi Agricultural University, Nanchang, China.
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MN, MD, and MR wrote the draft. MA, SUR, KN, and MFN conceptualized the study and reviewed it. MFN and KN drew the figures and graphical abstract.
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Nisar, M. et al. (2024). Current Trends in the Development and Biochemistry of Drugs. In: Hashmi, M.Z., Saeed, A., Musharraf, S.G., Shuhong, W. (eds) Recent Advances in Industrial Biochemistry. Springer, Cham. https://doi.org/10.1007/978-3-031-50989-6_13
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