Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
43
elements is crucial for identifying and advancing promising candidates that hold the potential for
effective pharmaceutical development. The interplay of these factors underscores the complexity
and nuanced decision-making required in the pursuit of successful drug discovery. Researchers
must balance these factors to identify promising candidates for further development into effective
pharmaceuticals [77, 97].
2.7 Conclusion
In conclusion, a vibrant and exciting area of pharmaceutical research is the investigation of bioac-
tive small molecules in drug discovery. The enormous variety of tiny molecules present in nature,
along with developments in computational techniques and synthetic chemistry, have completely
changed how we approach medication creation. Researchers have learned a great deal about dis-
ease causes, biological processes, and possible treatment targets by studying bioactive small mol-
ecules. NPs are still a great source of inspiration for drug discovery because of their wide range of
chemical structures and biological activity. Furthermore, the development of high-throughput
screening methods has sped up the process of developing new drugs by facilitating the quick dis-
covery and optimization of lead compounds. The development of multidisciplinary methods such
as computer modeling, medicinal chemistry, and structural biology has accelerated the search for
bioactive small molecules with improved pharmacokinetic, potent, and selective characteristics.
Additionally, the development of target-based screening techniques and fragment-based drug
design has made it easier to identify innovative treatment candidates with previously unheard-of
accuracy.
Drug development has a bright future ahead of it as we work to understand biological systems
and realize the potential of tiny compounds. Through the utilization of bioactive small-molecules’
extensive chemical variety, we can tackle unfulfilled medical requirements, counteract medication
resistance, and enhance patient results. In the quest for new treatments, bioactive small molecules
will surely continue to be essential instruments when working together and utilizing creative
approaches.
References
1 Zhou, S.F. and Zhong, W.Z. (2017). Drug design and discovery: principles and applications.
Molecules (Basel, Switzerland) 22 (2): 279.
2 Bonam, S.R., Sekar, M., Guntuku, G.S. et al. (2021). Role of pharmaceutical sciences in future drug
discovery. Future Drug Discovery 3 (3): FDD64.
3 Romano, J.D. and Tatonetti, N.P. (2019). Informatics and computational methods in natural product
drug discovery: a review and perspectives. Frontiers in Genetics 10: 368.
4 Talevi, A. and Bellera, C.L. (2020). Challenges and opportunities with drug repurposing: finding
strategies to find alternative uses of therapeutics. Expert Opinion on Drug Discovery 15 (4): 397–401.
5 Daina, A., Michielin, O., and Zoete, V. (2017). SwissADME: a free web tool to evaluate
pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific
Reports 7 (1): 42717.
6 Gu, M., Sun, S., You, Q., and Wang, L. (2023). Forward or backward: lessons learned from small
molecule drugs approved by FDA from 2012 to 2022. Molecules (Basel, Switzerland) 28 (24): 7941.
44
7 Li, H., Yang, Y., Hong, W. et al. (2020). Applications of genome editing technology in the targeted
therapy of human diseases: mechanisms, advances and prospects. Signal Transduction and
Targeted Therapy 5 (1): 1.
8 Seyhan, A.A. and Carini, C. (2019). Are innovation and new technologies in precision medicine
paving a new era in patients centric care? Journal of Translational Medicine 17 (1): 114.
9 Stielow, M., Witczyńska, A., Kubryń, N. et al. (2023). The bioavailability of drugs – the current
state of knowledge. Molecules (Basel, Switzerland) 28 (24): 8038.
10 Alqahtani, M.S., Kazi, M., Alsenaidy, M.A., and Ahmad, M.Z. (2021). Advances in oral drug
delivery. Frontiers in Pharmacology 12: 618411.
11 Mitchell, M.J., Billingsley, M.M., Haley, R.M. et al. (2021). Engineering precision nanoparticles for
drug delivery. Nature Reviews Drug Discovery 20 (2): 101–124.
12 Carpio, L.E., Sanz, Y., Gozalbes, R., and Barigye, S.J. (2021). Computational strategies for the
discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review.
Molecular Diversity 25 (3): 1425–1438.
13 Sliwoski, G., Kothiwale, S., Meiler, J., and Lowe, E.W. Jr. (2014). Computational methods in drug
discovery. Pharmacological Reviews 66 (1): 334–395.
14 Adelusi, T.I., Oyedele, A.-Q.K., Boyenle, I.D. et al. (2022). Molecular modeling in drug discovery.
Informatics in Medicine Unlocked 29: 100880.
15 Shah, A.P., Parmar, B.M., Ghodawala, M.A., and Seth, A. (2018). In silico drug discovery of novel
small lead compounds targeting Nipah virus attachment glycoprotein. Journal of Integrated Health
Sciences 6 (2): 60.
16 Sadybekov, A.V. and Katritch, V. (2023). Computational approaches streamlining drug discovery.
Nature 616 (7958): 673–685.
17 Shah, A., Sanghvi, K., Sureja, D., and Seth, A. (2018). In silico drug design and molecular docking
studies of some natural products as tyrosine kinase inhibitors. International Journal of
Pharmaceutical Research 10 (2).
18 Shah, A., Choudhary, A., Jain, M. et al. (2024). Discovery of novel anticancer flavonoids as
potential HDAC2 inhibitors: virtual screening approach based on molecular docking, DFT and
molecular dynamics simulations studies. 3 Biotech 14 (3): 83.
19 Shah, A. and Seth, K.A. (2021). In silico identification of novel flavonoids targeting epidermal
growth factor receptor. Current Drug Discovery Technologies 18 (1): 75–82.
20 Manigrasso, J., Marcia, M., and De Vivo, M. (2021). Computer-aided design of RNA-targeted small
molecules: a growing need in drug discovery. Chem 7 (11): 2965–2988.
21 Raschka, S. and Kaufman, B. (2020). Machine learning and AI-based approaches for bioactive
ligand discovery and GPCR-ligand recognition. Methods (San Diego, Calif) 180: 89–110.
22 Agu, P.C., Afiukwa, C.A., Orji, O.U. et al. (2023). Molecular docking as a tool for the
discovery of molecular targets of nutraceuticals in diseases management. Scientific Reports
13 (1): 13398.
23 Bissantz, C., Kuhn, B., and Stahl, M. (2010). A medicinal chemist’s guide to molecular interactions.
Journal of Medicinal Chemistry 53 (14): 5061–5084.
24 Vázquez, J., López, M., Gibert, E. et al. (2020). Merging ligand-based and structure-based methods
in drug discovery: an overview of combined virtual screening approaches. Molecules (Basel,
Switzerland) 25 (20): 4723.
25 Cereto-Massagué, A., Ojeda, M.J., Valls, C. et al. (2015). Molecular fingerprint similarity search in
virtual screening. Methods (San Diego, Calif) 71: 58–63.
26 Fiscon, G., Conte, F., Farina, L., and Paci, P. (2018). Network-based approaches to explore complex
biological systems towards network medicine. Genes 9 (9): 437.
45
27 Ginsberg, S.D., Sharma, S., Norton, L., and Chiosis, G. (2023). Targeting stressor-induced
dysfunctions in protein–protein interaction networks via epichaperomes. Trends in
Pharmacological Sciences 44 (1): 20–33.
28 Basar, M.A., Hosen, M.F., Kumar Paul, B. et al. (2023). Identification of drug and protein–protein
interaction network among stress and depression: a bioinformatics approach. Informatics in
Medicine Unlocked 37: 101174.
29 Kuzmanov, U. and Emili, A. (2013). Protein–protein interaction networks: probing disease
mechanisms using model systems. Genome Medicine 5 (4): 37.
30 Kenanova, D.N., Visser, E.J., Virta, J.M. et al. (2023). A systematic approach to the discovery of
protein–protein interaction stabilizers. ACS Central Science 9 (5): 937–946.
31 Yuan, H., Ma, Q., Ye, L., and Piao, G. (2016). The traditional medicine and modern medicine from
natural products. Molecules (Basel, Switzerland) 21 (5): 559.
32 Dias, D.A., Urban, S., and Roessner, U. (2012). A historical overview of natural products in drug
discovery. Metabolites 2 (2): 303–336.
33 Zhang, S., Zeng, X., Ren, M. et al. (2017). Novel metabolic and physiological functions of branched
chain amino acids: a review. Journal of Animal Science and Biotechnology 8: 10.
34 Wink, M. (2015). Modes of action of herbal medicines and plant secondary metabolites. Medicines
(Basel, Switzerland) 2 (3): 251–286.
35 Shahidi, F. and Ambigaipalan, P. (2015). Phenolics and polyphenolics in foods, beverages and
spices: antioxidant activity and health effects – a review. Journal of Functional Foods 18: 820–897.
36 Mahadik, N., Bhattacharya, D., Padmanabhan, A. et al. (2022). Targeting steroid hormone
receptors for anti-cancer therapy – a review on small molecules and nanotherapeutic approaches.
Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology 14 (2): e1755.
37 Ibrahim, S.R.M., Mohamed, G.A., Al Haidari, R.A. et al. (2018). Potential anti-malarial agents from
endophytic fungi: a review. Mini Reviews in Medicinal Chemistry 18 (13): 1110–1132.
38 Albuquerque, B.R., Heleno, S.A., Oliveira, M.B.P.P. et al. (2021). Phenolic compounds: current
industrial applications, limitations and future challenges. Food & Function 12 (1): 14–29.
39 Costa, G., Francisco, V., Lopes, M.C. et al. (2012). Intracellular signaling pathways modulated by
phenolic compounds: application for new anti-inflammatory drugs discovery. Current Medicinal
Chemistry 19 (18): 2876–2900.
40 Al-Salihi, S.A.A. and Alberti, F. (2021). Naturally occurring terpenes: a promising class of organic
molecules to address influenza pandemics. Natural Products and Bioprospecting 11 (4): 405–419.
41 Suleria, H.A.R., Gobe, G., Masci, P., and Osborne, S.A. (2016). Marine bioactive compounds and
health promoting perspectives; innovation pathways for drug discovery. Trends in Food Science &
Technology 50: 44–55.
42 Karpiński, T.M. and Adamczak, A. (2018). Anticancer activity of bacterial proteins and peptides.
Pharmaceutics 10 (2): 54.
43 Akram, M., Tahir, I.M., Shah, S.M.A. et al. (2018). Antiviral potential of medicinal plants against
HIV, HSV, influenza, hepatitis, and coxsackievirus: a systematic review. Phytotherapy Research
32 (5): 811–822.
44 Choudhari, A.S., Mandave, P.C., Deshpande, M. et al. (2019). Phytochemicals in cancer treatment:
from preclinical studies to clinical practice. Frontiers in Pharmacology 10: 1614.
45 Asma, S.T., Acaroz, U., Imre, K. et al. (2022). Natural products/bioactive compounds as a source of
anticancer drugs. Cancers 14 (24): 6203.
46 Dhyani, P., Quispe, C., Sharma, E. et al. (2022). Anticancer potential of alkaloids: a key emphasis
to colchicine, vinblastine, vincristine, vindesine, vinorelbine and vincamine. Cancer Cell
International 22 (1): 206.
46
47 Gao, Y., Shang, Q., Li, W. et al. (2020). Antibiotics for cancer treatment: a double-edged sword.
Journal of Cancer 11 (17): 5135–5149.
48 Razonable, R.R. (2011). Antiviral drugs for viruses other than human immunodeficiency virus.
Mayo Clinic Proceedings 86 (10): 1009–1026.
49 De Clercq, E. (2004). Antiviral drugs in current clinical use. Journal of Clinical Virology 30 (2):
115–133.
50 Suwannarach, N., Kumla, J., Sujarit, K. et al. (2020). Natural bioactive compounds from fungi as
potential candidates for protease inhibitors and immunomodulators to apply for coronaviruses.
Molecules (Basel, Switzerland) 25 (8): 1800.
51 Shibeshi, M.A., Kifle, Z.D., and Atnafie, S.A. (2020). Antimalarial drug resistance and novel targets
for antimalarial drug discovery. Infection and Drug Resistance 13: 4047–4060.
52 Cui, L., Mharakurwa, S., Ndiaye, D. et al. (2015). Antimalarial drug resistance: literature review
and activities and findings of the ICEMR network. The American Journal of Tropical Medicine and
Hygiene 93 (3 Suppl): 57–68.
53 Azmi, W.A., Rizki, A.F.M., Djuardi, Y. et al. (2023). Molecular insights into artemisinin resistance
in Plasmodium falciparum: an updated review. Infection, Genetics and Evolution 112: 105460.
54 Shah, A.P., Parmar, G.R., Sailor, G.U., and Seth, A.K. (2019). Antimalarial phytochemicals
identification from Euphorbia hirta against Plasmepsin protease: an in silico approach. Folia
Medica 61 (4): 584–593.
55 Parmar, G., Chudasama, M.J., Shah, V. et al. (2023). A mini-review on marine bioactive compounds
as a source of antiviral drug lead. Current Traditional Medicine 9 (5): 116–119.
56 Shah, A.P., Doshi, K., Fatepurwala, H. et al. (2023). Naturally occurring diterpenes as anti-
hypertensive agents: a review. Multidisciplinary Approaches in Pharmaceutical Sciences 201–213.
57 Amdal, C.D., Falk, R.S., Singer, S. et al. (2023). A multicenter international prospective study of the
validity and reliability of a COVID-19-specific health-related quality of life questionnaire. Quality
of Life Research 32 (2): 447–459.
58 Lin, X., Li, X., and Lin, X. (2020). A review on applications of computational methods in drug
screening and design. Molecules (Basel, Switzerland) 25 (6): 1375.
59 Shah, A., Parmar, G., Shah, U., and Perumal, S. (2023). Virtual screening, molecular docking
studies and DFT calculations of novel anticancer flavonoids as potential VEGFR-2 inhibitors.
Chemistry Africa 6 (4): 1847–1861.
60 De Souza, L.A., Nogueira, C.A.S., Lopes, J.F. et al. (2013). DFT study of cisplatin@carbon
nanohorns complexes. Journal of Inorganic Biochemistry 129: 71–83.
61 Abkari, A., Chaabane, I., and Guidara, K. (2016). DFT (B3LYP/LanL2DZ and B3LYP/6311G+(d,p))
comparative vibrational spectroscopic analysis of organic–inorganic compound bis(4-
acetylanilinium) tetrachlorocuprate(II). Physica E: Low-Dimensional Systems and Nanostructures
81: 136–144.
62 El-Demerdash, S.H., Halim, S.A., El-Nahas, A.M., and El-Meligy, A.B. (2023). A density functional
theory study of the molecular structure, reactivity, and spectroscopic properties of 2-(2-
mercaptophenyl)-1-azaazulene tautomers and rotamers. Scientific Reports 13 (1): 15626.
63 Mumit, M.A., Pal, T.K., Alam, M.A. et al. (2020). DFT studies on vibrational and electronic spectra,
HOMO–LUMO, MEP, HOMA, NBO and molecular docking analysis of benzyl-3-N-(2,4,5-
trimethoxyphenylmethylene)hydrazinecarbodithioate. Journal of Molecular Structure
1220: 128715.
64 Vidhya, V., Austine, A., and Arivazhagan, M. (2019). Quantum chemical determination of
molecular geometries and spectral investigation of 4-ethoxy-2, 3-difluoro benzamide. Heliyon
5 (11): e02365.
47
65 Pandey, J., Prajapati, P., Srivastava, A. et al. (2018). Spectroscopic and molecular structure
(monomeric and dimeric model) investigation of Febuxostat: a combined experimental and
theoretical study. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 203: 1–12.
66 Renj, D.P.L., Geetha, R.R.J., Seles, R.F. et al. (2024). Density functional theory calculations,
vibrational spectral analysis and topological analysis of 1-acethyl-2(4-isopropoxy-3-
methoxyphenyl) cyclopropane with docking studies. Chemical Physics Impact 100524.
67 Frenking, G. and Froehlich, N. (2000). The nature of the bonding in transition–metal compounds.
Chemical Reviews 100 (2): 717–774.
68 Ayisha Begam, K., Kanagathara, N., Marchewka, M.K., and Lo, A.Y. (2022). DFT, Hirshfeld and
molecular docking studies of a hybrid compound – 2,4-diamino-6-methyl-1,3,5-triazin-1-ium
hydrogen oxalate as a promising anti-breast cancer agent. Heliyon 8 (8): e10355.
69 Hirschi, J.S., Bashirova, D., and Zuehlsdorff, T.J. (2023). Opening the density functional theory
black box: a collection of pedagogic Jupyter notebooks. Journal of Chemical Education 100 (11):
4496–4503.
70 Haunschild, R., Barth, A., and French, B. (2019). A comprehensive analysis of the history of DFT
based on the bibliometric method RPYS. Journal of Cheminformatics 11 (1): 72.
71 Schenone, M., Dančík, V., Wagner, B.K., and Clemons, P.A. (2013). Target identification and
mechanism of action in chemical biology and drug discovery. Nature Chemical Biology 9 (4): 232–240.
72 Zhao, Z., Ukidve, A., Kim, J., and Mitragotri, S. (2020). Targeting strategies for tissue-specific drug
delivery. Cell 181 (1): 151–167.
73 Batool, M., Ahmad, B., and Choi, S. (2019). A structure-based drug discovery paradigm.
International Journal of Molecular Sciences 20 (11): 2783.
74 Adeluwa, T., McGregor, B.A., Guo, K., and Hur, J. (2021). Predicting drug-induced liver injury
using machine learning on a diverse set of predictors. Frontiers in Pharmacology 12: 648805.
75 Vora, L.K., Gholap, A.D., Jetha, K. et al. (2023). Artificial intelligence in pharmaceutical
technology and drug delivery design. Pharmaceutics 15 (7): 1916.
76 Bender, A., Fergus, S., Galloway, W.R. et al. (2006). Diversity oriented synthesis: a challenge for
synthetic chemists. Ernst Schering Research Foundation Workshop 58: 47–60.
77 Saha, C.N. and Bhattacharya, S. (2011). Intellectual property rights: an overview and implications
in pharmaceutical industry. Journal of Advanced Pharmaceutical Technology & Research
2 (2): 88–93.
78 Anderson, A.C. (2003). The process of structure-based drug design. Chemistry & Biology 10 (9): 787–797.
79 Salo-Ahen, O.M.H., Alanko, I., Bhadane, R. et al. (2020). Molecular dynamics simulations in drug
discovery and pharmaceutical development. Processes 9 (1): 71.
80 Agamah, F.E., Mazandu, G.K., Hassan, R. et al. (2020). Computational/in silico methods in drug
target and lead prediction. Briefings in Bioinformatics 21 (5): 1663–1675.
81 Bashir, S., Fitaihi, R., and Abdelhakim, H.E. (2023). Advances in formulation and manufacturing
strategies for the delivery of therapeutic proteins and peptides in orally disintegrating dosage
forms. European Journal of Pharmaceutical Sciences 182: 106374.
82 Barman, P., Joshi, S., Sharma, S. et al. (2023). Strategic approaches to improvise peptide drugs as
next generation therapeutics. International Journal of Peptide Research and Therapeutics 29 (4): 61.
83 Zhang, Z. and Tang, W. (2018). Drug metabolism in drug discovery and development. Acta
Pharmaceutica Sinica B 8 (5): 721–732.
84 Schneider, G. (2013). Prediction of drug-like properties. In: Madame Curie Bioscience Database
[Internet]. Landes Bioscience.
85 Bickerton, G.R., Paolini, G.V., Besnard, J. et al. (2012). Quantifying the chemical beauty of drugs.
Nature Chemistry 4 (2): 90–98.
48
86 Hughes, J.P., Rees, S., Kalindjian, S.B., and Philpott, K.L. (2011). Principles of early drug discovery.
British Journal of Pharmacology 162 (6): 1239–1249.
87 Taniguchi, C.M., Armstrong, S.R., Green, L.C. et al. (2008). Drug toxicity. In: Principles of
Pharmacology: The Pathophysiologic Basis of Drug Therapy, 63–69. Lippincott Williams & Wilkins.
88 Pognan, F., Beilmann, M., Boonen, H.C.M. et al. (2023). The evolving role of investigative
toxicology in the pharmaceutical industry. Nature Reviews Drug Discovery 22 (4): 317–335.
89 Baell, J.B. and Holloway, G.A. (2010). New substructure filters for removal of pan assay
interference compounds (PAINS) from screening libraries and for their exclusion in bioassays.
Journal of Medicinal Chemistry 53 (7): 2719–2740.
90 Baell, J.B. and Nissink, J.W.M. (2018). Seven year itch: pan-assay interference compounds (PAINS)
in 2017 – utility and limitations. ACS Chemical Biology 13 (1): 36–44.
91 Yan, X., Liu, X., Zhao, C., and Chen, G.-Q. (2023). Applications of synthetic biology in medical and
pharmaceutical fields. Signal Transduction and Targeted Therapy 8 (1): 199.
92 Patra, J.K., Das, G., Fraceto, L.F. et al. (2018). Nano based drug delivery systems: recent
developments and future prospects. Journal of Nanobiotechnology 16 (1): 71.
93 Galloway, W.R., Isidro-Llobet, A., and Spring, D.R. (2010). Diversity-oriented synthesis as a tool for
the discovery of novel biologically active small molecules. Nature Communications 1: 80.
94 Pavlinov, I., Gerlach, E.M., and Aldrich, L.N. (2019). Next generation diversity-oriented synthesis:
a paradigm shift from chemical diversity to biological diversity. Organic & Biomolecular Chemistry
17 (7): 1608–1623.
95 Atanasov, A.G., Zotchev, S.B., Dirsch, V.M. et al. (2021). Natural products in drug discovery:
advances and opportunities. Nature Reviews Drug Discovery 20 (3): 200–216.
96 Giugni, D. and Giugni, V. (2010). Intellectual property: a powerful tool to develop biotech research.
Microbial Biotechnology 3 (5): 493–506.
97 Paul, D., Sanap, G., Shenoy, S. et al. (2021). Artificial intelligence in drug discovery and
development. Drug Discovery Today 26 (1): 80–93.
49
3.1 Introduction
A drug target can be defined as a molecule, usually a protein that is associated with a specific
disease process in the body and can be addressed by drugs to produce therapeutic effects. The
drug target must exhibit the following properties: participate in the main pathway of the biologi-
cal system; have functional features and characteristics; and have efficacy (its ability to bind to
small molecules means there is a site for binding). Traditionally, analytical methods have been
used to find effective drug targets, leading to the concept of “druggability.” Drug targets are gener-
ally defined as proteins with protein structures that facilitate interaction with drugs. Many pro-
teins can be drugged based on their structure, but their combination does not provide medical
assistance. Over the past two decades, many efforts have been made to analyze and classify drug
targets. The most common proteins include proteases, kinases, G protein-coupled receptors
(GPCRs), and nuclear hormone receptors. Druggability is not a necessary characteristic that
defines a “good” drug. In fact, Scientists often choose initial drug candidates based on their
involvement in biological processes important for disease. Groups such as enzymes, substrates,
metabolites, proteins, receptors, ion channels, transporters, DNA, RNA, and ribosomes are targets
of monoclonal antibodies. Although proteins have historically formed the basis of the most effec-
tive drugs, there are now many emerging drug targets such as nucleic acids, control DNA content,
and noncoding RNAs (ncRNAs). Its importance in medicine and precision medicine is rapidly
increasing. In fact, drugs that target nucleic acids already exist, especially in the fields of antibac-
terial and anticancer therapy. RNA is now recognized as an important factor in many regulatory
processes, just like proteins. In fact, RNA plays an important role in transcriptional regulation,
translation regulation, catalysis, protein function, protein transport, peptide bonds, and RNA
splicing. Compared to DNA, RNA may provide better therapeutic results because RNA exhibits
higher diversity patterns and fewer repairs. Like proteins, RNA has three-dimensional folds that
form complex structures that enable specific binding of drug molecules. RNA targeting has been
used successfully in the field of antibiotics. Additionally, with the emergence of new RNA classes
and properties in animal control systems, their applications are rapidly expanding. Among these
new drug targets, ncRNA is attracting increasing attention. ncRNA refers to a broad class of
3
Novel Drug Targets for Small Molecule-based Drug Discovery
Raghu Ram Achar
1,2
, Ipsita Panigrahi
1,2
, Aditi Singh
1,2
, N. Chandana
1,2
,
and Shivananju Nanjunda Swamy
2
1
Division of Biochemistry, School of Life Sciences, JSS Academy of Higher Education & Research, Mysuru, Karnataka, India
2
Department of Biotechnology, JSS Science and Technology University, Mysuru, Karnataka, India
3 Novel Drug Targets for Small Molecule-based Drug Discovery50
endogenous RNA molecules that cannot code for proteins but have specific biological functions.
Although ncRNA cannot code for proteins, they can influence the expression of other genes
through various mechanisms. In some cases, the methods of operation are well known and the
strategies for managing these activities are correct. The ability of ncRNAs to regulate gene expres-
sion makes them a target for drug development. But uncertainty about how (or whether) ncRNAs
work makes the drug development process even more difficult. Targeting RNA has the opportu-
nity to modulate many cellular processes, including targeting of “weak” proteins. Examples
include proteins with many important functions that are difficult to block using a single molecule
or proteins that resemble other proteins, making them difficult to adequately select. Drug targets
are important substances that affect biological pathways specific to a disease or an organism. The
difference between drug targets and other biomolecules involved in the same pathway is only
their location and action. The target of the putative drug should be disease related, meaning it
should have minimal effects on other diseases. However, human diseases are often complex and
involve many interacting pathways, which can hinder the identification of molecular targets.
However, new drug targets are biomolecules whose roles are not fully understood and for which
established drugs do not exist. These targets deserve more attention because they could lead to
new treatments. The focus of preclinical drug discovery is often on the delivery of biomolecules
(e.g., DNA, RNA, proteins, and peptides), including known and novel targets. Candidate drug
targets should be characterized by a balance of efficacy and safety (“unsafe drugs”). Efficacy
should focus on how well the biomolecule works for the drug target. The “druggability” target
should be taken into account in the evaluation [1]. Identifying drug–target interactions (DTIs) is
an important step in the development of new drugs and understanding their side effects. Two
experimental methods are commonly used to identify DTIs: affinity chromatography and protein
microarrays. Due to the increasing number of synthetic antibodies produced against many pro-
teins and biological processes, identification of DTIs using biological assays is time consuming
and expensive, and very few true DTIs have been discovered through such a process. Therefore, in
recent years, researchers have tried to use methods to analyze DTI. There are many ways that
drugs interact with receptors; different relationships lead to different biochemical reactions. For
example, agonists generally have higher activity and can cause biological effects, while antago-
nists have lower activity in the body and then do not bind to receptors and cause biological effects,
but they can block the binding of agonists The type of relationship between the drug and the
protein is important in determining the drugs [2]. The term “drug” used here refers only to medi-
cal devices and includes all small molecules and biologics currently (or previously approved)
approved by the US FDA (prior to June 2015). Health and antimalarial drugs are approved else-
where in the world. The word “prescription” does not include prescription drugs, nutritional sup-
plements, sunscreens, or vaccines. James Black’s famous quote from 2000: “The best way to find
a new drug is to start with an old drug.” If possible, a deeper understanding of the effects of vac-
cines will continue to inform drug discovery, clinical trials, and efforts to address drug resistance.
Therefore, maintaining accurate and up-to-date information about proposed drugs and their
effective targets (e.g., prevention targets at which the drug exerts its therapeutic effect) is impor-
tant to guide future drug development and innovation. Possibly, the first attempt to compile the
list dates back to 1996, when Drews and Ryser estimated the number of human targets for approval
of small drugs. From this paper and subsequent reviews, the concept of “essential” protein fami-
lies with a good history and value in drug discovery began to emerge. Many repositories now
provide information on drug use, and each has a different focus. The first is Treatment Target
Database Drug Bank, which is the most advanced drug database that uses data to match drugs to
the proteins they are reported to bind to; Super Target, on the other hand, is a text mining-based
collection of direct and indirect targets of drugs. Recently, RaskAndersen and colleagues have
3.2 Drug Target Identification 51
provided a new perspective on the current drug and the human target thought to be responsible
for its results. In addition, Munos outlines the main trends in drug class and target innovation
over the past decade and examines the overlap and specificity of the types of drug plans pursued.
However, although many useful drugs are available online, it is still a challenge to be consistent
and visionary about the target of approved drugs (including small molecules and biologics) and
their associated molecular performance-intended purpose through medicine (human and patho-
genic). In addition, although the concept of purpose is a natural concept for researchers working
in the field, there are significant problems in practice in ongoing studies on the concept of pur-
pose for seeds and products [3]. Target identification is an important step in new drug discovery
and development as it allows researchers to understand the mechanism of drug action. Therefore,
much of the progress made in drug discovery and development over the past few hundred years
can be attributed to advances in target identification. Target identification is also important to
optimize drug selection and reduce side effects. There are many types of biomolecules that can
serve as therapeutic targets, including enzymes, cell receptors, ion channels, DNA, and transcrip-
tion factors (TFs). Determining the biological target of a drug can be tedious as there are vast
reserves of potential protein and nonprotein targets; therefore, recent advances in bioinformatics
overtake the traditional target discovery methods. In addition to a large research database con-
taining different physical and chemical properties of various ligands and targets, recent advances
in the gene/vaccine field provide various methods of drug identification. This article will review
various new drug targets for small-molecule drug discovery [4].
3.2 Drug Target Identification
Drug target is a biological protein or nonprotein component that interacts with its potential drug
candidate to bring about physiological changes usually targeting a disease.
An ideal drug target candidate would withhold the following properties [5]:
● Functionality of target on binding of drug.
● DTI should be able to bring about pathophysiological modifications in favor of the disease.
● The target should be druggable and favor simpler assayability methods for enabling its high-
throughput screening.
● Favorable prediction of target binding sites of drugs for efficient interaction.
● Target structure and function should be drug driven.
Target-based drug discovery is the basic strategy for drug development. Recent development in
computational biology has made several open web databases and bioinformatics tools available for
linking molecular drug targets to diseases. Analysis of “omics” datatypes like genomics, transcrip-
tomics, proteomics, and metabolomics has increased the efficacy of drug target discovery more
than the traditional methodologies [1]. Cellular or genetic chemicals in the system usually interact
with drug molecules, therefore acting as drug targets [6]. Phenotype screening is another target
identification method in which the drug target is unknown; yet, the validated drug candidate is
discovered on the basis of 3D modeling method. Most first-in-class drugs discovered before
1980 were based on phenotypic screening like aspirin that was synthesized in 1897 but its mecha-
nism of action and target was identified in 1976 [7].
Various tools and technologies are used for target identification and validation. Protein and gene
expression profiling to identify novel targets and validate the existing ones can be performed using
microarray techniques, whereas validation can be performed using gene knockout model and anti-
sense RNA technology [8].
3 Novel Drug Targets for Small Molecule-based Drug Discovery52
Drug target identification imposes another challenge to druggability of the target. An ideal drug-
gable target binds to ligands that obey Ro5; however, the absence of Ro5-associated ligand does not
make a target undruggable. Therefore, structure-based druggability assessment is performed by
identification of potential binding sites, function discrimination based on the physiological prop-
erty of binding pocket, and by assessing the difficulty of druggability [9] (Figure 3.1).
Information about disease is
extracted from disease database
like OMIM, GAD, DisGeNET
Detection of probable drug target
based on literature and data from
databases like PDB, DrugBank
Assay development for screening
novel drug targets using high
throughput screening
Check for
the target
availability
of disease target
information
YES
NO
Target based discovery
Phenotype screening
Assay development to identify
and check druggability of selected
targets using HTS
Target validation using chemical
and bioinformatics tools
Structure prediction of target
based on template amino acid
sequence information
Template sequence
available
Homology
modeling
Template sequence
not available
Ab - initio
Target identification
Target validation
Figure 3.1 Canonical steps in drug target identification.