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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5938_Библиотеки_им_академика_М_И_Перельмана
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310 10 Open Access Databases – An Industrial View
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● Guidances
● International information
Other FDA institutions are the Center for Drug Evaluation and Research (CDER)
(https://www.fda.gov/about-fda/fda-organization/center-drug-evaluation-andresearch-cder) that is responsible for public health is and nonprescription
and prescription drugs, generics, and biological therapeutics. The Center for
Biologics Evaluation and Research (CBER) (https://www.fda.gov/about-fda/
fda-organization/center-biologics-evaluation-and-research-cber) has specic
responsibility for biological products.
Further information on drugs and their development status can be found on the
websites of the regulatory authorities in every country.
10.2 Scaffold-Hopping
The aim of medicinal chemistry is to synthesize new active ingredients with
improved properties. One method is scaold hopping or rescaolding, in which
the basic structure of a known active ingredient is changed or replaced [34]. This
method is very similar to the bioisoster approach, but instead of replacing individual
atoms or functional groups, the central scaold is replaced. Through this exchange,
a higher activity toward the receptor is to be achieved, the ADME-Tox prole is to
be improved in order to reduce side eects. It is also possible to circumvent existing
patent protection, such as the example of the phosphodiesterase 5 (PDE5) inhibitor
Sildenal 1 from Pzer, which was approved in 1998 under the name Viagra®.
Chemically, sildenal is a 1H-pyrazolo[4,3-d]pyrimidin-7(4H)-one.
O O
N
N
In 2003, Bayer AG received approval for its PDE5 inhibitor Vardenal,
which was marketed under the name Levitra®. The new core structure is an
imidazo[5,1-f ][1,2,4]triazin-4(3H)-one, which allowed Bayer to circumvent patent
protection on sildenal.
Scaold hopping can be simplied and accelerated by using computer-assisted
methods. The commercial software ReCore was developed by the company
BioSolveIT (https://www.biosolveit.de/ (accessed 27 April 2022)) [35].
When searching for a new scaold, a drug molecule (e.g. COX2 inhibitor celecoxib 3) is imported into ReCore. By designating cleavage sites on the pyrazole
backbone at positions 1 and 5, the two substituents are xed. ReCore’s algorithm
O
N
N
S
O
1
N
N
H
O O
S
N
N
HN
2
O
N
N
N
O
10.3 Virtual-Screening 311
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A
S
O
N
N
CF
3
N
N
CF
3
O
C
S
O
O
3
S
O
O
S
O
B
O
S
O
O
Scheme 10.5 Principle of scaffold-hopping.
S
O
O
Celecoxib
(Celebrex, Searle)
approved 1998
O
N
N
CF
3
S
O
O
Rofecoxib
(Vioxx, Merck)
approved 1999
O
N
Etoricoxib
(Arcoxia, Merck)
approved 2004
Cl
N
Scheme 10.6 COX2 inhibitors.
compares the unsubstituted pyrazole fragment with 3D structures from imported
libraries and nds possible new scaolds A, B, or C (Schemes 10.5 and 10.6):
Other COX2 inhibitors, such as rofecoxib and etoricoxib, were found by applying
scaold hopping.
In principle, any substance library from known online databases can be used, such
as some of the listed ones:
● in-House repositories
● ZINC (https://zinc.docking.org/) – 230 million purchasable compounds
● PDB (Protein Data Bank) (https://www.rcsb.org/)
● CSD (The Cambridge Structural Database) (https://www.ccdc.cam.ac.uk/
solutions/csd-core/components/csd/)
10.3 Virtual-Screening
Another method of computer-aided drug design (CADD) in the search for new active
ingredients is in-silico or virtual screening (VS) [36]. In this computer-based method,
compound libraries are searched to identify new structures that are suitable for further investigation and development [37]. VS is used to:
312 10 Open Access Databases – An Industrial View
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● Finding substances from in-house databases for a HTS
● to order substances from external suppliers
● to decide which substances are synthesized.
Several open access databases are available for virtual screening [38]. A variety of
programs are used in the drug discovery process [39, 40].
Abbreviations
ADME absorption, distribution, Metabolism, and Excretion (ADME)
BMRB Biological Magnetic Resonance Bank
CADD Computer-aided drug design
CAS Chemical abstract service
CBER Center for biologics evaluation and research
CDER Center for drug evaluation and research
CI Competitive intelligence
CMDO Contract manufacturing and development organization
CNIPA China national intellectual property administration
COCONUT COlleCtion of open natural ProdUcTs
COD Crystallography open database
CRO Contract research organization
CSD Cambridge structural database
CYP Cytochrom peroxidase P450
DDBJ DNA Data Bank of Japan
DPMA Deutsches Patent- und Markenamt
DMPK Drug Metabolism and Pharmacokinetic
EMA European Medicines Agency
ENA European Nucleotide Archive
EPO European Patent oce
FDA U.S. Food and Drug Administration
GPCRs G-Protein Coupled Receptors
HTS High-Throughput Screening
IDG Illuminating the Druggable Genome
IGE-IPI Swiss Federal Institute of Intellectual Property
JPO Japanese Patent Oce - https://www.jpo.go.jp/e/
KMC Knowledge Management Center
NAKB Nucleic Acid Knowledge Base
NDB Nuclear Database
NIH National Institute of Health
PDB Protein Data Bank
PDBe Protein Data Bank in Europe
PDBj Protein Data Bank Japan
PDE Phosphodiesterase
PK Pharmacokinetic
sdf Structure Data File
References 313
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SULT Sulfotransferase
TA Target Assessment
UGT UDP-Glucuronosyltransferase
USPTO United States Patent and Trademark Oce
VS Virtual Screening
wwPDB Worldwide Protein Data Bank
References
1 Wiggins, G. (1996). Caught in a CrossFire: academic libraries and Beilstein. Jour-
nal of Chemical Information and Modeling 36: 746–749. https://doi.org/10.1021/
ci950250c.
2 Zass, E. (1996). From handbooks to databases on the net: new solutions and old
problems in information retrieval for chemists. Journal of Chemical Information
and Modeling 36: 942–948. https://doi.org/10.1021/ci950249d.
3 The World’s Largest Collection of Chemistry Insights. https://www.cas.org/
about/cas-content
4 Jacobs, A., Williams, D., Hickey, K. et al. (2022). CAS common chemistry in
2021: expanding access to trusted chemical information for the scientic community. Journal of Chemical Information and Modeling 62: 2737–2743. https://doi
.org/10.1021/acs.jcim.2c00268.
5 Ridley, D.D. (2015). Information Retrieval: SciFinder, 2e. Wiley.
6 Hübner, K. (2019). Chemical abstracts service - 150 millionen substanzen.
Chemie in unserer Zeit 53: 140–147. https://doi.org/10.1002/ciuz.201980052.
7 Berman, H.M. (2008). The Protein Data Bank: a historical perspective. Acta Crys-
tallographica Section A 64: 88–95. https://doi.org/10.1107/S0108767307035623.
8 The UniProt Consortium (2017). UniProt: the universal protein knowledgebase.
Nucleic Acids Research 45 (Database Issue): D158–D169. https://doi.org/10.1093/
nar/gkw1099.
9 Narayanan, B.C., Westbrook, J., Ghosh, S. et al. (2014). The nucleic acid
database: new features and capabilities. Nucleic Acids Research 42 (Database
Issue): D114–D122. https://doi.org/10.1093/nar/gkt980.
10 Berman, H.M., Lawson, C.L., and Schneider, B. (2022). Developing community
resources for nucleic acid structures. Lifestyles 12 (4): 540. https://doi.org/10
.3390/life12040540.
11 Okido, T., Kodama, Y., Mashima, J. et al. (2022). DNA Data Bank of Japan
(DDBJ) update report 2021. Nucleic Acids Research 50 (Database Issue):
D102–D105. https://doi.org/10.1093/nar/gkab995.
12 Cummins, C., Ahamed, A., Aslam, R. et al. (2022). The European nucleotide
archive in 2021. Nucleic Acids Research 50 (Database Issue): D106–D110. https://
doi.org/10.1093/nar/gkab1051.
13 Wishart, D.S., Knox, C., and Guo, A.C. (2008). DrugBank: a knowledgebase for
drugs, drug actions and drug targets. Nucleic Acids Research 36 (Database issue):
D901–D906. https://doi.org/10.1093/nar/gkm958.
314 10 Open Access Databases – An Industrial View
https://t.me/medicina_free
14 Gilson, M.K., Liu, T., Baitaluk, M. et al. (2016). BindingDB in 2015: a public
database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Research 44 (Database Issue): D1045–D1053. https://doi.org/
10.1093/nar/gkl999.
15 Groom, C.R., Bruno, I.J., Lightfoot, M.P., and Ward, S.C. (2016). The Cambridge
structural database. Acta Crystallographica Section B 72 (2): 171–179. https://doi
.org/10.1107/S2052520616003954.
16 Gražulis, S., Daškeviˇc, A., Merkys, A. et al. (2012). Crystallography Open
Database (COD): an open-access collection of crystal structures and platform for
world-wide collaboration. Nucleic Acids Research 40 (D1): D420–D427. https://doi
.org/10.1093/nar/gkr900.
17 Davies, M., Nowotka, M., Papadatos, G. et al. (2015). ChEMBL web services:
streamlining access to drug discovery data and utilities. Nucleic Acids Research 43
(Web Server Issue): W612–W620. https://doi.org/10.1093/nar/gkv352.
18 Kim, S., Chen, J., Cheng, T. et al. (2021). PubChem in 2021: new data con-
tent and improved web interfaces. Nucleic Acids Research 49 (Database Issue):
D1388–D1395. https://doi.org/10.1093/nar/gkaa971.
19 Butkiewicz, M., Wang, Y. , Bryant, S.H. et al. (2017). High-Throughput screening
assay datasets from the PubChem database. Chemical Informatics 3 (1): 1–12.
https://doi.org/10.21767/2470-6973.100022.
20 Nguyen, D.-T., Mathias, S., Bologa, C. et al. (2017). Pharos: collating protein
information to shed light on the druggable genome. Nucleic Acids Research 45
(D1): D995–D1002. https://doi.org/10.1093/nar/gkw1072.
21 Sorokina, M., Merseburger, P., Rajan, K. et al. (2021). COCONUT online: collec-
tion of open natural products database. Journal of Cheminformatics 13: 2. https://
doi.org/10.1186/s13321-020-00478-9 .
22 Testa, B., Krämer, S.D., Wunderli-Allenspach, H., and Folkers, G. (ed.) (2006).
Pharmacokinetic Proling in Drug Research - Biological, Physicochemical, and
Computational Strategies. Zürich (Switzerland): VHCA, Verlag Helvetica Chimica
Acta.
23 Ortiz de Montellano, P.R. (ed.) (2015). Cytochrome P450 - Structure, Mechanism,
and Biochemistry, 4the. Switzerland: Springer International Publishing.
24 Wishart, D.S., Djoumbou Feunang, Y., Marcu, A. et al. (2018). HMDB 4.0: the
human metabolome database for 2018. Nucleic Acids Research 46 (Database
Issue): D608–D617. https://doi.org/10.1093/nar/gkx1089.
25 Caspi, E., Billington, R., Keseler, I.M. et al. (2020). The MetaCyc database of
metabolic pathways and enzymes - a 2019 update. Nucleic Acids Research 48
(Database Issue): D445–D453. https://doi.org/10.1093/nar/gkz862.
26 Romero, P. (ed.) (2012). The HumanCyc pathway-genome database and path-
way tools software as tools for imaging and analyzing metabolomics data. In:
Fan, T.M., Lane A., Higashi R. (eds) The Handbook of Metabolomics. Methods
in Pharmacology and Toxicology. Humana Press, Totowa, NJ. https://doi.org/10
.1007/978-1-61779-618-0_13 .
References 315
https://t.me/medicina_free
27 Kanehisa, M., Goto, S., Sato, Y. et al. (2014). Data, information, knowledge and
principle: back to metabolism in KEGG. Nucleic Acids Research 42 (Database
Issue): D199–D205. https://doi.org/10.1093/nar/gkt1076.
28 Jewison, T., Su, Y. , Disfany, F.M. et al. (2014). SMPDB 2.0: big Improvements
to the small molecule pathway database. Nucleic Acids Research 42 (Database
Issue): D478–D484. https://doi.org/10.1093/nar/gkt1067.
29 ADME = Administration, Distribution, Metabolism, Excretion.
30 Canault, B., Bourg, S., Vayer, P. et al. (2017). Comprehensive Network Map of
ADME-Tox Databases. Molecular Informatics 36 (10): 1700029. https://doi.org/10
.1002/minf.201700029.
31 Said Faqi, A. (ed.) (2017). Comprehensive Guide to Toxicology in Nonclinical Drug
Development, 2e. Amsterdam: Elsevier Inc.
32 Will, Y. , McDue, J.E., Olaharski, A.J., and Jey, J.B. (ed.) (2016). Drug Discov-
ery Toxicology - From Target Assessment to Translational Biomarkers.NJ:John
Wiley & Sons, Inc.
33 https://www.roche.com/innovation/process/clinical-trials/about#b74e294d-1765-
419e-8b38-96aaa88d62fa (accessed 26 April 2022).
34 Brown, N. (ed.) (2014). Scaold Hopping in Medicinal Chemistry (Methods and
Principles in Medicinal Chemistry), vol. 58. Weinheim, Germany: Wiley-VCH
Verlag GmbH & Co. KGaA.
35 Maass, P. , Schulz-Gasch, T., Stahl, M., and Rarey, M. (2007). Recore: a fast and
versatile method for scaold hopping based on small molecule crystal structure
conformations. Journal of Chemical Information and Modeling 47 (2): 390–399.
https://doi.org/10.1021/ci060094h.
36 Varnek, A. and Tropsha, A. (ed.) (2008). Chemoinformatics Approaches to Virtual
Screening. Cambridge: Royal Society of Chemistry.
37 Murugan, N.A., Podobas, A., Gadioli, D. et al. (2022). A review on parallel virtual
screening softwares for high-performance computers. Pharmaceuticals 15 (1): 63.
https://doi.org/10.3390/ph15010063.
38 Shaker, B., Ahmad, S., Lee, Y. et al. In silico methods and tools for drug discov-
ery. Computers in Biology and Medicine 137: 104851. https://doi.org/10.1016/j
.compbiomed.2021.104851.
39 Maia, E.H.B., Assis, C., de Oliveira, T.A. et al. (2020). Structure-based virtual
screening: from classical to articial intelligence. Frontiers in Chemistry 8 (4):
343. https://doi.org/10.3389/fchem.2020.00343.
40 https://en.wikipedia.org/wiki/List_of_protein-ligand_docking_software (accessed
25 April 2022).
https://t.me/medicina_free
Index
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317
a
academic vs. industrial research 299–310
Binding Database 305
Cambridge Structural Database (CSD)
305
clinical trials 308
Competitive Intelligence (CI) 301
Crystallography Open Database (COD)
305
R&D process 301
Acetaminophen 70, 71
ADME-Tox 274, 307, 310
AlphaFold Database 177, 179–180, 192,
279–280
AlphaFold program 272, 279
American Chemical Society (ACS) 299
angiotensin-converting enzyme (ACE)
inhibitors 80, 82, 84
Apixaban 117
Approved Drugs component 244
articial intelligence/machine learning
(AI/ML) tools 68
Asinex 305
Assay ID (AID) 44
AutoDock Vina 275
automatic rebuilding of protein backbone
and side chains 203–204
b
BCR–ABL kinase inhibitor 122, 123
Beilstein 299
BindingDB Database 283, 305
BioAssay data collections 43
BioChemGraph project 166
bioisostere 101
classical vs. non-classical 102–105
bioisosteric replacement, in drug
discovery 105–106
bioisosterism 101, 102, 105, 106
BioMagResBank (BMRB) 141
BLAST (blastp) algorithm 241
Boltzmann-Enhanced Discrimination of
ROC (BEDROC) 287
c
Cambridge Structural Database (CSD)
42, 152, 305, 311
carbohydrates 149–150, 155, 204, 214
Center for Biologics Evaluation and
Research (CBER) 310
Center for Drug Evaluation and Research
(CDER) 310
Charles Rive 305
ChemAxon 73, 86, 240
ChEMBL 2, 4, 56, 57, 107, 109, 111, 118,
232, 234, 283, 284, 288, 306, 307
ChemBridge 305
chemical abstracts service (CAS) 45, 299
Chemical Taxonomy 73, 91
China National Intellectual Property
Administration (CNIPA) 302
ciprooxacin 118, 120
classical vs. non-classical bioisostere
102–105
Open Access Databases and Datasets for Drug Discovery, First Edition.
Edited by Antoine Daina, Michael Przewosny, and Vincent Zoete.
© 2024 WILEY-VCH GmbH. Published 2024 by WILEY-VCH GmbH.
318 Index
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Classication Browser 51, 52
3C-like protease (3CLpro) 94
COlleCtion of Open Natural ProdUcTs
(COCONUT) 281, 306
color-Tanimoto (CT) score 46, 48
Combo-Tanimoto (ComboT) score 48
competitive intelligence (CI) 301
computer-aided drug design (CADD) 1,
2, 311
computer-aided structure-based drug
design 190–191
COVID-19 94, 111, 164, 191
COVID Moonshot campaign 164
COX2 inhibitors 311
cytochrome peroxidase P450 (CYP) 306
cytochrome P450 monooxygenase
enzymes 212
d
Dene Secondary Structure of Proteins
(DSSP) analysis 220
details pages 236
Deutsches Patent-und Markenamt
(DPMA) 302
Die Pathway Datenbank HumanCyc 307
Disease Novelty component 246, 248
DrugBank 2, 68, 305
categories section 73
drug cards 70
identication section 70–71
knowledgebase 69
overview of 68–69
pharmacology 71–73
properties section 73
research using 94
Targets, Enzymes, Carriers, and
Transporters section 73–77
DrugBank Online’s Advanced Search
Functionality 80–83
drug metabolism and pharmacokinetic
prole (DMPK) 306
drug-related non-classical bioisosteres
103
Drug Target Ontology (DTO) 242
e
EGFR 104
Electron Microscopy Data Bank (EMDB)
141
Enamine 305
enrichment factor (EF) 286
enzyme inhibitors 84, 85
Estrogen-related receptor gamma
ligand-binding domain 179
EU Clinical Trials Register 308
European Nucleotide Archive (ENA)
305
European Patent Oce (EPO) 302
European Union Drug Regulating
Authorities Clinical Trials
Database (EudraCT) 309
Evotec 305
Expression Data component 243
f
Filter Value Enrichment 248–251, 263
ndability, accessibility, interoperability,
and reuse (FAIR) principles 3,
141, 220, 223
Find Predicted Targets 252
ngerprint-based 2-D similarity search
method 45–46, 48
g
Gaussian-shape overlay-based 3-D
similarity methods 45
Getinib 104
GeneCards 233
Gene Ontology (GO) 52, 246
Gene, Protein, Pathway, and Taxonomy
collections 43
Generated DataBases (GDBs) 282
genome-wide association studies (GWAS)
67, 246
glycoprotein structure model rebuilding
214
Gmelin 299
Google Patents 303
GWAS Traits component 246
Index 319
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h
high-throughput screening (HTS) 2, 41,
67, 105, 111, 271, 305
histidine ip and improved ligand
parameterization 208–210
hit nding 105, 106, 108, 117, 133, 305
human B-raf protein kinase 181
Human Metabolome Database (HMDB)
306
i
Identier Exchange Service 52
isosteres 101
isosterism 101, 102
j
Japanese Patent Oce (JPO) 302
k
KEGG Pathway Database 307
l
lead compounds 105, 106, 118, 124, 129
lead optimization 105, 106, 108, 120, 133
Ligand-Based VS (LBVS) 272
Lipinski’s rule of ve (Ro5), for
drug-likeness 55
Literature Knowledge Panels 49–50, 60
m
machine learning (ML) 1, 58, 93, 165,
166, 288
main protease protein (Mpro) 164
MarvinJS Widget 240, 252
Maybridge 305
metabolism 73, 74, 76, 79, 113, 212
MetaCyc Metabolic Pathway Database
307
metal binding sites 214–216
2mFo-DFcdensity map 154
mitogen activated protein kinase (MAPK)
signaling pathway 208
mmCIF format 3, 4, 218, 278
ModelArchive 176, 177, 180–181, 192,
193
molecular mechanics-generalized Born
surface area (MM-GBSA) strategy
94
molecule-based discovery 68
MONDO disease 261
morpholine 104, 105, 129
n
NCATS Predictor 241, 252, 253
non-classical vs. classical bioisostere
102–105
NorA eux pump, inhibitor design of
118
normalized ratios of principal moments of
inertia (NPR) 115
Nucleic Acid Database (NDB) 304
Nucleic Acid Knowledge Base (NAKB)
304
o
OneDep 145–146, 150, 151, 154
OpenEye 147
OpenTargets 233
p
papain-like protease (PLpro) 94
Patent collection 43
Pathways 73, 79, 246, 251
PDBeChem service 158
PDBe-KnowledgeBase (PDBe-KB) 142
PDBe tools for ligand analysis 155–158
PDB identier (PDB ID) 278
PDB-REDO databank 278
automated model completion
approaches 204–205
automatic rebuilding of protein
backbone and side chains
203–204
building new compounds into density
212–213
creating datasets 222
data available in PDB-REDO entries
220
downloading and inspecting individual
PDB-REDO entries 218–220
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