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10.1 Academic vs. Industrial Research 309
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Scheme 10.4 Search mask for an advanced search pm https://clinicaltrials.gov/ct2/ search/advanced?cond=&term=&cntry=&state=&city=&dist=.
EudraCT (European Union Drug Regulating Authorities Clinical Trials Database, https://eudract.ema.europa.eu/ (accessed 30 April 2022)) is the European database where all clinical trials on medicinal products authorized in the European Union are listed. This database contains information on ongoing clinical trials provided by the trial sponsors.
An overview of research activities and the development status of candidate sub­stances from the competition is possible with a simple internet search; search terms “company name” and “pipeline” deliver current results.
Additional information on all phases and issues of drug development can be obtained from regulatory bodies, such as the European Medicines Agency (EMA), https://www.ema.europa.eu/en (accessed 26 April 2022)), located in Amsterdam (Netherlands) since 2019 (formerly in London (United Kingdom)) and the U.S. Food and Drug Administration (FDA), https://www.fda.gov/ (accessed 26 April
2022)), located in Silver Springs, Maryland (United States). Information on the following topics is available on both websites:
● Submissions
● Registration
● Recent drug approvals
● Manufacturing
● Medication Guides
● Drug applications
● Drug compounding
● Drug safety communications
● Shortages
● Warning letters
● Recalls
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 specic 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 scaold hopping or rescaolding, 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 scaold is replaced. Through this exchange, a higher activity toward the receptor is to be achieved, the ADME-Tox prole is to be improved in order to reduce side eects. It is also possible to circumvent existing patent protection, such as the example of the phosphodiesterase 5 (PDE5) inhibitor Sildenal Chemically, sildenal is a 1H-pyrazolo[4,3-d]pyrimidin-7(4H)-one.
1 from Pzer, which was approved in 1998 under the name Viagra®.
O O
S
N
N
In 2003, Bayer AG received approval for its PDE5 inhibitor Vardenal, 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 sildenal.
Scaold hopping can be simplied 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 scaold, a drug molecule (e.g. COX2 inhibitor cele­coxib 3) is imported into ReCore. By designating cleavage sites on the pyrazole backbone at positions
O
N
N
O
1
N
N H
O O
S
N
N
HN
2
O
N
N
N
O
1 and 5, the two substituents are xed. ReCore’s algorithm
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 scaolds A, B, or C (Schemes 10.5 and 10.6):
Other COX2 inhibitors, such as rofecoxib and etoricoxib, were found by applying
scaold 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:
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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 oce 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 Oce - 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 Oce VS Virtual Screening wwPDB Worldwide Protein Data Bank
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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 articial 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 ciprooxacin 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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Classication 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
Dene 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 identication 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
prole (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 Oce (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 Getinib 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
Identier Exchange Service 52 isosteres 101 isosterism 101, 102
j
Japanese Patent Oce (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
-DFcdensity map 154
2mF
o
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 eux 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 identier (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