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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5927_Библиотеки_им_академика_М_И_Перельмана
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Index
Cisplatin, 17
Daptomycin, 17
Ebselen, 17
Manidipine, 17
Tabipenem, 17
Verapamil, 17
inflammatory diseases
Budesonide, 16
Cefadroxil, 16
Dextofisopam, 16
IBD (Inflammatory Bowel Disease), 16
Penicillamine, 16
RA (Rheumatoid Arthritis), 16
SLE (Systemic Lupus erythematosus),
16
Tofisopam, 16
models, validation of
AUROC (area under the receiver
operating characteristic), 11
positive-predictive value (PPV), 12
origin and significance, 3
strict regulations, 4
Zidovudine, 4
Swanson (ABC) model, 8
three-step process, 2–3
Drug repositioning, 244–245
computational chemistry methods
deep learning (DL), 249
drug pair knowledge, 250
logistic regression, 248
machine learning (ML), 248
network models, 249
neural network (NN), 248–249
support vector machine (SVM), 248
systems biology, 250
text mining and semantic inference,
249–250
general strategies for, 246
knowledge-based repurposing
genome strategy, 247
pathway-based drug repurposing, 247
TABLE, 247
target mechanism-based drug
repurposing, 247
target-based drug repurposing, 247
orphan drugs
with approval for another orphan
disease indication, 256–257
designated products with, 257
reviving withdrawn drugs, 257
SARS-COV-2 pandemic, challenges in,
258–260
phenotype-based repurposing, 248
validation of, 250
area under the precision-recall curve
(AUPRC), 252–253
AUROC plot, 251–252
common drug repositioning for, 255–256
computational validation, 251
experimental validation, 253
orphan diseases (ODs), 253–255
Orphan Drug Act (ODA), 253–255
orphan drug repositioning, 256
positive predictive value (PPV), 252
sensitivity, 252
specificity, 252
Drug repurposing
BEAR (Binding Estimation After
Refinement), 214
case study
macromolecular targets, 39–40
challenges and limitations, 48–49
computational analysis of novel drug
opportunities (CANDO), 218–220
computational drug discovery
acquisition of test compound, 201
ADMET (absorption, distribution,
metabolism, excretion, and toxicity),
199–200
bioactive compounds, selection, 198
carbonic anhydrase (CA), 201–202
drug development, 201
International Classification of Diseases
(ICD-10), 200
lead optimization, 198
marketing, 200, 201
phase IV, 200
registration, 201
screening, 198
test compound, 201
computational strategies, 28
chemical space, 33
chemioinformatics, 33
chemistry, 32–33
ligand-based and structure-based
approaches, 29
271 Index
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
ligand-based drug design (LBDD),
30–31
molecular docking, 36
molecular dynamics, 37
molecular modeling, 36
pharmacophore modeling, 33–34
protein structure predictions, 36
QSAR analysis, 31
in silico screening, 34
software tools and databases, 30
computer-aided drug design (CADD),
207–208
connectivity map (CMAP), 225–228
determinants of LB-CADD, 216
docking approaches, 210–211
electronic health record (EHR) data, 228
high-throughput screening (HTS), 206
and its mechanisms, 205
ligand-based CADD (LBCADD), 214–215
molecular docking method, 209–210
molecular dynamics (MD) simulation,
213–214
pharmacophore screening, 222
quantitative structure–activity relationship
(QSAR), 216–218
reverse docking, 222–223
reverse screening, 221, 223
shape screening, 222
signature-based approach, 224–225
sildenafil, 203–204
similarity measures, 216
structure-based CADD, 208–209
thalidomide, 204
virtual screening techniques, 220–221
H
Heart disease model, 151
Heparin, 139
Herbal medicines, 152
High-performance liquid chromatography
(HPLC), 192
High-throughput screening (HTS), 206
I
IBD (Inflammatory Bowel Disease), 16
Inflammatory diseases
asthma
Heparin, 139
Rapamycin, 138–139
atopic dermatitis
Artemisia Apiacea Hance, 139
Rifampicin, 139
computational approach
absorption, distribution, metabolism,
excretion, and toxicity (ADMET), 140
drugs and role, 142–143
epurposing approaches for, 135
repositioned drugs, 136–137
zebrafish screening, 136
global incidence of, 133
patients with, 134
repurposing of drugs, advantages of,
140–142
SEPSIS
Mangiferin, 138
Methylthiouracil (MTU), 137
Simvastatin, 138
targeting inflammatory mediators, 141
International Classification of Diseases
(ICD-10), 200
E
Ebola virus infection, 69
Ebselen, 17
Electronic health record (EHR) data, 228
ENL (erythema nodosumleprosum), 13
G
GLP1 (Glucagon Linked Peptide-1) cardio
protection, 156
Glucagon-like peptide 1 (GLP-1) regulators,
173
Graphics processing units (GPUs), 28
Ligand-based CADD (LBCADD), 214–215
Ligand-based drug design
artificial intelligence (AI), 93–94
pharmacophore modeling, 92–93
virtual screening (VS), 91–92
Ligand-based drug design (LBDD), 30–31
Malignant diseases
boost anticancer drug development
research, 112–113
L
M
Index
target-based cancer therapy, 114
computational tools and resources
anticancer drug target prediction,
115–116
artificial intelligence (AI), 115
cancer identification, 119
and computational tools, 116
drug target database, 116
ligand-based techniques, 118–119
structure-based drug discovery
approach, 117–118
drug-repurposing approach in cancer, 120
personalized medicine, importance, 122
in silico drug, role of, 121–122
OMICS studies
The Cancer Genome Atlas (TCGA)
study, 123–124
cancer targets, 124
targeted cancer therapy, 114
advantages of, 115
Manidipine, 17
Metformin, 13
Methylthiouracil (MTU), 137
Molecular docking method, 209–210
Molecular docking simulation studies
(MDSS), 89–90
Molecular dynamics (MD) simulation,
90–91, 213–214
Molecular property diagnostic suite
(MPDS), 184–185
Multi Drug Resistance (MDR), 112
N
Neglected tropical diseases (NTDs), 77
computational approaches and
techniques, 87–88
computational techniques used, 95–99
computer-aided drug design (CADD), 79
drug repurposing process, 80
ligand-based drug design
artificial intelligence (AI), 93–94
pharmacophore modeling, 92–93
virtual screening (VS), 91–92
repurposed drugs for, 79–80, 82–87
structure-based drug design
molecular docking simulation studies
(MDSS), 89–90
molecular dynamics (MD) simulation,
90–91
Neurodegenerative diseases
Alzheimer’s disease, 195–196
amyotrophic lateral sclerosis (ALS),
196–198
neuroinflammation/neuro-inflamm-aging,
193–194
oxi-inflamm-aging, 193
Parkinson’s disease (PD), 194–195
reactive oxygen species (ROS), 193
Nucleo magnetic resonance (NMR), 192
O
OMICS studies
The Cancer Genome Atlas (TCGA) study,
123–124
cancer targets, 124
Orphan Drug Act (ODA), 253–255
Oxi-inflamm-aging, 193
P
Parasitic diseases, 78
computational approaches and
techniques, 87–88
computational techniques used, 95–99
computer-aided drug design (CADD), 79
drug repurposing process, 80
ligand-based drug design
artificial intelligence (AI), 93–94
pharmacophore modeling, 92–93
virtual screening (VS), 91–92
repurposed drugs for, 80–82
structure-based drug design
molecular docking simulation studies
(MDSS), 89–90
molecular dynamics (MD) simulation,
90–91
Parkinson’s disease (PD), 194–195
Penicillamine, 16
Positive predictive value (PPV), 252
Positive-predictive value (PPV), 12
Proteochemometric (PCM) modeling, 38
Q
Quantitative structure–activity relationship
(QSAR), 43, 216–218
R
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
RA (Rheumatoid Arthritis), 16
Rapamycin, 138–139
Rare Disease Repurposing Database
(RDBD), 150
Reactive oxygen species (ROS), 193
Repurposing of different drugs
anti interleukin drugs, 158
antidiabetic drugs, 155–156
Atherosclerosis, 154–155
Colchicine, 153–154
GLP1 (Glucagon Linked Peptide-1)
cardio protection, 156
Rifampicin, 139
S
SEPSIS
Mangiferin,
Methylthiouracil (MTU), 137
Simvastatin, 138
Simplified molecular-input line-entry
system (SMILES), 67
SLE (Systemic Lupus erythematosus), 16
Sodium-glucose transporter 2 (SGLT2)
inhibitors, 173
Structure-based drug design (SBDD)
methods, 35–36
Support vector machine (SVM), 248
Swanson (ABC) model, 8
138
T
Tabipenem, 17
Targeting inflammatory mediators, 141
The Cancer Genome Atlas (TCGA) study,
123–124
Thiazolidinediones (TZD), 172
Three-dimensional quantitative structure
activity relationship (3D QSAR), 28
Tofisopam, 16
Type 1 diabetes (T1D), 170
Type 2 diabetes (T2D), 170
V
Verapamil, 17
Viral infections and Coronavirus
disease-2019
antiviral capabilities, 61
approved and candidate drugs, 62–63
CMV, 69–70
computer-aided drug discovery
deep learning (DL)-based repurposing
strategies, 65–67
new target–new indication, 65
same target–new indication, 65
same target–new virus, 65
simplified molecular-input line-entry
system (SMILES), 67
virus-targeting approaches, 64–65
drug repurposing strategy, 61
Ebola virus infection, 69
Food and Drug Administration (FDA), 60
HCV infections, 69–70
HIV, 69–70
host-targeting approaches
network-based approaches, 68
signature-based approaches, 67
HSV, 69–70
in influenza and dengue, 70–71
SARS COV-2, 71
traditional drug development, 61
virus-targeting, workflows of, 62
Zika virus (ZIKV), 68–69
Virtual screening (VS), 91–92
techniques, 220–221
Z
Zebrafish screening, 136
Zidovudine, 4
Zika virus (ZIKV), 68–69
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