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Prediction of Binding Poses 247–249 499 Index a algorithms 6 Å Rule 243–245 – Algorithm Builder (Pharma – demonstration 244 Algorithms) 379 – limitations 245 – Bayesian classification algorithms 339 – prediction of binding poses 247–249 – docking algorithm 246 – prediction of SoM 244, 245 – genetic algorithm k-nearest neighbor ––methodological approaches 245–247 (GA-kNN) 388 – protein flexibility 249–253 – SMART-Cyp’s SoM algorithm 67 – validity 245 amine 299 abacavir 406 – acetylation reactions 400 absolute reasoning domain 307–310 – E-state index 384 – relative confidence levels 309 – rule about demethylation of 299 absorption, distribution, metabolism, and ANOVA (analysis of variance) 325, 326 excretion/and toxicity (ADME/ antioxidants 421 ADMET) 40, 43, 60, 325, 327, 367, 441 antiretrovirals 441 – ADMET Predictor 42, 47, 65, 70 aprepitant 405 – ADMEWORKS Predictor 48 area under the curve (AUC) ratios 280, 352, – data on most FDA-approved drugs 64 452, 473, 476 – measurements 321 aromatic/aliphatic hydroxylation 42 – physiological ADMET data for approved artemisinin 405 drugs 62 artificial intelligence 19 – QikProp, software module for predicting 39 artificial neural network models 334 acceptor capability 379, 382 aryl hydrocarbon receptor (AhR) 364, 460 N-acetyltransferases (NATs) 29, 402, 444 aryl hydrocarbon receptor nuclear activation energy 151, 266, 267, 271 translocator (ARNT) 460, 466 ADRs, see adverse drug reactions (ADRs) atorvastatin 189 adverse drug reactions (ADRs) 7, 58, 59, 337, ATP hydrolysis 374, 376 403, 404 AutoDock Vina software 31, 40, 285 – caused by reactive metabolites 8 autooxidation 9 – off-target 8 AZD5438 494 – on-target 7 azole compounds, PXR antagonist model aglycones 420, 421 based on 365 AhR/ARNT signaling transduction pathway 466 b AhR-inducible gene battery 461 basic-helix-loop-helix (bHLH) protein 460 ALCHEM 299 Bayesian classifiers 334 alcohol consumption 443, 460 BEH C18 column 494 aldosterone synthase 118, 363 Bennett acceptance ratio (BAR) 185 Drug Metabolism Prediction, First Edition. Edited by Johannes Kirchmair. 2014 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2014 by Wiley-VCH Verlag GmbH & Co. KGaA. 500 Index benzodiazepines 6 comprehensive expert software packages 16 benzphetamine 190 computer prediction models B-factors 179 – from multiple sources 310–312 1β-hydroxytestosterone 203, 204, 207 confusion matrix 386 – space-filling model 207 conjugation reactions 10, 29, 205, 398, 400 bile acid esters 11 constitutive androstane receptor (CAR) 336, bile salt export pump (BSEP) 374 402, 460 bioactivation 7, 406 – pharmacophore model 366 bioactivity-based mechanistic models 403 cyclohexyl ring, oxidation 297 bioactivity-mediated toxic compounds cyclosporine 87, 106, 122, 378, 441 – rationalization of 404 CYP1A2 bioavailability (F) 226 – expression 471 biological oxygen demand (BOD) 301 – inhibition, flavonoid derivatives 333 biological system 3 – isoform 335 – used in drug metabolism investigations, – ligands 355 classification 13 CYP3A biotransformations 3, 10, 29, 41, 44, 237, – enzyme family 400 299, 301, 422 – pregnane X receptor (PXR) 404 black box models 323 CYP3A4 blood–brain barrier 70, 374 – activity 443, 468 bomb calorimetry 463 – chronic alcohol consumption 460 bootstrap aggregation 380 – diabetes 460 breast cancer resistance protein (BCRP) 374, – fasting 460 375, 427 – GRID MIFs of 224 bupropion 474 – inhibition ––models 334, 360 c ––quantitative pharmacophore models calcium channel blocker 106, 110, 441 360 calculated molar refractivity (CMR) 379, – inhibitor (see ritonavir) 382, 383 – isoforms, classification model 335 Cambridge Crystallographic Database – MD simulations 164 (CSD) 225 – mechanism-based inhibitor of 459 capillary electrophoresis (CE) 486 – structure 189 catechol-O-methyltransferase (COMT) 363 – substrate Caucasian hepatoblastoma 427 ––models 364 cell-based assays 465 CYP3A5 354, 360, 473 cetirizine 6 – primary enzyme expressed in 422 chemical ionization (CI) 487 CYP1A2 CoMFA model 336 chemical transformations 64, 182, 183, 407 CYP assays – achieved by CYPs 134 – experimental errors for 323 5-chloro-1,3-benzodioxol-4-amine – high-throughput screening assays 328 – positive ion mass spectrum 489 CYP2B6 474 chloroperoxidase (CPO) 135 – crystal structure 356 cholesterol esters 11 – substrates model 355 chorismate mutase 192 CYP2C9 40, 43, 47, 78, 86, 92, 93, 95, 123, circular dichroism (CD) 206 249, 329, 354, 357, 457, 469 cisplatin 7 – activity 406 classification and regression trees (CART) – heteroactivation model 363 325 – inhibitors 332, 336, 356, 363 comparative molecular field analysis ––pharmacophore models 356 (CoMFA) 326 – ligands 356, 363, 364 comparative molecular similarity indices – substrates analysis (CoMSIA) 326 ––pharmacophore model 357 Index 501 CYP2C19 83, 126, 329, 333, 334, 358, 444 – human CYPs classification based on major – inhibitor pharmacophore model 358 substrate class 200 – inhibitors 358 – inhibition 321 CYP2D6 ––assays 491, 492 – activity 92, 93, 112, 244, 256, 333, 335, – issues in predictions 213, 214 406, 442 – Km and kcat values – inhibition models 334 ––Michaelis–Menten interpretation 125 – isoforms ––and relationship with substrate and ––classification model 335 protein structure 124–127 – pharmacophore model 358 ––for various substrate–isoform – quantitative model 359 combinations 121–123 CYP-electron transfer protein – known substrates of various human CYP interactions 81, 82 isoforms 105, 106 CYP from a GRID perspective 224–226 – methods for analysis of products of CYP inhibition 126, 128, 234, 236, 266, 323, drugs 203, 204 325, 332, 447 – molecular dynamics studies 89–91 – assays 323 – number of CYPs present in various CYP isoforms 32, 33, 35, 36, 111, 189, 281, species 107 321, 329, 334, 358, 431, 434, 473 – pathways 400 CYP-mediated site of metabolism prediction – protein–ligand crystal structures – applications to SoM prediction 280, 281 119, 120 ––isoform-specific models 281–283 – SAR of reaction rates 213 ––isoform-unspecific models 283, 284 – structural data 199 – combinations of structure-based models – structural insight into substrate recognition and reactivity 284, 285 by 107, 108 – machine learning methods applied to 278 ––CYP2A6 108, 109 ––atomic descriptors 278, 279 ––CYP2A13 109, 110 ––machine learning methods and ––CYP3A4 115 optimization criteria 279, 280 ––CYP8A1 115, 116 – reactivity-based methods applied to 274 ––CYP11A1 116–118 ––comprehensive methods 276–278 ––CYP19A1 118, 119 ––methods only applicable to carbon ––CYP46A1 119 atoms 274–276 ––CYP1A1, CYP1A2, and CYP1B1 108 CYP polymorphism 64, 323 ––CYP11B2 118 CypScore software 41 ––CYP2C8 110–112 CYP model-based prediction, FDA ––CYP2C9 112 guidance 452 ––CYP2D6 112, 113 cysteine 496 ––CYP2E1 113 cytochrome P450 (CYP) enzymes 10, 16, 29, ––CYP2R1 113–115 77, 103, 199, 492. See also various isoforms – structure–activity relationships based on – analysis methods 204 products 210, 211 ––circular dichroism (CD) 206 ––knowledge-based SAR 212 ––HPLC–UV 204, 205 ––SARs based on chemical bond ––LC–HRMS 205 energy 211 ––LC–MS 205 ––SARs based on docking 211, 212 ––LC–MS/MS 205 – structure-based approaches to study ––NMR 205–207 metabolism of substrates by 243 ––X-ray diffraction 206 ––6 Å Rule 243–245 – binding site of isoforms 117 – substrate binding 199 – complex CYP products 208–210 – substrate identity in various species 104, – docking for predicting kinetic 106, 107 parameters 120 – substrate properties for various human ––challenges 120 isoforms 120, 123, 124 502 Index – substrate recognition in catalytic cycle 103, – software packages for 40 104 donor capability 379 – systems for production of reaction products dopamine and analysis of systems 200 – mechanism of formation 160–163 ––membranes from heterologous expression 3D-QSAR modeling 15 systems 202, 203 DrugBank 57, 58 ––purified CYPs in reconstituted – capecitabine drug metabolism systems 201, 202 pathway 58 ––tissue microsomal systems 201 drug-drug interaction (DDI) 12, 64, 70, 323, ––in vivo systems 201 441 – untargeted searches for CYP reactions 208 – additional factors influencing drug cytosolic enzyme systems 428 metabolism 442, 443 – drug metabolism enzymes, in vitro d models 463–474 databases 11 – drug metabolism, inhibition of 441 – Cambridge Crystallographic Database – metabolic 441 (CSD) 225 – metabolism, transcriptional regulation – Human Metabolome Database (HMDB) 59 460–463 data mining and machine learning – pharmacokinetics (PKs) 441 approaches 41, 42 – primary hepatocyte culture models 459 – disadvantage of mining approach 41 drug–food interactions 64, 70 – FAst MEtabolizer (FAME) 42 drug metabolism enzymes – Metaprint2D 41 – drug’s clearance 441 – P450 Regioselectivity module of drug metabolism enzymes, in vitro models Percepta 42 – cellular models, gene expression 471–474 – RegioSelectivity (RS)-WebPredictor 42 – control/test compounds, treatment 470, dealkylation 29 471 deglycosylation 29 – gene reporter assays 465, 466 dehydrogenases 17 – induction assays, in cellular models demethylation 299 468–470 density functional theory (DFT) 134 – induction studies, cellular models for desloratadine 6 466–468 detoxification 3, 9, 133, 321, 418, 421, 423 – ligand binding assays 463–465 diazepam 7 drug metabolism, factors affecting 12 diclofenac 86 – epigenetic mechanisms 13 dietary component – intra-individual factors 13 – bioavailability/elimination, flow chart 417 – pharmacodynamic/pharmacokinetic drug dietary tyramines responses 12 – degradation of 362 drug metabolism, inhibition of 441 dihydrotestosterone – cytosol 456, 457 – multistep
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