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 – 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 battery 461 basic-helix-loop-helix (bHLH) protein 460 ALCHEM 299 Bayesian classifiers 334 alcohol consumption 443, 460 BEH C18 column 494 118, 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, – 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 (CYP) 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, 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 pathway of oxidation, catalyzed – human liver microsomes (HLMs) by CYP19A1 210 445–456 – sites of oxidation by CYP3A4 212 – predicting inhibition, in vitro models 444, dimethyl sulfoxide (DMSO) 445 concentrations 447 – primary hepatocytes 458, 459 dioleoylphosphatidylcholine (DOPC) 96 – recombinant enzymes 457, 458 DME system 436 – S9 fraction 456, 457 docking 39, 40 drug metabolism, pharmacokinetics – AutoDock 40 (DMPK) 321 – AutoDock Vina 40 drug-metabolizing enzymes, in vitro – Glide 40 inducers 470 – GOLD 40 drug’s fate in body 5 – IDSite 40 dynamics of CYPs 88 Index 503

– active site access and egress pathways Fourier transform ion cyclotron resonance 93–95 (FT-ICR) instruments 487 – active site flexibility 88, 92, 93 free energy cycle 184 – active site solvation 93 free energy methods 183 – endpoint methods 186 e ––linear interaction energy 187 EC50 values 324, 434, 464, 465, 473 ––molecular mechanics-generalized born efflux ratio (ER) 377, 378 surface area 186, 187 electron impact (EI) 487 ––QM endpoint methods 187 electrophiles 8 – pathway methods 183, 184 electrophoresis-based separation 486 ––Bennett acceptance ratio 185 electrospray ionization (ESI) 487 ––free energy perturbation 185 electrostatic effect 163 ––pathway planning 184, 185 encainide 7 ––thermodynamic integration 185, 186 encoding rules in a knowledge base 299 free energy perturbation 185 endoplasmatic reticulum (ER) 423 free radicals 9 endoxifen 7 FuFo actives endpoint methods 186 – bioavailability 415 – linear interaction energy 187 – high concentrations 422 – molecular mechanics-generalized Born – ingredients 415 surface area (MM-GBSA) 186, 187 – pharmacokinetics (PK) of 415 – QM endpoint methods 187 functional 4, 12, 13, 16 epoxidation 113, 133, 209, 266, 283 – aromatic and double bonded carbon g atoms 271, 272 GALAS modeling approach 42 ergodic hypothesis 181 gas chromatography (GC) 204, 486 estradiol 10, 105, 106 gas chromatography–mass spectrometry estrone 10, 11 (GC–MS) 302 experimental drug metabolism 18 gene expression 12 expert software packages 11 GeneGo pathway 405 exponential averaging (EXP) 185 gene products 12 – regulatory 12 f genetic algorithm k-nearest neighbor fadrozole 118 (GA-kNN) 388 Fa2N-4 cell line 466 genetic polymorphisms 13 FAst MEtabolizer (FAME) 42 Gibbs energy 182 fatty acids 77 Gilbert’s syndrome 442 felodipine 106, 110, 441 GIT enzymes 416 fexofenadine 106, 444 Glide software 40 flavin-containing monooxygenases 17, 444 glucuronidation 6, 9, 10, 29, 295, 337, 354, – catalyzed N-oxygenation of tertiary 361, 362, 422, 442, 443, 493 amines 10 glutathione (GSH) 9, 496 flavonols – hepatic 400 – glucuronidation of 361 – trapping 235 – UGT1A9 pharmacophore model 361, 362 glutathione S-transferases 9, 17, 29, 422 flurbiprofen 43 glycosylated flavonoids 421 – ligands 357 GOLD software 31, 40, 248, 254 food bioactives graphical processing unit (GPU) – bioavailability (BA) 416 technologies 179 – first-pass metabolism 418 GRID/GOLPE QSAR methodology 337 – intestinal absorption 416–418 GRID software 223 – metabolism, in vitro models gut microflora classification 418, 419 – complex intestinal models (TIM-2) 421 504 Index

– exposure time 419 human endogenous biosynthesis 363 – fecal slurry 421 human ether-a-go-go-related gene – isolated pure bacterial cultures 421 (hERG) 321 – modifications 419 human hepatoma cell line HepG2 465 – several microbial enzymes 420 human liver microsomal stability – in vitro models 420, 421 – QSAR models of 339 gut wall human liver microsomes (HLMs) 445–456 – clearance 422 – CYP inhibitors 449 – metabolism (see intestinal metabolism) – direct inhibition 447–452 – physiological illustration depicting 416 – liver S9 fractions 456, 458 – time-dependent inhibition 452–456 h – time-dependent inhibition experimental HepaRG cell culture model 467, 468, 471 design 454 hepatic clearance 423 – in vitro to in vivo extrapolation hepatic enzyme leakage 471 (IV/IVE) 449 hepatic metabolism Human Metabolome Database (HMDB) 59 – cryopreserved hepatocytes vs. human organic cation transporter 1 microsomes 428, 429 (hOCT1) 375 – FuFo ingredients, pharmacokinetics 423 hydrogen bonding 85, 110, 134, 151, 224, – hepatocyte cell lines 426, 427 249, 382, 391 – hepatocytes, in culture 429–431 – capacity 385 – microsomes 424 hydrolases 16, 17 – physiological illustration depicting 416 3-hydroxylated benzodiazepines 6 – primary cultures 427, 428 hydroxylation 29 – S9 fractions 426 – of aliphatic carbon atoms 268 – supersomes 424 – aromatic and double bonded carbon – in vitro liver metabolism models atoms 271, 272 ––advantages/disadvantages of 425, 426 hydroxymethylbenzene 293 – in vitro models 424 4-hydroxytamoxifen 7, 405 hepatocyte nuclear factor 4 α 353 hepatocytes 11, 13, 373, 423, 427–429, 433, i 445, 446, 458, 463, 468, 470, 475, 491 ibuprofen 11, 86

hERG potassium channel 404 IC50 inhibition study design 450 heterologous expression systems 202 IC50 values 434 – membranes from 202 idiosyncratic drug reactions (IDRs) 8 ––insect cell systems 202 IDSite 40 ––mammalian cells 202 indomethacin 86 ––microbial membrane systems 202, 203 inducer models 363 heteronuclear multiple bond correlation – hetero/autoactivation 363 (HMBC) 206 ––CYP3A4 substrate model 364 heteronuclear single quantum coherence ––CYP2C9 heteroactivation model (HSQC) 206 363, 364 high-performance computing (HPC) – nuclear receptors 364 facilities 180 ––CAR ligands 366 high-performance liquid chromatography ––pregnane X receptor 364–366 (HPLC) 203, 204, 434 inhibition assays 329

high-resolution mass spectrometry inhibitor concentrations (IC50) curve 447 (HRMS) 205 interactions with metabolizing enzymes human CYPs 78 – methods for predicting 31–36 – based on major substrate class 200 inter-individual factors 12 – encoding 77 intestinal metabolism 421 – structural features 78–81 – FuFo actives 422 – three-dimensional structures 78 – subcellular/cellular models 423 Index 505

– tissue intact models 423 – metabolites 11 – in vitro models 422, 423 – xenobiotics 353 in vitro hepatocyte variation. 430 liquid chromatography–mass spectrometry in vitro liver metabolism models (LC–MS) 302 – advantages and disadvantages of 425, 426 liquid chromatography–mass spectroscopy/ in vitro metabolism models mass spectroscopy (LC–MS/MS) 447 – mathematical models for 432, 433 – direct inhibition experimental scheme – measurement methodology 432 448 – pharmacokinetic analysis 431, 432 liver isothermal titration calorimetry (ITC) 180 – clearance parameter (CLH) 431 – cytosol 457 k – microsomes 339 KBS, see knowledge-based system (KBS) – toxicity 496 ketoconazole 462, 463 – in vitro cellular models 446 kinetic isotope effects (KIEs) 213, 214 logic of argumentation, usage 303–307 knowledge-based application 299 loratadine 6 knowledge-based software 16, 37–39 lorazepam 6 knowledge-based system (KBS) 37–39, 44, luciferase 464–466 45, 293, 313, 407 – absolute and relative reasoning 307–310 m – basic structure of 294 machine learning approaches 41, 42 – building/maintaining 295–299 major histocompatibility complex – encoding rules 299, 300 (MHC) 406 – logic of argumentation 303–307 MassMetaSite 236–239 – MYCIN 295 mass spectrometry (MS) 485 – performance, validation/assessment of Matthews correlation coefficient (MCC) 327, 312–314 334, 387 – predictions from multiple sources 310–312 Maximum Unbiased Validation (MUV) – ways of working 301, 302 database 352 Kupffer cells 445 McGowans characteristic volume 382 MD simulations 247, 248 l – CYPs in bilayers 96 lactase phlorizin hydrolase (LPH) 421 MetabolExpert software 15, 16, 301 L-742 694 compound 405 metabolic lead optimization 226 – enzymes, phase I 362, 363 – challenges 226 – intermediate complex 360 leave-many-out (LMO) 326 – profiling, toxicological effects 353 leave-one-out (LOO) 326 – stability 491, 492 level of quantification (LOQ) 434 – toxification 7 libraries 53, 447 ––mechanisms of toxicity, parameters 437 – precalculated fragment-based 37 metabolism ligand-based approach 30, 48, 351, 355 – transcriptional regulation ligand binding assays 463–465 ––gene induction pathways 460–462 ligand-derived models 37 ––gene repression/suppression 462, 463 ligand parameterization 188, 189 Metabolite Database 41 ligand–protein interactions 223, 225, 248 metabolite detection/profiling 485–496 linear interaction energy (LIE) 247 – chromatography 486, 487 linking experiment and simulation 180 – cytochrome P450 inhibition assays 491, lipid peroxidation 9, 10 492 lipophilic – liquid chromatography-mass – anionic substrates 86, 95, 97 spectrometry 487– 490 – drugs 8 – metabolic stability 491, 492 – interactions 79 – reactive metabolite detection 496 506 Index

– sample preparation for 490, 491 MIF-based technologies 224 – sample preparation for LC–MS 490, 491 MIF discretization 227 – in vivo/in vitro studies, identification MIFs, see molecular interaction fields (MIFs) 492–495 molecular docking 69. See also docking metabolite identification (MetID) 224 molecular dynamics (MD) 78, 179 metabolites – simulations 37 – classification of drugs without/with molecular interaction fields (MIFs) 39, 46, active 7 223 – detection and profiling (see metabolite molecular orbital (MO) methods 16 detection/profiling) monoamine oxidase (MAO) 362 – discrepancy between numbers of observed morphine 7 and possible metabolites 298 morphine 6-O-glucuronide 7 – formation multidrug resistance (MDR) 374, 375, 378 ––vs. substrate depletion 432 multidrug resistance–associated proteins 374 – major determinants of metabolite multiple linear regression (MLR) 323 formation 267 – CYP2C9 substrates 329 – metabolite predictors 66 mutations 78, 86, 91, 256 – methods for predicting SoMs, structures – analysis of CYP2B4 82 of 31–36 – CYP1A2 T124S 85 – online databases 55 – CYP2D6 F483A 192 – predicting toxic effects (see predicting toxic – effect of 256–258 metabolites) – predicting substrate formation and 258 – software for predicting (see software for predicting metabolites) n – spectroscopy 206–208 N-acetyltransferases (NATs) 29 – structure prediction 30 NADPH-regenerating system 447 ––methods for 31–36 naproxen 85, 86, 105 – of toluene (methylbenzene) 293 negative predictive value (NPV) 386 Metabolizer 45, 65, 297 Netherlands Cancer Institute (NKI) 378 MetaPred server 68 nicotinamide adenine dinucleotide phosphate MetaPrint2D 41, 66, 67, 68 oxidase 445 MetaPrint2D-React 66, 401 NMR relaxation 179 MetaSite software 15, 16, 39, 226, 227 N/O-dealkylation 42 – accessibility function 227–229 non-CYP oxidoreductases 16 – automated metabolite identification (see nordazepam 7, 60 MassMetaSite) nuclear magnetic resonance (NMR) – prediction for PH-302 CYP3A4 spectroscopy 248, 486 inhibition 237 nuclear overhauser correlated spectroscopy – prediction of CYP inhibition 234–236 (NOESY) spectra 206 – reactivity function 229, 230 nuclear receptors (NRs) 353 – site of metabolism prediction 230, 231 – CYP induction 353 – validation 231 nucleophiles 8 META software 15, 16, 44 METEOR software 15, 16, 302 o 3-methoxy-O-desmethyl encainide 7 O-desmethyl encainide 7 2-methyl-3-(3,5-diiodo-4-hydroxybenzoyl) O-desmethyltramadol 7, 60 benzofuran 356 omeprazole 358 4-methyl(hydroxymethyl)benzene 293 online drug metabolism databases 53, 54, MEXAlert software 39 56, 57 Michaelis–Menten constant 424, 431 – categories 56 micronutrients 415 – DrugBank database 57, 58 midazolam 474 – Human Metabolome Database 59 MIF-based SoM predictor 46 – PharmGKB 59, 60 Index 507

– PubChem 61 – definitions of 352 – specialized databases 63 – determination of chemical features 354 ––PK/DB 64 – inducer models 363–366 ––PKKB 64 – qualitative models 352 ––SuperCYP 64 – substrate and inhibitor 354–363 ––UM-BBD 63 pharmacophore models 15, 284, 340, 352, – synoptic databases 61 353 ––BindingDB 63 – substrate and inhibitor 354–363 ––ChEBI 62 phase space 181 ––ChEMBL 62 phenacetin 6, 474 ––KEGG 62, 63 phenobarbital 462 – Wikipedia 60, 61 N-(1-phenylcyclohexyl)-2- online drug metabolism prediction servers 65 ethoxyethanamine 296 – ADMET predictors 70 3´-phosphoadenosine-5´-phosphosulfate – metabolite predictors 66 (PAPS) 338 – SoM predictors 66–68 phosphoglycoprotein (P-gp)-mediated – specialized predictors 68–70 disposition 6, 373, 374 oral contraceptives 441 – adenosine triphosphate (ATP) 374 organic anion transporters (OATs) 374, 427 – ATPase binding cassette (ABC) 373 organic anion transporting polypeptides – compound structure, influence 380–385 (OATPs) 427 – cytochrome P450 3A4 (CYP3A4) 375 organic cation transporters (OCTs) – drug discovery, application 388–391 373, 427 – drug transporter 374 oxazepam 6, 7 – QSAR models (see QSAR models) oxidative hydroxylation 299 – QSAR prediction 376 oxidative stress 8, 9 – solute carrier (SLC) superfamilies 373 oxidoreductases 8, 9, 16 – substrate identification 389 – in vitro/in silico approaches 374 p physiologically based pharmacokinetics 1-palmitoyl-2-oleoyl-sn-glycro-3- (PBPK) 392, 451, 452 phosphocholine (POPC) 94, 96 pioglitazone 235 paracetamol 6, 400, 407 – GSH adducts 235 parameterization – inhibition for CYP isoforms 235, 236 – ligand 188 polymorphisms 17, 77 – linear interaction energy 187 polyunsaturated fatty acids 9 partial least-squares discriminant analysis positive predictive value (PPV) 386 (PLS-DA) 332 potential energy surface (PES) 183 PASS (prediction of the activity spectra of PreADMET 70 substances) 403 predicting toxic metabolites 397, 401 pathways-based information 404 – absolute metabolism likelihoods and PBPK models 452 rates 401, 402 Percepta module 42 – anticipating toxic effects of 398 peroxidases 9, 17 – bioactivity-based mechanistic models 403, P-glycoprotein (P-gp) 418, 427 404 P-gp Predictor 69 – CYPs, polymorphisms 402 pharmacodynamic (PD) effects 5 – cytochrome P450 (CYP) pathways 400 Pharmacogenomics Knowledge Base – endogenous/exogenous substances 397 (PharmGKB) 59 – hepatic glutathione (GSH) 400 pharmacokinetic (PK) effects 5 – incorporating pathway information pharmacokinetic–pharmacodynamic (PKPD) 404–406 modeling 5 – knowledge-based systems 407 pharmacophore-based methods – MetaPrint2D-React 401 – CYP induction, nuclear receptors 353 – pharmacogenetic data 402 508 Index

– political developments 408 ––QM methods 137, 138 – reactive metabolites 407, 408 ––QM/MM boundary treatments – relative and absolute rates 399, 401, 402 139, 140 – in silico methods 397 ––QM/MM energy versus free energy – toxic effects 402 calculations 141 ––bioactivity-based mechanistic ––QM/MM geometry optimization 140 models 402, 403 ––QM/MM molecular dynamics and free ––incorporating pathway information energy calculations 140, 141 404–406 ––QM/MM partitioning 136, 137 ––knowledge-based systems 407 ––subtractive versus additive QM/MM ––reactive metabolites 407, 408 schemes 139 ––toxicogenetic/pharmacogenomic – practical issues in studies 141 approaches 406, 407 ––accuracy of QM/MM results 143 – toxicogenetic/pharmacogenomic ––extracting insights from QM/MM approaches 406, 407 calculations 144 – UDP-glucuronosyltransferases (UGTs) ––QM/MM geometry optimization 143, 402 144 – workflow for 399 ––QM/MM setup 142, 143 P450 regioselectivity module of percepta 42 ––QM/MM software 141, 142 pregnane X receptor (PXR) 336, 364, 402, QSAR models 322, 353, 375, 405 427, 460 – applicability domains 328 procarcinogens 77 – assessment and validation 327, 328 prodrugs 6, 7 – black box models 323 propranolol 493 – classical 326, 329–333 prostaglandins 77, 115 – conjugative metabolizing enzymes 337 (PDB) 225, 355 – for cytochrome P450 328 protein flexibility 249–253 ––SARs 328, 329 protein–ligand complexes 37, 107, 354 – 3D models 335, 336 protein–ligand crystal structures 119, 120 – 3D QSAR methods 340 protein–ligand interactions 39 – enzyme induction 336, 337 PubChem 61 – of human hepatic microsomal intrinsic purified CYPs in reconstituted systems 201, clearance 340 202 – local vs. global 325, 326 pyrazoles 294 – machine learning 327, 333 ––models 325 q – MLR-based 326 QikProp software 39 – models, classification 334, 335 QM/MM studies 135 – molecular descriptors 324 – applications to cytochrome P450 – multiple linear regression (MLR) 323 enzymes 144–146 – predictive ability 327 ––conversion of Cpd 0 into Cpd I in T252X – SAR 326 mutants 148–151 – in silico 324 ––Cpd I species of different cytochrome ––prediction 324 P450s 154, 155 – sulfotransferases 338, 339 ––formation of Cpd I from Cpd 0 146–148 – training SAR 325 ––mechanism of cytochrome P450 StaP – uridine diphosphate glucosyltransferase 155–160 (UGT) 338 ––mechanism of dopamine formation – in vitro clearance 339, 340 160–163 QSAR prediction ––properties of Cpd I 151–154 – experimental data/assays 376–378 – methodological issues in studies 136 – phosphoglycoprotein (P-gp)-mediated ––electrostatic QM/MM interactions 139 disposition 376, 380, 385–388 ––MM methods 138 – substrate identification 378–380 Index 509 quantitative reverse transcription polymerase s chain reaction (qRT-PCR) 473 sandwich culture model 429 quantitative structure–activity relationship SAR, see structure-activity relationship (SAR) (QSAR), see QSAR models Scutellaria baicalensis 359 quantum mechanical (QM) shape-focused approaches 42, 43 – calculations 37 single-nucleotide polymorphisms (SNPs) 456 – tool 134 site of metabolism (SoM) prediction 30, 38 quenching 9 – competition between different SoMs with quinone reductases 9 regard to 267 – Metabolism module of ADMET r Predictor 42 random forest (RF) 335, 380 – methods for 31–36 reactive nitrogen species (RNSs) 9 – software 38 reactive oxygen species (ROSs) 8, 9 – SoM predictors 56, 66 reactivity models 40, 41 – structure-based predictions 243 – for CYP reactions 268 SMARTCyp approach 45, 67, 248 ––combined carbon atom models 273 – for CYP isoforms 2D6 and 2C9 248 ––comprehensive models 273, 274 – for descriptor calculation 42 ––epoxidation of aromatic and double – software 40 bonded carbon atoms 271, 272 SMIRKS rules 267 ––hydroxylation sodium taurocholate cotransporting –––aliphatic carbon atoms 268–270 polypeptide (NTCP) 373 –––aromatic and double bonded carbon soft drugs 6 atoms 271, 272 software for predicting interactions of small – CypScore 41 molecules – SMARTCyp 40 – ADMET Predictor Metabolism module – software for 40, 41 47 – StarDrop 40, 41 – ADMEWORKS Predictor 48 receiver operating characteristic (ROC) – isoCyp 47 curves 38, 312 – with metabolizing enzymes 46–48 recombinant enzymes 232, 233, 238, – MetaDrug 47 457, 458 – MetaPred 47 recursive partitioning (RP) 335, 380 – PASS 48 regioselectivity – Percepta software package 46, 47 – P450 Regioselectivity module of – VirtualToxLab 48 Percepta 42 – WhichCyp 47 – RegioSelectivity (RS)-WebPredictor 42, 67, software for predicting metabolites 43 281 – data mining and machine learning registration, evaluation, and authorization of approaches 46 chemicals (REACH) 323 ––Metaprint2D-React 46 reliability index (RI) 42 – knowledge-based systems 44, 45 retinoid X receptor (RXR) 461 ––JChem Metabolizer 45 reversed-phase (RP) chromatography 487, ––META 44 494 ––MetabolExpert 44 rhodamine-123 375 ––MetaDrug 45 rifampicin 365, 441, 461, 469, 470 ––Meteor 44, 45 ritonavir 87, 88, 105, 106, 115, 122, 453, 459, ––SyGMa 45 469, 470 ––TIMES 45 root mean square error (RMSE) 324 ––UM-PPS 45 rosiglitazone 235 – molecular interaction fields 46 – GSH adducts 235 ––MetaSite 46 – in vivo liver toxicity 235 software for predicting sites of RS-WebPredictor 42, 67, 68 metabolism 38 510 Index

– data mining and machine learning – molecular docking (see molecular docking) approaches 41, 42 – for predicting metabolism 30 ––FAst MEtabolizer (FAME) 42 – for predicting sites and products of ––Metaprint2D 41 metabolism (see 6 Å Rule; protein ––P450 Regioselectivity module of flexibility) Percepta 42 structure-based prediction 40 ––RegioSelectivity (RS)-WebPredictor 42 structure–function relationships 77 – docking 39, 40 structure–metabolism relationships ––IDSite 40 (SMRs) 30 – knowledge-based systems 38, 39 substrate and inhibitor pharmacophore ––MEXAlert 39 models ––QikProp 39 – cytochrome P450 enzymes 354 – molecular interaction fields 39 ––CYP3A5 inhibitors 360 ––MetaSite 39 ––CYP3A7 inhibitors 360 – reactivity models 40, 41 ––CYP3A4 substrate pharmacophore ––CypScore 41 model 359, 360 ––SMARTCyp 40 ––CYP1A2, substrates/inhibitors ––StarDrop 40 354, 355 – shape-focused approaches 42, 43 ––CYP2B6 substrates 355, 356 ––ROCS 43 ––CYP2C9 ligands 356, 357 solid-phase extraction (SPE) 491 ––CYP2C19, substrates 357, 358 S-oxidation 42 ––CYP2D6 model 358, 359 specificity (sp) 386 – interference with phase I metabolic StarDrop software 40, 41 enzymes 362, 363 statins 441 – UDP-glucuronosyltransferases (UGTs) 9, statistical mechanics 180 29, 361, 402, 422 77 ––UGT1A1 substrates 361 structural variability 83, 97 ––UGT1A4 substrates 361 – CYP1A2 83–85 ––UGT1A9 substrates 361, 362 – CYP2A6 85 ––UGT2B7 substrates 362 – CYP3A4 87, 88 substrate recognition sites (SRSs) 82, 83 – CYP2C9 85, 86 substrate specificity profiles 83 – CYP2D6 86 sulfation 337, 400, 422, 490 – CYP2E1 87 sulfotransferases (SULTs) 9, 17, 29, 337, 338, structure-activity relationship (SAR) 39, 322 339, 422 – based on chemical bond energy 211 sulfoxidation 10, 133 – based on docking 211, 212 SuperCYP database 64 – based on products 210, 211 supernatant (S9) 446 ––knowledge-based SAR 212 superoxide dismutase (SOD) 9 ––SARs based on chemical bond Supersomes 424, 426, 428, 434 energy 211 support vector machine (SVM) 67, 380 ––SARs based on docking 211, 212 – classification models 334 – CYP inhibitor SyGMa (Systematic Generation of potential ––SAR, related to ionization state 330 Metabolites) 34, 45, 297, 311 ––SAR, related to molecular size 331 – 3D-QSAR modeling 15 t – GRID/GOLPE QSAR methodology 337 tamoxifen 7, 105, 106 – knowledge-based 212 temazepam 6 – PLS-based QSAR model 340 terfenadine 444 – reaction rates 213 testosterone 164, 203 – SAR of reaction rates 213 – hydroxylation 201 – training SAR 325 – sites of oxidation by CYP3A4 212 structure-based methods 37 tetrachlorodibenzo-p-dioxin (TCDD) 460 Index 511 thermodynamic integration 185 – model validation 312–314, 327 thin layer chromatography (TLC) 486 van der Waals (vdW) interactions 185 time-dependent inhibition (TDI) 266 very low-density lipoprotein (VLDL) 437 time-of-flight (TOF) 238, 487, 488 vitamin K epoxide reductase complex TIMES (Tissue Metabolism Simulator) 34, (VKORC1) 406 45, 301 VolSurf descriptors 380, 384, 388 tissue microsomal systems 201 TNO developed Intestinal Model w (TIM-2) 421 warfarin 441 total free energy 249 – crystal structure 363 toxicity pathways 408 – ligands 357 toxicophores 9 water 254 toxification 3, 7–9 – computational studies 254 toxophores 9 – effect of crystallographic water molecule in ToxTree software 267 CYP1A2 254 tramadol 7 – effect of water molecules transactivation assays 465 ––on predicted docking poses 255 transcription factor activators 464 –––SoM predictions, effects sorted in – in vitro screening methods 464 classes 255 transferases 16 ––and protein structures 256 transferase–transporter coupling 10 – entropic effect 254 transition state theory (TST) 183 – x-ray structure of CYP1A2 254 trapping assays 496 web-accessible databases 54 trichloroacetic acid (TCA) 491 web servers 53, 54, 71 troglitazone 105, 111, 112, 122, 234, 235 well-stirred model 432 trovofloxacin 405 whole-cell hepatocyte assays 339 Wikipedia 53 u WikiProjects 60 UDP-glucuronosyltransferases 17, 444 World Wide Web 53 UGT1A1 enzyme 442, 446 UGT1A gene 473 x UM-BBD database 45, 69 xenobiotics 104, 415, 417 UM-PPS web server 45, 69 – classification of human CYPs based on 200 uridine diphosphate glucosyltransferases – CYPs induction 336 (UGTs) 337 – definition 415 – detoxification process 418 v – drug’s clearance 441 validation 30 – harmful 367 – assay validation – metabolism 3, 30 ––analytics 434 ––enzymes expression of 459, 460, 463 ––reference compounds, selection ––factors influencing 18 433, 434 ––genes, mRNA expression of 471 ––in vitro model, theoretical steps 434, ––principal metabolic proteins 322 435 – metabolite prediction 66 – maximum unbiased validation (MUV) – response elements (XREs) 460 database 352 XMetDB database 266 – MetaSite 231 x-ray diffraction 206