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In silico ADME/Tox in drug design “Bioinformatics IV” (Computational Drug Discovery) Wednesday 7 June 2006 CMBI, University of Nijmegen Lars Ridder, Organon What makes a good drug ? • Good activity/selectivity on the right target BUT ALSO !!! • Absorption • Distribution • Metabolism • Excretion • Toxicity ADME/Tox Reasons for drug failure in Clinical Development (>80%) ADME/Tox Role of in silico ADME/Tox Research Development $300m $500m 4-5yrs (30%) 8-10yrs (70%) Does the compound work in man? Identify ADME/Tox problems Failure rate over 80-90% earlier in the process (safety, efficacy) More emphasis on ADME/Tox properties in lead optimization Market In-house design cycle Guide optimisation based on in silico models Screening, hit-optimization, lead selection, lead optimization, SOPP, development Validate/refine models based on new pharmacological data Absorption/Distribution/Metabolism Pharmacokinetic parameters • • • • Oral bioavailability = fraction of dose that enters blood circulation (after 1st pass metabolism in the liver) Absorption = fraction of dose that passes the gut wall Clearance (CL) = amount of blood cleared per time unit Volume of distribution (Vd) = (I.V.) Dose / Initial plasma concentration Absorption MW < 500, non-polar Most common route of drug absorption Membrane permeation Water C1 Membrane C2 Penetration rate = P x A x (C1-C2) Depends on physicochemical properties of drug, e.g. lipophilicity, MW, hydrogen bonding, etc. P = partition into membrane A = effective surface area of membrane C1-C2 = concentration gradient Hydrogen bond donors and acceptors H O O H H H H O O H R H O H O H Absorption requires desolvation, which becomes more difficult with an increasing number of hydrogen bonds The octanol/water model water O O OH octanol OH log P log [ AH ]oct [ AH ]wat The octanol/water model water O octanol O OH OH log P log [ AH ]oct [ AH ]wat [ AH ]oct [ A ]oct log D log [ AH ]wat [ A ]wat O O O O The octanol/water model water O octanol O OH OH log P log [ AH ]oct [ AH ]wat [ AH ]oct [ A ]oct log D log [ AH ]wat [ A ]wat O O O O logD = logP - log(1 + 10pH-pKa) logD = logP + log(1 - fionized) LogD depends on pH ! pH-range in GI tract pH (fed) pH (fasted) 3-7 1.4-2.1 5-6.5 6.5 6.5-8 6.5-8 5-8 ClogP Calculating logP from structure: • Fragmentation of solute molecule by identifying Isolating Carbons (IC = not doubly or triply bonded to a hetero atom) • Remaining fragments are characterized by topology and “environment” (i.e. the type of IC’s bound to it) • ClogP is a sum of (tabulated or estimated) contributions of all fragments + isolating carbons + ”corrections” • Where “corrections” are made for intramolecular polar, dipolar and hydrogen bond interactions as well as electronic (aromatic) interactions (modified Hammett approach) ClogP - examples O OH HO Fragment Value 6 x IC (arom) 0.78 Carboxy -0.03 Hydroxy -0.44 4 x Hydrogen 0.91 Electronic int. 0.34 ClogP 1.56 Exp. logP 1.58 ClogP - examples O OH OH Fragment Value 6 x IC (arom) 0.78 Carboxy -0.03 Hydroxy -0.44 4 x Hydrogen 0.91 Electronic int. 0.34 H-bonding 0.63 ClogP 2.19 Exp. logP 2.26 ClogP vs. Caco-2 Caco-2 = in vitro assay to measure absorption rate 10 y = -1.4107x + 7.3791 R2 = 0.4115 9 8 clogP 7 6 5 4 3 2 1 0 0 0.5 1 1.5 log(caco-2) 2 2.5 3 Lipinski’s Rule-of-5 • Lipinski (1997) selected 2245 orally active drugs from the World Drug Index (WDI) • Distribution analysis suggested that poor absorption is more likely when: – Mol. Weight > 500 – ClogP > 5 – Nr. of H-bond donors > 5 – Nr. of H-bond acceptors > 10 Correlation to in vivo (rat) absorption In-house rules based on: ClogP MW H-bond donors H-bond acceptors But also: • • Polar surface area Nr. of rotatable bonds These simple physicochemical properties largely determine bioavailaility ! number of compounds • • • • Bioavailability 120 100 80 60 < 30% > 30% 40 20 0 Good Moderate Bad Monika classification Good = no properties out of range Medium = 1 property out of range Bad = > 1 property out of range Pharmacokinetic modeling • • • • PK-sim Cloe PKexpress Gastroplus Advanced Drug Delivery Reviews 50 (2001) S41–S67 Distribution Most important organ: The brain • Drugs acting on the central nervous system (CNS) must cross the blood-brain barrier (BBB) • Peripheral drugs may be required not to pass the BBB to avoid CNS side effects • Physicochemical properties are important (again) • Efflux by P-gp mediated active transport Metabolism/Excretion Metabolic enzymes Lipophylic metabolites Cytochrome P450 e.g. Flavin monooxygenases Dehydrogenases Hydroxylation, dealkylation, N-oxidation, epoxidation, dehydrogenation, etc. Phase I: (mostly) oxidation Polar metabolites +glutathione +H2O +glucuronate +sulphate +acetate Phase II: conjugation Hydrophylic metabolites +methyl Contributions of Phase I and Phase II enzymes to drug metabolism ADH, alcohol dehydrogenase; ALDH, aldehyde dehydrogenase; CYP, cytochrome P450; DPD, dihydropyrimidine dehydrogenase; NQO1, NADPH:quinone oxidoreductase or DT diaphorase; COMT, catechol O-methyltransferase; GST, glutathione S-transferase; HMT, histamine methyltransferase; NAT, N-acetyltransferase; STs, sulfotransferases; TPMT, thiopurine methyltransferase; UGTs, uridine 59-triphosphate glucuronosyltransferases. [Evans (1999) Science 286: 487] Cellular localisation of metabolic enzymes • Endoplasmitic reticulum (ER) of intestinal- and liver cells contain P450 • Cytosol contains Phase II metabolic enzymes Xray structures of P450 • CYP 2C5 from rabbit was 1st mammalian P450 to be crystallized in 2000 * • the substrate access channel is likely to be buried in the membrane • Structures of most important human CYPs (2C9, 3A4 and 2D6) * [Williams et al. (2000) Mol. Cell 5:121] Structure of P450 Substrate access Heme = catalytic centre Cytochrome P450 (CYP) Reactive iron-oxo intermediate: “Compound 1” In-house data: Compounds tend to be very stable or very unstable Lipophilicity is an important factor in microsomal stability Unstable Stable 10 20 30 40 50 60 70 80 T1/2 (mins) 90 100 110 120 70 70 60 60 50 50 Stable Stable 40 40 Unstable Unstable 30 30 20 20 10 10 99 77 66 44 55 88 ClogP ClogD 33 22 11 00 -1 -1 0 0 -2-2 10 10 -3 -3 compounds compounds (ClogD discriminated better between stable and unstable than ClogP) 160 140 120 100 80 60 40 20 0 0 -4 -4 In vitro measurement of metabolic stability in microsomes = ER membrane fraction of liver cells Ncompounds Phase 1 metabolism vs. lipophilicity The Cytochrome P450 family CYP3A4 Family Subfamily Individual protein Isoenzyme specificity • Various isoenzymes have different but overlapping substrate specificities • (CR indicates flatness of molecule) [Lewis (2002) Drug Disc. Today 7:918] Individual variation in P450 activity • Genetic polymorphism – Defective gene: “poor metabolizer” (e.g. CYP 2C19: >20% in Asians) – Gene multiplication: “extensive metabolizer” (e.g. CYP 2D6) • Enzyme induction -> Increased protein synthesis • Enzyme inhibition • Enzyme activation (CYP 3A4) Drug-drug interactions ! Avoid drugs being metabolized via a single route ! Occurrence of major polymorphisms Ingelman-Sundberg et al. (1999) Trends in Pharm. Sciences20:342 Impact of P450 polymorphism Ingelman-Sundberg et al. (1999) Trends in Pharm. Sciences20:342 Metabolite identification • It is often important to identify the metabolites formed by P450’s: – Identification of toxic metabolites – Knowledge about the site of metabolism can be used to design metabolically more stable compounds (e.g. by modifying/blocking the labile site in a molecule) P450 metabolism • Which metabolites are formed by P450’s depends on: – If and how (i.e. in what orientation) a compound is bound to the active sites of individual CYP’s – The chemical reactivity of various sites of a molecule towards CYP catalyzed mechanisms In silico methods • Binding in CYP active site – Docking – Pharmacophore • Reactivity of ligand sites – QM methods • Metabolism rules – Expert knowledge – Empirical scoring Modeling Ligand binding to CYP2C19 by homology modeling and docking Drug Reaction Amitriptyline Diazepam Flunitrazepam Ifosfamide Imipramine Indomethacin Lansoprazole Methoxychlor Moclobemide Omeprazole Pantoprazole Phenobarbital Phenytoin Rabeprazole R-mephobarbital R-warfarin Sertraline Testosterone Venlafaxine Nirvanol Clomipramine Nordazepam Nortriptyline Trimipramine Citalopram Carisoprodol S-mephenytoin Fluoxetine Hexobarbital Tolbutamide Cyclophosphamide Desogestrel Progesterone N-deMe N-deMe N-deMe C-hydrox, alifatic N-deMe O-deMe C-hydrox, aromatic O-deMe C-hydrox, alifatic O-deMe O-deMe C-hydrox, aromatic C-hydrox, aromatic O-deMe C-hydrox, aromatic C-hydrox, aromatic N-deMe ox, -OH to =O O-deMe C-hydrox, aromatic N-deMe C-hydrox, alifatic N-deMe N-deMe N-deMe N-deMe C-hydrox, aromatic N-deMe C-hydrox, aromatic C-hydrox, alifatic C-hydrox, alifatic C-hydrox, alifatic C-hydrox, alifatic Reactive conformation top ranked top ranked top ranked top ranked top ranked top ranked top ranked top ranked top ranked top ranked top ranked top ranked top ranked top ranked top ranked top ranked top ranked top ranked top ranked top ranked found found found found found found found not found not found not found docking failed docking failed docking failed Assessing chemical susceptibility towards CYP metabolism based on QM calculations Many CYP reactions begin with abstraction of aliphatic H• sites of oxidation Primary/benzylic Benzylic/CI-methyl benzylic Benzylic/O-demethylation Benzylic/O-demethylation Benzylic/secondary 1/2 Hexanol 1/2 Octanol Benzylic/substituted Benzylic/substituted Benzylic/substituted Gact -4.39 -0.74 0.4 0.72 1.28 -1.85 -2.14 0.51 0 1.81 5 R2 = 0.86 4 calculated (AM1) substrate Ethylbenzene a-Chloro-p-xylene 2-Methylanisole 4-Methylanisole 1,3-Diphenylpropane Hexane Octane 1-Phenyl-3-(4-F-phenyl)propane 1-Phenyl-3-(4-Me-phenyl)propane 1-Phenyl-3-(4-F3Me-phenyl)propane 3 O 2 1 0 site 2 RT ln x1 H rad x2 IPrad site1 0 1 2 3 -1 experimental (kcal/mol) Works for small molecules – for larger drug molecules a combination of high level modeling and QM calculations will ultimately result in more accurate predictions 4 5 Derivation of metabolic rules • Example: rule for N-acetylation R NH2 H N R O [NH2:1] >> [N:1]C(=O)C • Apply on training set of 7307 reactions metabolites generated in total metabolites match experimental product probability 1223 122 122/1223 = 0.10 Refined rules for N-acetylation Three more specific rules for N-acetylation Arom NH2 H N Arom 79 / 357 = 0.22 R NH2 H N R 122/1223 = 0.10 Aliph NH2 O O H N Aliph 33 / 417 = 0.08 Hetero NH2 O H N Hetero 10 / 88 = 0.11 O Refined rules for N-demethylation H N NH2 CH3 0.8 10/13 = 0.77 0.6 H N N CH3 CH3 11/20=0.55 Probability CH3 0.4 0.2 H N aliph CH3 aliph NH2 102/266 = 0.38 0 20 CH3 CH3 aliph CH3 109/434 = 0.25 CH3 H N N R R N 22 23 24 H (hydrogen abstraction) H N N aliph 21 R 182/1052 = 0.17 R CH3 NH 2/87 = 0.02 25 Current rule base at Organon 19 • 148 rules • phase I and phase II metabolism • Probabilities range from 0.006 (glycination of aliphatic carboxyls) to 0.77 anilines) (demethylation of methyl- 19 12 24 22 14 4 13 dealkylation carbon oxidation reductions condensation glucuronidation 8 13 hydroxylation hetero oxidation hydrolysis other phase I other phase II Evaluation: Sulfadimidine 1 H N S O N O O N H H N 2 O H2N N O S S N H S N H O N H N O N O N O O Sulfadimidine H2N 5 N N OH N 3 H2N O S O 4 H2N N H O N Prediction(rank) N O S OH N N H OH N O OH Application: metabolic stability Predicted Rank 1 O N N N N N O N HO N N N N O O -> Confirm experimentally by mass spectroscopy Med Chem optimisation: increased metabolic stability O N F N N N N O Toxicity Systemic Toxicity Organ Specific Toxicity • • • • • • • Blood/Cardiovascular Toxicity • Hepatotoxicity • Immunotoxicity • Reproductive Toxicity • Respiratory Toxicity • Nephrotoxicity • Neurotoxicity • Dermal/Ocular Toxicity Acute Toxicity Subchronic Toxicity Chronic Toxicity Many endpoints Genetic Toxicity Many mechanisms Carcinogenicity Developmental Toxicity -> Tough problem • Photo toxicity Prediction of toxicity Biology Activity (Toxicity) Rules/ToxQSAR icophores Statistics Analytical methods Chemistry Structure Reaction mechanisms • Expert or rule-based systems • QSAR or “correlative” methods Example expert system: Derek • 303 knowledge based alerts or toxicophores • 35 tox. endpoints • refs to literature included • Works well e.g. for mutagenicity Example expert system: Derek output LHASA PREDICTIONS Carcinogenicity Alert overview: 107 Aromatic amide R1 N O R2 R2 R1 = C (aryl) R2 = C, H Cellular metabolism required for activity. The best evidence indicates that hydroxylamino compounds are proximate carcinogenic forms. The above functional group can be converted to hydroxylamine by hydrolases, oxidases, or reductases endogenous to most tissues. References: General principles for evaluating the safety of compounds used in food-producing animals. Title: Author: Food and Drug Administration (FDA). Source: Food and Drug Administration Report, 1986, III-7-III-17, July 1994 revision available at "http://www.fda.gov/cvm/guidance/guideline3toc.html". Locations: Paracetamol ! Example QSAR method: APA Acute toxicity model • 37400 IP-mouse LD50 data • Classification – Knowledge from literature – Properties identified form decision trees • QSAR based on fragments • Overall R=0.8 for test-set • • • • • Absorption Distribution Metabolism Excretion Toxicity Research Guide optimisation based on in silico models Screening, hit-optimization, lead selection, lead optimization, SOPP, development Validate/refine models based on new pharmacological data Development Decrease failure rate ! Market