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In silico ADME/Tox in drug design

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 ?. BUT ALSO !!! A bsorption D istribution M etabolism E xcretion Tox icity.

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In silico ADME/Tox in drug design

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  1. In silico ADME/Tox in drug design “Bioinformatics IV” (Computational Drug Discovery) Wednesday 7 June 2006 CMBI, University of Nijmegen Lars Ridder, Organon

  2. What makes a good drug ? BUT ALSO !!! • Absorption • Distribution • Metabolism • Excretion • Toxicity • Good activity/selectivity on the right target ADME/Tox

  3. ADME/Tox Reasons for drug failure in Clinical Development (>80%)

  4. Identify ADME/Tox problems earlier in the process More emphasis on ADME/Tox properties in lead optimization Role of in silico ADME/Tox Market Research Development $300m 4-5yrs (30%) $500m 8-10yrs (70%) Does the compound work in man? Failure rate over 80-90% (safety, efficacy)

  5. 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

  6. 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

  7. Absorption MW < 500, non-polar Most common route of drug absorption

  8. Membrane permeation Water C1 Membrane C2 Penetration rate = P x A x (C1-C2) P = partition into membrane A = effective surface area of membrane C1-C2 = concentration gradient Depends on physicochemical properties of drug, e.g. lipophilicity, MW, hydrogen bonding, etc.

  9. Hydrogen bond donors and acceptors Absorption requires desolvation, which becomes more difficult with an increasing number of hydrogen bonds

  10. The octanol/water model water octanol

  11. The octanol/water model water octanol

  12. logD = logP - log(1 + 10pH-pKa) logD = logP + log(1 - fionized) The octanol/water model water octanol LogD depends on pH !

  13. pH-range in GI tract pH pH (fed) (fasted) 3-7 1.4-2.1 5-6.5 6.5 6.5-8 6.5-8 5-8

  14. 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)

  15. ClogP - examples 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

  16. ClogP - examples 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

  17. ClogP vs. Caco-2 Caco-2 = in vitro assay to measure absorption rate

  18. 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

  19. 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 Good = no properties out of range Medium = 1 property out of range Bad = > 1 property out of range These simple physico-chemical properties largely determine bioavailaility !

  20. Pharmacokinetic modeling • PK-sim • Cloe • PKexpress • Gastroplus Advanced Drug Delivery Reviews 50 (2001) S41–S67

  21. 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

  22. Metabolism/Excretion

  23. Metabolic enzymes Lipophylic metabolites Cytochrome P450 Hydroxylation, dealkylation, N-oxidation, epoxidation, dehydrogenation, etc. e.g. Flavin monooxygenases Dehydrogenases Phase I: (mostly) oxidation Polar metabolites +glutathione +H2O +glucuronate +sulphate +acetate +methyl Phase II: conjugation Hydrophylic metabolites

  24. 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]

  25. Cellular localisation of metabolic enzymes • Endoplasmitic reticulum (ER) of intestinal- and liver cells contain P450 • Cytosol contains Phase II metabolic enzymes

  26. 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]

  27. Structure of P450 Substrate access Heme = catalytic centre

  28. Cytochrome P450 (CYP) Reactive iron-oxo intermediate: “Compound 1”

  29. Stable Unstable 160 140 120 100 Ncompounds 80 Lipophilicity is an important factor in microsomal stability 60 40 20 (ClogD discriminated better between stable and unstable than ClogP) 0 0 10 20 30 40 50 60 70 80 90 100 110 120 T1/2 (mins) Phase 1 metabolism vs. lipophilicity In vitro measurement of metabolic stability in microsomes = ER membrane fraction of liver cellsIn-house data: Compounds tend to be very stable or very unstable

  30. The Cytochrome P450 family CYP3A4 Family Subfamily Individual protein

  31. Isoenzyme specificity • Various isoenzymes have different but overlapping substrate specificities • (CR indicates flatness of molecule) [Lewis (2002) Drug Disc. Today 7:918]

  32. 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) Avoid drugs being metabolized via a single route ! Drug-drug interactions !

  33. Occurrence of major polymorphisms Ingelman-Sundberg et al. (1999) Trends in Pharm. Sciences20:342

  34. Impact of P450 polymorphism Ingelman-Sundberg et al. (1999) Trends in Pharm. Sciences20:342

  35. 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)

  36. 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

  37. In silico methods • Binding in CYP active site • Docking • Pharmacophore • Reactivity of ligand sites • QM methods • Metabolism rules • Expert knowledge • Empirical scoring

  38. Modeling Ligand binding to CYP2C19by homology modeling and docking

  39. Assessing chemical susceptibility towards CYP metabolism based on QM calculations Many CYP reactions begin with abstraction of aliphatic H• Works for small molecules – for larger drug molecules a combination of high level modeling and QM calculations will ultimately result in more accurate predictions

  40. Derivation of metabolic rules • Example: rule for N-acetylation [NH2:1] >> [N:1]C(=O)C • Apply on training set of 7307 reactionsmetabolites generated in total 1223metabolites match experimental product 122probability 122/1223 = 0.10

  41. Refined rules for N-acetylation Three more specific rules for N-acetylation 79 / 357 = 0.22 33 / 417 = 0.08 122/1223 = 0.10 10 / 88 = 0.11

  42. Refined rules for N-demethylation 10/13 = 0.77 11/20=0.55 102/266 = 0.38 109/434 = 0.25 182/1052 = 0.17 2/87 = 0.02

  43. Current rule base at Organon • 148 rules • phase I and phaseII metabolism • Probabilities range from0.006 (glycination of aliphatic carboxyls)to 0.77(demethylation of methyl-anilines)

  44. Evaluation: Sulfadimidine 1 5 Sulfadimidine 2 3 Prediction(rank) 4

  45. Application: metabolic stability Predicted Rank 1 -> Confirm experimentally by mass spectroscopy Med Chem optimisation: increased metabolic stability

  46. Systemic Toxicity Acute Toxicity Subchronic Toxicity Chronic Toxicity Genetic Toxicity Carcinogenicity Developmental Toxicity Photo toxicity Organ Specific Toxicity Blood/Cardiovascular Toxicity Hepatotoxicity Immunotoxicity Reproductive Toxicity Respiratory Toxicity Nephrotoxicity Neurotoxicity Dermal/Ocular Toxicity Toxicity Many endpoints Many mechanisms -> Tough problem

  47. Statistics Analytical methods QSAR Prediction of toxicity • Expert or rule-based systems • QSAR or “correlative” methods Biology Activity (Toxicity) Rules/Tox-icophores Chemistry Structure Reaction mechanisms

  48. Example expert system:Derek • 303 knowledge based alerts or toxicophores • 35 tox. endpoints • refs to literature included • Works well e.g. for mutagenicity

  49. Paracetamol ! Example expert system:Derek output

  50. 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

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