1 / 46

Flow of information in a drug discovery pipeline

CZ3253: Computer Aided Drug design Lecture 10: Overview of Drug Testing Methods I: ADME Test Prof. Chen Yu Zong Tel: 6874-6877 Email: csccyz@nus.edu.sg http://xin.cz3.nus.edu.sg Room 07-24, level 7, SOC1, National University of Singapore. Flow of information in a drug discovery pipeline.

amadis
Download Presentation

Flow of information in a drug discovery pipeline

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CZ3253: Computer Aided Drug designLecture 10: Overview of Drug Testing Methods I: ADME Test Prof. Chen Yu ZongTel: 6874-6877Email: csccyz@nus.edu.sghttp://xin.cz3.nus.edu.sgRoom 07-24, level 7, SOC1, National University of Singapore

  2. Flow of information in adrug discovery pipeline Bioinformatics Toxicity Computational and Combinatorial Chemisty

  3. Predictive ADME Absorption Distribution Metabolism Elimination Pharmacokinetic Bioavailability

  4. Why is the prediction of ADME parameters so important ? reasons that cause the failure of a potential drug candidate

  5. Bioavailablity of Drugs (I)

  6. Bioavailability of Drugs (II) Uptake of orally administered drug proceeds after the stomach passage via the small intestine. In the liver, a series of metabolic transformation occurs.

  7. 1. What Is Absorption? “the drug passing from the lumen into the tissue of the GIT” (Sietsema) Human Intestinal Absorption (HIA) 1,2 – Stability + Solubility 3 – Passive + Active Tr. 4 – Pgp efflux + CYP 3A4 5 – 1st Pass in liver Oral Bioavailability (%F) Frequently HIA is confused with either “Passive Absorption or “Oral %F”

  8. Theoreticalconcepts Exp. values from in vivotests Different “Absorption Types” Passive Absorption (PA) Passive transport across intestinal membrane in vivo PA = f (PeIntestine) Absorbed Fraction (FAbs) “Passive Absorption” that depends on solubility (SW): FAbs = f (PeIntestine, SW) Human Intestinal Absorption (HIA) HIA = f (PeIntestine, SW, AT, Pgp efflux, gut 1st pass) Human Oral Bioavailability (%F) %F = f (HIA, liver 1st pass)

  9. HIA is usually measured as the ratio of cumulative urinary excretion of drug-related material following oral and intravenous administrations: %HIA = (ExcrOral / ExcrIV )URINE This method is not always applicable (e.g., when biliary excretion interferes). All in vivo data types are poorly reproducible (dosing, formulation, physiology) Most HIA and %F values are qualitative How They Are Measured? %F is the ratio of cumulative plasma concentrations after oral and intravenous administrations: %F = (AUCOral / AUCIV )PLASMA

  10. How They Are Predicted? 1. “Direct” informatics Structure  “Absorption = f (PeIntestinein vivo)” -- SARs or QSARs %F = f (Pe , SW, 1st Pass, etc.) – “knowledge bases” In vitro tests are not used, SW is frequently ignored 2. “Pe + SW ” simulations Structure  FAbsusing in vitroPe and SW tests 3. “PB - PK” simulations Structure  %Fusing a wide array of in vitro tests: Kinetic dissolution rates, various types of polarized transport, metabolic stability tests, PBP, etc.

  11. How They Are Predicted? 4. Statistical Learning Methods Structure  Molecular Descriptors Training of Prediction System  Trained using samples of absorption and non-absorption compounds Like any other statistical learning method, prediction accuracy dependent on the diversity and representativity of training data

  12. Maximum FAbs Oral %F from CP Which Methods to Use and When? Conventional approach: New approach: Computational Informatics “Pe + SW “ Drug Development “PB - PK” Accuracy & Relevance In reality various types of simulations can be used during the earliest development stages

  13. Correlations by Jurs, Oprea, and many others: • Did not clearly verify possible dependences on a compound’s dose, stability, solubility, AT or 1st pass • Used incorrect functions of %HIA or %F values • Used “abstract” descriptors that rely on statistics (rather than knowledge) QSAR-Based Methods “One-step” models using ANNs or PLS: “One-step” kinetic scheme: f (%HIA or %F) = ao +  aixi

  14. %HIA = 92 – 22  – 21  + 11 V+ 3 E+ 4  + 0 f (pKa) Incorrect function (calc. HIA may exceed 100%) H-Bonding, f (TPSA) Polarity –polarizability ~ Zero effect of ionization Size, f (MW) Qualitative agreement with C-SAR models Deserves attention! Not everything is “perfect”, but the results are much more useful than from any other QSAR works Example of QSAR Model 1. Considered “passive absorption” only 2. Used good physicochemical descriptors

  15. Deservecriticism AB/ADME Boxes > 800 compounds with exp %HIA (passive absorption only) (independently – SW, AT, Pgp, 1st Pass, and Oral %F) Rule-Based Methods Simple rules using “data mining”, PCA, or recursive partitioning: Lipinski > 2,000 compounds that passed 2nd phase of clinical trials Absorbable if MW 500, log P 5, (OH + NH)  5, (O + N)  10 Veber > 1,000 compounds with rat %F Absorbable if PSA 140 A2, (O+N+OH+NH)  12, Rot-Bonds  10

  16. A very rough approximation: IF (MW < 250 OR MW < 580 AND TPSA < 150) THEN “POSITIVE” IF (MW < 580 AND TPSA > 150 OR TPSA > 290) THEN “NEGATIVE” Example Of Rule-Based Predictions Three types of passive absorption considered: Large natural compounds Traditional leads “Very small” molecules

  17. Generalization of the Rules:The “Rule of Five” Formulation for Drug-Like Molecules Poor absorption or permeation are more likely when: • There are more than 5 H-bond donors. • The molecular weight is over 500. • The LogP is over 5. • There are more than 10 H-bond acceptors.

  18. Exception to the rule of five Compound classes that are substrates for biological transporters: • Antibiotics • Fungicides-Protozoacides -antiseptics • Vitamins • Cardiac glycosides.

  19. Computational calculations for new chemical entities • Applied to entities introduced between 1990-1993 • Average values: • MlogP=1.80 • H-bond donor sum=2.53 • Molecular weight =408 • H-bond acceptor sum=6.95 • Alerts for possible poor absorption-12%

  20. Intrinsic Limitations No matter how good our rules are, “marginal” compounds will create false predictions Qualitative rules cannot accurately model continuous processes We must also know probabilities that our rules will be obeyed

  21. SVM Prediction System for HIA J. Chem. Inf. Comput. Sci. 44,1630-1638 (2004)

  22. Prediction Accuracy Measurement • Common measure (other measures also exist) • Sensitivity SE=TP/(TP+FN) • Specificity SP=TN/(TN+FP) • For example, prediction of binding peptides to a particular receptor • Experimental Predicted Class • Example 1 Binder Binder True positive (TP) • Example 2 Non-binder Non-binder True negative (TN) • Example 3 Binder Non-binder False negative (FN) • Example 4 Non-binder Binder False positive (FP) • Prediction system that has SE=0.8 and SP=0.9 will correctly predict 8 of 10 experimental positives, and for each 10 experimental negatives it will make one false prediction. This prediction accuracy may be very good for prediction of peptide binding, but is not very good for some other predictions, for example gene prediction.

  23. SVM Prediction Results J. Chem. Inf. Comput. Sci. 44,1630-1638 (2004)

  24. Cytochrome P450 The super-family of cytochrome P450 enzymes has a crucial role in the metabolism of drugs. Almost every drug is processed by some of these enzymes.This causes a reduced bioavailability. Cytochrome P450 enzymes show extensive structural polymorphism (differences in the coding region).

  25. Cytochrome P450 metabolisms (I) During first liver passage: First pass effectextensive chemical transformation of lipophilic or heavy (MW >500) compounds. They become more hydrophilic (increased water solubility) and are therefore easier to excreat. Predominantly cytochrome P450 (CYP) enzymes are responsible for the reactions belonging to phase I.Usually, the reaction is a monooxygenation.

  26. Cytochrome P450 Metabolisms (II) The substrates are monooxygenated in a catalytic cycle. The iron is part of a HEM moiety

  27. Cytochrome P450 Metabolisms (III) The cytochromes involved in the metabolism are mainly monooxygenases that evolved from the steroid and fatty acid biosynthesis. So far, 17 families of CYPs with about 50 isoforms have been characterized in the human genome. classification: CYP 3 A 4 *15 A-B family>40% sequence-homology isoenzyme allel sub-family>55% sequence-homology

  28. Cytochrome P450 gene families Human 14+ Molluscs 1 CYP450 Plants 22 Insects 3 Bacteria 18 Yeasts 2 Nematodes 3 Fungi 11

  29. Human cytochrome P450 family Of the super-family of all cytochromes, the following families were confirmed in humans: CYP 1-5, 7, 8, 11, 17, 19, 21, 24, 26, 27, 39, 46, 51 Function: CYP 1, 2A, 2B, 2C, 2D, 2E, 3 metabolism of xenobiotics CYP 2G1, 7, 8B1, 11, 17, 19, 21, 27A1, 46, 51 steroid metabolism CYP 2J2, 4, 5, 8A1 fatty acid metabolism CYP 24 (vitamine D), 26 (retinoic acid), 27B1 (vitamine D), ...

  30. Cytochrome P450 enzymes (I) Flavin Monooxygenase Isoenzyme Alkohol Dehydrogenase Aldehyd Oxidase Monoamin Dehydrogenase (MAO) The redox activity is mediated by an iron porphyrin in the active center

  31. Cytochrome P450 enzymes (II) Despite the low sequence identity between CYPs from different organisms, the tertiary structure is highy conserved. Superposition ofhCYP 2C9 (1OG5.pdb) andCYP 450 BM3 (2BMH.pdb) Bacillus megaterium In contrast to bacterial CYPs, the microsomal mammalian CYPs possess an additional transmembrane helix that serves as an anchor in the membrane

  32. Cytochrome P450 enzymes (III) The structures of several mammalian CYPs have now been determined in atomistic detail and are available from the Brookhaven Database: http://www.pdb.mdc-berlin.de/pdb/ 1DT6.pdb CYP 2C5 rabbit Sep 2000 1OG5.pdb CYP 2C9 human Jul 2003 1PO5.pdb CYP 2B4 rabbit Oct 2003 1PQ2.pdb CYP 2C8 human Jan 2004 They are suitable templates for deriving homology models of further CYPs

  33. Cytochrome P450 enzymes (IV) The majority of CYPs is found in the liver, but certain CYPs are also present in the wall cells of the inestine The mammalian CYPs are bound to the endoplasmic reticulum, and are therefore membrane bound.

  34. Cytochrome P450 enzymes (V) Especially CYP 3A4, CYP 2D6, and CYP 2C9 are involved in the metabolism of xenobiotics and drugs.

  35. Substrate specificity of CYPs (I) specific substrates of particular human CYPs CYP 1A2 verapamil, imipramine, amitryptiline, caffeine (arylamine N-oxidation) CYP 2A6 nicotine CYP 2B6 cyclophosphamid CYP 2C9 diclofenac, naproxen, piroxicam, warfarin CYP 2C19 diazepam, omeprazole, propanolol CYP 2D6 amitryptiline, captopril, codeine, mianserin, chlorpromazine CYP 2E1 dapsone, ethanol, halothane, paracetamol CYP 3A4 alprazolam, cisapride, terfenadine, ... see also http://medicine.iupui.edu/flockhart/

  36. Substrate specificity of CYPs (II) Decision tree for human P450 substrates CYP 1A2, CYP 2A-E, CYP 3A4 high low CYP 3A4 CYP 2E1 Volume medium acidic basic pKa CYP 2D6 CYP 2C9 neutral CYP 1A2, CYP 2A, 2B low high planarity CYP 2B6 CYP 1A2 medium CYP 2A6 Lit: D.F.V. Lewis Biochem. Pharmacol.60 (2000) 293

  37. Cytochrome P450 polymorphisms „Every human differs (more or less) “ The phenotype can be distinguished by the actual activity or the amount of the expressed CYP enzyme. The genotype, however, is determined by the individual DNA sequence. Human: two sets of chromosomes That means: The same genotype enables different phenotypes Depending on the metabolic activity, three major catagories of metabolizers are separated: extensive metabolizer (normal), poor metabolizer, and ultra-rapid metabolizer (increased metabolism of xenobiotics) Lit: K. Nagata et al. Drug Metabol. Pharmacokin3 (2002) 167

  38. CYP 2D6 Polymorphism (I) The polymorphisms of CYP 2D6 has been studied in great detail, as metabolic differences have first been described for certain antipsychotics Localized on chromosome 22Of the 75 allels, 26 are associated with adverse effectssee http://www.imm.ki.se/CYPalleles/cyp2d6.htm

  39. CYP 2D6 Polymorphism (II) Lit: J. van der Weide et al. Ann. Clin. Biochem36 (1999) 722

  40. CYP 2D6 Polymorphism (III) MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQ LRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVF LARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDK AVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKV LRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVA DLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVI HEVQRFGDIVPLGMTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHF LDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGV FAFLVSPSPYELCAVPR poor debrisoquine metabolism S R impaired mechanism of sparteine poor debrisoquine metabolism I poor debrisoquine metabolism R missing in CYP2D6*9 allele P loss of activity in CYP2D6*7 T impaired metabolism of sparteine in alleles 2, 10, 12, 14 and 17 of CYP2D6 see http://www.expasy.org/cgi-bin/niceprot.pl?P10635

  41. CYP 2D6 Polymorphism (III) Variability of debrisoquine-4-hydroxylation = number of individuals (european population) Homocygote extensive metabolizers Homocygote poor metabolizers = metabolic rate heterocygote extensive metabolizers Lit: T. Winkler Deutsche Apothekerzeitung 140 (2000) 38

  42. Polymorphisms of Other CYPs • CYP 1A2 individual: fast, medium, and slow turnover of caffeine • CYP 2B6 missing in 3-4 % of the caucasian population • CYP 2C9 deficit in 1-3 % of the caucasian population • CYP 2C19 individuals with inactive enzyme (3-6 % of the caucasian and 15-20 % of the asian population) • CYP 2D6 poor metabolizers in 5-8 % of the european, 10 % of the caucasian, and <1% of the japanese population. Over expression (gene duplication) among parts of the african and oriental population. • CYP 3A4 only few mutations

  43. Typical inhibitors of various CYPs CYP 1A2 cimetidine, ciprofloxacine, enoxacine...grapefruit juice (naringin, 6‘,7‘-dihydroxy- bergamottin) CYP 2C9 chloramphenicol, amiodarone, omeprazole,... CYP 2C19 fluoxetine, fluvastatin, sertraline,... CYP 2D6 fluoxetine, paroxetine, quinidine, haloperidol, ritonavir,... CYP 2E1 disulfiram, cimetidine,... CYP 3A4 cannabinoids, erythromycin, ritonavir, ketokonazole, grapefruit juice see also http://medicine.iupui.edu/flockhart/

  44. SVM Prediction of Cytochrome P450 3A4, 2D6, 2C9 Inhibitors and Substrates Dataset Statistics

  45. SVM Prediction of Cytochrome P450 3A4, 2D6, 2C9 Inhibitors and Substrates Distribution of types of forces involved in ligand-enzyme interactions

  46. SVM Prediction of Cytochrome P450 3A4, 2D6, 2C9 Inhibitors and Substrates Prediction Results

More Related