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 ? ADME/Tox
Good activity/selectivity on the right target
BUT ALSO !!!
Absorption
Distribution
Metabolism
Excretion
Toxicity
ADME/Tox

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

4. Role of in silico ADME/Tox
Research
Development
Market
$300m
4-5yrs (30%)
$500m
8-10yrs (70%)
Identify ADME/Tox problems earlier in the process
More emphasis on ADME/Tox properties in lead optimization
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

11. The octanol/water model

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

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 CYP2C19 by 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 reactions metabolites generated in total metabolites match experimental product 122 probability /1223 = 0.10

41. Refined rules for N-acetylation
Three more specific rules for N-acetylation
79 / 357 = 0.22
122/1223 = 0.10
33 / 417 = 0.08
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 phase II metabolism
Probabilities range from (glycination of aliphatic carboxyls) to 0.77 (demethylation of methyl-anilines)

44. Evaluation: Sulfadimidine1 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. Toxicity Systemic Toxicity Organ Specific Toxicity Many endpoints
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
Many endpoints
Many mechanisms
-> Tough problem

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

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. Example expert system: Derek output
Paracetamol !

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

51. Absorption Distribution Metabolism Excretion Toxicity
Screening, hit-optimization, lead selection, lead optimization, SOPP, development
Guide optimisation
based on in silico models
Validate/refine models based on new pharmacological data
Research
Development
Market
Decrease failure rate !