In silico ADME/Tox in drug design

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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

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What makes a good drug ?

• Good activity/selectivity on the right target

• BUT ALSO !!!
• Absorption
• Distribution
• Metabolism
• Excretion
• Toxicity

ADME/Tox
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Reasons for drug failure in Clinical Development
(gt80)
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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)
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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
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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

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Absorption
MW lt 500, non-polar
Most common route of drug absorption
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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.
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Hydrogen bond donors and acceptors
Absorption requires desolvation, which becomes
more difficult with an increasing number of
hydrogen bonds
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The octanol/water model
water
octanol
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The octanol/water model
water
octanol
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The octanol/water model
water
octanol
LogD depends on pH !
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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
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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 ICs 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)

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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
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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
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ClogP vs. Caco-2
Caco-2 in vitro assay to measure absorption rate
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Lipinskis 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 gt 500
• ClogP gt 5
• Nr. of H-bond donors gt 5
• Nr. of H-bond acceptors gt 10

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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 gt 1 property
out of range
These simple physico-chemical properties largely
determine bioavailaility !
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Pharmacokinetic modeling

• PK-sim
• Cloe
• PKexpress
• Gastroplus

Advanced Drug Delivery Reviews 50 (2001) S41S67
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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

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Metabolism/Excretion
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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
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Contributions of Phase I and Phase II enzymes to
drug metabolism
ADH, alcohol dehydrogenase ALDH, aldehyde
dehydrogenase CYP, cytochrome P450
DPD, dihydropyrimidine dehydrogenase NQO1,
NADPHquinone 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
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Cellular localisation of metabolic enzymes

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

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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 5121

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Structure of P450
Substrate access
Heme catalytic centre
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Cytochrome P450 (CYP)
Reactive iron-oxo intermediate Compound 1
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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
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The Cytochrome P450 family
CYP3A4
Family
Subfamily
Individual protein
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Isoenzyme specificity

• Various isoenzymes have different but overlapping
  substrate specificities
• (CR indicates flatness of molecule)
• Lewis (2002) Drug Disc. Today 7918

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Individual variation in P450 activity

• Genetic polymorphism
• Defective gene poor metabolizer (e.g. CYP
  2C19 gt20 in Asians)
• Gene multiplication extensive metabolizer
  (e.g. CYP 2D6)
• Enzyme induction
• -gt Increased protein synthesis
• Enzyme inhibition
• Enzyme activation
• (CYP 3A4)
• Avoid drugs being metabolized via a single route
  !

Drug-drug interactions !
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Occurrence of major polymorphisms
Ingelman-Sundberg et al. (1999) Trends in Pharm.
Sciences20342
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Impact of P450 polymorphism
Ingelman-Sundberg et al. (1999) Trends in Pharm.
Sciences20342
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Metabolite identification

• It is often important to identify the metabolites
  formed by P450s
• 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)

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P450 metabolism

• Which metabolites are formed by P450s depends
  on
• If and how (i.e. in what orientation) a compound
  is bound to the active sites of individual CYPs
• The chemical reactivity of various sites of a
  molecule towards CYP catalyzed mechanisms

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In silico methods

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

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Modeling Ligand binding to CYP2C19by homology
modeling and docking
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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
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Derivation of metabolic rules

• Example rule for N-acetylation
• NH21 gtgt N1C(O)C
• Apply on training set of 7307 reactionsmetabolite
  s generated in total 1223metabolites match
  experimental product 122probability 122/1223
  0.10

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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
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Refined rules for N-demethylation
10/13 0.77
11/200.55
102/266 0.38
109/434 0.25
182/1052 0.17
2/87 0.02
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Current rule base at Organon

• 148 rules
• phase I and phase II metabolism
• Probabilities range from0.006 (glycination of
  aliphatic carboxyls)to 0.77 (demethylation of
  methyl-anilines)

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Evaluation Sulfadimidine
1
5
Sulfadimidine
2
3
Prediction(rank)
4
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Application metabolic stability
Predicted Rank 1
-gt Confirm experimentally by mass spectroscopy
Med Chem optimisation increased metabolic
stability
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Toxicity

• 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

Many endpoints Many mechanisms -gt Tough problem
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Prediction of toxicity
Biology Activity (Toxicity)
Rules/Tox-icophores
Chemistry Structure Reaction mechanisms

• Expert or rule-based systems
• QSAR or correlative methods

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Example expert systemDerek

• 303 knowledge based alerts or toxicophores
• 35 tox. endpoints
• refs to literature included
• Works well e.g. for mutagenicity

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Example expert systemDerek output
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Example QSAR methodAPA Acute toxicity model

• 37400 IP-mouse LD50 data
• Classification
• Knowledge from literature
• Properties identified form decision trees
• QSAR based on fragments
• Overall R0.8 for test-set

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• Absorption
• Distribution
• Metabolism
• Excretion
• Toxicity

Market
Research
Development
Decrease failure rate !