In silicoIn silico ADME/Tox in drug ADME/Tox in drug designdesign

“Bioinformatics IV”(Computational Drug Discovery)

Wednesday 7 June 2006CMBI, University of Nijmegen

Lars Ridder, Organon

What makes a good drug ?What makes a good drug ?

BUT ALSO !!!BUT ALSO !!!• AAbsorption• DDistribution• MMetabolism• EExcretion• ToxToxicity

• Good activity/selectivity on the right target

ADME/Tox

Reasons for drug failure in Reasons for drug failure in Clinical Development (>80%)Clinical Development (>80%)

ADME/Tox

Role of Role of in silicoin silico ADME/Tox ADME/Tox

MarketResearch Development

$300m4-5yrs (30%)

$500m8-10yrs (70%)

Failure rate over 80-90% (safety, efficacy)

Does the compound work in

man?Identify ADME/Tox problems earlier in the processMore emphasis on ADME/Tox properties in lead optimization

In-house design cycleIn-house design cycle

Screening, hit-optimization, lead selection, lead optimization, SOPP, development

Guide optimisationbased on in silico models

Validate/refine models based on new pharmacological data

Absorption/Distribution/Absorption/Distribution/MetabolismMetabolism

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

AbsorptionAbsorption

Most common route of drug absorption

MW < 500, non-polarMW < 500, non-polar

Membrane permeationMembrane permeation

Penetration rate = P x A x (C1-C2)P = partition into membrane

A = effective surface area of membraneC1-C2 = concentration gradient

C1

C2

Depends on physicochemicalDepends on physicochemicalproperties of drug, e.g. lipophilicity, properties of drug, e.g. lipophilicity, MW, hydrogen bonding, etc.MW, hydrogen bonding, etc.

WaterWater

MembraneMembrane

Hydrogen bond donors and Hydrogen bond donors and acceptorsacceptors

R OH

OH

O

H

OH H

H

OH

H

O

H

Absorption requires desolvation, which becomes more difficult with an increasing number of hydrogen bonds

The octanol/water modelThe octanol/water model

wat

oct

AH

AHP

][

][loglog OH

O

OH

Owater octanol

The octanol/water modelThe octanol/water model

wat

oct

AH

AHP

][

][loglog OH

O

OH

Owater octanol

watwat

octoct

AAH

AAHD

][][

][][loglog

O

O

O

O

The octanol/water modelThe octanol/water model

wat

oct

AH

AHP

][

][loglog OH

O

OH

O

loglogDD = log = logPP - log(1 + 10 - log(1 + 10pH-ppH-pKKaa))loglogDD = log = logPP + log(1 - f + log(1 - fionizedionized))

water octanol

watwat

octoct

AAH

AAHD

][][

][][loglog

O

O

O

O

LogD depends LogD depends on pH !on pH !

pH-range in GI tractpH-range in GI tract

pHpH pHpH(fed)(fed) (fasted)(fasted)

3-73-7 1.4-2.11.4-2.1

5-6.55-6.5 6.56.56.5-86.5-8 6.5-86.5-8

5-85-8

ClogPClogPCalculating 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 - examplesClogP - examples

Fragment Value6 x IC (arom) 0.78

Hydroxy -0.444 x Hydrogen 0.91Electronic int. 0.34

ClogP 1.56

Exp. logP 1.58

OH

O

OH

Carboxy -0.03

ClogP - examplesClogP - examples

OH

O

OH

Fragment Value6 x IC (arom) 0.78Carboxy -0.03Hydroxy -0.444 x Hydrogen 0.91Electronic int. 0.34H-bonding 0.63

ClogP 2.19

Exp. logP 2.26

ClogP vs. Caco-2ClogP vs. Caco-2

y = -1.4107x + 7.3791R2 = 0.4115

0

1

2

3

4

5

6

7

8

9

10

0 0.5 1 1.5 2 2.5 3

log(caco-2)

clo

gP

Caco-2 = in vitro assay to measure absorption rateCaco-2 = in vitro assay to measure absorption rate

Lipinski’s Rule-of-5Lipinski’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) Correlation to in vivo (rat) absorptionabsorption

In-house rulesbased on:• ClogP

• MW

• H-bond donors

• H-bond acceptors

But also:• Polar surface area

• Nr. of rotatable bonds

Bioavailability

0

20

40

60

80

100

120

Good Moderate Bad

Monika classification

nu

mb

er o

f co

mp

ou

nd

s

< 30%

> 30%

Good = no properties out of rangeMedium = 1 property out of rangeBad = > 1 property out of range

These simple physico-These simple physico-chemical properties largely chemical properties largely determine bioavailaility !determine bioavailaility !

Pharmacokinetic modelingPharmacokinetic modeling

• PK-sim

• Cloe

• PKexpress

• Gastroplus

Advanced Drug Delivery Reviews 50 (2001) S41–S67

DistributionDistributionMost 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/ExcretionMetabolism/Excretion

Metabolic enzymesMetabolic enzymes

Phase II: conjugation

Lipophylic metabolites

Cytochrome P450Hydroxylation, dealkylation, N-oxidation, epoxidation, dehydrogenation, etc.

e.g. Flavin monooxygenases Dehydrogenases

Polar metabolites

Phase I: (mostly) oxidation

+glutathione +H2O +glucuronate +sulphate +acetate +methyl

Hydrophylic metabolites

Contributions of Phase I and Phase II Contributions of Phase I and Phase II enzymes to drug metabolismenzymes 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 Cellular localisation of metabolic enzymesenzymes

• Endoplasmitic reticulum (ER) of intestinal- and liver cells contain P450

• Cytosol contains Phase II metabolic enzymes

Xray structures of P450Xray 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 P450Structure of P450Substrate access Heme = catalytic centre

Cytochrome P450 (CYP)Cytochrome P450 (CYP)

Reactive iron-oxo intermediate:“Compound 1”

Phase 1 metabolism vs. Phase 1 metabolism vs. lipophilicitylipophilicity

020406080

100120140160

0 10 20 30 40 50 60 70 80 90 100 110T1/2 (mins)

Nco

mp

ou

nd

s

120

In vitro measurement of In vitro measurement of metabolic stability in metabolic stability in microsomes = ER membrane microsomes = ER membrane fraction of liver cellsfraction of liver cells

In-house data: Compounds In-house data: Compounds tend to be very stable or very tend to be very stable or very unstableunstable

-4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9

10

0

10

20

30

40

50

60

70

com

po

un

ds

ClogP

Stable

Unstable

Lipophilicity is an Lipophilicity is an important factor in important factor in microsomal stabilitymicrosomal stability

-4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9

10

0

10

20

30

40

50

60

70

com

po

un

ds

ClogD

Stable

Unstable

(ClogD discriminated (ClogD discriminated better between stable better between stable and unstable than ClogP)and unstable than ClogP)

StableUnstable

The Cytochrome P450 The Cytochrome P450 familyfamily

CYP3A4

Family

Subfamily

Individualprotein

Isoenzyme specificityIsoenzyme 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 Individual variation in P450 activity 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 !

Occurrence of major Occurrence of major polymorphismspolymorphisms

Ingelman-Sundberg et al. (1999) Trends in Pharm. Sciences20:342

Impact of P450 Impact of P450 polymorphismpolymorphism

Ingelman-Sundberg et al. (1999) Trends in Pharm. Sciences20:342

Metabolite identificationMetabolite 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 metabolismP450 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 methodsIn 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 CYP2C19Modeling Ligand binding to CYP2C19by homology modeling and dockingby homology modeling and docking

Drug ReactionReactiveconformation

Amitriptyline N-deMe top rankedDiazepam N-deMe top rankedFlunitrazepam N-deMe top rankedIfosfamide C-hydrox, alifatic top rankedImipramine N-deMe top rankedIndomethacin O-deMe top rankedLansoprazole C-hydrox, aromatic top rankedMethoxychlor O-deMe top rankedMoclobemide C-hydrox, alifatic top rankedOmeprazole O-deMe top rankedPantoprazole O-deMe top rankedPhenobarbital C-hydrox, aromatic top rankedPhenytoin C-hydrox, aromatic top rankedRabeprazole O-deMe top rankedR-mephobarbital C-hydrox, aromatic top rankedR-warfarin C-hydrox, aromatic top rankedSertraline N-deMe top rankedTestosterone ox, -OH to =O top rankedVenlafaxine O-deMe top rankedNirvanol C-hydrox, aromatic top rankedClomipramine N-deMe foundNordazepam C-hydrox, alifatic foundNortriptyline N-deMe foundTrimipramine N-deMe foundCitalopram N-deMe foundCarisoprodol N-deMe foundS-mephenytoin C-hydrox, aromatic foundFluoxetine N-deMe not foundHexobarbital C-hydrox, aromatic not foundTolbutamide C-hydrox, alifatic not foundCyclophosphamide C-hydrox, alifatic docking failedDesogestrel C-hydrox, alifatic docking failedProgesterone C-hydrox, alifatic docking failed

Assessing chemical susceptibility Assessing chemical susceptibility towards CYP metabolism based on towards CYP metabolism based on

QM calculationsQM calculations

substrate sites of oxidation Gact

Ethylbenzene Primary/benzylic -4.39a-Chloro-p-xylene Benzylic/CI-methyl benzylic -0.742-Methylanisole Benzylic/O-demethylation 0.44-Methylanisole Benzylic/O-demethylation 0.721,3-Diphenylpropane Benzylic/secondary 1.28Hexane 1/2 Hexanol -1.85Octane 1/2 Octanol -2.141-Phenyl-3-(4-F-phenyl)propane Benzylic/substituted 0.511-Phenyl-3-(4-Me-phenyl)propane Benzylic/substituted 01-Phenyl-3-(4-F3Me-phenyl)propane Benzylic/substituted 1.81

radrad IPxHxsite

siteRT

211

2ln

Many CYP reactions begin with abstraction of aliphatic H•Many CYP reactions begin with abstraction of aliphatic H•

O

R2 = 0.86

-1

0

1

2

3

4

5

0 1 2 3 4 5

experimental (kcal/mol)

calc

ula

ted

(A

M1)

Works for small molecules – for larger drug molecules a Works for small molecules – for larger drug molecules a combination of high level modeling and QM calculations will combination of high level modeling and QM calculations will ultimately result in more accurate predictionsultimately result in more accurate predictions

Derivation of metabolic Derivation of metabolic rulesrules

• Example: rule for N-acetylation

[NH2:1] >> [N:1]C(=O)C

• Apply on training set of 7307 reactions

metabolites generated in total 1223

metabolites match experimental product 122

probability 122/1223 =

0.10

RNH2

RNH

O

Refined rules for N-Refined rules for N-acetylationacetylation

AliphNH2

AliphNH

O

AromNH2

AromNH

O

HeteroNH2

HeteroNH

O

79 / 357 = 0.22

33 / 417 = 0.08

10 / 88 = 0.11

Three more specific rules for N-acetylation

RNH2

RNH

O122/1223 = 0.10

Refined rules for N-Refined rules for N-demethylationdemethylation

NH

CH3

NH2

NCH3

CH3

NH

CH3

aliphNH

CH3 aliphNH2

aliphN

CH3

CH3

aliphNH

CH3

RN

R

CH3

RNH

R

NCH3

NH

10/13 = 0.77

11/20=0.55

102/266 = 0.38

109/434 = 0.25

182/1052 = 0.17

2/87 = 0.02

0

0.2

0.4

0.6

0.8

20 21 22 23 24 25

H (hydrogen abstraction)

Pro

bab

ility

Current rule base at Current rule base at OrganonOrganon

• 148 rules

• phase I and phase II metabolism

• Probabilities range from0.006 (glycination of aliphatic carboxyls)

to 0.77 (demethylation of methyl-anilines)

19

24

14

138

134

22

12

19

dealkylation hydroxylation carbon oxidation hetero oxidation reductions hydrolysis condensation other phase I glucuronidation other phase II

Evaluation: SulfadimidineEvaluation: Sulfadimidine

S NH

O

ON

NNH2S N

HO

ON

NNH2

S NH

O

ON

NNH

O

S NH

O

ON

NNH2

OH

Sulfadimidine

S NH

O

ON

NNH2

OH

S NH

O

ON

NNH2

O

OH

S NH

O

ON

NNH

OHO

11

22

33

44

55

Prediction(rank)Prediction(rank)

Application: metabolic Application: metabolic stabilitystability

N

N

N N N

O

O

N

N

N N N

O

ON

N

N N N

O

O

OHN

N

N N N

O

O

OH

N

N

N N N

O

O

FN

N

N N N

O

O

F

PredictedRank 1

Med Chem optimisation:increased metabolic stability

-> Confirm experimentallyby mass spectroscopy

ToxicityToxicitySystemic 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

-> Tough problem

Prediction of toxicityPrediction of toxicity

• Expert or rule-based systems

• QSAR or “correlative” methods

BiologyActivity(Toxicity)

ChemistryStructureReaction mechanisms

Rules/Tox-icophores

StatisticsAnalytical methods

QSAR

Example expert system:Example expert system:DerekDerek

• 303 knowledge based alerts or toxicophores

• 35 tox. endpoints• refs to literature

included• Works well e.g. for

mutagenicity

Example expert system:Example expert system:Derek outputDerek output

LHASA PREDICTIONS

Carcinogenicity

Alert overview: 107 Aromatic amide

R1 = C (aryl)

R1 N O

R2 R2

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: Title: General principles for evaluating the safety of compounds used in food-producing animals. 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:Example QSAR method:APA Acute toxicity modelAPA 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

• AAbsorption

• DDistribution

• MMetabolism

• EExcretion

• ToxToxicity

Screening, hit-optimization, lead selection, lead optimization, SOPP, development

Guide optimisationbased on in silico models

Validate/refine models based on new pharmacological data

MarketResearch Development

Decrease failure rate !