EBI is an Outstation of the European Molecular Biology Laboratory.

Predictive ADMET - Promise and Reality

Anne Hersey – ChEMBL Group

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Overview

•  Why predict ADMET

•  Promise of Predictive ADMET

•  Reality – what can predictions do

•  Conclusions “How modern state of the art methods can accelerate the process of drug design”

Examples from ChEMBL Database www.ebi.ac.uk/chembldb

Do we need ADMET Prediction?

•  Relatively lower amounts of ADMET data than potency data

•  Multiple properties to take account of •  Absorption, Distribution, Metabolism., Elimination, Toxicity

•  in-vivo experiments •  expensive, ethical issues

•  Application in compound design •  understanding not just screening

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Tarbit 2002

Drug needs to balance potency, exposure and safety

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More of a tug of war than a balance!

Potency ADMET

Example - Data from ChEMBL

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pIC50bindingdata~200Kcompounds

mw

t

ALogP

Ratbioavailability~3.6K

mw

t ALogP

Opposing Properties

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MWT

MWT

Potency

Bioavailability

Same Trend on Data on Specific Target (P38alpha)

IC50 - 2000 compounds %F - 150 compounds

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P38alpha – Opposing Properties

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MWT

MWT

Properties of Drug

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Drug Potencies median = 20nM Overington et al Nat Rev Drug Disc 2006

CLOGP

coun

t

MWt

coun

t

Promise of ADMET Prediction

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(Data from SCOPUS Search – any fields)

No. of Published ADMET Modelling papers

Year

Num

ber

1990’s •  ADMET models showed promise that ADMET could be

predicted by simple descriptors and methodology •  Intestinal Absorption

•  Lipinski Rule of 5, Adv Drug Disc Rev 1997 •  PSA – K Palm, Pharm Res 1997

•  Brain/Blood Ratio •  MH Abraham, J Pharm Sci 1994

•  logBB=-0.04+0.20E-0.69S-0.72A-0.70B+1.00V (n=57, r2= 0.91) •  D Clark, J Pharm Sci 1999

•  logBB=-0.015PSA+0.15ClogP + 0.14 (n=55, r2=0.79)

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ChEMBL ADMET Data

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ChEMBL ADMET Data

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• Currently ~200K datapoints • Many in-vivo and in-vitro endpoints • Manual curation in progress to “standardise” activity_types • Extract from database where assay_type=‘A’

Data: • P450 3A4 & 2C9 • PPB • hERG • Volume of distribution • BBratio

MWt>1000 removed multiple values averaged

Can ADMET Models be built using ChEMBL data?

Descriptors: •  simple physchem & topological

descriptors from pipeline pilot • logP/logD/pKa from ACDlabs

PLS models

ChEMBL Models - Results

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Plasma Protein Binding (logB/F) n=731

pred

mea

s

ChEMBL Models - Results

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mea

s

pred

P450 2C9 Inhibition n=616

In-vivo Models from ChEMBL Database

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Volume of Distribution (logVd) n=2227

pred

mea

s

In-vivo Models from ChEMBL Database

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Brian/Blood Ratio (logBB) n=596

pred

mea

s

Descriptor Trends

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logVd

logBB

PPB

PSA

logD logVd

logB

ound

/free

Cou

nt

Cou

nt

Reality of ADMET Prediction

•  Do we use them? •  How good are they? •  What are the issues? •  Are they useful?

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Do we use them? •  ChEMBL extracts data from peer reviewed MedChem Journals e.g

JMedChem 1980 onwards, EurJMedChem from 2007 •  485K Compounds (MWT>1000 not included)

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Year

% o

f com

pds

in Y

ear

RO5

More Detail

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For oral drugs Average CLogP ~2.5, MWt ~337 (Leeson Nat Rev Drug Disc 2007)

logP=2.5

MWt=337

How Good are they?

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•  Lombardo J.Med Chem., 2009 Valko et al, J MedChem 2006

Good models at identifying trends Less useful within a chemotype Why is this?

Example – Vd Models

Why is it Difficult?

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HBA logP Charge

PSA frag1 F

Vd %Bound

Ki Rate

IC50

Issues - Molecular Descriptors

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Mannhold et al J Pharm Sci 2008

Most descriptors there is no independent way of measuring them

logP

Model Prediction Space •  Helps to identify which compounds are well predicted •  Molecules outside training set property space are poorly predicted •  Models get worse with time as “property space” of molecules changes •  Difficult to do for properties not represented in training set •  Simple example - BBB models •  Abraham (1994) n=57 diverse compounds •  New compounds – model descriptors in original range but obvious outliers

•  Platts (2001) n=148 new compounds

25 acids – class not represented in original dataset

Data •  Do we measure enough? •  Is it the right data (project cascade effect) •  More mechanistic information?

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liability targets vs drug targets distinct compds >500 target_type=protein

hERG 2D6

3A4 2C9

Pgp

protein target

Com

poun

ds

Are Predictions Useful?

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Yes but need realistic expectations

Mea

sure

d Predicted

Predicted

Mea

sure

d

“How modern state of the art methods can accelerate the process of drug design?”

•  Use the information from the simple predictions •  Reduce logP & MWT •  Focus more on designing molecules with good ADMET

and less on increasing potency

•  More comprehensive data •  Data sharing – publish more ADMET data •  Better descriptors

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Acknowledgements

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Louisa Bellis Patricia Bento Jon Chambers Mark Davies Anna Gaulton Kaz Ikeda

Felix Krueger Yvonne Light Shaun McGlinchey Karen Bonner

Bissan Al-Lazikani

John Overington