Predictive ADMET - Promise and Reality - MGMS
Transcript of Predictive ADMET - Promise and Reality - MGMS
EBI is an Outstation of the European Molecular Biology Laboratory.
Predictive ADMET - Promise and Reality
Anne Hersey – ChEMBL Group
2
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
3
Tarbit 2002
Drug needs to balance potency, exposure and safety
4
More of a tug of war than a balance!
Potency ADMET
Example - Data from ChEMBL
5
pIC50bindingdata~200Kcompounds
mw
t
ALogP
Ratbioavailability~3.6K
mw
t ALogP
Opposing Properties
6
MWT
MWT
Potency
Bioavailability
Same Trend on Data on Specific Target (P38alpha)
IC50 - 2000 compounds %F - 150 compounds
7
P38alpha – Opposing Properties
8
MWT
MWT
Properties of Drug
9
Drug Potencies median = 20nM Overington et al Nat Rev Drug Disc 2006
CLOGP
coun
t
MWt
coun
t
Promise of ADMET Prediction
10
(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)
11
ChEMBL ADMET Data
12
ChEMBL ADMET Data
13
• 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
14
Plasma Protein Binding (logB/F) n=731
pred
mea
s
ChEMBL Models - Results
15
mea
s
pred
P450 2C9 Inhibition n=616
In-vivo Models from ChEMBL Database
16
Volume of Distribution (logVd) n=2227
pred
mea
s
In-vivo Models from ChEMBL Database
17
Brian/Blood Ratio (logBB) n=596
pred
mea
s
Descriptor Trends
18
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?
19
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)
20
Year
% o
f com
pds
in Y
ear
RO5
More Detail
21
For oral drugs Average CLogP ~2.5, MWt ~337 (Leeson Nat Rev Drug Disc 2007)
logP=2.5
MWt=337
How Good are they?
22
• 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?
23
HBA logP Charge
PSA frag1 F
Vd %Bound
Ki Rate
IC50
Issues - Molecular Descriptors
24
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?
26
liability targets vs drug targets distinct compds >500 target_type=protein
hERG 2D6
3A4 2C9
Pgp
protein target
Com
poun
ds
Are Predictions Useful?
27
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
28
Acknowledgements
29
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