Transcript Document

Memories and the future:
From experimental to in silico
physical chemistry
Han van de Waterbeemd
AstraZeneca,
DMPK Alderley Park, Macclesfield, UK
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Overview
• Why physchem data?
• Wet screening (in vitro)
• Web screening (in silico)
• Future developments
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Medchem evolution
<1980
target affinity/binding using intuition and experience
protein crystallography
>1980
structure-based design
attrition analyses
>1995
drug/lead filters such as rule of five
physchem/DMPK considerations
>2000
property-based design
HT property screening
>2005
in silico/in vitro (in combo) approaches
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Key ADME questions
• Drugability
• Attrition
• Appropriate PK
Optimisation problem
Poor systemic exposure
Distribution
vs
First-pass clearance
Clearance
Volume of Distribution
Target affinity
Poor oral bioavailability
Renal Plasma Hepatic
Gut Stability
Physicochemical
Properties
Biliary
Blood Brain
Plasma Protein
Barrier
Binding
Metabolic
Transporters
Others pKa
Cytochromes P-450
ADME
P-gp MRP OATP OCTP
Which
P-450?
Which
Conjugate?
1A2, 2C9, 2C19, 2D6, 3A4
Regiospecificity
Lability
Affinity
Carlson
and Segall, Curr.Drug Disc. 34-36 (2002)
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Glucuronide
Membrane
permeation
Solubility LogP/D
Paracellular
Transcellular
Sulphate Amino Acids
Induction
PXR
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Absorption
CAR AHR Type II binding
Inhibition
Mechanistic
ADMET screening strategy
•
•
•
•
Biopharmaceutical (physchem) profiling
Pharmacokinetics
Metabolism
Early toxicology
•
•
•
•
In vitro = wet screening
In silico = web screening
In combo
In cerebro
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Wet screening
(in vitro measurement)
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Han very early days
Leiden (PhD)
• log P vs log k
• Are rate constants of partitioning
useful in QSAR?
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Han early days
Lausanne (post-doc with Bernard Testa)
pKa - Apple III, IBM PC
log kHPLC - first attempts to HT
log P = aV + L
= hydrophobicity + polarity
= size + hydrogen bonding
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Han early days
Roche (Molecular Properties Group)
pKa (GLpKa101, John Comer, Colin Peake)
log kHPLC
log Papp (artificial membranes pre-PAMPA,
Gian Camenisch)
PAMPA (Manfred Kansy)
PSA – polar surface area
Van de Waterbeemd and Kansy, Chimia 46 (1992) 299-303
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Han more recent days
Pfizer (automated ADME screening)
log D - 96 well plates
log S
PAMPA
Pfizer (in silico ADME)
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Lessons learned
•
•
•
•
•
Calculation goes faster
Computed data often good enough
No need to measure too much
In silico for virtual compounds
But, good quality experimental data
are needed to build robust models
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Kinetic vs equilibrium
Caco-2
PAMPA
(cm/s)
Water
log P
log D
Membrane
Water
log k (w/o) = a log P + b log (bP+1) + c
Kubinyi, 1978
Van de Waterbeemd et al, 1981
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Permeability = lipophilicity scale
Caco-2
PAMPA
Absorption
Permeability?
log Ddodecane
log Doct
Lipophilicity (log P/D)
In reality sigmoidal relationships
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Web screening
(in silico prediction)
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Why in silico ?
• Lots of compounds (libraries, parallel synthesis)
• Lots of data (in vitro ADME/physchem screening)
• Screening is expensive
• In vitro models not always predictive for in vivo
(e.g. Caco-2, PAMPA)
• In silico models to complement and/or replace
in vitro/in vivo
• Only option for virtual compounds
• Guide in decision-making
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In silico
• Sound QSAR and molecular modeling
methods/tools are available
• Commercial and in-house solutions for
physchem and ADME screening data
• Modeling and simulation for human PK
• Confidence is growing
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In silico solubility ?
 Artificial GI fluid and buffered water are models
for solubility in human GI
 In silico models of these surrogate conditions are
therefore a model of a model
 What is predictive power of such solubility models?
 We don’t take solid state properties into account!
Human GI
Artificial GI
r2 = 0.7
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Aqueous buffer
r2 = 0.7
r2=0.5
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In silico PAMPA and Caco-2 ?
 Caco-2 and PAMPA are models for oral absorption
 In silico models of Caco-2 and PAMPA are
therefore a model of a model
 What is predictive power of such models?
in vivo
in vitro
in silico
Human %A Caco-2/PAMPA
r2 = 0.7
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r2=0.5
Caco-2/PAMPA models
r2 = 0.7
model x model = random
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100
C. Lupfert, A. Reichel,
Chem.Biodivers. 2 (2005)
1462-1486
FA (%)
80
good
60
40
uncertain
20
0
0
50
100
poor
150
200 250
300
350
400
Papp (10-7 cm/s)
Typical range of Papp values in the Caco-2 permeation assay
“blind spot”
Papp values with acceptable in vivo predictivity
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values
2006,in this region have a highly
19 ambiguous in vivo relevance,
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i.e. the fraction dose absorbed may be anything between 10-100%!
ADME
Unravelling the processes
Bioavailability
Liver first-pass metabolism
Absorption
Transporters
Gut-wall metabolism
Permeability
Lipophilicity
Molecular size
Molecular shape
Flexibility
Hydrogen bonding
Solubility
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In vitro and
in silico
screens?
Prediction of A%
Design
Lead
Profiling
Lead
Optimization
Clinical
Candidate
ACAT
PBPK
R-o-5
Single
QSAR
Descriptors
MW<500
ClogP<5
HBA<10
HBD<5
MW<500
0<ClogP<4
0<logD<3
PSA<140A2
Structural ppb
Descriptors pKa
logD
Caco-2
PAMPA
Peff
Vmax, Km
Solubility
>60%
80-90%
75%
78%
A% human measured = 76 + 15% !!
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Development
Population
78 + 10%
Towards prediction paradise?
Solubility
A%
CL
log D
F%
T1/2
Dose
Vd
ADME
Activity
Toxicity
IC50
Tox
Van de Waterbeemd and Gifford,
Nature Revs. Drug Disc. 2 (2003) 192-204
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Future developments
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Property-based design is best practise
In combo approach established in drug discovery
Further progress in silico QSAR technology
New ADME/T world
• Pharma industry fully adapts in silico approach to
design, screening, and optimisation
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In vitro + in silico = in combo
Integration of experimental and computational
technologies
- Reducing cost of screening
- Maximising data information
Yu and Adedoyin, Drug Disc.Today 8, 852-861 (2003)
Dickins and Van de Waterbeemd, DDT: Biosilico, 2, 38-45 (2004)
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ADME technologies - autoQSAR
Automated model building and updating
Data
in combo
Build in silico model
Update in silico model
in vitro priorities
J.Cartmell et al, J.Comp.-Aid.Mol.Des. 19 (2005) 821-833
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In vitro: logP conferences
Great series of meetings,
Excellent Proceedings
Lausanne 1995, 2000
Zurich 2004, 2009
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In silico: EuroQSAR
conferences
QSAR has its attraction …
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References
Volume 5 ADME-Tox Approaches
(B. Testa and H. van de Waterbeemd),
Elsevier, November 2006
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Thanks
et bon appetit……
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