physchem forum, 29 nov 2006, newhouse 1 memories and the future: from experimental to in silico...

PhysChem Forum, 29 Nov 2006, Newhouse

1PhysChem Forum, 29 Nov 2006, Newhouse

Memories and the future:From experimental to in silico

physical chemistry

Han van de WaterbeemdAstraZeneca,

DMPK Alderley Park, Macclesfield, UK



Overview

• Why physchem data?

• Wet screening (in vitro)

• Web screening (in silico)

• Future developments



Medchem evolution

<1980 target affinity/binding using intuition and experience

>1980 structure-based design

>1995 drug/lead filters such as rule of five

>2000 property-based design

>2005 in silico/in vitro (in combo) approaches

protein crystallography

attrition analyses

physchem/DMPK considerations

HT property screening



Key ADME questions

Blood Brain Barrier Metabolic Biliary

Plasma Protein Binding

Volume of DistributionRenal Hepatic Gut Stability

Solubility

Membranepermeation

Physicochemical Properties

LogP/DpKaCytochromes P-450

WhichP-450?

WhichConjugate?

Regiospecificity Lability Affinity

Glucuronide Sulphate Amino Acids

Others

Paracellular Transcellular

Plasma

Transporters

P-gp OATP OCTPMRP

Poor systemic exposure Poor oral bioavailability

Distribution Clearance Absorption

Optimisation problem

1A2, 2C9, 2C19, 2D6, 3A4

Induction

CAR AHRPXR

First-pass clearance

Inhibition

Type II binding Mechanistic

Carlson and Segall, Curr.Drug Disc. 34-36 (2002)

• Drugability• Attrition• Appropriate PK

Target affinityvsADME



ADMET screening strategy

• Biopharmaceutical (physchem) profiling• Pharmacokinetics• Metabolism• Early toxicology

• In vitro = wet screening• In silico = web screening• In combo• In cerebro



Wet screening (in vitro measurement)



Han very early days

Leiden (PhD)

• log P vs log k

• Are rate constants of partitioning useful in QSAR?



Han early days

Lausanne (post-doc with Bernard Testa)

pKa - Apple III, IBM PC

log kHPLC - first attempts to HT

log P = aV +

= hydrophobicity + polarity

= size + hydrogen bonding



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 areaVan de Waterbeemd and Kansy, Chimia 46 (1992) 299-303



Han more recent days

Pfizer (automated ADME screening)

log D - 96 well plates

log S

PAMPA

Pfizer (in silico ADME)



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



Kinetic vs equilibrium

Water Membrane Water

Caco-2PAMPA(cm/s)

log Plog D

log k (w/o) = a log P + b log (P+1) + c

Kubinyi, 1978Van de Waterbeemd et al, 1981



Permeability = lipophilicity scale

Lipophilicity (log P/D)

Absorption

log Doct

log Ddodecane

PAMPA

Caco-2

In reality sigmoidal relationships

Permeability?



Web screening(in silico prediction)



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



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



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 Aqueous buffer

r2 = 0.7 r2 = 0.7 r2=0.5

In silico solubility ?



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 silicoHuman %A Caco-2/PAMPA Caco-2/PAMPA models

r2 = 0.7 r2 = 0.7 r2=0.5

model x model = random



0

20

40

60

80

100

0 50 100 150 200 250 300 350 400

Papp (10-7 cm/s)

FA

(%

)

Typical range of Papp values in the Caco-2 permeation assay

“blind spot”

Papp values with acceptable in vivo predictivity

Papp values in this region have a highly ambiguous in vivo relevance,i.e. the fraction dose absorbed may be anything between 10-100%!

C. Lupfert, A. Reichel,Chem.Biodivers. 2 (2005)1462-1486good

uncertain

poor



Unravelling the processes

Bioavailability

Liver first-pass metabolism

Absorption

Transporters

Gut-wall metabolism

Permeability

Lipophilicity

Molecular size

Molecular shape

Flexibility

Hydrogen bonding

Solubility

In vitro and in silico screens?

ADME



Design Clinical

CandidateLead

Optimization Development

Lead Profiling

A% human measured = 76 + 15% !!

R-o-5

MW<500ClogP<5HBA<10HBD<5

>60%

SingleDescriptors

MW<5000<ClogP<40<logD<3PSA<140A2

80-90%

QSAR

StructuralDescriptors

75%

ACATPBPK

ppbpKalogDCaco-2PAMPAPeff

Vmax, KmSolubility

78%

Population

78 + 10%

Prediction of A%



Towards prediction paradise?

Solubility Solubility A% F%A% F%

log Dlog D

CLCL

VdVdTT1/21/2

ICIC5050

Dose

Tox

Van de Waterbeemd and Gifford, Nature Revs. Drug Disc. 2 (2003) 192-204

ADMEActivityToxicity



Future developments

• 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



In vitro + in silico = in combo

Yu and Adedoyin, Drug Disc.Today 8, 852-861 (2003)Dickins and Van de Waterbeemd, DDT: Biosilico, 2, 38-45 (2004)

Integration of experimental and computationaltechnologies

- Reducing cost of screening- Maximising data information



ADME technologies - autoQSAR

Automated model building and updating

Data Build in silico model

Update in silico model

J.Cartmell et al, J.Comp.-Aid.Mol.Des. 19 (2005) 821-833

in combo

in vitro priorities



In vitro: logP conferences

Great series of meetings,

Excellent Proceedings

Lausanne 1995, 2000

Zurich 2004, 2009



QSAR has its attraction …

In silico: EuroQSAR conferences



References

Volume 5 ADME-Tox Approaches (B. Testa and H. van de Waterbeemd),Elsevier, November 2006



Thanks

et bon appetit……

physchem forum, 29 nov 2006, newhouse 1 memories and the future: from experimental to in silico...

Documents

physchem forum

physchem data

silico adme slide

pampa cms log p log

log p b log p

silico prediction slide

decisionmaking slide

cerebro slide