1. PhysChem Forum, 29 Nov 2006, Newhouse 1 Memories and the future: From experimental to in silico physical chemistry Han van de Waterbeemd AstraZeneca, DMPK Alderley Park, Macclesfield, UK

2. PhysChem Forum, 29 Nov 2006, Newhouse 2 Overview Why physchem data? Wet screening (in vitro) Web screening (in silico) Future developments

3. PhysChem Forum, 29 Nov 2006, Newhouse 3 Medchem evolution <1980target affinity/binding using intuition and experience >1980structure-based design >1995drug/lead filters such as rule of five >2000property-based design >2005in silico/in vitro (in combo) approaches protein crystallography attrition analyses physchem/DMPK considerations HT property screening

4. PhysChem Forum, 29 Nov 2006, Newhouse 4 Key ADME questions Carlson and Segall, Curr.Drug Disc. 34-36 (2002) Drugability Attrition Appropriate PK Target affinity vs ADME

5. PhysChem Forum, 29 Nov 2006, Newhouse 5 ADMET screening strategy Biopharmaceutical (physchem) profiling Pharmacokinetics Metabolism Early toxicology In vitro = wet screening In silico = web screening In combo In cerebro

6. PhysChem Forum, 29 Nov 2006, Newhouse 6 Wet screening (in vitro measurement)

7. PhysChem Forum, 29 Nov 2006, Newhouse 7 Han very early days Leiden (PhD) log P vs log k Are rate constants of partitioning useful in QSAR?

8. PhysChem Forum, 29 Nov 2006, Newhouse 8 Han early days Lausanne ( post-doc with Bernard Testa) pKa - Apple III, IBM PC log k HPLC - first attempts to HT log P = aV +  = hydrophobicity + polarity = size + hydrogen bonding

9. PhysChem Forum, 29 Nov 2006, Newhouse 9 Han early days Roche (Molecular Properties Group) pKa (GLpKa101, John Comer, Colin Peake) log k HPLC log P app (artificial membranes pre-PAMPA, Gian Camenisch) PAMPA (Manfred Kansy) PSA – polar surface area Van de Waterbeemd and Kansy, Chimia 46 (1992) 299-303

10. PhysChem Forum, 29 Nov 2006, Newhouse 10 Han more recent days Pfizer ( automated ADME screening) log D - 96 well plates log S PAMPA Pfizer (in silico ADME)

11. PhysChem Forum, 29 Nov 2006, Newhouse 11 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

12. PhysChem Forum, 29 Nov 2006, Newhouse 12 Kinetic vs equilibrium Water Membrane Water Caco-2 PAMPA (cm/s) log P log D log k (w/o) = a log P + b log (  P+1) + c Kubinyi, 1978 Van de Waterbeemd et al, 1981

13. PhysChem Forum, 29 Nov 2006, Newhouse 13 Permeability = lipophilicity scale Lipophilicity (log P/D) Absorption log D oct log D dodecane PAMPA Caco-2 In reality sigmoidal relationships Permeability?

14. PhysChem Forum, 29 Nov 2006, Newhouse 14 Web screening (in silico prediction)

15. PhysChem Forum, 29 Nov 2006, Newhouse 15 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

16. PhysChem Forum, 29 Nov 2006, Newhouse 16 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

17. PhysChem Forum, 29 Nov 2006, Newhouse 17  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 r 2 = 0.7r 2 = 0.7 r 2 =0.5 In silico solubility ?

18. PhysChem Forum, 29 Nov 2006, Newhouse 18 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 %ACaco-2/PAMPACaco-2/PAMPA models r 2 = 0.7r 2 = 0.7 r 2 =0.5 model x model = random

19. PhysChem Forum, 29 Nov 2006, Newhouse 19 C. Lupfert, A. Reichel, Chem.Biodivers. 2 (2005) 1462-1486 good uncertain poor

20. PhysChem Forum, 29 Nov 2006, Newhouse 20 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

21. PhysChem Forum, 29 Nov 2006, Newhouse 21 Design Clinical Candidate Lead Optimization Development Lead Profiling A% human measured = 76 + 15% !! R-o-5 MW<500 ClogP<5 HBA<10 HBD<5 >60% Single Descriptors MW<500 0<ClogP<4 0<logD<3 PSA<140A 2 80-90% QSAR Structural Descriptors 75% ACAT PBPK ppb pKa logD Caco-2 PAMPA P eff Vmax, Km Solubility 78% Population 78 + 10% Prediction of A%

22. PhysChem Forum, 29 Nov 2006, Newhouse 22 Towards prediction paradise? Solubility A% F% log D CLVd T 1/2 IC 50 Dose Tox Van de Waterbeemd and Gifford, Nature Revs. Drug Disc. 2 (2003) 192-204 ADME Activity Toxicity

23. PhysChem Forum, 29 Nov 2006, Newhouse 23 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

24. PhysChem Forum, 29 Nov 2006, Newhouse 24 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 computational technologies - Reducing cost of screening - Maximising data information

25. PhysChem Forum, 29 Nov 2006, Newhouse 25 ADME technologies - autoQSAR Automated model building and updating DataBuild 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

26. PhysChem Forum, 29 Nov 2006, Newhouse 26 In vitro: logP conferences Great series of meetings, Excellent Proceedings Lausanne 1995, 2000 Zurich 2004, 2009

27. PhysChem Forum, 29 Nov 2006, Newhouse 27 QSAR has its attraction … In silico: EuroQSAR conferences

28. PhysChem Forum, 29 Nov 2006, Newhouse 28 References Volume 5 ADME-Tox Approaches (B. Testa and H. van de Waterbeemd), Elsevier, November 2006

29. PhysChem Forum, 29 Nov 2006, Newhouse 29 Thanks et bon appetit……

30. PhysChem Forum, 29 Nov 2006, Newhouse 30