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Memories and the future: From experimental to in silico physical chemistry

Memories and the future: From experimental to in silico physical chemistry. Han van de Waterbeemd AstraZeneca, DMPK Alderley Park, Macclesfield, UK. Overview. Why physchem data? Wet screening (in vitro) Web screening (in silico) Future developments. Medchem evolution.

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Memories and the future: From experimental to in silico physical chemistry

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

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

  3. 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 >2005in silico/in vitro (in combo) approaches protein crystallography attrition analyses physchem/DMPK considerations HT property screening PhysChem Forum, 29 Nov 2006, Newhouse

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

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

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

  9. 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 PhysChem Forum, 29 Nov 2006, Newhouse

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

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

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

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

  17. 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 Aqueous buffer r2 = 0.7 r2 = 0.7 r2=0.5 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 %A Caco-2/PAMPA Caco-2/PAMPA models r2 = 0.7 r2 = 0.7 r2=0.5 model x model = random PhysChem Forum, 29 Nov 2006, Newhouse

  19. 100 80 60 FA (%) 40 20 0 0 50 100 150 200 250 300 350 400 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 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) 1462-1486 good uncertain poor PhysChem Forum, 29 Nov 2006, Newhouse

  20. Unravelling the processes ADME 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? PhysChem Forum, 29 Nov 2006, Newhouse

  21. Prediction of A% Design Lead Profiling Lead Optimization Clinical Candidate Development ACAT PBPK ppb pKa logD Caco-2 PAMPA Peff Vmax, Km Solubility 78% Single Descriptors MW<500 0<ClogP<4 0<logD<3 PSA<140A2 80-90% QSAR Structural Descriptors 75% R-o-5 MW<500 ClogP<5 HBA<10 HBD<5 >60% Population 78 + 10% A% human measured = 76 + 15% !! PhysChem Forum, 29 Nov 2006, Newhouse

  22. Towards prediction paradise? Solubility A% F% CL Vd Dose T1/2 log D ADME Activity Toxicity IC50 Tox Van de Waterbeemd and Gifford, Nature Revs. Drug Disc. 2 (2003) 192-204 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 PhysChem Forum, 29 Nov 2006, Newhouse

  24. 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) PhysChem Forum, 29 Nov 2006, Newhouse

  25. ADME technologies - autoQSAR Automated model building and updating in combo Data Build in silico model Update in silico model in vitro priorities J.Cartmell et al, J.Comp.-Aid.Mol.Des. 19 (2005) 821-833 PhysChem Forum, 29 Nov 2006, Newhouse

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

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

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

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

  30. PhysChem Forum, 29 Nov 2006, Newhouse

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