Memories and the future From experimental to in

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

3/16/2018 Overview • Why physchem data? • Wet screening (in vitro) • Web screening (in silico) • Future developments Phys. Chem Forum, 29 Nov 2006, Newhouse 2

3/16/2018 <1980 Medchem evolution 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 Phys. Chem Forum, 29 Nov 2006, Newhouse 3

3/16/2018 Key ADME questions • Drugability • Attrition • Appropriate PK Target affinity vs ADME Carlson and Segall, Curr. Drug Disc. 34 -36 (2002) Phys. Chem Forum, 29 Nov 2006, Newhouse 4

3/16/2018 ADMET screening strategy • • Biopharmaceutical (physchem) profiling Pharmacokinetics Metabolism Early toxicology • • In vitro = wet screening In silico = web screening In combo In cerebro Phys. Chem Forum, 29 Nov 2006, Newhouse 5

3/16/2018 Wet screening (in vitro measurement) Phys. Chem Forum, 29 Nov 2006, Newhouse 6

3/16/2018 Han very early days Leiden (Ph. D) • log P vs log k • Are rate constants of partitioning useful in QSAR? Phys. Chem Forum, 29 Nov 2006, Newhouse 7

3/16/2018 Han early days Lausanne (post-doc with Bernard Testa) p. Ka - Apple III, IBM PC log k. HPLC - first attempts to HT log P = a. V + L = hydrophobicity + polarity = size + hydrogen bonding Phys. Chem Forum, 29 Nov 2006, Newhouse 8

3/16/2018 Han early days Roche (Molecular Properties Group) p. Ka (GLp. Ka 101, John Comer, Colin Peake) log k. HPLC 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 Phys. Chem Forum, 29 Nov 2006, Newhouse 9

3/16/2018 Han more recent days Pfizer (automated ADME screening) log D - 96 well plates log S PAMPA Pfizer (in silico ADME) Phys. Chem Forum, 29 Nov 2006, Newhouse 10

3/16/2018 • • • 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 Phys. Chem Forum, 29 Nov 2006, Newhouse 11

3/16/2018 Kinetic vs equilibrium Caco-2 PAMPA (cm/s) Water log P log D Membrane Water log k (w/o) = a log P + b log (b. P+1) + c Kubinyi, 1978 Van de Waterbeemd et al, 1981 Phys. Chem Forum, 29 Nov 2006, Newhouse 12

3/16/2018 Permeability = lipophilicity scale Caco-2 PAMPA Absorption Permeability? log Ddodecane log Doct Lipophilicity (log P/D) In reality sigmoidal relationships Phys. Chem Forum, 29 Nov 2006, Newhouse 13

3/16/2018 Web screening (in silico prediction) Phys. Chem Forum, 29 Nov 2006, Newhouse 14

3/16/2018 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 Phys. Chem Forum, 29 Nov 2006, Newhouse 15

3/16/2018 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 Phys. Chem Forum, 29 Nov 2006, Newhouse 16

3/16/2018 In silico solubility ? q Artificial GI fluid and buffered water are models for solubility in human GI q In silico models of these surrogate conditions are therefore a model of a model v What is predictive power of such solubility models? v We don’t take solid state properties into account! Human GI Artificial GI r 2 = 0. 7 Phys. Chem Forum, 29 Nov 2006, Newhouse Aqueous buffer r 2 = 0. 7 r 2=0. 5 17

3/16/2018 In silico PAMPA and Caco-2 ? q Caco-2 and PAMPA are models for oral absorption q In silico models of Caco-2 and PAMPA are therefore a model of a model v What is predictive power of such models? in vivo in vitro in silico Human %A Caco-2/PAMPA r 2 = 0. 7 Phys. Chem Forum, 29 Nov 2006, Newhouse r 2=0. 5 Caco-2/PAMPA models r 2 = 0. 7 model x model = random 18

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 Phys. Chem Forum, 29 Nov 2006, this region have a highly ambiguous in vivo relevance, Papp values in Newhouse i. e. the fraction dose absorbed may be anything between 10 -100%!

3/16/2018 ADME Unravelling the processes Bioavailability Liver first-pass metabolism Absorption Transporters Gut-wall metabolism Permeability Lipophilicity Molecular size Molecular shape Flexibility Hydrogen bonding Solubility Phys. Chem Forum, 29 Nov 2006, Newhouse 20 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 Clog. P<5 HBA<10 HBD<5 MW<500 060% 80 -90% 75% Development 78% A% human measured = 76 + 15% !! Phys. Chem Forum, 29 Nov 2006, Newhouse Population 78 + 10%

3/16/2018 Towards prediction paradise? Solubility A% CL log D F% T 1/2 Dose Vd ADME Activity Toxicity IC 50 Tox Van de Waterbeemd and Gifford, Nature Revs. Drug Disc. 2 (2003) 192 -204 Phys. Chem Forum, 29 Nov 2006, Newhouse 22

3/16/2018 • • 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 Phys. Chem Forum, 29 Nov 2006, Newhouse 23

3/16/2018 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) Phys. Chem Forum, 29 Nov 2006, Newhouse 24

3/16/2018 ADME technologies - auto. QSAR 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 Phys. Chem Forum, 29 Nov 2006, Newhouse 25

3/16/2018 In vitro: log. P conferences Great series of meetings, Excellent Proceedings Lausanne 1995, 2000 Zurich 2004, 2009 Phys. Chem Forum, 29 Nov 2006, Newhouse 26

3/16/2018 In silico: Euro. QSAR conferences QSAR has its attraction … Phys. Chem Forum, 29 Nov 2006, Newhouse 27

3/16/2018 References Volume 5 ADME-Tox Approaches (B. Testa and H. van de Waterbeemd), Elsevier, November 2006 Phys. Chem Forum, 29 Nov 2006, Newhouse 28

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

Phys. Chem Forum, 29 Nov 2006, Newhouse