In Silico Methods for ADMET and Solubility Prediction

1. In Silico Methods for ADMET and Solubility Prediction Dr John Mitchell University of St Andrews

2. Outline • Part 1: Computational Toxicology • Part 2: Aqueous Solubility

3. 1. Toxicological Relationships Between Proteins Obtained Froma Molecular Spam Filter Florian Nigsch & John Mitchell Now at Novartis Institutes, Boston

7. Spam • Unsolicited (commercial) email • Approx. 90% of all email traffic is spam • Where are the legitimate messages? • Filtering

8. Analogy to Drug Discovery • Huge number of possible candidates • Virtual screening to help in selection process

9. High affinity to protein target Soluble Permeable Absorbable High bioavailability Specific rate of metabolism Renal/hepatic clearance? Volume of distribution? Low toxicity Plasma protein binding? Blood-Brain-Barrier penetration? Dosage (once/twice daily?) Synthetic accessibility Formulation (important in development) Properties of Drugs

10. Multiobjective Optimisation Synthetic accessibility Bioactivity Solubility Toxicity Permeability Metabolism Huge number of candidates …

11. Multiobjective Optimisation Synthetic accessibility Bioactivity Drug Solubility Toxicity U S E L E S S Permeability Metabolism Huge number of candidates … most of which are useless!

12. Feature Space - Chemical Space m = (f1,f2,…,fn) f3 f3 f2 COX2 CDK2 f1 Feature spaces of high dimensionality CDK1 f2 DHFR f1

13. Features of Molecules Based on circular fingerprints

14. Combinations of Features Combinations of molecular features to account for synergies.

15. Winnow Algorithm

16. Protein Target Prediction • Which protein does a given molecule bind to? • Virtual Screening • Multiple endpoint drugs - polypharmacology • New targets for existing drugs • Prediction of adverse drug reactions (ADR) • Computational toxicology

17. Predicted Protein Targets • Selection of 233 classes from the MDL Drug Data Report • ~90,000 molecules • 15 independent 50%/50% splits into training/test set

18. Predicted Protein Targets Cumulative probability of correct prediction within the three top-ranking predictions: 82.1% (±0.5%)

19. Model for target prediction Annotated library of toxic molecules MDL Toxicity database ~150,000 molecules For each molecule we predict the likely target Correlations between predicted protein targets and known toxicity codes Canonical (23) Full (490) Computational Toxicology

20. Toxicological Relationships Outline (1) • Protein target prediction allows us to link (predictively) 150,000 toxic organic molecules to 233 specific protein targets • Each target is treated as a single protein, although may be sets of related proteins • Toxicological databases link (experimentally) these 150,000 molecules to 23 toxicity classes • Combining these two sources of data matches the 233 proteins with the 23 toxicity classes

21. Toxicity Annotations FULL TOXICITY CODES (490) Y41 : Glycolytic < Metabolism (intermediary) < Biochemical CANONICAL TOXICITY CODES (23)

22. Toxicological Relationships Outline (2) • For each protein target, we have a profile of association with the 23 toxicity classes • Proteins with similar profiles are clustered together • We demonstrate that these clusters of proteins can be physiologically meaningful.

23. Predictions Obtained Highest ranking one IS predicted protein target Protein code j Target Prediction L70 - Changes in liver weight<Liver Y07 - Hepatic microsomal oxidase<Enzyme inhibition M30 - Other changes<Kidney, Urether, and Bladder L30 - Other changes<Liver Toxicity codesi Result matrix R = (rij) rij incremented for each prediction. Protein targets Toxcodes ( ) … r11 r12 r21

24. Cardiac - G Kainic acid receptor Adrenergic alpha2 Phosphodiesterase III cAMP Phosphodiesterase O6-Alkylguanine-DNA alkyltransferase Vascular - H Angiotensin II AT2 Dopamine (D2) Bombesin Adrenergic alpha2 5-HT antagonist Proteins by Toxicity

25. Top 5 Proteins by Toxicity 68 distinct proteins for 23 toxicity classes, i.e., 3 proteins per canonical toxicity code. Lanosterol 14alpha-Methyl Demethylase 5 Glucose-6-phosphate Translocase 4 IL-6 4 Benzodiazepine Antagonist 3 Kainic Acid Receptor 3 Proteins and their connectivities

26. Correlation Between Proteins Correlations between proteins: 233 by 233 correlation matrix Cluster 1 (proteins 6-11)

27. Carbonic Anhydrase Inhibitor Estrogen Receptor Modulator LHRH Agonist Aromatase Inhibitor Cysteine Protease Inhibitor DHFR Inhibitor Cluster 1 • Cluster 1 • Within-cluster correlation (without auto-correlation) • r = 0.95 Proteins involved in breast cancer

28. Cluster 1 Proteins involved in breast cancer

29. Literature-based links between these proteins Tissue-specific transcripts of human steroid sulfatase are under control of estrogen signaling pathways in breast carcinoma, Zaichuk 2007 “aim of this study was to characterize carbonic anhydrase II (CA2), as novel estrogen responsive gene” Caldarelli 2005 CA This led to premature expression of CAII, a possible explanation for the toxic effects of overexpressed ER. The Transactivation Domain AF-2 but not the DNA-Binding Domain of the Estrogen Receptor Is Required to Inhibit Differentiation of Avian Erythroid Progenitors, Marieke von Lindern 1998 ER LHRH Controversies of adjuvant endocrine treatment for breast cancer and recommendations of the 2007 St Gallen conference, Rabaglio 2007 Cathepsin L Gene Expression and Promoter Activation in Rodent Granulosa Cells, Sriraman 2004 showed that cathepsin L expression in granulosa cells of small, growing follicles in- creased in periovulatory follicles after human chorionic gonadotropin stimulation. Merchenthaler 2005 Summary of aromatase inhibitor trials: The past and future, Goss 2007 Regulation of collagenolytic cysteine protease synthesis by estrogen in osteoclasts, Furuyama 2000 Aromatase Cysteine Prot. Induction by estrogens of methotrexate resistance in MCF-7 breast cancer cells, Thibodeau 1998 DHFR Antimalarials?

30. Breast Cancer Proteins

31. Cluster 4

32. This cluster links treatment of stomach ulcers to loss of bone mass!

33. Proton Pump Inhibitors etc. Correlation above 0.98

34. Proton Pump Inhibitors etc. Correlation above 0.98 Correlation above 0.99

35. Proton Pump Inhibitors etc. PTH = Parathyroid hormone (84 aa mini-protein) • Proton pump inhibitors used to limit production of gastric acid • PTH is important in the developent/regulation of osteoclasts (cells for bone resorption) • PTH controls levels of Ca2+ in the blood; increased PTH levels are associated with age-related decrease of bone mass Recent clinical studies showed increased risk of hip fractures resulting from long-term use of proton pump inhibitors. Hence link between PTH and proton pump inhibitors.

36. Conclusions from Part 1 • Successful adaptation of algorithm formerly not used in chemoinformatics • Can find correct protein targets for molecules • Hence link proteins together vialigand-binding properties and associations of ligands with toxicities • Identify clinically relevant toxicological relationships between proteins

37. 2. In silico calculation of aqueous solubility Dr John Mitchell University of St Andrews

38. Our Methods … (a) Random Forest (informatics)

39. References

40. Our Random Forest Model … We want to construct a model that will predict solubility for druglike molecules … We don’t expect our model either to use real physics and chemistry or to be easily interpretable … We do expect it to be fast and reasonably accurate …

41. Random Forest Machine Learning Method

42. Random Forest for Predicting Solubility A Forest of Regression Trees • Dataset is partitioned into consecutively smaller subsets (of similar solubility) • Each partition is based upon the value of one descriptor • The descriptor used at each split is selected so as to minimise the MSE • High predictive accuracy • Includes descriptor selection • No training problems – largely immune from overfitting • “Out-of-bag” validation – using those molecules not in the bootstrap samples. Leo Breiman, "Random Forests“, Machine Learning 45, 5-32 (2001).

43. Dataset • Literature Data • Compiled from Huuskonen dataset and AquaSol database – pharmaceutically relevant molecules • All molecules solid at room temperature • n = 988 molecules • Training = 658 molecules • Test = 330 molecules • MOE descriptors 2D/3D ●Intrinsic aqueous solubility – the thermodynamic solubility of the neutral form in unbuffered water at 25oC Datasets compiled from diverse literature data may have significant random and systematic errors.

44. These results are competitive with any other informatics or QSPR solubility prediction method Random Forest: Solubility Results RMSE(oob)=0.68 r2(oob)=0.90 Bias(oob)=0.01 RMSE(te)=0.69 r2(te)=0.89 Bias(te)=-0.04 RMSE(tr)=0.27 r2(tr)=0.98 Bias(tr)=0.005 DS Palmer et al., J. Chem. Inf. Model., 47, 150-158 (2007)

45. Part 2a, Solubility by Random Forest: Conclusions ● Random Forest gives an RMS error of 0.69 logS units. ● These results are competitive with any other informatics or QSPR solubility prediction method. ● The nature of the model is predictive, without offering much insight.

46. Our Methods … (b) Thermodynamic Cycle (A hybrid of theoretical chemistry & informatics)

47. Reference

48. Our Thermodynamic Cycle method … We want to construct a theoretical model that will predict solubility for druglike molecules … We expect our model to use real physics and chemistry and to give some insight … We may need to include some empirical parameters… We don’t expect it to be fast by informatics or QSPR standards, but it should be reasonably accurate …

49. For this study Toni Llinàs measured 30 solubilities using the CheqSol method and took another 30 from other high quality studies (Bergstrom & Rytting). We use a Sirius glpKa instrument

50. Can we use theoretical chemistry to calculate solubility via a thermodynamic cycle?