Predicting Phospholipidosis Using Machine Learning

1. Lowe et al., Molec. Pharmaceutics, 7, 1708 (2010) 1 Predicting Phospholipidosis Using Machine Learning • Robert Lowe (Cambridge) • John Mitchell (St Andrews) • Robert Glen (Cambridge) • HamseMussa (Cambridge) • Florian Nigsch (Novartis)

2. John Mitchell; James McDonagh; Neetika Nath; Luna de Ferrari; Lazaros Mavridis; Rosanna Alderson Rob Lowe; Richard Marchese Robinson

3. John Mitchell; James McDonagh; Neetika Nath; Luna de Ferrari; Lazaros Mavridis; Rosanna Alderson Rob Lowe; Richard Marchese Robinson

4. Lowe et al., Molec. Pharmaceutics, 7, 1708 (2010) 4 Phospholipidosis • An adverse effect caused by drugs • Excess accumulation of phospholipids • Often by cationic amphiphilic drugs • Affects many cell types • Causes delay in the drug development process

5. Lowe et al., Molec. Pharmaceutics, 7, 1708 (2010) 5 Phospholipidosis • Causes delay in the drug development process • May or may not be related to human pathologies such as Niemann-Pick disease

6. Electron micrographs of alveolar macrophages (A and B) and peritoneal macrophages (C and D) obtained from 3-month-old Lpla2+/+ and Lpla2-/- mice Hiraoka, M. et al. 2006. Mol. Cell. Biol. 26(16):6139-6148

7. Tomizawa et al.,

8. Literature Mined Dataset • Produced our own dataset of 185 compounds (from literature survey) • 102 PPL+ and 83PPL- • Each compound is an experimentally confirmed positive or negative R. Lowe, R.C. Glen, J.B.O. Mitchell Mol. Pharm. 2010 VOL. 7, NO. 5, 1708–1714

9. Some PPL+ molecules, from Reasor et al., Exp Biol Med, 226, 825 (2001)

10. 10001101010011001101 10110101000011101101 10111101010001001100 10000001110011100111 10100101011101001110 10011111110001001010 Represent molecules using descriptors (we used E-Dragon & Circular Fingerprints)

11. Experimental Design Split data into N folds, then train on (N-2) of them, keeping one for parameter optimisation and one for unseen testing. Average results over all runs (each molecule is predicted once per N-fold validation). We also repeat the whole process several times with randomly different assignments of which molecules are in which folds.

12. Models are built using machine learning techniques such as Random Forest …

13. … or Support Vector Machine

14. Results Average MCC Values: RF SVM 0.619 0.650

16. So we have built a good predictive model that can learn the features that predispose a molecule to being PPL+, and can make predictions from chemical structure. This is useful – one could add it to a virtual screening protocol. But can we understand anything new about how phospholipidosis occurs?

17. Read up on gene expression studies related to phospholipidosis …

18. Sawada et al. listed genes which they found to be up- or down- regulated in phospholipidosis

19. As with all gene expression experiments, some of these will be highly relevant, others will be noise. Can we help interpret these data?

20. Mechanism? H. Sawada, K. Takami, S. Asahi Toxicological Sciences2005 282-292

21. What expertise do we have available amongst our team, colleagues & collaborators? • Multiple target prediction • Maths • Programming Florian Nigsch Hamse Mussa Rob Lowe

22. 22 Predicting Targets using ChEMBL: Application to the Mechanism of Phospholipidosis

23. 23 • Multiple target prediction • Predicting off-target interactions of drugs. Not with the primary pharmaceutical target, but with other targets relevant to side effects.

24. CHEMBL Data mining and filtering Filtered CHEMBL, 241145 compounds & 1923 targets Random 99:1 split of the whole dataset, 10 repeats 10 models Phospholipidosis dataset: 100 PPL+, 82 PPL- compounds Predicted target associations Target PS scores

25. ChEMBL Mining • Mined the ChEMBL (03) database for compounds and targets they interact with • Target description included the word "enzyme", "cytosolic", "receptor", "agonist" or "ion channel" • A high cut-off (weak binding) was used on Ki/Kd/IC50 values (< 500μM) to define activity

26. CHEMBL Data mining and filtering Filtered CHEMBL, 241145 compounds & 1923 targets

27. 27 Method • Number of Compounds : 241145 • Number of Targets : 1923 • Split the data into 10 different partitions of training and validation • Used circular fingerprints with SYBYL atom types to define similarities between molecules

28. 28 Multi-class Classification • Algorithms: • Parzen-Rosenblatt window • Naive Bayes

29. Parzen-Rosenblatt window • Rank likely targets using estimates of class-condition probabilities using a Gaussian kernel K(xi, xj) = (xi- xj)T(xi- xj) corresponds to the number of features in which xi and xj disagree

30. When we test the two methods, PRW ranks known targets better than Naïve Bayes does. Hence we use PRW for our study.

31. Filtered CHEMBL, 241145 compounds & 1923 targets Random 99:1 split of the whole dataset, 10 repeats 10 models So we generate 10 separate validated models which we will use to predict off-target interactions for our PPL+/PPL- set.

32. Assemble List of Targets Relevant to Sawada’s Suggested Mechanisms Mechanisms: 1. Inhibition of lysosomal phospholipase activity; 2. Inhibition of lysosomal enzyme transport; 3. Enhanced phospholipid biosynthesis; 4. Enhanced cholesterol biosynthesis.

33. Assemble List of Targets Relevant to Sawada’s Suggested Mechanisms Inhibition of lysosomal phospholipase activity Enhanced phospholipid biosynthesis Enhanced cholesterol biosynthesis

34. Assigning Scores to Targets • Use these 10 models of target interactions • Predict targets for phospholipidosis dataset • Score targets according to the likelihood of involvement in phospholipidosis • Use the top 100 predicted targets per compound as we seek off-target interactions

35. Score measures tendency of target to interact with PPL+ rather than PPL- compounds.

36. 10 models Phospholipidosis dataset: 100 PPL+, 82 PPL- compounds Predicted target associations Target PS scores

38. M1 & M5 are involved in phospholipase C regulation & may be relevant; but not in Sawada’s list.

40. Our Scores for 8 of Sawada’s PPL-Relevant Targets Inhibition of lysosomal phospholipase activity Enhanced phospholipid biosynthesis Enhanced cholesterol biosynthesis

41. 41 We consider a PS score significant if the target is predicted to interact with at least 50 more PPL+ compounds than PPL- compounds.

42. Our Scores for Sawada’s PPL-Relevant Targets Inhibition of lysosomal phospholipase activity Enhanced phospholipid biosynthesis Enhanced cholesterol biosynthesis

43. Assemble List of Targets Relevant to Sawada’s Suggested Mechanisms Mechanisms: 1. Inhibition of lysosomal phospholipase activity; 2. Inhibition of lysosomal enzyme transport; 3. Enhanced phospholipid biosynthesis; 4. Enhanced cholesterol biosynthesis.

44. Sawada’s Suggested Mechanisms • Mechanism: • Inhibition of lysosomal phospholipase activity • We find evidence for this mechanism operating through three target proteins: • Sphingomyelinphosphodiesterase (SMPD) • Lysosomalphospholipase A1 (LYPLA1) • Phospholipase A2 (PLA2)

45. Sawada’s Suggested Mechanisms Mechanisms: 2. Inhibition of lysosomal enzyme transport; There were no targets relevant to this mechanism with sufficient data to test.

46. Sawada’s Suggested Mechanisms Mechanisms: 3. Enhanced phospholipid biosynthesis We were able to test two targets relevant to this mechanism and found no evidence linking them to phospholipidosis.

47. Sawada’s Suggested Mechanisms Mechanisms: 4. Enhanced cholesterol biosynthesis We find evidence for this mechanism operating through one target protein: Lanosterol synthase (LSS)

48. Sawada’s Suggested Mechanisms • The mechanisms and targets suggested here are insufficient to explain all the PPL+ compounds in our data set. • We expect that other targets and possibly mechanisms are important. • Our method can’t test direct compound – phospholipid binding.

50. 50 Acknowledgements • Alexios Koutsoukas • Andreas Bender • Richard Marchese-Robinson