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

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

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

4. Phospholipidosis 4 Lowe et al., Molec. Pharmaceutics, 7, 1708 (2010) 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. Phospholipidosis 5 Lowe et al., Molec. Pharmaceutics, 7, 1708 (2010) Causes delay in the drug development process May or may not be related to human pathologies such as Niemann-Pick disease

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

7. Tomizawa et al.,

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

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

10. Represent molecules using descriptors (we used E-Dragon & Circular Fingerprints) 1000110101001100110110110101000011101101 1011110101000100110010000001110011100111 1010010101110100111010011111110001001010

11. 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. Experimental Design

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

13. … or Support Vector Machine

14. Average MCC Values: RFSVM 0.6190.650 Results

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 Sciences 2005 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 Filtered CHEMBL, 241145 compounds & 1923 targets Data mining and filtering 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 K i /K d /IC50 values (< 500μM) to define activity

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

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 using a Gaussian kernel K(x i, x j ) = (x i - x j ) T (x i - x j ) corresponds to the number of features in which x i and x j disagree Rank likely targets using estimates of class- condition probabilities

30. Partition No.PRW RankNB Rank 117.04974.104 216.34376.251 318.42479.078 416.21273.539 517.33973.535 618.63077.244 720.69478.560 818.87074.464 916.58476.235 1018.20078.077 Average17.83576.109 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 MechanismTargetRankPS  1 Sphingomyelin phosphodiesterase (SMPD) (h)22555 Lysosomal Phospholipase A1 (LYPLA1) (r)163=90 Phospholipase A2 (PLA2) (h)152=97 3 Elongation of very long chain fatty acids protein 6 (ELOVL6) (h)1203=-10 Acyl-CoA desaturase (SCD) (m)610=0 4 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) (h)456=10 Squalene monooxygenase (SQLE) (h)437=14 Lanosterol synthase (LSS) (h)114=134 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 MechanismTargetRankPS  1 Sphingomyelin phosphodiesterase (SMPD) (h)22555 Lysosomal Phospholipase A1 (LYPLA1) (r)163=90 Phospholipase A2 (PLA2) (h)152=97 3 Elongation of very long chain fatty acids protein 6 (ELOVL6) (h)1203=-10 Acyl-CoA desaturase (SCD) (m)610=0 4 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) (h)456=10 Squalene monooxygenase (SQLE) (h)437=14 Lanosterol synthase (LSS) (h)114=134 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: 1.Inhibition of lysosomal phospholipase activity We find evidence for this mechanism operating through three target proteins: Sphingomyelin phosphodiesterase (SMPD) Lysosomal phospholipase 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