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![Page 1: Predicting Phospholipidosis Using Machine Learning 1 Lowe et al., Molec. Pharmaceutics, 7, 1708 (2010) Robert Lowe (Cambridge) John Mitchell (St Andrews)](https://reader036.fdocuments.net/reader036/viewer/2022062719/56649ec05503460f94bcbcdd/html5/thumbnails/1.jpg)
Predicting Phospholipidosis Using Machine Learning
1Lowe et al., Molec. Pharmaceutics, 7, 1708 (2010)
• Robert Lowe (Cambridge)• John Mitchell (St Andrews)• Robert Glen (Cambridge)• Hamse Mussa (Cambridge)• Florian Nigsch (Novartis)
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2
John Mitchell; James McDonagh; Neetika Nath; Luna de Ferrari; Lazaros Mavridis; Rosanna Alderson
Rob Lowe; Richard Marchese Robinson
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3
John Mitchell; James McDonagh; Neetika Nath; Luna de Ferrari; Lazaros Mavridis; Rosanna Alderson
Rob Lowe; Richard Marchese Robinson
![Page 4: Predicting Phospholipidosis Using Machine Learning 1 Lowe et al., Molec. Pharmaceutics, 7, 1708 (2010) Robert Lowe (Cambridge) John Mitchell (St Andrews)](https://reader036.fdocuments.net/reader036/viewer/2022062719/56649ec05503460f94bcbcdd/html5/thumbnails/4.jpg)
Phospholipidosis
4Lowe 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
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Phospholipidosis
5Lowe 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
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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
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Tomizawa et al.,
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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
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Some PPL+ molecules, from Reasor et al., Exp Biol Med, 226, 825 (2001)
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Represent molecules using descriptors (we used E-Dragon & Circular Fingerprints)
10001101010011001101 10110101000011101101
10111101010001001100 10000001110011100111
10100101011101001110 10011111110001001010
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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
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Models are built using machine learning techniques such as Random Forest …
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… or Support Vector Machine
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Average MCC Values:
RF SVM
0.619 0.650
Results
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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?
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Read up on gene expression studies related to phospholipidosis …
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Sawada et al. listed genes which they found to be up- or down- regulated in phospholipidosis
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As with all gene expression experiments, some of these will be highly relevant, others will be noise. Can we help interpret these data?
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Mechanism?
H. Sawada, K. Takami, S. Asahi Toxicological Sciences 2005 282-292
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What expertise do we have available amongst our team, colleagues & collaborators?
•Multiple target prediction
•Maths
•Programming
Florian Nigsch
Hamse Mussa
Rob Lowe
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Predicting Targets using ChEMBL: Application to the
Mechanism of Phospholipidosis
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• Multiple target prediction
Predicting off-target interactions of drugs. Not with the primary pharmaceutical target, but with other targets relevant to side effects.
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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
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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
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CHEMBL
Filtered CHEMBL, 241145 compounds & 1923 targets
Data mining and filtering
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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
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Multi-class Classification
Algorithms:
• Parzen-Rosenblatt window• Naive Bayes
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Parzen-Rosenblatt window
jx
jii KN
xp xx ,1
)|(
using a Gaussian kernel
K(xi, xj) =
22 2
)()(
)(
1
hexp
hji
Tji
d
xxxx
(xi - xj)T(xi - xj) corresponds to the number of features in which xi and xj disagree
• Rank likely targets using estimates of class-condition probabilities
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Partition No. PRW Rank NB Rank
1 17.049 74.104
2 16.343 76.251
3 18.424 79.078
4 16.212 73.539
5 17.339 73.535
6 18.630 77.244
7 20.694 78.560
8 18.870 74.464
9 16.584 76.235
10 18.200 78.077
Average 17.835 76.109
When we test the two methods, PRW ranks known targets better than Naïve Bayes does. Hence we use PRW for our study.
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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.
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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.
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Assemble List of Targets Relevant to Sawada’s Suggested Mechanisms
Inhibition of lysosomal phospholipase activity
Enhanced phospholipid biosynthesis
Enhanced cholesterol biosynthesis
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Assigning Scores to Targets
N
iip xCPS
1
)()(
• 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
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N
iip xCPS
1
)()(
• Score measures tendency of target to interact with PPL+ rather than PPL- compounds.
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10 models
Phospholipidosis dataset: 100 PPL+, 82 PPL- compounds
Predicted target associations
Target PS scores
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M1 & M5 are involved in phospholipase C regulation & may be relevant; but not in Sawada’s list.
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Our Scores for 8 of Sawada’s PPL-Relevant Targets
Mechanism Target Rank PS
1 Sphingomyelin phosphodiesterase (SMPD) (h) 225 55
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
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We consider a PS score significant if the target is predicted to interact with at least 50 more PPL+ compounds than PPL- compounds.
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Our Scores for Sawada’s PPL-Relevant Targets
Mechanism Target Rank PS
1 Sphingomyelin phosphodiesterase (SMPD) (h) 225 55
Lysosomal Phospholipase A1 (LYPLA1) (r) 163= 90
Phospholipase A2 (PLA2) (h) 152= 97
3Elongation 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
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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.
![Page 44: Predicting Phospholipidosis Using Machine Learning 1 Lowe et al., Molec. Pharmaceutics, 7, 1708 (2010) Robert Lowe (Cambridge) John Mitchell (St Andrews)](https://reader036.fdocuments.net/reader036/viewer/2022062719/56649ec05503460f94bcbcdd/html5/thumbnails/44.jpg)
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)
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Sawada’s Suggested Mechanisms
Mechanisms:
2. Inhibition of lysosomal enzyme transport;
There were no targets relevant to this mechanism with sufficient data to test.
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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.
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Sawada’s Suggested Mechanisms
Mechanisms:
4. Enhanced cholesterol biosynthesis
We find evidence for this mechanism operating through one target protein:
Lanosterol synthase (LSS)
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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.
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Acknowledgements
• Alexios Koutsoukas
• Andreas Bender
• Richard Marchese-Robinson