predicting phospholipidosis using machine learning 1 lowe et al., molec. pharmaceutics, 7, 1708...

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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John Mitchell; James McDonagh; Neetika Nath; Luna de Ferrari; Lazaros Mavridis; Rosanna Alderson

Rob Lowe; Richard Marchese Robinson

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John Mitchell; James McDonagh; Neetika Nath; Luna de Ferrari; Lazaros Mavridis; Rosanna Alderson

Rob Lowe; Richard Marchese Robinson

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

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

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

Tomizawa et al.,

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

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

Represent molecules using descriptors (we used E-Dragon & Circular Fingerprints)

10001101010011001101 10110101000011101101

10111101010001001100 10000001110011100111

10100101011101001110 10011111110001001010

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

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

… or Support Vector Machine

Average MCC Values:

RF SVM

0.619 0.650

Results

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?

Read up on gene expression studies related to phospholipidosis …

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

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

Mechanism?

H. Sawada, K. Takami, S. Asahi Toxicological Sciences 2005 282-292

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.

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

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

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

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

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.

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.

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.

Assemble List of Targets Relevant to Sawada’s Suggested Mechanisms

Inhibition of lysosomal phospholipase activity

Enhanced phospholipid biosynthesis

Enhanced cholesterol biosynthesis

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

N

iip xCPS

1

)()(

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

10 models

Phospholipidosis dataset: 100 PPL+, 82 PPL- compounds

Predicted target associations

Target PS scores

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

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.

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

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.

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)

Sawada’s Suggested Mechanisms

Mechanisms:

2. Inhibition of lysosomal enzyme transport;

There were no targets relevant to this mechanism with sufficient data to test.

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.

Sawada’s Suggested Mechanisms

Mechanisms:

4. Enhanced cholesterol biosynthesis

We find evidence for this mechanism operating through one target protein:

Lanosterol synthase (LSS)

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