Demo

1. SideFinder: Predicting Drug Side Effects
Aqeel Ahmed
Insight Data Science FellowProf. Heather A. Carlson Group
College of Pharmacy
University of Michigan

2. Motivations: Drug Side Effects Prediction
 Developing a new drug is challenging
 Requires billions of dollars
 Often more than 13 years of efforts
 Side effects are one of the main causes of
Drug failure
Drug withdrawal
 Predictions can save time/money
 Major public health concern
 Estimated: 100,000 deaths per year
 Selecting better drug candidates

3. DEMO: www.SideFinder.info

4. SIDER: http://sideeffects.embl.deDSigDB: http://tanlab.ucdenver.edu/DSigDB
Data Resources
 DSigDB
 Drug Protein Interactions
 From: ChEMBL and PubChem
SIDER
 Drug side effects
 From: public documents and package inserts

5. Data Representation
 Each drug is represented by ~700 dimensional interaction vector
 Each element encodes for the interactions (1: interaction)
 Each drug is also associated with output side effect profile
 Aim is to predict side effect profile for a new drug
1 2 3 . . . . . . 705
1 0 1 0 0 0 1 0 1 0
0 0 1 0 0 1 0 0 1 1
0 0 1 0 1 0 0 1 1 1
1 0 1 0 1 1 0 1 1 0
1 0 1 0 1 0 0 0 1 1
1 0 1 0 0 0 1 0 1 0
Protein interactions
Drugs
1 2 3 . . . . . . . . . . 1000
0 0 1 0 0 0 1 0 0 0 1 0 1 0
1 0 0 1 1 0 0 1 1 0 0 1 1 0
0 0 0 1 1 0 1 0 0 0 1 0 0 0
1 0 1 0 0 0 0 1 1 0 0 0 1 0
0 0 0 1 1 0 1 0 0 0 1 0 1 1
1 0 0 1 1 0 1 0 0 0 1 0 1 0
Side effects

6. Machine learning
 Built 1000 models (one for each side effect)
 Random forest classifier
 Logistic regression classifier
 Validation
 5-fold cross validation
 Distributions of ROC AUC, Accuracy

7. Area Under the Curve (AUC)

9. Drug associations

10. Side effects
Protein interactions
Feature vectors for predictions

11. Accuracy

12. Roc (AUC)

13. Literature support for the predicted side effect
Mefenamic Acid: Treats pain, including menstrual pain
Ref: Drug.com
Lovastatin: Lowering cholesterol
Ref: Indian J Endocrinol Metab. Safety of statins 2013; 17(4): 636–646

14. Summary
 Predicting side effect in drug development process
 Challenging
 Very important
 Can save time and money
 Improve health care
 Developed a machine learning approach
 Used drug-protein interactions
 Can predict many side effects
 Future directions
 Incorporate other biological data
 Drug structure features
 Gene expression profiles
 Integrate models in a consensus based approach
 Group side effects into classes on similarity and severity levels