SideFinder: Predicting Drug Side Effects

Aqeel AhmedInsight Data Science

FellowProf. Heather A. Carlson GroupCollege of PharmacyUniversity of Michigan

Motivations: Drug Side Effects Prediction

Developing a new drug is challengingRequires billions of dollarsOften more than 13 years of effortsSide 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 yearSelecting better drug candidates

DEMO: www.SideFinder.info

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

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 00 0 1 0 0 1 0 0 1 10 0 1 0 1 0 0 1 1 11 0 1 0 1 1 0 1 1 01 0 1 0 1 0 0 0 1 11 0 1 0 0 0 1 0 1 0

Protein interactions

Drug

s

1 2 3 . . . . . . . . . . 1000

0 0 1 0 0 0 1 0 0 0 1 0 1 01 0 0 1 1 0 0 1 1 0 0 1 1 00 0 0 1 1 0 1 0 0 0 1 0 0 01 0 1 0 0 0 0 1 1 0 0 0 1 00 0 0 1 1 0 1 0 0 0 1 0 1 11 0 0 1 1 0 1 0 0 0 1 0 1 0

Side effects

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

Area Under the Curve (AUC)

Drug associations

Side effects

Protein interactionsFeature vectors for predictions

Accuracy

Roc (AUC)

Literature support for the predicted side effectMefenamic 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

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