Data-driven visualization of drug interactions

Adverse Drug Events Almost 1 million deaths/injuries each year in the US [1] Some fraction of ADEs are caused by previously unknown drug-drug interactions Clinical trials aren’t large enough to detect many potential interactions FDA, WHO, pharmaceutical companies maintain databases of reported [2] ADEs You can download a sample of the FDA data from the Adverse Event Reporting System website [3] We can analyze the reported data to identify suspicious drug interactions Copyright 2011 Cloudera Inc. All rights reserved

Challenges in Analyzing Adverse Drug Events Biased Sample Adverse event reporting is voluntary We don’t see events from patients who took the drugs and nothing happened Correlation != Causation No controlled trials, some correlations are coincidences Requires Advanced Statistical Modeling Skills Multi-item Gamma Poisson Shrinkage Estimator is used to score the significance of a drug interactions The model is too complex to solve directly, we use Expectation Maximization (EM) to estimate its parameters Copyright 2011 Cloudera Inc. All rights reserved

The Hard Problem: Counting It is a “small” data problem… 250,000+ events reported to the FDA annually …that explodes when we consider: Multi-drug, multi-symptom interactions Analyzed by strata (e.g., month of report, patient age, patient gender, etc.) ~1 million reports => ~360 million buckets Analysts typically filter the data to only consider a few adverse reactions at a time… …but that is not the way of the data scientist Copyright 2011 Cloudera Inc. All rights reserved

Solving the Hard Problem MapReduce on Hadoop 20 MapReduce jobs Filter, aggregate, join, aggregate again Model the resulting data in R Use MapReduce to apply the model parameters to the data, score each drug-drug interaction, and then filter the data to obtain the highest scoring interactions Visualizing the Results Even applying a restrictive filter on the scores, we end up with 20,000+ statistically significant drug-drug-reaction triples Copyright 2011 Cloudera Inc. All rights reserved

The Drug-Drug Interaction Graph Copyright 2011 Cloudera Inc. All rights reserved

HIV Medications Copyright 2011 Cloudera Inc. All rights reserved

Cancer Medications Copyright 2011 Cloudera Inc. All rights reserved

Exploring the Graph Copyright 2011 Cloudera Inc. All rights reserved

Bridges Between Dense Clusters Copyright 2011 Cloudera Inc. All rights reserved

Acknowledgments and References Thanks to Josh Wills, Director of Data Science at Cloudera, for the data collection and analysis shown here. References: [1] ADE instances/year: [2] AERS reporting site: [3] Download ADE instance data: tion/Surveillance/AdverseDrugEffects/ucm htm tion/Surveillance/AdverseDrugEffects/ucm htm Other resources: Copyright 2011 Cloudera Inc. All rights reserved12