Research project on Adverse Drug Reactions by Danika Gupta

1. RxPredict: A Novel Pipeline for Accurate Adverse Drug
Reaction Prediction and Mitigation via Knowledge Graphs
and Deep Learning.
Danika Gupta
The Harker School

2. The Problem - Adverse Drug Reactions
● Substantial cause of death and morbidity
● Fourth leading cause of death in the
US and Canada pre-pandemic.
● Despite efforts to detect side effects
during drug development and track
reactions afterwards, ADRs in the
general population are high
● Over 770 000 injuries or deaths in
U.S. hospitals each year.
FDA reports >11 million ADRs in the last 10
years, with >1.6 million deaths. Costs to US
Healthcare are over $30 billion annually.

3. ADRs Past and Present
Thalidomide: licensed worldwide in
1956, stopped in 1962 due to birth
defects. Affected over 10000 babies
and resulted in new rules for drug
testing.
However, even the last 2 years have
seen notable serious ADR incidents
including issues with COVID vaccines.
Recent example – In 2021 Pepaxto
received an FDA alert 5 months after
accelerated approval.

4. My Hypothesis
Multiple modalities (chemical
structures, multiple side effects,
and contextual information -
targets and indications) and a
holistic approach will improve
forecasting over just using drug
chemical structures.

5. Datasets
● SIDER4.1: 1427 drugs and side effects. The side
effects include Hepatobiliary Disorders, Eye
Disorders, Immune System Disorders, and others,
classified into the 27 MedDRA top level reaction
groups.
● Drugbank: 14000 drugs. Comprehensive
molecular information, mechanisms, interactions
and targets.
● PubChem connects the information from SIDER
and Drugbank.
Case Study Validation: For validation of 10 case studies (drugs not seen during training),
compared the predictions with the FDA Adverse Events Reporting System (FAERS) database’s

6. Research Methodology
● Incrementally add information (starting with SMILES structures (experiment A), then
side effect interrelationships (experiment B) and then targets and indications
(experiment C)
● Combine best models into Multi-Modal Ensemble
● Evaluate ensemble on the 10 case study drugs and FAERS reports

7. Methodology Flowchart

8. Methodology (Algorithms)
Experiment Algorithms used Hyper-Parameters Tools
Correlation Analysis Pearson Correlation, Histograms N/A
SciKit Learn, Pandas,
Matplotlib
A
ECFP featurization, KNN, Random Forest,
MLP Classifier
K, NumTrees,
Layers/Neurons
DeepChem (ECFP),
SciKit Learn
(Algorithms)
B
ECFP/GraphConv Featurization,
Multi-Task Classifier, Robust Multi-Task
Classifier, Graph CNN
Layers, Learning
Rate, Epochs DeepChem
C TransE, RotateE Epochs, K PyKeen
Ensemble Ensemble with AND, OR None Python

9. Correlation Analysis
This observation supported the theory that
relationships between side effects could be
useful for ADR forecasting.

10. Using SMILES To Predict Each Side Effect Group
Some side effect groups could be predicted but many could not. This approach was not
considered further.

11. Using SMILES And Side Effect Interrelationships
Three multilabel classifiers:
- MultiTask Classifiert
- Robust MultiTask Classifier
- Graph Convolutional Neural Network
The results were similar but the basic
MultiTask Classifier outperformed the
other two with a test accuracy of ~75%
across all 27 categories of side effects
Result demonstrated that SMILES plus side
effect interrelationships could effectively
predict all 27 side effect groups

12. Using Drug Targets and Indications
Knowledge Graph with side effects inter-relationships, targets and
indications generated good performance.

13. Multi-Modal Ensemble

14. Case Studies of 10 Drugs
Drug Common use
Lodoxamide Eye disorders caused by allergies.
Tetrofosmin Imaging blood flow to evaluate heart related conditions.
Sufentanil Managing severe pain
Apraclonidine Glaucoma and manage eye fluids/pressure.
Dalfampridine Walking in adults with multiple sclerosis (MS)
Tetracaine Local anesthetic
Dopamine Correct low blood pressure and vascular conditions.
Iohexol Contrast agent used in imaging procedures such as X-rays.
Midodrine Treat low blood pressure
Nitazoxanide Treat infections caused by parasites and viruses.

15. Multi-Modal Ensemble Performance
A hybrid of Multi-Task Classifier
and Knowledge Graph was
able to reduce damaging False
Positives by 25-100% (2.44x)in
a majority of the 10 case study
drugs.
False Positives increased but
not substantially.

16. Validation: FDA Adverse Events Reporting System (FAERS)
Compared to FDA ADR reports
from 1968-2023, the hybrid
pipeline predicted >88% of all
cases, and over 95% of all
cases for 7 of the 10 drugs.
Across all 10 drugs, there were
~170000 ADRs of which
~150000 were predicted
Demonstrated real-world
effectiveness of the full
solution.

17. Conclusions and Future Work
The results confirm my hypothesis that combining drug chemical structure data and
contextual information such as targets, indications and side effects provides an
effective way to forecast all 27 side effect groups..
Future Work
- In the future I plan to study how ADRs affect specific age groups (such as children and
the elderly) and also understand the role of enzymes and pathways in forecasting ADRs.

18. Impact
Even drugs such as Thalidomide, which caused over 10,000 birth defects in the
1950s, are still under (severely restricted) use and with some ADRs.
All drugs will have some ADRs for some patients. The key is to manage the ADRs with
awareness and forecasting.
My results can make it easier to forecast ADRs with any available subset of
information, and further improve forecasting as more is learned about the drug. It
has potential for use at every level of the drug lifecycle from manufacturing to
prescription, and be a tool for doctors, pharmacists, and researchers to reduce
the massive human and fiscal impact of ADRs.

19. Related Work
Past approaches: using gene expression profiles and classification methods to predict
drug ADR results, using only the drug’s chemical formula to predict ADRs, adding a
process to make the prediction interpretable. using feature matrices and machine learning
targeting specific types of ADRs, using BERT language models to classify ADRs from
Twitter, using language and multi-task learning approaches to extract ADRs from drug
labels.
My research differs from the above work primarily in that I seek to combine multiple
modalities (chemical structures, multiple side effects, and contextual information - targets
and indications) to create a holistic approach to ADR forecasting via a hybrid approach
that utilizes the best deep learning models for each type of information.

20. Acknowledgements
I would like to thank my mentor Dr. Talagala, and thank Dr. Ramsundar for his feedback on
my research, and DrugBank for providing access to the dataset.

21. Thank You!!