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Real World Evidence –

The Holistic Approach and the Practice

Dorit Dekel Rotman

© 2017. DATA2LIFE LTD.

“THE FUTURE OF REAL-WORLD INSIGHTS (RWI) IS BEING PROPELLED BY THE COMBINATION OF INCREASINGLY ABUNDANT DATA (SUPPLY) AND RISING STAKEHOLDER REQUIREMENTS FOR

DEEPER INSIGHTS (DEMAND)”

Sources: “A new wave of innovation for real-world evidence", AccessPoint, Volume 7, Issue 13, May 2017. Quintiles IMS Health

Our Data VISION

3

Social Media WearablesGenomicePrescribingMonitoring

Devices

Big Data

Clinical Trial

Data

Medical

Claims

Clinical EHRsMedical

Literature

3rd Party

Regulatory

Reporting

Systems

Internal

Safety

Database

Individual

Case Safety

Reports

Internal

Connecting

the Dots

Generating

Actionable

Insights

Data

Digestion

Plenty of REAL WORLD DATA Need to REDUCE “TIME-TO-EVIDENCE”

4

Data CollectionNLP, Machine Learning, &

Algorithms

Dynamic

Semantic

System

Crawler &

Ingestion

Free

Text

Analytics

Pattern

Recognition

Data

Lake

Data Connections and

Knowledge Graph

Cross-

References

&

Ontologies

Cutting-edge Teck Stack & Process

Evaluation of Immuno-Oncology product – Use Case

5

CHALLENGE

Evaluate new Immuno-Oncology product to be introduced into the Healthcare

system / market

SOLUTION

OVERVIEW

Comparing the incumbent product to the new product in a safety prism

using evidence from real patients in other approved markets

Multi-source real world evidence report comparing multiple products based

on data generated by clinical EHRs and Spontaneous reporting systems

(AERS & social media)

Data Analyzed

6

100 PATIENTS

1,517 REPORTS

228 POSTS

Data Availability

2010 2013 2016

160 PATIENTS

1,367 REPORTS

487 POSTS

Data Availability

2010 2013 2016

Drug A

Drug B

Patient Characteristics

7

51%49%

Male Female

Sex Age (Y)

53%47%

Male Female

98

54

1

97

62

12

average

average

• Exposed population cohort defined

by exposure period algorithm.

• Age diagram displays the

minimum, maximum and average

ages of patients in the cohort.

• Indications sorted by diagnoses of

unique patients, stratified by sex.

Indication for Use

0%

10%

20%

30%

40%

50%

Cancer A Cancer B Other

Male Female

0%

20%

40%

60%

80%

100%

Cancer A Cancer B Other

Male Female

Clinical EHRs

ADHERENCE and SWITCHING Patterns

8

YERVOY (11.2%)

GEMZAR (21.5%)

SUTENT (13.1%)

VOTRIENT (12.1%)

12% switched FROM DRUG A to

45%switched TO DRUG A from

100 patients in cohort

NAVELBINE (10.3%)

YERVOY (34.5%)

TAFINLAR (13.8%)

TAXOTERE (10.3%)

AFINITOR (10.3%)

Clinical EHRs

49.551

50

Total Male Female

Average Duration of Therapy (Days)

Additional Therapy Factors

9

• Top 10 concomitant medications are

additional medications dispensed at the time

patient dispensed with the product of

interest

ROSUVASTATIN

(11%)

OMEPRAZOLE

(11%) (11%)

ATORVASTAT

IN

(22%)

INSULIN

ASPART

(25%)

RAMIPRIL

(9%)

METFORMIN

(23%)

INFLUENZA

VACCINE

(21%)

ROSUVASTATI

N

(14%)

SIMVASTATIN

(15%)

OMEPRAZOLE

(16%)

RAMIPRIL

(13%)

ENALAPRIL

(12%)

INSULIN

ASPART

(16%)

Top 10 Concomitant Medications

SIMVASTATIN

BISOPROLOL

(12%)

ENALAPRIL

(10%)

METFORMIN

(39%)

INFLUENZA

VACCINE

(29%)

ATORVASTATI

N

(33%)

BISOPROLOL

(17%)

Clinical EHRs

Quality of Life (QOL) and Serious Suspected Events

10

5.00%

4.40%

3.70%

2.77%

5.44%

1.12%0.99%

0.56%

25.02%

49.00%

Blindness Deafness Toxic optic neuropathy

Hepatic infarction Other serious events

51%49%

Serious

Events Distribution

Not serious

Serious

Clinical EHRs

of patients

experienced

medical events that

are considered

serious.

51%

5.00%

4.40%

3.70%

2.77%

5.44%

1.12%0.99%

0.56%

0.22%

1.00%

25.00%

Dyspnea Pain Nausea and vomiting

Appetite loss Constipation Diarrhea

25%

75%

QOL Events

Distribution

of adverse

events that are

considered as

quality of life-

relevant.

25%

Safety Report Submission Trends

11

Regulatory Spontaneous Reporting Systems

Patient Discussions

12

Treatment Recommendation

Quality Of Life aspects

Cost

Trials

Drug switch/stop

Concomitant

Adverse Events

Serious AEs

Personal

News / Informational

Questions

• Discussion types classify posts reporting

actual experiences of the author

("Personal"), news/articles mentioning

the drug ("News") and inquiries about the

product ("Questions").

• Treatment discussions indicate what

issues were discussed by the users, e.g.

clinical trials involvement, cost and

disease management aspects and advice.

• Product experiences are classifying the

posts mentioning events of Drug switch /

Stop, AEs and additional factors.

*these classifications are based on our IP algorithm employing tailored

machine learning and NLP to the complex context of medical /health and

therapeutics

Number of posts

0

20

40

60

80

100

120

Discussion Clasifications Treatment Discussions Product Experiences

Social Media

2

1

11

3

13

Drug Stop Insights– Drilling in

Drug Stop

No Response to Treatment

Disease related death

Adverse Events

Adverse Event

Pneumonitis

Gastro issues

Diarrhea

Blood pressure

Itching, rashes

Social Media

52

1

Drug Stop

Drug Stop Detailed Analysis

14

“I just started on DRUG Z and my tongue is swollen and getting worse every

day. Today I am calling the doctor about it and got this page when I googled

the symptoms. I have not had real good luck with adjunct chemo drugs

because of the side effects. I had to stop taking DRUG A because of

blood pressure issues and itching and rashes that were so bad

I couldn't sleep. I was red all over for a few weeks after going

off of it like I had a sunburn. So far after just one infusion of DRUG Z

I have the swollen tongue, mouth sores, and neuropathy of my right arm. The

day after chemo I had what felt like the flu without the fever but luckily it

was just for a day. I have flushing and sudden sweating for no reason where

my hair will get soaked with sweat and my face turns red. I also have been

itchy off and on but nothing like DRUG A itching. I'm staying on DRUG Z as

long as I can stand the side effects because I am only 52 with terminal lung

cancer and I want as much (time) as I can squeeze out of this cancer,

to spend with my son”

Social Media

Signal Detection Analytics

Disproportionality Analysis View

Data Source Filters

Signal Detection

Algorithms

Confidence Intervals

Hover Summary of Results

Summary view of Signal Scores

MedDRA Dictionary

SOC to PT Hierarchical

Browsing

Branching by Hierarchy

Change Views

dorit@data2life.com

Thank You!