There are over 7,000 rare and orphan diseases known to impact approximately 1 in 17 individuals globally, or 50 million in the EU and USA alone. The development of safe, effective, and accessible therapies against these diseases has been challenged by manufacturing, clinical and regulatory hurdles. Despite these obstacles, increased awareness, greater funding, and new research technologies are driving discoveries in this area. Join this webinar to learn how various research groups are working in this space. Dr. Zabinski discusses how Artificial Intelligence (AI) and Real-World Datasets (RWD) can work in synergy to address many of the challenges facing rare disease researchers, including better describing real-world epidemiology; identifying meaningful patterns in rare disease patient journeys; and assisting in finding patients, plus those not yet diagnosed. This presentation will briefly explore the ways AI and RWD together can enhance visibility into patient trajectories, improve rare disease patient identification for clinical trial recruitment and observational research, and shorten time to diagnosis. Dr. Kish discusses how precision medicine is redefining how we evaluate and treat rare diseases. No longer is cancer defined by its organ of origin, tissue or cell type but rather its genotype. Over just a few decades lung cancer has been transformed from one disease affecting 100,000+ in the U.S. annually to dozens of cancers that each inflict just a few thousand. But, finding patients and RWD to improve outcomes can be challenging. Learn how Cardinal Health Real-World Evidence and Insights takes a decentralized approach to identify hard to find patients and RWD.

1. Welcome!
Addressing Orphan and Rare
Diseases
Joseph Zabinski, PhD, MEM
AI and Precision Medicine
OM1
Senior Director
Jonathan Kish, PhD, MPH
Real-World Evidence and Insights
Cardinal Health
Senior Director

2. Copyright 2022. All Rights Reserved. Contact Presenter for Permission
Using Real-World Data + AI
to Identify and Understand
Rare Disease Patients
Joseph Zabinski, PhD, MEM
AI and Precision Medicine
OM1
Senior Director

3. ©OM1 Confidential and Proprietary
Using Real-World Data + AI to Identify and
Understand Rare Disease Patients
February 15, 2022
Joseph Zabinski, Ph.D.
Sr. Director, AI & Personalized Medicine, OM1

4. ©OM1 Confidential and Proprietary
RWD + AI work together
Rich data for training, testing,
and validation
Data augmentation and
amplification
4
Providers
Open &
Closed Claims
Labs
Death
Drugs &
Devices
Procedures
SES
Consumer
PRO
Clinical

5. ©OM1 Confidential and Proprietary
Three areas where AI + RWD can help in rare disease
5
Improving insight into understudied subpopulations and phenotypes
Enhancing real-world epidemiology through visibility into patient populations
Finding patients to shorten time-to-diagnosis

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Better understanding rare patients
With 300M+ patients, the OM1 Real-World Data Cloud is a good place to start when looking for rare
diseases and subtypes. AI can help distinguish these groups and isolate important characteristics.
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An example: in SLE, we’ve used subtyping to isolate groups with disease
manifestations disproportionately concentrated in certain organ systems.
SLE (lupus)

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Enhancing visibility
In some rare diseases, most research and outreach focus on a ‘classic’ phenotype. RWD + AI can help
find, study, and reach other underserved phenotypes.
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Patients with heterogenous
disease manifestations –
including those who exhibit
some, but not all, ‘classic’
symptoms
Patients with demographic
characteristics that are
underrepresented in prior work
– for example, women in some
conditions
Patients in geographic locations
far from key treatment centers,
who may be ‘off the radar’
(including within and among
countries)

8. ©OM1 Confidential and Proprietary
Patient finding in rare disease
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• Clinicians often don’t see enough cases during their
practice to move towards a diagnosis quickly, especially in
primary care settings
• Symptomology can be diffuse and nonspecific
• Diagnosis can require specialized, non-routine tests or
examinations
• Often, patients experience long wait times to diagnosis:
Source: Shire
4.8yr
Average years elapsed
from symptom onset to
accurate diagnosis
Average number of
physicians patients
see before a
diagnosis is made
7.3

9. ©OM1 Confidential and Proprietary
OM1™ Patient Finder: how it works
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Learn from millions of datapoints to
understand common themes
Combine data into useful patterns to identify
key disease characteristics and utilization
Uses these patterns to identify patients
matching characteristics
OM1 Patient
Finder
Calibration
Health history data provide deep insight into rare disease symptoms and characteristics, as well as healthcare
utilization patterns. These patterns are hard to spot on their own, but can be seen by the AI at scale.
OM1 Patient Finder combines strong analytic performance with medical explainability and real-world clinical utility,
facilitating deeper insight into the most relevant populations.

10. ©OM1 Confidential and Proprietary
Identify and characterize patients who have a rare
lysosomal storage disease but are undiagnosed.
Patient Finder: rare disease
• Strong predictive performance in identifying
patients (AUC of 0.82)
• Found more than 20x patients in the riskiest 1%
of people analyzed relative to the general
population
• Recognized and categorized diffuse and
nonspecific signals known in the disease, but
hard to pin down as contributing to a diagnosis
• Generated new hypotheses about relationships
between the patient journey and the disease state
AI Solution
Challenges
Rare Lysosomal
Storage Disease
OM1™ Patient Finder AUC: 0.82
10

11. ©OM1 Confidential and Proprietary
OM1™ Patient Finder is designed to ingest real-world patient history data from
appropriate partner sites to find patients
Patient Finder: clinical deployment
Patient Finder analyzes patient records,
identifies those most likely to have the rare
disease, and flags them to the health system
for follow-up
Patient Finder receives data from the health
system
Study team and local study coordinators
receive counts and outreach is facilitated
locally in compliance with IRB
1
2
3
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12. ©OM1 Confidential and Proprietary
Real progress in rare disease
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Real-world data can work together with AI to help us better understand rare disease patients, and to find them
sooner in their journeys. The data and technology are ready, and broad-scale impact is on the horizon.

13. ©OM1 Confidential and Proprietary
Thank you
Joseph Zabinski, PhD
Sr. Director, AI & Personalized Medicine
jzabinski@om1.com

14. Copyright 2022. All Rights Reserved. Contact Presenter for Permission
Rare Patients and Rare Data:
Leveraging Decentralized
Networks in Cancer
Jonathan Kish, PhD, MPH
Real-World Evidence and Insights
Cardinal Health
Senior Director

15. © 2022 Cardinal Health. All Rights Reserved.
JONATHAN KISH
RARE PATIENTS AND RARE DATA:
LEVERAGING DECENTRALIZED
NETWORKS IN CANCER
FEBRUARY 15, 2022

16. 16 © 2020 Cardinal Health. All Rights Reserved.
Agenda
 Oncology tumor types and precision medicine
 Case Studies – How real-world data can be applied in rare patient populations
 Understanding decentralized community provider networks
 Applying innovative research methodology

17. 17 © 2020 Cardinal Health. All Rights Reserved.
Oncology tumor types
Bc
Breast
Cancer
Ln
Lung
Cancer
Crc
Colon/Rec
Cancer
Pn
Pancreatic
Cancer
Rcc
Renal Cell
Carcinoma
Ov
Ovarian
Cancer
CML
Chronic
Myeloid
Leukemia
DLBCL
Diffuse
Large B-cell
Lymphoma
HL
Hodgkin
Lymphoma
LEk
Leukemia
AML
Acute
Myeloid
Leukemia
Lm
Lymphoma
NHL
Indolent
Non-
Hodgkin
Lymphoma
ALL
Acute
Lymphocytic
Leukemia
CLL
Chronic
Lymphocytic
Leukemia
Mm
Multiple
Myeloma
Bn
Brain
Cancer
Mn
Melanoma
Bd
Bladder
Cancer
Hcc
Hepato-
cellular
Carcinoma
Biosimilars CAR-T Biomarker testing Pathways Value based care
Pr
Prostate
Cancer

18. 18 © 2020 Cardinal Health. All Rights Reserved.
Precision medicine leading to paradigm shift in the
classification of lung cancer
2004 2015 2019
Source: Pao W and Hutchinson KE. Nat Med. 2012;18(3):349-351. 2. Lovely C, Horn
L, Pao W. My Cancer Genome. 2018.

19. 19 © 2020 Cardinal Health. All Rights Reserved.
19 © 2022 Cardinal Health. All Rights Reserved.
Case Studies: How real-world
data can be applied in rare
patient populations

20. 20 © 2020 Cardinal Health. All Rights Reserved.
Applying real-world data in rare patient populations
Challenge
 Identifying patients diagnosed with squamous
cell carcinoma of the lung who received first-
line pembrolizumab plus chemotherapy,
followed by afatinib
Solution
 Implement a real-world retrospective
observational study leveraging decentralized
provider networks and applying innovative
research methodology

21. 21 © 2020 Cardinal Health. All Rights Reserved.
• Difficulty in achieving necessary sample size for
matching purposes from single center sources
(9 covariates)
• Case report form adapted to Cardinal Health
infrastructure
• Required collection of data for examining disease
response according to standardized criteria
Nowakowski, et al. RE-MIND study: a propensity score-based 1:1- matched comparison of tafasitumab + lenalidomide (L-MIND) versus
lenalidomide monotherapy (real-world data) in transplant-ineligible patients with relapsed/refractory diffuse large b-cell lymphoma. DOI:
10.1200/JCO.2020.38.15_suppl.8020 Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020) 8020-8020.
OBJECTIVES: IDENTIFY MATCHED COHORT FOR COMPARISON TO PHASE 2 STUDY
OF NOVEL DLBCL THERAPY
Case study – synthetic control arm for FDA submission
Conclusion: FDA Approval of agent on July 31, 2020

22. 22 © 2020 Cardinal Health. All Rights Reserved.
A network of community and academic providers cultivated over the last 10 years, allowing:
• Identification of hard-to-find patients through a decentralized approach
• Collection of difficult to obtain data through electronic case report forms
• Generation of fully customized datasets to answer YOUR research question
• Unique research methodologies to reach the patients and providers you need for a particular therapy areas
Specialty Provider Extended Networks
Utilizing decentralized provider networks
OPEN
(Oncology Network)
Rheumatology
Network
Neurology
Network
Rare Diseases/
Specialties

23. 23 © 2020 Cardinal Health. All Rights Reserved.
Oncology Provider Extended Network (OPEN)
FACILITATING UNRIVALED REACH TO KEY ONCOLOGY PROVIDERS
Geographically expansive
Representative of multiple specialties,
tumor types and practice settings
GPO agnostic
500+
Private community
oncology practices
Reach
Engagement
Insights

24. 24 © 2020 Cardinal Health. All Rights Reserved.
Applying innovative research methodology
PHYSICIAN LED CHART REVIEW
Randomized
clinical trials
Involves the
treating
physician
Real-world
evidence
Representative
patient
population
Electronic Medical Records have
limitations. Information vital to
value-based care and comparative
effectiveness is neither billed
nor charted.
When a patient discontinues therapy,
we don’t know the “why?”
• Nonresponse?
• Side effects?
• Nonadherence?
• Intercurrent illness, e.g., heart attack?

25. 25 © 2020 Cardinal Health. All Rights Reserved.
Leading insights driven by real-world data and solutions
June 2019 Feb 2021
Oct 2021

26. 26 © 2020 Cardinal Health. All Rights Reserved.
Thank you
JONATHAN KISH JONATHAN.KISH@CARDINALHEALTH.COM
SENIOR DIRECTOR, REAL-WORLD EVIDENCE AND INSIGHTS
WWW.CARDINALHEALTH.COM/RWE

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