Anne C. Bay-Jensen

1. Personalized Therapies for OA: Can Biomarkers Get Us There?
Anne C. Bay-Jensen
OARSI 2019, Toronto

2. Disclosures
• Full-time employee and shareholder at Nordic Bioscience
• EPMT member of the APPROACH consortium
• The presentation is based on opinion and perspectives of the
presenters, and do not necessarily reflect the views of either
companies
• Funders have not reviewed or censored the presentation

3. • Personalized Therapies for OA: Can Biomarkers Get Us There? Used
as diagnostic tools?
• YES!
• But the road will be long and bumpy
3

4. Definitions
• Personalized therapies
– A form of medicine that uses information about a person’s endotype or phenotype (genes,
proteins, and environment to prevent, diagnose, and treat disease).
– In cancer, personalized medicine uses specific information about a person’s tumor to help
diagnose, plan treatment, find out how well treatment is working, or make a prognosis.
4
https://www.cancer.gov/publications/dictionaries/cancer-terms/def/personalized-medicine
https://www.gesundheitsindustrie-bw.de/en/article/dossier/with-molecular-diagnostics-to-biomarker-based-personalised-therapy/

5. Definitions
• Biomarker
– Objective, quantifiable characteristics of biological processes.
– Evidence of link to biology, but not necessarily correlate with a patient's experience and sense
of wellbeing
– In contrast, clinical endpoints are variables that reflect or characterize how a subject in a study
or after treatment “feels, functions, or survives”
– Surrogate endpoints are a small subset of well-characterized biomarkers with validated clinical
relevance - solid scientific evidence from epidemiological, therapeutic, and/or
pathophysiological - that a biomarker consistently and accurately predicts a clinical outcome,
either a benefit or harm
• In this presentation will focus on biochemical markers (biomarkers).
5
Clin Pharmacol Ther. 2001 Mar;69(3):89-95.

6. Phenotypes and Endotypes
Phenotype
610-7-2019
• Observable properties of an organism
that are produced by the interactions of
the genotype and the environment.
• Patients with common characteristics
are grouped together in an attempt to
guide therapy and management.
Endotype
• A specific biological pathway is identified
that explains the observable properties of
a phenotype.
• It’s defining subgroups or phenotypes by
specific cells or molecules in blood or
other fluids.
• It’s a more specific, more accurate, way
of defining subgroups.
Example (imaginary, simplistic and generalizing example)
Phenotype: knee Pain
Endotype: SF level of the synovial marker Pain x
Wenzel S., Nature Med 2012

7. The WHY and WHAT
• A lack of appropriate measures that can robustly identify right treatment for the
individual patients, from a heterogenous population of patients with symptomatic OA,
which are more likely to respond
Karsdal MA, et al. AC&R 2014

8. The WHY and WHAT
• A lack of appropriate measures that can robustly identify right treatment for the individual
patients, from a heterogenous population of patients with symptomatic OA, which are
more likely to respond
 Prognostic selection/enrichement1:
 Choosing patients with a greater likelihood of having a disease-related endpoint event (for event-
driven studies), TJRs, or
 A substantial worsening in condition (for continuous measurement endpoints).
 EXAMPLE
– Women with a deleterious BRCA 1 or 2 mutation have a lifetime incidence of breast cancer and
ovarian cancer of 60% and 15-40%, respectively, compared to a risk of 12% and 1.4%,
respectively, in women without a BRCA mutation.
1 Adapted from “Guidance for Industry. Enrichment Strategies for Clinical Trials to Support Approval of Human
Drugs and Biological Products. Dec. 2012.
http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm

9. The WHY and WHAT
• A lack of appropriate measures that can robustly identify right treatment for the individual
patients, from a heterogenous population of patients with symptomatic OA, which are
more likely to respond
 Prognostic selection/enrichement1:
 Choosing patients with a greater likelihood of having a disease-related endpoint event (for event-
driven studies), TJRs, or
 A substantial worsening in condition (for continuous measurement endpoints).
 Predictive selection/enrichment1:
 Larger effect size and permit use of a smaller study population.
 Selection of patients could be based on a specific aspect of a patient’s physiology or a disease
characteristic that is related to the study drug’s mechanism.
• EXAMPLE
– Proteomic markers, such as the HER 2/neu marker in breast cancer indicating potential for
response to trastuzumab
1 Adapted from “Guidance for Industry. Enrichment Strategies for Clinical Trials to Support Approval of Human
Drugs and Biological Products. Dec. 2012.
http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm

10. The WHY and WHAT
• A lack of appropriate measures that can robustly identify right treatment for the individual
patients, from a heterogenous population of patients with symptomatic OA, which are
more likely to respond
 Prognostic selection/enrichement1:
 Choosing patients with a greater likelihood of having a disease-related endpoint event (for event-
driven studies), TJRs, or
 A substantial worsening in condition (for continuous measurement endpoints).
 Predictive selection/enrichment1:
 Larger effect size and permit use of a smaller study population.
 Selection of patients could be based on a specific aspect of a patient’s physiology or a disease
characteristic that is related to the study drug’s mechanism.
 A lack of validated non-invasive and objective measures for patient monitoring
 Following disease activity
1 Adapted from “Guidance for Industry. Enrichment Strategies for Clinical Trials to Support Approval of Human
Drugs and Biological Products. Dec. 2012.
http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm

11. The WHY and WHAT
• A lack of appropriate measures that can robustly identify right treatment for the individual
patients, from a heterogenous population of patients with symptomatic OA, which are
more likely to respond
 Prognostic selection/enrichement1:
 Choosing patients with a greater likelihood of having a disease-related endpoint event (for event-
driven studies), TJRs, or
 A substantial worsening in condition (for continuous measurement endpoints).
 Predictive selection/enrichment1:
 Larger effect size and permit use of a smaller study population.
 Selection of patients could be based on a specific aspect of a patient’s physiology or a disease
characteristic that is related to the study drug’s mechanism.
 A lack of validated non-invasive and objective measures for patient monitoring
 Cost: Patient, payers and regulators
1 Adapted from “Guidance for Industry. Enrichment Strategies for Clinical Trials to Support Approval of Human
Drugs and Biological Products. Dec. 2012.
http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm

12. The HOW - The long and bumpy road
Biomarker discovery
Assay development
Biological linking and hypothesis
generation
Clinical hypothesis testing
Technical performance
validation
Clinical validation
Dx
generation
Commerci
alizing
RCTs needed

13. Biomarker discovery
• Characterization of the IL-17 EFFECT on articular cartilage in a translational model and
identification of novel biomarker target
Sinkeviciut D, et al, OARSI 2019, #417
Cartilage explant
model
Mass
spectrometry
Differential
regulated
proteins
Novel
candidate
biomarker
target

14. Biomarker discovery
• Through understanding skeletal protein and their regulators
– RQ: can we develop a biomarker that reflects hypertrophy and cartilage calcification
Regenerative Medicine and Tissue Engineering 2013https://www.slideshare.net/trufflemedia/dr-laura-amundson-initiation-of-bone-lesions-
in-young-growing-pigs-and-the-importance-of-maternal-nutrition
Type X collagen seem to be an
obvious target

15. Assay development, Biological linking and hypothesis generation
• Type X collagen degradation marker
15
He Y, et al. OAC 2019

16. Biological linking and hypothesis generation
16
Kjelgaard-Petersen et al 2018 Biochem. Pharm.
Effect of anti-inflammatory inhibitors on type II collagen degradation
measured by C2M
p38i JakiSyki Ikki
• Hypothesis: C2M is a biomarker that reflects whether small molecule inhibitors have an
effect on cartilage remodeling.

17. ”Back-Translating” efficacy and treatment response from ex vivo testing to
clinical application
17
Cartilage tissue turnover in response to
fostamatinib treatment in the OSKIRA-1 study
Group A: 2x100mg/day + MTX
Group B: 4 weeks 2x100mg/day + MTX
then 2x150mg/day + MTX
Cartilage tissue turnover in response to
fostamatinib (API) treatment in bovine cartilage
C2M C2M
Ex vivo efficacy translates to clinical biomarker reduction in patients
Note:
In patients 160mg/bidaily results in steady state plasma conc. of 1 µM in
patients
Cartilage tissue concentration in patients is unknown

18. Biological linking and hypothesis generation
Biological link: Activation by a TLR2 agonist in human OA synovial explants leads to synovial
turnover, and TLR2 with ADAMTS-5 degraded cartilage can activate TLR2
Hypothesis: Markers of ADAMTS-5 mediate cartilage degradation (e.g. ARGS) reflect
activation of the innate system
Sharma N, et al, OARSI 2019 #092
Sharma et al., ACR 2017
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T L R 2 a c t i v a t i o n
Percentofcontrol
2 4 h o u r s 4 8 h o u r s 7 2 h o u r s
0 . 0 0 1 0 . 0 0 1 < 0 . 0 5 < 0 . 0 1

19. Hypothesis testing
• Hypothesis: A third of the OA patients have elevated tissue inflammation, measured by
serum CRPM and are more likely to progress
Table 2 Early
rheumatoid
arthritis
patients
Moderate-Severe Rheumatoid arthritis
patients
Phase III OA
study 1
Phase III OA
study 2
CRP
metabolites
Quartiles No. of patients (%)
Before treatment After treatment* n1 n2
Low <9 ng/mL 49 (8.2%) 87 (18%) 267 (59%) 233 (69%)
Moderate 9-12 100 (17%) 123 (25%) 137 (31%) 71 (21%)
High 12 – 15 132 (22%) 105 (21%) 26 (6%) 23 (7%)
Very high >=15 318 (53%) 175 (36%) 19 (4%) 11 (3%)
Bay-Jensen, Ladel et al. OARSI 2017

20. Hypothesis testing
• Hypothesis: A third of the OA patients have elevated tissue inflammation, measured by
serum CRPM and are more likely to respond to anti-IL1
Phase 1 studies of anti-interleukin-1 dual-variable domain immunoglobulin in healthy subjects and patients with osteoarthritis.
Osteoarthritis and Cartilage, Volume 23, Supplement 2, April 2015, Pages A398-A399. S.X. Wang, W. Liu, P. Jiang, M. Okun, R.A. Preston, C.J.
Lozada, D. Carter, J.K. Medema

21. Hypothesis generation and testing
• Hypothesis: U-CTX-II can be used as a prognostic enrichment tool to identify patients
with “active” and progressive disease, which are more likely to progress and undergo TJR
21
Greater
therapeutic
window =
greater
possible effect
size
Progressors
Non-progressors Baseline uCTX-II
Predictive validity of biochemical biomarkers in knee osteoarthritis: data from the FNIH OA
Biomarkers Consortium. Kraus VB1, Collins JE2, Hargrove D, et al ARD 2017, 76
TJR
JJ. Bjerre-Baston, abstract #012

22. Hypothesis generation and testing
• Hypothesis: Prognostic enrichment strategies - Identifying high-risk patients by Selection
of patients with “active” and progressive disease
22
TJR cases
non-TJR cases
Decision tree
AUC = 0.83, p<0.0001
Identification of serological biomarker profiles associated with total joint replacement in osteoarthritis patients.
R.H.G.P. Arends y, M.A. Karsdal et al. OAC 2017

23. Hypothesis generation and testing
• Hypothesis: Biomarkers can in combination be used for endotyping patients
for better prediction of cause of progression
Thudium poster #122
Blair J, et al, PlosOne 2019 (in press)
Risk of progression (ΔJSW>0.7)
Predictor OR (95% CI)
E vs D 1.9 (1.20 - 3.03)*
E vs C 1.2 (0.63 - 2.53)
Male gender 3.2 (2.13 - 4.77)***
Age 1.0 (0.97- 1.03)
BMI 1.0 (0.99-1.08)

24. Hypothesis generation
• For the calcitonin trials:
– An anti-resorptive… High CTX-I?
• For the anti-IL-1 DVD:
– A cytokine specific drug… IL1-driven inflammatory OA endotype?
Endotypes  Diagnostic tool for
defining phenotypes (PHC)

25. What we can do today
• Endotyping1 patients ?
– Understanding the underlaying mechanism that allow us to target
the disease
• Drug-pathway interaction? Translational medicine?
– Pharmacodynamics/target engagement
• Disease activity ?
– When which mechanisms are activated, flares, patient monitoring
• Surrogate endpoint?
– Early decision-making predictor of treatment endpoints;
symptomatic benefit and joint failure
• Phenotyping2 and diagnosis?
– Treatment selection by doctor
YES!
Maybe!
Dx precision
1A specific biological pathway is identified that explains the observable properties of a phenotype. It’s defining subgroups or phenotypes
by specific cells or molecules in blood or other fluids. It’s a more specific, more accurate, way of defining subgroups. 2Observable
properties of an organism that are produced by the interactions of the genotype and the environment. Patients with common
characteristics are grouped together in an attempt to guide therapy and management. Wenzel S., Nature Med 2012

26. Dx and commercialization
When all the science is done…..Practical challenges
• Intended use: trial enrichment, treatment guidance or selection, treatment monitoring
• GMP production of the kit and key reagents
• Global assessible? Automation, Kit a box, point of care test (PoCT)
• Get regulatory approval; clinical trial assay (CTA), complementary or companion diagnostic
(CDx)?
• Studies for test and validation
Laboratory
developed test
Patient careBiomarker development = like drug development

27. Conclusion
• Biomarkers may be used for personalized medicine, but need to be tested and validated in
prospective studies

28. Thank you for listning
Acknowledgement
The research leading to this presention has received support from Nordic Bioscience and
the Innovative Medicines Initiative Joint Undertaking under grant Agreement n° 115770,
resources of which are composed of financial contribution from the European Union's
Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind
contribution. See https://www.imi.europa.eu.