Personalized Therapies for OA: Can Biomarkers Get Us There?
- 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 C o n tr o l A D A M T S -5 A D A M T S -5 + M 6 4 9 5C o n tr o l A D A M T S -5 A D A M T S -5 + M 6 4 9 5C o n tr o l A D A M T S -5 A D A M T S -5 + M 6 4 9 5 - 4 0 0 - 2 0 0 0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 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.