Pilot study linking CMC Analytical data with Clinical data

Transcription

1 Pilot study linking CMC Analytical data with Clinical data John O Hara, UCB-Celltech, UK 5 May 2015

2 Support for pilot study 2 individuals New team Project support Pilot Study Company policy CQA

3 What did we want to achieve by linking CMC Analytical and Clinical Data? 3 UCB drives decisions to add value for the patient Facilitate the understanding of levels of process and product related impurities that patients have been exposed to Correlating Clinical events to CMC data e.g. responders/non responders vs CMC data Supporting scientific rationale for identifying Critical Quality Attributes Setting end-of-shelf life specifications not specifically asked of this Pilot study

4 Pilot Study Clinical datasets have been used to explore correlations (using Discoverant) between process monitoring, product quality profiles and clinical results. Cell / RawMat API Product Patient CMC data Process parameters Release & stability data Batch traceability Clinical data Number and type of adverse events Responders/Non responders Physiological data (e.g IgG levels)

5 Architecture 5 A single repository for the data, independent of the origin (geographical or business)

6 Hierarchy organization 6 Reflects complexity of described processes Going from Raw material to Patient Response Hierarchy includes Process setup Release Analysis Clinical observations Material genealogy IPC analysis Stability Analysis Adverse events Product / Patient link (as genealogy) Characterisation End of shelf life Char

7 Project Challenges 7 Retroactive Format External? Merging data Silos

8 Hierarchy organization 8 Analytical Goals Creating patient value : from noise to signal What is the acceptable level of degradation products in product for patients? Are process parameters, product / material quality profiles settings able to meet UCB patient needs? Can we estimate degradation products in drug product at the date of injection? Does quality attribute impact patient response? Does quality profile at injection have an impact on increase the risk of adverse events? Can we correlate immunogenicity (safety) with quality attribute?

9 Background on the molecule 9 IgG Levels of Acidic species determined by CEX Subspecies within CEX peak characterised by mass spectrometry etc % of contributing subspecies understood (not available here) CEX test used on stability to allow correlation with levels of AS at time of dose End of shelf life batches analysed by mass spectrometry to determine Which post-translational modifications increase New sites of post-translational modifications increase

10 Results PREDICT INNOVATE ANALYZE

11 Focus on 11 Acidic species, aggregates and potency : 1. Prediction of stability levels 2. Site of injection response 3. Concentration in body 4. Biological response: CRP and Immunogenicity 5. Adverse events 6. Responders versus non-responders

12 1. Acidic species levels on stability 12 Use of hierarchy to predict quality profile at use date Linearization of stability data, to create predictive model Model Linearization We can predict quality profile after storage, based on stability model.

13 2. Effect of acidic species on drug concentration in body Level of drug in patient is not affected by APG level 13

14 3. Effect of acidic species on adverse events 14 We don't observe a correlation with increase of AE or drug efficacy with decreasing acidic species Increase in Mab A in blood divided by unit injected % APG at injection

15 4. i. CMC data for responders (Y) and non-responders (N) 15 Acidic species Is the quality profile different in groups of patients responding to treatment compared to groups not responding? Acidic species do not appear to be different between responders and nonresponders

16 4. ii. CMC data for responders and non-responders FRAGMENTS AGGREGATES 16 Amount of fragment and aggregate does not appear to be different between responders and non-responders for HP-SEC

17 5. Impact of Acidic species on Biological results (CRP, HAHA) No correlation was observed between biological responses and acidic species injected into the patient. 17

18 Learnings from the study: collaboration needed 18 Potency assay Value of collaboration between informaticians and statisticians and biologists: software package determined that there was a significant difference but these values are within the acceptance criteria for this potency assay nM.

19 Do Responders and Non responders have the same product quality? 19 Potency assay vs. Patients Response No significant difference

20 Does age of DP batches at date of injection influence analytical results? 20 Red square : non-responders Green triangle : responders Mean of non-red monomers is not influenced by DP batch age at injection date

21 Conclusions of Pilot Study 21 Demonstrated feasibility of linking CMC data with Clinical data Correlation shown with CMC analytical data and responders/nonresponders and Adverse Events To date we have focused on correlation not causation Needs enhanced infrastructure Needs cooperation of informaticians, statisticians and technical experts to interpret data This approach could help define critical quality attributes Increased burden on detailed characterisation?

22 22

23 Acknowledgements 23 Jean- Etienne Fortier Nicolas Ballois Carl Jone Jolanda Westerlaken Laurence Vroye