2018-07-31 Project Team Meeting - Agenda & Minutes
- Richard Norman (Unlicensed)
Date
Attendees
- Jocelyne Fiaux (Deactivated)
- Laura hook (Deactivated)
- Chris Lloyd (Deactivated)
- Abhinandan Raghavan (Deactivated)
Apologies
Discussion items
| Time | Item | Who | Output |
|---|---|---|---|
| 17:00 - 18:00 (60 mins) | Assay workshop table, Questions and Discussion points | All | Resolve outstanding discussion points and agree table format |
Minutes
Assay workshop table, questions and discussion points
>Current table layout, where all assays fall into one or both categories (intrinsic properties and AS readout), is fit for purpose.
>Discussion on which parameters we will have a higher chance of being able to compare;
-Current view is that we have a higher chance of being able to compare intrinsic (biophysical) properties of molecules directly. One suggestion is to focus on selected parameters first to increase chance of success of data comparison. Once we get underway, one of the initial outcomes of the project will be to make sense of all the data available and understand how much of the data is directly (vs. Indirectly) comparable.
-For data which is indirectly comparable we will need to agree a way to do this.
-For either type of data, relative differences should be comparable irrespective of different conditions used at various organisations.
-We need to highlight which assay readouts will be most impacted by initial molecule production process e.g. Low, medium, high impact. Can we reach/agree a level of process consistency not just internally but across all organisations involved? e.g. At MedI, most Abs expressed in CHO cells and need to reach certain level of purity to be moved forward. Other process categories include: cell line used, glycolsylation pattern, other post-translational modifications, purity reached.
-Jain et al paper shows a high level of agreement between data generated using molecules produced in HEK cells by Adimab and selected marketed drugs, suggesting that production process may not be a limiting factor for comparisons;
p.945 ..."To test this hypothesis, we measured the full biophysical property set of nine vialed monoclonal antibody drugs and found that, for the most part, the numerical values for these measures are not significantly different from those assessed with our preparations (Table S1);..."
>Post-meeting summary - potential approach to account for molecule production process, with increasing level of complexity e.g.
-Level 1: Assume that production process does not impact comparability (supported to some extent by Jain et al).
-Level 2: Establish initial cut-offs for common pre-screening process criteria across organisations e.g. % purity reached, # purification steps, cell lines used, glycosylation levels/patterns (if know).
-Level 3: Track process criteria as they change through Early Selection, Final Profiling, Endpoint Data stages and agree cut-offs across organisations.
>Regarding 'TPP-like' definition for each development phase the sense is that defining purity, expression levels and DSF (or other thermal stability) data for molecules at each stage is sufficient.
>Regarding comparison of data from different assays which are used for the same purpose (these fall mostly under the AS properties category) - it should be easier to compare data as this will be a direct comparison between relative changes between two or more timepoints. In theory we need to capture which phenomenon is being observed by which assays, as only relative changes between assays which observe the same phenomenon(s) will be comparable. To simplify, we could highlight certain parameters (e.g. Chemical stability) and assays which measure these parameters (e.g. cIEF, CEC, CZE) and agree that these overlap enough that relative changes should be comparable.
Actions
Richard - Include buffer conditions for all assays (intrinsic and AS)
Richard - add assay Descriptions
Richard - add extra columns with 'impact of process' and 'likelihood of comparability'
*Highlight your contribution in a different colour*
All - check all assays are present in assay table and highlight which assays fall under which of the two main categories
All - enter information for all assays, focus on: purpose, data needs, measure of (units), tech platform.
All - capture the stress conditions applied ahead of running AS assays: stress stimulus, buffer conditions and temperature for AS storage (sample treatment), AS sampling timepoints
All - accept or decline the meetings in August based on availability