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Partner proposal - October 2018
Based on discussions and the information contained in these wiki pages we have compiled a pre-read document (linked below) and presentation pack with which we are engaging relevant stakeholders to seek endorsement for the creation of an industry-wide repository of 'developability' information on antibody drug candidates.
View file name AbVance II partner proposal October 2018 FINAL.pdf height 250
Proposal
The successful development of antibody molecules into approved drugs relies on optimizing a series of biophysical and pharmacological properties before the start of clinical trials. Failure during clinical trials or during the associated later stages of development is expensive. As such, early prediction of ‘developability’ risk is of key importance.
Our confidence in the ability to predict developability problems early by surrogate biophysical assays is limited by the low number of molecules with associated biophysical data and an agreed standard way of measuring this data across the industry. In addition, key pharmacological properties such as human and cyno PK (clearance), viscosity and solubility, and an understanding of whether these endpoint data could be predicted using biophysical surrogate data is lacking. Similar assays are used across the industry to predict developability risk (see Jain et al., 2017: http://www.pnas.org/content/114/5/944), but institutions use different methodologies. Nevertheless, commonality can be expected among differently implemented assays of the same type. For example we can expect some level of consistency in rank ordering of molecules by Hydrophobic Interaction Chromatography (HIC) even if different resins are used.
There is an opportunity to collectively identify the most predictive biophysical surrogate assays to understand variability among these, benchmark and establish best practices. In the first instance, this would act as an incentive for methods standardization and could subsequently encourage the building and growth of a common database to impact future antibody drug development.
Objectives
Based on the proposal we are working on our approach to achieving our objectives:
>Agree a standard/common way across industry of measuring data for 'well-known' biophysical assays.
>Share biophysical data generated by the above assays for sets of molecules contributed by industrial partners.
>Share associated endpoint data (e.g. human PK) for the above sets of molecules.
>Understand whether there is line of sight (LoS) from early selection (high-throughput) assay data to endpoint data.
>Understand whether we can predict endpoint data from sequence/structure.
The team
Project Team Meeting: Agendas & Minutes
Our Project Team Meetings are held every Tuesday, 17:00-18:00 (Central European Time)
.
Name
Organisation
Location
Relevant experience & interests
Qing Chai
Eli Lilly
San Diego, US
Craig Dickinson
Eli Lilly
San Diego, US
Francisco J. Fernández
Abvance Biotech
Madrid, ES
Chief Executive Officer
Laura Hook
GSK
Stevenage, UK
Bryan Jones
Eli Lilly
San Diego, US
Research Fellow
Helen Li
Eli Lilly
San Diego, US
Chris Lloyd
MedImmune
Cambridge, UK
Carmen Nitsche
Pistoia Alliance
Business Development Lead
Richard Norman
Pistoia Alliance
Gjøvik, NO
Consultant Project Manager
Abhinandan Raghavan
Novartis
Basel, CH
Kieran Todd
GSK
Stevenage, UK
Problem statements (and scale)
“Our confidence in the ability to predict developability problems early by surrogate biophysical assays is limited by the low number of molecules with associated biophysical data and an agreed standard way of measuring this data across the industry.” |
|---|
| 92% of industry members polled AGREE |
|---|
| 8% of industry members polled DISAGREE |
|---|
“Key pharmacological properties such as human and cyno PK (clearance), viscosity and solubility, and an understanding of whether these endpoint data could be predicted using biophysical surrogate data is lacking.” |
|---|
| 67% of industry members polled AGREE |
|---|
| 33% of industry members polled DISAGREE |
|---|
Value proposition
>Sharing data from mAbs which are poorly behaved would provide added value at least beyond the Jain et al., 2017 publication as they considered only reasonably well behaved mAbs.
>Developing a minimum set of ‘value’ assays, which can be used going forward, having previously understood the data output overlaps and correlations between assays currently used in each organisation.
>Organisations have a variety of early high-throughput assays which provide a qualitative sense of whether a molecule should be progressed but what is lacking is rigour in endpoint prediction from these assays. Generating enough data from enough molecules with varying properties/behaviours will enable higher confidence in endpoint data prediction early thus enabling earlier decision-making on developability of a molecule.
Scope
>Molecules: standard and non-standard mAbs (i.e. bi-/multi-specific's, ADCs, IgG/Fc fusions).
>Data: Chemical & structural stability, Aggregation, Toxicity, PK and Immunogenicity.
>Assays: early selection (high-throughput), detailed profiling (bespoke, low-throughput) and endpoint (pre-candidate nomination).
Strategy
>Creating a database of 'value' assays and associated data (focus on standard mAbs)
- Agree important criteria (associated with developability risk) e.g. from Target Product Profile (TPP).
- Identify assays which provide information on criteria.
- Prioritise which assays provide most 'value' (bang for buck) information with respect to predictive power (critical, decision-making, risk data).
- Select a set of molecules including some with interesting/poor properties
- Share SOPs, guideline metrics and data for 'value' assays on selected set of molecules.
- Test, validate and show correlation with endpoint data
- Generate standard assay data for selected (public or internal from 'dead' projects) molecules (index and normalise using standard molecules)
- Test if correlation hold for non-standard mAbs
Vision
To create an industry-wide repository of biophysical information for antibodies which will facilitate earlier selection of drug candidates.
Value proposition
- Increasing the confidence of selecting the 'right' molecules early based on their sequence, structure and biophysical properties;
- Being more effective at identifying which molecules will require the most/least resources to develop into potential lead candidates.
Objectives
1. Be able to compare data across industry partners.
2. Identify a minimum set of ‘value’ assays which will provide sufficient information to run a 'standard' campaign.
3. Better predict which molecules are likely to succeed based on early biophysical data.
4. Predict ideal manufacturing/developability conditions (e.g. formulation) from early biophysical data.
5. Define which sequence & structure elements, and biophysics properties contribute to liabilities or undesired effects.
6. Predicting endpoint data (e.g. in vivo nonspecific clearance rates) from sequence/structure
/structure
descriptors
Early risk assessment (challenges)
>Likely biggest challenge is IP and what is feasible to share.
Mitigation: identify low to high sensitivity data and pressure test with Legal departments (internal legal sponsor).
>First hurdle may be willingness within organisations to share data and assay method details.
Mitigation: target appropriate management level (internal executive sponsor) early and pressure test using 'basecase/upscale' scenarios and survey questions.
>Related to the above; willingness to share assay data correlation with endpoint data.
Mitigation: select 1-3 assays for which data is being shared and establish their correlation with PK, viscosity, solubility etc. internally, i.e. build model which can be shared thus circumventing the requirement for sharing primary data.
>Willingness to share sequence/structure information on molecules.
Mitigation: use information from molecules in the public domain and/or 'dead' internal projects. Define and use structure descriptors.
>Reaching agreement on how and where data is stored.
>Reaching agreement on model for ownership of data.
Questions
>What level of agreement is there with the problem statements?
>What data/analysis is there to support/refute the problem statements?
>What level of detail are organisations prepared to share with regards the assays, data, established correlations and sequence/structure?
>How much data do we need to make a difference and how are we going to test this?
>Who will ‘own’ and manage data?
>What internal related efforts are ongoing?Follow this link to our related initiative which aims to compile a 'gold standard' data set of PK, Immunogenicity and Phys Chem properties for Biologics (primarily antibodies) based on information in the public domain:
Biologics database collaboration
This parallel initiative originated at the EMBL-EBI Industry Programme workshop on Pharmacokinetics (PK) prediction and design for Biologics, held on the 27-28 June 2018 at the Wellcome Trust Genome Campus in Hinxton, Cambridge, UK