Welcome to the Abvance II wiki home page.
This page was created on 30 April 2018.
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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.
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.” | 100% industry members polled AGREE | 0% industry members polled DISAGREE |
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“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.” | 43% industry members polled AGREE | 57% industry members polled DISAGREE |
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Vision
To create an industry-wide repository of biophysical information for antibodies which will facilitate earlier selection of drug candidates.
Value proposition
Having greater ability to select the 'right' molecules early: be more effective at identifying which molecules will require the most/least resources to develop into potential lead candidates.
Objectives
1. Identify a minimum set of ‘value’ assays which will provide minimum required information to run a 'standard' campaign.
2. Highlight the variability between the same assays at different organisations.
3. Understand which sequences, structural elements, biophysics properties contribute to liabilities or undesired effects.
Follow the links below for further information:
The team - including links to Project Team Meetings: Agendas & Minutes.
Scope - including BASECASE scenario.
Risk assessment
Plans (short term)
Outstanding Questions
Plans (short term)
ACTIVITIES
Apr-Aug 2018
>Building the team (need critical mass)
Current members: Abvance Biotech, Eli Lilly, GSK, MedImmune, Novartis,
Potential members: Pfizer, UCB pharma, BMS, Roche, Lonza
>Validating and refining the problem statements
>Clarifying and validation on the value proposition
>Scope definition
>Define our objectives
>Identify key risks (challenges)
>Compile list of questions, discussion topics, activities
Jun-Oct 2018
>Internal executive and legal sponsor engagement (approval and validation)
>Defined user & business requirements
EVENTS
27-28th Jun 2018
>EMBL-EBI workshop - Chris Lloyd and Richard Norman will present the project with a view to obtaining further buy-in
Oct 2018 (to coincide with Pistoia Alliance US conference in Boston?)
>F2F Project workshop - aim is to use this as a Kick-off for the delivery phase of the project
Approach
>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
>Predicting endpoint data (e.g. in vivo nonspecific clearance rates) from sequence/structure
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.
>Too great a variability and reproducibility between same assays leading to inability to effectively compare data (establish correlations).
Mitigation: start with assays with least variability, for other assays 'standardisation' may be of value.
>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?