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 statement (and scale)
What level of agreement is there with the current problem statements?
“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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What data/analysis is there to support/refute these statements?
How much data do we need to make difference and how are we going to test this?
Scope
Strategy
Challenges
Questions
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