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The team
Name | Organisation | Location | Role | Experience/Interests |
|---|---|---|---|---|
Qing Chai | Eli Lilly | San Diego, US | ||
Craig Dickinson | Eli Lilly | San Diego, US | ||
Francisco Fernández | Abvance Biotech | Madrid, ES | ||
Bryan Jones | Eli Lilly | San Diego, US | ||
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 | |
Kieran Todd | GSK | Stevenage, UK |
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.
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