AbVance II: focus on developability risk prediction of antibody drug candidates

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.

About us

Based on the proposal we are working on our approach to achieving our draft 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 a line of sight (LoS) from biophysical data to endpoint data.

>Understand whether we can predict endpoint data from sequence/structure.

Join us and help shape and deliver the project: our Project Team Meetings are held every Tuesday, 17:00-18:00 (Central European Time)

If you are interested, contact the Project Manager: richard.norman@pistoiaalliance.org