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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)

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.

Follow the links below for further information:

The team - including links to Project Team Meetings: Agendas & Minutes.

Scope - including BASECASE scenario.

Approach - ROADMAP to achieving the Objectives.

Risk assessment

Plans (currently short term)

Outstanding Questions