Community Research and Development Information Service - CORDIS

H2020

PIPPI Report Summary

Project ID: 675074
Funded under: H2020-EU.1.3.1.

Periodic Reporting for period 1 - PIPPI (Protein-excipient Interactions and Protein-Protein Interactions in formulation)

Reporting period: 2016-01-01 to 2017-12-31

Summary of the context and overall objectives of the project

The increasing number of protein therapeutics on the market and in industrial pipelines is driving a need for a better understanding of their formulation to meet emerging trends such as ultra-high protein concentrations, novel formats and train specialists in the burgeoning biopharmaceuticals sector. This has led to the formation of the ITN network PIPPI (Protein-excipient Interactions and Protein-Protein Interactions in formulation), where a major focus area is the training of 15 young researchers within protein formulation, with an emphasis in biophysical and structural characterization of protein therapeutics.

The consortium consists of both academic and industrial partners located in Denmark, Sweden, Germany and United Kingdom. Hence, the young researchers in PIPPI will work in an international and interdisciplinary network and they will learn to combine systematic investigations of physicochemical behavior of a number of proteins with an in-depth understanding of the molecular interactions behind the macroscopic behavior.

Our vision is to generate a comprehensive database for diverse protein architectures, which can be interrogated for the prediction of their properties and stability when complemented with a minimum number of experiments. This vision depends on meeting several aims: i) create a representative library of structurally diverse proteins; ii) characterize the physical stability of the protein library using a variety of high throughput state-of-the-art techniques; ii) define critical formulation attributes for the protein library, measured over a range of formulation conditions and with various excipients; iii) determine the molecular behaviour for representatives of each protein structural class using high resolution structural analyses and in silico models.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The protein molecules for the representative library were selected upon public availability and library diversity:

The selected molecules cover a very large size range from approximately 4 kDalton all the way up to 150 kDalton. Their isoelectric points range from 4.7 to around 9, with intermediates also represented. Most importantly, the PIPPI protein library covers over 80% of the higher order protein folds found in biologics approved by the FDA.

A systematic study of the overall behaviour of the candidate proteins as a function of pH and ionic strength is almost completed, and will form the basis for further investigations. The protein molecular behaviour has been investigated by structural analyses and their critical formulation attributes. Furthermore, the database is under construction.

We have organised four workshops covering formulation development, light scattering, X-ray scattering, nuclear magnetic resonance and molecular dynamics simulations illustrated by lectures, exercises and case studies. For dissemination of our work, our ESR students have already presented their concepts and first results on a large number of international conferences; a first publication from the ITN has been published.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The large amount of data collected on the PIPPI proteins has made it possible to test if we based on these data could predict protein stability parameters, such as melting temperature and aggregation temperature using Artificial Neural Networks (ANNs). ANNs represent a promising modeling technique for data set with non-linear relationships. Consequently, an ANN approach was applied for the data sets and an ANN model was successfully fitted to the full screening data set of seven monoclonal antibodies and used to predict three important protein stability indicators. ANNs compared to classical statistical method leads to better fit and prediction. This work is ongoing.

The massive collaborative efforts in collecting comprehensive data on the same proteins, in the same conditions – stability, modeling, nuclear magnetic resonance, small-angle X-ray scattering, light scattering, rheology to mention some, opens the possibility for unique studies of the interplay between the microscopic atomic structure and the macroscopic overall behavior and stability of the proteins in the library. The data will go into a publicly available database which will make it possible also for other researchers in the community to benefit from these data in the future. We expect that based on these data we will be able to introduce new tools into the world of biologics, which might accelerate protein drug development.
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