Effective interactions between monoclonal antibodies
Title: Effective interactions between monoclonal antibodies
SNIC Project: SNIC 2022/5-160
Project Type: SNIC Medium Compute
Principal Investigator: Peter Schurtenberger <peter.schurtenberger@fkem1.lu.se>
Affiliation: Lunds universitet
Duration: 2022-03-29 – 2023-04-01
Classification: 10402 10603
Keywords:

Abstract

Therapeutic proteins such as monoclonal antibodies (mAbs) represent an important tool for the treatment of numerous diseases such as cancer, autoimmune disorders, and viral infections. An example is the anti-SARS-CoV-2 mAbs recently authorized by the Food and Drug Administration (FDA) to be used for the treatment of COVID-19 symptoms, but more than one hundred of mAbs have been already approved in the last 35 years and many more are under investigation. However, developing therapeutic mAbs is not an easy task. Phenomena like protein aggregation, high viscosity and opalescence represent serious obstacles when trying to achieve a good formulation, and they can cause undesired immune responses in patients treated with them. Given the high costs and the limited amounts of material available during the early stages of drug development, there is a considerable interest to develop a more fundamental knowledge that helps to link the molecular structure of mAbs to their solution properties and to predict high concentration behavior of mAbs for different solvent conditions. While a number of studies exist that have aimed at using low concentration experimental results combined with course grained computer simulations for this purpose, these attempts have not been very successful and the current situation is far from satisfactory. In the framework of an antibody research program led by LINXS (Lund Institute of Advanced Neutron and X-Ray scattering), we have teamed up with an international consortium of leading researchers in this field to tackle this problem and received access to an ideal model mAb from the American National Institute of Standards and Technology NIST that supports this LINXS program. At the same time, we have also obtained significant amounts of an additional four well-defined mAbs through a collaboration with an international pharmaceutical company. This has allowed us to create a unique set of experimental data covering mAb solution static and dynamic properties using dynamic and static light scattering (DLS/SLS), small-angle X-ray scattering, and microrheology experiments. At the same time we are now developing computational models to reproduce our data and thus gain insights into the mAb formulation process for these mAbs. Our computational models are constructed starting from an all-atom description of the mAbs and then coarse-grained at different levels based on the solution property that needs to be investigated. In order to have a clear overview of the molecular phenomena acting at different concentration regimes, we will use either one-protein, two-protein, or many-protein computational models. The starting point for each system will be a fully relaxed mAb structure obtained from molecular dynamics (MD) simulations. Then the structure will be coarse-grained and used to perform Metropolis Monte Carlo simulations (MC). Our models will be first validated by direct comparison with experimental data, and will subsequently be used to predict features of mAb solutions such as osmotic second virial coefficients B2, and protein structure factors, S(q), as a function of solution conditions such as pH, ionic strength and mAb concentration.