Analytical Workflows to Unlock Predictive Power in Biotherapeutic Developability.

ABSTRACT:

Purpose

Forming accurate data models that assist the design of developability assays is one area that requires a deep and practical understanding of the problem domain. We aim to incorporate expert knowledge into the model building process by creating new metrics from instrument data and by guiding the choice of input parameters and Machine Learning (ML) techniques.

Methods

We generated datasets from the biophysical characterisation of 5 monoclonal antibodies (mAbs). We explored combinations of techniques and parameters to uncover the ones that better describe specific molecular liabilities, such as conformational and colloidal instability. We also employed ML algorithms to predict metrics from the dataset.

Results

We found that the combination of Differential Scannin

SUBMITTER: Trikeriotis M 

PROVIDER: S-EPMC9944381 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature