Title: Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy,
Abstract: Monoclonal antibodies constitute one of the most important strategies to
treat patients suffering from cancers such as hematological malignancies and
solid tumors. In order to guarantee the quality of those preparations prepared
at hospital, quality control has to be developed. The aim of this study was to
explore a noninvasive, nondestructive, and rapid analytical method to ensure
the quality of the final preparation without causing any delay in the process.
We analyzed four mAbs (Inlfiximab, Bevacizumab, Ramucirumab and Rituximab)
diluted at therapeutic concentration in chloride sodium 0.9% using Raman
spectroscopy. To reduce the prediction errors obtained with traditional
chemometric data analysis, we explored a data-driven approach using statistical
machine learning methods where preprocessing and predictive models are jointly
optimized. We prepared a data analytics workflow and submitted the problem to a
collaborative data challenge platform called Rapid Analytics and Model
Prototyping (RAMP). This allowed to use solutions from about 300 data
scientists during five days of collaborative work. The prediction of the four
mAbs samples was considerably improved with a misclassification rate and the
mean error rate of 0.8% and 4%, respectively.