Ignite Academic Theater Presentation
Monoclonal antibody based biologics are gaining immense popularity as therapeutic agents to treat a wide array of diseases such as cancer and inflammation. Such a rise in demand for biologics necessitate the development of rapid, label-free and automated characterization tools for meeting stringent quality control requirements during their manufacturing. To meet regulatory requirements and reduce business risk associated with fill operations, accurate identification of the drug products is a critical and necessary analysis during multiple stages of manufacturing and distribution. However, due to high similarity in the chemical structures of these drugs, establishing product identification is challenging and the traditional wet lab techniques are destructive, labor intensive and expensive to perform multiple times during production and, even more so, for fill finish testing. Therefore, there is an urgent need for quick, inexpensive and reliable methods for biologics identification. Here, we report the first application of spontaneous and label-free plasmon-enhanced Raman spectroscopy coupled with multivariate data analysis for identification of a cohort of closely related human and murine antibody drugs. Building on finite difference time domain simulations, we synthesized nanoparticles of optimal morphology to compare the feasibility of performing SERS-based bulk sample detection with that of spontaneous Raman spectroscopy. We have developed partial least squares-discriminant analysis derived decision algorithms that provide near-perfect classification accuracy in predicting the identity of these drugs based on the subtle, but consistent, differences in their spectra, which are otherwise invisible to gross visual inspection. We have shown that the performance of the decision algorithm with plasmon-enhanced Raman spectroscopy, even at much lower biologic concentrations, is comparable with that of spontaneous Raman spectroscopy. Together, these results establish the feasibility of developing an automated non-perturbative spectroscopic pipeline for rapid identification and quality control of biologics. Translation of the developed technique to manufacturing and fill-finish sites and automation for alleviating the need of human expertise will be made possible by collaboration with industry experts.