Results obtained during Spectro-metrics have yield new methodologies for the study of drug mode of action and cellular responses using vibrational spectroscopy. Before the project, the tools available for treating the data in this context were generally limited. They included, for example, the use of unsupervised data (e.g. PCA), which could fail to extract important information when the spectral features of interest are not the main source of spectral variation. Another example is the use of the regression vector of a PLSR, which, as we demonstrated during the project, could fail to model non-linear responses of data. Spectro-metrics has provided a toolbox of novel methodologies for elucidating drug uptake and cellular responses from Raman and IR data of time dependent incubation experiments, highlighting:
- The validation of MCR-ALS as a technique to extract the concentration profiles and pure spectra of the drug and the biochemical changes induced, including initial binding and subsequent cellular responses.
- Creation of hard-soft models to incorporate within the process the kinetic equations, improving the interpretability of the model and providing an estimation of the equation constants.
- Methods for the treatment of multimodal vibrational data, allowing to combine the signals from Raman (More sensitive to resonant drugs) and IR (More sensitive to changes in the biochemical composition of the cell). Figure 3 depicts the cross correlation map for Raman and IR spectra for a incubation time experiment.
- The development of “ATR-Spin” concept to be able to measure the IR spectra of the cells with a single step of preconcentration and isolation, thus enabling the measurement without the need of an expensive FTIR microscope.
In general, the set of methodologies created is going to expand our knowledge about cellular processes. For example, works are in due course to study the beta-oxydation process in hepatic cells. In the case of the study of drugs, the better understanding of modes and mechanisms of action will assist in the development of better and personalized therapies against cancer.
