Calibration-free PAT: Locating selective crystallization or precipitation sweet spot in screenings with multi-way PARAFAC models

When developping selective crystallization or precipitation processes, biopharmaceutical modalities require empirical screenings and analytics tailored to the specific needs of the target molecule. The multi-way chemometric approach called parallel factor analysis (PARAFAC) coupled with ultraviolet visible light (UV/Vis) spectroscopy is able to predict specific concentrations and spectra from highly structured data sets without the need for calibration samples and reference analytics. These calculated models can provide exploratory information on pure species spectra and concentrations in all analyzed samples by representing one model component with one species. In this work, protein mixtures, monoclonal antibodies, and virus-like particles in chemically defined and complex solutions were investigated in three high-throughput crystallization or precipitation screenings with the aim to construct one PARAFAC model per case. Spectroscopic data sets of samples after the selective crystallization or precipitation, washing, and redissolution were recorded and arranged into a four-dimensional data set per case study. Different reference analytics and pure species spectra served as validation. Appropriate spectral preprocessing parameters were found for all case studies allowing even the application of this approach to the third case study in which quantitative concentration analytics are missing. Regardless of the modality or the number of species present in complex solutions, all models were able to estimate the specific concentration and find the optimal process condition regarding yield and product purity. It was shown that in complex solutions, species demonstrating similar phase behavior can be clustered as one component and described in the model. PARAFAC as a calibration-free approach coupled with UV/Vis spectroscopy provides a fast overview of species present in complex solution and of their concentration during selective crystallization or precipitation, washing, and redissolution.

High-throughput preparation of hexagonally ordered mesoporous silica and gadolinosilicate nanoparticles for use as MRI contrast agents

The development of biomedical nanoparticulate materials for use in diagnostics is a delicate balance between performance, particle size, shape, and stability. To identify materials that satisfy all of the criteria it is useful to employ automated high-throughput (HT) techniques for the study of these materials. The structure and performance of surfactant templated mesoporous silica is very sensitive to a wide number of variables. Variables, such as the concentration of the structure-directing agent, the cosolvent and dopant ions and also the temperature and concentration of quenching all have an influence on the structure, surface chemistry, and therefore, the performance of the mesoporous silica nanoparticles generated. Using an automated robotic synthetic platform, a technique has been developed for the high-throughput preparation of mesoporous silica and gadolinium-doped silicate (gadoliniosilicate) nanoparticulate MRI contrast agents. Twelve identical repeats of both the mesoporous silica and gadolinosilicate were synthesized to investigate the reproducibility of the HT technique. Very good reproducibility in the production of the mesoporous silica and the gadolinosilcate materials was obtained using the developed method. The performance of the gadolinosilicate materials was comparable as a T(1) agent to the commercial MRI contrast agents. This HT methodology is highly reproducible and an effective tool that can be translated to the discovery of any sol-gel derived nanomaterial.