Dynamic Chemical Imaging and Analysis within Biologically Active Materials

A thorough understanding of pharmaceutical and therapeutic products and materials is important for an improved quality of life. By probing the complex behaviors and properties of these systems, new insights can allow for a better understanding of current treatments, improved design and synthesis of new drug products, and the development of new treatments for various health conditions. Often, the impact of these new insights are limited by current technology and instrumentation and by the methods in which existing data is processed. Additionally, current standards for characterization of pharmaceuticals and therapeutics are time-consuming and can delay the timeline in which these products become available to the consumer. By addressing the limitations in current instrumentation and data science methods, faster and improved characterization is possible. Development and improvement in optical instrumentation provides potential solutions to the current limitations of characterization methods by conventional instrumentation. Limitations in speed can be addressed through the use of nonlinear optical (NLO) methods, such as second harmonic generation (SHG) and two-photon excited ultraviolet fluorescence (TPE-UVF) microscopy, or by linear methods such as fluorescence recovery after photobleaching (FRAP). For these methods, ahigh signal-to-noise ratio (SNR) and a nondestructive nature decrease the overall sample size requirements and collections times of these methods. Furthermore, by combination of these optical techniques with other techniques, such as thermal analysis (e.g. differential scanning calorimetry (DSC)), polarization modulation, or patterned illumination, the collection of more complex and higher quality data is possible while retaining the improved speed of these methods. Thus, this modified instrumentation can allow for improved characterization of properties such as stability, structure, and mobility of pharmaceutical and therapeutic products. With an increase in data quantity and complexity, improvements to existing methods of analysis, as well as development of new data science methods, is essential. Machine learning (ML) architectures and empirically validated models for the analysis of existing data can provide improved quantification. Using the aforementioned optical instrumentation, auto-calibration of data acquired by SHG microscopy is one such method in which quantification of sample crystallinity is enabled by these ML and empirical models. Additionally, ML approaches utilizing generative adversarial networks (GANs) are able to improve on identification of data tampering in order to retain data security. By use of GANs to tamper with experimentally collected and/or simulated data used in existing spectral classifiers, knowledge of adversarial methods and weakness in spectral classification can be ascertained. Likewise, perturbations in physical illumination can be used to ascertain information on classification of real objects by use of GANs. Use of this knowledge can then be used to prevent further data tampering or by improving identification of data tampering