Cao, Ziyi Data driven techniques for the analysis of oral dosage drug formulations Journal Article In: 2024. @article{noKey,
title = {Data driven techniques for the analysis of oral dosage drug formulations},
author = {Cao, Ziyi},
url = {https://hammer.purdue.edu/articles/thesis/DATA_DRIVEN_TECHNIQUES_FOR_THE_ANALYSIS_OF_ORAL_DOSAGE_DRUG_FORMULATIONS/24142605?file=42411960},
doi = {https://doi.org/10.25394/PGS.24142605.v1},
year = {2024},
date = {2024-09-20},
abstract = {This thesis focusses on developing novel data driven oral drug formulation analysis methods by employing technologies such as Fourier transform analysis and generative adversarial learning. Data driven measurements have been addressing challenges in advanced manufacturing and analysis for pharmaceutical development for the last two decade. Data science combined with analytical chemistry holds the future to solving key problems in the next wave of industrial research and development. Data acquisition is expensive in the realm of pharmaceutical development, and how to leverage the capability of data science to extract information in data deprived circumstances is a key aspect for improving such data driven measurements. Among multiple measurement techniques, chemical imaging is an informative tool for analyzing oral drug formulations. However, chemical imaging can often fall into data deprived situations, where data could be limited from the time-consuming sample preparation or related chemical synthesis. An integrated imaging approach, which folds data science techniques into chemical measurements, could lead to a future of informative and cost-effective data driven measurements. In this thesis, the development of data driven chemical imaging techniques for the analysis of oral drug formulations via Fourier transformation and generative adversarial learning are elaborated. Chapter 1 begins with a brief introduction of current techniques commonly implemented within the pharmaceutical industry, their limitations, and how the limitations are being addressed. Chapter 2 discusses how Fourier transform fluorescence recovery after photobleaching (FT-FRAP) technique can be used for monitoring the phase separated drug-polymer aggregation. Chapter 3 follows the innovation presented in Chapter 1 and illustrates how analysis can be improved by incorporating diffractive optical elements in the patterned illumination. While previous chapters discuss dynamic analysis aspects of drug product formulation, Chapter 4 elaborates on the innovation in composition analysis of oral drug products via use of novel generative adversarial learning methods for linear analyses.},
keywords = {FRAP},
pubstate = {published},
tppubtype = {article}
}
This thesis focusses on developing novel data driven oral drug formulation analysis methods by employing technologies such as Fourier transform analysis and generative adversarial learning. Data driven measurements have been addressing challenges in advanced manufacturing and analysis for pharmaceutical development for the last two decade. Data science combined with analytical chemistry holds the future to solving key problems in the next wave of industrial research and development. Data acquisition is expensive in the realm of pharmaceutical development, and how to leverage the capability of data science to extract information in data deprived circumstances is a key aspect for improving such data driven measurements. Among multiple measurement techniques, chemical imaging is an informative tool for analyzing oral drug formulations. However, chemical imaging can often fall into data deprived situations, where data could be limited from the time-consuming sample preparation or related chemical synthesis. An integrated imaging approach, which folds data science techniques into chemical measurements, could lead to a future of informative and cost-effective data driven measurements. In this thesis, the development of data driven chemical imaging techniques for the analysis of oral drug formulations via Fourier transformation and generative adversarial learning are elaborated. Chapter 1 begins with a brief introduction of current techniques commonly implemented within the pharmaceutical industry, their limitations, and how the limitations are being addressed. Chapter 2 discusses how Fourier transform fluorescence recovery after photobleaching (FT-FRAP) technique can be used for monitoring the phase separated drug-polymer aggregation. Chapter 3 follows the innovation presented in Chapter 1 and illustrates how analysis can be improved by incorporating diffractive optical elements in the patterned illumination. While previous chapters discuss dynamic analysis aspects of drug product formulation, Chapter 4 elaborates on the innovation in composition analysis of oral drug products via use of novel generative adversarial learning methods for linear analyses. |
Hilditch, Alexander T., Romanyuk, Andrey Assembling membraneless organelles from de novo designed proteins Journal Article In: 2023. @article{noKey,
title = {Assembling membraneless organelles from de novo designed proteins},
author = {Hilditch, Alexander T., Romanyuk, Andrey},
url = {https://www.biorxiv.org/content/10.1101/2023.04.18.537322v1.abstract},
doi = {https://doi.org/10.1101/2023.04.18.537322},
year = {2023},
date = {2023-04-18},
abstract = {Recent advances in de novo protein design have delivered a diversity of discrete de novo protein structures and complexes. A new challenge for the field is to use these designs directly in cells to intervene in biological process and augment natural systems. The bottom-up design of self-assembled objects like microcompartments and membraneless organelles is one such challenge, which also presents opportunities for chemical and synthetic biology. Here, we describe the design of genetically encoded polypeptides that form membraneless organelles in Escherichia coli (E. coli). To do this, we combine de novo α-helical sequences, intrinsically disordered linkers, and client proteins in single-polypeptide constructs. We tailor the properties of the helical regions to shift protein assembly from diffusion-limited assemblies to dynamic condensates. The designs are characterised in cells and in vitro using biophysical and soft-matter physics methods. Finally, we use the designed polypeptide to co-compartmentalise a functional enzyme pair in E. coli.},
keywords = {FRAP},
pubstate = {published},
tppubtype = {article}
}
Recent advances in de novo protein design have delivered a diversity of discrete de novo protein structures and complexes. A new challenge for the field is to use these designs directly in cells to intervene in biological process and augment natural systems. The bottom-up design of self-assembled objects like microcompartments and membraneless organelles is one such challenge, which also presents opportunities for chemical and synthetic biology. Here, we describe the design of genetically encoded polypeptides that form membraneless organelles in Escherichia coli (E. coli). To do this, we combine de novo α-helical sequences, intrinsically disordered linkers, and client proteins in single-polypeptide constructs. We tailor the properties of the helical regions to shift protein assembly from diffusion-limited assemblies to dynamic condensates. The designs are characterised in cells and in vitro using biophysical and soft-matter physics methods. Finally, we use the designed polypeptide to co-compartmentalise a functional enzyme pair in E. coli. |
Cao, Ziyi, Harmon, Dustin M. Periodic Photobleaching with Structured Illumination for Diffusion Imaging Journal Article In: 2023. @article{noKey,
title = {Periodic Photobleaching with Structured Illumination for Diffusion Imaging},
author = {Cao, Ziyi, Harmon, Dustin M.},
url = {https://pubs.acs.org/doi/full/10.1021/acs.analchem.2c02950?casa_token=nK9Ckj6YhgMAAAAA%3AC5pKduwdgP8RqdRI3Kr7PYMi4Qtu97katiZoy3fKCp_SlYPDQF6nq24-aUhPEyOIdxx6kqZg-VU4dzqd},
doi = {https://doi.org/10.1021/acs.analchem.2c02950},
year = {2023},
date = {2023-01-19},
abstract = {The use of periodically structured illumination coupled with spatial Fourier-transform fluorescence recovery after photobleaching (FT-FRAP) was shown to support diffusivity mapping within segmented domains of arbitrary shape. Periodic “comb-bleach” patterning of the excitation beam during photobleaching encoded spatial maps of diffusion onto harmonic peaks in the spatial Fourier transform. Diffusion manifests as a simple exponential decay of a given harmonic, improving the signal to noise ratio and simplifying mathematical analysis. Image segmentation prior to Fourier transformation was shown to support pooling for signal to noise enhancement for regions of arbitrary shape expected to exhibit similar diffusivity within a domain. Following proof-of-concept analyses based on simulations with known ground-truth maps, diffusion imaging by FT-FRAP was used to map spatially-resolved diffusion differences within phase-separated domains of model amorphous solid dispersion spin-cast thin films. Notably, multi-harmonic analysis by FT-FRAP was able to definitively discriminate and quantify the roles of internal diffusion and exchange to higher mobility interfacial layers in modeling the recovery kinetics within thin amorphous/amorphous phase-separated domains, with interfacial diffusion playing a critical role in recovery. These results have direct implications for the design of amorphous systems for stable storage and efficacious delivery of therapeutic molecules.},
keywords = {FRAP},
pubstate = {published},
tppubtype = {article}
}
The use of periodically structured illumination coupled with spatial Fourier-transform fluorescence recovery after photobleaching (FT-FRAP) was shown to support diffusivity mapping within segmented domains of arbitrary shape. Periodic “comb-bleach” patterning of the excitation beam during photobleaching encoded spatial maps of diffusion onto harmonic peaks in the spatial Fourier transform. Diffusion manifests as a simple exponential decay of a given harmonic, improving the signal to noise ratio and simplifying mathematical analysis. Image segmentation prior to Fourier transformation was shown to support pooling for signal to noise enhancement for regions of arbitrary shape expected to exhibit similar diffusivity within a domain. Following proof-of-concept analyses based on simulations with known ground-truth maps, diffusion imaging by FT-FRAP was used to map spatially-resolved diffusion differences within phase-separated domains of model amorphous solid dispersion spin-cast thin films. Notably, multi-harmonic analysis by FT-FRAP was able to definitively discriminate and quantify the roles of internal diffusion and exchange to higher mobility interfacial layers in modeling the recovery kinetics within thin amorphous/amorphous phase-separated domains, with interfacial diffusion playing a critical role in recovery. These results have direct implications for the design of amorphous systems for stable storage and efficacious delivery of therapeutic molecules. |
