Bruce Wareham - Academia.edu (original) (raw)
Uploads
Papers by Bruce Wareham
University of Strathclyde, 2019
I would like to express my thanks to my primary supervisor Dr Blair Johnston who has always given... more I would like to express my thanks to my primary supervisor Dr Blair Johnston who has always given me his time when I have asked for it. He gave me the enormous freedom and trust to pursue my project independently and for that I am extremely thankful. I would also like to thank my second supervisor Prof. Alastair Florence. I would like to thank EPSRC (Grant Ref: EP/K503289/1) for funding this work. I would also like to thank Dr. Murray Robertson who had the misfortune of putting up with all my incessant questions. I'd also like to thank Dr. Antony Vassileiou for all the times I have annoyed him as I thoroughly enjoyed annoying him. For supporting my fragile ego I'd like to thank
In the ESI there are two folders: gPROMS prediction and solubility prediction. In gPROMS predicti... more In the ESI there are two folders: gPROMS prediction and solubility prediction. In gPROMS prediction folder there are the model B and the different simulations of model B, with the data analysis. I solubility prediction folder there are the COSMOtherm simulations. Binary plot prediction solvent selection paper excel file contains the solubility binary plots used in model B. PCM-Dry excel files contain the particle size analysis of the two paracetamol grades. The solvent selection paper and the visio solvent selection paper file contain the workflow diagram. In solvent selection paper calculation from gPROM there is the data analysis of model A and model B simulations. The remaining pfd and excel files are the HPLC analysis of the different samples used for the validation.
Organic Process Research & Development
A predictive tool was developed to aid process design and to rationally select optimal solvents f... more A predictive tool was developed to aid process design and to rationally select optimal solvents for isolation of active pharmaceutical ingredients. The objective was to minimize the experimental work required to design a purification process by (i) starting from a rationally selected crystallization solvent based on maximizing yield and minimizing solvent consumption (with the constraint of maintaining a suspension density which allows crystal suspension); (ii) for the crystallization solvent identified from step 1, a list of potential isolation solvents (selected based on a series of constraints) is ranked, based on thermodynamic consideration of yield and predicted purity using a mass balance model; and (iii) the most promising of the predicted combinations is verified experimentally, and the process conditions are adjusted to maximize impurity removal and maximize yield, taking into account mass transport and kinetic considerations. Here, we present a solvent selection workflow based on logical solvent ranking supported by solubility predictions, coupled with digital tools to transfer material property information between operations to predict the optimal purification strategy. This approach addresses isolation, preserving the particle attributes generated during crystallization, taking account of the risks of product precipitation and particle dissolution during washing, and the selection of solvents, which are favorable for drying.
University of Strathclyde, 2019
I would like to express my thanks to my primary supervisor Dr Blair Johnston who has always given... more I would like to express my thanks to my primary supervisor Dr Blair Johnston who has always given me his time when I have asked for it. He gave me the enormous freedom and trust to pursue my project independently and for that I am extremely thankful. I would also like to thank my second supervisor Prof. Alastair Florence. I would like to thank EPSRC (Grant Ref: EP/K503289/1) for funding this work. I would also like to thank Dr. Murray Robertson who had the misfortune of putting up with all my incessant questions. I'd also like to thank Dr. Antony Vassileiou for all the times I have annoyed him as I thoroughly enjoyed annoying him. For supporting my fragile ego I'd like to thank
In the ESI there are two folders: gPROMS prediction and solubility prediction. In gPROMS predicti... more In the ESI there are two folders: gPROMS prediction and solubility prediction. In gPROMS prediction folder there are the model B and the different simulations of model B, with the data analysis. I solubility prediction folder there are the COSMOtherm simulations. Binary plot prediction solvent selection paper excel file contains the solubility binary plots used in model B. PCM-Dry excel files contain the particle size analysis of the two paracetamol grades. The solvent selection paper and the visio solvent selection paper file contain the workflow diagram. In solvent selection paper calculation from gPROM there is the data analysis of model A and model B simulations. The remaining pfd and excel files are the HPLC analysis of the different samples used for the validation.
Organic Process Research & Development
A predictive tool was developed to aid process design and to rationally select optimal solvents f... more A predictive tool was developed to aid process design and to rationally select optimal solvents for isolation of active pharmaceutical ingredients. The objective was to minimize the experimental work required to design a purification process by (i) starting from a rationally selected crystallization solvent based on maximizing yield and minimizing solvent consumption (with the constraint of maintaining a suspension density which allows crystal suspension); (ii) for the crystallization solvent identified from step 1, a list of potential isolation solvents (selected based on a series of constraints) is ranked, based on thermodynamic consideration of yield and predicted purity using a mass balance model; and (iii) the most promising of the predicted combinations is verified experimentally, and the process conditions are adjusted to maximize impurity removal and maximize yield, taking into account mass transport and kinetic considerations. Here, we present a solvent selection workflow based on logical solvent ranking supported by solubility predictions, coupled with digital tools to transfer material property information between operations to predict the optimal purification strategy. This approach addresses isolation, preserving the particle attributes generated during crystallization, taking account of the risks of product precipitation and particle dissolution during washing, and the selection of solvents, which are favorable for drying.