Drug development is a long and expensive process. Potential drug candidates can fail clinical trials due to numerous issues, including metabolic stability and efficacy issues, wasting years of research effort and resource. This thesis detailed the development of in silico methods to predict the metabolic stability of structures and their bioavailability. Coralie Atom-based Statistical SOM Identifier (CASSI) is a site of metabolism (SOM) predictor which provides a SOM prediction based on statistical information gathered about previously seen atoms present in similar environments. CASSI is a real-time SOM predictor accessible via graphical user interface (GUI), allowing users to view the prediction results and likelihood of each atom to undergo different types of metabolic transformation. Fast Metabolizer (FAME)1 is a ligand-based SOM predictor developed around the same time by Kirchmair et al. In the course of the evaluation of CASSI and FAME performance, the two concepts were combined to produce FamePrint. FamePrint is a tool developed within the Coralie Cheminformatics Platform developed by Lhasa Limited. which can carry out SOM predictions, as well as bioisosteric replacement identification. Same as CASSI, this is available via the Coralie application GUI. The bioavailability issues caused by the metabolic enzyme, cytochrome P450 3A4, and transporter protein P-gylcoprotein are also investigated in this work, along with the potential synergistic relationship between the two systems. In silico classifiers to distinguish substrates against non-substrates of the two systems are produced and it was envisaged that these classifiers can be integrated into FamePrint as an additional layer of information available to the user when deciding on bioisosteric replacements to use when optimising a compound.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:753416 |
| Date | January 2018 |
| Creators | Choy, Alison Pui Ki |
| Contributors | Glen, Robert |
| Publisher | University of Cambridge |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.repository.cam.ac.uk/handle/1810/278798 |
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