The overarching goal of my thesis work was to utilize the combination of mathematical and experimental models towards an effort to resolve chemotherapy-induced peripheral neuropathy (CIPN), one of the most common adverse effects of cancer chemotherapy. In chapter two, we have developed quantitative-structure toxicity relationship (QSTR) models using machine learning algorithms that enable the prediction of peripheral neuropathy incidence solely from a chemicals molecular structure. The QSTR models enable the prediction of clinical neurotoxicity, which could be potentially useful in early drug discovery to screen out compounds that are highly neurotoxic and identify safer drug candidates to move forward into further development. The QSTR model was used to suggest modifications to the molecular structure of bortezomib that may reduce the number of patients who develop peripheral neuropathy from bortezomib therapy. In the third chapter, we conducted a network-based comparative systems pharmacology analysis of proteasome inhibitions. The concept behind this work was to use in silico pharmacological interaction networks to elucidate the neurotoxic differences between bortezomib and carfilzomib. Our theoretical results suggested the importance of the unfolded protein response in bortezomib neurotoxicity and that the mechanisms of neurotoxicity by proteasome inhibitors closely relate to the pathogenesis of Guillian-Barré syndrome caused by the Epstein-Barr virus. In chapter four we have written a review article to introduce the concept of Boolean network modeling in systems pharmacology. Due to the lack of knowledge about parameter values that govern the cellular dynamic processes involved in peripheral nerve damage, the development of a quantitative systems pharmacology model would not be feasible. Therefore, in chapter five, we developed a Boolean network-based systems pharmacology model of intracellular signaling and gene regulation in peripheral neurons. The model was used to simulate the neurotoxic effects of bortezomib and to identify potential treatment strategies for proteasome-inhibitor induced peripheral neuropathy. A novel combinatorial treatment strategy was identified that consists of a TNF? inhibitor, NMDA receptor antagonist, and reactive oxygen species inhibitor. Our subsequent goals were aimed towards translating this finding with the endeavor to hopefully one-day impact human health. Initially we had proposed to use three separate agents for each of these targets, however the clinical administration of three agents to prevent the neurotoxicity of one is likely unfeasible. We then came across a synthetic cannabinoid derivative, dexanabinol, that promiscuously inhibits all three of these targets and was previously developed for its intended use to treat traumatic brain injury. We believe that this drug candidate was worth investigating due to the overlapping pharmacological activity with suggested targets from network analyses, previously established favorable safety profile in humans, notable in vitro/vivo neuroprotective properties, and rising popularity for the therapeutic potential of cannabinoids to treat CIPN. In chapter six we assessed the efficacy of dexanabinol for preventing the neurotoxic effects of bortezomib in various experimental models. Due to the limited translatability of 2D cell culture techniques, we investigated the pharmacodynamics of dexanabinol using a microphysiological model of the peripheral nerve. Bortezomib caused a reduction in electrophysiological endpoints, which were partially restored by dexanabinol. In chapter 7 we evaluated the possible interaction of dexanabinol on the anti-cancer effects of bortezomib. We observed no significant differences in tumor volume between bortezomib alone and in combination with dexanabinol in a multiple myeloma mouse model. Lastly, we are currently investigating the efficacy of dexanabinol in well-established rat model of bortezomib-induced peripheral neuropathy. We believe that positive results would warrant a clinical trial. In conclusion, the statistical and mechanistic models of peripheral neuropathy that were developed could be used to reduce the overall burden of CIPN through the design of safer chemotherapeutics and discovery of novel neuroprotective treatment strategies.
|Advisor:||Mager, Donald E.|
|Commitee:||Krzyzanski, Wojciech, Ramananthan, Murali|
|School:||State University of New York at Buffalo|
|School Location:||United States -- New York|
|Source:||DAI-B 80/07(E), Dissertation Abstracts International|
|Subjects:||Pharmaceutical sciences, Artificial intelligence|
|Keywords:||Boolean network, Machine learning, Peripheral neuropathy, Proteasome inhibitors, Systems pharmacology, Systems toxicology|
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