Design, Synthesis, and Biological Characterization of Largazole Analogues

Histone deacetylases (HDACs) have been shown to play key roles in tumorigenesis, and

have been validated as effective enzyme target for cancer treatment. Largazole, a marine natural

product isolated from the cyanobacterium Symploca, is an extremely potent HDAC inhibitor that

has been shown to possess high differential cytotoxicity towards cancer cells along with excellent

HDAC class-selectivity. However, improvements can be made in the isoform-selectivity and

pharmacokinetic properties of largazole.

In attempts to make these improvements and furnish a more efficient biochemical probe

as well as a potential therapeutic, several largazole analogues have been designed, synthesized,

and tested for their biological activity. Three different types of analogues were prepared. First,

different chemical functionalities were introduced at the C2 position to probe the class Iselectivity profile of largazole. Additionally, docking studies led to the design of a potential

HDAC8-selective analogue. Secondly, the thiol moiety in largazole was replaced with a wide

variety of othe zinc-binding group in order to probe the effect of Zn2+ affinity on HDAC

inhibition. Lastly, three disulfide analogues of largazole were prepared in order to utilize a

different prodrug strategy to modulate the pharmacokinetic properties of largazole.

Through these analogues it was shown that C2 position can be modified significantly

without a major loss in activity while also eliciting minimal changes in isoform-selectivity. While

the Zn2+-binding group plays a major role in HDAC inhibition, it was also shown that the thiol

can be replaced by other functionalities while still retaining inhibitory activity. Lastly, the use of

a disulfide prodrug strategy was shown to affect pharmacokinetic properties resulting in varying

functional responses in vitro and in vivo.

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Largazole is already an impressive HDAC inhibitor that shows incredible promise.

However, in order to further develop this natural product into an anti-cancer therapeutic as well as

a chemical probe, improvements in the areas of pharmacokinetics as well as isoform-selectivity

are required. Through these studies we plan on building upon existing structure–activity

relationships to further our understanding of largazole’s mechanism of inhibition so that we may

improve these properties and ultimately develop largazole into an efficient HDAC inhibitor that

may be used as an anti-cancer therapeutic as well as a chemical probe for the studying of

biochemical systems.

On the Advancement of Probabilistic Models of Decompression Sickness

The work presented in this dissertation is focused on applying engineering methods to develop and explore probabilistic survival models for the prediction of decompression sickness in US NAVY divers. Mathematical modeling, computational model development, and numerical optimization techniques were employed to formulate and evaluate the predictive quality of models fitted to empirical data. In Chapters 1 and 2 we present general background information relevant to the development of probabilistic models applied to predicting the incidence of decompression sickness. The remainder of the dissertation introduces techniques developed in an effort to improve the predictive quality of probabilistic decompression models and to reduce the difficulty of model parameter optimization.

The first project explored seventeen variations of the hazard function using a well-perfused parallel compartment model. Models were parametrically optimized using the maximum likelihood technique. Model performance was evaluated using both classical statistical methods and model selection techniques based on information theory. Optimized model parameters were overall similar to those of previously published Results indicated that a novel hazard function definition that included both ambient pressure scaling and individually fitted compartment exponent scaling terms.

We developed ten pharmacokinetic compartmental models that included explicit delay mechanics to determine if predictive quality could be improved through the inclusion of material transfer lags. A fitted discrete delay parameter augmented the inflow to the compartment systems from the environment. Based on the observation that symptoms are often reported after risk accumulation begins for many of our models, we hypothesized that the inclusion of delays might improve correlation between the model predictions and observed data. Model selection techniques identified two models as having the best overall performance, but comparison to the best performing model without delay and model selection using our best identified no delay pharmacokinetic model both indicated that the delay mechanism was not statistically justified and did not substantially improve model predictions.

Our final investigation explored parameter bounding techniques to identify parameter regions for which statistical model failure will not occur. When a model predicts a no probability of a diver experiencing decompression sickness for an exposure that is known to produce symptoms, statistical model failure occurs. Using a metric related to the instantaneous risk, we successfully identify regions where model failure will not occur and identify the boundaries of the region using a root bounding technique. Several models are used to demonstrate the techniques, which may be employed to reduce the difficulty of model optimization for future investigations.