Browsing by Subject "Pharmacokinetics"
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Item Embargo Innovations in Decompression Sickness Prediction and Adaptive Ascent Algorithms(2023) Di Muro, GianlucaDecompression Sickness (DCS) is a potentially serious medical condition which can occur in humans when there is a decrease in ambient pressure. While it is generally accepted that DCS is initiated by the formation and growth of inert gas bubbles in the body, the mechanisms of its various forms are not completely understood. Complicating matters, divers often face challenges in adhering to predetermined safe ascent paths due to unpredictable environmental conditions. Therefore, the challenge of improving dive safety is twofold: 1) enhancing the accuracy of models in predicting DCS risk for a given dive profile; 2) developing algorithms, recommending safe ascent profiles, and capable of adapting in real time to new unforeseen diving conditions. This dissertation addresses both problems in the context of diving applications.First, we examine how the DCS risk is partitioned in air decompression dives to identify which portion of the dive is the most challenging. Our findings show that most of the risk might be accrued at surface, or during the ascent phase, depending on the specific mission parameters. Subsequently, we conducted a comprehensive investigation into DCS models incorporating inter-tissue perfusion dynamics. We proposed a novel algorithm to optimize these models efficiently. Our results determined that a model neglecting the coupling of faster tissue to slower tissues outperformed all other models on O2 surface decompression dive profiles. We further conducted experiments with various compartment tissue connections, involving diffusion phenomena and introducing delayed dynamics, while also exploring different risk functions. By adopting the Akaike Information Criterion, we found that the best performing model on the training set was BQE22AXT4, a four-compartment model featuring a risk threshold term only in the fourth compartment. Conversely, the classical Linear-Exponential model demonstrated superior performance on the extrapolation set. Finally, we introduce a groundbreaking real-time algorithm that delivers a secure and time optimized ascent path capable of adapting to unanticipated conditions. Our approach harnesses the power of advanced machine learning techniques and backward optimal control. Through our comprehensive analysis, we demonstrate that this innovative methodology attains a safety level on par with precomputed NAVY tables, while offering the added advantage of dynamic adaptation in response to unexpected events.
Item Open Access Microdosing and drug development: past, present and future.(Expert Opin Drug Metab Toxicol, 2013-07) Lappin, Graham; Noveck, Robert; Burt, TalINTRODUCTION: Microdosing is an approach to early drug development where exploratory pharmacokinetic data are acquired in humans using inherently safe sub-pharmacologic doses of drug. The first publication of microdose data was 10 years ago and this review comprehensively explores the microdose concept from conception, over the past decade, up until the current date. AREAS COVERED: The authors define and distinguish the concept of microdosing from similar approaches. The authors review the ability of microdosing to provide exploratory pharmacokinetics (concentration-time data) but exclude microdosing using positron emission tomography. The article provides a comprehensive review of data within the peer-reviewed literature as well as the latest applications and a look into the future, towards where microdosing may be headed. EXPERT OPINION: Evidence so far suggests that microdosing may be a better predictive tool of human pharmacokinetics than alternative methods and combination with physiologically based modelling may lead to much more reliable predictions in the future. The concept has also been applied to drug-drug interactions, polymorphism and assessing drug concentrations over time at its site of action. Microdosing may yet have more to offer in unanticipated directions and provide benefits that have not been fully realised to date.Item Open Access On the Advancement of Probabilistic Models of Decompression Sickness(2016) Hada, Ethan AlexanderThe 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.
Item Open Access Time to Analgesia Onset and Pharmacokinetics After Separate and Combined Administration of Liposome Bupivacaine and Bupivacaine HCl: Considerations for Clinicians.(Open Orthop J, 2016) Gadsden, Jeffrey; Long, William JBACKGROUND: Liposome bupivacaine is a prolonged-release bupivacaine formulation indicated for single-dose administration into the surgical site to produce postsurgical analgesia. METHODS: An overview of time to onset of analgesia observed with liposome bupivacaine in human studies is provided, as well as a summary of data from pharmacokinetic studies including those that assessed pharmacokinetics after separate versus coadministration of liposome bupivacaine and bupivacaine HCl. RESULTS: Data from multiple studies show that local administration of liposome bupivacaine is associated with rapid onset and effective analgesia after surgery. However, the efficacy profile observed in controlled settings may not replicate the profile observed in clinical practice; time to onset may be impacted by nonpharmacologic factors, such as amount of drug given, location and relative vascularity, and variances in surgical techniques. Some clinicians coadminister or admix bupivacaine HCl and liposome bupivacaine based on the supposition that adjuvant use will result in more rapid onset of efficacy. To date, no clinical studies have been conducted comparing pain-related outcomes following coadministration versus liposome bupivacaine alone. Preclinical pharmacokinetic studies have assessed the potential impact of combined use, which resulted in predictable, additive systemic exposure without compromising the prolonged-release profile of liposome bupivacaine, and without signs of toxicity. CONCLUSION: Based on available data and approved package insert, in the setting of wound infiltration, clinicians have the flexibility to administer liposome bupivacaine alone, coadminister separately with bupivacaine HCl, or admix with bupivacaine HCl prior to injection, providing the bupivacaine HCl dose does not exceed 50% of the liposome bupivacaine dose.