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Thesis (Ph.D.)--University of Tennessee Health Science Center, 2007. / Title from title page screen (viewed on July 17, 2008). Research advisor: Atul J. Shukla, Ph.D. Document formatted into pages (ix, 146 p. : ill.). Vita. Abstract. Includes bibliographical references (p. 137-146).

A method of designing early clinical trials is developed for finding an optimum dose level of a new drug to be recommended for use in later phases. During the trial, the efficacious doses are allocated to the patients more often and those with a high probability of toxicity are less likely to be chosen. The method proposed is adaptive in the sense that the statistical models are updated after the data from each cohort of patients are collected and the dose level is adjusted at each stage based on the current data. Two classes of designs are presented. Although both are for efficacy and toxicity responses, one of them also considers pharmacokinetic information. The dose optimisation criteria are based on the probability of success and on the determinant of the Fisher information matrix for estimation of the dose-response parameters. They can be constrained by both acceptable levels of the probability of toxicity and desirable levels of the area under the concentration curve or the maximum concentration. The method presented is general and can be applied to various dose-response and pharmacokinetic models. To illustrate the methodology, it is applied to two different classes of models. In both cases, the pharmacokinetic model incorporates the population variability by making appropriate assumptions about the model parameters, while the dose responses are assumed to be either trinomial or bivariate binomial. Various design properties of the method are examined by simulation studies. Efficiency measures and the sensitivity of the designs to the assumed prior parameter values are presented. All of the computations are conducted in R, where the D- v optimal sampling time points are obtained by using the package PFIM. The results show that the proposed adaptive method works well and could be appropriate as a seamless phase IB/IIA trial design.

Thesis (Ph. D.)--Virginia Commonwealth University, 2007. / Prepared for: Dept. of Biostatistics. Bibliography: leaves 179-181. Also available online via the Internet.

Medication related errors are a significant cause of morbidity and mortality. In Brazil, most errors are related to prescribing, preparing, and administering medications. One way to deal with this barrier to safe care is through assessment and education of medication calculation dosage skills. Considering the Brazilian reality, this dissertation is a context and language adaptation of an evidence-based intervention called safeMedicate, a program that reinforces learning synthesis in crucial elements of medication dosage problem solving and provides the foundation for development in remaining levels of the hierarchy of learning. A guideline for medication calculation skills development or improvement based on the seven research-based principles for smart teaching was developed. Teaching approaches are beneficial for multiple methods of learning by addressing cognitive, motivational, and developmental goals. Web-based software would be a strong ally on adopting those approaches by complementing the class practice and providing opportunities for practice learning. The two-phases of adaptation and preliminary evaluation of safeMedicate for use in Brazil were guided by the Participatory and Iterative Process Framework for Language Adaptation (PIPFLA) cross-cultural equivalence model. A triangulation method of face validity survey, journaling, and multiple focus groups was used. The focus groups were (1) language adaptation team, (2) panel of experts, and (3) student panel. In order to analyze focus group data, a systematic coding procedure was performed through an iterative process, solving any differences between coders in order to guarantee internal consistency. The main themes were language, visual, content, programing, and data while discussing necessary adaptations of safeMedicate for use in Brazil.

<p>Progress in individualized drug treatment is of increasing importance, promising to avoid much human suffering and reducing medical treatment costs for society. The strategy is to maximize the therapeutic effects and minimize the negative side effects of a drug on individual or group basis. To reach the goal, interactions between the human body and different drugs must be further clarified, for instance by using mathematical models. Whether clinical studies or laboratory experiments are used as primary sources of information, greatly</p><p>influences the possibilities of obtaining data. This must be considered both prior and during model development and different strategies must be used. The character of the data may also restrict the level of complexity for the models, thus limiting their usage as tools for individualized treatment.</p><p>In this thesis work two case studies have been made, each with the aim to develop a model for a specific human-drug interaction. The first case study concerns treatment of inflammatory bowel disease with thiopurines, whereas the second is about treatment of ovarian cancer with paclitaxel. Although both case studies make use of similar amounts of experimental data, model development depends considerably on prior knowledge about the systems, the character of the data and the choice of modelling tools. All these factors are presented for</p><p>each of the case studies along with current results. Further, a system for classifying different but related models is also proposed with the intention that an increased understanding will contribute to advancement in individualized drug dosage.</p> / Report code: LiU-Tek-Lic-2007:41.

Progress in individualized drug treatment is of increasing importance, promising to avoid much human suffering and reducing medical treatment costs for society. The strategy is to maximize the therapeutic effects and minimize the negative side effects of a drug on individual or group basis. To reach the goal, interactions between the human body and different drugs must be further clarified, for instance by using mathematical models. Whether clinical studies or laboratory experiments are used as primary sources of information, greatly influences the possibilities of obtaining data. This must be considered both prior and during model development and different strategies must be used. The character of the data may also restrict the level of complexity for the models, thus limiting their usage as tools for individualized treatment. In this thesis work two case studies have been made, each with the aim to develop a model for a specific human-drug interaction. The first case study concerns treatment of inflammatory bowel disease with thiopurines, whereas the second is about treatment of ovarian cancer with paclitaxel. Although both case studies make use of similar amounts of experimental data, model development depends considerably on prior knowledge about the systems, the character of the data and the choice of modelling tools. All these factors are presented for each of the case studies along with current results. Further, a system for classifying different but related models is also proposed with the intention that an increased understanding will contribute to advancement in individualized drug dosage. / <p>Report code: LiU-Tek-Lic-2007:41.</p>

Current research on HIV therapy is diverse and multi-disciplinary. Engineers however, were late in joining the research movement and as such, engineering literature related to HIV chemotherapy is limited. Control engineers in particular, should have risen to the challenge, as it is apparent that HIV chemotherapy and control engineering have a lot in common. From a control theoretic point of view, HIV chemotherapy is control of a time varying nonlinear dynamical system with constrained controls. Once a suitable model has been developed or identified, control system theoretical concepts and design principles can be applied. The adopted control approach or strategy depends primarily on the control objectives, performance specifications and the control constraints. In principle, the designed control system can then be validated with clinical data. Obtaining measurements of the controlled variables however, has the potential to hinder effective control. The first part of this research focused on the application of control system analytical tools to HIV/AIDS models. The intention was to gain some insights into the HIV infection dynamics from a control theoretic perspective. The issues that needed to be addressed are: Persistent virus replication under potent HAART, variability in response to therapy between individuals on the same regimen, transient rebounds of plasma viremia after periods of suppression, the attainment, or lack thereof, of maximal and durable suppression of the viral load. The questions to answer were: When are the above mentioned observed responses to therapy most likely to occur as the HIV infection progresses, and does attaining one necessarily imply the other? Furthermore, the prognostic markers of virologic success, the possibility of individualizing therapy and timing the initiation of antiretroviral therapy such that the benefits of therapy are maximized, are matters that were also investigated. The primary objective of this thesis was to analyze models for the eventual control of the HIV infection. HIV therapy has multiple and often conflicting objectives, and these objectives had to be prioritized. The intention of the proposed control strategy was to produce practical solutions to the current antiretroviral problems. To this end, the second part of the research focused on addressing the HIV/AIDS control issues of sampling for effective control given the invasive nature of drawing blood from a patient and the derivation of drug dosage sequences to strike a balance between maximal suppression and toxicity reduction, when multiple drugs are concomitantly used to treat the infection. / Thesis (PhD)--University of Pretoria, 2006. / Electrical, Electronic and Computer Engineering / Unrestricted