Tesis sobre el tema "Metabolic P system"

Thesis (Ph. D.)--West Virginia University, 2001.
Title from document title page. Document formatted into pages; contains viii, 199 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 176-195).

Thesis (Ph. D.)--West Virginia University, 2008.
Title from document title page. Document formatted into pages; contains viii, 174 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 164-174).

La 3,4-metilenodioximetanfetamina (MDMA, éxtasis) es una anfetamina sustituida de consumo frecuente y abusivo. La enzima principal que participa en el metabolismo de fase I de la MDMA, la isoforma 2D6 (CYP2D6) del citocromo P450, resulta también inhibida por la MDMA. Además, ésta es a la vez metabolizada por otras isoformas de CYP, por ejemplo la CYP1A2. La contribución de esta enzima y los posibles cambios en su actividad tras una administración de MDMA nunca han sido estudiados in-vivo. En consecuencia, se realizó un ensayo clínico donde los marcadores, dextrometorfano y cafeína se administraron tras una dosis oral de MDMA. En base a la farmacocinética de ambos marcadores se evaluaron posibles cambios en la actividad de las enzimas. En base al ratio metabólico urinario de dextrometorfano i dextrorfano (MR) se calculó la vida-media de degradación de CYP2D6. Tras una dosis de MDMA, el Cmax i AUC del dextrometorfano aumentó aproximadamente 10 veces con la correspondiente disminución en los parámetros farmacocineticos de dextrorfano. Se aumentó el MR casi 100 veces después de una dosis de MDMA, con un 67% de los sujetos superando la antimoda de 0.3 para la asignación del fenotipo de metabolizador lento de CYP2D6. La actividad de CYP2D6 se recuperó después de 10 días con una vida media de degradación de CYP2D6 de 46.6 h. La farmacocinética de la cafeína y sus metabolitos no fue afectada por la MDMA. Se debería avisar los consumidores de MDMA de las consecuencias de tal inhibición. A pesar de que hay muchas evidencias en animales sugiriendo que el MDMA es una neurotoxina serotonergica, todavía hay mucho debate sobre cuál es la causa de estos cambios cerebrales a largo plazo. La investigación apunta a la producción excesiva de especies reactivos de oxigeno (ROS) en el cerebro después de la administración de MDMA. La MDMA induce hipertermia, la liberación excesiva de dopamina cerebral y lleva a la desregulación energética del metabolismo. Conjuntamente, el metabolismo de MDMA produce un catecol reactivo, cuyos productos causan neurotoxicidad serotonergica en ratas. Cualquiera de los factores anteriores podrían ser la causa de la excesiva producción de ROS y los consecuentes cambios serotonergicos. A tenor de estas hipótesis, se investigó si diferentes temperaturas corporales afectarían el metabolismo de MDMA. Se administró MDMA a ratas a tres temperaturas ambientales distintas con el fin de prevenir o exacerbar la hipertermia inducida por MDMA. Se determinaron las concentraciones plasmáticas de MDMA y sus metabolitos principales durante las 6 h posteriores a la administración de la droga. Después de siete días, se sacrificaron los animales y se determinaron las cantidades de índoles cerebral. La administración de MDMA a 15ºC bloqueó la respuesta hipertermica y la disminución a largo plazo de 5-HT encontrada en ratas administradas a 21.5 ºC. A 15ºC, las concentraciones plasmáticas de MDMA aumentaron significativamente mientras que las concentraciones de sus metabolitos disminuyeron en comparación con ratas administradas a 21.5ºC. En contraste, la hipertermia y las deficiencias de indoles fueron exacerbadas en ratas tratadas a 30ºC. Se observó que las concentraciones plasmáticas de metabolitos de MDMA aumentaron significativamente en estos animales. La depleción a largo plazo de 5-HT no estuvo potenciada por la perfusión intrastriatal de MDMA después de una dosis sistémica de MDMA. Además, la interferencia del metabolismo de MDMA con la administración del inhibidor de catecol-o-metiltransferasa, entacapona, potenció la neurotoxicidad de MDMA, indicando que los metabolitos que son sustratos para este enzima podrían contribuir a la neurotoxicidad. Estos resultados tienen implicaciones tanto con el papel de la temperatura en el mecanismo del desarrollo de la neurotoxicidad del MDMA como en el abuso en humanos donde la hipertermia esta asociado con casos de toxicidad aguda. Se ha sugerido también que la causa de la depleción de 5-HT por MDMA es debido a un aumento de niveles de tirosina en el cerebro, cuyo hidroxilación no enzimática conduce a la formación de radicales libres derivados de la dopamina. En consecuencia, se propuso que el metabolismo de MDMA en compuestos pro-oxidantes fuera el paso limitante del proceso. En una serie de experimentos se encontraron niveles más altos de hipertermia aguda, concentraciones plasmáticas de tirosina, MDMA y sus metabolitos después de una dosis toxica de MDMA (15 mg/kg i.p.) versus una dosis no-toxica (7.5 mg/kg i.p.). La administración de una dosis no-toxica de MDMA (7.5 mg/kg i.p.) en conjunto con L-tirosina (0.2 mmol/kg i.p.) produció un aumento similar de niveles de tirosina en el suero con los niveles encontrados tras una dosis toxica de MDMA, sin embargo, los niveles de 5-HT cerebral permanecieron en niveles normales. Una dosis no-toxica de MDMA en combinación con una dosis alta de tirosina (0.5 mmol/kg i.p.) causó depleciones a largo plazo en ratas administradas a 21.5ºC pero no a 15ºC, condiciones conocidas por disminuir el metabolismo de MDMA. Al mismo tiempo, la perfusión estriatal de MDMA en combinación con tirosina (0.5 mmol/kg i.p.) en ratas hipertermicas no causaron depleciones de 5-HT. En contraste, se observaron reducciones significativas en 5-HT cerebral tras la administración de una dosis no-toxica de MDMA en ratas en condiciones de hipertermia en combinación con entacapona o acivisina, compuestos capaces de interferir con el metabolismo de MDMA o aumentar la disponibilidad de sus metabolitos en el cerebro, respectivamente. En conjunto estos datos indican que a pesar de que la tirosina y la hipertermia pueden contribuir a la neurotoxicidad inducida por la MDMA, el metabolismo de la droga parece ser el paso limitante.

Thesis (Ph. D.)--West Virginia University, 2010.
Title from document title page. Document formatted into pages; contains xiv, 180 p. : ill. (some col.). Includes abstract. Includes bibliographical references.

I sistemi biologici sono gruppi di entità biologiche (es. molecole ed organismi), che interagiscono producendo specifiche dinamiche. Questi sistemi sono solitamente caratterizzati da una elevata complessità perchè coinvolgono un elevato numero di componenti con molte interconnessioni. La comprensione dei meccanismi che governano i sistemi biologici e la previsione dei loro comportamenti in condizioni normali e patologiche è una sfida cruciale della biologia dei sistemi (in inglese detta systems biology), un'area di ricerca al confine tra biologia, medicina, matematica ed informatica. In questa tesi i P sistemi metabolici, detti brevemente sistemi MP, sono stati utilizzati come modello discreto per l'analisi di dinamiche biologiche. Essi sono una classe deterministica dei P sistemi classici, che utilizzano regole di riscrittura per rappresentare le reazioni chimiche e "funzioni di regolazioni di flusso" per regolare la reattività di ciascuna reazione rispetto alla quantita' di sostanze presenti istantaneamente nel sistema. Dopo un excursus sulla letteratura relativa ad alcuni modelli convenzionali (come le equazioni differenziali ed i modelli stocastici proposti da Gillespie) e non-convenzionali (come i P sistemi ed i P sistemi metabolici), saranno presentati i risultati della mia ricerca. Essi riguardano tre argomenti principali: i) l'equivalenza tra sistemi MP e reti di Petri ibride funzionali, ii) le prospettive statistiche e di ottimizzazione nella generazione di sistemi MP a partire da dati sperimentali, iii) lo sviluppo di un laboratorio virtuale chiamato MetaPlab, un software Java basato sui sistemi MP. L'equivalenza tra i sistemi MP e le reti di Petri ibride funzionali è stata dimostrata per mezzo di due teoremi ed alcuni esperimenti al computer per il caso di studio del meccanismo regolativo del gene operone lac nella pathway glicolitica. Il secondo argomento di ricerca concerne nuovi approcci per la sintesi delle funzioni di regolazione di flusso. La regressione stepwise e le reti neurali sono state impiegate come approssimatori di funzioni, mentre algoritmi di ottimizzazione classici ed evolutivi (es. backpropagation, algoritmi genetici, particle swarm optimization ed algoritmi memetici) sono stati impiegati per l'addestramento dei modelli. Una completo workflow per l'analisi dei dati sperimentali è stato presentato. Esso gestisce ed indirizza l'intero processo di sintesi delle funzioni di regolazione, dalla preparazione dei dati alla selezione delle variabili, fino alla generazione dei modelli ed alla loro validazione. Le metodologie proposte sono state testate con successo tramite esperimenti al computer sui casi di studio dell'oscillatore mitotico negli embrioni anfibi e del non photochemical quenching (NPQ). L'ultimo tema di ricerca è infine piu' applicativo e riguarda la progettazione e lo sviluppo di una architettura Java basata su plugin e di una serie di plugin che consentono di automatizzare varie fasi del processo di modellazione con sistemi MP, come la simulazione di dinamiche, la determinazione dei flussi e la generazione delle funzioni di regolazione.
Biological systems are groups of biological entities, (e.g., molecules and organisms), that interact together producing specific dynamics. These systems are usually characterized by a high complexity, since they involve a large number of components having many interconnections. Understanding biological system mechanisms, and predicting their behaviors in normal and pathological conditions is a crucial challenge in systems biology, which is a central research area on the border among biology, medicine, mathematics and computer science. In this thesis metabolic P systems, also called MP systems, have been employed as discrete modeling framework for the analysis of biological system dynamics. They are a deterministic class of P systems employing rewriting rules to represent chemical reactions and "flux regulation functions" to tune reactions reactivity according to the amount of substances present in the system. After an excursus on the literature about some conventional (i.e., differential equations, Gillespie's models) and unconventional (i.e., P systems and metabolic P systems) modeling frameworks, the results of my research are presented. They concern three research topics: i) equivalences between MP systems and hybrid functional Petri nets, ii) statistical and optimization perspectives in the generation of MP models from experimental data, iii) development of the virtual laboratory MetaPlab, a Java software based on MP systems. The equivalence between MP systems and hybrid functional Petri nets is proved by two theorems and some in silico experiments for the case study of the lac operon gene regulatory mechanism and glycolytic pathway. The second topic concerns new approaches to the synthesis of flux regulation functions. Stepwise linear regression and neural networks are employed as function approximators, and classical/evolutionary optimization algorithms (e.g., backpropagation, genetic algorithms, particle swarm optimization, memetic algorithms) as learning techniques. A complete pipeline for data analysis is also presented, which addresses the entire process of flux regulation function synthesis, from data preparation to feature selection, model generation and statistical validation. The proposed methodologies have been successfully tested by means of in silico experiments on the mitotic oscillator in early amphibian embryos and the non photochemical quenching (NPQ). The last research topic is more applicative, and pertains the design and development of a Java plugin architecture and several plugins which enable to automatize many tasks related to MP modeling, such as, dynamics computation, flux discovery, and regulation function synthesis.

Le reti biologiche hanno un ruolo cruciale in ogni processo vitale: meccanismi di regolazione genetica, differenziazione cellulare, metabolismo, ciclo cellulare, e trasduzione intracellulare del segnale. Progressi nei metodi sperimentali hanno permesso studi su larga scala di queste reti e possono rivelare la loro logica. Di conseguenza, i biologi devono integrare grandi quantità di dati sperimentali a analizzare reti complesse. I modelli matematici sono strumenti essenziali per collegare i comportamenti di un sistema con le interazioni tra le sue componenti. Modelli di reti biochimiche possono portare benefici in diversi campi. In medicina, malattie legate a disfunzioni di meccanismi genetici possono essere delucidate. La farmaceutica, potrebbe essere avvantaggiata nella ricerca di nuovi trattamenti e medicinali. Progetti biotecnologici possono trarre benefici da modelli predittivi che sostituiranno noiosi e costosi esperimenti di laboratorio. E, analisi computazionali possono contribuire alla ricerca biologica di base. Perciò, il successo della Biologia dei Sistemi certamente richiederà nuovi strumenti di simulazione e modellazione, nonché nuovi approcci di reverse-engineering. Negli ultimi anni, molti tools e modelli computazionali sono stati sviluppati per l'analisi di sistemi biologici. Tuttavia, probabilmente la nostra corrente visione di come le regolazioni vengono svolte non sta prendendo in considerazioni alcuni elementi. Nuovi e ulteriori esperimenti sono necessari, mentre i risultati sperimentali devono essere incorporati in nuovi modelli. Questo è legato alla necessità di migliorare gli approcci di reverse-engineering che usano serie temporali. Rimarchiamo inoltre che l'integrazione di diversi tipi di reti biologiche sarà un passo fondamentale verso l'obiettivo che si è posto la Biologia dei Sistemi. Questo trarrà beneficio dallo sviluppo di nuovi strumenti di modellazione che tengono in considerazione diverse entità (geni, proteine, metaboliti, ecc...) e relazioni (reazioni metaboliche, interazioni, regolazione, ecc...). Questo rappresenta un settore dove nuovi formalismi di modellazione e strumenti di simulazione avranno un grande valore aggiunto. Diversi problemi e richieste emergono in questa direzione, come, per esempio, l'utilizzo di informazioni incomplete, la manipolazione, l'uso e l'analisi di grandi e complessi modelli, l'estrazione di conoscenze relative ai meccanismi regolativi, l'inferenza di utili modelli a partire da dati sperimentali. Tuttavia, i frameworks esistenti difficilmente possono soddisfare interamente queste richieste, e questo si riflette nella ricerca di nuovi modelli computazionali. Inoltre, una delle più importanti necessità nell'affrontare la complessità dei sistemi biologici sembra essere la possibilità di osservare questi sistemi da adeguati livelli di astrazione. In questa direzione, i Metabolic P systems (MP systems) sono stati introdotti. Essi sono un nuovo modello computazionale che fornisce una macroscopica, globale, e tempo-discreto prospettiva nell'analisi di processi biologici e delle relative dinamiche. Gli MP systems offrono questi vantaggi: i) una naturale mappatura tra elementi reali ed elementi del modello, ii) la possibilità di adattare la prospettiva del modello alla risoluzione temporale dei dati osservati, e iii) la teoria Log-Gain. Grazie a questa teoria, il processo di modelling mediante l'utilizzo degli MP systems può essere ricondotto ad un problema di reverse-engineering. Tuttavia, problemi aperti rimangono da risolvere. In questa Tesi, proponiamo soluzioni per questi problemi. Essa parte dal punto di vista dell'informazione biologica e di come viene processata negli organismi viventi. Quindi, dopo un a visione d'insieme delle tecniche di modellazione in biologica computazionale e dei tools che le supportano, la Tesi si focalizza sul tema principale: la modellazione e il reverse-engineering di fenomeni biologici mediate l'utilizzo degli MP systems. I primi risultati dimostrano l'utilità degli MP systems per modellare diverse classi di fenomeni. Infatti, i) abbiamo modellato la glicolisi nel Saccharomyces cerevisiae e una rete genetica sintetica, ii) abbiamo sviluppato una pipeline per la stima di MP systems di sistemi bistabili/multistabili. Altri risultati riguardano MetaPlab, uno strumento software Java implementato per automatizzare la fase di modellazione, reverse-engineering, e analisi di fenomeni biochimici mediante l'utilizzo di MP systems. L'autore ha contribuito allo sviluppo di un plug-in per il computo dei flussi e nella realizzazione della guida di MetaPlab e di tutorials di diversi plu-ins. I rimanenti risultati rappresentano il cuore di questo lavoro e sono legati alla teoria Log-Gain, la quale rappresenta il primo passo per ottenere un MP system a partire da dati sperimentali. Abbiamo proposto soluzioni per i problemi aperti e i tasks cruciali, in maniera tale da sviluppare uno strumento che potesse essere applicato per l'inferenza di modelli MP che potessero spiegare dinamiche osservate. In particolare, i) abbiamo dimostrato che la teoria Log-Gain può essere applicata anche in caso di mancanza di informazioni circa i meccanismi regolativi, ii) abbiamo raggiunto risultati teorici riguardanti la computazione efficiente dei flussi reattivi, iii) abbiamo proposto un algoritmo euristico per calcolare i flussi iniziali, elementi indispensabili per l'applicazione della teoria Log-Gain, iv) abbiamo sviluppato una pipeline per l'analisi dei dati che affronta l'intero processo di sintesi delle funzioni di regolazione degli MP systems dalla preparazione dei dati alla validazione del modello. Inoltre, questa Tesi presenta il primo modello MP dedotto applicando la teoria Log-Gain applicata a dati sperimentali. Infatti, abbiamo definito il modello MP di un importante fenomeno fotosintetico chiamato Non Photochemical Quenching, il quale determina l'adattamento delle piante alle luce ambientale. Visto che nessun modello per questo fenomeno era stato precedentemente sviluppato, questo risultato mostra i vantaggi della teoria Log-Gain per la deduzione di modelli matematici di sistemi complessi. In questa maniera la teoria degli MP systems si dimostra essere un nuovo strumento per la costruzione di modelli, in particolare in quei casi dove la valutazione di parametri cinetici risulta difficoltosa. Questo perché la teoria Log-Gain permette di evitare analisi a livello microscopico. Ricordiamo anche i modelli che abbiamo ottenuto per la mitosi cellulare negli embrioni anfibi e il signaling pathway legato al metabolismo dell'insulina. I risultati raggiunti per il primo modello dimostrano che il nostro framework e abile nel catturare le caratteristiche salienti di un sistema anche quando viene osservato da un punto di vista macroscopico. Diversamente, i risultati del secondo modello presentano investigazioni relative all'implementazione su Graphic Processing Units (GPU) degli algoritmi per la stima dei flussi sviluppati nel contesto della teoria Log-Gain. Questa piattaforma hardware permette computazione massivamente parallele. Il problema del calcolo dei flussi in sistemi biologici può chiaramente ottenere benefici dalla parallelizzazione. Studi su simulazioni e una comparazione con MatLab dimostra che un implementazione GPU supera un'implementazione puramente sequenziale. In conclusione, richiamiamo che in cerca di soluzioni per i problemi aperti relativi alla teoria Log-Gain, il legame degli MP systems con una varietà di metodi, che vanno dall'algebra lineare, all'ottimizzazione su spazi vettoriali e reti neurali, è stata evidenziata.
Biological networks have a crucial role in each process of life, including gene regulatory mechanisms, cell differentiation, metabolism, the cell cycle, and signal transduction. Advances in experimental methods have enabled large-scale studies of these networks and can reveal the logic that underlies them. Consequently, biologists must integrate great quantities of experimental data and analyze complex networks. Mathematical models are essential tools to link the behaviours of a system to the interaction between its components. Models of biochemical networks are expected to benefit several fields. In medicine, mechanisms of diseases which are characterized by dysfunctions of regulatory processes can be elucidated. Pharmaceutics could take advantage in the search of new treatments and drugs. Biotechnological projects can benefit from predictive models that will replace some tedious and costly experiments in laboratory. And, computational analysis may contribute to basic biological research. Therefore, the success of Systems Biology will certainly require new modelling, simulation tools, and reverse-engineering approaches. In the last years, many tools and computational models have been developed for biological systems analysis. Nonetheless, our current picture of how regulations are carried out is probably still missing several significant pieces. More experimental work is needed, and these experimental results must be incorporated in improved models. This is linked with the necessity to improve reverse-engineering approaches which use time-series data. We emphasize that integration of different types of biological networks will be a fundamental step to the goal of Systems Biology. This would benefit from the creation of a common modelling framework which takes into account different entities, such as genes, proteins, metabolites, etc..., and relationships, like metabolic reactions, interactions, regulations, transports, etc... This represents a field where novel modelling formalisms and simulation tools will have great added value. Several problems and requirements arise toward this advance, such as how to deal with incomplete information, how to manipulate large models, how to extract valuable information about the regulative mechanisms, how to analyse these models, and how to infer suitable models from experimental data. However, the existing frameworks can hardly fulfill such demands, which reflects the need to search for suitable computational models. Moreover, one of the most important features for handling the high complexity of biological phenomena seems to be the possibility to observe these systems from an adequate abstraction level. Along this direction, the Metabolic P systems (MP systems) have been introduced. They are a new computational model which provides a macroscopic, global and time-discrete perspective on metabolic processes and related dynamics. Advantages of this approach are a i) natural mapping between real elements and model elements, ii) the possibility to adapt the model perspective to the temporal grain of observed data, and iii) the Log-Gain theory. According with this theory, the MP modelling process of a biochemical systems can be reduced to a reverse-engineering problem. However, crucial tasks and open problems remained to be performed for a complete discovery of the underlying MP system which explains an observed dynamics. In this Thesis, we propose solutions for these problems and tasks. It starts from the standpoint of biological information and its processing in living organisms. Then, after an overview about modelling and tools in computational biosystems, the Thesis is focused on the main theme: modelling and reverse-engineering of biological phenomena by means of MP systems. The first results prove the usefulness of MP systems to model several classes of phenomena. In fact, i) we modelled the upper part of the glycolysis in Saccharomyces cerevisiae and a synthetic oscillatory genetic network, ii) we developed a work-flow for the estimation of MP systems describing the dynamics of bistable/multistable phenomena. Others results concern MetaPlab, a Java software implemented to automatize modelling, reverse-engineering, and analysis of biochemical phenomena by means of MP systems. The author contributes to develop a flux discovery plugin and to realize a MetaPlab user guide and plugin tutorials. The remaining results represent the core of this work and are related to the Log-Gain theory, which represents the first step to obtain an MP system starting from experimental data. We performed the crucial tasks and solved the open problems of this theory, in order to have a framework useful for a complete discovery of the underlying MP system explaining an observed dynamics. In particular, i) we proved that the Log-Gain theory can be applied even with a lack of information about the regulative mechanisms; ii) we reported results regarding the efficient computations of reaction fluxes; iii) we proposed a heuristic algorithm to compute initial reaction fluxes, which are needed for the application of the Log-Gain theory; iv) we developed a complete pipeline for data analysis which addresses the entire process of flux regulation function synthesis and regulators discovery from data preparation to model validation. Moreover, this Thesis provides the first MP model deduced by means of the Log-Gain theory from experimental data. In fact, we defined an MP model of an important photosynthetic phenomenon called Non Photochemical Quenching, which determines the plant accommodation to the environmental light. Since no previous mathematical models of this phenomenon were available, this result shows the advantage of the Log-Gain theory for deducing mathematical models describing complex systems. In this manner the theory of MP systems can be seen as a new tool for constructing models, where the difficulty of kinetic rate constants evaluation is solved by the log-gain procedure, avoiding analysis at microscopic level. We also recall the models that we obtained for the mitotic oscillator in early amphibian embryos and the metabolic insulin signaling pathway. The results achieved for the first model prove that our framework is able to capture the salient characteristics of a system, also when it is observed from a macroscopic point of view. Differently, the results of the second model present investigations on the use of Graphic Processing Units (GPU) in the context of flux estimation by means of Log-Gain theory. These results are relevant in the framework of fluxes estimation since they highlight the potentialities of MP systems to infer biological fluxes when the size of a phenomenon increases. Simulation studies and a comparison with MatLab clearly shows that the (GPU) implementation outperforms pure sequential counterparts. Finally, we point out that in the search of solutions for the open problems of the Log-Gain theory, a variety of methods naturally occurred, going from vector algebra and vector optimization to artificial neural networks.

Grace S.N. Lau.
Thesis (Ph.D.)--Chinese University of Hong Kong, 1994.
Includes bibliographical references (leaves 335-362).
List of Abbreviations --- p.i
Abstract --- p.1
Chapter Chapter 1 --- General Introduction and Study Objectives
Chapter 1.1 --- Metabolic activation - role in drug toxicity and carcinogenesis --- p.5
Chapter 1.2 --- Hepatocellular carcinoma --- p.12
Chapter 1.2.1 --- Epidemiology --- p.12
Chapter 1.2.2 --- Aetiological factors --- p.17
Chapter 1.2.2.1 --- Hepatitis B virus infection --- p.17
Chapter 1.2.2.2 --- Cirrhosis --- p.24
Chapter 1.2.2.3 --- Aflatoxins --- p.25
Chapter 1.2.2.4 --- Other factors --- p.26
Chapter 1.2.2.5 --- Summary --- p.29
Chapter 1.3 --- Study objectives --- p.30
Chapter Chapter 2 --- The Metabolism of Paracetamol in Healthy Subjects andin Patients with Liver Disease and Hepatocellular Carcinoma
Chapter 2.1 --- Introduction --- p.34
Chapter 2.1.1. --- History of paracetamol --- p.34
Chapter 2.1.2 --- Pharmacology of paracetamol --- p.37
Chapter 2.1.3 --- "Absorption, Distribution, Metabolism and Excretion" --- p.38
Chapter 2.1.3.1 --- Absorption --- p.38
Chapter 2.1.3.2 --- Distribution --- p.41
Chapter 2.1.3.3 --- Metabolism --- p.42
Chapter 2.1.3.4 --- Excretion --- p.57
Chapter 2.1.4 --- Toxicity and Overdosage --- p.59
Chapter 2.2 --- Estimation of paracetamol and its metabolites in plasma and urine by high performance liquid chromatography --- p.72
Chapter 2.2.1 --- Introduction --- p.72
Chapter 2.2.2 --- Analytical method --- p.76
Chapter 2.2.2.1 --- Materials --- p.76
Chapter 2.2.2.2 --- Instrumentation --- p.77
Chapter 2.2.2.3 --- Collection and storage of samples --- p.79
Chapter 2.2.2.4 --- Chromatographic conditions --- p.79
Chapter 2.2.3 --- Urine assay --- p.79
Chapter 2.2.3.1 --- Preparation of standards and test samples for urine assay --- p.79
Chapter 2.2.3.2 --- Calculation of results for urine assay --- p.80
Chapter 2.2.3.3 --- Results of urine assay --- p.81
Chapter 2.2.3.4 --- Validation of urine assay --- p.81
Chapter 2.2.4 --- Plasma assay --- p.83
Chapter 2.2.4.1 --- Preparation of standards and test samples for plasma assay --- p.83
Chapter 2.2.4.2 --- Calculation of results for plasma assay --- p.91
Chapter 2.2.4.3 --- Results of plasma assay --- p.91
Chapter 2.2.4.4 --- Validation of plasma assay --- p.93
Chapter 2.2.5 --- Summary --- p.99
Chapter 2.3 --- The pharmacokinetics of paracetamol in healthy subjects --- p.103
Chapter 2.3.1 --- Introduction --- p.103
Chapter 2.3.2 --- Study protocol --- p.103
Chapter 2.3.3 --- Methods --- p.103
Chapter 2.3.3.1 --- Subjects --- p.103
Chapter 2.3.3.2 --- Drug administration and sampling --- p.104
Chapter 2.3.3.3 --- Drug analysis --- p.108
Chapter 2.3.3.4 --- Calculations --- p.108
Chapter 2.3.4 --- Pharmacokinetic analysis --- p.109
Chapter 2.3.5 --- Statistical analysis --- p.113
Chapter 2.3.6 --- Results --- p.114
Chapter 2.3.6.1 --- Plasma Results --- p.114
Chapter 2.3.6.2 --- Urine Results --- p.118
Chapter 2.3.6.3 --- Pharmacokinetic Results --- p.125
Chapter 2.3.6.4 --- Statistical Results --- p.134
Chapter 2.3.7 --- Discussion --- p.145
Chapter 2.4 --- "The pharmacokinetics of paracetamol in healthy subjects, patients with liver disease and hepatocellular carcinoma" --- p.155
Chapter 2.4.1 --- Introduction --- p.155
Chapter 2.4.2 --- Study protocol --- p.156
Chapter 2.4.3 --- Methods --- p.156
Chapter 2.4.3.1 --- Subjects --- p.156
Chapter 2.4.3.2 --- Drug administration and sampling --- p.157
Chapter 2.4.3.3 --- Drug analysis --- p.160
Chapter 2.4.3.4 --- Calculations --- p.160
Chapter 2.4.4 --- Pharmacokinetic analysis --- p.161
Chapter 2.4.6 --- Results --- p.162
Chapter 2.4.6.1 --- Plasma Results --- p.162
Chapter 2.4.6.2 --- Urine Results --- p.162
Chapter 2.4.6.3 --- Pharmacokinetic Results --- p.179
Chapter 2.4.7 --- Discussion --- p.194
Chapter 2.4.8 --- Summary --- p.203
Chapter Chapter 3 --- Metabolic Activation of Aflatoxin B1 in Healthy Subjects and in Patients with Liver Disease and Hepatocellular Carcinoma
Chapter 3.1 --- General introduction --- p.206
Chapter 3.1.1 --- Chemical structures and properties --- p.207
Chapter 3.1.2 --- Contamination of food by aflatoxins --- p.209
Chapter 3.1.3 --- Metabolism of aflatoxins --- p.210
Chapter 3.1.4 --- Human diseases possibly related to exposure to aflatoxins --- p.226
Chapter 3.1.4.1 --- Acute aflatoxicosis --- p.226
Chapter 3.1.4.2 --- Reye's syndrome --- p.227
Chapter 3.1.4.3 --- Kwashiorkor --- p.228
Chapter 3.1.4.4 --- Impaired immune function --- p.229
Chapter 3.1.4.5 --- Hepatocellular carcinoma --- p.230
Chapter 3.1.5 --- Biochemical and molecular epidemiology of aflatoxins --- p.232
Chapter 3.2 --- Development of an ELISA method to monitor AFB1 exposure in human serum --- p.237
Chapter 3.2.1 --- Introduction --- p.237
Chapter 3.2.2 --- Preparation of all the components necessary for analysing AFB1-albumin adducts by ELISA --- p.243
Chapter 3.2.2.1 --- Materials --- p.243
Chapter 3.2.2.2 --- Preparation of rabbit AFB1 antiserum --- p.244
Chapter 3.2.2.3 --- Preparation of the rat monoclonal antibody --- p.244
Chapter 3.2.2.4 --- Concentration of cell culture supernatant by ammonium sulphate precipitation --- p.246
Chapter 3.2.2.5 --- Preparation of the BSA-AFB1 conjugate --- p.248
Chapter 3.2.2.6 --- Preparation of the immunoaffinity gel --- p.250
Chapter 3.2.2.7 --- Preparation of the ELISA plates --- p.251
Chapter 3.2.3 --- General procedures used in the analysis of AFB1- albumin adducts --- p.252
Chapter 3.2.3.1 --- Competitive ELISA binding assay --- p.253
Chapter 3.2.3.2 --- Sep-pak C18 cartridge --- p.254
Chapter 3.2.3.3 --- Immunoaffinity column --- p.255
Chapter 3.2.3.4 --- Evaporation process --- p.255
Chapter 3.2.3.5 --- HPLC --- p.256
Chapter 3.2.3.6 --- Radioactive counting --- p.256
Chapter 3.2.3.7 --- Albumin isolation --- p.257
Chapter 3.2.3.8 --- Digestion of albumin --- p.257
Chapter 3.2.3.9 --- Animal procedures --- p.258
Chapter 3.2.4 --- Validations --- p.259
Chapter 3.2.4.1 --- Analysis of standard AFB1 and AFB1- lysine in ELISA --- p.259
Chapter 3 2.4.2 --- Optimisation of antiserum dilution and concentration of coating antigenin ELISA --- p.259
Chapter 3 2.4.3 --- Elution characteristics and capacity of the immunoaffinity column --- p.261
Chapter 3.2.4.4 --- Comparison of immunoaffinity gels prepared with different affinity gels --- p.261
Chapter 3.2.4.5 --- Immunoaffinity column experiment of AFB1-lysine --- p.263
Chapter 3.2.4.6 --- HPLC Analysis of fractions from immunoaffinity column --- p.263
Chapter 3.2.4.8 --- HPLC analysis of fractions from Sep- Pak C18 cartridge --- p.264
Chapter 3.2.4.9 --- Digestion of serum albumin by proteinase K --- p.264
Chapter 3.2.4.10 --- Effect of ethanol in samples to be loaded onto Sep-Pak C18 cartridge --- p.265
Chapter 3.2.4.11 --- Effect of drying in a vacuum concentrator on recovery of radioactivity of 3H-AFB1 --- p.266
Chapter 3.2.4.12 --- Evaluation of the overall procedure for the analysis of serum albumin adducts of AFB1 --- p.267
Chapter 3.2.4.13 --- HPLC analysis of samples obtained after digestion and all clean-up procedures --- p.268
Chapter 3.2.5 --- Results and discussion --- p.268
Chapter 3.2.5.1 --- BSA-AFB1 conjugate --- p.268
Chapter 3.2.5.2 --- Treatment of experimental animals with 3H-AFB1 --- p.270
Chapter 3.2.5.3 --- Optimisation of antiserum dilution and concentration of coating antigenin ELISA --- p.272
Chapter 3.2.5.4 --- Analysis of standard AFB1 and AFB1- lysine in ELISA --- p.275
Chapter 3.2.5.5 --- Sep-Pak C18 cartridge - elution characteristics and capacity --- p.279
Chapter 3.2.5.6 --- Elution characteristics of immunoaffinity columns --- p.282
Chapter 3.2.5.7 --- Immunoaffinity column experiment of AFB1-lysine --- p.290
Chapter 3.2.5.8 --- Digestion of serum albumin by proteinase K --- p.295
Chapter 3.2.5.9 --- Effect of ethanol in samples to be applied onto Sep-Pak C18 cartridges --- p.297
Chapter 3.2.5.10 --- Recovery of radioactivity after dryingin a vacuum concentrator --- p.300
Chapter 3.2.5.11 --- Recovery of the overall clean-up procedure for the analysis of serum albumin adducts of AFB1 --- p.300
Chapter 3.2.5.12 --- HPLC analysis of samples obtained after all clean-up procedures --- p.305
Chapter 3.2.5.13 --- The use of rabbit anti-AFB1 anti-serum and rat anti-AFB1 monoclonal antibody --- p.308
Chapter 3.2.6 --- Summary --- p.309
Chapter 3.3 --- Monitoring of AFBralbumin adducts in plasma of patients with liver disease and hepatocellular carcinoma --- p.311
Chapter 3.3.1 --- Introduction --- p.311
Chapter 3.3.2 --- Material and methods --- p.314
Chapter 3.3.2.1 --- Subject --- p.314
Chapter 3.3.2.2 --- Sample collections --- p.315
Chapter 3.3.2.4 --- Assay for AFB1-albumin adducts --- p.315
Chapter 3.3.2.5 --- Statistical analysis --- p.318
Chapter 3.3.3 --- Results and discussion --- p.318
Chapter Chapter 4 --- Summary and Ideas for Further Studies --- p.330
Acknowledgements --- p.333
References --- p.335
Appendices --- p.364

Il tema principale di questa tesi di dottorato riguarda la soluzione di problemi di dinamica inversa nel campo dei P sistemi metabolici (sistemi MP). I sistemi MP, basati sui P sistemi classici di Paun, sono stati introdotti da Manca nel 2004 per permettere la modellazione di sistemi metabolici per mezzo di grammatiche di riscrittura su multi-insiemi. In questo tipo di grammatiche, le trasformazioni di multi-insiemi di oggetti sono regolate, in modo deterministico, da particulari funzioni di stato chiamate regolatori. Il risultato chiave presentato in questa tesi riguarda la definizione di un algoritmo di regressione, chiamato LGSS (Log-gain Stoichiometric Stepwise regression), il quale implementa un ambiente di lavoro completo per affrontare problemi di dinamica inversa con sistemi MP. In particolare, LGSS è in grado di definire nuovi modelli MP, partendo dalle serie temporali di una dinamica osservata, combinando ed estendendo il principio di log-gain, sviluppato nella teoria dei sistemi MP, con il metodo classico di regressione stepwise, il quale è basato sull'uso di approssimazioni ai minimi quadrati e di test F. Nella parte finale della tesi, sono inoltre presentate tre applicazioni dei sistemi MP nella scoperta della logica di regolazione in fenomeni importanti nel campo della systems biology. Nonostante le evidenti differenze riscontrabili tra i fenomeni considerati, i quali spaziano da metabolismi a reti di regolazione genica, in tutti i casi affrontati è stato possibile definire modelli che esibiscono una buona approssimazione delle serie temporali osservate e che evidenziano fenomeni regolativi totalmente nuovi o che erano stati solamente teorizzati in precedenza.
The main theme of this Ph.D. thesis is focused on the solution of dynamical inverse problems in the context of Metabolic P systems (MP systems). Metabolic P systems, based on Paun's P systems, were introduced by Manca in 2004 for modelling metabolic systems by means of suitable multiset rewriting grammars. In such kind of grammars, multiset transformations are regulated, in a deterministic way, by particular functions called regulators. The key result presented in the thesis is the definition of a regression algorithm, called LGSS (Log-gain Stoichiometric Stepwise regression), which provides a complete statistical regression framework for dealing with inverse dynamical problems in the MP context. In particular, LGSS derives MP models from the time series of observed dynamics by combining and extending the log-gain principle, developed in the MP system theory, with the classical method of Stepwise Regression, which is a statistical regression technique based on least squares approximation and statistical F-tests. In the last part of the thesis, three applications of MP systems are also presented for discovering, by means of LGSS, the internal regulation logic of phenomena relevant in systems biology. Despite the differences between the considered phenomena, which comprise both metabolic and gene regulatory processes, in all the cases a model was found that exhibits good approximation of the observed time series and highlights results which are new or that have been only theorized before.

碩士
國立雲林科技大學
防災與環境工程研究所
95
Abstract Starting-up of newly established Enhanced Biological Phosphorus Removal (EPBR) system requires seeding sludge from other biological treatment plant so as to obtain a PAO-enriched sludge. Some scholars believe PAOs have to coexist with anaerobic bacteria, as PAOs can only take up short chain fatty acid in the anaerobic stage. Some researches mention that micro flora structure of PAO-enriched sludge is very complicated while Betaproteobacteria and Actinobacteria are the main superiority micro flora. However, whether seeding with waste sludge from traditional biological treatment system (sewage treatment, industry waste water treatment plants) can have the biologically phosphorus removal result of the newly established biological system is still left for further study. Therefore, the research aims at verifying this by seeding different sludge to anaerobic-oxic (A/O) activated sludge system. After continuous operation of 3 folds of SRT (i.e. 40 to 60 days), the A/O systems become stable and we have similar process performance for the EBPR system. From the results of the batch tests before seeding, almost no phosphorous release/uptake was obseved. However, batch tests after seeding demonstrated excellent phosphorous removal；which means PAOs exist in various traditional active sludge system. Finally, same influent substrate and operation conditions can obtain, PAO-enriched sludge with similar microbial. It has long been prove although the sludge source is different that anaerobic-oxic (A/O) activated sludge system enhances biologically phosphorus removal, although the effect is often hindered due to some factors. The main reason is the competition between Glycogen Accumulating Organisms (GAOs) and Phosphate Accumulating Organisms (PAOs). The organic substances in influent water of common waste water treatment plants are complicated. When the influent water is in anaerobic stage, fermentation reaction starts. The fermentation products includes acetate, propionate, and lactate, etc. It is, therefore, suspected whether different organic substances affect the biologically phosphorus removal of the system. Therefore, this study investigated the stoichiometries and kinetic constants of PAO-enriched sludge by using batch tests fed with acetate, propionate, glucose, and latate. When acetate and glucose are the single substrate, they do not inhibit the substrate uptake of sludge in three different P/C ratios. However, with propionate and lactic as single substrate, higher substrate concentration exhibited self inhibition effects. We have listed the kinetic parameter of sludge from three ratios of P/C on various substrates in the following table. Besides, results show that different substrate concentrations do change their stoichiometry relation. carbon kinetic parameters GAOs (2.8/600) PAOs (11/600) PAOs (22/600) source Acetate qm a 0.55 0.70 0.69 Ks b 153.98 132.13 93.67 Glucose qm 4.00 4.17 4.04 Ks 176.65 211.80 253.89 Propionate qm 1.47 1.60 1.30 Ks 70.73 47.85 20.26 KSI c 176.18 124.42 111.48 Lactate qm 0.84 0.83 0.76 Ks 22.86 23.60 44.41 KSI 219.94 138.48 82.75 a..maximum specific substrate utilization rates (mg/g MLSS/min) b. saturation constant (mg/L) c. inhibition constants (mg/L) Experimented results infer that acetate in biological treatment influent does help biologically phosphorus removal. As for glucose, the maximum specific substrate utilization rates of the three sludge are similar. It is speculated that glucose influent shall not make impacts on EPBR. Although maximum specific substrate utilization rates in propionate has no fixed trend, as PAOs is more affinitive to propionate from KS, it is speculated that propionate in influent does not make much difference on EPBR. In lactic experiment, it is found GAOs has better maximum specific substrate utilization rates and KS. Yet, PAOs can accumulate more PHAs. Therefore, one can still not be sure of the impact on EPBR from lactate influent.

Sun Huimin.
Thesis submitted in: December 2002.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.
Includes bibliographical references (leaves 77-89).
Abstracts in English and Chinese.
TITLE PAGE --- p.i
ACKNOWLEDGEMENTS --- p.ii
ABSTRACT --- p.iii
ABSTRACT IN CHINESE --- p.v
LIST OF PUBLICATIONS --- p.vii
ABBREVIATIONS --- p.viii
TABLE OF CONTENTS --- p.ix
Chapter CHAPTER 1. --- General Introduction --- p.1
Chapter 1.1 --- Current Status of Herbal Product Use --- p.1
Chapter 1.2 --- Herb-drug interactions --- p.2
Chapter 1.2.1. --- Mechanisms of herb-drug interaction --- p.3
Chapter 1.2.2. --- Pharmacodynamic interaction --- p.3
Chapter 1.2.3. --- Pharmacokinetic interaction --- p.4
Chapter 1.2.4. --- Herb-drug interaction involving drug metabolizing enzymes --- p.5
Chapter 1.3 --- Methodologies for studying herb-drug interactions involving CYP enzymes --- p.7
Chapter 1.3.1. --- Animal studies (Ex vivo approach) --- p.7
Chapter 1.3.2. --- In vitro inhibition/induction studies --- p.8
Chapter 1.3.3. --- Clinical studies --- p.9
Chapter CHAPTER 2. --- Effect of flavonoid-containing herbs on CYP 450 enzyme activities: a screening study in rat --- p.11
Chapter 2.1 --- Introduction --- p.11
Chapter 2.2 --- Materials and Methods --- p.12
Chapter 2.2.1. --- Chemicals --- p.12
Chapter 2.2.2. --- Herbs --- p.12
Chapter 2.2.3. --- Preparation of herbal extracts --- p.13
Chapter 2.2.3.1. --- Preparation of Green Tea extract --- p.13
Chapter 2.2.3.2. --- Preparation of Decaffeinated Green Tea (DGT) and its extracts --- p.13
Chapter 2.2.3.3. --- "Preparation of extracts of Huang Qin, Ge Gen and Huai Mi" --- p.14
Chapter 2.2.3.4. --- Preparation of Ginkgo biloba extract suspension --- p.14
Chapter 2.2.4. --- Animal treatment --- p.14
Chapter 2.2.5. --- Preparation of rat liver microsomes --- p.15
Chapter 2.2.6. --- Determination of protein content of liver microsomes --- p.16
Chapter 2.2.7. --- Determination of microsomal CYP content --- p.17
Chapter 2.2.8. --- Statistical analysis --- p.19
Chapter 2.3 --- Results --- p.19
Chapter 2.4 --- Discussion --- p.24
Chapter 2.5 --- Conclusion --- p.25
Chapter CHAPTER 3 --- Rationale of the clinical study --- p.26
Chapter CHAPTER 4 --- Development of HPLC methods for simultaneous determination of multiple probe drugs and their metabolites in human plasma or urine --- p.30
Chapter 4.1 --- Introduction --- p.30
Chapter 4.2 --- Materials and Methods --- p.33
Chapter 4.2.1. --- Chemicals and reagents --- p.33
Chapter 4.2.2. --- Preparation of stock and working solutions --- p.33
Chapter 4.2.3. --- Equipment and chromatographic conditions --- p.34
Chapter 4.2.4. --- Treatment of plasma and urine samples with β-glucuronidase --- p.35
Chapter 4.2.5. --- Extraction procedures --- p.36
Chapter 4.2.6. --- Preparation of working solutions for calibration curve --- p.37
Chapter 4.3 --- Results --- p.39
Chapter 4.3.1. --- Separation of the analytes --- p.39
Chapter 4.3.2. --- Calibration and linearity --- p.39
Chapter 4.3.3. --- Sensitivity --- p.39
Chapter 4.3.4 --- Accuracy and precision --- p.50
Chapter 4.4 --- Discussion --- p.52
Chapter 4.5 --- Conclusions --- p.53
Chapter CHAPTER 5 --- Stability study of probe drugs --- p.54
Chapter 5.1 --- Introduction --- p.54
Chapter 5.2 --- Materials and Methods --- p.54
Chapter 5.2.1. --- Preparation of standard solutions of probe drugs --- p.54
Chapter 5.2.2. --- Preparation of stability study mediums --- p.54
Chapter 5.2.2.1. --- Gastric juice (pH=1.2) --- p.54
Chapter 5.2.2.2. --- Intestine fluid (pH=6.8) --- p.54
Chapter 5.2.2.3. --- Human plasma (pH=7.4) --- p.55
Chapter 5.2.2.4. --- Phosphate buffer (pH=7.4) --- p.55
Chapter 5.2.3. --- Incubation --- p.55
Chapter 5.2.4. --- Determination of probe drug concentrations in incubation samples --- p.56
Chapter 5.3 --- Results --- p.57
Chapter 5.4 --- Discussion --- p.59
Chapter 5.5 --- Conclusion --- p.59
Chapter CHAPTER 6 --- Effect of the extract of Ginkgo biloba leaf (761) on CYP isozymes in human subjects --- p.60
Chapter 6.1 --- Introduction --- p.60
Chapter 6.2 --- Materials and Methods --- p.60
Chapter 6.2.1. --- Drugs --- p.60
Chapter 6.2.2. --- Subjects --- p.61
Chapter 6.2.3. --- Study design --- p.62
Chapter 6.2.4. --- Determination of probe drugs/metabolites in the plasma and urine --- p.63
Chapter 6.2.5. --- Data analysis --- p.65
Chapter 6.2.6. --- Statistical analysis --- p.66
Chapter 6.3 --- Results --- p.66
Chapter 6.3.1. --- Effect of EGb761on CYP1A2 activity --- p.66
Chapter 6.3.2. --- Effect of EGb761on CYP2E1 activity --- p.67
Chapter 6.3.3. --- Effect of EGb761 on CYP450 3A activity --- p.68
Chapter 6.3.4. --- Effect of EGb761 on NAT2 activity --- p.69
Chapter 6.3.5. --- Effect of EGb761 on CYP2D6 activity --- p.70
Chapter 6.3.6. --- Effects of EGb761 on CYP2C19 activity --- p.71
Chapter 6.4 --- Discussion --- p.72
Chapter 6.5 --- Conclusion --- p.76
References --- p.77
Appendix --- p.90

by Wong Che-cheuk, Dobe.
Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.
Includes bibliographical references (leaves 98-112).
Abstract also in Chinese.
Acknowledgements --- p.i
Abstracts --- p.ii
List of Abbreviation --- p.vi
Table of Contents --- p.vii
Chapter 1. --- Introduction
Chapter 1.1 --- Stages of Cancer Development --- p.1
Chapter 1.2 --- Comparison of Breast Cancer in Hong Kong & the United States --- p.2
Chapter 1.2.1 --- Statistics of Breast Cancer in the United States --- p.2
Chapter 1.2.2 --- Statistics of Breast Cancer in Hong Kong --- p.2
Chapter 1.3 --- Factors for Breast Cancer --- p.6
Chapter 1.3.1 --- Genetic Factor --- p.6
Chapter 1.3.2 --- Hormonal Factor --- p.7
Chapter 1.3.3 --- Genetic Bias --- p.9
Chapter 1.3.4 --- Influence of Diet --- p.10
Chapter 1.3.5 --- Obesity --- p.14
Chapter 1.3.6 --- Xenoestrogen --- p.14
Chapter 1.4 --- Hormonal Therapy in Breast Cancer --- p.15
Chapter 1.4.1 --- Antiestrogen --- p.15
Chapter 1.4.2 --- Progestin Antagonist --- p.19
Chapter 1.4.3 --- Aromatase Inhibitor --- p.20
Chapter 1.4.4 --- Gonadotropin Releasing Hormone (GnRH) Analogue --- p.23
Chapter 1.5 --- Metabolism of Estrogen --- p.25
Chapter 1.6 --- Substance with Chemopreventive Properties towards Breast Cancer --- p.29
Chapter 1.7 --- Aryl Hydrocarbon Receptor --- p.33
Chapter 1.8 --- Cytochrome P450s --- p.34
Chapter 1.9 --- Yuehchukene --- p.36
Chapter 1.10 --- Objectives of the Present Study --- p.38
Chapter 2. --- Materials and Methods
Chapter 2.1 --- Animals --- p.40
Chapter 2.2 --- Animal Treatment --- p.40
Chapter 2.3 --- Preparation of Crude Microsomal Fraction --- p.41
Chapter 2.4 --- Protein Assay --- p.41
Chapter 2.5 --- Ethoxyresorufm-O-deethylase Assay --- p.41
Chapter 2.6 --- Methoxyresorufin-O-deethylase Assay --- p.42
Chapter 2.7 --- Estradiol-2-hydroxylase Assay --- p.42
Chapter 2.8 --- Progesterone Hydroxylase Assay --- p.43
Chapter 2.9 --- Hepatic Aromatase Activity Assay --- p.43
Chapter 2.10 --- Inhibition of Ethoxyresorufm-O-deethylase and Estradiol-2-hydroxylase --- p.44
Chapter 2.11 --- Free Radicals Scavenging Assay --- p.44
Chapter 2.12 --- Chemicals --- p.45
Chapter 3. --- Result
Chapter 3.1 --- Optimization of Condition --- p.47
Chapter 3.1.1 --- Dosage --- p.47
Chapter 3.1.2 --- Time for Sacrifice --- p.47
Chapter 3.2 --- "Effect of Isoprenyl Compounds on the Body Weight, Liver Weight and Hepatic Microsomal Protein Content" --- p.50
Chapter 3.3 --- Hepatic Enzyme Activities --- p.54
Chapter 3.3.1 --- Ethoxyresorufm-O-deethylase --- p.54
Chapter 3.3.2 --- Methoxyresorufm-O-deethylase --- p.57
Chapter 3.3.3 --- Estradiol-2-hydroxylase --- p.60
Chapter 3.3.4 --- Progesterone Hydroxylase --- p.62
Chapter 3.3.5 --- Aromatase --- p.65
Chapter 3.4 --- Effect of Inhibitors in Ethoxyresorufin-O-deethylase and Estradiol-2-hydroxylase Activity --- p.65
Chapter 3.5 --- Free Radical Scavenging Activity --- p.72
Chapter 4. --- Discussion --- p.77
Chapter 5. --- Conclusion --- p.95
Chapter 6. --- References --- p.98
Chapter 7. --- Appendix --- p.113

Here we report the identification and approximate quantification of cytochrome P450 (CYP) proteins in human liver microsomes as determined by nano-LC-MS/MS with application of the exponentially modified protein abundance index (emPAI) algorithm during database searching. Protocols based on 1D-gel protein separation and 2D-LC peptide separation gave comparable results. In total, 18 CYP isoforms were unambiguously identified based on unique peptide matches. Further, we have determined the absolute quantity of two CYP enzymes (2E1 and 1A2) in human liver microsomes using stable-isotope dilution mass spectrometry, where microsomal proteins were separated by 1D-gel electrophoresis, digested with trypsin in the presence of either a CYP2E1- or 1A2-specific stable-isotope labeled tryptic peptide and analyzed by LC-MS/MS. Using multiple reaction monitoring (MRM) for the isotope-labeled tryptic peptides and their natural unlabeled analogues quantification could be performed over the range of 0.1-1.5 pmol on column. Liver microsomes from four individuals were analyzed for CYP2E1 giving values of 88-200 pmol/mg microsomal protein. The CYP1A2 content of microsomes from a further three individuals ranged from 165 to 263 pmol/mg microsomal protein. Although, in this proof-of-concept study for CYP quantification, the two CYP isoforms were quantified from different samples, there are no practical reasons to prevent multiplexing the method to allow the quantification of multiple CYP isoforms in a single sample.

PURPOSE: Cytochrome P450 2W1 (CYP2W1) is a monooxygenase detected in 30% of colon cancers, whereas its expression in nontransformed adult tissues is absent, rendering it a tumor-specific drug target for development of novel colon cancer chemotherapy. Previously, we have identified duocarmycin synthetic derivatives as CYP2W1 substrates. In this study, we investigated whether two of these compounds, ICT2705 and ICT2706, could be activated by CYP2W1 into potent antitumor agents. EXPERIMENTAL DESIGN: The cytotoxic activity of ICT2705 and ICT2706 in vitro was tested in colon cancer cell lines expressing CYP2W1, and in vivo studies with ICT2706 were conducted on severe combined immunodeficient mice bearing CYP2W1-positive colon cancer xenografts. RESULTS: Cells expressing CYP2W1 suffer rapid loss of viability following treatment with ICT2705 and ICT2706, whereas the CYP2W1-positive human colon cancer xenografts display arrested growth in the mice treated with ICT2706. The specific cytotoxic metabolite generated by CYP2W1 metabolism of ICT2706 was identified in vitro. The cytotoxic events were accompanied by an accumulation of phosphorylated H2A.X histone, indicating DNA damage as a mechanism for cancer cell toxicity. This cytotoxic effect is most likely propagated by a bystander killing mechanism shown in colon cancer cells. Pharmacokinetic analysis of ICT2706 in mice identified higher concentration of the compound in tumor than in plasma, indicating preferential accumulation of drug in the target tissue. CONCLUSION: Our findings suggest a novel approach for treatment of colon cancer that uses a locoregional activation of systemically inactive prodrug by the tumor-specific activator enzyme CYP2W1.