A modelagem farmacocinética permite prever a concentração de medicamentos em diferentes tecidos do organismo humano. O desenvolvimento de modelos matemáticos é importante para verificar a adequação de certos procedimentos realizados na administração de medicamentos. O objetivo deste trabalho é o desenvolvimento de um modelo farmacocinético capaz de prever a concentração plasmática de drogas no organismo para diversas formas de infusão. Foram utilizados dois tipos de abordagem. Inicialmente, na abordagem monocompartimental, considerou-se que a droga adentra ao organismo diretamente no compartimento sanguíneo, que representa todo o corpo humano. Já na abordagem bicompartimental foram considerados os seguintes compartimentos: um representando o meio pelo qual a droga é infundida no organismo (podendo ser via gastrointestinal, transdermal ou pulmonar) e outro representando o plasma sanguíneo. Em ambos os casos, foi considerada a hipótese de concentração homogênea da droga nos compartimentos em questão. O modelo foi estruturado na forma de diagramas de blocos e a solução foi feita com a utilização da Transformada de Laplace. Foi feita a validação dos modelos e verificou-se que os resultado gerados foram muito próximos dos resultados presentes na literatura. A utilização do modelo monocompartimental permitiu comparar os resultados da administração da mesma quantidade de droga por infusão constante e por infusão periódica. A análise dos resultados gerados pelo modelo mostrou que as concentrações atingidas pelos dois métodos não são as mesmas. O modelo bicompartimental permitiu simular administrações orais e transdermais, e inalação. Foi possível prever a concentração sanguínea após a interrupção da terapia com anti-concepcionais e anti-depressivos e foi verificado o tempo necessário para que esta concentração seja atingida novamente. Foram propostos métodos para que esta concentração fosse atingida em um menor período de tempo. Outra aplicação foi na comparação entre o tratamento com comprimidos inteiros e tomados pela metade em um intervalo menor de tempo. Verificou-se que a concentração atingida é diferente mesmo que a massa ingerida seja a mesma. O modelo também foi utilizado para calcular a concentração de nicotina após o consumo de cigarros e verificou-se que, o indivíduo que fuma a cada três horas não consegue eliminar totalmente a nicotina de seu organismo. Além disso, foi possível simular a sobredosagem de um anti-inflamatório e verificar o tempo em que a concentração fica acima do nível terapêutico. Foi proposto um método para obtenção do parâmetro farmacocinético relacionado à absorção, que pode ser obtido facilmente a partir de dados presentes nas bulas dos medicamentos. Este método é muito mais simples e preciso do que e proposto na literatura, que utiliza análise gráfica e dados clínicos que não são obtidos com tanta facilidade. / The pharmacokinetic modeling can predict the concentration of drug in different tissues of the human body. The development of mathematical models is an important tool to verify the appropriateness of certain procedures performed in medication administration. The objective of this work is to develop a pharmacokinetic model able to predict the plasma concentration of drug in the body after various forms of infusion. Two approaches were used. Initially, in the one-compartment approach it was considered that the drug enters the body directly into the blood compartment, which represents the entire human body. In the two-compartment approach it was considered the following compartments: one representing the means by which the drug is infused into the body (either via the gastrointestinal tract, lung, or transdermal) and one representing the blood plasma. In both cases, it was considered homogeneous concentration of the drug in the compartments. The model was built by using block diagrams and the solution was obtained using the Laplace Transform. The model was validated by comparing its results to literature data, with very good agreement. The model allowed comparing the one-compartment constant infusion of drug in the body with the periodic infusion. The analysis of the results generated by the model showed that the concentrations achieved by these methods are not the same. The two-compartment model allowed simulating oral and transdermal administration, and inhalation. It was possible to predict blood concentration after interruption of therapy with anti-depressants and anti-conceptional drugs. The model was able to verify the time it takes to reach the former level. Methods have been proposed to achieve the same concentration in a shorter period of time. Another application was the comparison of the treatment with whole tablets and taken by half in a smaller interval of time. It was found that the concentration achieved is different even though the same mass is ingested in both cases. The model was also used to calculate the concentration of nicotine after cigarette smoking and it was found that the individual who smokes every three hours, nicotine is not entirely eliminated from body. Furthermore, it was possible to simulate overdose of an anti-inflammatory and the period of time when the concentration is above the therapeutic level. It has been proposed a method to obtain pharmacokinetic parameter related to absorption, which can be easily obtained based on data present in the drug bull. This method is much simpler and more accurate than the method proposed in the, which uses graphical analysis and clinical data that are not so easy to be obtained.

Submitted by Maria Cristina (library@lncc.br) on 2015-04-07T14:04:08Z No. of bitstreams: 1 thesis_RLC.pdf: 14146223 bytes, checksum: 3e764cd68f4c65f0572940fb339e5708 (MD5) / Approved for entry into archive by Maria Cristina (library@lncc.br) on 2015-04-07T14:04:34Z (GMT) No. of bitstreams: 1 thesis_RLC.pdf: 14146223 bytes, checksum: 3e764cd68f4c65f0572940fb339e5708 (MD5) / Made available in DSpace on 2015-04-07T14:04:50Z (GMT). No. of bitstreams: 1 thesis_RLC.pdf: 14146223 bytes, checksum: 3e764cd68f4c65f0572940fb339e5708 (MD5) Previous issue date: 2014-10-31 / Recent developments in molecular assays to study the transcriptome associated with statistical, mathematics and computational methods, introduced great advances in the comprehension of biological systems, in understanding the viral infectious process associated with immune response and the choice of targets for the development of vaccines and antiviral therapies. On one hand side, the modelling process involves different stages, including transcriptome acquisition, the integration with information available in biological databases and the visualization and analysis of networks therein obtained. On the other hand side, during the modelling process, many software systems are employed with differences in structure, design assumptions and heterogeneity in input data, making the whole analysis process, besides laborious and fragmented, error-prone. In this context, infrastructure to support e-science such as scientific workflows and databases are used in automating, structuring, execution, organization and management of scientific experiments in silico. In this thesis, we studied gene expression data (DNA microarray) of primates infected with SIV (Simian Virus Imunodeficiency) composing a time-series reflecting the evolution of infection. SIV infects more than 40 species in African continent that are known as natural hosts, as they do not develop the immunodeficiency syndrome (AIDS). However, when SIV strains infect non African primates, known as non-natural hosts, among them Asian rhesus monkey (Macaca mulatta), they develop a syndrome similar to the human immunodeficiency virus HIV. The evolutionary closeness of the virus, SIV and HIV, and between the hosts, human and non-human primates, enables biological studies in non-human models relevant to understanding the biological mechanisms of various innate and adaptive immunity in the host. Thus, we used data corresponding to sampling points in three different stages of SIV infection: before infection, acute and chronic phases of infection. Data process analysis was based on systems biology approaches. These analyzes included steps such as, data normalization, filtering, annotation, clustering, enrichment, interactions biological database and visualizations of the results in gene interaction networks. Among the main biological results, we selected: identification of co-expressed genes; functional characterization profiles from ontologies related to biological processes; interactions between genes host-host and virus-host and differences in the timing of immune responses during acute phase of infection between the different types of hosts. We implemented the analytical process described above in a framework denominated GenNet that consisting of a scientific workflow, GenNet.W, responsible for the automation of scientific experiments in silico and a database, GenNet.DB. The database adopted a graph data model, within a NoSQL based system, to store the inferred gene interaction networks, as well as other information generated by the scientific workflow. The graph data model natively supports the representation of gene interaction networks and enables the comparison between different inferred networks, as much as path explorations such as co??-expression genes in high-level declarative query language. / O recente desenvolvimento de ensaios moleculares para estudo do transcriptoma, associados a métodos estatísticos, matemáticos e computacionais, trouxeram grandes avanços no entendimento de sistemas biológicos, dentre os quais, a compreensão de processos infecciosos virais associados à resposta imune e escolha de alvos para o desenvolvimento de vacinas e terapias antivirais. De um lado, o processo de modelagem envolve diferentes etapas, desde a aquisição dos dados de transcriptoma, integração de informações disponíveis em bancos de dados biológicos até visualizações e análises das redes obtidas. Por outro lado, durante o processo de modelagem, são utilizados diversos sistemas de software com diferentes pressupostos de organização e forma de dados de entrada, fazendo com que todo esse processo seja, além de trabalhoso e fragmentado, passível de erros. Nesse sentido, infraestruturas de apoio a e-science como workflows científicos e banco de dados são utilizados na automação, estruturação, execução, organização e gerenciamento de experimentos científicos in silico. Nessa tese, utilizamos diferentes dados de séries temporais de expressão gênica (microarranjo de DNA) em primatas infectados pelo SIV (do inglês, Simian Imunodeficiency Virus). O SIV infecta várias espécies de primatas africanos, conhecidos como hospedeiros naturais, que não desenvolvem doença. Entretanto, quando linhagens de SIV infectam primatas não africanos, conhecidos como hospedeiros não naturais, dentre os quais o macaco asiático rhesus (Macaca mulatta), esses desenvolvem uma imunodeficiência semelhante a que ocorre em humanos pelo HIV. A proximidade evolutiva entre os vírus, SIV e HIV, e entre os hospedeiros, humanos e primatas não humanos, possibilitam estudos em modelos biológicos não humanos relevantes na compreensão dos diferentes mecanismos da imunidade inata e adaptativa nos hospedeiros. Os dados utilizados corresponderam a pontos amostrais em três diferentes fases da infecção pelo SIV: antes da infecção, fase aguda e fase crônica. O processo de análise desses dados baseou-se em abordagens de biologia de sistemas. Tais análises incluíram etapas de normalização dos dados, filtragem, anotação, agrupamento, enriquecimento, integração com base de dados biológicas e visualização dos resultados em redes de interações gênicas. Dentre os principais resultados biológicos obtidos, selecionamos: a identificação dos genes co-expressos nos perfis de expressão gênica, caracterização funcional a partir das ontologias relacionadas aos processos biológicos, interações entre os genes hopedeiro-hospedeiro e vírus-hospedeiro e diferenças nos tempos das respostas imunes na fase aguda da infecção entre os tipos de hospedeiros. Inserimos o processo analítico descrito acima em uma framework chamada GenNet consistindo de um workflow científico GenNet.W responsável pela automação do experimento científico in silico e um banco de dados GenNet.DB. O banco de dados adotou um modelo de dados em grafos, NoSQL, para armazenar as redes de interações gênicas inferidas, bem como outras informações geradas pelo workflow científico. O modelo de dados em grafo suporta nativamente a representação das redes de interações gênicas e permite a comparação entre diferentes redes inferidas e a exploração de vias de como co-expressão gênica, usando consultas que expressam em alto nível de linguagem que o sistema suporta.

High-throughput time series data characterizing magnitudes of gene expression, levels of protein activity, and the accumulation of select metabolites in vivo are being generated with increased frequency. These time profiles contain valuable information about the structure, dynamics and underlying regulatory mechanisms that govern the behavior of cellular systems. However, extraction and integration of this information into fully functional, computational and explanatory models has been a daunting task. Three types of issues have prevented successful outcomes in this inverse task of system identification. The first type pertains to the algorithmic and computational difficulties encountered in parameter estimation, be it using a genetic algorithm, nonlinear regression, or any other technique. The second type of issues stems from implicit assumptions that are made about the system topology and/or the functional model representing the biological system. These include the choice of intermediate pathway steps to be accounted for in the model, decisions on the irreversibility of a step, and the inclusion of ill-characterized regulatory signals. The third type of issue arises from the fact that there is often no unique set of parameter values, which when fitted to a model, reproduces the observed dynamics under one or several different sets of experimental conditions. This latter issue raises intriguing questions about the validity of the parameter values and the model itself. The central focus of my research has been to design a workflow for parameter estimation and system identification from biological time series data that resolves the issues outlined above. In this thesis I present the theory and application of a novel framework, called Dynamic Flux Estimation (DFE), for system identification from biological time-series data.

The key to understanding any system, including physiologic and pathologic systems, is to obtain a truly comprehensive view of the system. The purpose of this dissertation was to develop foundational analytical and modeling tools, which would enable such a comprehensive view to be obtained of any physiological or pathological system by combining experimental, clinical, and theoretical viewpoints. Specifically, we focus on the development of analytical and modeling techniques capable of predicting and prioritizing the mechanisms, emergent dynamics, and underlying principles necessary in order to obtain a comprehensive system understanding. Since physiologic systems are inherently complex systems, our approach was to translate the philosophy of complex systems into a set of applied and quantitative methods, which focused on the relationships within the system that result in the system's emergent properties and behavior. The result was a set of developed techniques, referred to as relational modeling and analysis that utilize relationships as either a placeholder or bridging structure from which unknown aspects of the system can be effectively explored. These techniques were subsequently tested via the construction and analysis of models of five very different systems: synaptic neurotransmitter spillover, secondary spinal cord injury, physiological and pathological axonal transport, and amyotrophic lateral sclerosis and to analyze neurophysiological data of in vivo cat spinal motoneurons. Our relationship-based methodologies provide an equivalent means by which the different perspectives can be compared, contrasted, and aggregated into a truly comprehensive viewpoint that can drive research forward.

The theoretical part of the Master´s thesis describes ab initio methods in quantum chemistry and semiempirical methods, which represents a way in overcoming of main disadvantages in ab initio methods (costs, speed). The experimental part was focused on comparison highly accurate CCSD(T) method with used semiempirical methods (AM1, PM3, PM6, and PM7). The data were mostly compared on small model systems with ions, which are an essential part of many biological systems. Furthermore, the applicability of semiempirical methods was examined for the description of intra- and intermolecular hydrogen bonds and van der Waals interactions.

Many diseases, such as cardiovascular diseases, cancer and diabetes, originate from several malfunctions in biological systems. The human body is regulated by a wide range of biological systems, composed of biological entities interacting in complex networks, responsible for carrying out specific functions. Some parts of the networks, such as hubs serving as master regulators, are more important for maintaining a function. To find the cause of diseases, where hubs are possible disease regulators, it is critical to know the structure of these biological systems. Such structures can be reverse engineered from high-throughput data with measured levels of biological entities. However, the complexity of biological systems makes inferring their structure a complicated task, demanding the use of computational methods, called network inference methods. Today, many network inference methods have been developed, that predicts the interactions of biological networks, with varying degree of success. In the DREAM5 challenge 35 network inference methods were evaluated on how well interactions in gene regulatory networks (GRNs) were predicted. Herein, in contrast to the DREAM5 challenge, we have evaluated network inference methods’ ability to predict hubs in GRNs. In accordance with the DREAM5 challenge, different methods performed the best on different data sets. Moreover, we discovered that network inference methods were not able to identify hubs from groups of similarly expressed genes. Also, we noticed that hubs in GRNs had a distinct expression in the data, leading to the development of a new method (the PCA method) for the prediction of hubs. Furthermore, the DREAM5 challenge showed that community predictions, combining the predictions from many network inference methods, resulted in more robust predictions of interactions. Herein, the community approach was applied on predicting hubs, with the conclusion that community predictions is the more robust approach. However, we also concluded that it was enough to combine 6-7 network inference methods to achieve robust predictions of hubs.

L’objectif de cette thèse est la modélisation et l’étude de systèmes biologiques par l’intermédiaire de méthodes formelles. Les systèmes biologiques démontrent des comportements continus mais sont aussi susceptibles de montrer des changements abrupts dans leur dynamiques. Les équations différentielles ordinaires, ainsi que les systèmes dynamiques hybrides, sont deux formalismes mathématiques utilisés pour modéliser clairement de tels comportements. Un point critique de la modélisation de systèmes biologiques est la recherche des valeurs des paramètres du modèle afin de reproduire de manière précise un ensemble de données expérimentales. Si aucun jeu de paramètres valides n’est trouvé, il est nécessaire de réviser le modèle. Une possibilité est alors de remplacer un paramètre, ou un ensemble de paramètres, définissant un processus biologique par une fonctiondépendante du temps.Dans le cadre de cette thèse, nous exposons tout d’abord une méthode pour la révision de modèles hybrides. Pour cela, nous proposons une approche gloutonne appliquée à une méthode de contrôle optimal utilisant les mesures d’occupations etla relaxation convexe. Ensuite, nous étudions comment analyser les propriétés dynamiques d’un modèle à temps discret en utilisant la simulation ensembliste. Dans cet objectif, nous proposons deux méthodes basées sur deux outils mathématiques.La première méthode, qui se repose sur les polynômes de Bernstein, est une extension aux systèmes dynamiques hybrides, de l’outil de calcul ensembliste Sapo [1]. La seconde méthode utilise les représentations de Krivine-Stengle [2] pour permettre l’analyse d’atteignaiblité de systèmes dynamiques polynomiaux. Enfin, nous proposons aussi une méthodologie pour générer des systèmes dynamiques hybrides modélisant des protocoles biologiques expérimentaux. Les méthodes précédemment proposées sont appliquées sur divers études biologiques. Nous étudions tout d’abord un modèle de la production d’hémoglobinedurant la différentiation des érythrocytes dans la moelle [3]. Pour permettre la construction de ce modèle, nous avons dans un premier temps généré un ensemble de jeux de paramètres valides à l’aide d’une méthode de type Monte-Carlo. Dans un second temps, nous avons appliqué la méthode de révision de modèle afin de reproduire plus précisément les données expérimentales [4]. Nous proposons aussi un modèle préliminaire des effets à faibles doses du Cadmium sur la réponse du métabolisme à différentes étapes de la vie d’un rat. Enfin, nous appliquons les techniques d’analyse ensembliste pour la validation d’hypothèses sur un modèle d’homéostasie du fer [6] dans le cas où des paramètres varient dans de larges intervalles.Dans cette thèse, nous montrons aussi que le protocole associé à l’étude de la production d’hémoglobine, ainsi que le protocole étudiant l’intégration du Cadmium durant la vie d’un rat, peuvent être formalisés comme des systèmes dynamiques hybrides, et servent ainsi de preuves de concepts pour notre méthode de modélisation de protocoles expérimentaux / The purpose of this thesis is the modelling and analysis of biological systems with mechanistic models (in opposition with black-box models).In particular we use two mathematical formalisms for mechanism modelling: hybrid dynamical systems and polynomial Ordinary Differential Equations (ODEs).Biological systems modelling give rise to numerous problem and in this work we address three of them.First, the parameters in the differential equations are often uncertain or unknown.Consequently, we aim at generating a subset of valid parameter sets such that the models satisfy constraints deducted from some experimental data.This problem is addressed in the literature under the denomination of parameter synthesis, parameter estimation, parameter fitting, or parameter identification following the context.Then, if no valid parameter is found, one solution is to revise the model. This can be done by substituting a law in place of a constant parameter.In the literature, models with uncertain parts are known as grey models, and their studies can be found under the term of model identification.Finally, it may be necessary to ensure the correctness of the built models using validation, or verification, methods for a continuous over-approximation of the determined valid parameters.In this thesis we study the parameter synthesis problem, in the Haemoglobin production model case study, using an adaptation of the classical method based on Monte-Carlo sampling, and numerical simulations.To perform model revision of hybrid dynamical systems we propose an iterative scheme of an optimal control method based on occupation measures, and convex relaxations.Finally, we assess the quality of a model using set-based simulations, and reachability analysis.For this purpose, we propose two methods: the first one, which relies on Bernstein expansion, is an extension for hybrid dynamical systems of the reachability tool sapo , while the other uses Krivine-Stengle representations to perform the reachability analysis of polynomial ODEs.We also provide a methodology to generate hybrid dynamical systems modelling biological experimental protocols.All of these proposed methods were applied in different case studies.We first propose a model of haemoglobin production during the differentiation of an erythrocyte in the bone marrow.To develop this model we first applied the Monte-Carlo based parameters synthesis, followed by the model revision to correctly fit to the experimental data.We also propose a hybrid model of Cadmium flux in rats in the context of an experimental protocol, as well as a preliminary study of the effect of low dose Cadmium on a Glucose response.Finally, we apply the reachability analysis techniques for the validation on large parameters set of the iron homoeostasis model developed by Nicolas Mobilia during his Phd.We note the haemoglobin production model, as well as the glucose reponse model can be formalised, in their experimental context, as hybrid dynamical systems. Thus, they serve as proof of concept for the methodology of biological experimental protocols modelling.

Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro / Neste trabalho apresentamos as etapas para a utilização do método da Programação Dinâmica, ou Princípio de Otimização de Bellman, para aplicações de controle ótimo. Investigamos a noção de funções de controle de Lyapunov (FCL) e sua relação com a estabilidade de sistemas autônomos com controle. Uma função de controle de Lyapunov deverá satisfazer a equação de Hamilton-Jacobi-Bellman (H-J-B). Usando esse fato, se uma função de controle de Lyapunov é conhecida, será então possível determinar a lei de realimentação ótima; isto é, a lei de controle que torna o sistema globalmente assintóticamente controlável a um estado de equilíbrio. Como aplicação, apresentamos uma modelagem matemática adequada a um problema de controle ótimo de certos sistemas biológicos. Este trabalho conta também com um breve histórico sobre o desenvolvimento da Teoria de Controle de forma a ilustrar a importância, o progresso e a aplicação das técnicas de controle em diferentes áreas ao longo do tempo. / This dissertation presents the steps for using the method of Dynamic Programming or Bellman Optimization Principle for optimal control applications. We investigate the notion of control-Lyapunov functions (CLF) and its relation to the stability of autonomous systems with control. A control-Lyapunov function must satisfy the Hamilton-Jacobi- Bellman equation (H-J-B). Using this fact, if a control-Lyapunov function is known, it is possible to determine the optimal feedback law, in other words, the control law which makes the system globally asymptotically controllable at an equilibrium state. As an application, we present a mathematical model suitable for an optimal control problem of certain biological systems. This dissertation also presents a brief historic about the development of the Control Theory in a way of illustrate the importance and the progress of the control techniques, specially where it can be applied, according to the diverse areas and different times that this techniques were discovered and used.

Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro / Neste trabalho apresentamos as etapas para a utilização do método da Programação Dinâmica, ou Princípio de Otimização de Bellman, para aplicações de controle ótimo. Investigamos a noção de funções de controle de Lyapunov (FCL) e sua relação com a estabilidade de sistemas autônomos com controle. Uma função de controle de Lyapunov deverá satisfazer a equação de Hamilton-Jacobi-Bellman (H-J-B). Usando esse fato, se uma função de controle de Lyapunov é conhecida, será então possível determinar a lei de realimentação ótima; isto é, a lei de controle que torna o sistema globalmente assintóticamente controlável a um estado de equilíbrio. Como aplicação, apresentamos uma modelagem matemática adequada a um problema de controle ótimo de certos sistemas biológicos. Este trabalho conta também com um breve histórico sobre o desenvolvimento da Teoria de Controle de forma a ilustrar a importância, o progresso e a aplicação das técnicas de controle em diferentes áreas ao longo do tempo. / This dissertation presents the steps for using the method of Dynamic Programming or Bellman Optimization Principle for optimal control applications. We investigate the notion of control-Lyapunov functions (CLF) and its relation to the stability of autonomous systems with control. A control-Lyapunov function must satisfy the Hamilton-Jacobi- Bellman equation (H-J-B). Using this fact, if a control-Lyapunov function is known, it is possible to determine the optimal feedback law, in other words, the control law which makes the system globally asymptotically controllable at an equilibrium state. As an application, we present a mathematical model suitable for an optimal control problem of certain biological systems. This dissertation also presents a brief historic about the development of the Control Theory in a way of illustrate the importance and the progress of the control techniques, specially where it can be applied, according to the diverse areas and different times that this techniques were discovered and used.

A modelagem farmacocinética permite prever a concentração de medicamentos em diferentes tecidos do organismo humano. O desenvolvimento de modelos matemáticos é importante para verificar a adequação de certos procedimentos realizados na administração de medicamentos. O objetivo deste trabalho é o desenvolvimento de um modelo farmacocinético capaz de prever a concentração plasmática de drogas no organismo para diversas formas de infusão. Foram utilizados dois tipos de abordagem. Inicialmente, na abordagem monocompartimental, considerou-se que a droga adentra ao organismo diretamente no compartimento sanguíneo, que representa todo o corpo humano. Já na abordagem bicompartimental foram considerados os seguintes compartimentos: um representando o meio pelo qual a droga é infundida no organismo (podendo ser via gastrointestinal, transdermal ou pulmonar) e outro representando o plasma sanguíneo. Em ambos os casos, foi considerada a hipótese de concentração homogênea da droga nos compartimentos em questão. O modelo foi estruturado na forma de diagramas de blocos e a solução foi feita com a utilização da Transformada de Laplace. Foi feita a validação dos modelos e verificou-se que os resultado gerados foram muito próximos dos resultados presentes na literatura. A utilização do modelo monocompartimental permitiu comparar os resultados da administração da mesma quantidade de droga por infusão constante e por infusão periódica. A análise dos resultados gerados pelo modelo mostrou que as concentrações atingidas pelos dois métodos não são as mesmas. O modelo bicompartimental permitiu simular administrações orais e transdermais, e inalação. Foi possível prever a concentração sanguínea após a interrupção da terapia com anti-concepcionais e anti-depressivos e foi verificado o tempo necessário para que esta concentração seja atingida novamente. Foram propostos métodos para que esta concentração fosse atingida em um menor período de tempo. Outra aplicação foi na comparação entre o tratamento com comprimidos inteiros e tomados pela metade em um intervalo menor de tempo. Verificou-se que a concentração atingida é diferente mesmo que a massa ingerida seja a mesma. O modelo também foi utilizado para calcular a concentração de nicotina após o consumo de cigarros e verificou-se que, o indivíduo que fuma a cada três horas não consegue eliminar totalmente a nicotina de seu organismo. Além disso, foi possível simular a sobredosagem de um anti-inflamatório e verificar o tempo em que a concentração fica acima do nível terapêutico. Foi proposto um método para obtenção do parâmetro farmacocinético relacionado à absorção, que pode ser obtido facilmente a partir de dados presentes nas bulas dos medicamentos. Este método é muito mais simples e preciso do que e proposto na literatura, que utiliza análise gráfica e dados clínicos que não são obtidos com tanta facilidade. / The pharmacokinetic modeling can predict the concentration of drug in different tissues of the human body. The development of mathematical models is an important tool to verify the appropriateness of certain procedures performed in medication administration. The objective of this work is to develop a pharmacokinetic model able to predict the plasma concentration of drug in the body after various forms of infusion. Two approaches were used. Initially, in the one-compartment approach it was considered that the drug enters the body directly into the blood compartment, which represents the entire human body. In the two-compartment approach it was considered the following compartments: one representing the means by which the drug is infused into the body (either via the gastrointestinal tract, lung, or transdermal) and one representing the blood plasma. In both cases, it was considered homogeneous concentration of the drug in the compartments. The model was built by using block diagrams and the solution was obtained using the Laplace Transform. The model was validated by comparing its results to literature data, with very good agreement. The model allowed comparing the one-compartment constant infusion of drug in the body with the periodic infusion. The analysis of the results generated by the model showed that the concentrations achieved by these methods are not the same. The two-compartment model allowed simulating oral and transdermal administration, and inhalation. It was possible to predict blood concentration after interruption of therapy with anti-depressants and anti-conceptional drugs. The model was able to verify the time it takes to reach the former level. Methods have been proposed to achieve the same concentration in a shorter period of time. Another application was the comparison of the treatment with whole tablets and taken by half in a smaller interval of time. It was found that the concentration achieved is different even though the same mass is ingested in both cases. The model was also used to calculate the concentration of nicotine after cigarette smoking and it was found that the individual who smokes every three hours, nicotine is not entirely eliminated from body. Furthermore, it was possible to simulate overdose of an anti-inflammatory and the period of time when the concentration is above the therapeutic level. It has been proposed a method to obtain pharmacokinetic parameter related to absorption, which can be easily obtained based on data present in the drug bull. This method is much simpler and more accurate than the method proposed in the, which uses graphical analysis and clinical data that are not so easy to be obtained.