Sulfones are useful bioisosteres in drug discovery, and have an unusual ability to engage in binding with both polar and nonpolar regions of target proteins. Despite this, they have seen limited use in drug-screening campaigns, compared with other functional groups. With the goal of generating a library of bicyclo[3.2.1]sulfone-containing molecules to probe biological function, a tandem 1,2-addition/anionic oxy-Cope/1,2-addition reaction proceeding from 3-sulfolene and discovered by previous members of our group was used to prepare highly substituted scaffolds for diversification. Functional group manipulations on this scaffold were partially successful, but ultimately provided limited scope for exploring three-dimensional space. Moving to a less-substituted bicyclo[3.2.1]sulfone scaffold that could be accessed using methodology developed by the Chou group, it was found that a greater range of chemical diversification could be achieved. Using both substrate-directed methods and intrinsic functional group reactivity, about 70% of the skeletal framework was functionalized with high levels of regioselectivity and (in some cases) good levels of diastereoselectivity. Chemoinformatic analysis was performed on our collection of synthesized bicyclo[3.2.1]sulfone-containing molecules, and diverse molecular descriptors were obtained. Collaborations were established with industrial partners and non-profit institutions for the purpose of determining biological properties in medicinally relevant areas. Significantly, each of these partners joined the project with therapeutic expertise in a different field (oncology, neurodegenerative diseases, antimicrobial agents, and skin inflammation), thereby maximizing the chances of finding useful lead compounds for future development. Preliminary biological screening data were obtained, which suggest future potential for sulfone-containing conformationally restricted small molecules to be impactful in therapeutic development. / Graduate

La sélection de sous-ensembles de molécules diverses est un enjeu très important de la recherche pharmaceutique. En effet, de la qualité de cette sélection, dépendra la découverte efficace d'un médicament. De nombreuses méthodes existent pour répondre à cette demande. Certaines sont basées sur la création de groupes de molécules, d'autres sur le principe de dissimilarité inter-moléculaire. Nous proposons dans ce travail, une nouvelle technique à la croisée de ces méthodes, qui permet d'obtenir des sous-ensembles à la fois divers dans l'espace et représentatifs de l'ensemble initial duquel ils sont extraits. Pour créer cette méthode de sélection, nous avons tout d'abord défini et formalisé mathématiquement un critère de diversité, puis nous nous sommes appuyés sur des heuristiques connues en apprentissage artificiel pour concevoir l'algorithme. Celui-ci a été comparé à d'autres types de sélections de diversité couramment utilisées en chémoinformatique telles que les k-medoïds, Maximum-Dissimilarity, Sphere-Exclusion. La formalisation du critère de diversité nous a enfin permis de proposer un nouveau critère d'évaluation de la qualité des sélections. La méthode et le critère présentées dans ce travail donnent des échantillons divers et représentatifs d'un espace chimique. / The selection of diverse molecules'subsets is a very important stake in the pharmaceutical research. Indeed, the effective discovery of a drug will depend of the quality of this selection. Several methods exist to address this problem. Some of them are based on the creation of groups of molecules, the others on the principle of dissimilarity between chemical compounds. In this work, we propose a new technique, between these two concepts, which allows to obtain subsets, at the same time, diverse in the space and representative from the initial set which they are extracted. First of all, to create this selection method, we defined and formalized mathematically a diversity criterion, then we used heuristics known in machine learning to conceive our algorithm. This one was compared with the other types of diversity selections usually used in chemoinformatic such as k-medoïds, Maximum-Dissimilarity, Sphere-Exclusion. The formalization of the diversity criterion finally allowed us to propose a new criterion of evaluation of the quality of the selections. The algorithm and the criterion presented in this work give diverse and representative samples of a chemical space.

L’énantiomérisme est un sujet majeur dans des domaines divers, en particulier celui de la pharmacie (principe actifs et métabolites). La commercialisation de médicaments sous forme racémique a longtemps été privilégiée au dépend d’éventuels effets secondaires. Depuis les années 90, le développement du nombre d’entités énantiomériquement pures est en hausse, notamment grâce au développement des techniques de séparation. La chromatographie chirale, à l’aide de support énantiosélectif, s’est très vite imposée pour la résolution rapide et directe de stéréoisomères. Cependant, la faible compréhension des mécanismes intervenant dans la reconnaissance chirale limite un choix rationnel reposant sur la structure des composés chiraux, conduisant à une procédure de développement de méthode de séparation chirale systématique, fastidieuse et coûteuse d’essais et erreurs. Ce projet de recherche se situe à l’interface des sciences analytiques et de la chémo-informatique. Il consiste en une amélioration de nos connaissances sur le mécanisme de reconnaissance chirale afin d’aborder le développement de méthode chirale de manière plus rigoureuse. / The subject of enantiomer separation is a major issue in various fields, particularly pharmaceuticals (metabolites and active principle). To prepare pharmaceutical formulations, the racemic form has long been favored at the expense of possible side effects. Since the 90s, the development of the number of enantiomerically pure entities is rising, particularly through the development of separation techniques. Chiral chromatography using enantioselective stationary phases is an excellent technique for fast and direct resolution of stereoisomers. However, limited understanding of the mechanism leads to tedious and expensive trial-and-error systematic chiral method development. No clear guideline for choosing a chromatographic system is available for a new chiral drug. In this project, we wish to achieve a better knowledge of enantioselective separation techniques in order to help in the choice of separation method that will be the most appropriate for any given chiral separation. This project is based on the rationalization of large amounts of experimental data with the help of modelling and chemometric techniques to unravel the enantioselective recognition mechanism.

La chémoinformatique et la bioinformatique sont des disciplines devenues indispensables à la découverte de médicaments. De nos jours, les industries pharmaceutiques consacrent près de 10% de leur budget de recherche et développement, à la recherche de médicaments assisté par ordinateur (Kapetanovic 2008). Cette émergence peut s’expliquer à la fois par le développement des architectures de calculs mais aussi par le faible coup qu’engendrent des analyses in silico par rapport à des tests in-vitro.Les essais biologiques qui ont été menés depuis des décennies afin d’identifier des médicaments potentiels, commencent à former une source très importante de données et plusieurs bases de données commencent à les répertorier. La disponibilité de ce type de données a favorisé le développement d’un nouvel axe de recherche appelé la "chémogénomique" et qui s’intéresse à l’étude et à l’identification des associations possibles entre plusieurs molécules et plusieurs cibles. Ainsi, la chémogénomique permet de déterminer le profil biologique d’une molécule et nous renseigne sur sa capacité à devenir une touche intéressante mais aussi à identifier ses possibles effets indésirables. Des méthodes de chémoinformatique permettent d’utiliser ces sources de données à des fins d’apprentissage et établir des modèles prédictifs qui permettront par la suite de faire des prédictions pour connaitre l’activité d’une molécule.Cette thèse a porté sur le développement et l'utilisation de méthodes de prédictions d’association protéine-ligand. La prédiction d’une association est importante en vue d’un criblage virtuel et peut s’effectuer à l’aide de plusieurs méthodes. Au sein du laboratoire, on s’intéresse plus particulièrement au profilage de bases de données de molécules (chimiothèques) contre une série de cibles afin d’établir leur profil biologique. J’ai donc essayé au cours de ma thèse de mettre au point des modèles prédictifs d’association protéine-ligand pour un grand nombre de cibles, valider des méthodes de criblage virtuel récentes à des fins de profilage mais aussi établir un protocole de profilage automatisé, qui décide du choix de la méthode de criblage la plus adaptée en s’appuyant sur les propriétés physico-chimiques du ligand à profiler et de l’éventuelle cible. / Chemoinformatics and bioinformatics methods are now necessary in every drug discovery program. Pharmaceutical industries dedicate more than 10% of their research and development investment in computer aided drug design (Kapetanovic 2008). The emergence of these tools can be explained by the increasing availability of high performance calculating machines and also by the low cost of in silico analysis compared to in vitro tests.Biological tests that were performed over last decades are now a valuable source of information and a lot of databases are trying to list them. This huge amount of information led to the birth of a new research field called “chemogenomics”. The latter is focusing on the identification of all possible associations between all possible molecules and all possible targets. Thus, using chemogenomics approaches, one can obtain a biological profile of a molecule and even anticipate possible side effects.This thesis was focused on the development of approaches that aim to predict the binding of molecules to targets. In our lab, we focus on profiling molecular databases in order to get their full biological profile. Thus, my main work was related to this context and I tried to develop predictive models to assess the binding of ligands to proteins, to validate some virtual screening methods for profiling purpose, and finally, I developed an automatic hybrid profiling workflow that selects the best fitted virtual screening approach to use according the ligand/target context.

A doença de Chagas, causada pelo Trypanosoma cruzi, é uma doença tropical que aflige milhões de pessoas, gerando consequências sócio-econômicas devastadoras. Ela tem sido considerada uma doença tropical super-negligenciada, já que os únicos fármacos disponíveis para o seu tratamento apresentam baixa eficácia e causam vários efeitos colaterais. Além disso, os mesmos foram introduzidos há mais de três décadas. Com esse cenário, é evidente a necessidade da descoberta, desenvolvimento e introdução de novos fármacos para o tratamento eficiente e seguro da doença de Chagas. A enzima gliceraldeído-3-fosfato desidrogenase (GAPDH) é um alvo biomacromolecular atraente para a descoberta de novos fármacos contra os tripanossomatídeos, em virtude das enzimas da via glicolítica exercerem um papel fundamental no fornecimento de energia para a sobrevivência do parasito. Essa enzima foi selecionada neste trabalho de tese para a realização de estudos em química medicinal com base em quiminformática com o objetivo de identificar potenciais inibidores enzimáticos e do T. cruzi. Na primeira etapa desta tese, o ensaio virtual baseado na estrutura do alvo (SBVS) foi usado na identificação e seleção dos compostos. Como resultado do planejamento in silico, vinte compostos foram selecionados e avaliados experimentalmente na segunda etapa do trabalho empregando a técnica de calorimetria de titulação isotérmica (ITC). Destes, onze inibiram a GAPDH de T. cruzi resultando numa elevada taxa de acerto (>= 20%). Os novos inibidores apresentam excelente eficiência do ligante (LE), bem como mostram ligeira seletividade pela enzima do parasito. O ensaio dos inibidores contra a forma tripomastigota do T. cruzi identificou dois compostos capazes de inibir essa forma infectiva e um deles também mostrou ser um potente inibidor da forma amastigota do parasito, além de apresentar baixa toxidez. As duas melhores classes de inibidores da GAPDH e do parasito foram selecionadas para o estabelecimento de relações quantitativas entre a estrutura química e a atividade biológica (QSAR). Estudos de QSAR 2D (HQSAR) forneceram modelos com elevada capacidade preditiva e proporcionaram a identificação de características estruturais importantes para a otimização dos ligantes a compostos-matrizes. / Chagas disease, caused by Trypanosoma cruzi, is a tropical disease, which afflicts millions of people, thus generating devastating socio-economic consequences. It has been pointed out that it is a super-neglected tropical disease, based on available drugs with low efficacy and that give rise to many side effects. In addition, these drugs were introduced three decades ago. With this scenario, it is clear the necessity of the discovery, development and introduction of new efficient drugs to treat Chagas disease. The enzyme glyceraldehyde-3-phosphate dehydrogenase is a promising target for the development of new drugs against trypanosomatides, since the enzymes of the glycolytic pathway display a fundamental role in the energy supply to parasite survival. In this thesis, this enzyme was selected for medicinal chemistry within the cheminformatics framework aiming at the identification of potential enzymatic and parasite inhibitors. In the first part, structure-based virtual screening (SBVS) methods were employed in the selection and identification of compounds. Based on the in silico design, twenty compounds were selected and experimentally evaluated in the second part using the isothermal titration calorimetry (ITC) technique. Out of these, eleven compounds inhibited the T. cruzi GAPDH, resulting in high hit rates (>= 20 %). The new selected inhibitors display excellent ligand efficiency (LE), as well as some selectivity for the parasite enzyme. The inhibitors assay against the trypomastigote form of T. cruzi was used to identify two compounds able to inhibit this infective form, and one showed to be a strong amastigote parasite inhibitor, also disclosing low cytotoxicity profile. The best two classes of GAPDH and parasite inhibitors were selected for the establishment of a quantitative structure-activity relationship (QSAR). 2D QSAR (HQSAR) studies resulted in linear models with high predictive power, amenable for the identification of important structural features in the process of hit-to-lead optimization.

A Química Medicinal é ciência multidisciplinar com ação direta sobre conhecimentos específicos focalizando Química, Biologia, Medicina, Fisiologia, entre outras áreas de estudos no domínio fundamental e tecnológico. Esta ciência atua ainda entre várias interfaces científicas tais como a Bioquímica, Biofísica, Biologia Molecular, Química Biológica e outras. <br /> A investigação no metabolismo de fármacos é a primeira e essencial fase na moderna farmacologia, uma vez que os parâmetros Farmacocinéticos são os mais relevantes dados iniciais a serem considerados no início da Fase 3 em testes clínicos com humanos. Em sua totalidade os dados farmacocinéticos são conhecidos como ADME (Absorção, Distribuição, Metabolismo e Excreção) e, ao lado dos Parâmetros Toxicológicos responde por no mínimo 70 % das avaliações finais negativas de fármacos (não recomendáveis) durante a Fase 3 em testes clínicos com humanos. <br /> Esta dissertação emprega Métodos Quiminformáticos para obter parâmetros metabólicos de fármacos conhecidos com o objetivo de se chegar a modelos de metabolismo preditivos baseados em correlações entre estrutura e atividade e, por intermédio desta avaliação, desenvolver abordagem similar para fármacos desconhecidos tentando obter modelos metabólicos preditivos baseando-se em correlações de estrutura e reatividade, envolvendo as enzimas citocromo P450 dentro do grupo de enzimas CYP1A2, CYP2C9, CYP2C19, CYP2D6 e CYP3A4. A validação do modelo in silico foi desenvolvida por meio de estudos comparativos de perfis metabólicos empregando-se o critério de superposição de dados de compostos com estruturas de referência que mostrem as melhores correlações de estrutura e reatividade considerando enzimas CYP2C9, CYP2D6 e CYP3A4. <br /> Os modelos obtidos podem ser muito úteis na previsão de metabolismo considerando enzimas CYP2C9, CYP2D6 e CYP3A4 para novos tipos de possíveis fármacos, pois, o comportamento referente a tendências de metabolismo de novas entidades químicas pode levar a análises por antecipação de reações enzimáticas. Certamente, este estudo preditivo na Fase I do estudo de fármacos na farmacoterapia reduzirá drasticamente o perfil temporal e o impacto de custos no desenvolvimento de novas substâncias bio-ativas no planejamento da gênese de novos fármacos. / The Medicinal Chemistry is a multidisciplinary science with direct action over specific knowledge focusing Chemistry, Biology, Medicine, Physiology, among others domains of fundamental and technologic studies. This science also acts between several scientific interfaces like Biochemistry, Biophysics, Molecular Biology, Biologic Chemistry, and others <br /> The investigation on drugs metabolism is the first and essential phase on modern Pharmacotherapy since the pharmacokinetics\' parameters are the most relevant impute on the beginning of the Phase 3 on Human Clinical Tests. <br /> The overall pharmacokinetics data base is known as ADME (Absorption, Distribution, Metabolism and Excretion) and side by side with the Toxicological Parameters responds at least of 70 % of the total final evaluation of drugs negatively evaluated (not recommended) during the Phase 3 on Human Clinical Tests. <br /> This dissertation employs Chemoinformatic Methods to obtain metabolic parameters of known drugs with the mean objective of to ends up a predictive metabolic pattern based on correlation of structure and activity, and by mean of this evaluation, to perform similar approaches on unknown drugs trying to get predictive metabolic pattern based on correlation of structure and reactivity, involving the cytochrome enzymes P450 on the group of CYP1A2, CYP2C9, CYP2C19, CYP2D6 e CYP3A4. The pattern in silico validation was developed by mean of a comparative studies of metabolic profile ad by using the superposition criteria of the reference structures compounds data base having better correlation of structure and reactivity considering the enzymes CYP2C9, CYP2D6 e CYP3A4. <br /> The obtained pattern can be useful on metabolism prediction considering enzymes CYP2C9, CYP2D6 e CYP3A4 for new kinds of possible drugs, since this behavior concerning metabolic trends of newer chemical entities can arise anticipated analysis of enzymatic reactions. Surely, this predictive studies on Phase 1 of drugs on Pharmacotherapy will reduces drastically the time profile and the costs impacts on developing of new bioactive substances on planning genesis of new drugs.

A doença de Chagas, causada pelo Trypanosoma cruzi, é uma doença tropical que aflige milhões de pessoas, gerando consequências sócio-econômicas devastadoras. Ela tem sido considerada uma doença tropical super-negligenciada, já que os únicos fármacos disponíveis para o seu tratamento apresentam baixa eficácia e causam vários efeitos colaterais. Além disso, os mesmos foram introduzidos há mais de três décadas. Com esse cenário, é evidente a necessidade da descoberta, desenvolvimento e introdução de novos fármacos para o tratamento eficiente e seguro da doença de Chagas. A enzima gliceraldeído-3-fosfato desidrogenase (GAPDH) é um alvo biomacromolecular atraente para a descoberta de novos fármacos contra os tripanossomatídeos, em virtude das enzimas da via glicolítica exercerem um papel fundamental no fornecimento de energia para a sobrevivência do parasito. Essa enzima foi selecionada neste trabalho de tese para a realização de estudos em química medicinal com base em quiminformática com o objetivo de identificar potenciais inibidores enzimáticos e do T. cruzi. Na primeira etapa desta tese, o ensaio virtual baseado na estrutura do alvo (SBVS) foi usado na identificação e seleção dos compostos. Como resultado do planejamento in silico, vinte compostos foram selecionados e avaliados experimentalmente na segunda etapa do trabalho empregando a técnica de calorimetria de titulação isotérmica (ITC). Destes, onze inibiram a GAPDH de T. cruzi resultando numa elevada taxa de acerto (>= 20%). Os novos inibidores apresentam excelente eficiência do ligante (LE), bem como mostram ligeira seletividade pela enzima do parasito. O ensaio dos inibidores contra a forma tripomastigota do T. cruzi identificou dois compostos capazes de inibir essa forma infectiva e um deles também mostrou ser um potente inibidor da forma amastigota do parasito, além de apresentar baixa toxidez. As duas melhores classes de inibidores da GAPDH e do parasito foram selecionadas para o estabelecimento de relações quantitativas entre a estrutura química e a atividade biológica (QSAR). Estudos de QSAR 2D (HQSAR) forneceram modelos com elevada capacidade preditiva e proporcionaram a identificação de características estruturais importantes para a otimização dos ligantes a compostos-matrizes. / Chagas disease, caused by Trypanosoma cruzi, is a tropical disease, which afflicts millions of people, thus generating devastating socio-economic consequences. It has been pointed out that it is a super-neglected tropical disease, based on available drugs with low efficacy and that give rise to many side effects. In addition, these drugs were introduced three decades ago. With this scenario, it is clear the necessity of the discovery, development and introduction of new efficient drugs to treat Chagas disease. The enzyme glyceraldehyde-3-phosphate dehydrogenase is a promising target for the development of new drugs against trypanosomatides, since the enzymes of the glycolytic pathway display a fundamental role in the energy supply to parasite survival. In this thesis, this enzyme was selected for medicinal chemistry within the cheminformatics framework aiming at the identification of potential enzymatic and parasite inhibitors. In the first part, structure-based virtual screening (SBVS) methods were employed in the selection and identification of compounds. Based on the in silico design, twenty compounds were selected and experimentally evaluated in the second part using the isothermal titration calorimetry (ITC) technique. Out of these, eleven compounds inhibited the T. cruzi GAPDH, resulting in high hit rates (>= 20 %). The new selected inhibitors display excellent ligand efficiency (LE), as well as some selectivity for the parasite enzyme. The inhibitors assay against the trypomastigote form of T. cruzi was used to identify two compounds able to inhibit this infective form, and one showed to be a strong amastigote parasite inhibitor, also disclosing low cytotoxicity profile. The best two classes of GAPDH and parasite inhibitors were selected for the establishment of a quantitative structure-activity relationship (QSAR). 2D QSAR (HQSAR) studies resulted in linear models with high predictive power, amenable for the identification of important structural features in the process of hit-to-lead optimization.

A Química Medicinal é ciência multidisciplinar com ação direta sobre conhecimentos específicos focalizando Química, Biologia, Medicina, Fisiologia, entre outras áreas de estudos no domínio fundamental e tecnológico. Esta ciência atua ainda entre várias interfaces científicas tais como a Bioquímica, Biofísica, Biologia Molecular, Química Biológica e outras. <br /> A investigação no metabolismo de fármacos é a primeira e essencial fase na moderna farmacologia, uma vez que os parâmetros Farmacocinéticos são os mais relevantes dados iniciais a serem considerados no início da Fase 3 em testes clínicos com humanos. Em sua totalidade os dados farmacocinéticos são conhecidos como ADME (Absorção, Distribuição, Metabolismo e Excreção) e, ao lado dos Parâmetros Toxicológicos responde por no mínimo 70 % das avaliações finais negativas de fármacos (não recomendáveis) durante a Fase 3 em testes clínicos com humanos. <br /> Esta dissertação emprega Métodos Quiminformáticos para obter parâmetros metabólicos de fármacos conhecidos com o objetivo de se chegar a modelos de metabolismo preditivos baseados em correlações entre estrutura e atividade e, por intermédio desta avaliação, desenvolver abordagem similar para fármacos desconhecidos tentando obter modelos metabólicos preditivos baseando-se em correlações de estrutura e reatividade, envolvendo as enzimas citocromo P450 dentro do grupo de enzimas CYP1A2, CYP2C9, CYP2C19, CYP2D6 e CYP3A4. A validação do modelo in silico foi desenvolvida por meio de estudos comparativos de perfis metabólicos empregando-se o critério de superposição de dados de compostos com estruturas de referência que mostrem as melhores correlações de estrutura e reatividade considerando enzimas CYP2C9, CYP2D6 e CYP3A4. <br /> Os modelos obtidos podem ser muito úteis na previsão de metabolismo considerando enzimas CYP2C9, CYP2D6 e CYP3A4 para novos tipos de possíveis fármacos, pois, o comportamento referente a tendências de metabolismo de novas entidades químicas pode levar a análises por antecipação de reações enzimáticas. Certamente, este estudo preditivo na Fase I do estudo de fármacos na farmacoterapia reduzirá drasticamente o perfil temporal e o impacto de custos no desenvolvimento de novas substâncias bio-ativas no planejamento da gênese de novos fármacos. / The Medicinal Chemistry is a multidisciplinary science with direct action over specific knowledge focusing Chemistry, Biology, Medicine, Physiology, among others domains of fundamental and technologic studies. This science also acts between several scientific interfaces like Biochemistry, Biophysics, Molecular Biology, Biologic Chemistry, and others <br /> The investigation on drugs metabolism is the first and essential phase on modern Pharmacotherapy since the pharmacokinetics\' parameters are the most relevant impute on the beginning of the Phase 3 on Human Clinical Tests. <br /> The overall pharmacokinetics data base is known as ADME (Absorption, Distribution, Metabolism and Excretion) and side by side with the Toxicological Parameters responds at least of 70 % of the total final evaluation of drugs negatively evaluated (not recommended) during the Phase 3 on Human Clinical Tests. <br /> This dissertation employs Chemoinformatic Methods to obtain metabolic parameters of known drugs with the mean objective of to ends up a predictive metabolic pattern based on correlation of structure and activity, and by mean of this evaluation, to perform similar approaches on unknown drugs trying to get predictive metabolic pattern based on correlation of structure and reactivity, involving the cytochrome enzymes P450 on the group of CYP1A2, CYP2C9, CYP2C19, CYP2D6 e CYP3A4. The pattern in silico validation was developed by mean of a comparative studies of metabolic profile ad by using the superposition criteria of the reference structures compounds data base having better correlation of structure and reactivity considering the enzymes CYP2C9, CYP2D6 e CYP3A4. <br /> The obtained pattern can be useful on metabolism prediction considering enzymes CYP2C9, CYP2D6 e CYP3A4 for new kinds of possible drugs, since this behavior concerning metabolic trends of newer chemical entities can arise anticipated analysis of enzymatic reactions. Surely, this predictive studies on Phase 1 of drugs on Pharmacotherapy will reduces drastically the time profile and the costs impacts on developing of new bioactive substances on planning genesis of new drugs.

New experimental strategies and algorithmic approaches were devised and tested to improve the analysis of pharmaceutically relevant materials. These new methods were developed to address key bottlenecks in the design of amorphous solid dispersions for the delivery of low-solubility active pharmaceutical ingredients in the final dosage forms exhibiting high bioavailability. <br>