Estudos de relações preditivas entre a estrutura e a atividade para inibição enzimática e analgesia /

Tese (Doutorado) - Universidade Federal de Santa Catarina, Centro de Ciências Físicas e Matemáticas

Structural and chemical basis for anticancer activity of a series of 'beta'-tubulin ligands: molecular modeling and 3D QSAR studies

An important approach to cancer therapy is the design of small molecule modulators that interfere with microtubule dynamics through their specific binding to the ²-subunit of tubulin. In the present work, comparative molecular field analysis (CoMFA) studies were conducted on a series of discodermolide analogs with antimitotic properties. Significant correlation coefficients were obtained (CoMFA(i), q² =0.68, r²=0.94; CoMFA(ii), q² = 0.63, r²= 0.91), indicating the good internal and external consistency of the models generated using two independent structural alignment strategies. The models were externally validated employing a test set, and the predicted values were in good agreement with the experimental results. The final QSAR models and the 3D contour maps provided important insights into the chemical and structural basis involved in the molecular recognition process of this family of discodermolide analogs, and should be useful for the design of new specific ²-tubulin modulators with potent anticancer activity.Uma estratégia importante para a terapia do câncer é o planejamento de modulares que interferem na dinâmica dos microtúbulos através de sua ligação específica à subunidade ² da tubulina. No presente trabalho, estudos de análise comparativa dos campos moleculares (CoMFA) foram realizados com uma série de análogos do discodermolídeo com ação antimitótica. Resultados significativos foram obtidos (CoMFA(i), q² =0,68, r² =0,94; CoMFA(ii), q² = 0,63, r² =0,91), indicando a elevada consistência interna e externa dos modelos gerados empregando duas estratégias independentes de alinhamento estrutural. Os modelos foram validados externamente com um conjunto teste e os valores preditos apresentaram boa concordância com os resultados experimentais. Os modelos de QSAR e os mapas de contorno 3D forneceram importantes informações sobre as bases químicas e estruturais envolvidas no processo de reconhecimento molecular dessa família de análogos do discodermolídeo, sendo uma valiosa ferramenta no planejamento de novos moduladores específicos da ²-tubulina com potente atividade antitumoral.Conselho Nacional Desenvolvimento Científico e Technológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Fragment-based QSAR: perspectives in drug design

Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure–activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design

Integrating virtual and high-throughput screening: opportunities and challenges in drug research and development

High-throughput screening (HTS) and virtual screening (VS) are useful methods employed in drug discovery, allowing the identification of promising hits for lead optimization. The efficiency of these approaches depends on a number of factors, such as the organization of high quality databases of compounds and the parameterization of essential components of the screen process. This brief review presents the basic principles of the HTS and VS methods, as well as a perspective of the utility and integration of these drug design approaches, highlighting current opportunities and future challenges in medicinal chemistry.FAPESPCNP

Descriptor-and fragment-based QSAR models for a series of Schistosoma mansoni purine nucleoside inhibitors

The enzyme purine nucleoside phosphorylase from Schistosoma mansoni (SmPNP) is an attractive molecular target for the treatment of major parasitic infectious diseases, with special emphasis on its role in the discovery of new drugs against schistosomiasis, a tropical disease that affects millions of people worldwide. In the present work, we have determined the inhibitory potency and developed descriptor- and fragment-based quantitative structure-activity relationships (QSAR) for a series of 9-deazaguanine analogs as inhibitors of SmPNP. Significant statistical parameters (descriptor-based model: r² = 0.79, q² = 0.62, r²pred = 0.52; and fragment-based model: r² = 0.95, q² = 0.81, r²pred = 0.80) were obtained, indicating the potential of the models for untested compounds. The fragment-based model was then used to predict the inhibitory potency of a test set of compounds, and the predicted values are in good agreement with the experimental resultsA enzima purina nucleosídeo fosforilase de Schistosoma mansoni (SmPNP) é um alvo molecular atrativo para o tratamento de importantes doenças infecciosas parasitárias, com especial ênfase para o seu papel na descoberta de novos fármacos contra a esquistossomose, uma doença tropical que afeta cerca de 200 milhões de pessoas em 74 áreas endêmicas no mundo todo. No presente trabalho, a potência inibitória foi determinada e estudos das relações quantitativas entre a estrutura e atividade (QSAR), baseados em descritores e fragmentos, foram desenvolvidos para uma série de 9-deazaguaninas que atuam como inibidores da SmPNP. Parâmetros estatísticos significantes (modelo baseado em descritor: r² = 0,79; q² = 0,62, r²pred = 0,52; e modelo baseado em fragmento: r² = 0,95; q² = 0,81; r²pred = 0,80) foram obtidos, indicando o potencial dos modelos para compostos ainda não testados. O modelo baseado em fragmento foi então usado para predizer a potência inibitória de um conjunto teste de compostos, e os valores preditos estão em boa concordância com os resultados experimentais.Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB)(FAPESP) São Paulo Research Foundation(CNPq) National Council for Scientific and Technological Developmen

Structure-based drug design strategies in medicinal chemistry

A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of\ud leads, as well as to accelerate the development of high quality drug candidates. Structure-based drug design (SBDD)\ud methods are becoming increasingly powerful, versatile and more widely used. This review summarizes current\ud developments in structure-based virtual screening and receptor-based pharmacophores, highlighting achievements as well\ud as challenges, along with the value of structure-based lead optimization, with emphasis on recent examples of successful\ud applications for the identification of novel active compounds.CNPqFAPES

Structure-based drug design studies on a series of aldolase inhibitors

Human African trypanosomiasis, also known as sleeping sickness, is a major cause of death in Africa, and for which there are no safe and effective treatments available. The enzyme aldolase from Trypanosoma brucei is an attractive, validated target for drug development. A series of alkyl‑glycolamido and alkyl-monoglycolate derivatives was studied employing a combination of drug design approaches. Three-dimensional quantitative structure-activity relationships (3D QSAR) models were generated using the comparative molecular field analysis (CoMFA). Significant results were obtained for the best QSAR model (r2 = 0.95, non-cross-validated correlation coefficient, and q2 = 0.80, cross-validated correlation coefficient), indicating its predictive ability for untested compounds. The model was then used to predict values of the dependent variables (pKi) of an external test set,the predicted values were in good agreement with the experimental results. The integration of 3D QSAR, molecular docking and molecular dynamics simulations provided further insight into the structural basis for selective inhibition of the target enzyme.FAPESPCNP

Structure- and ligand-based drug design approaches for neglected tropical diseases

Drug discovery has moved toward more rational strategies based on our increasing understanding of the fundamental principles of protein-ligand interactions. Structure( SBDD) and ligand-based drug design (LBDD) approaches bring together the most powerful concepts in modern chemistry and biology, linking medicinal chemistry with structural biology. The definition and assessment of both chemical and biological space have revitalized the importance of exploring the intrinsic complementary nature of experimental and computational methods in drug design. Major challenges in this field include the identification of promising hits and the development of high-quality leads for further development into clinical candidates. It becomes particularly important in the case of neglected tropical diseases (NTDs) that affect disproportionately poor people living in rural and remote regions worldwide, and for which there is an insufficient number of new chemical entities being evaluated owing to the lack of innovation and R&D investment by the pharmaceutical industry. This perspective paper outlines the utility and applications of SBDD and LBDD approaches for the identification and design of new small-molecule agents for NTDs.State of Sao Paulo Research Foundation (FAPESP)State of Sao Paulo Research Foundation (FAPESP)National Council for Scientific and Technological Development (CNPq), BrazilNational Council for Scientific and Technological Development (CNPq), Brazi

A fragment-based approach for the in silico prediction of blood-brain barrier permeation

Blood-brain barrier (BBB) permeation is an essential property for drugs that act in the central nervous system (CNS) for the treatment of human diseases, such as epilepsy, depression, Alzheimer's disease, Parkinson disease, schizophrenia, among others. In the present work, quantitative structure-property relationship (QSPR) studies were conducted for the development and validation of in silico models for the prediction of BBB permeation. The data set used has substantial chemical diversity and a relatively wide distribution of property values. The generated QSPR models showed good statistical parameters and were successfully employed for the prediction of a test set containing 48 compounds. The predictive models presented herein are useful in the identification, selection and design of new drug candidates having improved pharmacokinetic properties

Modern drug discovery technologies: opportunities and challenges in lead discovery

The identification of promising hits and the generation of high quality leads are crucial steps in the early stages of drug discovery projects. The definition and assessment of both chemical and biological space have revitalized the screening process model and emphasized the importance of exploring the intrinsic complementary nature of classical and\ud modern methods in drug research. In this context, the widespread use of combinatorial chemistry and sophisticated screening methods for the discovery of lead compounds has created a large demand for small organic molecules that act on specific drug targets.\ud Modern drug discovery involves the employment of a wide variety of technologies and expertise in multidisciplinary research teams. The synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds emphasize the importance of the integration of advanced technologies in drug discovery programs. These technologies (VS, HTS, SBDD, LBDD, QSAR, and so on) are complementary in the sense that they have mutual goals, thereby the combination of both empirical and in silico efforts is feasible at many different levels of lead optimization and new chemical entity (NCE) discovery. This paper provides a brief perspective on the evolution and use of key drug design technologies, highlighting opportunities and challenges