Modelo de Tomada de Decisão de Kortland no Delineamento de Atividade Didática para o Ensino de Bioquímica

A aplicação da metodologia de estudo de casos no ensino de ciências tem sido alvo de atenção de vários educadores nos últimos anos. No presente trabalho, relatamos a aplicação de uma atividade didática dessa natureza em disciplina de Bioquímica, oferecida no curso de Bacharelado em Química do Instituto de Química de São Carlos, da Universidade de São Paulo. Para que a atividade fosse levada a cabo fez-se necessária, inicialmente, a produção de um caso investigativo, denominado O Mal do Século, que aborda a obesidade infantil. A resolução do caso foi apresentada pelos alunos no formato de relatório, cujo roteiro foi construído com base no Modelo de Tomada de Decisão de Kortland (1996). Nessa perspectiva, os estudantes pesquisaram e analisaram múltiplas fontes de dados fazendo uso de critérios desenvolvidos por eles para solucionar o casoThe use of case-study method in science education has been the focus of attention of many educators in recent years. In this work we describe a didactic activity based on this method in a biochemistry discipline offered to undergraduate chemistry students at the São Carlos Institute of Chemistry, University of São Paulo. Thus, we developed an investigative case entitled The Plague of the Century, which addresses childhood obesity. In order to solve the case, the students wrote a formal report according to the script produced by us, based on Kortland’s Model of a Decision-making Procedure (1996). In view of this, the students researched and evaluated multiple sources of data, using criteria developed by them to solve the cas

Structure-Property Optimization of a Series of Imidazopyridines for Visceral Leishmaniasis

Leishmaniasis is a collection of diseases caused by more than 20 Leishmania parasite species that manifest as either visceral, cutaneous, or mucocutaneous leishmaniasis. Despite the significant mortality and morbidity associated with leishmaniasis, it remains a neglected tropical disease. Existing treatments have variable efficacy, significant toxicity, rising resistance, and limited oral bioavailability, which necessitates the development of novel and affordable therapeutics. Here, we report on the continued optimization of a series of imidazopyridines for visceral leishmaniasis and a scaffold hop to a series of substituted 2-(pyridin-2-yl)-6,7-dihydro-5H-pyrrolo[1,2-a]imidazoles with improved absorption, distribution, metabolism, and elimination properties

Molecular design, synthesis and evaluation of cruzain inhibitors and antitrypanosomal agents based on imidazopyridines

No capítulo 1, a modelagem HQSAR, a docagem e os estudos de ROCS foram construídos utilizando uma série de 57 inibidores de cruzaína. O melhor modelo HQSAR (q2 = 0,70, r2 = 0,95, r2test = 0,62, q2rand. = 0,09 and r2rand. = 0,26) foi utilizado para predizer a potência de 121 compostos extraídos da literatura (conjunto de dados V1), resultando em um valor de r2 satisfatório de 0,65 para essa validação externa. Uma validação externa adicional foi empregada utilizando uma série de 1223 compostos extraído dos bancos de dados ChEMBL e CDD (conjunto de dados V3); nessa validação externa o valor de AUC (área sob a curva) para a curva ROC foi de 0,70. Os mapas de contribuição, obtidos para o melhor modelo HQSAR 3.4, estão de acordo com as predições do modo de interação e com as bioatividades dos compostos estudados. Nos estudos de ROCS, a forma molecular utilizada como filtro, foi útil na rápida identificação de modificações moleculares promissoras para inibidores de cruzaína. O valor de AUC obtido com a curva ROC foi de 0,72, isso indica que o método foi muito eficiente na distinção entre inibidores ativos e inativos da enzima cruzaína. Em seguida, o melhor modelo HQSAR foi utilizado para predizer os valores de pIC50 para novos compostos. Alguns dos compostos identificados, utilizando esse método, demonstraram valores de potência calculada maior do que a série de treinamento em estudo. No capítulo 2, os efeitos sobre a potência na inibição da enzima cruzaína pela substituição de um grupo nitrila como warhead por outros grupos foi avaliada. Com a síntese de 20 compostos do tipo dipeptidil, avaliou-se a relação estrutura-atividade (SAR), baseado na troca do grupo warhead na porção P1\'. O grupo oxima foi mais potente que o grupo correspondente nitrila em 0,7 unidades logarítmicas. Os compostos do tipo dipeptidil aldeídos e azanitrila obtiveram potências mais elevadas do que o correspondente dipeptidil nitrila em duas de magnitude. Os compostos dipeptidil alfa-beta insaturados foram menos potentes do que o correspondente dipeptidil nitrila. No capítulo 3, estratégias de química medicinal foram empregadas nas sínteses de 23 novos análogos, contendo o esqueleto básico de imidazopiridina. Sete e doze compostos sintetizados exibiram EC50 <= 1µM in vitro contra os parasitos Tripanosoma cruzi (T. cruzi) e brucei (T. brucei), respectivamente. Com os resultados promissores de atividade biológica in vitro, citotoxicidade, estabilidade metabólica, ligação proteica e propriedades farmacocinéticas, o composto 41 foi selecionado como candidato para os estudos de eficácia in vivo. Esse composto foi submetido em um modelo agudo da infecção com T. cruzi em ratos (cepa Tulahuen). Depois de estabelecida a infecção, os ratos foram dosados duas vezes ao dia, durante 5 dias; e monitorados por 6 semanas usando um sistema de imagem in vivo IVIS (do inglês, \"In Vivo Imaging System\"). O composto 41 demonstrou inibição parasitária comparável com o grupo de treinamento dosado com benzonidazol. O composto 41 representa um potencial líder para o desenvolvimento de novos fármacos para o tratamento de tripanossomíases.In chapter 1, the HQSAR, molecular docking and ROCS were applied to a dataset of 57 cruzain inhibitors. The best HQSAR model (q2 = 0.70, r2 = 0.95, r2test = 0.62, q2rand. = 0.09 and r2rand. = 0.26) was then used to predict the potencies of 121 unknown compounds (the V1 database), giving rise to a satisfactory predictive r2 value of 0.65 (external validation). By employing an extra external dataset comprising 1223 compounds (the V3 database) either retrieved from the ChEMBL or CDD databases, an overall ROC AUC (area under the curve) score well over 0.70 was obtained. The contribution maps obtained with the best HQSAR model (model 3.4) are in agreement with the predicted binding mode and with the biological potencies of the studied compounds. We also screened these compounds using the ROCS method, a Gaussian-shape volume filter able to identify quickly the shapes that match a query molecule. The AUC obtained with the ROC curves (ROC AUC) was 0.72, indicating that the method was very efficient in distinguishing between active and inactive cruzain inhibitors. These set of information guided us to propose novel cruzain inhibitors to be synthesized. Then, the best HQSAR model obtained was used to predict the pIC50 values of these new compounds. Some compounds identified using this method has shown calculated potencies higher than those which have originated them. In chapter 2, the effects on potency of cruzain inhibition of replacing a nitrile group with alternative warheads were explored; with the syntheses of 20 dipeptidyl compounds, we explored the structure activity relationships (SAR) based on exchanging of the warhead portion (P1\'). The oxime was 0.7 units more potent than the corresponding nitrile. Dipeptide aldehydes and azadipeptide nitriles were found to be two orders of magnitude more potent than the corresponding dipeptide nitriles. The vinyl esters and amides were less potent than the corresponding nitrile by between one and two orders of magnitude. In chapter 3, we synthesized 23 new imidazopyridine analogues arising from medicinal chemistry optimization at different sites on the molecule. Seven and twelve compounds exhibited an in vitro EC50 <= 1µM against Trypanosoma cruzi (T. cruzi) and Trypanosoma brucei (T. brucei) parasites, respectively. Based on promising results of in vitro activity (EC50 &lt; 100 nM), cytotoxicity, metabolic stability, protein binding and pharmacokinetics (PK) properties, compound 41 was selected as a candidate for in vivo efficacy studies. This compound was screened in an acute mouse model against T.cruzi (Tulahuen strain). After established infection, mice were dosed twice a day for 5 days, and then monitored for 6 weeks using an in vivo imaging system (IVIS). Compound 41 demonstrated parasite inhibition comparable to the benznidazole treatment group. Compound 41 represents a potential lead for the development of drugs to treat trypanosomiasis

Molecular design, synthesis and evaluation of cruzain inhibitors and antitrypanosomal agents based on imidazopyridines

No capítulo 1, a modelagem HQSAR, a docagem e os estudos de ROCS foram construídos utilizando uma série de 57 inibidores de cruzaína. O melhor modelo HQSAR (q2 = 0,70, r2 = 0,95, r2test = 0,62, q2rand. = 0,09 and r2rand. = 0,26) foi utilizado para predizer a potência de 121 compostos extraídos da literatura (conjunto de dados V1), resultando em um valor de r2 satisfatório de 0,65 para essa validação externa. Uma validação externa adicional foi empregada utilizando uma série de 1223 compostos extraído dos bancos de dados ChEMBL e CDD (conjunto de dados V3); nessa validação externa o valor de AUC (área sob a curva) para a curva ROC foi de 0,70. Os mapas de contribuição, obtidos para o melhor modelo HQSAR 3.4, estão de acordo com as predições do modo de interação e com as bioatividades dos compostos estudados. Nos estudos de ROCS, a forma molecular utilizada como filtro, foi útil na rápida identificação de modificações moleculares promissoras para inibidores de cruzaína. O valor de AUC obtido com a curva ROC foi de 0,72, isso indica que o método foi muito eficiente na distinção entre inibidores ativos e inativos da enzima cruzaína. Em seguida, o melhor modelo HQSAR foi utilizado para predizer os valores de pIC50 para novos compostos. Alguns dos compostos identificados, utilizando esse método, demonstraram valores de potência calculada maior do que a série de treinamento em estudo. No capítulo 2, os efeitos sobre a potência na inibição da enzima cruzaína pela substituição de um grupo nitrila como warhead por outros grupos foi avaliada. Com a síntese de 20 compostos do tipo dipeptidil, avaliou-se a relação estrutura-atividade (SAR), baseado na troca do grupo warhead na porção P1\'. O grupo oxima foi mais potente que o grupo correspondente nitrila em 0,7 unidades logarítmicas. Os compostos do tipo dipeptidil aldeídos e azanitrila obtiveram potências mais elevadas do que o correspondente dipeptidil nitrila em duas de magnitude. Os compostos dipeptidil alfa-beta insaturados foram menos potentes do que o correspondente dipeptidil nitrila. No capítulo 3, estratégias de química medicinal foram empregadas nas sínteses de 23 novos análogos, contendo o esqueleto básico de imidazopiridina. Sete e doze compostos sintetizados exibiram EC50 <= 1µM in vitro contra os parasitos Tripanosoma cruzi (T. cruzi) e brucei (T. brucei), respectivamente. Com os resultados promissores de atividade biológica in vitro, citotoxicidade, estabilidade metabólica, ligação proteica e propriedades farmacocinéticas, o composto 41 foi selecionado como candidato para os estudos de eficácia in vivo. Esse composto foi submetido em um modelo agudo da infecção com T. cruzi em ratos (cepa Tulahuen). Depois de estabelecida a infecção, os ratos foram dosados duas vezes ao dia, durante 5 dias; e monitorados por 6 semanas usando um sistema de imagem in vivo IVIS (do inglês, \"In Vivo Imaging System\"). O composto 41 demonstrou inibição parasitária comparável com o grupo de treinamento dosado com benzonidazol. O composto 41 representa um potencial líder para o desenvolvimento de novos fármacos para o tratamento de tripanossomíases.In chapter 1, the HQSAR, molecular docking and ROCS were applied to a dataset of 57 cruzain inhibitors. The best HQSAR model (q2 = 0.70, r2 = 0.95, r2test = 0.62, q2rand. = 0.09 and r2rand. = 0.26) was then used to predict the potencies of 121 unknown compounds (the V1 database), giving rise to a satisfactory predictive r2 value of 0.65 (external validation). By employing an extra external dataset comprising 1223 compounds (the V3 database) either retrieved from the ChEMBL or CDD databases, an overall ROC AUC (area under the curve) score well over 0.70 was obtained. The contribution maps obtained with the best HQSAR model (model 3.4) are in agreement with the predicted binding mode and with the biological potencies of the studied compounds. We also screened these compounds using the ROCS method, a Gaussian-shape volume filter able to identify quickly the shapes that match a query molecule. The AUC obtained with the ROC curves (ROC AUC) was 0.72, indicating that the method was very efficient in distinguishing between active and inactive cruzain inhibitors. These set of information guided us to propose novel cruzain inhibitors to be synthesized. Then, the best HQSAR model obtained was used to predict the pIC50 values of these new compounds. Some compounds identified using this method has shown calculated potencies higher than those which have originated them. In chapter 2, the effects on potency of cruzain inhibition of replacing a nitrile group with alternative warheads were explored; with the syntheses of 20 dipeptidyl compounds, we explored the structure activity relationships (SAR) based on exchanging of the warhead portion (P1\'). The oxime was 0.7 units more potent than the corresponding nitrile. Dipeptide aldehydes and azadipeptide nitriles were found to be two orders of magnitude more potent than the corresponding dipeptide nitriles. The vinyl esters and amides were less potent than the corresponding nitrile by between one and two orders of magnitude. In chapter 3, we synthesized 23 new imidazopyridine analogues arising from medicinal chemistry optimization at different sites on the molecule. Seven and twelve compounds exhibited an in vitro EC50 <= 1µM against Trypanosoma cruzi (T. cruzi) and Trypanosoma brucei (T. brucei) parasites, respectively. Based on promising results of in vitro activity (EC50 &lt; 100 nM), cytotoxicity, metabolic stability, protein binding and pharmacokinetics (PK) properties, compound 41 was selected as a candidate for in vivo efficacy studies. This compound was screened in an acute mouse model against T.cruzi (Tulahuen strain). After established infection, mice were dosed twice a day for 5 days, and then monitored for 6 weeks using an in vivo imaging system (IVIS). Compound 41 demonstrated parasite inhibition comparable to the benznidazole treatment group. Compound 41 represents a potential lead for the development of drugs to treat trypanosomiasis

Strategies towards expansion of chemical space of natural product‑based compounds to enable drug discovery

Natural products (NPs) are an excellent source of biologically active molecules that provide many biologically biased features that enable innovative designing of synthetic compounds. NPs are characterized by high content of sp3 -hybridized carbon atoms; oxygen; spiro, bridged, and linked systems; and stereogenic centers, with high structural diversity. To date, several approaches have been implemented for mapping and navigating into the chemical space of NPs to explore the different aspects of chemical space. The approaches providing novel opportunities to synthesize NP-inspired compound libraries involve NP-based fragments and ring distortion strategies. These methodologies allow access to areas of chemical space that are less explored, and consequently help to overcome the limitations in the use of NPs in drug discovery, such as lack of accessibility and synthetic intractability. In this review, we describe how NPs have recently been used as a platform for the development of diverse compounds with high structural and stereochemical complexity. In addition, we show developed strategies aiming to reengineer NPs toward the expansion of NP-based chemical space by fragment-based approaches and chemical degradation to yield novel compounds to enable drug discovery

Strategies towards expansion of chemical space of natural product-based compounds to enable drug discovery

Natural products (NPs) are an excellent source of biologically active molecules that provide many biologically biased features that enable innovative designing of synthetic compounds. NPs are characterized by high content of sp3-hybridized carbon atoms; oxygen; spiro, bridged, and linked systems; and stereogenic centers, with high structural diversity. To date, several approaches have been implemented for mapping and navigating into the chemical space of NPs to explore the different aspects of chemical space. The approaches providing novel opportunities to synthesize NP-inspired compound libraries involve NP-based fragments and ring distortion strategies. These methodologies allow access to areas of chemical space that are less explored, and consequently help to overcome the limitations in the use of NPs in drug discovery, such as lack of accessibility and synthetic intractability. In this review, we describe how NPs have recently been used as a platform for the development of diverse compounds with high structural and stereochemical complexity. In addition, we show developed strategies aiming to reengineer NPs toward the expansion of NP-based chemical space by fragment-based approaches and chemical degradation to yield novel compounds to enable drug discovery

Highly predictive hologram QSAR models of nitrile-containing cruzain inhibitors

<p>The HQSAR, molecular docking, and ROCS were applied to a data-set of 57 cruzain inhibitors. The best HQSAR model (<i>q</i>² = .70, <i>r</i>² = .95,  = .62,  = .09 and  = .26), employing well-balanced, diverse training (40) and test (17) sets, was obtained using atoms (A), bonds (B), and hydrogen (H) as fragment distinctions and 6–9 as fragment sizes. This model was then used to predict the unknown potencies of 121 compounds (the V1 database), giving rise to a satisfactory predictive <i>r</i>² value of .65 (external validation). By employing an extra external data-set comprising 1223 compounds (the V3 database) either retrieved from the ChEMBL or CDD databases, an overall ROC AUC score well over .70 was obtained. The contribution maps obtained with the best HQSAR model (model 3.4) are in agreement with the predicted binding mode and with the biological potencies of the studied compounds. We also screened these compounds using the ROCS method, a Gaussian-shape volume filter able to identify quickly the shapes that match a query molecule. The area under the curve (AUC) obtained with the ROC curves (ROC AUC) was .72, indicating that the method was very efficient in distinguishing between active and inactive cruzain inhibitors. These set of information guided us to propose novel cruzain inhibitors to be synthesized. Then, the best HQSAR model obtained was used to predict the pIC₅₀ values of these new compounds. Some compounds identified using this method have shown calculated potencies higher than those which have originated them.</p