RESUMO
As leishmanioses são doenças negligenciadas que afetam mais de um bilhão e meio de pessoas ao redor do mundo, principalmente nos países em desenvolvimento, provocando grandes impactos socioeconômicos. Os fármacos disponíveis para o tratamento dessas doenças são ineficazes e apresentam graves efeitos adversos. O processo de pesquisa de novos fármacos envolve, entre outras coisas, a seleção de alvos bioquímicos essenciais para a sobrevivência e desenvolvimento do agente causador. Neste sentido, a Sirtuína 2, uma enzima epigenética com atividade hidrolase essencial para a sobrevivência dos parasitas do gênero Leishmania se apresenta como um alvo validado na busca de novos fármacos contra essas parasitoses. O planejamento de fármacos baseado na estrutura do receptor requer o conhecimento da estrutura tridimensional da proteína alvo. Desta forma, a elucidação estrutural e um estudo minucioso das Sirtuínas das várias espécies do gênero Leishmania apresenta-se como uma importante abordagem na aplicação desta estratégia na busca por agentes quimioterápicos. Até o momento, na família Trypanosomatidae, a única estrutura tridimensional resolvida experimentalmente de uma enzima Sirtuína 2 é a da espécie L. infantum. Assim, este trabalho aplicou a abordagem de Modelagem Comparativa utilizando o software Modeller na construção de modelos da Sir2rp1 das espécies L. infantum, L. major e L. braziliensis, cujas sequências de aminoácidos foram extraídas do banco de dados UNIProt. Os modelos construídos foram validados por meio da função de escore DOPE do Modeller e dos servidores PROCHECK, MolProbity e QMEAN, avaliando sua qualidade estereoquímica e seu enovelamento. Os ligantes naturais da enzima foram sobrepostos nos modelos construídos por alinhamento estrutural utilizando o software PyMol e os complexos validados foram submetidos a simulações de Dinâmica Molecular através do pacote GROMACS. Os complexos refinados foram então analisados por meio dos softwares PyMol e LigPlotPlus e dos pacotes GROMACS e gmx_MMPBSA, e foram estudados os sítios de ligação dos substratos e os resíduos de aminoácidos relevantes envolvidos em sua ligação e reconhecimento. A Modelagem Comparativa da Sirtuína 2 humana e seus homólogos das espécies L. infantum, L. major e L. braziliensis, as simulações de Dinâmica Molecular realizadas com os modelos enzimáticos construídos e validados complexados com seus ligantes naturais, os cálculos de energia de interação entre os modelos e seus substratos e o estudo estrutural comparativo realizado entre eles nos fornecem uma base teórica para a busca de novos inibidores da Sirtuína 2 que sejam mais seletivos e potentes contra as enzimas parasitárias, abrindo caminho para o desenvolvimento de candidatos a fármacos leishmanicidas mais seguros e eficazes
Leishmaniasis are neglected diseases that affect more than one and a half billion people around the world, mainly in developing countries, causing major socioeconomic impacts. The drugs available for the treatment of these diseases are ineffective and have serious adverse effects. The process of researching new drugs involves, among other things, the selection of biochemical targets essential for the survival and development of the causative agent. In this sense, Sirtuin 2, an epigenetic enzyme with hydrolase activity essential for the survival of parasites of the Leishmania genus, presents itself as a validated target in the search for new drugs against these parasites. Structure-Based Drug Design requires knowledge of the three-dimensional structure of the target protein. In this way, structural elucidation and a detailed study of Sirtuins from various species of the genus Leishmania presents itself as an important approach in the application of this strategy in the search for chemotherapeutic agents. To date, in the Trypanosomatidae family, the only experimentally resolved three-dimensional structure of a Sirtuin 2 enzyme is that of the species L. infantum. Thus, this work applied the Comparative Modeling approach using the Modeller software in the construction of Sir2rp1 models of the species L. infantum, L. major and L. braziliensis, whose amino acid sequences were retrieved from the UNIProt database. The constructed models were validated using Modeller's DOPE score function and the PROCHECK, MolProbity and QMEAN servers, evaluating their stereochemical quality and folding. The enzyme's natural ligands were superimposed on the built models by structural alignment using the PyMol software and the validated complexes were subjected to Molecular Dynamics simulations using the GROMACS package. The refined complexes were then analyzed using the PyMol and LigPlotPlus softwares and the GROMACS and gmx_MMPBSA packages, and the substrate binding sites and relevant amino acid residues involved in their binding and recognition were studied. The Comparative Modeling of human Sirtuin 2 and its homologues from the species L. infantum, L. major and L. braziliensis, the Molecular Dynamics simulations carried out with the constructed and validated enzymatic models complexed with their natural ligands, the interaction energy calculations between the models and their substrates and the comparative structural study carried out between them provide us with a theoretical basis for the search for new Sirtuin 2 inhibitors that are more selective and potent against the parasitic enzymes, paving the way for the development of safer and more effective leishmanicidal drug candidates
Assuntos
Preparações Farmacêuticas/análise , Leishmaniose/patologia , Sirtuínas/análise , Simulação de Dinâmica Molecular/estatística & dados numéricos , Doenças Negligenciadas/complicações , Epigenômica/classificação , Leishmania/classificaçãoRESUMO
The emergence of artificial intelligence (AI) tools has transformed the landscape of drug discovery, providing unprecedented speed, efficiency, and cost-effectiveness in the search for new therapeutics. From target identification to drug formulation and delivery, AI-driven algorithms have revolutionized various aspects of medicinal chemistry, significantly accelerating the drug design process. Despite the transformative power of AI, this perspective article emphasizes the limitations of AI tools in drug discovery, requiring inventive skills of medicinal chemists. However, the article highlighted that there is a need for a harmonious integration of AI-based tools and human expertise in drug discovery. Such a synergistic approach promises to lead to groundbreaking therapies that address unmet medical needs and benefit humankind. As the world evolves technologically, the question remains: When will AI tools effectively design and develop drugs? The answer may lie in the seamless collaboration between AI and human researchers, unlocking transformative therapies that combat diseases effectively.
RESUMO
BACKGROUND: Neglected diseases require special attention when looking for new therapeutic alternatives, as these are diseases of extreme complexity and severity that affect populations belonging to lower social classes who lack access to basic rights, such as sanitation. INTRODUCTION: Among the alternatives available for obtaining new drugs is Medicinal Chemistry, which is responsible for the discovery, identification, invention, and preparation of prototypes. In this perspective, the present work aims to make a bibliographic review on the recent studies of Medicinal Chemistry applied to neglected diseases. METHODS: A literature review was carried out by searching the "Web of Sciences" database, including recent articles published on the Neglected Drug Design. RESULTS: In general, it was noticed that the most studied neglected diseases corresponded to Chagas disease and leishmaniasis, with studies on organic synthesis, optimization of structures, and molecular hybrids being the most used strategies. It is also worth mentioning the growing number of computationally developed studies, providing speed and optimization of costs in the procurement process. CONCLUSION: The CADD approach and organic synthesis studies, when applied in the area of Medicinal Chemistry, have proven to be important in the research and discovery of drugs for Neglected Diseases, both in terms of planning the experimental methodology used to obtain it and in the selection of compounds with higher activity potential.
Assuntos
Química Farmacêutica , Desenho de Fármacos , Doenças Negligenciadas/tratamento farmacológico , Doença de Chagas/tratamento farmacológico , Humanos , Leishmaniose/tratamento farmacológicoRESUMO
Over recent years, many outbreaks caused by (re)emerging RNA viruses have been reported worldwide, including life-threatening Flaviviruses, such as Dengue (DENV) and Zika (ZIKV). Currently, there is only one licensed vaccine against Dengue, Dengvaxia®. However, its administration is not recommended for children under nine years. Still, there are no specific inhibitors available to treat these infectious diseases. Among the flaviviral proteins, NS5 RNA-dependent RNA polymerase (RdRp) is a metalloenzyme essential for viral replication, suggesting that it is a promising macromolecular target since it has no human homolog. Nowadays, several NS5 RdRp inhibitors have been reported, while none inhibitors are currently in clinical development. In this context, this review constitutes a comprehensive work focused on RdRp inhibitors from natural, synthetic, and even repurposing sources. Furthermore, their main aspects associated with the structure-activity relationship (SAR), proposed mechanisms of action, computational studies, and other topics will be discussed in detail.
Assuntos
Antivirais/farmacologia , Vírus da Dengue/efeitos dos fármacos , Inibidores Enzimáticos/farmacologia , RNA Polimerase Dependente de RNA/antagonistas & inibidores , Zika virus/efeitos dos fármacos , Antivirais/síntese química , Antivirais/química , Vírus da Dengue/enzimologia , Relação Dose-Resposta a Droga , Inibidores Enzimáticos/síntese química , Inibidores Enzimáticos/química , Testes de Sensibilidade Microbiana , Estrutura Molecular , RNA Polimerase Dependente de RNA/metabolismo , Relação Estrutura-Atividade , Zika virus/enzimologiaRESUMO
Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating diseases such as neutropenia induced by chemotherapy, prostate, and breast cancer. Recently, a series of compounds that promote the activation of c-Abl has been identified, opening a promising ground for c-Abl drug development. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) methodologies have significantly impacted recent drug development initiatives. Here, we combined SBDD and LBDD approaches to characterize critical chemical properties and interactions of identified c-Abl's activators. We used molecular docking simulations combined with tree-based machine learning models-decision tree, AdaBoost, and random forest to understand the c-Abl activators' structural features required for binding to myristoyl pocket, and consequently, to promote enzyme and cellular activation. We obtained predictive and robust models with Matthews correlation coefficient values higher than 0.4 for all endpoints and identified characteristics that led to constructing a structure-activity relationship model (SAR).
Assuntos
Aprendizado de Máquina , Simulação de Acoplamento Molecular , Simulação de Dinâmica Molecular , Inibidores de Proteínas Quinases/química , Proteínas Proto-Oncogênicas c-abl/química , Sítios de Ligação , Desenho de Fármacos , Humanos , Ligantes , Estrutura Molecular , Ligação Proteica , Inibidores de Proteínas Quinases/farmacologia , Proteínas Proto-Oncogênicas c-abl/metabolismo , Relação Quantitativa Estrutura-AtividadeRESUMO
Natural products based on imidazole scaffolds have inspired the discovery of a wide variety of bioactive compounds. Herein, a series of imidazoles that act as competitive and potent cruzain inhibitors was investigated using a combination of ligand- and structure-based drug design strategies. Quantitative structure-activity relationships (QSARs) were generated along with the investigation of enzyme-inhibitor molecular interactions. Predictive hologram QSAR (HQSAR, r2pred = 0.80) and AutoQSAR (q2 = 0.90) models were built, and key structural properties that underpin cruzain inhibition were identified. Moreover, comparative molecular field analysis (CoMFA, r2pred = 0.81) and comparative molecular similarity indices analysis (CoMSIA, r2pred = 0.73) revealed 3D molecular features that strongly affect the activity of the inhibitors. These findings were examined along with molecular docking studies and were highly compatible with the intermolecular contacts that take place between cruzain and the inhibitors. The results gathered herein revealed the main factors that determine the activity of the imidazoles studied and provide novel knowledge for the design of improved cruzain inhibitors.
Assuntos
Cisteína Endopeptidases/química , Imidazóis/farmacologia , Simulação de Acoplamento Molecular , Inibidores de Proteases/química , Proteínas de Protozoários/química , Relação Quantitativa Estrutura-Atividade , Sítios de Ligação , Cisteína Endopeptidases/metabolismo , Desenho de Fármacos , Imidazóis/química , Inibidores de Proteases/farmacologia , Ligação Proteica , Proteínas de Protozoários/antagonistas & inibidores , Proteínas de Protozoários/metabolismoRESUMO
The development of new drugs is becoming notably harder each decade. To overcome the present pitfalls in the drug development pipeline, such as those related to potency, selectivity, or absorption, distribution, metabolism, excretion and toxicity properties, medicinal chemistry strategies need to be in continuous evolution and need to become even more multidisciplinary. In this review, we present how structure-based, ligand-based, and fragment-based drug design (SBDD, LBDD, and FBDD, respectively) and their respective techniques were used for the design and optimization of successful cases of New Molecular Entities (NMEs) approved by the Food and Drug Administration (FDA).
Assuntos
Química Farmacêutica , Aprovação de Drogas , Desenho de Fármacos , Humanos , Ligantes , Estados Unidos , United States Food and Drug Administration/legislação & jurisprudênciaRESUMO
BACKGROUND: Peroxisome proliferator-activated receptors (PPAR) are nuclear receptors activated by endogenous fatty acids and prostaglandins that are classified into three types: α, γ and δ, which have different functions and tissue distribution. PPAR modulators have been exploited to the treatment of important metabolic diseases, such as type 2 diabetes mellitus and metabolic syndrome, which are considered relevant epidemic diseases currently. Along the last decades, several studies have reported structural differences between the three PPAR subtypes associated with the discovery of selective ligands, dual and pan-agonists. Nowadays, there are several approved drugs that activate PPARα (fibrates) and PPARγ (glitazones), but up to now there is none clinically used drug targeting PPARδ. Additionally, several side-effects associated with the use of PPARα and γ agonists are reported by regulatory agencies, which do not indicate anymore their use as first-line drugs. OBJECTIVE: A significant new market has grown in the last years, focusing on the development of new PPARδ agonists as drug candidates to treat metabolic diseases and, in this sense, this study proposes to review the structural requirements to achieve selective PPARδ activation, as well to discuss the most relevant agonists in clinical trials, providing information on the current phase in the drug discovery and design targeting PPARδ. CONCLUSION: Several PPARδ ligands with high potency were reported in the literature and were designed or discovered by a combination of experimental and computational approaches. Furthermore, the reported importance of pockets and individual residues at PPARδ binding site as well as the importance of substituent and some physicochemical properties that could help to design of new classes of agonists.
Assuntos
Desenho de Fármacos , Drogas em Investigação , PPAR delta/agonistas , Animais , Diabetes Mellitus Tipo 2/tratamento farmacológico , Diabetes Mellitus Tipo 2/metabolismo , Drogas em Investigação/química , Drogas em Investigação/farmacologia , Humanos , Ligantes , Síndrome Metabólica/tratamento farmacológico , Síndrome Metabólica/metabolismo , Simulação de Acoplamento Molecular , Relação Quantitativa Estrutura-AtividadeRESUMO
Doenças causadas por agentes infecciosos e parasitários são chamadas negligenciadas por não despertarem interesse das indústrias farmacêuticas para o desenvolvimento de novas alternativas terapêuticas. Essas doenças são responsáveis por levar milhões de pessoas à morte todos os anos e afetam principalmente os países pobres e em desenvolvimento. Dentre estas, a doença de Chagas e as leishmanioses, parasitoses causadas por parasitas flagelados pertencentes à família Trypanosomatidae, T. cruzi e Leishmaina sp., respectivamente, se apresentam como um sério problema de saúde pública mundial. Endêmicas em vários países e causando milhões de mortes anualmente, ainda hoje não existem fármacos eficientes e seguros para o tratamento dessas doenças. Este panorama torna eminente a necessidade de pesquisa e desenvolvimento de novos fármacos para essas parasitoses. A busca por agentes quimioterápicos envolve a seleção de vias metabólicas essenciais à sobrevivência dos parasitas. Dentre estas, destacamse cisteíno-proteases presentes nesses tripanossomatídeos, deste modo a cruzaína no T. cruzi, e a CPB2.8 na Leishmania mexicana, se mostram como alvos bioquímicos promissores. A disponibilidade de estruturas cristalográficas da cruzaína e do sequenciamento genômico da CPB2.8, nos permite utilizar estratégias de planejamento de fármacos baseado no receptor (SBDD) na identificação de candidatos a fármacos para essas doenças. Entre as técnicas modernas de SBDD utilizadas, a triagem virtual possibilita identificar promissores candidatos a novos fármacos. Assim neste trabalho, obteve-se por meio da técnica de modelagem comparativa o modelo da enzima CPB2.8 de L. mexicana, visto a indisponibilidade da estrutura cristalográfica no Protein Data Bank (PDB). De modo a refinar o modelo construído realizou-se a simulação por dinâmica molecular de 100ns, apresentando estabilização a partir de 80ns. A simulação por dinâmica molecular foi validada por meio do gráfico de Ramachandran, gráfico de raio de giro, RMSD, gráfico de superfície hidrofóbica. Foram calculados os mapas de interação molecular no programa GRID das seguintes proteínas: cruzaína, CPB2.8, catepsina B e catepsina L, e, posteriormente, foi construído um modelo farmacofórico baseado no sítio ativo das enzimas cruzaína e CPB2.8. O modelo farmacofórico da cruzaína foi validado por curva ROC apresentando valor de AUC 61%. A triagem virtual foi realizada para ambas as proteínas e foram obtidos 369 compostos para a cuzaína e 225 compostos para a CPB2.8. Foi realizado o ancoramento molecular desses compostos obtidos pela triagem virtual a fim de diminuir a quantidade de compostos a serem avaliados experimentalmente
Neglected diseases are caused by parasites and infectious agents and affect mainly people in poor areas being prevalent in 149 countries and causing 534,000 deaths per year. Among neglected diseases we can highlight Chagas Disease and Leishmaniasis, both have a high rate of morbidity and mortality and both are addressed in this project in the search of new drugs against a NTD. Nowadays, the search for new drugs involves the selection of biological pathways essential for parasite survival, in this class of parasites we can suggest the cysteine proteases, a proteases family present in Trypanosoma cruzi and and Leishmania ssp. In order to obtain a new agent against Neglected Disease in this work was obtained the model of the enzyme CPB2.8 of L. mexicana using the comparative modeling technique, due to the unavailability of the crystallographic structure in the Protein Data Bank (PDB). In order to refine the constructed model was performed the molecular dynamics simulation of 100ns, stabilization was achieved from 80ns. Molecular dynamics simulation was validated using the Ramachandran graph, radius of rotation graph, RMSD, hydrophobic surface area graph. The molecular interaction fields were calculated in the GRID program to cruzain, CPB2.8, cathepsin B and cathepsin L. Based on molecular interaction fields generated pharmacophoric models were constructed using information about the active site of the enzymes cruzain and CPB2.8. The pharmacophoric model of cruzain was validated by ROC curve presenting AUC value of 61%. Virtual screening was performed for both proteins and 369 compounds were obtained for cuzain and 225 compounds for CPB2.8. Docking studies of these compounds was performed in order to decrease the amount of compounds to be evaluated experimentally
Assuntos
Trypanosoma cruzi/classificação , Triagem , Cisteína Proteases/análise , Doenças Negligenciadas/prevenção & controle , Preparações Farmacêuticas , Trypanosomatina/classificação , Descoberta de Drogas , Leishmania/classificaçãoRESUMO
A doença de Chagas, causada pelo parasita Trypanosoma cruzi, acomete entre 6 a 8 milhões de pessoas em todo o mundo. Conhecida como tripanossomíase americana, por ter sido considerada endêmica apenas na América Latina, esta doença, se espalhou para outros continentes devido aos movimentos migratórios se tornando um problema de sáude mundial. Estima-se que 56.000 novos casos e cerca de 12.000 mortes por complicações relacionadas à doença de Chagas anualmente. A quimioterapia disponível para o tratamento é composta apenas por dois fármacos, nifurtimox e benznidazol, no entanto são pouco eficazes na fase crônica da doença. Estes fármacos apresentarem, ainda, efeitos adversos graves e resistência por parte de algumas cepas do parasita. Diante deste panorama, é iminente a necessidade da busca de novos fármacos contra T. cruzi. Para a busca racional de novos quimiterapicos antiparasitários é fundamental a identificação e caracterização de vias metabólicas essenciais à sobrevivência dos parasitas. Assim, a enzima sirtuína 2 - Silent Information Regulator 2 (Sir2), tem importante papel para a infecção por T. cruzi, pois está totalmente envolvida no seu ciclo celular do parasita. Esta é uma enzima NAD+ dependente da classe III histona desacetilases, e se mostra como um interessante alvo bioquímico para o desenvolvimento de antichagásicos. A disponibilidade do sequenciamento genômico da Sir2 nos permite utilizar estratégias de planejamento de fármaco baseado no receptor (SBDD - Structure Based Drug Design) na identificação de candidatos a fármacos para essa doença. Entre as técnicas modernas de SBDD utilizadas, a triagem virtual possibilita identificar e selecionar inibidores enzimáticos potentes e seletivos para o alvo escolhido. Assim, neste trabalho, foi construído por meio da técnica de modelagem comparativa o modelo da enzima Sir2 de T. cruzi. Uma simulação por dinâmica molecular de 200ns, foi realizada para averiguar a estabilidade do modelo obtido. Diante da estabilização do modelo a partir de 100ns, o mesmo foi validado utilizando análise de clusters, RMSD (Root-mean-square Deviation) e análises de frequência de ligações de hidrogênio com o Cofator (NAD+) e os aminoácidos do sítio de catálise foram observadas, estes passos de simulação e validação foram realizados no programa DESMOND. Com o modelo robusto, os campos de interações moleculares (MIFs) foram gerados no programa GRID (Molecular Discovery v2.1) com o intuito de elucidar as regiões favoráveis a interação com a enzima em relação a propriedades físico-químicas da Sir2. A partir dos MIFs favoráveis a Sir2 de T. cruzi foi possível a construção de dois modelos farmacofóricos, o qual se baseou nas interações do Cofator (NAD+) e o sítio de catálise (Nicotinamida). O mesmo foi apliacdo como filtro para Triagem Virtual no programa UNITY da plataforma SYBYL X 2.0, utilizando os bancos de dados ZINC15 e GSK. A triagem resultou na seleção de 8 compostos candidatos a inibidores. Destes foram adquiridos 6 compostos por serem considerados mais promissores devido a complementariedade molecular. Estes foram testados contra a enzima de T. cruzi Sri2. Após o ensaio foi possível avaliar a potência de 4 compostos, sendo o composto CDMS-01 (IC50 = 39,9uM) o mais promissor que será submetido à processos de otimização molecular
Chagas disease, caused by the parasite Trypanosoma cruzi, affects between 6 and 8 million people worldwide. Also known as American trypanosomiasis, because it is considered endemic only in Latin America, but has spread to other continents due to migratory movements. It is estimated that 56,000 new cases and about 12,000 deaths from complications related to Chagas disease annually. The chemotherapy available for treatment consists of only two drugs, nifurtimox and benznidazole, however these are poorly effective in the chronic phase. These drugs also have serious adverse effects and resistance from strains of the parasite. Faced with this scenario, the need to search for new drugs against T. cruzi is imminent. For the drug planning for new antiparasitic chemotherapics, the identification and characterization of metabolic pathways essential to the survival of parasites is fundamental. Therewith, the sirtuin 2 - Silent Information Regulator 2 (Sir2) enzyme has an important role for T. cruzi infection, since Sir2 in the parasite is totally involved in its cell cycle. This is an NAD+-dependent enzyme of class III histone deacetylases, and it shows an interesting biochemical target for the development of antichagasic. The availability of Sir2 genomic sequencing allows us to use SBDD (Structure Based Drug Design) strategies in identifying drug candidates for this disease. Among the modern techniques of SBDD used, virtual screening makes it possible to identify and select potent and selective enzyme inhibitors for the chosen target. The model of the T. cruzi Sir2 enzyme was constructed using the comparative modeling technique. A molecular dynamics simulation of 200ns was performed to ascertain the stability of the obtained model. Considering the stabilization of the model from 100ns, it was validated using cluster analysis, Root-mean-square Deviation (RMSD) and hydrogen bond frequency analyzes with Cofator (NAD+) and the amino acids of the catalysis site were observed, these simulation and validation steps were performed in the DESMOND program. With the robust model, the molecular interaction fields (MIFs) were generated in the GRID program (Molecular Discovery v2.1) in order to elucidate the regions favorable to the interaction with the enzyme in relation to the physicalchemical properties of Sir2. From the MIFs favorable to Sir2 of T. cruzi it was possible to construct two pharmacophoric models, which was based on the interactions of Cofator (NAD+) and the catalysis site (Nicotinamide). It was also applied as a Virtual screening filter in the UNITY program of the SYBYL X 2.0 platform, using the ZINC15 and GSK databases. Screening resulted in the selection of 8 inhibitor candidate compounds. Six compounds were obtained from the screening, because they were considered more promising, and were tested against T. cruzi Sri2 enzyme. After the assay it was possible to evaluate the potency of 4 compounds, the most promising compound being CDMS-01 (IC50 = 39.9 µM) that will be submitted to molecular optimization processes
Assuntos
Trypanosoma cruzi/patogenicidade , Sirtuína 2/análise , Estudo de Validação , Composição de Medicamentos , Sirtuína 2/antagonistas & inibidores , Simulação de Dinâmica Molecular , AntiparasitáriosRESUMO
AIM: Chagas disease is endemic in Latin America and no effective treatment is available. Efforts in drug research have focused on several enzymes from Trypanosoma cruzi, among which cruzain is a validated pharmacological target. METHODOLOGY: Chemometric analyses were performed on the data set using the hologram quantitative structure-activity relationship, comparative molecular field analysis and comparative molecular similarity index analysis methods. Docking simulations were executed using the crystallographic structure of cruzain in complex with a benzimidazole inhibitor. The top-scoring enzyme-inhibitor complexes were selected for the development of the 3D quantitative structure-activity relationship (QSAR) models and to assess the inhibitor binding modes and intermolecular interactions. RESULTS: Benzimidazole derivatives as cruzain inhibitors were used in molecular docking and QSAR studies. Significant statistical indicators were obtained, and the best models demonstrated high predictive ability for an external test set (r 2pred = 0.65, 0.94 and 0.82 for hologram QSAR, comparative molecular field analysis and comparative molecular similarity index analysis, respectively). Additionally, the graphical information of the chemometric analyses demonstrated substantial complementarity with the enzyme-binding site. CONCLUSION: These results demonstrate the relevance of the QSAR models to guide the design of structurally related benzimidazole derivatives with improved potency.
Assuntos
Benzimidazóis/farmacologia , Doença de Chagas/tratamento farmacológico , Inibidores de Cisteína Proteinase/farmacologia , Proteínas de Protozoários/antagonistas & inibidores , Tripanossomicidas/farmacologia , Trypanosoma cruzi/efeitos dos fármacos , Benzimidazóis/síntese química , Benzimidazóis/química , Benzimidazóis/metabolismo , Sítios de Ligação , Doença de Chagas/metabolismo , Cisteína Endopeptidases/metabolismo , Inibidores de Cisteína Proteinase/síntese química , Inibidores de Cisteína Proteinase/química , Inibidores de Cisteína Proteinase/metabolismo , Descoberta de Drogas , Humanos , Modelos Moleculares , Simulação de Acoplamento Molecular , Estrutura Molecular , Ligação Proteica , Proteínas de Protozoários/metabolismo , Relação Quantitativa Estrutura-Atividade , América do Sul , Tripanossomicidas/síntese química , Tripanossomicidas/química , Tripanossomicidas/metabolismo , Trypanosoma cruzi/metabolismoRESUMO
Fragment-based drug discovery (FBDD) is a broadly used strategy in structure-guided ligand design, whereby low-molecular weight hits move from lead-like to drug-like compounds. Over the past 15 years, an increasingly important role of the integration of these strategies into industrial and academic research platforms has been successfully established, allowing outstanding contributions to drug discovery. One important factor for the current prominence of FBDD is the better coverage of the chemical space provided by fragment-like libraries. The development of the field relies on two features: (i) the growing number of structurally characterized drug targets and (ii) the enormous chemical diversity available for experimental and virtual screenings. Indeed, fragment-based campaigns have contributed to address major challenges in lead optimization, such as the appropriate physicochemical profile of clinical candidates. This perspective paper outlines the usefulness and applications of FBDD approaches in medicinal chemistry and drug design.
Assuntos
Química Farmacêutica , Descoberta de Drogas , Bibliotecas de Moléculas Pequenas/química , Bibliotecas de Moléculas Pequenas/farmacologia , Estrutura Molecular , Proteínas/antagonistas & inibidores , Proteínas/química , Bibliotecas de Moléculas Pequenas/síntese químicaRESUMO
INTRODUCTION: The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds. AREAS COVERED: This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening. EXPERT OPINION: Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.
Assuntos
Desenho de Fármacos , Descoberta de Drogas/métodos , Aprendizado de Máquina , Sítios de Ligação , Árvores de Decisões , Humanos , Ligantes , Modelos Biológicos , Simulação de Acoplamento Molecular , Redes Neurais de Computação , Máquina de Vetores de SuporteRESUMO
Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.