RESUMEN
1-Phenyl-5-p-tolyl-1H-1, 2, 3-triazole (PPTA) was a synthesized compound. The result of acute toxicities to mice of PPTA by intragastric administration indicated that PPTA did not produce any significant acute toxic effect on Kunming strain mice. It exhibited the various potent inhibitory activities against two kinds of bananas pathogenic bacteria, black sigatoka and freckle, when compared with that of control drugs and the inhibitory rates were up to 64.14% and 43.46%, respectively, with the same concentration of 7.06 mM. The interaction of PPTA with human serum albumin (HSA) was studied using fluorescence polarization, absorption spectra, 3D fluorescence, and synchronous spectra in combination with quantum chemistry and molecular modeling. Multiple modes of interaction between PPTA and HSA were suggested to stabilize the PPTA-HSA complex, based on thermodynamic data and molecular modeling. Binding of PPTA to HSA induced perturbation in the microenvironment around HSA as well as secondary structural changes in the protein.
Asunto(s)
Antiinfecciosos/farmacología , Evaluación Preclínica de Medicamentos/métodos , Albúmina Sérica Humana/metabolismo , Triazoles/metabolismo , Triazoles/farmacología , Animales , Sitios de Unión , Femenino , Polarización de Fluorescencia , Fungicidas Industriales/farmacología , Humanos , Masculino , Ratones , Modelos Moleculares , Musa/microbiología , Estructura Secundaria de Proteína , Albúmina Sérica Humana/química , Pruebas de Toxicidad Aguda , Triazoles/toxicidadRESUMEN
In diagnosis of lung cancer, rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important. Serum markers, including lactate dehydrogenase (LDH), C-reactive protein (CRP), carcino-embryonic antigen (CEA), neurone specific enolase (NSE) and Cyfra21-1, are reported to reflect lung cancer characteristics. In this study classification of lung tumors was made based on biomarkers (measured in 120 NSCLC and 60 SCLC patients) by setting up optimal biomarker joint models with a powerful computerized tool - gene expression programming (GEP). GEP is a learning algorithm that combines the advantages of genetic programming (GP) and genetic algorithms (GA). It specifically focuses on relationships between variables in sets of data and then builds models to explain these relationships, and has been successfully used in formula finding and function mining. As a basis for defining a GEP environment for SCLC and NSCLC prediction, three explicit predictive models were constructed. CEA and NSE are frequently- used lung cancer markers in clinical trials, CRP, LDH and Cyfra21-1 have significant meaning in lung cancer, basis on CEA and NSE we set up three GEP models-GEP 1(CEA, NSE, Cyfra21-1), GEP2 (CEA, NSE, LDH), GEP3 (CEA, NSE, CRP). The best classification result of GEP gained when CEA, NSE and Cyfra21-1 were combined: 128 of 135 subjects in the training set and 40 of 45 subjects in the test set were classified correctly, the accuracy rate is 94.8% in training set; on collection of samples for testing, the accuracy rate is 88.9%. With GEP2, the accuracy was significantly decreased by 1.5% and 6.6% in training set and test set, in GEP3 was 0.82% and 4.45% respectively. Serum Cyfra21-1 is a useful and sensitive serum biomarker in discriminating between NSCLC and SCLC. GEP modeling is a promising and excellent tool in diagnosis of lung cancer.
Asunto(s)
Biomarcadores de Tumor/sangre , Carcinoma de Pulmón de Células no Pequeñas/sangre , Carcinoma de Pulmón de Células no Pequeñas/diagnóstico , Neoplasias Pulmonares/diagnóstico , Carcinoma Pulmonar de Células Pequeñas/sangre , Carcinoma Pulmonar de Células Pequeñas/diagnóstico , Anciano , Biomarcadores de Tumor/genética , Proteína C-Reactiva/análisis , Antígeno Ca-125/sangre , Antígeno Carcinoembrionario/sangre , Carcinoma de Pulmón de Células no Pequeñas/genética , Estudios de Cohortes , Diagnóstico Diferencial , Femenino , Perfilación de la Expresión Génica/métodos , Regulación Neoplásica de la Expresión Génica , Humanos , Neoplasias Pulmonares/sangre , Masculino , Persona de Mediana Edad , Invasividad Neoplásica/patología , Estadificación de Neoplasias , Fosfopiruvato Hidratasa/sangre , Valor Predictivo de las Pruebas , Estudios Retrospectivos , Sensibilidad y Especificidad , Carcinoma Pulmonar de Células Pequeñas/genéticaRESUMEN
Quantitative structure-property relationships (QSPR) were developed to predict the pK(a) values of sulfa drugs via heuristic method (HM) and gene expression programming (GEP). The descriptors of 31 sulfa drugs were calculated by the software CODESSA, which can calculate constitutional, topological, geometrical, electrostatic, and quantum chemical descriptors. HM was also used for the preselection of 4 appropriate molecular descriptors. Linear and nonlinear QSPR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R) of 0.90 and 0.95. The two QSPR models are tseful in predicting pK(a) during the discovery of new drugs and providing theory information for studying the new drugs.
Asunto(s)
Algoritmos , Modelos Químicos , Programas Informáticos , Sulfonamidas/química , Expresión Génica , Relación Estructura-Actividad CuantitativaRESUMEN
The gene expression programming, a novel machine learning algorithm, is used to develop quantitative model as a potential screening mechanism for a series of 1,4-dihydropyridine calcium channel antagonists for the first time. The heuristic method was used to search the descriptor space and select the descriptors responsible for activity. A nonlinear, six-descriptor model based on gene expression programming with mean-square errors 0.19 was set up with a predicted correlation coefficient (R2) 0.92. This paper provides a new and effective method for drug design and screening.