RESUMO

Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19-95.25% for training and 89.22-95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.

RESUMO

Nanoparticles (NPs) decorated with coating agents (polymers, gels, proteins, etc.) form Nanoparticle Drug Delivery Systems (DDNS), which are of high interest in nanotechnology and biomaterials science. There have been increasing reports of experimental data sets of biological activity, toxicity, and delivery properties of DDNS. However, these data sets are still dispersed and not as large as the datasets of DDNS components (NP and drugs). This has prompted researchers to train Machine Learning (ML) algorithms that are able to design new DDNS based on the properties of their components. However, most ML models reported up to date predictions of the specific activities of NP or drugs over a determined target or cell line. In this paper, we combine Perturbation Theory and Machine Learning (PTML algorithm) to train a model that is able to predict the best components (NP, coating agent, and drug) for DDNS design. In so doing, we downloaded a dataset of >30 000 preclinical assays of drugs from ChEMBL. We also downloaded an NP data set formed by preclinical assays of coated Metal Oxide Nanoparticles (MONPs) from public sources. Both the drugs and NP datasets of preclinical assays cover multiple conditions of assays that can be listed as two arrays, namely, cjdrug and cjNP. The cjdrug array includes >504 biological activity parameters (c0drug), >340 target proteins (c1drug), >650 types of cells (c2drug), >120 assay organisms (c3drug), and >60 assay strains (c4drug). On the other hand, the cjNP array includes 3 biological activity parameters (c0NP), 40 types of proteins (c1NP), 10 shapes of nanoparticles (c2NP), 6 assay media (c3NP), and 12 coating agents (c4NP). After downloading, we pre-processed both the data sets by separate calculation PT operators that are able to account for changes (perturbations) in the drug, coating agents, and NP chemical structure and/or physicochemical properties as well as for the assay conditions. Next, we carry out an information fusion process to form a final dataset of above 500 000 DDNS (drug + MONP pairs). We also trained other linear and non-linear PTML models using R studio scripts for comparative purposes. To the best of our knowledge, this is the first multi-label PTML model that is useful for the selection of drugs, coating agents, and metal or metal-oxide nanoparticles to be assembled in order to design new DDNS with optimal activity/toxicity profiles.

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RESUMO

Osteosarcoma is the most common subtype of primary bone cancer, affecting mostly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has still not been determined with precision. Therefore, we applied a consensus strategy with the use of several bioinformatics tools to prioritize genes involved in its pathogenesis. Subsequently, we assessed the physical interactions of the previously selected genes and applied a communality analysis to this protein-protein interaction network. The consensus strategy prioritized a total list of 553 genes. Our enrichment analysis validates several studies that describe the signaling pathways PI3K/AKT and MAPK/ERK as pathogenic. The gene ontology described TP53 as a principal signal transducer that chiefly mediates processes associated with cell cycle and DNA damage response It is interesting to note that the communality analysis clusters several members involved in metastasis events, such as MMP2 and MMP9, and genes associated with DNA repair complexes, like ATM, ATR, CHEK1, and RAD51. In this study, we have identified well-known pathogenic genes for osteosarcoma and prioritized genes that need to be further explored.

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In this work, we developed a general perturbation theory and machine learning method for data mining of proteomes to discover new B-cell epitopes useful for vaccine design. The method predicts the epitope activity εq(cqj) of one query peptide (q-peptide) under a set of experimental query conditions (cqj). The method uses as input the sequence of the q-peptide. The method also uses as input information about the sequence and epitope activity εr(crj) of a peptide of reference (r-peptide) assayed under similar experimental conditions (crj). The model proposed here is able to classify 1 048 190 pairs of query and reference peptide sequences from the proteome of many organisms reported on IEDB database. These pairs have variations (perturbations) under sequence or assay conditions. The model has accuracy, sensitivity, and specificity between 71 and 80% for training and external validation series. The retrieved information contains structural changes in 83 683 peptides sequences (Seq) determined in experimental assays with boundary conditions involving 1448 epitope organisms (Org), 323 host organisms (Host), 15 types of in vivo process (Proc), 28 experimental techniques (Tech), and 505 adjuvant additives (Adj). Afterward, we reported the experimental sampling, isolation, and sequencing of 15 complete sequences of Bm86 gene from state of Colima, Mexico. Last, we used the model to predict the epitope immunogenic scores under different experimental conditions for the 26 112 peptides obtained from these sequences. The model may become a useful tool for epitope selection toward vaccine design. The theoretical-experimental results on Bm86 protein may help the future design of a new vaccine based on this protein.

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Hasta los años 70 la obstrucción biliar fue tratada con derivaciones biliodigestivas. El abordaje percutáneo se ha venido utilizando con fi nes diagnósticos y terapéuticos cada vez más prometedores. Los métodos combinados que utilizan endoscopia (Rendezvous) pueden realizarse vía transparietohepática, eco endoscópica, laparoscopica o transKehr. Objetivo: Evaluar el abordaje de la vía biliar a través de la combinación de la técnica endoscópica y transkehr (Rendezvous). Métodos: Se evaluaron pacientes entre enero 2004 y febrero 2012 a quienes se les realizó colecistectomía más coledocotomía y colocación de tubo de Kehr, y con deformidad postquirúrgica, canulación difícil y dificultad del paso del contraste a duodeno vía transkehr que imposibilitan la colangiografía retrógrada endoscópica. Resultados: De 1146 colangiografías retrógrada endoscópicas, 12 (1.04%) fueron realizadas en pacientes que cumplían los criterios de inclusión. 75% del sexo femenino. La etiología más frecuente fue la colédocolitiasis (83.3%) y 16.7% estenosis de papila. En todos los pacientes el drenaje biliar fue exitoso. No hubo complicaciones ni mortalidad asociada al procedimiento. Conclusiones: El procedimiento combinado endoscópico-transKehr es efectivo, sencillo y seguro en el abordaje biliar alternativo cuando fracasa o no es posible la técnica convencional, asociado a menor trauma papilar y menos incidencia de pancreatitis.

Until the 1970s, biliary obstruction was resolved surgically. Percutaneous approach has been used for diagnostic and therapeutic purposes with more and more promising results. Combined methods using endoscopy (Rendezvous) can be made via transparietohepatic, endoscopic ultrasound, laparoscopic, or transKehr. Objective: Evaluate the approach of the biliary tract through the combination of the endoscopic technique and transkehr (Rendezvous). Methods: Evaluated patients between January 2004 and February 2012 those who underwent both cholecystectomy more coledocotomy combined with Kehr tube placement, because of postoperative deformity, difficult cannulation or difficulty of the passage from the contrast to duodenum through transkehr tube, that therefore preclude cholangiography retrograde endoscopic. Results: from 1146 retrograde cholangiography endoscopic, 12 (1.04%) were performed in patients who fulfilled the inclusion criteria. 75% were female. The most frequent etiology was choledocholithiasis (83.3%) and stenosis of duodenal papilla 16.7%. Biliary drainage was successful in all patients. There were no complications or mortality associated with the procedure. Conclusions: The combined procedure endoscopic-transKehr is effective, simple and secure alternative biliary approach when it fails or is not possible the conventional technique, associated with minor trauma papillary and less incidence of pancreatitis.