Study of modeling and optimization for predicting the acute toxicity of carbamate pesticides using the binding information with carrier protein.

To predict drug acute toxicity using the binding information with human serum albumin, our research group established a new method (Carrier protein binding information-toxicity relationship, CPBITR). Unfortunately, the previous model had too few data sets which may affect the accuracy and credibility of the model. In this paper, therefore, we measured the binding modes of three carbamate pesticides, Bendiocarb, Butocarboxim and Dioxacarb with human serum albumin (HSA) to supplement the previously modeled training set. Multispectral methods and molecular docking were used to study their binding modes. We built and optimized the previous models with the combined information of three different toxicity pesticides and HSA in order to find better prediction method. The results showed that Back-propagation Artificial Neural Network model has the best fitting effect among these models. In conclusion, the proposed model effectively improves the accuracy and credibility of the existing model. It results in significant predict drug acute toxicity using the binding information with carrier protein and contribute to drug development and research.

A new method for predicting the acute toxicity of carbamate pesticides based on the perspective of binding information with carrier protein.

Toxicity is one of the most important factors limiting the success of new drug development. In this paper, we built a fast and convenient new method (Carrier protein binding information-toxicity relationship, CPBITR) for predicting drug acute toxicity based on the perspective of binding information with carrier protein. First, we studied the binding information between carbamate pesticides and human serum albumin (HSA) through various spectroscopic methods and molecular docking. Then a total of 16 models were established to clarify the relationship between binding information with HSA and drug toxicity. The results showed that the binding information was related to toxicity. Finally we obtained the effective toxicity prediction model for carbamate pesticides. And the "Platform for Predicting Drug Toxicity Based on the Information of Binding with Carrier Protein" was established with the Back-propagation neural network model. We proposed and proved that it was feasible to predict drug toxicity from this new perspective: binding with carrier protein. According to this new perspective, toxicity prediction model of other drugs can also be established. This new method has the advantages of convenience and fast, and can be used to screen out low-toxic drugs quickly in the early stage. It is helpful for drug research and development.

Comprehensive identification and characterization of somatic copy number alterations in triple­negative breast cancer.

Triple­negative breast cancer (TNBC) accounts for ~15% of all breast cancer diagnoses each year. Patients with TNBC tend to have a higher risk for early relapse and a worse prognosis. TNBC is characterized by extensive somatic copy number alterations (CNAs). However, the DNA CNA profile of TNBC remains to be extensively investigated. The present study assessed the genomic profile of CNAs in 201 TNBC samples, aiming to identify recurrent CNAs that may drive the pathogenesis of TNBC. In total, 123 regions of significant amplification and deletion were detected using the Genomic Identification of Significant Targets in Cancer algorithm, and potential driver genes for TNBC were identified. A total of 31 samples exhibited signs of chromothripsis and revealed chromosome pulverization hotspot regions. The present study further determined 199 genomic locations that were significantly enriched for breakpoints, which indicated TNBC­specific genomic instability regions. Unsupervised hierarchical clustering of tumors resulted in three main subgroups that exhibited distinct CNA profiles, which may reveal the heterogeneity of molecular mechanisms in TNBC subgroups. These results will extend the molecular understanding of TNBC and will facilitate the discovery of therapeutic and diagnostic target candidates.