Discrimination of approved drugs from experimental drugs by learning methods

<p>Abstract</p> <p>Background</p> <p>To assess whether a compound is druglike or not as early as possible is always critical in drug discovery process. There have been many efforts made to create sets of 'rules' or 'filters' which, it is hoped, will help chemists to identify 'drug-like' molecules from 'non-drug' molecules. However, among the chemical space of the druglike molecules, the minority will be approved drugs. Classifying approved drugs from experimental drugs may be more helpful to obtain future approved drugs. Therefore, discrimination of approved drugs from experimental ones has been done in this paper by analyzing the compounds in terms of existing drugs features and machine learning methods.</p> <p>Results</p> <p>Four methodologies were compared by their performance to classify approved drugs from experimental ones. The best results were obtained by SVM, in which the accuracy is 0.7911, the sensitivity is 0.5929, and the specificity is 0.8743. Based on the results, consensus model was developed to effectively discriminate drugs, which further pushed the correct classification rate up to 0.8517, sensitivity up to 0.7242, specificity up to 0.9352. The applications on the Traditional Chinese Medicine Ingredients Database (TCM-ID) tested the methods. Therefore this model has been proven to be a potent tool for identifying drug molecules.</p> <p>Conclusion</p> <p>The studies would have potential applications in the research of combinatorial library design and virtual high throughput screening for drug discovery.</p

The altering cellular components and function in tumor microenvironment during remissive and relapsed stages of anti-CD19 CAR T-cell treated lymphoma mice

Anti-CD19 chimeric antigen receptor (CAR) T cells represent a highly promising strategy for B-cell malignancies. Despite the inspiring initial achievement, remission in a notable fraction of subjects is short-lived, and relapse remains a major challenge. Tumor microenvironment (TME) was proved to be aroused by CAR T cells; however, little is known about the dynamic characteristics of cellular components in TME especially during the different phases of disease after anti-CD19 CAR T-cell treatment. We took advantage of an immunocompetent model receiving syngeneic A20 lymphoma cells to dissect the changes in TME with or without CAR T-cell injection. We found that anti-CD19 CAR T-cell treatment attenuated the symptoms of lymphoma and significantly prolonged mice survival through eradicating systemic CD19+ cells. Increased myeloid subsets, including CD11c+ DCs and F4/80+ macrophages with higher MHC II and CD80 expression in bone marrow, spleen, and liver, were detected when mice reached remission after anti-CD19 CAR T treatment. Compared to mice without anti-CD19 CAR T administration, intrinsic T cells were triggered to produce more IFN-γ and TNF-α. However, some lymphoma mice relapsed by day 42 after therapy, which coincided with CAR T-cell recession, decreased myeloid cell activation and increased Treg cells. Elevated intrinsic T cells with high PD-1 and TIGIT exhaust signatures and attenuated cytotoxicity in TME were associated with the late-stage relapse of CAR T-cell treatment. In summary, the cellular compositions of TME as allies of CAR T cells may contribute to the anti-tumor efficacy at the initial stage, whereas anti-CD19 CAR T-cell disappearance and host response immunosuppression may work together to cause lymphoma relapse after an initial, near-complete elimination phase

Potential metabolic mechanism of girls' central precocious puberty: a network analysis on urine metabonomics data

BACKGROUND: Central precocious puberty (CPP) is a common pediatric endocrine disease caused by early activation of hypothalamic-putuitary-gonadal (HPG) axis, yet the exact mechanism was poorly understood. Although there were some proofs that an altered metabolic profile was involved in CPP, interpreting the biological implications at a systematic level is still in pressing need. To gain a systematic understanding of the biological implications, this paper analyzed the CPP differential urine metabolites from a network point of view. RESULTS: In this study, differential urine metabolites between CPP girls and age-matched normal ones were identified by LC-MS. Their basic topological parameters were calculated in the background network. The network decomposition suggested that CPP differential urine metabolites were most relevant to amino acid metabolism. Further proximity analysis of CPP differential urine metabolites and neuro-endocrine metabolites showed a close relationship between CPP metabolism and neuro-endocrine system. Then the core metabolic network of CPP was successfully constructed among all these differential urine metabolites. As can be demonstrated in the core network, abnormal aromatic amino acid metabolism might influence the activity of HPG and hypothalamic pituitary adrenal (HPA) axis. Several adjustments to the early activation of puberty in CPP girls could also be revealed by urine metabonomics. CONCLUSIONS: The present article demonstrated the ability of urine metabonomics to provide several potential metabolic clues for CPP's mechanism. It was revealed that abnormal metabolism of amino acid, especially aromatic amino acid, might have a close correlation with CPP's pathogenesis by activating HPG axis and suppressing HPA axis. Such a method of network-based analysis could also be applied to other metabonomics analysis to provide an overall perspective at a systematic level

CE-BLAST makes it possible to compute antigenic similarity for newly emerging pathogens

Major challenges in vaccine development include rapidly selecting or designing immunogens for raising cross-protective immunity against different intra-or inter-subtypic pathogens, especially for the newly emerging varieties. Here we propose a computational method, Conformational Epitope (CE)-BLAST, for calculating the antigenic similarity among different pathogens with stable and high performance, which is independent of the prior binding-assay information, unlike the currently available models that heavily rely on the historical experimental data. Tool validation incorporates influenza-related experimental data sufficient for stability and reliability determination. Application to dengue-related data demonstrates high harmonization between the computed clusters and the experimental serological data, undetectable by classical grouping. CE-BLAST identifies the potential cross-reactive epitope between the recent zika pathogen and the dengue virus, precisely corroborated by experimental data. The high performance of the pathogens without the experimental binding data suggests the potential utility of CE-BLAST to rapidly design cross-protective vaccines or promptly determine the efficacy of the currently marketed vaccine against emerging pathogens, which are the critical factors for containing emerging disease outbreaks.Peer reviewe

The Effects of SNCA rs894278 on Resting-State Brain Activity in Parkinson’s Disease

The pathogenesis of Parkinson’s disease (PD) is not well established. The rs894278 polymorphism of SNCA has been associated with PD. We performed this study to investigate the relationship between rs894278 and PD status on resting-state brain activity, by analyzing the amplitude of low-frequency fluctuation (ALFF). A total of 81 PD patients and 64 healthy controls were recruited. Disease severity and PD stage were evaluated in PD patients using the unified Parkinson’s disease rating scale (UPDRS) and the Hoehn and Yahr (HY) scale, while the cognitive function of all participants was assessed using the mini-mental state examination (MMSE). All participants were genotyped for the rs894278 SNP and underwent a resting state functional magnetic resonance imaging scan. We found that the ALFF values of PD patients in the lingual gyrus and left caudate were lower than those of HCs; and the ALFF values for the right fusiform of participants with G allele were lower than those of participants without G allele. And we further revealed higher ALFF values in bilateral fusiform in rs894278-G carriers than in rs894278-G non-carriers in the PD group and lower ALFF values in bilateral fusiform in rs894278-G carriers than in rs894278-G non-carriers in the HC group. Our findings show that rs894278 and PD status interactively affect the brain activity of PD patients and HCs, and changes in the brain connectomes may play a key role in the pathogenesis of PD. Thus, our work sheds light on the mechanism underlying PD pathogenesis

Induced cultivation pattern enhanced the phycoerythrin production in red alga Porphyridium purpureum.

Porphyridium purpureum is a rich source for producing phycoerythrin (PE); however, the PE content is greatly affected by culture conditions. Researchers have aimed to optimize the cultivation of P. purpureum for accumulation of PE. When traditional optimized culture conditions were used to cultivate P. purpureum, high PE contents were not usually achieved. In this study, an induced cultivation pattern was applied to P. purpureum for PE biosynthesis (i.e., an incremental approach by altering temperatures, light intensities, and nitrate concentrations). Results revealed that the induced pattern greatly improved the PE biosynthesis. The optimized PE content of 229 mg/L was achieved on the 12th cultivation day, which was a maximum PE content within one cultivation period and accounted for approximately 3.05% of the dry biomass. The induced cultivation pattern was highly suitable for PE synthesis in P. purpureum, which provided an important reference value to the large-scale production of PE