Deep learning for in vitro prediction of pharmaceutical formulations

Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error approach by individual experiences of pharmaceutical scientists, which is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of this research is to use deep learning to predict pharmaceutical formulations. In this paper, two different types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assessing the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. The result shows the accuracies of both two deep neural networks were above 80% and higher than other machine learning models, which showed good prediction in pharmaceutical formulations. In summary, deep learning with the automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was firstly developed for the prediction of pharmaceutical formulations. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical researches from experience-dependent studies to data-driven methodologies

A synthetic peptide from Sipunculus nudus promotes bone formation via Estrogen/MAPK signal pathway based on network pharmacology

The tripeptide Leu-Pro-Lys (LPK), derived from the Sipunculus nudus protein, was synthesized and studied to investigate its potential protective effect on bone formation. The effect and mechanism of LPK were analyzed through network pharmacology, bioinformatics, and experimental pharmacology. The study found that LPK at concentrations of 25 μg/mL and 50 μg/mL significantly increased ALP activity and mineralization in C3H10 cells. LPK also increased the expression of COL1A1 and promoted bone formation in zebrafish larvae. Network pharmacology predicted 148 interaction targets between LPK and bone development, and analysis of the protein-protein interaction network identified 13 hub genes, including ESR1, MAPK8, and EGFR, involved in bone development. Through KEGG enrichment pathways analysis, it was determined that LPK promotes bone development by regulating endocrine resistance, the relaxin signaling pathway, and the estrogen signaling pathway. Molecular docking results showed direct interactions between LPK and ESR1, MAPK8, and MAPK14. Additional verification experiments using western blot assay revealed that LPK significantly upregulated the expression of genes related to bone formation, including COL1A1, OPG, RUNX2, ESR1, phosphorylated MAPK14, and phosphorylated MAPK8 in C3H10 cells. These results suggest that LPK promotes bone formation by activating the estrogen/MAPK signaling pathway

Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models

Over 100 genome-scale metabolic networks (GSMNs) have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficult to use the GSMNs for precise optimal pathway design. Therefore, it is necessary to compare and identify the discrepancies among networks and build a consensus metabolic network for an organism. Here we proposed a process for systematic comparison of metabolic networks at pathway level. We compared four published GSMNs of Pseudomonas putida KT2440 and identified the discrepancies leading to inconsistent pathway calculation results. The mistakes in the models were corrected based on information from literature so that all the calculated synthesis and uptake pathways were the same. Subsequently we built a pathway-consensus model and then further updated it with the latest genome annotation information to obtain modelPpuQY1140 for P. putida KT2440, which includes 1140 genes, 1171 reactions and 1104 metabolites. We found that even small errors in a GSMN could have great impacts on the calculated optimal pathways and thus may lead to incorrect pathway design strategies. Careful investigation of the calculated pathways during the metabolic network reconstruction process is essential for building proper GSMNs for pathway design

Influence of Land Use and Point Source Pollution on Water Quality in a Developed Region: A Case Study in Shunde, China

To design and implement policy to manage water quality, it is important to investigate land use and possible sources of pollution. In this study, using Pearson regression analysis, redundancy analysis and multiple regression analysis, we assess the influence of land use and point sources on water quality in the river system in Shunde district in 2000 and 2010. The results show that water quality was related positively with water surface but negatively with impervious and urban greening area. Additionally, water quality was related negatively to point source emissions of chemical oxygen demand (COD) and ammonium-nitrogen (NH4-N). The total explanatory power of spatial variation of water quality was improved from 43.4% to 60.0% in 2000 and from 31.3% to 57.8% in 2010, respectively, when the influence of point sources was added into redundancy analysis between water quality and land use. Thus, both land use management and point source pollution control should be considered for improving river water quality

Comparative Evaluation of Magnesium Bisulfite Pretreatment under Different pH Values for Enzymatic Hydrolysis of Corn Stover

During pretreatment, the pretreatment pH often plays an important role in removing hemicelluloses and lignin for improving the conversion of biomass to sugars. In this study, corn stover was subjected to magnesium bisulfite pretreatment (MBSP) under various pH conditions. The obtained data showed that the hemicelluloses and lignin were solubilized by MBSP, which led to changes in the structural and chemical properties of the pretreated material. The pretreatment pH could alter the existing forms of SO2, and magnesium bisulfite was the most effective reagent for removing lignin. A relatively neutral MBSP (pH 5.13) not only considerably improved the enzymatic hydrolysis yield (80.18%), but also produced a large amount of high-value xylo-oligosaccharides in the spent liquor. Furthermore, only the hemicellulose removal showed a linear relationship with the enzymatic hydrolysis yield. These results suggest that removal of all the lignin might not be necessary to improve the hydrolysis efficiency

Biomass-Templated Fabrication of Metallic Materials for Photocatalytic and Bactericidal Applications

In this paper, we report a simple, feasible and low-cost method to fabricate self-standing metallic materials using cellulose-based biomass as sacrificial templates. This process involves the impregnation of metallic precursors to the cellulose fibers of biomass templates and the transformation of the precursors to corresponding metals or metal oxides (as well as the removal of the cellulose framework) at an elevated temperature. The structures of the metallic materials as fabricated take the form of architectures of biomass templates (e.g., chromatography paper, medical absorbent cotton, catkins of reed, seed balls of oriental plane, and petals of peach blossom), and the various kinds of metals and metal oxides fabricated with these templates include silver, gold, anatase, cupric oxide, zinc oxide, etc. We have demonstrated photocatalytic and bactericidal applications of such metallic materials, and they should find more applications in electronics, catalysis, energy storage, biomedicine and so on

Establishment of Epidemiological Resistance Cut-Off Values of Aquatic Aeromonas to Eight Antimicrobial Agents

The abuse of antibiotics in aquaculture has led to the increasing rate of antibiotic resistance of aquatic bacteria including Aeromonas, which is an increasing threat to environmental and human health. To date, no epidemiological cut-off values (COWT) for Aeromonas spp. have been established by the Clinical and Laboratory Standards Institute nor the European Commission on Antimicrobial Susceptibility Testing. In this study, commercially prepared minimum inhibitory concentration (MIC) test 96-well plates (dry-form plates) were used to determine the MIC of eight antimicrobial agents against 556 Aeromonas strains. The obtained MIC distributions were simulated and analyzed by NRI and ECOFFinder to obtain tentative COWT values for Aeromonas spp. The COWT values of eight kinds of representative antimicrobial agents including trimethoprim–sulfamethoxazole, erythromycin, doxycycline, neomycin, colistin, florfenicol, enrofloxacin, and ceftazidime for Aeromonas spp. were established and were 0.25, 64/32, 4/2, 8, 4, 1, 0.062/0.125, and 0.5 μg/mL, respectively. Results showed that Aeromonas spp. had a very high proportion of non-wild-type strains to enrofloxacin, florfenicol, and doxycycline, which are the most widely used antimicrobials in aquaculture. The COWT values for Aeromonas spp. obtained in this study can contribute to the final establishment of COWT for Aeromonas spp. internationally