Supersaturation is a promising strategy to improve gastrointestinal absorption of poorly water-soluble drugs. Supersaturation is a metastable state and therefore dissolved drugs often quickly precipitate again. Precipitation inhibitors can prolong the metastable state. Supersaturating drug delivery systems (SDDS) are commonly formulated with precipitation inhibitors, hence the supersaturation is effectively prolonged for absorption, leading to improved bioavailability. This review summarizes the theory of and systemic insight into supersaturation, with the emphasis on biopharmaceutical aspects. Supersaturation research has developed from the generation of supersaturation (pH-shift, prodrug and SDDS) and the inhibition of precipitation (the mechanism of precipitation, the character of precipitation inhibitors and screening precipitation inhibitors). Then, the evaluation approaches to SDDS are discussed, including in vitro, in vivo and in silico studies and in vitro–in vivo correlations. In vitro aspects involve biorelevant medium, biomimetic apparatus and characterization instruments; in vivo aspects involve oral absorption, intestinal perfusion and intestinal content aspiration and in silico aspects involve molecular dynamics simulation and pharmacokinetic simulation. More physiological data of in vitro studies should be taken into account to simulate the in vivo environment. The supersaturation theory should be further completed, especially with regard to physiological conditions.

As a subclass of the biopharmaceutical classification system (BCS) class II, basic drugs (BCS IIB) exhibit pH-dependent solubility and tend to generate supersaturation in the gastrointestinal tract, leading to less qualified in vitro–in vivo correlation (IVIVC). This study aims to develop a physiologically based multi-cup dissolution approach to improve the evaluation of the supersaturation for a higher quality of IVIVC and preliminarily explores the molecular mechanism of supersaturation and precipitation of ketoconazole affected by Polyvinylpyrrolidone–vinyl acetate copolymer (PVPVA) and hydroxypropyl methyl-cellulose (HPMC). The concentration of ketoconazole in each cup of the dynamic gastrointestinal model (DGIM) was measured using fiber optical probes. Molecular interactions between ketoconazole and PVPVA or HPMC were simulated by Materials Studio. The results demonstrated that PVPVA and HPMC improved and maintained the supersaturation of ketoconazole. PVPVA exhibited superior precipitation inhibitory effect on ketoconazole molecule aggregation due to slightly stronger van der Waals forces as well as unique electrostatic forces, thereby further enhancing in vitro drug absorption, which correlated well with in vivo drug absorption. Compared with a conventional dissolution apparatus paddle method, the DGIM improved the mean prediction error through the IVIVC from 19.30% to 9.96%, reaching the qualification criteria. In conclusion, the physiologically based multi-cup dissolution approach enables improved evaluation of supersaturation in gastrointestinal transportation of BCS IIB drug ketoconazole, enabling screening screen precipitation inhibitors and achieving qualified IVIVC for drug formulation studies.

The amount of medical text data is increasing dramatically. Medical text data record the progress of medicine and imply a large amount of medical knowledge. As a natural language, they are characterized by semistructured, high-dimensional, high data volume semantics and cannot participate in arithmetic operations. Therefore, how to extract useful knowledge or information from the total available data is very important task. Using various techniques of data mining can extract valuable knowledge or information from data. In the current study, we reviewed different approaches to apply for medical text data mining. The advantages and shortcomings for each technique compared to different processes of medical text data were analyzed. We also explored the applications of algorithms for providing insights to the users and enabling them to use the resources for the specific challenges in medical text data. Further, the main challenges in medical text data mining were discussed. Findings of this paper are benefit for helping the researchers to choose the reasonable techniques for mining medical text data and presenting the main challenges to them in medical text data mining.

Background: Most lung cancer patients worldwide (stage IV non-small cell lung cancer, NSCLC) have a poor survival: 25%-30% patients die < 3 months. Yet, of those surviving > 3 months, 10%-15% patients survive (very) long. Astragali radix (AR) is an effective traditional Chinese medicine widely used for non-small cell lung cancer (NSCLC). However, the pharmacological mechanisms of AR on NSCLC remain to be elucidated. Methods: Ultra Performance Liquid Chromatography system coupled with Q-Orbitrap HRMS (UPLC-Q-Orbitrap HRMS) was performed for the qualitative analysis of AR components. Then, network module analysis and molecular docking-based approach was conducted to explore underlying mechanisms of AR on NSCLC. The target genes of AR were obtained from four databases including TCMSP (Traditional Chinese Medicine Systems Pharmacology) database, ETCM (The Encyclopedia of TCM) database, HERB (A high-throughput experiment- and reference-guided database of TCM) database and BATMAN-TCM (a Bioinformatics Analysis Tool for Molecular mechanism of TCM) database. NSCLC related genes were screened by GEO (Gene Expression Omnibus) database. The STRING database was used for protein interaction network construction (PIN) of AR-NSCLC shared target genes. The critical PIN were further constructed based on the topological properties of network nodes. Afterwards the hub genes and network modules were analyzed, and enrichment analysis were employed by the R package clusterProfiler. The Autodock Vina was utilized for molecular docking, and the Gromacs was utilized for molecular dynamics simulations Furthermore, the survival analysis was performed based on TCGA (The Cancer Genome Atlas) database. Results: Seventy-seven AR components absorbed in blood were obtained. The critical network was constructed with 1447 nodes and 28,890 edges. Based on topological analysis, 6 hub target genes and 7 functional modules were gained. were obtained including TP53, SRC, UBC, CTNNB1, EP300, and RELA. After module analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that AR may exert therapeutic effects on NSCLC by regulating JAK-STAT signaling pathway, PI3K-AKT signaling pathway, ErbB signaling pathway, as well as NFkB signaling pathway. After the intersection calculation of the hub targets and the proteins participated in the above pathways, TP53, SRC, EP300, and RELA were obtained. These proteins had good docking affinity with astragaloside IV. Furthermore, RELA was associated with poor prognosis of NSCLC patients. Conclusions: This study could provide chemical component information references for further researches. The potential pharmacological mechanisms of AR on NSCLC were elucidated, promoting the clinical application of AR in treating NSCLC. RELA was selected as a promising candidate biomarker affecting the prognosis of NSCLC patients.

According to Traditional Chinese Medicine (TCM), Aconiti Radix Cocta (AC) is clinically employed to expel wind, remove dampness, and relieve pain. We evaluated the antirheumatoid arthritis (RA) activities and underlying mechanisms of AC. The chemical constituents of AC were analyzed by high-performance liquid chromatography (HPLC) using three reference compounds (benzoylaconitine, benzoylmesaconine, and benzoylhypacoitine). The anti-RA effects of AC were evaluated in adjuvant-induced arthritis (AIA) rats by hind paw volume and histopathological analysis. The effects of AC on inflammatory cytokines (IL-1β and IL-17A) were determined by enzyme-linked immunosorbent assay. The regulation of cyclooxygenases (COX-1 and/or COX-2) was determined by Western blot and real-time quantitative reverse transcription polymerase chain reaction analyses. AC significantly reduced paw swelling, attenuated the inflammation and bone destruction in joint tissues, and reduced IL-1β and IL-17A in the serum. Moreover, AC downregulated the expression of COX-1 and COX-2 in the synovial tissues. We also identified that AC possesses significant anti-RA activities on AIA, which may be ascribed to the regulation of inflammatory cytokines IL-1β and IL-17, as well as to the inhibition of arachidonic acid signaling pathways. Our findings provide theoretical support for AC as an effective nature-derived therapeutic agent for RA treatment.

Background. The Coronavirus Disease 2019 (COVID‐19) outbreak in Wuhan, China, was caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Anisodamine hydrobromide injection (AHI), the main ingredient of which is anisodamine, is a listed drug for improving microcirculation in China. Anisodamine can improve the condition of patients with COVID‐19. Materials and Methods. Protein‐protein interactions obtained from the String databases were used to construct the protein interaction network (PIN) of AHI using Cytoscape. The crucial targets of AHI PIN were screened by calculating three topological parameters. Gene ontology and pathway enrichment analyses were performed. The intersection between the AHI component proteins and angiotensin‐converting enzyme 2 (ACE2) coexpression proteins was analyzed. We further investigated our predictions of crucial targets by performing molecular docking studies with anisodamine. Results. The PIN of AHI, including 172 nodes and 1454 interactions, was constructed. A total of 54 crucial targets were obtained based on topological feature calculations. The results of Gene Ontology showed that AHI could regulate cell death, cytokine‐mediated signaling pathways, and immune system processes. KEGG disease pathways were mainly enriched in viral infections, cancer, and immune system diseases. Between AHI targets and ACE2 coexpression proteins, 26 common proteins were obtained. The results of molecular docking showed that anisodamine bound well to all the crucial targets. Conclusion. The network pharmacological strategy integrated molecular docking to explore the mechanism of action of AHI against COVID‐19. It provides protein targets associated with COVID‐19 that may be further tested as therapeutic targets of anisodamine.