Ban-Xia-Xie-Xin-Tang (BXXXT) is a classical formula from Shang-Han-Lun which is one of the earliest books of TCM clinical practice. In this work, we investigated the therapeutic mechanisms of BXXXT for the treatment of multiple diseases using a network pharmacology approach. Here three BXXXT representative diseases (colitis, diabetes mellitus, and gastric cancer) were discussed, and we focus on in silico methods that integrate drug-likeness screening, target prioritizing, and multilayer network extending. A total of 140 core targets and 72 representative compounds were finally identified to elucidate the pharmacology of BXXXT formula. After constructing multilayer networks, a good overlap between BXXXT nodes and disease nodes was observed at each level, and the network-based proximity analysis shows that the relevance between the formula targets and disease genes was significant according to the shortest path distance (SPD) and a random walk with restart (RWR) based scores for each disease. We found that there were 22 key pathways significantly associated with BXXXT, and the therapeutic effects of BXXXT were likely addressed by regulating a combination of targets in a modular pattern. Furthermore, the synergistic effects among BXXXT herbs were highlighted by elucidating the molecular mechanisms of individual herbs, and the traditional theory of “Jun-Chen-Zuo-Shi” of TCM formula was effectively interpreted from a network perspective. The proposed approach provides an effective strategy to uncover the mechanisms of action and combinatorial rules of BXXXT formula in a holistic manner.

P-glycoprotein (P-gp) is regarded as an important factor in determining the ADMET (absorption, distribution, metabolism, elimination, and toxicity) characteristics of drugs and drug candidates. Successful prediction of P-gp inhibitors can thus lead to an improved understanding of the underlying mechanisms of both changes in the pharmacokinetics of drugs and drug-drug interactions. Therefore, there has been considerable interest in the development of in silico modeling of P-gp inhibitors in recent years. Considering that a large number of molecular descriptors are used to characterize diverse structural moleculars, efficient feature selection methods are required to extract the most informative predictors. In this work, we constructed an extensive available data set of 2428 molecules that includes 1518 P-gp inhibitors and 910 P-gp noninhibitors from multiple resources. Importantly, a two-step feature selection approach based on a genetic algorithm and a greedy forward-searching algorithm was employed to select the minimum set of the most informative descriptors that contribute to the prediction of P-gp inhibitors. To determine the best machine learning algorithm, 18 classifiers coupled with the feature selection method were compared. The top three best-performing models (flexible discriminant analysis, support vector machine, and random forest) and their ensemble model using respectively only 3, 9, 7, and 14 descriptors achieve an overall accuracy of 83.2%-86.7% for the training set containing 1040 compounds, an overall accuracy of 82.3%-85.5% for the test set containing 1039 compounds, and a prediction accuracy of 77.4%-79.9% for the external validation set containing 349 compounds. The models were further extensively validated by DrugBank database (1890 compounds). The proposed models are competitive with and in some cases better than other published models in terms of prediction accuracy and minimum number of descriptors. Applicability domain then was addressed by developing an ensemble classification model to obtain more reliable predictions. Finally, we employed these models as a virtual screening tool for identifying potential P-gp inhibitors in Traditional Chinese Medicine Systems Pharmacology (TCMSP) database containing a total of 13 051 unique compounds from 498 herbs, resulting in 875 potential P-gp inhibitors and 15 inhibitor-rich herbs. These predictions were partly supported by a literature search and are valuable not only to develop novel P-gp inhibitors from TCM in the early stages of drug development, but also to optimize the use of herbal remedies.

BackgroundColorectal cancer remains one of the leading causes of cancer death worldwide. Traditional Chinese Medicine (TCM) has played a positive role in colorectal cancer treatment. There is a great need to establish effective herbal formula for colorectal cancer treatment. Based on TCM principles and clinical practices, we have established an eight herbs composed formula for colorectal cancer treatment, which is Teng-Long-Bu-Zhong-Tang (TLBZT). We have demonstrated the anticancer effects of TLBZT against colorectal carcinoma in vitro. In present study, we evaluated the anticancer potential of TLBZT, used alone or in combination with low dose of 5-Fluorouracil (5-Fu), in CT26 colon carcinoma in vivo.MethodsCT26 colon carcinoma was established in BALB/c mice and treated with TLBZT, 5-Fu, or TLBZT plus 5-Fu. The tumor volumes were observed. Apoptosis was detected by TUNEL assay. Caspases activities were detected by colorimetric assay. Cell senescence was indentified by senescence β-galactosidase staining. Gene expression and angiogenesis was observed by immunohistochemistry or western blot.ResultsTLBZT significantly inhibited CT26 colon carcinoma growth. TLBZT elicited apoptosis in CT26 colon carcinoma, accompanied by Caspase-3, 8, and 9 activation and PARP cleavage, and downregulation of XIAP and Survivin. TLBZT also induced cell senescence in CT26 colon carcinoma, with concomitant upregulation of p16 and p21 and downregulation of RB phosphorylation. In addition, angiogenesis and VEGF expression in CT26 colon carcinoma was significantly inhibited by TLBZT treatment. Furthermore, TLBZT significantly enhanced anticancer effects of 5-Fu in CT26 colon carcinoma.ConclusionsTLBZT exhibited significantly anticancer effect, and enhanced the effects of 5-Fu in CT26 colon carcinoma, which may correlate with induction of apoptosis and cell senescence, and angiogenesis inhibition. The present study provides new insight into TCM approaches for colon cancer treatment that are worth of further study.

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The purpose of this study was to investigate the absorption properties of isorhamnetin (IS), quercetin (QU), and kaempferol (KA) in total flavones of Hippophaë rhamnoides L. (TFH) by an in situ single-pass intestinal perfusion model. The results indicated that IS, QU, and KA in TFH were absorbed site-dependently, and both enterohepatic circulation and intestinal flora could participate in their absorption processes. The absorption mechanisms of IS, QU, and KA in TFH were involved in both passive diffusion and active transport, and the mediation of efflux transporter multidrug resistance-associated proteins (MRPs) should not be neglected.