Advances in the study of berberine and its derivatives: a focus on anti-inflammatory and anti-tumor effects in the digestive system.

It has been widely recognized that inflammation, particularly chronic inflammation, can increase the risk of cancer and that the simultaneous treatment of inflammation and cancer may produce excellent therapeutic effects. Berberine, an alkaloid isolated from Rhizoma coptidis, has broad applications, particularly as an antibacterial agent in the clinic with a long history. Over the past decade, many reports have demonstrated that this natural product and its derivatives have high activity against both cancer and inflammation. In this review, we summarize the advances in studing berberine and its derivatives as anti-inflammatory and anti-tumor agents in the digestive system; we also discuss their structure-activity relationship. These data should be useful for the development of this natural product as novel anticancer drugs with anti-inflammation activity.

Multi-algorithm and multi-model based drug target prediction and web server.

AIM: To develop a reliable computational approach for predicting potential drug targets based merely on protein sequence. METHODS: With drug target and non-target datasets prepared and 3 classification algorithms (Support Vector Machine, Neural Network and Decision Tree), a multi-algorithm and multi-model based strategy was employed for constructing models to predict potential drug targets. RESULTS: Twenty one prediction models for each of the 3 algorithms were successfully developed. Our evaluation results showed that ∼30% of human proteins were potential drug targets, and ∼40% of putative targets for the drugs undergoing phase II clinical trials were probably non-targets. A public web server named D3TPredictor (http://www.d3pharma.com/d3tpredictor) was constructed to provide easy access. CONCLUSION: Reliable and robust drug target prediction based on protein sequences is achieved using the multi-algorithm and multi-model strategy.