Multiple fingerprint profiling for quality evaluation of polysaccharides and related biological activity analysis of Chinese patent drugs: Zishen Yutai Pills as a case study.

ETHNOPHARMACOLOGICAL RELEVANCE: Zishen Yutai Pills (ZYP), a famous traditional Chinese patent medicine, has been widely applied to avoid recurrent miscarriage and treat threatened abortion. Polysaccharides of ZYP (ZYPPs) play an essential role in the theraprutic effects of ZYP. However, the complex compositions of ZYP and the complicated structure of ZYPPs have posed great challenges and barriers to the quality evaluation of ZYP. AIM OF THE STUDY: To identify and characterize the ZYPPs for better quality control of ZYP, a reliable and valid quality control system was established in this study. MATERIALS AND METHODS: A multi-fingerprint profile strategy based on HPSEC-MALL-RID, FT-IR, and HPLC (complete acid digested fingerprint, partial acid digested fingerprint and enzymatically digested fingerprint) was established to identify and discriminate the chemical structure of ZYPPs. Besides, the purpose of revealing the relationships between structure and biological activity of ZYPPs, their chemical characteristics, in vitro antioxidant and anti-glycation activities were investigated and discussed. RESULTS: The similarity evaluation of ZYPPs indicated ZYPPs from different batches showed a high similarity based on the correlation coefficient values of multi-fingerprints. Furthermore, ZYPPs exhibited remarkable antioxidant and antiglycation properties, which might be attributed to their molecular weights and the content of uronic acids. CONCLUSIONS: These results indicated that the multiple fingerprint technique was reliable and effective for the improvement of quality control of ZYPPs, suggesting the multiple fingerprint technique could also be potentially applied as a valid and feasible strategy to control the quality of polysaccharide-enriched herbal medicines.

Accelerating Drug Discovery by Early Protein Drug Target Prediction Based on a Multi-Fingerprint Similarity Search.

In this continuing work, we have updated our recently proposed Multi-fingerprint Similarity Search algorithm (MuSSel) by enabling the generation of dominant ionized species at a physiological pH and the exploration of a larger data domain, which included more than half a million high-quality small molecules extracted from the latest release of ChEMBL (version 24.1, at the time of writing). Provided with a high biological assay confidence score, these selected compounds explored up to 2822 protein drug targets. To improve the data accuracy, samples marked as prodrugs or with equivocal biological annotations were not considered. Notably, MuSSel performances were overall improved by using an object-relational database management system based on PostgreSQL. In order to challenge the real effectiveness of MuSSel in predicting relevant therapeutic drug targets, we analyzed a pool of 36 external bioactive compounds published in the Journal of Medicinal Chemistry from October to December 2018. This study demonstrates that the use of highly curated chemical and biological experimental data on one side, and a powerful multi-fingerprint search algorithm on the other, can be of the utmost importance in addressing the fate of newly conceived small molecules, by strongly reducing the attrition of early phases of drug discovery programs.