A new pimarane-type diterpene obtained by biotransformation inhibits human HCT-116 colorectal carcinoma through inhibition of LTA4H activity

Cordyceps sinensis-mediated biotransformation of notoginsenoside R1 into 25-OH-20(S/R)-R2 with elevated cardioprotective effect against DOX-induced cell injury

Notoginsenoside R1 is a dammarane saponin in Panax notoginseng with promising cardioprotective effects. The bioactivity–structure relationship of such saponins suggested that the presence of a hydroxyl group at C25 could elevate its performance. To fulfill that goal, bioconversion of notoginsenoside R1 was mediated by a biocatalytic system of Cordyceps sinensis that had successfully produced multiple 25-OH derivatives from ginsenoside Re and Rg1. The major metabolic products of notoginsenoside R1 were identified as 25-OH-20(S/R)-R2 via the techniques of HRMS, ¹³C-NMR, ¹H-NMR, HSQC and HMBC. Time-course experiments were designed to monitor the reaction process, establishing a biocatalytic pathway of “R1→20(S/R)-R2→25-OH-20(S/R)-R2”. The bioconversion rate of these 25-OH derivatives added up to 69.87% which greatly precedes the previous report. Afterwards, the effect of these biocatalytic products against doxorubicin-induced cardiotoxicity was evaluated, indicating a significant increase in efficacy after the hydration of the C24–C25 double bond on the dammarane skeleton. In conclusion, the biocatalytic system employed in this paper is able to harvest 25-OH-20(S/R)-R2 in high yield from notoginsenoside R1, which will provide lead compounds or drug candidates to alleviate myocardial injury caused by doxorubicin.

The biocatalytic system in this paper preferably yielded 25-OH notoginsenoside R2 from R1 in a regioselective manner. Such a process significantly elevated the effects of these 25-OH derivatives against DOX-induced cardiomyocyte injury.

Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods

Leukotriene A4 hydrolase (LTA4H) is an important anti-inflammatory target which can convert leukotriene A4 (LTA4) into pro-inflammatory substance leukotriene B4 (LTB4). In this paper, we built 18 classification models for 463 LTA4H inhibitors by using support vector machine (SVM), random forest (RF) and K-Nearest Neighbour (KNN). The best classification model (Model 2A) was built from RF and MACCS fingerprints. The prediction accuracy of 88.96% and the Matthews correlation coefficient (MCC) of 0.74 had been achieved on the test set. We also divided the 463 LTA4H inhibitors into six subsets using K-Means. We found that the highly active LTA4H inhibitors mostly contained diphenylmethane or diphenyl ether as the scaffold and pyridine or piperidine as the side chain. In addition, six quantitative structure-activity relationship (QSAR) models for 172 LTA4H inhibitors were built by multiple linear regression (MLR) and SVM. The best QSAR model (Model 6A) was built by using SVM and CORINA Symphony descriptors. The coefficients of determination of the training set and the test set were equal to 0.81 and 0.79, respectively. Classification and QSAR models could be used for subsequent virtual screening, and the obtained fragments that were important for highly active inhibitors would be helpful for designing new LTA4H inhibitors.