A rapid and reproducible liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed and validated to simultaneously determine sacubitril, valsartan and a metabolite of sacubitril (LBQ657) in human plasma using sacubitril-d4 and valsartan-d3 as the internal standards. Following protein precipitation, the analytes were operated on an Ultimate® XB-C18 column (2.1 × 50 mm, 3.5 μm, Welch) with a gradient elution with acetonitrile, and 5 mM ammonium acetate and 0.1% formic acid in water as the mobile phase. The detection was performed on a Triple Quad™ 4000 mass spectrometer coupled with an electrospray ionization source (ESI) under positive-ion multiple reaction monitoring mode. The linearities are 2.00-4000, 5.00-10000 and 5.00-10000 ng mL−1 for sacubitril, valsartan and LBQ657, respectively. The accuracy and precision of intra- and inter-day, dilution accuracy, recovery and stability of the method were all within the acceptable limits and no matrix effect or carryover was observed. The suitability of the method was successfully demonstrated in terms of the quantification of sacubitril, valsartan and LBQ657 in plasma samples collected from healthy Chinese volunteers in a clinical trial.

Background A sequential statistical strategy was used to optimize tannase production from Aspergillus tubingensis using tea stalks by solid-state fermentation. Results First, using a Plackett-Burman design, inoculum size and incubation time (among seven tested variables) were identified as the most significant factors for tannase yield. The effects of significant variables were further evaluated through a single steepest ascent experiment and central composite design with response surface analysis. Under optimal conditions, the experimental value of 84.24 units per gram of dry substrate (U/gds) closely matched the predicted value of 87.26 U/gds. Conclusions The result of the statistical approach was 2.09 times higher than the basal medium (40.22 U/gds). The results were fitted onto a second-order polynomial model with a correlation coefficient (R²) of 0.9340, which implied an adequate credibility of the model.