Sensitive and effective method with 96-well plate for determination of levamlodipine in human plasma using LC-MS/MS.

we developed an effective protein precipitation method for determination of levamlodipine in human plasma using LC-MS/MS. Sample extraction was carried out by using liquid-liquid extraction in 96-well plate format. (S)-Amlodipine-d4 was used as internal standard (IS). The chromatographic separation was achieved using Philomen Chiral MX (2) column (3 µm, 2.1 × 100 mm). Mobile phase A was comprised of Acetonitrile (ACN), Mono ethanol amine (MEA) and Iso-Propyl alcohol (IPA) (1000:1:10, v/v/v), Mobile phase B was IPA-ACN (2:1, v/v). The flow rate was 0.4 mL/min. The total run time of each sample was 4.0 min with gradient elution. LC-MS/MS spectra were generated in positive ion mode, and multiple reaction monitoring (MRM) was used to detect the following transitions: m/z 409.20 â†’ 238.15 for levamlodipine and 415.25 â†’ 240.20 for (S)-Amlodipine-d4 (the IS). The method was linear from 50 to 10000 pg/mL（R2＝0.9988489）,and the lower limit of quantification (LLOQ) was 50 pg/mL. This method was applied to a bioequivalence study of levamlodipine.

Safety, tolerability, pharmacokinetics and neutrophil elastase inhibitory effects of Sivelestat: A randomized, double-blind, placebo-controlled single- and multiple-dose escalation study in Chinese healthy subjects.

BACKGROUND AND OBJECTIVE: Neutrophil elastase has been identified as a potential therapeutic target for acute lung injury or acute respiratory distress syndrome, and Sivelestat is a selective, reversible and competitive neutrophil elastase inhibitor. This study was designed to investigate the safety, tolerability, pharmacokinetics and neutrophil elastase inhibitory effects of Sivelestat in healthy Chinese subjects. METHODS: A randomized, double-blind, placebo-controlled single- and multiple-dose escalation clinical trial was carried out. Briefly, healthy volunteers in twelve cohorts with 8 per cohort received 1.0-20.2 mg/kg/h Sivelestat or placebo in an intravenous infusion manner for two hours, and healthy volunteers in four cohorts received two hours intravenous infusion of 2.0-5.0 mg/kg/h Sivelestat or placebo with an interval of twelve hours for seven times. The safety and tolerability were evaluated and serial blood samples were collected for pharmacokinetics and neutrophil elastase inhibitory effects analysis at the specified time-point. RESULTS: A total of 128 subjects were enrolled and all participants completed the study except one. Sivelestat exhibited satisfactory safety and tolerability up to 20.2 mg/kg/h in single-dose cohorts and 5.0 mg/kg/h in multiple-dose cohorts. Even so, more attention should be paid to the safety risks when using high doses. The Cmax and AUC of Sivelestat increased in a dose dependent manner, and Tmax was similar for different dose cohorts. In multiple-dose cohorts, the plasma concentrations reached steady state 48 h after first administration and the accumulation of Cmax and AUC was not obvious. Furthermore, the Cmin_ss of 5.0 mg/kg/h dose cohort could meet the needs of clinical treatment. For some reason, the pharmacodynamics data revealed that the inhibitory effect of Sivelestat on neutrophil elastase content in healthy subjects was inconclusive. CONCLUSION: Sivelestat was safe and well tolerated with appropriate pharmacokinetic parameters, which provided support for more diverse dosing regimen in clinical application. CLINICAL TRIAL REGISTRATION: www.chinadrugtrials.org.cn identifier is CTR20210072.

Tenofovir Alafenamide for Pregnant Chinese Women With Active Chronic Hepatitis B: A Multicenter Prospective Study.

BACKGROUND & AIMS: Data on long-term tenofovir alafenamide (TAF) therapy for pregnant women with active chronic hepatitis B (CHB) (immune clearance and reactivation phases, currently and previously diagnosed) and their infants are lacking. METHODS: Pregnant women with active CHB treated with TAF and tenofovir disoproxil fumarate (TDF) were enrolled in this multicenter prospective study, and infants received immunoprophylaxis. The primary outcomes were rates of adverse (safety) events in pregnant women and defects in infants and fetuses. The secondary outcomes were virologic responses in pregnant women, infants' safety, hepatitis B surface antigen (HBsAg) status, and growth conditions. RESULTS: One hundred three and 104 pregnant women were enrolled and 102 and 104 infants were born in the TAF and TDF groups, respectively. In the TAF group, the mean age, gestational age, alanine aminotransferase level, and viral loads at treatment initiation were 29.3 years, 1.3 weeks, 122.2 U/L, and 5.1 log10 IU/mL, respectively. TAF was well-tolerated, and the most common adverse event was nausea (29.1%) during a mean of 2 years of treatment. Notably, 1 (1.0%) TAF-treated pregnant woman underwent induced abortion due to noncausal fetal cleft lip and palate. No infants in either group had birth defects. In the TAF group, the hepatitis B e antigen seroconversion rate was 20.7% at postpartum month 6, infants had normal growth parameters, and no infants were positive for HBsAg at 7 months. The TDF group had comparable safety and effectiveness profiles. CONCLUSIONS: TAF administered throughout or beginning in early pregnancy is generally safe and effective for pregnant women with active CHB and their infants.

Detection of sivelestat and its metabolite in small volumes of plasma from Chinese ALI/ARDS patients with SIRS via high-throughput UPLC-MS/MS: A pharmacokinetic study.

In this study, we developed a sensitive and efficient analytical approach combining a 96-well plate-based protein precipitation strategy with ultra-performance liquid chromatography electrospray ionization tandem mass spectrometry (UPLC-MS/MS) in order to assess the pharmacokinetic (PK) properties of sivelestat and its metabolite XW-IMP-A in samples of plasma from ALI/ARDS patients with SIRS. The samples were separated via gradient elution with a C18 column (Phenomenex Kinetex, C18, 2.6 µm, 100 Å, 50 × 2.1 mm) using 0.1 % formic acid aqueous solution (A) and acetonitrile-methanol (1:1, V:V) (B) as a mobile phase at a 0.6 mL/min flow rate. UPLC-MS/MS spectra were generated in positive ion mode, and multiple reaction monitoring (MRM) was used to detect the following transitions: m/z 435.1 → 360.0 for sivelestat, m/z 469.0 → 394.0 for sivelestat-IS, m/z 351.0 → 276.0 for XW-IMP-A, and m/z 384.9 → 310.0 for XW-IMP-A-IS. This assay was run for 2.5 min in total, and achieved lowest limit of quantitation values of 2.0 ng/mL and 0.5 ng/mL for sivelestat and XW-IMP-A, respectively, while remaining highly linear from 2-500 ng/mL for sivelestat (r2 ≥ 0.9900) and from 0.5-125 ng/mL for XW-IMP-A (r2 ≥ 0.9900). These validated data were consistent with US Food and Drug Administration (FDA) and European Medicines Agency (EMA) acceptance criteria. In addition, this method was successfully applied to the steady-state PK evaluation of ALI/ARDS patients with SIRS.

Biosensing with microbial fuel cells and artificial neural networks: laboratory and field investigations.

In this study microbial fuel cell-based biosensing was integrated with artificial neural networks (ANNs) in laboratory and field testing of water samples. Inoculation revealed two types of anode-respiring bacteria (ARB) induction profiles, a relatively slow gradual profile and a faster profile that was preceded by a significant lag time. During laboratory testing, the MFCs generated well-organized normally distributed profiles but during field experiments the peaks had irregular shapes and were smaller in magnitude. Generally, the COD concentration correlated better with peak area than with peak height. The ANN predicted the COD concentration (R(2) = 0.99) with one layer of hidden neurons and for concentrations as low as 5 mg acetate-COD/L. Adding 50 mM of 2-bromoethanesulfonate amplified the electrical signals when glucose was the substrate. This report is the first to identify two types of ARB induction profiles and to demonstrate the power of ANNs for interpreting a wide variety of electrical response peaks.

Neural network processing of microbial fuel cell signals for the identification of chemicals present in water.

Biosensing is emerging as an important element of water quality monitoring. This research demonstrated that microbial fuel cell (MFC)-based biosensing can be integrated with artificial neural networks (ANNs) to identify specific chemicals present in water samples. The non-fermentable substrates, acetate and butyrate, induced peak areas (PA) and peak heights (PH) that were generally larger than those caused by the injection of fermentable substrates, glucose and corn starch. The ANN successfully identified peaks associated with these four chemicals under a variety of experimental conditions and for two MFCs that had different levels of sensitivity. ANNs that employ the hyperbolic tangent sigmoid transfer function performed better than those using non-continuous transfer functions. ANNs should be integrated into water quality monitoring efforts for smart biosensing.

Using microbial fuel cell output metrics and nonlinear modeling techniques for smart biosensing.

Microbial fuel cells (MFCs) are promising tools for water quality monitoring but the response peaks have not been characterized and the data processing methods require improvement. In this study MFC-based biosensing was integrated with two nonlinear programming methods, artificial neural networks (ANN) and time series analysis (TSA). During laboratory testing, the MFCs generated well-organized normally-distributed peaks when the influent chemical oxygen demand (COD) was 150 mg/L or less, and multi-peak signals when the influent COD was 200 mg/L. The area under the response peak correlated well with the influent COD concentration. During field testing, we observed normally-distributed and multi-peak profiles at low COD concentrations. The ANN predicted the COD concentration without error with just one layer of hidden neurons, and the TSA model predicted the temporal trends present in properly functioning MFCs and in a device that was gradually failing. This report is the first to integrate ANN and TSA with MFC-based biosensing.