Pharmacokinetic Study of Nalbuphine in Surgical Patients Undergoing General Anesthesia with Varying Degrees of Liver Dysfunction

Hepatotoxic assessment in a microphysiological system: Simulation of the drug absorption and toxic process after an overdosed acetaminophen on intestinal-liver-on-chip

To compensate the limitation of animal models, new models were proposed for drug safety evaluation to refine and reduce existing models. To mimic drug absorption and metabolism and predict toxicokinetic and toxic effects in an in vitro intestinal-liver microphysiological system (MPS), we constructed an intestinal-liver-on-chip and detected the acute liver injury process after an overdose of acetaminophen (APAP). Caco-2 and HT29-MTX-E12 cell lines were utilized to establish intestinal equivalents, along with HepG2, HUVEC-T1, and THP-1 induced by PMA and human hepatic stellate cell to establish liver equivalents. The APAP concentration was determined using high-performance liquid chromatography, and the toxicokinetic parameters were fitted using the non-compartmental analysis method by Phoenix. Changes in liver injury biomarkers aspartate aminotransferase and alanine aminotransferase, and liver function marker albumin indicated that the short-term culture of the two organs-on-chip model was stable for 4 days. Reactive oxygen species signaling was enhanced after APAP administration, along with decreased mitochondrial membrane potential, activated caspase-3, and enhanced p53 signaling, indicating a toxic response induced by APAP overdose. In the gut-liver MPS model, we fitted the toxicokinetic parameters and simulated the hepatotoxicity procedure following an APAP overdose, which will facilitate the organ-on-chips application in drug toxicity assays.

The effect of nalbuphine on prevention of emergence delirium in children: a systematic review with meta-analysis

BACKGROUND

Emergence delirium remains a major postoperative concern for children undergoing surgery. Nalbuphine is a synthetic mixed agonist-antagonist opioid, which is believed to reduce the incidence of emergence delirium in children. The primary objective was to examine the effect of nalbuphine on emergence delirium in children undergoing surgery.

METHODS

Databases of MEDLINE, EMBASE, and CENTRAL were searched from their starting dates until April 2023. Randomized Clinical Trials (RCT) and observational studies comparing nalbuphine and control in children undergoing surgery were included.

RESULTS

Eight studies (n = 1466 patients) were eligible for inclusion of data analysis. Compared to the control, our pooled data showed that the nalbuphine group was associated with lower incidence of emergence delirium (RR = 0.38, 95% CI [0.30, 0.47], p < 0.001) and reduced postoperative pain scores (MD = -0.98, 95% CI [-1.92, -0.04], p = 0.04).

CONCLUSIONS

This review showed the administration of nalbuphine is associated with significant decrease in the incidence of emergence delirium and postoperative pain scores among children undergoing surgery. However, due to limited sample size, high degree of heterogeneity and low level of evidence, future adequately powered trials are warranted to explore the efficacy of nalbuphine on emergence delirium among the pediatric population.

CPhaMAS: The first pharmacokinetic analysis cloud platform developed by China

OBJECTIVES

Software for pharmacological modeling and statistical analysis is essential for drug development and individualized treatment modeling. This study aims to develop a pharmacokinetic analysis cloud platform that leverages cloud-based benefits, offering a user-friendly interface with a smoother learning curve.

METHODS

The platform was built using Rails as the framework, developed in Julia language, and employs PostgreSQL 14 database, Redis cache, and Sidekiq for asynchronous task management. Four commonly used modules in clinical pharmacology research were developed: Non-compartmental analysis, bioequivalence/bioavailability analysis, compartment model analysis, and population pharmacokinetics modeling. The platform ensured comprehensive data security and traceability through multiple safeguards, including data encryption, access control, transmission encryption, redundant backups, and log management. The platform underwent basic function, performance, reliability, usability, and scalability testing, along with practical case studies.

RESULTS

The CPhaMAS cloud platform successfully implemented the 4 module functionalities. The platform provides a list-based navigation for users, featuring checkbox-style interactions. Through cloud computing, it allows direct online data analysis, saving computer storage and minimizing performance requirements. Modeling and visualization do not require programming knowledge. Basic functionality achieved 100% completion, with an average annual uptime of over 99%. Server response time was between 200 to 500 ms, and average CPU usage was maintained below 30%. In a practical case study, cefotaxime sodium/tazobactam sodium injection (6꞉1 ratio) displayd near-linear pharmacokinetics within a dose range of 1.0 to 4.0 g, with no significant effect of tazobactam on the pharmacokinetic parameters of cefotaxime, validating the platform's usability and reliability.

CONCLUSIONS

CPhaMAS provides an integrated modeling and statistical tool for educators, researchers, and industrial professionals, enabling non-compartmental analysis, bioequivalence/bioavailability analysis, compartmental model building, and population pharmacokinetic modeling and simulation.

CPhaMAS: An online platform for pharmacokinetic data analysis based on optimized parameter fitting algorithm

BACKGROUND AND OBJECTIVE

Clinical pharmacological modeling and statistical analysis software is an essential basic tool for drug development and personalized drug therapy. The learning curve of current basic tools is steep and unfriendly to beginners. The curve is even more challenging in cases of significant individual differences or measurement errors in data, resulting in difficulties in accurately estimating pharmacokinetic parameters by existing fitting algorithms. Hence, this study aims to explore a new optimized parameter fitting algorithm that reduces the sensitivity of the model to initial values and integrate it into the CPhaMAS platform, a user-friendly online application for pharmacokinetic data analysis.

METHODS

In this study, we proposed an optimized Nelder-Mead method that reinitializes simplex vertices when trapped in local solutions and integrated it into the CPhaMAS platform. The CPhaMAS, an online platform for pharmacokinetic data analysis, includes three modules: compartment model analysis, non-compartment analysis (NCA) and bioequivalence/bioavailability (BE/BA) analysis. Our proposed CPhaMAS platform was evaluated and compared with existing WinNonlin.

RESULTS

The platform was easy to learn and did not require code programming. The accuracy investigation found that the optimized Nelder-Mead method of the CPhaMAS platform showed better accuracy (smaller mean relative error and higher R2) in two-compartment and extravascular administration models when the initial value was set to true and abnormal values (10 times larger or smaller than the true value) compared with the WinNonlin. The mean relative error of the NCA calculation parameters of CPhaMAS and WinNonlin was <0.0001 %. When calculating BE for conventional, high-variability and narrow-therapeutic drugs. The main statistical parameters of the parameters Cmax, AUCt, and AUCinf in CPhaMAS have a mean relative error of <0.01% compared to WinNonLin.

CONCLUSIONS

In summary, CPhaMAS is a user-friendly platform with relatively accurate algorithms. It is a powerful tool for analysing pharmacokinetic data for new drug development and precision medicine.

Population pharmacokinetics of nalbuphine in patients undergoing general anesthesia surgery

Purpose: The aim of this study was to build a population pharmacokinetics (PopPK) model of nalbuphine and to estimate the suitability of bodyweight or fixed dosage regimen. Method: Adult patients who were undergoing general anesthetic surgery using nalbuphine for induction of anesthesia were included. Plasma concentrations and covariates information were analyzed by non-linear mixed-effects modeling approach. Goodness-of-fit (GOF), non-parametric bootstrap, visual predictive check (VPC) and external evaluation were applied for the final PopPK model evaluation. Monte Carlo simulation was conducted to assess impact of covariates and dosage regimens on the plasma concentration to nalbuphine. Results: 47 patients aged 21-78 years with a body weight of 48-86 kg were included in the study. Among them, liver resection accounted for 14.8%, cholecystectomy for 12.8%, pancreatic resection for 36.2% and other surgeries for 36.2%. 353 samples from 27 patients were enrolled in model building group; 100 samples from 20 patients were enrolled in external validation group. The results of model evaluation showed that the pharmacokinetics of nalbuphine was adequately described by a two-compartment model. The hourly net fluid volume infused (HNF) was identified as a significant covariate about the intercompartmental clearance (Q) of nalbuphine with objective function value (OFV) decreasing by 9.643 (p < 0.005, df = 1). Simulation results demonstrated no need to adjust dosage based on HNF, and the biases of two dosage methods were less than 6%. The fixed dosage regimen had lower PK variability than the bodyweight regimen. Conclusion: A two-compartment PopPK model adequately described the concentration profile of nalbuphine intravenous injection for anesthesia induction. While HNF can affect the Q of nalbuphine, the magnitude of the effect was limited. Dosage adjustment based on HNF was not recommended. Furthermore, fixed dosage regimen might be better than body weight dosage regimen.