Population Pharmacokinetic‐Pharmacodynamic Modeling for Propofol Anesthesia Guided by the Bispectral Index (BIS)

Population pharmacokinetic/pharmacodynamic modeling and exposure-response analysis of ciprofol in the induction and maintenance of general anesthesia in patients undergoing elective surgery: A prospective dose optimization study

AIM

This study aimed to establish a population pharmacokinetic and pharmacodynamic (PK-PD) model to explore the optimal maintenance dose and appropriate starting time of maintenance dose after induction of ciprofol and investigate the efficacy and safety of ciprofol for general anesthesia induction and maintenance in patients undergoing elective surgery.

METHOD

A total of 334 subjects with 3092 concentration measurements from nine clinical trials and 115 subjects with 5640 bispectral index (BIS) measurements from two clinical trials were used in the population PK-PD analysis. Exposure-response relationships for both efficacy endpoints (duration of anesthesia successful induction, time to recovery from anesthesia, time to respiratory recovery, and time from discontinuation to the 1st/3rd consecutive Aldrete score ≥ 9) and safety variables (hypotension, bradycardia, and injection site pain) were evaluated based on the data gathered from 115 subjects in two clinical trials.

RESULT

Ciprofol pharmacokinetics (PK) were adequately described by a three-compartment model with first-order elimination from the central compartment and redistribution from the deep and shallow peripheral compartments. An inhibitory sigmoidal Emax model best described the relationship between ciprofol effect-site concentrations and BIS measurements. Body weight, age, sex, blood sampling site, and study type (short-term infusion vs long-term infusion) were identified as statistically significant covariates on the PK of ciprofol. No covariates were found to have a significant effect on the pharmacodynamic (PD) parameters. The PK-PD simulation results showed that the optimal maintenance dose was 0.8 mg/kg/h and the appropriate time to start the maintenance dose was 4-5 mins after the induction dose of ciprofol. Within the exposure range of this study, no meaningful correlations between ciprofol exposures and efficacy or safety endpoints were observed.

CONCLUSION

A population PK-PD model was successfully developed to describe the ciprofol PK and BIS changes. Efficacy was consistent across the exposure range with a well-tolerated safety profile indicating no maintenance dose adjustment is required for patients undergoing elective surgery.

General purpose models for intravenous anesthetics, the next generation for target-controlled infusion and total intravenous anesthesia?

PURPOSE OF REVIEW

There are various pharmacokinetic-dynamic models available, which describe the time course of drug concentration and effect and which can be incorporated into target-controlled infusion (TCI) systems. For anesthesia and sedation, most of these models are derived from narrow patient populations, which restricts applicability for the overall population, including (small) children, elderly, and obese patients. This forces clinicians to select specific models for specific populations.

RECENT FINDINGS

Recently, general purpose models have been developed for propofol and remifentanil using data from multiple studies and broad, diverse patient groups. General-purpose models might reduce the risks associated with extrapolation, incorrect usage, and unfamiliarity with a specific TCI-model, as they offer less restrictive boundaries (i.e., the patient "doesn't fit in the selected model") compared with the earlier, simpler models. Extrapolation of a model can lead to delayed recovery or inadequate anesthesia. If multiple models for the same drug are implemented in the pump, it is possible to select the wrong model for that specific case; this can be overcome with one general purpose model implemented in the pump.

SUMMARY

This article examines the usability of these general-purpose models in relation to the more traditional models.

Population total and unbound pharmacokinetics and pharmacodynamics of ciprofol and M4 in subjects with various renal functions

AIMS

The aim of this study was to develop a population pharmacokinetic (PK) model to simultaneously describe both total and unbound concentrations of ciprofol and its major glucuronide metabolite, M4, and to link it to the population pharmacodynamics (PD) model in subjects with various renal functions.

METHODS

A total of 401 and 459 pairs of total and unbound plasma concentrations of ciprofol and M4, respectively, as well as 2190 bispectral index (BIS) data from 24 Chinese subjects with various renal functions were available. Covariates that may potentially contribute to the PK and PD variability of ciprofol were screened using a stepwise procedure. The optimal ciprofol induction dosing regimen was determined by model-based simulations.

RESULTS

The PK of unbound ciprofol could best be described by a three-compartment model, while a two-compartment model could adequately describe unbound M4 PK. The concentrations of total and unbound ciprofol and M4 were linked using a linear protein binding model. The relationship between plasma concentrations of ciprofol and BIS data was best described by an inhibitory sigmoidal E_max model with a two-compartment biophase distribution compartment. Hemoglobin was the identified covariate determining the central compartment clearance of ciprofol; uric acid was a covariate affecting the central compartment clearance of M4 and protein binding rate, k_B . The included covariates had no effect on the PD of ciprofol. Simulation results indicated that the label-recommended dose regimen was adequate for anaesthesia induction.

CONCLUSIONS

The developed model fully characterized the population PK and PD profiles of ciprofol. No dose adjustment is required in patients with mild and moderate renal impairment.

Robust PID control of propofol anaesthesia: Uncertainty limits performance, not PID structure

BACKGROUND AND OBJECTIVE

New proposals to improve the regulation of hypnosis in anaesthesia based on the development of advanced control structures emerge continuously. However, a fair study to analyse the real benefits of these structures compared to simpler clinically validated PID-based solutions has not been presented so far. The main objective of this work is to analyse the performance limitations associated with using a filtered PID controller, as compared to a high-order controller, represented through a Youla parameter.

METHODS

The comparison consists of a two-steps methodology. First, two robust optimal filtered PID controllers, considering the effect of the inter-patient variability, are synthesised. A set of 47 validated paediatric pharmacological models, identified from clinical data, is used to this end. This model set provides representative inter-patient variability Second, individualised filtered PID and Youla controllers are synthesised for each model in the set. For fairness of comparison, the same performance objective is optimised for all designs, and the same robustness constraints are considered. Controller synthesis is performed utilising convex optimisation and gradient-based methods relying on algebraic differentiation. The worst-case performance over the patient model set is used for the comparison.

RESULTS

Two robust filtered PID controllers for the entire model set, as well as individual-specific PID and Youla controllers, were optimised. All considered designs resulted in similar frequency response characteristics. The performance improvement associated with the Youla controllers was not significant compared to the individually tuned filtered PID controllers. The difference in performance between controllers synthesized for the model set and for individual models was significantly larger than the performance difference between the individual-specific PID and Youla controllers. The different controllers were evaluated in simulation. Although all of them showed clinically acceptable results, the robust solutions provided slower responses.

CONCLUSION

Taking the same clinical and technical considerations into account for the optimisation of the different controllers, the design of individual-specific solutions resulted in only marginal differences in performance when comparing an optimal Youla parameter and its optimal filtered PID counterpart. The inter-patient variability is much more detrimental to performance than the limitations imposed by the simple structure of the filtered PID controller.