Simulation of residual sedation effect of remimazolam: pharmacokinetic–pharmacodynamic simulation can be an additional standard anesthesia monitoring method

Abstract

Monitoring the sedation level during general anesthesia is important to avoid intraoperative awareness and recall or overdose of anesthetics. For that purpose, along with the traditional observation of autonomic responses, using processed electroencephalogram monitors, including bispectral index monitors, has become the standard recently. However, this method can only estimate the depth of anesthesia during measurements. Particularly, in the case of overdose, even if the sedation level seems adequate or slightly deep, potential drug accumulation can cause delayed recovery from anesthesia; therefore, predicting the potential anesthetic effects by estimating the effect-site concentration of anesthetics and drug effect using pharmacokinetic–pharmacodynamic (PK–PD) simulations is necessary. Anesthesia practitioners use many standalone applications (e.g., TIVA trainer, EuroSIVA, Netherlands) and anesthesia information management systems with similar functionality. Patient and equipment monitoring is used to titrate the administration of anesthetics to detect physiological perturbations, allow interventions before the patient experiences harmful effects, and detect and rectify equipment malfunction [1]. Additionally, the term “monitoring” is defined as the observation of a patient by a physician and analysis of the quality of sedation or anesthesia over a period [2]. Given the intent of the aforementioned definition, although the standard monitoring procedure during anesthesia (e.g., continuous evaluation of the patient’s oxygenation, ventilation, circulation, and temperature) [3] excludes it, PK–PD simulations can be an additional monitoring method. Recently, in the Journal of Anesthesia, Morimoto et al. [4] have conducted a randomized controlled trial to evaluate the utility of SmartPilot View (SPV) (Draeger, Lübeck, Germany), a PK–PD simulator. SPV automatically records the administrations of anesthetics from the anesthesia workstation and shows the estimated effect-site concentrations and PD interaction between sedative and analgesic drugs as isobolograms. The authors have compared the recovery time in SPV-guided general anesthesia with that in usual practice in patients with desflurane general anesthesia. SPVguided anesthesia enabled faster recovery of orientation (451 ± 100 s in the control group and 316 ± 57 s in the SPV group). Several similar clinical studies have used PK–PD simulation systems [5–8]. As these studies have examined the effects of reduced anesthetic use and shortened recovery time as outcomes, the use of PK–PD simulation systems could prevent delayed recovery due to drug accumulation from the clinical viewpoint. Recently, anesthetics were expected to have characteristics that make their effects easily adjustable, as desflurane does for inhalation anesthetics. In intravenous anesthetics, this attribute is increasingly achieved using the soft drug approach, in which novel active compounds are specifically designed to be susceptible to rapid biotransformation to inactive metabolites [9]. For example, nonspecific esterases distributed throughout the body rapidly metabolize remifentanil. Carboxylesterases rapidly metabolize Remimazolam in the liver. These features would reduce the risk of accumulation. Then, the question arises whether the importance of PK–PD simulation will diminish as such drugs are used increasingly. As shown by Morimoto et al. above, the difference in recovery time between the groups using * Shinju Obara [email protected]