| Literature DB >> 30019172 |
Marko M Sahinovic1,2, Michel M R F Struys3,4, Anthony R Absalom3.
Abstract
Propofol is an intravenous hypnotic drug that is used for induction and maintenance of sedation and general anaesthesia. It exerts its effects through potentiation of the inhibitory neurotransmitter γ-aminobutyric acid (GABA) at the GABAA receptor, and has gained widespread use due to its favourable drug effect profile. The main adverse effects are disturbances in cardiopulmonary physiology. Due to its narrow therapeutic margin, propofol should only be administered by practitioners trained and experienced in providing general anaesthesia. Many pharmacokinetic (PK) and pharmacodynamic (PD) models for propofol exist. Some are used to inform drug dosing guidelines, and some are also implemented in so-called target-controlled infusion devices, to calculate the infusion rates required for user-defined target plasma or effect-site concentrations. Most of the models were designed for use in a specific and well-defined patient category. However, models applicable in a more general population have recently been developed and published. The most recent example is the general purpose propofol model developed by Eleveld and colleagues. Retrospective predictive performance evaluations show that this model performs as well as, or even better than, PK models developed for specific populations, such as adults, children or the obese; however, prospective evaluation of the model is still required. Propofol undergoes extensive PK and PD interactions with both other hypnotic drugs and opioids. PD interactions are the most clinically significant, and, with other hypnotics, tend to be additive, whereas interactions with opioids tend to be highly synergistic. Response surface modelling provides a tool to gain understanding and explore these complex interactions. Visual displays illustrating the effect of these interactions in real time can aid clinicians in optimal drug dosing while minimizing adverse effects. In this review, we provide an overview of the PK and PD of propofol in order to refresh readers' knowledge of its clinical applications, while discussing the main avenues of research where significant recent advances have been made.Entities:
Mesh:
Substances:
Year: 2018 PMID: 30019172 PMCID: PMC6267518 DOI: 10.1007/s40262-018-0672-3
Source DB: PubMed Journal: Clin Pharmacokinet ISSN: 0312-5963 Impact factor: 6.447
Fig. 1Propofol metabolic pathway. CYP cytochrome P450, UDP uridine 5′-diphosphate
Fig. 2Overview of a three-compartment pharmacokinetic/pharmacodynamic model
Methods
| Principal investigator (publication year) | Population characteristics | Setting | Comedication | Target population | |||||
|---|---|---|---|---|---|---|---|---|---|
| Sex (M/F) | Age (years)a | TBW (kg)a | Height (cm)a | Adults | Obese | Paediatric | |||
| Gepts [ | 13/5 | 29–65 | 49–82 | 155–175 | PTN | Glycopyrrolate 0.4 mg IM, lidocaine/bupivacaine intrathecal | X | ||
| Marsh [ | Idem Gepts | Idem Gepts | Idem Gepts | Idem Gepts | Idem Gepts | Idem Gepts | X | ||
| Kataria [ | 28/25 | 3–11 | 15–61 | NA | PTN | Rocuronium, fentanyl, atropine, bupivacaine, neostigmine + atropine | X | ||
| Short [ | 6/4 | 4–7 | 15–22 | NA | PTN | EMLA, N2O, bupivacaine wound infiltration | X | ||
| Schnider [ | 13/11 | 26–81 | 44–123 | 155–196 | HV | No comedication administered | X | ||
| Schuttler [ | 150/120 | 2–88 | 12–100 | – | PTN and HV | X1 | X | X | |
| Knibbe [ | R: 24 | R: x | R: 0.25–0.3 | – | R: HV | X2 | X | ||
| Paedfusor [ | NA | NA | NA | NA | NA | Yes, details not published | X | ||
| van Kralingen [ | 20/44 | 48.5 | 55–167 | – | PTN | Lidocaine, fentanyl, atracurium, remifentanil | X | X | |
| Cortinez [ | 7/11 | 28–56 | 82–134 | 139–185 | PTN | X3 | X | X | |
| Eleveld [ | 672/361 | 0–88 | 0.68–160 | NA | PTN and HV | X4 | X | X | X |
| Sahinovic [ | 21/19 | 53 | 81.5 | 175.5 | PTN | Rocuronium | X | ||
X1: multiple datasets were analysed (Schuttler [52], Cockshott [51, 190], Glass [184], White [65, 191], Shafer [74]). Each dataset involved patients receiving different comedications
X2: Multiple datasets were analysed (Cox [192], Knibbe [193], Knibbe [194], Knibbe [195]). Each dataset involved patients receiving different comedications
X3: Multiple datasets were analysed (Schnider [66], Servin [196], Cortínez [69]). Each dataset involved patients receiving different comedications
X4: Data from 30 previously published studies were used. Each dataset involved patients receiving different comedications
HV healthy volunteers, PTN patient, R rat, C children, A adults, NA not available, TBW total body weight, M male, F female, IM intramuscularly
aRange/average
Model equations
| Gepts [ | Marsh [ | ||
|---|---|---|---|
|
| 16.92 L |
| 0.228 × TBW |
|
| 35.07 L |
| 0.464 × TBW |
|
| 215.3 L |
| 2.89 × TBW |
| 0.119 | 0.119 | ||
| 0.114 | 0.112 | ||
| 0.0419 | 0.042 | ||
| 0.0550 | 0.055 | ||
| 0.0033 | 0.0033 |
CL1 = V1 × K10; CL2 = V2 × K21; CL3 = V3 × K31; V2 = V1 × K12/K21; V3 = V1 × K13/K31
M male, F female, Y young, E elderly, TBW total body weight, FFM fat-free mass, PMA postmenstrual age
| Propofol is a potent intravenous hypnotic drug. It exerts its effects through potentiation of the inhibitory neurotransmitter, γ-Aminobutyric acid (GABA). Much experience with its clinical use has been amassed since it was introduced over three decades ago. |
| A general purpose pharmacokinetic (PK) and pharmacodynamic (PD) propofol model recently published by Eleveld and colleagues might replace PK/PD models currently used in clinical practice. |
| Defining the nature of interaction between propofol and other drugs remains a challenge. Response surface model studies can help to elucidate these interactions. |