Evaluation of the estimation of midazolam concentrations and pharmacokinetic parameters in intensive care patients using a bayesian pharmacokinetic software (PKS) according to sparse sampling approach

Prolonged infusion of sedatives and analgesics in adult intensive care patients: A systematic review of pharmacokinetic data reporting and quality of evidence

Although pharmacokinetic (PK) data for prolonged sedative and analgesic agents in intensive care unit (ICU) has been described, the number of publications in this important area appear relatively few, and PK data presented is not comprehensive. Known pathophysiological changes in critically ill patients result in altered drug PK when compared with non-critically ill patients. ClinPK Statement was recently developed to promote consistent reporting in PK studies, however, its applicability to ICU specific PK studies is unclear. In this systematic review, we assessed the overall ClinPK Statement compliance rate, determined the factors affecting compliance rate, graded the level of PK evidence and assessed the applicability of the ClinPK Statement to future ICU PK studies. Of the 33 included studies (n=2016), 22 (67%) were low evidence quality descriptive studies (Level 4). Included studies had a median compliance rate of 80% (IQR 66% to 86%) against the ClinPK Statement. Overall pooled compliance rate (78%, 95% CI 73% to 83%) was stable across time (P=0.38), with higher compliance rates found in studies fitting three compartments models (88%, P<0.01), two compartments models (83%, P<0.01) and one compartment models (77%, P=0.17) than studies fitting noncompartmental or unspecified models (69%) (P<0.01). Data unique to the interpretation of PK data in critically ill patients, such as illness severity (48%), organ dysfunction (36%) and renal replacement therapy use (32%), were infrequently reported. Discrepancy between the general compliance rate with ClinPK Statement and the under-reporting of ICU specific parameters suggests that the applicability of the ClinPK Statement to ICU PK studies may be limited in its current form.

Scaling clearance in paediatric pharmacokinetics: All models are wrong, which are useful?

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This article is commented on in the editorial by Holford NHG and Anderson BJ. Why standards are useful for predicting doses. Br J Clin Pharmacol 2017; 83: 685–7. doi: 10.1111/bcp.13230

Aim

When different models for weight and age are used in paediatric pharmacokinetic studies it is difficult to compare parameters between studies or perform model‐based meta‐analyses. This study aimed to compare published models with the proposed standard model (allometric weight^0.75 and sigmoidal maturation function).

Methods

A systematic literature search was undertaken to identify published clearance (CL) reports for gentamicin and midazolam and all published models for scaling clearance in children. Each model was fitted to the CL values for gentamicin and midazolam, and the results compared with the standard model (allometric weight exponent of 0.75, along with a sigmoidal maturation function estimating the time in weeks of postmenstrual age to reach half the mature value and a shape parameter). For comparison, we also looked at allometric size models with no age effect, the influence of estimating the allometric exponent in the standard model and, for gentamicin, using a fixed allometric exponent of 0.632 as per a study on glomerular filtration rate maturation. Akaike information criteria (AIC) and visual predictive checks were used for evaluation.

Results

No model gave an improved AIC in all age groups, but one model for gentamicin and three models for midazolam gave slightly improved global AIC fits albeit using more parameters: AIC drop (number of parameters), –4.1 (5), –9.2 (4), –10.8 (5) and –10.1 (5), respectively. The 95% confidence interval of estimated CL for all top performing models overlapped.

Conclusion

No evidence to reject the standard model was found; given the benefits of standardised parameterisation, its use should therefore be recommended.

Optimal sampling strategy development methodology using maximum a posteriori Bayesian estimation

Maximum a posteriori Bayesian (MAPB) pharmacokinetic parameter estimation is an accurate and flexible method of estimating individual pharmacokinetic parameters using individual blood concentrations and prior information. In the past decade, many studies have developed optimal sampling strategies to estimate pharmacokinetic parameters as accurately as possible using either multiple regression analysis or MAPB estimation. This has been done for many drugs, especially immunosuppressants and anticancer agents. Methods of development for optimal sampling strategies (OSS) are diverse and heterogeneous. This review provides a comprehensive overview of OSS development methodology using MAPB pharmacokinetic parameter estimation, determines the transferability of published OSSs, and compares sampling strategies determined by MAPB estimation and multiple regression analysis. OSS development has the following components: 1) prior distributions; 2) reference value determination; 3) optimal sampling time identification; and 4) validation of the OSS. Published OSSs often lack all data necessary for the OSS to be clinically transferable. MAPB estimation is similar to multiple regression analysis in terms of predictive performance but superior in flexibility.

Mild hypothermia alters midazolam pharmacokinetics in normal healthy volunteers

The clinical use of therapeutic hypothermia has been rapidly expanding due to evidence of neuroprotection. However, the effect of hypothermia on specific pathways of drug elimination in humans is relatively unknown. To gain insight into the potential effects of hypothermia on drug metabolism and disposition, we evaluated the pharmacokinetics of midazolam as a probe for CYP3A4/5 activity during mild hypothermia in human volunteers. A second objective of this work was to determine whether benzodiazepines and magnesium administered intravenously would facilitate the induction of hypothermia. Subjects were enrolled in a randomized crossover study, which included two mild hypothermia groups (4 degrees C saline infusions and 4 degrees C saline + magnesium) and two normothermia groups (37 degrees C saline infusions and 37 degrees C saline + magnesium). The lowest temperatures achieved in the 4 degrees C saline + magnesium and 4 degrees C saline infusions were 35.4 +/- 0.4 and 35.8 +/- 0.3 degrees C, respectively. A significant decrease in the formation clearance of the major metabolite 1'-hydroxymidazolam was observed during the 4 degrees C saline + magnesium compared with that in the 37 degrees C saline group (p < 0.05). Population pharmacokinetic modeling identified a significant relationship between temperature and clearance and intercompartmental clearance for midazolam. This model predicted that midazolam clearance decreases 11.1% for each degree Celsius reduction in core temperature from 36.5 degrees C. Midazolam with magnesium facilitated the induction of hypothermia, but shivering was minimally suppressed. These data provided proof of concept that even mild and short-duration changes in body temperature significantly affect midazolam metabolism. Future studies in patients who receive lower levels and a longer duration of hypothermia are warranted.

Physiologically-based minimal model of agitation-sedation dynamics

Agitation-sedation cycling in critically ill patients, characterized by oscillations between states of agitation and over-sedation, damages patient health and increases length of stay and cost. The model presented captures the essential dynamics of the agitation-sedation system, is physiologically representative, and is validated by accurately simulating patient response for 37 critical care patients. The model provides a platform to develop and test controllers that offer the potential of improved agitation management.

Automated agitation management accounting for saturation dynamics

Agitation-sedation cycling in critically ill is damaging to patient health and increases length of and cost. A physiologically representative model of the agitation-sedation system is used as a platform to evaluate feedback controllers offering improved agitation management. A heavy-derivative controller with upper and infusion rate bounds maintains minimum plasma concentrations through a low constant infusion, and minimizes outbursts of agitation through strong, timely boluses. controller provides improved agitation management using from 37 critically ill patients, given the saturation of effect at high concentration. Approval was obtained the Canterbury Ethics Board for this research.

Parameter identification and sedative sensitivity analysis of an agitation-sedation model

Sedation administration and agitation management are fundamental activities in any intensive care unit. A lack of objective measures of agitation and sedation, as well as poor understanding of the underlying dynamics, contribute to inefficient outcomes and expensive healthcare. Recent models of agitation-sedation dynamics have enhanced understanding of the underlying dynamics and enable development of advanced protocols for semi-automated sedation administration. In this research, the agitation-sedation model parameters are identified using an integral-based fitting method developed in this work. Parameter variance is then analysed over 37 intensive care unit patients. The parameter identification method is shown to be effective and computationally inexpensive, making it suited to real-time clinical control applications. Sedative sensitivity, an important model parameter, is found to be both patient-specific and time-varying. However, while the variation between patients is observed to be as large as a factor 10, the observed variation in time is smaller, and varies slowly over a period of days rather than hours. The high fitted model performance across all patients show that the agitation-sedation model presented captures the fundamental dynamics of the agitation-sedation system. Overall, these results provide additional insight into the system and clinical dynamics of sedation management.

Physiological modelling of agitation–sedation dynamics including endogenous agitation reduction

Sedation administration and agitation management are fundamental activities in any intensive care unit. A lack of objective measures of agitation and sedation, as well as poor understanding of the underlying dynamics, contribute to inefficient outcomes and expensive healthcare. Recent models of agitation-sedation pharmacodynamics have enhanced understanding of the underlying dynamics and enable development of advanced protocols for semi-automated sedation administration. However, these initial models do not capture all observed dynamics, particularly periods of low sedative infusion. A physiologically representative model that incorporates endogenous agitation reduction (EAR) dynamics is presented and validated using data from 37 critical care patients. High median relative average normalised density (RAND) values of 0.77 and 0.78 support and minimum RAND values of 0.51 and 0.55 for models without and with EAR dynamics respectively show that both models are valid representations of the fundamental agitation-sedation dynamics present in a broad spectrum of intensive care unit (ICU) patients. While the addition of the EAR dynamic increases the ability of the model to capture the observed dynamics of the agitation-sedation system, the improvement is relatively small and the sensitivity of the model to the EAR dynamic is low. Although this may represent a limitation of the model, the inclusion of EAR is shown to be important for accurately capturing periods of low, or no, sedative infusion, such as during weaning prior to extubation.

Physiological modelling of agitation–sedation dynamics

Agitation-sedation cycling in critically ill patients, characterized by oscillations between states of agitation and over-sedation, damages patient health and increases length of stay and cost. A model that captures the essential dynamics of the agitation-sedation system and is physiologically representative is developed, and validated using data from 37 critical care patients. It is more physiologically representative than a previously published agitation-sedation model, and captures more realistic and complex dynamics. The median time in the 90% probability band is 90%, and the total drug dose, relative to recorded drug dose data, is a near ideal 101%. These statistical model validation metrics are 5-13% better than a previously validated model. Hence, this research provides a platform to develop and test semi-automated sedation management controllers that offer the significant clinical potential of improved agitation management and reduced length of stay in critical care.