Prediction of unbound propofol concentrations in a diabetic population

Pharmacometabonomic profiling as a predictor of toxicity in patients with inoperable colorectal cancer treated with capecitabine

PURPOSE

Endogenous metabolic profiles have been shown to predict the fate and toxicity of drugs such as acetaminophen in healthy individuals. However, the clinical utility of metabonomics in oncology remains to be defined. We aimed to evaluate the effect of pretreatment serum metabolic profiles generated by (1)H NMR spectroscopy on toxicity in patients with inoperable colorectal cancer receiving single agent capecitabine.

EXPERIMENTAL DESIGN

Serum was collected from 54 patients with a diagnosis of locally advanced or metastatic colorectal cancer prior to treatment with single agent capecitabine. (1)H NMR spectroscopy was used to generate metabolic profile data for each patient. Toxicities were graded according to National Cancer Institute Common Toxicity Criteria version 2.0.

RESULTS

Higher levels of low-density lipoprotein-derived lipids, including polyunsaturated fatty acids and choline phospholipids predicted for higher grade toxicity over the treatment period. Statistical analyses revealed a "pharmacometabonomic" lipid profile that correlated with severity of toxicity.

CONCLUSIONS

This study suggests that metabolic profiles can delineate subpopulations susceptible to adverse events and have a potential role in the assessment of treatment viability for cancer patients prior to commencing chemotherapy.

Altered dose-to-effect of propofol due to pharmacokinetics in rats with experimental diabetes mellitus

Pathology related alterations in the pharmacokinetics or the pharmacodynamics of propofol could contribute to the observed large variability in the hypnotic dose. We have tested the influence of diabetes mellitus on the induction dose and the pharmacokinetics and pharmacodynamics of propofol in rats. Diabetes was induced in rats by administration of streptozotocin (60 mg kg(-1), i. p.) while control rats received vehicle intraperitoneally. All animals had glucose, cholesterol, triglycerides and albumin levels measured. In-vitro protein binding was determined by ultrafiltration. Rats were randomly split into set 1 (dose-concentration-effect study) with control and streptozotocin rats, and set 2 (pharmacokinetic study), with control and streptozotocin rats. Rats in the effect set received either a variable infusion of 6 mg kg(-1) min(-1) propofol until onset (induction dose) of the hypnotic effect (loss of the righting reflex), or a 15 mg kg(-1) bolus to assess offset time (recovery of the righting reflex). Blood (C(blood)) and brain (C(brain)) propofol concentrations at onset and offset were assayed by HPLC. In the pharmacokinetic study, propofol was administered intravenously at 6 mg kg(-1) min(-1) for 2 min. Arterial blood samples were collected between 0.5 and 540 min and assayed for propofol. A mixed effects compartmental pharmacokinetic modelling method (NONMEM) was used to analyse the observations and variabilities. The dose necessary for onset differed between streptozotocin and controls, and so did the pharmacokinetics with two- and three-compartment descriptions, respectively. C(blood) and C(brain) at onset and offset were similar, possibly rejecting changes in pharmacodynamics. The total and unbound volume of distribution was significantly lower in the streptozotocin group with no differences in clearance (CL) between streptozotocin and controls, (mean (inter-animal CV%)) CL = 0.026 (17%) and 0.025 (62%) L min(-1), respectively. Individual Bayes Vd(ss) (volume of distribution at steady state) were different, (mean (s. d.)) Vd(ss) = 7.7 (2.67) and 1.11 (0.09) L, respectively. The pharmacokinetic model was validated by comparison with the data from set 1. Simulations of total and unbound C(blood), for both groups, at the hypnotic dose for the controls, revealed differences throughout the time course of the pharmacokinetics. The difference observed in the induction dose of propofol to streptozotocin and control rats was due to alterations in the pharmacokinetics, secondary to the pathology.

Human physiologically based pharmacokinetic model for propofol

Background

Propofol is widely used for both short-term anesthesia and long-term sedation. It has unusual pharmacokinetics because of its high lipid solubility. The standard approach to describing the pharmacokinetics is by a multi-compartmental model. This paper presents the first detailed human physiologically based pharmacokinetic (PBPK) model for propofol.

Methods

PKQuest, a freely distributed software routine , was used for all the calculations. The "standard human" PBPK parameters developed in previous applications is used. It is assumed that the blood and tissue binding is determined by simple partition into the tissue lipid, which is characterized by two previously determined set of parameters: 1) the value of the propofol oil/water partition coefficient; 2) the lipid fraction in the blood and tissues. The model was fit to the individual experimental data of Schnider et. al., Anesthesiology, 1998; 88:1170 in which an initial bolus dose was followed 60 minutes later by a one hour constant infusion.

Results

The PBPK model provides a good description of the experimental data over a large range of input dosage, subject age and fat fraction. Only one adjustable parameter (the liver clearance) is required to describe the constant infusion phase for each individual subject. In order to fit the bolus injection phase, for 10 or the 24 subjects it was necessary to assume that a fraction of the bolus dose was sequestered and then slowly released from the lungs (characterized by two additional parameters). The average weighted residual error (WRE) of the PBPK model fit to the both the bolus and infusion phases was 15%; similar to the WRE for just the constant infusion phase obtained by Schnider et. al. using a 6-parameter NONMEM compartmental model.

Conclusion

A PBPK model using standard human parameters and a simple description of tissue binding provides a good description of human propofol kinetics. The major advantage of a PBPK model is that it can be used to predict the changes in kinetics produced by variations in physiological parameters. As one example, the model simulation of the changes in pharmacokinetics for morbidly obese subjects is discussed.

Influence of formulation on propofol pharmacokinetics and pharmacodynamics in anesthetized patients

BACKGROUND

In anesthesia with propofol, variability persists besides sophisticated effect targeting. Drug formulation may be another factor. We have analyzed, retrospectively, the pharmacokinetics (PK) and pharmacodynamics (PD) in monitored surgery patients anesthetized with one each of five formulations of propofol.

METHODS

Propofol 1% ('form' 1: Diprivan(Zeneca Limited, Macclesfield, UK), 2: Recofol(Schering Espana, Madrid, Spain), 3: Ivofol(Juste, Madrid, Spain), 4: Propofol Abbott (Abbott Laboratories, Madrid, Spain), 5: Fresenius (Fresenius Kabi Espana, Barcelona, Spain)) was administered to 77 ASA I-II patients of age [mean (range) 44 (18-65) years]. Induction of anesthesia was with varying propofol doses up to endpoints of either 60 on the Bispectral Index system (BIS) in group I (n = 48, model development) or standard clinical signs in group II (n = 29, validation). Maintenance was with three 10-min infusions of 10, 8 and 6 mg kg(-1) h(-1). Three blood samples were obtained from each subject, immediately after induction, and at 15 and 30 min on maintenance, with BIS and hemodynamic variables recorded at these times also. Total and free blood concentrations (Cb) of propofol were determined with HPLC. Pharmacokinetic and PD models with link equilibration rate ke0, were studied with a mixed-effects procedure (NONMEM).

RESULTS

The induction dose (group I) showed large interindividual variability [mean (range) 163 (90-290 mg)] that correlated significantly with age, basal systolic blood pressure and formulation. The PK of propofol (basic model) was described by a one-compartment model with (typical value [interindividual coefficient of variation percent (CV%)]) CL=2.30 l min(-1) (27%) and V=8.40 l (80%). Weight (WT) and formulation, within NONMEM, were found to be significant covariates for CL and V, reducing their CV% to 25% and 74%, respectively. The final PK/PD model, which includes formulation, showed a 50% reduction in the CV% for both the ke0 and the residual error. This PK/PD model was validated in group II with 33% precision and no bias.

CONCLUSION

The PK and PD are not equal for all formulations, which contributes to an increase in variability of the observed effect.

Clinical issues in the care of critically ill diabetic patients

Critically ill patients with a preexisting diagnosis of diabetes mellitus are very common and they have several inherent disorders that make their ICU care challenging. There is increasing evidence that hyperglycemia is all too common in the critically ill and that improved glucose control at presentation and during an ICU stay improves mortality. Glucose control after acute myocardial infarction and coronary artery bypass grafting in particular affects patient outcome and mortality. Diabetic patients are more prone to several medical problems that can predispose them to critical illness, prolong hospitalization, and increase mortality. Delayed gastric emptying and altered metabolism that affect medication dosing, absorption, and efficacy are common in this population. In addition, many patients with diabetes have renal dysfunction that can cause difficulties with drug excretion and glucose monitoring. Finally, patients with diabetes have an increased rate and severity of life-threatening infections and venous thromboembolism than do patients with normal glucose metabolism. There are many treatment options for patients with diabetes, and hyperglycemia in the ICU should be corrected in order to avoid adverse outcomes.