Bayesian estimation of pharmacokinetic parameters: an important component to include in the teaching of clinical pharmacokinetics and therapeutic drug monitoring

Antifungal drug penetration in soft tissue abscesses: a comparative analysis

BACKGROUND

Invasive fungal infections (IFIs) are severe and difficult-to-treat infections affecting immunocompromised patients. Antifungal drug penetration at the site of infection is critical for outcome and may be difficult to achieve. Data about antifungal drug distribution in infected human tissues under real circumstances of IFI are scarce.

METHODS

Multiple samples were obtained from soft tissue abscesses of a lung transplant patient with Candida albicans invasive candidiasis who underwent recurrent procedures of drainage, while receiving different consecutive courses of antifungal therapy [itraconazole (ITC), fluconazole, caspofungin]. Antifungal drug concentrations were measured simultaneously at the site of infection (surrounding inflammatory tissue and fluid content of the abscess) and in plasma for calculation of the tissue/plasma ratio (R). The concentration within the infected tissue was interpreted as appropriate if it was equal or superior to the MIC of the causal pathogen.

RESULTS

A total of 30 tissue samples were collected for measurements of ITC (n = 12), fluconazole (n = 17) and caspofungin (n = 1). Variable concentrations were observed in the surrounding tissue of the lesions with median R of 2.79 (range 0.51-15.9) for ITC and 0.94 (0.21-1.37) for fluconazole. Concentrations ranges within the fluid content of the abscesses were 0.39-1.83 for ITC, 0.66-1.02 for fluconazole and 0.23 (single value) for caspofungin. The pharmacodynamic target (tissue concentration ≥ MIC) was achieved in all samples for all three antifungal drugs.

CONCLUSIONS

This unique dataset of antifungal drug penetration in infected human soft tissue abscesses suggests that ITC, fluconazole and caspofungin could achieve appropriate concentrations in soft tissue abscesses.

Development and validation of individualized tacrolimus dosing software for Chinese pediatric liver transplantation patients: a population pharmacokinetic approach

OBJECTIVE

We aim to describe the population pharmacokinetics (PPK) of tacrolimus in Chinese pediatric patients under 4 years old after liver transplantation and to develop individualized tacrolimus dosing software.

METHODS

A total of 663 blood concentrations from 85 patients aged 4.57 months to 3.97 years were collected in this study. PPK analysis was performed using a nonlinear mixed effects modeling approach with the software, Phoenix. Using C#, an individualized tacrolimus dosing software was created. The software was then used to predict the concentrations of another ten pediatric liver transplantation patients to verify the accuracy of said software. The predictive error (PE) and the absolute predictive error (APE) for each predicted time point were computed.

RESULTS

A one-compartment model with first-order elimination best fitted the data. The apparent volume of distribution (V/F) and apparent clearance (CL/F) were 198.65 L and 2.41 L/h. Postoperative days (POD), total bilirubin (TBIL), and the use of voriconazole significantly influenced tacrolimus apparent clearance. The incorporation of an increasing number of actual blood drug concentrations into the prediction resulted in a decrease in both PE (72%, 17%, 7%) and APE (87%, 53%, 26%).

CONCLUSIONS

A qualified PPK model of tacrolimus was developed in Chinese pediatric patients. The individualized tacrolimus dosing software could be used as a suitable tool for the personalization of tacrolimus dosing for pediatric patients after liver transplantation.

Tutorial on model selection and validation of model input into precision dosing software for model-informed precision dosing

There has been rising interest in using model-informed precision dosing to provide personalized medicine to patients at the bedside. This methodology utilizes population pharmacokinetic models, measured drug concentrations from individual patients, pharmacodynamic biomarkers, and Bayesian estimation to estimate pharmacokinetic parameters and predict concentration-time profiles in individual patients. Using these individualized parameter estimates and simulated drug exposure, dosing recommendations can be generated to maximize target attainment to improve beneficial effect and minimize toxicity. However, the accuracy of the output from this evaluation is highly dependent on the population pharmacokinetic model selected. This tutorial provides a comprehensive approach to evaluating, selecting, and validating a model for input and implementation into a model-informed precision dosing program. A step-by-step outline to validate successful implementation into a precision dosing tool is described using the clinical software platforms Edsim++ and MwPharm++ as examples.

A two-sample approach to retrograde extrapolation of blood THC concentrations - Is it feasible?

BACKGROUND

Retrograde extrapolation of drug concentrations in blood can be relevant in cases of drug-impaired driving and is regularly used in forensic toxicology in Norway. Δ9-tetrahydrocannabinol (THC) has complex, multi-compartmental pharmacokinetics, which makes retrograde extrapolation of blood THC concentrations problematic. In the present study, we evaluated an approach to retrograde extrapolation in which momentary rates of decrease of THC were estimated from two consecutive blood samples in apprehended drivers.

MATERIAL AND METHODS

Data were collected from apprehended drivers in Norway 2000-2020. We included 548 cases in which THC was detected in two consecutive blood samples collected ≥ 20 min apart. THC concentrations were measured by GC-MS and UHPLC-MS/MS. In each case, THC concentrations and the time between the two sampling points (Δt) were used to estimate the rate constant k. The relationship between THC concentration and k was modelled by linear regression.

RESULTS

The median Δt was 31 min (interquartile range, IQR = 9). The median blood THC concentration was 2.4 μg/L (IQR = 3.4) at the first sampling point and 2.3 μg/L (IQR =3.1) at the second. The concentration decreased in 62% and increased in 38% of all cases. However, considering measurement uncertainty, the changes were not statistically significant in 87% of cases. The mean of k was 0.12 h-1, corresponding to an apparent t1/2 of 6.0 h. The t1/2 predicted from linear regression of k against THC concentration ranged from 0.93 to 13 h for the highest and lowest concentrations observed (36 and 0.63 μg/L, respectively). The time from driving to blood collection had a median of 1.7 h (IQR = 1.5), and did not correlate with k.

CONCLUSIONS

The apparent t1/2 of THC calculated from the mean of k was 6.0 h, which is shorter than the terminal elimination t1/2 suggested in previous population studies. This indicates that blood samples were often taken during the late distribution phase of THC. Because Δt was short relative to the rates of decrease expected in the late distribution and elimination phases, the underlying true concentration changes related to in vivo pharmacokinetics were small and masked by the relatively larger "false" changes introduced by random analytical and pre-analytical error. Therefore, individual values of k calculated from only two blood samples taken a short time apart are unreliable, and a two-sample approach to retrograde extrapolation of THC cannot be recommended.

Use of a common spreadsheet program to demonstrate the ability of Bayesian forecasting to estimate the pharmacokinetic parameters of antibiotics

OBJECTIVES

Recent guidelines for vancomycin have incorporated the use of Bayesian forecasting, reinforcing the need to inform students in pharmacy and clinical pharmacology of its use in therapeutic drug monitoring. The goal was to devise a PharmD research project that could demonstrate to students through simulation and data generation the utility of the Bayesian approach in estimating the pharmacokinetics of gentamicin and vancomycin.

METHODS

A series of steps were devised using Microsoft Excel to simulate patient data based on study-derived means and variances, pharmacokinetic modelling, random selection of sparse blood samples, introduce random error into the selected concentrations based on assay variability measure, and finally, inputting of the information into an add-in computer program to find the pharmacokinetic estimates using Bayesian forecasting.

KEY FINDINGS

Excellent correlations were seen between Bayesian estimates and true clearances. Lower assay variability tended to provide better estimates than larger assay variability for gentamicin, and for vancomycin, selecting a sample during the distribution phase and near the trough values tended to provide estimates with less bias and greater precision.

CONCLUSIONS

The approach used was able to demonstrate all aspects involved in Bayesian forecasting, and the results supported its use for these antibiotics.

Driving under the influence of zopiclone: Elimination between two consecutive blood samples

AIM

Zopiclone is a widely used hypnotic drug which is frequently detected in apprehended drivers. For assessments in forensic cases, the elimination half-life (t1/2) of a drug is sometimes important. A t1/2 of 3.5-6.5 h for zopiclone is previously reported in healthy individuals, but different factors like age and drug-interactions can affect the t1/2 of zopiclone. The aim of this study was to describe concentrations of zopiclone and co-ingestion of additional drugs in apprehended drivers, and to investigate the t1/2 of zopiclone based on two consecutive blood samples.

METHODS

Data was collected from apprehended drivers in Norway between 2003 and 2021. All cases where zopiclone was detected were included. In a subset of the material, two consecutive whole blood samples were collected ≥ 20 and < 60 min apart. Concentrations of zopiclone in blood were determined by LC-MS or UHPLC-MS/MS. The elimination and t1/2 of zopiclone was estimated from the concentration change of zopiclone and the time interval between the two consecutive blood samples, under the assumption of first order kinetics.

RESULTS

The median concentration among all zopiclone positive cases was 0.044 mg/L (IQR 0.070 mg/L) (n = 2401). The most frequent additional drugs detected were ethanol (36%), diazepam (22%), amphetamine (14%) and THC (14%). In zopiclone-only cases (n = 364), the median concentration of zopiclone was 0.066 mg/L (IQR 0.115 mg/L). In 112 cases, two consecutive blood samples were collected. Of these, 28 cases showed increasing concentrations of zopiclone between the two sampling time points. Among the cases in which the concentration decreased (n = 84), the median C1 was 0.048 mg/L (IQR 0.062 mg/L) and the median C2 was 0.043 mg/L (IQR 0.056 mg/L). A Bayesian statistical model was used to obtain the posterior distribution of t1/2. The posterior median of t1/2 was estimated to 3.1 h (IQR=0.39 h) when including only the cases showing decreasing concentrations, and this increased to 3.8 h (IQR=0.52 h) when also including samples showing non outlying increase in concentrations. There was no statistically significant gender difference in the calculated half-lives (two-sided Mann-Whitney U test, p = .525).

CONCLUSIONS

This study showed that zopiclone is frequently detected in apprehended drivers in supra therapeutic concentrations and poly drug cases. The elimination of zopiclone in blood from two consecutive blood samples indicated an apparent t1/2 of between 3.1 and 3.8 h, which is within the lower range of what previous experimental studies on healthy individuals have reported.

Precision Oncology by Point-of-Care Therapeutic Drug Monitoring and Dosage Adjustment of Conventional Cytotoxic Chemotherapies: A Perspective

Therapeutic drug monitoring (TDM) of conventional cytotoxic chemotherapies is strongly supported yet poorly implemented in daily practice in hospitals. Analytical methods for the quantification of cytotoxic drugs are instead widely presented in the scientific literature, while the use of these therapeutics is expected to keep going for longer. There are two main issues hindering the implementation of TDM: turnaround time, which is incompatible with the dosage profiles of these drugs, and exposure surrogate marker, namely total area under the curve (AUC). Therefore, this perspective article aims to define the adjustment needed from current to efficient TDM practice for cytotoxics, namely point-of-care (POC) TDM. For real-time dose adjustment, which is required for chemotherapies, such POC TDM is only achievable with analytical methods that match the sensitivity and selectivity of current methods, such as chromatography, as well as model-informed precision dosing platforms to assist the oncologist with dose fine-tuning based on quantification results and targeted intervals.

Efficacy and safety of FLOT regimen vs DCF, FOLFOX, and ECF regimens as perioperative chemotherapy treatments for resectable gastric cancer patients; a report from the middle east

Background and purpose

This study aimed to compare the efficacy and toxicity of perioperative chemotherapy regimens including epirubicin, cisplatin, 5-fluorouracil (ECF), docetaxel, cisplatin, 5-fluorouracil (DCF), leucovorin, 5-fluorouracil, oxaliplatin (FOLFOX), and 5-fluorouracil, leucovorin, oxaliplatin, and docetaxel (FLOT) to identify the most effective chemotherapy regimen with less toxicity.

Experimental approach

This retrospective cohort study (2014-2021) was based on 152 eligible resectable gastric cancer patients who had received one of the perioperative mentioned chemotherapy regimens and followed for at least two years. The primary endpoint of this study was overall survival (OS), progression-free survival (PFS), overall response rate (ORR), and R0 resection.

Findings / Results

Of included patients, 21%, 33.7%, 24.3%, and 21% had received ECF, DCF, FOLFOX and FLOT, respectively. After the median 30-month follow-ups, OS was higher with the FLOT regimen in comparison with other regimens (hazard ratio = 0. 276). The median OS of the FLOT regimen was 39 months. Besides, the median OS was 28, 25, and 21 months for DCF, FOLOFX, and ECF regimens, respectively. Moreover, a median PFS of 24, 18, 17, and 14 months was observed for FLOT, DCF, FOLFOX, and ECF regimens, respectively (Log-rank < 0.001). FLOT regimen showed 84. 4% ORR which was notably higher than other groups.

Conclusions and implications

For resectable gastric cancer patients, the perioperative FLOT regimen led to a significant improvement in patients' OS and PFS versus ECF, DCF, and FOLFOX regimens. As such, the FLOT regimen could be considered the optimal option for managing resectable gastric cancer patients.

Challenges associated with test dose pharmacokinetic predictions of high dose melphalan exposure in patients with multiple myeloma

Aim

To evaluate the accuracy of melphalan test dose pharmacokinetic (PK) predictions of the subsequent high dose (HDM) area under the concentration-versus-time curve (AUC) and to identify sources of prediction error (PE).

Methods

A prospective multicentre PK study was conducted in 40 myeloma patients of median age 60 (range:35–71) years using a 20 mg/m² test dose administered 1–3 days prior to HDM (predominantly 180 mg/m²). PK data were collected post the test and high doses to compare predicted versus actual AUCs determined using the trapezoidal rule. Test and high dose infusion concentration, volume and duration and the time from preparation to infusion were compared using the paired Wilcoxin rank sign test. The impact of Melphalan administration parameters on PE was evaluated using the Mann–Whitney test. The predictive capacity of a previously published population PK (PopPK) model was also examined.

Results

Predicted HDM AUC was within 15% of the observed values in only 63% of patients when analysed using the trapezoidal rule and 70% of patients using PopPK. Test dose infusion concentration, volume, duration and time from preparation to infusion were significantly lower than for HDM (p < 0.005). Test dose administration within 15 min of reconstitution (n = 5) was associated with significantly lower PE than administration times of 16–60 min (n = 22), p < 0.05. Test and HDM infusion concentrations were lower in patients with large PE (> ± 15%), but the differences were not significant (p = 0.078, 0.228, respectively).

Conclusion

Test dose PK has the potential to predict subsequent HDM exposure to achieve a target AUC once melphalan administration parameters are optimised to account for stability issues in the formulation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00228-022-03396-x.

Monitoring of Dabrafenib and Trametinib in Serum and Self-Sampled Capillary Blood in Patients with BRAFV600-Mutant Melanoma

Patients treated with dabrafenib and trametinib for BRAFV600-mutant melanoma often experience dose reductions and treatment discontinuations. Current knowledge about the associations between patient characteristics, adverse events (AE), and exposure is inconclusive. Our study included 27 patients (including 18 patients for micro-sampling). Dabrafenib and trametinib exposure was prospectively analyzed, and the relevant patient characteristics and AE were reported. Their association with the observed concentrations and Bayesian estimates of the pharmacokinetic (PK) parameters of (hydroxy-)dabrafenib and trametinib were investigated. Further, the feasibility of at-home sampling of capillary blood was assessed. A population pharmacokinetic (popPK) model-informed conversion model was developed to derive serum PK parameters from self-sampled capillary blood. Results showed that (hydroxy-)dabrafenib or trametinib exposure was not associated with age, sex, body mass index, or toxicity. Co-medication with P-glycoprotein inducers was associated with significantly lower trough concentrations of trametinib (p = 0.027) but not (hydroxy-)dabrafenib. Self-sampling of capillary blood was feasible for use in routine care. Our conversion model was adequate for estimating serum PK parameters from micro-samples. Findings do not support a general recommendation for monitoring dabrafenib and trametinib but suggest that monitoring can facilitate making decisions about dosage adjustments. To this end, micro-sampling and the newly developed conversion model may be useful for estimating precise PK parameters.