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Daptomycin, an anti-methicillin-resistant Staphylococcus aureus drug, causes exposure-dependent muscle toxicity and eosinophilic pneumonia. Although the area under the concentration-time curve (AUC)-guided dosing is crucial, an optimal blood sampling strategy is lacking. This study aimed to identify an optimal limited sampling strategy using Bayesian forecasting to rapidly achieve the target AUC. Two validated population pharmacokinetic models generated a virtual population of 1000 individuals (models 1 and 2 represent diverse patients and kidney transplant recipients, respectively). The AUC for each blood sample was assessed using the probability of achieving the estimated/reference AUC ratio on the second day (AUC 24-48 ) and at the steady state (AUC ss ). In Model 1, Bayesian posterior probabilities for AUC 24-48 increased from 50.7% (a priori) to 59.4% and for AUC ss from 48.9% (a priori) to 61.9%, with one-point C trough sampling at 24 h. With two-point sampling at 7 and 24 h, the probabilities increased to 73.8% for AUC 24-48 and 69.7% for AUC ss . In Model 2, the probabilities for both AUC 24-48 and AUC ss with one-point C trough or two-point sampling incorporating C trough sampling increased compared to a priori probabilities. These results suggest that two-point sampling incorporating C trough during initial dosing enhanced achieving the target AUC 24-48 and AUC ss rapidly., (© 2024 Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society). Published by John Wiley & Sons Ltd.)

Purpose: S-warfarin is used to phenotype cytochrome P450 (CYP) 2C9 activity. This study evaluated S-warfarin limited sampling strategy with a population pharmacokinetic (PK) approach to estimate CYP2C9 activity in healthy adults., Methods: In 6 previously published studies, a single oral dose of warfarin 10 mg was administered alone or with a CYP2C9 inducer to 100 healthy adults. S-warfarin concentrations were obtained from adults during conditions when subjects were not on any prescribed medications. A population PK model was developed using non-linear mixed effects modeling. Limited sampling models (LSMs) using single- or 2-timepoint concentrations were compared with full PK profiles from intense sampling using empiric Bayesian post hoc estimations of S-warfarin AUC derived from the population PK model. Preset criterion for LSM selection and validation were a correlation coefficient (R 2 ) >0.9, relative percent mean prediction error (%MPE) >-5 to <5%, relative percent mean absolute error (%MAE) ≤ 10%, and relative percent root mean squared error (%RMSE) ≤ 15%., Results: S-warfarin concentrations (n=2540) were well described with a two-compartment model. Mean apparent oral clearance was 0.56 L/hr and volume of distribution was 35.5 L. Clearance decreased 33% with the CYP2C9 *3 allele and increased 42% with lopinavir/ritonavir co-administration. During CYP2C9 constitutive conditions, LSMs at 48 hr and at 72 hr as well as 2-timepoint LSMs were within acceptable limits for R 2 , %MPE, %MAE, and %RMSE. During CYP2C9 induction, S-warfarin LSMs had unacceptable %MPE, %MAE, and %RMSE., Conclusions: Phenotyping studies with S-warfarin in healthy subjects can utilize a single- and/or a 2-timepoint LSM with a population PK approach to estimate constitutive CYP2C9 activity., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Irinotecan (CPT-11) is an anticancer agent widely used in the treatment of a variety of adult solid tumors. The objective of this study was to develop an optimal sampling strategy model that accurately estimates pharmacokinetic parameters of CPT-11 and its active metabolite, SN-38. This study included 221 patients with advanced solid tumors or lymphoma receiving CPT-11 single or combination therapy with 5-fluorouracil (5-FU)/leucovorin (LV) (FOLFIRI) plus bevacizumab from 4 separate clinical trials. Population pharmacokinetic analysis of CPT-11 and SN-38 was performed by non-linear mixed effects modeling. The optimal sampling strategy model was developed using D-optimality with expected distribution approach. The pharmacokinetic profiles of CPT-11 and SN-38 were best described by a 3- and 2-compartment model, respectively, with first-order elimination. Body surface area and co-administration with 5-FU/LV plus bevacizumab were significant covariates (p < 0.01) for volumes of the central compartment of CPT-11 and SN-38, and clearance of CPT-11. Pre-treatment total bilirubin and co-administration with 5-FU/LV and bevacizumab were significant covariates (p < 0.01) for clearance of SN-38. Accurate and precise predictive performance (r 2 > 0.99, -2 < bias (%ME) < 0, precision (% RMSE) < 12) of both CPT-11 and SN-38 was achieved using: (i) 6 fixed sampling times collected at 1.5, 3.5, 4, 5.75, 22, 23.5 hours post-infusion; or (ii) 1 fixed time and 2 sampling windows collected at 1.5, [3-5.75], [22-23.5] hours post-infusion. The present study demonstrates that an optimal sampling design with three blood samples achieves accurate and precise pharmacokinetic parameter estimates for both CPT-11 and SN-38.

Purpose: Pemetrexed is a widely used cytostatic agent with an established exposure-response relationship. Although dosing is based on body surface area (BSA), large interindividual variability in pemetrexed plasma concentrations is observed. Therapeutic drug monitoring (TDM) can be a feasible strategy to reduce variability in specific cases leading to potentially optimized pemetrexed treatment. The aim of this study was to develop a limited sampling schedule (LSS) for the assessment of pemetrexed pharmacokinetics., Methods: Based on two real-life datasets, several limited sampling designs were evaluated on predicting clearance, using NONMEM, based on mean prediction error (MPE %) and normalized root mean squared error (NRMSE %). The predefined criteria for an acceptable LSS were: a maximum of four sampling time points within 8 h with an MPE and NRMSE ≤ 20%., Results: For an accurate estimation of clearance, only four samples in a convenient window of 8 h were required for accurate and precise prediction (MPE and NRMSE of 3.6% and 5.7% for dataset 1 and of 15.5% and 16.5% for dataset 2). A single sample at t = 24 h performed also within the criteria with MPE and NRMSE of 5.8% and 8.7% for dataset 1 and of 11.5% and 16.4% for dataset 2. Bias increased when patients had lower creatinine clearance., Conclusions: We presented two limited sampling designs for estimation of pemetrexed pharmacokinetics. Either one can be used based on preference and feasibility.

Background: Cytochrome P450 3A is the most relevant drug-metabolizing enzyme in humans as it is involved in the elimination of 50% of marketed drugs. Nothing is known about the activity of cytochrome P450 3A in palliative care patients who have complicated symptoms often associated with a terminal illness., Aim: In order to improve drug dosing in end-of-life care and to avoid drug interactions, cytochrome P450 3A activity was determined in patients of a palliative care unit under real-life clinical conditions., Design: As midazolam is an established marker substance for cytochrome P450 3A activity, this single-arm prospective trial was designed to obtain a 4-h pharmacokinetic profile of midazolam after oral administration of a 10-µg dose from each enrolled patient. Plasma concentrations of midazolam and its primary metabolite 1'-hydroxy-midazolam were quantified by mass spectrometry techniques. Cytochrome P450 3A activity was calculated as partial metabolic clearance from a limited sampling area under the curve. All other drugs taken by the participating patients were considered, as well as recent blood test results and patients' diagnoses. The trial was registered at German Clinical Trials Register ( www.drks.de ): DRKS00011753., Setting/participants: The trial was carried out at a university palliative care unit under real-life clinical conditions. Every patient admitted to the ward was screened for possible participation, independent of the individual performance status., Results: Partial metabolic clearance of midazolam in palliative care patients was 31.7 ± 32.1 L/h. This was a highly significant 40% reduction ( p  < 0.0001) in comparison with the cytochrome P450 3A activity of healthy subjects., Conclusion: Dosing of cytochrome P450 3A substrate drugs (e.g. macrolide antibiotics, benzodiazepines, calcium channel blockers) needs to be adjusted in palliative care patients; otherwise, escalation of debilitating symptoms due to drug interactions might occur.

Aims: Tacrolimus has been associated with notable extrarenal adverse effects (AEs), which are unpredictable and impact patient morbidity. The association between model-predicted tacrolimus exposure metrics and standardized extrarenal AEs in stable renal transplant recipients was investigated and a limited sampling strategy (LSS) was developed to predict steady-state tacrolimus area under the curve over a 12-h dosing period (AUC ss,0-12h )., Methods: All recipients receiving tacrolimus and mycophenolic acid ≥6 months completed a 12-h cross-sectional observational pharmacokinetic-pharmacodynamic study. Patients were evaluated for the presence of individual and composite gastrointestinal, neurological, and aesthetic AEs during the study visit. The associations between AEs and tacrolimus exposure metrics generated from a published population pharmacokinetic model were investigated using a logistic regression analysis in NONMEM 7.3. An LSS was determined using a Bayesian estimation method with the same patients., Results: Dose-normalized tacrolimus AUC ss,0-12h and apparent clearance were independently associated with diarrhoea, dyspepsia, insomnia and neurological AE ratio. Dose-normalized tacrolimus maximum concentration was significantly correlated with skin changes and acne. No AE associations were found with trough concentrations. Using limited sampling at 0, 2h; 0, 1, 4h; and 0, 1, 2, 4h provided a precise and unbiased prediction of tacrolimus AUC (root mean squared prediction error < 10%), which was not well characterized using trough concentrations only (root mean squared prediction error >15%)., Conclusions: Several AEs (i.e. diarrhoea, dyspepsia, insomnia and neurological AE ratio) were associated with tacrolimus dose normalized AUC ss,0-12h and clearance. Skin changes and acne were associated with dose-normalized maximum concentrations. To facilitate clinical implementation, a LSS was developed to predict AUC ss,0-12h values using sparse patient data to efficiently assess projected immunosuppressive exposure and potentially minimize AE manifestations., (© 2018 The British Pharmacological Society.)

Purpose: Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar., Methods: Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out., Findings: None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation (r) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC 0-t prediction of saroglitazar. The same models, when applied to the AUC 0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error <30% and correlation (r) was at least 0.9339 in the same pool of healthy subjects., Implications: A 3-concentration-time points limited sampling model predicts the exposure of saroglitazar (ie, AUC 0-t ) within predefined acceptable bias and imprecision limit. Same model was also used to predict AUC 0-∞ . The same limited sampling model was found to predict the exposure of saroglitazar sulfoxide within predefined criteria. This model can find utility during late-phase clinical development of saroglitazar in the patient population., (Copyright © 2018 Elsevier HS Journals, Inc. All rights reserved.)

Ertapenem is a broad-spectrum carbapenem antibiotic whose activity against Mycobacterium tuberculosis is being explored. Carbapenems have antibacterial activity when the plasma concentration exceeds the MIC at least 40% of the time (40% T MIC in multidrug-resistant tuberculosis (MDR-TB) patients, a limited sampling strategy was developed using a population pharmacokinetic model based on data for healthy volunteers. A two-compartment population pharmacokinetic model was developed with data for 42 healthy volunteers using an iterative two-stage Bayesian method. External validation was performed by Bayesian fitting of the model developed with data for volunteers to the data for individual MDR-TB patients (in which the fitted values of the area under the concentration-time curve from 0 to 24 h [AUC T MIC = 1,000) was used to evaluate limited sampling strategies. Additionally, the 40% 0-24, fit values] were used) using the population model developed for volunteers as a prior. A Monte Carlo simulation ( n = 1,000) was used to evaluate limited sampling strategies. Additionally, the 40% T MIC ) in MDR-TB patients by 6.8% (range, -17.2 to 30.7%). The best-performing limited sampling strategy, which had a time restriction of 0 to 6 h, was found to be sampling at 1 and 5 h ( f 40% T MIC would have been exceeded in 9 out of 12 patients. A population pharmacokinetic model and limited sampling strategy, developed using data from healthy volunteers, were shown to be adequate to predict ertapenem exposure in MDR-TB patients.0-24 ) in MDR-TB patients by 6.8% (range, -17.2 to 30.7%). The best-performing limited sampling strategy, which had a time restriction of 0 to 6 h, was found to be sampling at 1 and 5 h ( r 2 = 0.78, mean prediction error = -0.33%, root mean square error = 5.5%). Drug exposure was overestimated by a mean percentage of 4.2% (range, -15.2 to 23.6%). When a free fraction of 5% was considered and the MIC was set at 0.5 mg/liter, the minimum f 40% T MIC would have been exceeded in 9 out of 12 patients. A population pharmacokinetic model and limited sampling strategy, developed using data from healthy volunteers, were shown to be adequate to predict ertapenem exposure in MDR-TB patients., (Copyright © 2017 American Society for Microbiology.)

The aim of this study was to establish a limited sample strategy (LSS) to predict the mycophenolic acid (MPA) area under the curve (AUC) (0-12) in children with systemic lupus erythematosus (SLE). Three months after initiation of mycophenolate mofetil (MMF) 26 children with SLE presented for therapeutic drug monitoring of MPA. On the day of the test, 10 specimens were collected, analyzed, and MPA AUC (0-12) was calculated. Using step-wise regression analysis, LSS equations were developed. Using bootstrap validation, the predictive performance was calculated. The measured mean (standard deviation) for the trough concentration and AUC (0-12) were 2.55 (1.57) μg/ml and 62.6 (21.67) mg.h/L, respectively. The range of trough concentrations and AUC (0-12) were 0.7-5.54 μg/ml and 22.1-104.8 mg.h/L, respectively. The interindividual variability (%CV) for dose normalized AUC (0-12) and dose normalized C trough was 46.5% and 61.1%, respectively. The correlation between the concentrations at the different time points and MPA AUC (0-12) ranged from 0.05 (1.5 h) to 0.56 (4 h). Two LSS equations that included 4 or 5 time points up to 3 h were developed and validated. The 4 point LSS had a correlation ( R 2 ) of 0.88 and the 5 point LSS an R 2 of 0.87. With respect to the 4 point and 5 point MPA LSS AUC (0-12) , the bias was 1.92% and 1.96%, respectively, and the imprecision was 11.24% and 11.28%, respectively. A 4 point LSS which concludes within 3 h after the administration of the MMF dose was developed and validated, to determine the MPA AUC (0-12) in children with SLE., Competing Interests: There are no conflicts of interest.

Introduction: Tuberculosis remains a global health problem and pharmacokinetic variability has been postulated as one of the causes of treatment failure and acquired drug resistance. New developments enable implementation of therapeutic drug monitoring, a strategy to evaluate drug exposure in order to tailor the dose to the individual patient, in tuberculosis treatment., Areas Covered: Literature on pharmacokinetics and pharmacodynamics of anti-tuberculosis drugs was explored to evaluate the effect of drug exposure in relation to drug susceptibility, toxicity and efficacy. New, down-sized strategies, like dried blood spot analysis and limited sampling strategies are reviewed. In addition, molecular resistance testing of Mycobacteria tuberculosis, combining a short turn-around time with relevant information on drug susceptibility of the causative pathogen was explored. Newly emerging host biomarkers provide information on the response to treatment., Expert Opinion: Therapeutic drug monitoring can minimize toxicity and increase efficacy of tuberculosis treatment and prevent the development of resistance. Dried blood spot analysis and limited sampling strategies, can be combined to provide us with a more patient friendly approach. Furthermore, rapid information on drug susceptibility by molecular testing, and information from host biomarkers on the bacteriological response, can be used to further optimize tuberculosis treatment.

Amikacin and kanamycin are considered important and effective drugs in the treatment of multidrug-resistant tuberculosis (MDR-TB). Unfortunately, the incidence of toxicity is high and is related to elevated drug exposure. In order to achieve a balance between efficacy and toxicity, a population pharmacokinetic (PPK) model may help to optimise drug exposure. Patients with MDR-TB who had received amikacin or kanamycin as part of their treatment and who had routinely received therapeutic drug monitoring were evaluated. A PPK model was developed and subsequently validated. Using this model, a limited sampling model was developed. Eleven patients receiving amikacin and nine patients receiving kanamycin were included in this study. The median observed 24-h area under the concentration-time curve (AUC0-24h) was 77.2 mg h/L [interquartile range (IQR) 64.7-96.2 mg h/L] for amikacin and 64.1 mg h/L (IQR 55.6-92.1 mg h/L) for kanamycin. The PPK model was developed and validated using n-1 cross-validation. A robust population model was developed that is suitable for predicting the AUC0-24h of amikacin and kanamycin. This model, in combination with the limited sampling strategy developed, can be used in daily routine to guide dosing but also to assess AUC0-24h in phase 3 studies., (Copyright © 2015 Elsevier B.V. and the International Society of Chemotherapy. All rights reserved.)

Understanding the relationship between drug dose and exposure (pharmacokinetics, PK) and the relationship between exposure and effect (pharmacodynamics) is an important component of pharmacology when attempting to predict clinical effects of anticancer drugs. PK studies can provide a better understanding of these relationships; however, they often involve intensive sampling over an extended period of time, resulting in increased cost and decreased compliance. Doxorubicin (DOX), one of the most widely used antineoplastic agents in veterinary cancer therapy, is characterized by large interpatient variability in overall drug exposure and the development and degree of myelosuppression following equivalent dosages. We have developed and validated a limited-sampling strategy for DOX, in which three blood samples are obtained over 1 h post-treatment, that accurately predicts patient exposure. This strategy could allow for refining of dosing variables and utilization of therapeutic drug monitoring to ensure optimized dosing., (© 2012 John Wiley & Sons Ltd.)