8 research outputs found

    Population plasma and urine pharmacokinetics and the probability of target attainment of fosfomycin in healthy male volunteers

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    Purpose: A population pharmacokinetic model of fosfomycin was developed in healthy volunteers after intravenous administration, and different dosing regimens were evaluated in terms of the probability of target attainment for Escherichia coli using both plasma and urinary pharmacokinetic/pharmacodynamic targets. Methods: Eight healthy men received fosfomycin as both intermittent 8 g q8h and continuous infusion 1 g/h with a loading dose of 8 g in a crossover study design. Dense sampling was conducted during both regimens. Population pharmacokinetic modelling was performed using NONMEM. Monte Carlo simulations were conducted to evaluate the Probability of Target Attainment (PTA) of different dosing regimens using bactericidal (AUC24h/MIC of 83 and 75%T&gt;MIC) and bacteriostatic (AUC24h/MIC of 25) plasma targets and bacteriostatic (AUC24h/MIC of 3994) urine target. Results: A total of 176 plasma and 86 urine samples were available for PK analysis. A two-compartment model with a urine compartment best described the data. Glomerular filtration rate (GFR) showed a significant correlation with renal clearance and was implemented in the final model. Simulation results show that the dose of 4 g q8h reached 100% of PTA using bactericidal and bacteriostatic targets for MIC up to 16 mg/L. Conclusion: For the clinical breakpoint of 32 mg/L, the standard dosing regimen (4 g q8h) might not be sufficient to reach the bactericidal target. Higher dosing of 8 g q8h as an intermittent infusion or 0.75 g/h as a continuous infusion might be required. Continuous infusion resulted in better attainment of the %T&gt;MIC target than intermittent infusion.</p

    Individualized dosing algorithms for tacrolimus in kidney transplant recipients:current status and unmet needs

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    Introduction: Tacrolimus is a potent immunosuppressive drug with many side effects including nephrotoxicity and post-transplant diabetes mellitus. To limit its toxicity, therapeutic drug monitoring (TDM) is performed. However, tacrolimus’ pharmacokinetics are highly variable within and between individuals, which complicates their clinical management. Despite TDM, many kidney transplant recipients will experience under- or overexposure to tacrolimus. Therefore, dosing algorithms have been developed to limit the time a patient is exposed to off-target concentrations. Areas Covered: Tacrolimus starting dose algorithms and models for follow-up doses developed and/or tested since 2015, encompassing both adult and pediatric populations. Literature was searched in different databases, i.e. Embase, PubMed, Web of Science, Cochrane Register, and Google Scholar, from inception to February 2023 Expert Opinion: Many algorithms have been developed, but few have been prospectively evaluated. These performed better than bodyweight-based starting doses, regarding the time a patient is exposed to off-target tacrolimus concentrations. No benefit in reduced tacrolimus toxicity has yet been observed. Most algorithms were developed from small datasets, contained only a few tacrolimus concentrations per person, and were not externally validated. Moreover, other matrices should be considered which might better correlate with tacrolimus toxicity than the whole-blood concentration, e.g. unbound plasma or intra-lymphocytic tacrolimus concentrations.</p

    Population Pharmacokinetic Modelling of Intravenous Immunoglobulin Treatment in Patients with Guillain–BarrĂ© Syndrome

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    BACKGROUND AND OBJECTIVE: Intravenous immunoglobulin (IVIg) at a standard dosage is the treatment of choice for Guillain–BarrĂ© syndrome. The pharmacokinetics, however, is highly variable between patients, and a rapid clearance of IVIg is associated with poor recovery. We aimed to develop a model to predict the pharmacokinetics of a standard 5-day IVIg course (0.4 g/kg/day) in patients with Guillain–BarrĂ© syndrome. METHODS: Non-linear mixed-effects modelling software (NONMEM(Âź)) was used to construct a pharmacokinetic model based on a model-building cohort of 177 patients with Guillain–BarrĂ© syndrome, with a total of 589 sequential serum samples tested for total immunoglobulin G (IgG) levels, and evaluated on an independent validation cohort that consisted of 177 patients with Guillain–BarrĂ© syndrome with 689 sequential serum samples. RESULTS: The final two-compartment model accurately described the daily increment in serum IgG levels during a standard IVIg course; the initial rapid fall and then a gradual decline to steady-state levels thereafter. The covariates that increased IgG clearance were a more severe disease (as indicated by the Guillain–BarrĂ© syndrome disability score) and concomitant methylprednisolone treatment. When the current dosing regimen was simulated, the percentage of patients who reached a target ∆IgG > 7.3 g/L at 2 weeks decreased from 74% in mildly affected patients to only 33% in the most severely affected and mechanically ventilated patients (Guillain–BarrĂ© syndrome disability score of 5). CONCLUSIONS: This is the first population-pharmacokinetic model for standard IVIg treatment in Guillain–BarrĂ© syndrome. The model provides a new tool to predict the pharmacokinetics of alternative regimens of IVIg in Guillain–BarrĂ© syndrome to design future trials and personalise treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40262-022-01136-z

    Towards precision dosing of aripiprazole in children and adolescents with autism spectrum disorder:Linking blood levels to weight gain and effectiveness

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    Aims: Aripiprazole is one of the most commonly prescribed antipsychotic drugs to children and adolescents worldwide, but it is associated with serious side-effects, including weight gain. This study assessed the population pharmacokinetics of aripiprazole and its active metabolite and investigated the relationship between pharmacokinetic parameters and body mass index (BMI) in children and adolescents with autism spectrum disorder (ASD) and behavioural problems. Secondary outcomes were metabolic, endocrine, extrapyramidal and cardiac side-effects and drug effectiveness. Methods: Twenty-four children and adolescents (15 males, 9 females) aged 6–18 years were included in a 24-week prospective observational trial. Drug plasma concentrations, side-effects and drug effectiveness were measured at several time points during follow-up. Relevant pharmacokinetic covariates, including CYP2D6, CYP3A4, CYP3A5 and P-glycoprotein (ABCB1) genotypes, were determined. Nonlinear mixed-effects modelling (NONMEM¼) was used for a population pharmacokinetic analysis with 92 aripiprazole and 91 dehydro-aripiprazole concentrations. Subsequently, model-based trough concentrations, maximum concentrations and 24-h area under the curves (AUCs) were analysed to predict outcomes using generalized and linear mixed-effects models. Results: For both aripiprazole and dehydro-aripiprazole, one-compartment models best described the measured concentrations, with albumin and BMI as significant covariates. Of all the pharmacokinetic parameters, higher sum (aripiprazole plus dehydro-aripiprazole) trough concentrations best predicted higher BMI z-scores (P &lt;.001) and higher Hb1Ac levels (P =.03) during follow-up. No significant association was found between sum concentrations and effectiveness. Conclusions: Our results indicate a threshold with regard to safety, which suggests that therapeutic drug monitoring of aripiprazole could potentially increase safety in children and adolescents with ASD and behavioural problems.</p

    Meropenem Model-Informed Precision Dosing in the Treatment of Critically Ill Patients: Can We Use It?

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    The number of pharmacokinetic (PK) models of meropenem is increasing. However, the daily role of these PK models in the clinic remains unclear, especially for critically ill patients. Therefore, we evaluated the published meropenem models on real-world ICU data to assess their suitability for use in clinical practice. All models were built in NONMEM and evaluated using prediction and simulation-based diagnostics for the ability to predict the subsequent meropenem concentrations without plasma concentrations (a priori), and with plasma concentrations (a posteriori), for use in therapeutic drug monitoring (TDM). Eighteen PopPK models were included for evaluation. The a priori fit of the models, without the use of plasma concentrations, was poor, with a prediction error (PE)% of the interquartile range (IQR) exceeding the ±30% threshold. The fit improved when one to three concentrations were used to improve model predictions for TDM purposes. Two models were in the acceptable range with an IQR PE% within ±30%, when two or three concentrations were used. The role of PK models to determine the starting dose of meropenem in this population seems limited. However, certain models might be suitable for TDM-based dose adjustment using two to three plasma concentrations

    Population pharmacokinetics of oxycodone and metabolites in patients with cancer‐related pain

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    Oxycodone is frequently used for treating cancer‐related pain, while not much is known about the factors that influence treatment outcomes in these patients. We aim to unravel these factors by developing a population‐pharmacokinetic model to assess the pharmacokinetics of oxycodone and its metabolites in cancer patients, and to associate this with pain scores, and adverse events. Hospitalized patients with cancer‐related pain, who were treated with oral oxycodone, could participate. Pharmacokinetic samples and patient‐reported pain scores and occurrence and severity of nine adverse events were taken every 12 h. In 28 patients, 302 pharmacokinetic samples were collected. A one‐compartment model for oxycodone and each metabolite best described oxycodone, nor‐oxycodone, and nor‐oxymorphone pharmacokinetics. Furthermore, oxycodone exposure was not associated with average and maximal pain scores, and oxycodone, nor-oxycodone, and nor‐oxymorphone exposure were not associated with adverse events (all p > 0.05). This is the first model to describe the pharmacokinetics of oxycodone including the metabolites nor-oxycodone and nor‐oxymorphone in hospitalized patients with cancer pain. Additional research, including more patients and a more timely collection of pharmacodynamic data, is needed to further elucidate oxycodone (metabolite) pharmacokinetic/pharmacodynamic relationships. This model is an important starting point for further studies to optimize oxycodone dosing regiments in patients with cancer‐related pain

    Prospective real-world study on the pharmacokinetics of pembrolizumab in patients with solid tumors

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    Background Dosing schemes of pembrolizumab (anti-programmed cell death protein 1 monoclonal antibody) are solely based on pharmacokinetic (PK) modelling derived from phase I–III trials. The current study aimed to determine factors affecting PK and its relationship with clinical outcome in the real-world setting.Methods Advanced-stage cancer patients, who were treated with pembrolizumab monotherapy (2 mg/kg Q3W or 200 mg flat Q3W), were prospectively included for serial sampling to obtain trough concentrations. A PK model was generated, covariate effects assessed and internally validated by a bootstrap procedure. PK parameters were related to overall survival (OS) and the occurrence of immune-related adverse events (irAEs).Results 588 serum samples derived from 122 patients with (non-)small-cell lung cancer ([N]SCLC), malignant pleural mesothelioma (MPM), melanoma and urothelial cell cancer (UCC) were analyzed. Median follow-up was 2.2 years. A one-compartment PK model was generated: body surface area (BSA) and serum albumin had a significant effect on drug clearance (CL; covariate estimate 1.46 and −1.43, respectively), and serum lactate dehydrogenase (LDH) on the distribution volume(Vd; 0.34). A significant inverse CL–OS relationship was determined for NSCLC (HR:1.69; 95%CI1.07–2.68; p=0.024) and MPM (HR: 3.29; 95% CI 1.08 to 10.09; p=0.037), after correction for prognostic factors, which could not confirmed for melanoma (p=0.22) or UCC (p=0.34). No relationship could be determined between CL and grade &gt;3 irAEs (p=0.70).Conclusions High interpatient variability of pembrolizumab PK is determined by BSA and serum albumin (on CL) and LDH (on Vd). A strong inverse CL–OS relationship was demonstrated for NSCLC and MPM, which could not be observed for melanoma and UCC. The findings suggest that personalized dosing should be prospectively explored
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