39 research outputs found

    Adaptation of a population pharmacokinetic model to inform tacrolimus therapy in heart transplant recipients

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    Aim: Existing tacrolimus population pharmacokinetic models are unsuitable for guiding tacrolimus dosing in heart transplant recipients. This study aimed to develop and evaluate a population pharmacokinetic model for tacrolimus in heart transplant recipients that considers the tacrolimus-azole antifungal interaction. Methods: Data from heart transplant recipients (n = 87) administered the oral immediate-release formulation of tacrolimus (Prograf®) were collected. Routine drug monitoring data, principally trough concentrations, were used for model building (n = 1099). A published tacrolimus model was used to inform the estimation of Ka, V2/F, Q/F and V3/F. The effect of concomitant azole antifungal use on tacrolimus CL/F was quantified. Fat-free mass was implemented as a covariate on CL/F, V2/F, V3/F and Q/F on an allometry scale. Subsequently, stepwise covariate modelling was performed. Significant covariates influencing tacrolimus CL/F were included in the final model. Robustness of the final model was confirmed using prediction-corrected visual predictive check (pcVPC). The final model was externally evaluated for prediction of tacrolimus concentrations of the fourth dosing occasion (n = 87) from one to three prior dosing occasions. Results: Concomitant azole antifungal therapy reduced tacrolimus CL/F by 80%. Haematocrit (∆OFV = −44, P <.001) was included in the final model. The pcVPC of the final model displayed good model adequacy. One recent drug concentration is sufficient for the model to guide tacrolimus dosing. Conclusion: A population pharmacokinetic model that adequately describes tacrolimus pharmacokinetics in heart transplant recipients, considering the tacrolimus–azole antifungal interaction was developed. Prospective evaluation is required to assess its clinical utility to improve patient outcomes.</p

    Therapeutic drug monitoring in oncology - What's out there: A bibliometric evaluation on the topic

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    International audiencePharmacological therapy is the mainstay of treatment for cancer patients. Despite wide interpatient variability in systemic drug concentrations for numerous antineoplastics, dosing based on body size remains the predominant approach. Therapeutic drug monitoring (TDM) is used for few antineoplastics in specific scenarios. We conducted a rapid bibliometric evaluation of TDM in oncology to capture a snapshot of research in this area over time and explore topics that reflect development in the field. Reports with the composite, indexed term 'therapeutic drug monitoring' in the title and abstract were extracted from MEDLINE (inception to August 2021). Reports related to applications in cancer were selected for inclusion and were tagged by study design, antineoplastic drugs and concepts related to TDM. We present a timeline from 1980 to the present indicating the year of first report of antineoplastic agents and key terms. The reports in our sample primarily reflected development and validation of analytical methods with few relating to clinical outcomes to support implementation. Our work emphasises evidence gaps that may contribute to poor uptake of TDM in oncology

    Evaluation of published population pharmacokinetic models to inform tacrolimus dosing in adult heart transplant recipients

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    Background and Aim: Identification of the most appropriate population pharmacokinetic model-based Bayesian estimation is required prior to its implementation in routine clinical practice to inform tacrolimus dosing decisions. This study aimed to determine the predictive performances of relevant population pharmacokinetic models of tacrolimus developed from various solid organ transplant recipient populations in adult heart transplant recipients, stratified based on concomitant azole antifungal use. Concomitant azole antifungal therapy alters tacrolimus pharmacokinetics substantially, necessitating dose adjustments. Methods: Population pharmacokinetic models of tacrolimus were selected (n = 17) for evaluation from a recent systematic review. The models were transcribed and implemented in NONMEM version 7.4.3. Data from 85 heart transplant recipients (2387 tacrolimus concentrations) administered the oral immediate-release formulation of tacrolimus (Prograf) were obtained up to 391 days post-transplant. The performance of each model was evaluated using: (i) prediction-based assessment (bias and imprecision) of the individual predicted tacrolimus concentration of the fourth dosing occasion (MAXEVAL = 0, FOCE-I) from 1–3 prior dosing occasions; and (ii) simulation-based assessment (prediction-corrected visual predictive check). Both assessments were stratified based on concomitant azole antifungal use. Results: Regardless of the number of prior dosing occasions (1–3) and concomitant azole antifungal use, all models demonstrated unacceptable individual predicted tacrolimus concentration of the fourth dosing occasion (n = 152). The prediction-corrected visual predictive check graphics indicated that these models inadequately predicted observed tacrolimus concentrations. Conclusion: All models evaluated were unable to adequately describe tacrolimus pharmacokinetics in adult heart transplant recipients included in this study. Further work is required to describe tacrolimus pharmacokinetics for our heart transplant recipient cohort.</p

    Molecular Determinants for Substrate Interactions with the Glycine Transporter GlyT2

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    Transporters in the SLC6 family play key roles in regulating neurotransmission and are the targets for a wide range of therapeutics. Important insights into the transport mechanisms and the specificity of drug interactions of SLC6 transporters have been obtained from the crystal structures of a bacterial homologue of the family, LeuTAa, and more recently the Drosophila dopamine transporter and the human serotonin transporter. However, there is disputed evidence that the bacterial leucine transporter, LeuTAa, contains two substrate binding sites that work cooperatively in the mechanism of transport, with the binding of a second substrate being required for the release of the substrate from the primary site. An alternate proposal is that there may be low affinity binding sites that serve to direct the flow of substrates to the primary site. We have used a combination of molecular dynamics simulations of substrate interactions with a homology model of GlyT2, together with radiolabeled amino acid uptake assays and electrophysiological analysis of wild-type and mutant transporters, to provide evidence that substrate selectivity of GlyT2 is determined entirely by the primary substrate binding site and, furthermore, if a secondary site exists then it is a low affinity nonselective amino acid binding site.This work was supported by a National Health and Medical Research Council Project Grant (APP1082570) and the Merit Allocation Scheme on the NCI National Facility at the ANU. M.L.O. is supported by an ARC DECRA (DE120101550). S.M. is supported by an Australian Postgraduate Award

    A pharmacokinetic‐pharmacodynamic study of a single dose of febuxostat in healthy subjects

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    Aims To examine the pharmacokinetic‐phamacodynamic (PK‐PD) relationships of plasma febuxostat and serum urate and the effect of a single dose of the drug on renal excretion and fractional clearance of urate (FCU). Methods Blood and urine samples were collected at baseline and up to 145 h following administration of febuxostat (80 mg) to healthy subjects (n=9). Plasma febuxostat and serum and urinary urate and creatinine concentrations were determined. Febuxostat pharmacokinetics were estimated using a two‐compartment model with first order absorption. An Emax PK‐PD model was fitted to mean febuxostat and urate concentrations. Urinary urate excretion and FCU were calculated pre‐ and post‐dose. Results Maximum mean plasma concentration of febuxostat (2.7 mg l‐1) was observed 1.2 h after dosage. Febuxostat initial and terminal half‐lives were 2.0 ± 1.0 h and 14.0 ± 4.7 h (mean ± SD), respectively. The majority (81%) of the drug was eliminated in the 9 h after dosing. Serum urate declined slowly achieving mean nadir (0.20 mmol l‐1) at 24 h. The IC50 (plasma febuxostat concentration that inhibits urate production by 50%) was 0.11 ± 0.09 mg l‐1 (mean ± SD). Urinary urate excretion changed in parallel with serum urate. There was no systematic or significant change in FCU from baseline. Conclusion The PK‐PD model could potentially be used to individualise febuxostat treatment and improve clinical outcomes. Single dose of febuxostat does not affect the efficiency of the kidney to excrete urate. Further investigations are required to confirm the present results following multiple dosing with febuxostat

    Clinical pharmacokinetics in kidney disease: application to rational design of dosing regimens

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    A change in pharmacokinetics can alter drug exposure and predispose the patient to either over- or underdosing, potentially resulting in adverse drug reactions or therapeutic failure. Kidney disease is characterized by multiple physiologic effects, which induce clinically significant changes in pharmacokinetics. These vary between individuals and may be quantitated in certain instances. An understanding of pharmacokinetic concepts is, therefore, important for a rational approach to the design of drug dosing regimens for the delivery of personalized medical care. Whether kidney disease is acute or chronic, drug clearance decreases and the volume of distribution may remain unchanged or increase. AKI is defined by dynamic changes in kidney function, which complicates attempts to accurately quantify drug clearance. In contrast, changes in drug clearance progress more slowly with CKD. In general, kidney replacement therapies increase drug clearance, but the extent to which this occurs depends on the modality used and its duration, the drug’s properties, and the timing of drug administration. However, the changes in drug handling associated with kidney disease are not isolated to reduced kidney clearance and an appreciation of the scale of potential derangements is important. In most instances, the first dose administered in patients with kidney disease is the same as in patients with normal kidney function. However, in some cases, a higher (loading) initial dose is given to rapidly achieve therapeutic concentrations, followed by a lower maintenance dose, as is well described when prescribing anti-infectives to patients with sepsis and AKI. This review provides an overview of how pharmacokinetic principles can be applied to patients with kidney disease to personalize dosage regimens. Patients with kidney disease are a vulnerable population and the increasing prevalence of kidney disease means that these considerations are important for all prescribers.Acknowledgments D.M.R. is a recipient of the Jacquot Research Establishment Fellowship, Royal Australasian College of Physicians and the Clinician “Buy-Out” Program, St. Vincent’s Centre for Applied Medical Research

    Determination of febuxostat in human plasma by high performance liquid chromatography (HPLC) with fluorescence-detection

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    Febuxostat prevents gout attacks by lowering serum urate. Aspects of the pharmacokinetic-pharmacodynamic relationship of febuxostat concentrations to urate in gout patients need further elucidation. In order to undertake these studies, the assay methodology for febuxostat has been enhanced and validated to meet FDA standards. An HPLC method with fluorescence-detection has been modified to increase sensitivity, reduce complexity, shorten the sample preparation process and improve the inter-day coefficient of variation of the lowest quality control sample (0.03 μg/L). Protein in plasma samples (200 μL) is now precipitated with acetonitrile (400 μL) containing the internal standard (2-naphthoic acid). The supernatant is analysed at excitation and emission wavelengths of 320 and 380 nm, respectively as in the previous method. A Luna C18 column (Phenomenex, Australia) at 40 °C with mobile phase of glacial acetic acid (0.032%) in acetonitrile:water (60:40, v:v), an injection volume of 10 μL and a flow rate of 1.5 mL/min is employed. Analysis time is 8 min. Calibration curves in drug-free plasma range from 0.005 to 10.00 μg/mL. Data points are fitted using linear regression with a weighting factor of 1/concentration. The inter-day accuracy and imprecision of the quality control samples (0.0075, 0.015, 3.00 and 9.80 μg/mL) is 90–115% and  ≤ 14.5%, respectively

    Identification of a 3rd Na+ binding site of the glycine transporter, GlyT2

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    The Na+/Cl- dependent glycine transporters GlyT1 and GlyT2 regulate synaptic glycine concentrations. Glycine transport by GlyT2 is coupled to the co-transport of three Na+ ions, whereas transport by GlyT1 is coupled to the co-transport of only two Na+ ions. These differences in ion-flux coupling determine their respective concentrating capacities and have a direct bearing on their functional roles in synaptic transmission. The crystal structures of the closely related bacterial Na+-dependent leucine transporter, LeuTAa, and the Drosophila dopamine transporter, dDAT, have allowed prediction of two Na+ binding sites in GlyT2, but the physical location of the third Na+ site in GlyT2 is unknown. A bacterial betaine transporter, BetP, has also been crystallized and shows structural similarity to LeuTAa. Although betaine transport by BetP is coupled to the co-transport of two Na+ ions, the first Na+ site is not conserved between BetP and LeuTAa, the so called Na1' site. We hypothesized that the third Na+ binding site (Na3 site) of GlyT2 corresponds to the BetP Na1' binding site. To identify the Na3 binding site of GlyT2, we performed molecular dynamics (MD) simulations. Surprisingly, a Na+ placed at the location consistent with the Na1' site of BetP spontaneously dissociated from its initial location and bound instead to a novel Na3 site. Using a combination of MD simulations of a comparative model of GlyT2 together with an analysis of the functional properties of wild type and mutant GlyTs we have identified an electrostatically favorable novel third Na+ binding site in GlyT2 formed by Trp263 and Met276 in TM3, Ala481 in TM6 and Glu648 in TM10
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