Accurate and precise plasma clearance measurement using four 99mTc-DTPA plasma samples over 4 h

Wanasundara, Surajith N.; Wesolowski, Michal J.; Barnfield, Mark C.; Waller, Michael L.; Murray, Anthony W.; Burniston, Maria; Babyn, Paul; Wesolowski, Carl A.

doi:10.1097/mnm.0000000000000405

Cited by 9 publications

(16 citation statements)

References 29 publications

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“…The 1/ C ( t ) 2 least squares weighting fits biexponential washout models favour fitting the later data to obtain more accurate half-lives [39]. Although some researchers have, without much discussion, used WLS E2 rather than OLS E2 in their patient series [13, 40, 41], direct numerical evidence of improved correlation against better or more complete models, such as that in Table 4, is currently limited. From the Monte Carlo testing—see Table 3—the GDC MRT WO had a 0.69% (CV) precision and 0.46% accuracy, so that comparison to GDC as a measure is not obviously error prone.…”

Section: Discussionmentioning

confidence: 99%

“…The use of convolution to create concentration models inclusive of early and late data is hardly new [15]. However, this has usually been done using sums of exponential terms for the washout models, which latter are less accurate and precise washout models than gamma distribution models see Table 4 and [13, 31, 46]. The current work is possibly a first application of a GDC radioactivity (or concentration) model.…”

Section: Discussionmentioning

confidence: 99%

“…One of these latter, Tk-GV; the Tikhonov regularized gamma variate herein adaptively minimizes the relative error of β or alternatively the minimum relative error of plasma clearance [12, 13, 32]. Briefly, Tk-GV is an approximate inverse solution to Eq.…”

Section: Methodsmentioning

confidence: 99%

“…Exponential distributions are not the preferred shapes to explain organ bolus input function shapes, for which GDs or other functions are more useful [27–29]. To model the prolonged washout kinetics, which models are only fit following peak organ activity, GDs, one of several possible generalizations of EDs, have better fidelity than mono- and bi-exponential models [12, 13, 30–33]. One reason for this is that GD washout models imply zero initial drug volume with no initial mixing [33].…”

Section: Theorymentioning

confidence: 99%

“…Thus, at the earliest times following a bolus, there is a severe mismatch between a washout model and the signal it models. Consequently, most of the routinely used pharmacokinetic models, e.g., classic compartmental washout models, do a poor job of fitting early-time organ activity [12, 13]. Thus, washout functions do not fit concentration or time-activity curves well enough to serve as accurate simulation study models.…”

Section: Introductionmentioning

confidence: 99%

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A gamma-distribution convolution model of 99mTc-MIBI thyroid time-activity curves

et al. 2016

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BackgroundThe convolution approach to thyroid time-activity curve (TAC) data fitting with a gamma distribution convolution (GDC) TAC model following bolus intravenous injection is presented and applied to 99mTc-MIBI data. The GDC model is a convolution of two gamma distribution functions that simultaneously models the distribution and washout kinetics of the radiotracer.The GDC model was fitted to thyroid region of interest (ROI) TAC data from 1 min per frame 99mTc-MIBI image series for 90 min; GDC models were generated for three patients having left and right thyroid lobe and total thyroid ROIs, and were contrasted with washout-only models, i.e., less complete models. GDC model accuracy was tested using 10 Monte Carlo simulations for each clinical ROI.ResultsThe nine clinical GDC models, obtained from least counting error of counting, exhibited corrected (for 6 parameters) fit errors ranging from 0.998% to 1.82%. The range of all thyroid mean residence times (MRTs) was 212 to 699 min, which from noise injected simulations of each case had an average coefficient of variation of 0.7% and a not statistically significant accuracy error of 0.5% (p = 0.5, 2-sample paired t test). The slowest MRT value (699 min) was from a single thyroid lobe with a tissue diagnosed parathyroid adenoma also seen on scanning as retained marker. The two total thyroid ROIs without substantial pathology had MRT values of 278 and 350 min overlapping a published 99mTc-MIBI thyroid MRT value. One combined value and four unrelated washout-only models were tested and exhibited R-squared values for MRT with the GDC, i.e., a more complete concentration model, ranging from 0.0183 to 0.9395.ConclusionsThe GDC models had a small enough TAC noise-image misregistration (0.8%) that they have a plausible use as simulations of thyroid activity for querying performance of other models such as washout models, for altered ROI size, noise, administered dose, and image framing rates. Indeed, of the four washout-only models tested, no single model approached the apparent accuracy of the GDC model using only 90 min of data. Ninety minutes is a long gamma-camera acquisition time for a patient, but a short a time for most kinetic models. Consequently, the results should be regarded as preliminary.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A gamma-distribution convolution model of 99mTc-MIBI thyroid time-activity curves

et al. 2016

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Comparison of the gamma-Pareto convolution with conventional methods of characterising metformin pharmacokinetics in dogs

Wesolowski

Wanasundara

Babyn

et al. 2019

J Pharmacokinet Pharmacodyn

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A model was developed for long term metformin tissue retention based upon temporally inclusive models of serum/plasma concentration (C) having power function tails called the gamma-Pareto type I convolution (GPC) model and was contrasted with biexponential (E2) and noncompartmental (NC) metformin models. GPC models of C have a peripheral venous first arrival of drug-times parameter, early C peaks and very slow washouts of C. The GPC, E2 and NC models were applied to a total of 148 serum samples drawn from 20 min to 72 h following bolus intravenous metformin in seven healthy mongrel dogs. The GPC model was used to calculate area under the curve (AUC), clearance (CL), and functions of time, f(t), for drug mass remaining (M), apparent volume of distribution (V d), as well as t 1=2 f ðtÞ for C, M and V d. The GPC models of C yielded metformin CL-values that were 84.8% of total renal plasma flow (RPF) as estimated from meta-analysis. The GPC CL-values were significantly less than the corresponding NC and E2 CL-values of 104.7% and 123.7% of RPF, respectively. The GPC plasma/serum only model predicted 78.9% drug M average urinary recovery at 72 h; similar to prior human urine drug M collection results. The GPC model t 1=2 of M, C and V d , were asymptotically proportional to elapsed time, with a constant limiting t 1=2 ratio of M/C averaging 7.0 times, a result in keeping with prior simultaneous C and urine M collection studies and exhibiting a rate of apparent volume growth of V d that achieved limiting constant values. A simulated constant average drug mass multidosing protocol exhibited increased V d and t 1=2 with elapsing time, effects that have been observed experimentally during same-dose multidosing. The GPC heavy-tailed models explained multiple documented phenomena that were unexplained with lighter-tailed models. Keywords Metformin Á Pharmacokinetics Á Mathematical modelling pdf Á Clearance Á Serum concentration Á Drug mass Á Loading dose regimen Abbreviations AUC Area under the curve C(t) Concentration (model) CDF Cumulative density function CI Confidence interval, default 95% C in ðtÞ Concentration, constant infusion CL Plasma or serum clearance C max Local peak concentration C obs ðtÞ Observed concentrations (vector) C SS Steady-state concentration, terminal C in ðtÞ CV% Coefficient of variation D Dose mass df Degrees of freedom DF(n) Fractional dose multiplier D R An acronym of dosing rate E1 Monoexponential c e Àk t E2 Biexponential ED n Exponential density of n terms E PAH Renal extraction fraction PAH GD Gamma distribution GM Geometric mean GPC Gamma Pareto distribution H-MRT Harmonic mean residence time Electronic supplementary material The online version of this article (

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A series acceleration algorithm for the gamma-Pareto (type I) convolution and related functions of interest for pharmacokinetics

Wesolowski

Alcorn

Tucker

2021

J Pharmacokinet Pharmacodyn

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The gamma-Pareto type I convolution (GPC type I) distribution, which has a power function tail, was recently shown to describe the disposition kinetics of metformin in dogs precisely and better than sums of exponentials. However, this had very long run times and lost precision for its functional values at long times following intravenous injection. An accelerated algorithm and its computer code is now presented comprising two separate routines for short and long times and which, when applied to the dog data, completes in approximately 3 min per case. The new algorithm is a more practical research tool. Potential pharmacokinetic applications are discussed. Graphic abstract

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Accurate and precise plasma clearance measurement using four 99mTc-DTPA plasma samples over 4 h

Abstract: Objectives Glomerular filtration rate can be measured as the plasma clearance (CL) of a glomerular filtration rate marker despite body fluid disturbances using numerous, prolonged time samples. We desire a simplified technique without compromised accuracy and precision.

Cited by 9 publications

References 29 publications

A gamma-distribution convolution model of 99mTc-MIBI thyroid time-activity curves

A gamma-distribution convolution model of 99mTc-MIBI thyroid time-activity curves

Comparison of the gamma-Pareto convolution with conventional methods of characterising metformin pharmacokinetics in dogs

A series acceleration algorithm for the gamma-Pareto (type I) convolution and related functions of interest for pharmacokinetics

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