Deconvolution of Plasma Pharmacokinetics from Dynamic Heart Imaging Data Obtained by Single Positron Emission Computed Tomography/Computed Tomography Imaging

Abstract

Plasma pharmacokinetic (PK) data are required as an input function for graphical analysis of single positron emission computed tomography/computed tomography (SPECT/CT) and positron emission tomography/CT (PET/CT) data to evaluate tissue influx rate of radiotracers. Dynamic heart imaging data are often used as a surrogate of plasma PK. However, accumulation of radiolabel in the heart tissue may cause overprediction of plasma PK. Therefore, we developed a compartmental model, which involves forcing functions to describe intact and degraded radiolabeled proteins in plasma and their accumulation in heart tissue, to deconvolve plasma PK of ¹²⁵I-amyloid beta 40 (¹²⁵I-Aβ ₄₀) and ¹²⁵I-insulin from their dynamic heart imaging data. The three-compartment model was shown to adequately describe the plasma concentration-time profile of intact/degraded proteins and the heart radioactivity time data obtained from SPECT/CT imaging for both tracers. The model was successfully applied to deconvolve the plasma PK of both tracers from their naïve datasets of dynamic heart imaging. In agreement with our previous observations made by conventional serial plasma sampling, the deconvolved plasma PK of ¹²⁵I-Aβ ₄₀ and ¹²⁵I-insulin in young mice exhibited lower area under the curve than aged mice. Further, Patlak plot parameters extracted using deconvolved plasma PK as input function successfully recapitulated age-dependent plasma-to-brain influx kinetics changes. Therefore, the compartment model developed in this study provides a novel approach to deconvolve plasma PK of radiotracers from their noninvasive dynamic heart imaging. This method facilitates the application of preclinical SPECT/PET imaging data to characterize distribution kinetics of tracers where simultaneous plasma sampling is not feasible. SIGNIFICANCE STATEMENT: Knowledge of plasma pharmacokinetics (PK) of a radiotracer is necessary to accurately estimate its plasma-to-brain influx. However, simultaneous plasma sampling during dynamic imaging procedures is not always feasible. In the current study, we developed approaches to deconvolve plasma PK from dynamic heart imaging data of two model radiotracers, ¹²⁵I-amyloid beta 40 (¹²⁵I-Aβ ₄₀) and ¹²⁵I-insulin. This novel method is expected to minimize the need for conducting additional plasma PK studies and allow for accurate estimation of the brain influx rate.

Graphical abstract

Fig. 1.

Apparent heart pharmacokinetics obtained by dynamic SPECT/CT imaging overestimates plasma pharmacokinetics of radiolabeled tracers ¹²⁵I-insulin and ¹²⁵I-Aβ₄₀. Female B6SJLF1/J mice were intravenously injected with around 500 μCi ¹²⁵I-insulin (n = 8, 9 to 12 months old) or ¹²⁵I-Aβ₄₀ (n = 9, 7 to 8 months old), and the heart of each animal was imaged up to 42 minutes by dynamic SPECT/CT. (A, B) Representative images of mice after injection with (A) ¹²⁵I-insulin and (B) ¹²⁵I-Aβ₄₀ at 3, 4, 5, 6, 12, and 42 minutes and example regions of interest for data sampling are shown. (C, D) Comparison of apparent heart PK and plasma PK of (C) ¹²⁵I-insulin and (D) ¹²⁵I-Aβ₄₀. Data are presented as mean ± S.D. Apparent heart PK was calculated as the ratio of heart radioactivity (μCi) and heart cavity volume V_h (mL; 0.036 mL for ¹²⁵I-insulin and 0.099 mL for ¹²⁵I-Aβ₄₀) as described in the Materials and Methods section.

Fig. 2.

Model scheme and workflow. A three-compartment model was constructed, including intact and degraded radiolabeled proteins in plasma (defined by forcing functions) and their accumulation in heart tissue. The model was fitted to the heart radioactivity-time data, and transfer rate constants were estimated using SAAM II software. The model predictions were further verified using data from naïve animal group. Compartment 1 is intact protein (precipitate in TCA assay) and compartment 2 is degraded protein (supernatant in TCA assay) in the plasma. Compartment 3 is accumulation of radiotracers in the heart tissue. The parameters k₁, k₂, and k₀ are first-order constants that describe transfer rates from compartments.

Fig. 3.

Forcing functions describing plasma concentration-time profile of intact and degraded ¹²⁵I-insulin or ¹²⁵I-Aβ₄₀. Female B6SJLF1/J mice (n = 12, 9–12 months old for ¹²⁵I-insulin; n = 12, 7 to 8 months old for ¹²⁵I-Aβ₄₀) were intravenously injected with 100 μCi of ¹²⁵I-insulin or ¹²⁵I-Aβ_40. Time series plasma samples were taken at regular intervals and are subjected to TCA precipitation. The plasma concentration (μCi/ml) in precipitate (intact) and supernatant (degraded) were determined by gamma counter. Forcing functions were fitted to the plasma concentration-time data. (A) Intact ¹²⁵I-insulin; (B) degraded ¹²⁵I-insulin; (C) intact ¹²⁵I-Aβ₄₀; (D) degraded ¹²⁵I-Aβ₄₀. Data are presented as mean ± S.D.

Fig. 4.

Model fit of the dynamic heart radioactivity data. The model shown in Fig. 2 was fitted to heart radioactivity-time data of (A) ¹²⁵I-insulin and (B) ¹²⁵I-Aβ₄₀. Data are presented as mean ± S.D. Parameter estimates and relative standard error were presented in (C) ¹²⁵I-insulin and (D) ¹²⁵I-Aβ_40. Volume of heart cavity V_h was determined separately as described in the Materials and Methods section and fixed during model fitting. k₁, k₂, and k₀ are the first-order rate constants between the respective compartments.

Fig. 5.

Prediction of ¹²⁵I-insulin plasma PK in young and old mice from independent dynamic heart radioactivity datasets using the calibrated model. (A) Curve fitting of the dynamic heart radioactivity data and (B) deconvolved plasma PK profile for each independent animal. (C) Comparison of various PK parameters derived from deconvolved plasma PK profile in young (n = 3, 3-month-old females) and old (n = 3, 24-month-old females) mice. *P < 0.05, **P < 0.01, student’s t-test. Data are presented as mean ± S.D.

Fig. 6.

Prediction of ¹²⁵I-Aβ₄₀ plasma PK in young and old mice from independent dynamic heart radioactivity datasets using the calibrated model. (A) Curve fitting of the dynamic heart radioactivity data and (B) deconvolved plasma PK profile for each independent animal. (C) Comparison of various PK parameters derived from deconvolved plasma PK profile in young (n = 3, 3-month-old females) and old (n = 3, 24-month-old females) mice. *P < 0.05, **P < 0.01, student’s t-test. Data are presented as mean ± S.D.

Fig. 7.

Patlak plot analysis of ¹²⁵I-insulin in young and aged mice using different input functions. (A–C) Representative Patlak plot using (A) observed plasma PK (B) deconvolved plasma PK and (C) apparent heart PK as input function. (D) Comparison of predicted plasma-to-brain influx clearance values (Ki, mean ± S.D.) from linear regression of respective Patlak plot. *P < 0.05, **P < 0.01, two-way ANOVA followed by multiple comparisons using two-stage set-up method of Benjamini, Krieger, and Yekutieli.

Fig. 8.

Patlak plot analysis of ¹²⁵I-Aβ₄₀ in young and aged mice using different input functions. (A–C) Representative Patlak plot using (A) observed plasma PK, (B) deconvolved plasma PK, and (C) apparent heart PK as input function. (D) Comparison of predicted plasma-to-brain influx clearance values (Ki, mean ± S.D.) from linear regression of respective Patlak plot. **P < 0.01, ***P < 0.001, two-way ANOVA followed by multiple comparisons using two-stage set-up method of Benjamini, Krieger, and Yekutieli.