EVALUATING RENAL TIME-INTEGRATED ACTIVITY COEFFICIENT IN [177Lu]Lu-DOTA-TATE THERAPY: SIMULTANEOUS VS. SEPARATE KIDNEY MODELING USING NON-LINEAR MIXED-EFFECTS MODELING
DOI:
https://doi.org/10.21009/03.1401.FA01Abstrak
This study aimed to compare renal time-integrated activity coefficients (TIACs) in [177Lu]Lu-DOTA- TATE therapy using non-linear mixed-effects modeling (NLMEM), by evaluating the effect of the fitting setting method of left and right kidney biokinetic data to TIAC calculation. Renal biokinetic data of [177Lu]Lu-DOTA-TATE were collected from ten patients with neuroendocrine tumors from literature (PMID:33443063). SPECT/CT imaging was performed between days 1 and 7 after-injection. The bi-exponential function parameters were fitted to biokinetic data using NLMEM performed using NONMEM software, with two fitting approaches: simultaneous fitting of both kidneys and separate fitting of the left and right kidneys. TIACs from the simultaneous fitting were defined as simultaneous TIACs (siTIACs), and those from separate fitting as separated TIACs (seTIACs). The differences between siTIACs and seTIACs were assessed using relative deviations (RDs), with seTIACs considered equivalent to siTIACs if RD was below 5%. The bi-exponential function successfully describes the renal biokinetic data. seTIACs showed good agreement with the siTIACs, with median[min, max] RD of -1.6[-4.5, 0.8]%. Simultaneous fitting of left and right kidneys biokinetic data using the NLMEM approaches produced similar TIACs to those obtained from separate fittings. Therefore, TIACs from simultaneous and separate fittings of renal biokinetic data are comparable and clinically applicable.
Referensi
[1] M. Lassmann, C. Chiesa, G. Flux, and M. Bardiès, ‘EANM Dosimetry Committee guidance
document: good practice of clinical dosimetry reporting’, EJNMMI, vol. 38, no. 1, pp. 192–200,
Jan. 2011, doi: 10.1007/s00259-010-1549-3.
[2] G. Glatting, M. Bardiès, and M. Lassmann, ‘Treatment planning in molecular radiotherapy’,
ZMP, vol. 23, no. 4, pp. 262–269, Dec. 2013, doi: 10.1016/j.zemedi.2013.03.005.
[3] D. Hardiansyah et al., ‘The role of patient-based treatment planning in peptide receptor
radionuclide therapy’, EJNMMI, vol. 43, no. 5, pp. 871–880, May 2016, doi: 10.1007/s00259-
015-3248-6.
[4] M. T. Madsen, Y. Menda, T. M. O’Dorisio, and M. S. O’Dorisio, ‘Technical Note: Single time
point dose estimate for exponential clearance’, Med Phys, vol. 45, no. 5, pp. 2318–2324, May
2018, doi: 10.1002/mp.12886.
[5] T. P. Devasia, Y. K. Dewaraja, K. A. Frey, K. K. Wong, and M. J. Schipper, ‘A Novel Time–
Activity Information-Sharing Approach Using Nonlinear Mixed Models for Patient-Specific
Dosimetry with Reduced Imaging Time Points: Application in SPECT/CT After 177Lu-
DOTATATE’, JNM, vol. 62, no. 8, pp. 1118–1125, Aug. 2021, doi:
10.2967/jnumed.120.256255.
[6] D. Hardiansyah, A. Riana, M. Eiber, A. J. Beer, and G. Glatting, ‘Population-based model
selection for an accurate estimation of time-integrated activity using non-linear mixed-effects
modelling’, ZMP, Feb. 2023, doi: 10.1016/j.zemedi.2023.01.007.
[7] A. F. Jundi, M. D. Naqiyyun, B. B. Patrianesha, I. A. S. Mu’minah, A. Riana, and D.
Hardiansyah, ‘Uncertainty Analysis of Time-Integrated Activity Coefficient in Single-Time-
Point Dosimetry Using Bayesian Fitting Method’, Nucl Med Mol Imaging, vol. 58, no. 3, pp.
120–128, May 2024, doi: 10.1007/s13139-024-00851-8.
[8] B. B. Patrianesha et al., ‘Single-time-point dosimetry using model selection and the Bayesian
fitting method: A proof of concept’, Phys Med, vol. 129, p. 104868, Jan. 2025, doi:
10.1016/j.ejmp.2024.104868.
[9] P. Kletting et al., ‘Optimized Peptide Amount and Activity for 90 Y-Labeled DOTATATE
Therapy’, JNM, vol. 57, no. 4, pp. 503–508, Apr. 2016, doi: 10.2967/jnumed.115.164699.
[10] P. Kletting et al., ‘Investigating the Effect of Ligand Amount and Injected Therapeutic
Activity: A Simulation Study for 177Lu-Labeled PSMA-Targeting Peptides’, PLoS One, vol. 11,
no. 9, p. e0162303, Sept. 2016, doi: 10.1371/journal.pone.0162303.
[11] D. Hardiansyah, A. Riana, A. J. Beer, and G. Glatting, ‘Single-time-point estimation of
absorbed doses in PRRT using a non-linear mixed-effects model’, ZMP, vol. 33, no. 1, pp. 70–
81, Feb. 2023, doi: 10.1016/j.zemedi.2022.06.004.
[12] I. Budiansah, D. Hardiansyah, A. Riana, S. A. Pawiro, A. J. Beer, and G. Glatting, ‘Accuracy
and precision analyses of single-time-point dosimetry utilising physiologically-based
pharmacokinetic modelling and non-linear mixed-effects modelling’, EJNMMI Phys, vol. 12,
no. 1, p. 26, Mar. 2025, doi: 10.1186/s40658-025-00726-7.
[13] P. L. Bonate, ‘Nonlinear Mixed Effects Models: Theory’, in Pharmacokinetic-
Pharmacodynamic Modeling and Simulation, P. L. Bonate, Ed., Boston, MA: Springer US,
2011, pp. 233–301. doi: 10.1007/978-1-4419-9485-1_7.
[14] D. R. Mould and R. N. Upton, ‘Basic Concepts in Population Modeling, Simulation, and
Model-Based Drug Development—Part 2: Introduction to Pharmacokinetic Modeling
Methods’, CPT: PSP, vol. 2, no. 4, p. e38, Apr. 2013, doi: 10.1038/psp.2013.14.
[15] D. Hardiansyah, A. Riana, A. J. Beer, and G. Glatting, ‘Single-time-point dosimetry using
model selection and nonlinear mixed-effects modelling: a proof of concept’, EJNMMI Physics,
vol. 10, no. 1, p. 12, Feb. 2023, doi: 10.1186/s40658-023-00530-1.
[16] D. Hardiansyah et al., ‘Single-Time-Point Renal Dosimetry Using Nonlinear Mixed-Effects
Modeling and Population-Based Model Selection in [177Lu]Lu-PSMA-617 Therapy’, JNM, vol.
65, no. 4, pp. 566–572, Apr. 2024, doi: 10.2967/jnumed.123.266268.
[17] P. L. Bonate, ‘Nonlinear Mixed Effects Models: Practical Issues’, in Pharmacokinetic-
Pharmacodynamic Modeling and Simulation, P. L. Bonate, Ed., Boston, MA: Springer US,
2011, pp. 303–358. doi: 10.1007/978-1-4419-9485-1_8.
[18] D. Mould and R. Upton, ‘Basic Concepts in Population Modeling, Simulation, and Model-
Based Drug Development’, CPT: PSP, vol. 1, no. 9, p. 6, 2012, doi: 10.1038/psp.2012.4.
[19] P. Kletting et al., ‘Molecular radiotherapy: The NUKFIT software for calculating the time-
integrated activity coefficient’, Med Phys, vol. 40, no. 10, p. 102504, 2013, doi:
10.1118/1.4820367.
[20] P. Kletting et al., ‘The NUKDOS software for treatment planning in molecular radiotherapy’,
ZMP, vol. 25, no. 3, pp. 264–274, Sept. 2015, doi: 10.1016/j.zemedi.2015.01.001.
[21] O. V. Ivashchenko et al., ‘Time-Activity data fitting in molecular Radiotherapy: Methodology
and pitfalls’, Phys Med, vol. 117, p. 103192, Jan. 2024, doi: 10.1016/j.ejmp.2023.103192.
[22] D. Hardiansyah et al., ‘A population-based method to determine the time-integrated activity in
molecular radiotherapy’, EJNMMI Phys, vol. 8, no. 1, p. 82, Dec. 2021, doi: 10.1186/s40658-
021-00427-x.
[23] S.-E. Strand, P. Zanzonico, and T. K. Johnson, ‘Pharmacokinetic modeling’, Med Phys, vol. 20,
no. 2, pp. 515–527, 1993, doi: 10.1118/1.597047.
[24] G. Glatting, ‘Time-activity Curves: Data, Models, Curve Fitting, and Model Selection’, in
Handbook of Nuclear Medicine and Molecular Imaging for Physicists, CRC Press, 2022.
[25] G. Glatting, P. Kletting, S. N. Reske, K. Hohl, and C. Ring, ‘Choosing the optimal fit function:
Comparison of the Akaike information criterion and the F-test’, Med Phys, vol. 34, no. 11, pp.
4285–4292, 2007, doi: 10.1118/1.2794176.
[26] P. Kletting and G. Glatting, ‘Model selection for time-activity curves: The corrected Akaike
information criterion and the F-test’, ZMP, vol. 19, no. 3, pp. 200–206, Aug. 2009, doi:
10.1016/j.zemedi.2009.05.003.
[27] D. Hardiansyah, A. Riana, H. Hänscheid, A. J. Beer, M. Lassmann, and G. Glatting, ‘Non-
linear mixed-effects modelling and population-based model selection for 131I kinetics in benign
thyroid disease’, EJNMMI Phys, vol. 12, no. 1, p. 37, Apr. 2025, doi: 10.1186/s40658-025-
00735-6.
