UNLABELLED: To improve radioimmunotherapy with anti-CD66 antibody, a physiologically based pharmacokinetic (PBPK) model was developed that was capable of describing the biodistribution and extrapolating between different doses of anti-CD66 antibody. METHODS: The biodistribution of the (111)In-labeled anti-CD66 antibody of 8 patients with acute leukemia was measured. The data were fitted to 2 PBPK models. Model A incorporated effective values for antibody binding, and model B explicitly described mono- and bivalent binding. The best model was selected using the corrected Akaike information criterion. The predictive power of the model was validated comparing simulations and (90)Y-anti-CD66 serum measurements. The amount of antibody (range, 0.1-4 mg) leading to the most favorable therapeutic distribution was determined using simulations. RESULTS: Model B was better supported by the data. The fits of the selected model were good (adjusted R(2) > 0.91), and the estimated parameters were in a physiologically reasonable range. The median deviation of the predicted and measured (90)Y-anti-CD66 serum concentration values and the residence times were 24% (range, 17%-31%) and 9% (range, 1%-64%), respectively. The validated model predicted considerably different biodistributions for dosimetry and therapeutic settings. The smallest (0.1 mg) simulated amount of antibody resulted in the most favorable therapeutic biodistribution. CONCLUSION: The developed model is capable of adequately describing the anti-CD66 antibody biodistribution and accurately predicting the time-activity serum curve of (90)Y-anti-CD66 antibody and the therapeutic serum residence time. Simulations indicate that an improvement of radioimmunotherapy with anti-CD66 antibody is achievable by reducing the amount of administered antibody; for example, the residence time of the red marrow could be increased by a factor of 1.9 +/- 0.3 using 0.27 mg of anti-CD66 antibody.
UNLABELLED: To improve radioimmunotherapy with anti-CD66 antibody, a physiologically based pharmacokinetic (PBPK) model was developed that was capable of describing the biodistribution and extrapolating between different doses of anti-CD66 antibody. METHODS: The biodistribution of the (111)In-labeled anti-CD66 antibody of 8 patients with acute leukemia was measured. The data were fitted to 2 PBPK models. Model A incorporated effective values for antibody binding, and model B explicitly described mono- and bivalent binding. The best model was selected using the corrected Akaike information criterion. The predictive power of the model was validated comparing simulations and (90)Y-anti-CD66 serum measurements. The amount of antibody (range, 0.1-4 mg) leading to the most favorable therapeutic distribution was determined using simulations. RESULTS: Model B was better supported by the data. The fits of the selected model were good (adjusted R(2) > 0.91), and the estimated parameters were in a physiologically reasonable range. The median deviation of the predicted and measured (90)Y-anti-CD66 serum concentration values and the residence times were 24% (range, 17%-31%) and 9% (range, 1%-64%), respectively. The validated model predicted considerably different biodistributions for dosimetry and therapeutic settings. The smallest (0.1 mg) simulated amount of antibody resulted in the most favorable therapeutic biodistribution. CONCLUSION: The developed model is capable of adequately describing the anti-CD66 antibody biodistribution and accurately predicting the time-activity serum curve of (90)Y-anti-CD66 antibody and the therapeutic serum residence time. Simulations indicate that an improvement of radioimmunotherapy with anti-CD66 antibody is achievable by reducing the amount of administered antibody; for example, the residence time of the red marrow could be increased by a factor of 1.9 +/- 0.3 using 0.27 mg of anti-CD66 antibody.
Authors: Deni Hardiansyah; Christian Maass; Ali Asgar Attarwala; Berthold Müller; Peter Kletting; Felix M Mottaghy; Gerhard Glatting Journal: Eur J Nucl Med Mol Imaging Date: 2015-11-18 Impact factor: 9.236
Authors: Jonathan I Gear; Maurice G Cox; Johan Gustafsson; Katarina Sjögreen Gleisner; Iain Murray; Gerhard Glatting; Mark Konijnenberg; Glenn D Flux Journal: Eur J Nucl Med Mol Imaging Date: 2018-09-14 Impact factor: 9.236
Authors: F Morschhauser; B Dekyndt; C Baillet; C Barthélémy; E Malek; J Fulcrand; P Bigot; D Huglo; B Décaudin; N Simon; P Odou Journal: Sci Rep Date: 2018-10-05 Impact factor: 4.379