A Meta-Analysis Methodology in Stan to Estimate Population Pharmacokinetic Parameters from Multiple Aggregate Concentration–Time Datasets: Application to Gevokizumab mPBPK Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Clinical Data of Gevokizumab
2.2. Simulation Study
2.3. Second-Generation mPBPK Model for Gevokizumab
2.4. Application to the Real Datasets of Gevokizumab
3. Results and Discussion
3.1. Gevokizumab’s Simulation Study
3.2. Application to the Real Datasets of Gevokizumab
3.2.1. Dosage Group of 7 mg
3.2.2. Five Dosage Groups
3.3. Application of the Method to a Variety of Drugs and PK Models
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameters | Dataset I | Dataset II | Dataset III |
---|---|---|---|
N | 24 | 24 | 10 |
rv (%) | 5 | 10 | 5 |
Dose (mg) | 7 | 7 | 7 |
CLp (L/h) | 0.00668 | 0.00668 | 0.00668 |
rc1 | 0.931 | 0.931 | 0.931 |
rc2 | 0.837 | 0.837 | 0.837 |
(%) | 20 | 20 | 20 |
(%) | 20 | 20 | 20 |
Dataset I (rv = 5%, N = 24) | Dataset II (rv = 10%, N = 24) | Dataset III (rv = 5%, N = 10) | |
---|---|---|---|
Mean PopPK parameters | |||
rc1 | |||
%RBIAS | 0.05727019 | 0.3271362 | −0.007274277 |
%RMSE | 1.227154 | 1.588526 | 2.048936 |
%RAE | 0.924102 | 1.293161 | 1.535167 |
rc2 | |||
%RBIAS | −0.08333982 | −0.2648748 | −0.5783198 |
%RMSE | 1.627635 | 2.211325 | 2.688598 |
%RAE | 1.278485 | 1.754442 | 2.116847 |
CLp | |||
%RBIAS | −0.01624675 | −0.4528178 | 0.07686021 |
%RMSE | 0.5339084 | 1.005213 | 0.8498672 |
%RAE | 0.4371228 | 0.805272 | 0.6663747 |
IIV terms | |||
ωCLp | |||
%RBIAS | 0.7168948 | 9.489459 | −0.341962 |
%RMSE | 5.340278 | 12.17255 | 9.716216 |
%RAE | 4.408732 | 10.12623 | 7.257112 |
ωV | |||
%RBIAS | 5.636213 | 20.4582 | 4.685937 |
%RMSE | 8.651427 | 22.61808 | 12.41132 |
%RAE | 6.85914 | 20.4582 | 9.153764 |
Parameter | Mean | SE_mean | SD | 2.5% | 50% | 97.5% | Neff |
---|---|---|---|---|---|---|---|
sigma_1 | 0.0758 | 0.0004 | 0.019 | 0.049 | 0.072 | 0.123 | 1989.956 |
sigma_2 | 0.2316 | 0.0012 | 0.056 | 0.151 | 0.222 | 0.370 | 2133.417 |
CLp_mean | 0.0065 | 0.0000 | 0.000 | 0.006 | 0.007 | 0.007 | 1157.409 |
rc1_mean | 0.9584 | 0.0011 | 0.033 | 0.877 | 0.966 | 0.999 | 967.447 |
rc2_mean | 0.7645 | 0.0008 | 0.031 | 0.709 | 0.762 | 0.830 | 1415.577 |
ωCLp | 0.0775 | 0.0002 | 0.010 | 0.059 | 0.077 | 0.100 | 2162.507 |
ωV | 0.0699 | 0.0001 | 0.007 | 0.057 | 0.070 | 0.084 | 2994.706 |
Parameter | Mean | SE_Mean | SD | 2.5% | 50% | 97.5% | Neff |
---|---|---|---|---|---|---|---|
sigma_1 | 0.0734 | 0.0001 | 0.007 | 0.061 | 0.073 | 0.089 | 3739.926 |
sigma_2 | 0.2706 | 0.0005 | 0.033 | 0.215 | 0.268 | 0.343 | 4736.458 |
CLp_mean | 0.0064 | 0.0000 | 0.000 | 0.006 | 0.006 | 0.007 | 910.859 |
rc1_mean | 0.9504 | 0.0006 | 0.025 | 0.895 | 0.954 | 0.990 | 1680.591 |
rc2_mean | 0.7674 | 0.0013 | 0.058 | 0.647 | 0.767 | 0.896 | 2126.448 |
γCLp | 0.1254 | 0.0029 | 0.101 | 0.029 | 0.102 | 0.368 | 1191.566 |
γrc1 | 0.0338 | 0.0008 | 0.037 | 0.003 | 0.023 | 0.130 | 2152.609 |
γrc2 | 0.1810 | 0.0029 | 0.125 | 0.054 | 0.148 | 0.496 | 1872.394 |
ωCLp [1] | 0.1813 | 0.0020 | 0.109 | 0.010 | 0.176 | 0.401 | 2872.073 |
ωCLp [2] | 0.1138 | 0.0010 | 0.049 | 0.011 | 0.118 | 0.201 | 2430.177 |
ωCLp [3] | 0.0798 | 0.0002 | 0.012 | 0.059 | 0.079 | 0.104 | 5461.348 |
ωCLp [4] | 0.0374 | 0.0002 | 0.009 | 0.022 | 0.037 | 0.056 | 2932.790 |
ωCLp [5] | 0.2002 | 0.0004 | 0.028 | 0.147 | 0.199 | 0.258 | 3961.537 |
ωV [1] | 0.1676 | 0.0003 | 0.021 | 0.123 | 0.168 | 0.207 | 4222.692 |
ωV [2] | 0.3213 | 0.0027 | 0.111 | 0.060 | 0.332 | 0.505 | 1657.621 |
ωV [3] | 0.0733 | 0.0001 | 0.008 | 0.058 | 0.073 | 0.090 | 3424.664 |
ωV [4] | 0.0960 | 0.0001 | 0.010 | 0.078 | 0.096 | 0.117 | 5452.132 |
ωV [5] | 0.1000 | 0.0010 | 0.045 | 0.008 | 0.105 | 0.181 | 1906.016 |
log_CLp [1] | −5.0572 | 0.0013 | 0.068 | −5.205 | −5.056 | −4.928 | 2925.386 |
log_CLp [2] | −4.9818 | 0.0009 | 0.040 | −5.060 | −4.981 | −4.903 | 2007.067 |
log_CLp [3] | −5.0418 | 0.0005 | 0.030 | −5.101 | −5.041 | −4.985 | 4136.353 |
log_CLp [4] | −5.1067 | 0.0004 | 0.028 | −5.162 | −5.106 | −5.054 | 4340.950 |
log_CLp [5] | −5.1280 | 0.0006 | 0.033 | −5.197 | −5.127 | −5.065 | 3607.724 |
log_rc1 [1] | −0.0588 | 0.0008 | 0.037 | −0.152 | −0.052 | −0.007 | 1944.272 |
log_rc1 [2] | −0.0528 | 0.0007 | 0.030 | −0.123 | −0.049 | −0.007 | 1890.579 |
log_rc1 [3] | −0.0441 | 0.0006 | 0.026 | −0.103 | −0.041 | −0.004 | 1731.245 |
log_rc1 [4] | −0.0489 | 0.0006 | 0.026 | −0.106 | −0.046 | −0.006 | 1789.105 |
log_rc1 [5] | −0.0513 | 0.0006 | 0.028 | −0.115 | −0.047 | −0.007 | 2002.731 |
log_rc2 [1] | −0.4421 | 0.0011 | 0.055 | −0.544 | −0.444 | −0.326 | 2731.710 |
log_rc2 [2] | −0.2947 | 0.0009 | 0.043 | −0.378 | −0.295 | −0.210 | 2546.313 |
log_rc2 [3] | −0.2639 | 0.0007 | 0.035 | −0.330 | −0.266 | −0.194 | 2263.931 |
log_rc2 [4] | −0.1878 | 0.0006 | 0.031 | −0.245 | −0.189 | −0.123 | 2316.835 |
log_rc2 [5] | −0.2086 | 0.0007 | 0.035 | −0.273 | −0.210 | −0.140 | 2365.040 |
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Karakitsios, E.; Dokoumetzidis, A. A Meta-Analysis Methodology in Stan to Estimate Population Pharmacokinetic Parameters from Multiple Aggregate Concentration–Time Datasets: Application to Gevokizumab mPBPK Model. Pharmaceutics 2024, 16, 1129. https://doi.org/10.3390/pharmaceutics16091129
Karakitsios E, Dokoumetzidis A. A Meta-Analysis Methodology in Stan to Estimate Population Pharmacokinetic Parameters from Multiple Aggregate Concentration–Time Datasets: Application to Gevokizumab mPBPK Model. Pharmaceutics. 2024; 16(9):1129. https://doi.org/10.3390/pharmaceutics16091129
Chicago/Turabian StyleKarakitsios, Evangelos, and Aristides Dokoumetzidis. 2024. "A Meta-Analysis Methodology in Stan to Estimate Population Pharmacokinetic Parameters from Multiple Aggregate Concentration–Time Datasets: Application to Gevokizumab mPBPK Model" Pharmaceutics 16, no. 9: 1129. https://doi.org/10.3390/pharmaceutics16091129