A simple quantitative model for the prediction of exposure of renally excreted drugs in pregnant women: a comparison with whole body PBPK model
Aim: The objective of this study was to develop a simple quantitative model (SQM) to predict maximum plasma concentration (Cmax) and the area under the curve (AUC) of renally excreted drugs (n = 16) in pregnant women from non-pregnant women. Methods: The SQM was developed using 6 physiological param...
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| Format: | Article |
| Language: | English |
| Published: |
Open Exploration
2025-07-01
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| Series: | Exploration of Drug Science |
| Subjects: | |
| Online Access: | https://www.explorationpub.com/uploads/Article/A1008115/1008115.pdf |
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| Summary: | Aim: The objective of this study was to develop a simple quantitative model (SQM) to predict maximum plasma concentration (Cmax) and the area under the curve (AUC) of renally excreted drugs (n = 16) in pregnant women from non-pregnant women. Methods: The SQM was developed using 6 physiological parameters and the fraction unbound protein in plasma (fup) as the product characteristic. The six physiological parameters used in this study were total body water, blood volume, cardiac output, glomerular filtration rate (GFR), volume of the fetoplacental unit and blood flow of the fetoplacental unit. A factor was derived based on the average values of the physiological parameters and fup for different gestational ages to predict Cmax and AUC values in pregnant women from non-pregnant women. The predicted values from SQM were then compared with the dedicated clinical studies as well as predicted values by a physiologically-based pharmacokinetic (PBPK) model. Results: Out of 17 Cmax data points, 15 (88.2%), 15 (88.2%), and 12 (70.6%) data points were within 0.5–2.0-fold, 0.5–1.5-fold and 0.7–1.30-fold prediction error, respectively, by SQM, whereas, 17 (100%), 15 (88.2%), and 13 (76.5%) data points were within 0.5–2.0-fold, 0.5–1.5-fold and 0.7–1.30 fold prediction error, respectively, by PBPK. Out of 36 AUC data points, 36 (100%), 34 (94.4%), and 30 (83.3%) data points were within 0.5–2.0-fold, 0.5–1.5-fold and 0.7–1.30-fold prediction error, respectively, by SQM, whereas, 35 (97.2%), 33 (91.7%), and 27 (75%) data points were within 0.5–2.0-fold, 0.5–1.5-fold and 0.7–1.30-fold prediction error, respectively, by PBPK. The results of the study indicated that the predictive power of both models was very good. Conclusions: The results of the study indicate that the SQM in its predictive performance is as robust and accurate as whole body PBPK. |
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| ISSN: | 2836-7677 |