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PBPK/PD Modeling of Nifedipine for Precision Medicine in Pregnant Women: Enhancing Clinical Decision-Making for Optimal Drug Therapy

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This study aims to develop physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) predictive models for nifedipine in pregnant women, enhancing precision medicine and reducing adverse reactions for both mothers and infants.


A PBPK/PD model was constructed using PK-Sim, MoBi, and MATLAB software, integrating literature and pregnancy-specific physiological information. The process involved: (1) establishing and validating a PBPK model for serum clearance after intravenous administration in non-pregnant individuals, (2) establishing and validating a PBPK model for serum clearance after oral administration in non-pregnant individuals, (3) constructing and validating a PBPK model for enzyme clearance after oral administration in non-pregnant individuals, and (4) adjusting the PBPK model structure and enzyme parameters according to pregnant women and validating it in oral administration. (5) PK/PD model was explored through MATLAB, and the PBPK and PK/PD models were integrated to form the PBPK/PD model.


The Nifedipine PBPK model's predictive accuracy was confirmed by non-pregnant and pregnant validation studies. The developed PBPK/PD model accurately predicted maximum antihypertensive effects for clinical doses of 5, 10, and 20 mg. The model suggested peak effect at 0.86 h post-administration, achieving blood pressure reductions of 5.4 mmHg, 14.3 mmHg, and 21.3 mmHg, respectively. This model provides guidance for tailored dosing in pregnancy-induced hypertension based on targeted blood pressure reduction.


Based on available literature data, the PBPK/PD model of Nifedipine in pregnancy demonstrated good predictive performance. It will help optimize individualized dosing of Nifedipine, improve treatment outcomes, and minimize the risk of adverse reactions in mothers and infants.

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Data Availability

The datasets used and/or analyzed during the current study are derived from publicly available databases and previously published literature. All data sources are appropriately cited within the manuscript. Additional information regarding data availability can be obtained from the corresponding author upon reasonable request.


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This research was supported by grants from the Shenzhen-Hong Kong-Macau Science and Technology Program (Category C) of Shenzhen Science and Technology Innovation Commission (SGDX20210823103802016), Industry-University-Research Cooperation Project and Zhuhai-Hong Kong-Macao Cooperation Project from Zhuhai Science and Technology Innovation Bureau (ZH22017002210010PWC) and the General Program of Guangdong Province Natural Science Foundation under Grant No. 2022A1515012405.

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Xinyang Liu, Wei Wang, Jingsi Chen, Dunjin Chen, Yong Tao and Defang Ouyang contributed to the conception and design of the study. Xinyang Liu, Wei Wang and Defang Ouyang were involved in data collection, analysis, and interpretation. Xinyang Liu contributed to drafting the manuscript. Wei Wang and Defang Ouyang provided critical revisions. All authors approved the final version of the manuscript and agreed to be accountable for all aspects of the work.

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Correspondence to Defang Ouyang.

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Liu, X., Wang, W., Chen, J. et al. PBPK/PD Modeling of Nifedipine for Precision Medicine in Pregnant Women: Enhancing Clinical Decision-Making for Optimal Drug Therapy. Pharm Res 41, 63–75 (2024).

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