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Journal articles on the topic 'Physiologically- based pharmacokinetic modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Andersen, Melvin E. "Physiologically-Based Pharmacokinetic Modeling." Drug Information Journal 28, no. 1 (1994): 247–54. http://dx.doi.org/10.1177/009286159402800131.

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2

Yang, Raymond S. H., Louis W. Chang, Chung Shi Yang, and Pinpin Lin. "Pharmacokinetics and Physiologically-Based Pharmacokinetic Modeling of Nanoparticles." Journal of Nanoscience and Nanotechnology 10, no. 12 (2010): 8482–90. http://dx.doi.org/10.1166/jnn.2010.2687.

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3

Yuan, Dongfen, Hua He, Yun Wu, Jianghong Fan, and Yanguang Cao. "Physiologically Based Pharmacokinetic Modeling of Nanoparticles." Journal of Pharmaceutical Sciences 108, no. 1 (2019): 58–72. http://dx.doi.org/10.1016/j.xphs.2018.10.037.

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4

Li, Mingguang, Khuloud T. Al-Jamal, Kostas Kostarelos, and Joshua Reineke. "Physiologically Based Pharmacokinetic Modeling of Nanoparticles." ACS Nano 4, no. 11 (2010): 6303–17. http://dx.doi.org/10.1021/nn1018818.

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5

Yu, Yanke, Cho-Ming Loi, Justin Hoffman, and Diane Wang. "Physiologically Based Pharmacokinetic Modeling of Palbociclib." Journal of Clinical Pharmacology 57, no. 2 (2016): 173–84. http://dx.doi.org/10.1002/jcph.792.

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6

Sidhu, Pardeep, Henry T. Peng, Bob Cheung, and Andrea Edginton. "Simulation of differential drug pharmacokinetics under heat and exercise stress using a physiologically based pharmacokinetic modeling approach." Canadian Journal of Physiology and Pharmacology 89, no. 5 (2011): 365–82. http://dx.doi.org/10.1139/y11-030.

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Under extreme conditions of heat exposure and exercise stress, the human body undergoes major physiological changes. Perturbations in organ blood flows, gastrointestinal properties, and vascular physiology may impact the body’s ability to absorb, distribute, and eliminate drugs. Clinical studies on the effect of these stressors on drug pharmacokinetics demonstrate that the likelihood of pharmacokinetic alteration is dependent on drug properties and the intensity of the stressor. The objectives of this study were to use literature data to quantify the correlation between exercise and heat expos

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7

TANAKA, Chiaki, Ryosei KAWAI, Christoph SAXER, et al. "PHYSIOLOGICALLY-BASED PHARMACOKINETIC MODELING OF PSC 833." Drug Metabolism and Pharmacokinetics 12, supplement (1997): 102–3. http://dx.doi.org/10.2133/dmpk.12.supplement_102.

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8

Wong, Harvey, and Timothy W. Chow. "Physiologically Based Pharmacokinetic Modeling of Therapeutic Proteins." Journal of Pharmaceutical Sciences 106, no. 9 (2017): 2270–75. http://dx.doi.org/10.1016/j.xphs.2017.03.038.

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9

Molnar, Janos. "Physiologically Based Pharmacokinetic Modeling???Science and Applications." American Journal of Therapeutics 13, no. 2 (2006): 177. http://dx.doi.org/10.1097/00045391-200603000-00018.

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10

Barrett, J. S., O. Della Casa Alberighi, S. Läer, and B. Meibohm. "Physiologically Based Pharmacokinetic (PBPK) Modeling in Children." Clinical Pharmacology & Therapeutics 92, no. 1 (2012): 40–49. http://dx.doi.org/10.1038/clpt.2012.64.

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11

Emoto, Chie, Brooks T. McPhail, and Tsuyoshi Fukuda. "Clinical Applications of Physiologically Based Pharmacokinetic Modeling." Therapeutic Drug Monitoring 42, no. 1 (2020): 157–58. http://dx.doi.org/10.1097/ftd.0000000000000714.

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12

Song, Yoo-Kyung, Yun-Hwan Seol, Min Ju Kim, et al. "Pharmacokinetic Characterization of Supinoxin and Its Physiologically Based Pharmacokinetic Modeling in Rats." Pharmaceutics 13, no. 3 (2021): 373. http://dx.doi.org/10.3390/pharmaceutics13030373.

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Supinoxin is a novel anticancer drug candidate targeting the Y593 phospho-p68 RNA helicase, by exhibiting antiproliferative activity and/or suppression of tumor growth. This study aimed to characterize the in vitro and in vivo pharmacokinetics of supinoxin and attempt physiologically based pharmacokinetic (PBPK) modeling in rats. Supinoxin has good permeability, comparable to that of metoprolol (high permeability compound) in Caco-2 cells, with negligible net absorptive or secretory transport observed. After an intravenous injection at a dose range of 0.5–5 mg/kg, the terminal half-life (i.e.,

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13

Ploeger, Bart, Tjeert Mensinga, Adriënne Sips, Willem Seinen, Jan Meulenbelt, and Joost DeJongh. "THE PHARMACOKINETICS OF GLYCYRRHIZIC ACID EVALUATED BY PHYSIOLOGICALLY BASED PHARMACOKINETIC MODELING†." Drug Metabolism Reviews 33, no. 2 (2001): 125–47. http://dx.doi.org/10.1081/dmr-100104400.

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14

Shaik, Abdul Naveed, and Ansar Ali Khan. "Physiologically based pharmacokinetic (PBPK) modeling and simulation in drug discovery and development." ADMET and DMPK 7, no. 1 (2019): 1–3. http://dx.doi.org/10.5599/admet.667.

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Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic or physiology based mathematical modeling technique which integrates the knowledge from both drug-based properties including physiochemical and biopharmaceutical properties and system based or physiological properties to generate a model for predicting the absorption, distribution, metabolism and excretion (ADME) properties of a drug as well as pharmacokinetic behavior of a drug in preclinical species and humans.

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15

KAWAI, Ryosei. "PHYSIOLOGICALLY-BASED PHARMACOKINETIC MODELING OF CYCLOSPORINE DERIVATIVES IV." Drug Metabolism and Pharmacokinetics 9, supplement (1994): 66–69. http://dx.doi.org/10.2133/dmpk.9.supplement_66.

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16

Li, Min, Peng Zou, Katherine Tyner, and Sau Lee. "Physiologically Based Pharmacokinetic (PBPK) Modeling of Pharmaceutical Nanoparticles." AAPS Journal 19, no. 1 (2016): 26–42. http://dx.doi.org/10.1208/s12248-016-0010-3.

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17

Clewell, Harvey J., and Melvin E. Andersen. "Physiologically-Based Pharmacokinetic Modeling and Bioactivation of Xenobiotics." Toxicology and Industrial Health 10, no. 1-2 (1994): 1–24. http://dx.doi.org/10.1177/074823379401000101.

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18

Tucker, Geoffrey T. "Physiologically Based Pharmacokinetic–Pharmacodynamic Modeling to the Rescue." Anesthesiology 120, no. 4 (2014): 795–96. http://dx.doi.org/10.1097/aln.0000000000000143.

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19

Feng, Kairui, and Robert H. Leary. "Toward personalized medicine with physiologically based pharmacokinetic modeling." International Journal of Pharmacokinetics 2, no. 1 (2017): 1–4. http://dx.doi.org/10.4155/ipk-2016-0014.

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20

Sonne, Christian, Kim Gustavson, Frank F. Rigét, Rune Dietz, Tanja Krüger, and Eva C. Bonefeld-Jørgensen. "Physiologically based pharmacokinetic modeling of POPs in Greenlanders." Environment International 64 (March 2014): 91–97. http://dx.doi.org/10.1016/j.envint.2013.12.006.

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21

Missel, Paul J., and Ramesh Sarangapani. "Physiologically based ocular pharmacokinetic modeling using computational methods." Drug Discovery Today 24, no. 8 (2019): 1551–63. http://dx.doi.org/10.1016/j.drudis.2019.05.039.

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22

Grass, George M., and Patrick J. Sinko. "Physiologically-based pharmacokinetic simulation modelling." Advanced Drug Delivery Reviews 54, no. 3 (2002): 433–51. http://dx.doi.org/10.1016/s0169-409x(02)00013-3.

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23

Xia, Binfeng, Tycho Heimbach, Tsu-han Lin, Handan He, Yanfeng Wang, and Eugene Tan. "Novel physiologically based pharmacokinetic modeling of patupilone for human pharmacokinetic predictions." Cancer Chemotherapy and Pharmacology 69, no. 6 (2012): 1567–82. http://dx.doi.org/10.1007/s00280-012-1863-5.

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24

Kovar, Lukas, Christina Schräpel, Dominik Selzer, et al. "Physiologically-Based Pharmacokinetic (PBPK) Modeling of Buprenorphine in Adults, Children and Preterm Neonates." Pharmaceutics 12, no. 6 (2020): 578. http://dx.doi.org/10.3390/pharmaceutics12060578.

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Buprenorphine plays a crucial role in the therapeutic management of pain in adults, adolescents and pediatric subpopulations. However, only few pharmacokinetic studies of buprenorphine in children, particularly neonates, are available as conducting clinical trials in this population is especially challenging. Physiologically-based pharmacokinetic (PBPK) modeling allows the prediction of drug exposure in pediatrics based on age-related physiological differences. The aim of this study was to predict the pharmacokinetics of buprenorphine in pediatrics with PBPK modeling. Moreover, the drug-drug i

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25

Shin, Na-Young. "Application of Physiologically Based Pharmacokinetic (PBPK) Modeling in Prediction of Pediatric Pharmacokinetics." Yakhak Hoeji 59, no. 1 (2015): 29–39. http://dx.doi.org/10.17480/psk.2015.59.1.29.

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26

Glassman, Patrick M., and Joseph P. Balthasar. "Physiologically-based pharmacokinetic modeling to predict the clinical pharmacokinetics of monoclonal antibodies." Journal of Pharmacokinetics and Pharmacodynamics 43, no. 4 (2016): 427–46. http://dx.doi.org/10.1007/s10928-016-9482-0.

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27

Yamamoto, Yumi, Pyry A. Välitalo, Yin Cheong Wong, et al. "Prediction of human CNS pharmacokinetics using a physiologically-based pharmacokinetic modeling approach." European Journal of Pharmaceutical Sciences 112 (January 2018): 168–79. http://dx.doi.org/10.1016/j.ejps.2017.11.011.

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28

Grzegorzewski, Jan, Janosch Brandhorst, Kathleen Green, et al. "PK-DB: pharmacokinetics database for individualized and stratified computational modeling." Nucleic Acids Research 49, no. D1 (2020): D1358—D1364. http://dx.doi.org/10.1093/nar/gkaa990.

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Abstract A multitude of pharmacokinetics studies have been published. However, due to the lack of an open database, pharmacokinetics data, as well as the corresponding meta-information, have been difficult to access. We present PK-DB (https://pk-db.com), an open database for pharmacokinetics information from clinical trials. PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, bodyweight, smoking status, genetic variants); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) pharmacokinetic parameters (e.g. cle

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29

Kovar, Lukas, Andreas Weber, Michael Zemlin, et al. "Physiologically-Based Pharmacokinetic (PBPK) Modeling Providing Insights into Fentanyl Pharmacokinetics in Adults and Pediatric Patients." Pharmaceutics 12, no. 10 (2020): 908. http://dx.doi.org/10.3390/pharmaceutics12100908.

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Fentanyl is widely used for analgesia, sedation, and anesthesia both in adult and pediatric populations. Yet, only few pharmacokinetic studies of fentanyl in pediatrics exist as conducting clinical trials in this population is especially challenging. Physiologically-based pharmacokinetic (PBPK) modeling is a mechanistic approach to explore drug pharmacokinetics and allows extrapolation from adult to pediatric populations based on age-related physiological differences. The aim of this study was to develop a PBPK model of fentanyl and norfentanyl for both adult and pediatric populations. The adu

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30

Ota, Miki, Makiko Shimizu, Yusuke Kamiya, Chie Emoto, Tsuyoshi Fukuda, and Hiroshi Yamazaki. "Adult and infant pharmacokinetic profiling of dihydrocodeine using physiologically based pharmacokinetic modeling." Biopharmaceutics & Drug Disposition 40, no. 9 (2019): 350–57. http://dx.doi.org/10.1002/bdd.2209.

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31

Deng, Linjing, Hui Liu, Yongsheng Ma, Yufeng Miao, Xiaoli Fu, and Qihong Deng. "Endocytosis mechanism in physiologically-based pharmacokinetic modeling of nanoparticles." Toxicology and Applied Pharmacology 384 (December 2019): 114765. http://dx.doi.org/10.1016/j.taap.2019.114765.

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32

Haddad, Sami, James Withey, Sylvain Laparé, Francis Law, and Kannan Krishnan. "Physiologically-based pharmacokinetic modeling of pyrene in the rat." Environmental Toxicology and Pharmacology 5, no. 4 (1998): 245–55. http://dx.doi.org/10.1016/s1382-6689(98)00008-8.

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33

Gentry, P. Robinan, Tammie R. Covington, Sabine Mann, Annette M. Shipp, Janice W. Yager, and Harvey J. Clewell III. "Physiologically Based Pharmacokinetic Modeling of Arsenic in the Mouse." Journal of Toxicology and Environmental Health, Part A 67, no. 1 (2004): 43–71. http://dx.doi.org/10.1080/15287390490253660.

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34

Peck, C., D. Conner, N. Fleischer, V. Hill, and G. Murphy. "Physiologically Based Pharmacokinetic Modeling of Noninvasive Transcutaneous Chemical Dosimetry." Health Physics 57 (July 1989): 157. http://dx.doi.org/10.1097/00004032-198907001-00019.

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35

Plawecki, Martin H., Ulrich S. Zimmermann, Victor Vitvitskiy, Peter C. Doerschuk, David Crabb, and Sean O'Connor. "Alcohol Exposure Rate Control Through Physiologically Based Pharmacokinetic Modeling." Alcoholism: Clinical and Experimental Research 36, no. 6 (2012): 530–42. http://dx.doi.org/10.1111/j.1530-0277.2011.01706.x.

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36

Johnston, C., C. M. Kirkpatrick, A. J. McLachlan, and S. N. Hilmer. "Physiologically Based Pharmacokinetic Modeling at the Extremes of Age." Clinical Pharmacology & Therapeutics 93, no. 2 (2012): 148. http://dx.doi.org/10.1038/clpt.2012.176.

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37

Rioux, N., and N. J. Waters. "Physiologically Based Pharmacokinetic Modeling in Pediatric Oncology Drug Development." Drug Metabolism and Disposition 44, no. 7 (2016): 934–43. http://dx.doi.org/10.1124/dmd.115.068031.

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38

Dong, Michael H. "Microcomputer programs for physiologically-based pharmacokinetic (PB-PK) modeling." Computer Methods and Programs in Biomedicine 45, no. 3 (1994): 213–21. http://dx.doi.org/10.1016/0169-2607(94)90205-4.

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39

Krishnan, Kannan, Lee C. B. Crouse, Matthew A. Bazar, Michael A. Major, and Gunda Reddy. "Physiologically based pharmacokinetic modeling of cyclotrimethylenetrinitramine in male rats." Journal of Applied Toxicology 29, no. 7 (2009): 629–37. http://dx.doi.org/10.1002/jat.1455.

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40

Bae, Dong-Jun, Sang-Yeob Kim, Sang Mun Bae, et al. "Whole-Body Physiologically Based Pharmacokinetic Modeling of Trastuzumab and Prediction of Human Pharmacokinetics." Journal of Pharmaceutical Sciences 108, no. 6 (2019): 2180–90. http://dx.doi.org/10.1016/j.xphs.2019.01.024.

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41

Matsumoto, Yuki, Tamara Cabalu, Punam Sandhu, et al. "Application of Physiologically Based Pharmacokinetic Modeling to Predict Pharmacokinetics in Healthy Japanese Subjects." Clinical Pharmacology & Therapeutics 105, no. 4 (2018): 1018–30. http://dx.doi.org/10.1002/cpt.1240.

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42

Chen, K. F., P. Milgrom, and Y. S. Lin. "Silver Diamine Fluoride in Children Using Physiologically Based PK Modeling." Journal of Dental Research 99, no. 8 (2020): 907–13. http://dx.doi.org/10.1177/0022034520917368.

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Silver diamine fluoride (SDF) is used topically to prevent or arrest dental caries and has been tested clinically in toddlers to elderly adults. Following SDF application, small quantities of silver can be swallowed and absorbed. To monitor silver concentrations, pharmacokinetic studies can be performed. However, pharmacokinetic studies are time-consuming, resource intensive, and challenging to perform in young children. The objective of this study was to develop a physiologically based pharmacokinetic (PBPK) model to predict silver disposition in children. The PBPK model for silver was develo

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43

Dostalek, Miroslav, Iain Gardner, Brian M. Gurbaxani, Rachel H. Rose, and Manoranjenni Chetty. "Pharmacokinetics, Pharmacodynamics and Physiologically-Based Pharmacokinetic Modelling of Monoclonal Antibodies." Clinical Pharmacokinetics 52, no. 2 (2013): 83–124. http://dx.doi.org/10.1007/s40262-012-0027-4.

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44

Mallick, Pankajini, Gina Song, Alina Y. Efremenko, et al. "Physiologically Based Pharmacokinetic Modeling in Risk Assessment: Case Study With Pyrethroids." Toxicological Sciences 176, no. 2 (2020): 460–69. http://dx.doi.org/10.1093/toxsci/kfaa070.

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Abstract The assessment of potentially sensitive populations is an important application of risk assessment. To address the concern for age-related sensitivity to pyrethroid insecticides, life-stage physiologically based pharmacokinetic (PBPK) modeling supported by in vitro to in vivo extrapolation was conducted to predict age-dependent changes in target tissue exposure to 8 pyrethroids. The purpose of this age-dependent dosimetry was to calculate a Data-derived Extrapolation Factor (DDEF) to address age-related pharmacokinetic differences for pyrethroids in humans. We developed a generic huma

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45

Savoca, Adriana, and Davide Manca. "Physiologically-based pharmacokinetic simulations in pharmacotherapy: selection of the optimal administration route for exogenous melatonin." ADMET and DMPK 7, no. 1 (2019): 44–59. http://dx.doi.org/10.5599/admet.625.

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The benefits of melatonin on human body are drawing increasing attention from several researchers in different fields. While its role as cure for sleep disturbances (e.g., jet lag, insomnia) is well documented and established, new functions in physiological and pathophysiological processes are emerging. To investigate these effects, there is need for the characterization of melatonin transport processes in the body and resulting pharmacokinetics. Although recent works propose physiologically-based pharmacokinetic modelling of melatonin, no work has yet highlighted the potential of PBPK simulat

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46

Matthews, Jessica L., Irvin R. Schultz, Michael R. Easterling, and Ronald L. Melnick. "Physiologically based pharmacokinetic modeling of dibromoacetic acid in F344 rats." Toxicology and Applied Pharmacology 244, no. 2 (2010): 196–207. http://dx.doi.org/10.1016/j.taap.2009.12.033.

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47

Ni, Zhanglin, Arjang Talattof, Jianghong Fan, et al. "Physiologically Based Pharmacokinetic and Absorption Modeling for Osmotic Pump Products." AAPS Journal 19, no. 4 (2017): 1045–53. http://dx.doi.org/10.1208/s12248-017-0075-7.

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48

Staschen, Carl-Michael. "A Review of: “Physiologically Based Pharmacokinetic Modeling. Science and Applications”." Clinical Toxicology 45, no. 5 (2007): 598–99. http://dx.doi.org/10.1080/15563650701396821.

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49

Maharaj, A. R., and A. N. Edginton. "Physiologically Based Pharmacokinetic Modeling and Simulation in Pediatric Drug Development." CPT: Pharmacometrics & Systems Pharmacology 3, no. 11 (2014): 1–13. http://dx.doi.org/10.1038/psp.2014.45.

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50

Huang, S.-M., and M. Rowland. "The Role of Physiologically Based Pharmacokinetic Modeling in Regulatory Review." Clinical Pharmacology & Therapeutics 91, no. 3 (2012): 542–49. http://dx.doi.org/10.1038/clpt.2011.320.

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