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Research Landscape of Physiologically Based Pharmacokinetic Model Utilization in Different Fields: A Bibliometric Analysis (1999–2023)

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Abstract

Purpose

The physiologically based pharmacokinetic (PBPK) modeling has received increasing attention owing to its excellent predictive abilities. However, there has been no bibliometric analysis about PBPK modeling. This research aimed to summarize the research development and hot points in PBPK model utilization overall through bibliometric analysis.

Methods

We searched for publications related to the PBPK modeling from 1999 to 2023 in the Web of Science Core Collection (WoSCC) database. The Microsoft Office Excel, CiteSpace and VOSviewers were used to perform the analyses.

Results

A total of 4,649 records from 1999 to 2023 were identified, and the largest number of publications focused in the period 2018–2023. The United States was the leading country, and the Environmental Protection Agency (EPA) was the leading institution. The journal Drug Metabolism and Disposition published and co-cited the most articles. Drug–drug interactions, special populations, and new drug development are the main topics in this research field.

Conclusion

We first visualize the research landscape and hotspots of the PBPK modeling through bibliometric methods. Our study provides a better understanding for researchers, especially beginners about the dynamization of PBPK modeling and presents the relevant trend in the future.

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Data Availability

The original contributions presented in the study are included in the article material. Further inquiries can be directed to the corresponding author.

References

  1. Utembe W, Clewell H, Sanabria N, Doganis P, Gulumian M. Current approaches and techniques in physiologically based pharmacokinetic (PBPK) modelling of nanomaterials. Nanomaterials (Basel). 2020;10(7):1267.

    Article  CAS  PubMed  Google Scholar 

  2. Jones H, Rowland-Yeo K. Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacometrics Syst Pharmacol. 2013;2(8):e63.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Peters SA, Dolgos H. Requirements to establishing confidence in physiologically based pharmacokinetic (PBPK) models and overcoming some of the challenges to meeting them. Clin Pharmacokinet. 2019;58(11):1355–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, et al. Computational approaches in preclinical studies on drug discovery and development. Front Chem. 2020;8:726.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Polak S, Tylutki Z, Holbrook M, Wisniowska B. Better prediction of the local concentration-effect relationship: the role of physiologically based pharmacokinetics and quantitative systems pharmacology and toxicology in the evolution of model-informed drug discovery and development. Drug Discov Today. 2019;24(7):1344–54.

    Article  CAS  PubMed  Google Scholar 

  6. Zhu Q, Chen Z, Paul PK, Lu Y, Wu W, Qi J. Oral delivery of proteins and peptides: Challenges, status quo and future perspectives. Acta Pharm Sin B. 2021;11(8):2416–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Traber GM, Yu AM. RNAi-based therapeutics and novel RNA bioengineering technologies. J Pharmacol Exp Ther. 2023;384(1):133–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Smits A, De Cock P, Vermeulen A, Allegaert K. Physiologically based pharmacokinetic (PBPK) modeling and simulation in neonatal drug development: how clinicians can contribute. Expert Opin Drug Metab Toxicol. 2019;15(1):25–34.

    Article  CAS  PubMed  Google Scholar 

  9. Verscheijden LFM, Koenderink JB, Johnson TN, de Wildt SN, Russel FGM. Physiologically-based pharmacokinetic models for children: Starting to reach maturation? Pharmacol Ther. 2020;211:107541.

    Article  CAS  PubMed  Google Scholar 

  10. Fairman K, Choi MK, Gonnabathula P, Lumen A, Worth A, Paini A, et al. An overview of physiologically-based pharmacokinetic models for forensic science. Toxics. 2023;11(2):126.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lin W, Chen Y, Unadkat JD, Zhang X, Wu D, Heimbach T. Applications, challenges, and outlook for PBPK modeling and simulation: A Regulatory, industrial and academic perspective. Pharm Res. 2022;39(8):1701–31.

    Article  CAS  PubMed  Google Scholar 

  12. Tsakalozou E, Alam K, Babiskin A, Zhao L. Physiologically-based pharmacokinetic modeling to support determination of bioequivalence for dermatological drug products: scientific and regulatory considerations. Clin Pharmacol Ther. 2022;111(5):1036–49.

    Article  PubMed  Google Scholar 

  13. Anand O, Pepin XJH, Kolhatkar V, Seo P. The use of physiologically based pharmacokinetic analyses-in biopharmaceutics applications -regulatory and industry perspectives. Pharm Res. 2022;39(8):1681–700.

    Article  CAS  PubMed  Google Scholar 

  14. Wang B, Xing D, Zhu Y, Dong S, Zhao B. The state of exosomes research: A global visualized analysis. Biomed Res Int. 2019;2019:1495130.

    PubMed  PubMed Central  Google Scholar 

  15. Poletto VC, Faraco Junior IM. Bibliometric study of articles published in a Brazilian journal of pediatric dentistry. Braz Oral Res. 2010;24(1):83–8.

    Article  PubMed  Google Scholar 

  16. Chen CM, Ibekwe-SanJuan F, Hou JH. The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis. J Am Soc Inf Sci Tec. 2010;61(7):1386–409.

    Article  Google Scholar 

  17. Pei Z, Chen S, Ding L, Liu J, Cui X, Li F, et al. Current perspectives and trend of nanomedicine in cancer: A review and bibliometric analysis. J Control Release. 2022;352:211–41.

    Article  CAS  PubMed  Google Scholar 

  18. Chen P, Zhong C, Jin S, Zhang Y, Li Y, Xia Q, et al. Global trends in research of lipid metabolism in T lymphocytes from 1985 to 2022: A bibliometric analysis. Front Immunol. 2022;13:884030.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Chen C, Dubin R, Kim MC. Emerging trends and new developments in regenerative medicine: a scientometric update (2000–2014). Expert Opin Biol Ther. 2014;14(9):1295–317.

    Article  PubMed  Google Scholar 

  20. Chen CM. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Tec. 2006;57(3):359–77.

    Article  Google Scholar 

  21. van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84(2):523–38.

    Article  PubMed  Google Scholar 

  22. Bradford S. Sources of information on specific subjects. Engineering. 1934;137:85–6.

    Google Scholar 

  23. Venable GT, Shepherd BA, Loftis CM, McClatchy SG, Roberts ML, Fillinger ME, et al. Bradford’s law: identification of the core journals for neurosurgery and its subspecialties. J Neurosurg. 2016;124(2):569–79.

    Article  PubMed  Google Scholar 

  24. Egghe L. Applications of the theory of Bradford’s Law to the calculation of Leimkuhler’s Law and to the completion of bibliographies. J Am Soc Inf Sci. 1990;41:469–92.

    Article  Google Scholar 

  25. Chen CM. Science mapping: A systematic review of the literature. J Data Inf Sci. 2017;2:1–40.

    CAS  Google Scholar 

  26. Zheng MQ, Li XX, Xu R, Liu S, Rui ZY, Guo ZY, et al. Bibliometric analysis of tuberculosis molecular epidemiology based on CiteSpace. Front Public Health. 2022;10:1040176.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Sabe M, Chen C, Perez N, Solmi M, Mucci A, Galderisi S, et al. Thirty years of research on negative symptoms of schizophrenia: A scientometric analysis of hotspots, bursts, and research trends. Neurosci Biobehav Rev. 2023;144:104979.

    Article  PubMed  Google Scholar 

  28. Rowland M, Peck C, Tucker G. Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol. 2011;51:45–73.

    Article  CAS  PubMed  Google Scholar 

  29. Shebley M, Sandhu P, EmamiRiedmaier A, Jamei M, Narayanan R, Patel A, et al. Physiologically based pharmacokinetic model qualification and reporting procedures for regulatory submissions: A consortium perspective. Clin Pharmacol Ther. 2018;104(1):88–110.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Jones HM, Chen Y, Gibson C, Heimbach T, Parrott N, Peters SA, et al. Physiologically based pharmacokinetic modeling in drug discovery and development: a pharmaceutical industry perspective. Clin Pharmacol Ther. 2015;97(3):247–62.

    Article  CAS  PubMed  Google Scholar 

  31. Grimstein M, Yang Y, Zhang X, Grillo J, Huang SM, Zineh I, et al. Physiologically based pharmacokinetic modeling in regulatory science: An update from the U.S. food and drug administration’s office of clinical pharmacology. J Pharm Sci. 2019;108(1):21–5.

    Article  CAS  PubMed  Google Scholar 

  32. Sager JE, Yu J, Ragueneau-Majlessi I, Isoherranen N. Physiologically Based Pharmacokinetic (PBPK) modeling and simulation approaches: A systematic review of published models, applications, and model verification. Drug Metab Dispos. 2015;43(11):1823–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Zhao P, Zhang L, Grillo JA, Liu Q, Bullock JM, Moon YJ, et al. Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review. Clin Pharmacol Ther. 2011;89(2):259–67.

    Article  CAS  PubMed  Google Scholar 

  34. Wagner C, Zhao P, Pan Y, Hsu V, Grillo J, Huang SM, et al. Application of Physiologically Based Pharmacokinetic (PBPK) modeling to support dose selection: Report of an FDA public workshop on PBPK. CPT Pharmacometrics Syst Pharmacol. 2015;4(4):226–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhuang X, Lu C. PBPK modeling and simulation in drug research and development. Acta Pharm Sin B. 2016;6(5):430–40.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Jamei M. Recent advances in development and application of Physiologically-Based Pharmacokinetic (PBPK) Models: a Transition from Academic Curiosity to Regulatory Acceptance. Curr Pharmacol Rep. 2016;2(3):161–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, et al. Applied concepts in PBPK modeling: How to build a PBPK/PD model. CPT Pharmacometrics Syst Pharmacol. 2016;5(10):516–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Peng C, Kuang L, Zhao J, Ross AE, Wang Z, Ciolino JB. Bibliometric and visualized analysis of ocular drug delivery from 2001 to 2020. J Control Release. 2022;345:625–45.

    Article  CAS  PubMed  Google Scholar 

  39. Chen C, Hu Z, Liu S, Tseng H. Emerging trends in regenerative medicine: a scientometric analysis in CiteSpace. Expert Opin Biol Ther. 2012;12(5):593–608.

    Article  PubMed  Google Scholar 

  40. Qin YF, Ren SH, Shao B, Qin H, Wang HD, Li GM, et al. The intellectual base and research fronts of IL-37: A bibliometric review of the literature from WoSCC. Front Immunol. 2022;13:931783.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Brown RP, Delp MD, Lindstedt SL, Rhomberg LR, Beliles RP. Physiological parameter values for physiologically based pharmacokinetic models. Toxicol Ind Health. 1997;13(4):407–84.

    Article  CAS  PubMed  Google Scholar 

  42. Zhao P. Report from the EMA workshop on qualification and reporting of physiologically based pharmacokinetic (PBPK) modeling and simulation. CPT Pharmacometrics Syst Pharmacol. 2017;6(2):71–2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Al-Tabakha MM, Alomar MJ. In vitro dissolution and in Silico Modeling shortcuts in bioequivalence testing. Pharmaceutics. 2020;12(1):45.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Rostami-Hodjegan A, Toon S. Physiologically based pharmacokinetics as a component of model-informed drug development: Where we were, where we are, and where we are heading. J Clin Pharmacol. 2020;60(Suppl 1):S12–6.

    CAS  PubMed  Google Scholar 

  45. Wu F, Gao J, Kang J, Wang X, Niu Q, Liu J, et al. Knowledge mapping of exosomes in autoimmune diseases: A bibliometric analysis (2002–2021). Front Immunol. 2022;13:939433.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Williamson B, Riley RJ. Hepatic transporter drug-drug interactions: an evaluation of approaches and methodologies. Expert Opin Drug Metab Toxicol. 2017;13(12):1237–50.

    Article  CAS  PubMed  Google Scholar 

  47. Vijaywargi G, Kollipara S, Ahmed T, Chachad S. Predicting transporter mediated drug-drug interactions via static and dynamic physiologically based pharmacokinetic modeling: A comprehensive insight on where we are now and the way forward. Biopharm Drug Dispos. 2023;44(3):195–220.

    Article  CAS  PubMed  Google Scholar 

  48. Chetty M, Johnson TN, Polak S, Salem F, Doki K, Rostami-Hodjegan A. Physiologically based pharmacokinetic modelling to guide drug delivery in older people. Adv Drug Deliv Rev. 2018;135:85–96.

    Article  CAS  PubMed  Google Scholar 

  49. Kovar L, Schrapel C, Selzer D, Kohl Y, Bals R, Schwab M, et al. Physiologically-Based Pharmacokinetic (PBPK) modeling of buprenorphine in adults, children and preterm neonates. Pharmaceutics. 2020;12(6):578.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Kovar L, Weber A, Zemlin M, Kohl Y, Bals R, Meibohm B, et al. Physiologically-Based Pharmacokinetic (PBPK) modeling providing insights into fentanyl pharmacokinetics in adults and pediatric patients. Pharmaceutics. 2020;12(10):908.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Li Z, Fisher C, Gardner I, Ghosh A, Litchfield J, Maurer TS. Modeling exposure to understand and predict kidney injury. Semin Nephrol. 2019;39(2):176–89.

    Article  CAS  PubMed  Google Scholar 

  52. Guinn D, Sahin L, Fletcher EP, Choi SY, Johnson T, Dinatale M, et al. Pharmacokinetic evaluation in pregnancy-current status and future considerations: Workshop summary. J Clin Pharmacol. 2023;63(Suppl 1):S7–17.

    PubMed  Google Scholar 

  53. Coppola P, Kerwash E, Cole S. Use of physiologically based pharmacokinetic modeling for hepatically cleared drugs in pregnancy: Regulatory perspective. J Clin Pharmacol. 2023;63(Suppl 1):S62–80.

    PubMed  Google Scholar 

  54. Wang W, Ye Z, Gao H, Ouyang D. Computational pharmaceutics - A new paradigm of drug delivery. J Control Release. 2021;338:119–36.

    Article  CAS  PubMed  Google Scholar 

  55. Wang W, Ouyang D. Opportunities and challenges of physiologically based pharmacokinetic modeling in drug delivery. Drug Discov Today. 2022;27(8):2100–20.

    Article  CAS  PubMed  Google Scholar 

  56. Lutz JD, Mathias A, German P, Pikora C, Reddy S, Kirby BJ. Physiologically-based pharmacokinetic modeling of remdesivir and Its metabolites to support dose selection for the treatment of pediatric patients with COVID-19. Clin Pharmacol Ther. 2021;109(4):1116–24.

    Article  CAS  PubMed  Google Scholar 

  57. Vieira MLT, Kim MJ, Apparaju S, Sinha V, Zineh I, Huang SM, et al. PBPK model describes the effects of comedication and genetic polymorphism on systemic exposure of drugs that undergo multiple clearance pathways. Clin Pharmacol Ther. 2014;95(5):550–7.

    Article  CAS  PubMed  Google Scholar 

  58. Fairman K, Li M, Ning B, Lumen A. Physiologically based pharmacokinetic (PBPK) modeling of RNAi therapeutics: Opportunities and challenges. Biochem Pharmacol. 2021;189:114468.

    Article  CAS  PubMed  Google Scholar 

  59. Jones HM, Zhang Z, Jasper P, Luo H, Avery LB, King LE, et al. A physiologically-based pharmacokinetic model for the prediction of monoclonal antibody pharmacokinetics from in vitro data. CPT Pharmacometrics Syst Pharmacol. 2019;8(10):738–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kumar M, Kulkarni P, Liu S, Chemuturi N, Shah DK. Nanoparticle biodistribution coefficients: A quantitative approach for understanding the tissue distribution of nanoparticles. Adv Drug Deliv Rev. 2023;194:114708.

    Article  CAS  PubMed  Google Scholar 

  61. D’Mello SR, Cruz CN, Chen ML, Kapoor M, Lee SL, Tyner KM. The evolving landscape of drug products containing nanomaterials in the United States. Nat Nanotechnol. 2017;12(6):523–9.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We are grateful to the Department of Pharmacy, the First Affiliated Hospital of Chongqing Medical University and the School of Pharmacy, Chongqing Medical University for supporting this study.

Funding

This work is supported by the Graduate Research Innovation Project of Chongqing, China [grant number CYS23328].

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Authors and Affiliations

  1. Department of Pharmacy, The First Affiliated Hospital of Chongqing Medical University, No. 1, Youyi Road, Yuzhong District, Chongqing, 400016, China

    Xin Wang & Shenyin Zhu

  2. School of Pharmacy, Chongqing Medical University, Chongqing, China

    Jiangfan Wu & Xiaofang Zhao

  3. Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

    Hongjiang Ye

  4. Qiandongnan Miao and Dong Autonomous Prefecture People’s Hospital, Guizhou, 556000, China

    Xiaofang Zhao

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  1. Xin Wang
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  5. Shenyin Zhu
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Contributions

Xin Wang: Conceptualization, Methodology, Formal analysis, Writing- Original draft preparation. Jiangfan Wu and Hongjiang Ye: Formal analysis, Validation. Xiaofang Zhao: Review and Editing. Shenyin Zhu: Conceptualization, Review and Editing, Supervision, Funding. All the authors have read and approved the final content of this manuscript.

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Correspondence to Shenyin Zhu.

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Wang, X., Wu, J., Ye, H. et al. Research Landscape of Physiologically Based Pharmacokinetic Model Utilization in Different Fields: A Bibliometric Analysis (1999–2023). Pharm Res 41, 609–622 (2024). https://doi.org/10.1007/s11095-024-03676-4

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