Physiologically Based Pharmacokinetic Modeling of Regorafenib Monohydrate-Loaded PEGylated PLGA Nanoparticles Employing GastroPlusTM Software
Abstract
Nanoparticles (NPs) have transformed drug delivery by altering the pharmacokinetics of small-molecule therapeutics by dramatically enhancing solubility, bioavailability and tumour specificity. By leveraging the enhanced permeability and retention EPR effect characteristics of tumour vasculature, nanoparticles exhibit preferential localisation within neoplastic tissues, enabling deep intratumoral penetration and the targeted delivery of therapeutic agents with high spatial precision. When wrapped in hydrophilic polymers like polyethylene glycol (PEG), these smart carriers evade immune detection, extending systemic circulation and amplifying drug accumulation at the disease site. Yet the very properties that make NPs so effective, such as their tunable size, shape, surface charge, and chemistry, also render their in vivo behaviour highly complex and difficult to predict from traditional in vitro assays. To bridge this gap, advanced modelling methodologiessuch as physiologically based pharmacokinetic modelling offer robust avenues for predicting the outcome, efficacy, and safety of these nanoengineered therapies. In these research findings, a robust rabbit physiologically based pharmacokinetic model or toxicokinetic model, has been calibrated and validated against reported literature to predict the pharmacokinetics of regorafenib monohydrate-loaded PEGylated PLGA nanoparticles. The model demonstrates a high degree of predictive reliability, with Cmax and AUC estimations achieving fold errors near unity, underscoring its potential as a high-value asset for preclinical simulation, risk assessment, and design optimisation of nano-formulation. This innovative modelling approach accelerates the path from bench to bedside, offering a powerful tool through which the intricate dance of nanoparticles within the body can be precisely understood.
Keywords:
Nanoparticles, PBPK Modeling, Pegylated PLGA Nanoparticles, Pharmacokinetics, Regorafenib Monohydrate
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References
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Ma, C., Wei, T., Hua, Y., Wang, Z., & Zhang, L. (2021). Effective antitumor of orally intestinal targeting penetrating peptide-loaded tyroserleutide/PLGA nanoparticles in hepatocellular carcinoma. International Journal of Nanomedicine, 4495-4513. https://doi.org/10.2147/IJN.S315713
Mavroudis, P. D., Hermes, H. E., Teutonico, D., Preuss, T. G., & Schneckener, S. (2018). Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits. PLoS One, 13(3), e0194294. https://doi.org/10.1371/journal.pone.0194294
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Panigrahi, D., Sahu, P. K., Swain, S., & Verma, R. K. (2021). Quality by design prospects of pharmaceuticals application of double emulsion method for PLGA loaded nanoparticles. SN Applied Sciences, 3(6), 638.https://doi.org/10.1007/s42452-021-04609-1
Panigrahi, D., Swain, S., Sahu, P. K., Ghose, D., & Jena, B. R. (2024). Quality by design enabled formulation development of regorafenib monohydrate loaded PEGylated PLGA polymeric nanoparticles: Enhanced oral bioavailability and biopharmaceutical attributes. Nanomedicine Journal, 11(4). 401-416. https://doi.org/10.22038/nmj.2024.76842.1867
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Xu, W., Yu, C., Qing, X., Liu, S., Lv, X., Wang, W., Dong, X., & Zhang, Y. (2022). Multi-target TKI nanoparticle delivery systems. Materials Today Bio, 16, 100358. https://doi.org/10.1016/j.mtbio.2022.100358
Yuan, D., He, H., Wu, Y., Fan, J., & Cao, Y. (2019). Physiologically based pharmacokinetic modeling of nanoparticles. Journal of Pharmaceutical Sciences, 108(1), 58-72. https://doi.org/10.1016/j.xphs.2018.10.037
Yuan, S., Ye, Z. W., Liang, R., Tang, K., Zhang, A. J., Lu, G., ... & Chan, J. F. W. (2022). Pathogenicity, transmissibility, and fitness of SARS-CoV-2 Omicron in Syrian hamsters. Science, 377(6604), 428-433. https://doi.org/10.1126/science.abn8939
Bassani, D., Parrott, N. J., Manevski, N., & Zhang, J. D. (2024). Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules. Expert Opinion on Drug Discovery, 19(6), 683-698. https://doi.org/10.1080/17460441.2024.2348157
Chou, P., Shannar, A., Pan, Y., Dave, P. D., Xu, J., & Kong, A. N. T. (2025). Application of physiologically based pharmacokinetic (PBPK) model in drug development and in dietary phytochemicals. Current Pharmacology Reports, 11(1), 45. https://doi.org/10.1007/s40495-025-00427-w
da Silva, T. M., Sarcinelli, M. A., Cunha Chaves, M. H., Dias, A. D. A., Patricio, B. F. D. C., Prado, L. D., ... & Rocha, H. V. (2025). Physiologically Based Pharmacokinetic Modeling of Efavirenz Nanoparticles: from Animal Model to Human Extrapolation. ACS Omega, 10(39), 45382-45399. https://doi.org/10.1021/acsomega.5c05211
De Buck, S. S., Sinha, V. K., Fenu, L. A., Nijsen, M. J., Mackie, C. E., & Gilissen, R. A. (2007). Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs. Drug Metabolism and Disposition, 35(10), 1766-1780. https://doi.org/10.1124/dmd.107.015644
Dong, D., Wang, X., Wang, H., Zhang, X., Wang, Y., & Wu, B. (2015). Elucidating the in vivo fate of nanocrystals using a physiologically based pharmacokinetic model: a case study with the anticancer agent SNX-2112. International Journal of Nanomedicine, 2521-2535. https://doi.org/10.2147/IJN.S79734
Franz, M., Jairam, R. K., Kuepfer, L., & Hanke, N. (2024). PBPK-based translation from preclinical species to humans for the full-size IgG therapeutic efalizumab. Frontiers in Pharmacology, 15, 1418870. https://doi.org/10.3389/fphar.2024.1418870
Fujita, K., Miura, M., & Shibata, H. (2016). Quantitative determination of regorafenib and its two major metabolites in human plasma with high‐performance liquid chromatography and ultraviolet detection. Biomedical Chromatography, 30(10), 1611-1617. https://doi.org/10.1002/bmc.3730
Gerisch, M., Hafner, F. T., Lang, D., Radtke, M., Diefenbach, K., Cleton, A., & Lettieri, J. (2018). Mass balance, metabolic disposition, and pharmacokinetics of a single oral dose of regorafenib in healthy human subjects. Cancer Chemotherapy and Pharmacology, 81(1), 195-206. https://doi.org/10.1007/s00280-017-3480-9
Jimoh, O. O., Ajuwon, T., Okonkwo, S. S., Awoyemi, R. F., Olaosebikan, I., Olahanmi, O. A., ... & Ifijen, I. H. (2026). PLGA nanoparticles in otoprotection and inner ear regeneration: A new frontier in nanomedicine for hearing disorders. RSC Advances, 16(1), 76-106. https://doi.org/10.1039/D5RA06007A
Karwasra, R., Bano, N., Ahmad, S., Singh, S., Khanna, K., Sharma, N., ... & Verma, S. (2026). Multimodal evaluation of mannose engineered poly lactic glycolic acid nanoparticles with granulocyte colony stimulating factor focused delivery to bone marrow for neutropenia. Scientific Reports, 16(1), 2849. https://doi.org/10.1038/s41598-025-29790-w
Kim, J. S., Cho, J. H., & Choi, H. G. (2021). Development of a simple, precise, and validated HPLC method for the anticancer drug, regorafenib: application to pharmacokinetics in rats and stability study. Bulletin of the Korean Chemical Society, 42(9), 1239-1244. https://doi.org/10.1002/bkcs.12355
Kollipara, S., Bhattiprolu, A. K., Boddu, R., Ahmed, T., & Chachad, S. (2023). Best practices for integration of dissolution data into physiologically based biopharmaceutics models (PBBM): a biopharmaceutics modeling scientist perspective. AAPS Pharm Sci Tech, 24(2), 59. https://doi.org/10.1208/s12249-023-02521-y
Kollipara, S., Sivadasu, P., Rashmi, S. R., Ravi, P. R., & Guntupalli, C. (2025). Role of physiologically based biopharmaceutics modeling in predicting and circumventing the drug-drug interactions of tyrosine kinase inhibitors with acid-reducing agents. Journal of Applied Pharmaceutical Science, 15(6), 055-069.https://dx.doi.org/10.7324/JAPS.2025.234298
Kostewicz, E. S., Aarons, L., Bergstrand, M., Bolger, M. B., Galetin, A., Hatley, O., ... & Dressman, J. (2014). PBPK models for the prediction of in vivo performance of oral dosage forms. European Journal of Pharmaceutical Sciences, 57, 300-321. https://doi.org/10.1016/j.ejps.2013.09.008
Kutumova, E. O., Akberdin, I. R., Kiselev, I. N., Sharipov, R. N., Egorova, V. S., Syrocheva, A. O., ... & Kolpakov, F. A. (2022). Physiologically based pharmacokinetic modeling of nanoparticle biodistribution: a review of existing models, simulation software, and data analysis tools. International Journal of Molecular Sciences, 23(20), 12560. https://doi.org/10.3390/ijms232012560
Ladumor, M. K., Storelli, F., Liang, X., Lai, Y., Enogieru, O. J., Chothe, P. P., ... & Unadkat, J. D. (2023). Predicting changes in the pharmacokinetics of CYP3A‐metabolized drugs in hepatic impairment and insights into factors driving these changes. CPT: Pharmacometrics & Systems Pharmacology, 12(2), 261-273. https://doi.org/10.1002/psp4.12901
Li, Y., Sun, H., & Zhang, Z. (2025). The Evolution and Future Directions of PBPK Modeling in FDA Regulatory Review. Pharmaceutics, 17(11), 1413. https://doi.org/10.3390/pharmaceutics17111413
Loisios-Konstantinidis, I., Huth, F., Hoch, M., & Einolf, H. J. (2025). Physiologically Based Pharmacokinetic Modeling and Simulations in Lieu of Clinical Pharmacology Studies to Support the New Drug Application of Asciminib. Pharmaceutics, 17(10), 1266. https://doi.org/10.3390/pharmaceutics17101266
Ma, C., Wei, T., Hua, Y., Wang, Z., & Zhang, L. (2021). Effective antitumor of orally intestinal targeting penetrating peptide-loaded tyroserleutide/PLGA nanoparticles in hepatocellular carcinoma. International Journal of Nanomedicine, 4495-4513. https://doi.org/10.2147/IJN.S315713
Mavroudis, P. D., Hermes, H. E., Teutonico, D., Preuss, T. G., & Schneckener, S. (2018). Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits. PLoS One, 13(3), e0194294. https://doi.org/10.1371/journal.pone.0194294
Mun, E. J., Babiker, H. M., Weinberg, U., Kirson, E. D., & Von Hoff, D. D. (2018). Tumor-treating fields: a fourth modality in cancer treatment. Clinical Cancer Research, 24(2), 266-275. https://doi.org/10.1158/1078-0432.CCR-17-1117
Panigrahi, D., Sahu, P. K., Swain, S., & Verma, R. K. (2021). Quality by design prospects of pharmaceuticals application of double emulsion method for PLGA loaded nanoparticles. SN Applied Sciences, 3(6), 638.https://doi.org/10.1007/s42452-021-04609-1
Panigrahi, D., Swain, S., Sahu, P. K., Ghose, D., & Jena, B. R. (2024). Quality by design enabled formulation development of regorafenib monohydrate loaded PEGylated PLGA polymeric nanoparticles: Enhanced oral bioavailability and biopharmaceutical attributes. Nanomedicine Journal, 11(4). 401-416. https://doi.org/10.22038/nmj.2024.76842.1867
Rey, J. B., Launay-Vacher, V., & Tournigand, C. (2015). Regorafenib as a single-agent in the treatment of patients with gastrointestinal tumors: an overview for pharmacists. Targeted Oncology, 10(2), 199-213. https://doi.org/10.1007/s11523-014-0333-x
Sarkar, S., Horn, G., Moulton, K., Oza, A., Byler, S., Kokolus, S., & Longacre, M. (2013). Cancer development, progression, and therapy: An epigenetic overview. International Journal of Molecular Sciences, 14(10), 21087-21113. https://doi.org/10.3390/ijms141021087
Sartore-Bianchi, A., Zeppellini, A., Amatu, A., Ricotta, R., Bencardino, K., & Siena, S. (2014). Regorafenib in metastatic colorectal cancer. Expert Review of Anticancer Therapy, 14(3), 255-265. https://doi.org/10.1586/14737140.2014.894887
Shardlow, C. E., Generaux, G. T., Patel, A. H., Tai, G., Tran, T. T., & Bloomer, J. C. (2013). Impact of PBPK modeling in drug development. Drug Metabolism and Disposition, 41(12), 1994–2003. https://doi.org/10.1124/dmd.113.052803
Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 71(3), 209-249. https://doi.org/10.3322/caac.21660
Thun, M. J., DeLancey, J. O., Center, M. M., Jemal, A., & Ward, E. M. (2010). The global burden of cancer: priorities for prevention. Carcinogenesis, 31(1), 100-110. https://doi.org/10.1093/carcin/bgp263
Xia, B., Heimbach, T., Lin, T. H., Li, S., Zhang, H., Sheng, J., & He, H. (2013). Utility of physiologically based modeling and preclinical in vitro/in vivo data to mitigate positive food effect in a BCS class 2 compound. AAPS PharmSciTech, 14(3), 1255-1266. https://doi.org/10.1208/s12249-013-0018-2
Xu, W., Yu, C., Qing, X., Liu, S., Lv, X., Wang, W., Dong, X., & Zhang, Y. (2022). Multi-target TKI nanoparticle delivery systems. Materials Today Bio, 16, 100358. https://doi.org/10.1016/j.mtbio.2022.100358
Yuan, D., He, H., Wu, Y., Fan, J., & Cao, Y. (2019). Physiologically based pharmacokinetic modeling of nanoparticles. Journal of Pharmaceutical Sciences, 108(1), 58-72. https://doi.org/10.1016/j.xphs.2018.10.037
Yuan, S., Ye, Z. W., Liang, R., Tang, K., Zhang, A. J., Lu, G., ... & Chan, J. F. W. (2022). Pathogenicity, transmissibility, and fitness of SARS-CoV-2 Omicron in Syrian hamsters. Science, 377(6604), 428-433. https://doi.org/10.1126/science.abn8939
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Panigrahi, D., Swain, S., Jena, B. and Sahu, P. (2026) “Physiologically Based Pharmacokinetic Modeling of Regorafenib Monohydrate-Loaded PEGylated PLGA Nanoparticles Employing GastroPlusTM Software”, International Journal of Advancement in Life Sciences Research, 9(2), pp. 53-67. doi: https://doi.org/10.31632/ijalsr.2026.v09i02.005.
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