Issue
Date Log
Copyright (c) 2024 Hamad Abdulelah H Alkhayyal, AMAL Ibrahim Samah Alruwaili, Mohammed Mousa Muyidi, Mousa Mohammed Ali Bahkali, Raef Abdu Ahmad Kamli, Sami Musaad Alotaibi, Maher Omran Ali Alotaibi, Dalal Ali Saleh Almadeh, Talal Mubarak Shuraim Al-Mughairi, Ibrahim Mohamed Hamed Somaily, Amani Ayeed Madee Alshahrani, Meshal Muflih Almutairi, Maryam Mohammed harthi

This work is licensed under a Creative Commons Attribution 4.0 International License.
Quantitative Imaging Biomarkers for Pharmacokinetic/Pharmacodynamic (PK/PD) Modeling in Clinical Trials: A Review of Methodologies and Applications
Corresponding Author(s) : Hamad Abdulelah H Alkhayyal
Saudi Journal of Medicine and Public Health,
Vol. 1 No. 2 (2024)
Abstract
Background: The high cost and failure rate of late-stage oncology and neurology drug trials underscore the critical need for robust early-phase decision-making tools. Traditional pharmacokinetic/pharmacodynamic (PK/PD) modeling, which relates drug exposure to a biological effect, often relies on plasma drug levels and sparse tissue biopsies, providing an incomplete picture of in vivo drug behavior and heterogeneity. Concurrently, advancements in medical imaging have moved beyond qualitative assessment to provide repeatable, non-invasive, and spatially resolved quantitative data on tumor morphology, cellularity, perfusion, and metabolism.
Aim: This narrative review aims to analyze the convergence of these two fields by examining how quantitative imaging biomarkers (QIBs) are integrated into PK/PD modeling frameworks within clinical trials.
Methods: A comprehensive literature search was conducted across PubMed, Scopus, and Web of Science for English-language articles (2010-2024).
Results: QIBs from modalities like Dynamic Contrast-Enhanced MRI (DCE-MRI), Diffusion-Weighted Imaging (DWI), and Positron Emission Tomography (PET) provide critical parameters (e.g., Ktrans, ADC, SUV) that can populate computational PK/PD models. These imaging-informed models enable the non-invasive estimation of drug delivery to tissues, receptor occupancy, and the time-course of pharmacologic effect, offering a powerful tool for dose selection, go/no-go decisions, and patient stratification in early-phase trials. However, significant challenges in standardization, validation, and regulatory acceptance persist.
Conclusion: The integration of QIBs into PK/PD modeling represents a transformative paradigm in translational drug development. It bridges molecular pharmacology and in vivo imaging, enabling a more holistic, mechanistic understanding of drug action. For this potential to be fully realized, concerted efforts in technical standardization, robust biomarker qualification, and the development of shared computational frameworks are urgently needed.
Keywords
Download Citation
Endnote/Zotero/Mendeley (RIS)BibTeX
- Burt, T., Yoshida, K., Lappin, G., Vuong, L., John, C., De Wildt, S. N., ... & Rowland, M. (2016). Microdosing and other phase 0 clinical trials: facilitating translation in drug development. Clinical and translational science, 9(2), 74. https://doi.org/10.1111/cts.12390
- Chauvie, S., Mazzoni, L. N., & O’Doherty, J. (2023). A review on the use of imaging biomarkers in oncology clinical trials: quality assurance strategies for technical validation. Tomography, 9(5), 1876-1902. https://doi.org/10.3390/tomography9050149
- Cho, N. S., Wong, W. K., Nghiemphu, P. L., Cloughesy, T. F., & Ellingson, B. M. (2023). The future glioblastoma clinical trials landscape: early phase 0, window of opportunity, and adaptive phase I–III studies. Current Oncology Reports, 25(9), 1047-1055. https://d oi.org/10.1007/s11912-023-01433-1
- Choi, S. H., Jung, S. C., Kim, K. W., Lee, J. Y., Choi, Y., Park, S. H., & Kim, H. S. (2016). Perfusion MRI as the predictive/prognostic and pharmacodynamic biomarkers in recurrent malignant glioma treated with bevacizumab: a systematic review and a time-to-event meta-analysis. Journal of Neuro-oncology, 128(2), 185-194. https://doi.org/10.1007/s11060-016-2102-4
- De Herder, W. W., Hofland, L. J., van der Lely, A. J., & Lamberts, S. W. J. (2003). Somatostatin receptors in gastroentero-pancreatic neuroendocrine tumours. Endocrine-related cancer, 10(4), 451-458.
- DiMasi, J. A., Grabowski, H. G., & Hansen, R. W. (2016). Innovation in the pharmaceutical industry: new estimates of R&D costs. Journal of health economics, 47, 20-33. https://doi.org/10.1016/j.jhealeco.2016.01.012
- Dunphy, M. P., & Pillarsetty, N. (2020). The unique pharmacometrics of small molecule therapeutic drug tracer imaging for clinical oncology. Cancers, 12(9), 2712. https://doi.org/10.3390/cancers12092712
- Falkenhagen, U., Knöchel, J., Kloft, C., & Huisinga, W. (2023). Deriving mechanism‐based pharmacodynamic models by reducing quantitative systems pharmacology models: An application to warfarin. CPT: pharmacometrics & systems pharmacology, 12(4), 432-443. https://doi.org/10.1002/psp4.12903
- FDA-NIH Biomarker Working Group. (2022). BEST (Biomarkers, EndpointS, and other Tools) Resource [Internet]. Silver Spring (MD): Food and Drug Administration (US); 2016 [cited 2020].
- Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016). Radiomics: images are more than pictures, they are data. Radiology, 278(2), 563-577. https://doi.org/10.1148/radiol.2015151169
- Hormuth, D. A., Phillips, C. M., Wu, C., Lima, E. A., Lorenzo, G., Jha, P. K., ... & Yankeelov, T. E. (2021). Biologically-based mathematical modeling of tumor vasculature and angiogenesis via time-resolved imaging data. Cancers, 13(12), 3008. https://doi.org/10.3390/cancers13123008
- Innis, R. B., Cunningham, V. J., Delforge, J., Fujita, M., Gjedde, A., Gunn, R. N., ... & Carson, R. E. (2007). Consensus nomenclature for in vivo imaging of reversibly binding radioligands. Journal of Cerebral Blood Flow & Metabolism, 27(9), 1533-1539. https://doi.org/10.1038/sj.jcbfm.9600493
- Kitajima, K., Maruyama, M., Minami, T., Yokoi, T., Kuribayashi, K., Kijima, T., ... & Yamakado, K. (2020). Comparison of modified Response Evaluation Criteria in Solid Tumors, European Organization for Research and Treatment of Cancer criteria, and PET Response Criteria in Solid Tumors for evaluation of tumor response to chemotherapy and prognosis prediction in patients with unresectable malignant pleural mesothelioma. Nuclear Medicine Communications, 41(8), 790-799. DOI: 10.1097/MNM.0000000000001223
- Koh, D. M., & Collins, D. J. (2007). Diffusion-weighted MRI in the body: applications and challenges in oncology. American Journal of Roentgenology, 188(6), 1622-1635. https://doi.org/10.2214/AJR.06.1403
- Lopci, E., Grassi, I., Chiti, A., Nanni, C., Cicoria, G., Toschi, L., ... & Fanti, S. (2014). PET radiopharmaceuticals for imaging of tumor hypoxia: a review of the evidence. American journal of nuclear medicine and molecular imaging, 4(4), 365.
- Miceli, A., Jonghi-Lavarini, L., Santo, G., Cassarino, G., Linguanti, F., Gazzilli, M., ... & Nappi, A. G. (2023). [18F] FDG PET/CT criteria for treatment response assessment: EORTC and beyond. Clinical and Translational Imaging, 11(5), 421-437. https://doi.org/10.1007/s40336-023-00578-0
- Mould, D. R., & Upton, R. N. (2013). Basic concepts in population modeling, simulation, and model‐based drug development—part 2: introduction to pharmacokinetic modeling methods. CPT: pharmacometrics & systems pharmacology, 2(4), 1-14. https://doi.org/10.1038/psp.2013.14
- Nass, S. J., Rothenberg, M. L., Pentz, R., Hricak, H., Abernethy, A., Anderson, K., ... & Schilsky, R. L. (2018). Accelerating anticancer drug development—opportunities and trade-offs. Nature Reviews Clinical Oncology, 15(12), 777-786. https://doi.org/10.1038/s41571-018-0102-3
- Nerella, S. G., Singh, P., Sanam, T., & Digwal, C. S. (2022). PET molecular imaging in drug development: the imaging and chemistry perspective. Frontiers in medicine, 9, 812270. https://doi.org/10.3389/fmed.2022.812270
- O'Connor, J. P., Jackson, A., Parker, G. J., & Jayson, G. C. (2007). DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents. British journal of cancer, 96(2), 189-195. https://doi.org/10.1038/sj.bjc.6603515
- O'Connor, J. P., Rose, C. J., Waterton, J. C., Carano, R. A., Parker, G. J., & Jackson, A. (2015). Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clinical Cancer Research, 21(2), 249-257. https://doi.org/10.1158/1078-0432.CCR-14-0990
- O'connor, J. P. B., Tofts, P. S., Miles, K. A., Parkes, L. M., Thompson, G., & Jackson, A. (2011). Dynamic contrast-enhanced imaging techniques: CT and MRI. The British journal of radiology, 84(special_issue_2), S112-S120. https://doi.org/10.1259/bjr/55166688
- O'connor, J. P., Aboagye, E. O., Adams, J. E., Aerts, H. J., Barrington, S. F., Beer, A. J., ... & Waterton, J. C. (2017). Imaging biomarker roadmap for cancer studies. Nature reviews Clinical oncology, 14(3), 169-186. https://doi.org/10.1038/nrclinonc.2016.162
- Saleem, A., Harte, R. J., Matthews, J. C., Osman, S., Brady, F., Luthra, S. K., ... & Aboagye, E. O. (2001). Pharmacokinetic evaluation of N-[2-(dimethylamino) ethyl] acridine-4-carboxamide in patients by positron emission tomography. Journal of clinical oncology, 19(5), 1421-1429. https://doi.org/10.1200/JCO.2001.19.5.1421
- Sander, C. Y., & Hesse, S. (2017). News and views on in vivo imaging of neurotransmission using PET and MRI. The quarterly journal of nuclear medicine and molecular imaging: official publication of the Italian Association of Nuclear Medicine (AIMN)[and] the International Association of Radiopharmacology (IAR),[and] Section of the Society of..., 61(4), 414. https://doi.org/10.23736/S1824-4785.17.03019-9
- Schuhmacher, A., Hinder, M., und Stein, A. V. S., Hartl, D., & Gassmann, O. (2023). Analysis of pharma R&D productivity–a new perspective needed. Drug Discovery Today, 28(10), 103726. https://doi.org/10.1016/j.drudis.2023.103726
- Sullivan, D. C., Obuchowski, N. A., Kessler, L. G., Raunig, D. L., Gatsonis, C., Huang, E. P., ... & RSNA-QIBA Metrology Working Group. (2015). Metrology standards for quantitative imaging biomarkers. Radiology, 277(3), 813-825. https://doi.org/10.1148/radiol.2015142202
- Thoeny, H. C., Forstner, R., & De Keyzer, F. (2012). Genitourinary applications of diffusion-weighted MR imaging in the pelvis. Radiology, 263(2), 326-342. https://doi.org/10.1148/radiol.12110446
- Wang, C., Bai, R., Liu, Y., Wang, K., Wang, Y., Yang, J., ... & Yang, P. (2023). Multi-region sequencing depicts intratumor heterogeneity and clonal evolution in cervical cancer. Medical Oncology, 40(2), 78. https://doi.org/10.1007/s12032-022-01942-2
- Wichtmann, B. D., Harder, F. N., Weiss, K., Schönberg, S. O., Attenberger, U. I., Alkadhi, H., ... & Baeßler, B. (2023). Influence of image processing on radiomic features from magnetic resonance imaging. Investigative Radiology, 58(3), 199-208. DOI: 10.1097/RLI.0000000000000921
- Yap, T. A., Bjerke, L., Clarke, P. A., & Workman, P. (2015). Drugging PI3K in cancer: refining targets and therapeutic strategies. Current opinion in pharmacology, 23, 98-107. https://doi.org/10.1016/j.coph.2015.05.016
- Zhao, M., Guo, L. L., Huang, N., Wu, Q., Zhou, L., Zhao, H., ... & Fu, K. (2017). Quantitative analysis of permeability for glioma grading using dynamic contrastenhanced magnetic resonance imaging. Oncology letters, 14(5), 5418-5426. https://doi.org/10.3892/ol.2017.6895
References
Burt, T., Yoshida, K., Lappin, G., Vuong, L., John, C., De Wildt, S. N., ... & Rowland, M. (2016). Microdosing and other phase 0 clinical trials: facilitating translation in drug development. Clinical and translational science, 9(2), 74. https://doi.org/10.1111/cts.12390
Chauvie, S., Mazzoni, L. N., & O’Doherty, J. (2023). A review on the use of imaging biomarkers in oncology clinical trials: quality assurance strategies for technical validation. Tomography, 9(5), 1876-1902. https://doi.org/10.3390/tomography9050149
Cho, N. S., Wong, W. K., Nghiemphu, P. L., Cloughesy, T. F., & Ellingson, B. M. (2023). The future glioblastoma clinical trials landscape: early phase 0, window of opportunity, and adaptive phase I–III studies. Current Oncology Reports, 25(9), 1047-1055. https://d oi.org/10.1007/s11912-023-01433-1
Choi, S. H., Jung, S. C., Kim, K. W., Lee, J. Y., Choi, Y., Park, S. H., & Kim, H. S. (2016). Perfusion MRI as the predictive/prognostic and pharmacodynamic biomarkers in recurrent malignant glioma treated with bevacizumab: a systematic review and a time-to-event meta-analysis. Journal of Neuro-oncology, 128(2), 185-194. https://doi.org/10.1007/s11060-016-2102-4
De Herder, W. W., Hofland, L. J., van der Lely, A. J., & Lamberts, S. W. J. (2003). Somatostatin receptors in gastroentero-pancreatic neuroendocrine tumours. Endocrine-related cancer, 10(4), 451-458.
DiMasi, J. A., Grabowski, H. G., & Hansen, R. W. (2016). Innovation in the pharmaceutical industry: new estimates of R&D costs. Journal of health economics, 47, 20-33. https://doi.org/10.1016/j.jhealeco.2016.01.012
Dunphy, M. P., & Pillarsetty, N. (2020). The unique pharmacometrics of small molecule therapeutic drug tracer imaging for clinical oncology. Cancers, 12(9), 2712. https://doi.org/10.3390/cancers12092712
Falkenhagen, U., Knöchel, J., Kloft, C., & Huisinga, W. (2023). Deriving mechanism‐based pharmacodynamic models by reducing quantitative systems pharmacology models: An application to warfarin. CPT: pharmacometrics & systems pharmacology, 12(4), 432-443. https://doi.org/10.1002/psp4.12903
FDA-NIH Biomarker Working Group. (2022). BEST (Biomarkers, EndpointS, and other Tools) Resource [Internet]. Silver Spring (MD): Food and Drug Administration (US); 2016 [cited 2020].
Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016). Radiomics: images are more than pictures, they are data. Radiology, 278(2), 563-577. https://doi.org/10.1148/radiol.2015151169
Hormuth, D. A., Phillips, C. M., Wu, C., Lima, E. A., Lorenzo, G., Jha, P. K., ... & Yankeelov, T. E. (2021). Biologically-based mathematical modeling of tumor vasculature and angiogenesis via time-resolved imaging data. Cancers, 13(12), 3008. https://doi.org/10.3390/cancers13123008
Innis, R. B., Cunningham, V. J., Delforge, J., Fujita, M., Gjedde, A., Gunn, R. N., ... & Carson, R. E. (2007). Consensus nomenclature for in vivo imaging of reversibly binding radioligands. Journal of Cerebral Blood Flow & Metabolism, 27(9), 1533-1539. https://doi.org/10.1038/sj.jcbfm.9600493
Kitajima, K., Maruyama, M., Minami, T., Yokoi, T., Kuribayashi, K., Kijima, T., ... & Yamakado, K. (2020). Comparison of modified Response Evaluation Criteria in Solid Tumors, European Organization for Research and Treatment of Cancer criteria, and PET Response Criteria in Solid Tumors for evaluation of tumor response to chemotherapy and prognosis prediction in patients with unresectable malignant pleural mesothelioma. Nuclear Medicine Communications, 41(8), 790-799. DOI: 10.1097/MNM.0000000000001223
Koh, D. M., & Collins, D. J. (2007). Diffusion-weighted MRI in the body: applications and challenges in oncology. American Journal of Roentgenology, 188(6), 1622-1635. https://doi.org/10.2214/AJR.06.1403
Lopci, E., Grassi, I., Chiti, A., Nanni, C., Cicoria, G., Toschi, L., ... & Fanti, S. (2014). PET radiopharmaceuticals for imaging of tumor hypoxia: a review of the evidence. American journal of nuclear medicine and molecular imaging, 4(4), 365.
Miceli, A., Jonghi-Lavarini, L., Santo, G., Cassarino, G., Linguanti, F., Gazzilli, M., ... & Nappi, A. G. (2023). [18F] FDG PET/CT criteria for treatment response assessment: EORTC and beyond. Clinical and Translational Imaging, 11(5), 421-437. https://doi.org/10.1007/s40336-023-00578-0
Mould, D. R., & Upton, R. N. (2013). Basic concepts in population modeling, simulation, and model‐based drug development—part 2: introduction to pharmacokinetic modeling methods. CPT: pharmacometrics & systems pharmacology, 2(4), 1-14. https://doi.org/10.1038/psp.2013.14
Nass, S. J., Rothenberg, M. L., Pentz, R., Hricak, H., Abernethy, A., Anderson, K., ... & Schilsky, R. L. (2018). Accelerating anticancer drug development—opportunities and trade-offs. Nature Reviews Clinical Oncology, 15(12), 777-786. https://doi.org/10.1038/s41571-018-0102-3
Nerella, S. G., Singh, P., Sanam, T., & Digwal, C. S. (2022). PET molecular imaging in drug development: the imaging and chemistry perspective. Frontiers in medicine, 9, 812270. https://doi.org/10.3389/fmed.2022.812270
O'Connor, J. P., Jackson, A., Parker, G. J., & Jayson, G. C. (2007). DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents. British journal of cancer, 96(2), 189-195. https://doi.org/10.1038/sj.bjc.6603515
O'Connor, J. P., Rose, C. J., Waterton, J. C., Carano, R. A., Parker, G. J., & Jackson, A. (2015). Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clinical Cancer Research, 21(2), 249-257. https://doi.org/10.1158/1078-0432.CCR-14-0990
O'connor, J. P. B., Tofts, P. S., Miles, K. A., Parkes, L. M., Thompson, G., & Jackson, A. (2011). Dynamic contrast-enhanced imaging techniques: CT and MRI. The British journal of radiology, 84(special_issue_2), S112-S120. https://doi.org/10.1259/bjr/55166688
O'connor, J. P., Aboagye, E. O., Adams, J. E., Aerts, H. J., Barrington, S. F., Beer, A. J., ... & Waterton, J. C. (2017). Imaging biomarker roadmap for cancer studies. Nature reviews Clinical oncology, 14(3), 169-186. https://doi.org/10.1038/nrclinonc.2016.162
Saleem, A., Harte, R. J., Matthews, J. C., Osman, S., Brady, F., Luthra, S. K., ... & Aboagye, E. O. (2001). Pharmacokinetic evaluation of N-[2-(dimethylamino) ethyl] acridine-4-carboxamide in patients by positron emission tomography. Journal of clinical oncology, 19(5), 1421-1429. https://doi.org/10.1200/JCO.2001.19.5.1421
Sander, C. Y., & Hesse, S. (2017). News and views on in vivo imaging of neurotransmission using PET and MRI. The quarterly journal of nuclear medicine and molecular imaging: official publication of the Italian Association of Nuclear Medicine (AIMN)[and] the International Association of Radiopharmacology (IAR),[and] Section of the Society of..., 61(4), 414. https://doi.org/10.23736/S1824-4785.17.03019-9
Schuhmacher, A., Hinder, M., und Stein, A. V. S., Hartl, D., & Gassmann, O. (2023). Analysis of pharma R&D productivity–a new perspective needed. Drug Discovery Today, 28(10), 103726. https://doi.org/10.1016/j.drudis.2023.103726
Sullivan, D. C., Obuchowski, N. A., Kessler, L. G., Raunig, D. L., Gatsonis, C., Huang, E. P., ... & RSNA-QIBA Metrology Working Group. (2015). Metrology standards for quantitative imaging biomarkers. Radiology, 277(3), 813-825. https://doi.org/10.1148/radiol.2015142202
Thoeny, H. C., Forstner, R., & De Keyzer, F. (2012). Genitourinary applications of diffusion-weighted MR imaging in the pelvis. Radiology, 263(2), 326-342. https://doi.org/10.1148/radiol.12110446
Wang, C., Bai, R., Liu, Y., Wang, K., Wang, Y., Yang, J., ... & Yang, P. (2023). Multi-region sequencing depicts intratumor heterogeneity and clonal evolution in cervical cancer. Medical Oncology, 40(2), 78. https://doi.org/10.1007/s12032-022-01942-2
Wichtmann, B. D., Harder, F. N., Weiss, K., Schönberg, S. O., Attenberger, U. I., Alkadhi, H., ... & Baeßler, B. (2023). Influence of image processing on radiomic features from magnetic resonance imaging. Investigative Radiology, 58(3), 199-208. DOI: 10.1097/RLI.0000000000000921
Yap, T. A., Bjerke, L., Clarke, P. A., & Workman, P. (2015). Drugging PI3K in cancer: refining targets and therapeutic strategies. Current opinion in pharmacology, 23, 98-107. https://doi.org/10.1016/j.coph.2015.05.016
Zhao, M., Guo, L. L., Huang, N., Wu, Q., Zhou, L., Zhao, H., ... & Fu, K. (2017). Quantitative analysis of permeability for glioma grading using dynamic contrastenhanced magnetic resonance imaging. Oncology letters, 14(5), 5418-5426. https://doi.org/10.3892/ol.2017.6895