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McKenna MT, Weis JA, Quaranta V, et al. Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric. Front Physiol. 2019;10:616doi: 10.3389/fphys.2019.00616.
McKenna, M. T., Weis, J. A., Quaranta, V., & Yankeelov, T. E. (2019). Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric. Frontiers in physiology, 10616. https://doi.org/10.3389/fphys.2019.00616
McKenna, Matthew T, et al. "Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric." Frontiers in physiology vol. 10 (2019): 616. doi: https://doi.org/10.3389/fphys.2019.00616
McKenna MT, Weis JA, Quaranta V, Yankeelov TE. Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric. Front Physiol. 2019 May 22;10:616. doi: 10.3389/fphys.2019.00616. eCollection 2019. PMID: 31178753; PMCID: PMC6538812.
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Front Physiol. 2019 May 22;10:616. doi: 10.3389/fphys.2019.00616. eCollection 2019.

Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric.

Frontiers in physiology

Matthew T McKenna, Jared A Weis, Vito Quaranta, Thomas E Yankeelov

Affiliations

  1. Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States.
  2. Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States.
  3. Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC, United States.
  4. Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, NC, United States.
  5. Department of Cancer Biology, Vanderbilt University School of Medicine, Vanderbilt University, Nashville, TN, United States.
  6. Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States.
  7. Department of Diagnostic Medicine, Dell Medical School, The University of Texas at Austin, Austin, TX, United States.
  8. Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States.
  9. Oden Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX, United States.
  10. Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, United States.

PMID: 31178753 PMCID: PMC6538812 DOI: 10.3389/fphys.2019.00616

Abstract

Treatment response assays are often summarized by sigmoidal functions comparing cell survival at a single timepoint to applied drug concentration. This approach has a limited biophysical basis, thereby reducing the biological insight gained from such analysis. In particular, drug pharmacokinetic and pharmacodynamic (PK/PD) properties are overlooked in developing treatment response assays, and the accompanying summary statistics conflate these processes. Here, we utilize mathematical modeling to decouple and quantify PK/PD pathways. We experimentally modulate specific pathways with small molecule inhibitors and filter the results with mechanistic mathematical models to obtain quantitative measures of those pathways. Specifically, we investigate the response of cells to time-varying doxorubicin treatments, modulating doxorubicin pharmacology with small molecules that inhibit doxorubicin efflux from cells and DNA repair pathways. We highlight the practical utility of this approach through proposal of the "equivalent dose metric." This metric, derived from a mechanistic PK/PD model, provides a biophysically-based measure of drug effect. We define equivalent dose as the functional concentration of drug that is bound to the nucleus following therapy. This metric can be used to quantify drivers of treatment response and potentially guide dosing of combination therapies. We leverage the equivalent dose metric to quantify the specific intracellular effects of these small molecule inhibitors using population-scale measurements, and to compare treatment response in cell lines differing in expression of drug efflux pumps. More generally, this approach can be leveraged to quantify the effects of various pharmaceutical and biologic perturbations on treatment response.

Keywords: breast cancer; doxorubicin; mathematical modeling; pharmacodynamics; pharmacokinetic modeling; treatment response

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