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Abstract 1503: A physiologically-based pharmacokinetic (PBPK) model of hydroxychloroquine
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Abstract
Hydroxychloroquine (HCQ) is a lysotropic autophagy inhibitor that is being used in over 45 clinical trials either alone or in combination with another chemotherapeutic. Pharmacokinetic (PK) and pharmacodynamic (PD) studies with HCQ have shown that drug exposure in the blood does not correlate with autophagy inhibition in either peripheral blood mononuclear cells (PBMCs) or tumor tissue (Autophagy 10:1415). HCQ exhibits primarily pH-driven PK and has been shown, by way of heightened levels of autophagy markers, to generate a therapeutic effect longer than PK data suggests. A physiologically-based pharmacokinetic model (PBPK) was developed for HCQ to describe the tissue-specific absorption, distribution, metabolism, and excretion as well as lysosome-specific sequestration. Physiologic parameters were adapted from literature, or obtained from experimental data when necessary, and used to simulate physiologically-based HCQ PK following designated dosing regimen in mice and rats. Experimentally derived PK data from these species were compared to simulation generated data to drive model development and subsequently determine model accuracy, achieving statistically-similar PK predictions of blood and tissues. Through allometric scaling and species-specific parameter modifications this model can be easily adapted for accurate prediction of HCQ PK in dogs and humans, as determined by comparison with respective blood levels. The value of this model lies in its ability to simulate HCQ PK in cancer patients with tumor types deemed autophagy-dependent. Model data simulating HCQ uptake in a neutral tumor compartment (pH = 7.2) shows that peak concentration in the lysosomes, the active site of the drug, is roughly four-fold higher than the peak concentration of lysosomes in an acidic tumor compartment (pH = 6.8), yet there is only a small change between whole-tumor concentrations. This suggests that the ability of HCQ to inhibit autophagy in acidic tumors would be significantly reduced, which is currently being investigated through in vitro and in vivo uptake of HCQ in MDA-MB-231 and MCF-7 tumor cell lines. Additionally, HCQ PK exhibits large interpatient variability, thus model utilization is beneficial in determining if therapeutic levels of HCQ are achieved at the tumor site and the impact of variability in the local tumor environment on HCQ disposition. The flexibility and simulation capabilities of the developed PBPK model also allows for investigation of how HCQ PK and subsequent autophagy inhibition may potentially be modified by other treatment modalities and suggest dosing schedules to optimize therapeutic response.
Citation Format: Keagan P. Collins, Kristen M. Jackson, Daniel L. Gustafson. A physiologically-based pharmacokinetic (PBPK) model of hydroxychloroquine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1503. doi:10.1158/1538-7445.AM2017-1503
American Association for Cancer Research (AACR)
Title: Abstract 1503: A physiologically-based pharmacokinetic (PBPK) model of hydroxychloroquine
Description:
Abstract
Hydroxychloroquine (HCQ) is a lysotropic autophagy inhibitor that is being used in over 45 clinical trials either alone or in combination with another chemotherapeutic.
Pharmacokinetic (PK) and pharmacodynamic (PD) studies with HCQ have shown that drug exposure in the blood does not correlate with autophagy inhibition in either peripheral blood mononuclear cells (PBMCs) or tumor tissue (Autophagy 10:1415).
HCQ exhibits primarily pH-driven PK and has been shown, by way of heightened levels of autophagy markers, to generate a therapeutic effect longer than PK data suggests.
A physiologically-based pharmacokinetic model (PBPK) was developed for HCQ to describe the tissue-specific absorption, distribution, metabolism, and excretion as well as lysosome-specific sequestration.
Physiologic parameters were adapted from literature, or obtained from experimental data when necessary, and used to simulate physiologically-based HCQ PK following designated dosing regimen in mice and rats.
Experimentally derived PK data from these species were compared to simulation generated data to drive model development and subsequently determine model accuracy, achieving statistically-similar PK predictions of blood and tissues.
Through allometric scaling and species-specific parameter modifications this model can be easily adapted for accurate prediction of HCQ PK in dogs and humans, as determined by comparison with respective blood levels.
The value of this model lies in its ability to simulate HCQ PK in cancer patients with tumor types deemed autophagy-dependent.
Model data simulating HCQ uptake in a neutral tumor compartment (pH = 7.
2) shows that peak concentration in the lysosomes, the active site of the drug, is roughly four-fold higher than the peak concentration of lysosomes in an acidic tumor compartment (pH = 6.
8), yet there is only a small change between whole-tumor concentrations.
This suggests that the ability of HCQ to inhibit autophagy in acidic tumors would be significantly reduced, which is currently being investigated through in vitro and in vivo uptake of HCQ in MDA-MB-231 and MCF-7 tumor cell lines.
Additionally, HCQ PK exhibits large interpatient variability, thus model utilization is beneficial in determining if therapeutic levels of HCQ are achieved at the tumor site and the impact of variability in the local tumor environment on HCQ disposition.
The flexibility and simulation capabilities of the developed PBPK model also allows for investigation of how HCQ PK and subsequent autophagy inhibition may potentially be modified by other treatment modalities and suggest dosing schedules to optimize therapeutic response.
Citation Format: Keagan P.
Collins, Kristen M.
Jackson, Daniel L.
Gustafson.
A physiologically-based pharmacokinetic (PBPK) model of hydroxychloroquine [abstract].
In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC.
Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1503.
doi:10.
1158/1538-7445.
AM2017-1503.
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