Estimating meat withdrawal times in pigs exposed to melamine contaminated feed using a physiologically based pharmacokinetic model

Violative chemical residues in animal-derived food products affect food safety globally and have impact on the trade of international agricultural products. The Food Animal Residue Avoidance Databank program has been developing scientific tools to provide appropriate withdrawal interval (WDI) estimations after extralabel drug use in food animals for the past three decades. One of the tools is physiologically based pharmacokinetic (PBPK) modeling, which is a mechanistic-based approach that can be used to predict tissue residues and WDIs. However, PBPK models are complicated and difficult to use by non-modelers. Therefore, a user-friendly PBPK modeling framework is needed to move this field forward. Flunixin was one of the top five violative drug residues identified in the United States from 2010 to 2016. The objective of this study was to establish a web-based user-friendly framework for the development of new PBPK models for drugs administered to food animals. Specifically, a new PBPK model for both cattle and swine after administration of flunixin meglumine was developed. Population analysis using Monte Carlo simulations was incorporated into the model to predict WDIs following extralabel administration of flunixin meglumine. The population PBPK model was converted to a web-based interactive PBPK (iPBPK) framework to facilitate its application. This iPBPK framework serves as a proof-of-concept for further improvements in the future and it can be applied to develop new models for other drugs in other food animal species, thereby facilitating the application of PBPK modeling in WDI estimation and food safety assessment.

Pharmacokinetic (PK) modeling is a useful method for investigating drug absorption, distribution, metabolism, and excretion. The most commonly used mathematical models in PK modeling are the compartment model and physiologically based pharmacokinetic (PBPK) model. Although the theoretical characteristics of each model are well known, there have been few comparative studies of the compatibility of the models. Therefore, we evaluated the compatibility of PBPK and compartment models using the lumping method with 20 model compounds. The PBPK model was theoretically reduced to the lumped model using the principle of grouping tissues and organs that show similar kinetic behaviors. The area under the concentration–time curve (AUC) based on the simulated concentration and PK parameters (drug clearance [CL], central volume of distribution [Vc], peripheral volume of distribution [Vp]) in each model were compared, assuming administration to humans. The AUC and PK parameters in the PBPK model were similar to those in the lumped model within the 2-fold range for 17 of 20 model compounds (85%). In addition, the relationship of the calculated Vd/fu (volume of distribution [Vd], drug-unbound fraction [fu]) and the accuracy of AUC between the lumped model and compartment model confirmed their compatibility. Accordingly, the compatibility between PBPK and compartment models was confirmed by the lumping method. This method can be applied depending on the requirement of compatibility between the two models.

Comparisons of PK-Sim and R program for physiologically based pharmacokinetic model development for broiler chickens and laying hens: meloxicam as a case study.

Physiologically based pharmacokinetic (PBPK) models allow integrated representations of the time course of drug concentrations in the important organs and sites of drug action, toxicity, absorption, metabolism, and excretion. PBPK models have multiple compartments representing defined organs or physiological spaces, with parameters that are potentially directly measurable. They lie on a continuum of pharmacokinetic model types, which includes Compartmental (least complex), Semi-PBPK, PBPK, and Systems Biology (most complex) models. Compartmental models are typically parameterized using a “top-down” approach, where the model is fitted to a specific data set to estimate parameter values. PBPK models are typically parameterized using a “bottom-up” approach, where model parameters are derived from literature, in vitro data, or scaled data from another species. Although there is increasing availability and use of commercial software for PBPK modeling, it is feasible to construct bespoke PBPK for specific projects using general-purpose software platforms. The challenge here is often collecting, collating, and justifying the data used to parameterize the model. In this chapter, the fundamental equations for a simple PBPK models are presented with respect to key “submodels” of an example whole-body model. The current rise in the rate of publication and application of PBPK models is likely to be sustained in the foreseeable future. Contributing factors will likely be the wider use of commercial PBPK models, the advent of broader collective efforts to advance and coordinate PBPK and Systems Biology modeling, and the increasing ease with which bespoke PBPK models can be coded and shared.

Intratumoral pharmacokinetic (PK) and pharmacodynamic (PD) heterogeneity contribute to variability in NB tumor response to chemotherapy and can be responsible for tumor relapse. Herein we propose to develop a whole body PBPK model with an individualized tumor compartment to derive individual tumor specific concentration-time profiles for the NB standard of care drug TPT. This model can then relate intratumoral heterogeneity in tumor blood flow to PD response and antitumor effects. PK studies of TPT (0.6, 1.25, 5, and 20 mg/kg, IV bolus) will be performed in CD1 nude mice (n = 3 mice/time point) bearing orthotopic NB (NB5) xenograft. Blood samples will be collected at predetermined time points using cardiac puncture, and plasma separated and stored until analysis. Animals will be perfused using saline solution to remove residual blood, and tissue samples including tumor, muscle, adipose, bone, liver, gallbladder, kidney, spleen, lungs, brain, heart, duodenum, and large intestine collected. TPT concentrations in plasma and tissue homogenate samples will be quantified using a validated HPLC fluorescence spectrophotometry method. Tumor samples will be divided into two sections each, one for TPT quantification and one for immunohistochemistry of PD markers for DNA damage (γ-H2AX) and apoptosis (CASP3). A cohort of mice will be used to quantify tumor blood flow using contrast-enhanced ultrasound (CEUS) using MicroMarker® microbubbles prior to dosing the mice for the PK study. TPT plasma and tissue concentration-time data will be used to develop the whole-body PBPK model with an individualized tumor compartment using NONMEM. Individual tumor perfusion data obtained using CEUS will be combined with the PBPK model to derive tumor specific concentration-time profiles. A preliminary study conducted in non-tumor bearing mice receiving TPT 5 mg/kg showed that TPT plasma and tissue concentration-time data were reasonably described by our PBPK model. As expected from our previous studies, the brain tissue was found to have the lowest exposure to TPT with a brain to plasma partition coefficient (Kp,brain ∼ 8%). We also observed high permeability of TPT (Kp > 1) into the gallbladder, duodenum, large intestine, spleen, liver and kidney. In future we will study the correlations between individual tumor concentrations based on our comprehensive PBPK model and γ-H2AX and CASP3 activity. Citation Format: Yogesh T. Patel, Megan O. Jacus, Abbas Shirinifard, Abigail D. Davis, Suresh Thiagarajan, Stacy L. Throm, Vinay M. Daryani, Andras Sablauer, Clinton F. Stewart. Development of a whole body physiologically-based pharmacokinetic (PBPK) model with individualized tumor compartment for topotecan (TPT) in mice bearing neuroblastoma (NB). [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4519. doi:10.1158/1538-7445.AM2015-4519

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

The objective of this study was to compare the predictive performance of an allometric model with that of a physiologically based pharmacokinetic (PBPK) model to predict clearance or area under the concentration-time curve (AUC) of drugs in subjects from neonates to adolescents. From the literature, 10 studies were identified in which clearance or AUC of drugs from neonates to adolescents was predicted by PBPK models. In these published studies, drugs were given to children either by intravenous or oral route. The allometric model was an age-dependent exponent (ADE) model for the prediction of clearance across the age groups. The predicted clearance or AUC values from the PBPK and ADE models were compared with the experimental values. The acceptable prediction error was the percentage of subjects within an 0.5- to 2-fold or 0.5- to 1.5-fold prediction error. There were 73 drugs with a total of 372 observations. From PBPK and allometric models, 91.1% and 90.6% of observations were within 0.5- to 2-fold prediction error, respectively. For children ≤2 years old (n = 130), PBPK and allometric models had 89% and 87% of observations within the 0.5- to 2-fold prediction error, respectively. This study indicates that the predictive power of PBPK and allometric models was essentially similar for the prediction of clearance or AUC in pediatric subjects ranging from neonates to adolescents.

Development of a physiologically based pharmacokinetic model for flunixin in cattle and swine following dermal exposure.

A flow-limited, physiologically based pharmacokinetic (PBPK) model for predicting the plasma and tissue concentrations of valnemulin after a single oral administration to rats was developed, and then the data were extrapolated to pigs so as to predict withdrawal interval in edible tissues. Blood/tissue pharmacokinetic data and blood/tissue partition coefficients for valnemulin in rats and pigs were collected experimentally. Absorption, distribution and elimination of the drug were characterized by a set of mass-balance equations. Model simulations were achieved using a commercially available software program. The rat PBPK model better predicted plasma and tissue concentrations. The correlation coefficients of the predicted and experimentally determined values for plasma, liver, kidney, lung and muscle were 0.96, 0.94, 0.96, 0.91 and 0.91, respectively. The rat model parameters were extrapolated to pigs to estimate valnemulin residue withdrawal interval in edible tissues. Correlation (R(2) ) between predicted and observed liver, kidney and muscle were 0.95, 0.97 and 0.99, respectively. Based on liver tissue residue profiles, the pig model estimated a withdrawal interval of 10 h under a multiple oral dosing schedule (5.0 mg/kg, twice daily for 7.5 days). PBPK models, such as this one, provide evidence of the usefulness in interspecies PK data extrapolation over a range of dosing scenarios and can be used to predict withdrawal interval in pigs.

ObjectiveThe objective of this study was to compare the predictive performances of a glomerular filtration rate (GFR) model with a physiologically based pharmacokinetic (PBPK) model to predict total or renal clearance or area under the curve of renally excreted drugs in subjects with varying degrees of renal impairment.MethodsFrom the literature, 11 studies were randomly selected in which total or renal clearance or area under the curve of drugs in subjects with different degrees of renal impairment were predicted by PBPK models. In these published studies, drugs were given to subjects intravenously or orally. The PBPK model was generally a whole-body model whereas the GFR model was as follows: Predicted total clearance (CLT) = CLT in healthy subjects × (GFR in RI/GFR in H), Predicted AUC = AUC in healthy subjects × (GFR in H/GFR in RI), where H is the healthy subjects and RI is renal impairment. The predicted clearance or area under the curve values using PBPK and GFR models were compared with the observed (experimental pharmacokinetic) values. The acceptable prediction error was within the 0.5- to 2-fold or 0.5- to 1.5-fold prediction error.ResultsThere were 33 drugs with a total number of 101 observations (area under the curve, total and renal clearance in subjects with mild, moderate, and severe renal impairment). From PBPK and GFR models, out of 101 observations, 94 (93.1%) and 96 (95.0%) observations were within the 0.5- to 2-fold prediction error, respectively.ConclusionsThis study indicates that the predictive power of a simple GFR model is similar to a PBPK model for the prediction of clearance or area under the curve in subjects with renal impairment. The GFR method is simple, robust, and reliable and can replace complex empirical PBPK models.

Cefquinome is widely used to treat respiratory tract diseases of swine. While extra-label dosages of cefquinome could improve clinical efficacy, they might lead to excessively high residues in animal-derived food. In this study, a physiologically based pharmacokinetic (PBPK) model was calibrated based on the published data and a microdialysis experiment to assess the dosage efficiency and food safety. For the microdialysis experiment, in vitro/in vivo relative recovery and concentration-time curves of cefquinome in the lung interstitium were investigated. This PBPK model is available to predict the drug concentrations in the muscle, kidney, liver, plasma, and lung interstitial fluid. Concentration-time curves of 1000 virtual animals in different tissues were simulated by applying sensitivity and Monte Carlo analyses. By integrating pharmacokinetic/pharmacodynamic target parameters, cefquinome delivered at 3-5 mg/kg twice daily is advised for the effective control of respiratory tract infections of nursery pig, which the bodyweight is around 25 kg. Based on the predicted cefquinome concentrations in edible tissues, the withdrawal interval is 2 and 3 days for label and the extra-label doses, respectively. This study provides a useful tool to optimize the dosage regimen of cefquinome against respiratory tract infections and predicts the concentration of cefquinome residues in edible tissues. This information would be helpful to improve the food safety and guide rational drug usage.

Objective: Preclinical studies have demonstrated that CFAK-C4 has anti-tumor efficacy in a variety of malignancies. To maximize its efficacy, it is necessary to understand the pharmacokinetic (PK) properties of CFAK-C4 in the body. A PK study was conducted to characterize CFAK-C4 disposition in plasma and various tissues, including brain, heart, liver, lung, muscle, spleen, and sternum. Subsequently, a physiologically-based pharmacokinetic (PBPK) model was developed to simultaneously characterize and predict plasma and tissue CFAK-C4 concentrations and thus help guide future dosing strategies alone and in combination. Methods: Female CD-1 mice received a single IP injection of CFAK-C4 with a dose of 50mg/kg, and then plasma and tissue samples were collected at serial time points after injection. Three mice were sacrificed at each time point. CFAK-C4 concentrations were determined by a validated LC-MS/MS method. Noncompartmental PK analysis was performed using WinNonlin (Pharsight, Version 5.3) for PK parameters. CFAK-C4 concentration-time profiles were fitted with a PBPK model composed of compartments for plasma, all the measured tissues, peritoneum, and a remainder compartment which represented all other tissues where CFAK-C4 was not measured, using ADAPT5 (BMSR, USC). The PBPK model was assessed by goodness-of-fit plots together with agreement of estimated parameters with noncompartmental analysis. Results: CFAK-C4 concentrations followed a monoexponential decay in plasma, while there was a longer elimination phase observed in tissues. As a result, CFAK-C4 concentration-time profiles in plasma and tissues were simultaneously fitted into a plasma-flow-rate-limited PBPK model successfully. Partition coefficients (Kp), as a measure of the extent of tissue distribution, and plasma clearance (Cl) were estimated by the PBPK model, while the volumes of distribution and plasma flow rates of tissues were fixed to physiological values. The model predicts that CFAK-C4 can be well distributed to various tissues quickly, and Cmax is achieved within half an hour after IP injection. The estimated Cl, 0.111 (±7.4%) l/h, was similar to the value from non-compartmental analysis (NCA) (0.0925 L/h). The model also predicts CFAK-C4 has the highest penetration to lung, with a Kp of 28.1 (±15.2%), followed by brain, with a Kp of 16.6 (±11.6%), and it has the lowest penetration to muscle, with a Kp of 3.6 (±8.3%). Conclusions: The wide tissue distribution of CFAK-C4 provides a great advantage for maximizing its anti-tumor efficacy. The PBPK model predicts CFAK-C4 plasma and tissue concentrations reasonably well. This PBPK model will be used as a tool to build PK/PD models to characterize the temporal relationship between CFAK-C4 pharmacokinetics and its antitumor efficacy and thus help decide future dosing strategies for the treatment of various malignancies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3786. doi:1538-7445.AM2012-3786

Physiologically based pharmacokinetic (PBPK) models are powerful tools to predict tissue distribution and depletion of veterinary drugs in food animals. However, most models only simulate the pharmacokinetics of the parent drug without considering their metabolites. In this study, a PBPK model was developed to simultaneously describe the depletion in pigs of the food animal antimicrobial agent cyadox (CYA), and its marker residue 1,4-bisdesoxycyadox (BDCYA). The CYA and BDCYA sub-models included blood, liver, kidney, gastrointestinal tract, muscle, fat and other organ compartments. Extent of plasma-protein binding, renal clearance and tissue-plasma partition coefficients of BDCYA were measured experimentally. The model was calibrated with the reported pharmacokinetic and residue depletion data from pigs dosed by oral gavage with CYA for five consecutive days, and then extrapolated to exposure in feed for two months. The model was validated with 14 consecutive day feed administration data. This PBPK model accurately simulated CYA and BDCYA in four edible tissues at 24–120 h after both oral exposure and 2-month feed administration. There was only slight overestimation of CYA in muscle and BDCYA in kidney at earlier time points (6–12 h) when dosed in feed. Monte Carlo analysis revealed excellent agreement between the estimated concentration distributions and observed data. The present model could be used for tissue residue monitoring of CYA and BDCYA in food animals, and provides a foundation for developing PBPK models to predict residue depletion of both parent drugs and their metabolites in food animals.

Oseltamivir phosphate (OP, Tamiflu®) is a widely used prodrug for the treatment of influenza viral infections. Orally administered OP is rapidly hydrolyzed by the carboxylesterases in animals to oseltamivir carboxylate (OC), a potent influenza virus neuraminidase inhibitor. The goals of this study were to develop and validate a physiologically-based pharmacokinetic (PBPK) model of OP/OC in rats and humans, and to predict the internal tissue doses for OP and OC in humans after receiving OP orally. To this end, a PBPK model of OP/OC was first developed in the rat, which was then scaled up to humans by replacing the physiological and biochemical parameters with human-specific values. The proposed PBPK model consisted of an OP and an OC sub-models each containing nine first-order, flow-limited tissue/organ compartments. OP metabolism to OC was assumed to carry out mainly by hepatic carboxylesterases although extra-hepatic metabolism also occurred especially in the plasma. The PBPK model was developed and validated by experimental data from our laboratories and from the literature. The proposed PBPK model accurately predicted the pharmacokinetic behavior of OP and OC in humans and rats after receiving a single or multiple doses of OP orally or an OC dose i.v. The PBPK model was used to predict the internal tissue doses of OP and OC in a hypothetical human after receiving the recommended dose of 75 mg/kg OP b.i.d. for 6 days. Steady-state OC concentrations in the plasma and major organs such as the lung and the brain were higher than the minimum in vitro IC50 reported for H1N1 influenza virus neuraminidase, confirming OP is an effective, anti-viral agent. OP side-effects in the gastrointestinal tract and brain of humans were explainable by the tissue doses found in these organs. The PBPK model provides a quantitative tool to evaluate the relationship between an externally applied dose of OP and the internal tissue doses in humans. As such the model can be used to adjust the dose regimens for adult patients in disease states e.g., renal failure and liver damage.

There has been relatively little attention given to incorporating knowledge of mode of action or of dosimetry of active toxic chemical to target tissue sites in the calculation of noncancer exposure guidelines. One exception is the focus in the revised reference con centration (RfC) process on delivered dose adjustments for inhaled materials. The studies reported here attempt to continue in the spirit of the new RfC guidelines by incorporating both mechanistic and delivered dose information using a physiologically based pharmaco kinetic (PBPK) model, along with quantitative dose-response information using the bench mark dose (BMD) method, into the noncancer risk assessment paradigm. Two examples of the use of PBPK and BMD techniques in noncancer risk assessment are described: methylene chloride, and trichloroethylene. Minimal risk levels (MRLs) based on PBPK analysis of these chemicals were generally similar to those based on the traditional process, but individual MRLs ranged from roughly 10-fold higher to more than 10-fold lower than existing MRLs that were not based on PBPK modeling. Only two MRLs were based on critical studies that presented adequate data for BMD modeling, and in these two cases the BMD models were unable to provide an acceptable fit to the overall dose-response of the data, even using pharmacokinetic dose metrics. A review of 10 additional chemicals indicated that data reporting in the toxicological literature is often inadequate to support BMD modeling. Three general observations regarding the use of PBPK and BMD modeling in noncancer risk assessment were noted. First, a full PBPK model may not be necessary to support a more accurate risk assessment; often only a simple pharmacokinetic description, or an understanding of basic pharmacokinetic principles, is needed. Second, pharmacokinetic and mode of action considerations are a crucial factor in conducting non cancer risk assessments that involve animal-to-human extrapolation. Third, to support the application of BMD modeling in noncancer risk assessment, reporting of toxicity results in the toxicological literature should include both means and standard deviations for each dose group in the case of quantitative endpoints, such as relative organ weights or testing scores, and should report the number of animals affected in the case of qualitative endpoints.