Sampling Site Has a Critical Impact on Physiologically Based Pharmacokinetic Modeling

An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science

A physiologically-based pharmacokinetic (PBPK) model represents the structural components of the body with physiologically relevant compartments connected via blood flow rates described by mathematical equations to determine drug disposition. PBPK models are used in the pharmaceutical sector for drug development, precision medicine, and the chemical industry to predict safe levels of exposure during the registration of chemical substances. However, one area of application where PBPK models have been scarcely used is forensic science. In this review, we give an overview of PBPK models successfully developed for several illicit drugs and environmental chemicals that could be applied for forensic interpretation, highlighting the gaps, uncertainties, and limitations.

Designing drug occupancy studies with PET neuroimaging: Sample size, occupancy ranges and analytical methods

Molecular neuroimaging is today considered essential for evaluation of novel CNS drugs; it is used to quantify blood-brain barrier permeability, verify interaction with key target and determine the drug dose resulting in 50% occupancy, IC₅₀. In spite of this, there has been limited data available to inform on how to optimize study designs. Through simulations, we here evaluate how IC₅₀ estimation is affected by the (i) range of drug doses administered, (ii) number of subjects included, and (iii) level of noise in the plasma drug concentration measurements. Receptor occupancy is determined from PET distribution volumes using two different methods: the Lassen plot and Likelihood estimation of occupancy (LEO). We also introduce and evaluate a new likelihood-based estimator for direct estimation of IC₅₀ from PET distribution volumes. For estimation of IC₅₀, we find very limited added benefit in scanning individuals who are given drug doses corresponding to less than 40% receptor occupancy. In the range of typical PET sample sizes (5-20 subjects) each extra individual clearly reduces the error of the IC₅₀ estimate. In all simulations, likelihood-based methods gave more precise IC₅₀ estimates than the Lassen plot; four times the number of subjects were required for the Lassen plot to reach the same IC₅₀ precision as LEO.

A Permeability- and Perfusion-based PBPK model for Improved Prediction of Concentration-time Profiles

To improve predictions of concentration‐time (C‐t) profiles of drugs, a new physiologically based pharmacokinetic modeling framework (termed ‘PermQ’) has been developed. This model includes permeability into and out of capillaries, cell membranes, and intracellular lipids. New modeling components include (i) lumping of tissues into compartments based on both blood flow and capillary permeability, and (ii) parameterizing clearances in and out of membranes with apparent permeability and membrane partitioning values. Novel observations include the need for a shallow distribution compartment particularly for bases. C‐t profiles were modeled for 24 drugs (7 acidic, 5 neutral, and 12 basic) using the same experimental inputs for three different models: Rodgers and Rowland (RR), a perfusion‐limited membrane‐based model (K_p,mem), and PermQ. K_p,mem and PermQ can be directly compared since both models have identical tissue partition coefficient parameters. For the 24 molecules used for model development, errors in V_ss and t_1/2 were reduced by 37% and 43%, respectively, with the PermQ model. Errors in C‐t profiles were reduced (increased EOC) by 43%. The improvement was generally greater for bases than for acids and neutrals. Predictions were improved for all 3 models with the use of parameters optimized for the PermQ model. For five drugs in a test set, similar results were observed. These results suggest that prediction of C‐t profiles can be improved by including capillary and cellular permeability components for all tissues.

Predicting Regional Respiratory Tissue and Systemic Concentrations of Orally Inhaled Drugs through a Novel PBPK Model

Oral inhalation (OI) of drugs is the route of choice to treat respiratory diseases or for recreational drug use (e.g., cannabis). After OI, the drug is deposited in and systemically absorbed from various regions of the respiratory tract. Measuring regional respiratory tissue drug concentrations at the site of action is important for evaluating the efficacy and safety of orally inhaled drugs (OIDs). Because such a measurement is routinely not possible in humans, the only alternative is to predict these concentrations, for example by physiologically based pharmacokinetic (PBPK) modeling. Therefore, we developed an OI-PBPK model to integrate the interplay between regional respiratory drug deposition and systemic absorption to predict regional respiratory tissue and systemic drug concentrations. We validated our OI-PBPK model by comparing the simulated and observed plasma concentration-time profiles of two OIDs, morphine and nicotine. Furthermore, we performed sensitivity analyses to quantitatively demonstrate the impact of key parameters on the extent and pattern of regional respiratory drug deposition, absorption, and the resulting regional respiratory tissue and systemic plasma concentrations. Our OI-PBPK model can be applied to predict regional respiratory tissue and systemic drug concentrations to optimize OID formulations, delivery systems, and dosing regimens. Furthermore, our model could be used to establish the bioequivalence of generic OIDs for which systemic plasma concentrations are not measurable or are not a good surrogate of the respiratory tissue drug concentrations. SIGNIFICANCE STATEMENT: Our OI-PBPK model is the first comprehensive model to predict regional respiratory deposition, as well as systemic and regional tissue concentrations of OIDs, especially at the drug's site of action, which is difficult to measure in humans. This model will help optimize OID formulations, delivery systems, dosing regimens, and bioequivalence assessment of generic OID. Furthermore, this model can be linked with organs-on-chips, pharmacodynamic and quantitative systems pharmacology models to predict and evaluate the safety and efficacy of OID.

Bridging the gap between in silico and in vivo by modeling opioid disposition in a kidney proximal tubule microphysiological system

Opioid overdose, dependence, and addiction are a major public health crisis. Patients with chronic kidney disease (CKD) are at high risk of opioid overdose, therefore novel methods that provide accurate prediction of renal clearance (CL_r) and systemic disposition of opioids in CKD patients can facilitate the optimization of therapeutic regimens. The present study aimed to predict renal clearance and systemic disposition of morphine and its active metabolite morphine-6-glucuronide (M6G) in CKD patients using a vascularized human proximal tubule microphysiological system (VPT-MPS) coupled with a parent-metabolite full body physiologically-based pharmacokinetic (PBPK) model. The VPT-MPS, populated with a human umbilical vein endothelial cell (HUVEC) channel and an adjacent human primary proximal tubular epithelial cells (PTEC) channel, successfully demonstrated secretory transport of morphine and M6G from the HUVEC channel into the PTEC channel. The in vitro data generated by VPT-MPS were incorporated into a mechanistic kidney model and parent-metabolite full body PBPK model to predict CL_r and systemic disposition of morphine and M6G, resulting in successful prediction of CL_r and the plasma concentration–time profiles in both healthy subjects and CKD patients. A microphysiological system together with mathematical modeling successfully predicted renal clearance and systemic disposition of opioids in CKD patients and healthy subjects.

Predicting Maternal-Fetal Disposition of Fentanyl Following Intravenous and Epidural Administration Using Physiologically Based Pharmacokinetic Modeling

Fentanyl is an opioid analgesic used to treat obstetrical pain in parturient women through epidural or intravenous route, and unfortunately can also be abused by pregnant women. Fentanyl is known to cross the placental barrier, but how the route of administration and time after dosing affects maternal-fetal disposition kinetics at different stages of pregnancy is not well characterized. To address this knowledge gap, we developed a maternal-fetal physiologically based pharmacokinetic (mf-PBPK) model for fentanyl to evaluate the feasibility to predict the maternal and fetal plasma concentration-time profiles of fentanyl after various dosing regimens. As fentanyl is typically given via the epidural route to control labor pain, an epidural dosing site was developed using alfentanil as a reference drug and extrapolated to fentanyl. Fetal hepatic clearance of fentanyl was predicted from CYP3A7-mediated norfentanyl formation in fetal liver microsomes (intrinsic clearance = 0.20 ± 0.05 µl/min/mg protein). The developed mf-PBPK model successfully captured fentanyl maternal and umbilical cord concentrations after epidural dosing and was used to simulate the concentrations after intravenous dosing (in a drug abuse situation). The distribution kinetics of fentanyl were found to have a considerable impact on the time course of maternal:umbilical cord concentration ratio and on interpretation of observed data. The data show that mf-PBPK modeling can be used successfully to predict maternal disposition, transplacental distribution, and fetal exposure to fentanyl. SIGNIFICANCE STATEMENT: This study establishes the modeling framework for predicting the time course of maternal and fetal exposures of fentanyl opioids from mf-PBPK modeling. The model was validated based on fentanyl exposure data collected during labor and delivery after intravenous or epidural dosing. The results show that mf-PBPK modeling is a useful predictive tool for assessing fetal exposures to fentanyl opioid therapeutic regimens and potentially can be extended to other drugs of abuse.

Physiologically Based Pharmacokinetics of Lysosomotropic Chloroquine in Rat and Human

A semimechanistic physiologically based pharmacokinetic (PBPK) model for chloroquine (CQ), a highly lysosomotropic weak base, was applied to digitized rat and human concentration versus time data. The PBPK model in rat featured plasma and red blood cell (RBC) concentrations, extensive and apparent nonlinear tissue distribution, fitted hepatic and renal intrinsic clearances, and a plasma half-life of about 1 day. Tissue-to-plasma CQ ratios at 50 hours after dosing were highest in lung, kidney, liver, and spleen (182-318) and lower in heart, muscle, brain, eye, and skin (11-66). The RBC-to-plasma ratio of 11.6 was assumed to reflect cell lipid partitioning. A lysosome-based extended model was used to calculate subcellular CQ concentrations based on tissue mass balances, fitted plasma, interstitial and free cytosol concentrations, and literature-based pH and pKa values. The CQ tissue component concentrations ranked as follows: lysosome > > acidic phospholipid > plasma = interstitial = cytosol ≥ neutral lipids. The extensive lysosome sequestration appeared to change over time and was attributed to lowering pH values caused by proton pump influx of hydrogen ions. The human-to-rat volume of distribution (Vss) ratio of 7 used to scale rat tissue partitioning to human along with estimation of hepatic clearance allowed excellent fitting of oral-dose PK (150-600 mg) of CQ with a 50-day half-life in humans. The prolonged PK of chloroquine was well characterized for rat and human with this PBPK model. The calculated intratissue concentrations and lysosomal effects have therapeutic relevance for CQ and other cationic drugs. SIGNIFICANCE STATEMENT: Sequestration in lysosomes is a major factor controlling the pharmacokinetics and pharmacology of chloroquine and other cationic drugs. This report provides comprehensive physiologic modeling of chloroquine distribution in tissues and overall disposition in rat and human that reveals expected complexities and inferences related to its subcellular association with various tissue components.

Dose Optimization in Kidney Disease: Opportunities for PBPK Modeling and Simulation

Kidney disease affects pharmacokinetic (PK) profiles of not only renally cleared drugs but also nonrenally cleared drugs. The impact of kidney disease on drug disposition has not been fully elucidated, but describing the extent of such impact is essential for conducting dose optimization in kidney disease. Accurate evaluation of kidney function has been a clinical interest for dose optimization, and more scientists pay attention and conduct research for clarifying the role of drug transporters, metabolic enzymes, and their interplay in drug disposition as kidney disease progresses. Physiologically based pharmacokinetic (PBPK) modeling and simulation can provide valuable insights for dose optimization in kidney disease. It is a powerful tool to integrate discrete knowledge from preclinical and clinical research and mechanistically investigate system- and drug-dependent factors that may contribute to the changes in PK profiles. PBPK-based prediction of drug exposures may be used a priori to adjust dosing regimens and thereby minimize the likelihood of drug-related toxicity. With real-time clinical studies, parameter estimation may be performed with PBPK approaches that can facilitate identification of sources of interindividual variability. PBPK modeling may also facilitate biomarker research that aids dose optimization in kidney disease. U.S. Food and Drug Administration guidances related to conduction of PK studies in kidney impairment and PBPK documentation provide the foundation for facilitating model-based dose-finding research in kidney disease.

Novel Mechanistic PBPK Model to Predict Renal Clearance in Varying Stages of CKD by Incorporating Tubular Adaptation and Dynamic Passive Reabsorption

Chronic kidney disease (CKD) has significant effects on renal clearance (CLr ) of drugs. Physiologically-based pharmacokinetic (PBPK) models have been used to predict CKD effects on transporter-mediated renal active secretion and CLr for hydrophilic nonpermeable compounds. However, no studies have shown systematic PBPK modeling of renal passive reabsorption or CLr for hydrophobic permeable drugs in CKD. The goal of this study was to expand our previously developed and verified mechanistic kidney model to develop a universal model to predict changes in CLr in CKD for permeable and nonpermeable drugs that accounts for the dramatic nonlinear effect of CKD on renal passive reabsorption of permeable drugs. The developed model incorporates physiologically-based tubular changes of reduced water reabsorption/increased tubular flow rate per remaining functional nephron in CKD. The final adaptive kidney model successfully (absolute fold error (AFE) all < 2) predicted renal passive reabsorption and CLr for 20 permeable and nonpermeable test compounds across the stages of CKD. In contrast, use of proportional glomerular filtration rate reduction approach without addressing tubular adaptation processes in CKD to predict CLr generated unacceptable CLr predictions (AFE = 2.61-7.35) for permeable compounds in severe CKD. Finally, the adaptive kidney model accurately predicted CLr of para-amino-hippuric acid and memantine, two secreted compounds, in CKD, suggesting successful integration of active secretion into the model, along with passive reabsorption. In conclusion, the developed adaptive kidney model enables mechanistic predictions of in vivo CLr through CKD progression without any empirical scaling factors and can be used for CLr predictions prior to assessment of drug disposition in renal impairment.

Mechanistic PBPK Modeling of Urine pH Effect on Renal and Systemic Disposition of Methamphetamine and Amphetamine

The effect of urine pH on renal excretion and systemic disposition has been observed for many drugs and metabolites. When urine pH is altered, tubular ionization, passive reabsorption, renal clearance, and systemic exposure of drugs and metabolites may all change dramatically, raising clinically significant concerns. Surprisingly, the urine pH effect on drug disposition is not routinely explored in humans, and regulatory agencies have neither developed guidance on this issue nor required industry to conduct pertinent human trials. In this study, we hypothesized that physiologically based pharmacokinetic (PBPK) modeling could be used as a cost-effective method to examine potential urine pH effect on drug and metabolite disposition. Our previously developed and verified mechanistic kidney model was integrated with a full-body PBPK model to simulate renal clearance and area under the plasma concentration-time curve (AUC) with varying urine pH statuses using methamphetamine and amphetamine as model compounds. We first developed and verified drug models for methamphetamine and amphetamine under normal urine pH condition [absolute average fold error (AAFE) < 1.25 at study level]. Then, acidic and alkaline urine scenarios were simulated. Our simulation results show that the renal excretion and plasma concentration-time profiles for methamphetamine and amphetamine could be recapitulated under different urine pH (AAFE < 2 at individual level). The methamphetamine-amphetamine parent-metabolite full-body PBPK model also successfully simulated amphetamine plasma concentration-time profiles (AAFE < 1.25 at study level) and amphetamine/methamphetamine urinary concentration ratios (AAFE < 2 at individual level) after dosing methamphetamine. This demonstrates that our mechanistic PBPK model can predict urine pH effect on systemic and urinary disposition of drugs and metabolites. SIGNIFICANCE STATEMENT: Our study shows that integrating mechanistic kidney model with full-body physiologically based pharmacokinetic model can predict the magnitude of alteration in renal excretion and area under the plasma concentration-time curve (AUC) of drugs and metabolites when urine pH is changed. This provides a cost-effective method to evaluate the likelihood of renal and systemic disposition changes due to varying urine pH. This is important because multiple drugs and diseases can alter urine pH, leading to quantitatively and clinically significant changes in drug and metabolite disposition that may require adjustment of therapy.