Physiologically Based Pharmacokinetic Models of 2′,3′-Dideoxyinosine

Purpose. The goal of this study was to develop physiologically based pharmacokinetic (PBPK) models for 2,3-dideoxyinosine (ddI) in rats when the drug was administered alone (ddI model) and with pentamidine (ddI + pentamidine model), and to use these models to evaluate the effect of our previously reported pentamidine-ddI interaction on tissue ddI exposure in humans. Methods. The PBPK models consisted of pharmacologically relevant tissues (blood, brain, gut, spleen, pancreas, liver, kidney, lymph nodes, muscle) and used the assumptions of perfusion-rate limited tissue distribution and linear tissue binding of ddI. The required physiologic model parameters were obtained from the literature, whereas the pharmacokinetic parameters and the tissue-to-plasma partition coefficients were calculated using plasma and tissue data. Results. The ddI model in rats yielded model-predicted concentration-time profiles that were in close agreement with the experimentally determined profiles after an intravenous ddI dose (5% deviation in plasma and 20% deviation in tissues). The ddI + pentamidine model incorporated the pentamidine-induced increases of ddI partition in pancreas and muscle. The two PBPK models were scaled-up to humans using human physiologic and pharmacokinetic parameters. A comparison of the model-predicted plasma concentration-time profiles with the observed profiles in AIDS patients who often received ddI with pentamidine showed that the ddI model underestimated the terminal half-life (t_1/2,) by 39% whereas the ddI + pentamidine model yielded identical t_1/2, and area-under-the-curve as the observed values (<1% deviation). Simulations of ddI concentration-time profiles in human tissues using the two models showed that pancreas and lymph nodes received about 2- to 30-fold higher ddI concentration than spleen and brain, and that coadministration of pentamidine increased the AUC of ddl in the pancreas by 20%. Conclusions. Data of the present study indicate that the plasma ddI concentration-time profile in patients were better described by the ddI + pentamidine model than by the ddI model, suggesting that the pentamidine-induced changes in tissue distribution of ddI observed in rats may also occur in humans.     

Does the Systemic Plasma Profile Inform the Liver Profile? Analysis Using a Physiologically Based Pharmacokinetic Model and Individual Compounds

The physiologically based pharmacokinetic (PBPK) model for liver transporter substrates has been established previously and used for predicting drug–drug interactions (DDI) and for clinical practice guidance. So far, nearly all the published PBPK models for liver transporter substrates have one or more hepatic clearance processes (i.e., active uptake, passive diffusion, metabolism, and biliary excretion) estimated by fitting observed systemic data. The estimated hepatic clearance processes are then used to predict liver concentrations and DDI involving either systemic or liver concentration. However, the accuracy and precision of such predictions are unclear. In this study, we try to address this question by using the PBPK model to generate simulated compounds for which we know both systemic and liver profiles. We then developed an approach to assess the accuracy and precision of predicted liver concentration. With hepatic clearance processes estimated using plasma data, model predictions of liver are typically accurate (i.e., true value is bounded by predicted maximum and minimum); however, only for a few compounds are predictions also precise. The results of the current study indicate that extra attention is required when using the current PBPK approach to predict liver concentration and DDI for transporter substrates dependent upon liver concentrations.     

Physiologically Based Pharmacokinetic Modelling of Drug Penetration Across the Blood–Brain Barrier—Towards a Mechanistic IVIVE-Based Approach

Predicting the penetration of drugs across the human blood–brain barrier (BBB) is a significant challenge during their development. A variety of in vitro systems representing the BBB have been described, but the optimal use of these data in terms of extrapolation to human unbound brain concentration profiles remains to be fully exploited. Physiologically based pharmacokinetic (PBPK) modelling of drug disposition in the central nervous system (CNS) currently consists of fitting preclinical in vivo data to compartmental models in order to estimate the permeability and efflux of drugs across the BBB. The increasingly popular approach of using in vitro–in vivo extrapolation (IVIVE) to generate PBPK model input parameters could provide a more mechanistic basis for the interspecies translation of preclinical models of the CNS. However, a major hurdle exists in verifying these predictions with observed data, since human brain concentrations can’t be directly measured. Therefore a combination of IVIVE-based and empirical modelling approaches based on preclinical data are currently required. In this review, we summarise the existing PBPK models of the CNS in the literature, and we evaluate the current opportunities and limitations of potential IVIVE strategies for PBPK modelling of BBB penetration.     

Physiologically Based Pharmacokinetic Modeling of Fimasartan,Amlodipine, and Hydrochlorothiazide for the Investigation of Drug–Drug Interaction Potentials

Purpose

To build a physiologically based pharmacokinetic (PBPK) model for fimasartan, amlodipine, and hydrochlorothiazide, and to investigate the drug–drug interaction (DDI) potentials.

Methods

The PBPK model of each drug was developed using Simcyp software (Version 15.0), based on the information obtained from literature sources and in vitro studies. The predictive performance of the model was assessed by comparing the predicted PK profiles and parameters with the observed data collected from healthy subjects after multiple oral doses of fimasartan, amlodipine, and hydrochlorothiazide. The DDI potentials after co-administration of three drugs were simulated using the final model.

Results

The predicted-to-observed ratios of all the pharmacokinetic parameters met the acceptance criterion. The PBPK model predicted no significant DDI when fimasartan was co-administered with amlodipine or hydrochlorothiazide, which is consistent with the observed clinical data. In the simulation of DDI at steady-state after co-administration of three drugs, the model predicted that fimasartan exposure would be increased by ~24.5%, while no changes were expected for the exposures of amlodipine and hydrochlorothiazide.

Conclusions

The developed PBPK model adequately predicted the pharmacokinetics of fimasartan, amlodipine, and hydrochlorothiazide, suggesting that the model can be used to further investigate the DDI potential of each drug.

     

Physiologically Based Modeling of the Pharmacokinetics of “Catch-and-Release” Anti-Carcinoembryonic Antigen Monoclonal Antibodies in Colorectal Cancer Xenograft Mouse Models

Engineered monoclonal antibodies (mAbs) with pH-sensitive target release, or “catch-and-release” (CAR) binding, have shown promise in decreasing the extent of target-mediated mAb elimination, increasing mAb exposure, and increasing dose potency. This study developed a mechanistic physiologically based pharmacokinetic (PBPK) model to evaluate the effects of pH-sensitive CAR target binding on the disposition of anti-carcinoembryonic antigen (CEA) mAbs in mouse models of colorectal cancer. The PBPK model was qualified by comparing model-predicted plasma concentration-time data with data observed in tumor-bearing mice following the administration of T84.66, a “standard” anti-CEA mAb that demonstrates strong binding at pH 7.4 and 5.5. Further simulations evaluated the effects CAR pH-dependent binding, with decreasing CEA affinity with decreasing pH, on anti-CEA mAb plasma pharmacokinetics. Simulated data were compared with data observed for a novel CAR mAb, 10H6. The PBPK model provided precise parameter estimates, and excellent data characterization (median prediction error 18.4%) following fitting to T84.66 data. Simulations well predicted 10H6 data (median prediction error 21.4%). Sensitivity analyses demonstrated that key determinants of the disposition of CAR mAbs include the following: antigen binding affinity, the rate constant of mAb-CEA dissociation in acidified endosomes, antigen concentration, and the tumor vasculature reflection coefficient.     

Mechanistic Physiologically Based Pharmacokinetic Modeling of the Dissolution and Food Effect of a Biopharmaceutics Classification System IV Compound—The Venetoclax Story

Venetoclax, a selective B-cell lymphoma-2 inhibitor, is a biopharmaceutics classification system class IV compound. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to mechanistically describe absorption and disposition of an amorphous solid dispersion formulation of venetoclax in humans. A mechanistic PBPK model was developed incorporating measured amorphous solubility, dissolution, metabolism, and plasma protein binding. A middle-out approach was used to define permeability. Model predictions of oral venetoclax pharmacokinetics were verified against clinical studies of fed and fasted healthy volunteers, and clinical drug interaction studies with strong CYP3A inhibitor (ketoconazole) and inducer (rifampicin). Model verification demonstrated accurate prediction of the observed food effect following a low-fat diet. Ratios of predicted versus observed C_max and area under the curve of venetoclax were within 0.8- to 1.25-fold of observed ratios for strong CYP3A inhibitor and inducer interactions, indicating that the venetoclax elimination pathway was correctly specified. The verified venetoclax PBPK model is one of the first examples mechanistically capturing absorption, food effect, and exposure of an amorphous solid dispersion formulated compound. This model allows evaluation of untested drug-drug interactions, especially those primarily occurring in the intestine, and paves the way for future modeling of biopharmaceutics classification system IV compounds.     

Physiologically Based Pharmacokinetic Modeling in Regulatory Science: An Update From the U.S. Food and Drug Administration’s Office of Clinical Pharmacology

This commentary provides an update on the status of physiologically based pharmacokinetic modeling and simulation at the U.S. Food and Drug Administration's Office of Clinical Pharmacology. Limitations and knowledge gaps in integration of physiologically based pharmacokinetic approach to inform regulatory decision making, as well as the importance of scientific engagement with drug developers who intend to use this approach, are highlighted.     

Quantifying the Interaction between Angiotensin-Converting Enzyme Inhibitors and Aspirin: Are We Using the Right Method?

Angiotensin-converting enzyme (ACE) inhibitors reduce mortality in patients with heart failure and coronary artery disease. Recently, there has been growing concern about the possible interaction between ACE inhibitors and aspirin. Numerous investigators have addressed this issue; however, results are equivocal. Most researchers used a statistical test of interaction, but the use of this method has been criticized. To assess the interaction between ACE inhibitors and aspirin properly, an additive model-more specifically, the Rothman Synergy Index-should be used. Further investigation with this model, however, is needed.     

Investigating the Impact of Drug Crystallinity in Amorphous Tacrolimus Capsules on Pharmacokinetics and Bioequivalence Using Discriminatory In Vitro Dissolution Testing and Physiologically Based Pharmacokinetic Modeling and Simulation

Delivering a drug in amorphous form in a formulated product is a strategy used to enhance the apparent solubility of a drug substance and its oral bioavailability. Drug crystallization in such products may occur during the manufacturing process or on storage, reducing the solubility advantage of the amorphous drug. However, the impact of partial drug crystallization in the drug product on the resulting bioavailability and pharmacokinetics is unknown. In this study, dissolution testing of commercial tacrolimus capsules (which are formulated to contain amorphous drug), both fresh and those containing different amounts of crystalline drug, was conducted using both United States Pharmacopeia and noncompendial dissolution tests with different dissolution media and volumes. A physiologically based pharmacokinetic (PBPK) absorption model was developed to predict the impact of crystallinity extent on the oral absorption of the products and to evaluate the discriminatory ability of the different dissolution methods. Virtual bioequivalence simulations between partially crystallized tacrolimus capsules versus fresh Prograf or generic tacrolimus capsules were performed using the PBPK model and in vitro dissolution data of the various fresh and partially crystallized capsules under United States Pharmacopeia and noncompendial dissolution conditions. The results suggest that compendial dissolution tests may not be sufficiently discriminatory with respect to the presence of crystallinity in an amorphous formulation. Nonsink dissolution tests using lower dissolution volumes generate more discriminatory profiles that predict different pharmacokinetics of tacrolimus capsules containing different extents of drug crystallinity. In conclusion, the PBPK modeling approach can be used to assess the impact of partial drug crystallinity in the formulated product and to guide the development of appropriate dissolution methods.     

A Valid Equation for the Well-Stirred Perfusion Limited Physiologically Based Pharmacokinetic Model that Consistently Accounts for the Blood–Tissue Drug Distribution in the Organ and the Corresponding Valid Equation for the Steady State Volume of Distribution

A consistent account of the assumptions of the well-stirred perfusion limited model leads to the equation for the organ tissue that does not coincide with that often presented in books and papers. The difference in pharmacokinetic profiles calculated by the valid and the commonly used equations could be quite significant, particularly due to contribution of the organs with relatively large perfusion volume, and especially for drugs with small tissue–plasma partition coefficient and high blood–plasma concentration ratio. Application of the valid equation may result in much faster initial drop of drug plasma concentration time curve and significantly longer terminal half-life, especially for low extraction ratio drugs. An equation for the steady state volume of distribution consistent with the well-stirred model described by the valid equation is provided. © 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 99:475–485, 2010     

On the Nature of Physiologically-Based Pharmacokinetic Models –<Emphasis Type="Italic">A Priori</Emphasis> or <Emphasis Type="Italic">A Posteriori</Emphasis>? Mechanistic or Empirical?

Physiologically-based pharmacokinetic (PBPK) models explicitly incorporate tissue-specific blood flows, partition coefficients, and metabolic processes. Since PBPK models are derived using physiologic parameters and interactions of the compound with tissue components, these models are considered to be “bottom up” as opposed to “top down”. Modeling approaches can be characterized as either a posteriori (observational) or a priori (based solely on theory). Furthermore, approaches can be mechanistic (structure and components based on mechanisms) or empirical (based on observations alone). Both “bottom up” and “top down” approaches can incorporate either empirical or mechanistic components. In this perspective, we discuss some of the methods and assumptions of current PBPK modeling approaches. Specifically, we discuss drug partitioning into phospholipids and neutral lipids, use of blood-plasma ratios to estimate basic drug tissue partitioning, and clearance of neutral and acidic drugs. Based on these discussions, we believe that current PBPK models are mechanistic but a posteriori and semi-empirical.