Physiologically Based Pharmacokinetic Modeling of a Homologous Series of Barbiturates in the Rat: A Sensitivity Analysis

Sensitivity analysis studies the effects of the inherent variability and uncertainty in model parameters on the model outputs and may be a useful tool at all stages of the pharmacokinetic modeling process. The present study examined the sensitivity of a whole-body physiologically based pharmacokinetic (PBPK) model for the distribution kinetics of nine 5-n-alkyl-5-ethyl barbituric acids in arterial blood and 14 tissues (lung, liver, kidney, stomach, pancreas, spleen, gut, muscle, adipose, skin, bone, heart, brain, testes) after iv bolus administration to rats. The aims were to obtain new insights into the model used, to rank the model parameters involved according to their impact on the model outputs and to study the changes in the sensitivity induced by the increase in the lipophilicity of the homologues on ascending the series. Two approaches for sensitivity analysis have been implemented. The first, based on the Matrix Perturbation Theory, uses a sensitivity index defined as the normalized sensitivity of the 2-norm of the model compartmental matrix to perturbations in its entries. The second approach uses the traditional definition of the normalized sensitivity function as the relative change in a model state (a tissue concentration) corresponding to a relative change in a model parameter. Autosensitivity has been defined as sensitivity of a state to any of its parameters; cross-sensitivity as the sensitivity of a state to any other states' parameters. Using the two approaches, the sensitivity of representative tissue concentrations (lung, liver, kidney, stomach, gut, adipose, heart, and brain) to the following model parameters: tissue-to-unbound plasma partition coefficients, tissue blood flows, unbound renal and intrinsic hepatic clearance, permeability surface area product of the brain, have been analyzed. Both the tissues and the parameters were ranked according to their sensitivity and impact. The following general conclusions were drawn: (i) the overall sensitivity of the system to all parameters involved is small due to the weak connectivity of the system structure; (ii) the time course of both the auto- and cross-sensitivity functions for all tissues depends on the dynamics of the tissues themselves, e.g., the higher the perfusion of a tissue, the higher are both its cross-sensitivity to other tissues' parameters and the cross-sensitivities of other tissues to its parameters; and (iii) with a few exceptions, there is not a marked influence of the lipophilicity of the homologues on either the pattern or the values of the sensitivity functions. The estimates of the sensitivity and the subsequent tissue and parameter rankings may be extended to other drugs, sharing the same common structure of the whole body PBPK model, and having similar model parameters. Results show also that the computationally simple Matrix Perturbation Analysis should be used only when an initial idea about the sensitivity of a system is required. If comprehensive information regarding the sensitivity is needed, the numerically expensive Direct Sensitivity Analysis should be used.     

Fuzzy Simulation of Pharmacokinetic Models: Case Study of Whole Body Physiologically Based Model of Diazepam

The aim of the present study is to develop and implement a methodology that accounts for parameter variability and uncertainty in the presence of qualitative and semi-quantitative information (fuzzy simulations) as well as when some parameters are better quantitatively defined than others (fuzzy-probabilistic approach). The fuzzy simulations method consists of (i) representing parameter uncertainty and variability by fuzzy numbers and (ii) simulating predictions by solving the pharmacokinetic model. The fuzzy-probabilistic approach includes an additional transformation between fuzzy numbers and probability density functions. To illustrate the proposed method a diazepam WBPBPK model was used where the information for hepatic intrinsic clearance determined by in vitro-in vivo scaling was semi-quantitative. The predicted concentration time profiles were compared with those resulting from a Monte Carlo simulation. Fuzzy simulations can be used as an alternative to Monte Carlo simulation.     

Reducing Whole Body Physiologically Based Pharmacokinetic Models Using Global Sensitivity Analysis: Diazepam Case Study

Abtract There are situations in drug development where one may wish to reduce the dimensionality and complexity of whole body physiologically based pharmacokinetic models. A technique for formal reduction of such models, based on global sensitivity analysis, is suggested. Using this approach mean and variance of tissue(s) and/or blood concentrations are preserved in the reduced models. Extended Fourier amplitude sensitivity test (FAST), a global sensitivity technique, takes a sampling approach, acknowledging parameter variability and uncertainty, to calculate the impact of parameters on concentration variance. We used existing literature rules for formal model reduction to identify all possible smaller dimensionally models. To discriminate among those competing mechanistic models extended FAST was used, whereby we treated model structural uncertainty as another factor contributing to the overall uncertainty. A previously developed 14 compartment whole body physiologically based model for diazepam disposition in rat was reduced to three alternative reduced models, with preserved arterial mean and variance concentration profiles.     

Sensitivity Analysis of Pharmacokinetic and Pharmacodynamic Systems: I. A Structural Approach to Sensitivity Analysis of Physiologically Based Pharmacokinetic Models

Based on a frequency response approach to the sensitivity analysis of pharmacokinetic models, the concept of structural sensitivity is introduced. The core of this concept is the factorization of the system sensitivity into two multipliers. The first one, called structural sensitivity index, has an analytical form, which depends solely on the structure and connectivity of the system and does not depend on the drug administered or the factor perturbed. The second multiplier, the parameter sensitivity index, depends on the drug properties, the tissue of interest and the parameter perturbed, but is largely independent of the structure of the system. The structural and parametric sensitivity indices can be evaluated and analyzed separately. The most important feature of the proposed approach is that the conclusions drawn from the analysis of the structural sensitivity index are valid across all mammalian species, as the latter share a common anatomical and physiological structure. The concept of structural sensitivity is illustrated on the commonly used structure of the whole body physiologically based pharmacokinetic models by showing that the factorization of the sensitivity carried out arises naturally from the mechanism of the distribution of perturbations throughout the organism. The concept of structural sensitivity has interesting practical implications. It enables the formal proof of relationships and facts that have been observed previously. Moreover, the conclusions drawn introduce in fact a ranking of the tissues or subsystems with respect to their impact on the model outputs. From this ranking, direct recommendations regarding the design of experiments for whole-body physiologically based pharmacokinetic models are derived.     

The Quantitative Prediction of CYP-mediated Drug Interaction by Physiologically Based Pharmacokinetic Modeling

PURPOSE: The objective is to confirm if the prediction of the drug-drug interaction using a physiologically based pharmacokinetic (PBPK) model is more accurate. In vivo Ki values were estimated using PBPK model to confirm whether in vitro Ki values are suitable. METHOD: The plasma concentration-time profiles for the substrate with coadministration of an inhibitor were collected from the literature and were fitted to the PBPK model to estimate the in vivo Ki values. The AUC ratios predicted by the PBPK model using in vivo Ki values were compared with those by the conventional method assuming constant inhibitor concentration. RESULTS: The in vivo Ki values of 11 inhibitors were estimated. When the in vivo Ki values became relatively lower, the in vitro Ki values were overestimated. This discrepancy between in vitro and in vivo Ki values became larger with an increase in lipophilicity. The prediction from the PBPK model involving the time profile of the inhibitor concentration was more accurate than the prediction by the conventional methods. CONCLUSION: A discrepancy between the in vivo and in vitro Ki values was observed. The prediction using in vivo Ki values and the PBPK model was more accurate than the conventional methods.     

A Physiologically Based Pharmacokinetic Model Incorporating Dispersion Principles to Describe Solute Distribution in the Perfused Rat Hindlimb Preparation

A physiologically based pharmacokinetic model incorporating dispersion principles has been developed to describe outflow data from the isolated perfused rat hindlimb preparation, for the three reference markers ¹⁴ C-sucrose, ¹⁴ C-urea, and ³ H-water and three ¹⁴ C-labeled 5-n-alkyl-5-ethyl barbiturates; the methyl, butyl, and nonyl homologues. Also ⁵¹ Cr-RBC and ¹²⁵ I-albumin were studied. The model consists of four parallel components representing each of the tissues comprising the hindlimb: skeletal muscle, skin, bone, and adipose. Attempts to simplify the model by using the principle of tissue lumping were made by examining the tissue equilibration rate constant k _T for each of respective tissues for each compound. It was found that simplification was only possible in the case of ³ H-water data. The model took into account a possible shunting component in the skin tissue and incomplete mass but not volumetric recovery from the system. The dispersion model characterizes the relative spreading of solute on transit through a tissue bed by a dimension-less parameter D _N. The estimated dispersion numbers (D_N) obtained were in the region of 2.7–4.72, 8.39–15.54, 0.61–2.74, and 6.02–14.0 for skeletal muscle, skin, bone, and adipose, respectively, and were independent of the compound studied. These values are much larger than the range reported in the literature for hepatic outflow data, D _N = 0.2–0.5, and suggest a greater heterogeneity of vascular flow in the different component tissues of the rat hindlimb.     

Successful Prediction of Human Pharmacokinetics by Improving Calculation Processes of Physiologically Based Pharmacokinetic Approach

The physiologically based pharmacokinetics (PBPK) model is a major mechanistic approach for predicting human pharmacokinetics (PK) using drug-specific and physiological parameters but has been difficult to use for human PK prediction with acceptable accuracy. Here, we report a newly developed PBPK approach that incorporates the mechanism of albumin-mediated membrane penetration in the liver and interspecies correlation for unbound tissue fractions. To verify the utility of our PBPK approach, we used 12 drugs that are mainly eliminated by hepatic metabolism to compare the prediction accuracy with a conventional PBPK approach and to observe human PK parameters. We found the predictive accuracy for total clearance (CL_tot), distribution volume at the steady state (V_ss), elimination half-life (t_1/2), and plasma concentration at the last measurable time point (C_last) of our PBPK approach to show better absolute average fold error and percentage within 2-fold error (1.6-1.8 and 67%-92%, respectively) compared with values obtained from the conventional PBPK approach (2.1-2.4 and 42%-67%, respectively). As our approach can use parameters obtained in early drug screening, it could help accelerate successful nomination of drug candidates by optimizing the pharmacokinetics of new chemical entities by directly using predicted human PK profiles.     

Diazepam Pharamacokinetics from Preclinical to Phase I Using a Bayesian Population Physiologically Based Pharmacokinetic Model with Informative Prior Distributions in Winbugs

Modelling is an important applied tool in drug discovery and development for the prediction and interpretation of drug pharmacokinetics. Preclinical information is used to decide whether a compound will be taken forwards and its pharmacokinetics investigated in human. After proceeding to human little to no use is made of these often very rich data. We suggest a method where the preclinical data are integrated into a whole body physiologically based pharmacokinetic (WBPBPK) model and this model is then used for estimating population PK parameters in human. This approach offers a continuous flow of information from preclinical to clinical studies without the need for different models or model reduction. Additionally, predictions are based upon single parameter values, but making realistic predictions involves incorporating the various sources of variability and uncertainty. Currently, WBPBPK modelling is undertaken as a two-stage process: (i) estimation (optimisation) of drug-dependent parameters by either least squares regression or maximum likelihood and (ii) accounting for the existing parameter variability and uncertainty by stochastic simulation. To address these issues a general Bayesian approach using WinBUGS for estimation of drug-dependent parameters in WBPBPK models is described. Initially applied to data in rat, this approach is further adopted for extrapolation to human, which allows retention of some parameters and updating others with the available human data. While the issues surrounding the incorporation of uncertainty and variability within prediction have been explored within WBPBPK modeling methodology they have equal application to other areas of pharmacokinetics, as well as to pharmacodynamics.     

A Whole-Body Physiologically Based Pharmacokinetic Model Incorporating Dispersion Concepts: Short and Long Time Characteristics

In whole-body physiologically based pharmacokinetic (PBPK) models, each tissue or organ is frequently portrayed as a single well-mixed compartment with distribution, perfusion rate limited. However, single-pass profiles from isolated organ studies are more adequately described by models which display an intermediate degree of mixing. One such model is the dispersion model, which successfully describes the outflow profiles from the liver and the perfused hindlimb of many compounds, under a variety of conditions. A salient parameter of this model is the dispersion number, a dimensionless term, which characterizes the relative axial spreading of compound on transit through the organ. We have developed a whole-body PBPK model wherein the distribution of drug on transit through each organ is described by the dispersion model with closed boundary conditions incorporated. The model equations were numerically solved using finite differencing methods, in particular, the method of lines. An integrating routine suitable for solving stiff sets of equations was used. Physiological parameters, blood flows, and tissue volumes, were taken from the literature, as were the tissue dispersion numbers, which characterize the mixing properties of each tissue; where none could be found, the value was set as that for liver. On solution, tissue, venous and arterial blood concentration–time profiles are generated. The profiles exhibited both short and long time characteristics. Oscillations were observed in the venous and arterial profiles over the first 10 min of simulation for the rat. On scale-up to human, the effects were seen over a 30 min period. Longer time effects of tissue distribution involve buildup of drug in the large tissues of distribution: skeletal muscle, skin, and adipose. The extent of distribution in the large tissues was somewhat dependent on the magnitude of the dispersion number, the lower the dispersion number, the greater the extent of distribution after an intravenous bolus dose. The model has a distinct advantage over the well-stirred organ whole-body PBPK model in its ability to describe both short and long time characteristics.     

Lumping of Whole-Body Physiologically Based Pharmacokinetic Models

Lumping is a common pragmatic approach aimed at the reduction of whole-body physiologically based pharmacokinetic (PBPK) model dimensionality and complexity. Incorrect lumping is equivalent to model misspecification with all the negative consequences to the subsequent model implementation. Proper lumping should guarantee that no useful information about the kinetics of the underlying processes is lost. To enforce this guarantee, formal standard lumping procedures and techniques need to be defined and implemented. This study examines the lumping process from a system theory point of view, which provides a formal basis for the derivation of principles and standard procedures of lumping. The lumping principle in PBPK modeling is defined as follows: Only tissues with identical model specification, and occupying identical positions in the system structure should be lumped together at each lumping iteration. In order to lump together parallel tissues, they should have similar or close time constants. In order to lump together serial tissues, they should equilibrate very rapidly with one another. The lumping procedure should include the following stages: (i) tissue specification conversion (when tissues with different model specifications are to be lumped together); (ii) classification of the tissues into classes with significantly different kinetics, according to the basic principle of lumping above; (iii) calculation of the parameters of the lumped compartments; (iv) simulation of the lumped system; (v) lumping of the experimental data; and (vi) verification of the lumped model. The use of the lumping principles and procedures to be adopted is illustrated with an example of a commonly implemented whole-body physiologically based pharmacokinetic model structure to characterize the pharmacokinetics of a homologous series of barbiturates in the rat.     

Food Effect Projections via Physiologically Based Pharmacokinetic Modeling: Predictive Case Studies

Food can alter the absorption of orally administered drugs. Biopharmaceutics physiologically based pharmacokinetic (PBPK) modeling offers the possibility to simulate a compound's pharmacokinetics under fasted or fed states. To advance the utility of PBPK modeling, with a view to regulatory impact, we have pooled our experience across 4 pharmaceutical companies to propose a general multistep PBPK workflow leveraging pre-existing clinical data for immediate-release formulations of Biopharmaceutics Classification System I and II compounds. With this strategy, we wish to promote pragmatic PBPK approaches for compounds where absorption is well understood, that is, compounds with moderate-to-high permeability that are not substrates for uptake transporters. Five case studies demonstrate how food effect can be well predicted using appropriately established and validated models. The case studies integrate solubility and dissolution data for initial model development and apply a “middle-out” validation with clinical data in one prandial state. Then, whenever possible, a validation against both fasted and fed state data is recommended before application of the models prospectively for to-be-marketed formulations. Thus, when combined with limited clinical data, PBPK models could be used to simulate outcomes for new doses, formulations, or active pharmaceutical ingredient forms, in lieu of a clinical food-effect study.     

Population-Based Analysis of Methadone Distribution and Metabolism Using an Age-Dependent Physiologically Based Pharmacokinetic Model

Limited pharmacokinetic (PK) and pharmacodynamic (PD) data are available to use in methadone dosing recommendations in pediatric patients for either opioid abstinence or analgesia. Considering the extreme inter-individual variability of absorption and metabolism of methadone, population-based PK would be useful to provide insight into the relationship between dose, blood concentrations, and clinical effects of methadone. To address this need, an age-dependent physiologically based pharmacokinetic (PBPK) model has been constructed to systematically study methadone metabolism and PK. The model will facilitate the design of cost-effective studies that will evaluate methadone PK and PD relationships, and may be useful to guide methadone dosing in children. The PBPK model, which includes whole-body multi-organ distribution, plasma protein binding, metabolism, and clearance, is parameterized based on a database of pediatric PK parameters and data collected from clinical experiments. The model is further tailored and verified based on PK data from individual adults, then scaled appropriately to apply to children aged 0–24 months. Based on measured variability in CYP3A enzyme expression levels and plasma orosomucoid (ORM2) concentrations, a Monte–Carlo-based simulation of methadone kinetics in a pediatric population was performed. The simulation predicts extreme variability in plasma concentrations and clearance kinetics for methadone in the pediatric population, based on standard dosing protocols. In addition, it is shown that when doses are designed for individuals based on prior protein expression information, inter-individual variability in methadone kinetics may be greatly reduced.     

Reduction and Lumping of Physiologically Based Pharmacokinetic Models: Prediction of the Disposition of Fentanyl and Pethidine in Humans by Successively Simplified Models

Physiologically based pharmacokinetic (PBPK) models can be used to predict drug disposition in humans from animal data and the influence of disease or other changes in physiology on the pharmacokinetics of a drug. The potential usefulness of a PBPK model must however be balanced against the considerable effort needed for its development. Proposed methods to simplify PBPK modeling include predicting the necessary tissue:blood partition coefficients (k_p) from physicochemical data on the drug instead of determining them in vivo, formal lumping of model compartments, and replacing the various k_p values of the organs and tissues by only two values, for fat and lean tissues, respectively. The aim of this study was to investigate the effects of simplifying complex PBPK models on their ability to predict drug disposition in humans. Arterial plasma concentration curves of fentanyl and pethidine were simulated by means of a number of successively reduced models. Median absolute prediction errors were used to evaluate the performance of each model, in relation to arterial plasma concentration data from clinical studies, and the Wilcoxon matched pairs test was used for comparison of predictions. An originally diffusion-limited model for fentanyl was simplified to perfusion-limitation, and this model was either lumped, reducing 11 organ/tissue compartments to six, or changed to a model based on only two k_p values, those of fat (used for fat and lungs) and muscle (used for all other tissues). None of these simplifications appreciably changed the predictions of arterial drug concentrations in the 10 patients. Perfusion-limited models for pethidine were set up using either experimentally determined [Gabrielsson et al. 1986] or theoretically calculated [Davis and Mapleson 1993] k_p values, and predictions using the former were found to be significantly better. Lumping of the models did not appreciably change the predictions; however, going from a full set of k_p values to only two (fat and lean) had an adverse effect. Using a k_p for lungs determined either in rats or indirectly in humans [Persson et al. 1988], i.e., a total of three k_p values, improved these predictions. In con- clusion, this study strongly suggested that complex PBPK models for lipophilic basic drugs may be considerably reduced with marginal loss of power to predict standard plasma pharmacokinetics in humans. Determination of only two or three k_p values instead of a full set can mean an important reduction of experimental work to define a basic model. Organs of particular pharmacological or toxicological interest should of course be investigated separately as needed. This study also suggests and applies a simple method for statistical evaluation of the predictions of PBPK models.     

Modeling and Simulation of Hepatic Drug Disposition Using a Physiologically Based,Multi-agent In Silico Liver

Purpose Validate a physiologically based, mechanistic, in silico liver (ISL) for studying the hepatic disposition and metabolism of antipyrine, atenolol, labetalol, diltiazem, and sucrose administered alone or in combination. Materials and Methods Autonomous software objects representing hepatic components such as metabolic enzymes, cells, and microarchitectural details were plugged together to form a functioning liver analogue. Microarchitecture features were represented separately from drug metabolizing functions. Each ISL component interacts uniquely with mobile objects. Outflow profiles were recorded and compared to wet-lab data. A single ISL structure was selected, parameterized, and held constant for all compounds. Parameters sensitive to drug-specific physicochemical properties were tuned so that ISL outflow profiles matched in situ outflow profiles. Results ISL simulations were validated separately and together against in situ data and prior physiologically based pharmacokinetic (PBPK) predictions. The consequences of ISL parameter changes on outflow profiles were explored. Selected changes altered outflow profiles in ways consistent with knowledge of hepatic anatomy and physiology and drug physicochemical properties. Conclusions A synthetic, agent-oriented in silico liver has been developed and successfully validated, enabling us to posit that static and dynamic ISL mechanistic details, although abstract, map realistically to hepatic mechanistic details in PBPK simulations. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Structural Identifiability of Physiologically Based Pharmacokinetic Models

When starting a project in drug kinetics it is necessary to test a priori whether there is sufficient information in the experimental input-output design to estimate unique values of internal rate constants. This is an important test if the pharmacokinetics of a drug are to be characterised in some way by the parameter values estimated from the observed plasma or blood concentration profile. Various modifications of the well-perfused Physiologically Based Pharmacokinetic model (PBPK) are considered here. More complex PBPK models can be considered to consist of subsystems, representing groups of tissues, which are connected in parallel to the central compartment. A novel method of structural identifiability analysis is presented here that considers these subsystems individually. This makes analysis of subsequently modified models much simpler. It is found in a number of cases that these more complex systems remain globally identifiable and at worst reduce to locally identifiable for the additional parameters. A caveat is added about having more than one eliminating peripheral tissue.     

A Physiologically Based Pharmacokinetic Analysis of Capecitabine,a Triple Prodrug of 5-FU,in Humans: The Mechanism for Tumor-Selective Accumulation of 5-FU

Purpose. To identify the factors governing the dose-limiting toxicity in the gastrointestine (GI) and the antitumor activity after oral administration of capecitabine, a triple prodrug of 5-FU, in humans. Method. The enzyme kinetic parameters for each of the four enzymes involved in the activation of capecitabine to 5-FU and its elimination were measured experimentally in vitro to construct a physiologically based pharmacokinetic model. Sensitivity analysis for each parameter was performed to identify the parameters affecting tissue 5-FU concentrations. Results. The sensitivity analysis demonstrated that (i) the dihydropyrimidine dehydrogenase (DPD) activity in the liver largely determines the 5-FU AUC in the systemic circulation, (ii) the exposure of tumor tissue to 5-FU depends mainly on the activity of both thymidine phosphorylase (dThdPase) and DPD in the tumor tissues, as well as the blood flow rate in tumor tissues with saturation of DPD activity resulting in 5-FU accumulation, and (iii) the metabolic enzyme activity in the GI and the DPD activity in liver are the major determinants influencing exposure to 5-FU in the GI. The therapeutic index of capecitabine was found to be at least 17 times greater than that of other 5-FU-related anticancer agents, including doxifluridine, the prodrug of 5-FU, and 5-FU over their respective clinical dose ranges. Conclusions. It was revealed that the most important factors that determine the selective production of 5-FU in tumor tissue after capecitabine administration are tumor-specific activation by dThdPase, the nonlinear elimination of 5-FU by DPD in tumor tissue, and the blood flow rate in tumors.     

Prediction of Ticagrelor and its Active Metabolite in Liver Cirrhosis Populations Using a Physiologically Based Pharmacokinetic Model Involving Pharmacodynamics

Ticagrelor, a P2Y₁₂ receptor antagonist, has been highly recommended for use in acute coronary syndrome. The major active metabolite (AM) is similar to the parent drug, which exhibits antiplatelet activity. The inhibition of platelet aggregation (IPA) is used as an assay to demonstrate the anticoagulant efficacy of ticagrelor. In this study, we developed a physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of ticagrelor and its AM and combined this model with a pharmacodynamics model to reflect potential pharmacodynamic alterations in liver cirrhosis populations. The simulated results obtained using the PBPK model were validated by fold error values, which were all smaller than 2. Comparisons of exposure in different classifications of liver cirrhosis indicated that exposure to ticagrelor increased significantly with an increase in the degree of cirrhosis severity, whereas exposure to AM was decreased. The total concentration of ticagrelor and AM was related to the IPA included in the Sigmoid E_max model. The PBPK model of ticagrelor and AM could predict the pharmacokinetics of all populations, and a combination of PD models was used to extrapolate for predicting unknown scenarios. Liver cirrhosis may result in prolonged IPA, depending on the severity degree of this disease. The combined PBPK model including IPA can reveal changes in pharmacokinetics and pharmacodynamics in populations affected by liver cirrhosis and indicate the risk potential.     

A Physiologically Based Pharmacokinetic Modeling Approach to Predict Drug-Drug Interactions of Buprenorphine After Subcutaneous Administration of CAM2038 With Perpetrators of CYP3A4

CAM2038, FluidCrystal injection depot, is an extended release formulation of buprenorphine given subcutaneously every 1 week (Q1W) or every 4 weeks (Q4W). The purpose of this research was to predict the magnitude of drug-drug interaction (DDI) after coadministration of a strong CYP3A4 inducer or inhibitor using physiologically based pharmacokinetic (PBPK) modeling. A PBPK model was developed for CAM2038 based on the previously published buprenorphine PBPK model after intravenous and sublingual administration and the PK profiles after subcutaneous administration of CAM2038 from 2 phase I clinical trials. The strong CYP3A4 inhibitor ketoconazole was predicted to increase the buprenorphine exposure by 35% for the Q1W formulation and 34% for Q4W formulation, respectively. Also, the strong CYP3A4 inducer rifampin was predicted to decrease the buprenorphine exposure by 26% for both the Q1W and Q4W formulations. The results provided insight into the potential DDI effect for CAM2038 and suggested a lack of clinically meaningful DDI when CAM2038 is coadministered with CYP3A4 inhibitor or inducer. Therefore, no dose adjustment is required when CAM2038 is coadministered with CYP3A4 perpetrators.     

Physiologically Based Synthetic Models of Hepatic Disposition

Current physiologically based pharmacokinetic (PBPK) models are inductive. We present an additional, different approach that is based on the synthetic rather than the inductive approach to modeling and simulation. It relies on object-oriented programming. A model of the referent system in its experimental context is synthesized by assembling objects that represent components such as molecules, cells, aspects of tissue architecture, catheters, etc. The single pass perfused rat liver has been well described in evaluating hepatic drug pharmacokinetics (PK) and is the system on which we focus. In silico experiments begin with administration of objects representing actual compounds. Data are collected in a manner analogous to that in the referent PK experiments. The synthetic modeling method allows for recognition and representation of discrete event and discrete time processes, as well as heterogeneity in organization, function, and spatial effects. An application is developed for sucrose and antipyrine, administered separately and together. PBPK modeling has made extensive progress in characterizing abstracted PK properties but this has also been its limitation. Now, other important questions and possible extensions emerge. How are these PK properties and the observed behaviors generated? The inherent heuristic limitations of traditional models have hindered getting meaningful, detailed answers to such questions. Synthetic models of the type described here are specifically intended to help answer such questions. Analogous to wet-lab experimental models, they retain their applicability even when broken apart into sub-components. Having and applying this new class of models along with traditional PK modeling methods is expected to increase the productivity of pharmaceutical research at all levels that make use of modeling and simulation.     

曲妥珠单抗-药物共轭物(T-DM1)临床前药物代谢动力学研究及人体药物代谢动力学预测

本试验通过临床前药物代谢动力学(PK)和毒理学研究结果,对曲妥珠单抗-药物共轭物(T-DM1)的人体PK特性进行了预测,并探讨了目前广泛采用的预测方法的不足和可能的解决途径。本试验首先进行了动物试验,包括大鼠急性毒性试验和食蟹猴PK试验,通过试验获得了T-DM1的总抗体、偶联抗体和游离小分子药物emtansine(DM1)的PK参数,随后基于这些参数,使用异速增长模型和种属-时间不变法,对总抗体和偶联抗体的人体PK特性进行了预测。此外,通过参考近年的一些相关研究,探讨了如何基于动物生理药动学(PBPK)模型,更科学地预测抗体偶联药物中小分子药物的人体PK和分布特性。