脑组织生理药代动力学模型研究进展

在新药研发过程中,了解药物在脑内的转运与分布情况可以预测药物效应和不良反应。然而,人脑内的药物浓度难以通过现有分析技术直接测定。生理药代动力学（PBPK）模型通过数学建模的方式模拟药物在脑内的转运与分布情况,为预测脑内药物浓度提供帮助。综述影响药物在脑内转运分布的生理因素,以及近年来文献中报道的研究药物脑组织分布的PBPK模型及其在药物研发中的应用。     

A Physiologically-Based Pharmacokinetic Model of Drug Detoxification by Nanoparticles

Nanoparticles (NPs) may be capable of reversing the toxic effects of drug overdoses in humans by adsorbing/absorbing drug molecules. This paper develops a model to include the kinetic effects of treating drug overdoses by NPs. Depending on the size and the nature of the NPs, they may either pass through the capillary walls and enter the tissue space or remain only inside the capillaries and other blood vessels; models are developed for each case. Furthermore, the time scale for equilibration between the NP and the blood will vary with the specific type of NP. The NPs may sequester drug from within the capillaries depending on weather this time scale is larger or smaller than the residence time of blood within the capillary. Models are developed for each scenario. The results suggest that NPs are more effective at detoxification if they are confined to the blood vessels and do not enter the tissues. The results also show that the detoxification process is faster if drug uptake occurs within the capillaries. The trends shown by the model predictions can serve as useful guides in the design of the optimal NP for detoxification.     

Development of a Physiologically-Based Pharmacokinetic Model for Whole-Body Disposition of MMAE Containing Antibody-Drug Conjugate in Mice

Pharmaceutical Research - To quantitate and mathematically characterize the whole-body pharmacokinetics (PK) of different ADC analytes following administration of an MMAE-conjugated ADC in...     

Population Pharmacokinetic Modeling: The Importance of Informative Graphics

Purpose. The usefulness of several modelling methods were examined in the development of a population pharmacokinetics model for cefepime. Methods. The analysis was done in six steps: (1) exploratory data analysis to examine distributions and correlations among covariates, (2) determination of a basic pharmacokinetic model using the NON-MEM program and obtaining Bayesian individual parameter estimates, (3) examination of the distribution of parameter estimates, (4) multiple linear regression (MLR) with case deletion diagnostics, generalized additive modelling (GAM), and tree-based modelling (TBM) for the selection of covariates and revealing structure in the data, (5) final NONMEM modelling to determine the population PK model, and (6) the evaluation of final parameter estimates. Results. An examination of the distribution of individual clearance (CL) estimates suggested bimodality. Thus, the mixture model feature in NONMEM was used for the separation of subpopulations. MLR and GAM selected creatinine clearance (CRCL) and age, while TBM selected both of these covariates and weight as predictors of CL. The final NONMEM model for CL included only a linear relationship with CRCL. However, two subpopulations were identified that differed in slope and intercept. Conclusions. The findings suggest that using informative graphical and statistical techniques enhance the understanding of the data structure and lead to an efficient analysis of the data.     

General Moments in Linear Pharmacokinetic Models

Abstract

The communication presents the derivation and application of formulas for estimating the parameters MRT and VRT of the linear or linearised complex time invariant pharmacokinetic system, with or without shunt and time delays, defined on the animal body after any route of drug administration. the derivation is based on the model of the system transfer function and/or the model of the system weighting function. Thus the estimation of the system parameters MRT and VRT according to the formulas presented is not restricted only to using experimental data corresponding to the instantaneous system input. the approach provides feasibility of MRT and VRT estimation and may increase the accuracy of the estimates.     

Performance and Robustness of the Monte Carlo Importance Sampling Algorithm Using Parallelized S-ADAPT for Basic and Complex Mechanistic Models

The Monte Carlo Parametric Expectation Maximization (MC-PEM) algorithm can approximate the true log-likelihood as precisely as needed and is efficiently parallelizable. Our objectives were to evaluate an importance sampling version of the MC-PEM algorithm for mechanistic models and to qualify the default estimation settings in SADAPT-TRAN. We assessed bias, imprecision and robustness of this algorithm in S-ADAPT for mechanistic models with up to 45 simultaneously estimated structural parameters, 14 differential equations, and 10 dependent variables (one drug concentration and nine pharmacodynamic effects). Simpler models comprising 15 parameters were estimated using three of the ten dependent variables. We set initial estimates to 0.1 or 10 times the true value and evaluated 30 bootstrap replicates with frequent or sparse sampling. Datasets comprised three dose levels with 16 subjects each. For simultaneous estimation of the full model, the ratio of estimated to true values for structural model parameters (median [5-95% percentile] over 45 parameters) was 1.01 [0.94-1.13] for means and 0.99 [0.68-1.39] for between-subject variances for frequent sampling and 1.02 [0.81-1.47] for means and 1.02 [0.47-2.56] for variances for sparse sampling. Imprecision was ≤25% for 43 of 45 means for frequent sampling. Bias and imprecision was well comparable for the full and simpler models. Parallelized estimation was 23-fold (6.9-fold) faster using 48 threads (eight threads) relative to one thread. The MC-PEM algorithm was robust and provided unbiased and adequately precise means and variances during simultaneous estimation of complex, mechanistic models in a 45 dimensional parameter space with rich or sparse data using poor initial estimates.     

药代动力学/药效动力学模型在降糖药物评价中的应用

本文通过检索国外相关文献,对采用PK/PD方法对降糖药物及其制剂进行评价的研究进行归纳整理。以介绍药代动力学/药效动力学（pharmacokinetic/pharmacodynamic,PK/PD）模型在降糖药物及其制剂评价中的应用。检索结果表明血糖和胰岛素的相关性是PK/PD模型中的重点部分,PK/PD模型能够评价不同制剂或给药方式下的降糖效果。PK/PD模型是降糖药物及其制剂研究中一种较好的评价方法。     

A Multiscale Physiologically-Based Pharmacokinetic Model for Doxorubicin to Explore its Mechanisms of Cytotoxicity and Cardiotoxicity in Human Physiological Contexts

Purpose

The mechanisms underlying doxorubicin cytotoxicity and cardiotoxicity were broadly explored but remain incompletely understood. A multiscale physiologically-based pharmacokinetic (PBPK) model was developed to assess doxorubicin dispositions at levels of system, tissue interstitial, cell, and cellular organelles. This model was adopted to explore the mechanisms-of-action/toxicity of doxorubicin in humans.

Methods

The PBPK model was developed by analyzing data from mice and the model was verified by scaling up to predict doxorubicin multiscale dispositions in rats and humans. The multiscale dispositions of doxorubicin in human heart and tumors were explicitly simulated to elucidate the potential mechanisms of its cytotoxicity and cardiotoxicity.

Results

The developed PBPK model was able to adequately describe doxorubicin dispositions in mice, rats and humans. In humans, prolonged infusion, a dosing regimen with less cardiotoxicity, was predicted with substantially reduced free doxorubicin concentrations at human heart interstitium, which were lower than the concentrations associated with oxidative stress. However, prolonged infusion did not reduce doxorubicin-DNA adduct at tumor nucleus, consistent with clinical observations that prolonged infusion did not compromise anti-tumor effect, indicating that one primary anti-tumor mechanism was DNA torsion.

Conclusions

A multiscale PBPK model for doxorubicin was developed and further applied to explore its cytotoxic and cardiotoxic mechanisms.

     

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.     

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.     

Physiologically-Based Pharmacokinetic Modeling to Predict Methylphenidate Exposure Affected by Interplay Among Carboxylesterase 1 Pharmacogenetics,Drug-Drug Interactions,and Sex

Background and objectiveThe pharmacokinetics (PK) of methylphenidate (MPH) differ significantly among individuals. Carboxylesterase 1 (CES1) is the primary enzyme metabolizing MPH, and its function is affected by genetic variants, drug-drug interaction (DDI), and sex. The object of this study is to evaluate CES1 pharmacogenetics as related to MPH metabolism using human liver samples and develop a physiologically-based pharmacokinetic (PBPK) modeling approach to investigate the influence of CES1 genotypes and other factors on MPH PK.MethodsThe effect of the CES1 variant G143E (rs71647871) on MPH metabolism was studied utilizing 102 individual human liver S9 (HLS9) fraction samples. PBPK models were developed using the population-based PBPK software PK-Sim® by incorporating the HLS9 incubation data. The established models were applied to simulate MPH PK profiles under various clinical scenarios, including different genotypes, drug-alcohol interactions, and the difference between males and females.ResultsThe HLS9 incubation study showed that subjects heterozygous for the CES1 variant G143E metabolized MPH at a rate of approximately 50% of that in non-carriers. The developed PBPK models successfully predicted the exposure alteration of MPH from the G143E genetic variant, ethanol-MPH DDI, and sex. Importantly, the study suggests that male G143E carriers who are alcohol consumers are at a higher risk of MPH overexposure.ConclusionPBPK modeling provides a means for better understanding the mechanisms underlying interindividual variability in MPH PK and PD and could be utilized to develop a safer and more effective MPH pharmacotherapy regimen.     

药动学模型的辨识问题研究

目的:研究药动学房室模型的辨识问题。方法:采用Laplace变换方法对一次性给药的经典房室模型的辨识问题进行系统讨论。结果:一次性给药的经典房室模型一般不具有唯一性,存在房室模型的辨识问题。结论:在两种经典房室模型不可辨识的情况下,血药浓度与分布器官中药物浓度成正比,此时通过测定血药浓度来预测分布器官或靶器官中药物浓度是有理论根据和有意义的;反之,在两种经典房室模型可辨识的情况下,血药浓度与分布器官或靶器官中药物浓度不成比例关系,此时欲通过血药浓度监测来预测分布器官或靶器官中药物浓度,就需首先确定血药浓度与分布器官或靶器官中药物浓度之间的关系,否则是没有理论根据和无意义的。     

Bayesian Population Pharmacokinetic and Pharmacodynamic Analyses Using Mixture Models

Population studies of the pharmacokinetics or pharmacodynamics or drugs help us learn about the variability in drug disposition and effects, information that can be used to treat future patients at safe and effective doses. We present a new approach to population modeling based on a weighted mixture of normal distributions having random weights and means. This method allows estimation of underlying continuous population distributions without prespecifying the parametric form or shape of these probability distributions. Additionally, this method can carry out nonparametric regression of pharmacokinetic or dynamic parameters on patient covariates while estimating the underlying distributions. Two examples illustrate the method and its flexibility.     

Implementation of EPA Revised Cancer Assessment Guidelines: Incorporation of Mechanistic and Pharmacokinetic Data

A workshop entitled "Implementation of EPA Revised Cancer AssessmentGuidelines: Incorporation of Mechanistic and PharmacokineticData" was held in Anaheim, California, in 1996 at the 35th AnnualMeeting of the Society of Toxicology (SOT). This workshop wasjointly sponsored by the Carcinogenesis, Risk Assessment, andVeterinary Specialty Sections of the SOT. The thrust of theworkshop was to discuss the scientific basis for the revisionsto the EPA Guidelines for cancer assessment and EPA's plansfor their implementation. This is the first revision to theoriginal EPA guidelines which have been in use by EPA since1986. The principal revisions are intended to provide a frameworkfor an increased ability to incorporate biological data intothe risk assessment process. Two cases were presented, for chloroformand trichloroethylene, that demonstrated the use of the revisedguidelines for specific cancer risk assessments. Using thesenew guidelines, nonlinear margin of exposure analyses were proposedfor these chemicals instead of the linearized multistage modelpreviously used by the EPA as the default method. The workshopparticipants generally applauded the planned revisions to theEPA guidelines. For the most part, they considered that therevised guidelines represented a positive step which shouldallow for and encourage the use of biological information inthe conduct of cancer risk assessments. Several participantscautioned however that the major problem with cancer risk assessmentswould continue to be the inadequacy of available data on whichto conduct more scientific risk assessments.     

Approaches for Applications of Physiologically Based Pharmacokinetic Models in Risk Assessment

Physiologically based pharmacokinetic (PBPK) models are particularly useful for simulating exposures to environmental toxicants for which, unlike pharmaceuticals, there is often little or no human data available to estimate the internal dose of a putative toxic moiety in a target tissue or an appropriate surrogate. This article reviews the current state of knowledge and approaches for application of PBPK models in the process of deriving reference dose, reference concentration, and cancer risk estimates. Examples drawn from previous U.S. Environmental Protection Agency (EPA) risk assessments and human health risk assessments in peer-reviewed literature illustrate the ways and means of using PBPK models to quantify the pharmacokinetic component of the interspecies and intraspecies uncertainty factors as well as to conduct route to route, high dose to low dose and duration extrapolations. The choice of the appropriate dose metric is key to the use of the PBPK models for the various applications in risk assessment. Issues related to whether uncertainty factors are most appropriately applied before or after derivation of human equivalent dose (or concentration) continue to be explored. Scientific progress in the understanding of life stage and genetic differences in dosimetry and their impacts on variability in susceptibility, as well as ongoing development of analytical methods to characterize uncertainty in PBPK models, will make their use in risk assessment increasingly likely. As such, it is anticipated that when PBPK models are used to express adverse tissue responses in terms of the internal target tissue dose of the toxic moiety rather than the external concentration, the scientific basis of, and confidence in, risk assessments will be enhanced.     

Bootstrapping for Pharmacokinetic Models: Visualization of Predictive and Parameter Uncertainty

Purpose. We explore use of "bootstrapping methods to obtain a measure of reliability of predictions made in part from fits of individual drug level data with a pharmacokinetic (PK) model, and to help clarify parameter identifiability for such models. Methods. Simulation studies use four sets (A-D) of drug concentration data obtained following a single oral dose. Each set is fit with a two compartment PK model, and the "bootstrap is employed to examine the potential predictive variation in estimates of parameter sets. This yields an empirical distribution of plausible steady state (SS) drug concentration predictions that can be used to form a confidence interval for a prediction. Results. A distinct, narrow confidence region in parameter space is identified for subjects A and B. The bootstrapped sets have a relatively large coefficient of variation (CV) (35-90% for A), yet the corresponding SS drug levels are tightly clustered (CVs only 2-9%). The results for C and D are dramatically different. The CVs for both the parameters and predicted drug levels are larger by a factor of 5 and more. The results reveal that the original data for C and D, but not A and B, can be represented by at least two different PK model manifestations, yet only one provides reliable predictions. Conclusions. The insights gained can facilitate making decisions about parameter identifiability. In particular, the results for C and D have important implications for the degree of implicit overparameterization that may exist in the PK model. In cases where the data support only a single model manifestation, the "bootstrap method provides information needed to form a confidence interval for a prediction.     

Simplification of Complex Physiologically Based Pharmacokinetic Models of Monoclonal Antibodies

Monoclonal antibodies (mAbs) exhibit biexponential profiles in plasma that are commonly described with a standard two-compartment model with elimination from the central compartment. These models adequately describe mAb plasma PK. However, these models ignore elimination from the peripheral compartment. This may lead to underestimation of the volume of distribution of the peripheral compartment and thus over-predicts concentration in the peripheral compartment. We developed a simple and physiologically relevant model that incorporates information on binding and dissociation rates between mAb and FcRn receptor, mAb uptake, reflection, and catabolic degradation. We employed a previously published PBPK model and, with assumptions regarding rates of processes controlling mAb disposition, reduced the complex PBPK model to a simpler circular model with central, peripheral, and lymph compartments specifying elimination from both central and peripheral. We successfully applied the model to describe the PK of an investigational mAb. Our model presents an improvement over standard two-compartmental models in predicting whole-body average tissue concentrations while adequately describing plasma PK with minimal complexity and physiologically more meaningful parameters.     

Similarities and Differences in Gastrointestinal Physiology Between Neonates and Adults: a Physiologically Based Pharmacokinetic Modeling Perspective

Physiologically based pharmacokinetic (PBPK) modeling holds great promise for anticipating the quantitative changes of pharmacokinetics in pediatric populations relative to adults, which has served as a useful tool in regulatory reviews. Although the availability of specialized software for PBPK modeling has facilitated the widespread applications of this approach in regulatory submissions, challenges in the implementation and interpretation of pediatric PBPK models remain great, for which controversies and knowledge gaps remain regarding neonatal development of the gastrointestinal tract. The commentary highlights the similarities and differences in the gastrointestinal pH and transit time between neonates and adults from a PBPK modeling prospective. Understanding the similarities and differences in these physiological parameters governing oral absorption would promote good practice in the use of pediatric PBPK modeling to assess oral exposure and pharmacokinetics in neonates.     

Pharmacokinetic and Pharmacodynamic Evaluation of Different PEGylated Human Interleukin-11 Preparations in Animal Models

Treating thrombocytopenia induced by chemotherapy remains an unmet-medical need. The use of recombinant human interleukin-11 (rhIL-11) requires repeated injections and induces significant fluid retention in some patients. Modification of human interleukin-11 with chemically inert polyethylene glycol polymer (PEG) may extend the peripheral circulation half-life leading to an improved pharmacokinetic and pharmadynamic profile. In this study, a number of rhIL-11 PEG conjugates were created to determine the optimal approach to prolong circulating half-life with the most robust pharmacological effect. The lead candidate was found to be a single 40-kDa Y-shaped PEG linked to the N-terminus, which produced a long-lasting circulating half-life, enhanced efficacy and alleviated side effect of dilutional anemia in healthy rat models. This candidate was also shown to be effective in myelosuppressive rats in preventing the occurrence of severe thrombocytopenia while ameliorating dilutional anemia, compared to rats receiving daily administration of unmodified rhIL-11 at the same dose. These data indicated that a single injection of the selected modified rhIL-11 for each cycle of chemotherapy regimen is potentially feasible. This approach may also be useful in treating patients of acute radiation syndrome when frequent administration is not feasible in a widespread event of a major radiation exposure.