Compartmental Modeling Approach Research Articles

Impacts of optimal control strategies on the HBV and COVID-19 co-epidemic spreading dynamics

Different cross-sectional and clinical research studies investigated that chronic HBV infected individuals’ co-epidemic with COVID-19 infection will have more complicated liver infection than HBV infected individuals in the absence of COVID-19 infection. The main objective of this study is to investigate the optimal impacts of four time dependent control strategies on the HBV and COVID-19 co-epidemic transmission using compartmental modeling approach. The qualitative analyses of the model investigated the model solutions non-negativity and boundedness, calculated all the models effective reproduction numbers by applying the next generation operator approach, computed all the models disease-free equilibrium point (s) and endemic equilibrium point (s) and proved their local stability, shown the phenomenon of backward bifurcation by applying the Center Manifold criteria. By applied the Pontryagin’s Maximum principle, the study re-formulated and analyzed the co-epidemic model optimal control problem by incorporating four time dependent controlling variables. The study also carried out numerical simulations to verify the model qualitative results and to investigate the optimal impacts of the proposed optimal control strategies. The main finding of the study reveals that implementation of protections, COVID-19 vaccine, and treatment strategies simultaneously is the most effective optimal control strategy to tackle the HBV and COVID-19 co-epidemic spreading in the community.

Dynamics of Feline Coronavirus and FIP: A Compartmental Modeling Approach.

The investigation of infectious agents invading human and nonhuman populations represents a rich research domain within the framework of mathematical biology, captivating the interest of scientists across various disciplines. In this work, we examine the endemic equilibrium of feline coronavirus and feline infectious peritonitis by using a modified susceptible-infected-susceptible epidemiological model. We incorporate the concept of mutations from FCoV to FIP to enrich our analysis. We establish that the model, when subjected to reasonable parameter ranges, supports an endemic equilibrium wherein the FCoV group dominates. To demonstrate the stability of the equilibria under typical parameters and initial conditions, we employ the model SCF presented by Dobie in 2022 (Dobie, 2022). We ascertain that the equilibrium values reside within the interior domains of stability. Additionally, we displayed perturbed solutions to enhance our understanding. Remarkably, our findings align qualitatively with existing literature, which reports the prevalence of seropositivity to FCoV among stray cats (Tekelioglu et al. 2015, Oğuzoğlu et al. 2010, Pratelli 2008, Arshad et al. 2004).

Functional FDG-PET: Measurement of Task Related Neural Activity in Humans-A Compartment Model Approach and Comparison to fMRI.

Neuroimaging holds an essential position in global healthcare, as brain-related disorders are a substantial and growing burden. Non-degenerative disorders such as stress, depression and anxiety share common function related traits of diffuse and fluctuating changes, such as change in brain-based functions of mood, behavior and cognitive abilities, where underlying physiological mechanism remain unresolved. In this study we developed a novel application for studying intra-subject task-activated brain function by the quantitative physiological measurement of the change in glucose metabolism in a single scan setup. Data were acquired on a PET/MR-scanner. We implemented a functional [18F]-FDG PET-scan with double boli-tracer administration and finger-tapping activation, as proof-of-concept, in five healthy participants. The [18F]-FDG data were analyzed using a two-tissue compartment double boli kinetic model with an image-derived input function. For stand-alone visual reference, blood oxygenation level dependent (BOLD) functional MRI (fMRI) was acquired in the same session and analyzed separately. We were able to measure the cerebral glucose metabolic rate during baseline as well as activation. Results showed increased glucose metabolic rate during activation by 36.3-87.9% mean 62.0%, locally in the peak seed region of M1 in the brain, on an intra-subject level, as well as very good spatial accuracy on group level, and localization compared to the BOLD fMRI result at subject and group level. Our novel method successfully determined the relative increase in the cerebral metabolic rate of glucose on a voxel level with good visual association to fMRI at the subject-level, holding promise for future individual clinical application. This approach will be easily adapted in future clinical perspectives and pharmacological interventions studies.

The dynamics of prisoner population model in Indonesia with a rehabilitation regulation for drug users to overcome prison overcapacity issue

This paper discusses about a mathematical model of the prisoner population with a new regulation regarding punishments to the drug users in an effort to overcome the prison overcapacity issue in Indonesia. This new regulation is launched by the Indonesia government after a fact about overcapacity of prison in Indonesia is revealed through a fire incident in a prison on 8 September 2021 that causes 41 people died and a number of people were injured. Besides, prisons in Indonesia are mostly occupied by the drug user. The model is constructed by using a compartmental model approach. The stability analysis of the equilibrium points is carried out along with its existence conditions. The analytical studies are equipped by calculate the basic reproduction number. Furthermore, this study is also equipped by numerical simulations with some scenarios. The results of this study confirm that the effect of the new regulation is able to reduce overcrowded issue in prisons in Indonesia. However, if it compare to recent prison capacity, this new regulation has not been able to suppress the number of prisoner below to its capacity limit in the short time so that it is needed to consider other solutions as the additional regulation and policy

Levetiracetam Pharmacokinetics and Brain Uptake in a Lateral Fluid Percussion Injury Rat Model.

Post-traumatic epilepsy (PTE) occurs in some patients after moderate/severe traumatic brain injury (TBI). Although there are no approved therapies to prevent epileptogenesis, levetiracetam (LEV) is commonly given for seizure prophylaxis due to its good safety profile. This led us to study LEV as part of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) Project. The objective of this work is to characterize the pharmacokinetics (PK) and brain uptake of LEV in naïve control rats and in the lateral fluid percussion injury (LFPI) rat model of TBI after either single intraperitoneal doses or a loading dose followed by a 7-day subcutaneous infusion. Sprague-Dawley rats were used as controls and for the LFPI model induced at the left parietal region using injury parameters optimized for moderate/severe TBI. Naïve and LFPI rats received either a bolus injection (intraperitoneal) or a bolus injection followed by subcutaneous infusion over 7 days. Blood and parietal cortical samples were collected at specified time points throughout the study. LEV concentrations in plasma and brain were measured using validated high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) methods. Noncompartmental analysis and a naive-pooled compartmental PK modeling approach were used. Brain-to-plasma ratios ranged from 0.54 to 1.4 to 1. LEV concentrations were well fit by one-compartment, first-order absorption PK models with a clearance of 112 ml/h per kg and volume of distribution of 293 ml/kg. The single-dose pharmacokinetic data were used to guide dose selection for the longer-term studies, and target drug exposures were confirmed. Obtaining LEV PK information early in the screening phase allowed us to guide optimal treatment protocols in EpiBioS4Rx. SIGNIFICANCE STATEMENT: The characterization of levetiracetam pharmacokinetics and brain uptake in an animal model of post-traumatic epilepsy is essential to identify target concentrations and guide optimal treatment for future studies.

Modelling of Hepatitis B Virus vertical transmission dynamics in Ethiopia: a compartmental modelling approach

BackgroundHepatitis B (HB) is a virus which causes a potentially fatal liver infection. It is a DNA virus belonging to the Hepadnaviridae virus family. Africa, after Asia, has the second highest number of chronic HBV carriers and is considered a high-endemic region. Ethiopia is classified as a country with a high prevalence of viral hepatitis and with nations that lack a systematic strategy for viral hepatitis surveillance.MethodsS-I-C-R deterministic model was developed and the numerical simulations were done in “R” statistical and programming software. Fixed population assumption was considered so as to develop a simple model which could predict the HBV vertical transmission for the next 5 decades.ResultsThe model revealed that significant number of populations will be infected and become carrier till the end the next 49 years even though it has decreasing trend. It was predicted that 271,719 people will die of HBV complications if no intervention will be made on its vertical transmission. The sensitivity analysis result showed that the force of infection has the most important parameter in the vertical transmission dynamics of hepatitis B. Provision of hepatitis B immunoglobulin (HBVIG) and vaccines at the time of delivery could decrease the force of infection by more than half and 51,892 lives will be saved if the intervention is offered for 50% of deliveries in Ethiopia.ConclusionDespite the fact that the incidence of HBV vertical transmission is substantial, it is expected to decline during the next five decades. However, the situation necessitates immediate attention, since it results in thousands of deaths if no action is taken. Offering HBVIG and vaccinations to the 50% of infants can save many lives and reduces the force of infection by more than a half.

A network-based model of dynamic cerebral autoregulation.

Cerebrovascular diseases continue to be one of the leading causes of morbidity and mortality in humans. Abnormalities in dynamic cerebral autoregulation (dCA) have been implicated in many of these disease conditions. Accurate models are therefore needed to better understand the complex pathophysiology behind impaired dCA. We thus present here a simple framework for modelling a vessel-driven network model of dCA in the microvasculature, as opposed to the conventional compartmental modelling approach. Network models incorporate the actual connectivity and anatomy of the vasculature, thereby allowing us to include and trace changes in the calibre and morphology of individual vessels, investigate the spatial specificity and heterogeneity of the various control mechanisms to help disentangle their contributions, and link the model parameters to the actual network physiology. The proposed control feedback mechanisms are incorporated at the level of the individual vessel, and the dynamic pressure and flow fields are solved for here within a simple vessel network. In response to an upstream pressure drop, the network is found to be able to recover cerebral blood flow (CBF) while exhibiting the characteristic autoregulatory behaviour in terms of changes in vessel calibre and the biphasic flow response. We assess the feasibility of our formulation in larger networks by comparing the simulation results to those obtained using a one-dimensional (1D) model of CBF applied to the same microvasculature network and find that our model results are in very good agreement with the 1D solution, while significantly reducing the computational cost, thus enabling more detailed models of network behaviour to be adopted in the future. Accurate and computationally feasible models of dCA that are more representative of the vasculature can help increase the translatability of haemodynamic models into the clinical environment, which would help develop more informed treatment guidelines for patients with cerebrovascular diseases.

Exploring Bipartite Network Approach in Hand, Foot and Mouth Disease Hotspot Identification

Mathematical modeling of hand, foot, and mouth disease (HFMD) mainly focuses on compartmental modeling approaches. It classifies human population into compartments and assumes homogeneity that regards every human has equal chance of contacting other individuals in the population. However, the transmission of HFMD is complicated and dynamic with the interactions of the intertwined biomedical and social factors. Describing the disease transmission dynamic that involves high-dimensional space is mathematically challenging. The graph theoretic bipartite network modeling (BNM) approach has the potential to handle this challenge by abstracting the real-world disease transmission system and incorporating the individual features of the bipartite nodes. This study aims to seize the advantages portrayed by the BNM approach in capturing the heterogeneous features of the entities within a disease transmission system. It intends to explore adopting the BNM approach in modeling the transmission of HFMD at Kuching, Malaysia and identify the hotspot by employing the BNM approach comprising a four-stage methodology adapted from the BNM methodology framework. The bipartite HFMD contact (BHC) network is formulated with the basic building block consisting of the location and human nodes. The individual parameters of the location and human node are incorporated. The resulting BHC network formulated comprises 10 human nodes, 20 location nodes, and 23 edges. Then, six top-ranked location nodes were identified and agreed with the chosen benchmark system. The potential HFMD hotspots are thus identified by determining the location nodes ranking. The result from this study has enabled timely and effective measures and policies to be customized accordingly by the public health authorities and related policymakers.

Preparation, characterization, and pharmacokinetics of oridonin-loaded liposomes.

The aim of this study was to prepare oridonin liposomes and evaluate the physicochemical characteristics and pharmacokinetics in rats. A three-level, three-factor Box-Behnken design was used to optimize the preparation of oridonin liposomes. A highly sensitive high-performance liquid chromatographic quantification method using ultraviolet detection was established and validated for the determination of oridonin in rat plasma. Twelve Sprague-Dawley rats were randomly assigned and injected with 15 mg/kg of oridonin or oridonin liposomes via the tail vein. Pharmacokinetic parameters were estimated using a compartmental modeling approach using PKsolver software. The optimum conditions were as follows: soybean phospholipids/cholesterol ratio, 3.9:1; soybean phospholipids/drug ratio, 8.5:1; and soybean phospholipid concentration, 1.1%. Under these conditions, the mean particle size, polydispersity index, zeta potential, and encapsulation efficiency of oridonin liposomes were 170.5nm, 0.246, -30.3mV, and 76.15%, respectively. The pharmacokinetic results showed that liposomes could significantly prolong the elimination half-life (from 2.88 ± 0.55 to 13.67 ± 3.52 h), increase the area under the concentration-time curve (from 1.65 ± 0.17 to 6.22 ± 0.83 μg h/ml), and decrease the clearance (from 6.62 ± 1.38 to 1.96 ± 0.24L/kg h). The oridonin liposomes increased the elimination half-life and area under the concentration-time curve and provided a reference for the development of drugs with a short half-life.

Fair Dynamic Rationing

We study the allocative challenges that governmental and nonprofit organizations face when tasked with equitable and efficient rationing of a social good among agents whose needs (demands) realize sequentially and are possibly correlated. As one example, early in the COVID-19 pandemic, the Federal Emergency Management Agency faced overwhelming, temporally scattered, a priori uncertain, and correlated demands for medical supplies from different states. In such contexts, social planners aim to maximize the minimum fill rate across sequentially arriving agents, where each agent’s fill rate (i.e., its fraction of satisfied demand) is determined by an irrevocable, one-time allocation. For an arbitrarily correlated sequence of demands, we establish upper bounds on the expected minimum fill rate (ex post fairness) and the minimum expected fill rate (ex ante fairness) achievable by any policy. Our upper bounds are parameterized by the number of agents and the expected demand-to-supply ratio, yet we design a simple adaptive policy called projected proportional allocation (PPA) that simultaneously achieves matching lower bounds for both objectives (ex post and ex ante fairness) for any set of parameters. Our PPA policy is transparent and easy to implement, as it does not rely on distributional information beyond the first conditional moments. Despite its simplicity, we demonstrate that the PPA policy provides significant improvement over the canonical class of nonadaptive target-fill-rate policies. We complement our theoretical developments with a numerical study motivated by the rationing of COVID-19 medical supplies based on a standard compartmental modeling approach that is commonly used to forecast pandemic trajectories. In such a setting, our PPA policy significantly outperforms its theoretical guarantee and the optimal target-fill-rate policy. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4700 .

Does a One Health approach to human African trypanosomiasis control hasten elimination? A stochastic compartmental modeling approach

Background. In response to large strides in the control of human African trypanosomiasis (HAT), in the early 2000s the WHO set targets for elimination of both the gambiense (gHAT) and rhodesiense (rHAT) forms as a public health (EPHP) problem by 2020, and elimination of gHAT transmisson (EOT) by 2030. While global EPHP targets have been met, and EOT appears within reach, current control strategies may fail to achieve gHAT EOT in the presence of animal reservoirs, the role of which is currently uncertain. Furthermore, rHAT is not targeted for EOT due to the known importance of animal reservoirs for this form. Methods. To evaluate the utility of a One Health approach to gHAT and rHAT EOT, we built and parameterized a compartmental stochastic model, using the Institute for Disease Modeling's Compartmental Modeling Software, to six HAT epidemics: the national rHAT epidemics in Uganda and Malawi, the national gHAT epidemics in Uganda and South Sudan, and two separate gHAT epidemics in Democratic Republic of Congo distinguished by dominant vector species. In rHAT foci the reservoir animal sub-model was stratified on four species groups, while in gHAT foci domestic swine were assumed to be the only competent reservoir. The modeled time horizon was 2005-2045, with calibration performed using HAT surveillance data and Optuna. Interventions included insecticide and trypanocide treatment of domestic animal reservoirs at varying coverage levels. Results. Validation against HAT surveillance data indicates favorable performance overall, with the possible exception of DRC. EOT was not observed in any modeled scenarios for rHAT, however insecticide treatment consistently performed better than trypanocide treatment in terms of rHAT control. EOT was not observed for gHAT at 0% coverage of domestic reservoirs with trypanocides or insecticides, but was observed by 2030 in all test scenarios; again, insecticides demonstrated superior performance to trypanocides. ConclusionsEOT likely cannot be achieved for rHAT without control of wildlife reservoirs, however insecticide treatment of domestic animals holds promise for improved control. In the presence of domestic animal reservoirs, gHAT EOT may not be achieved under current control strategies.

Dynamic Interplay between O2 Availability, Growth Rates, and the Transcriptome of Yarrowia lipolytica

Industrial-sized fermenters differ from the laboratory environment in which bioprocess development initially took place. One of the issues that can lead to reduced productivity on a large scale or even early termination of the process is the presence of bioreactor heterogeneities. This work proposes and adopts a design–build–test–learn-type workflow that estimates the substrate, oxygen, and resulting growth heterogeneities through a compartmental modelling approach and maps Yarrowia lipolytica-specific behavior in this relevant range of conditions. The results indicate that at a growth rate of 0.1 h−1, the largest simulated volume (90 m3) reached partial oxygen limitation. Throughout the fed-batch, the cells experienced dissolved oxygen values from 0 to 75% and grew at rates of 0 to 0.2 h−1. These simulated large-scale conditions were tested in small-scale cultivations, which elucidated a transcriptome with a strong downregulation of various transporter and central carbon metabolism genes during oxygen limitation. The relation between oxygen availability and differential gene expression was dynamic and did not show a simple on–off behavior. This indicates that Y. lipolytica can differentiate between different available oxygen concentrations and adjust its transcription accordingly. The workflow presented can be used for Y. lipolytica-based strain engineering, thereby accelerating bioprocess development.

Pharmacokinetic Modeling of VV116 for Treatment of COVID-19

SARS-Cov-2 is still impacting global health and potentially will live with human in the near future. Therefore, effective oral medicines such as VV116 will play an important role in treating numerous patients and ending the pandemic. From this research, these simulated results can be used to guide clinical decisions to maximize therapeutic benefits of VV116 while avoiding potential toxicities. This compartmental modeling approach can be readily used for other drugs that have identical physiochemical properties in the clinical trial. However, caution should be exercised when the dose exceeds the linear PK range (up to 1200 mg in this case), where the model becomes invalid. As such, future study is needed to examine mixed-order or zero order PK where the dose-response is not linear. The importance of biomedical research has also inspired me to pursue a future career to fighting diseases with unmet medical needs.

Data-based dynamic compartment model: Modeling ofE. colifed-batch fermentation in a 600 m3 bubble column

Mathematical modeling is a powerful and inexpensive approach to provide a quantitative basis for improvements that minimize the negative effects of bioreactor heterogeneity. For a model to accurately represent a heterogeneous system, a flow model that describes how mass is channeled between different zones of the bioreactor volume is necessary. In this study, a previously developed compartment model approach based on data from flow-following sensor devices was further developed to account for dynamic changes in volume and flow rates and thus enabling simulation of the widely used fed-batch process. The application of the dynamic compartment model was demonstrated in a study of an industrial fermentation process in a 600 m3 bubble column bioreactor. The flow model was used to evaluate the mixing performance by means of tracer simulations and was coupled with reaction kinetics to simulate concentration gradients in the process. The simulations showed that despite the presence of long mixing times and significant substrate gradients early in the process, improving the heterogeneity did not lead to overall improvements in the process. Improvements could, however, be achieved by modifying the dextrose feeding profile.

CFD‐based compartment modeling approach for continuous polymer reactors by means of the mean‐age theory

CFD‐based compartment modeling approach for continuous polymer reactors by means of the mean‐age theory

Influences of longitudinal gradients on methane-driven membrane biofilm reactor for complete nitrogen removal: A model-based investigation

Integrating anammox with denitrifying anaerobic methane oxidation (DAMO) in the membrane biofilm reactor (MBfR) is a promising technology capable of achieving complete nitrogen removal from wastewater. However, it remains unknown whether reactor configurations featuring longitudinal gradients parallel to the membrane surface would affect the performance of the CH4-driven MBfR. To this end, this work aims to study the impacts of longitudinal heterogeneity potentially present in the gas and liquid phases on a representative CH4-driven MBfR performing anammox/DAMO by applying the reported modified compartmental modeling approach. Through comparing the modeling results of different reactor configurations, this work not only offered important guidance for better design, operation and monitoring of the CH4-driven MBfR, but also revealed important implications for prospective related modeling research. The total nitrogen removal efficiency of the MBfR at non-excessive CH4 supply (e.g., surface loading of ≤0.064 g-COD m−2 d−1 in this work) was found to be insensitive to both longitudinal gradients in the liquid and gas phases. Comparatively, the longitudinal gradient in the liquid phase led to distinct longitudinal biomass stratification and therefore played an influential role in the effective CH4 utilization efficiency, which was also related to the extent of reactor compartmentation considered in modeling. When supplied with non-excessive CH4, the MBfR is recommended to be designed/operated with both the biofilm reactor and the membrane lumen as plug flow reactors (PFRs) with co-current flow of wastewater and CH4, which could mitigate dissolved CH4 discharge in the effluent. For the reactor configurations with the biofilm reactor designed/operated as a PFR, multi-spot sampling in the longitudinal direction is needed to obtain a correct representation of the microbial composition of the MBfR.

Looking into mobility in the Covid-19 ‘eye of the storm’: Simulating virus spread and urban resilience in the Wuhan city region travel flow network

Recent urban and regional studies have focused on identifying positive spillover effects from intensifying flows of people in city region networks. However, potential negative spillover effects have lacked attention. The article addresses this research gap focusing on the negative spillover effects represented by Covid-19 contagion in the Wuhan regional travel flow network, China. Drawing on central place theory and central flow theory, Covid-19 spatial spread simulation scenarios are explored using a combined micro-level epidemic compartment model and urban network approach. It is found that not only centrally positioned primate but secondary cities are highly risk exposed to contagion. In addition, these cities have enhanced transmission capacity in a balanced, well-connected travel flow network, whereas a centralised or locally clustered network would be more spread resilient. Both hierarchical position and horizontal flows are found relevant for explaining Covid-19 uneven spread and for informing mobility interventions for a potential future outbreak.

Compartmental Modeling Approach: Application on Transdermal Delivery for In Vitro Drug Permeation Mechanism Analysis

Compartmental modeling analysis was used to understand the transport mechanism of drugs in biological systems by computation presented as intercompartmental flows or material. This study was aimed to implement compartmental modeling analysis for in vitro permeation studies in transdermal delivery. The cumulative drug transported versus time were obtained based on the previous report and implemented the two structural different proposed models. WinSAAM software (Windows-based Simulation Analysis and Modeling-WinSAAM Project Group, University of Pennsylvania) was used to analyze data with compartmental analysis. The chosen models were selected through visual and numeric evaluation. The best model had been chosen and could figure out the drug transport kinetics in the biological system. The compartmental modeling approach was helpful used in understanding drug transport mechanisms in transdermal delivery and effectively estimate the drug transport parameter.

On some fundamental challenges in monitoring epidemics.

Epidemic models often reflect characteristic features of infectious spreading processes by coupled nonlinear differential equations considering different states of health (such as susceptible, infectious or recovered). This compartmental modelling approach, however, delivers an incomplete picture of the dynamics of epidemics, as it neglects stochastic and network effects, and the role of the measurement process, on which the estimation of epidemiological parameters and incidence values relies. In order to study the related issues, we combine established epidemiological spreading models with a measurement model of the testing process, considering the problems of false positives and false negatives as well as biased sampling. Studying a model-generated ground truth in conjunction with simulated observation processes (virtual measurements) allows one to gain insights into the fundamental limitations of purely data-driven methods when assessing the epidemic situation. We conclude that epidemic monitoring, simulation, and forecasting are wicked problems, as applying a conventional data-driven approach to a complex system with nonlinear dynamics, network effects and uncertainty can be misleading. Nevertheless, some of the errors can be corrected for, using scientific knowledge of the spreading dynamics and the measurement process. We conclude that such corrections should generally be part of epidemic monitoring, modelling and forecasting efforts.This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

Population Pharmacokinetic Modelling to Support the Evaluation of Preclinical Pharmacokinetic Experiments with Lorlatinib

The effect of transporters and enzymes on drug pharmacokinetics is increasingly evaluated using genetically modified animals that have these proteins either knocked-out or their human orthologues transgenically expressed. Analysis of pharmacokinetic data obtained in such experiments is typically performed using non-compartmental analysis (NCA), which has limitations such as not being able to identify the PK parameter that is affected by the genetic modification of the enzymes or transporters and the requirement of intense and homogeneous sampling of all subjects. Here we used a compartmental population pharmacokinetic modeling approach using PK data from a series of genetically modified mouse experiments with lorlatinib to extend the results and conclusions from previously reported NCA analyses. A compartmental population pharmacokinetic model was built and physiologically plausible covariates were evaluated for the different mouse strains. With the model, similar effects of the strains on the area under the concentration-time curve (AUC) from 0 to 8 hours were found as for the NCA. Additionally, the differences in AUC between the strains were explained by specific effects on clearance and bioavailability for the strain with human expressing CYP3A4. Finally, effects of multidrug efflux transporters ATP-binding cassette (ABC) sub-family B member 1 (ABCB1) and G member 2 (ABCG2) on brain efflux were quantified. Use of compartmental population PK modeling yielded additional insight into the role of drug-metabolizing enzymes and drug transporters in mouse experiments compared to the NCA. Furthermore, these models allowed analysis of heterogeneous pooled datasets and the sparse organ concentration data in contrast to classical NCA analyses.