Application of Physiologically Based Absorption Modeling to Formulation Development of a Low Solubility,Low Permeability Weak Base: Mechanistic Investigation of Food Effect

Physiologically based pharmacokinetic (PBPK) modeling has been broadly used to facilitate drug development, hereby we developed a PBPK model to systematically investigate the underlying mechanisms of the observed positive food effect of compound X (cpd X) and to strategically explore the feasible approaches to mitigate the food effect. Cpd X is a weak base with pH-dependent solubility; the compound displays significant and dose-dependent food effect in humans, leading to a nonadherence of drug administration. A GastroPlus Opt logD Model was selected for pharmacokinetic simulation under both fasted and fed conditions, where the biopharmaceutic parameters (e.g., solubility and permeability) for cpd X were determined in vitro, and human pharmacokinetic disposition properties were predicted from preclinical data and then optimized with clinical pharmacokinetic data. A parameter sensitivity analysis was performed to evaluate the effect of particle size on the cpd X absorption. A PBPK model was successfully developed for cpd X; its pharmacokinetic parameters (e.g., C _max, AUC_inf, and t _max) predicted at different oral doses were within ±25% of the observed mean values. The in vivo solubility (in duodenum) and mean precipitation time under fed conditions were estimated to be 7.4- and 3.4-fold higher than those under fasted conditions, respectively. The PBPK modeling analysis provided a reasonable explanation for the underlying mechanism for the observed positive food effect of the cpd X in humans. Oral absorption of the cpd X can be increased by reducing the particle size (<100 nm) of an active pharmaceutical ingredient under fasted conditions and therefore, reduce the cpd X food effect correspondingly.     

Utility of Physiologically Based Modeling and Preclinical In Vitro/In Vivo Data to Mitigate Positive Food Effect in a BCS Class 2 Compound

Physiologically based pharmacokinetic (PBPK) modeling has become a useful tool to estimate the performance of orally administrated drugs. Here, we described multiple in silico/in vitro/in vivo tools to support formulation development toward mitigating the positive food effect of NVS123, a weak base with a pH-dependent and limited solubility. Administered orally with high-fat meal, NVS123 formulated as dry filled capsules displayed a positive food effects in humans. Three alternative formulations were developed and assessed in in vitro and in vivo preclinical and/or clinical studies. By integrating preclinical in vitro and in vivo data, the PBPK model successfully estimated the magnitude of food effects and the predicted values were within ±30% of the observed results. A model-guided parameter sensitivity analysis illustrated that enhanced solubility and longer precipitation times under fed condition were the main reason for enhanced NVS123's exposure in presence of food. Eventually, exposure after an amorphous formulation was found to be not significantly altered because of remarkably enhanced intestinal solubility and reduced precipitation. Gastroplus population simulations also suggested that the amorphous formulation is promising in mitigating a clinically significant food effect. Overall, these efforts supported the rationale of clinical investigation of the new formulation, and more importantly, highlighted a practical application of PBPK modeling solving issues of undesirable food effects in weakly basic compounds based on preclinical in vitro/in vivo data.     

Development and application of physiologically based pharmacokinetic-modeling tools to support drug discovery

Physiologically based pharmacokinetic (PBPK) modeling integrates physicochemical (PC) and in vitro pharmacokinetic (PK) data using a mechanistic framework of principal ADME (absorption, distribution, metabolism, and excretion) processes into a physiologically based whole-body model. Absorption, distribution, and clearance are modeled by combining compound-specific PC and PK properties with physiological processes. Thereby, isolated in vitro data can be upgraded by means of predicting full concentration-time profiles prior to animal experiments. The integrative process of PBPK modeling leads to a better understanding of the specific ADME processes driving the PK behavior in vivo, and has the power to rationally select experiments for a more focussed PK project support. This article presents a generic disposition model based on tissue-composition-based distribution and directly scaled hepatic clearance. This model can be used in drug discovery to identify the critical PK issues of compound classes and to rationally guide the optimization path of the compounds toward a viable development candidate. Starting with a generic PBPK model, which is empirically based on the most common PK processes, the model will be gradually tailored to the specifics of drug candidates as more and more experimental data become available. This will lead to a growing understanding of the 'drug in the making', allowing a range of predictions to be made for various purposes and conditions. The stage is set for a wide penetration of PK modeling and simulations to form an intrinsic part of a project starting from lead discovery, to lead optimization and candidate selection, to preclinical profiling and clinical trials.     

Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations

Interindividual variability in anatomical and physiological properties results in significant differences in drug pharmacokinetics. The consideration of such pharmacokinetic variability supports optimal drug efficacy and safety for each single individual, e.g. by identification of individual-specific dosings. One clear objective in clinical drug development is therefore a thorough characterization of the physiological sources of interindividual variability. In this work, we present a Bayesian population physiologically-based pharmacokinetic (PBPK) approach for the mechanistically and physiologically realistic identification of interindividual variability. The consideration of a generic and highly detailed mechanistic PBPK model structure enables the integration of large amounts of prior physiological knowledge, which is then updated with new experimental data in a Bayesian framework. A covariate model integrates known relationships of physiological parameters to age, gender and body height. We further provide a framework for estimation of the a posteriori parameter dependency structure at the population level. The approach is demonstrated considering a cohort of healthy individuals and theophylline as an application example. The variability and co-variability of physiological parameters are specified within the population; respectively. Significant correlations are identified between population parameters and are applied for individual- and population-specific visual predictive checks of the pharmacokinetic behavior, which leads to improved results compared to present population approaches. In the future, the integration of a generic PBPK model into an hierarchical approach allows for extrapolations to other populations or drugs, while the Bayesian paradigm allows for an iterative application of the approach and thereby a continuous updating of physiological knowledge with new data. This will facilitate decision making e.g. from preclinical to clinical development or extrapolation of PK behavior from healthy to clinically significant populations.     

PBPK models in risk assessment--A focus on chloroprene

Mathematical models are increasingly being used to simulate events in the exposure-response continuum, and to support quantitative predictions of risks to human health. Physiologically based pharmacokinetic (PBPK) models address that portion of the continuum from an external chemical exposure to an internal dose at a target site. Essential data needed to develop a PBPK model include values of key physiological parameters (e.g., tissue volumes, blood flow rates) and chemical specific parameters (rate of chemical absorption, distribution, metabolism, and elimination) for the species of interest. PBPK models are commonly used to: (1) predict concentrations of an internal dose over time at a target site following external exposure via different routes and/or durations; (2) predict human internal concentration at a target site based on animal data by accounting for toxicokinetic and physiological differences; and (3) estimate variability in the internal dose within a human population resulting from differences in individual pharmacokinetics. Himmelstein et al. [M.W. Himmelstein, S.C. Carpenter, P.M. Hinderliter, Kinetic modeling of beta-chloroprene metabolism. I. In vitro rates in liver and lung tissue fractions from mice, rats, hamsters, and humans, Toxicol. Sci. 79 (1) (2004) 18-27; M.W. Himmelstein, S.C. Carpenter, M.V. Evans, P.M. Hinderliter, E.M. Kenyon, Kinetic modeling of beta-chloroprene metabolism. II. The application of physiologically based modeling for cancer dose response analysis, Toxicol. Sci. 79 (1) (2004) 28-37] developed a PBPK model for chloroprene (2-chloro-1,3-butadiene; CD) that simulates chloroprene disposition in rats, mice, hamsters, or humans following an inhalation exposure. Values for the CD-PBPK model metabolic parameters were obtained from in vitro studies, and model simulations compared to data from in vivo gas uptake studies in rats, hamsters, and mice. The model estimate for total amount of metabolite in lung correlated better with rodent tumor incidence than did the external dose. Based on this PBPK model analytical approach, Himmelstein et al. [M.W. Himmelstein, S.C. Carpenter, M.V. Evans, P.M. Hinderliter, E.M. Kenyon, Kinetic modeling of beta-chloroprene metabolism. II. The application of physiologically based modeling for cancer dose response analysis, Toxicol. Sci. 79 (1) (2004) 28-37; M.W. Himmelstein, R. Leonard, R. Valentine, Kinetic modeling of beta-chloroprene metabolism: default and physiologically-based modeling approaches for cancer dose response, in: IISRP Symposium on Evaluation of Butadiene & Chloroprene Health Effects, September 21, 2005, TBD--reference in this proceedings issue of Chemical-Biological Interactions] propose that observed species differences in the lung tumor dose-response result from differences in CD metabolic rates. The CD-PBPK model has not yet been submitted to EPA for use in developing the IRIS assessment for chloroprene, but is sufficiently developed to be considered. The process that EPA uses to evaluate PBPK models is discussed, as well as potential applications for the CD-PBPK model in an IRIS assessment.     

Prediction of Pharmacokinetic Profile of Valsartan in Humans Based on in vitro Uptake‐Transport Data

The aim of this study was to evaluate a physiologically based pharmacokinetic (PBPK) model for predicting PK profiles in humans based on a model refined in rats and humans in vitro uptake‐transport data using valsartan as a probe substrate. Valsartan is eliminated unchanged, mostly through biliary excretion, both in humans and rats. It was, therefore, chosen as model compound to predict in vivo elimination based on in vitro hepatic uptake‐transport data using a fully mechanistic PBPK model. Plated rat and human hepatocytes, and cell lines overexpressing human OATP1B1 and OATP1B3 were used for in vitro uptake experiments. A mechanistic two‐compartment model was used to derive the active and passive transport parameters, namely uptake Michaelis–Menten parameters (V_max and K_m,u) together with passive diffusion (P_dif). These transport parameters were then used as input in a whole body physiologically based pharmacokinetic (PBPK) model. The uptake rate of valsartan was higher for rat hepatocytes (K_m,u=28.4±3.7 μM , V_max=1320±180 pmol/mg/min, and P_dif =1.21±0.42 μl/mg/min) compared to human hepatocytes (K_m,u=44.4±14.6 μM , V_max=304±85 pmol/mg/min, and P_dif=0.724±0.271 μl/mg/min). OATP1B1 and ‐1B3 parameters were correlated to human hepatocyte data, using experimentally established relative activity factors (RAF). Resulting PBPK simulations were compared for plasma‐ (humans and rats) and bile‐ (rats) concentration–time profiles following iv bolus administration of valsartan. Plasma clearances (CL_P) for rats and humans were predicted within twofold relative to predictions based on respective in vitro data. The simulations were extended to simulate the impact of either OATP1B1 or ‐1B3 inhibition on plasma profile. The limited data set indicates that the mechanistic model allowed for accurate evaluation of in vitro transport data; and the resulting hepatic uptake transport kinetic parameters enabled the prediction of in vivo PK profiles and plasma clearances, using PBPK modelling. Moreover, the interspecies difference in elimination rate observed in vivo was correctly reflected in the transport parameters determined in vitro.     

Use of Physiologically-Based Pharmacokinetic Modeling to Simulate the Profiles of 3-Hydroxybenzo(a)pyrene in Workers Exposed to Polycyclic Aromatic Hydrocarbons

Biomathematical modeling has become an important tool to assess xenobiotic exposure in humans. In the present study, we have used a human physiologically-based pharmacokinetic (PBPK) model and an simple compartmental toxicokinetic model of benzo(a)pyrene (BaP) kinetics and its 3-hydroxybenzo(a)pyrene (3-OHBaP) metabolite to reproduce the time-course of this biomarker of exposure in the urine of industrially exposed workers and in turn predict the most plausible exposure scenarios. The models were constructed from in vivo experimental data in rats and then extrapolated from animals to humans after assessing and adjusting the most sensitive model parameters as well as species specific physiological parameters. Repeated urinary voids from workers exposed to polycyclic aromatic hydrocarbons (PAHs) have been collected over the course of a typical workweek and during subsequent days off work; urinary concentrations of 3-OHBaP were then determined. Based on the information obtained for each worker (BaP air concentration, daily shift hours, tasks, protective equipment), the time courses of 3-OHBaP in the urine of the different workers have been simulated using the PBPK and toxicokinetic models, considering the various possible exposure routes, oral, dermal and inhalation. Both models were equally able to closely reproduce the observed time course of 3-OHBaP in the urine of workers and predicted similar exposure scenarios. Simulations of various scenarios suggest that the workers under study were exposed mainly by the dermal route. Comparison of measured air concentration levels of BaP with simulated values needed to obtain a good approximation of observed time course further pointed out that inhalation was not the main route of exposure for most of the studied workers. Both kinetic models appear as a useful tool to interpret biomonitoring data of PAH exposure on the basis of 3-OHBaP levels.     

Population pharmacokinetics of Artemether and dihydroartemisinin in pregnant women with uncomplicated Plasmodium falciparum malaria in Uganda

ABSTRACT: BACKGROUND: Malaria in pregnancy increases the risk of maternal anemia, abortion and low birth weight. Approximately 85.3 million pregnancies occur annually in areas with Plasmodium falciparum transmission. Pregnancy has been reported to alter the pharmacokinetic properties of many anti-malarial drugs. Reduced drug exposure increases the risk of treatment failure. The objective of this study was to evaluate the population pharmacokinetic properties of artemether and its active metabolite dihydroartemisinin in pregnant women with uncomplicated P. falciparum malaria in Uganda. METHODS: Twenty-one women with uncomplicated P. falciparum malaria in the second and third trimesters of pregnancy received the fixed oral combination of 80 mg artemether and 480 mg lumefantrine twice daily for three days. Artemether and dihydroartemisinin plasma concentrations after the last dose administration were quantified using liquid chromatography coupled to tandem mass-spectroscopy. A simultaneous drug-metabolite population pharmacokinetic model for artemether and dihydroartemisinin was developed taking into account different disposition, absorption, error and covariate models. A separate modeling approach and a non-compartmental analysis (NCA) were also performed to enable a comparison with literature values and different modeling strategies. RESULTS: The treatment was well tolerated and there were no cases of recurrent malaria. A flexible absorption model with sequential zero-order and transit-compartment absorption followed by a simultaneous one-compartment disposition model for both artemether and dihydroartemisinin provided the best fit to the data. Artemether and dihydroartemisinin exposure was lower than that reported in non-pregnant populations. An approximately fourfold higher apparent volume of distribution for dihydroartemisinin was obtained by noncompartmental analysis and separate modeling compared to that from simultaneous modeling of the drug and metabolite. This highlights a potential pitfall when analyzing drug/metabolite data with traditional approaches. CONCLUSION: The population pharmacokinetic properties of artemether and dihydroartemisinin, in pregnant women with uncomplicated P. falciparum malaria in Uganda, were described satisfactorily by a simultaneous drug-metabolite model without covariates. Concentrations of artemether and its metabolite dihydroartemisinin were relatively low in pregnancy compared to literature data. However, this should be interpreted with caution considered the limited literature available. Further studies in larger series are urgently needed for this vulnerable group.     

Estimation of Lead Elimination Rates for Liver and Kidney Using a Physiologically- Based Pharmacokinetic Model for Improved Human Risk Analysis

This research has studied the uncertainties in a physiologically-based pharmacokinetic (PBPK) model that describes uptake, accumulation, and elimination of Pb in the human body and to estimate the model's parameters. The model's application required probabilistic Pb exposure to humans which was accomplished by determining Pb content in various food items and food consumption patterns in a rural site near Kanpur, India. The important model parameters that varied were excretion constants, KE_LI and KE_KI (1/d), for elimination of Pb from liver and kidney. For estimating these parameters, the PBPK model's equations were reorganized by incorporating steady state conditions. Measured blood and urine Pb levels were used for estimating these parameters. A significant variability was observed in estimated parameters, KE_LI (0.112 to 0.248/day) and KE_KI (0.390 to 0.794/day). This research suggested that excretion parameters must be taken in a stochastic sense for obtaining proper estimates of human risk. In addition to KE_LI and KE_KI, variability (food quantity, Pb concentration in food items, and bodyweight) was considered for estimating blood Pb concentrations through PBPK modeling and Monte-Carlo simulation. It was demonstrated that by not considering the variability, health risk was underestimated (compare 8.98 × 10^?5 [no variability] to 9.34 × 10^?3 [with variability]).     

Hologram QSAR model for the prediction of human oral bioavailability

A drug intended for use in humans should have an ideal balance of pharmacokinetics and safety, as well as potency and selectivity. Unfavorable pharmacokinetics can negatively affect the clinical development of many otherwise promising drug candidates. A variety of in silico ADME (absorption, distribution, metabolism, and excretion) models are receiving increased attention due to a better appreciation that pharmacokinetic properties should be considered in early phases of the drug discovery process. Human oral bioavailability is an important pharmacokinetic property, which is directly related to the amount of drug available in the systemic circulation to exert pharmacological and therapeutic effects. In the present work, hologram quantitative structure–activity relationships (HQSAR) were performed on a training set of 250 structurally diverse molecules with known human oral bioavailability. The most significant HQSAR model (q² = 0.70, r² = 0.93) was obtained using atoms, bond, connection, and chirality as fragment distinction. The predictive ability of the model was evaluated by an external test set containing 52 molecules not included in the training set, and the predicted values were in good agreement with the experimental values. The HQSAR model should be useful for the design of new drug candidates having increased bioavailability as well as in the process of chemical library design, virtual screening, and high-throughput screening.     

Consensus hologram QSAR modeling for the prediction of human intestinal absorption

Consistent in silico models for ADME properties are useful tools in early drug discovery. Here, we report the hologram QSAR modeling of human intestinal absorption using a dataset of 638 compounds with experimental data associated. The final validated models are consistent and robust for the consensus prediction of this important pharmacokinetic property and are suitable for virtual screening applications.     

Physiologically based pharmacokinetic modeling of hydrogen cyanide in humans following the oral administration of potassium cyanide and cyanogenic glycosides from food

Abstract

Cyanogenic glycosides (CGs) are commonly found in some edible plants and seeds. After ingestion, CGs can release toxic hydrogen cyanide (HCN) in humans. At present, unfortunately, there is no tool capable of predicting the cyanide concentration in human blood and organs following oral administration of CG-containing food. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model of cyanide following the ingestion of CG-containing food in humans. To develop this model, pharmacokinetic data concerning cyanide concentration levels in humans exposed to potassium cyanide (KCN) and CG-containing foods (persipan paste, linseed, cassava, and bitter apricot kernels) were obtained from published data. This study created a model structure consisting of four organ compartments including the lungs, kidneys, liver, and slowly perfused tissues by employing Berkeley Madonna software to extract three unknown parameters including the maximum velocity of rhodanese, the absorption rate constant, and the bioavailability for oral administration of KCN and the four CG-containing foods (equivalent a 6.8?mg dose of cyanide). The model was then validated by comparing the simulated results for the concentration-time courses of cyanide levels in venous blood with data from two clinical studies covering the oral administration of KCN and linseed at three other doses.     

PKQuest: volatile solutes - application to enflurane,nitrous oxide,halothane, methoxyflurane and toluene pharmacokinetics

BACKGROUND: The application of physiologically based pharmacokinetic models (PBPK) to human studies has been limited by the lack of the detailed organ information that is required for this analysis. PKQuest is a new generic PBPK that is designed to avoid this problem by using a set of "standard human" default parameters that are applicable to most solutes. RESULTS: PKQuest is used to model the human pharmacokinetics of the volatile solutes. A "standard human" value for the lipid content of the blood and each organ (klip) was chosen. This set of klip and the oil/water partition coefficient then specifies the organ/blood partition for each organ. Using this approach, the pharmacokinetics of inert volatile solute is completely specified by just 2 parameters: the water/air and oil/water partition coefficients. The model predictions of PKQuest were in good agreement with the experimental data for the inert solutes enflurane and nitrous oxide and the metabolized solutes halothane and toluene. METHODS: The experimental data that was modeled was taken from previous publications. CONCLUSIONS: This approach greatly increases the predictive power of the PBPK. For inert volatile solutes the pharmacokinetics are determined just from the water/air and oil/water partition coefficient. Methoxyflurane cannot be modeled by this PBPK because the arterial and end tidal partial pressures are not equal (as assumed in the PBPK). This inequality results from the "washin-washout" artifact in the large airways that is established for solutes with large water/air partition coefficients.PKQuest and the worked examples are available on the web www.pkquest.com.     

A mechanistic framework for a priori pharmacokinetic predictions of orally inhaled drugs

The fate of orally inhaled drugs is determined by pulmonary pharmacokinetic processes such as particle deposition, pulmonary drug dissolution, and mucociliary clearance. Even though each single process has been systematically investigated, a quantitative understanding on the interaction of processes remains limited and therefore identifying optimal drug and formulation characteristics for orally inhaled drugs is still challenging. To investigate this complex interplay, the pulmonary processes can be integrated into mathematical models. However, existing modeling attempts considerably simplify these processes or are not systematically evaluated against (clinical) data. In this work, we developed a mathematical framework based on physiologically-structured population equations to integrate all relevant pulmonary processes mechanistically. A tailored numerical resolution strategy was chosen and the mechanistic model was evaluated systematically against data from different clinical studies. Without adapting the mechanistic model or estimating kinetic parameters based on individual study data, the developed model was able to predict simultaneously (i) lung retention profiles of inhaled insoluble particles, (ii) particle size-dependent pharmacokinetics of inhaled monodisperse particles, (iii) pharmacokinetic differences between inhaled fluticasone propionate and budesonide, as well as (iv) pharmacokinetic differences between healthy volunteers and asthmatic patients. Finally, to identify the most impactful optimization criteria for orally inhaled drugs, the developed mechanistic model was applied to investigate the impact of input parameters on both the pulmonary and systemic exposure. Interestingly, the solubility of the inhaled drug did not have any relevant impact on the local and systemic pharmacokinetics. Instead, the pulmonary dissolution rate, the particle size, the tissue affinity, and the systemic clearance were the most impactful potential optimization parameters. In the future, the developed prediction framework should be considered a powerful tool for identifying optimal drug and formulation characteristics.     

Separating Pharmacokinetic and Pharmacodynamic Components in Empirical Adjustment Factor Distributions

For a particular chemical, one can treat the chemical-by-chemical variation in relative doses for equal toxicity in experimental animals and humans as a characterization of the likelihoods of extrapolation factors of different magnitudes. An emerging approach to noncancer risk assessment is to use such empirical distributions in place of fixed Uncertainty Factors. This paper discusses dividing the overall variation into two sub-distributions representing pharmacokinetic (PK) and pharmacodynamic (PD) contributions to the variation among chemicals in the animal-to-human toxicologically equivalent dose. If a physiologically based pharmacokinetic model (PBPK model) is used to derive a compound specific adjustment factor (CSAF) for the pharmacokinetic component, the deconvolution of the PK and PD components allows one to remove the PK component (to be replaced with the CSAF), while retaining the uncertainty in pharmacodynamics that PBPK models do not address. One must then add back the uncertainty in the PBPK determination of the CSAF (which may be considerable). A candidate criterion for whether one can use an uncertain PBPK model is whether the generic uncertainty about cross-species pharmacokinetics (reflected in the PK component of the overall empirical distribution) is larger than the chemical-specific uncertainty in the determination of kinetically equivalent doses in experimental animals and humans.     

Tissue expression profile of human neonatal Fc receptor (FcRn) in Tg32 transgenic mice

The neonatal Fc receptor (FcRn) is a homeostatic receptor responsible for prolonging immunoglobulin G (IgG) half-life by protecting it from lysosomal degradation and recycling it to systemic circulation. Tissue-specific FcRn expression is a critical parameter in physiologically-based pharmacokinetic (PBPK) modeling for translational pharmacokinetics of Fc-containing biotherapeutics. Using online peptide immuno-affinity chromatography coupled with high resolution mass spectrometry, we established a quantitative FcRn tissue protein expression profile in human FcRn (hFcRn) transgenic mice, Tg32 homozygous and hemizygous strains. The concentration of hFcRn across 14 tissues ranged from 3.5 to 111.2 pmole per gram of tissue. Our hFcRn quantification data from Tg32 mice will enable a more refined PBPK model to improve the accuracy of human PK predictions for Fc-containing biotherapeutics.     

The design and fabrication of three-chamber microscale cell culture analog devices with integrated dissolved oxygen sensors

Whole animal testing is an essential part in evaluating the toxicological and pharmacological profiles of chemicals and pharmaceuticals, but these experiments are expensive and cumbersome. A cell culture analog (CCA) system, when used in conjunction with a physiologically based pharmacokinetic (PBPK) model, provides an in vitro supplement to animal studies and the possibility of a human surrogate for predicting human response in clinical trials. A PBPK model mathematically simulates animal metabolism by modeling the absorption, distribution, metabolism, and elimination kinetics of a chemical in interconnected tissue compartments. A CCA uses mammalian cells cultured in interconnected chambers to physically represent the corresponding PBPK. These compartments are connected by recirculating tissue culture medium that acts as a blood surrogate. The purpose of this article is to describe the design and basic operation of the microscale manifestation of such a system. Microscale CCAs offer the potential for inexpensive, relatively high throughput evaluation of chemicals while minimizing demand for reagents and cells. Using microfabrication technology, a three-chamber ("lung"-"liver"-"other") microscale cell culture analog (microCCA) device was fabricated on a 1 in. (2.54 cm) square silicon chip. With a design flow rate of 1.76 microL/min, this microCCA device achieves approximate physiological liquid-to-cell ratio and hydrodynamic shear stress while replicating the liquid residence time parameters in the PBPK model. A dissolved oxygen sensor based on collision quenching of a fluorescent ruthenium complex by oxygen molecules was integrated into the system, demonstrating the potential to integrate real-time sensors into such devices.     

Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current,past, and future climates

How climate constrains species’ distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat‐balance model, to convert macroclimate data to pika‐specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8–19% less habitat loss in response to annual temperature increases of ~3–5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect: climate‐imposed restrictions on activity. This more complete understanding is necessary to inform climate adaptation actions, management strategies, and conservation plans.     

A parametric study of thermal therapy of skin tissue

A thermal therapy for cancer in skin tissue is numerically investigated using three bioheat conduction models, namely Pennes, thermal wave and dual-phase lag models. A laser is applied at the surface of the skin for cancer ablation, and the temperature and thermal damage distributions are predicted using the three bioheat models and two different modeling approaches of the laser effect. The first one is a prescribed surface heat flux, in which the tissue is assumed to be highly absorbent, while the second approach is a volumetric heat source, which is reasonable if the scattering and absorption skin effects are of similar magnitude. The finite volume method is applied to solve the governing bioheat equation. A parametric study is carried out to ascertain the effects of the thermophysical properties of the cancer on the thermal damage. The temperature distributions predicted by the three models exhibit significant differences, even though the temperature distributions are similar when the laser is turned off. The type of bioheat model has more influence on the predicted thermal damage than the type of modeling approach used for the laser. The phase lags of heat flux and temperature gradient have an important influence on the results, as well as the thermal conductivity of the cancer. In contrast, the uncertainty in the specific heat and blood perfusion rate has a minor influence on the thermal damage.