A physiologically based pharmacokinetic model of alvespimycin in mice and extrapolation to rats and humans

A whole-body mechanistic physiologically-based pharmacokinetic modeling of intravenous iron

Iron is essential for every cell of the mammalian organism. Iron deficiency is a major public health issue worldwide. Intravenous (IV) iron therapy has been used to treat anemia. However, IV iron therapy is known far away from ideal because the quantitative relationship between the pharmacokinetics and biodistribution of IV iron under different iron statuses remains unclear. Patients are known to suffer adverse effects from excessive iron accumulation. Our objective was to develop a physiologically based pharmacokinetic (PBPK) model of iron in mice and validate its application for predicting iron disposition in rats and humans. Previously published data on iron were collected for constructing the PBPK model of iron in mice, and then extrapolated to rats and humans based on physiologically and chemically specific parameters relevant to each species. The PBPK model characterized the distribution of iron in mice successfully. The model based on extrapolation to rats accurately simulated the ferric carboxymaltose (FCM) PK profiles in rat tissues. Similarly, the observed and simulated serum PK of FCM in humans were in reasonable agreement. This mechanistic whole-body PBPK model is useful for understanding and predicting iron effects on different species. It also establishes a foundation for future research that incorporates iron kinetics and biodistribution, along with related clinical experiments. This approach could lead to the development of effective and personalized iron deficiency anemia treatments.

Physiologically based toxicokinetic modelling of Tri(2-chloroethyl) phosphate (TCEP) in mice accounting for multiple exposure routes

Exposure routes are important for health risk assessment of chemical risks. The application of physiologically based toxicokinetic (PBTK) models to predict concentrations in vivo can determine the effects of harmful substances and tissue accumulation on the premise of saving experimental costs. In this study, Tri(2-chloroethyl) phosphate (TCEP), an organophosphate ester (OPE), was used as an example to study the PBTK model of mice exposed to different exposure doses by multiple routes. Different routes of exposure (gavage and intradermal injection) can cause differences in the concentration of chemicals in the organs. TCEP that enters the body through the mouth is mainly concentrated in the gastrointestinal tract and liver. However, the concentrations of chemicals that enter the skin into the mice are higher in skin, rest of body, and blood. In addition, TCEP was absorbed and accumulated very rapidly in mice, within half an hour after a single exposure. We have successfully established a mouse PBTK model of the TCEP accounting for multiple exposure Routes and obtained a series of kinetic parameters. The model includes blood, liver, kidney, stomach, intestine, skin, and rest of body compartments. Oral and dermal exposure route was considered for PBTK model. The PBTK model established in this study has a good predictive ability. More than 70% of the predicted values deviated from the measured values by less than 5-fold. In addition, we extrapolated the model to humans. A human PBTK model is built. We performed a health risk assessment for world populations based on human PBTK model. The risk of TCEP in dust is greater through mouth than through skin. The risk of TCEP in food of Chinese population is greater than dust.

PBPK Modeling as an Alternative Method of Interspecies Extrapolation that Reduces the Use of Animals: A Systematic Review

INTRODUCTION

Physiologically based pharmacokinetic (PBPK) modeling is a computational approach that simulates the anatomical structure of the studied species and presents the organs and tissues as compartments interconnected by arterial and venous blood flows.

AIM

The aim of this systematic review was to analyze the published articles focused on the development of PBPK models for interspecies extrapolation in the disposition of drugs and health risk assessment, presenting to this modeling an alternative to reduce the use of animals.

METHODS

For this purpose, a systematic search was performed in PubMed using the following search terms: "PBPK" and "Interspecies extrapolation". The revision was performed according to PRISMA guidelines.

RESULTS

In the analysis of the articles, it was found that rats and mice are the most commonly used animal models in the PBPK models; however, most of the physiological and physicochemical information used in the reviewed studies were obtained from previous publications. Additionally, most of the PBPK models were developed to extrapolate pharmacokinetic parameters to humans and the main application of the models was for toxicity testing.

CONCLUSION

PBPK modeling is an alternative that allows the integration of in vitro and in silico data as well as parameters reported in the literature to predict the pharmacokinetics of chemical substances, reducing in large quantity the use of animals that are required in traditional studies.

Using the LeiCNS-PK3.0 Physiologically-Based Pharmacokinetic Model to Predict Brain Extracellular Fluid Pharmacokinetics in Mice

INTRODUCTION

The unbound brain extracelullar fluid (brainECF) to plasma steady state partition coefficient, Kp,uu,BBB, values provide steady-state information on the extent of blood-brain barrier (BBB) transport equilibration, but not on pharmacokinetic (PK) profiles seen by the brain targets. Mouse models are frequently used to study brain PK, but this information cannot directly be used to inform on human brain PK, given the different CNS physiology of mouse and human. Physiologically based PK (PBPK) models are useful to translate PK information across species.

AIM

Use the LeiCNS-PK3.0 PBPK model, to predict brain extracellular fluid PK in mice.

METHODS

Information on mouse brain physiology was collected from literature. All available connected data on unbound plasma, brainECF PK of 10 drugs (cyclophosphamide, quinidine, erlotonib, phenobarbital, colchicine, ribociclib, topotecan, cefradroxil, prexasertib, and methotrexate) from different mouse strains were used. Dosing regimen dependent plasma PK was modelled, and Kpuu,BBB values were estimated, and provided as input into the LeiCNS-PK3.0 model to result in prediction of PK profiles in brainECF.

RESULTS

Overall, the model gave an adequate prediction of the brainECF PK profile for 7 out of the 10 drugs. For 7 drugs, the predicted versus observed brainECF data was within two-fold error limit and the other 2 drugs were within five-fold error limit.

CONCLUSION

The current version of the mouse LeiCNS-PK3.0 model seems to reasonably predict available information on brainECF from healthy mice for most drugs. This brings the translation between mouse and human brain PK one step further.

Development and Evaluation of Amorphous Solid Dispersion of Riluzole with PBPK Model to Simulate the Pharmacokinetic Profile

In the current work, screening of polymers viz. polyacrylic acid (PAA), polyvinyl pyrrolidone vinyl acetate (PVP VA), and hydroxypropyl methyl cellulose acetate succinate (HPMC AS) based on drug-polymer interaction and wetting property was done for the production of a stable amorphous solid dispersion (ASD) of a poorly water-soluble drug Riluzole (RLZ). PAA showed maximum interaction and wetting property hence, was selected for further studies. Solid state characterization studies confirmed the formation of ASD with PAA. Saturation solubility, dissolution profile, and in vivo pharmacokinetic data of the ASD formulation were generated in rats against its marketed tablet Rilutor. The RLZ:PAA ASD showed exponential enhancement in the dissolution of RLZ. Predicted and observed pharmacokinetic data in rats showed enhanced area under curve (AUC) and Cmax in plasma and brain with respect to Rilutor. Furthermore, a physiologically based pharmacokinetic (PBPK) model of rats for Rilutor and RLZ ASD was developed and then extrapolated to humans where physiological parameters were changed along with a biochemical parameter. The partition coefficient was kept similar in both species. The model was used to predict different exposure scenarios, and the simulated data was compared with observed data points. The PBPK model simulated Cmax and AUC was within two times the experimental data for plasma and brain. The Cmax and AUC in the brain increased with ASD compared to Rilutor for humans showing its potential in improving its biopharmaceutical performance and hence enhanced therapeutic efficacy. The model can predict the RLZ concentration in multiple compartments including plasma and liver.

Quantitative Characterization of Clinically Relevant Drug-Metabolizing Enzymes and Transporters in Rat Liver and Intestinal Segments for Applications in PBPK Modeling

Rats are extensively used as a preclinical model for assessing drug pharmacokinetics (PK) and tissue distribution; however, successful translation of the rat data requires information on the differences in drug metabolism and transport mechanisms between rats and humans. To partly fill this knowledge gap, we quantified clinically relevant drug-metabolizing enzymes and transporters (DMETs) in the liver and different intestinal segments of Sprague-Dawley rats. The levels of DMET proteins in rats were quantified using the global proteomics-based total protein approach (TPA) and targeted proteomics. The abundance of the major DMET proteins was largely comparable using quantitative global and targeted proteomics. However, global proteomics-based TPA was able to detect and quantify a comprehensive list of 66 DMET proteins in the liver and 37 DMET proteins in the intestinal segments of SD rats without the need for peptide standards. Cytochrome P450 (Cyp) and UDP-glycosyltransferase (Ugt) enzymes were mainly detected in the liver with the abundance ranging from 8 to 6502 and 74 to 2558 pmol/g tissue. P-gp abundance was higher in the intestine (124.1 pmol/g) as compared to that in the liver (26.6 pmol/g) using the targeted analysis. Breast cancer resistance protein (Bcrp) was most abundant in the intestinal segments, whereas organic anion transporting polypeptides (Oatp) 1a1, 1a4, 1b2, and 2a1 and multidrug resistance proteins (Mrp) 2 and 6 were predominantly detected in the liver. To demonstrate the utility of these data, we modeled digoxin PK by integrating protein abundance of P-gp and Cyp3a2 into a physiologically based PK (PBPK) model constructed using PK-Sim software. The model was able to reliably predict the systemic as well as tissue concentrations of digoxin in rats. These findings suggest that proteomics-informed PBPK models in preclinical species can allow mechanistic PK predictions in animal models including tissue drug concentrations.

A quantitative high-throughput screen identifies compounds that lower expression of the SCA2-and ALS-associated gene ATXN2

CAG repeat expansions in the ATXN2 (ataxin-2) gene can cause the autosomal dominant disorder spinocerebellar ataxia type 2 (SCA2) as well as increase the risk of ALS. Abnormal molecular, motor, and neurophysiological phenotypes in SCA2 mouse models are normalized by lowering ATXN2 transcription, and reduction of nonmutant Atxn2 expression has been shown to increase the life span of mice overexpressing the TDP-43 (transactive response DNA-binding protein 43 kDa) ALS protein, demonstrating the potential benefits of targeting ATXN2 transcription in humans. Here, we describe a quantitative high-throughput screen to identify compounds that lower ATXN2 transcription. We screened 428,759 compounds in a multiplexed assay using an ATXN2-luciferase reporter in human embryonic kidney 293 (HEK-293) cells and identified a diverse set of compounds capable of lowering ATXN2 transcription. We observed dose-dependent reductions of endogenous ATXN2 in HEK-293 cells treated with procillaridin A, 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG), and heat shock protein 990 (HSP990), known inhibitors of HSP90 and Na+/K+-ATPases. Furthermore, HEK-293 cells expressing polyglutamine-expanded ATXN2-Q58 treated with 17-DMAG had minimally detectable ATXN2, as well as normalized markers of autophagy and endoplasmic reticulum stress, including STAU1 (Staufen 1), molecular target of rapamycin, p62, LC3-II (microtubule-associated protein 1A/1B-light chain 3II), CHOP (C/EBP homologous protein), and phospho-eIF2α (eukaryotic initiation factor 2α). Finally, bacterial artificial chromosome ATXN2-Q22 mice treated with 17-DMAG or HSP990 exhibited highly reduced ATXN2 protein abundance in the cerebellum. Taken together, our study demonstrates inhibition of HSP90 or Na+/K+-ATPases as potentially effective therapeutic strategies for treating SCA2 and ALS.

Drug Exposure to Establish Pharmacokinetic-Response Relationships in Oncology

In the oncology field, understanding the relationship between the dose administered and the exerted effect is particularly important because of the narrow therapeutic index associated with anti-cancer drugs and the high interpatient variability. Therefore, in this review, we provide a critical perspective of the different methods of characterising treatment exposure in the oncology setting. The increasing number of modelling applications in oncology reflects the applicability and the impact of pharmacometrics on all phases of the drug development process and patient management as well. Pharmacometric modelling is a worthy component within the current paradigm of model-based drug development, but pharmacometric modelling techniques are also accessible for the clinician in the optimisation of current oncology therapies. Consequently, the application of population models in a hospital setting by generating close collaborations between physicians and pharmacometricians is highly recommended, providing a systematic means of developing and assessing model-based metrics as 'drivers' for various responses to treatments, which can then be evaluated as predictors for treatment success. Characterising the key determinants of variability in exposure is of particular importance for anticancer agents, as efficacy and toxicity are associated with exposure. We present the different strategies to describe and predict drug exposure that can be applied depending on the data available, with the objective of obtaining the most useful information in the patients' favour throughout the full drug cycle. Therefore, the objective of the present article is to review the different approaches used to characterise a patient's exposure to oncology drugs, which will result in a better understanding of the time course of the response and the magnitude of interpatient variability.

Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues

Metformin is the primary drug for type 2 diabetes treatment and a promising candidate for other disease treatment. It has significant deviations between individuals in therapy efficiency and pharmacokinetics, leading to the administration of an unnecessary overdose or an insufficient dose. There is a lack of data regarding the concentration-time profiles in various human tissues that limits the understanding of pharmacokinetics and hinders the development of precision therapies for individual patients. The physiologically based pharmacokinetic (PBPK) model developed in this study is based on humans’ known physiological parameters (blood flow, tissue volume, and others). The missing tissue-specific pharmacokinetics parameters are estimated by developing a PBPK model of metformin in mice where the concentration time series in various tissues have been measured. Some parameters are adapted from human intestine cell culture experiments. The resulting PBPK model for metformin in humans includes 21 tissues and body fluids compartments and can simulate metformin concentration in the stomach, small intestine, liver, kidney, heart, skeletal muscle adipose, and brain depending on the body weight, dose, and administration regimen. Simulations for humans with a bodyweight of 70kg have been analyzed for doses in the range of 500-1500mg. Most tissues have a half-life (T_1/2) similar to plasma (3.7h) except for the liver and intestine with shorter T_1/2 and muscle, kidney, and red blood cells that have longer T_1/2. The highest maximal concentrations (C_max) turned out to be in the intestine (absorption process) and kidney (excretion process), followed by the liver. The developed metformin PBPK model for mice does not have a compartment for red blood cells and consists of 20 compartments. The developed human model can be personalized by adapting measurable values (tissue volumes, blood flow) and measuring metformin concentration time-course in blood and urine after a single dose of metformin. The personalized model can be used as a decision support tool for precision therapy development for individuals.

Impact of CNS Diseases on Drug Delivery to Brain Extracellular and Intracellular Target Sites in Human: A "WHAT-IF" Simulation Study

The blood-brain barrier (BBB) is equipped with unique physical and functional processes that control central nervous system (CNS) drug transport and the resulting concentration-time profiles (PK). In CNS diseases, the altered BBB and CNS pathophysiology may affect the CNS PK at the drug target sites in the brain extracellular fluid (brainECF) and intracellular fluid (brainICF) that may result in changes in CNS drug effects. Here, we used our human CNS physiologically-based PK model (LeiCNS-PK3.0) to investigate the impact of altered cerebral blood flow (CBF), tight junction paracellular pore radius (pararadius), brainECF volume, and pH of brainECF (pHECF) and of brainICF (pHICF) on brainECF and brainICF PK for 46 small drugs with distinct physicochemical properties. LeiCNS-PK3.0 simulations showed a drug-dependent effect of the pathophysiological changes on the rate and extent of BBB transport and on brainECF and brainICF PK. Altered pararadius, pHECF, and pHICF affected both the rate and extent of BBB drug transport, whereas changes in CBF and brainECF volume modestly affected the rate of BBB drug transport. While the focus is often on BBB paracellular and active transport processes, this study indicates that also changes in pH should be considered for their important implications on brainECF and brainICF target site PK.

Development of a Physiologically Based Pharmacokinetic Model of Mitragynine, Psychoactive Alkaloid in Kratom (Mitragyna Speciosa Korth.), In Rats and Humans

Mitragynine is a major psychoactive alkaloid in leaves of kratom (Mitragyna speciosa Korth.). To understand its disposition in organs, this study aimed to develop a physiologically based pharmacokinetic (PBPK) model that predicts mitragynine concentrations in plasma and organ of interests in rats and humans. The PBPK model consisted of six organ compartments (i.e. lung, brain, liver, fat, slowly perfused tissues, and rapidly perfused tissue). From systematic searching, three pharmacokinetic studies of mitragynine (two studies in rats and 1 study in humans) were retrieved from the literature. Berkeley Madonna Software (version 8.3.18) was used for model development and model simulation. The developed PBPK model consisted of biologically relevant features following involvement of (i) breast cancer-resistant protein (BCRP) in brain, (ii) a hepatic cytochrome P450 3A4 (CYP3A4)-mediated metabolism in the liver, and (iii) a diffusion-limited transport in fat. The simulations adequately describe simulated and observed data in the two species with different dosing regimens. PBPK models of mitragynine in rats and humans were successfully developed. The models may be used to guide optimal mitragynine dosing regimens.

Physiologically based pharmacokinetic-pharmacodynamic modeling for prediction of vonoprazan pharmacokinetics and its inhibition on gastric acid secretion following intravenous/oral administration to rats, dogs and humans

Vonoprazan is characterized as having a long-lasting antisecretory effect on gastric acid. In this study we developed a physiologically based pharmacokinetic (PBPK)-pharmacodynamic (PD) model linking to stomach to simultaneously predict vonoprazan pharmacokinetics and its antisecretory effects following administration to rats, dogs, and humans based on in vitro parameters. The vonoprazan disposition in the stomach was illustrated using a limited-membrane model. In vitro metabolic and transport parameters were derived from hepatic microsomes and Caco-2 cells, respectively. We found the most predicted plasma concentrations and pharmacokinetic parameters of vonoprazan in rats, dogs and humans were within twofold errors of the observed data. Free vonoprazan concentrations (f_u × C₂) in the stomach were simulated and linked to the antisecretory effects of the drug (I) (increases in pH or acid output) using the fomula dI/dt = k × f_u × C₂ × (I_max − I) − k_d × I. The vonoprazan dissociation rate constant k_d (0.00246 min⁻¹) and inhibition index K_I (35 nM) for H⁺/K⁺-ATPase were obtained from literatures. The vonoprazan-H⁺/K⁺-ATPase binding rate constant k was 0.07028 min⁻¹· μM⁻¹ using ratio of k_d to K_I. The predicted antisecretory effects were consistent with the observations following intravenous administration to rats (0.7 and 1.0 mg/kg), oral administration to dogs (0.3 and 1.0 mg/kg) and oral single dose or multidose to humans (20, 30, and 40 mg). Simulations showed that vonoprazan concentrations in stomach were 1000-fold higher than those in the plasma at 24 h following administration to human. Vonoprazan pharmacokinetics and its antisecretory effects may be predicted from in vitro data using the PBPK-PD model of the stomach. These findings may highlight 24-h antisecretory effects of vonoprazan in humans following single-dose or the sustained inhibition throughout each 24-h dosing interval during multidose administration.

Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review

PURPOSE

Physiologically based pharmacokinetic (PBPK) modeling, a mathematical modeling approach which uses a pharmacokinetic model to mimick human physiology to predict drug concentration-time profiles, has been used for the discover and development of drugs in various fields, including oncology, since 2000. There have been a few general review articles on the utilization of PBPK in the development of oncology drugs, but these do not include an evaluation of model prediction accuracy. We therefore conducted a systematic review to define the accuracy of PBPK model prediction and its utility throughout all the developmental phases of oncology drugs.

METHODS

A systematic search was performed in the PubMed, PubMed Central and Cochrane Library databases from 1980 to February 2017 for articles (1) written in English, (2) focused on oncology or antineoplastic or anticancer drugs, tumor or cancer or anticancer drugs listed in the U.S. National Institutes of Health and (3) involving a PBPK model. The absolute-average-folding-errors (AAFEs) of the area under the curve (AUC) between predicted and observed values in each article were calculated to assess model prediction accuracy.

RESULTS

Of the 2341 articles initially identified by our search of the databases, 40 were included in the review analysis. These articles reported on six types of studies, i.e. in vivo (n = 4), first-in-human (n = 5), phase II/III clinical trials (n = 9), organ impairment (n = 3), pediatrics (n = 4) and drug-drug interactions (n = 15). AAFEs of the predicted AUC for all groups of studies were within 1.3-fold of each other despite variations in experimental methodologies.

CONCLUSION

PBPK modeling is a potential tool which can be effectively applied throughout all phases of oncology drug development. The number of experimental animals and human participants enrolled in the studies can be reduced using PBPK modeling and PBPK-population-PK modeling. The limited number of publications of unsuccessful model application to date may contribute to bias toward the usefulness of modeling.

Integration of a plasma protein binding factor to the Chemical-Specific Adjustment Factor (CSAF) for facilitating the estimation of uncertainties in interspecies extrapolations when deriving health-based exposure limits for active pharmaceutical ingredients: Investigation of recent drug datasets

The objective was to challenge cross-species extrapolation factors with which to scale animal doses to human by any route for non-carcinogenic endpoints. The conventional hypothesis of the toxicokinetics (TK)-toxicodynamics (TD) relationship was equal toxicity at equal plasma level of the total drug moiety in each species, but this should also follow the free drug assumption, which states that only the unbound drug moiety in plasma may elicit a TD effect in tissue. Therefore, a protein binding factor (PBF) was combined with the Chemical-Specific Adjustment Factor (CSAF) (i.e., CSAF x PBF). The value of PBF of each drug was set equal to the ratio between human and animals of the unbound fraction in plasma (fu_p). Recent drug datasets were investigated. Our results indicate that any CSAF value would be increased or decreased while PBF deviates to the unity, and this required more attention. Accordingly, further testing indicated that the CSAF values set equal to basic allometric uncertainty factors according to the conventional hypothesis (dog∼2, monkey∼3.1, rat∼7, mouse∼12) would increase by including PBF for 30% of the drugs tested that showed a superior fu_p value in human compared to animals. However, default uncertainty factors in the range of 10-100 were less frequently exceeded. Overall, PBF could be combined with any other uncertainty factor to get reliable estimate of CSAF for each bound drug in deriving health-based exposure limits.

Prediction of Deoxypodophyllotoxin Disposition in Mouse, Rat, Monkey, and Dog by Physiologically Based Pharmacokinetic Model and the Extrapolation to Human

Deoxypodophyllotoxin (DPT) is a potential anti-tumor candidate prior to its clinical phase. The aim of the study was to develop a physiologically based pharmacokinetic (PBPK) model consisting of 13 tissue compartments to predict DPT disposition in mouse, rat, monkey, and dog based on in vitro and in silico inputs. Since large interspecies difference was found in unbound fraction of DPT in plasma, we assumed that K_t:pl,u (unbound tissue-to-plasma concentration ratio) was identical across species. The predictions of our model were then validated by in vivo data of corresponding preclinical species, along with visual predictive checks. Reasonable matches were found between observed and predicted plasma concentrations and pharmacokinetic parameters in all four animal species. The prediction in the related seven tissues of mouse was also desirable. We also attempted to predict human pharmacokinetic profile by both the developed PBPK model and interspecies allometric scaling across mouse, rat and monkey, while dog was excluded from the scaling. The two approaches reached similar results. We hope the study will help in the efficacy and safety assessment of DPT in future clinical studies and provide a reference to the preclinical screening of similar compounds by PBPK model.

Use of a semi-physiological pharmacokinetic model to investigate the influence of itraconazole on tacrolimus absorption, distribution and metabolism in mice

1. The aim of this study was to investigate the influence of itraconazole (ITCZ) on tacrolimus absorption, distribution and metabolism by developing a semi-physiological pharmacokinetic model of tacrolimus in mice. 2. Mice were randomly divided into four groups, namely control group (CG, taking 3 mg kg^-1 tacrolimus only), low-dose group (LDG, taking tacrolimus with 12.5 mg kg^-1 ITCZ), medium-dose group (MDG, taking tacrolimus with 25 mg kg^-1 ITCZ) and high-dose group (HDG, taking tacrolimus with 50 mg kg^-1 ITCZ). 3. Liver clearance (CL_li) decreased significantly (**p < 0.01) in LDG (35.3%), MDG (45.2%) and HDG (58.7%) mice compared to CG mice. With respect to gut clearance (CL_gu), significant (**p < 0.01) decrease was also revealed in LDG (35.9%), MDG (50.2%) and HDG (64.6%) mice. A significant (**p < 0.01) higher tacrolimus brain-to-blood partition coefficient (K_t,br) was found in MDG (25.3%) and HDG (55.9%) mice than in CG mice. Moreover, a significant (*p < 0.05) increase (16.3%) was found in the absorption rate constant (K_a) in HDG mice compared to CG mice. There was a significant (**p < 0.01) association between ITCZ dose and the change in CL_gu (ΔCL_gu, r= -0.790), the change in CL_li (ΔCL_li, r= -0.787) and the change in K_t,br (ΔK_t,br, r = 0.727), while the association between ITCZ dose and the change in K_a (ΔK_a) was not significant (p > 0.05). 4. These findings could be useful in predicting the efficacy and toxicity of tacrolimus, and drug-drug interaction of ITCZ and tarcolimus in human.

Physiologically Based Pharmacokinetic Modeling of Fluorescently Labeled Block Copolymer Nanoparticles for Controlled Drug Delivery in Leukemia Therapy

A physiologically based pharmacokinetic (PBPK) model was developed that describes the concentration and biodistribution of fluorescently labeled nanoparticles in mice used for the controlled delivery of dexamethasone in acute lymphoblastic leukemia (ALL) therapy. The simulated data showed initial spikes in nanoparticle concentration in the liver, spleen, and kidneys, whereas concentration in plasma decreased rapidly. These simulation results were consistent with previously published in vivo data. At shorter time scales, the simulated data predicted decrease of nanoparticles from plasma with concomitant increase in the liver, spleen, and kidneys before decaying at longer timepoints. Interestingly, the simulated data predicted an unaccounted accumulation of about 50% of the injected dose of nanoparticles. Incorporation of an additional compartment into the model justified the presence of unaccounted nanoparticles in this compartment. Our results suggest that the proposed PBPK model can be an excellent tool for prediction of optimal dose of nanoparticle-encapsulated drugs for cancer treatment.