Time Scale Calculus: a new approach to multi-dose pharmacokinetic modeling.

In this paper, we use Time Scale Calculus (TSC) to formulate and solve pharmacokinetic models exploring multiple dose dynamics. TSC is a mathematical framework that allows the modeling of dynamical systems comprising continuous and discrete processes. This characteristic makes TSC particularly suited for multi-dose pharmacokinetic problems, which inherently feature a blend of continuous processes (such as absorption, metabolization, and elimination) and discrete events (drug intake). We use this toolkit to derive analytical expressions for blood concentration trajectories under various multi-dose regimens across several flagship pharmacokinetic models. We demonstrate that this mathematical framework furnishes an alternative and simplified way to model and retrieve analytical solutions for multi-dose dynamics. For instance, it enables the study of blood concentration responses to arbitrary dose regimens and facilitates the characterization of the long-term behavior of the solutions, such as their steady state.

Revising Pharmacokinetics of Oral Drug Absorption: II Bioavailability-Bioequivalence Considerations.

PURPOSE: To explore the application of the parameters of the physiologically based finite time pharmacokinetic (PBFTPK) models subdivided in first-order (PBFTPK)1 and zero-order (PBFTPK)0 models to bioavailability and bioequivalence. To develop a methodology for the estimation of absolute bioavailability, F, from oral data exclusively. METHODS: Simulated concentration time data were generated from the Bateman equation and compared with data generated from the (PBFTPK)1 and (PBFTPK)0 models. The blood concentration Cb(τ) at the end of the absorption process τ, was compared to Cmax; the utility of [Formula: see text] and [Formula: see text] in bioequivalence assessment was also explored. Equations for the calculation of F from oral data were derived for the (PBFTPK)1 and (PBFTPK)0 models. An estimate for F was also derived from an areas proportionality using oral data exclusively. RESULTS: The simulated data of the (PBFTPK)0 models exhibit rich dynamics encountered in complex drug absorption phenomena. Both (PBFTPK)1 and (PBFTPK)0 models result either in Cmax = Cb(τ) or Cmax > Cb(τ) for rapidly- and not rapidly-absorbed drugs, respectively; in the latter case, Cb(τ) and τ are meaningful parameters for drug's rate of exposure. For both (PBFTPK)1 and (PBFTPK)0 models, [Formula: see text] or portions of it cannot be used as early exposure rate indicators. [Formula: see text] is a useful parameter for the assessment of extent of absorption for very rapidly absorbed drugs. An estimate for F for theophylline formulations was found close to unity. CONCLUSION: The (PBFTPK)1 and (PBFTPK)0 models are more akin to in vivo conditions. Estimates for F can be derived from oral data exclusively.

Lumbar cerebrospinal fluid-to-brain extracellular fluid surrogacy is context-specific: insights from LeiCNS-PK3.0 simulations.

Predicting brain pharmacokinetics is critical for central nervous system (CNS) drug development yet difficult due to ethical restrictions of human brain sampling. CNS pharmacokinetic (PK) profiles are often altered in CNS diseases due to disease-specific pathophysiology. We previously published a comprehensive CNS physiologically-based PK (PBPK) model that predicted the PK profiles of small drugs at brain and cerebrospinal fluid compartments. Here, we improved this model with brain non-specific binding and pH effect on drug ionization and passive transport. We refer to this improved model as Leiden CNS PBPK predictor V3.0 (LeiCNS-PK3.0). LeiCNS-PK3.0 predicted the unbound drug concentrations of brain ECF and CSF compartments in rats and humans with less than two-fold error. We then applied LeiCNS-PK3.0 to study the effect of altered cerebrospinal fluid (CSF) dynamics, CSF volume and flow, on brain extracellular fluid (ECF) pharmacokinetics. The effect of altered CSF dynamics was simulated using LeiCNS-PK3.0 for six drugs and the resulting drug exposure at brain ECF and lumbar CSF were compared. Simulation results showed that altered CSF dynamics changed the CSF PK profiles, but not the brain ECF profiles, irrespective of the drug's physicochemical properties. Our analysis supports the notion that lumbar CSF drug concentration is not an accurate surrogate of brain ECF, particularly in CNS diseases. Systems approaches account for multiple levels of CNS complexity and are better suited to predict brain PK.

A latent variable approach to account for correlated inputs in global sensitivity analysis.

In drug development decision-making is often supported through model-based methods, such as physiologically-based pharmacokinetics (PBPK). Global sensitivity analysis (GSA) is gaining use for quality assessment of model-informed inference. However, the inclusion and interpretation of correlated factors in GSA has proven an issue. Here we developed and evaluated a latent variable approach for dealing with correlated factors in GSA. An approach was developed that describes the correlation between two model inputs through the causal relationship of three independent factors: the latent variable and the unique variances of the two correlated parameters. The latent variable approach was applied to a set of algebraic models and a case from PBPK. Then, this method was compared to Sobol's GSA assuming no correlations, Sobol's GSA with groups and the Kucherenko approach. For the latent variable approach, GSA was performed with Sobol's method. By using the latent variable approach, it is possible to devise a unique and easy interpretation of the sensitivity indices while maintaining the correlation between the factors. Compared methods either consider the parameters independent, group the dependent variables into one unique factor or present difficulties in the interpretation of the sensitivity indices. In situations where GSA is called upon to support model-informed decision-making, the latent variable approach offers a practical method, in terms of ease of implementation and interpretability, for applying GSA to models with correlated inputs that does not violate the independence assumption. Prerequisites and limitations of the approach are discussed.

Automated proper lumping for simplification of linear physiologically based pharmacokinetic systems.

Physiologically based pharmacokinetic (PBPK) models are an important type of systems model used commonly in drug development before commencement of first-in-human studies. Due to structural complexity, these models are not easily utilised for future data-driven population pharmacokinetic (PK) analyses that require simpler models. In the current study we aimed to explore and automate methods of simplifying PBPK models using a proper lumping technique. A linear 17-state PBPK model for fentanyl was identified from the literature. Four methods were developed to search the optimal lumped model, including full enumeration (the reference method), non-adaptive random search (NARS), scree plot plus NARS, and simulated annealing (SA). For exploratory purposes, it was required that the total area under the fentanyl arterial concentration-time curve (AUC) between the lumped and original models differ by 0.002% at maximum. In full enumeration, a 4-state lumped model satisfying the exploratory criterion was found. In NARS, a lumped model with the same number of lumped states was found, requiring a large number of random samples. The scree plot provided a starting lumped model to NARS and the search completed within a short time. In SA, a 4-state lumped model was consistently delivered. In simplify an existing linear fentanyl PBPK model, SA was found to be robust and the most efficient and may be suitable for general application to other larger-scale linear systems. Ultimately, simplified PBPK systems with fundamental mechanisms may be readily used for data-driven PK analyses.

[Applicability analysis and evaluation of aglycones in single-pass intestinal perfusion technique based on PBPK model].

Single-pass intestinal perfusion( SPIP) is the common carrier of biopharmaceutics classification system( BCS) to study compound permeability. With the application and deepening study of BCS in the field of traditional Chinese medicine( TCM),SPIP model is becoming more and more common to study the intestinal absorption of TCM ingredients. Based on the limitations of the SPIP model in some researches on TCM permeability,it was speculated in this study that aglycone may be more suitable than the glycoside to study the intestinal absorption problem by using SPIP model. Furthermore,applicability of aglycone components was analyzed and evaluated. In this study,with quercetin,daidzein,formononetin,genistein and glycyrrhetinic acid used as research objects,the quantitative study of SPIP was used to evaluate the intestinal permeability of these aglycones and to predict the effective permeability coefficient( Peff) and absorption fraction( Fa) in human body. By combining studies comparison and analysis on multiple permeability research methods and prediction of human body absorption of aglycones in physiological-based pharmacokinetic models,this paper can further illustrate that the SPIP model is a good tool for studying the permeability of aglycones and predicting human absorption,which can provide data foundation and theoretical reference for researches on SPIP technique and BCS in intestinal absorption of TCM ingredients.

Ecological and human exposure assessment to PBDEs in Adige River.

The interest for environmental issues and the concern resulting from the potential exposure to contaminants were the starting point to develop methodologies in order to evaluate the consequences that those might have over both the environment and human health. Considering the feature of POPs, including PBDEs, such as bioaccumulation, biomagnification, long-range transport and adverse effects even long time after exposure, risk assessment of POPs requires specific approaches and tools. In this particular context, the MERLIN-Expo tool was used to assess the aquatic environmental exposure of Adige River to PBDEs and the accumulation of PBDEs in humans through the consumption of possible contaminated local aquatic food. The aquatic food web models provided as output of the deterministic simulation the time trend of concentrations for twenty years of BDE-47 and total PBDEs, expressed using the physico-chemical properties of BDE-47, in aquatic organisms of the food web of Adige River. For BDE-47, the highest accumulated concentrations were detected for two benthic species: Thymallus thymallus and Squalius cephalus whereas the lowest concentrations were obtained for the pelagic specie Salmo trutta marmoratus. The trend obtained for the total PBDEs, calculated using the physico-chemical properties of BDE-47, follows the one of BDE-47. For human exposure, different BDE-47 and total PBDEs concentration trends between children, adolescent, adults and elderly were observed, probably correlated with the human intake of fish products in the daily diet and the ability to metabolize these contaminants. In detail, for the adolescents, adults and elderly a continuous accumulation of the target contaminants during the simulation's years was observed, whereas for children a plateau at the end of the simulation period was perceived.

Application of a Bayesian approach to physiological modelling of mavoglurant population pharmacokinetics.

Mavoglurant (MVG) is an antagonist at the metabotropic glutamate receptor-5 currently under clinical development at Novartis Pharma AG for the treatment of central nervous system diseases. The aim of this study was to develop and optimise a population whole-body physiologically-based pharmacokinetic (WBPBPK) model for MVG, to predict the impact of drug-drug interaction (DDI) and age on its pharmacokinetics. In a first step, the model was fitted to intravenous (IV) data from a clinical study in adults using a Bayesian approach. In a second step, the optimised model was used together with a mechanistic absorption model for exploratory Monte Carlo simulations. The ability of the model to predict MVG pharmacokinetics when orally co-administered with ketoconazole in adults or administered alone in 3-11 year-old children was evaluated using data from three other clinical studies. The population model provided a good description of both the median trend and variability in MVG plasma pharmacokinetics following IV administration in adults. The Bayesian approach offered a continuous flow of information from pre-clinical to clinical studies. Prediction of the DDI with ketoconazole was consistent with the results of a non-compartmental analysis of the clinical data (threefold increase in systemic exposure). Scaling of the WBPBPK model allowed reasonable extrapolation of MVG pharmacokinetics from adults to children. The model can be used to predict plasma and brain (target site) concentration-time profiles following oral administration of various immediate-release formulations of MVG alone or when co-administered with other drugs, in adults as well as in children.

In vitro assessment of inhibitory effects of kinase inhibitors on CYP2C9, 3A and 1A2: Prediction of drug-drug interaction risk with warfarin and direct oral anticoagulants.

OBJECTIVE: This study aimed to evaluate the cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) potential of kinase inhibitors with warfarin and direct oral anticoagulants (DOACs). METHODS: An in vitro CYP probe substrate cocktail assay was used to study the inhibitory effects of fifteen kinase inhibitors on CYP2C9, 3A, and 1A2. Then, DDI predictions were performed using both mechanistic static and physiologically-based pharmacokinetic (PBPK) models. RESULTS: Linsitinib, masitinib, regorafenib, tozasertib, trametinib, and vatalanib were identified as competitive CYP2C9 inhibitors (Ki = 1.4, 1.0, 1.1, 3.8, 0.5, and 0.1 µM, respectively). Masitinib and vatalanib were competitive CYP3A inhibitors (Ki = 1.3 and 0.2 µM), and vatalanib noncompetitively inhibited CYP1A2 (Ki = 2.0 µM). Moreover, linsitinib and tozasertib were CYP3A time-dependent inhibitors (KI = 26.5 and 400.3 µM, kinact = 0.060 and 0.026 min-1, respectively). Only linsitinib showed time-dependent inhibition of CYP1A2 (KI = 13.9 µM, kinact = 0.018 min-1). Mechanistic static models identified possible DDI risks for linsitinib and vatalanib with (S)-/(R)-warfarin, and for masitinib with (S)-warfarin. PBPK simulations further confirmed that vatalanib may increase (S)- and (R)-warfarin exposure by 4.37- and 1.80-fold, respectively, and that linsitinib may increase (R)-warfarin exposure by 3.10-fold. Mechanistic static models predicted a smaller risk of DDIs between kinase inhibitors and apixaban or rivaroxaban. The greatest AUC increases (1.50-1.74) were predicted for erlotinib in combination with apixaban and rivaroxaban. Linsitinib, masitinib, and vatalanib were predicted to have a smaller effect on apixaban and rivaroxaban AUCs (AUCR 1.22-1.53). No kinase inhibitor was predicted to increase edoxaban exposure. CONCLUSIONS: Our results suggest that several kinase inhibitors, including vatalanib and linsitinib, can cause CYP-mediated drug-drug interactions with warfarin and, to a lesser extent, with apixaban and rivaroxaban. The work provides mechanistic insights into the risk of DDIs between kinase inhibitors and anticoagulants, which can be used to avoid preventable DDIs in the clinic.

In vivo determination of the bioavailability of folic acid through the utilization of the PBPK model in conjunction with UPLC.

This paper employed a physiologically based pharmacokinetic model (PBPK) to investigate the transformations of folic acid and its metabolites in vivo. Additionally, an ultra-performance liquid chromatography (UPLC) method was developed to accurately measure the body's retention rate and conversion rate of folic acid, tetrahydrofolate, and 5-methyltetrahydrofolate. Furthermore, the bioavailability of folic acid in the body was assessed by combining this method with an evaluation technique for animal models. The study found that the gastric metabolism time was 2 h, while the small intestinal metabolism duration was 4 h. The maximum conversion rate was observed in plasma and liver after 6 h, and in the brain after 8 h. This serves as a framework for creating a model to assess the bioavailability of folic acid in living organisms, to enhance the safety and efficacy of folic acid intake.

Incorporating Breastfeeding-Related Variability with Physiologically Based Pharmacokinetic Modeling to Predict Infant Exposure to Maternal Medication Through Breast Milk: a Workflow Applied to Lamotrigine.

Current methods to assess risk in infants exposed to maternal medication through breast milk do not specifically account for infants most vulnerable to high drug exposure. A workflow applied to lamotrigine incorporated variability in infant anatomy and physiology, milk intake volume, and milk concentration to predict infant exposure. An adult physiologically based pharmacokinetic model of lamotrigine was developed and evaluated. The model was scaled to account for growth and maturation of a virtual infant population (n=100). Daily infant doses were simulated using milk intake volume and concentration models described by a nonlinear equation of weight-normalized intake across infant age and a linear function on the relationship of observed milk concentrations and maternal doses, respectively. Average infant plasma concentration at steady state was obtained through simulation. Models were evaluated by comparing observed to simulated infant plasma concentrations from breastfeeding infants based on a 90% prediction interval (PI). Upper AUC ratio (UAR) was defined as a novel risk metric. Twenty-five paired (milk concentrations measured) and 18 unpaired (milk concentrations unknown) infant plasma samples were retrieved from the literature. Forty-four percent and 11% of the paired and unpaired infant plasma concentrations were outside of the 90% PI, respectively. Over all ages (0-7 months), unpaired predictions captured more observed infant plasma concentrations within 90% PI than paired. UAR was 0.18-0.44 when mothers received 200 mg lamotrigine, suggesting that infants can receive 18-44% of the exposure per dose as compared to adults. UARs determined for further medications could reveal trends to better classify at-risk mother-infant pairs.

Prediction of Drug Clearance from Enzyme and Transporter Kinetics.

Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.

Development of a quantitative relationship between CAR-affinity, antigen abundance, tumor cell depletion and CAR-T cell expansion using a multiscale systems PK-PD model.

The development of mechanism-based, multiscale pharmacokinetic-pharmacodynamic (PK-PD) models for chimeric antigen receptor (CAR)-T cells is needed to enable investigation of in vitro and in vivo correlation of CAR-T cell responses and to facilitate preclinical-to-clinical translation. Toward this goal, we first developed a cell-level in vitro PD model that quantitatively characterized CAR-T cell-induced target cell depletion, CAR-T cell expansion and cytokine release. The model accounted for key drug-specific (CAR-affinity, CAR-densities) and system-specific (antigen densities, E:T ratios) variables and was able to characterize comprehensive in vitro datasets from multiple affinity variants of anti-EGFR and anti-HER2 CAR-T cells. Next, a physiologically based PK (PBPK) model was developed to simultaneously characterize the biodistribution of untransduced T-cells, anti-EGFR CAR-T and anti-CD19 CAR-T cells in xenograft -mouse models. The proposed model accounted for the engagement of CAR-T cells with tumor cells at the site of action. Finally, an integrated PBPK-PD relationship was established to simultaneously characterize expansion of CAR-T cells and tumor growth inhibition (TGI) in xenograft mouse model, using datasets from anti-BCMA, anti-HER2, anti-CD19 and anti-EGFR CAR-T cells. Model simulations provided potential mechanistic insights toward the commonly observed multiphasic PK profile (i.e., rapid distribution, expansion, contraction and persistence) of CAR-T cells in the clinic. Model simulations suggested that CAR-T cells may have a steep dose-exposure relationship, and the apparent Cmax upon CAR-T cell expansion in blood may be more sensitive to patient tumor-burden than CAR-T dose levels. Global sensitivity analysis described the effect of other drug-specific parameters toward CAR-T cell expansion and TGI. The proposed modeling framework will be further examined with the clinical PK and PD data, and the learnings can be used to inform design and development of future CAR-T therapies.

The Path to Perfect Pediatric Posology - Drug Development in Pediatrics.

Reluctance to enroll pediatric subjects in clinical trials has left gaps in information about dosing, safety, and efficacy of medications. Pharmacotherapeutic information for pediatric patients may be available for only a small range of ages and may be deficient, as children respond differently as they grow and mature from prematurity to adolescence. Current regulations, however, require early planning for the participation of children in drug development, as pediatric plans must be submitted at the end of phase 1 (European Union) or the end of phase 2 (United States). These plans are extensive, outlining planned studies, subjects to be enrolled, dose and dosage form justification, planned observations, and statistical analysis as well as planned modeling, simulation, and extrapolation analyses. The extent to which efficacy information in adults can be extrapolated to children depends on how similar the disease is in adults and each of the 5 pediatric age groups. Extrapolation may not be possible for conditions that do not occur in adults, requiring a complete development plan in adults, or extrapolation may be complete because of similar pathology and response to treatment. Pharmacokinetic and safety information cannot be extrapolated and must be collected in children of all ages, unless a waiver is granted. Physiologically based pharmacokinetic modeling, optimal design, population pharmacokinetics, and scavenged samples are all examples of new methodologies being used to study pediatric therapeutics. Clinicaltrials.gov and EU Clinical Trials registry are good sources of results of pediatric trials, although sponsors are also working toward prompt publication of study results in peer-reviewed journals.

Systems pharmacology and enhanced pharmacodynamic models for understanding antibody-based drug action and toxicity.

Pharmacokinetic (PK) and pharmacodynamic (PD) models seek to describe the temporal pattern of drug exposures and their associated pharmacological effects produced at micro- and macro-scales of organization. Antibody-based drugs have been developed for a large variety of diseases, with effects exhibited through a comprehensive range of mechanisms of action. Mechanism-based PK/PD and systems pharmacology models can play a major role in elucidating and integrating complex antibody pharmacological properties, such as nonlinear disposition and dynamical intracellular signaling pathways triggered by ligation to their cognate targets. Such complexities can be addressed through the use of robust computational modeling techniques that have proven powerful tools for pragmatic characterization of experimental data and for theoretical exploration of antibody efficacy and adverse effects. The primary objectives of such multi-scale mathematical models are to generate and test competing hypotheses and to predict clinical outcomes. In this review, relevant systems pharmacology and enhanced PD (ePD) models that are used as predictive tools for antibody-based drug action are reported. Their common conceptual features are highlighted, along with approaches used for modeling preclinical and clinically available data. Key examples illustrate how systems pharmacology and ePD models codify the interplay among complex biology, drug concentrations, and pharmacological effects. New hybrid modeling concepts that bridge cutting-edge systems pharmacology models with established PK/ePD models will be needed to anticipate antibody effects on disease in subpopulations and individual patients.

A Physiologically Based Pharmacokinetic Model for Ganciclovir and Its Prodrug Valganciclovir in Adults and Children.

A physiologically based pharmacokinetic (PBPK) model has been developed for ganciclovir and its prodrug valganciclovir. Initial bottom-up modeling based on physicochemical drug properties and measured in vitro inputs was verified in preclinical animal species, and then, a clinical model was verified in a stepwise fashion with pharmacokinetic data in adult, children, and neonatal patients. The final model incorporated conversion of valganciclovir to ganciclovir through esterases and permeability-limited tissue distribution of both drugs with active transport processes added in gut, liver, and kidney. A PBPK model which accounted for known age-related tissue volumes, composition and blood flows, and renal filtration clearance was able to simulate well the measured plasma exposures in adults and pediatric patients. Overall, this work illustrates the stepwise development of PBPK models which could be used to predict pharmacokinetics in infants and neonates, thereby assisting drug development in a vulnerable patient population where clinical data are challenging to obtain.

Reduction of a Whole-Body Physiologically Based Pharmacokinetic Model to Stabilise the Bayesian Analysis of Clinical Data.

Whole-body physiologically based pharmacokinetic (PBPK) models are increasingly used in drug development for their ability to predict drug concentrations in clinically relevant tissues and to extrapolate across species, experimental conditions and sub-populations. A whole-body PBPK model can be fitted to clinical data using a Bayesian population approach. However, the analysis might be time consuming and numerically unstable if prior information on the model parameters is too vague given the complexity of the system. We suggest an approach where (i) a whole-body PBPK model is formally reduced using a Bayesian proper lumping method to retain the mechanistic interpretation of the system and account for parameter uncertainty, (ii) the simplified model is fitted to clinical data using Markov Chain Monte Carlo techniques and (iii) the optimised reduced PBPK model is used for extrapolation. A previously developed 16-compartment whole-body PBPK model for mavoglurant was reduced to 7 compartments while preserving plasma concentration-time profiles (median and variance) and giving emphasis to the brain (target site) and the liver (elimination site). The reduced model was numerically more stable than the whole-body model for the Bayesian analysis of mavoglurant pharmacokinetic data in healthy adult volunteers. Finally, the reduced yet mechanistic model could easily be scaled from adults to children and predict mavoglurant pharmacokinetics in children aged from 3 to 11 years with similar performance compared with the whole-body model. This study is a first example of the practicality of formal reduction of complex mechanistic models for Bayesian inference in drug development.

The Application of Physiologically Based Pharmacokinetic Modeling to Predict the Role of Drug Transporters: Scientific and Regulatory Perspectives.

Transporters play an important role in drug absorption, disposition, and drug action. The evaluation of drug transporters requires a comprehensive understanding of transporter biology and pharmacology. Physiologically based pharmacokinetic (PBPK) models may offer an integrative platform to quantitatively evaluate the role of drug transporters and its interplay with other drug disposition processes such as passive drug diffusion and elimination by metabolizing enzymes. To date, PBPK modeling and simulations integrating drug transporters lag behind that for drug-metabolizing enzymes. In addition, predictive performance of PBPK has not been well established for predicting the role of drug transporters in the pharmacokinetics of a drug. To enhance overall predictive performance of transporter-based PBPK models, it is necessary to have a detailed understanding of transporter biology for proper representation in the models and to have a quantitative understanding of the contribution of transporters in the absorption and metabolism of a drug. This article summarizes PBPK-based submissions evaluating the role of drug transporters to the Office of Clinical Pharmacology of the US Food and Drug Administration.

Insights From an Integrated Physiologically Based Pharmacokinetic Model for Brain Penetration.

Central-nervous-system, physiologically based pharmacokinetic (PBPK) models predict exposure profiles in the brain, that is, the rate and extent of distribution. The current work develops one such model and presents improved methods for determining key input parameters. A simple linear regression statistical model estimates the passive permeability at the blood-brain barrier from brain uptake index data and descriptors, and a novel analysis extracts the relative active transport parameter from in vitro assays taking into consideration both paracellular transport and unstirred water layers. The integrated PBPK model captures the concentration profiles of both rate-restricted and effluxed compounds with high passive permeability. In many cases, compounds distribute rapidly into the brain and are, therefore, not rate limited. The PBPK model is then simplified to a straightforward equation to describe brain-to-plasma ratios at steady state. The equation can estimate brain penetration either from in vitro efflux data or from in vivo results from another species and, therefore, is a valuable tool in the discovery setting.

Prediction of in-vivo pharmacokinetic profile for immediate and modified release oral dosage forms of furosemide using an in-vitro-in-silico-in-vivo approach.

OBJECTIVES: To develop a physiologically based pharmacokinetic (PBPK) model for furosemide immediate release (IR) tablets and modified release (MR) capsules by coupling biorelevant dissolution testing results with pharmacokinetic (PK) and physiologic parameters, and to investigate the key factors influencing furosemide absorption using simulation approaches and the PBPK model. METHODS: Using solubility, dissolution kinetics, gastrointestinal (GI) parameters and disposition parameters, a PBPK model for furosemide was developed with STELLA software. Solubility and dissolution profiles for both formulations were evaluated in biorelevant and compendial media. The simulated plasma profiles were compared with in-vivo profiles using point estimates of area under plasma concentration-time curve, maximal concentration after the dose and time to maximal concentration after the dose. KEY FINDINGS: Simulated plasma profiles of both furosemide IR tablets and MR capsules were similar to the observed in-vivo profile in terms of PK parameters. Sensitivity analysis of the IR tablet model indicated that both the gastric emptying and absorption rate have an influence on the plasma profile. For the MR capsules, the sensitivity analysis suggested that the release rate in the small intestine, gastric emptying and the absorption rate all have an influence on the plasma profile. CONCLUSIONS: A predictive model to describe both IR and MR dosage forms containing furosemide was attained. Because sensitivity analysis of the model is able to identify key factors influencing the plasma profile, this in-vitro-in-silico-in-vivo approach could be a useful tool for facilitating formulation development of drug products.