Drug-drug interactions and synergy: from pharmacological models to clinical application.

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts such as concentration-response curves, additive effects, and predictive models are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. While various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected at bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. Significance Statement Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug interaction research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

Linezolid Population Pharmacokinetic Model in Plasma and Cerebrospinal Fluid Among Patients With Tuberculosis Meningitis.

BACKGROUND: Linezolid is evaluated in novel treatment regimens for tuberculous meningitis (TBM). Linezolid pharmacokinetics have not been characterized in this population, particularly in cerebrospinal fluid (CSF), as well as, following its co-administration with high-dose rifampicin. We aimed to characterize linezolid plasma and CSF pharmacokinetics in adults with TBM. METHODS: In the LASER-TBM pharmacokinetic substudy, the intervention groups received high-dose rifampicin (35 mg/kg) plus 1200 mg/day of linezolid for 28 days, which was then reduced to 600 mg/day. Plasma sampling was done on day 3 (intensive) and day 28 (sparse). A lumbar CSF sample was obtained on both visits. RESULTS: Thirty participants contributed 247 plasma and 28 CSF observations. Their median age and weight were 40 years (range, 27-56) and 58 kg (range, 30-96). Plasma pharmacokinetics was described by a 1-compartment model with first-order absorption and saturable elimination. Maximal clearance was 7.25 L/h, and the Michaelis-Menten constant was 27.2 mg/L. Rifampicin cotreatment duration did not affect linezolid pharmacokinetics. CSF-plasma partitioning correlated with CSF total protein up to 1.2 g/L, where the partition coefficient reached a maximal value of 37%. The plasma-CSF equilibration half-life was ∼3.5 hours. CONCLUSIONS: Linezolid was readily detected in CSF despite high-dose rifampicin coadministration. These findings support continued clinical evaluation of linezolid plus high-dose rifampicin for the treatment of TBM in adults. Clinical Trials Registration. ClinicalTrials.gov (NCT03927313).

Phase plane dynamics of ERK phosphorylation.

The extracellular signal-regulated kinase (ERK) controls multiple critical processes in the cell and is deregulated in human cancers, congenital abnormalities, immune diseases, and neurodevelopmental syndromes. Catalytic activity of ERK requires dual phosphorylation by an upstream kinase, in a mechanism that can be described by two sequential Michaelis-Menten steps. The estimation of individual reaction rate constants from kinetic data in the full mechanism has proved challenging. Here, we present an analytically tractable approach to parameter estimation that is based on the phase plane representation of ERK activation and yields two combinations of six reaction rate constants in the detailed mechanism. These combinations correspond to the ratio of the specificities of two consecutive phosphorylations and the probability that monophosphorylated substrate does not dissociate from the enzyme before the second phosphorylation. The presented approach offers a language for comparing the effects of mutations that disrupt ERK activation and function in vivo. As an illustration, we use phase plane representation to analyze dual phosphorylation under heterozygous conditions, when two enzyme variants compete for the same substrate.

Non-equilibrium structural and dynamic behaviors of active polymers in complex and crowded environments.

Active matter systems, which convert internal chemical energy or energy from the environment into directed motion, are ubiquitous in nature and exhibit a range of emerging non-equilibrium behaviors. However, most of the current works on active matter have been devoted to particles, and the study of active polymers has only recently come into the spotlight due to their prevalence within living organisms. The intricate interplay between activity and conformational degrees of freedom gives rise to novel structural and dynamical behaviors of active polymers. Research in active polymers remarkably broadens diverse concepts of polymer physics, such as molecular architecture, dynamics, scaling and so on, which is of significant importance for the development of new polymer materials with unique performance. Furthermore, active polymers are often found in strongly interacting and crowded systems and in complex environments, so that the understanding of this behavior is essential for future developments of novel polymer-based biomaterials. This review thereby focuses on the study of active polymers in complex and crowded environments, and aims to provide insights into the fundamental physics underlying the adaptive and collective behaviors far from equilibrium, as well as the open challenges that the field is currently facing.

Use of Model-Informed Drug Development to Streamline Development of Long-Acting Products: Can These Successes Be Translated to Long-Acting Hormonal Contraceptives?

Long-acting contraceptives are the most effective reversible contraceptive methods. Increasing patients' access to these contraceptives may translate into fewer unintended pregnancies and lead to substantial individual and public health benefits. However, development of long-acting products can be complex and challenging. This review provides (a) an overview of representative development programs for long-acting antipsychotics as cases for conceptual translation to long-acting contraceptives, (b) several case examples on how modeling and simulation have been used to streamline the development of long-acting products, and (c) examples of challenges andopportunities in developing long-acting contraceptives and information on how exposure-response relationships of commonly used progestins may enable regulators and developers to rely on prior findings of effectiveness and safety from an approved contraceptive to streamline the development of long-acting contraceptives. The US Food and Drug Administration is seeking assistance from stakeholders to provide data from studies in which pharmacokinetic and pharmacodynamic or clinical outcomes of hormonal contraceptives were evaluated and not previously submitted.

Predicting clearance with simple and permeability-limited PBPK frameworks: comparison of well-stirred, dispersion and parallel tube liver models.

One-compartment (1C) and permeability-limited models were used to evaluate the ability of microsomal and hepatocyte intrinsic clearances to predict hepatic clearance. Well-stirred (WSM), parallel tube (PTM), and dispersion (DM) models were evaluated within the liver as well as within whole-body physiologically based pharmacokinetic frameworks. It was shown that a linear combination of well-stirred and parallel-tube average liver blood concentrations accurately approximates dispersion model blood concentrations. Using a flow/permeability-limited model, a large systematic error was observed for acids and no systematic error for bases. A scaling factor that reduced interstitial fluid (ISF) plasma protein binding could greatly decrease the absolute average-fold error (AAFE) for acids. Using a 1C model, a scalar to reduce plasma protein binding decreased the microsomal clearance AAFE for both acids and bases. With a permeability-limited model, only acids required this scalar. The mechanism of the apparent increased cytosolic concentrations for acids remains unknown. We also show that for hepatocyte intrinsic clearance in vitro-in vivo correlations (IVIVCs), a 1C model is mechanistically appropriate since hepatocyte clearance should represent the net clearance from ISF to elimination. A relationship was derived that uses microsomal and hepatocyte intrinsic clearance to solve for an active hepatic uptake clearance, but the results were inconclusive. Finally, the PTM model generally performed better than the WSM or DM models, with no clear advantage between microsomes and hepatocytes. Significance Statement Prediction of drug clearance from microsomes or hepatocytes remains challenging. Various liver models (e.g. WSM, PTM, and DM) have been mathematically incorporated into liver as well as whole-body PBPK frameworks. Although the resulting models allow incorporation of pH partitioning, permeability, and active uptake for prediction of drug clearance, including these processes did not improve clearance predictions for both microsomes and hepatocytes.

Physiologically Based Pharmacokinetic (PBPK) Modeling of small molecules: How Much Progress Have We Made?

Physiologically based pharmacokinetic (PBPK) models of small molecules have become mainstream in drug development and academic research. The use of PBPK models is continuously expanding with the majority of work now focusing on predictions of drug-drug interactions, drug-disease interactions, and changes in drug disposition across lifespan. Recently, publications that use PBPK modeling to predict drug disposition during pregnancy and in organ impairment have increased reflecting the advances in incorporating diverse physiological changes into the models. Due to the expanding computational power and diversity of modeling platforms available, the complexity of PBPK models has also increased. Academic efforts have provided clear advances in better capturing human physiology in PBPK models and incorporating more complex mathematical concepts into PBPK models. Examples of such advances include the segregated gut model with a series gut compartments allowing modeling of physiological blood flow distribution within an organ and zonation of metabolic enzymes, and series compartment liver models allowing simulations of hepatic clearance for high extraction drugs. Despite these advances in academic research, the progress in assessing model quality and defining model acceptance criteria based on the intended use of the models has not kept pace. This review suggests that awareness of the need for predefined criteria for model acceptance has increased but many manuscripts still lack description of scientific justification and/or rationale for chosen acceptance criteria. As artificial intelligence and machine learning approaches become more broadly accepted, these tools offer promise for development of comprehensive assessment for existing observed data and analysis of model performance. Significance Statement PBPK modeling has become a mainstream application in academic literature and is broadly used for predictions, analysis and evaluation of pharmacokinetic data. Many significant advances have been made in developing advanced PBPK models that better capture human physiology but oftentimes sufficient justification for the chosen model acceptance criterion and model structure is still missing. This review provides a summary of the current landscape of PBPK applications used and highlights the needs for advancing PBPK modeling science and training in academia.

Modeling the heterogeneous apoptotic response of caspase-mediated signaling in tumor cells.

Resisting apoptosis is a hallmark of cancer. For this reason, it may be possible to force cancer cells to die by targeting components along the apoptotic signaling pathway. However, apoptosis signaling is challenging to understand due to dynamic and complex behaviors of ligands, receptors, and intracellular signaling components in response to cancer therapy. In this work, we forecast the apoptotic response based on the combined impact of these features. We expanded a previously established mathematical model of caspase-mediated apoptosis to include extracellular activation and receptor dynamics. In addition, three potential threshold values of caspase-3 necessary for the activation of apoptosis were selected to forecast which cells become apoptotic over time. We first vary ligand and receptor levels with the number of intracellular signaling proteins remaining consistent. Then, we vary the intracellular protein molecules in each simulated tumor cell to forecast the response of a heterogeneous population. By leveraging the benefits of computational modeling, we investigate the combined effect of several factors on the onset of apoptosis. This work provides quantitative insights for how the apoptotic signaling response can be forecasted, and precisely triggered, amongst heterogeneous cells via extracellular activation.

Importance of Considering Fed-State Gastrointestinal Physiology in Predicting the Reabsorption of Enterohepatic Circulation of Drugs.

PURPOSE: The purpose of this study was to develop a simulation model for the pharmacokinetics (PK) of drugs undergoing enterohepatic circulation (EHC) with consideration to the environment in the gastrointestinal tract in the fed state in humans. The investigation particularly focused on the necessity of compensating for the permeability rate constant in the reabsorption process in consideration of drug entrapment in bile micelles. METHODS: Meloxicam and ezetimibe were used as model drugs. The extent of the entrapment of drugs inside bile micelles was evaluated using the solubility ratio of Fed State Simulated Intestinal Fluid version 2 (FeSSIF-V2) to Fasted State Simulated Intestinal Fluid version 2 (FaSSIF-V2). Prediction accuracy was evaluated using the Mean Absolute Percentage Error (MAPE) value, calculated from the observed and predicted oral PK profiles. RESULTS: The solubilization of ezetimibe by bile micelles was clearly observed while that of meloxicam was not. Assuming that only drugs in the free fraction of micelles permeate through the intestinal membrane, PK simulation for ezetimibe was performed in both scenarios with and without compensation by the permeation rate constant. The MAPE value of Zetia® tablet, containing ezetimibe, was lower with compensation than without compensation. By contrast, Mobic® tablet, containing meloxicam, showed a relatively low MAPE value even without compensation. CONCLUSION: For drugs which undergo EHC and can be solubilized by bile micelles, compensating for the permeation rate constant in the reabsorption process based on the free fraction ratio appears an important factor in increasing the accuracy of PK profile prediction.

Bioequivalence study followed by model-informed dose optimization of a powder for oral suspension of 6-mercaptopurine.

BACKGROUND: 6-Mercaptopurine (6MP) is the mainstay chemotherapy for acute lymphoblastic leukemia (ALL) and is conventionally available as 50 mg tablets. A new 6MP powder for oral suspension (PFOS 10 mg/mL) was developed recently by IDRS Labs, India, intended for pediatric use. A comparative pharmacokinetics of PFOS with T. mercaptopurine was conducted to determine the dose equivalence. METHODS: An open-label, randomized, two-treatment, two-period, two-sequence, single oral dose, crossover, bioequivalence study was conducted on 51 healthy adult subjects. Post hoc, a population pharmacokinetic (PopPK) model was developed using the healthy volunteer data to perform simulations with various PFOS doses and select a bioequivalent dose. Further, to confirm the safety of PFOS in pediatrics, a simulation of 6MP and 6-thioguanine exposures was performed by incorporating the formulation-specific parameters derived from the healthy volunteer study into the PopPK model in childhood ALL available in literature. RESULTS: The 6MP PFOS had 47% higher oral bioavailability compared to the reference product. Simulations using a two-compartmental PopPK model with dissolution and transit compartments showed that 40 mg of PFOS was found to be equivalent to 50 mg tablets. The simulated 6-thioguanine nucleotide concentrations in children using the dose adjusted for PFOS were between 114 and 703.6 pmol/8 × 108 RBC, which was within the range reported in pediatric ALL studies. CONCLUSION: 6MP PFOS 10 mg/mL should be administered at a 20% lower dose than the tablet to achieve comparable exposure. 6MP PFOS addresses an unmet medical need for a liquid formulation of 6MP in the Indian subcontinent.

Processionary Caterpillars at the Edge of Complexity.

This article deals with individuals moving in procession in real and artificial societies. A procession is a minimal form of society in which individual behavior is to go in a given direction and the organization is structured by the knowledge of the one ahead. This simple form of grouping is common in the living world, and, among humans, procession is a very circumscribed social activity whose origins are certainly very remote. This type of organization falls under microsociology, where the focus is on the study of direct interactions between individuals within small groups. In this article, we focus on the particular case of pine tree processionary caterpillars (Thaumetopoea pityocampa). In the first part, we propose a formal definition of the concept of procession and compare field experiments conducted by entomologists with agent-based simulations to study real caterpillars' processionaries as they are. In the second part, we explore the life of caterpillars as they could be. First, by extending the model beyond reality, we can explain why real processionary caterpillars behave as they do. Then we report on field experiments on the behavior of real caterpillars artificially forced to follow a circular procession; these experiments confirm that each caterpillar can either be the leader of the procession or follow the one in front of it. In the third part, by allowing variations in the speed of movement on an artificial circular procession, computational simulations allow us to observe the emergence of unexpected mobile spatial structures built from regular polygonal shapes where chaotic movements and well-ordered forms are intimately linked. This confirms once again that simple rules can have complex consequences.

Differences in Glenohumeral Joint Contact Forces Between Recovery Hand Patterns During Wheelchair Propulsion With and Without Shoulder Muscle Weakness: A Simulation Study.

The majority of manual wheelchair users (MWCU) develop shoulder pain or injuries, which is often caused by impingement. Because propulsion mechanics are influenced by the recovery hand pattern used, the pattern may affect shoulder loading and susceptibility to injury. Shoulder muscle weakness is also correlated with shoulder pain, but how shoulder loading changes with specific muscle group weakness is unknown. Musculoskeletal modeling and simulation were used to compare glenohumeral joint contact forces (GJCFs) across hand patterns and determine how GJCFs vary when primary shoulder muscle groups are weakened. Experimental data were analyzed to classify individuals into four hand pattern groups. A representative musculoskeletal model was then developed for each group and simulations generated to portray baseline strength and six muscle weakness conditions. Three-dimensional GJCF peaks and impulses were compared across hand patterns and muscle weakness conditions. The semicircular pattern consistently had lower shear (anterior-posterior and superior-inferior) GJCFs compared to other patterns. The double-loop pattern had the highest superior GJCFs, while the single-loop pattern had the highest anterior and posterior GJCFs. These results suggest that using the semicircular pattern may be less susceptible to shoulder injuries such as subacromial impingement. Weakening the internal rotators and external rotators resulted in the greatest increases in shear GJCFs and decreases in compressive GJCF, likely due to decreased force from rotator cuff muscles. These findings suggest that strengthening specific muscle groups, especially the rotator cuff, is critical for decreasing the risk of shoulder overuse injuries.

Training the next generation of pharmacometric modelers: a multisector perspective.

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

Dynamic Simulation and Modeling of a Novel NeuRaiSya for Railway Monitoring System Using Petri Nets.

This research introduces the NeuRaiSya (Neural Railway System Application), an innovative railway signaling system integrating deep learning for passenger analysis. The objectives of this research are to simulate the NeuRaiSya and evaluate its effectiveness using the GreatSPN tool (graphical editor for Petri nets). GreatSPN facilitates evaluations of system behavior, ensuring safety and efficiency. Five models were designed and simulated using the Petri nets model, including the Dynamics of Train Departure model, Train Operations with Passenger Counting model, Timestamp Data Collection model, Train Speed and Location model, and Train Related-Issues model. Through simulations and modeling using Petri nets, the study demonstrates the feasibility of the proposed NeuRaiSya system. The results highlight its potential in enhancing railway operations, ensuring passenger safety, and maintaining service quality amidst the evolving railway landscape in the Philippines.

Soft sensor for substrate characterization through the reverse application of the ADM1 model for anaerobic digestion plant operations.

Accurately characterizing the substrate used in anaerobic digestion is crucial for predicting the biogas plant's performance. This issue makes particularly challenging the application of modeling in codigestion plants. In this work, a novel methodology called substrate prediction module (SPM) has been developed and tested, using virtual codigestion data. The SPM aims to estimate the inlet properties of the substrate based on the reverse application of the anaerobic digestion model n1 (ADM1). The results show that, while the SPM can estimate some properties of the substrate based on certain output parameters, there are limitations in accurately determining all required variables.

Predicting Human Dermal Drug Concentrations Using PBPK Modeling and Simulation: Clobetasol Propionate Case Study.

Quantitative in silico tools may be leveraged to mechanistically predict the dermato-pharmacokinetics of compounds delivered from topical and transdermal formulations by integrating systems of rate equations that describe permeation through the formulation and layers of skin and pilo-sebaceous unit, and exchange with systemic circulation via local blood flow. Delivery of clobetasol-17 propionate (CP) from DermovateTM cream was simulated using the Transdermal Compartmental Absorption & Transit (TCATTM) Model in GastroPlus®. The cream was treated as an oil-in-water emulsion, with model input parameters estimated from publicly available information and quantitative structure-permeation relationships. From the ranges of values available for model input parameters, a set of parameters was selected by comparing model outputs to CP dermis concentration-time profiles measured by dermal open-flow microperfusion (Bodenlenz et al. Pharm Res. 33(9):2229-38, 2016). Predictions of unbound dermis CP concentrations were reasonably accurate with respect to time and skin depth. Parameter sensitivity analyses revealed considerable dependence of dermis CP concentration profiles on drug solubility in the emulsion, relatively less dependence on dispersed phase volume fraction and CP effective diffusivity in the continuous phase of the emulsion, and negligible dependence on dispersed phase droplet size. Effects of evaporative water loss from the cream and corticosteroid-induced vasoconstriction were also assessed. This work illustrates the applicability of computational modeling to predict sensitivity of dermato-pharmacokinetics to changes in thermodynamic and transport properties of a compound in a topical formulation, particularly in relation to rate-limiting steps in skin permeation. Where these properties can be related to formulation composition and processing, such a computational approach may support the design of topically applied formulations.

Lysosomal calcium loading promotes spontaneous calcium release by potentiating ryanodine receptors.

Spontaneous calcium release by ryanodine receptors (RyRs) due to intracellular calcium overload results in delayed afterdepolarizations, closely associated with life-threatening arrhythmias. In this regard, inhibiting lysosomal calcium release by two-pore channel 2 (TPC2) knockout has been shown to reduce the incidence of ventricular arrhythmias under ß-adrenergic stimulation. However, mechanistic investigations into the role of lysosomal function on RyR spontaneous release remain missing. We investigate the calcium handling mechanisms by which lysosome function modulates RyR spontaneous release, and determine how lysosomes are able to mediate arrhythmias by its influence on calcium loading. Mechanistic studies were conducted using a population of biophysically detailed mouse ventricular models including for the first time modeling of lysosomal function, and calibrated by experimental calcium transients modulated by TPC2. We demonstrate that lysosomal calcium uptake and release can synergistically provide a pathway for fast calcium transport, by which lysosomal calcium release primarily modulates sarcoplasmic reticulum calcium reuptake and RyR release. Enhancement of this lysosomal transport pathway promoted RyR spontaneous release by elevating RyR open probability. In contrast, blocking either lysosomal calcium uptake or release revealed an antiarrhythmic impact. Under conditions of calcium overload, our results indicate that these responses are strongly modulated by intercellular variability in L-type calcium current, RyR release, and sarcoplasmic reticulum calcium-ATPase reuptake. Altogether, our investigations identify that lysosomal calcium handling directly influences RyR spontaneous release by regulating RyR open probability, suggesting antiarrhythmic strategies and identifying key modulators of lysosomal proarrhythmic action.

In Silico Modeling Approaches Coupled with In Vitro Characterization in Predicting In Vivo Performance of Drug Delivery System Formulations.

Optimization of the in vivo performance of dosage forms in humans is essential in developing not only conventional formulations but also drug delivery system (DDS) formulations. Although animal experiments are still useful for these formulations, in silico approaches have become increasingly important for DDS formulations with regard to species-specific differences in physiology that can affect the in vivo performance of dosage forms between animals and humans. Furthermore, it is also important to couple in vitro characterizations with in silico models to predict in vivo performance in humans precisely. In this review article, I summarized in vitro-in silico approaches to predicting the in vivo performance of oral DDS formulations (amorphous solid dispersions, lipid-based formulations, nanosized formulations, cyclodextrins-based formulations, sustained release products, enteric coat products, and orally disintegrating tablets) and parenteral DDS formulations (cyclodextrins-based formulations, liposomes, and inhaled formulations).

Dynamic Changes in Gastrointestinal Fluid Characteristics after Food Ingestion Are Important for Quantitatively Predicting the In Vivo Performance of Oral Solid Dosage Forms in Humans in the Fed State.

The aim of this study was to develop a simulation model to predict the in vivo performance of solid oral dosage forms in humans in the fed state. We focused on investigating the effect of dynamic changes in gastrointestinal (GI) fluid characteristics in the fed state on the in vivo performance of solid dosage forms. We used six solid dosage forms containing weak base drugs as model formulations, two with positive food effects in humans, two with negative food effects, and two which are not affected by food ingestion. These model drug formulations were used to perform biorelevant dissolution tests in the stomach and small intestine under both prandial states. The in vitro properties of the drug products obtained from these tests were then coupled with in silico models (fasted or fed) to predict food effects in humans. We successfully incorporated the dynamic changes in GI fluid characteristics and their effects on the in vivo dissolution of drugs into the prediction model for the fed state. This newly designed physiologically based biopharmaceutics modeling approach provided the precise and quantitative prediction of food effects (i.e., changes in Cmax and AUC after food ingestion) in humans while considering the dynamic changes in fluid characteristics in the fed state.

Mechanisms of Obesity-Induced Changes in Pharmacokinetics of IgG in Rats.

PURPOSE: To evaluate how obesity affects the pharmacokinetics of human IgG following subcutaneous (SC) and intravenous (IV) administration to rats and the homeostasis of endogenous rat IgG. METHODS: Differences in body weight and size, body composition, and serum concentration of endogenous rat IgG in male Zucker obese (ZUC-FA/FA) and control (ZUC-LEAN) rats were measured from the age of 5 weeks up to 30 weeks. At the age of 23-24 weeks animals received a single IV or SC dose of human IgG (1 g/kg of total body weight), and serum pharmacokinetics was followed for 7 weeks. A mechanistic model linking obesity-related changes in pharmacokinetics with animal growth and changes in body composition was developed. RESULTS: Significant differences were observed in both endogenous and exogenous IgG pharmacokinetics between obese and control groups. The AUC for human IgG was lower in obese groups (57.6% of control after IV and 48.1% after SC dosing), and clearance was 1.75-fold higher in obese animals. The mechanistic population model successfully captured the data and included several major components: endogenous rat IgG homeostasis with age-dependent synthesis rate; competition of human IgG and endogenous rat IgG for FcRn binding and its effect on endogenous rat IgG concentrations following injection of a high dose of human IgG; and the effect of body size and composition (changing over time and dependent on the obesity status) on pharmacokinetic parameters. CONCLUSIONS: We identified important obesity-induced changes in the pharmacokinetics of IgG. Results can potentially facilitate optimization of the dosing of IgG-based therapeutics in the obese population.