Incorporating target-mediated drug disposition in a minimal physiologically-based pharmacokinetic model for monoclonal antibodies

Dose Optimization in Oncology Drug Development: An International Consortium for Innovation and Quality in Pharmaceutical Development White Paper

The landscape of oncology drug development has witnessed remarkable advancements over the last few decades, significantly improving clinical outcomes and quality of life for patients with cancer. Project Optimus, introduced by the U.S. Food and Drug Administration, stands as a groundbreaking endeavor to reform dose selection of oncology drugs, presenting both opportunities and challenges for the field. To address complex dose optimization challenges, an Oncology Dose Optimization IQ Working Group was created to characterize current practices, provide recommendations for improvement, develop a clinical toolkit, and engage Health Authorities. Historically, dose selection for cytotoxic chemotherapeutics has focused on the maximum tolerated dose, a paradigm that is less relevant for targeted therapies and new treatment modalities. A survey conducted by this group gathered insights from member companies regarding industry practices in oncology dose optimization. Given oncology drug development is a complex effort with multidimensional optimization and high failure rates due to lack of clinically relevant efficacy, this Working Group advocates for a case-by-case approach to inform the timing, specific quantitative targets, and strategies for dose optimization, depending on factors such as disease characteristics, patient population, mechanism of action, including associated resistance mechanisms, and therapeutic index. This white paper highlights the evolving nature of oncology dose optimization, the impact of Project Optimus, and the need for a tailored and evidence-based approach to optimize oncology drug dosing regimens effectively.

A Minimal PBPK/PD Model with Expansion-Enhanced Target-Mediated Drug Disposition to Support a First-in-Human Clinical Study Design for a FLT3L-Fc Molecule

FLT3L-Fc is a half-life extended, effectorless Fc-fusion of the native human FLT3-ligand. In cynomolgus monkeys, treatment with FLT3L-Fc leads to a complex pharmacokinetic/pharmacodynamic (PK/PD) relationship, with observed nonlinear PK and expansion of different immune cell types across different dose levels. A minimal physiologically based PK/PD model with expansion-enhanced target-mediated drug disposition (TMDD) was developed to integrate the molecule's mechanism of action, as well as the complex preclinical and clinical PK/PD data, to support the preclinical-to-clinical translation of FLT3L-Fc. In addition to the preclinical PK data of FLT3L-Fc in cynomolgus monkeys, clinical PK and PD data from other FLT3-agonist molecules (GS-3583 and CDX-301) were used to inform the model and project the expansion profiles of conventional DC1s (cDC1s) and total DCs in peripheral blood. This work constitutes an essential part of our model-informed drug development (MIDD) strategy for clinical development of FLT3L-Fc by projecting PK/PD in healthy volunteers, determining the first-in-human (FIH) dose, and informing the efficacious dose in clinical settings. Model-generated results were incorporated in regulatory filings to support the rationale for the FIH dose selection.

Utility of Minimal Physiologically Based Pharmacokinetic Models for Assessing Fractional Distribution, Oral Absorption, and Series-Compartment Models of Hepatic Clearance

Minimal physiologically based pharmacokinetic (mPBPK) models are physiologically relevant, require less information than full PBPK models, and offer flexibility in pharmacokinetics (PK). The well-stirred hepatic model (WSM) is commonly used in PBPK, whereas the more plausible dispersion model (DM) poses computational complexities. The series-compartment model (SCM) mimics the DM but is easier to operate. This work implements the SCM and mPBPK models for assessing fractional tissue distribution, oral absorption, and hepatic clearance using literature-reported blood and liver concentration-time data in rats for compounds mainly cleared by the liver. Further handled were various complexities, including nonlinear hepatic binding and metabolism, differing absorption kinetics, and sites of administration. The SCM containing one to five (n) liver subcompartments yields similar fittings and provides comparable estimates for hepatic extraction ratio (ER), prehepatic availability (Fg ), and first-order absorption rate constants (ka ). However, they produce decreased intrinsic clearances (CLint ) and liver-to-plasma partition coefficients (Kph ) with increasing n as expected. Model simulations demonstrated changes in intravenous and oral PK profiles with alterations in Kph and ka and with hepatic metabolic zonation. The permeability (PAMPA P) of the various compounds well explained the fitted fractional distribution (fd ) parameters. The SCM and mPBPK models offer advantages in distinguishing systemic, extrahepatic, and hepatic clearances. The SCM allows for incorporation of liver zonation and is useful in assessing changes in internal concentration gradients potentially masked by similar blood PK profiles. Improved assessment of intraorgan drug concentrations may offer insights into active moieties driving metabolism, biliary excretion, pharmacodynamics, and hepatic toxicity. SIGNIFICANCE STATEMENT: The minimal physiologically based pharmacokinetic model and the series-compartment model are useful in assessing oral absorption and hepatic clearance. They add flexibility in accounting for various drug- or system-specific complexities, including fractional distribution, nonlinear binding and saturable hepatic metabolism, and hepatic zonation. These models can offer improved insights into the intraorgan concentrations that reflect physiologically active moieties often driving disposition, pharmacodynamics, and toxicity.

Mechanistically Weighted Metric to Predict In Vivo Antibody-Receptor Occupancy: An Analytical Approach

In situ clinical measurement of receptor occupancy (RO) is challenging, particularly for solid tumors, necessitating the use of mathematical models that predict tumor receptor occupancy to guide dose decisions. A potency metric, average free tissue target to initial target ratio (AFTIR), was previously described based on a mechanistic compartmental model and is informative for near-saturating dose regimens. However, the metric fails at clinically relevant subsaturating antibody doses, as compartmental models cannot capture the spatial heterogeneity of distribution faced by some antibodies in solid tumors. Here we employ a partial differential equation (PDE) Krogh cylinder model to simulate spatiotemporal receptor occupancy and derive an analytical solution, a mechanistically weighted global AFTIR, that can better predict receptor occupancy regardless of dosing regimen. In addition to the four key parameters previously identified, a fifth key parameter, the absolute receptor density (targets/cell), is incorporated into the mechanistic AFTIR metric. Receptor density can influence equilibrium intratumoral drug concentration relative to whether the dose is saturating or not, thereby influencing the tumor penetration depth of the antibody. We derive mechanistic RO predictions based on distinct patterns of antibody tumor penetration, presented as a global AFTIR metric guided by a Thiele Modulus and a local saturation potential (drug equivalent of binding potential for positron emissions tomography imaging) and validate the results using rigorous global and local sensitivity analysis. This generalized AFTIR serves as a more accurate analytical metric to aid clinical dose decisions and rational design of antibody-based therapeutics without the need for extensive PDE simulations. SIGNIFICANCE STATEMENT: Determining antibody-receptor occupancy (RO) is critical for dosing decisions in pharmaceutical development, but direct clinical measurement of RO is often challenging and invasive, particularly for solid tumors. Significant efforts have been made to develop mathematical models and simplified analytical metrics of RO, but these often require complex computer simulations. Here we present a mathematically rigorous but simplified analytical model to accurately predict RO across a range of affinities, doses, drug, and tumor properties.

Mechanism-Based Pharmacokinetic Modeling of Absorption and Disposition of a Deferoxamine-Based Nanochelator in Rats

Deferoxamine (DFO) is an effective FDA-approved iron chelator. However, its use is considerably limited by off-target toxicities and an extremely cumbersome dose regimen with daily infusions. The recent development of a deferoxamine-based nanochelator (DFO-NP) with selective renal excretion has shown promise in ameliorating animal models of iron overload with a substantially improved safety profile. To further the preclinical development of this promising nanochelator and to inform on the feasibility of clinical development, it is necessary to fully characterize the dose and administration-route-dependent pharmacokinetics and to develop predictive pharmacokinetic (PK) models describing absorption and disposition. Herein, we have evaluated the absorption, distribution, and elimination of DFO-NPs after intravenous and subcutaneous (SC) injection at therapeutically relevant doses in Sprague Dawley rats. We also characterized compartment-based model structures and identified model-based parameters to quantitatively describe the PK of DFO-NPs. Our modeling efforts confirmed that disposition could be described using a three-compartment mamillary model with elimination and saturable reabsorption both occurring from the third compartment. We also determined that absorption was nonlinear and best described by parallel saturable and first-order processes. Finally, we characterized a novel pathway for saturable SC absorption of an ultrasmall organic nanoparticle directly into the systemic circulation, which offers a novel strategy for improving drug exposure for nanotherapeutics.

Mathematical Models to Characterize the Absorption, Distribution, Metabolism, and Excretion of Protein Therapeutics

Therapeutic proteins (TPs) have ranked among the most important and fastest-growing classes of drugs in the clinic, yet the development of successful TPs is often limited by unsatisfactory efficacy. Understanding pharmacokinetic (PK) characteristics of TPs is key to achieving sufficient and prolonged exposure at the site of action, which is a prerequisite for eliciting desired pharmacological effects. PK modeling represents a powerful tool to investigate factors governing in vivo disposition of TPs. In this mini-review, we discuss many state-of-the-art models that recapitulate critical processes in each of the absorption, distribution, metabolism/catabolism, and excretion pathways of TPs, which can be integrated into the physiologically-based pharmacokinetic framework. Additionally, we provide our perspectives on current opportunities and challenges for evolving the PK models to accelerate the discovery and development of safe and efficacious TPs. SIGNIFICANCE STATEMENT: This minireview provides an overview of mechanistic pharmacokinetic (PK) models developed to characterize absorption, distribution, metabolism, and elimination (ADME) properties of therapeutic proteins (TPs), which can support model-informed discovery and development of TPs. As the next-generation of TPs with diverse physicochemical properties and mechanism-of-action are being developed rapidly, there is an urgent need to better understand the determinants for the ADME of TPs and evolve existing platform PK models to facilitate successful bench-to-bedside translation of these promising drug molecules.

Recent Advances in Translational Pharmacokinetics and Pharmacodynamics Prediction of Therapeutic Antibodies Using Modeling and Simulation

Therapeutic monoclonal antibodies (mAbs) have been a promising therapeutic approach for several diseases and a wide variety of mAbs are being evaluated in clinical trials. To accelerate clinical development and improve the probability of success, pharmacokinetics and pharmacodynamics (PKPD) in humans must be predicted before clinical trials can begin. Traditionally, empirical-approach-based PKPD prediction has been applied for a long time. Recently, modeling and simulation (M&S) methods have also become valuable for quantitatively predicting PKPD in humans. Although several models (e.g., the compartment model, Michaelis–Menten model, target-mediated drug disposition model, and physiologically based pharmacokinetic model) have been established and used to predict the PKPD of mAbs in humans, more complex mechanistic models, such as the quantitative systemics pharmacology model, have been recently developed. This review summarizes the recent advances and future direction of M&S-based approaches to the quantitative prediction of human PKPD for mAbs.

A Physiologically Based Pharmacokinetic Framework for Quantifying Antibody Distribution Gradients from Tumors to Tumor-Draining Lymph Nodes

Immune checkpoint blockades prescribed in the neoadjuvant setting are now under active investigation for many types of tumors, and many have shown early success. The primary tumor (PT) and tumor-draining lymph node (TDLN) immune factors, along with adequate therapeutic antibody distributions to the PT and TDLN, are critical for optimal immune activation and anti-tumor efficacy in neoadjuvant immunotherapy. However, it remains largely unknown how much of the antibody can be distributed into the PT-TDLN axis at different clinical scenarios. The goal of the current work is to build a physiologically based pharmacokinetic (PBPK) model framework capable of characterizing antibody distribution gradients in the PT-TDLN axis across various clinical and pathophysiological scenarios. The model was calibrated using clinical data from immuno-PET antibody-imaging studies quantifying antibody pharmacokinetics (PK) in the blood, PTs, and TDLNs. The effects of metastatic lesion location, tumor-induced compression, and inflammation, as well as surgery, on antibody concentration gradients in the PT-TDLN axis were characterized. The PBPK model serves as a valuable tool to predict antibody exposures in various types of tumors, metastases, and the associated lymph node, supporting effective immunotherapy.

Physiological Considerations for Modeling in vivo Antibody-Target Interactions

The number of therapeutic antibodies in development pipelines is increasing rapidly. Despite superior success rates relative to small molecules, therapeutic antibodies still face many unique development challenges. There is often a translational gap from their high target affinity and specificity to the therapeutic effects. Tissue microenvironment and physiology critically influence antibody-target interactions contributing to apparent affinity alterations and dynamic target engagement. The full potential of therapeutic antibodies will be further realized by contextualizing antibody-target interactions under physiological conditions. Here we review how local physiology such as physical stress, biological fluid, and membrane characteristics could influence antibody-target association, dissociation, and apparent affinity. These physiological factors in the early development of therapeutic antibodies are valuable toward rational antibody engineering, preclinical candidate selection, and lead optimization.

An Extended Model Including Target Turnover, Ligand-Target Complex Kinetics, and Binding Properties to Describe Drug-Receptor Interactions

Since the beginning of this century, target-mediated drug disposition has become a central concept in modeling drug action in drug development. It combines a range of processes, such as turnover, protein binding, internalization, and non-specific elimination, and often serves as a nucleus of more complex pharmacokinetic models. It is simple enough to comprehend but complex enough to be able to describe a wide range of phenomena and data sets. However, the complexity comes at a price: many parameters. In this chapter, we present an overview of the temporal development of the compounds involved after different types of drug doses and offer convenient handles for dissecting data sets in a sophisticated manner in order to estimate the values of these parameters, such as rate constants and pertinent concentrations.

A minimal physiologically based pharmacokinetic model to characterize colon TNF suppression and treatment effects of an anti-TNF monoclonal antibody in a mouse inflammatory bowel disease model

Biotherapeutic drugs against tumor necrosis factor (TNF) are effective treatments for moderate to severe inflammatory bowel disease (IBD). Here, we evaluated CNTO 5048, an antimurine TNF surrogate monoclonal antibody (mAb), in a CD45RB^high adoptive T cell transfer mouse colitis model, which allows examination of the early immunological events associated with gut inflammation and the therapeutic effects. The study was designed to quantitatively understand the effects of IBD on CNTO 5048 disposition, the ability of CNTO 5048 to neutralize pathogenic TNF at the colon under disease conditions, and the impact of dosing regimen on CNTO 5048 treatment effect. CNTO 5048 and TNF concentrations in both mice serum and colon homogenate were also measured. Free TNF concentrations in colon, but not in serum, were shown to correlate well with the colon pharmacodynamic readout, such as the summed histopathology score and neutrophil score. A minimal physiologically based pharmacokinetic (mPBPK) model was developed to characterize CNTO 5048 PK and disposition, as well as colon soluble TNF target engagement (TE). The mPBPK/TE model reasonably captured the observed data and provided a quantitative understanding of an anti-TNF mAb on its colon TNF suppression and therapeutic effect in a physiologically relevant IBD animal model. These results also provided insights into the potential benefits of using induction doses for the treatment of IBD patients.

Preclinical characterization of the ADME properties of a surrogate anti-IL-36R monoclonal antibody antagonist in mouse serum and tissues

The decision to pursue a monoclonal antibody (mAb) as a therapeutic for disease intervention requires the assessment of many factors, such as target-biology, including the total target burden and its accessibility at the intended site of action, as well as mAb-specific properties like binding affinity and the pharmacokinetics in serum and tissue. Interleukin-36 receptor (IL-36 R) is a member of the IL-1 family cytokine receptors and an attractive target to treat numerous epithelial-mediated inflammatory conditions, including psoriatic and rheumatoid arthritis, asthma, and chronic obstructive pulmonary disease. However, information concerning the expression profile of IL-36 R at the protein level is minimal, so the feasibility of developing a therapeutic mAb against this target is uncertain. Here, we present a characterization of the properties associated with absorption, distribution, metabolism, and excretion of a high-affinity IL-36 R-targeted surrogate rat (IgG2a) mAb antagonist in preclinical mouse models. The presence of IL-36 R in the periphery was confirmed unequivocally as the driver of non-linear pharmacokinetics in blood/serum, although a predominant site of tissue accumulation was not observed based upon the kinetics of radiotracer. Additionally, the contribution of IL-36 R-mediated catabolism of mAb in kidney was tested in a 5/6 nephrectomized mouse model where minimal effects on serum pharmacokinetics were observed, although analysis of functional mAb in urine suggests that target can influence the amount of mAb excreted. Our data highlight an interesting case of target-mediated drug disposition (TMDD) where low, yet broadly expressed levels of membrane-bound target result in a cumulative effect to drive TMDD behavior typical of a large, saturable target sink. The potential differences between our mouse model and IL-36 R target profile in humans are also presented.

Which factors matter the most? Revisiting and dissecting antibody therapeutic doses

Factors such as antibody clearance and target affinity can influence antibodies' effective doses for specific indications. However, these factors vary considerably across antibody classes, precluding direct and quantitative comparisons. Here, we apply a dimensionless metric, the therapeutic exposure affinity ratio (TEAR), which normalizes the therapeutic doses by antibody bioavailability, systemic clearance and target-binding property to enable direct and quantitative comparisons of therapeutic doses. Using TEAR, we revisited and dissected the doses of up to 60 approved antibodies. We failed to detect a significant influence of target baselines, turnovers or anatomical locations on antibody therapeutic doses, challenging the traditional perceptions. We highlight the importance of antibodies' modes of action for therapeutic doses and dose selections; antibodies that work through neutralizing soluble targets show higher TEARs than those working through other mechanisms. Overall, our analysis provides insights into the factors that influence antibody doses, and the factors that are crucial for antibodies' pharmacological effects.

Modeling Pharmacokinetics and Pharmacodynamics of Therapeutic Antibodies: Progress, Challenges, and Future Directions

With more than 90 approved drugs by 2020, therapeutic antibodies have played a central role in shifting the treatment landscape of many diseases, including autoimmune disorders and cancers. While showing many therapeutic advantages such as long half-life and highly selective actions, therapeutic antibodies still face many outstanding issues associated with their pharmacokinetics (PK) and pharmacodynamics (PD), including high variabilities, low tissue distributions, poorly-defined PK/PD characteristics for novel antibody formats, and high rates of treatment resistance. We have witnessed many successful cases applying PK/PD modeling to answer critical questions in therapeutic antibodies’ development and regulations. These models have yielded substantial insights into antibody PK/PD properties. This review summarized the progress, challenges, and future directions in modeling antibody PK/PD and highlighted the potential of applying mechanistic models addressing the development questions.

Physiologically based pharmacokinetic (PBPK) modeling of RNAi therapeutics: Opportunities and challenges

Physiologically based pharmacokinetic (PBPK) modeling is a powerful tool with many demonstrated applications in various phases of drug development and regulatory review. RNA interference (RNAi)-based therapeutics are a class of drugs that have unique pharmacokinetic properties and mechanisms of action. With an increasing number of RNAi therapeutics in the pipeline and reaching the market, there is a considerable amount of active research in this area requiring a multidisciplinary approach. The application of PBPK models for RNAi therapeutics is in its infancy and its utility to facilitate the development of this new class of drugs is yet to be fully evaluated. From this perspective, we briefly discuss some of the current computational modeling approaches used in support of efficient development and approval of RNAi therapeutics. Considerations for PBPK model development are highlighted both in a relative context between small molecules and large molecules such as monoclonal antibodies and as it applies to RNAi therapeutics. In addition, the prospects for drawing upon other recognized avenues of PBPK modeling and some of the foreseeable challenges in PBPK model development for these chemical modalities are briefly discussed. Finally, an exploration of the potential application of PBPK model development for RNAi therapeutics is provided. We hope these preliminary thoughts will help initiate a dialogue between scientists in the relevant sectors to examine the value of PBPK modeling for RNAi therapeutics. Such evaluations could help standardize the practice in the future and support appropriate guidance development for strengthening the RNAi therapeutics development program.

A Phase 1 Study of KHK4083: A Single-Blind, Randomized, Placebo-Controlled Single-Ascending-Dose Study in Healthy Adults and an Open-Label Multiple-Dose Study in Patients With Ulcerative Colitis

OX40 plays an essential role in maintaining late T‐cell proliferation and survival by suppressing apoptosis and by inducing T‐cell memory formation. Here, we report the results of the phase 1 study of KHK4083, a fully human antimonoclonal antibody specific for OX40. In this study, we aimed to assess the safety and tolerability of a single intravenous or subcutaneous administration of KHK4083 compared with placebo in healthy Japanese and Caucasian subjects and determined the pharmacokinetics (PK) and immunogenicity. Also, we assessed the preliminary efficacy and pharmacodynamics of multiple intravenous doses in Japanese patients with moderate to severe ulcerative colitis (UC). Drug‐related treatment emergent adverse events occurred in 21 healthy subjects (58.3%) and 5 patients with UC (62.5%) after administration of KHK4083. There were no serious adverse events. The PK profile of a single intravenous dose of 10 mg/kg KHK4083 was similar in healthy Japanese and Caucasian subjects. Of 8 UC patients, a clinical response was observed in 3 patients (37.5%) and clinical remission in 2 patients (25.0%) in week 6. Our study demonstrated the safety and tolerability of single and multiple administrations of KHK4083 in healthy Japanese and Caucasian subjects and Japanese patients with moderate to severe UC. It also indicated favorable pharmacological properties of the drug.

A target-mediated drug disposition population pharmacokinetic model of GC1118, a novel anti-EGFR antibody, in patients with solid tumors

Abstract

GC1118 is a monoclonal antibody for epidermal growth factor receptor (EGFR) that is currently under clinical development to treat patients with solid tumors. In this study, the pharmacokinetics (PKs) of GC1118 were modeled in solid tumor patients who received a 2‐h intravenous infusion of GC1118 at 0.3, 1, 3, 5, or 4 mg/kg once‐weekly (Q1W) on days 1, 8, 15, and 22 or 8 mg/kg every other week on days 1 and 15. A target‐mediated drug disposition population PK model adequately described the concentration‐time profiles of GC1118. Monte‐Carlo simulation experiments of the PK profiles and EGFR occupancies (ROs) by GC1118 based on the final model showed that Q1W at 4 or 5 mg/kg will produce a better antitumor effect than Q2W at 8 mg/kg. Because GC1118 was safer at 4 mg/kg than 5 mg/kg in the phase I study, we suggest to test the 4 mg/kg Q1W regimen in further clinical trials with GC1118.

Pharmacokinetics and pharmacodynamics of therapeutic antibodies in tumors and tumor-draining lymph nodes

The signaling axis from the primary tumor to the tumor-draining lymph node (TDLN) has emerged as a crucial mediator for the efficacy of immunotherapies in neoadjuvant settings, challenging the primary use of immunotherapy in adjuvant settings. TDLNs are regarded as highly opportunistic sites for cancer cell dissemination and promote further spread via several primary tumor-dependent mechanisms. Lesion-level mixed responses to antibody immunotherapy have been traced to local immune signatures present in the TDLN and the organ-specific primary tumors that they drain. However, the pharmacokinetics (PK) and biodistribution gradients of antibodies in primary tumors and TDLNs have not been systemically evaluated. These concentration gradients are critical in ensuring adequate antibody pharmacodynamic (PD) T-cell activation and/or anti-tumor response. The current work reviews the knowledge for developing physiologically-based PK and pharmacodynamic (PBPK/PD) models to quantify antibody biodistribution gradients in anatomically distinct primary tumors and TDLNs as a means to characterize the clinically observed heterogeneous responses to antibody therapies. Several clinical and pathophysiological considerations in modeling the primary tumor-TDLN axis, as well as a summary of both preclinical and clinical PK/PD lymphatic antibody disposition studies, will be provided.

Transitioning from Basic toward Systems Pharmacodynamic Models: Lessons from Corticosteroids

Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.

PK/PD Mediated Dose Optimization of Emactuzumab, a CSF1R Inhibitor, in Patients With Advanced Solid Tumors and Diffuse-Type Tenosynovial Giant Cell Tumor

Targeted biological therapies may achieve maximal therapeutic efficacy at doses below the maximum tolerated dose (MTD); therefore, the search for the MTD in clinical studies may not be ideal for these agents. Emactuzumab is an investigational monoclonal antibody that binds to and inhibits the activation of the cell surface colony‐stimulating factor‐1 receptor. Here, we show how modeling target‐mediated drug disposition coupled with pharmacodynamic end points was used to optimize the dose of emactuzumab without defining an MTD. The model could be used to recommend doses across different disease indications. The approach recommended an optimal biological dose of emactuzumab for dosing every 2 weeks (q2w) ≥ 900 mg, approximately three‐fold lower than the highest dose tested clinically. The model predicted that emactuzumab doses ≥ 900 mg q2w would achieve target saturation in excess of 90% over the entire dosing cycle. Subsequently, a dose of 1,000 mg q2w was used in the extension phase of a phase I study of emactuzumab in patients with advanced solid tumors or diffuse‐type tenosynovial giant cell tumor. Clinical data from this study were consistent with model predictions. The model was also used to predict the optimum dose of emactuzumab for use with dosing every 3 weeks, enabling dosing flexibility with respect to comedications. In summary, this work demonstrates the value of quantitative clinical pharmacology approaches to dose selection in oncology as opposed to traditional MTD methods.

The biodistribution of therapeutic proteins: Mechanism, implications for pharmacokinetics, and methods of evaluation

Therapeutic proteins (TPs) are a diverse drug class that include monoclonal antibodies (mAbs), recombinantly expressed enzymes, hormones and growth factors, cytokines (e.g. chemokines, interleukins, interferons), as well as a wide range of engineered fusion scaffolds containing IgG1 Fc domain for half-life extension. As the pharmaceutical industry advances more potent and selective protein-based medicines through discovery and into the clinical stages of development, it has become widely appreciated that a comprehensive understanding of the mechanisms of TP biodistribution can aid this endeavor. This review aims to highlight the literature that has advanced our understanding of the determinants of TP biodistribution. A particular emphasis is placed on the multi-faceted role of the neonatal Fc receptor (FcRn) in mAb and Fc-fusion protein disposition. In addition, characterization of the TP-target interaction at the cell-level is discussed as an essential strategy to establish pharmacokinetic-pharmacodynamic (PK/PD) relationships that may lead to more informed human dose projections during clinical development. Methods for incorporation of tissue and cell-level parameters defining these characteristics into higher-order mechanistic and semi-mechanistic PK models will also be presented.

Assessment of romosozumab efficacy in the treatment of postmenopausal osteoporosis: Results from a mechanistic PK-PD mechanostat model of bone remodeling

This paper introduces a theoretical framework for the study of the efficacy of romosozumab, a humanized monoclonal antibody targeting sclerostin for the treatment of osteoporosis. We developed a comprehensive mechanistic pharmacokinetic-pharmacodynamic (PK-PD) model of the effect of drug treatment on bone remodeling in postmenopausal osteoporosis (PMO). We utilized a one-compartment PK model to represent subcutaneous injections of romosozumab and subsequent absorption into serum. The PD model is based on a recently-developed bone cell population model describing the bone remodeling process at the tissue scale. The latter accounts for mechanical feedback by incorporating nitric oxide (NO) and sclerostin (Scl) as biochemical feedback molecules. Utilizing a competitive binding model, where Wnt and Scl compete for binding to LRP5/6, allows to regulate anabolic bone remodeling responses. Here, we extended this model with respect to romosozumab binding to sclerostin. For the currently approved monthly injections of 210 mg, the model predicted a 6.59%, 10.38% and 15.25% increase in BMD at the lumbar spine after 6, 12 and 24 months, respectively. These results are in good agreement with the data reported in the literature. Our model is also able to distinguish the bone-site specific drug effects. For instance, at the femoral neck, our model predicts a BMD increase of 3.85% after 12 months of 210 mg injections, which is consistent with literature observations. Finally, our simulations indicate rapid bone loss after treatment discontinuation, indicating that some additional interventions such as use of bisphosphonates are required to maintain bone mass.

Antibody Coadministration as a Strategy to Overcome Binding-Site Barrier for ADCs: a Quantitative Investigation

It has been proposed that the binding-site barrier (BSB) for antibody-drug conjugates (ADCs) can be overcome with the help of antibody coadministration. However, broad utility of this strategy remains in question. Consequently, here, we have conducted in vivo experiments and pharmacokinetics-pharmacodynamics (PK-PD) modeling and simulation (M&S) to further evaluate the antibody coadministration hypothesis in a quantitative manner. Two different Trastuzumab-based ADCs, T-DM1 (no bystander effect) and T-vc-MMAE (with a bystander effect), were evaluated in high-HER2 (N87) and low-HER2 (MDA-MB-453) expressing tumors, with or without the coadministration of 1, 3, or 8-fold higher Trastuzumab. The tumor growth inhibition (TGI) data was quantitatively characterized using a semi-mechanistic PK-PD model to determine the nature of drug interaction for each coadministration regimen, by estimating the interaction parameter ψ. It was found that the coadministration strategy improved ADC efficacy under certain conditions and had no impact on ADC efficacy in others. The benefit was more pronounced for N87 tumors with very high antigen expression levels where the effect on treatment was synergistic (a synergistic drug interaction, ψ = 2.86 [2.6-3.12]). The benefit was diminished in tumor with lower antigen expression (MDA-MB-453) and payload with bystander effect. Under these conditions, the coadministration regimens resulted in an additive or even less than additive benefit (ψ ≤ 1). As such, our results suggest that while antibody coadministration may be helpful for ADCs in certain circumstances, one should not broadly apply this strategy to all the scenarios without first identifying the costs and benefits of this approach.

A Minimal Physiologically-Based Pharmacokinetic Model Demonstrates Role of the Neonatal Fc Receptor (FcRn) Competition in Drug-Disease Interactions With Antibody Therapy

Disease trajectories following antibody therapy can have a significant impact on the pharmacokinetics of the antibody. Although this phenomenon can often be explained by reduced target-expressing cells, other mechanisms may play a role. We use a novel minimal physiologically-based pharmacokinetic model to evaluate an alternative drug-disease interaction mechanism involving competitive inhibition of neonatal Fc receptor (FcRn)-mediated Immunoglobulin G recycling by paraproteins. The model is validated with clinical data from the anti-FcRn antibody M281 and is used to conduct a scenario test to quantify the interaction among M-protein, the characteristic paraprotein of multiple myeloma (MM), and the anti-CD38 antibody daratumumab indicated for MM treatment. Simulations predict up to a 3.6-fold increase in daratumumab half-life following M-protein reduction, which lends credence to the hypothesis that FcRn competition in MM can manifest as time-dependent reduction of clearance for daratumumab. This model can inform optimal dosing strategies for antibodies in MM and other pathologies of paraprotein excess.

ATLAS mPBPK: A MATLAB-Based Tool for Modeling and Simulation of Minimal Physiologically-Based Pharmacokinetic Models

Minimal physiologically-based pharmacokinetic (mPBPK) models are frequently used to model plasma pharmacokinetic (PK) data and utilize and yield physiologically relevant parameters. Compared with classical compartment and whole-body physiologically-based pharmacokinetic modeling approaches, mPBPK models maintain a structure of intermediate physiological complexity that can be adequately informed by plasma PK data. In this tutorial, we present a MATLAB-based tool for the modeling and simulation of mPBPK models (ATLAS mPBPK) of small and large molecules. This tool enables the users to perform the following: (i) PK data visualization, (ii) simulation, (iii) parameter optimization, and (iv) local sensitivity analysis of mPBPK models in a simple and efficient manner. In addition to the theoretical background and implementation of the different tool functionalities, this tutorial includes simulation and sensitivity analysis showcases of small and large molecules with and without target-mediated drug disposition.

A Bioluminescence Resonance Energy Transfer-Based Approach for Determining Antibody-Receptor Occupancy In Vivo

Elucidating receptor occupancy (RO) of monoclonal antibodies (mAbs) is a crucial step in characterizing the therapeutic efficacy of mAbs. However, the in vivo assessment of RO, particularly within peripheral tissues, is greatly limited by current technologies. In the present study, we developed a bioluminescence resonance energy transfer (BRET)-based system that leverages the large signal:noise ratio and stringent energy donor-acceptor distance dependency to measure antibody RO in a highly selective and temporal fashion. This versatile and minimally invasive system enables longitudinal monitoring of the in vivo antibody-receptor engagement over several days. As a proof of principle, we quantified cetuximab-epidermal growth factor receptor binding kinetics using this system and assessed cetuximab RO in a tumor xenograft model. Incomplete ROs were observed, even at a supratherapeutic dose of 50 mg/kg, indicating that fractional target accessibility is achieved. The BRET-based imaging approach enables quantification of antibody in vivo RO and provides critical information required to optimize therapeutic mAb efficacy.

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Nano-BRET was used to longitudinally quantify cetuximab-binding kinetics to EGFR

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Incomplete EGFR occupancy in solid tumors was observed even at supratherapeutic doses

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A kinetic disassociation exists between plasma antibody and bound targets in tumors

Physiologically-based modeling of monoclonal antibody pharmacokinetics in drug discovery and development

Over the past few decades, monoclonal antibodies (mAbs) have become one of the most important and fastest growing classes of therapeutic molecules, with applications in a wide variety of disease areas. As such, understanding of the determinants of mAb pharmacokinetic (PK) processes (absorption, distribution, metabolism, and elimination) is crucial in developing safe and efficacious therapeutics. In the present review, we discuss the use of physiologically-based pharmacokinetic (PBPK) models as an approach to characterize the in vivo behavior of mAbs, in the context of the key PK processes that should be considered in these models. Additionally, we discuss current and potential future applications of PBPK in the drug discovery and development timeline for mAbs, spanning from identification of potential target molecules to prediction of potential drug-drug interactions. Finally, we conclude with a discussion of currently available PBPK models for mAbs that could be implemented in the drug development process.

MPBPK-TMDD models for mAbs: alternative models, comparison, and identifiability issues

The aim of the present study was to evaluate model identifiability when minimal physiologically-based pharmacokinetic (mPBPK) models are integrated with target mediated drug disposition (TMDD) models in the tissue compartment. Three quasi-steady-state (QSS) approximations of TMDD dynamics were explored: on (a) antibody-target complex, (b) free target, and (c) free antibody concentrations in tissue. The effects of the QSS approximations were assessed via simulations, taking as reference the mPBPK-TMDD model with no simplifications. Approximation (a) did not affect model-derived concentrations, while with the inclusion of approximation (b) or (c), target concentration profiles alone, or both drug and target concentration profiles respectively deviated from the reference model profiles. A local sensitivity analysis was performed, highlighting the potential importance of sampling in the terminal pharmacokinetic phase and of collecting target concentration data. The a priori and a posteriori identifiability of the mPBPK-TMDD models were investigated under different experimental scenarios and designs. The reference model and QSS approximation (a) on antibody-target complex were both found to be a priori identifiable in all scenarios, while under the further inclusion of QSS approximation (b) target concentration data were needed for a priori identifiability to be preserved. The property could not be assessed for the model including all three QSS approximations. A posteriori identifiability issues were detected for all models, although improvement was observed when appropriate sampling and dose range were selected. In conclusion, this work provides a theoretical framework for the assessment of key properties of mathematical models before their experimental application. Attention should be paid when applying integrated mPBPK-TMDD models, as identifiability issues do exist, especially when rich study designs are not feasible.

In vivo affinity and target engagement in skin and blood in a first-time-in-human study of an anti-oncostatin M monoclonal antibody

AIMS

The oncostatin M (OSM) pathway drives fibrosis, inflammation and vasculopathy, and is a potential therapeutic target for inflammatory and fibrotic diseases. The aim of this first-time-in-human experimental medicine study was to assess the safety, tolerability, pharmacokinetics and target engagement of single subcutaneous doses of GSK2330811, an anti-OSM monoclonal antibody, in healthy subjects.

METHODS

This was a phase I, randomized, double-blind, placebo-controlled, single-dose escalation, first-time-in-human study of subcutaneously administered GSK2330811 in healthy adults (NCT02386436). Safety and tolerability, GSK2330811 pharmacokinetic profile, OSM levels in blood and skin, and the potential for antidrug antibody formation were assessed. The in vivo affinity of GSK2330811 for OSM and target engagement in serum and skin blister fluid (obtained via a skin suction blister model) were estimated using target-mediated drug disposition (TMDD) models in combination with compartmental and physiology-based pharmacokinetic (PBPK) models.

RESULTS

Thirty subjects were randomized to receive GSK2330811 and 10 to placebo in this completed study. GSK2330811 demonstrated a favourable safety profile in healthy subjects; no adverse events were serious or led to withdrawal. There were no clinically relevant trends in change from baseline in laboratory values, with the exception of a reversible dose-dependent reduction in platelet count. GSK2330811 exhibited linear pharmacokinetics over the dose range 0.1-6 mg kg-1 . The estimated in vivo affinity (nM) of GSK2330811 for OSM was 0.568 [95% confidence interval (CI) 0.455, 0.710] in the compartmental with TMDD model and 0.629 (95% CI 0.494, 0.802) using the minimal PBPK with TMDD model.

CONCLUSIONS

Single subcutaneous doses of GSK2330811 were well tolerated in healthy subjects. GSK2330811 demonstrated sufficient affinity to achieve target engagement in systemic circulation and target skin tissue, supporting the progression of GSK2330811 clinical development.

Predicting the Onset of Nonlinear Pharmacokinetics

When analyzing the pharmacokinetics (PK) of drugs, one is often faced with concentration C vs. time curves, which display a sharp transition at a critical concentration C_crit. For C > C_crit, the curve displays linear clearance and for C < C_crit clearance increases in a nonlinear manner as C decreases. Often, it is important to choose a high enough dose such that PK remains linear in order to help ensure that continuous target engagement is achieved throughout the duration of therapy. In this article, we derive a simple expression for C_crit for models involving linear and nonlinear (saturable) clearance, such as Michaelis‐Menten and target‐mediated drug disposition (TMDD) models.

Study Highlights

Role of Interstitial Fluid Turnover on Target Suppression by Therapeutic Biologics Using a Minimal Physiologically Based Pharmacokinetic Model

For therapeutic biologics against soluble ligands, the magnitude and duration of target suppression affect their therapeutic efficacy. Many factors have been evaluated in relation to target suppression but the interstitial fluid turnover rate in target tissues has not been considered. Inspired by the fact that etanercept exerts limited efficacy in Crohn's disease despite its high efficacy in rheumatoid arthritis, we developed a minimal physiologically based pharmacokinetic model to investigate the role of the tissue fluid turnover rate on soluble target suppression and assessed the interrelationships between binding constants and tissue fluid turnover. Interstitial fluid turnover rates in target tissues were found to strongly influence target binding kinetics. For tissues with low fluid turnover, stable binders (low k_off) exhibit greater target suppression, but efficacy is often restricted by accumulation of the drug-target complex. For tissues with high fluid turnover, fast binders (high k_on) are generally favored, but a plateau effect is present for antibodies with low dissociation rates (k_off). Etanercept is often regarded as a fast tumor necrosis factor-α (TNF-α) binder (high k_on) despite comparable binding affinity (K_D, k_off/k_on) with adalimumab and infliximab. Crohn's disease largely involves the colon, a tissue with relatively slower fluid turnover than arthritis-associated joint synovium; this may explain why etanercept exerts poor TNF-α suppressive effect in Crohn's disease. This study highlights the importance of tissue interstitial fluid turnover in evaluation of therapeutic antibodies bound to soluble antigens.

Sensitive, High-Throughput, and Robust Trapping-Micro-LC-MS Strategy for the Quantification of Biomarkers and Antibody Biotherapeutics

For LC-MS-based targeted quantification of biotherapeutics and biomarkers in clinical and pharmaceutical environments, high sensitivity, high throughput, and excellent robustness are all essential but remain challenging. For example, though nano-LC-MS has been employed to enhance analytical sensitivity, it falls short because of its low loading capacity, poor throughput, and low operational robustness. Furthermore, high chemical noise in protein bioanalysis typically limits the sensitivity. Here we describe a novel trapping-micro-LC-MS (T-μLC-MS) strategy for targeted protein bioanalysis, which achieves high sensitivity with exceptional robustness and high throughput. A rapid, high-capacity trapping of biological samples is followed by μLC-MS analysis; dynamic sample trapping and cleanup are performed using pH, column chemistry, and fluid mechanics separate from the μLC-MS analysis, enabling orthogonality, which contributes to the reduction of chemical noise and thus results in improved sensitivity. Typically, the selective-trapping and -delivery approach strategically removes >85% of the matrix peptides and detrimental components, markedly enhancing sensitivity, throughput, and operational robustness, and narrow-window-isolation selected-reaction monitoring further improves the signal-to-noise ratio. In addition, unique LC-hardware setups and flow approaches eliminate gradient shock and achieve effective peak compression, enabling highly sensitive analyses of plasma or tissue samples without band broadening. In this study, the quantification of 10 biotherapeutics and biomarkers in plasma and tissues was employed for method development. As observed, a significant sensitivity gain (up to 25-fold) compared with that of conventional LC-MS was achieved, although the average run time was only 8 min/sample. No appreciable peak deterioration or loss of sensitivity was observed after >1500 injections of tissue and plasma samples. The developed method enabled, for the first time, ultrasensitive LC-MS quantification of low levels of a monoclonal antibody and antigen in a tumor and cardiac troponin I in plasma after brief cardiac ischemia. This strategy is valuable when highly sensitive protein quantification in large sample sets is required, as is often the case in typical biomarker validation and pharmaceutical investigations of antibody therapeutics.

Development and Translational Application of a Minimal Physiologically Based Pharmacokinetic Model for a Monoclonal Antibody against Interleukin 23 (IL-23) in IL-23-Induced Psoriasis-Like Mice

The interleukin (IL)-23/T_h17/IL-17 immune pathway has been identified to play an important role in the pathogenesis of psoriasis. Many therapeutic proteins targeting IL-23 or IL-17 are currently under development for the treatment of psoriasis. In the present study, a mechanistic pharmacokinetics (PK)/pharmacodynamics (PD) study was conducted to assess the target-binding and disposition kinetics of a monoclonal antibody (mAb), CNTO 3723, and its soluble target, mouse IL-23, in an IL-23-induced psoriasis-like mouse model. A minimal physiologically based pharmacokinetic model with target-mediated drug disposition features was developed to quantitatively assess the kinetics and interrelationship between CNTO 3723 and exogenously administered, recombinant mouse IL-23 in both serum and lesional skin site. Furthermore, translational applications of the developed model were evaluated with incorporation of human PK for ustekinumab, an anti-human IL-23/IL-12 mAb developed for treatment of psoriasis, and human disease pathophysiology information in psoriatic patients. The results agreed well with the observed clinical data for ustekinumab. Our work provides an example on how mechanism-based PK/PD modeling can be applied during early drug discovery and how preclinical data can be used for human efficacious dose projection and guide decision making during early clinical development of therapeutic proteins.

gPKPDSim: a SimBiology®-based GUI application for PKPD modeling in drug development

Modeling and simulation (M&S) is increasingly used in drug development to characterize pharmacokinetic-pharmacodynamic (PKPD) relationships and support various efforts such as target feasibility assessment, molecule selection, human PK projection, and preclinical and clinical dose and schedule determination. While model development typically require mathematical modeling expertise, model exploration and simulations could in many cases be performed by scientists in various disciplines to support the design, analysis and interpretation of experimental studies. To this end, we have developed a versatile graphical user interface (GUI) application to enable easy use of any model constructed in SimBiology^® to execute various common PKPD analyses. The MATLAB^®-based GUI application, called gPKPDSim, has a single screen interface and provides functionalities including simulation, data fitting (parameter estimation), population simulation (exploring the impact of parameter variability on the outputs of interest), and non-compartmental PK analysis. Further, gPKPDSim is a user-friendly tool with capabilities including interactive visualization, exporting of results and generation of presentation-ready figures. gPKPDSim was designed primarily for use in preclinical and translational drug development, although broader applications exist. gPKPDSim is a MATLAB^®-based open-source application and is publicly available to download from MATLAB^® Central™. We illustrate the use and features of gPKPDSim using multiple PKPD models to demonstrate the wide applications of this tool in pharmaceutical sciences. Overall, gPKPDSim provides an integrated, multi-purpose user-friendly GUI application to enable efficient use of PKPD models by scientists from various disciplines, regardless of their modeling expertise.

In vivo potency revisited - Keep the target in sight

Potency is a central parameter in pharmacological and biochemical sciences, as well as in drug discovery and development endeavors. It is however typically defined in terms only of ligand to target binding affinity also in in vivo experimentation, thus in a manner analogous to in in vitro studies. As in vivo potency is in fact a conglomerate of events involving ligand, target, and target-ligand complex processes, overlooking some of the fundamental differences between in vivo and in vitro may result in serious mispredictions of in vivo efficacious dose and exposure. The analysis presented in this paper compares potency measures derived from three model situations. Model A represents the closed in vitro system, defining target binding of a ligand when total target and ligand concentrations remain static and constant. Model B describes an open in vivo system with ligand input and clearance (Cl_(L)), adding in parallel to the turnover (k_syn, k_deg) of the target. Model C further adds to the open in vivo system in Model B also the elimination of the target-ligand complex (k_e(RL)) via a first-order process. We formulate corresponding equations of the equilibrium (steady-state) relationships between target and ligand, and complex and ligand for each of the three model systems and graphically illustrate the resulting simulations. These equilibrium relationships demonstrate the relative impact of target and target-ligand complex turnover, and are easier to interpret than the more commonly used ligand-, target- and complex concentration-time courses. A new potency expression, labeled L₅₀, is then derived. L₅₀ is the ligand concentration at half-maximal target and complex concentrations and is an amalgamation of target turnover, target-ligand binding and complex elimination parameters estimated from concentration-time data. L₅₀ is then compared to the dissociation constant K_d (target-ligand binding affinity), the conventional Black & Leff potency estimate EC₅₀, and the derived Michaelis-Menten parameter K_m (target-ligand binding and complex removal) across a set of literature data. It is evident from a comparison between parameters derived from in vitro vs. in vivo experiments that L₅₀ can be either numerically greater or smaller than the K_d (or K_m) parameter, primarily depending on the ratio of k_deg-to-k_e(RL). Contrasting the limit values of target R and target-ligand complex RL for ligand concentrations approaching infinity demonstrates that the outcome of the three models differs to a great extent. Based on the analysis we propose that a better understanding of in vivo pharmacological potency requires simultaneous assessment of the impact of its underlying determinants in the open system setting. We propose that L₅₀ will be a useful parameter guiding predictions of the effective concentration range, for translational purposes, and assessment of in vivo target occupancy/suppression by ligand, since it also encompasses target turnover - in turn also subject to influence by pathophysiology and drug treatment. Different compounds may have similar binding affinity for a target in vitro (same K_d), but vastly different potencies in vivo. L₅₀ points to what parameters need to be taken into account, and particularly that closed-system (in vitro) parameters should not be first choice when ranking compounds in vivo (open system).

Impact of mathematical pharmacology on practice and theory: four case studies

Drug-discovery has become a complex discipline in which the amount of knowledge about human biology, physiology, and biochemistry have increased. In order to harness this complex body of knowledge mathematics can play a critical role, and has actually already been doing so. We demonstrate through four case studies, taken from previously published data and analyses, what we can gain from mathematical/analytical techniques when nonlinear concentration-time courses have to be transformed into their equilibrium concentration-response (target or complex) relationships and new structures of drug potency have to be deciphered; when pattern recognition needs to be carried out for an unconventional response-time dataset; when what-if? predictions beyond the observational concentration-time range need to be made; or when the behaviour of a semi-mechanistic model needs to be elucidated or challenged. These four examples are typical situations when standard approaches known to the general community of pharmacokineticists prove to be inadequate.

A novel model for the pharmacokinetic studies of bevacizumab and etanercept in healthy volunteers and patients

Therapeutic monoclonal antibodies (mAbs) have been successfully applied to treat various diseases and shown a promising prospect in medical treatment. MAbs have some unique characteristics when compared with small chemical drugs, and their pharmacokinetic (PK) properties are much more complex than those of small chemical drugs, whose eliminations are usually linear. In this study, a new model was established through taking into account the mechanisms of the elimination of mAbs. The proposed model was applied to the modeling and simulation of two kinds of mAbs, including bevacizumab and etanercept, in PK studies of healthy volunteers and eligible patients, and the classical linear compartment model was set as control. The goodness-of-fit of the fitting concentration-time curve of mAbs was calculated to verify the accuracy of both models during the modeling and simulation. The accuracy of the proposed model was better than that of classical linear compartment model in healthy volunteers and even much better in patients. The proposed model demonstrates a stronger ability in the modeling and simulation of mAbs, and may provide a new option for the PK studies of those reagents.

Accelerating drug development by efficiently using emerging PK/PD data from an adaptable entry-into-human trial: example of lumretuzumab

PURPOSE

This study aimed at evaluating if pharmacokinetic and pharmacodynamic data from the first few patients treated with an investigational monoclonal antibody in a dose-escalation study can be used to guide the early initiation of potentially more efficacious combination regimens.

METHODS

Emerging pharmacokinetic and pharmacodynamic data from the first nine patients treated with lumretuzumab (a glycoengineered anti-HER3 monoclonal antibody) monotherapy at doses from 100 to 400 mg q2w were used along with a pharmacokinetic model that incorporated target-mediated drug disposition to guide the selection of the starting dose for use in combination regimens.

RESULTS

The dose-escalation study investigated lumretuzumab doses up to 2000 mg q2w and a maximum tolerated dose was not reached. However, the model described in this report predicted linear lumretuzumab pharmacokinetics and >95% target saturation at doses ≥400 mg q2w. These data, along with safety data, contributed to the decision to begin dose-escalation studies in combination with cetuximab and erlotinib using a starting dose of 400 mg lumretuzumab. Pharmacokinetic data from patients treated with lumretuzumab 400-2000 mg q2w in combination regimens were consistent with the model predictions.

CONCLUSION

PK/PD modelling of emerging clinical data might accelerate development programs by enabling additional parts of a trial to commence before completion of the monotherapy part. The dose and schedule of lumretuzumab were optimised for concomitant therapy at doses substantially below the highest dose investigated.

Interrelationships between Infliximab and Recombinant Tumor Necrosis Factor-α in Plasma Using Minimal Physiologically Based Pharmacokinetic Models

The soluble cytokine tumor necrosis factor-α (TNF-α) is an important target for many therapeutic proteins used in the treatment of rheumatoid arthritis. Biologics targeting TNF-α exert their pharmacologic effects through binding and neutralizing this cytokine and preventing it from binding to its cell surface receptors. The magnitude of their pharmacologic effects directly corresponds to the extent and duration of free TNF-α suppression. However, endogenous TNF-α is of low abundance, so it is quite challenging to assess the free TNF-α suppression experimentally. Here we have applied an experimental approach to bypass this difficulty by giving recombinant human TNF-α (rhTNF-α) to rats by s.c. infusion. This boosted TNF-α concentration enabled quantification of TNF-α in plasma. Free rhTNF-α concentrations were measured after separation from the infliximab-rhTNF-α complex using Dynabeads Protein A. The interrelationship of infliximab and TNF-α was assessed with minimal physiologically based pharmacokinetic models for TNF-α and infliximab with a target-mediated drug disposition component. Knowledge of TNF-α pharmacokinetics allows reliable prediction of the free TNF-α suppression with either free or total TNF-α concentration profiles. The experimental and modeling approaches in our study may aid in the development of next-generation TNF-α inhibitors with improved therapeutic effects.

Population PBPK modelling of trastuzumab: a framework for quantifying and predicting inter-individual variability

In this work we proposed a population physiologically-based pharmacokinetic (popPBPK) framework for quantifying and predicting inter-individual pharmacokinetic variability using the anti-HER2 monoclonal antibody (mAb) trastuzumab as an example. First, a PBPK model was developed to account for the possible mechanistic sources of variability. Within the model, five key factors that contribute to variability were identified and the nature of their contribution was quantified with local and global sensitivity analyses. The five key factors were the concentration of membrane-bound HER2 ([Formula: see text]), the convective flow rate of mAb through vascular pores ([Formula: see text]), the endocytic transport rate of mAb through vascular endothelium ([Formula: see text]), the degradation rate of mAb-HER2 complexes ([Formula: see text]) and the concentration of shed HER2 extracellular domain in circulation ([Formula: see text]). [Formula: see text] was the most important parameter governing trastuzumab distribution into tissues and primarily affected variability in the first 500 h post-administration. [Formula: see text] was the most significant contributor to variability in clearance. These findings were used together with population generation methods to accurately predict the observed variability in four experimental trials with trastuzumab. To explore anthropometric sources of variability, virtual populations were created to represent participants in the four experimental trials. Using populations with only their expected anthropometric diversity resulted in under-prediction of the observed inter-individual variability. Adapting the populations to include literature-based variability around the five key parameters enabled accurate predictions of the variability in the four trials. The successful application of this framework demonstrates the utility of popPBPK methods to understand the mechanistic underpinnings of pharmacokinetic variability.

Pharmacokinetic Steady-States Highlight Interesting Target-Mediated Disposition Properties

In this paper, we derive explicit expressions for the concentrations of ligand L, target R and ligand-target complex RL at steady state for the classical model describing target-mediated drug disposition, in the presence of a constant-rate infusion of ligand. We demonstrate that graphing the steady-state values of ligand, target and ligand-target complex, we obtain striking and often singular patterns, which yield a great deal of insight and understanding about the underlying processes. Deriving explicit expressions for the dependence of L, R and RL on the infusion rate, and displaying graphs of the relations between L, R and RL, we give qualitative and quantitive information for the experimentalist about the processes involved. Understanding target turnover is pivotal for optimising these processes when target-mediated drug disposition (TMDD) prevails. By a combination of mathematical analysis and simulations, we also show that the evolution of the three concentration profiles towards their respective steady-states can be quite complex, especially for lower infusion rates. We also show how parameter estimates obtained from iv bolus studies can be used to derive steady-state concentrations of ligand, target and complex. The latter may serve as a template for future experimental designs.

Target-mediated drug disposition with drug-drug interaction, Part I: single drug case in alternative formulations

Target-mediated drug disposition (TMDD) describes drug binding with high affinity to a target such as a receptor. In application TMDD models are often over-parameterized and quasi-equilibrium (QE) or quasi-steady state (QSS) approximations are essential to reduce the number of parameters. However, implementation of such approximations becomes difficult for TMDD models with drug-drug interaction (DDI) mechanisms. Hence, alternative but equivalent formulations are necessary for QE or QSS approximations. To introduce and develop such formulations, the single drug case is reanalyzed. This work opens the route for straightforward implementation of QE or QSS approximations of DDI TMDD models. The manuscript is the first part to introduce DDI TMDD models with QE or QSS approximations.

A Philosophical Framework for Integrating Systems Pharmacology Models Into Pharmacometrics

The framework for systems pharmacology style models does not naturally sit with the usual scientific dogma of parsimony and falsifiability based on deductive reasoning. This does not invalidate the importance or need for overarching models based on pharmacology to describe and understand complicated biological systems. However, it does require some consideration on how systems pharmacology fits into the overall scientific approach.

Using Model-Based "Learn and Confirm" to Reveal the Pharmacokinetics-Pharmacodynamics Relationship of Pembrolizumab in the KEYNOTE-001 Trial

Evaluation of pharmacokinetic/pharmacodynamic (PK/PD) properties played an important role in the early clinical development of pembrolizumab. Because analysis of data from a traditional 3 + 3 dose‐escalation design revealed several critical uncertainties, a model‐based approach was implemented to better understand these properties. Based on anticipated scenarios for potency and PK nonlinearity, a follow‐up study was designed and thoroughly evaluated. Execution of 14,000 virtual trials led to the selection and implementation of a robust design that extended the low‐dose range by 200‐fold. Modeling of the resulting data demonstrated that pembrolizumab PKs are nonlinear at <0.3 mg/kg every 3 weeks, but linear in the clinical dose range. Saturation of ex vivo target engagement in blood began at ≥1 mg/kg every 3 weeks, and a steady‐state dose of 2 mg/kg every 3 weeks was needed to reach 95% target engagement, supporting examination of 2 mg/kg every 3 weeks in ongoing trials in melanoma and other advanced cancers.

Fab-dsFv: A bispecific antibody format with extended serum half-life through albumin binding

An antibody format, termed Fab-dsFv, has been designed for clinical indications that require monovalent target binding in the absence of direct Fc receptor (FcR) binding while retaining substantial serum presence. The variable fragment (Fv) domain of a humanized albumin-binding antibody was fused to the C-termini of Fab constant domains, such that the VL and VH domains were individually connected to the Cκ and CH1 domains by peptide linkers, respectively. The anti-albumin Fv was selected for properties thought to be desirable to ensure a durable serum half-life mediated via FcRn. The Fv domain was further stabilized by an inter-domain disulfide bond. The bispecific format was shown to be thermodynamically and biophysically stable, and retained good affinity and efficacy to both antigens simultaneously. In in vivo studies, the serum half-life of Fab-dsFv, 2.6 d in mice and 7.9 d in cynomolgus monkeys, was equivalent to Fab'-PEG.