Leveraging a physiologically-based quantitative translational modeling platform for designing B cell maturation antigen-targeting bispecific T cell engagers for treatment of multiple myeloma

Whole-Body Physiologically Based Pharmacokinetic Modeling Framework for Tissue Target Engagement of CD3 Bispecific Antibodies

Background: T-cell-engaging bispecific (TCB) antibodies represent a promising therapy that utilizes T-cells to eliminate cancer cells independently of the major histocompatibility complex. Despite their success in hematologic cancers, challenges such as cytokine release syndrome (CRS), off-tumor toxicity, and resistance limit their efficacy in solid tumors. Optimizing biodistribution is key to overcoming these challenges. Methods: A physiologically based pharmacokinetic (PBPK) model was developed that incorporates T-cell transmigration, retention, receptor binding, receptor turnover, and cellular engagement. Preclinical biodistribution data were modeled using two TCB formats: one lacking tumor target binding and another with target arm binding, each with varying CD3 affinities in a transgenic tumor-bearing mouse model. Results: The PBPK model successfully described the distribution of activated T-cells and various TCB formats. It accurately predicted preclinical biodistribution patterns, demonstrating that higher CD3 affinity leads to faster clearance from the blood and increased accumulation in T-cell-rich organs, often reducing tumor exposure. Simulations of HER2-CD3 TCB doses (0.1 µg to 100 mg) revealed monotonic increases in synapse AUC within the tumor. A bell-shaped dose-Cmax relationship for synapse formation was observed, and Tmax was delayed at higher doses. Blood PK was a reasonable surrogate for tumor synapse at low doses but less predictive at higher doses. Conclusions: We developed a whole-body PBPK model to simulate the biodistribution of T-cells and TCB molecules. The insights from this model provide a comprehensive understanding of the factors affecting PK, synapse formation, and TCB activity, aiding in dose optimization and the design of effective therapeutic strategies.

General quasi-equilibrium multivalent binding model to study diverse and complex drug-receptor interactions of biologics

Pharmacokinetics and pharmacodynamics of many biologics are influenced by their complex binding to biological receptors. Biologics consist of diverse groups of molecules with different binding kinetics to its receptors including IgG with simple one-to-one drug receptor bindings, bispecific antibody (BsAb) that binds to two different receptors, and antibodies that can bind to six or more identical receptors. As the binding process is typically much faster than elimination (or internalization) and distribution processes, quasi-equilibrium (QE) binding models are commonly used to describe drug-receptor binding kinetics of biologics. However, no general QE modeling framework is available to describe complex binding kinetics for diverse classes of biologics. In this paper, we describe novel approaches of using differential algebraic equations (DAE) to solve three QE multivalent drug-receptor binding (QEMB) models. The first example describes the binding kinetics of three-body equilibria of BsAb that binds to 2 different receptors for trimer formation. The second example models an engineered IgG variant (Multabody) that can bind to 24 identical target receptors. The third example describes an IgG with modified neonatal Fc receptor (FcRn) binding affinity that competes for the same FcRn receptor as endogenous IgG. The model parameter estimates were obtained by fitting the model to all data simultaneously. The models allowed us to study potential roles of cooperative binding on bell-shaped drug exposure-response relationships of BsAb, and concentration-depended distribution of different drug-receptor complexes for Multabody. This DAE-based QEMB model platform can serve as an important tool to better understand complex binding kinetics of diverse classes of biologics.

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development

Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Developing a mechanistic translational PK/PD model for a trifunctional NK cell engager to predict the first-in-human dose for acute myeloid leukemia

Natural killer cell engagers (NKCEs), a treatment that stimulates innate immunity, have lately gained attention owing to their favorable safety profile, and their efficacy. Natural killer (NK) cell activation is driven by immune synapse formation between drugs, NK cells, and tumor cells. However, no clear translational modeling approach has been reported for first-in-human (FIH) dose estimation of humanized NKCEs. We developed the first translational mechanistic synapse-driven pharmacokinetic/pharmacodynamic (PK/PD) model for a trifunctional NKp46/CD16a-CD123 (CD123-NKCE) by integrating (i) in vitro target cell cytotoxicity in MOLM-13 tumor cell lines at varying effector-to-tumor cell ratios and incubation intervals; (ii) nonhuman primate PK and profiles of CD123+ cells and NKP46+ NK cells; and (iii) healthy human or patients with acute myeloid leukemia system-specific parameters. To depict direct tumor cell killing by the innate immunity, no transit compartment was included in PK/PD model structures. Model predictions suggested an intrapatient dose escalation of 10/30/100 μg/kg twice weekly to be selected as the starting dose in the FIH trial. However, sensitivity analyses revealed that CD123+ cell growth rate constant and maximal tumor killing rate constant were the key uncertainties to the recommended active dose. This novel translational model structure can be used as the basis to predict clinical PK/PD data for CD123-NKCE, and the translational strategy may serve as a foundation for future advancements of NKCEs.

Development of bispecific T cell engagers: harnessing quantitative systems pharmacology

Bispecific T cell engagers (bsTCEs) have emerged as a promising class of cancer immunotherapy. Several bsTCEs have achieved marketing approval; dozens more are under clinical investigation. However, the clinical development of bsTCEs remains rife with challenges, including nuanced pharmacology, limited translatability of preclinical findings, frequent on-target toxicity, and convoluted dosing regimens. In this opinion article we present a distinct perspective on how quantitative systems pharmacology (QSP) can serve as a powerful tool for overcoming these obstacles. Recent advances in QSP modeling have empowered developers of bsTCEs to gain a deeper understanding of their context-dependent pharmacology, bridge gaps in experimental data, guide first-in-human (FIH) dose selection, design dosing regimens with expanded therapeutic windows, and improve long-term treatment outcomes. We use recent case studies to exemplify the potential of QSP techniques to support future bsTCE development.

Mechanism-based pharmacokinetic and pharmacodynamic modeling for bispecific antibodies: challenges and opportunities

INTRODUCTION

Unlike conventional antibodies, bispecific antibodies (bsAbs) are engineered antibody- or antibody fragment-based molecules that can simultaneously recognize two different epitopes or antigens. Over the past decade, there has been an explosion of bsAbs being developed across therapeutic areas. Development of bsAbs presents unique challenges and mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) modeling has served as a powerful tool to optimize their development and realize their clinical utility.

AREAS COVERED

In this review, the guiding principles and case examples of how fit-for-purpose, mechanism-based PK/PD models have been applied to answer questions commonly encountered in bsAb development are presented. Such models characterize the key pharmacological elements of bsAbs, and they can be utilized for model-informed drug development. We also include the discussion of challenges, knowledge gaps and future direction for such models.

EXPERT OPINION

Mechanistic PK/PD modeling is a powerful tool to support the development of bsAbs. These models can be extrapolated to predict treatment outcomes based on mechanisms of action (MoA) and clinical observations to form positive learn-and-confirm cycles during drug development, due to their abilities to differentiate system- and drug-specific parameters. Meanwhile, the models should keep being adapted according to novel drug design and MoA, providing continuous opportunities for model-informed drug development.

A next generation mathematical model for the in vitro to clinical translation of T-cell engagers

T-cell engager (TCE) molecules activate the immune system and direct it to kill tumor cells. The key mechanism of action of TCEs is to crosslink CD3 on T cells and tumor associated antigens (TAAs) on tumor cells. The formation of this trimolecular complex (i.e. trimer) mimics the immune synapse, leading to therapeutic-dependent T-cell activation and killing of tumor cells. Computational models supporting TCE development must predict trimer formation accurately. Here, we present a next-generation two-step binding mathematical model for TCEs to describe trimer formation. Specifically, we propose to model the second binding step with trans-avidity and as a two-dimensional (2D) process where the reactants are modeled as the cell-surface density. Compared to the 3D binding model where the reactants are described in terms of concentration, the 2D model predicts less sensitivity of trimer formation to varying cell densities, which better matches changes in EC₅₀ from in vitro cytotoxicity assay data with varying E:T ratios. In addition, when translating in vitro cytotoxicity data to predict in vivo active clinical dose for blinatumomab, the choice of model leads to a notable difference in dose prediction. The dose predicted by the 2D model aligns better with the approved clinical dose and the prediction is robust under variations in the in vitro to in vivo translation assumptions. In conclusion, the 2D model with trans-avidity to describe trimer formation is an improved approach for TCEs and is likely to produce more accurate predictions to support TCE development.

Strategies for clinical dose optimization of T cell-engaging therapies in oncology

Innovative approaches in the design of T cell-engaging (TCE) molecules are ushering in a new wave of promising immunotherapies for the treatment of cancer. Their mechanism of action, which generates an in trans interaction to create a synthetic immune synapse, leads to complex and interconnected relationships between the exposure, efficacy, and toxicity of these drugs. Challenges thus arise when designing optimal clinical dose regimens for TCEs with narrow therapeutic windows, with a variety of dosing strategies being evaluated to mitigate key side effects such as cytokine release syndrome, neurotoxicity, and on-target off-tumor toxicities. This review evaluates the current approaches to dose optimization throughout the preclinical and clinical development of TCEs, along with perspectives for improvement of these strategies. Quantitative approaches used to aid the understanding of dose-exposure-response relationships are highlighted, along with opportunities to guide the rational design of next-generation TCE molecules, and optimize their dose regimens in patients.