Mechanistic Quantitative Pharmacology Strategies for the Early Clinical Development of Bispecific Antibodies in Oncology

Abstract

Bispecific antibodies (bsAbs) have become an integral component of the therapeutic research strategy to treat cancer. In addition to clinically validated immune cell re‐targeting, bsAbs are being designed for tumor targeting and as dual immune modulators. Explorative preclinical and emerging clinical data indicate potential for enhanced efficacy and reduced systemic toxicity. However, bsAbs are a complex modality with challenges to overcome in early clinical trials, including selection of relevant starting doses using a minimal anticipated biological effect level approach, and predicting efficacious dose despite nonintuitive dose response relationships. Multiple factors can contribute to variability in the clinic, including differences in functional affinity due to avidity, receptor expression, effector to target cell ratio, and presence of soluble target. Mechanistic modeling approaches are a powerful integrative tool to understand the complexities and aid in clinical translation, trial design, and prediction of regimens and strategies to reduce dose limiting toxicities of bsAbs. In this tutorial, the use of mechanistic modeling to impact decision making for bsAbs is presented and illustrated using case study examples.

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

“…For those novel antibody formats with unique PK/PD profiles, mechanism-based PK/PD modeling can be valuable in their development, including providing rationales for antibody engineering and candidate optimization [ 78 , 79 ], offering insights into their MoAs [ 25 , 75 , 79 , 80 , 81 , 82 , 83 ], and facilitating their transition from preclinical space to the clinic [ 75 , 80 , 81 ]. For instance, to explore the inter-dependency of dual-target engagement, mechanistic target binding models were developed, which helped find the optimal binding affinity as a function of target isotypes, abundances, and cellular membrane properties.…”

“…A very high affinity to CD3 can result in monovalent binding, leading to premature activation of T cells and accumulations of antibodies in the CD3-rich tissues [ 223 ]. PK/PD modeling can help evaluate optimal target affinity in different local tissue environments for decision-making in the early stage of antibody development [ 76 , 79 , 208 , 224 , 225 , 226 , 227 , 228 ]. For example, Jiang et al developed a trimer-based cell-killing model to describe blinatumomab PK/PD profiles in ALL patients [ 76 ].…”

“…One major challenge to develop mechanism-based PD models for antibodies is the lack of techniques to longitudinally assessing the immunomodulatory functions in the living system, particularly in the evolving TME [ 244 , 245 ]. In vitro measurements of immunomodulatory functions are mainly for mechanistic exploration and are limited in predicting the in vivo immune dynamics due to the lack of physiological contexts [ 79 , 246 ]. Many biomarkers only provide a time-frozen snapshot of the immune status but fail to reveal the dynamically immune functions [ 247 ].…”

The Changing Face of Oncology Research, Drug Development, and Clinical Practice: Toward Patient‐Focused Precision Therapeutics

“…Investigational immunotherapies, such as bispecific antibodies, have the potential for bell-shaped dose-response relationships and potential for cytokine release, making dose selection for first-in-human trials a nontrivial undertaking and emphasizing the central importance of QSP frameworks. 23 Systems models can revolutionize precision medicine by enabling adaptive personalization of treatment decisions for individual patients through iterative validation of "personal models" using biomarker and response data collected dynamically during individual patient journeys. 24 Given the explosive increase in the generation of "big data" across multiple dimensions (-omics, imaging, and clinical outcomes) in the development and use of oncology therapeutics, convergence and synergy across mechanistically driven (e.g., QSP), and biologically agnostic (e.g., statistical approaches leveraging artificial intelligence/machine learning) models will be key to success.…”

Quantitative modeling predicts competitive advantages of a next generation anti‐NKG2A monoclonal antibody over monalizumab for the treatment of cancer

“…Modeling and simulation of oncology therapies can facilitate design, selection, and preclinical to clinical translation, along with optimization of clinical trials 18 . Translational pharmacokinetic (PK)/pharmacodynamic modeling has been defined as the integration of in silico, in vitro, and in vivo preclinical data with mechanism‐based models to predict the effects of new drugs in humans 18,19 . These models are built by assembling a quantitative framework describing the relationship between the pharmacology of drug action and downstream biomarker or efficacy responses observed in the preclinical data.…”