Population dynamics of immunological synapse formation induced by bispecific T cell engagers predict clinical pharmacodynamics and treatment resistance.

Effector T cells need to form immunological synapses (IS) with recognized target cells to elicit cytolytic effects. Facilitating IS formation is the principal pharmacological action of most T cell-based cancer immunotherapies. However, the dynamics of IS formation at the cell population level, the primary driver of the pharmacodynamics of many cancer immunotherapies, remains poorly defined. Using classic immunotherapy CD3/CD19 bispecific T cell engager (BiTE) as our model system, we integrate experimental and theoretical approaches to investigate the population dynamics of IS formation and their relevance to clinical pharmacodynamics and treatment resistance. Our models produce experimentally consistent predictions when defining IS formation as a series of spatiotemporally coordinated events driven by molecular and cellular interactions. The models predict tumor-killing pharmacodynamics in patients and reveal trajectories of tumor evolution across anatomical sites under BiTE immunotherapy. Our models highlight the bone marrow as a potential sanctuary site permitting tumor evolution and antigen escape. The models also suggest that optimal dosing regimens are a function of tumor growth, CD19 expression, and patient T cell abundance, which confer adequate tumor control with reduced disease evolution. This work has implications for developing more effective T cell-based cancer immunotherapies.

Utilization of a Population Pharmacokinetic Model to Improve a Busulfan Test-Dose Strategy in Allogeneic Hematopoietic Cell Transplant Recipients.

Busulfan is an alkylating agent used as part of conditioning chemotherapy regimens prior to allogeneic hematopoietic cell transplant (allo-HCT). Pharmacokinetic (PK)-guided test-dose strategies have been shown to improve the number of patients achieving busulfan exposure goals and improve clinical outcomes. However, current practices require extensive PK sampling. In this study, PK data were retrospectively collected from busulfan drug monitoring records from adult allo-HCT recipients who received once-daily intravenous busulfan at the University of North Carolina Medical Center (UNCMC). A population pharmacokinetic (popPK) model was developed to identify sources of interindividual variability and evaluate alternative PK sampling strategies. A 2-compartment model, with covariate effects of actual body weight and sex, best described the data. The typical value of clearance for an 83 kg male was estimated to be 11.21 L/h. Fifty-nine percent of allo-HCT recipients were estimated to have met the UNCMC institutional myeloablative conditioning (MAC) exposure goal based on model post hoc estimates of clearance using all PK samples obtained following MAC dosing. Fifty-seven percent of patients were estimated to have met this goal based on post hoc estimates using a single PK sample. Our results indicate once-daily, intravenous busulfan PK in adult allo-HCT recipients receiving MAC dosing can be reasonably described by a popPK model, and the use of a sparse PK sampling strategy may be feasible for determining target exposure attainment following MAC dosing. Use of a popPK model and sparse PK sampling strategy to carry out busulfan test-dose procedures could reduce health care costs and inconvenience to patients.

Embracing Project Optimus: Can we Leverage Evolutionary Theory to Optimize Dosing in Oncology?

Project Optimus is a US Food and Drug Administration (FDA) initiative to reform dose selection in oncology drug development. Here, we focus on tumor evolution, a broadly observed phenomenon that invariably leads to therapeutic failure and disease relapse, and its effect on the exposure-response (E-R) relationships of oncology drugs. We propose a greater emphasis on tumor evolution during clinical development to facilitate the selection of optimal doses for molecularly targeted therapies and immunotherapies in oncology.

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.