Clinical Pharmacology Characterization and Dose Selection of Xaluritamig, a Next Generation XmAb® 2+1 T-Cell Engager, in Prostate Cancer Patients.

Bispecific T-cell engagers have revolutionized the treatment and management of hematological malignancies and more recently have started making similar strides for solid tumor indications, with opportunities to become best-in- class therapeutics for cancer. Xaluritamig is a novel bivalent XmAb® 2+1 T cell engager with two STEAP1 binding sites and one CD3 binding site being developed for solid tumors with the primary indication of metastatic castrate resistant prostate cancer (mCRPC). The First-In-Human (FIH) study showed promising anti-tumor activity in mCRPC patients, and the program is currently in late phase clinical development.

A novel Bayesian generative approach for estimating tumor dynamics from published studies.

Tumor growth inhibition (TGI) modeling attempts to describe the time course changes in tumor size for patients undergoing cancer therapy. TGI models present several advantages over traditional exposure-response models that are based explicitly on clinical end points and have become a popular tool in the pharmacometrics community. Unfortunately, the data required to fit TGI models, namely longitudinal tumor measurements, are sparse or often not available in literature or publicly accessible databases.

Noise suppression in stochastic genetic circuits using PID controllers.

Inside individual cells, protein population counts are subject to molecular noise due to low copy numbers and the inherent probabilistic nature of biochemical processes. We investigate the effectiveness of proportional, integral and derivative (PID) based feedback controllers to suppress protein count fluctuations originating from two noise sources: bursty expression of the protein, and external disturbance in protein synthesis. Designs of biochemical reactions that function as PID controllers are discussed, with particular focus on individual controllers separately, and the corresponding closed-loop system is analyzed for stochastic controller realizations.

Controlling organism size by regulating constituent cell numbers.

How living cells employ counting mechanisms to regulate their numbers or density is a long-standing problem in developmental biology that ties directly with organism or tissue size. Diverse cells types have been shown to regulate their numbers via secretion of factors in the extracellular space. These factors act as a proxy for the number of cells and function to reduce cellular proliferation rates creating a negative feedback.

Analysis of Noise Mechanisms in Cell-Size Control.

At the single-cell level, noise arises from multiple sources, such as inherent stochasticity of biomolecular processes, random partitioning of resources at division, and fluctuations in cellular growth rates. How these diverse noise mechanisms combine to drive variations in cell size within an isoclonal population is not well understood. Here, we investigate the contributions of different noise sources in well-known paradigms of cell-size control, such as adder (division occurs after adding a fixed size from birth), sizer (division occurs after reaching a size threshold), and timer (division occurs after a fixed time from birth).