Molkenbur, R. | Search bioRxiv

bioRxiv2026

Domain-resolved epitope mapping of B7-H3-targeting VHH-Fcs defines isoform-selective binders for antibody-drug conjugate development

The immune checkpoint molecule B7-H3 has emerged as one of the most actively pursued therapeutic targets in solid tumors, and multiple B7-H3-targeting antibody-drug conjugates have entered clinical evaluation. B7-H3 occurs as a 2Ig isoform carrying a single IgV-IgC domain pair and a predominant 4Ig isoform carrying two tandem pairs of near-identical sequence in their extracellular domains. Resolving which pair an antibody engages, or how antibodies distinguish the two isoforms, requires epitope mapping at residue level. Here we generated a panel of six B7-H3-targeting VHH-Fcs and characterized them by surface plasmon resonance, epitope binning, domain-resolved deep mutational scanning and receptor internalization, benchmarked against the clinically advanced antibodies ifinatamab, mirzotamab and vobramitamab. Most VHH-Fcs bound the membrane-proximal pair more tightly than the distal pair, and 4Ig 10- to 100-fold more tightly than 2Ig, whereas the three clinical antibodies showed no such discrimination. Displaying each domain pair independently on yeast identified the residues that account for this selectivity. Internalization varied several-fold among binders, including between VHH-Fc 620 and 636, which have near-identical footprints and comparable affinities. VHH-Fc 620 internalized most efficiently, also compared to ifinatamab, and was conjugated to exatecan through a protease-cleavable linker. The conjugate showed B7-H3-dependent cytotoxicity that scaled with B7-H3 cell surface density, though it was less potent than ifinatamab-exatecan. Affinity, epitope and internalization therefore behaved as independent characteristics rather than integrating to predict antibody-drug conjugate potency. Together, these results define the molecular basis for isoform-selective recognition of B7-H3 and identify VHH-Fc 620 as a starting point for isoform-selective targeting.

cancer biology↗

bioRxiv2025

Simulation-Based Inference of Cell Migration Dynamics in Complex Spatial Environments

To assess cell migration in complex spatial environments, microfabricated chips, such as mazes and pillar forests, are routinely used to impose spatial and mechanical constraints, and cell trajectories are followed within these structures by advanced imaging techniques. In systems mechanobiology, computational models serve as essential tools to uncover how physical geometry influences intracellular dynamics; however, decoding such complex behaviors requires advanced inference techniques. Here, we integrated experimental observations of dendritic cell migration in a geometrically constrained microenvironment into a Cellular Potts model. We demonstrated that these spatial constraints modulate the motility dynamics, including speed and directional changes. We show that classical summary statistics, such as mean squared displacement and turning angle distributions, can resolve key mechanistic features but fail to extract richer spatiotemporal patterns, limiting accurate parameter inference. To solve this, we applied neural posterior estimation with in-the-loop learning of summary features. This learned summary representation of the data enables robust and flexible parameter inference, providing a data-driven framework for model calibration and advancing quantitative analysis of cell migration in structured microenvironments.

cell biology↗