Rapid Biofabrication of an Advanced Microphysiological System Mimicking Phenotypical Heterogeneity and Drug Resistance in Glioblastoma.

Microphysiological systems (MPSs) reconstitute tissue interfaces and organ functions, presenting a promising alternative to animal models in drug development. However, traditional materials like polydimethylsiloxane (PDMS) often interfere by absorbing hydrophobic molecules, affecting drug testing accuracy. Additive manufacturing, including 3D bioprinting, offers viable solutions. GlioFlow3D, a novel microfluidic platform combining extrusion bioprinting and stereolithography (SLA) is introduced. GlioFlow3D integrates primary human cells and glioblastoma (GBM) lines in hydrogel-based microchannels mimicking vasculature, within an SLA resin framework using cost-effective materials. The study introduces a robust protocol to mitigate SLA resin cytotoxicity. Compared to PDMS, GlioFlow3D demonstrated lower small molecule absorption, which is relevant for accurate testing of small molecules like Temozolomide (TMZ). Computational modeling is used to optimize a pumpless setup simulating interstitial fluid flow dynamics in tissues. Co-culturing GBM with brain endothelial cells in GlioFlow3D showed enhanced CD133 expression and TMZ resistance near vascular interfaces, highlighting spatial drug resistance mechanisms. This PDMS-free platform promises advanced drug testing, improving preclinical research and personalized therapy by elucidating complex GBM drug resistance mechanisms influenced by the tissue microenvironment.

Protein language models enable prediction of polyreactivity of monospecific, bispecific, and heavy-chain-only antibodies.

Background: Early assessment of antibody off-target binding is essential for mitigating developability risks such as fast clearance, reduced efficacy, toxicity, and immunogenicity. The baculovirus particle (BVP) binding assay has been widely utilized to evaluate polyreactivity of antibodies. As a complementary approach, computational prediction of polyreactivity is desirable for counter-screening antibodies from in silico discovery campaigns. However, there is a lack of such models. Methods: Herein, we present the development of an ensemble of three deep learning models based on two pan-protein foundational protein language models (ESM2 and ProtT5) and an antibody-specific protein language model (PLM) (Antiberty). These models were trained in a transfer learning network to predict the outcomes in the BVP assay and the bovine serum albumin binding assay, which was developed as a complement to the BVP assay. The training was conducted on a large dataset of antibody sequences augmented with experimental conditions, which were collected through a highly efficient application system. Results: The resulting models demonstrated robust performance on canonical mAbs (monospecific with heavy and light chain), bispecific Abs, and single-domain Fc (VHH-Fc). PLMs outperformed a model built using molecular descriptors calculated from AlphaFold 2 predicted structures. Embeddings from the antibody-specific and foundational PLMs resulted in similar performance. Conclusion: To our knowledge, this represents the first application of PLMs to predict assay data on bispecifics and VHH-Fcs.