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The human antibody repertoire is a promising source for therapeutic-grade antibodies. Yet current methods for strategically mining these B cell repertoires are stymied by throughput and chain pairing considerations. This study presents advancements in fluidics and molecular biology that enable the multi-step encapsulation and capture of B cells from an immunized, humanized mouse in nanoliter sized droplets. Once singularly captured, antigen-specific B-cells can be lysed and individually manipulated via RT-PCR to splice cognate V genes and create a predominantly natively paired library. To explore the importance of these process improvements in library generation, we constructed natively-paired libraries against two therapeutically-relevant human proteins. Through deep sequencing, bioinformatics-driven screening and phage display, we selected functional, target-specific antibodies. Our findings reveal that natively paired libraries contain a higher percentage of target-specific antibodies and demonstrate enhanced potency and improved developability in both in silico and in vitro assessments relative to combinatorial library-derived antibodies. Furthermore, antibodies with native pairing show increased potency as well as improved in silico and in vitro developability compared to their randomly paired counterparts. To this end, we see this droplet microfluidic platform and its capacity to generate and facilitate the high-throughput interrogation of antigen-specific antibody repertoires as an important, orthogonal therapeutic antibody discovery approach.

Activity cliffs are formed by pairs or groups of structurally similar or analogous active compounds with large differences in potency. They can be defined in two or three dimensions by comparing graph-based molecular representations or compound binding modes, respectively. Through systematic analysis of publicly available compound activity data and ligand-target X-ray structures we have in a series of studies determined all currently available two- and three-dimensional activity cliffs (2D- and 3D-cliffs, respectively). Furthermore, we have systematically searched for 2D extensions of 3D-cliffs. Herein, we specify different categories of activity cliffs we have explored and introduce an open access data deposition in ZENODO (doi: 10.5281/zenodo.18490 ) that makes the entire knowledge base of current activity cliffs freely available in an organized form.

Agents that can simultaneously activate latent HIV, increase immune activation and enhance the killing of latently-infected cells represent promising approaches for HIV cure. Here, we develop and evaluate a trispecific antibody (Ab), N6/αCD3-αCD28, that targets three independent proteins: (1) the HIV envelope via the broadly reactive CD4-binding site Ab, N6; (2) the T cell antigen CD3; and (3) the co-stimulatory molecule CD28. We find that the trispecific significantly increases antigen-specific T-cell activation and cytokine release in both CD4 + and CD8 + T cells. Co-culturing CD4 + with autologous CD8 + T cells from ART-suppressed HIV + donors with N6/αCD3-αCD28, results in activation of latently-infected cells and their elimination by activated CD8 + T cells. This trispecific antibody mediates CD4 + and CD8 + T-cell activation in non-human primates and is well tolerated in vivo. This HIV-directed antibody therefore merits further development as a potential intervention for the eradication of latent HIV infection. One of the main hurdles to curing HIV infection are viral reservoirs. Here, the authors develop a trispecific antibody and demonstrate its ability to simultaneously activate and target latently HIV−1 infected cells for elimination by T cells as an alternative strategy for HIV cure.

Doc number: P13

Over the past 40 years, the discovery and development of therapeutic antibodies to treat disease has become common practice. However, as therapeutic antibody constructs are becoming more sophisticated (e.g., multi-specifics), conventional approaches to optimisation are increasingly inefficient. Machine learning (ML) promises to open up an in silico route to antibody discovery and help accelerate the development of drug products using a reduced number of experiments and hence cost. Over the past few years, we have observed rapid developments in the field of ML-guided antibody discovery and development (D&D). However, many of the results are difficult to compare or hard to assess for utility by other experts in the field due to the high diversity in the datasets and evaluation techniques and metrics that are across industry and academia. This limitation of the literature curtails the broad adoption of ML across the industry and slows down overall progress in the field, highlighting the need to develop standards and guidelines that may help improve the reproducibility of ML models across different research groups. To address these challenges, we set out in this perspective to critically review current practices, explain common pitfalls, and clearly define a set of method development and evaluation guidelines that can be applied to different types of ML-based techniques for therapeutic antibody D&D. Specifically, we address in an end-to-end analysis, challenges associated with all aspects of the ML process and recommend a set of best practices for each stage.