To identify and develop optimal lead candidates against specific members of the HLA ligandome remains difficult. We have used a combination of classical HLA-matched subtractive antibody phage display in combination with thermal and competitor challenged CDR and FR targeted engineering using our pIX-based NextCore display platform to develop highly specific TCR-like antibodies allowing for high-resolution T cell epitope targeting and sequestering.

Focusing antibody engineering and selection strategies towards the isolation of high-affinity molecules can sometimes come at the expense of a robust developability profile. Incorporating strategies to optimise both affinity and biophysical characteristics during the discovery phase is important in ensuring that we develop high-quality, safe, and efficacious drugs. In this presentation, we will describe the methods that we applied to improve the affinity and developability attributes of a diverse panel of C7 antibodies. Affinity improvements were engineered using CDR-targeted mutagenesis libraries constructed in the Adimab Yeast Platform. In parallel, developability liabilities were removed using molecular design and engineering approaches combined with early quality-based screening techniques.

Investigating the interaction of antibodies with membrane proteins under physiologically relevant conditions is a challenging task. We have explored different methods, such as surface plasmon resonance (SPR)-based biosensors, Ligand Tracer and KinExA, to investigate the interaction of antibodies with full-length membrane proteins embedded into a lipid membrane. The assays allowed the calculation of binding affinities (K_D), and hence improved the assessment of therapeutic antibodies.

This presentation provides an overview of the basic concept of recombinant protein expression, key factors and major challenges in this field, as well as the application of this technique in the development of SARS COV2 proteins. Strategies and methods for obtaining high-quality protein products are also discussed.

Methods generating highly specific antibodies against classical target molecules, as e.g., receptor tyrosine kinases or cytokines, are routinely established. Antibody compounds inhibiting these classical target classes are widely used in clinical development and as approved therapeutics. Innovative selection strategies like next-generation sequencing have been applied to enable broadening the target space and addressing new target classes such as e.g., HLA/peptide complexes.

Early-stage developability assessments of biologic drug candidates are often undertaken with minimal material availability. Such resource limitations complicate or prevent completely the characterization of macroscopic solution properties, such as viscosity and opalescence, which only emerge at high protein concentrations. Here, we present a strategy for the identification of molecules with a high propensity to self-associate in dilute solution, which is strongly predictive of poor solution behavior at elevated concentrations.

Making critical decisions that determine the success of a biopharmaceutical requires clear and precise results. Here, we discuss how Prometheus empowers experienced organizations to improve their biologic discovery and development by using high-quality data to make better decisions.

Development of monoclonal antibodies as anticancer agents requires optimization of their safety for use in humans. Among optimization avenues for specific tumor targeting is the slightly higher acidity of solid tumors relative to normal tissues. A structure-based computational approach was applied to engineer antibody fragments with selective binding in an acidic environment relative to physiological pH. Designed full-size antibodies exhibit binding and functional selectivities between tumor and normal cell models.

We will review published examples where antibody developability metrics were assessed on large antibody sets and sequence information provided.  We will apply existing and in-development computational approaches aimed at assessing some of those metrics from sequence information alone.

Traditional antibody discovery typically investigates membrane-bound antigen-receptors encoded by a pool of B cells. Alternatively, mass spectrometry can identify high-affinity, bioactive antibody proteins secreted by a select subset of the total B-cell pool. Here, I present: (i) two techniques which can comprehensively determine cellular and serological antibody repertoires; and (ii) data illustrating the connectivity between them in the context of two primary immune responses–malaria vaccination and SARS-CoV-2 infection.

The use of descriptors averaged over an ensemble of molecular conformations has improved the accuracy of property predictions key to biologics’ utility and developability as therapeutics. Using an increasing database of clinical stage therapeutics, we present some useful guidelines for developable biologicals, similar to the Lipinski rules for small molecules.

Aspartate isomerization and asparagine deamidation are spontaneous post- translational modifications that adversely affect therapeutics function and long-term stability. In this talk, I will present a comprehensive analysis of 1000+ isomerization and deamidation sites across 130+ antibodies. A total of ~500 microsecond, unrestrained molecular dynamics simulations, along with extensive quantum mechanics (QM) calculations at the peptide level, were utilized to understand the mechanistic roles of structure and chemical environment promoting the isomerization and deamidation reaction. Such physics-based classification models require no heavy training against the data set, thus they can be leveraged to predict deamidation and isomerization propensity of therapeutic proteins in external data sets.

In the development of an antibody therapeutic, candidates are often chosen for their desired functional properties rather than their stability and manufacturability. This talk will describe the use of rational design of a bispecific antibody to improve properties including heterogeneity, stability, and purity. Additionally, I will present predictive in-vitro and in-silico developability techniques for early detection of liabilities that can affect biological function, clearance, and homogeneity.

Mouse LIGHT (targeting the Lymphotoxin beta receptor, LTBR) showed significant PK liability in mice, therefore in vivo studies could not be interpreted. Homology modeling identified a positive charge cluster on the mouse ligand. We engineered two alternative variants where the cluster was removed. The variants showed no impact on the binding and in vitro activity with substantial improvement in exposure, and an increase in Ccl19 (biomarker for agonizing LTBR pathway). We will present novel work, where an interdisciplinary scientific team (structural biology, protein chemistry, pharmaceutical science, and immuno-oncology) synergistically improved the therapeutic potential of a Fc-fusion protein.