Structural information provides critical insights to accelerate antibody discovery and development. Structure analysis helps us select better antibody clones by evaluating binder diversity while predicting functional and developability profiles. I will also highlight structure-guided engineering tools using a combination of with physics-based and machine learning methods for affinity maturation, cross-reactivity optimization, liability mitigation, and humanization. This should provide a comprehensive overview for leveraging structural biology to reduce costs, compress timelines, and ultimately improve success rates in biologics discovery and optimization.

Ancora Innovation, a collaboration focused on the union of Vanderbilt University’s innovative life science discovery efforts and Deerfield Management’s commitment to accelerating state-of-the-art drug development, conducted H2L and LID campaigns to discover new voltage-gated K+ ion channel inhibitors for the treatment of rare pediatric epilepsies, such as EIMFS. We employed a computationally driven approach to identify and optimize novel scaffolds that led to the discovery of a low nM inhibitor with promising in vivo PK. The presentation will highlight the CADD and medicinal chemistry strategy undertaken.

In recent years, there has been significant growth in the field of protein therapeutics, encompassing various complex modalities such as multi-specifics, fusion proteins, and protein conjugates. However, the expansion into these diverse modalities poses several challenges for purification process development, which stems from the unique biophysical properties of these novel modalities relative to monoclonal antibodies (mAbs) as well as distinct impurities profiles. Given these challenges, it is imperative to rapidly develop non-platform polishing chromatography processes that can effectively isolate the desired product with adequate yield and purity.

Experimental high throughput screening (HTS) capabilities such as slurry plate screens and robocolumn chromatography have become important tools to rapidly assess operating conditions for polishing chromatography. Predictive modeling enhances HTS capabilities for identifying operating conditions by enabling exploration and prioritization of large design spaces in silico and therefore picking promising conditions to screen experimentally. This talk presents the development and implementation of a quantitative structure activity relationship (QSAR) model that leverages over 10 years of internal Kp screening data and can predict the partition coefficient as a function of protein, resin, and mobile phase conditions. The model contains ~10,000 screened conditions on more than 40 resins. A diverse set of >40 proteins are represented in the data, including mAbs, Fc-fusion proteins, host cell proteins, viruses, and other modalities. Overall, the model has a test set R2=0.91 and can predict elution and strong binding conditions across the protein property and resin space with 95% classification accuracy, enabling extensive design space reduction for HTS experiments. Intentional diversification of the training data has expanded the applicability window of the model and enabled the prediction of both product and impurity partitioning, including aggregates, low molecular weight fragments, and HCPs. The prediction of both product and impurity partitioning facilitates computational assessment of other metrics describing the purification process, including separability and orthogonality between multiple steps. Overall, this work outlines how predictive modeling can be used in conjunction with HTS experimentation to guide the development of polishing chromatography steps. These models can be applied to a diverse set of proteins and across the development lifecycle, either by proposing novel approaches for a given separation or supporting process changes later in development.

Finding selective small molecule modulators against members of closely related enzyme families can be challenging, especially if high degrees of selectivity are required. Molecular glues can uniquely address this challenge by leveraging the binding surface of a partner protein to effectively discriminate between enzymatic family members. Using structure-guided tools, we designed a novel series of molecular glues that stabilize a weak, endogenous interaction of a protein of interest with an E3 ligase complex component, thereby inducing its rapid degradation. The unique interface between the protein pair enables designs that are active only in the presence of both partners, with limited interactions in the absence of ternary complex formation, thereby avoiding liabilities associated with targeting the partner protein.

Subcutaneous (SC) delivery of therapeutic antibodies can offer multiple benefits to patients and healthcare providers, including convenience, time savings, and cost reduction. To improve the SC injection experience, drug developers may seek a low injection volume (1-2 mL), which for some antibody drugs necessitates a high concentration solution (≥100 mg/mL) to meet dosage requirements. Several molecular-level challenges hinder the development of high concentration antibody drug products, including high viscosity caused by reversible self-association (RSA). Here, we take an enhanced rational design approach to reduce RSA via protein engineering. Using hydrogen-deuterium exchange mass spectrometry (HDX-MS) and in silico modeling in Molecular Operating Environment (MOE), we identified potential self-interaction hotspots on the surface of an in-house IgG1 which has known viscosity issues at high concentration. With this information, several variants were rationally designed which vastly improved on the parent antibody’s viscosity issues and offer insight into the mechanism of self-association. This workflow and strategy will be discussed alongside results and implications for future molecules.

The developability evaluation for antibody-related modalities includes a wide range of factors, such as non-specific binding, self-interaction, hydrophobicity, electrostatic properties, and structural stability. This evaluation is crucial in biologics drug discovery research for the early identification of candidates with high manufacturability as well as favorable behavior in human, and it is providing numerous benefits like improvements in drug discovery speed and reductions in duration and costs associated with bioprocess development.

In particular, the in-silico approach to predict developability from sequence information has grown exponentially alongside advancements in antibody-related technology platforms and is actively researched in both academia and the pharmaceutical industry. Recently, we established a wet evaluation system for high-throughput analysis of multiple developability parameters and created in-silico workflow for predicting developability by leveraging accumulated wet data and machine learning techniques. This machine learning utilizes protein feature quantities generated by MOE. The details and accuracy of this in-silico developability workflow will be presented.