The current race to develop better drugs faster has led biopharmaceutical companies into optimizing all drug discovery and development processes. As part of this effort, machine learning algorithms are being developed to identify correlations between amino acid sequences and physicochemical properties observed during drug development. Here, we describe our ongoing efforts aiming at benchmarking such machine learning algorithms to facilitate our drug discovery process.

Choosing one protein quantification tool that spans upstream and downstream workflows means that sample volume, throughput, and accuracy are all critical. Stunner delivers fast, automation-compatible protein quantification to within 2% accuracy and 1% precision on 2 µL of sample. And when moving downstream, Stunner will ace the USP and Ph. Eur. standards for UV/Vis.

Multivalent binding of antibodies and bispecifics to cell surface targets can be strongly modulated by leveraging avidity to affect target antigen binding. Avidity-driven target engagement can be exploited to improve properties, such as drug potency and target-cell or tissue selectivity. We demonstrate examples utilizing mathematical modeling as a powerful tool to make mechanistically driven predictions and guide experiments to optimize multivalent proteins, guide dosing, and select desirable targets.

Monoclonal antibodies display variable and difficult-to-predict levels of nonspecific and self-interactions that lead to various drug development challenges, including antibody aggregation, abnormally high viscosity, and fast antibody clearance. In this presentation, we will report experimental and computational methods for identifying, engineering, and predicting antibody variants with drug-like biophysical properties for diverse panels of preclinical and clinical antibodies.

As the initial stages of biologics discovery become geared toward generating large numbers of small quantities of material, there is increasing need for sensitive and high-throughput structural assays. Thermostability is an important property for therapeutic proteins, particularly for new biologics formats of increasing complexity. We will describe our implementation of differential scanning fluorimetry to interrogate thermostability in both our protein production and protein engineering workflows.

Automated and high-throughput analysis of biologic stability spans the entire workflow of biologic development, from early stage developability assessments through protein engineering to formulation and manufacturing decisions. The necessity for high-quality data to inform modeling and pipeline decisions is pervasive. Here we discuss how Prometheus empowers organizations to improve their biologic development through the use of high-quality data to make better decisions.

An ideal therapeutic scaffold should possess both high folding stability and minimal conformational fluctuations, but to date it has not been possible to measure conformational fluctuations on a large scale. We developed a multiplexed hydrogen-deuterium exchange mass spectrometry-based approach for measuring stability and conformational fluctuations for thousands of designed protein scaffolds in parallel. These data should reveal the structural determinants of conformational fluctuations and enable the design of optimized scaffolds.

The ability to identify and characterize therapeutic antibodies targeting multi-pass membrane proteins is hampered by the often difficult expression and purification of membrane proteins in their native conformation. We examined the utility of novel methods for membrane protein stabilization, including SMALPs, nanodiscs, and amphipols, to isolate a model multi-pass membrane protein. Following successful incorporation into the membrane mimetics, we evaluated their utility in FACS to facilitate lead identification.

In this presentation, we will discuss the benefits of early stage kinetic characterization from crude samples ranging from peptides to membrane proteins and relevant clinical samples.

Save time, work with suboptimal assay conditions and validate your ELISA data.

Rapid technological growth in analytical instrumentation and data processing has enabled researchers to develop powerful new methods for use in the biopharmaceutical industry. Unfortunately, sample preparation methods have progressed at a much slower rate, leading to a bottleneck in high-throughput analytics. By incorporating automation into our sample preparation and data analysis methods, we address the bottleneck issue while supporting harmonization of workflows across multiple sites.

NRC has developed nanobodies that act as carriers to shuttle therapeutic payloads across the blood-brain barrier. The antibodies bind to IGF1R on the surface of brain endothelial cells and trigger transcytosis. HDX-MS, integrated with NMR and imaging based structural techniques, is being deployed to better understand the mechanisms by which these antibodies bind to IGF1R and how this differs from interactions with its endogenous ligand, IGF-1.

We will introduce an entirely new class of high-sensitivity, inline mid-IR liquid analyzers. Based on ultra-high-brightness tunable quantum cascade lasers (QCL), this new platform technology offers fast scan rates, a large dynamic range and an ability to easily measure small sample volumes. We will present the physical operating principles of these new analyzers and provide several application examples including their use in fractionated HPLC measurements and chemical and biologic reactor monitoring.

The next generation of medicines depends upon our ability to quickly assess the structures and stabilities of protein and protein complexes, as well as protein-based biotherapeutics. Such endeavors are nearly insurmountable with current tools without exhaustive orthogonal experimentation. In this presentation, we will discuss recently developed ion mobility-mass spectrometry tools aimed at bridging this gap in basic technology to characterize novel biotherapeutics and their generic analogs (biosimilars).

In-depth, quantitative characterization of protein interactions is crucial in engineering and optimizing new biologics that can act as effective therapeutics. Among the many biophysical techniques that have accelerated this process for scientists, surface plasmon resonance (SPR) is an industry standard that is both sensitive and powerful, enabling rapid and label-free detection of a wide range of molecular interactions. Yet, there has been minimal innovation in SPR platforms to date, considerably limiting their use and accessibility. Join us as we discuss how we’ve integrated digital microfluidics technology and AI-powered intelligence with SPR, and how this will eliminate challenges in efficiency and optimization to help you go-to-market faster with your biotherapeutics.