The Future of Biologics: Insights from PEGS Summit 2024

The PEGS Summit 2024 highlighted many advancements in biologics, emphasizing the shift from traditional methodologies to innovative, need-based designs. Below are some of our key takeaways from this year’s summit.

De novo design: engineering the future of biologics 

Biologics-based therapies have long improved human life through the discovery and mimicking of naturally occurring biologics. However, the current target-driven discovery approach is reaching its limits. The future lies in de novo design, where the focus shifts from imitation to innovation, creating biologics tailored to specific needs through engineering principles.

Key advancements in this area include:

Enhancing protein design with logic gating

Logic gating is revolutionizing protein design by introducing advanced control mechanisms that enhance specificity and safety. By employing AND, OR, and NOT logic gates, researchers can design proteins that precisely respond to multiple inputs, ensuring that therapeutic actions are activated only under specific conditions. This approach minimizes off-target effects and maximizes therapeutic efficacy. For example, AND gates can be used to ensure that a protein is activated only in the presence of two or more specific biomarkers, enhancing target specificity and safety. NOT gates can prevent activation in the presence of particular non-specific markers (e.g. in a non-target organ), thus increasing safety. Through logic gating, protein design is becoming more sophisticated, enabling the creation of next-generation biologics with improved performance and reduced side effects.

Optimized design for diffusion and kinetics

Optimized design for diffusion and kinetics is a strategy within de novo protein design, enabling the creation of biologics that are precisely tailored for specific actions and environments. By adjusting the size and shape of biologics, researchers can optimize their diffusion rates and interaction kinetics, ensuring effective delivery and activity within the target areas. Additionally, customizing biologics to suit different body micro-environments—such as the liver, ovaries, or brain—enhances their therapeutic potential. This precision design approach allows for the development of biologics that perform optimally within the unique conditions of each tissue, maximizing efficacy and minimizing side effects. De novo protein design leverages these advancements to engineer next-generation therapeutics that are finely tuned for specific clinical applications, paving the way for more effective and personalized treatments.

Innovating therapeutics with advanced payload design

Payload design is rapidly expanding, revolutionizing the development of biologics with targeted therapeutic capabilities. De novo protein design plays a crucial role in this advancement by enabling the creation of biologics that can carry specific payloads—such as Antibody-Drug Conjugates (ADCs) and targeted lipid nanoparticles (LNPs)—with unprecedented precision. This tailored approach allows researchers to engineer proteins that deliver drugs directly to diseased cells while sparing healthy tissue, enhancing efficacy and minimizing side effects.

Moreover, the concept of dual or multi-payload biologics is particularly exciting. By designing payload carriers from scratch, scientists can integrate multiple therapeutic agents into a single biologic, offering a powerful strategy to tackle complex diseases. These innovations in payload design are paving the way for next-generation therapeutics that are more effective, safer, and capable of addressing a broader range of medical conditions.

In silico discovery: harnessing AI and machine learning

The integration of Artificial Intelligence (AI) and Machine Learning (ML) is creating a third pillar in discovery methods alongside in vivo and in vitro approaches. Many biotech companies and large biopharma firms are investing in AI/ML for drug discovery. Although this is a highly debated topic in the community, there is a general consensus that this will play a significant role in the future of biologics design with the following considered:

• Accelerated design: AI and ML can expedite the design of current modalities, reducing time and costs.
• Innovative discoveries: AI has the potential to uncover new therapeutic avenues beyond human imagination.
• Need for iterative testing: Despite its promise, AI- and ML-driven discovery currently still requires rigorous functional screening and iterative refinement based on results obtained in the lab .
• Data biases: Acknowledging and addressing biases in data that is input into AI and ML models is crucial to ensure reliable outcomes.

Taking on the risk of innovation

The risk of de novo and in silico design versus traditional therapeutic design poses a strategic dilemma. While traditional approaches carry lower risk, de novo and in silico designs are essential for advancing the field. This risk-taking is more likely to be embraced by smaller biotech firms than larger, more risk-averse biopharmaceutical companies.

 

Exploring digital SPR: the future of biomolecular interaction analysis

Biomolecular interaction analysis remains a fundamental aspect of protein design and engineering, crucial for understanding how proteins interact and function. As researchers continuously strive to enhance this process, alternative technologies to traditional methods like Surface Plasmon Resonance (SPR) and Bio-Layer Interferometry (BLI) have emerged. Among these, digital SPR stands out as a promising solution. This cutting-edge technology offers numerous advantages, including: 

• No maintenance: There are no fluidics components inside the Alto, so the instrument bypasses frequent fluidics issues found in traditional platforms as well as the need for cleaning, priming, and maintenance. This simplifies user operation and reduces instrument downtime.
• Minimal sample volume requirement: Full kinetics can be obtained with a sample size as small as 2 μL.
• Crude sample compatibility: The Alto can analyze a broader range of sample types, including crude samples, nucleic acids, proteins, peptides, antibodies, viruses, virus-like particles, and lipid nano-particles.
• Low consumable costs: Lowest cost/interaction compared to other traditional platforms 
• No learning curves: Easy to adopt and use.

With its potential to streamline and optimize biomolecular interaction analysis, Digital SPR provides a more efficient, cost-effective, and user-friendly alternative to traditional methods.