What's Happening?
Viva Biotech's Dr. Susan Chen, Chief Technology Officer, and Dr. Yue Qian, Vice President and Head of the Multi-Modality AI-Rooted Solutions (MARS) division, have highlighted the scientific and operational challenges in developing and manufacturing peptide
therapeutics, particularly cyclic peptides. Peptides are fragile and sensitive to various production factors, including reactor geometry, resin swelling, and temperature shifts, with even a 5°C change potentially tripling impurities. Traditional Solid Phase Peptide Synthesis (SPPS) for long-chain peptides can be inefficient, leading to low yields and purity, requiring large, costly reactors. To address these issues, Viva Biotech has developed hybrid synthesis strategies that integrate solid-phase and liquid-phase approaches, enabling more efficient synthesis and scale-up, achieving high purity for GLP-1 analogues at kilogram scale. They emphasize the critical need for the discovery stage to be closely aligned with manufacturing to prevent later-stage issues. AI and computational tools are also being leveraged to improve predictive models, optimize peptide design, and prevent costly errors in synthesis and impurity prediction.
Why It's Important?
The discussion from Viva Biotech underscores significant hurdles in bringing peptide therapeutics, a rapidly growing area fueled by markets like GLP-1 obesity drugs, to patients. Overcoming manufacturing complexities is crucial for the U.S. pharmaceutical industry to capitalize on the potential of these drugs. Cyclic peptides offer unique advantages over small molecules and antibodies, such as the ability to target previously undruggable surfaces, cross cell membranes, and offer oral administration, which can significantly improve patient compliance and market reach. However, challenges like low bioavailability and limited pharmacokinetic (PK) data for cyclic peptides hinder their development. Viva Biotech's efforts in hybrid synthesis and AI integration aim to reduce costs, minimize risks, and accelerate the development of these promising drug candidates. Success in these areas could lead to a new generation of effective treatments for various diseases, impacting drug development pipelines and patient care across the U.S.
What's Next?
The future of peptide therapeutics, as outlined by Viva Biotech, will likely see increased investment in high-throughput automation and the introduction of more building blocks and amino acids into the market. Viva Biotech itself is developing novel synthesis technologies, including continuous flow, TAG technology, crystallization after tag, and new modalities like PDC, APDC, RDC, and POA, which are expected to enhance the field. The company's CRDMO platform, which combines AI-enabled peptide discovery and optimization with synthesis, process development, analytics, formulation, and manufacturing capabilities, is designed to support programs from early discovery through commercial manufacturing. This integrated approach suggests a trend towards more comprehensive solutions in drug development. As AI and computational tools become more sophisticated, they will play an increasingly vital role in optimizing peptide design, predicting impurities, and improving bioavailability, ultimately accelerating the translation of promising peptide candidates from research to clinical application.
Beyond the Headlines
The advancements in cyclic peptide development and manufacturing, particularly through hybrid synthesis and AI integration, point to a broader transformation in pharmaceutical R&D. This shift moves beyond traditional trial-and-error methods towards a more predictive and data-driven approach. The emphasis on 'Quality by Design' and 'Failure Mode and Effects Analysis' models highlights a proactive risk management strategy, crucial for complex biological molecules. Furthermore, the ability of cyclic peptides to overcome limitations of small molecules and antibodies could open up entirely new therapeutic avenues, potentially addressing diseases that are currently untreatable. The ethical implications of AI in drug discovery, such as ensuring unbiased data input and model validation, will become increasingly important. The need for extensive human expertise, even with automation, also underscores the enduring value of experienced scientists in navigating the nuances of peptide chemistry, suggesting a future where human ingenuity and advanced AI tools work in synergy to push the boundaries of medicine.
