As biopharma begins to scratch the surface of what AI-enabled protein design is capable of, Aizen Therapeutics is targeting the flipside of the coin. The San Diego-based startup, which emerged from stealth Thursday with $13 million in seed funding, aims to generate peptides that are the mirror image of naturally occurring molecules, and have the added bonus of improved stability and lower immunogenicity. Aizen's AI-powered DaX platform allows it to design D-amino acids, which are the exact opposite of nature's L-amino acids. The technology was spun out of the lab of California Institute of Technology professor David Van Valen, who is also the newco's scientific founder.With Thursday's financing, which is expected to provide more than two years of runway, Aizen is aiming to select an IND-enabling lead programme and secure a strategic partner that validates its mirror peptide technology. The company's backers include venture capital firms Wilson Hill, Madrona and Cercano.AI accelerationThe drug development space has been interested in D-amino acids for at least 20 years, Aizen CEO Ajay Kshatriya told FirstWord, given their therapeutically beneficial properties. When proteins composed of L-peptides are introduced to the body, they're quickly broken down, and the smaller pieces of amino acids are then clocked as foreign, triggering an immune response, he explained. However, proteases can't engage with mirror peptides, thus bypassing the breakdown process and avoiding alerting the immune system altogether. Several drugs that feature D-amino acids in their molecular makeup have been approved by the FDA, but designing entire mirror peptides has historically posed a time-consuming problem due to a lack of data. The entire process, if relying solely on wet lab work and a method called reverse mirror image phage display, could take one to three years, Kshatriya said. While Van Valen believed that the process could be sped up with generative AI (genAI), the next roadblock he faced was that the protein repositories assembled thus far relied completely on L-amino acids."AI needs data, and all the data in the Protein Data Bank, all the crystal structures and all the images, were all looking at L-proteins binding to L-receptors," Kshatriya said, adding that existing computational programmes such as AlphaFold and Rosetta were designed to work only for L-amino acids. "It's a pretty challenging transformation to leverage L-data and then design a D-model," he remarked. But, thanks to a "clever algorithmic design" and recent innovations in guided diffusion models, Van Valen was able to build and train a genAI programme to design D-peptides from scratch. More importantly, the Aizen team has shown that the algorithm works. The company's scientists synthesised D-peptides spit out by the model and then tested them against known receptors — and were able to demonstrate binding activity. With the scientific and technological groundwork laid for Aizen, the company has now begun to explore all the clinical applications of a compound that can now be designed in weeks, instead of years. Best of both therapeutic worldsAccording to Kshatriya, the startup's mirror peptides are "ideal targeting vehicles" because they have the "benefits of size and tissue penetration of a small molecule, but the specificity of binding of an antibody."One application Aizen is pursuing is the development of peptides that target receptors at the interface of the blood-brain barrier, thus acting as a delivery vehicle to transport a linked payload into the brain. Another area ripe for innovation is radioligand therapeutics (RLTs), a modality that has seen big-ticket investments and deals from pharma this year (see – ViewPoints: Exploring the strategy behind Sanofi's recent investment in RLTs with global head of oncology Olivier Nataf and ViewPoints: RadioMedix CEO sees lead as the future of RLTs).Mirror peptides could grant cancer cell surface-specific targeting to RLTs, while also being able to penetrate deep into the tumour microenvironment, Kshatriya explained.Beyond those two applications, Aizen's DaX platform is capable of designing peptides for a variety of receptor classes, including GPCRs, transmembrane proteins and ion channels."We're in the early innings of the potential for computation and AI to impact the design of proteins," Kshatriya commented.