Absci (ABSI) Morgan Stanley 23rd Annual Global Healthcare Conference summary

Strategic positioning and innovation

• Focused on leveraging AI for generative design to address hard-to-drug targets like ion channels and GPCRs, aiming to reduce timelines and costs while unlocking novel biology.

• Partnerships with major pharma companies and a robust internal pipeline, with the first asset in the clinic and another expected by year-end or early next year.

• Strategic shift influenced by global biotech innovation, particularly advancements in China, leading to a focus on differentiation and innovation in challenging biological targets.

• AI platform enables rapid iteration and model improvement, with a wet-lab-in-the-loop approach for continuous learning and innovation.

• Emphasis on data generation and model validation as key drivers for platform advancement and competitive edge.

Pipeline and clinical development

• ABS-101 (TL1A asset) is nearing a Phase 1A readout, focusing on safety, PK/PD, and ADA rates, with head-to-head advantages in potency, half-life, and bioavailability over first-gen molecules.

• ABS-201 targets androgenetic alopecia via the prolactin receptor, showing promising preclinical and early clinical results, with a pivotal Phase 2 readout expected in the second half of next year.

• ABS-301 and ABS-501 are transitioning to in vivo studies and are intended for out-licensing rather than internal clinical development.

• Plans to nominate and disclose a new drug candidate focused on INI or metabolism-based targets later this year or early next.

• ABS-201 is considered a co-lead program with ABS-101, with plans to take it deep into the clinic and potentially submit a BLA independently.

AI platform differentiation and future outlook

• Platform's wet-lab-in-the-loop enables rapid data generation and model refinement, reducing campaign size and increasing efficiency.

• AI-driven design allows for engineering of smart antibody features, such as pH-dependent binding and agonism/antagonism, moving beyond traditional trial-and-error methods.

• Anticipates eventual static models for specific problems, but continuous innovation is required as new challenges arise.

• Regulatory environment has had minimal negative impact; recent FDA initiatives align with platform strengths in safety modeling.

• Partnerships provide non-dilutive capital, data for model improvement, and diversification into therapeutic areas outside internal focus.