PeptiDream (4587) Investor Day 2024 summary

Strategic focus and pipeline expansion

• Expanding a robust pipeline in radiopharmaceuticals and non-radiopharmaceutical peptide therapeutics, with strong internal and partnered R&D efforts, including collaborations with Novartis, RayzeBio/BMS, Curium, and LinqMed.

• Announced a new wholly owned program targeting Claudin 18.2, following the CA9 program, aiming to launch at least one internal program per year.

• Strategic acquisitions and partnerships have secured a strong position in the Japanese radiopharmaceutical sector, leveraging high entry barriers and regulatory access.

• Accelerating non-radiopharmaceutical programs, including peptide therapeutics for acromegaly, immuno-oncology, multiple myeloma, inflammatory diseases, and COVID-19.

• Maintains a dual approach: developing in-house programs and collaborating with global pharma, often retaining Japan rights for partnered assets.

Clinical and preclinical program updates

• Advancing multiple programs: 64Cu-ATSM (glioma, phase III), 177Lu/64Cu-PSMA-I&T (prostate cancer, phase III US data to support Japan launch), Glypican-3 (HCC, IND filed), and CA9 (RCC, phase zero completed, IND in 2025).

• Claudin 18.2 program (PD-29875) for gastric and pancreatic cancer is in IND-enabling and phase zero studies, with preclinical and first-in-human data expected in 2025.

• Myostatin inhibitor program shows first-in-class oral efficacy in obesity models, preserving lean mass in combination with GLP-1 agonists, with ongoing preclinical development and partnering discussions.

• Other notable programs: AZP-3813 (acromegaly, phase II in 2025), Merck collaborations (two phase I programs), and COVID-19 peptide inhibitor (phase I in elderly volunteers).

• Initiating Phase 3 trials for 64Cu-ATSM in glioblastoma (Japan), with product filing anticipated around 2027 and SAKIGAKE designation potential.

Platform innovation and technology advancements

• PDPS platform enables discovery of macrocyclic peptides with non-canonical amino acids, leveraging a proprietary library of over 3,000 amino acids and next-generation sequencing.

• Integration of automation, machine learning, and AI is accelerating hit-to-lead optimization, with ambitions to sequence over 100 million peptides per selection in 2025.

• Proprietary clustering algorithms and predictive models are enhancing drug-like property selection and reducing optimization cycles, aiming to shorten drug discovery cycles from 3–7 to 2–3 years by 2025.

• Focus on generative peptide design and target/application-tailored libraries to further differentiate the platform and pipeline.

• Next-generation data analysis and machine learning models deployed to predict drug-like properties and accelerate candidate optimization.