Neumora Therapeutics (NMRA) Status Update summary

Program overview and clinical rationale

• Navacaprant is a highly selective, once-daily kappa-opioid receptor antagonist developed as a monotherapy for major depressive disorder (MDD) and other neuropsychiatric conditions, with a favorable safety and tolerability profile in phase II studies.

• Phase II data showed significant improvements in depressive symptoms and anhedonia, with no weight gain, sexual dysfunction, or other common antidepressant side effects.

• The COASTAL Program consists of three replicate phase III studies (COASTAL-1, -2, -3) and a long-term safety extension, all designed to maximize the probability of regulatory success.

• The primary endpoint is change in MADRS score at week six, with key secondary endpoints including the SHAPS for anhedonia, aligning with FDA guidance for MDD studies.

• Top-line data from COASTAL-1 is expected in Q4, with additional readouts and a potential NDA submission in 2025.

Differentiation and competitive landscape

• Navacaprant demonstrates over 300-fold selectivity for kappa-opioid receptors versus mu-opioid receptors, potentially enabling higher dosing and fewer side effects compared to other KORAs like aticaprant and icalcaprant.

• Unlike aticaprant, which is studied as adjunctive therapy, navacaprant is being evaluated as monotherapy, offering greater clinical flexibility.

• The robust safety profile and lack of abuse potential have been confirmed by FDA feedback, with no requirement for further abuse liability studies.

Clinical trial design and execution

• The COASTAL studies employ rigorous patient and site selection, central raters, video compliance monitoring, and real-time data oversight to minimize placebo response and ensure data integrity.

• Studies are powered at approximately 90% for the primary outcome, with measures in place to intervene quickly if inconsistencies or compliance issues are detected.

• The design includes measures to ensure stability of depressive symptoms at baseline and minimize expectation bias.