PI-082 - PLASMA METHYLMALONIC ACID CONCENTRATION IS POSITIVELY ASSOCIATED WITH METABOLIC DECOMPENSATION EVENTS IN PATIENTS WITH METHYLMALONIC ACIDEMIA.

M. Liang¹, J. Calderón², V. Ivaturi², N. Carillo¹, H. Attarwala¹; ¹Moderna Therapeutics, Inc., Cambridge, MA, USA, ²Pumas-AI Inc., Baltimore, MD, USA.

Background: Methylmalonic acidemia due to methylmalonyl-CoA mutase deficiency is a rare and severe inherited metabolic disease with no effective therapies. The disorder typically occurs in infants and is characterized biochemically by the accumulation of methylmalonic acid (MMA) and clinically by recurrent metabolic decompensation events (MDEs). The objective of this study was to identify predictive biomarkers of methylmalonic acidemia disease severity and prognosis.

Methods: Data from a prospective, observational, longitudinal, natural history study of people with propionic acidemia and methylmalonic acidemia (MaP study; NCT03484767) were analyzed to model the relationship between plasma MMA levels and MDEs in methylmalonic acidemia. The modeling approach targeted the start of an MDE, with the hazard function being a function of MMA concentration, a smoothed term for the days since the end of the previous MDE, and a smoothed random effect term for the participant identifier. A generalized additive mixed model approach explored the relationship between MMA and MDEs. Model performance was evaluated using visual predictive checks.

Results: Data from 44 participants with methylmalonic acidemia were included in the model. Age was selected as a covariate. The probability of no MDEs decreased exponentially over time in all MMA level strata. Lower MMA levels resulted in a higher probability of no MDEs. The repeated time-to-event model predicted that 50% MMA reduction is associated with a 28% (95% CI, 17%-38%) reduction in MDE rate.

Conclusion: This study reinforces the association between plasma MMA levels and MDE risk in patients with methylmalonic acidemia, suggesting that MMA could potentially be used as a surrogate biomarker to predict the risk and rate of MDEs in clinical trial design for novel therapies.