On April 23, 2021, the FDA approved a supplemental NDA (sNDA) for a new dose (60 mg) of INGREZZA (valbenazine) for the treatment of tardive dyskinesia (TD). Valbenazine is a vesicular monoamine transporter 2 (VMAT2) inhibitor indicated for the treatment of adults with TD. The availability of a valbenazine 60 mg dose to complement the previously approved doses of 40 and 80 mg fills an existing medical need for patients with TD who could benefit from an intermediate dosing option.
In lieu of a clinical trial, the efficacy of valbenazine 60 mg versus placebo in subjects with TD was demonstrated through clinical trial simulations based on a longitudinal exposure-response (E-R) model for valbenazine. Briefly, a longitudinal E-R model characterized the relationship between exposure of valbenazine’s active metabolite and change from baseline in Abnormal Involuntary Movement Scale dyskinesia total score in patients with TD. Utilizing known information from the safety profile of the valbenazine 40 and 80 mg doses, as established in clinical studies and supplemented by available data from the post-approval setting, the valbenazine 60 mg dose was projected to be well-tolerated in patients with TD. The modeling and simulation results have been described in Section 14 (Clinical Studies) of the updated prescribing information linked below.
Detailed information regarding valbenazine dosage and administration, drug interactions, and important warnings and precautions, including somnolence, QT prolongation, and parkinsonism can be found in the full prescribing information linked below.
The development program for this sNDA was facilitated via meetings under the Prescription Drug User Fee Act (PDUFA) VI Model-Informed Drug Development (MIDD) Paired Meeting Pilot Program. MIDD is an approach that involves developing and applying exposure-based, biological, and statistical models derived from preclinical and clinical data sources to inform drug development and decision-making. It aims to integrate information from diverse data sources to help decrease uncertainty and lower failure rates and to develop information that cannot or would not be generated experimentally.
