Physiologically-Based Pharmacokinetic Model-Informed Labeling for Cariprazine Drug Interactions With CYP3A Inhibitors

Area of Science:

• Pharmacokinetics and Drug Metabolism
• Psychiatric Pharmacology
• Computational Biology

Background:

• Cariprazine is a dopamine D3/D2 receptor partial agonist used for psychiatric disorders.
• Short-term drug-drug interaction (DDI) studies informed initial cariprazine dosing, but didesmethyl-cariprazine (DDCAR) requires longer to reach steady-state.
• Clinical DDI studies are challenging, prompting the need for alternative modeling approaches.

Purpose of the Study:

• To develop and validate physiologically based pharmacokinetic (PBPK) models for cariprazine and its metabolites (DCAR, DDCAR).
• To predict steady-state cariprazine exposure changes with concomitant use of strong, moderate, and weak CYP3A4 inhibitors.
• To inform optimal cariprazine dosing adjustments for improved treatment efficacy and safety.

Main Methods:

• Development of PBPK models for cariprazine, DCAR, and DDCAR.
• Validation of models using clinical data from Phase 1/2 studies with cariprazine alone or with CYP3A inhibitors (ketoconazole, erythromycin).
• Prediction of steady-state total cariprazine exposure with varying CYP3A4 inhibition strengths.

Main Results:

• PBPK models accurately described cariprazine and metabolite exposures in clinical studies.
• Predicted increases in total cariprazine exposure at steady state: 6.0-fold (strong), 2.9-fold (moderate), and 1.1-fold (weak) CYP3A4 inhibitors.
• Model predictions supported updated US prescribing information for cariprazine dosing adjustments.

Conclusions:

• Validated PBPK models effectively predict cariprazine DDIs with CYP3A4 inhibitors.
• PBPK modeling enables more precise cariprazine dose adjustments for short-term and long-term coadministration.
• Updated dosing recommendations aim to balance treatment efficacy with reduced adverse effect risk.