(M-011) A Survey of Model Informed Approaches in Neuroscience Drug Development

Vishnu Sharma, Ph.D. – Pharmacometrics Reviewer, US Food and Drug Administration; Jie Liu, Ph.D. – Pharmacometrics Reviewer, US Food and Drug Administration; Bilal AbuAsal, Ph.D. – Clinical Pharmacology Team Leader, US Food and Drug Administration; Venkateswaran C Pillai, Ph.D. – Clinical Pharmacology Team Lead, US Food and Drug Administration; Gopichand Gottipati, Ph.D. – Clinical Pharmacology Team Leader, US Food and Drug Administration; Yun Xu, Ph.D. – Clinical Pharmacology Team Leader, US Food and Drug Administration; Atul Bhattaram, Ph.D. – Pharmacometrics Team Leader, US Food and Drug Administration

1.

Objectives: Model-informed approaches offer key support for drug development and regulatory approval. This research is aimed to assess how the model informed approaches aided regulatory recommendations and approval of neuroscience therapeutics. Key examples are provided. This reflects the views of the authors and should not be construed to represent FDA’s views or policies.

2.

Methods: Model-informed analyses from Office of Clinical Pharmacology, FDA over the last 10 years were reviewed. The authors screened regulatory reviews of NDAs, BLAs and INDs in neurology, psychiatry, addiction, anesthesia, and analgesia. The authors identified instances where model-informed approaches had a significant impact on the approval or directions of use of a drug or biologic assessment.

3.

Results: Multiple submissions applied population pharmacokinetic, population pharmacokinetic pharmacodynamic (PKPD), or exposure-response modeling, and simulation to influence decision making. Applications include:

Efficacy Extrapolation:
• selecting dosing in pediatric patients based on exposure-matching to older pediatric patients or adults at approved adult dosing,
• optimizing dose determination (fixed dose versus mg/kg dosing),
• approving a buprenorphine dose regimen not assessed in the Phase 3 program,
• updating labeled diazepam dosing for status epilepticus by assessing the labeled dosing versus different dosing that is commonly used in clinical practice,
• supporting dose interpolation for valbenazine.

Surrogate endpoints:
• selection of beta amyloid plaque reduction as a reasonably likely surrogate endpoint for biologics targeting anti-beta-amyloid plaques in Alzheimer’s disease patients,
• analyses of a surrogate endpoint and an efficacy endpoint to support efficacy of tofersen.

Exposure-Response and Pharmacokinetic Pharmacodynamic (PKPD) Analyses:
• informing the duration of driving impairment associated with zuranolone treatment,
• informing robustness of single pivotal study to support efficacy of givinostat,
• modeling of the reversal of opioid-induced respiratory depression to support efficacy of a naloxone autoinjector.

4.

Conclusions: Model-informed analyses informed decision making across the spectrum of neuroscience drug development. These analyses have provided supportive evidence for drug efficacy and safety during NDA or BLA review.

Citations: None