Background: This study investigated the potential of external control (EC) arms to replace concurrent control (CC) arms in organ impairment (OI) studies. We evaluated statistical matching (SM) and pooling methods for EC construction, considering EC data availability both at the time of OI study conduct (ATT) and at new drug application filing (ALL). We further evaluated the conditions that may enable or necessitate the use of EC. Methods: This two-part study first analyzed 13 OI studies from Takeda's database. In Part A, we developed ECs using two approaches: (1) pooling, which combined EC data from eligible studies available ATT or ALL, and (2) statistical matching (SM), where we evaluated 8 matching methods and 13 distance metrics to identify the optimal combination that minimized standardized mean differences. In Part B, we simulated drug exposure data for 11,000 renal impairment (RI) studies and 11,000 corresponding EC datasets, using demographic distributions from Takeda's internal database of RI patients and healthy volunteers, respectively. The base scenario assumed low between-subject variability (CV of 30%), 8 subjects per RI arm, and 80 available EC subjects. We explored 11 variations of these parameters. Assessment criteria included geometric mean ratios (GMRs) of PK parameters (e.g. AUCinf and Cmax) between CC and EC arms, and consistency of dosing recommendations based on AUC GMRs for each OI arm versus EC or CC. Results: Both SM and pooling produced GMRs of EC versus CC within 0.8-1.25 for 11/13 (85%), with consistent results for ATT and ALL scenarios. Dosing recommendations aligned for all OI arms (N=27), regardless of EC or CC use. Simulations revealed that higher between-subject variability (CV > 50%) could cause inconsistencies in AUC and Cmax distributions between EC and CC arms. Varying EC size (10-100 subjects) or RI arm size (5-20 subjects) had minimal impact on outcomes. Conclusion: Our findings validate both SM and pooling for EC arm construction in OI studies. However, drugs with moderate to high PK variability may necessitate augmenting CC with EC due to potential distributional disparities. These insights can inform more efficient OI study designs in drug development, potentially reducing resource requirements and accelerating the drug development process.
