Expediting MIDD evidence generation to support quizartinib approval in newly diagnosed AML patients.
Anaïs Glatard1, Enrica Mezzalana1, Ming Zheng2, Martin Bergstrand1, Giovanni Smania1
1Pharmetheus AB, 2Quantitative Clinical Pharmacology Department Daiichi Sankyo, Inc.
Introduction: Quizartinib is a highly potent type II FMS-like receptor tyrosine kinase 3 (FLT3) inhibitor. The Phase 3 QuANTUM-First trial demonstrated that the addition of quizartinib to standard induction and consolidation chemotherapy in newly diagnosed FLT3-ITD-positive acute myeloid leukemia (AML) patients, followed by continuation with quizartinib monotherapy for up to 3 years, resulted in improved survival compared to placebo [1]. Speedy and high-quality submissions including pharmacometrics analyses following the completion of pivotal trials are crucial to faster access to new medicines for patients and return of investment for applicants. Because of their complexity, pharmacometric analyses often lie on the critical path to submission. Using quizartininb as a case-study, the aims of the present work were: •To summarise how MIDD evidence contributed to support the proposed dosing regimen •To illustrate a workflow that can be used to accelerate the generation of submission-ready MIDD evidence Methods: The population PK of quizartinib and its exposure-response (ER) relationship with 6 efficacy and 11 safety endpoints were characterized. The PK analysis included data from 13 studies [2], while the ER analyses were based on data from the pivotal phase 3 study QuANTUM-First. The relationship between quizartinib exposure and Fridericia corrected QT (QTcF) interval was of particular interest due to the known QT interval prolongation by quizartinib [3]. To increase efficiency in the pharmacometric analyses, from dataset preparation to report writing, the following measures were adopted. First, the data programming was performed in parallel for the different endpoints. Second, the population PK analysis and reporting was first performed on 12 available studies (representing 85% of the total data included in the final model), and was followed by a model/report update after receiving data from the pivotal study. Third, the ER analyses were conducted in parallel by 3 different pharmacometricians. Finally, reproducible writing was used to generate the analysis reports which, besides increasing efficiency, allowed to enhance accuracy and reliability of the reported results [4]. Throughout the process, independent quality control checkpoints combined with scientific peer reviews were included to ensure that the quality level of the generated MIDD evidence met regulatory standards. Results: The population PK analysis provided supportive evidence that no dose adjustments were necessary for various patient groups based on age, sex, body weight or race groups. In addition, it confirmed the need to reduce the quizartinib dose during concomitant treatment with strong cytochrome P450 3A (CYP3A) inhibitors [2]. The concentration-QTcF analysis supported the dose-modification algorithm used in the QuANTUM-First trial, basing dose adjustments on observed QTcF prolongation and/or concomitant administration of strong CYP3A inhibitors [5]. After receiving the QuANTUM-First source data, the ER report was finalized within 16 weeks. The source data was later updated and, due to the efficiency-gaining measures that were employed in the analysis, updated PK and ER reports could be finalized in 4 weeks. Overall, the analyses supported the successful approval of quizartinib by the European Medicines Agency, the US Food and Drug Administration and the Japanese Pharmaceuticals and Medical Devices Agency. Conclusions: The MIDD evidence generated from the above analyses supported a positive benefit-risk profile for the proposed quizartinib dosing regimens in adult patients with newly diagnosed AML, and contributed to its approval by three major regulatory agencies. When pharmacometric analyses are on the critical path to submission, timelines can be optimized by utilizing the proposed approach.
[1] Erba HP, Montesinos P, Kim HJ, et al. Lancet. 2023; 401(10388): 1571-1583. doi: 10.1016/S0140-6736(23)00464-6. [2] Vaddady P., Glatard A., Smania G. et al. Clin Transl Sci. 2024 Dec;17(12):e70074. doi: 10.1111/cts.70074. [3] Kang D, Ludwig E, Jaworowicz D, et al. Cancer Chemother Pharmacol. 2021;87(4):513-523. doi:10.1007/s00280- 020- 04204- y [4] Moroso V., Magnusson MO., Jonsson EN. Medical Writing. 2023;32(3):56–63. https://doi.org/10.56012/wmqy8556 [5] Vaddady P., Smania G., Nakayama S. et al. Clin Transl Sci. 2024 Nov;17(11):e70065. doi: 10.1111/cts.70065.