A Practical Alternative to Refine the Estimate of fmCYP3A4 and Evaluate Drug–Drug Interaction Potential for Ziftomenib Using PBPK Modeling to Inform Labeling
- 1PBPK Consultancy, Simcyp Division Certara UK Ltd Sheffield UK
- 2Kura Oncology, Inc. San Diego California USA
Abstract
Accurate physiologically based pharmacokinetic (PBPK) simulation of drug–drug interaction (DDI) potential requires estimation of the relative contribution of the impacted pathway. While fraction metabolized by CYP enzymes is usually estimated using dedicated clinical DDI studies with strong CYP inhibitors, this approach might not be available in some situations. In the case of the menin inhibitor, ziftomenib, a dedicated DDI study in healthy subjects was infeasible due to the potential for toxicity associated with the mechanism of action, so an alternate approach was used. AML patients are typically immuno‐compromised and hence are at a high risk of fungal infections. For this reason, co‐administration of azole antifungals (moderate and strong CYP3A4 inhibitors) was permitted in Clinical Study KO‐MEN‐001. The PK data obtained in the presence or absence of CYP3A4 inhibitors was used to refine the estimate of ziftomenib fmCYP3A4 to 60%–70%. Once refined, the model was applied to predict the victim DDI liability of ziftomenib in the presence of CYP3A4 inhibitors and inducers. Moderate or weak interaction (2.6‐fold and 1.4‐fold increase in AUC) was predicted with itraconazole and isavuconazole, respectively. Approximately 80% reduction in ziftomenib AUC was predicted with rifampicin. Ziftomenib was predicted to be a weak CYP3A4 inhibitor, causing a 1.9‐fold increase in midazolam exposure. In the absence of data from dedicated DDI clinical trials, these results were used to support regulatory interactions with the US FDA regarding concomitant administration of ziftomenib with other medications such as CYP3A4 modulators. These modeling results ultimately supported a range of DDI language in ziftomenib label.
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BibTeX
@article{templeton2026practical,
title = {{A Practical Alternative to Refine the Estimate of fmCYP3A4 and Evaluate Drug–Drug Interaction Potential for Ziftomenib Using PBPK Modeling to Inform Labeling}},
author = {Ian E. Templeton and Chara Litou and Hannah M. Jones and Julie Mackey Ahsan and Marilyn Tabachri and Mollie Leoni and Amitava Mitra},
journal = {CPT: Pharmacometrics \& Systems Pharmacology},
year = {2026},
month = sep,
doi = {10.1002/psp4.70327},
url = {https://doi.org/10.1002/psp4.70327},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - A Practical Alternative to Refine the Estimate of fmCYP3A4 and Evaluate Drug–Drug Interaction Potential for Ziftomenib Using PBPK Modeling to Inform Labeling
AU - Ian E. Templeton
AU - Chara Litou
AU - Hannah M. Jones
AU - Julie Mackey Ahsan
AU - Marilyn Tabachri
AU - Mollie Leoni
AU - Amitava Mitra
PY - 2026
DA - 2026/09/02
JO - CPT: Pharmacometrics & Systems Pharmacology
DO - 10.1002/psp4.70327
UR - https://doi.org/10.1002/psp4.70327
AB - Accurate physiologically based pharmacokinetic (PBPK) simulation of drug–drug interaction (DDI) potential requires estimation of the relative contribution of the impacted pathway. While fraction metabolized by CYP enzymes is usually estimated using dedicated clinical DDI studies with strong CYP inhibitors, this approach might not be available in some situations. In the case of the menin inhibitor, ziftomenib, a dedicated DDI study in healthy subjects was infeasible due to the potential for toxicity associated with the mechanism of action, so an alternate approach was used. AML patients are typically immuno‐compromised and hence are at a high risk of fungal infections. For this reason, co‐administration of azole antifungals (moderate and strong CYP3A4 inhibitors) was permitted in Clinical Study KO‐MEN‐001. The PK data obtained in the presence or absence of CYP3A4 inhibitors was used to refine the estimate of ziftomenib fmCYP3A4 to 60%–70%. Once refined, the model was applied to predict the victim DDI liability of ziftomenib in the presence of CYP3A4 inhibitors and inducers. Moderate or weak interaction (2.6‐fold and 1.4‐fold increase in AUC) was predicted with itraconazole and isavuconazole, respectively. Approximately 80% reduction in ziftomenib AUC was predicted with rifampicin. Ziftomenib was predicted to be a weak CYP3A4 inhibitor, causing a 1.9‐fold increase in midazolam exposure. In the absence of data from dedicated DDI clinical trials, these results were used to support regulatory interactions with the US FDA regarding concomitant administration of ziftomenib with other medications such as CYP3A4 modulators. These modeling results ultimately supported a range of DDI language in ziftomenib label.
SN - 2163-8306
ER -