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Assessment of Telisotuzumab Vedotin Drug-Drug Interaction Potential Using Physiologically-Based Pharmacokinetic
Md Mahbubul Huq Riad1, Priya Brunsdon1, Rajeev Menon1
1Clinical Pharmacology, AbbVie Inc., North Chicago, IL, USA.
None:
Telisotuzumab vedotin (Teliso-V; ABBV-399) is an antibody-drug conjugate (ADC) comprised of the c-Met targeting antibody telisotuzumab (ABT-700) conjugated to the potent cytotoxic monomethyl auristatin E (MMAE). Teliso-V has been evaluated for treating solid tumors and is approved for adults with locally-advanced or metastatic non-squamous non-small cell lung cancer with high c-Met protein overexpression (≥50% tumor cells with strong [+3] staining; determined by FDA-approved test), who have received prior systemic therapy. Here, physiologically-based pharmacokinetic (PBPK) modeling was utilized to evaluate Teliso-V drug-drug interaction (DDI) potential. A published PBPK-model for MMAE as the primary metabolite and a newly-developed telisotuzumab model from existing pre-clinical and clinical trial data were used to create a novel Teliso-V PBPK-model. Unconjugated MMAE release was modeled with drug-to-antibody ratio-specific deconjugation rates, with non-specific and catabolic clearance added to capture half-life and overall PK profile. The Teliso-V model was calibrated and validated using observed clinical trial data, requiring dose-normalized exposure %PEs ≤50%. CYP3A-mediated DDI simulations demonstrated that when Teliso-V was modeled as the victim, a 43% increase and 70% decrease in MMAE AUC were predicted with ketoconazole (strong CYP3A4-inhibitor) and rifampin (strong CYP3A4-inducer) coadministration, respectively. DDI magnitude was comparable to that observed between another approved ADC with the same MMAE payload (brentuximab vedotin) and ketoconazole and rifampin. The current PBPK simulations demonstrated a lack of perpetrator effect of Teliso-V on midazolam, a sensitive CYP3A substrate. The current analysis provides important information on Teliso-V DDI potential and further demonstrates the utility of PBPK models, particularly in oncology, where dedicated DDI studies are challenging.
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