Daniel Bending, Dr. Venkatesh Pilla Reddy, Dr. Eunice Yuen and Dr. Raimund Peter
Eli Lilly and Company
Introduction/Objectives: Clinical Utility Indexes (CUIs) offer a quantitative and combined measure of efficacy and safety within a highly expandable platform. In an evolving oncology landscape, where treatment is less constrained by the maximum tolerable dose, a robust tool for predicting and selecting optimal doses for later-stage clinical trials is paramount. Traditionally, benefit and risk are evaluated quantitatively in exposure-response (ER) analyses, which are qualitatively compared to choose a dose. CUIs change this by creating reproducible and flexible combinations of multiple ER geometries to produce a singular dose/PK exposure-dependent metric. These often display as a bell-shaped curve with the maximum CUI value being mappable onto a nominal dosage or regimen. This modelling work displays the CUI for three approved antibody drug conjugates (ADCs): Enfortumab Vedotin (EV), Sacituzumab Govitecan (SG) and Mirvetuximab Soravtansine (MS), as well as one phase one ADC: AZD5335. These CUIs were further evaluated for how they differ when the ER analyses are combined arithmetically or geometrically.1-4 Methods: The data used in the analyses were digitised from published literature describing the pharmacokinetics, efficacy (overall response rate), and safety (primarily anaemia and neutropenia). Logistic regression was performed to characterise the exposure response for a set of exposure metrics: the area under the curve (AUC), maximum concentration (Cmax), minimum concentration (Cmin), and average concentration (Cav), for both ADC concentration and deconjugated payload concentration were data permitted. For known variation within doses for each exposure metric, these ER analyses were combined in a multiplicative (geometric) and additive (arithmetic) manner, weighted by user-defined weights between safety and efficacy to produce a singular CUI value. These CUI values could then be compared to the approved dosage for these ADCs to determine parity between the quantitative and qualitative dose selection routines. These ER analyses and CUI calculations were built into an R Shiny app for easy use by clinicians to select weightings between safety and efficacy. Results: The ER analyses displayed a range of associations between the exposure metric and the response rates, where good data density and strong correlations resulted in more interpretable CUI outputs and a strong bell-shaped distribution, notable for the three approved ADCs. Too sparse data without a clear trend resulted in flat ER geometries, which, when combined, produced flat CUI plots, as displayed for AZD5335. Because the difference in CUI values between doses was low, these could not be used to quantitatively determine an optimal dose, but a qualitative method would also fail here, outside of utilising prior trends in different ADCs. For EV, SG, and MG, the CUI was able to replicate the approved doses of 1.25 mg/kg thrice Q3W, 10 mg/kg twice Q2W and 6 mg/kg Q3W respectively. Conclusions: The evaluation of Clinical Utility Indexes for ADCs demonstrates the potential of CUIs as a robust tool for ADC dose selection in oncology. Our findings indicate that CUIs can effectively combine multiple exposure-response analyses into a singular, dose-dependent metric, providing a quantitative approach to dose optimisation. However, the utility of CUIs is contingent on the quality and density of the underlying data. Sparse data with weak correlations result in less interpretable CUI outputs, highlighting the need for comprehensive and high-quality datasets. Despite these limitations, CUIs offer a promising alternative to traditional qualitative methods, particularly when integrated into user-friendly platforms like the R Shiny app.
1. Wang C et al., Meta-Analysis of Exposure-Adverse Event Relationships for Antibody-Drug Conjugates. J Clin Pharmacol. (2024). DOI: 10.1002/jcph.6160. 2. Fostvedt, Luke K et al., Pharmacokinetic/Pharmacodynamic Modeling to Support the Re-approval of Gemtuzumab Ozogamicin. Clinical pharmacology and therapeutics vol. 106,5 (2019): 1006-1017. DOI:10.1002/cpt.1500 3. Yiming Cheng et al., Exposure-Response–Based Multiattribute Clinical Utility Score Framework to Facilitate Optimal Dose Selection for Oncology Drugs. JCO 42, 4145-4152 (2024). DOI:10.1200/JCO.24.00349 4. Assimwe, Innocent et al. Postmarketing Assessment of Antibody-Drug Conjugates: Proof-of-Concept Using Model-Based Meta-Analysis and a Clinical Utility Index Approach. CPT Pharmacometrics Syst Pharmacol. 2025 Mar 4. doi: 10.1002/psp4.70013. Epub ahead of print. PMID: 40040312.
Reference: PAGE 33 (2025) Abstr 11343 [www.page-meeting.org/?abstract=11343]
Poster: Drug/Disease Modelling - Oncology