(W-100) Pharmacometrics-Based Evaluation of Fixed versus Weight-Based Dosing Strategies for Approved Antibody Drug Conjugates (ADC)

Andrew Santulli, Pharmacometrician – Pharmacometrician, Enhanced Pharmacodynamics, LLC; Buffalo, New York, 14203, USA.; Scott Van Wart, CSO – Vice President, Enhanced Pharmacodynamics, LLC; Buffalo, New York, 14203, USA.; Cindy Bednasz, Assistant Director – Assistant Director, Clinical Pharmacology, Enhanced Pharmacodynamics, LLC; Buffalo, New York, 14203, USA.; Karthik Venkatakrishnan, Vice President – Global Head of Quantitative Pharmacology, EMD Serono Research & Development Institute, Inc., Billerica, MA, USA, an affiliate of Merck KGaA, Darmstadt, Germany; Akash Khandelwal, Senior Director – Senior Director, Pharmacometrics, EMD Serono Research & Development Institute, Inc., Billerica, MA, USA, an affiliate of Merck KGaA, Darmstadt, Germany; Donald Mager, CEO – President, Department of Pharmaceutical Sciences, University at Buffalo, SUNY, Buffalo New York, 14214, USA., Enhanced Pharmacodynamics, LLC; Buffalo, New York, 14203, USA.; Wei Gao, Global Head of Pharmacometrics – Portfolio Section Head, Quantitative Pharmacology, EMD Serono Research & Development Institute, Inc., Billerica, MA, USA, an affiliate of Merck KGaA, Darmstadt, Germany

All FDA approved ADCs have utilized weight- or BSA-based dosing regimens, with inotuzumab ozogamicin and gemtuzumab ozogamicin being the only ADCs approved with mg/m2 dosing. Dose capping was also implemented for 3 out of 12 ADCs. The objective was to evaluate the impact of body weight (BW) or size-based dosing versus fixed dosing for 12 FDA-approved ADCs.
Literature search was performed to identify published population PK models and exposure-response (E-R) models for the ADCs. FDA review summaries were prioritized for final parameter estimates. Population PK models were reconstructed and validated in R version 4.1.0 [1] using mrgsolve version 0.11.1 [2]. A large virtual cancer patient population was generated in R using methods described by Cheeti et al. [3]. Simulations were performed for each ADC at their FDA-approved body dosing regimen and for the fixed dose equivalent based upon a typical 80 kg adult. Cycle 1 PK exposures metrics for intact ADC and unconjugated payload were calculated for each virtual patient and presented as boxplots by overall population, weight quartiles, and weight bins to compare the two dosing regimens.

PK characterization of the 12 ADCs utilized compartmental PK modeling with a linear and stationary clearance, 3 incorporated MM kinetics to capture nonlinear clearance, and 6 included a time-varying decline in CL for the intact ADC. Population estimates for typical subject intact ADC clearance and central volume were approximately 0.36 L/day (range: 0.2-1.4 L/day) and 2.98 L (range: 2.63-3.8 L), respectively. PK of the unconjugated payload was commonly described using a compartmental approach, with time-varying DAR incorporated when needed to reflect systemic deconjugation. Covariates significantly impacting intact ADC and apparent unconjugated payload clearance (CL) and central volume (Vc) included BW or BSA and albumin. The average exponent value for scaling body weight on ADC CL was 0.629 (range: 0.370-1.05), and the average exponent value for scaling body weight on Vc was 0.626 (range: 0.426-1). Simulations were used to compare the projected variability between weight-based and fixed dosing. The variability in PK exposure metrics, when summarized across the entire range of body weights, was similar between the regimens. Projections also suggested a trend in which heavier patients exhibited lower systemic exposures for the fixed regimen compared to the weight-based regimen, and lighter patients had higher systemic exposures for the fixed dose regimen compared to the weight-based regimen. Of note, variability in exposures should be placed within the context of E-R relationships.

We conclude that decisions regarding posology should be made on a case-by-case basis. Using fixed dosing in early clinical development may be a feasible approach for adult populations, provided that population-based analyses of PK, and E-R analyses of safety, and efficacy data are used to assess whether weight-based dosing is warranted.

Citations:

References:
1. R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
2. Kyle T Baron (2021). mrgsolve: Simulate from ODE-Based Models. R package version 0.11.1. https://CRAN.R-project.org/package=mrgsolve
3. Cheeti S, Budha NR, Rajan S, Dresser MJ, Jin JY. A physiologically based pharmacokinetic (PBPK) approach to evaluate pharmacokinetics in patients with cancer. Biopharm Drug Dispos. 2013 Apr;34(3):141-54. doi: 10.1002/bdd.1830. Epub 2013 Feb 26. PMID: 23225350.