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Platform model describing pharmacokinetic properties of vc-MMAE antibody-drug conjugates.

Matts KågedalLeonid GibianskyJian XuXin WangDivya SamineniShang-Chiung ChenDan LuPriya AgarwalBei WangOla SaadNeelima KoppadaBernard M FineJin Y JinSandhya GirishChunze Li
Published in: Journal of pharmacokinetics and pharmacodynamics (2017)
Antibody-drug conjugates (ADCs) developed using the valine-citrulline-MMAE (vc-MMAE) platform, consist of a monoclonal antibody (mAb) covalently bound with a potent anti-mitotic toxin (MMAE) through a protease-labile vc linker. Recently, clinical data for a variety of vc-MMAE ADCs has become available. The goal of this analysis was to develop a platform model that simultaneously described antibody-conjugated MMAE (acMMAE) pharmacokinetic (PK) data from eight vc-MMAE ADCs, against different targets and tumor indications; and to assess differences and similarities of model parameters and model predictions, between different compounds. Clinical PK data of eight vc-MMAE ADCs from eight Phase I studies were pooled. A population PK platform model for the eight ADCs was developed, where the inter-compound variability (ICV) was described explicitly, using the third random effect level (ICV), and implemented using LEVEL option of NONMEM 7.3. The PK was described by a two-compartment model with time dependent clearance. Clearance and volume of distribution increased with body weight; volume was higher for males, and clearance mildly decreased with the nominal dose. Michaelis-Menten elimination had only minor effect on PK and was not included in the model. Time-dependence of clearance had no effect beyond the first dosing cycle. Clearance and central volume were similar among ADCs, with ICV of 15 and 5%, respectively. Thus, PK of acMMAE was largely comparable across different vc-MMAE ADCs. The model may be applied to predict PK-profiles of vc-MMAE ADCs under development, estimate individual exposure for the subsequent PK-pharmacodynamics (PD) analysis, and project optimal dose regimens and PK sampling times.
Keyphrases
  • high throughput
  • monoclonal antibody
  • body weight
  • escherichia coli
  • big data
  • randomized controlled trial
  • clinical trial
  • machine learning
  • cancer therapy
  • artificial intelligence
  • study protocol
  • single cell