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Clinical pharmacology of vc-MMAE antibody-drug conjugates in cancer patients: learning from eight first-in-human Phase 1 studies.

Chunze LiCindy X ZhangZao LiDivya SamineniDan LuBei WangShang-Chiung ChenRong ZhangPriya AgarwalBernard M FineSandhya Girish
Published in: mAbs (2021)
vc-MMAE antibody-drug conjugates (ADCs) consist of a monoclonal antibody (mAb) covalently bound with a potent anti-mitotic toxin (MMAE) through a protease-labile valine-citrulline (vc) linker. The objective of this study was to characterize the pharmacokinetics (PK) and explore exposure-response relationships of eight vc-MMAE ADCs, against different targets and for diverse tumor indications, using data from eight first-in-human Phase 1 studies. PK parameters of the three analytes, namely antibody-conjugated MMAE (acMMAE), total antibody, and unconjugated MMAE, were estimated using non-compartmental approaches and compared across the eight vc-MMAE ADCs. Relationships between analytes were assessed by linear regression. Exposure-response relationships were explored with key efficacy (objective response rate) and safety (Grade 2+ peripheral neuropathy) endpoints. PK profiles of acMMAE, total antibody and unconjugated MMAE following the first dose of 2.4 mg/kg were comparable across the eight ADCs; the exposure differences between molecules were small relative to the inter-subject variability. acMMAE exposure was strongly correlated with total antibody exposure for all the eight ADCs, but such correlation was less evident between acMMAE and unconjugated MMAE exposure. For multiple ADCs evaluated, efficacy and safety endpoints appeared to correlate well with acMMAE exposure, but not with unconjugated MMAE over the doses tested. PK of vc-MMAE ADCs was well characterized and demonstrated remarkable similarity at 2.4 mg/kg across the eight ADCs. Results from analyte correlation and exposure-response relationship analyses suggest that measurement of acMMAE analyte alone might be adequate for vc-MMAE ADCs to support the clinical pharmacology strategy used during late-stage clinical development.
Keyphrases
  • monoclonal antibody
  • escherichia coli
  • drug delivery
  • machine learning
  • big data
  • cell cycle
  • artificial intelligence