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  2. Unraveling the pharmacokinetic interaction of ticagrelor and MEDI2452 (Ticagrelor antidote) by mathematical modeling

Unraveling the pharmacokinetic interaction of ticagrelor and MEDI2452 (Ticagrelor antidote) by mathematical modeling

  • CPT Pharmacometrics Syst Pharmacol. 2016 Jun;5(6):313-23. doi: 10.1002/psp4.12089.
J Almquist 1 2 3 M Penney 4 S Pehrsson 3 A-S Sandinge 3 A Janefeldt 3 S Maqbool 4 S Madalli 5 J Goodman 4 S Nylander 3 P Gennemark 3
Affiliations

Affiliations

  • 1 Fraunhofer-Chalmers Centre, Chalmers Science Park, Gothenburg, Sweden.
  • 2 Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
  • 3 Cardiovascular and Metabolic Diseases, Innovative Medicines, AstraZeneca R&D, Mölndal, Sweden.
  • 4 Clinical Pharmacology and DMPK, MedImmune, Cambridge, UK.
  • 5 Cardiovascular and Metabolic Diseases Research, MedImmune, Cambridge, UK.
Abstract

The investigational ticagrelor-neutralizing antibody fragment, MEDI2452, is developed to rapidly and specifically reverse the antiplatelet effects of ticagrelor. However, the dynamic interaction of ticagrelor, the ticagrelor active metabolite (TAM), and MEDI2452, makes pharmacokinetic (PK) analysis nontrivial and mathematical modeling becomes essential to unravel the complex behavior of this system. We propose a mechanistic PK model, including a special observation model for post-sampling equilibration, which is validated and refined using mouse in vivo data from four studies of combined ticagrelor-MEDI2452 treatment. Model predictions of free ticagrelor and TAM plasma concentrations are subsequently used to drive a pharmacodynamic (PD) model that successfully describes platelet aggregation data. Furthermore, the model indicates that MEDI2452-bound ticagrelor is primarily eliminated together with MEDI2452 in the kidneys, and not recycled to the plasma, thereby providing a possible scenario for the extrapolation to humans. We anticipate the modeling work to improve PK and PD understanding, experimental design, and translational confidence.

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