title: Physiologically based pharmacokinetic modeling as a tool to predict drug interactions for antibody-drug conjugates.
abstract: OBJECTIVE
  Monomethyl auristatin E (MMAE, a cytotoxic agent), upon releasing from valine-citrulline-MMAE (vc-MMAE) antibody-drug conjugates (ADCs), is expected to behave like small molecules. Therefore, evaluating the drug-drug interaction (DDI) potential associated with MMAE is important in the clinical development of ADCs. The objective of this work was to build a physiologically based pharmacokinetic (PBPK) model to assess MMAE-drug interactions for vc-MMAE ADCs.
  
  
  METHODS
  A PBPK model linking antibody-conjugated MMAE (acMMAE) to its catabolite unconjugated MMAE associated with vc-MMAE ADCs was developed using a mixed 'bottom-up' and 'top-down' approach. The model was developed using in silico and in vitro data and in vivo pharmacokinetic data from anti-CD22-vc-MMAE ADC. Subsequently, the model was validated using clinical pharmacokinetic data from another vc-MMAE ADC, brentuximab vedotin. Finally, the verified model was used to simulate the results of clinical DDI studies between brentuximab vedotin and midazolam, ketoconazole, and rifampicin.
  
  
  RESULTS
  The pharmacokinetic profile of acMMAE and unconjugated MMAE following administration of anti-CD22-vc-MMAE was well described by simulations using the developed PBPK model. The model's performance in predicting unconjugated MMAE pharmacokinetics was verified by successful simulation of the pharmacokinetic profile following brentuximab vedotin administration. The model simulated DDIs, expressed as area under the concentration-time curve (AUC) and maximum concentration (C max) ratios, were well within the two-fold of the observed data from clinical DDI studies.
  
  
  CONCLUSIONS
  This work is the first demonstration of the use of PBPK modelling to predict MMAE-based DDI potential. The described model can be extended to assess the DDI potential of other vc-MMAE ADCs.
authors.affiliation: Drug Metabolism and Pharmacokinetics, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA, chen.yuan@gene.com.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Chen
authors.forename: Divya
authors.initials: D
authors.lastname: Samineni
authors.forename: Sophie
authors.initials: S
authors.lastname: Mukadam
authors.forename: Harvey
authors.initials: H
authors.lastname: Wong
authors.forename: Ben-Quan
authors.initials: BQ
authors.lastname: Shen
authors.forename: Dan
authors.initials: D
authors.lastname: Lu
authors.forename: Sandhya
authors.initials: S
authors.lastname: Girish
authors.forename: Cornelis
authors.initials: C
authors.lastname: Hop
authors.forename: Jin Yan
authors.initials: JY
authors.lastname: Jin
authors.forename: Chunze
authors.initials: C
authors.lastname: Li
mesh_terms: D000079963:Brentuximab Vedotin; D003198:Computer Simulation; D004347:Drug Interactions; D006801:Humans; D018796:Immunoconjugates / Q000493:pharmacokinetics* / Q000494:pharmacology; D007654:Ketoconazole / Q000493:pharmacokinetics / Q000494:pharmacology; D008874:Midazolam / Q000493:pharmacokinetics / Q000494:pharmacology; D008954:Models, Biological; D009842:Oligopeptides / Q000493:pharmacokinetics* / Q000494:pharmacology; D012293:Rifampin / Q000493:pharmacokinetics / Q000494:pharmacology
chemical_list: D018796:Immunoconjugates; D009842:Oligopeptides; D000079963:Brentuximab Vedotin; D008874:Midazolam; D007654:Ketoconazole; C495575:monomethyl auristatin E; D012293:Rifampin