title: A priori prediction of tumor payload concentrations: preclinical case study with an auristatin-based anti-5T4 antibody-drug conjugate.
abstract: The objectives of this investigation were as follows: (a) to validate a mechanism-based pharmacokinetic (PK) model of ADC for its ability to a priori predict tumor concentrations of ADC and released payload, using anti-5T4 ADC A1mcMMAF, and (b) to analyze the PK model to find out main pathways and parameters model outputs are most sensitive to. Experiential data containing biomeasures, and plasma and tumor concentrations of ADC and payload, following A1mcMMAF administration in two different xenografts, were used to build and validate the model. The model performed reasonably well in terms of a priori predicting tumor exposure of total antibody, ADC, and released payload, and the exposure of released payload in plasma. Model predictions were within two fold of the observed exposures. Pathway analysis and local sensitivity analysis were conducted to investigate main pathways and set of parameters the model outputs are most sensitive to. It was discovered that payload dissociation from ADC and tumor size were important determinants of plasma and tumor payload exposure. It was also found that the sensitivity of the model output to certain parameters is dose-dependent, suggesting caution before generalizing the results from the sensitivity analysis. Model analysis also revealed the importance of understanding and quantifying the processes responsible for ADC and payload disposition within tumor cell, as tumor concentrations were sensitive to these parameters. Proposed ADC PK model provides a useful tool for a priori predicting tumor payload concentrations of novel ADCs preclinically, and possibly translating them to the clinic.
authors.affiliation: Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, 455 Kapoor Hall, Buffalo, New York, 14214, USA, dshah4@buffalo.edu.
authors.forename: Dhaval K
authors.initials: DK
authors.lastname: Shah
authors.forename: Lindsay E
authors.initials: LE
authors.lastname: King
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Han
authors.forename: Jo-Ann
authors.initials: JA
authors.lastname: Wentland
authors.forename: Yanhua
authors.initials: Y
authors.lastname: Zhang
authors.forename: Judy
authors.initials: J
authors.lastname: Lucas
authors.forename: Nahor
authors.initials: N
authors.lastname: Haddish-Berhane
authors.forename: Alison
authors.initials: A
authors.lastname: Betts
authors.forename: Mauricio
authors.initials: M
authors.lastname: Leal
mesh_terms: D062365:Aminobenzoates / Q000737:chemistry* / Q000493:pharmacokinetics*; D000818:Animals; D000911:Antibodies, Monoclonal / Q000493:pharmacokinetics; D061067:Antibodies, Monoclonal, Humanized / Q000737:chemistry* / Q000493:pharmacokinetics*; D045744:Cell Line, Tumor; D004305:Dose-Response Relationship, Drug; D006801:Humans; D018796:Immunoconjugates / Q000737:chemistry* / Q000493:pharmacokinetics*; D008562:Membrane Glycoproteins / Q000378:metabolism*; D051379:Mice; D008954:Models, Biological; D009842:Oligopeptides / Q000737:chemistry* / Q000493:pharmacokinetics*; D014018:Tissue Distribution; D023041:Xenograft Model Antitumor Assays
chemical_list: C581920:A1mcMMAF; D062365:Aminobenzoates; D000911:Antibodies, Monoclonal; D061067:Antibodies, Monoclonal, Humanized; D018796:Immunoconjugates; D008562:Membrane Glycoproteins; D009842:Oligopeptides; C543533:auristatin; C063709:trophoblastic glycoprotein 5T4, human