Literature DB >> 27198897

Preclinical to Clinical Translation of Antibody-Drug Conjugates Using PK/PD Modeling: a Retrospective Analysis of Inotuzumab Ozogamicin.

Alison M Betts1,2, Nahor Haddish-Berhane3, John Tolsma4, Paul Jasper4, Lindsay E King5, Yongliang Sun6, Subramanyam Chakrapani7, Boris Shor8, Joseph Boni9, Theodore R Johnson10.   

Abstract

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model was used for preclinical to clinical translation of inotuzumab ozogamicin, a CD22-targeting antibody-drug conjugate (ADC) for B cell malignancies including non-Hodgkin's lymphoma (NHL) and acute lymphocytic leukemia (ALL). Preclinical data was integrated in a PK/PD model which included (1) a plasma PK model characterizing disposition and clearance of inotuzumab ozogamicin and its released payload N-Ac-γ-calicheamicin DMH, (2) a tumor disposition model describing ADC diffusion into the tumor extracellular environment, (3) a cellular model describing inotuzumab ozogamicin binding to CD22, internalization, intracellular N-Ac-γ-calicheamicin DMH release, binding to DNA, or efflux from the tumor cell, and (4) tumor growth and inhibition in mouse xenograft models. The preclinical model was translated to the clinic by incorporating human PK for inotuzumab ozogamicin and clinically relevant tumor volumes, tumor growth rates, and values for CD22 expression in the relevant patient populations. The resulting stochastic models predicted progression-free survival (PFS) rates for inotuzumab ozogamicin in patients comparable to the observed clinical results. The model suggested that a fractionated dosing regimen is superior to a conventional dosing regimen for ALL but not for NHL. Simulations indicated that tumor growth is a highly sensitive parameter and predictive of successful outcome. Inotuzumab ozogamicin PK and N-Ac-γ-calicheamicin DMH efflux are also sensitive parameters and would be considered more useful predictors of outcome than CD22 receptor expression. In summary, a multiscale, mechanism-based model has been developed for inotuzumab ozogamicin, which can integrate preclinical biomeasures and PK/PD data to predict clinical response.

Entities:  

Keywords:  antibody-drug conjugate; clinical translation; inotuzumab ozogamicin; mechanistic modeling; pharmacokinetics-pharmacodynamics

Mesh:

Substances:

Year:  2016        PMID: 27198897     DOI: 10.1208/s12248-016-9929-7

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  46 in total

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6.  Therapeutic potential of CD22-specific antibody-targeted chemotherapy using inotuzumab ozogamicin (CMC-544) for the treatment of acute lymphoblastic leukemia.

Authors:  J F Dijoseph; M M Dougher; D C Armellino; D Y Evans; N K Damle
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Authors:  J K Peterson; P J Houghton
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Authors:  John F DiJoseph; Douglas C Armellino; Erwin R Boghaert; Kiran Khandke; Maureen M Dougher; Latha Sridharan; Arthur Kunz; Philip R Hamann; Boris Gorovits; Chandrasekhar Udata; Justin K Moran; Andrew G Popplewell; Sue Stephens; Philip Frost; Nitin K Damle
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2.  QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models.

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4.  Evolution of the Systems Pharmacokinetics-Pharmacodynamics Model for Antibody-Drug Conjugates to Characterize Tumor Heterogeneity and In Vivo Bystander Effect.

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Review 5.  Antibody-Drug Conjugates: Pharmacokinetic/Pharmacodynamic Modeling, Preclinical Characterization, Clinical Studies, and Lessons Learned.

Authors:  William D Hedrich; Tamer E Fandy; Hossam M Ashour; Hongbing Wang; Hazem E Hassan
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6.  Application of a PK-PD Modeling and Simulation-Based Strategy for Clinical Translation of Antibody-Drug Conjugates: a Case Study with Trastuzumab Emtansine (T-DM1).

Authors:  Aman P Singh; Dhaval K Shah
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9.  Quantitative systems pharmacology modeling provides insight into inter-mouse variability of Anti-CTLA4 response.

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Review 10.  New Strategies Using Antibody Combinations to Increase Cancer Treatment Effectiveness.

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