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Lewis Sheiner


2019
Stockholm, Sweden



2018
Montreux, Switzerland

2017
Budapest, Hungary

2016
Lisboa, Portugal

2015
Hersonissos, Crete, Greece

2014
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2013
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2012
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2011
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2010
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2009
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2008
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2007
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2006
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2005
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2004
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2003
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2002
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2001
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2000
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1999
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1998
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1997
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1996
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1995
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1994
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1993
Paris, France

1992
Basel, Switzerland



Printable version

PAGE. Abstracts of the Annual Meeting of the Population Approach Group in Europe.
ISSN 1871-6032

Reference:
PAGE 28 (2019) Abstr 8919 [www.page-meeting.org/?abstract=8919]


Oral: Drug/Disease modelling


D-07 James Lu Integrated efficacy-safety QSP model of acute myeloid leukemia (AML) generates insights into the role of clinical dose schedules on cytopenia

James Lu (1), Kyoung-Ae Kim (2), Paul Jasper (2), Nick Corr (3), Aaron Fullerton (3), Dale Miles (1), James Cooper (4), Divya Samineni (1), Bing Zheng (5), Chunze Li (1), Jin Y. Jin (1), Dan Lu (1)

(1) Modeling & Simulation/Clinical Pharmacology, Genentech, USA, (2) RES Group Inc, USA, (3) Investigative Toxicology, Genentech, USA, (4) Exploratory Clinical Development, Genentech, USA, (5) Translational Oncology, Genentech, USA

Introduction: Acute Myeloid Leukemia (AML) is an aggressive hematological malignancy, characterized by an accumulation of leukemic blast cells in the bone marrow and is often accompanied by anemia, thrombocytopenia and neutropenia [1]. Treatment of AML patients aims to eliminate leukemic blasts and hence revert their bone marrow function.  In the development of novel therapies for AML, a significant challenge is balancing efficacy with safety: a recent example being the discontinuation of vadastuximab talirine (VT) development due to increased hematologic toxicity and fatal infections [2,3], despite efficacy in blast reduction. The dual effects of therapeutic agents on both the blast reduction as well as hematologic toxicity requires a systems-level description of their interactions.

Objectives: We show how clinical development questions for AML therapies can be addressed by utilizing a quantitative systems pharmacology (QSP) model that describes the life-cycle of leukemic blasts in bone marrow and peripheral blood, as well as disease-induced cytopenia.

Methods: Firstly, a model of normal hematopoiesis across the three lineages (neutrophils, platelets and red blood cells) including regulation mechanisms (EPO, TPO and GCSF) was built and calibrated based on published data sets (e.g., [4,5]). Secondly, the proliferation rate of AML blasts in the bone marrow and their transit time into the blood compartment are informed by published tracer kinetic studies (e.g., [6,7])Finally, the model mechanisms by which leukemic blasts result in cytopenia were informed by key experimental publications [8,9] based upon clinical and preclinical data of AML. Therapies are modelled by adding killing effects on both the leukemic blasts and normal progenitor cells. In particular, to predict the hematologic toxicity of a given drug candidate, a novel multilineage hematopoiesis assay is used to generate treatment data across progenitors and mature cells along the three lineages (neutrophils, platelets and red-blood cells). A model of the in-vitro assay is used to estimate and translate toxicity parameters in-vivo [10].  We evaluated the platform model using VT as a test example, using Emax expressions for drug effects.

Results:  The QSP model recapitulates aspects of the AML disease, as well as demonstrates the reversal of cytopenias after the removal of leukemic blasts from the bone marrow.   We show that the published neutrophil and platelet recovery times under VT monotherapy [2] are in good consistency with the QSP model, which takes as inputs the estimated efficacy parameters based on data in [2] and hematologic toxicity parameters estimated from in-vitro model [10]. We simulate the model for both the single dose as well as fractionated dosing (days 0 and 3) scenarios, and explain the reported clinical outcomes.  The model findings suggest that in AML patients the dosing paradigm for an improved neutrophil recovery time may differ from that applicable to solid tumor patients. In the latter, patient baseline blood cell counts are close to normal and dose fractionation is an established approach to avoid blood count nadirs falling below the thresholds corresponding to grades-3/4 hematological AEs. In contrast, blood counts in AML patients prior to receiving treatment are already very low, and the aim of therapies is to remove leukemic blasts sufficiently quickly so that their suppressive effects on normal hematopoiesis can be removed, while not causing undue hematologic toxicity. Model simulations shed light on the important role of efficacy on the time to count recovery. 

Conclusion: AML is a disease where clinical response criteria entail not only leukemic blast elimination but also the recovery of normal blood counts. We demonstrate that the use of a QSP model which integrates both the efficacy and safety aspects generates valuable insights for optimizing the dosing schedule of AML therapies.



References:
[1] Lichtman, M. A., Kaushansky, K., Prchal, J. T., Levi, M. M., Burns, L. J., & Armitage, J. (2017). Williams manual of hematology. McGraw Hill Professional.
[2] Stein, E. M., Walter, R. B., Erba, H. P., Fathi, A. T., Advani, A. S., Lancet, J. E., ... & Faderl, S. (2018). A phase 1 trial of vadastuximab talirine as monotherapy in patients with CD33-positive acute myeloid leukemia. Blood, 131(4), 387-396.
[3] Fathi, A. T., Erba, H. P., Lancet, J. E., Stein, E. M., Ravandi, F., Faderl, S., ... & Jillella, A. (2018). A phase 1 trial of vadastuximab talirine combined with hypomethylating agents in patients with CD33-positive AML. Blood, 132(11), 1125-1133
[4] Busch, K., Klapproth, K., Barile, M., Flossdorf, M., Holland-Letz, T., Schlenner, S. M., ... & Rodewald, H. R. (2015). Fundamental properties of unperturbed haematopoiesis from stem cells in vivo. Nature, 518(7540), 542.
[5] Ho, T., Clermont, G., & Parker, R. S. (2013). A model of neutrophil dynamics in response to inflammatory and cancer chemotherapy challenges. Computers & Chemical Engineering, 51, 187-196.
[6] Preisler, H. D., Raza, A., Gopal, V., Ahmad, S., & Bokhari, J. (1995). Distribution of cell cycle times amongst the leukemia cells within individual patients with acute myelogenous leukemia. Leukemia research, 19(10), 693-698.
[7] Clarkson, B., Ohkita, T., Ota, K., & Fried, J. (1967). Studies of cellular proliferation in human leukemia. I. Estimation of growth rates of leukemic and normal hematopoietic cells in two adults with acute leukemia given single injections of tritiated thymidine. The Journal of clinical investigation, 46(4), 506-529.
[8] Akinduro, O., Weber, T. S., Ang, H., Haltalli, M. L. R., Ruivo, N., Duarte, D., ... & Celso, C. L. (2018). Proliferation dynamics of acute myeloid leukaemia and haematopoietic progenitors competing for bone marrow space. Nature communications, 9(1), 519.
[9] Miraki-Moud, F., Anjos-Afonso, F., Hodby, K. A., Griessinger, E., Rosignoli, G., Lillington, D., ... & Oakervee, H. (2013). Acute myeloid leukemia does not deplete normal hematopoietic stem cells but induces cytopenias by impeding their differentiation. Proceedings of the National Academy of Sciences, 110(33), 13576-13581.
[10] Lu, J., Corr, N., Fullerton, A., Miles, D., Jin, J., Lu, D. (2019). Systems modeling of a novel multilineage hematopoiesis assay for the deconvolution of mechanisms of drug-induced myelosuppression, ASCPT Annual Meeting, abstract QSP-009.