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Abstract 679: Quantitative systems pharmacology modeling of immunotherapies in B-cell acute lymphoblastic leukemia

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Abstract Introduction: Multiple T-cell engaging antibody-based molecules and CAR-T therapies are in development for the treatment of B-cell acute lymphoblastic leukemia (B-ALL). Moreover, two therapies have already been approved by FDA: blinatumomab (CD19/CD3 bispecific T-cell engager) and tisagenlecleucel (anti-CD19 CAR-T therapy). This work employs quantitative systems pharmacology (QSP) modeling approach to investigate factors affecting outcomes of administration of such types of therapy. Methods: Model was developed on the basis of Immune Response Template (QSP model of immune system). It describes leukemic blasts, hematopoiesis of normal B cells, CD4+ and CD8+ T-cell subsets, and cytokines (IL-6, TNFα, IFNγ, TGFβ, etc.) in bone marrow and blood. The model takes into account the different level of expression of CD19 on various subsets of normal B cells and leukemic cells. Model parameters were estimated and fitted against in vitro, in vivo human data. Small part of clinical data on blinatumomab and anti-CD19 CAR-T therapies was used for model calibration, other part was used for model validation. Results: Our model reproduced clinically relevant blinatumomab and anti-CD19 CAR-T hallmark data including depletion of CD19+ cells, T-cell subset dynamics and cytokine release post infusion. The model successfully described the clinically observed phenomenon on progressive cytokine release attenuation with subsequent blinatumomab treatment cycles. It confirmed the dose step strategy for decreasing initial cytokine release. Model showed that treatment outcome is very sensitive to target expression, level of immune cells, disease progression rate and expression of PD-L1 on leukemic cells. The proportion of CD19+ and CD19- leukemic blasts before treatment can be used as a biomarker to predict probability of relapse in patients to stratify patients. Conclusions: This work provides a quantitative modeling framework to predict responses to immunotherapies in B-ALL on the basis of realistic baseline conditions and reveal the factors affecting treatment outcome. Citation Format: Oleg Demin Jr, Antonina Nikitich, Oleg Demin. Quantitative systems pharmacology modeling of immunotherapies in B-cell acute lymphoblastic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 679.
American Association for Cancer Research (AACR)
Title: Abstract 679: Quantitative systems pharmacology modeling of immunotherapies in B-cell acute lymphoblastic leukemia
Description:
Abstract Introduction: Multiple T-cell engaging antibody-based molecules and CAR-T therapies are in development for the treatment of B-cell acute lymphoblastic leukemia (B-ALL).
Moreover, two therapies have already been approved by FDA: blinatumomab (CD19/CD3 bispecific T-cell engager) and tisagenlecleucel (anti-CD19 CAR-T therapy).
This work employs quantitative systems pharmacology (QSP) modeling approach to investigate factors affecting outcomes of administration of such types of therapy.
Methods: Model was developed on the basis of Immune Response Template (QSP model of immune system).
It describes leukemic blasts, hematopoiesis of normal B cells, CD4+ and CD8+ T-cell subsets, and cytokines (IL-6, TNFα, IFNγ, TGFβ, etc.
) in bone marrow and blood.
The model takes into account the different level of expression of CD19 on various subsets of normal B cells and leukemic cells.
Model parameters were estimated and fitted against in vitro, in vivo human data.
Small part of clinical data on blinatumomab and anti-CD19 CAR-T therapies was used for model calibration, other part was used for model validation.
Results: Our model reproduced clinically relevant blinatumomab and anti-CD19 CAR-T hallmark data including depletion of CD19+ cells, T-cell subset dynamics and cytokine release post infusion.
The model successfully described the clinically observed phenomenon on progressive cytokine release attenuation with subsequent blinatumomab treatment cycles.
It confirmed the dose step strategy for decreasing initial cytokine release.
Model showed that treatment outcome is very sensitive to target expression, level of immune cells, disease progression rate and expression of PD-L1 on leukemic cells.
The proportion of CD19+ and CD19- leukemic blasts before treatment can be used as a biomarker to predict probability of relapse in patients to stratify patients.
Conclusions: This work provides a quantitative modeling framework to predict responses to immunotherapies in B-ALL on the basis of realistic baseline conditions and reveal the factors affecting treatment outcome.
Citation Format: Oleg Demin Jr, Antonina Nikitich, Oleg Demin.
Quantitative systems pharmacology modeling of immunotherapies in B-cell acute lymphoblastic leukemia [abstract].
In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA.
Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 679.

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