Pharmacological Profiles of Acute Myeloid Leukemia Treatments in Patient Samples by Automated Flow Cytometry: A Bridge to Individualized Medicine

Bennett, Teresa; Montesinos, Pau; Moscardó, Federico; Martínez‐Cuadrón, David; Martínez, Joaquín; Sierra, Jorge; García, Raimundo; Oteyza, Jaime Pérez de; Fernández, Pascual; Serrano, Josefina; Fernández, Ana; Herrera, Pilar; Gonzalez, Ataulfo; Bethancourt, Concepcion; Rodríguez‐Macías, Gabriela; Alonso, Arancha; Vera, Juan Antonio; Navas, Begoña; Lavilla, Esperanza; López, Juan Antonio; Jiménez, Santiago; Simiele, Adriana; Vidriales, María‐Belén; González, Bernardo; Burgaleta, Carmen; Hernández-Rivas, José-Ángel; Mascunano, R. Cordoba; Bautista, Guiomar; Pérez-Simón, José A.; Fuente, Adolfo de la; Rayón, Consolación; Trocóniz, Iñaki F.; Janda, Álvaro; Bosanquet, Andrew G.; Hernández-Campo, Pilar; Primo, Daniel; López, Rocío; Liebana, Belen; Rojas, José Luis Vázquez; Gorrochategui, Julián; Sanz, Miguel Á.; Ballesteros, Joan

doi:10.1016/j.clml.2013.11.006

Cited by 30 publications

(50 citation statements)

References 29 publications

(38 reference statements)

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“…This uses high-throughput methods to screen a library of drugs for the ability to preferentially kill ex-vivo AML cells from a particular patient compared with normal bone marrow. This “individualized medicine” approach has the advantage that any therapy[9], or combination of therapy[10], selected as a result of such a screen already has pre-clinical evidence as justification for that particular patient. This approach may also be used iteratively at different time points in treatment and to adjust treatment, either by re-screening after treatment failure and/or based on overcoming resistance mechanisms as highlighted by acquired mutations [9].…”

Section: Ex-vivo Veritas? Drug Sensitivity and Resistance Testingmentioning

confidence: 99%

Precision medicine for acute myeloid leukemia

Lai

Karp

Hourigan

2015

Expert Review of Hematology

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The goal of precision medicine is to personalize therapy based on individual patient variation, to correctly select the right treatment, for the right patient, at the right time. Acute myeloid leukemia (AML) is a heterogeneous collection of myeloid malignancies with diverse genetic etiology and the potential for intra-patient clonal evolution over time. We discuss here how the precision medicine paradigm might be applied to the care of AML patients by focusing on the potential roles of targeting therapy by patient-specific somatic mutations and aberrant pathways, ex-vivo drug sensitivity and resistance testing, high sensitivity measurements of residual disease burden and biology along with potential clinical trial and regulatory constraints.

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Section: Ex-vivo Veritas? Drug Sensitivity and Resistance Testingmentioning

confidence: 99%

Precision medicine for acute myeloid leukemia

Lai

Karp

Hourigan

2015

Expert Review of Hematology

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“…AML blasts were characterized according to their light scatter properties and stained with antibodies to the following markers: CD11b, CD45, CD13, CD34, CD64, CD117 and HLA-DR as described previously [54, 55]. AML blasts were incubated in RPMI 1640 medium (20% FBS, 2% HEPES, 1% penicillin-streptomycin, and 1% L-glutamine) containing a human cytokine cocktail to stimulate proliferation of leukemic cells.…”

Section: Methodsmentioning

confidence: 99%

“…The cytokine cocktail included 0.1 ng/μL SCF (StemCell Technologies, catalog 02630), 0.05 ng/μL IL-3 (StemCell Technologies, catalog 02503), 0.04 ng/μL IL-6 (Miltenyi Biotech, catalog 130-095-365), 0.2 ng/μL GM-CSF (Peprotech, catalog 300–03), 0.2 ng/μL G-CSF (Peprotech, catalog 300–23), 0.004 U/mL Erithropoietin (StemCell, catalog 02625), 0.94 μg/μL Transferrin (Sigma Aldrich, catalog T8158), 0.1 ng/μL 2-Mercaptoethanol (Sigma Aldrich, catalog M7154). To identify live leukemic cells by flow cytometry, two antibodies that unequivocally identify the pathologic cell population in the patient samples were selected in combination with annexin V. Those cells without annexin V staining and with appropriate markers were considered live leukemic cells, as described previously [54, 55]. Proliferation inhibition was measured as the difference in the number of live leukemic cells in a well with drugs versus the vehicle control treated wells.…”

Section: Methodsmentioning

confidence: 99%

Selective Inhibitors of Histone Deacetylases 1 and 2 Synergize with Azacitidine in Acute Myeloid Leukemia

et al. 2017

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Acute myeloid leukemia (AML) is a heterogeneous group of hematopoietic stem cell disorders characterized by defects in myeloid differentiation and increased proliferation of neoplastic hematopoietic precursor cells. Outcomes for patients with AML remain poor, highlighting the need for novel treatment options. Aberrant epigenetic regulation plays an important role in the pathogenesis of AML, and inhibitors of DNA methyltransferase or histone deacetylase (HDAC) enzymes have exhibited activity in preclinical AML models. Combination studies with HDAC inhibitors plus DNA methyltransferase inhibitors have potential beneficial clinical activity in AML, however the toxicity profiles of non-selective HDAC inhibitors in the combination setting limit their clinical utility. In this work, we describe the preclinical development of selective inhibitors of HDAC1 and HDAC2, which are hypothesized to have improved safety profiles, for combination therapy in AML. We demonstrate that selective inhibition of HDAC1 and HDAC2 is sufficient to achieve efficacy both as a single agent and in combination with azacitidine in preclinical models of AML, including established AML cell lines, primary leukemia cells from AML patient bone marrow samples and in vivo xenograft models of human AML. Gene expression profiling of AML cells treated with either an HDAC1/2 inhibitor, azacitidine, or the combination of both have identified a list of genes involved in transcription and cell cycle regulation as potential mediators of the combinatorial effects of HDAC1/2 inhibition with azacitidine. Together, these findings support the clinical evaluation of selective HDAC1/2 inhibitors in combination with azacitidine in AML patients.

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“…PK/PD models can also be used to guide dose and regimen selection using isobolograms, which relate in vivo exposure to antitumor activity [17].…”

confidence: 99%

“…Monitored event types that had not Establishing the relationship between PK and BMK and/or TS dynamics (extensively reviewed in [23,29,36,37] Establishing the relationship between different (model-based) predictors and OS and PFS (Table 1) Establishing the relationship between drug exposure and toxicity in Dose optimization [77] PBPK [8][9][10] Hematological toxicities [51][52][53][54][55][56][57][58][59] CT measurement optimization [27] TMDD [12][13][14][15][16] Nonhematological toxicities [65][66][67][68][69][70] Drug selection [17] Abbreviations: BMK, biomarker; CT, computed tomography; OS, overall survival; PFS, progression free survival; PBPK, physiologically based pharmacokinetic model; PK, pharmacokinetics; TMDD, target-mediated drug disposition; TS, tumor size.…”

Section: Data Setsmentioning

confidence: 99%

Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications

et al. 2015

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Despite much investment and progress, oncology is still an area with significant unmet medical needs, with new therapies and more effective use of current therapies needed. The emergent field of pharmacometrics combines principles from pharmacology (pharmacokinetics [PK] and pharmacodynamics [PD]), statistics, and computational modeling to support drug development and optimize the use of already marketed drugs. Although it has gained a role within drug development, its use in clinical practice remains scarce. The aim of the present study was to review the principal pharmacometric concepts and provide some examples of its use in oncology. Integrated population PK/PD/disease progression models as part of the pharmacometrics platform provide a powerful tool to predict outcomes so that the right dose can be given to the right patient to maximize drug efficacy and reduce drug toxicity. Population models often can be developed with routinely collected medical record data; therefore, we encourage the application of such models in the clinical setting by generating close collaborations between physicians and pharmacometricians. The Oncologist 2016;21:220-232Implications for Practice: The present review details how the emerging field of pharmacometrics can integrate medical record data with predictive pharmacological and statistical models of drug response to optimize and individualize therapies. In order to make this routine practice in the clinic, greater awareness of the potential benefits of the field is required among clinicians, together with closer collaboration between pharmacometricians and clinicians to ensure the requisite data are collected in a suitable format for pharmacometrics analysis.

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Pharmacological Profiles of Acute Myeloid Leukemia Treatments in Patient Samples by Automated Flow Cytometry: A Bridge to Individualized Medicine

Abstract: We hypothesize that the use of the individual patient ex vivo pharmacological profiles may help to guide a personalized treatment selection.

Cited by 30 publications

References 29 publications

Precision medicine for acute myeloid leukemia

Precision medicine for acute myeloid leukemia

Selective Inhibitors of Histone Deacetylases 1 and 2 Synergize with Azacitidine in Acute Myeloid Leukemia

Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications

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