Personalizing Therapies with Ex Vivo Pharmacological
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PERSONALIZING THERAPIES WITH EX VIVO PHARMACOLOGICAL RESPONSES MAY UNCOVER THE DIFFERENCES BETWEEN IDA-DNR-MIT AMONG EUROPEAN AML PROTOCOLS Pau Montesinos1, David Martínez 1, Raimundo García2, Jaime Pérez de Oteyza3 , Pascual Fernández4, Josefina Serrano5 , Ángeles Fernández6, Pilar Herrrera7 , Arancha Alonso8, Ataulfo González9, Concepción Bethancourt10, Esperanza Lavilla11, Juan Antonio Vera12, Begoña Navas13, Gabriela Rodríguez14, Juan Antonio López15, Santiago Jiménez Bravo de la Laguna16, Adriana Simiele17, Bernardo González18, Jose Angel Hernández Rivas19, Raúl Córdoba20, Consolación Rayón21, Carmen Burgaleta22, Jorge Sierra23, Iñaki F. Trocóniz24, Ignacio Ortega25, Andrew G. Bosanquet26, Daniel Primo27, Pilar Hernández-Campo27, Julian Gorrochategui27, Jose L. Rojas27, Teresa A. Bennett27 , Belén Liébana27, Rocío López27, Joan Ballesteros Nobell27 , Federico Moscardó1, Miguel Ángel Sanz1, Joaquín Martínez López28 1Haematology, Hospital Universitari i politecnic La Fe, Valencia, Spain, 2Haematology, Hospital Universitario General de Castellón, Castellón, Spain, 3Haematology, Hospital de Madrid Norte Sanchinarro, Madrid, Spain, 4Haematology, Hospital General Universitario de Alicante, Alicante, Spain, 5Haematology, Hospital Universitario Reina Sofía, Córdoba, Spain, 6Haematology, Complejo Hospitalario Xeral Cíes de Vigo, Vigo, Spain, 7Haematology, Hospital Universitario Ramón y Cajal, Madrid, Spain, 8Haematology, Hospital Quirón Madrid, Spain, 9Haematology, Hospital Clínico San Carlos, Madrid, Spain, 10Hospital Regional Universitario Carlos Haya, Málaga, Spain, 11Hospital Universitario Lucus Augusti, Lugo, Spain, 12Hospital Universitario Virgen de la Macarena, Sevilla, Spain, 13Hospital Moncloa, Madrid, Spain, 14Haematology, Hospital Universitario Gregorio Marañón, Madrid, Spain, 15Haematology, Complejo Hospitalario de Jaén, Jaén, Spain, 16Haematology, Hospital Universitario de Gran Canaria Doctor Negrín, Gran Canaria, Spain, 17Haematology, Hospital Povisa, Vigo, Spain, 18Haematology, Hospital Universitario de Canarias, Tenerife, Spain, 19Haematology, Hospital Universitario Infanta Leonor, Madrid, Spain, 20Haematology, Hospital Universitario Infanta Sofía, Madrid, Spain, 21Haematology, Hospital Universitario Central de Asturias, Oviedo, Spain, 22Haematology, Hospital Universitario Príncipe de Asturias, Madrid, Spain, 23Haematology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain, 24Universidad de Navarra, Pamplona, Spain, 25Universidad del País Vasco, Bilbao, Spain, 26Bath Cancer Research, Bath, England, 27Vivia Biotech, Madrid, Spain, 28Haematology, Hospital Universitario Doce de Octubre, Madrid, Spain ABSTRACT METHODS ExviTech© Platform PLATE SETUP WHOLE SAMPLE vs. ISOLATED LYMPHOCYTES Background and objectives: Protocols for acute myeloid leukemia (AML) 1st line patients are centered on the combination of Cytarabine and an anthracycline; Idarubicin (IDA), Daunorubicin (DNR),or Mitoxantrone (MIT).Patients may be treated with IDA, DNR, or MIT Robotic Sample Automated FCM BeckmanCoulter Cyan Proprietary Activity Base Bioinformatics CYT-IDA Preparation Based Screening Flow Cytometer Analysis Results + Clinical Info A IDA-DAU-MIT depending on the country of residence, because multiple clinical trials have not found significant differences among them. A new Software Eight different concentrations of each drug or A. Dose-response curves for D Personalized Medicine (PM) test developed by Vivia Biotech based on pharmacological responses in patient samples(ex vivo)is uncovering drug combination is run for the used treatment IDA and CYT in isolated individual responses to these treatments. Our objective is to explore whether a significant % of individual patients may respond differently protocols. The max concentration used is listed leukocytes and whole sample. to IDA vs DNR vs MIT treatments, in spite that of their “on average” similar response shown by clinical trials. Data, from sample 6 below, Patients and Methods: Multicenter, prospective, non-interventional study of the PETHEMA group for treatment of AML. Bone Marrow 1 2 3 4 5 6 7 8 9 10 11 12 A displays a log difference in DMSO DMSO 0.47% (BM) samples were collected at diagnosis for 160 AML patients. Samples were incubated for 48 hours in 96well plates, each well B DMSO 0.47% MITOXANTRONE 7µM the EC50s for IDA, but equal C IDARUBICIN containing different drugs or drug combinations, each at 8 different concentrations, enabling calculation of dose response curves for each 3µM results for CYT. B. The EC50 1.5µM D DAUNORUBICIN single drug (CYT, IDA, DNR, MIT) and combination used in treatments(CYT-IDA, CYT-DNR, CYT-MIT). Drug response was evaluated as (y-axis) of the whole sample E CYT + IDA depletion of AML malignant cells in each well after 48 hours incubations. Annexin V-FITC was used to quantify the ability of the drugs to E F CYT + DNR and the isolated leukocyte B induce apoptosis. Malignant cells were identified with monoclonal antibodies and light scatter properties. 1)We use the whole bone G CYT + MIT Screening Setup and Workflow fraction from 9 patient H marrow sample, retaining the erythrocyte population and serum proteins, during the entire incubation period; and after 48h leukocytes DAY 1 samples with CYT. C. EC50 of are isolated prior to evaluation by flow cytometry. 2)We have pioneered development of a proprietary automated flow cytometry platform PB or BM DAY 3 the same samples to IDA. called ExviTech. 3)Pharmacological responses are calculated using pharmacokinetic population models. Analysis and Import Data Analysis: performed using the into ActivityBase population approach using NONMEM 7.2.: Results: Figure 3 shows dose responses for IDA (blue), DNR (red) and MIT (green) in 125AML patient samples. Although their average Split sample REPORT D-F. Dose response curves for population PD modelling of the ex vivo curves (Figure 2) are similar, the inter-patient variability of either drug is quite large. We hypothesized that some patients could show very GENERATED IDA (D) DAU (E) MIT (F) for 2 F response vs concentration data in differential sensitivities to these drugs, as illustrated in Figure 4 (panel A) where a patient sample is resistant to IDA (right shifted dose C samples (red, blue), in whole monotherapy (fig.2), establishing for each response curve) but sensitive to DNR (left shifted dose response curve). To identify these cases, Figure 5 panel A shows a comparison of sample (continuous line) and patient the 95% prediction intervals (PI) of the potency IDA vs DNR. Potency is represented by their EC50 (concentration that kills 50% of the cells). Most dots tend to line up, but red Sample Validation/ isolated leukocytes (dotted Cell Count Apoptotic the isobologram from each individual dots represent patient samples with a difference in potency between these drugs >30%. Repeating this exercise for IDA-MIT and DNR-MIT lines). Blue sample shows Plated Analysis with: parameter (fig.4) computation of the (panels B and C) to cover all alternatives among the 3 anthracyclines identifies 40% of patients samples with >30% different potency Annexin V Anti-CD45 small difference DAU vs large Drugs combination index using raw data descriptors among IDA-DNR-MIT. Repeating this exercise with the combination treatments CYT-IDA, CYT-DNR, CYT-MIT (Figure 6) increases to 58% the Drug-Sample Plates Annexin V Anti-CD14 difference IDA & MIT. Red 48H Anti-CD34 Anti-CD64 Live from combination experiments. Chou and population of patients whose samples have a differential sensitivity to these anthracyclines. A fraction of this 65% of patients may benefit Anti-CD117 Anti-CD13 sample all more similar. in if treatment selection among these 3 treatments were to be aided by this ex vivo testing sensitivities. To identify which fraction would Anti-HLA-DR Anti-CD11b Talalay. 2010. Cancer Research 70: 440-446. benefit we would need a trial specifically designed. Figure 1. Chemosensitivity test Figure 3. Inter-patient Figure 4. Examples of differential Figure 5. Overall statistical Figure 6. Overall statistical Figure 7. Drugs interaction in combination treatments by dose-response modeling analysis variability individual behavior differences among drugs differences among combinations Potency ranking based on EC50 Patient 1 A A 18/106 (16.7%) 40% of patients showed significant IDA differences among potency of three DAU Sensitive Resistant drugs. Surv_Idx (%) Surv_Idx Surv_Idx (%) Surv_Idx Surv_Idx (%) Surv_Idx IDARUBICINE DAUNORUBICIN 58% of patients showing any difference in Patient 2 26/106 (24.5%) B B synergy measurements among the three IDA combinations MIT Shift of dose-response curves to the left (sensitive) or right Figure 7. Inter-patient variability on drug interaction Sensitive (resistant) indicates differences of drug potency exvivo. measurement Resistant Surv_Idx (%) Surv_Idx IDARUBICINE Figure 2. Similar sensitivity IDA DAU MIT (%) Surv_Idx MITOXANTRONE Patient 3 C 24/104 (23.1%) C More than 65% of patients show DAU MIT Sensitive differences either on drug potency or Resistant synergy measurements among Surv_Idx (%) Surv_Idx (%) Surv_Idx Surv_Idx (%) Surv_Idx DAUNORUBICIN CYT-IDA, CYT-DAU, CYT-MIT. MITOXANTRONE IDA Each curve corresponds to the average exvivo effect of Wide Inter-patient variability shown on individual dose-response Examples show how inter-patient variability may result in one Red dots for patients with a response exvivo showing Red dots for patients and combinations indexes showing Box-plots showing population distribution of measured combination index