Population Pharmacological Profiles Of AML Treatments In Patient Samples By Automated Flow Cytometry; A Bridge To Individualized Medicine

Abstract

Background To aid in the identification of effective treatments for individual patients, ex vivo assays for detecting cell death inducible by drugs for hematological malignancies have been in development for over 20 years. We have developed a novel approach incorporating 4 key innovations; incubating drugs in whole bone marrow sample without isolating leukocytes, using flow cytometry enables identification of the malignant cells selectively, an automated flow cytometry-based platform (ExviTech) decreases errors and enables full pharmacological characterization, and analyzing the data using pharmacodynamic population models. Aim The purpose of this study is to derive the ex vivo pharmacological profiles across the AML patient population of single drugs and combination treatments as a tool for individualized treatment selection. Patients and Methods Bone-marrow samples from 160 patients diagnosed with AML were sent to Vivia from 24 hospitals across Spain within 24 hrs. The plates were incubated for 48-hours prior to analysis with ExviTech, The percentage of leukemic cell death was determined via labeling with monoclonal antibodies and AnnexinV-FITC. A survival index is computed for each drug, the lower the survival index, the more effective the drug. Dose-response curves of cytarabine, idarubicin, daunorubicine, etoposide, mitoxantrone, fludarabine, clofarabine, and 6-thioguanine were measured in 160 patient samples. The added benefit of combining these drugs into 12 combination treatments was assessed by measuring their synergy in each individual patient. In 39 patients treated with CYT IDA we had clinical data of response, and then we performed a blinded interpretation of this in vitro test by an expert hematologist, to predict the clinical response based in this test result. Results There was a large range of interpatient variability in the response to a single drug and even larger in the synergism between drugs. The Population Pharmacological Profiles for an individual patient is shown on the figure below. The relative drug potency in terms of their percentile ranking within the population is shown in the left panel from 0 (weakest) to 100 (most potent). Green lines represent the individual patient potency relative to the population ranking, with confidence intervals. Third column lists when a drug leaves a significant % of leukemic cells alive, potential resistant clones. The panel on the right side shows the synergism of the drug combinations treatments shown as box-plots at 10-25-75-90% to highlight their distribution. The synergism value for an individual patient in each combination is shown in green, with confidence interval as parallel dotted green lines. This representation of the Pharmacological Profile of an individual patient sample quickly identifies extreme values, when a drug or combination is very sensitive (rightward shift green lines, green boxes) or very resistant (leftward shift green lines, red boxes). This patient showed average sensitivities for most drugs though highly resistant to Clofarabine (red box) that leaves 45% alive. However this patient showed lack of synergism in multiple treatments (right, red boxes). CYT and IDA show average potencies but lack of synergism, suggesting CYT-DAU might be a more efficient treatment. These representations lead to clear guidelines in >90% samples, and based on hematologist's interpretation of these guidelines show a clinical correlation with clinical responses to CYT-IDA of 84%. Conclusion We have developed an improved a methodology to measure the pharmacological activity of drugs and drug combinations in AML patient samples as well as modeling their pharmacological behavior. This information may be useful in selecting the optimal treatment for the individual patient, especially relapse/refractory patients in need of therapeutic alternatives. By testing the drugs used in the treatment protocols for AML directly on patient samples, a pharmacological based model has been developed to infer drug resistance or sensitivity, patient by patient. Disclosures: Ballesteros: Vivia Biotech: Equity Ownership. Primo:Vivia Biotech: Employment. Hernandez-Campo:Vivia Biotech: Employment. Rojas:Vivia Biotech: Employment. Liebana:Vivia Biotech: Employment. Lopez:Vivia Biotech: Employment. Iñaki:Vivia Biotech: Consultancy. Bennett:Vivia Biotech: Employment.