Publications

//Publications
Publications2019-03-21T22:52:40+00:00

Conference presentations

“LB18 – Clinical Pharmacokinetics and Pharmacodynamics Characterization of ANAVEX™2-73 for Designing a Phase 2/3 Study in Mild-to-Moderate Alzheimer’s Disease”, Mohammad Afshar, MD, PhD[1], Frédéric Parmentier, PhD[1], Ene I Ette, PhD[2], Emmanuel O Fadiran, PhD[3], Christopher U Missling, PhD[3] [1]Ariana Pharma, Paris, France, [2]Anoixis Corp., Natick, MA, [3]Anavex Life Sciences Corp., New York, NY

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CTAD 2017 conference website
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“Anavex Life Sciences Reports PK and PD Data from Phase 2a Trial of ANAVEX®2-73 in Mild-to-Moderate Alzheimer’s Disease Patients”, Missling C.[1], Afshar M.[2], Perry G.[3] [1]Anavex Life Sciences, USA; [2]Ariana Pharma, France; [3]University of Texas, San Antonio, USA

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“P8: Comorbidities increase In-Hospital Mortality in Dengue Patients in Mexico”, TOH M.L.[1], BAURIN N.[1], MORLEY D.[2], RECAMIER V.[2], GUERGOVA-KURAS M.[2], PUENTES-ROSAS E[3], OCHIAI L.[4], COUDEVILLE L.[1], MASCAREÑAS C.[3] [1]Sanofi Pasteur, France; [2]Ariana Pharma, France; [3]Sanofi Pasteur, Mexico; [4]Sanofi Pasteur, Singapore

“P9: Comorbidities increase In-Hospital Mortality in Dengue Patients in Brazil”, TOH M.L.[1], BAURIN N.[1], MORLEY D.[2], RECAMIER V.[2], GUERGOVA-KURAS M.[2], PUENTES-ROSAS E[3], OCHIAI L.[4], COUDEVILLE L.[1], MASCAREÑAS C.[3] [1]Sanofi Pasteur, France; [2]Ariana Pharma, France; [3]Sanofi Pasteur, Mexico; [4]Sanofi Pasteur, Singapore

Links :

API 2017 conference website
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“O10.6 Initial perspectives from WINTHER, an international precision medicine trial using both DNA and RNA data to guide treatment”, Vladimir Lazar [1], Razelle Kurzrock [2], Raanan Berger [3], Jordi Rodon [4], Wilson Miller [5], Vincent Miller [6], Fanny Wunder [1], Eitan Rubin [7], Serge Koscielny [8], Mohammad Afshar [9], Jean-Charles Soria [8] [1] Worldwide Innovative Networking (WIN) Consortium, Villejuif, France [2] UC San Diego Moores Cancer Center, San Diego, USA [3] Chaim Sheba Medical Center, Tel-Hashomer, Israel [4] Vall d’Hebron Institute of Oncology Universidad Autonoma de Barcelona, Barcelona, Spain [5] Segal Cancer Centre at the Jewish General Hospital, Montreal, Canada [6] Foundation Medicine Inc., Cambridge, USA [7] Ben-Gurion University of the Negev, Be’er Sheva, Israel [8] Gustave Roussy, Villejuif, France [9] Ariana Pharma, Paris, France

tat2017_thumbThe WINTHER clinical trial is a cutting edge precision medicine prospective study, using not only genomic but also transcriptomic assays to guide treatment decisions. This multinational trial (4 countries) coordinated by the Worldwide Innovative Networking (WIN) Consortium is structured in two arms. Arm A uses Foundation Medicine’s Foundation One DNA gene panel to guide therapeutic decisions. If no actionable alterations are identified or therapies suggested in Arm A are not available, patients are moved to the Arm B that uses an innovative transcriptomic assay and algorithm further developed in a clinical decision support system (Onco KEM). The study has included a broad range of solid tumor patients heavily pretreated that exhausted conventional therapy. The main study endpoint is the comparison of the progression-free-survival (PFS) under the WINTHER selected therapy to the PFS of the last therapeutic line for which the patient had a progression. 303 patients were registered in the study. 107 patients were treated according to the WINTHER therapeutic decisions taken by the study’s Clinical Management Committees. Analysis of the treated patients shows that 64% (69) were treated using the identified DNA based panel alterations. The transcriptomic test enabled treatment selection for the remaining 36% (38). This group consisted of 22% (23) patients for whom an actionable DNA alteration was not identified and an additional 14% (15) patients for whom the DNA based suggested therapy was not accessible in the clinical center. Indeed, Arm B assay explores a large number of genes (7442) and therapies (178) both targeted and chemotherapies. In 45% of patients in Arm A and 45% in Arm B, best response was Stable Disease or better based on already available data. While awaiting the final prospective efficacy data, the study already demonstrates that the innovative transcriptomic based decision support system fills a gap left by traditional DNA based targeted therapy, enabling more patients to reach a larger pool of accessible drugs and potentially benefit from precision medicine, even at a late stage of the disease, when no more clinical guidelines exist.

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“117 – Combination Therapy in Melanoma: finding biomarkers of synergistic associations using Large Scale Drug Combination Screening and integrated Omics Data Analysis”, F. Parmentier [1], A. Amzallag [2], M. Guergova-Kuras [1], M. Afshar [1], C.H. Benes [2]
[1] Ariana Pharmaceuticals, Paris, France [2] Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, USA

poster117_thumbThe drive towards precision medicine in oncology is fueling the identification and characterization of an ever-increasing number of drugs specifically targeted to altered tumor targets. The lack of complete response and the emergence of resistance in large number of patients is pushing clinicians to search for combination therapies to prevent disease progression. The ability to perform large scale omic analysis against a large number of drugs is an opportunity to develop a systematic approach for identifying optimal drug combinations for a given patient. High throughput unbiased screening of targeted drug combinations, with appropriate library selection and mechanistic follow-up, can yield to clinically actionable combination therapeutic options. In this work, we screened a library of 24 melanoma cell lines against the 5778 pair combinations of 108 anticancer drugs. The synergistic effect across cell lines was assessed for each drug pair. The cell lines were characterized using 6916 omics features, including gene expression, copy number variations and single nucleotide polymorphism. Using a machine learning approach (KEM®) we systematically extracted all logical associations between omics features and drug pair synergies. Exploring a search space of around 150 million possible relations, we constructed a knowledge base by extracting 889 181 rules of interest for 4832 drug pairs. The knowledge base enables the exhaustive identification of specific markers linked to each synergistic pair. Detailed analysis of the combination of 104 drugs with PLX4720, a precursor of vemurafenib, a wellknown BRAFV600E inhibitor widely used in melanoma treatment, is presented showing specific markers that can gate the selection of a particular drug combination. We speculate that the identification of specific biomarkers linked to specific synergies may be transformed in the future into a therapeutic decision support system, identifying optimal combination therapies for melanoma patients.

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“212 – Management of dengue hospitalizations in Brazil during and outside epidemic periods: Insights from Data Mining”, Nicolas Baurin, David Morley, Leon Ochiai, Mariana Guergova-Kuras, Mohammad Afshar, Laurent Coudeville

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P212 abstract
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ASTMH 2016 conference website
“P667: Shorter multimarker signatures: a new tool to facilitate cancer diagnosis”, M.P. Schneider, N. Jullian, M. Afshar, M. Guergova-Kuras, Ariana Pharma.

Links :

EACR-23 conference website
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“P2.26: Computational biomarker models to predict response to lung cancer treatments from combined molecular data”, Marine Le Morvan, Maria del Pilar Schneider, Mariana Guergova-Kuras, Ariana Pharma.

“LBA2: Text mining workflow for extraction of paragraphs from full articles describing drug-gene interactions to support Onco KEM software platform for personalized treatments”, Fanny Perraudeau, David Morley, Mohammad Afshar, Mariana Guergova-Kuras, Ariana Pharma.

Links :

WIN 2014 conference website
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“Which baseline characteristics influence the response to milnacipran (Joncia®) in patients with fibromyalgia?”, O Vitton, P Bunouf, F Bonfils and L Girard, Pierre Fabre Research & Development Center, Toulouse, France.

Links :

APS 2013 conference website
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“A 4 serum protein signature for the diagnosis of mild fibrosis in chronic Hepatitis C (Hepachronix study)”, B Jardin Watelet, I Bieche, M Lapalus, A Duces, E Mathieu-Dupas, S De Muynck, N Lambert, I Baxtelli-Molina, B Sallenave, E Nicolas, E Estrabaud, N Jullian, J Balicchi, M Martinot-Peignoux, O Lada, V Paradis, D Valla, P Bedossa, D Laune, P Marcellin, M Vidaud, T Asselah*.

Links :

EASL 2013 conference website

“A liver gene molecular study rational to identify serum proteins signature to predict treatment response in chronic Hepatitis C”, T Asselah, I Bieche, I Molina, A Duces, M Lapalus, S De Muynck, E Mathieu-Dupas, N Jullian, N Lambert, D Laune, E Nicolas, B Sallenave, M Blanuet, M Estrabaud, S Pawlowski, V Moulin, M Martinot-Peignoux, O Lada, D Valla, P Bedossa, B Jardin-Watelet, P Marcellin, M Vidaud.

“Liver gene expression rational to build a serum proteins signature of mild fibrosis in chronic hepatitis C”, B Jardin-Watelet, I Bieche, M Lapalus, A Duces, EM Dupas, S De Muynck, N Lambert, I Molina, B Sallenave, E Nicolas, E Estrabaud, N Jullian, J Balicchi, M Martinot-Peignoux, O Lada, V Paradis, D Valla, P Bedossa, D Laune, P Marcellin, M Vidaud, T Asselah.

“A serum protein signature predict response to pegylated interferon and ribavirin in chronic Hepatitis C”, T Asselah, I Bieche, I Baxtelli-Molina, A Ducés, M Lapalus, S De Muynck, E Mathieu-Dupas, N Jullian, N Lambert, D Laune, E Nicolas, B Sallenave, M Blanuet, E Estrabaud, S Pawlowski, V Moulin, M Martinot-Peignoux, O Lada, D Valla, P Bedossa, B Jardin-Watelet, P Marcellin, M Vidaud.

“A serum protein signature detect early fibrosis in chronic Hepatitis C”, B Jardin-Watelet, I Bieche, M Lapalus, A Ducés, E Mathieu-Dupas, S De Muynck, N Lambert, I Baxtelli-Molina, B Sallenave, E Nicolas, E Estrabaud, N Jullian, J Balicchi, M Martinot-Peignoux, O Lada, V Paradis, D Valla, P Bedossa, D Laune, P Marcellin, M Vidaud, T Asselah.

“A two serum protein signature is a stronger predictor of treatment response than IL28B genotype in non-genotype 1 virus infected patients”, N Lambert, E Mathieu-Dupas, B Jardin-Watelet, I Batxelli-Molina, A Estampes-Barthelemy, I Bièche, A Ducès, M Lapalus, S De Muynck, N Jullian , E Nicolas, B Sallenave, M Blanuet, E Estrabaud, S Pawlowski, V Moulin, M Martinot-Peignoux, O Lada, D Valla, P Bedossa, D Laune, P Marcellin, M Vidaud, T Asselah.

“A four Gene molecular signature is better than IL28B polymorphism to predict treatment response to pegylated –interferon and ribavirin in chronic hepatitis C”, T Asselah, I Bieche, B Jardin-Watelet, A Duces, M Lapalus, S De Muynck, E Dupas, N Jullian, I Molina, N Lambert, E Nicolas, B Sallenave, M Blanuet, E Estrabaud, M Martinot-Peignoux, O Lada, D Valla, P Bedossa, D Laune, P Marcellin, M Vidaud.

“A rational approach identified a 4 gene molecular signature to predict response to treatment in hepatitis C”, T Asselah, I Bièche, B Jardin-Watelet, A Ducès, M Lapalus, S De Muynck, E Dupas, N Jullian, I Molina, N Lambert, E Nicolas, B Sallenave, M Blanluet, E Estrabaud, M Martinot-Peignoux, O Lada, D Valla, P Bedossa, D Laune, P Marcellin, M Vidaud.

“Liver gene expression signature of mild fibrosis in chronic hepatitis C”, A Ducès, M Lapalus, I Bièche, B Jardin Watelet, E Dupas, S De Muynck, I Molina, N Lambert, B Sallenave, E Nicolas, E Estrabaud, N Jullian, M Martinot-Peignoux, O Lada, V Paradis, D Valla, P Bedossa, D Laune, M Vidaud, P Marcellin, T Asselah.

“Liver gene expression signature to predict response to pegylated interferon plus ribavirin in chronic hepatitis C”, A Ducès, I Bièche, B Jardin-Watelet, M Lapalus, S De Muynck, E Dupas, I Molina, N Lambert, E Nicolas, B Sallenave, E Estrabaud, N Jullian, M Martinot-Peignoux, O Lada, V Paradis, D Valla, P Bedossa, D Laune, M Vidaud, P Marcellin, T Asselah.

Publications

“Is the Efficacy of Milnacipran in Fibromyalgia Predictable? A Data-Mining Analysis of Baseline and Outcome Variables”, L Abtroun, P Bunouf, RM Gendreau and O Vitton, Clin. J. Pain32 (2016) 435–440.

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For a recent review of FCA, see Proceedings of the 10th International Conference on Formal Concept Analysis, ICFCA 2012, Leuven, Belgium, May 7-10, 2012, F Domenach, D Ignatov and J Poelmans (Eds.), Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence, 7278 (2012).

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“Human Discovery and Machine Learning”, C Dartnell, E Martin, H Hagège and J Sallantin, International Journal of Cognitive Informatics and Natural Intelligence2 (2008) 55-69.

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“Multiobjective/Multicriteria Optimization and Decision Support in Drug Discovery”, M Afshar, A Lanoue and J Sallantin, Comprehensive Medicinal Chemistry II4 (2006) 767-774.

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“A pragmatic logic of scientific discovery”, J Sallantin, C Dartnell, and M Afshar in Proceedings of the 9th International Conference on Discovery Science, DS 2006, Barcelona, Spain, Oct 7-10, 2006, N Lavrač, L Todorovski and KP Jantke (Eds.), Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence, 4265 (2006) 231-242.

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Publications from other research groups that have cited KEM. “Automating Knowledge Discovery for Toxicity Prediction Using Jumping Emerging Pattern Mining”, R Sherhod, VJ Gillet, PN Judson and JD Vessey, J. Chem. Inf. Model., 52 (2012) 3074-87.

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“The CancerCell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity”, Nature, 483 (2012) 603–607.

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“Systematic identification of genomic markers of drug sensitivity in cancer cells”, Nature, 483 (2012) 570-575.

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“Navigating the kinome”, Nature Chemical Biology, 7 (2011) 200-202.

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“Predicting new molecular targets for known drugs”, Nature, 462 (2009) 175-181.

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“A quantitative analysis of kinase inhibitor selectivity”, Nature Biotechnology, 26 (2008) 127–132.

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