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5. Dynamic changes of depolarizing GABA in a computational model of epileptogenic brain: Insight for Dravet syndrome

Author

Olivier Dulac, Polina Kurbatova, Rima Nabbout, Fabrice Wendling, Anna Rosati, Renzo Guerrini, Patrice Nony, Catherine Chiron, Anna Kaminska, Catherine Cornu, Gérard Pons, Pascal Benquet, Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Epilepsies de l'Enfant et Plasticité Cérébrale (U1129), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Pediatric Neurology Unit and Laboratories, Università degli Studi di Firenze = University of Florence (UniFI)-Children's Hospital A. Meyer, IRCCS Fondazione Stella Maris [Pisa], Evaluation et modélisation des effets thérapeutiques, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), CIC CHU Lyon (inserm), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM), CRESIM/EpiCRESIM Study Group, Leon Aarons, Corinne Alberti, Agathe Bajard, Pascal Benque t, Yves Bertrand, Frank Bretz, Daan Caudri, Charlotte Castellan, Sylvie Chabaud, Catherine Chir on, Catherine Cornu, Frank Dufour, Nathalie Eymard, Roland Fisch, Renzo Guerrini, Vinc ent Jullien, Behrouz Kassai, Polina Kurbatova, Salma Malik, Rima Nabbout, Patrice Nony, Kayode Ogungbenro, David Pérol, Gérard Pons, Anna Rosati, Harm Tiddens, Fabrice Wendling., ANR-11-INBS-0011,NeurATRIS,Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences(2011), Senhadji, Lotfi, Infrastructures - Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences - - NeurATRIS2011 - ANR-11-INBS-0011 - INBS - VALID, Laboratoire de Biométrie et Biologie Evolutive ( LBBE ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Traitement du Signal et de l'Image ( LTSI ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Epilepsies de l'Enfant et Plasticité Cérébrale ( U1129 ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Children's Hospital A. Meyer-University of Florence, Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale ( INSERM ), ANR-11-INBS-0011/11-INBS-0011,NeurATRIS,Infrastructure de Recherche Translationnelle pour les Biothérapies en Neurosciences ( 2011 ), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI)-Children's Hospital A. Meyer

Subjects
0301 basic medicine, Male, Epilepsies, Myoclonic, Synaptic Transmission, Dravet, Membrane Potentials, 0302 clinical medicine, [ SDV.IB ] Life Sciences [q-bio]/Bioengineering, EEG, SCN1A, Child, gamma-Aminobutyric Acid, Paroxysmal depolarizing shift, GABAA receptor, Chemistry, musculoskeletal, neural, and ocular physiology, Pyramidal Cells, depolarizing GABA, Brain, Electroencephalography, stiripentol, medicine.anatomical_structure, Neurology, Child, Preschool, Anticonvulsants, Female, [SDV.IB]Life Sciences [q-bio]/Bioengineering, shunting inhibition, Shunting inhibition, medicine.drug, Interneuron, Adolescent, seizure, Models, Neurological, glutamate, interneuron, Inhibitory postsynaptic potential, Article, 03 medical and health sciences, Developmental Neuroscience, Dravet syndrome, Stiripentol, medicine, Animals, Humans, Ictal, Computer Simulation, [SDV.IB] Life Sciences [q-bio]/Bioengineering, Neural Inhibition, medicine.disease, Brain Waves, NAV1.1 Voltage-Gated Sodium Channel, 030104 developmental biology, nervous system, Mutation, excitatory GABA, epilepsy, fast-onset, Neuroscience, 030217 neurology & neurosurgery
Abstract

Abnormal reemergence of depolarizing GABAA current during postnatal brain maturation may play a major role in paediatric epilepsies, Dravet syndrome (DS) being among the most severe. To study the impact of depolarizing GABA onto distinct patterns of EEG activity, we extended a neural mass model as follows: one sub-population of pyramidal cells was added as well as two sub-populations of interacting interneurons, perisomatic-projecting interneurons (basket-like) with fast synaptic kinetics GABAA (fast, I1) and dendritic-projecting interneurons with slow synaptic kinetics GABAA (slow, I2). Basket-like cells were interconnected to reproduce mutual inhibition mechanisms (I1➔I1). The firing rate of interneurons was adapted to mimic the genetic alteration of voltage gated sodium channels found in DS patients, SCN1A(+/-). We implemented the "dynamic depolarizing GABAA" mediated post-synaptic potential in the model, as some studies reported that the chloride reversal potential can switch from negative to more positive value depending on interneuron activity. The "shunting inhibition" promoted by GABAA receptor activation was also implemented. We found that increasing the proportion of depolarizing GABAA mediated IPSP (I1➔I1 and I1➔P) only (i.e., other parameters left unchanged) was sufficient to sequentially switch the EEG activity from background to (1) interictal isolated polymorphic epileptic spikes, (2) fast onset activity, (3) seizure like activity and (4) seizure termination. The interictal and ictal EEG patterns observed in 4 DS patients were reproduced by the model via tuning the amount of depolarizing GABAA postsynaptic potential. Finally, we implemented the modes of action of benzodiazepines and stiripentol, two drugs recommended in DS. Both drugs blocked seizure-like activity, partially and dose-dependently when applied separately, completely and with a synergic effect when combined, as has been observed in DS patients. This computational modeling study constitutes an innovative approach to better define the role of depolarizing GABA in infantile onset epilepsy and opens the way for new therapeutic hypotheses, especially in Dravet syndrome.

Published
2016

6. Physiologically based pharmacokinetic modelling of methotrexate and 6-mercaptopurine in adults and children. Part 1: methotrexate

Author

Kayode, Ogungbenro, Leon, Aarons, and Polina, Kurbatova

Subjects
Antimetabolites, Antineoplastic, Physiologically based pharmacokinetic modelling, Adolescent, medicine.drug_class, Administration, Oral, Pharmacology, Models, Biological, Antimetabolite, Young Adult, Pharmacokinetics, Reference Values, medicine, Humans, Distribution (pharmacology), Computer Simulation, Tissue Distribution, Child, Kidney, medicine.diagnostic_test, business.industry, Infant, Newborn, Infant, Precursor Cell Lymphoblastic Leukemia-Lymphoma, Mercaptopurine, Methotrexate, medicine.anatomical_structure, Therapeutic drug monitoring, Child, Preschool, Administration, Intravenous, business, medicine.drug
Abstract

Methotrexate is an antimetabolite and antifolate drug that is widely used in the treatment of malignancies and auto-immune disorders. In childhood acute lymphoblastic leukaemia, methotrexate is often combined with 6-mercaptopurine and both of them have been shown to be very effective for maintenance of remission. Large variability in the pharmacokinetics of methotrexate has led to increasing use of therapeutic drug monitoring in its clinical use to identify patients with high risk of toxicity and optimise clinical outcome. A physiologically based pharmacokinetic model was developed for methotrexate for oral and intravenous dosing and adults and paediatric use. The model has compartments for stomach, gut lumen, enterocyte, gut tissue, spleen, liver vascular, liver tissue, gall bladder, systemic plasma, red blood cells, kidney vascular, kidney tissue, skin, bone marrow, thymus, muscle and rest of body. A mechanistic model was also developed for the kidney to account for renal clearance of methotrexate via filtration and secretion. Variability on system and drug specific parameters was incorporated in the model to reflect observed clinical data and assuming the same pathways in adults and children, age-dependent changes in body size, organ volumes and plasma flows, the model was scaled to children. The model was developed successfully for adults and parameters such as net secretion clearance, biliary transit time and red blood cell distribution and binding parameters were estimated from published adult profiles. A relationship between fraction absorbed and dose using reported mean bioavailability data in the literature was also established. The model also incorporates non-linear binding in some tissues that has been described in the literature. Predictions using this model provide adequate description of observed plasma concentration data in adults and children. The model can be used to predict plasma and tissue concentrations of methotrexate following intravenous and oral dosing in adults and children and therefore help to improve clinical outcome.

Published
2014

7. A methodological framework for drug development in rare diseases

Author

Polina Kurbatova, Charlotte Castellan, Salma Malik, Patrice Nony, Agathe Bajard, Behrouz Kassai, Nathalie Eymard, Sylvie Chabaud, Catherine Cornu, Vitaly Volpert, CCSD, Accord Elsevier, Evaluation et modélisation des effets thérapeutiques, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Unité de Biostatistique et d'Evaluation des Thérapeutiques (UBET), Centre Léon Bérard [Lyon], CIC CHU Lyon (inserm), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM), Multi-scale modelling of cell dynamics : application to hematopoiesis (DRACULA), Centre de génétique et de physiologie moléculaire et cellulaire (CGPhiMC), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Camille Jordan [Villeurbanne] (ICJ), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), Modélisation mathématique, calcul scientifique (MMCS), Institut Camille Jordan [Villeurbanne] (ICJ), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Camille Jordan (ICJ), Institut Camille Jordan (ICJ), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Inria Grenoble - Rhône-Alpes, Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Research design, medicine.medical_specialty, Orphan Drug Production, Computer science, education, Drug development, Translational research, Review, Pharmacology, Clinical trial simulation, law.invention, Orphan drug, 03 medical and health sciences, Rare Diseases, 0302 clinical medicine, Randomized controlled trial, law, medicine, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Humans, Genetics(clinical), Pharmacology (medical), Medical physics, 030212 general & internal medicine, [MATH.MATH-AP] Mathematics [math]/Analysis of PDEs [math.AP], Genetics (clinical), 030304 developmental biology, Medicine(all), Clinical Trials as Topic, 0303 health sciences, business.industry, General Medicine, 3. Good health, Integrative modeling, Clinical trial, Research Design, Personalized medicine, business
Abstract

Introduction Developing orphan drugs is challenging because of their severity and the requisite for effective drugs. The small number of patients does not allow conducting adequately powered randomized controlled trials (RCTs). There is a need to develop high quality, ethically investigated, and appropriately authorized medicines, without subjecting patients to unnecessary trials. Aims and Objectives The main aim is to develop generalizable framework for choosing the best-performing drug/endpoint/design combinations in orphan drug development using an in silico modeling and trial simulation approach. The two main objectives were (i) to provide a global strategy for each disease to identify the most relevant drugs to be evaluated in specific patients during phase III RCTs, (ii) and select the best design for each drug to be used in future RCTs. Methodological approach In silico phase III RCT simulation will be used to find the optimal trial design and was carried out in two steps: (i) statistical analysis of available clinical databases and (ii) integrative modeling that combines mathematical models for diseases with pharmacokinetic-pharmacodynamics models for the selected drug candidates. Conclusion There is a need to speed up the process of orphan drug development, develop new methods for translational research and personalized medicine, and contribute to European Medicines Agency guidelines. The approach presented here offers many perspectives in clinical trial conception.

Published
2014

8. An in silico approach helped to identify the best experimental design, population, and outcome for future randomized clinical trials

Author

Behrouz Kassai, Salma Malik, Polina Kurbatova, A.C. Castellan, Patrice Nony, Nathalie Eymard, Agathe Bajard, Vitaly Volpert, Catherine Cornu, and Sylvie Chabaud

Subjects
medicine.medical_specialty, Epidemiology, Migraine Disorders, Population, Crossover, Statistical power, law.invention, 03 medical and health sciences, 0302 clinical medicine, Randomized controlled trial, law, Econometrics, Medicine, Humans, Medical physics, Computer Simulation, 030212 general & internal medicine, education, Randomized Controlled Trials as Topic, education.field_of_study, Cross-Over Studies, business.industry, Sumatriptan, Crossover study, Clinical trial, Sample size determination, Research Design, Personalized medicine, business, 030217 neurology & neurosurgery, Forecasting
Abstract

Objectives The main objective of our work was to compare different randomized clinical trial (RCT) experimental designs in terms of power, accuracy of the estimation of treatment effect, and number of patients receiving active treatment using in silico simulations. Study Design and Setting A virtual population of patients was simulated and randomized in potential clinical trials. Treatment effect was modeled using a dose–effect relation for quantitative or qualitative outcomes. Different experimental designs were considered, and performances between designs were compared. One thousand clinical trials were simulated for each design based on an example of modeled disease. Results According to simulation results, the number of patients needed to reach 80% power was 50 for crossover, 60 for parallel or randomized withdrawal, 65 for drop the loser (DL), and 70 for early escape or play the winner (PW). For a given sample size, each design had its own advantage: low duration (parallel, early escape), high statistical power and precision (crossover), and higher number of patients receiving the active treatment (PW and DL). Conclusion Our approach can help to identify the best experimental design, population, and outcome for future RCTs. This may be particularly useful for drug development in rare diseases, theragnostic approaches, or personalized medicine.

Published
2014

9. PP270—Computational modeling of dravet syndrome

Author

Renzo Guerrini, Pierre Jean Octave Nony, Rima Nabbout, Catherine Cornu, Catherine Chiron, Anna Kaminska, Polina Kurbatova, Gérard Pons, Fabrice Wendling, Pascal Benquet, Olivier Dulac, Laboratoire de Biométrie et Biologie Evolutive ( LBBE ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Traitement du Signal et de l'Image ( LTSI ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Service de neurologie pédiatrique [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Épilepsie de l'enfant et plasticité cérébrale ( Inserm U663 ), Institut National de la Santé et de la Recherche Médicale-Université Paris Descartes - Paris 5 ( UPD5 ), Department of Pediatric Neurology, National Referral Center for Rare Epilepsies, CHU Necker - Enfants Malades [AP-HP], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), Evaluation et modélisation des effets thérapeutiques, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Épilepsie de l'enfant et plasticité cérébrale (Inserm U663), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), National Referral Center for Rare Epilepsies - Department of Pediatric Neurology, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Necker - Enfants Malades [AP-HP]

Subjects
Clomipramine, CYP2D6, Pregabalin, 02 engineering and technology, 030204 cardiovascular system & hematology, 03 medical and health sciences, 020210 optoelectronics & photonics, 0302 clinical medicine, Dravet syndrome, Pharmacokinetics, 0202 electrical engineering, electronic engineering, information engineering, Medicine, Clorazepate, Pharmacology (medical), Trough Concentration, [ SDV.IB ] Life Sciences [q-bio]/Bioengineering, ComputingMilieux_MISCELLANEOUS, Pharmacology, business.industry, Plasma levels, medicine.disease, 3. Good health, Anesthesia, [SDV.IB]Life Sciences [q-bio]/Bioengineering, business, medicine.drug
Abstract

e102 Volume 35 Number 8S clorazepate (20mg 2× /d), and pregabalin (100 mg 3× /d). Because of resurgence of severe anxio-depressive symptoms, without any change of the treatment, the patient was readmitted 2 months later. Despite increasing the dose of clomipramine up to 225 mg/d, there was no clinical improvement, and the patient finally attempted to her life by abusing drugs. She then improved after 2 weeks on clomipramine IV (50 mg/d). Compliance was estimated good and no pharmacokinetic interactions with the rest of the treatment were found. C and DC plasma levels were measured, and CYP2D6/CYP2C19 genotype analyzed. Results: The plasma levels of C and DC are given in the Table below. Measures were done at the steady state and at trough concentration for IV treatment and 10 hours after the last dose for oral treatment.

Published
2013

10. Experimental designs for small randomised clinical trials: an algorithm for choice

Author

Catherine, Cornu, Behrouz, Kassai, Roland, Fisch, Catherine, Chiron, Corinne, Alberti, Renzo, Guerrini, Anna, Rosati, Gerard, Pons, Harm, Tiddens, Sylvie, Chabaud, Daan, Caudri, Clément, Ballot, Polina, Kurbatova, Anne-Charlotte, Castellan, Agathe, Bajard, Patrice, Nony, Leon, Aarons, Yves, Bertrand, Frank, Bretz, Charlotte, Castellan, Frank, Dufour, Cornelia, Dunger-Baldauf, Jean-Marc, Dupont, Vincent, Jullien, Behrouz, Kassaï, Kayode, Ogungbenro, David, Pérol, Gérard, Pons, Rima, Nabbout, Service de Pharmacologie Clinique, CHU Lyon, Evaluation et modélisation des effets thérapeutiques, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), CIC CHU Lyon (inserm), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM), Senior Expert Statistical Methodologist, Novartis Pharma AG, Epilepsies de l'Enfant et Plasticité Cérébrale (U1129), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Unité d'Epidémiologie Clinique, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Robert Debré-Université Paris Diderot - Paris 7 (UPD7), Pediatric Neurology Unit and Laboratories, Children's Hospital A. Meyer-University of Florence, Erasmus University Medical Center [Rotterdam] (Erasmus MC), Unité de Biostatistique et d'Evaluation des Thérapeutiques (UBET), Centre Léon Bérard [Lyon], CRESim was funded by the ERA-NET PRIOMEDCHILD Joint Call in 2010., the CRESim & Epi-CRESim Project Groups, Pediatrics, BMC, Ed., Laboratoire de Biométrie et Biologie Evolutive ( LBBE ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Epilepsies de l'Enfant et Plasticité Cérébrale ( U1129 ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Assistance publique - Hôpitaux de Paris (AP-HP)-Hôpital Robert Debré-Université Paris Diderot - Paris 7 ( UPD7 ), Erasmus University Medical Center, Sophia Children's Hospital, and Unité de Biostatistique et d'Evaluation des Thérapeutiques ( UBET )

Subjects
Research design, Computer science, MEDLINE, Review, [SDV.GEN] Life Sciences [q-bio]/Genetics, law.invention, Orphan drug, 03 medical and health sciences, 0302 clinical medicine, Randomized controlled trial, law, Humans, media_common.cataloged_instance, Genetics(clinical), Pharmacology (medical), European Union, 030212 general & internal medicine, European union, Genetics (clinical), Randomized Controlled Trials as Topic, media_common, Medicine(all), [SDV.GEN]Life Sciences [q-bio]/Genetics, Cross-Over Studies, General Medicine, Crossover study, 3. Good health, Clinical trial, Identification (information), Research Design, [ SDV.GEN ] Life Sciences [q-bio]/Genetics, Algorithm, Algorithms, 030217 neurology & neurosurgery
Abstract

International audience; BACKGROUND: Small clinical trials are necessary when there are difficulties in recruiting enough patients for conventional frequentist statistical analyses to provide an appropriate answer. These trials are often necessary for the study of rare diseases as well as specific study populations e.g. children. It has been estimated that there are between 6,000 and 8,000 rare diseases that cover a broad range of diseases and patients. In the European Union these diseases affect up to 30 million people, with about 50% of those affected being children. Therapies for treating these rare diseases need their efficacy and safety evaluated but due to the small number of potential trial participants, a standard randomised controlled trial is often not feasible. There are a number of alternative trial designs to the usual parallel group design, each of which offers specific advantages, but they also have specific limitations. Thus the choice of the most appropriate design is not simple. METHODS: PubMed was searched to identify publications about the characteristics of different trial designs that can be used in randomised, comparative small clinical trials. In addition, the contents tables from 11 journals were hand-searched. An algorithm was developed using decision nodes based on the characteristics of the identified trial designs. RESULTS: We identified 75 publications that reported the characteristics of 12 randomised, comparative trial designs that can be used in for the evaluation of therapies in orphan diseases. The main characteristics and the advantages and limitations of these designs were summarised and used to develop an algorithm that may be used to help select an appropriate design for a given clinical situation. We used examples from publications of given disease-treatment-outcome situations, in which the investigators had used a particular trial design, to illustrate the use of the algorithm for the identification of possible alternative designs. CONCLUSIONS: The algorithm that we propose could be a useful tool for the choice of an appropriate trial design in the development of orphan drugs for a given disease-treatment-outcome situation.

Published
2013

11. Hybrid model of erythropoiesis

Author

Polina Kurbatova, Nathalie Eymard, and Vitaly Volpert

Subjects
Applied Mathematics, Cell, General Medicine, Biology, Models, Biological, General Biochemistry, Genetics and Molecular Biology, Cell biology, Philosophy, Red blood cell, medicine.anatomical_structure, Immunology, medicine, Erythropoiesis, Cell Lineage, Bone marrow, General Agricultural and Biological Sciences, Hybrid model, General Environmental Science
Abstract

A hybrid model of cell dynamics is presented. It is illustrated by model examples and applied to study erythropoiesis (red blood cell production). In this approach, cells are considered as discrete objects while intra-cellular proteins and extra-cellular biochemical substances are described with continuous models. Spatial organization of erythropoiesis occurring in specific structures of the bone marrow, called erythroblastic island, is investigated.

Published
2012

12. Asymptotic-numerical analysis of the diffusion- discrete absorption equation

Author

Polina Kurbatova, Grigory Panasenko, Vitaly Volpert, Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Institut Camille Jordan (ICJ), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), Multi-scale modelling of cell dynamics : application to hematopoiesis (DRACULA), Centre de génétique et de physiologie moléculaire et cellulaire (CGPhiMC), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Camille Jordan (ICJ), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Institut Camille Jordan [Villeurbanne] (ICJ), Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Camille Jordan [Villeurbanne] (ICJ), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL)

Subjects
Partial differential equation, Diffusion equation, Differential equation, General Mathematics, 010102 general mathematics, Mathematical analysis, General Engineering, First-order partial differential equation, Summation equation, 01 natural sciences, Burgers' equation, 010101 applied mathematics, Riccati equation, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Fokker–Planck equation, 0101 mathematics, ComputingMilieux_MISCELLANEOUS, Mathematics
Abstract

The diffusion-discrete absorption (DDA) equation is considered. This equation contains the standard diffusion term and the discrete sorption expressed by a sum of large number of δ-functions with the support at a non-uniform mesh multiplied by the unknown function (concentration). The main result of the paper is the homogenization (continualization) of this equation when the small parameter is the characteristic step h of the mesh. The error estimates are proved for the difference of the exact solution of the DDA equation and the solution of the homogenized differential equation. Copyright © 2012 John Wiley & Sons, Ltd.

Published
2012

13. Modelling Erythroblastic Islands: Using a Hybrid Model to Assess the Function of Central Macrophage

Author

Polina Kurbatova, Olivier Gandrillon, Nikolai Bessonov, Stephan Fischer, Fabien Crauste, Vitaly Volpert, COMputational BIology and data miNING (COMBINING), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Multi-scale modelling of cell dynamics : application to hematopoiesis (DRACULA), Institut Camille Jordan [Villeurbanne] (ICJ), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de génétique et de physiologie moléculaire et cellulaire (CGPhiMC), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institute of Mechanical Engineering Problems [St. Petersburg] (IPME), Russian Academy of Sciences [Moscow] (RAS), Centre de génétique et de physiologie moléculaire et cellulaire (CGPhiMC), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), ANR-09-JCJC-0100,ProCell(2009), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Camille Jordan (ICJ), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), and Institut Camille Jordan (ICJ)

Subjects
Erythroblasts, Macrophage, MESH: Feedback, Physiological, medicine.medical_treatment, Cell Communication, Quantitative Biology - Quantitative Methods, 0302 clinical medicine, Cell Behavior (q-bio.CB), Erythropoiesis, MESH: Erythroblasts, Quantitative Methods (q-bio.QM), Erythroblastic islands, Feedback, Physiological, 0303 health sciences, MESH: Bone Marrow Cells, Applied Mathematics, General Medicine, Cell biology, medicine.anatomical_structure, Biological Physics (physics.bio-ph), 030220 oncology & carcinogenesis, Modeling and Simulation, General Agricultural and Biological Sciences, Intracellular, Hybrid model, Statistics and Probability, FOS: Physical sciences, Bone Marrow Cells, Biology, Models, Biological, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, MESH: Cell Communication, medicine, Extracellular, Humans, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Physics - Biological Physics, Progenitor cell, 030304 developmental biology, MESH: Humans, General Immunology and Microbiology, Macrophages, Growth factor, MESH: Erythropoiesis, MESH: Models, Biological, MESH: Macrophages, Spindle apparatus, FOS: Biological sciences, Quantitative Biology - Cell Behavior, Bone marrow
Abstract

International audience; The production and regulation of red blood cells, erythropoiesis, occurs in the bone marrow where erythroid cells proliferate and differentiate within particular structures, called erythroblastic islands. A typical structure of these islands consists of a macrophage (white cell) surrounded by immature erythroid cells (progenitors), with more mature cells on the periphery of the island, ready to leave the bone marrow and enter the bloodstream. A hybrid model, coupling a continuous model (ordinary differential equations) describing intracellular regulation through competition of two key proteins, to a discrete spatial model describing cell-cell interactions, with growth factor diffusion in the medium described by a continuous model (partial differential equations), is proposed to investigate the role of the central macrophage in normal erythropoiesis. Intracellular competition of the two proteins leads the erythroid cell to either proliferation, differentiation, or death by apoptosis. This approach allows considering spatial aspects of erythropoiesis, involved for instance in the occurrence of cellular interactions or the access to external factors, as well as dynamics of intracellular and extracellular scales of this complex cellular process, accounting for stochasticity in cell cycle durations and orientation of the mitotic spindle. The analysis of the model shows a strong effect of the central macrophage on the stability of an erythroblastic island, when assuming the macrophage releases pro-survival cytokines. Even though it is not clear whether or not erythroblastic island stability must be required, investigation of the model concludes that stability improves responsiveness of the model, hence stressing out the potential relevance of the central macrophage in normal erythropoiesis.

Published
2011

14. Application of Hybrid Models to Blood Cell Production in the Bone Marrow

Author

Vitaly Volpert, Nick Bessonov, Fabien Crauste, Polina Kurbatova, Stephan Fischer, Institute of Mechanical Engineering Problems [St. Petersburg] (IPME), Russian Academy of Sciences [Moscow] (RAS), Multi-scale modelling of cell dynamics : application to hematopoiesis (DRACULA), Centre de génétique et de physiologie moléculaire et cellulaire (CGPhiMC), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Camille Jordan [Villeurbanne] (ICJ), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS), Institut Camille Jordan [Villeurbanne] (ICJ), Artificial Evolution and Computational Biology (BEAGLE), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), ANR grant ANR-09-JCJC- 0100-01, Volpert, Vitaly, Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Camille Jordan (ICJ), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), Institut Camille Jordan (ICJ), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes

Subjects
Bioinformatics, modèle hybride, regulatory mechanism, hybrid models, erythropoiesis, Biology, 01 natural sciences, Blood cell, 03 medical and health sciences, medicine, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Equations aux dérivées partielles, 0101 mathematics, 030304 developmental biology, 0303 health sciences, moelle osseuse, Applied Mathematics, Analysis of PDEs, érythropoièse, mécanisme de régulation, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Cell biology, 010101 applied mathematics, Red blood cell, medicine.anatomical_structure, Apoptosis, Modeling and Simulation, Immunology, Normal erythropoiesis, Bio-informatique, Erythropoiesis, Bone marrow, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Hybrid model
Abstract

International audience; A hybrid model of red blood cell production, where cells are considered as discrete objects while intra-cellular proteins and extra-cellular biochemical substances are described with continuous models, is proposed. Spatial organization and regulation of red blood cell production (erythropoiesis) are investigated. Normal erythropoiesis is simulated in two dimensions, and the influence on the output of the model of some parameters involved in cell fate (differentiation, self-renewal, and death by apoptosis) is studied.

Published
2011

15. HYBRID MODEL OF ERYTHROPOIESIS AND LEUKEMIA TREATMENT WITH CYTOSINE ARABINOSIDE

Author

Samuel Bernard, Fabien Crauste, Stephan Fischer, Vitaly Volpert, Polina Kurbatova, Ivan Demin, Nikolai Bessonov, Charles Dumontet, Institut Camille Jordan (ICJ), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), Multi-scale modelling of cell dynamics : application to hematopoiesis (DRACULA), Centre de génétique et de physiologie moléculaire et cellulaire (CGPhiMC), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Camille Jordan (ICJ), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Institute of Mechanical Engineering Problems [St. Petersburg] (IPME), Russian Academy of Sciences [Moscow] (RAS), Novartis Pharma AG, Oncogénèse et progression tumorale, Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM), COMputational BIology and data miNING (COMBINING), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), INRIA project-team 'Dracula', ANR project Mecamerge, ANR project Anatools, ANR-06-BLAN-0327,Mecamerge,Emergence de structures dans les systèmes à interactions locales(2006), Institut Camille Jordan [Villeurbanne] (ICJ), Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de génétique et de physiologie moléculaire et cellulaire (CGPhiMC), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Inria Grenoble - Rhône-Alpes, Kurbatova, Polina, Programme 'blanc' - Emergence de structures dans les systèmes à interactions locales - - Mecamerge2006 - ANR-06-BLAN-0327 - BLANC - VALID, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Camille Jordan [Villeurbanne] (ICJ)

Subjects
Cellular differentiation, [PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], Population, 92C30, 92C37, 92C50, 70F40, 68U20, 35Q70, 35Q92, 03 medical and health sciences, leukemia treatment, 0302 clinical medicine, regulatory networks, Extracellular, medicine, education, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], 030304 developmental biology, 0303 health sciences, education.field_of_study, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [PHYS.PHYS.PHYS-BIO-PH] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph], chronotherapy, Chemistry, Applied Mathematics, medicine.disease, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, Cell biology, Leukemia, Red blood cell, medicine.anatomical_structure, 030220 oncology & carcinogenesis, Erythropoiesis, cell cycle, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, Bone marrow, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Intracellular, Hybrid model
Abstract

International audience; A hybrid model of cell population dynamics, where cells are discrete elements whose dynamics depend on continuous intracellular and extracellular processes, is developed to simulate the evolution of immature red blood cells in the bone marrow. Cell differentiation, self-renewal or apoptosis are determined by an intracellular network, based on two proteins, Erk and Fas, and described by ordinary differential equations, and by local extracellular regulation performed by Fas- ligand, a protein produced by mature cells whose concentration evolution is represented by a partial differential equation. The model is used to study normal and leukemic red blood cell production (erythropoiesis), and treatment of leukemia. Normal cells are assumed to have a circadian rhythm that influences their cell cycle progression, whereas leukemic cells, are assumed to escape circadian rhythms. We consider a treatment based on periodic administration of Ara-C, an anti-cancer agent targeting cells in DNA synthesis. A detailed pharmacodynamic/pharmacokinetic model of Ara-C is proposed and used to simulate the treatment. Influence of the period of the treatment and the day delivery time on the outcome of the treatment is investigated and stress the relevance of considering chronotherapeutic treatments to treat leukemia.

Published
2011

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