Peer-Reviewed Articles




      1. Beil, M., Moreno, R., Fronczek, J. et al. Prognosticating the outcome of intensive care in older patients—a narrative review. Ann. Intensive Care 14, 97 (2024). [PDF]
      2. Albrecht M, Kogan Y, Kulms D, Sauter T. Mechanistically Coupled PK (MCPK) Model to Describe Enzyme Induction and Occupancy Dependent DDI of Dabrafenib Metabolism. Pharmaceutics 2022, 14, 310. [PDF]
      3. Kogan Y, Robinson A, Itelman E, Bar-Nur Y, Jakobson DJ, Segal G, et al. Developing and validating a machine learning prognostic model for alerting to imminent deterioration of hospitalized patients with COVID-19. Sci Rep (2022) 12(1):19220. [PDF]
      4. Agur Z. Separating the Wheat from the Chaff: On the Importance of Machine Learning Models in the Fight Against COVID-19 and on the Necessity to Scrutinize them. Isr Med Assoc J (2022) 24(11):705-07. [PDF]
      5. Forys U, Nahshony A, Elishmereni M. Mathematical model of hormone sensitive prostate cancer treatment using leuprolide: A small step towards personalization. PLoS One (2022) 17(2):e0263648. doi: 10.1371/journal.pone.0263648. PubMed PMID: 35167616. [PDF]
      6. Gillis A, Ben Yaacov A, Agur Z. A New Method for Optimizing Sepsis Therapy by Nivolumab and Meropenem Combination: Importance of Early Intervention and CTL Reinvigoration Rate as a Response Marker. Frontiers in Immunology. 2021-March-01 2021;12(468). [PDF]
      7. Agur Z, Elishmereni M, Foryś U, Kogan Y. Accelerating the Development of Personalized Cancer Immunotherapy by Integrating Molecular Patients’ Profiles with Dynamic Mathematical Models. Clinical Pharmacology & Therapeutics 2020. doi: [PDF] 
      8. Perlstein D, Shlagman O, Kogan Y, et al. Personal response to immune checkpoint inhibitors of patients with advanced melanoma explained by a computational model of cellular immunity, tumor growth, and drug. Plos One. 2019;14(12):e0226869. [PDF]
      9. Tsur N, Kogan Y, Rehm M, Agur Z. Response of Patients with Melanoma to Immune Checkpoint Blockade – Insights Gleaned from Analysis of a New Mathematical Mechanistic Model.  Journal of Theoretical Biology 2019:110033. doi: 10.1016/j.jtbi.2019.110033. PubMed PMID: 31580835.  [PDF]
      10. Tsur N, Kogan Y, Avizov-Khodak E, Vaeth D, Vogler N, Utikal J, Lotem M, Agur Z. Predicting response to pembrolizumab in metastatic melanoma by a new personalization algorithm.  Journal of Translational Medicine 2019;17(1):338. doi: 10.1186/s12967-019-2081-2. PubMed PMID: 31590677.    [PDF]
      11. Gillis A, Beil M, Halevi-Tobias K, van Heerden PV, Sviri S, Agur Z. Alleviation of exhaustion-induced immunosuppression and sepsis by immune checkpoint blockers sequentially administered with antibiotics—analysis of a new mathematical model. J Intensive Care Medicine Experimental. 2019 7(1):32. doi: 10.1186/s40635-019-0260-3.    [PDF]
      12. Hochman G, Halevi-Tobias K, Kogan Y, Agur Z. Extracellular inhibitors can attenuate tumorigenic Wnt pathway activity in adenomatous polyposis coli mutants: Predictions of a validated mathematical model. PLoS One 2017 Jul 14;12(7):e0179888. do    [PDF]
      13. Trahtemberg U, Sviri S, Mandel M, van Heerden PV, Agur Z, Beil M. Tracheostomy as a model for studying the systemic effects of local tissue injuries and the cytokine patterns of acute inflammation: design, rationale and analysis plan. J Anaesth Intensive Care. 2016 Nov;44(6):789-790. [PDF]
      14. Agur Z, Halevi-Tobias K, Kogan Y, et al. Employing Dynamical Computational Models for Personalizing Cancer Immunotherapy. J Expert Opinion on Biological Therapy 2016 16(11) pp.1373-1385    [PDF]
      15. Forys U, Bodnar M, Kogan Y.Asymptotic dynamics of some t-periodic one-dimensional model with application to prostate cancer immunotherapy. J Math Biol 2016 73(4) pp. 867-83 doi: 10.1007/s00285-016-0978-4   (PDF)
      16. Elishmereni M, Kheifetz Y, Shukrun I, Bevan GH, Nandy D, McKenzie KM, Kohli M, Agur Z. Predicting time to castration resistance in hormone sensitive prostate cancer by a personalization algorithm based on a mechanistic model integrating patient data. Prostate 2016 76(1) pp. 48-57    [PDF]
      17. Agur, Z., Elishmereni, M., Kheifetz, Y. Personalizing oncology treatments by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics, and their integration. Wiley Interdiscip Rev Syst Biol Med 2014 6(3): pp. 239-53    [PDF]
      18. Kheifetz, Y., Elishmereni, M., Agur, Z. Complex pattern of interleukin-11-induced inflammation revealed by mathematically modeling the dynamics of C-reactive protein. J Pharmacokinet Pharmacodyn 2014 41(5) pp. 479-491., PMID: 25231819 [PDF]
      19. Agur Z. The resonance phenomenon in population persistence: can the same theory guide both national security policies and personalized medicine? Croatian medical journal 2014 55(2) pp. 93-102    [PDF]
      20. Kogan Y, Agur Z, Elishmereni M. A mathematical model for the immunotherapeutic control of the Th1/Th2 imbalance in melanoma. Discrete and Continuous Dynamics – Series B 2013, 18(4) pp. 1017-1030.doi:10.3934/dcdsb.2013.18.1017.    [PDF]
      21. Agur Z, & Vuk Pavlović S. Personalizing immunotherapy: Balancing predictability and precision. Oncoimmunology 2012, 1(8) pp.1-3.    [PDF]
      22. Shukron O, Vainstein V, Kundgen A, Germing U, Agur. Analyzing transformation of myelodysplastic syndrome to secondary acute myeloid leukemia using a large patient database. American Journal of Hematology 2012, ISSN 1096-8652.    [PDF]
      23. Kogan Y, Halevi-Tobias K, Elishmereni M, Vuk Pavlović S, Agur Z. Reconsidering the Paradigm of Cancer Immunotherapy by Computationally Aided Real-Time Personalization Cancer Res. 2012 Mar 19. 72(9) pp.2218-2227, PMID: 22422938    [PDF]
      24. Kogan Y, Halevi-Tobias KE, Hochman G, Baczmanska AK, Leyns L, Agur, Z. A new validated mathematical model of the Wnt signaling pathway predicts effective combinational therapy by sFRP and Dkk Biochem J 2012. 444 pp. 115–125 PMID; 22356261    [PDF]
      25. Vainstein V, Kirnasovsky OU, Kogan Y, Agur Z. Strategies for cancer stem cell elimination: Insights from mathematical modeling. J Theor Biol. 2012 7(298) pp.32-41.    [PDF]
      26. Agur Z, and Vuk Pavlović, S. Mathematical Modeling in Immunotherapy of Cancer: Personalizing Clinical Trials. J. Molecular Therapy 2012 20(1) pp. 1-2.    [PDF]
      27. Kronik N, Kogan Y, Schlegel PG, Wölfl M. Improving T-cell Immunotherapy for Melanoma Through a Mathematically Motivated Strategy: Efficacy in Numbers? Journal of Immunotherapy 2012 35(2) 110.1097/CJI.1090b1013e318236054c.    [PDF]
      28. Elishmereni M, Kheifetz Y, Søndergaard H, Viig R. O, Agur Z. An integrated disease/pharmacokinetic/pharmacodynamic model suggests improved interleukin-21 regimens validated prospectively for mouse solid cancers.PLoS Computational Biology 2011 7(9) e1002206    [PDF]
      29. Agur Z, Kirnasovsky OU, Vasserman G, Tencer-Hershkowicz L, Kogan Y, Harrison H, et al. Dickkopf1 regulates fate decision and drives breast cancer stem cells to differentiation: an experimentally supported mathematical model. PLoS one 2011 6(9) e24225    [PDF]
      30. Jager E, van der Velden VH, Te Marvelde JG, Walter RB, Agur Z, Vainstein V. Targeted drug delivery by gemtuzumab ozogamicin: mechanism-based mathematical model for treatment strategy improvement and therapy individualization. PLoS one 2011 6(9) e24265    [PDF]
      31. Kronik N., Kogan Y., Elishmereni M., Halevi-Tobias K., Vuk Pavlović S., Agur Z. Predicting Effect of Prostate Cancer Immunotherapy by Personalized Mathematical Models PLoS one 2010 5(12) e15482    [PDF]
      32. Agur Z. From the evolution of toxin resistance to virtual clinical trials: the role of mathematical models in oncology. Future Oncol. 2010 6 pp.917-27    [PDF]
      33. Agur Z., Kogan Y., Levi L., Harrison H., Lamb R., Kirnasovsky O.U., Clarke R.B. Disruption of a Quorum Sensing Mechanism Triggers Tumorigenesis: a Simple Discrete Model Corroborated by Experiments in Mammary Cancer Stem Cells. Biol Direct 2010 5(1) pp.20-41    [PDF]
      34. Kogan Y., Forys U., Shukron O., Kronik N., Agur Z. Cellular immunotherapy for high grade Gliomas: mathematical analysis deriving efficacious infusion rates based on patient requirements. SIAM J. Appl. Math. 2010 70(6) pp. 1953-1976    [PDF]
      35. Gorelik B., Ziv I., Shohat R., Wick M., Webb C., Hankins D., Sidransky D., Agur Z. Efficacy of once weekly docetaxel combined with bevacizumab for patients with intense angiogenesis: validation of a new theranostic method in mesenchymal chondrosarcoma xenografs. Cancer Research 2008 68(21) pp. 9033-40    [PDF]
      36. Kirnasovsky O.U., Kogan Y, Agur Z. Analysis of a mathematical model for the molecular mechanism of mammary stem cell fate decision. Mathematical Modelling of Natural Phenomena 2008 3(7) pp. 78-89    [PDF]
      37. Kirnasovsky O.U., Kogan Y., Agur Z. Resilience in Stem Cell renewal: Development of the Agur – Daniel – Ginossar Model. Disc. Cont. Dyn. Systems 2008 10 pp.129- 148    [PDF]
      38. Kogan Y., Ribba B., Dahan N., Marron K., Vainstein V., Agur Z. Intensifed Doxorubicin-Based Regimen Efficacy in Residual Non-Hodgkin’s Lymphoma Disease: Towards a Computationally Supported Treatment Improvement. Mathematical Modelling of Natural Phenomena 2007 2(3), pp. 47-68    [PDF]
      39. Kronik N., Kogan Y., Vainstein V., Agur Z. Improving alloreactive CTL immunotherapy for malignant gliomas using a simulation model of their interactive dynamics. Cancer Immunol Immunother 2007 Mar;57(3):425-439 (2008). Epub 2007 Sep 7    [PDF]
      40. Cappuccio A., Elishmereni M., and Agur Z. Optimization of Interleukin-21 Immunotherapeutic Strategies. Jour Theor. Biol 2007 248 (2) pp. 259-66.    [PDF]
      41. Kheifetz Y, Elishmereni M., Horowitz S., and Agur Z. Fluid retention side-effects of the chemotherapy-supportive treatment interleukin11: Mathematical modelling as affected by data availability. GTHM Computational and Mathematical Methods in Medicine. 7 (2-3) 2006 pp. 71-84.   [PDF]
      42. Vainstein V., Ginosar Y., Shoham M., Ianovski A., Rabinovich A., Kogan Y., Selitser V., Agur Z. Improving cancer therapy by Doxorubicin and Granulocyte colony-stimulating factor: Insights from a Computerized Model of Human Granulopoiesis. Mathematical Modelling of Natural Phenomena 2006 1(2), pp.70-80.   [PDF]
      43. Agur Z., Hassin R., Levy S. Optimizing chemotherapy scheduling using local search heuristics. Operations Research  2006; 54 (5): 829-846.   [PDF]
      44. Cappuccio A., Elishmereni M., Agur Z. Cancer immunotherapy by interleukin-21 treatment strategies evaluated in a amathematical model. Can. Res.  2006; 66 (14): 7293-300.   [PDF]
      45. Agur Z., Biomathematics in the development of personalized medicine in oncology. Future Oncol. 2006; 2 (1): 39-42.   [PDF]
      46. Forys U., Kheifetz Y., Kogan Y. Critical-point analysis for three-variable cancer angiogenesis modeling. Mathematical Biosciences and Engineering. 2005; 2 (3): 511-525.   [PDF]
      47. Kheifetz Y., KoganY. & Agur Z. Long-range predictability in models of cell populations subjected to phase-specific drugs: Growth-rate approximation using properties of positive compact operators. Mathematical Models & Methods in the Applied Sciences. 2006; 16 (7), 1-18.  [PDF]
      48. Presnov E.V. & Agur Z . The role of time-delays, slow processes and chaos in modulating the cell cycle clock . Mathematical Biosc & Engi. 2005; 2 (3): 625-542.  [PDF]
      49. Vainstein V., Ginosar Y., Shoham M., Ranmar D., Ianovski A., Agur Z. The complex effect of granulocyte on human granulopoiesis analyzed by a new physiologically-based mathematical model. Jour Theor Biol. 2005; 234 (3); 311-27.   [PDF]
      50. Ribba B., Marron K., Alarcon T., Maini P., Agur Z. A mathematical model of doxorubicin treatment efficacy for non-Hodgkin’s lymphoma: Investigation of the current protocol through theoretical modelling results Bull.Math. Biol. 2005; 67, 79-99.  [PDF]
      51. Arakelyan L., Merbl Y., Agur Z. Vessel maturation effects on tumor growth: computer model validated in implanted human ovarian carcinoma spheroids. Eur. Jour. Cancer. 2005; 41: 159-167.   [PDF]
      52. Kheifetz Y., Kogan Y., Agur Z. Matrix and compact operator description of resonance and antiresonance in cell populations subjected to phase specific drugs. Jour. Medical Informatics & Technologies. 2004; 8, MM-11 – MM-29.  [PDF]
      53. Agur Z., Arakelyan L. Daugulis P., Ginosar Y. Hopf point analysis for angiogenesis models, Discrete and Continuous Dynamics – Series B, 2004, 4(1), pp.29-38.     [PDF]
      54. Skomorovski K., Harpak H., Ianovski A., Vardi M., Visser TP., Hartong S., Van Vliet H., Wagemaker G., Agur Z. New TPO treatment schedules of increased safety and efficacy: pre clinical validation of a thrombopoiesis simulation model. Br. Jour. Haematol, 123 (4), 2003 (pp. 683-691).,    [PDF]
      55. Arakelyan L., Vainstain V., and Agur Z. A computer algorithm describing angiogenesis and vessel maturation and its use for studying the effects of anti-angiogenic and anti-maturation therapy on vascular tumor growth, Angiogenesis 5(3), 2002 (pp.203-14).     [PDF]
      56. Agur Z., Daniel Y., and Ginosar Y. The universal properties of stem cells as pinpointed by a simple discrete model. Jour. Math. Biol2002; 44 (1): 79-86.     [PDF]
      57. Stone L., Shulgin B., and Agur Z. Theoretical Examination of the Pulse Vaccination Policy in the SIR Epidemic model. Jour. Math. Comp. Modelling, 31, 2000 (pp. 207-215). [PDF]
      58. Shochat. E., Hart D., & Agur, Z. Using Computer Simulations for Evaluating The Efficacy of Breast Cancer Chemotherapy Protocols Jour. Math. Models & Methods in Applied Sciences Vol. 9 (4) 1999 (pp. 599-615). [PDF]
      59. Hart D., Shochat E., Agur Z. The growth law of primary breast cancer as inferred from mammography screening trials data. Br J Cancer 1998; 78: 382-387    [PDF]
      60. Agur Z. Resonance and anti-resonance in the design of chemotherapeutic protocols. Journal of Theoretical Medicine  1998; 1: 237 – 245.   [PDF]
      61. Shulgin B., Stone L., Agur Z. Pulse vaccination strategy in the SIR epidemic model. Journal of Theoretical Medicine  1998; 1: 237-245.Bulletin of Mathematical Biology  1998; 60: 1123-1148.   [PDF]
      62. Agur Z., Mehr R. Modelling Trypanosoma congolense parasitaemia patterns during the chronic phase of infection in N’Dama cattle. Parasite Immunol. Parasite Immunol  1997; 19: 171-82.   [PDF]
      63. Mehr R., Agur Z. Temporal stochasticity leads to nondeterministic chaos in a model for blood cell production. in: Fluctuations and Order: The New Synthesis. (M.M. Millonas ed.) New-York, Springer.  1996; 419—427 .    [PDF]
      64. Ubezio P., Tagliabue G., Schechter B., et al. Increasing 1-beta-D-arabinofuranosylcytosine efficacy by scheduled dosing intervals based on direct measurements of bone marrow cell kinetics. Cancer Res  1994; 54:6446-6451.    [PDF]
      65. Agur Z., Dvir Y. Use of knowledge on series for predicting optimal chemotherapy treatment. Random & Computational Dynamics  1994; 2(3&4): 279-286.   [PDF]
      66. Harnevo L.E., Agur Z. Use of mathematical models for understanding the dynamics of gene amplification. Mutation Research/Environmental Mutagenesis and Related Subjects.  1993; 292:17-24.    [PDF]
      67. Agur Z., Danon Y. L., Anderson R. M., Cojocaru L., and May R. M. Measles Immunization Strategies for an Epidemiologically Heterogeneous Population: The Israeli Case Study Proceedings: Biological Sciences  1993; 252(1334):81-84.    [PDF]
      68. Agur Z., Cojocaru L., Mazor G., Anderson RM., Danon YL. Pulse mass measles vaccination across age cohorts. Proceedings: Proc Natl Acad Sci U S A.  1993; 90:11698-11702.    [PDF]
      69. Mehr R., Agur Z. Bone marrow regeneration under cytotoxic drug regimens: behaviour ranging from homeostasis to unpredictability in a model for hemopoietic differentiation. Biosystems  1992;26:231-237.    [PDF]
      70. Cojocaru L., Agur Z. A theoretical analysis of interval drug dosing for cell-cycle-phase-specific drugs. Math Biosci.  1992;109:85-97.    [PDF]
      71. Agur Z., Arnon R., Schechter B. Effect of the dosing interval on myelotoxicity and survival in mice treated by cytarabine. Eur J Cancer.  1992;28A:1085-1090.    [PDF]
      72. Harnevo L.E., Agur Z. Drug resistance as a dynamic process in a model for multistep gene amplification under various levels of selection stringency. Cancer Chemother Pharmacol.  1992;30:469-476.    [PDF]
      73. Agur Z. Fixed points of majority rule cellular automata with applied to plasticity and precision of the immune system. Complex Systems.  1988;2:351-357.    [PDF]
      74. Agur Z., Mazor G., Meilijson I. Maturation of the Humoral Immune Response as an Optimization Problem. Proc Biol Sci  , 1991. 245(1313): p. 147-50.    [PDF]
      75. Harnevo L.E., Agur Z. The dynamics of gene amplification described as a multitype compartmental model and as a branching process. Math Biosci.  1991;103:115-138.    [PDF]
      76. Norel R., Agur Z. A model for the adjustment of the mitotic clock by cyclin and MPF levels. Science  1991;251:1076-1078.    [PDF]
      77. Agur Z, Abiri D, Van der Ploeg LH. Ordered appearance of antigenic variants of African trypanosomes explained in a mathematical model based on a stochastic switch process and immune-selection against putative switch intermediates. Proc Natl Acad Sci U S A.  1989;86:9626-9630.    [PDF]
      78. Agur Z., Arnon R., Schechter B. Reduction of cytotoxicity to normal tissues by new regimens of cell-cycle phase-specific drugs. Mathematical Biosciences  1988;92:1-15.    [PDF]
      79. Agur Z., Fraenkel A.S., Klein S.T. The number of fixed points of the majority rule. Discrete Mathematics  1988;70:295-302..    [PDF]
      80. Agur Z. Resilience and Variability in Pathogens and Hosts. Mathematical Medicine and Biology 1987;4:295-307    [PDF]
      81. Agur Z., Kerszberg M. The emergence of phenotypic novelties through progressive genetic change. Amer. Natur. 1987;129(6):862—875    [PDF]
      82. Agur Z., Slobodkin L. Environmental fluctuations: How do they affect the topography of the adaptive landscape? Journal of Genetics 1986;65:45-54.   [PDF]..
      83. Agur Z. Randomness, synchrony and population persistence. Journal of Theoretical Biology 1985;112:677-6930.   [PDF]
      84. Agur Z., Deneubourg J.L. The effect of environmental disturbances on the dynamics of marine intertidal populations. Theoretical Population Biology.  1985;27:75-90.   [PDF]



Chapters in Books


      1. Hochman G., Kogan Y., Vainstein V., Shukron O., Lankenau A., Boysen B., Lamb R., Berkman T., Clarke R.,Duschl D., Agur Z. “Evidence for Power Law Tumor Growth and Implications for Cancer Radiotherapy” in Mathematical modelling of cancer growth and treatment. Eds. M Bachar, J Batzel, M Chaplain Springer Lecture Notes in Mathematics Biosciences (LNMBIOS) Series, Volume 4, In press.    [PDF]
      2. Agur Z., Kheifetz Y. “Optimizing Cancer Chemotherapy: from Mathematical Theories to Clinical Treatment.” New Challenges for Cancer Systems Biomedicine. Eds. d’Onofrio, A., Cerrai, P., Gandolfi, A. Springer-Verlag GmbH 2012. 285-299.    [PDF]
      3. Agur Z. “Interactive Clinical Trial Design: A Combined Mathematical and Statistical Simulation Method for Optimizing Drug Development in Mathematical Models and Methods.” Statistical Methods in Healthcare. Eds. F. Frederick, R. Kenett, R. Fabrizio. Wiley 2012. 78-102.    [PDF] [Book cover]
      4. Hochman G., Agur Z. “Deciphering Fate Decision in Normal and Cancer Stem Cells – Mathematical Models and Their Experimental Verification”. Mathematical Models and Methods in Biomedicine. Eds. A. Friedman, E. Kashdan, U. Ledzewicz and H. Schättler. Springer 2012. 203-232.    [PDF]
      5. Agur Z, Bloch N, Gorelik B, Kleiman M, Kogan Y, Sagi Y, Sidreansky D, Ronen Y. “Developing Oncology Drugs Using Virtual Patients of Vascular Tumor Diseases.” Systems Biology in Drug Discovery and Development. Eds. Young DL, Michelson S. Wiley, 2011. 203-231.    [PDF]
      6. Ribba B, Alarcon T, Marron K, Maini PK, Agur Z,. “The use of hybrid cellular automaton models for improving cancer therapy.” In Proceedings, Cellular Automata: 6th International Conference on Cellular Automata for Research and Industry, ACRI, 2004, Amsterdam, The Netherlands, eds P.M.A. Sloot, B. Chopard, A.G. Hoekstra. Lecture Notes in Computer Science 3305: pp 444-453.   [PDF]
      7. Zvia Agur, Moran Elishmereni, Yuri Kogan, Yuri Kheiffetz, Irit Ziv, Meir Shoham, and Vladimir Vainstein. “Mathematical modeling as a new approach for improving the efficacy/toxicity profile of drugs: the thrombocytopenia case study.” Preclinical Development Handbook. Ed. Shayne Gad. John Wiley and Sons, USA. 2008, 1229-1266.   [PDF]
      8. Zvia Agur, Keren Marron, Hanita Shai, Yehuda L. Danon. “Preparing for a smallpox bioterrorist attack: pulse vaccination as an optimal strategy.” Risk Assessment and Risk Communication Strategies in Bioterrorism Preparedness. Eds. M.S. Green et al.Springer 2007. 219-229.    [PDF]
      9. Agur Z, Kheiffets Y. “Resonance and Anti-Resonance: from mathematical theory to clinical cancer treatment design.” Handbook of Cancer Models With Applications to Cancer Screening, Cancer Traetment and Risk Assessment. World Scientific, Singapore and River Edge, New Jersey, (to appear).   [PDF]
      10. Castiglione F, Selitser V, Agur Z. “Analysing Hypersensitivity to Chemotherapy in a Cellular Automata Model of the Immune System.” Cancer Modelling and Simulation. Ed. Luigi Preziosi. CRC Press, LLC, (UK), 2003.   [PDF]
      11. Arakelyan L. et al., “Multi-Scale Analysis of Angiogenic Dynamics and Therapy.” Cancer Modelling and Simulation. Ed. Luigi Preziosi. CRC Press, LLC, (UK), 2003.     [PDF]





    1. Moran Elishmereni, Yuri Kheifetz, Antonio Cappuccio, Ori Inbar, Henrik S?ndergaard, Peter Thygesen, Rune Viig Overgaard, Zvia Agur, “IL-21 Immunotherapy in Solid Cancers: Therapeutic Insights from a Preclinically Validated Mathematical PK/PD Model.” In American Association for Cancer Research, AACR, Mar 16-19, 2008 Dead Sea, Jordan, pp 95-96.   [PDF]
    2. Fatima S.F. Aerts Kaya, Trudi T.P. Visser, Simone C.C. Hartong, Zvia Agur*, Gerard Wagemaker . Eficacy of recombinant human and rhesus Thrombopoietin stimulated blood transfusions in comparison to unstimulated whole blood or thrombocyte transfusions in a non-human primate model. European Hematology Association (EHA) 13th Congress, June 12-15, 2008, Copenhagen, Denmark.    [PDF]
    3. Zvia Agur, Irit Ziv, Revital Shochat, Michael Wick, Craig Webb, David Hankins, Levon Arakelyan and David Sidransky Using a Novel Computer Technology for Tailoring Targeted and Chemotherapeutic Drug Schedules to the Individual Patient. First American Association for Cancer Research (AACR) International Conference on Molecular Diagnostics in Cancer Therapeutic Development, Sep 12-15, 2006.   [PDF]
    4. Vainstein V., Ginosar Y., Shoham M., Ianovski A., Rabinovich A., Kogan Y., Selitser V., Ariad S., Chan S., Agur Z. Clinical validation of a physiologically-based computer model of human granulopoiesis and its use for improving cancer therapy by Doxorubicin and Granulocyte colony-stimulating factor (G-CSF). 48th Annual Meeting of the American Society of Hematology, Orlando, Florida, 9-12 December 2006.   [PDF]
    5. Cappuccio A, Elishmereni M, Agur Z, Cancer Immunotherapy by Interleukin-21: Theoretical Evaluation
    6. Ziv I., Arakelyan L., Shohat R., Wick M., Webb C., Hankins D. Sidransky D., Agur Z. Novel Virtual Patient technology for predicting response of breast cancer and mesenchymal chondrosarcoma patients to mono- and combination therapy by cytotoxic and targeted drugs. 18th EORTC-NCI-AACR Symposium on “Molecular Targets and Cancer Therapeutics”, Prague Czech republic, 7-10 November 2006.   [PDF]
    7. Mukherjee A., Chan S., Arakelyan L., Samuel A., Belianina E., Ellis I., Paish C., Dahan N., Agur Z. Virtual patient (VCP) for treatment personalization: Prediction accuracy in metastatic brest cancer (MBC) oatients. NCRI Cancer conference, 2006. Birmingham, U.K., 8-11 October 2006.   [PDF]
    8. Agur Z. , Ziv I. , Shohat R. , Wick M. , Webb C. , Hankins D. , Arkelyan L. , Sidransky D., Using a novel computer technology for tailoring and chemotherapeutic drug schedules to the individual patient. AACR conference on Molecular Diagnostics in Cancer Therapeutic Development. Chicago, Illinois, 12-15 September 2006.  [PDF]
    9. Arakelyan L., Merbl Y., Vainstein V., Agur Z. A new cancer drug regimen based on the interplay between tumor growth and angiogenesis – Predictions of a mathematical model. SIAM Conference on the Life Sciences . Raleigh, North Carolina, 31July – 4 August 2006.   [PDF]
    10. Ziv I., Arkelyan L., Shohat R., Wick M., Webb C., Hankins D. Sidransky D., Agur Z. Novel virtual patient technology for personalizing single-agent and combination therapies of chemotherapeutic and targeted drugs: Validation in xenografted biopsies of mesenchymal chondrosarcoma patient. 2nd Joint American-Israeli Conference (JAICC) on “Novel Therapeutic Approaches to Cancer,” Jerusalem, Israel, 28-30 June 2006.   [PDF]
    11. Agur Z., The Resonance effect: From Mathematical theory to clinical application in cancer drug design. The American Institute of Mathematics (AIM), The Modeling of Cancer Progression and Immunotherapy. Palo Alto, California, 12-16 December, 2005.    [PDF]
    12. Harpak H, Cohen I, Ginosar Y, Ianovski A, Kogan Y, Shani M, Shoham M, Skomorovski K, Selitser V, Vainstein V & Agur Z. Using In Silico thrombopoiesis tool for identifying mechanisms of drug-induced thrombocytopenia and for defining patients of higher risk, EHO(2003).   [PDF]
    13. Arakelyan L, Vainstain V, and Agur Z. 2002. Optimizing anti-angiogenic therapy using mathematical tools. Proceedings of American Society of Clinical Oncology (ASCO), 21, p. 440a.    [PDF]
    14. Skomorovski K, Agur Z. A new method for predicting and optimizing thrombopoietin (TPO) therapeutic protocols in thrombocytopenic patients and in platelet donors. 6 th Annual Meeting Of The European Haematology Association – Frankfurt, Germany, June 2001.   [PDF]
    15. Skomorovski K, Vardi M, Harpak H, Wagemaker G. and Agur Z. Using computational approach for optimizing thrombopoietin treatment effects – proof of concept in murine experiments. Proceedings of the 43rd Annual Meeting of the American Society of Hematology, 2001. 4275a.   [PDF]
    16. Skomorovski K., Vardi M., Harpak H., Agur Z. Using ‘in silico mouse’ for predicting therapeutic protocols on thrombopoiesis. Eur. Jour. Cancer, Vol. 37 (6), 2001(p.S362). (ECCO 11 – the European Cancer Conference, 21-25 October 2001, Lisbon, Portugal).   [PDF]
    17. Arakelyan L, Vainstain V, and Agur Z. Optimizing anti-angiogenic therapy using mathematical tools. Proceedings of American Society of Clinical Oncology (ASCO),2002 21, p. 440a.
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