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Accelerating the Development of Personalized Cancer Immunotherapy by Integrating Molecular Patients' Profiles with Dynamic Mathematical Models.
Agur, Zvia; Elishmereni, Moran; Forys, Urszula; Kogan, Yuri.
Afiliação
  • Agur Z; Institute for Medical Biomathematics (IMBM), Bene Ataroth, Israel.
  • Elishmereni M; Institute for Medical Biomathematics (IMBM), Bene Ataroth, Israel.
  • Forys U; Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland.
  • Kogan Y; Institute for Medical Biomathematics (IMBM), Bene Ataroth, Israel.
Clin Pharmacol Ther ; 108(3): 515-527, 2020 09.
Article em En | MEDLINE | ID: mdl-32535891
We review the evolution, achievements, and limitations of the current paradigm shift in medicine, from the "one-size-fits-all" model to "Precision Medicine." Precision, or personalized, medicine-tailoring the medical treatment to the personal characteristics of each patient-engages advanced statistical methods to evaluate the relationships between static patient profiling (e.g., genomic and proteomic), and a simple clinically motivated output (e.g., yes/no responder). Today, precision medicine technologies that have facilitated groundbreaking advances in oncology, notably in cancer immunotherapy, are approaching the limits of their potential, mainly due to the scarcity of methods for integrating genomic, proteomic and clinical patient information. A different approach to treatment personalization involves methodologies focusing on the dynamic interactions in the patient-disease-drug system, as portrayed in mathematical modeling. Achievements of this scientific approach, in the form of algorithms for predicting personal disease dynamics in individual patients under immunotherapeutic drugs, are reviewed as well. The contribution of the dynamic approaches to precision medicine is limited, at present, due to insufficient applicability and validation. Yet, the time is ripe for amalgamating together these two approaches, for maximizing their joint potential to personalize and improve cancer immunotherapy. We suggest the roadmap toward achieving this goal, technologically, and urge clinicians, pharmacologists, and computational biologists to join forces along the pharmaco-clinical track of this development.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biomarcadores Tumorais / Técnicas de Apoio para a Decisão / Técnicas de Diagnóstico Molecular / Medicina de Precisão / Antineoplásicos Imunológicos / Inibidores de Checkpoint Imunológico / Imunoterapia / Modelos Teóricos / Neoplasias Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biomarcadores Tumorais / Técnicas de Apoio para a Decisão / Técnicas de Diagnóstico Molecular / Medicina de Precisão / Antineoplásicos Imunológicos / Inibidores de Checkpoint Imunológico / Imunoterapia / Modelos Teóricos / Neoplasias Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article