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Step-by-step comparison of ordinary differential equation and agent-based approaches to pharmacokinetic-pharmacodynamic models.
Truong, Van Thuy; Baverel, Paul G; Lythe, Grant D; Vicini, Paolo; Yates, James W T; Dubois, Vincent F S.
Afiliación
  • Truong VT; Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Cambridge, UK.
  • Baverel PG; Department of Applied Mathematics, University of Leeds, Leeds, UK.
  • Lythe GD; Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Cambridge, UK.
  • Vicini P; Roche Pharma Research and Early Development, Clinical Pharmacology, Pharmaceutical Sciences, Roche Innovation Center Basel F. Hoffmann-La Roche Ltd., Basel, Switzerland.
  • Yates JWT; Department of Applied Mathematics, University of Leeds, Leeds, UK.
  • Dubois VFS; Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Cambridge, UK.
CPT Pharmacometrics Syst Pharmacol ; 11(2): 133-148, 2022 02.
Article en En | MEDLINE | ID: mdl-34399036
ABSTRACT
Mathematical models in oncology aid in the design of drugs and understanding of their mechanisms of action by simulation of drug biodistribution, drug effects, and interaction between tumor and healthy cells. The traditional approach in pharmacometrics is to develop and validate ordinary differential equation models to quantify trends at the population level. In this approach, time-course of biological measurements is modeled continuously, assuming a homogenous population. Another approach, agent-based models, focuses on the behavior and fate of biological entities at the individual level, which subsequently could be summarized to reflect the population level. Heterogeneous cell populations and discrete events are simulated, and spatial distribution can be incorporated. In this tutorial, an agent-based model is presented and compared to an ordinary differential equation model for a tumor efficacy model inhibiting the pERK pathway. We highlight strengths, weaknesses, and opportunities of each approach.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Modelos Teóricos / Neoplasias Límite: Humans Idioma: En Revista: CPT Pharmacometrics Syst Pharmacol Año: 2022 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Modelos Teóricos / Neoplasias Límite: Humans Idioma: En Revista: CPT Pharmacometrics Syst Pharmacol Año: 2022 Tipo del documento: Article País de afiliación: Reino Unido