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Interrogating and Quantifying In Vitro Cancer Drug Pharmacodynamics via Agent-Based and Bayesian Monte Carlo Modelling

The effectiveness of chemotherapy in cancer cell regression is often limited by drug resistance, toxicity, and neoplasia heterogeneity. However, due to the significant complexities entailed by the many cancer growth processes, predicting the impact of interference and symmetry-breaking mechanisms is...

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Autores principales: Demetriades, Marios, Zivanovic, Marko, Hadjicharalambous, Myrianthi, Ioannou, Eleftherios, Ljujic, Biljana, Vucicevic, Ksenija, Ivosevic, Zeljko, Dagovic, Aleksandar, Milivojevic, Nevena, Kokkinos, Odysseas, Bauer, Roman, Vavourakis, Vasileios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9029523/
https://www.ncbi.nlm.nih.gov/pubmed/35456583
http://dx.doi.org/10.3390/pharmaceutics14040749
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author Demetriades, Marios
Zivanovic, Marko
Hadjicharalambous, Myrianthi
Ioannou, Eleftherios
Ljujic, Biljana
Vucicevic, Ksenija
Ivosevic, Zeljko
Dagovic, Aleksandar
Milivojevic, Nevena
Kokkinos, Odysseas
Bauer, Roman
Vavourakis, Vasileios
author_facet Demetriades, Marios
Zivanovic, Marko
Hadjicharalambous, Myrianthi
Ioannou, Eleftherios
Ljujic, Biljana
Vucicevic, Ksenija
Ivosevic, Zeljko
Dagovic, Aleksandar
Milivojevic, Nevena
Kokkinos, Odysseas
Bauer, Roman
Vavourakis, Vasileios
author_sort Demetriades, Marios
collection PubMed
description The effectiveness of chemotherapy in cancer cell regression is often limited by drug resistance, toxicity, and neoplasia heterogeneity. However, due to the significant complexities entailed by the many cancer growth processes, predicting the impact of interference and symmetry-breaking mechanisms is a difficult problem. To quantify and understand more about cancer drug pharmacodynamics, we combine in vitro with in silico cancer models. The anti-proliferative action of selected cytostatics is interrogated on human colorectal and breast adenocarcinoma cells, while an agent-based computational model is employed to reproduce experiments and shed light on the main therapeutic mechanisms of each chemotherapeutic agent. Multiple drug administration scenarios on each cancer cell line are simulated by varying the drug concentration, while a Bayesian-based method for model parameter optimisation is employed. Our proposed procedure of combining in vitro cancer drug screening with an in silico agent-based model successfully reproduces the impact of chemotherapeutic drugs in cancer growth behaviour, while the mechanisms of action of each drug are characterised through model-derived probabilities of cell apoptosis and division. We suggest that our approach could form the basis for the prospective generation of experimentally-derived and model-optimised pharmacological variables towards personalised cancer therapy.
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spelling pubmed-90295232022-04-23 Interrogating and Quantifying In Vitro Cancer Drug Pharmacodynamics via Agent-Based and Bayesian Monte Carlo Modelling Demetriades, Marios Zivanovic, Marko Hadjicharalambous, Myrianthi Ioannou, Eleftherios Ljujic, Biljana Vucicevic, Ksenija Ivosevic, Zeljko Dagovic, Aleksandar Milivojevic, Nevena Kokkinos, Odysseas Bauer, Roman Vavourakis, Vasileios Pharmaceutics Article The effectiveness of chemotherapy in cancer cell regression is often limited by drug resistance, toxicity, and neoplasia heterogeneity. However, due to the significant complexities entailed by the many cancer growth processes, predicting the impact of interference and symmetry-breaking mechanisms is a difficult problem. To quantify and understand more about cancer drug pharmacodynamics, we combine in vitro with in silico cancer models. The anti-proliferative action of selected cytostatics is interrogated on human colorectal and breast adenocarcinoma cells, while an agent-based computational model is employed to reproduce experiments and shed light on the main therapeutic mechanisms of each chemotherapeutic agent. Multiple drug administration scenarios on each cancer cell line are simulated by varying the drug concentration, while a Bayesian-based method for model parameter optimisation is employed. Our proposed procedure of combining in vitro cancer drug screening with an in silico agent-based model successfully reproduces the impact of chemotherapeutic drugs in cancer growth behaviour, while the mechanisms of action of each drug are characterised through model-derived probabilities of cell apoptosis and division. We suggest that our approach could form the basis for the prospective generation of experimentally-derived and model-optimised pharmacological variables towards personalised cancer therapy. MDPI 2022-03-30 /pmc/articles/PMC9029523/ /pubmed/35456583 http://dx.doi.org/10.3390/pharmaceutics14040749 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Demetriades, Marios
Zivanovic, Marko
Hadjicharalambous, Myrianthi
Ioannou, Eleftherios
Ljujic, Biljana
Vucicevic, Ksenija
Ivosevic, Zeljko
Dagovic, Aleksandar
Milivojevic, Nevena
Kokkinos, Odysseas
Bauer, Roman
Vavourakis, Vasileios
Interrogating and Quantifying In Vitro Cancer Drug Pharmacodynamics via Agent-Based and Bayesian Monte Carlo Modelling
title Interrogating and Quantifying In Vitro Cancer Drug Pharmacodynamics via Agent-Based and Bayesian Monte Carlo Modelling
title_full Interrogating and Quantifying In Vitro Cancer Drug Pharmacodynamics via Agent-Based and Bayesian Monte Carlo Modelling
title_fullStr Interrogating and Quantifying In Vitro Cancer Drug Pharmacodynamics via Agent-Based and Bayesian Monte Carlo Modelling
title_full_unstemmed Interrogating and Quantifying In Vitro Cancer Drug Pharmacodynamics via Agent-Based and Bayesian Monte Carlo Modelling
title_short Interrogating and Quantifying In Vitro Cancer Drug Pharmacodynamics via Agent-Based and Bayesian Monte Carlo Modelling
title_sort interrogating and quantifying in vitro cancer drug pharmacodynamics via agent-based and bayesian monte carlo modelling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9029523/
https://www.ncbi.nlm.nih.gov/pubmed/35456583
http://dx.doi.org/10.3390/pharmaceutics14040749
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