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Open AccessArticle
MCMC Methods for Parameter Estimation in ODE Systems for CAR-T Cell Cancer Therapy
by
Elia Antonini
Elia Antonini
Elia Antonini is an intern in the department of Data Science Product Management at the Cilag GmbH & [...]
Elia Antonini is an intern in the department of Data Science Product Management at the Cilag GmbH International,Johnson & Johnson Family of Companies in Switzerland. He completed his Master studies in Mathematics at the University of Pisa and was awarded the highest grade, 110 cum laude. His interests include Monte Carlo Methods, ODEs, Marko chain, and neural networks, but also theoretical math like elliptic curves and algebraic geometry.
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,
Gang Mu
Gang Mu
Dr. Gang Mu is Director of Data Science Product Management at the Cilag GmbH International,Johnson & [...]
Dr. Gang Mu is Director of Data Science Product Management at the Cilag GmbH International,Johnson & Johnson Family of Companies in Switzerland. He completed his PhD studies in Mathematics at Tongji University. He led projects for collaboration with CERN, ETH, MIT, Harvard etc. from the Industry side. He has published papers in various journals and serves as a president for Swiss Network for Mathematics in Industry, as well as for the InnoSussie Project. His interests include Monte Carlo methods, backward stochastic equations, healthcare economics, etc.
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,
Sara Sansaloni-Pastor
Sara Sansaloni-Pastor
Dr Sara Sansaloni-Pastor is a Postdoc at the Research and Discovery Quality department from Actelion [...]
Dr Sara Sansaloni-Pastor is a Postdoc at the Research and Discovery Quality department from Actelion Pharmaceuticals Ltd. She completed her PhD studies in Sciences (Pharmaceutical technology) at University of Geneve, Switzerland. Her teaching disciplines include pharmaceutical formulation and galenic.
2
,
Vishal Varma
Vishal Varma
Dr. Vishal Varma is the Director of Data Science at Johnson & Johnson Inc. He completed his PhD in a [...]
Dr. Vishal Varma is the Director of Data Science at Johnson & Johnson Inc. He completed his PhD studies in Chemical Engineering at Purdue University West Lafayette, IN, in 2005. He has been awarded numerous professional accolades throughout his career. He recently led several significant projects in the field of data science and has published numerous papers in various reputable journals. He serves as a key member and advisor for multiple professional societies and research projects. His disciplines include stochastic process modeling, mathematical programming, and optimization techniques, and his research interests encompass a broad range of topics including metaheuristic optimization, actuarial risk modeling, and supply chain modeling.
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and
Ryme Kabak
Ryme Kabak
Ms. Ryme Kabak is a Senior Data Scientist at Johnson & Johnson. She completed her master's degree in [...]
Ms. Ryme Kabak is a Senior Data Scientist at Johnson & Johnson. She completed her master's degree in Business Analytics at the Massachusetts Institute of Technology (MIT). Ryme is deeply passionate about harnessing AI's potential to revolutionize the healthcare industry and enhance patient safety. She has been recognized for her contributions to healthcare data analytics and machine learning. Her disciplines include healthcare data analytics and machine learning applications, reflecting her broad interest in the intersections of healthcare and AI.
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1
Cilag GmbH International, 6300 Zug, Switzerland
2
Actelion Pharmaceuticals Ltd., 4123 Allschwil, Switzerland
3
Johnson & Johnson World Headqtrs US, Bridgewater, NJ 08807, USA
*
Author to whom correspondence should be addressed.
Cancers 2024, 16(18), 3132; https://doi.org/10.3390/cancers16183132 (registering DOI)
Submission received: 24 July 2024
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Revised: 6 September 2024
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Accepted: 9 September 2024
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Published: 11 September 2024
Simple Summary
Chimeric antigen receptor (CAR)-T cell therapy is a promising treatment for highly resistant blood cancers, using genetically modified T cells from the patient or a donor. While CAR-T therapy has been successful in pre-clinical and clinical stages for cancer treatment, it also presents challenges, including cytokine release syndrome. To improve the efficacy and reduce side effects, there is a need to better understand CAR-T cell behavior. We aimed to develop a mathematical framework that describes CAR-T behavior using ordinary differential equations (ODEs) and Bayesian parameter estimation (using advanced algorithms including Metropolis–Hastings, DEMetropolis, and DEMetropolisZ). This model will help to understand CAR-T behavior and, by extension, help to improve the effectiveness and efficacy of therapy in a clinical setting.
Abstract
Chimeric antigen receptor (CAR)-T cell therapy represents a breakthrough in treating resistant hematologic cancers. It is based on genetically modifying T cells transferred from the patient or a donor. Although its implementation has increased over the last few years, CAR-T has many challenges to be addressed, for instance, the associated severe toxicities, such as cytokine release syndrome. To model CAR-T cell dynamics, focusing on their proliferation and cytotoxic activity, we developed a mathematical framework using ordinary differential equations (ODEs) with Bayesian parameter estimation. Bayesian statistics were used to estimate model parameters through Monte Carlo integration, Bayesian inference, and Markov chain Monte Carlo (MCMC) methods. This paper explores MCMC methods, including the Metropolis–Hastings algorithm and DEMetropolis and DEMetropolisZ algorithms, which integrate differential evolution to enhance convergence rates. The theoretical findings and algorithms were validated using Python and Jupyter Notebooks. A real medical dataset of CAR-T cell therapy was analyzed, employing optimization algorithms to fit the mathematical model to the data, with the PyMC library facilitating Bayesian analysis. The results demonstrated that our model accurately captured the key dynamics of CAR-T cell therapy. This conclusion underscores the potential of parameter estimation to improve the understanding and effectiveness of CAR-T cell therapy in clinical settings.
Share and Cite
MDPI and ACS Style
Antonini, E.; Mu, G.; Sansaloni-Pastor, S.; Varma, V.; Kabak, R.
MCMC Methods for Parameter Estimation in ODE Systems for CAR-T Cell Cancer Therapy. Cancers 2024, 16, 3132.
https://doi.org/10.3390/cancers16183132
AMA Style
Antonini E, Mu G, Sansaloni-Pastor S, Varma V, Kabak R.
MCMC Methods for Parameter Estimation in ODE Systems for CAR-T Cell Cancer Therapy. Cancers. 2024; 16(18):3132.
https://doi.org/10.3390/cancers16183132
Chicago/Turabian Style
Antonini, Elia, Gang Mu, Sara Sansaloni-Pastor, Vishal Varma, and Ryme Kabak.
2024. "MCMC Methods for Parameter Estimation in ODE Systems for CAR-T Cell Cancer Therapy" Cancers 16, no. 18: 3132.
https://doi.org/10.3390/cancers16183132
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