Evidence map›Paper›PMID 32548108›Full record

ArticleFrontiers in bioengineering and biotechnology2020

An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments.

Fabrizio Angaroni, Alex Graudenzi, Marco Rossignolo, Davide Maspero, Tommaso Calarco, Rocco Piazza, Simone Montangero, Marco Antoniotti

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Treatment of evolving cancers will require dynamic decision support.Annals of oncology : official journal of the European Society for Medical Oncology · 2023
    Review
  7. Article
  8. Review
  9. Optimal control for colistin dosage selection.Journal of pharmacokinetics and pharmacodynamics · 2021
    Article
  10. Review
  11. Control Theory and Cancer Chemotherapy: How They Interact.Frontiers in bioengineering and biotechnology · 2020
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Fabrizio AngaroniDepartment of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy.
Alex GraudenziDepartment of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy.
Marco RossignoloCenter for Integrated Quantum Science and Technologies, Institute for Quantum Optics, Universitat Ulm, Ulm, Germany.
Davide MasperoDepartment of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy.
Tommaso CalarcoForschungszentrum Jülich, Institute of Quantum Control (PGI-8), Jülich, Germany.
Rocco PiazzaDepartment of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.
Simone MontangeroIstituto Nazionale di Fisica Nucleare (INFN), Padova, Italy.
Marco AntoniottiDepartment of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the key challenges in current cancer research is the development of computational strategies to support clinicians in the identification of successful personalized treatments. Control theory might be an effective approach to this end, as proven by the long-established application to therapy design and testing. In this respect, we here introduce the Control Theory for Therapy Design (CT4TD) framework, which employs optimal control theory on patient-specific pharmacokinetics (PK) and pharmacodynamics (PD) models, to deliver optimized therapeutic strategies. The definition of personalized PK/PD models allows to explicitly consider the physiological heterogeneity of individuals and to adapt the therapy accordingly, as opposed to standard clinical practices. CT4TD can be used in two distinct scenarios. At the time of the diagnosis, CT4TD allows to set optimized personalized administration strategies, aimed at reaching selected target drug concentrations, while minimizing the costs in terms of toxicity and adverse effects. Moreover, if longitudinal data on patients under treatment are available, our approach allows to adjust the ongoing therapy, by relying on simplified models of cancer population dynamics, with the goal of minimizing or controlling the tumor burden. CT4TD is highly scalable, as it employs the efficient dCRAB/RedCRAB optimization algorithm, and the results are robust, as proven by extensive tests on synthetic data. Furthermore, the theoretical framework is general, and it might be applied to any therapy for which a PK/PD model can be estimated, and for any kind of administration and cost. As a proof of principle, we present the application of CT4TD to Imatinib administration in Chronic Myeloid leukemia, in which we adopt a simplified model of cancer population dynamics. In particular, we show that the optimized therapeutic strategies are diversified among patients, and display improvements with respect to the current standard regime.

Indexed as

chronic myeloid leukemiaoptimal control theorypersonalized therapypharmacodynamicspharmacokineticsRedCRAB

Identifiers

PMID32548108
PMCPMC7270334

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.