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Optimal dynamic regimens with artificial intelligence : The case of temozolomide

Author

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  • Nicolas Houy

    (EM - EMLyon Business School)

  • François Le Grand

Abstract

We determine an optimal protocol for temozolomide using population variability and dynamic optimization techniques inspired by artificial intelligence. We use a Pharmacokinetics/Pharmacodynamics (PK/PD) model based on Faivre and coauthors (Faivre, et al., 2013) for the pharmacokinetics of temozolomide, as well as the pharmacodynamics of its efficacy. For toxicity, which is measured by the nadir of the normalized absolute neutrophil count, we formalize the myelosuppression effect of temozolomide with the physiological model of Panetta and coauthors (Panetta, et al., 2003). We apply the model to a population with variability as given in Panetta and coauthors (Panetta, et al., 2003). Our optimization algorithm is a variant in the class of Monte-Carlo tree search algorithms. We do not impose periodicity constraint on our solution. We set the objective of tumor size minimization while not allowing more severe toxicity levels than the standard Maximum Tolerated Dose (MTD) regimen. The protocol we propose achieves higher efficacy in the sense that –compared to the usual MTD regimen– it divides the tumor size by approximately 7.66 after 336 days –the 95% confidence interval being [7.36–7.97]. The toxicity is similar to MTD. Overall, our protocol, obtained with a very flexible method, gives significant results for the present case of temozolomide and calls for further research mixing operational research or artificial intelligence and clinical research in oncology.

Suggested Citation

  • Nicolas Houy & François Le Grand, 2018. "Optimal dynamic regimens with artificial intelligence : The case of temozolomide," Post-Print hal-02312154, HAL.
  • Handle: RePEc:hal:journl:hal-02312154
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    Cited by:

    1. Nicolas Houy & François Le Grand, 2019. "Optimizing treatment combination for lymphoma using an optimization heuristic," Post-Print halshs-02386445, HAL.
    2. Nicolas Houy & François Le Grand, 2019. "Personalized oncology with artificial intelligence: The case of temozolomide," Post-Print halshs-02386458, HAL.
    3. Nicolas Houy & Julien Flaig, 2021. "Hospital-wide surveillance-based antimicrobial treatments: A Monte-Carlo look-ahead method," Post-Print halshs-03506952, HAL.
    4. Hope Murphy & Gabriel McCarthy & Hana M Dobrovolny, 2020. "Understanding the effect of measurement time on drug characterization," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-15, May.

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