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公开(公告)号:EP4177902A1
公开(公告)日:2023-05-10
申请号:EP21206570.0
申请日:2021-11-04
发明人: SZINNAI, Gabor , KOCH, Gilbert , PFISTER, Marc , SCHROPP, Johannes , STEFFENS, Britta , BACHMANN, Freya
摘要: The present invention relates to a method for automatically determining an optimal individual dosing regimen of at least one drug for a patient suffering from a known disease, the optimal individual dosing regimen being optionally subject to at least one clinical constraint, wherein the method comprises the steps of: providing a mathematical model adapted to model a progression of said disease and an effect of the at least one drug on the progression of the disease, the model comprising individual model parameters associated with the patient; utilizing an empirical Bayesian estimation to automatically and numerically estimate the individual model parameters of the mathematical model utilizing patient data associated with the patient; automatically calculating an optimal individual dosing regimen for the mathematical model by solving an optimal control problem based on a desired progression of the disease, the estimated individual model parameters, and an initial guess for the dosing regimen; and adjusting the optimal individual dosing regimen to optionally account for at least one clinical constraint to yield the optimal individual dosing regimen subject to said at least one clinical constraint.
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公开(公告)号:EP4427233A1
公开(公告)日:2024-09-11
申请号:EP22813974.7
申请日:2022-11-04
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3.
公开(公告)号:EP4177902A8
公开(公告)日:2024-05-29
申请号:EP21206570.0
申请日:2021-11-04
发明人: SZINNAI, Gabor , KOCH, Gilbert , PFISTER, Marc , SCHROPP, Johannes , STEFFENS, Britta , BACHMANN, Freya
摘要: The present invention relates to a method for automatically determining an optimal individual dosing regimen of at least one drug for a patient suffering from a known disease, the optimal individual dosing regimen being optionally subject to at least one clinical constraint, wherein the method comprises the steps of: providing a mathematical model adapted to model a progression of said disease and an effect of the at least one drug on the progression of the disease, the model comprising individual model parameters associated with the patient; utilizing an empirical Bayesian estimation to automatically and numerically estimate the individual model parameters of the mathematical model utilizing patient data associated with the patient; automatically calculating an optimal individual dosing regimen for the mathematical model by solving an optimal control problem based on a desired progression of the disease, the estimated individual model parameters, and an initial guess for the dosing regimen; and adjusting the optimal individual dosing regimen to optionally account for at least one clinical constraint to yield the optimal individual dosing regimen subject to said at least one clinical constraint.
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