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- Nachgewiesen in: USPTO Patent Grants
- Sprachen: English
- Patent Number: 10910,084
- Publication Date: February 02, 2021
- Appl. No: 14/123252
- Application Filed: March 21, 2012
- Assignees: Albert-Ludwigs-Universität Freiburg (Freiburg, DE), Northern Institute for Cancer Research (Newcastle upon Tyne, GB)
- Claim: 1. A computerized method for treating an mTOR-related tumor disease with kinase inhibitors comprising the steps of: a. selecting network profile of a biological system based on a dynamic network model of insulin-mammalian Target Of Rapamycin (mTOR) kinase, wherein said biological system comprises a biological cell, and wherein said dynamic network profile comprises a set of biochemical reactions including mTOR kinase and kinetic rate constants and initial concentrations of reactants thereof; b. parameterizing the dynamic network model by (i) experimentally modifying at least one species of said dynamic network of said biological system containing mTOR, thereby modifying the dynamic behavior and generating observed time course data of changes of the species constituting said dynamic network model, and (ii) introducing the same modification of at least one species to the dynamic network model thereby generating simulated time course data for changes of the species constituting said dynamic network model; (iii) parameterizing, using a dedicated deterministic software mathematical application tool, the dynamic network model by assigning value to at least one species of said at dynamic network model, wherein said parameterizing comprises (iv) varying the values for kinetic reaction rates and/or initial concentrations of species to produce a plurality of simulated time course data of changes in species of said dynamic network model, (v) comparing said observed time course data of changes in said species with said plurality of simulated time course data, (vi) preferentially selecting the parameter sets producing simulated time course data most similar to the observed time course data and (vii) repeating steps (iv)-(vi) until no further increase in similarity beyond a predetermined threshold is achieved; and wherein a concentration of a species in the parameterized equations can be equal to zero, thereby producing a parameterized dynamic network model; c. stratifying patients with an mTOR-related tumor disease into a plurality of patient profiles characterized by different levels of at least one species of said at least one parametrized dynamic network model, thereby producing a plurality of parameterized dynamic network models representing the different patient profile characteristics; d. (i) selecting at least one drug intervention altering at least one kinetic reaction rate within at least one of said plurality of dynamic network models, (ii) simulating the drug intervention and the resulting changes in concentrations of species over time for said plurality of dynamic network models representing said different patient profiles, (iii) determining effective patient profile-drug intervention pairs according to the extent of inhibition of the target kinase of said drug intervention and the extent of activation of other species within the network, wherein inhibition of said target kinase is considered desirable and activation of other species within the network is undesirable, and; e. wherein said drug intervention is mTOR inhibition, assigning a patient to one of said patient profiles, and administering to said patient at least one drug intervention determined effective for said patient profile in step (d) (iii), wherein said drug intervention is selected from the group consisting of PI3K inhibitors; Akt inhibitors and mTORC1 inhibitors.
- Claim: 2. The computerized method according to claim 1 , wherein said biochemical reactions are kinase reactions and said reactants and products are phosphorylated or de-phosphorylated species.
- Claim: 3. The computerized method according claim 1 , wherein the dynamic time course model output is validated experimentally.
- Claim: 4. The computerized method of claim 1 , wherein said biological cell is a tumor cell.
- Claim: 5. The computerized method of claim 1 , wherein said mTOR-related tumor disease is breast cancer.
- Claim: 6. A computerized method for treating a mammalian Target Of Rapamycin (mTOR)-related tumor disease with kinase inhibitors comprising the steps of: a. (i) selecting a set of biochemical reactions including mTOR kinase and reactions, thereby defining a model of a dynamic network comprising biochemical kinetic reaction rate equations of which at least one contains mTOR; b. parameterizing the dynamic network model by (i) modifying experimentally at least one species of said dynamic network containing mTOR, thereby modifying the dynamic behavior and generating observed time course data of changes of the species constituting said dynamic network model, and (ii) introducing the same modification of at least one species to the dynamic network model thereby generating simulated time course data for changes of the species constituting said dynamic network model, (iii) parameterizing the set of biochemical kinetic reaction rate equations by (iv) varying the values for kinetic reaction rates and/or initial concentrations of species to produce a plurality of simulated time course data of changes in species of said dynamic network model, (v) comparing said observed time course data of changes in said species with said plurality of simulated time course data, (vi) preferentially selecting the parameter sets producing simulated time course data most similar to the observed time course data and (vii) repeating steps (iv)-(vi) until no further increase in similarity beyond a predetermined threshold is achieved; and wherein a concentration of a species in the parameterized equations can be equal to zero, thereby producing a parameterized dynamic network model; c. stratifying patients with an mTOR-related tumor disease into a plurality of patient profiles characterized by different levels of at least one species of said at least one parametrized dynamic network model, thereby producing a plurality of parameterized dynamic network models representing the different patient profile characteristics; d. (i) selecting at least one drug intervention altering at least one kinetic reaction rate within at least one of said plurality of dynamic network models, (ii) simulating the drug intervention and the resulting changes in concentrations of species over time for said plurality of dynamic network models representing said different patient profiles, (iii) classifying the patient profile-drug intervention pairs according to the effectiveness of inhibition of the target of said intervention and the extent of activation of other species within the network, wherein effectiveness of said inhibition of said target is considered a positive effect and activation of other species within the network is considered an adverse effect, and e. wherein said drug intervention is mTOR inhibition, assigning a patient to one of said patient profiles, and administering to said patient at least one intervention predicted to be effective and to minimize ineffective and/or adverse effects for said patient profile in step (d) (iii), wherein said drug intervention is selected from the group consisting of EGFR inhibitors, PI3K inhibitors; Akt inhibitors, CDK4/6 inhibitors and mTORC1 inhibitors.
- Patent References Cited: 2011/0264420 October 2011 Sander ; 2858446 February 2005 ; WO 2005/111905 November 2005
- Other References: Meric-Bernstam et al. (J. Clin. Oncology (2009) vol. 27:2278-2287). cited by examiner ; Tao et al. in Biochemistry (2010) vol. 49:8488-8498. cited by examiner ; Iadevaia et al. in Cancer Research (2010) vol. 70(17):6704-6714. cited by examiner ; International Search Report dated Nov. 23, 2012 From the International Searching Authority Re. Application No. PCT/EP2012/001236. cited by applicant ; Nyman et al. “Mechanistic Explanations for Counter-Intuitive Phosphorylation Dynamics of the Insulin Receptor and Insulin Receptor Substrate-1 in Response to Insulin in Murine Adipocytes”, The FEBS Journal, XP055043904, 279(6): 987-999, Feb. 15, 2012. cited by applicant ; Ruths et al. “The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks”, PLoS Computational Biology, XP055043916, 4(2): e1000005-1-e1000005-15, Feb. 29, 2008. cited by applicant
- Primary Examiner: Clow, Lori A.
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