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US 2026/0301880 A1Published application
Nose-to-brain physiologically based pharmacokinetic model
OwnerWilliam Ganleyinventor
Life of this patent
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Abstract
The present disclosure relates to the building and use of physiologically-based pharmacokinetic models for predicting absorption/distribution of a nasally administered compound of interest.
Claims67
A computer-implemented method of generating a trained nose-to-brain physiologically based pharmacokinetic (PBPK) model of delivery of a drug to brain tissue via nasal inhalation, the method comprising:
- (a) generating a multi-compartment PBPK model comprising a plurality of separate body compartments;
- (b) adding to the multi-compartment PBPK model a brain compartment and a nasal cavity compartment comprising one or more separate nasal sub-compartments, thereby generating an initial nose-to-brain PBPK model;
- (c) parameterizing the initial nose-to-brain PBPK model to include data for a plurality of physiological parameters for the plurality of separate body compartments and/or one or more or all of the separate nasal sub-compartments;
- (d) parameterizing the initial nose-to-brain PBPK model to include compound-specific parameters for a drug with known pharmacokinetic data for each body compartment and each nasal sub-compartment, to generate a parameterized nose-to-brain PBPK model;
- (e) running the parameterized nose-to-brain model and generating a report comprising drug concentration data for one or more of the body compartments and/or one or more of the separate nasal sub-compartments at one or more time points;
- and
- (f) analyzing and comparing the drug concentration data in the report with the known pharmacokinetic data for the drug, and updating the parameterized nose-to-brain model to obtain a trained nose-to-brain model.
4 more elements
A system comprising:
- a memory to store instructions that are executable;
- and
- one or more processing devices coupled to the memory, wherein the one or more processing devices are configured to execute the instructions to perform operations comprising:
- (a) generating a multi-compartment PBPK model comprising a plurality of separate body compartments;
- (b) adding to the multi-compartment PBPK model a brain compartment and a nasal cavity compartment comprising one or more separate nasal sub-compartments, thereby generating an initial nose-to-brain PBPK model;
- (c) parameterizing the initial nose-to-brain PBPK model to include data for a plurality of physiological parameters for the plurality of separate body compartments and/or one or more or all of the separate nasal sub-compartments;
- (d) parameterizing the initial nose-to-brain PBPK model to include compound-specific parameters for a drug with known pharmacokinetic data for each body compartment and each nasal sub-compartment, to generate a parameterized nose-to-brain PBPK model;
- (e) running the parameterized nose-to-brain model and generating a report comprising drug concentration data for one or more of the body compartments and/or one or more of the separate nasal sub-compartments at one or more time points;
- and
- (f) analyzing and comparing the drug concentration data in the report with the known pharmacokinetic data for the drug, and updating the parameterized nose-to-brain model to obtain a trained nose-to-brain model.
7 more elements
One or more non-transitory machine-readable storage media storing instructions that are executed to perform operations comprising:
- (a) generating a multi-compartment PBPK model comprising a plurality of separate body compartments;
- (b) adding to the multi-compartment PBPK model a brain compartment and a nasal cavity compartment comprising one or more separate nasal sub-compartments, thereby generating an initial nose-to-brain PBPK model;
- (c) parameterizing the initial nose-to-brain PBPK model to include data for a plurality of physiological parameters for the plurality of separate body compartments and/or one or more or all of the separate nasal sub-compartments;
- (d) parameterizing the initial nose-to-brain PBPK model to include compound-specific parameters for a drug with known pharmacokinetic data for each body compartment and each nasal sub-compartment, to generate a parameterized nose-to-brain PBPK model;
- (e) running the parameterized nose-to-brain model and generating a report comprising drug concentration data for one or more of the body compartments and/or one or more of the separate nasal sub-compartments at one or more time points;
- and
- (f) analyzing and comparing the drug concentration data in the report with the known pharmacokinetic data for the drug, and updating the parameterized nose-to-brain model to obtain a trained nose-to-brain model.
4 more elements
A trained nose-to-brain physiologically based pharmacokinetic (PBPK) model of delivery of a drug to brain tissue via nasal inhalation, wherein the trained nose-to-brain PBPK model is (1) encoded on one or more non-transitory machine-readable storage media and is (2) generated by operations comprising:
- (a) generating a multi-compartment PBPK model comprising a plurality of separate body compartments;
- (b) adding to the multi-compartment PBPK model a brain compartment and a nasal cavity compartment comprising one or more separate nasal sub-compartments, thereby generating an initial nose-to-brain PBPK model;
- (c) parameterizing the initial nose-to-brain PBPK model to include data for a plurality of physiological parameters for the plurality of separate body compartments and/or one or more or all of the separate nasal sub-compartments;
- (d) parameterizing the initial nose-to-brain PBPK model to include compound-specific parameters for a drug with known pharmacokinetic data for each body compartment and each nasal sub-compartment, to generate a parameterized nose-to-brain PBPK model;
- (e) running the parameterized nose-to-brain model and generating a report comprising drug concentration data for one or more of the body compartments and/or one or more of the separate nasal sub-compartments at one or more time points;
- and
- (f) analyzing and comparing the drug concentration data in the report with the known pharmacokinetic data for the drug, and updating the parameterized nose-to-brain model to obtain a trained nose-to-brain model.
4 more elements
Dependencies are inferred from the claim text.