Metabolic phenotyping
First Claim
1. A model system, wherein the model predicts, without dosing, the post-dose response of an individual where the response is dependent on the individual'"'"'s metabolic phenotype, the model system comprising:
- a) pre-dose data of a plurality of subjects or samples thereof before dosing with a dosing substance;
b) post-dose data of the plurality of subjects or samples thereof after dosing with the dosing substance; and
c) a processor that identifies and correlates inter-subject variation in the pre-dose data with inter-subject variation in the post-dose data using a pattern recognition (PR) technique, wherein the inter-subject variation in the post-dose data corresponds to different responses of the subjects to the dosed substance, thereby producing a pre-to-post-dose predictive model,wherein the processor applies the pre-to-post-dose predictive model to the individual'"'"'s pre-dose data and classifies the individual'"'"'s post-dose response, without dosing of the individual;
wherein the pre- and/or post-dose data comprise chemical composition data and/or physical parameter data of samples and/or subjects;
wherein the pre-to-post-dose predictive model consists of one or more mathematical equations defining the relationship between pre-dose and post-dose data.
0 Assignments
0 Petitions
Accused Products
Abstract
A method of generating models with which to characterize selected aspects of the metabolic phenotype of subjects without dosing a test substance to those subjects or with which to predict, without dosing, the post-dose responses of subjects where those responses are dependent on metabolic phenotype, the method comprising: obtaining pre-dose data relating to a plurality of subjects before dosing with a dosing substance; obtaining post-dose data relating to the plurality of subjects after dosing with the dosing substance; and correlating inter-subject variation in the pre-dose data with inter-subject variation in the post-dose data, and generating a pre-to-post-dose predictive model on the basis of the observed correlation. The models may be used to determine selected aspects of the metabolic phenotype of a subject or to predict, without dosing, the post-dose responses of subjects. This is achieved by analysing data relating to the un-dosed subject in relation to a model describing the correlation of pre-dose and post-dose data relating to a plurality of subjects when dosed with a particular substance which challenges the biochemical transformation or pathway of interest; and generating, according to the predetermined criteria of the model, a numerical measure or classification describing the metabolic phenotype of the un-dosed subject.
-
Citations
10 Claims
-
1. A model system, wherein the model predicts, without dosing, the post-dose response of an individual where the response is dependent on the individual'"'"'s metabolic phenotype, the model system comprising:
-
a) pre-dose data of a plurality of subjects or samples thereof before dosing with a dosing substance; b) post-dose data of the plurality of subjects or samples thereof after dosing with the dosing substance; and c) a processor that identifies and correlates inter-subject variation in the pre-dose data with inter-subject variation in the post-dose data using a pattern recognition (PR) technique, wherein the inter-subject variation in the post-dose data corresponds to different responses of the subjects to the dosed substance, thereby producing a pre-to-post-dose predictive model, wherein the processor applies the pre-to-post-dose predictive model to the individual'"'"'s pre-dose data and classifies the individual'"'"'s post-dose response, without dosing of the individual; wherein the pre- and/or post-dose data comprise chemical composition data and/or physical parameter data of samples and/or subjects; wherein the pre-to-post-dose predictive model consists of one or more mathematical equations defining the relationship between pre-dose and post-dose data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
-
Specification