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US 2021/0327553 A1Published application
Prediction of adverse drug reaction based on machine-learned models using protein function scores and clinical factors
AssigneeCipherome, Inc.
InventorsIn Gu LEE, Min Sang KIM
Life of this patent
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Abstract
The present disclosure predicts adverse reaction to drugs based on individual genetic and clinical information. The system receives as an input to the system gene sequence information and clinical information for a subject, and determines one or more scores (e.g., protein function score, clinical factor score, drug safety score) based on that information, where the scores can indicate subject's risk of having the adverse drug reaction. The system provides a representation of the prediction and/or information about the associated phenotype for display on a user interface in a client device (e.g., a physician's device).
Claims48
A method for treating a subject based on prediction of an adverse reaction to a drug, comprising the steps of:
- receiving, by a prediction system, clinical information of the subject related to a plurality of clinical factors (cj);
- for each of the clinical factors (cj), determining, by the prediction system, a clinical factor score (Scj) based on the clinical information;
- receiving, by the prediction system, individual gene sequence information of the subject;
- receiving, by the prediction system, information about a plurality of proteins, wherein each of the proteins is related to pharmacokinetics or pharmacodynamics of the drug;
- for each of the genes (gk) encoding the proteins,
- determining, by the prediction system, a gene sequence variation score (vj,k) for each gene sequence variation of the gene (gk) for the subject by using the individual gene sequence information;
- and
- calculating, by the prediction system, an individual protein function score (Fgk) associated with the protein by using Equation 2, wherein
- Equation 2 is:
- F
- gk
-
- (
- v
- 1
- ,
- …
- ,
- v
- n
- k
- )
- =
- (
- ∏
- j
- =
- 1
- n
- k
-
-
- v
- j
- ,
- k
- b
- j
- ,
- k
- )
- 1
- ∑
- j
- =
- 1
- n
- k
-
- b
- j
- ,
- k
- wherein Fgk is the individual protein function score of the protein encoded by the gene gk, nk is the number of sequence variations of the gene gk, vj,k is a gene sequence variation score of an jth gene sequence variation for the gene gk, and bj,k is a weighting assigned to the vj,k;
- and
- determining, by the prediction system, a drug safety score (DSS) by using Equation 7, wherein Equation 7 is:
- ln
-
- (
- DSS
- 1
- -
- DSS
- )
- =
- B
- 0
- +
- W
- g
-
- 1
-
- F
- g
-
- 1
- +
- W
- g
-
- 2
-
- F
- g
-
- 2
- +
- …W
- gm
-
- F
- gm
- +
- W
- c
-
- 1
-
- S
- c
-
- 1
- +
- W
- c
-
- 2
-
- S
- c
-
- 2
- +
- …W
- cp
-
- S
- cp
- wherein B0 is an intercept, Wgk is a weighting assigned to each protein function score Fgk, Wci is a weighting assigned to each clinical factor score Sci;
- and
- treating the subject with the drug, if the DSS compared to a threshold indicates a low risk of the adverse reaction, and treating the subject with an alternative drug, if the DSS compared to a threshold indicates a high risk of the adverse reaction.
119 more elements
A method for treating a subject based on prediction of an adverse reaction to a drug, comprising the steps of:
- receiving, by a prediction system, individual gene sequence information of the subject;
- receiving, by the prediction system, information about a protein, wherein the protein is related to pharmacokinetics or pharmacodynamics of the drug, and a gene (g) encoding the protein;
- determining, by the prediction system, a gene sequence variation score (v) for each of a gene sequence variation of the gene (g) for the subject by using the individual gene sequence information;
- calculating, by the prediction system, an individual protein function score associated with the protein by using Equation 2, wherein
- Equation 2 is:
- F
- g
-
- (
- v
- 1
- ,
- …
- ,
- v
- n
- )
- =
- (
- ∏
- i
- =
- 1
- n
-
-
- v
- i
- b
- i
- )
- 1
- ∑
- i
- =
- 1
- n
-
- b
- i
- wherein Fg is the individual protein function score of the protein encoded by the gene g, n is the number of sequence variations of the gene g, vi is a gene sequence variation score of an ith gene sequence variation, and bi is a weighting assigned to the gene sequence variation score vi of the ith gene sequence variation of the gene g, and
- wherein the weighting (bi) assigned to the gene sequence variation score vi is determined by:
- obtaining training data including a plurality of training instances for a particular protein, each training instance including a predetermined protein function score for the particular protein and a set of gene sequence variation scores for the particular protein,
- determining a loss function indicating a difference between the predetermined protein function scores and estimated protein function scores, an estimated protein function score for a training data instance generated by applying Equation 2 to the set of gene sequence variation scores for the training data instance, and
- reducing the loss function to determine the weightings assigned to the gene sequence variation scores;
- predicting, by the prediction system, likelihood of the adverse reaction to the drug based on the individual protein function score compared to a threshold;
- and
- treating the subject with the drug, if the prediction step indicates low likelihood of the adverse reaction, and treating the subject with an alternative drug, if the prediction step indicates high likelihood of the adverse reaction.
45 more elements
A system for predicting an adverse drug reaction of a subject to a drug, the system comprising:
- a processor;
- a computer readable storage medium for storing modules executable by a processor, the modules comprising:
- a communication module configured to receive clinical information of the subject related to a plurality of clinical factors (cj), individual gene sequence information for the subject and a plurality of proteins, wherein each of the proteins is related to pharmacokinetics or pharmacodynamics of the drug;
- an analysis module configured to:
- determine a clinical factor score (Sccj) for each of the clinical factors (cj),
- for each of the genes (gk) encoding the proteins, determine a gene sequence variation score (vj,k) for each of a gene sequence variation the gene (gk) for the subject by using the individual gene sequence information,
- calculate an individual protein function score (Fgk) associated with the protein by using Equation 2, wherein
- Equation 2 is:
- F
- gk
-
- (
- v
- 1
- ,
- …
- ,
- v
- n
- k
- )
- =
- (
- ∏
- j
- =
- 1
- n
- k
-
-
- v
- j
- ,
- k
- b
- j
- ,
- k
- )
- 1
- ∑
- j
- =
- 1
- n
- k
-
- b
- j
- ,
- k
- wherein Fgk is the individual protein function score of the protein encoded by the gene gk, nk is the number of sequence variations of the gene gk, vj,k is a gene sequence variation score of an jth gene sequence variation for the gene gk, and bj,k is a weighting assigned to the vj,k,
- determine a drug safety score (DSS) by using Equation 7, wherein Equation 7 is:
- ln
-
- (
- DSS
- 1
- -
- DSS
- )
- =
- B
- 0
- +
- W
- g
-
- 1
-
- F
- g
-
- 1
- +
- W
- g
-
- 2
-
- F
- g
-
- 2
- +
- …W
- gm
-
- F
- gm
- +
- W
- c
-
- 1
-
- S
- c
-
- 1
- +
- W
- c
-
- 2
-
- S
- c
-
- 2
- +
- …W
- cp
-
- S
- cp
- wherein B0 is an intercept, Wgk is a weighting assigned to each protein function score Fgk, Wci is a weighting assigned to each clinical factor score Sci, and
- predict the adverse drug reaction of the subject using the drug safety score (DSS);
- and
- an interface generation module configured to provide for display in a user interface on a client device a representation of the prediction for use in treatment of the subject.
118 more elements
A system for predicting an adverse drug reaction of a subject to a drug, the system comprising:
- a processor;
- a computer readable storage medium for storing modules executable by a processor, the modules comprising:
- a communication module configured to receive individual gene sequence information of a subject and information about a protein, wherein the protein is related to pharmacokinetics or pharmacodynamics of the drug, and a gene (g) encoding the protein;
- an analysis module configured to:
- determine a gene sequence variation score (v) for each of a gene sequence variation of the gene (g) for the subject by using the individual gene sequence information,
- calculate an individual protein function score associated with the protein by using Equation 2, wherein
- Equation 2 is:
- F
- g
-
- (
- v
- 1
- ,
- …
- ,
- v
- n
- )
- =
- (
- ∏
- i
- =
- 1
- n
-
-
- v
- i
- b
- i
- )
- 1
- ∑
- i
- =
- 1
- n
-
- b
- i
- wherein Fg is the individual protein function score of the protein encoded by the gene g, n is the number of sequence variations of the gene g, vi is a gene sequence variation score of an ith gene sequence variation, and bi is a weighting assigned to the gene sequence variation score vi of the ith gene sequence variation,
- wherein the weighting (bi) assigned to the gene sequence variation score vi is determined by:
- obtaining training data including a plurality of training instances for a particular protein, each training instance including a predetermined protein function score for the particular protein and a set of gene sequence variation scores for the particular protein,
- determining a loss function indicating a difference between the predetermined protein function scores and estimated protein function scores, an estimated protein function score for a training data instance generated by applying Equation 2 to the set of gene sequence variation scores for the training data instance, and
- reducing the loss function to determine the weightings assigned to the gene sequence variation scores, and
- predict the adverse reaction to the drug based on the individual protein function score;
- and
- an interface generation module configured to provide for display in a user interface on a client device a representation of the prediction for use in treatment of the subject.
47 more elements
A computer-readable medium comprising an execution module for executing a processor that performs an operation of predicting an adverse reaction of a subject to a drug, comprising the steps of:
- receiving clinical information of the subject related to a plurality of clinical factors (cj);
- for each of the clinical factors (cj), determining a clinical factor score (Scj) based on the clinical information;
- receiving individual gene sequence information of the subject;
- receiving information about a plurality of proteins, wherein each of the proteins is related to pharmacokinetics or pharmacodynamics of the drug;
- for each of the genes (gk) encoding the proteins,
- determining a gene sequence variation score (vj,k) for each of a gene sequence variation of the gene (gk) for the subject by using the individual gene sequence information;
- and
- calculating an individual protein function score (Fgk) associated with the protein by using Equation 2, wherein
- Equation 2 is:
- F
- gk
-
- (
- v
- 1
- ,
- …
- ,
- v
- n
- k
- )
- =
- (
- ∏
- j
- =
- 1
- n
- k
-
-
- v
- j
- ,
- k
- b
- j
- ,
- k
- )
- 1
- ∑
- j
- =
- 1
- n
- k
-
- b
- j
- ,
- k
- wherein Fgk is the individual protein function score of the protein encoded by the gene gk, nk is the number of sequence variations of the gene gk, vj,k is a gene sequence variation score of an jth gene sequence variation for the gene gk, and bj,k is a weighting assigned to the vj,k;
- and
- determining a drug safety score (DSS) by using Equation 7, wherein Equation 7 is:
- ln
-
- (
- DSS
- 1
- -
- DSS
- )
- =
- B
- 0
- +
- W
- g
-
- 1
-
- F
- g
-
- 1
- +
- W
- g
-
- 2
-
- F
- g
-
- 2
- +
- …W
- gm
-
- F
- gm
- +
- W
- c
-
- 1
-
- S
- c
-
- 1
- +
- W
- c
-
- 2
-
- S
- c
-
- 2
- +
- …W
- cp
-
- S
- cp
- wherein B0 is an intercept, Wgk is a weighting assigned to each protein function score Fgk, Wci is a weighting assigned to each clinical factor score Sci;
- and
- predict the adverse drug reaction of the subject using the drug safety score (DSS);
- and
- providing for display in a user interface on a client device a representation of the prediction for use in treatment of the subject.
121 more elements
A computer-readable medium comprising an execution module for executing a processor that performs an operation of predicting an adverse reaction of a subject to a drug, comprising the steps of:
- receiving individual gene sequence information of the subject;
- receiving information about a protein, wherein the protein is related to pharmacokinetics or pharmacodynamics of the drug, and a gene (g) encoding the protein;
- determining a gene sequence variation score (v) for each of a gene sequence variation of the gene (g) for the subject by using the individual gene sequence information;
- calculating an individual protein function score associated with the protein by using Equation 2, wherein
- Equation 2 is:
- F
- g
-
- (
- v
- 1
- ,
- …
- ,
- v
- n
- )
- =
- (
- ∏
- i
- =
- 1
- n
-
-
- v
- i
- b
- i
- )
- 1
- ∑
- i
- =
- 1
- n
-
- b
- i
- wherein Fg is the individual protein function score of the protein encoded by the gene g, n is the number of sequence variations of the gene g, vi, is a gene sequence variation score of an ith gene sequence variation of the gene g, and bi is a weighting assigned to the gene sequence variation score vi of the ith gene sequence variation, and
- wherein the weighting (bi) assigned to the gene sequence variation score vi is determined by:
- obtaining training data including a plurality of training instances for a particular protein, each training instance including a predetermined protein function score for the particular protein and a set of gene sequence variation scores for the particular protein,
- determining a loss function indicating a difference between the predetermined protein function scores and estimated protein function scores, an estimated protein function score for a training data instance generated by applying Equation 2 to the set of gene sequence variation scores for the training data instance, and
- reducing the loss function to determine the weightings assigned to the gene sequence variation scores;
- predicting, by the prediction system, the adverse reaction to the drug based on the individual protein function score;
- and
- providing for display in a user interface on a client device a representation of the prediction for use in treatment of the subject.
45 more elements
A method for selecting a treatment population from a plurality of subjects for treatment with a drug, comprising the steps of:
- for each subject in the plurality of subjects:
- receiving, by a prediction system, clinical information of the subject related to a plurality of clinical factors (cj);
- for each of the clinical factors (cj), determining, by the prediction system, a clinical factor score (Scj) based on the clinical information of the subject;
- receiving, by the prediction system, individual gene sequence information of the subject and information about a plurality of proteins, wherein each of the proteins is related to pharmacokinetics or pharmacodynamics of the drug;
- for each of the genes (gk) encoding the plurality of proteins,
- determining, by the prediction system, a gene sequence variation score (vj,k) for each of a gene sequence variation of the gene (gk) for the subject by using the individual gene sequence information;
- and
- calculating, by the prediction system, an individual protein function score (Fgk) associated with the protein by using Equation 2, wherein
- Equation 2 is:
- F
- gk
-
- (
- v
- 1
- ,
- …
- ,
- v
- n
- k
- )
- =
- (
- ∏
- j
- =
- 1
- n
- k
-
-
- v
- j
- ,
- k
- b
- j
- ,
- k
- )
- 1
- ∑
- j
- =
- 1
- n
- k
-
- b
- j
- ,
- k
- wherein Fgk is the individual protein function score of the protein encoded by the gene gk, nk is the number of sequence variations of the gene gk, vj,k is a gene sequence variation score of jth gene sequence variation for the gene gk, and bj,k is a weighting assigned to the vj,k;
- and
- determining, by the prediction system, a drug safety score (DSS) for the subject by using Equation 7, wherein Equation 7 is:
- ln
-
- (
- DSS
- 1
- -
- DSS
- )
- =
- B
- 0
- +
- W
- g
-
- 1
-
- F
- g
-
- 1
- +
- W
- g
-
- 2
-
- F
- g
-
- 2
- +
- …W
- gm
-
- F
- gm
- +
- W
- c
-
- 1
-
- S
- c
-
- 1
- +
- W
- c
-
- 2
-
- S
- c
-
- 2
- +
- …W
- cp
-
- S
- cp
- wherein B0 is an intercept, Wgk is a weighting assigned to each protein function score Fgk, Wci is a weighting assigned to each clinical factor score Sci;
- and
- selecting a treatment population from the plurality of subjects for treatment with the drug based on the determined DSS for the plurality of subjects, the DSS of the selected treatment population indicating a low risk of an adverse reaction to the drug.
119 more elements
Dependencies are inferred from the claim text.