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AssigneeScipher Medicine Corporation
InventorsSusan Ghiassian, Theodore R. Mellors, Marc Santolini, Asher Ameli, Nancy Schoenbrunner, Viatcheslav R. Akmaev, Keith J. Johnson
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Presented herein are systems and methods for developing classifiers useful for predicting response to particular treatments. For example, in some embodiments, the present disclosure provides a method of treating subjects suffering from an autoimmune disorder, the method comprising a step of: administering an anti-TNF therapy to subjects who have been determined to be responsive via a classifier established to distinguish between responsive and non-responsive prior subjects in a cohort who have received the anti-TNF therapy.
A method of treating a subject suffering from rheumatoid arthritis, the method comprising administering to the subject an anti-TNF therapy, wherein the subject has been predicted to be responsive to the anti-TNF therapy based at least in part on a trained machine learning classifier that distinguishes between responsive and non-responsive subjects who have received the anti-TNF therapy with an area under curve of at least about 70%.
A method of treating a subject suffering from rheumatoid arthritis, the method comprising administering to the subject an alternative to anti-TNF therapy, wherein the subject has been predicted to be non-responsive to the anti-TNF therapy based at least in part on a trained machine learning classifier that distinguishes between responsive and non-responsive subjects who have received the anti-TNF therapy, based at least in part on analyzing an expression level in the subject of a set of genes comprising a member selected from the group consisting of ARPC1A, ATAT1, CD27, MORN2, SNX8, SSNA1, and UBL7-AS1.
A method of treating a subject suffering from rheumatoid arthritis, the method comprising:
A method of treating a subject suffering from rheumatoid arthritis, the method comprising administering to the subject an anti-TNF therapy, wherein the subject has been predicted to be responsive to the anti-TNF therapy based at least in part on a trained machine learning classifier that distinguishes between responsive and non-responsive subjects who have received the anti-TNF therapy at an accuracy of at least about 90%.
Dependencies are inferred from the claim text.