US 11,783,913 B2
Methods of treating a subject suffering from rheumatoid arthritis with alternative to anti-TNF therapy based in part on a trained machine learning classifier
Susan Ghiassian, Boston, MA (US); Theodore R. Mellors, Boston, MA (US); Marc Santolini, Waltham, MA (US); Asher Ameli, Waltham, MA (US); Nancy Schoenbrunner, Charlestown, MA (US); Viatcheslav R. Akmaev, Sudbury, MA (US); and Keith J. Johnson, Wayland, MA (US)
Assigned to SCIPHER MEDICINE CORPORATION, Waltham, MA (US)
Filed by Scipher Medicine Corporation, Waltham, MA (US)
Filed on Aug. 4, 2022, as Appl. No. 17/881,441.
Application 17/881,441 is a continuation of application No. 17/517,521, filed on Nov. 2, 2021, granted, now 11,456,056.
Application 17/517,521 is a continuation of application No. 17/315,580, filed on May 10, 2021, granted, now 11,195,595, issued on Dec. 7, 2021.
Application 17/315,580 is a continuation of application No. PCT/US2020/039991, filed on Jun. 26, 2020.
Claims priority of provisional application 62/965,486, filed on Jan. 24, 2020.
Claims priority of provisional application 62/882,402, filed on Aug. 2, 2019.
Claims priority of provisional application 62/867,853, filed on Jun. 27, 2019.
Prior Publication US 2022/0375541 A1, Nov. 24, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. A61P 37/06 (2006.01); C07K 16/24 (2006.01); G16B 20/20 (2019.01); G16H 10/60 (2018.01); G16H 50/70 (2018.01); G16H 50/20 (2018.01); G16H 20/10 (2018.01); A61P 19/02 (2006.01); G16B 40/00 (2019.01); A61K 38/17 (2006.01)
CPC G16B 20/20 (2019.02) [A61K 38/1793 (2013.01); A61P 19/02 (2018.01); A61P 37/06 (2018.01); C07K 16/241 (2013.01); G16B 40/00 (2019.02); G16H 10/60 (2018.01); G16H 20/10 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); C07K 2317/21 (2013.01); C07K 2317/24 (2013.01); C07K 2317/55 (2013.01); C07K 2317/76 (2013.01)] 14 Claims
 
1. 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 subjects and non-responsive subjects who have received the anti-TNF therapy,
wherein the trained machine learning classifier distinguishes between the responsive subjects and the non-responsive subjects, based at least in part on analyzing an expression level in the subject of a set of genes.