Patent ES-2724100-T3 (ES)
Title: Factores de predicción genéticos de una respuesta al tratamiento con antagonistas de CRHR1
Inventor: HOLSBOER FLORIAN
IPC: C12Q1/68
Filed: 2016-04-01
Granted: 2019-09-06
Abstract: Método para predecir una respuesta al tratamiento de un sujeto a un tratamiento con un antagonista de CRHR1 no peptídico, comprendiendo el método: detectar la presencia o ausencia de uno o más genotipos de polimorfismos en una muestra biológica del sujeto, en el que el uno o más genotipos de polimorfismos comprenden: al menos un genotipo de polimorfismo seleccionado del grupo que consiste en rs34169260 (A/G), rs796287 (A/C), rs56149945 (A/G), rs6190 (T/C), rs7179092 (T/C), rs7614867 (A/G), rs920640 (T/C), rs7167722 (T/C), rs920638 (T/C), rs7165629 (T/C), rs80049044 (T/A), rs16941058 (A/G), rs112015971 (A/G), rs10894873 (T/C), rs117455294 (T/G), rs1170303 (T/C), rs16940681 (C/G), rs968519 (T/C), rs28381866 (T/C), rs79320848 (T/G), rs114653646 (T/G), rs2589496 (T/C), rs10482650 (A/G), rs17614642 (A/G), rs73200317 (T/C), rs1380146 (T/A), rs735164 (T/C), rs730976 (T/G), rs55934524 (T/G), rs4570614 (A/G), rs4458044 (C/G), rs77850169 (A/G), rs35339359 (A/G), rs34800935 (T/C), rs72945439 (T/C), rs113959523 (A/G), rs116798177 (A/G), rs11247577 (T/G), rs75869266 (T/C), rs74372553 (T/C), rs11691508 (A/G), rs6493965 (A/G), rs4869476 (T/C), rs3730170 (T/C), rs2145288 (A/C), rs2935752 (A/C), rs146512400 (A/G), rs62057097 (T/C), rs115061314 (T/C), rs34113594 (T/G), rs61751173 (A/G), rs74338736 (A/C), rs10851726 (T/C), rs4610906 (T/C), rs59485211 (T/C), rs7060015 (T/G), rs75710780 (T/G), rs6520908 (T/C), rs487011 (T/G), rs1383699 (A/C), rs67516871 (A/G), rs114106519 (T/C), rs7220091 (A/G), rs12489026 (A/G), rs876270 (T/C), rs4968161 (T/C), rs62056907 (A/G), rs2235013 (T/C), rs16878812 (A/G), rs6549407 (A/G), rs28381848 (A/G), rs79723704 (A/C), rs72814052 (A/G), rs10152908 (T/C), rs172769 (A/C), rs78596668 (T/C), rs73307922 (T/C), rs3842 (A/G), rs7210584 (A/C), rs62402121 (T/C), rs55709291 (A/G), rs72747088 (A/G), rs929610 (G/C), rs6766242 (T/C), rs1468552 (G/C), rs78838114 (T/C), rs62489862 (T/C), rs894342 (A/G), rs58882373 (T/C), rs3811939 (A/G), rs6984688 (T/G), rs1018160 (T/C), rs76602912 (A/G), rs80067508 (A/G), rs74888440 (T/C), rs12481583 (T/C), rs66794218 (A/G), rs16946701 (A/G), rs75726724 (A/G), rs67959715 (T/A), rs11871392 (T/G), rs2044070 (A/G), rs77612799 (T/C), rs6743702 (T/C), rs616870 (T/C), rs79590198 (A/G), rs75715199 (A/G), rs13087555 (T/C), rs4869618 (T/C), rs117397046 (A/G), rs8042817 (A/G), rs2258097 (T/C), rs2260882 (C/G), rs532996 (A/G), rs11747040 (T/C), rs10034039 (T/G), rs116909369 (A/G), rs79134986 (A/G), rs117615688 (T/C), rs8032253 (T/C), rs12818653 (T/A), rs4587884 (A/C), rs77122853 (T/C), rs117615061 (T/C), rs74682905 (A/G), rs2257468 (T/C), rs2032582 (T/G), rs2235015 (T/G), rs2729794 (T/C), rs77549514 (A/G), rs74790420 (A/C), rs73129579 (T/C), rs12913346 (A/C), rs117560908 (T/C), rs72747091 (A/G), rs2935751 (A/G), rs4331446 (A/G), rs7523266 (T/C), rs7648662 (T/C), rs117034065 (A/G), rs4836256 (T/C), rs80238698 (T/C), rs3730173 (T/C), rs11687884 (T/C), rs72693005 (T/C), rs2589476 (T/C), rs9813396 (T/C), rs10482667 (A/G), rs72784444 (A/G), rs75074511 (T/C), rs7951003 (A/G), rs79584784 (A/G), rs2214102 (T/C), rs28811003 (A/G), rs6100261 (A/T), rs77152456 (A/G), rs66624622 (T/G), rs140302965 (A/G), rs11653269 (T/C), rs74405057 (A/G), rs7121 (A/G), rs16977818 (A/C), rs12490095 (T/C), rs118003903 (A/G), rs62377761 (T/C), P1_M_061510_6_34_M (-/CACTTACCTTCTTTGTGCCACAGTTTCCCTATCTAAAACACAAGGTTATCAGTTATCAACATCTCTTGG GATTGTGAGGACTAAAGTAATGCACATAAAG), rs375115639 (-/AAATTACCCTGTTAGGTTTCAATGAAACACCTTTTCTCTTGTAACAAACATCTCC TCCAAGCTAGAATTTCAAAACAG), rs1002204 (A/C), rs10062367 (A/G), rs10482642 (A/G), rs10482658 (A/G), rs1053989 (A/C), rs10851628 (T/C), rs10947562 (T/C), rs11069612 (A/G), rs11071351 (T/C), rs11091175 (A/G), rs11638450 (T/C), rs11715827 (T/G), rs11745958 (T/C), rs11834041 (A/G), rs1202180 (T/C), rs12054781 (A/G), rs12539395 (A/G), rs12720066 (T/G), rs1279754 (A/C), rs12872047 (T/C), rs12876742 (A/C), rs12917505 (A/G), rs13066950 (T/G), rs13229143 (C/G), rs1383707 (T/C), rs1441824 (T/C), rs1652311 (A/G), rs17064 (T/A), rs17100236 (A/G), rs17149699 (A/G), rs1724386 (A/G), rs17250255 (A/G), rs17327624 (T/G), rs17616338 (A/G), rs17687796 (A/G), rs17740874 (T/C), rs17763104 (T/C), rs1880748 (T/C), rs1882478 (A/G), rs1944887 (T/C), rs2028629 (A/G), rs2143404 (A/G), rs2173530 (T/C), rs220806 (T/C), rs2235047 (A/C), rs2242071 (A/G), rs2257474 (T/C), rs2295583 (A/T), rs234629 (T/C), rs234630 (A/G), rs2436401 (A/G), rs258750 (T/C), rs2589487 (T/C), rs28364018 (T/G), rs28381774 (T/C), rs28381784 (A/G), rs2963155 (A/G), rs3133622 (T/G), rs32897 (T/C), rs33388 (A/T), rs3730168 (T/C), rs3735833 (T/G), rs3777747 (A/G), rs3786066 (T/C), rs3798346 (T/C), rs3822736 (A/G), rs389035 (T/C), rs3924144 (A/G), rs4148737 (T/C), rs4148749 (G/C), rs417968 (T/C), rs4458144 (T/C), rs4515335 (T/C), rs4728699 (A/G), rs4758040 (A/G), rs4812040 (A/G), rs4912650 (T/G), rs4957891 (T/C), rs5906392 (A/G), rs6026561 (T/C), rs6026565 (T/A), rs6026567 (A/G), rs6026593 (A/G), rs6092704 (T/G), rs6100260 (A/G), rs6128461 (T/C), rs6415328 (T/C), rs6610868 (T/C), rs6686061 (A/C), rs6730350 (T/G), rs6746197 (T/C), rs6963426 (T/C), rs7121326 (T/C), rs7721799 (A/G), rs7787082 (T/C), rs7799592 (A/C), rs796245 (T/C), rs809482 (A/C), rs8125112 (T/C), rs919196 (A/G), rs920750 (T/C), rs9332385 (A/G), rs930473 (T/G), rs9324921 (A/C), rs9348979 (A/G), rs9571939 (A/C) y rs9892359 (T/C), en el que la etapa de predicción comprende preferiblemente: (a) determinar si el sujeto responderá, o tiene una probabilidad aumentada de responder al tratamiento con el antagonista de CRHR1 no peptídico; y/o (b) determinar si el sujeto no responderá, o tiene una probabilidad disminuida de responder al tratamiento con el antagonista de CRHR1 no peptídico, y en el que la etapa de determinación comprende preferiblemente uno o más métodos de análisis estadístico seleccionados del grupo que consiste en aprendizaje de redes neuronales artificiales, aprendizaje de árbol de decisión, aprendizaje de bosque de árboles de decisión, análisis discriminante lineal, análisis discriminante no lineal, programación de expresión genética, máquinas de vectores de relevancia, modelos lineales, modelos lineales generalizados, ecuaciones de estimación generalizadas, modelos mixtos lineales generalizados, la red elástica, el aprendizaje de máquina de vectores de soporte de Lasso, aprendizaje de redes bayesianas, aprendizaje de redes neuronales probabilísticas, agrupamiento y análisis de regresión, opcionalmente en el que el método de análisis estadístico está implementado informáticamente.