Treatment and diagnostic methods for Parkinson's disease associated with wild-type LRRK2

Abstract

The present invention provides a method for treating patients with wild-type LRRK2-associated Parkinson's disease (PD). The present invention recognizes that by analyzing genetic modifiers of LRRK2 in such patients, it is possible to identify patients who will respond to LRRK2 inhibitors. Accordingly, the present invention provides a method for identifying PD patients who will respond to LRRK2 inhibitors and a method for treating such patients. In one embodiment, the present invention provides a method for treating a subject with wild-type LRRK2-associated Parkinson's disease by providing an LRRK2 inhibitor to a subject that exhibits Parkinson's disease and has wild-type LRRK2 and genetic modifiers of wild-type LRRK2, such that the subject will respond to the LRRK2 inhibitor, thereby treating the wild-type LRRK2-associated Parkinson's disease in the subject.

Description

[Technical Field]

[0001] Field of Invention The present invention relates to a method for treating and diagnosing patients with Parkinson's disease associated with wild-type LRRK2. [Background technology]

[0002] background Parkinson's disease (PD) is a progressive neurodegenerative disease that affects more than 6 million people worldwide. PD is usually first recognized by movement disorders, with basic symptoms including tremors, rigidity, slowness of movement, and difficulty walking. In later stages, PD can also lead to neuropsychiatric disorders, including dementia, depression, and anxiety. More than 1% of people over 60 years of age have PD, and it causes more than 100,000 deaths annually.

[0003] Parkinson's disease (PD) is thought to result from a combination of genetic and environmental factors. While numerous mutations associated with familial PD have been identified, 85–90% of PD cases are idiopathic. In PD cases potentially associated with known genetic factors, mutations in the LRRK2 gene are one of the most common causes in both familial and idiopathic PD. LRRK2 encodes a protein kinase expressed in multiple tissues, including brain regions associated with PD such as the basal ganglia, and disease-causing mutations result in enhanced kinase activity. However, recent evidence suggests that some cases of PD are associated with elevated activity of wild-type, or non-mutant, LRRK2. [Overview of the project] [Problems that the invention aims to solve]

[0004] summary Since there is no cure for Parkinson's disease (PD), current treatments focus on alleviating symptoms, particularly motor impairments. For decades, the primary approach has been to enhance dopaminergic function using dopamine precursor levodopa, dopamine agonists, or monoamine oxidase inhibitors. However, such drug therapies lose their effectiveness as the disease progresses, and eventually their side effects outweigh their benefits. More recently, the use of LRRK2 inhibitors has been studied in relation to the treatment of PD cases associated with variant forms of LRRK2 kinase. However, in the majority of PD cases, mutations in LRRK2 cannot be identified. Unfortunately, in PD patients with wild-type LRRK2, there is no way to identify a subset of patients whose disease is associated with elevated LRRK2 activity, and indiscriminately administering LRRK2 inhibitors to PD patients increases the risk of harming patients without pathological LRRK2 activity. As a result, existing treatments for the majority of PD patients are inadequate, and millions of people continue to suffer the progression and debilitating effects of this disease. [Means for solving the problem]

[0005] This invention provides a method for determining whether a PD patient with wild-type LRRK2 is more likely to respond to an LRRK2 inhibitor, using genetic modifiers of LRRK2 in the patient's genome as indicators. The invention recognizes that genetic modifiers of LRRK2 may alter the level or activity of the LRRK2 kinase, for example, by increasing or decreasing it, or otherwise alter the LRRK2 signaling pathway via upstream or downstream regulators, thus contributing to the pathogenesis of PD. Therefore, PD patients with one or more such modifiers may benefit from drug therapy using LRRK2 inhibitors, despite having LRRK2 alleles that produce a normal form of the kinase. Thus, genetic modifiers of LRRK2 activity serve as indicators for determining whether LRRK2 inhibitor therapy is appropriate for a given individual. The method of this invention is useful for both identifying PD patients as candidates for LRRK2 inhibitor therapy and for treating such patients.

[0006] In one embodiment, the present invention provides a method for treating subjects with wild-type LRRK2-related Parkinson's disease by providing an LRRK2 inhibitor to subjects exhibiting Parkinson's disease and possessing wild-type LRRK2 and genetic modifiers of wild-type LRRK2, wherein the subjects respond to the LRRK2 inhibitor.

[0007] Genetic data may include any type of data relating to the composition and / or expression of one or more genes in a subject. Genetic data may include one or more of the following: exome data, genomic data, genotype data, proteome data, sequence data, and transcriptome data.

[0008] Genetic modifiers can be any gene element that alters LRRK2 expression or activity, correlates with changes in activity, or causes changes in protein levels associated with disease burden (not related to increase or decrease). Genetic modifiers can increase or decrease LRRK2 expression and / or activity; they can also increase or decrease LRRK2 degradation. Genetic modifiers can be amplifications, deletions, duplications, fusions, insertions, inversions, rearrangements, single nucleotide polymorphisms (SNPs), substitutions, or translocations. Genetic modifiers can be located within coding or non-coding regions of the genome in question. Genetic modifiers may be associated with family history and genetically confirmed Ashkenazi status.

[0009] In certain embodiments, the genetic modifier is a SNP. In certain embodiments, the genetic modifier may be one or more SNPs selected from any of the SNPs in groups I to IV. In certain embodiments of the present invention, the SNP in group I is a rare variant of a harmful protein that encodes a change in the LRRK2 gene, which is known to increase kinase activity and significantly increase the risk of Parkinson's disease. In certain embodiments, the SNP in group II is associated with the level of lipid BMP in the urine of patients with Parkinson's disease. In certain embodiments, the SNP in group III is located around and / or near the LRRK2 gene. They can tag regulatory elements that can control LRRK2 expression. In certain embodiments, the SNP in group IV facilitates the optimal functioning of the detection system. Exemplary SNPs in groups I to IV are presented below: Group I: rs33939927, rs35801418, rs34805604, rs34637584, rs35870237, rs34995376, and rs34778348. Group II: rs11611119, rs1497046, rs12296462, rs549790, rs12423473, rs555740, rs2242367, rs7295598, rs2253736, rs11564274, rs2708419, rs17519419, rs76904798, rs17519573, rs11175847, rs10878452, rs17444612, rs11564264, rs11564235, rs17128233, rs7960976, and rs1918942. Group III: rs549790, rs611829, rs7531501, rs9793102, rs3755541, rs7578955, rs17738103, rs4676776, rs1259475, rs10477505, rs7380062, rs4636028, rs12704998, rs2768282, rs10283642, rs3750779, rs1362993, rs7089200, rs9783486, rs4763946, rs16920645, rs12312400, rs2708494, rs128132 79, rs11613339, rs7300813, rs166806, rs3794253, rs7960429, rs7955116, rs10491998, rs12814145, rs2708078, rs1922761, rs1373422, rs17691793, rs7301498, rs7967809, rs1866074, rs10507535, rs9300705, rs1642819, rs8048361, rs1152838, rs2331796, rs10515969, rs6566942, and rs2422956. グループIV:rs11175847、rs10737170、rs10514889、rs709184、rs2842258、rs2 982373、rs11585441、rs6680835、rs10737374、rs535175、rs9438994、rs75 42025、rs7533665、rs16828092、rs270728、rs12139976、rs12023577、rs11 208250、rs7552569、rs17399290、rs10873661、rs1325273、rs4907105、rs11 57148、rs17016501、rs11102944、rs1218593、rs2275081、rs17385169、rs6 427299、rs584025、rs11264477、rs2814774、rs2494262、rs2427825、rs1205 8214、rs16852055、rs2146609、rs10449267、rs2268147、rs2089891、rs374 8022、rs11117564、rs4844863、rs10916131、rs903687、rs1923822、rs67003 81、rs12060035、rs12144733、rs7521596、rs10924936、rs11892108、rs654 4507、rs4953527、rs6724638、rs6544601、rs7599740、rs10198317、rs17047 533、rs1016339、rs11677415、rs878539、rs12615868、rs2034136、rs13009 072、rs7602946、rs1257193、rs2710184、rs10496767、rs1921772、rs403523 3、rs2602191、rs13395562、rs12621063、rs3116536、rs4673710、rs126172 70、rs721358、rs6751814、rs1504073、rs1391920、rs9815996、rs9856251、r s3805036、rs7652766、rs6786115、rs6550967、rs12493220、rs10866048、r s6784559、rs10511119、rs12494049、rs6441729、rs11708297、rs13094003、rs1994128、rs1554513、rs16839317、rs9825996、rs4683618、rs6768143、rs11705701、rs2268833、rs9867755、rs4689190、rs10009272、rs6837434、rs10033492、rs4832818、rs2911908、rs1032984、rs7683552、rs1350246、rs17238982、rs13149946、rs1897814、rs3846448、rs11098653、rs13136491、rs4485803、rs4130505、rs6824404、rs17057224、rs4862385、rs11132255、rs6852642、rs6553197、rs7654439、rs10065645、rs7728112、rs2973522、rs258879、rs10471439、rs7714264、rs358532、rs283619、rs16877147、rs4541642、rs6894105、rs1384282、rs17149301、rs920387、rs9637846、rs6889919、rs11745517、rs3111491、rs7716878、rs6861937、rs4868495、rs6420088、rs4700746、rs6867221、rs458108、rs6900447、rs267802、rs1410366、rs9368072、rs12210896、rs728398、rs1161397、rs1327266、rs2653344、rs9294329、rs9400038、rs7774400、rs9320489、rs7757785、rs9968822、rs11154296、rs6933986、rs2297367、rs806383、rs17150761、rs11536481、rs17667596、rs1723808、rs1548577、rs4722576、rs7801967、rs216730、rs1008262、rs41304、rs2893375、rs10951888、rs12533618、rs6971934、rs2110290、rs259139、rs861006、rs5015756、rs7806537、rs11761624、rs2075668、rs10238218、rs17331134、rs4383914、rs11766415、rs2536501、rs6979121、rs4730333、rs17558874、rs3779707、rs11985845、rs6530980、rs2628289、rs916550、rs16888648、rs7002709、rs4305943、rs7015322、rs7004403、rs10505145、rs10103670、rs11779980、rs6994324、rs17194924、rs6997195、rs7018287、rs2233334、rs7002431、rs1853418、rs2038592、rs1475111、rs10511552、rs3808750、rs10963691、rs10757769、rs1954280、rs11141486、rs7864316、rs12379601、rs10978589、rs10978602、rs10816518、rs1324952、rs747066、rs7034837、rs1541332、rs7090790、rs943101、rs11816077、rs359322、rs7906048、rs2015190、rs4369307、rs7914692、rs16917302、rs2393967、rs2619634、rs2289310、rs11187664、rs4919276、rs12267798、rs7893754、rs2788178、rs11196386、rs4752582、rs7922033、rs7907856、rs3781411、rs17155120、rs6482940、rs3842752、rs10835333、rs373894、rs11025356、rs10444236、rs1463079、rs11037114、rs11231603、rs11235519、rs193170、rs618929、rs7482554、rs10789547、rs10891103、rs4937131、rs7111323、rs1040099、rs12276840、rs4937861、rs216340、rs2302365、rs2948、rs2239675、rs1344717、rs11045248、rs7301416、rs1049377、rs16919821、rs2733682、rs1995303、rs1875311、rs7295095、rs11052842、rs1705772、rs9705446、rs7133706、rs7311662、rs1349257、rs4500524、rs4768592、rs12831835、rs12582802、rs7138679、rs7964150、rs515205、rs7971218、rs10784387、rs2708435、rs10878249、rs2404581、rs10506151、rs1427263、rs7307562、rs2404835、rs10878405、rs994798、rs1820545、rs10878590、rs10784629、rs4768293、rs2405286、rs1918954、rs1373431、rs7309561、rs10879153、rs11178396、rs1838343、rs1899827、rs1305000、rs11178879、rs10879360、rs1546081、rs9645832、rs7966732、rs3747556、rs11181115、rs2167352、rs10785280、rs1546190、rs11181158、rs7312550、rs1391003、rs17666080、rs12820881、rs1669921、rs11181613、rs12312435、rs7297752、rs11181729、rs1402360、rs1167161、rs7311395、rs4251580、rs10880606、rs2060896、rs3742060、rs7958908、rs2239182、rs2232565、rs7314952、rs10400566、rs17749783、rs201396、rs12579750、rs10861138、rs12824323、rs1525967、rs6489166、rs3751385、rs500755、rs476697、rs475916、rs9579104、rs2254191、rs9539579、rs4773918、rs473930、rs9519161、rs11543947、rs3210043、rs1884235、rs12880654、rs6571690、rs723204、rs17660245、rs10146989、rs7144018, rs17092921, rs17767434, rs11620653, rs2362865, rs17111972, rs4900406, rs11635966, rs2703957, rs1533861, rs11631192, rs1015 3042, rs1495279, rs1816619, rs4287512, rs1446332, rs2606169, rs12915467, rs7169879, rs8038143, rs8039999, rs8052848, rs4786169, rs29073 63, rs12445976, rs9888840, rs4444329, rs17796129, rs5728, rs9939129, rs4788105, rs79030220, rs8052403, rs17248940, rs727628, rs4888947 , rs12933198, rs7184633, rs4598906, rs12930347, rs12946712, rs7207104, rs2309810, rs1860380, rs2347548, rs4795064, rs8073372, rs895245, rs8079215, rs62074125, rs2332933, rs2302782, rs9906395, rs12941384, rs6501537, rs8091121, rs7231384, rs8093359, rs1436906, rs4502305, rs5005414, rs1261094, rs486285, rs334449, rs4369780, rs2972574, rs7254351, rs6512186, rs8104468, rs4805409, rs4805410, rs12984266, rs81 12461, rs633924, rs8119133, rs11087720, rs6055279, rs2235244, rs17189752, rs2206809, rs200753, rs200768, rs6131683, rs1420440, rs4810699, rs1276459, rs6025744, rs7263962, rs2822691, rs2822701, rs415850, rs2825942, rs877118, rs2330579, rs9619407, rs470062, and rs5764726.

[0010] In certain embodiments, SNPs may be in linkage disequilibrium (LD) with these SNPs, which are suitable as surrogates for these SNPs.

[0011] In certain embodiments, the method of the present invention relies on additional patient characteristics, in addition to genetic data, when determining whether a PD patient with wild-type LRRK2 is more likely to respond to an LRRK2 inhibitor. In certain embodiments, a patient characteristic that may be considered when determining whether a PD patient with wild-type LRRK2 is more likely to respond to an LRRK2 inhibitor is the patient's age. In certain embodiments, a patient characteristic that may be considered when determining whether a PD patient with wild-type LRRK2 is more likely to respond to an LRRK2 inhibitor is the patient's age at the time of diagnosis.

[0012] In certain embodiments, the LRRK2 inhibitor may be CZC-25146, CZC-54252, DNL151, DNL201, GNE-7915, GNE-0877, GSK2578215A, HG-10-102-01, JH-II-127, K252A, K252B, LRRK2-IN-1, MLi-2, PF-06447475, or staurosporine.

[0013] In a particular embodiment, the LRRK2 inhibitor is one compound of formula (I), (II), (III), or (IV): [ka] (In the formula: A is NH, O, S, C=O, NR 3 or CR 4 R 5 And, X is an arylene, heteroarylene, cycloalkylene, heterocycloalkylene, alkylcycloalkylene, heteroalkylcycloalkylene, aralkylene, or heteroaralkylene group, which may be substituted as needed. R 1is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl group that is optionally substituted, R 2 is a hydrogen atom, a halogen atom, NO2, N3, OH, SH, NH2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl group, R 3 is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkyl-cycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl group, R 4 is a hydrogen atom, NO2, N3, OH, SH, NH2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl group, R 5 is a hydrogen atom, NO2, N3, OH, SH, NH2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl group, B is NH, O, S, C=O, NR 14 or CR 15 R 16 wherein, R 11 is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl group, R 12R is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl, or heteroaralkyl group, 12 It is bonded to the pyrimidine ring of formula (II) via a carbon-carbon bond, R 13 This is a hydrogen atom, halogen atom, NO2, N3, OH, SH, NH2 or alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl group. R 14 These are alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkyl-cycloalkyl, heterocycloalkyl, aralkyl, or heteroaralkyl groups. R 15 is a hydrogen atom, NO2, N3, OH, SH, NH2, or alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl, or heteroaralkyl group. R 16 is a hydrogen atom, NO2, N3, OH, SH, NH2, or alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl, or heteroaralkyl group. R 21 These are aryl or heteroaryl compounds, which are substituted as needed. R 22 H, Halo, OH, CN, CF3, C 1~6 Alkyl, C 1~6 Alkoxy, C 1~6 Haloalkyl, C 1~6 Thioalkyl, C 3~8 Cycloalkyl, C 2~8It is a heterocycloalkyl, aryl, or heteroaryl, Y is an aryl or a 5-membered or 6-membered heteroaryl. C 1~6 Alkyl, C 1~6 Alkoxy, C 1~6 Haloalkyl, C 1~6 Thioalkyl, C 3~8 Cycloalkyl, C 2~8 Heterocycloalkyl, aryl, and heteroaryl are, respectively, halo, OH, CN, CF3, NH2, NO2, and C. 1~6 Alkyl, C 1~6 Haloalkyl, C 1~6 Thioalkyl, C 3~8 Cycloalkyl, C 2~8 Heterocycloalkyl, C 2~8 Heterocycloalkenyl, C 2~6 Alkenil, C 2~6 Alkinyl, C 1~6 Alkoxy, C 1~6 Haloalkoxy, C 1~6 Alkylamino, C 2~6 Dialkylamino, C 7~12 Aralkil, C 1~12 It is optionally replaced by one or more parts selected from the group consisting of heteroaralkyl, aryl, heteroaryl, -C(O)R, -C(O)OR, -C(O)NRR', -C(O)NRS(O)2R', -C(O)NRS(O)2NR'R'', -OR, -OC(O)NRR', -NRR', -NRC(O)R', -NRC(O)NR'R'', -NRS(O)2R', -NRS(O)2NR'R'', -S(O)2R, and -S(O)2NRR'. R, R', and R'' are, independently, H, H, OH, and C, respectively. 1~6 Alkyl, C 1~6 Haloalkyl, C 1~6 Alkoxy, C 3~8 Cycloalkyl, C 2~8 Heterocycloalkyl, aryl, or heteroaryl, or R and R' or R' and R'' together with the nitrogen they are bonded to, C 2~8 Forms heterocycloalkyl groups, R 31 is C(O)CH2R 33 , optionally substituted cycloalkyl, optionally substituted cycloheteralkyl, optionally substituted cycloalkenyl, optionally substituted cycloheteralkenyl, optionally substituted aryl, or optionally substituted heteroaryl, R 32 Each example is independently a halo, haloalkyl, optionally substituted alkoxyl, optionally substituted alkyl, optionally substituted heteroalkyl, optionally substituted alkenyl, or optionally substituted heteroalkenyl. R 33 These are optionally substituted cycloalkyls, optionally substituted cycloheteralkyls, optionally substituted cycloalkenyls, optionally substituted cycloheteralkenyls, optionally substituted aryls, or optionally substituted heteroaryls. Z is a cycloalkyl, cycloheteroalkyl, cycloalkenyl, cycloheteroalkenyl, aryl, or heteroaryl. n is between 0 and 5. Alternatively, it may be a pharmaceutically acceptable salt of any of the above compounds.

[0014] In a particular embodiment, an LRRK2 inhibitor is presented in WO2021 / 048620, which is incorporated in whole by reference.

[0015] In a particular embodiment, the LRRK2 inhibitor is one of the compounds of formula (V): [ka] and its pharmaceutically acceptable salts. (In the formula: G1 is CF3, CHF2, CH2F, halogen, or cyclopropyl, ethyl, or isopropyl. G2 is H, substituted or unsubstituted C1-C6-alkyl, C3-C6-cycloalkyl, C1-C6 haloalkyl, C3-C6 halocycloalkyl, 1-6 membered heterocycle, heteroaryl, -CH2-cycloalkyl, -CF2-cycloalkyl (cycloalky), -CH(CH3)-cycloalkyl, -CH2-aryl, -CH2-heterocyclic, -CH2-heteroaryl, -CF2-aryl, and -CH(-CH3)-aryl. A and B are independently selected from 5-membered or 6-membered cycloalkyl or cycloaryl rings containing one or more heteroatoms and one or more substitutions. Both A and B contain at least two nitrogen heteroatoms cumulatively, A and B are condensed at two positions. One or more substitutions on rings A and B include H, halo, C1-C6-alkyl, branched alkyl, haloalkyl, substituted or unsubstituted alkenyl, substituted or unsubstituted alkynyl, alkoxy, cycloalkoxy, haloalkoxy, alkoxyalkoxy, cyano, -CH2-cycloalkyl, -CF2-cycloalkyl, -(CH2) 0~2 C(CH3)2-CN, -(CH2) 0~4 -CN, -(CH2) 0~4 SO2R', -CH(CH3)-cycloalkyl, -CH2-aryl, -CF2-aryl, -CH(-CH3)-aryl, -C(=O)-alkyl, -C(=O)-cycloalkyl, -C(=O)-NR'R'', -C(=O)-NH-alkyl, -C(=O)NH2, hydroxy, -COOH (and its esters), alkylsulfonyl, cycloalkylsulfonyl, arylsulfonyl, -S(=O)2-NR'R'', -NH2, -NR'R'', -NR'-S(=O)2R'', cyanoalkyl, haloalkyl, substituted or unsubstituted alkylsulfonyl, arylsulfonyl, -C(=O)-morpholine, -C(=O)-heterocycle, -C(H) 0~1 (-CH3) 1~2-OH, -CH2-C(=O)-NH2; independently selected from 3- to 6-membered heterocycles, each of which may have one or more substituents, and the 3- to 6-membered heterocycle contains at least one heteroatom independently selected from O, S, and N. R' and R'' are independently selected from the group consisting of H, alkyl, substituted or unsubstituted aryl, and heterocycles.

[0016] In a particular embodiment, the LRRK2 inhibitor is a compound of formula (VI): [ka] (In the formula, X1, X2, X3, and X4 are C or N, and X1, X2, and X3 contain either one N, two Ns, or three Ns. R1 is CF3, CHF2, CH2F, halogen, cyclopropyl, ethyl, isopropyl. R2 is H, substituted or unsubstituted C1-C6 alkyl, C3-C6-cycloalkyl, C3-C6-halocycloalkyl; branched alkyl, alkenyl, alkynyl, haloalkyl, alkoxy, cycloalkoxy, haloalkoxy, -CH2-cycloalkyl, -CF2-cycloalkyl, tetrahydrofuran, -CH(-CH3)-cycloalkyl, -CH2-aryl, -CF2-aryl, -CH(-CH3)-aryl, -C(=O)-alkyl, -C(=O)-cycloalkyl, -C(=O)-NH-alkyl, R3, R4, and R5 are independently selected from the group consisting of H, substituted or unsubstituted C1-C6 alkyl, C3-C6-cycloalkyl, C3-C6-halocycloalkyl; branched alkyl, alkenyl, alkynyl, haloalkyl, alkoxy, cycloalkoxy, haloalkoxy, nitro, cyano, alkylcyano, -CH2-cycloalkyl, -CF2-cycloalkyl, -CH(CH3)-cycloalkyl, -CH2-aryl, -CF2-aryl, -CH(-CH3)-aryl, -C(=O)-R', -C(=O)-NR'R'', -S=(O)2-R', -CH2CN, -C-(CH3)2-CN, -C(-CH3)2-OH, and -CH2-C(=O)-NH2. R6, R7 and R8 are H, halo, C1-C6-alkyl, branched alkyl, alkenyl, alkynyl, haloalkyl, alkoxy, cycloalkoxy, haloalkoxy, nitro, cyano, -CH2-cycloalkyl, -CF2-cycloalkyl, -CH(CH3)-cycloalkyl, -CH2-aryl, -CF2-aryl, -CH(-CH3)-aryl, -C(=O)-alkyl, -C(=O)-cycloalkyl, -C(=O)-NH-alkyl, -C(=O)NH2, hydroxy, -COOH (and its esters), -C(=O)-morpholine, -C(=O)-heterocyclic, -S=(O)2-R', ami -NR'R'', -NR'S=(O)2-R', cyanoalkyl, haloalkyl, -C(-CH3)2-OH, -CH2-C(=O)-NH2; 3-6 membered heterocycles (each of which may have one or more substituents, and the 3-6 membered heterocycle contains at least one heteroatom independently selected from O, S, and N); -S=(O)2-cyclopropyl, -C(=O)-morpholine, -C(-CH3)2-OH, -CH2-C(=O)-NH2, -CF3, -OCF3, tetrahydropyran, 3H-pyran, 2H-pyran, piperidine, alkyl-morpholine, independently selected from the group. R' and R'' are independently selected from the group consisting of H, unsubstituted or substituted C1-C8 alkyl, substituted or unsubstituted C1-C8 aryl, substituted or unsubstituted C1-C8 cycloalkyl or heterocycloalkyl. Substitutions R3, R4, R5, and R6 are present only if their valence allows. R3 and R6 optionally form 5- to 7-membered heteroalkyl rings. It is possible.

[0017] In a particular embodiment, the LRRK2 inhibitor is a compound of formula (VII): [ka] (In the formula, X5 is selected from C or N, R9, R 10 or R 11 This is independently selected from the group consisting of H, substituted or unsubstituted C1-C6 alkyl, C1-C6-cycloalkyl, C3-C6-halocycloalkyl; C1-C6 alkyl, branched alkyl, alkenyl, alkynyl, haloalkyl, alkoxy, cycloalkoxy, haloalkoxy, nitro, cyano, -CH2-cycloalkyl, -CF2-cycloalkyl, -CH(CH3)-cycloalkyl, -CH2-aryl, -CF2-aryl, -CH(-CH3)-aryl, C(=O)-alkyl, -C(=O)-cycloalkyl, -C(=O)-NH-alkyl, -S=(O)2-CH3, -C-(CH3)2-CN, -S=(O)2-cyclopropyl, -C(=O)-morpholine, -C(-CH3)2-OH, and -CH2-C(=O)-NH2. Replacement R9,R 10 and R 11 (It exists only if the valence allows it.) It is possible. In a particular embodiment, the LRRK2 inhibitor is a compound of formula (VIII): [ka] (In the formula, X6 and X7 are selected from C or N, and at least one of X6 and X7 is N, R 12 , R 13 and R 14This is independently selected from the group consisting of H, substituted or unsubstituted C1-C6 alkyl, C3-C6-cycloalkyl, C3-C6-halocycloalkyl; C1-C6 branched alkyl, C1-C6-alkenyl, C1-C6-alkynyl, C1-C6-haloalkyl, C1-C6-alkoxy, cycloalkoxy, haloalkoxy, halo, nitro, cyano, -CH2-cycloalkyl, -CF2-cycloalkyl, -CH(CH3)-cycloalkyl, -CH2-aryl, -CF2-aryl, -CH(-CH3)-aryl, C(=O)-alkyl, -C(=O) heterocycloalkyl and -C(=O)-morpholine. R 15 and R 16 The following are independently selected from the group consisting of H, halo, substituted or unsubstituted C1-C6 alkyl, alkyloxy, cyanoalkyl, C1-C6-branched alkyl, alkenyl, alkynyl, haloalkyl, alkoxy, cycloalkoxy, haloalkoxy, halo, nitro, cyano, -CH2-cycloalkyl, -CF2-cycloalkyl, -CH(CH3)-cycloalkyl, -C(=O) heterocycloalkyl, and -C(=O)-morpholine. replacement R 12 , R 13 and R 14 (It exists only if the valence allows it.) It is possible.

[0018] In certain embodiments, LRRK2 inhibitors are presented in WO2024 / 073073, which are incorporated in their entirety by reference.

[0019] In another embodiment, the present invention provides a method for determining whether a subject with wild-type LRRK2-associated Parkinson's disease responds to an LRRK2 inhibitor. The method includes the steps of: performing an assay on a sample from a subject with wild-type LRRK2-associated Parkinson's disease to obtain genetic data from the subject; preparing a report identifying one or more genetic modifiers of LRRK2 in the genetic data, wherein one or more genetic modifiers in the LRRK2 network indicate that the subject with wild-type LRRK2-associated Parkinson's disease is responsive to an LRRK2 inhibitor; and providing the report to a physician to prescribe or provide an LRRK2 inhibitor to the subject.

[0020] The genetic data may be any of the above types of genetic data.

[0021] The genetic modifier may be any type of LRRK2 genetic modifier described above. The genetic modifier may be any of the SNPs listed above.

[0022] The LRRK2 inhibitor may be any of the above.

[0023] In another embodiment, the present invention provides a method for treating a subject having wild-type LRRK2-associated Parkinson's disease. The method includes the steps of receiving genetic data identifying one or more genetic modifiers of LRRK2, wherein the one or more genetic modifiers indicate that the subject having wild-type LRRK2-associated Parkinson's disease is responsive to an LRRK2 inhibitor, and prescribing or providing an LRRK2 inhibitor to the subject.

[0024] The genetic data may be any of the above types of genetic data.

[0025] The genetic modifier may be any type of LRRK2 genetic modifier described above. The genetic modifier may be any of the SNPs listed above.

[0026] The LRRK2 inhibitor may be any of the above.

[0027] In another embodiment, the present invention provides an LRRK2 inhibitor for use in the treatment of PD associated with wild-type LRRK2.

[0028] The subjects may have one or more genetic modifiers of LRRK2, such as any of the above.

[0029] Use may include the step of receiving or obtaining genetic data, such as any of the genetic data described above.

[0030] The LRRK2 inhibitor may be any of the above. [Modes for carrying out the invention]

[0031] Detailed explanation Parkinson's disease (PD) is a progressive neurodegenerative disease caused by both genetic and environmental factors. One gene that plays a role in the development of some cases of PD is LRRK2, which encodes a kinase expressed in multiple tissues, including brain regions associated with PD, such as the basal ganglia. Mutations in LRRK2 are one of the most common known genetic causes of PD, yet patients with LRRK2 mutations constitute only a small fraction of the total number of PD cases. Nevertheless, the pathophysiology and phenotypes of some patients with wild-type, i.e., non-mutant LRRK2 appear similar to those of patients with mutant LRRK2. In particular, disease-causing mutations in LRRK2 result in increased LRRK2 kinase activity, and it has recently been shown that some PD patients with wild-type LRRK2 exhibit elevated LRRK2 activity.

[0032] Various LRRK2 inhibitors are currently being studied as treatments for Parkinson's disease (PD). Such drugs are promising for PD patients with LRRK2 mutations. However, the use of LRRK2 inhibitors to treat PD patients with wild-type LRRK2 presents challenges due to the diverse pathogenesis of the disease. Patients with enhanced wild-type LRRK2 activity may benefit from LRRK2 inhibitors, but LRRK2 inhibition may not be effective in PD patients with normal levels of LRRK2 activity whose disease pathogenesis is due to changes in other molecular pathways. Because neurons expressing LRRK2 are located in the midbrain and are extremely difficult to access, kinase activity cannot be easily assessed in living patients. As a result, to date, there has been no means to identify a subset of PD patients with wild-type LRRK2 who could still benefit from LRRK2 inhibition.

[0033] This invention solves this problem by using genetic modifiers of LRRK2 activity to determine whether PD patients with wild-type LRRK2 may benefit from LRRK2 inhibitors. The invention recognizes that genetic alterations outside the LRRK2 locus affect the expression or activity of LRRK2 kinase, and that the presence of certain genetic markers correlates with changes in LRRK2 expression or activity, e.g., increase or decrease. Therefore, the method of the invention makes it possible to identify candidate LRRK2 drug therapies based on readily available genetic data from patients. Thus, for a subset of PD patients, the invention reveals the therapeutic potential of a class of drugs previously not recommended for PD patients.

[0034] Parkinson's disease and its treatment Parkinson's disease (PD) is a progressive neurodegenerative disease of the central nervous system. In its early stages, the disease affects the motor system, and the basic symptoms are tremors, rigidity, slowness of movement, and difficulty walking. Cognitive and behavioral symptoms such as dementia, depression, and anxiety often appear in the later stages of PD. PD usually occurs in people over 60 years of age, affecting about 1% of the population, although so-called early-onset PD can occur before the age of 50.

[0035] Parkinson's disease (PD) is characterized by cell death in the basal ganglia, including dopamine-secreting neurons, astrocytes, and microglia in the substantia nigra. Five mechanisms have been proposed for neuronal death in PD. First, oligomerization of proteins such as alpha-synuclein into aggregates called Lewy bodies may directly lead to cell death. A second proposed cause is dysregulation of autophagy, particularly mitochondrial degradation. Another proposed mechanism is that mitochondrial dysfunction leads to decreased energy production and increased reactive oxygen species. A fourth proposed mechanism is neuroinflammation resulting from the secretion of pro-inflammatory factors by microglia. Finally, it has been proposed that disruption of the blood-brain barrier allows plasma proteins to leak into the substantia nigra, promoting apoptosis.

[0036] Parkinson's disease (PD) is thought to result from a combination of genetic and environmental factors. In some cases, genetic mutations that increase the risk of PD are hereditary, with approximately 10-15% of individuals with PD having a first-degree relative with the disease. However, most cases of PD are idiopathic or "sporadic." Genes with mutations associated with PD include CHCHD2, DJ1 / PARK7, DNAJC13, EIF4G1, GBA, LRRK2 / PARK8, PINK1, PRKN, SNCA, UCHL1, and VPS35. For both familial and sporadic PD, one of the most common known causes is mutations in LRRK2. Disease-causing mutations in LRRK2 result in elevated or abnormal kinase forms. Enhanced activity of wild-type LRRK2 has recently been linked to idiopathic PD as well. The role of LRRK2 in PD has been demonstrated, for example, by Chen, et al., Leucine-Rich Repeat Kinase 2 in Parkinson's Disease: Updated from Pathogenesis to Potential Therapeutic Target, Eur Neurol. 2018;79(5-6):256-265, doi: 10.1159 / 000488938. Epub 2018 Apr 27;Di Maio, et al. al., LRRK2 activation in idiopathic Parkinson's disease, Sci Transl Med. 2018 Jul 25;10(451):eaar5429, doi: 10.1126 / scitranslmed.aar5429;Taymans and Greggio, LRRK2 Kinase Inhibition as a Therapeutic Strategy for Parkinson's Disease, Where Do We Stand? Curr Neuropharmacol. The contents of 2016;14(3):214-25, doi: 10.2174 / 1570159x13666151030102847 are incorporated herein by reference.

[0037] Several behavioral and environmental conditions are known to increase the risk of developing Parkinson's disease (PD). Risk factors associated with PD include exposure to pesticides and a history of head injury. Caffeine consumption and tobacco use are associated with a reduced risk of PD. Low blood uric acid levels are associated with an increased risk of PD.

[0038] The management of Parkinson's disease (PD) typically involves pharmacological stimulation of the dopaminergic system. The most widely used drug for treating PD is levodopa, which is enzymatically converted to dopamine in dopaminergic neurons. Dopamine agonists such as bromocriptine, pergolide, pramipexole, ropinirole, pyribezil, cabergoline, apomorphine, and risulide may also be used to treat PD. A third class of drugs for treating PD includes monoamine oxidase inhibitors such as selegiline and rasagiline.

[0039] Identification of genetic modifiers from genetic data The present invention recognizes that genetic modifiers of LRRK2 can serve as an indicator of the likelihood that PD patients with wild-type LRRK2 are likely to benefit from drug therapy using one or more LRRK2 inhibitors. A genetic modifier of LRRK2 may be one or more gene elements (e.g., a single gene element or any combination of gene elements) that operably modify LRRK2 (e.g., wild-type LRRK2) in a subject, such as the LRRK2 gene, the transcript of the LRRK2 gene, and the polypeptide product of the LRRK2 gene, thereby altering the expression, degradation, localization (e.g., intracellular or cell-type), binding, or activity of LRRK2. For example and not limited to, a genetic modifier may alter, for example, increase or decrease the expression, activity, stability, binding, localization, degradation, transcription, or translation of LRRK2, including the LRRK2 gene, the transcript of the LRRK2 gene, and the polypeptide product of the LRRK2 gene. In certain embodiments, a genetic modifier may also result in epigenic changes. Such epigenic changes may be related to a patient's response to treatment with LRRK2 inhibitors. In certain embodiments, genetic modifiers of LRRK2 may be structural changes in the genome of interest. For example, and not limited to, genetic modifiers may be amplifications, deletions, duplications, fusions, insertions, inversions, rearrangements, single nucleotide polymorphisms (SNPs), substitutions, or translocations. SNPs that may be genetic modifiers of LRRK2 are listed in this application. In certain embodiments, the SNP may be one of the already known genetic modifiers of LRRK2, including, for example, the genetic modifiers presented in WO2022 / 093685. Furthermore, any other SNP in linkage disequilibrium (LD) with the SNP may be used as a genetic modifier. Genetic modifiers may be cis-regulatory elements such as promoters, enhancers, silencers, or operators. Cis-regulatory elements may regulate the binding of one or more proteins to DNA adjacent to LRRK2. The cis regulatory element may affect the binding of any of the components of histones, transcription factors, initiation factors, helicases, polymerases, or the aforementioned proteins.Genetic modifiers can be trans-acting factors. Trans-acting factors can affect the transcription or translation of LRRK2. Genetic modifiers can be located in any region of the genome in question. Genetic modifiers can be located within the coding or non-coding regions of the genome in question. The coding region may be located within LRRK2 or another gene. Genetic modifiers can be located within the LRRK2 coding region, but they do not alter the sequence of the LRRK2 polypeptide, the size of the LRRK2 polypeptide, or both.

[0040] The method of the present invention may include the identification or analysis of one or more genetic modifiers of LRRK2 in genetic data obtained from a subject. The genetic data may include any type of data relating to the composition and / or expression of one or more genes in the subject. The genetic data may include one or more of the following: exome data, genomic data, genotype data, proteome data, sequence data, and transcriptome data. The genetic data may include data relating to one or more genes known to be associated with PD, such as any of the genes mentioned above.

[0041] Genetic modifiers can be identified from genetic data using any preferred method. In some embodiments, genetic data collected from subjects are compared to a reference set of data to provide a probability of responsiveness to LRRK2 inhibitors. The reference set may include data collected from individuals without PD. Phenotypic data from both subjects and reference individuals may also be used. Phenotypic data may include PD-related traits, including PD symptoms or PD risk factors (such as those mentioned above). Data may include outcomes, such as whether the individual responded to LRRK2 inhibitor treatment.

[0042] The present invention provides a method and system for predicting a subject's responsiveness to an LRRK2 inhibitor based on the subject's phenotypic traits and / or genotype data. In some embodiments, the method and system of the present invention uses a diagnostic signature to predict responsiveness. The diagnostic predictor can be based on any suitable pattern recognition method that receives input data representing multiple responsiveness-related phenotypic traits, such as (1) LRRK2-like manifestation of PD observed in carriers of LRRK2 harmful variants, (2) PD of a clearly unknown mechanism, and (3) molecular signatures of a suitable control, and provides an output indicating the probability that the subject will respond to an LRRK2 inhibitor. The diagnostic predictor can be trained with data from multiple individuals for which phenotypic traits, medical interventions, and LRRK2 inhibitor response outcomes are known. The multiple individuals used to train the diagnostic predictor are also known as the training population. For each individual in the training population, the training data includes (a) data representing multiple phenotypic traits; (b) medical interventions; and (c) LRRK2 inhibitor response information. LRRK2 inhibitor response outcomes may not be required to generate a diagnostic signature. LRRK2 inhibitor responses can be evaluated in prospectively selected patient populations. Various diagnostic predictors that can be used in conjunction with the present invention are described below. In some embodiments, the accuracy of diagnostic predictors obtained using a training population can be tested using additional individuals with known trait profiles and LRRK2 response outcomes. Such additional patients are known as test populations.

[0043] In certain embodiments, the genetic modifier is a SNP. In certain embodiments, the genetic modifier may be one or more SNPs selected from any of the SNPs in groups I to IV. In certain embodiments of the present invention, the SNP in group I is a rare variant of a harmful protein that encodes a change in the LRRK2 gene, which is known to increase kinase activity and significantly increase the risk of Parkinson's disease. In certain embodiments, the SNP in group II is associated with the level of lipid BMP in the urine of patients with Parkinson's disease. In certain embodiments, the SNP in group III is located around and / or near the LRRK2 gene. They can tag regulatory elements that can control LRRK2 expression. In certain embodiments, the SNP in group IV facilitates the optimal functioning of the detection system. Exemplary SNPs in groups I to IV are presented below: Group I: rs33939927, rs35801418, rs34805604, rs34637584, rs35870237, rs34995376, and rs34778348. Group II: rs11611119, rs1497046, rs12296462, rs549790, rs12423473, rs555740, rs2242367, rs7295598, rs2253736, rs11564274, rs2708419, rs17519419, rs76904798, rs17519573, rs11175847, rs10878452, rs17444612, rs11564264, rs11564235, rs17128233, rs7960976, and rs1918942. Group III: rs549790, rs611829, rs7531501, rs9793102, rs3755541, rs7578955, rs17738103, rs4676776, rs1259475, rs10477505, rs7380062, rs4636028, rs12704998, rs2768282, rs10283642, rs3750779, rs1362993, rs7089200, rs9783486, rs4763946, rs16920645, rs12312400, rs2708494, rs128132 79, rs11613339, rs7300813, rs166806, rs3794253, rs7960429, rs7955116, rs10491998, rs12814145, rs2708078, rs1922761, rs1373422, rs17691793, rs7301498, rs7967809, rs1866074, rs10507535, rs9300705, rs1642819, rs8048361, rs1152838, rs2331796, rs10515969, rs6566942, and rs2422956.

[0044] グループIV:rs11175847、rs10737170、rs10514889、rs709184、rs2842258、rs2 982373、rs11585441、rs6680835、rs10737374、rs535175、rs9438994、rs75 42025、rs7533665、rs16828092、rs270728、rs12139976、rs12023577、rs11 208250、rs7552569、rs17399290、rs10873661、rs1325273、rs4907105、rs11 57148、rs17016501、rs11102944、rs1218593、rs2275081、rs17385169、rs6 427299、rs584025、rs11264477、rs2814774、rs2494262、rs2427825、rs1205 8214、rs16852055、rs2146609、rs10449267、rs2268147、rs2089891、rs374 8022、rs11117564、rs4844863、rs10916131、rs903687、rs1923822、rs67003 81、rs12060035、rs12144733、rs7521596、rs10924936、rs11892108、rs654 4507、rs4953527、rs6724638、rs6544601、rs7599740、rs10198317、rs17047 533、rs1016339、rs11677415、rs878539、rs12615868、rs2034136、rs13009 072、rs7602946、rs1257193、rs2710184、rs10496767、rs1921772、rs403523 3、rs2602191、rs13395562、rs12621063、rs3116536、rs4673710、rs126172 70、rs721358、rs6751814、rs1504073、rs1391920、rs9815996、rs9856251、r s3805036、rs7652766、rs6786115、rs6550967、rs12493220、rs10866048、r s6784559、rs10511119、rs12494049、rs6441729、rs11708297、rs13094003、rs1994128、rs1554513、rs16839317、rs9825996、rs4683618、rs6768143、rs11705701、rs2268833、rs9867755、rs4689190、rs10009272、rs6837434、rs10033492、rs4832818、rs2911908、rs1032984、rs7683552、rs1350246、rs17238982、rs13149946、rs1897814、rs3846448、rs11098653、rs13136491、rs4485803、rs4130505、rs6824404、rs17057224、rs4862385、rs11132255、rs6852642、rs6553197、rs7654439、rs10065645、rs7728112、rs2973522、rs258879、rs10471439、rs7714264、rs358532、rs283619、rs16877147、rs4541642、rs6894105、rs1384282、rs17149301、rs920387、rs9637846、rs6889919、rs11745517、rs3111491、rs7716878、rs6861937、rs4868495、rs6420088、rs4700746、rs6867221、rs458108、rs6900447、rs267802、rs1410366、rs9368072、rs12210896、rs728398、rs1161397、rs1327266、rs2653344、rs9294329、rs9400038、rs7774400、rs9320489、rs7757785、rs9968822、rs11154296、rs6933986、rs2297367、rs806383、rs17150761、rs11536481、rs17667596、rs1723808、rs1548577、rs4722576、rs7801967、rs216730、rs1008262、rs41304、rs2893375、rs10951888、rs12533618、rs6971934、rs2110290、rs259139、rs861006、rs5015756、rs7806537、rs11761624、rs2075668、rs10238218、rs17331134、rs4383914、rs11766415、rs2536501、rs6979121、rs4730333、rs17558874、rs3779707、rs11985845、rs6530980、rs2628289、rs916550、rs16888648、rs7002709、rs4305943、rs7015322、rs7004403、rs10505145、rs10103670、rs11779980、rs6994324、rs17194924、rs6997195、rs7018287、rs2233334、rs7002431、rs1853418、rs2038592、rs1475111、rs10511552、rs3808750、rs10963691、rs10757769、rs1954280、rs11141486、rs7864316、rs12379601、rs10978589、rs10978602、rs10816518、rs1324952、rs747066、rs7034837、rs1541332、rs7090790、rs943101、rs11816077、rs359322、rs7906048、rs2015190、rs4369307、rs7914692、rs16917302、rs2393967、rs2619634、rs2289310、rs11187664、rs4919276、rs12267798、rs7893754、rs2788178、rs11196386、rs4752582、rs7922033、rs7907856、rs3781411、rs17155120、rs6482940、rs3842752、rs10835333、rs373894、rs11025356、rs10444236、rs1463079、rs11037114、rs11231603、rs11235519、rs193170、rs618929、rs7482554、rs10789547、rs10891103、rs4937131、rs7111323、rs1040099、rs12276840、rs4937861、rs216340、rs2302365、rs2948、rs2239675、rs1344717、rs11045248、rs7301416、rs1049377、rs16919821、rs2733682、rs1995303、rs1875311、rs7295095、rs11052842、rs1705772、rs9705446、rs7133706、rs7311662、rs1349257、rs4500524、rs4768592、rs12831835、rs12582802、rs7138679、rs7964150、rs515205、rs7971218、rs10784387、rs2708435、rs10878249、rs2404581、rs10506151、rs1427263、rs7307562、rs2404835、rs10878405、rs994798、rs1820545、rs10878590、rs10784629、rs4768293、rs2405286、rs1918954、rs1373431、rs7309561、rs10879153、rs11178396、rs1838343、rs1899827、rs1305000、rs11178879、rs10879360、rs1546081、rs9645832、rs7966732、rs3747556、rs11181115、rs2167352、rs10785280、rs1546190、rs11181158、rs7312550、rs1391003、rs17666080、rs12820881、rs1669921、rs11181613、rs12312435、rs7297752、rs11181729、rs1402360、rs1167161、rs7311395、rs4251580、rs10880606、rs2060896、rs3742060、rs7958908、rs2239182、rs2232565、rs7314952、rs10400566、rs17749783、rs201396、rs12579750、rs10861138、rs12824323、rs1525967、rs6489166、rs3751385、rs500755、rs476697、rs475916、rs9579104、rs2254191、rs9539579、rs4773918、rs473930、rs9519161、rs11543947、rs3210043、rs1884235、rs12880654、rs6571690、rs723204、rs17660245、rs10146989、rs7144018, rs17092921, rs17767434, rs11620653, rs2362865, rs17111972, rs4900406, rs11635966, rs2703957, rs1533861, rs11631192, rs1015 3042, rs1495279, rs1816619, rs4287512, rs1446332, rs2606169, rs12915467, rs7169879, rs8038143, rs8039999, rs8052848, rs4786169, rs29073 63, rs12445976, rs9888840, rs4444329, rs17796129, rs5728, rs9939129, rs4788105, rs79030220, rs8052403, rs17248940, rs727628, rs4888947 , rs12933198, rs7184633, rs4598906, rs12930347, rs12946712, rs7207104, rs2309810, rs1860380, rs2347548, rs4795064, rs8073372, rs895245, rs8079215, rs62074125, rs2332933, rs2302782, rs9906395, rs12941384, rs6501537, rs8091121, rs7231384, rs8093359, rs1436906, rs4502305, rs5005414, rs1261094, rs486285, rs334449, rs4369780, rs2972574, rs7254351, rs6512186, rs8104468, rs4805409, rs4805410, rs12984266, rs81 12461, rs633924, rs8119133, rs11087720, rs6055279, rs2235244, rs17189752, rs2206809, rs200753, rs200768, rs6131683, rs1420440, rs4810699, rs1276459, rs6025744, rs7263962, rs2822691, rs2822701, rs415850, rs2825942, rs877118, rs2330579, rs9619407, rs470062, and rs5764726.

[0045] In certain embodiments, SNPs may be in linkage disequilibrium (LD) with these SNPs, which are suitable as surrogates for these SNPs.

[0046] In certain embodiments, the method of the present invention uses a diagnostic predictor, also called a classifier, to determine the probability of responding to LRRK2 inhibition. As described above, the diagnostic predictor may be based on any suitable pattern recognition method that receives a profile, such as a profile based on multiple phenotypic traits, and provides an output that includes data indicating whether a patient is likely or unlikely to respond to an LRRK2 inhibitor, and may include the potential risks and benefits of treatment with such an inhibitor. The profile may be obtained by completing a questionnaire that includes questions about a particular phenotypic trait, or by collecting a biological sample to obtain genotypic data or a combination thereof. The diagnostic predictor is trained with training data from a training population of individuals whose phenotypic traits, medical interventions, and LRRK2 inhibitor response outcomes are known.

[0047] In certain embodiments, the method of the present invention relies on additional patient characteristics, in addition to genetic data, when determining whether a PD patient with wild-type LRRK2 is more likely to respond to an LRRK2 inhibitor. In certain embodiments, a patient characteristic that may be considered when determining whether a PD patient with wild-type LRRK2 is more likely to respond to an LRRK2 inhibitor is the patient's age. In certain embodiments, a patient characteristic that may be considered when determining whether a PD patient with wild-type LRRK2 is more likely to respond to an LRRK2 inhibitor is the patient's age at the time of diagnosis.

[0048] Diagnostic predictors based on any of these methods can be constructed using profiles and diagnostic data of trained patients. Such diagnostic predictors can then be used to predict the LRRK2 inhibitor response of a subject based on its phenotypic traits, genotypic traits, or both. This method can also be used, using trait profiles and diagnostic data from a training population, to identify traits that distinguish between those that respond to and those that do not respond to LRRK2 inhibition.

[0049] In one embodiment, the diagnostic predictor may be prepared by (a) generating a reference set of individuals for which phenotypic traits, medical interventions, and LRRK2 response outcomes are known; (b) determining, for each trait, a metric of correlation between the trait and the LRRK2 response outcome in multiple individuals having known LRRK2 response outcomes at a given time; (c) selecting one or more traits based on the level of association; and (d) training the diagnostic predictor with training data from the reference set of subjects, including evaluations of the traits adopted from individuals, wherein the diagnostic predictor receives data representing the traits selected in the previous step and provides an output indicating the probability of responding to LRRK2 inhibition.

[0050] Various known statistical pattern recognition methods can be used in conjunction with the present invention. Suitable statistical methods include, but are not limited to, logic regression, ordinal logistic regression, linear or quadratic discriminant analysis, clustering, principal component analysis, nearest neighbor classifier analysis, and Cox proportional hazards regression. To demonstrate the implementation of statistical methods in conjunction with training sets, a non-limiting example of combining and performing specific diagnostic predictors is provided herein.

[0051] In some embodiments, the diagnostic predictor is based on a regression model, preferably a logistic regression model. Such a regression model includes coefficients for each of the markers in a selected set of markers of the present invention. In such embodiments, the coefficients of the regression model are calculated, for example, using a maximum likelihood approach.

[0052] Cox proportional hazards regression also includes coefficients for each of the markers in a selected set of markers of the present invention. Cox proportional hazards regression incorporates censored data (individuals in the reference set who did not return to treatment). In such embodiments, the coefficients of the regression model are calculated, for example, using a maximum partial likelihood approach.

[0053] Some embodiments of the present invention provide a generalization of logistic regression models that handle multi-category (multinomial) responses. Such embodiments can be used to classify organisms into one, three, or more diagnostic groups. Such regression models use a multi-category logit model that simultaneously references all pairs of categories and describes the odds of a response in one category rather than another. Once the model specifies a logit for a particular (J-1) pair of categories, the rest becomes redundant. See, for example, Agresti, An Introduction to Categorical Data Analysis, John Wiley & Sons, Inc., 1996, New York, Chapter 8, which is incorporated herein by reference. Linear discriminant analysis (LDA) attempts to classify an object into one of two categories based on a particular object characteristic. In other words, LDA tests whether an object attribute measured experimentally predicts the classification of the object. LDA typically requires a continuous independent variable and a binary categorical dependent variable. In the present invention, selected phenotypic traits serve as the required continuous independent variable. The diagnostic group classification of each member of the training group acts as a binary category dependent variable.

[0054] LDA uses grouping information to find a linear combination of variables that maximizes the ratio of inter-group variance to intra-group variance. Implicitly, the linear weights used by LDA depend on how the selected phenotypic trait manifests in two groups (e.g., a group that responds to LRRK2 inhibition and a group that does not) and how the selected trait correlates with the manifestation of the other trait. For example, LDA can be applied to a data matrix of N members in a training sample by K genes in the gene combination described in the invention. The linear discriminant expression for each member of the training population is then plotted. Ideally, members of the training population representing a first subgroup (e.g., subjects that do not respond to LRRK2 inhibition) are clustered to one range of linear discriminant values ​​(e.g., negative), and members of the training population representing a second subgroup (e.g., subjects that respond to LRRK2 inhibition) are clustered to a second range of linear discriminant values ​​(e.g., positive). LDA is considered more successful when the separation between clusters of discriminant values ​​is greater. For more information on linear discriminant analysis, see Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York; and Venables & Ripley, 1997, Modern Applied Statistics with s-plus, Springer, New York.

[0055] Quadratic discriminant analysis (QDA) takes the same input parameters and returns the same results as LDA. QDA uses a quadratic equation instead of a linear equation to generate its results. LDA and QDA are interchangeable, and the choice between them depends on the preference and / or availability of the software supporting the analysis. Logistic regression takes the same input parameters and returns the same results as LDA and QDA.

[0056] In some embodiments of the present invention, a decision tree is used to classify patients using expression data of a selected set of molecular markers of the present invention. The decision tree algorithm belongs to the class of supervised learning algorithms. The purpose of a decision tree is to derive a classifier (tree) from real-world example data. This tree can be used to classify unknown examples that were not used to derive the decision tree.

[0057] The decision tree is derived from the training data. One example includes the values ​​of different attributes and which class the example belongs to. In one embodiment, the training data represents multiple phenotypic traits, medical interventions, and LRRK2 inhibition response outcomes.

[0058] The following algorithm describes the derivation of a decision tree: Tree(Examples,Class,Attributes) Create world root node If all Examples have the same Class value, give the root this label Else if Attributes is empty label the root according to the most Common Value Elsebe Calculate the information gain for each attribute Select the attribute A with highest information gain and make this the root home For each possible value,v,of this attribute Add a new branch below the root,corresponding to A=v Let Examples(v) be those examples with A=v If Examples(v) is empty,make the new branch a leaf node labeled with the most common value among Examples Else let the new branch be the tree created by Tree(Examples(v),Class,Attributes-{A}) end

[0059] A more detailed explanation of the calculation of information gain is given below. If the possible class vi of the example has probability P(vi), then the information content I of the actual answer is given by: I(P(v1), ...,P(v n ))=nΣi=1-P(v i )log2×P(v i )

[0060] The I value indicates the amount of information needed to describe the classification results for a particular dataset used. Assuming the dataset contains p positive examples (e.g., responders) and n negative examples (e.g., non-responders), the information contained in the ground truth is as follows: I(p / p+n,n / p+n)=-p / p+n log2p / p+nn / p+n log2n / p+n (In the formula, log2 is the base-2 logarithm). By testing a single attribute, the amount of information required to make a correct classification can be reduced. The remainder of a particular attribute A (e.g., a trait) indicates how much of the required information can be reduced. Remainder (A) = vΣi = 1 p i +n i / p+n I(p i / Pi+n i ,n i / p i +n i )

[0061] "v" is the number of unique attribute values ​​for attribute A in a particular dataset, "i" is an attribute value, and "p" is the number of unique attribute values ​​for attribute A in a particular dataset. i " is the number of examples (e.g., responders) for attribute A whose classification is positive, and "n i This is the number of examples for attribute A whose classification is negative (e.g., non-responders).

[0062] The information gain of a particular attribute A is calculated as the difference between the information content of the class and the remainder of attribute A. Gain(A) = I(p / p+n, n / p+n) - Remainder(A)

[0063] Information gain is used to assess how important different attributes are for classification (how much they divide the examples) and which attribute contains the most information.

[0064] In general, there are several different decision tree algorithms, many of which are described in Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc. Decision tree algorithms often require consideration of feature processing, impurity scales, stopping criteria, and pruning. Specific decision tree algorithms include, but are not limited to, classification and regression trees (CART), multivariate decision trees, ID3, and C4.5.

[0065] In one approach, when an exemplary embodiment of a decision tree is used, data representing multiple phenotypic traits across a training population are standardized to have a mean of zero and a unit variance. Members of the training population are randomly divided into a training set and a test set. For example, in one embodiment, two-thirds of the members of the training population are placed in the training set and one-third of the members of the training population are placed in the test set. The expression values of the selected combination of traits are used to construct a decision tree. Next, the ability of the decision tree to correctly classify members within the test set is determined. In some embodiments, this calculation is performed several times for a given combination of molecular markers. In each iteration of the calculation, the members of the training population are randomly assigned to the training set and the test set. Then, the quality of the combination of traits is considered the average of such each iteration of the decision tree calculation.

[0066] In some embodiments, phenotypic traits and / or genotype data are used to cluster the training set. For example, consider the case of using the 10 genes described in the present invention. Each member m of the training population has an expression value for each of the 10 genes. Such values from member m within the training population define the following vector: X 1m X 2m X 3m X 4m X 5m X 6m X 7m X 8m X[[ID=,23]] 9m X 10m (where X im(where is the expression level of the i-th gene in organism m). If there are m organisms in the training set, the selection of i genes defines m vectors. It should be noted that in the method of the present invention, each expression value of any single trait used in the vectors does not need to be represented in any single vector m. In other words, data from subjects where one of the i-th traits is not found can still be used for clustering. In such cases, the missing expression value is assigned to either "0" or some other normalized value. In some embodiments, before clustering, the trait expression values ​​are normalized to have a mean and unit variance of 0.

[0067] Members of a training population who exhibit similar expression patterns across training groups tend to cluster together. Certain combinations of traits in the present invention are considered good classifiers in this embodiment of the invention if the vector clusters to a set of traits found in the training population. For example, if the training population includes patients with good or poor prognosis, the clustering classifier clusters the population into two groups, each uniquely representing either good or poor prognosis.

[0068] Clustering is described on pages 211–256 of Duda and Hart, *Pattern Classification and Scene Analysis*, 1973, John Wiley & Sons, Inc., New York. As described in Duda's section 6.7, the clustering problem is described as one of finding natural groupings within a dataset. Two problems are addressed in identifying natural groupings. First, a method for measuring similarity (or difference) between two samples is determined. This metric (measure of similarity) is used to ensure that samples in one cluster are more similar to each other than samples in other clusters. Second, a mechanism for dividing the data into clusters using the measure of similarity is determined.

[0069] The measure of similarity is discussed in section 6.7 of Duda, where it is stated that one way to begin a clustering investigation is to define a distance function and compute a matrix of distances between all pairs of samples in a dataset. If distance is a good measure of similarity, the distances between samples in the same cluster will be significantly smaller than the distances between samples in different clusters. However, as described on page 215 of Duda, clustering does not require the use of a distance metric. For example, a nonmetric similarity function s(x, x') can be used to compare two vectors x and x'. Traditionally, s(x, x') is a symmetric function with a large value when x and x' are somewhat "similar". An example of a nonmetric similarity function s(x, x') is provided on page 216 of Duda.

[0070] Once a method is chosen to measure the "similarity" or "difference" between points in a dataset, clustering requires a baseline function to measure the clustering quality of any given segment of the data. The segments of the dataset that extremize the baseline function are used to cluster the data. See page 217 of Duda. The baseline function is described in section 6.8 of Duda.

[0071] More recently, Duda et al., Pattern Classification, 2nd edition, John Wiley & Sons, Inc. New York has been published. Pages 537 - 563 describe clustering in detail. Further information on clustering techniques can be found in Kaufman and Rousseeuw, 1990, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York, N.Y.; Everitt, 1993, Cluster analysis (3d ed.), Wiley, New York, N.Y.; and Backer, 1995, Computer - Assisted Reasoning in Cluster Analysis, Prentice Hall, Upper Saddle River, N.J. Specific exemplary clustering techniques that can be used in the present invention include hierarchical clustering (agglomerative clustering using the nearest - neighbor algorithm, farthest - neighbor algorithm, average linkage algorithm, centroid algorithm, or sum - of - squares algorithm), k - means clustering, fuzzy k - means clustering algorithm, and Jarvis - Patrick clustering, but are not limited thereto.

[0072] The nearest - neighbor classifier is memory - based and does not require a fitting model. Given a query point x0, the k training points x (r)、 r,..., k are identified, and then the k nearest neighbors are used to classify the point x0. Ties can be broken randomly. In some embodiments, the Euclidean distance in the feature space is used to determine the distance as follows:

Equation

[0073] Typically, when a nearest neighbor algorithm is used, the expression data used to calculate the linear discriminant is standardized to have a mean of 0 and a variance of 1. In this invention, members of the training population are randomly divided into a training set and a test set. For example, in one embodiment, 2 / 3 of the members of the training population are placed in the training set and 1 / 3 of the members of the training population are placed in the test set. The profile represents the feature space on which the members of the test set are plotted. Next, the ability of the training set to correctly characterize the members of the test set is calculated. In some embodiments, the nearest neighbor calculation is performed several times for a given combination of phenotypic traits. In each iteration of the calculation, members of the training population are randomly assigned to the training set and the test set. The quality of the trait combination is then considered to be the average of each such iteration of the nearest neighbor calculation.

[0074] The nearest neighbor rule can be improved to address unequal class priors, differential misclassification costs, and feature selection problems. Many of these improvements involve some form of weighted voting for neighbors. For further information on nearest neighbor analysis, see Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York.

[0075] The pattern classification and statistical techniques described above are merely examples of the types of models that can be used to construct models for classification. It should be understood that any statistical method can be used in accordance with the present invention. Furthermore, these can be used in combination. Further details regarding other statistical methods and their implementations are described in U.S. Patent No. 10,181,009, which is incorporated herein by reference in its entirety.

[0076] It is understood that during the course of treatment, individuals constituting the reference set may drop out before their LRRK2 inhibition response can be determined. It is unknown whether these individuals will ultimately respond to LRRK2 inhibition. Simply excluding these individuals from the reference set would bias the reference dataset by excluding characteristics of individuals with a poor prognosis for response. Such bias would lead to reporting overly optimistic probabilities of response to treatment with LRRK2 inhibitors.

[0077] Rather than extensively omitting these subjects, the present invention utilizes specific methods of statistical analysis to account for dropouts. For example, the Kaplan-Meier method can be used to censor or exclude data from individuals in the reference set who did not return to treatment. Other forms of statistical analysis can be used according to the present invention to edit the data in the reference set. For example, logistic regression, ordinal logistic regression, Cox proportional hazards regression, and other methods can all be used to edit the data in the reference set. In addition, the reference set is intended to allow for censoring or considering dropouts based on individual traits rather than making comprehensive assumptions about the responsiveness of dropouts. For example, rather than simply assuming that dropouts have the same opportunity to respond as individuals who continued treatment, or rather assuming that dropouts do not have the opportunity to respond, the present invention can evaluate the traits of dropouts and, based on such information, censor dropouts in a way that provides useful information. In this way, overly optimistic estimates (resulting from the assumption that all dropouts have an equal chance of responding) or overly conservative estimates (resulting from the assumption that dropouts do not have the opportunity to respond) are avoided.

[0078] In certain embodiments, the present invention incorporates the use of artificial censoring to account for dropouts. In artificial censoring, participants are censored if they meet predetermined test criteria, such as exposure to the intervention, non-adherence to the treatment regimen, or occurrence of a competing outcome. Further analytical methods, such as the inverse-probability-of-censoring weight (IPCW), can then be used to determine what the survival experience of the artificially censored participants would have been if they had not been exposed to the intervention, had adhered to it, or had not produced a competing outcome. In some embodiments, methods encompassing the use of artificial censoring, and further the use of IPCW, are incorporated into the present invention to account for dropouts in the reference set. Further details regarding the use of artificial censoring and the use of IPCW are provided by reference to Howe et al., Limitation of inverse probability-of-censoring weights in estimating survival in the presence of strong selection bias, Am J Epidemiology, 2011, which is incorporated herein by reference in its entirety.

[0079] The embodiments of the present invention described herein may be carried out using any type of computing device, such as a computer including a processor, for example a central processing unit, or any combination of computing devices, each device performing at least a part of the process or method. In some embodiments, the systems and methods described herein may be carried out using a handheld device, for example a smart tablet or smartphone, or a special device manufactured for the system.

[0080] The methods of the present invention can be implemented using software, hardware, firmware, hardwiring, or any combination thereof. The features that implement the functionality can also be physically located in various locations, including being distributed so that some of the functionality is implemented in different physical locations (e.g., an imaging device in one room and a host workstation in another room, or these devices in separate buildings with, for example, wireless or wired connectivity).

[0081] Processors suitable for executing computer programs include, for example, both general-purpose and dedicated microprocessors, as well as any one or more processors in any type of digital computer. Generally, a processor receives instructions and data from read-only memory or random-access memory or both. Essential elements of a computer are a processor for executing instructions, and one or more memory devices for storing instructions and data. Generally, a computer also includes one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks or optical disks, or is operablely coupled to receive data from them, transfer data to them, or both. Suitable information carriers for embodying computer program instructions and data include, for example, all forms of non-volatile memory, including semiconductor memory devices (e.g., EPROM, EEPROM, solid-state drives (SSDs), and flash memory devices); magnetic disks (e.g., internal hard disks or removable disks); magneto-optical disks; and optical disks (e.g., CDs and DVDs). Processors and memory can be complemented by or incorporated into dedicated logic circuits.

[0082] To provide user interaction, the subject matter described herein can be implemented on a computer having I / O devices for displaying information to the user, such as a CRT, LCD, LED, or projection device, and input or output devices such as a keyboard and pointing device (e.g., mouse or trackball) to which the user can provide input to the computer. User interaction can also be provided using other types of devices. For example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user can be received in any form, including acoustic, voice, or tactile input.

[0083] The subject matter described herein can be implemented in a computing system comprising backend components (e.g., data servers), middleware components (e.g., application servers), or frontend components (e.g., client computers having a graphical user interface or web browser that allows a user to interact with an implementation of the subject matter described herein), or any combination of such backend, middleware, and frontend components. The components of the system can be interconnected over a network by digital data communication in any form or medium, such as a communication network. For example, a reference set of data may be stored in a remote location, and the computer communicates over the network to access the reference set and compare data derived from the subject with the reference set. However, in other embodiments, the reference set is stored locally within the computer, and the computer accesses the reference set in the CPU and compares the subject data with the reference set. Examples of communication networks include cell networks (e.g., 3G or 4G), local area networks (LANs), and wide area networks (WANs), such as the Internet.

[0084] The subject matter described herein may be implemented as one or more computer program products, such as one or more computer programs tangibly embodied in an information carrier (e.g., in a non-temporary computer-readable medium) for execution by or control of a data processing device (e.g., a programmable processor, a computer, or multiple computers). A computer program (also known as a program, software, software application, application, macro, or code) can be written in any form of programming language, including compiled or interpreted languages ​​(e.g., C, C++, Perl), and may be deployed as a standalone program or in any form, including modules, components, subroutines, or other units suitable for use in a computing environment. The systems and methods of the present invention may, not limited to, include instructions written in any suitable programming language known in the art, including C, C++, Perl, Java®, ActiveX, HTML5, Visual Basic, or JavaScript®.

[0085] Computer programs do not necessarily correspond to files. A program can be stored in a file or part of a file that holds other programs or data, in a single file dedicated to the program in question, or in multiple collaborative files (e.g., a file containing one or more modules, subprograms, or parts of code). Computer programs can be deployed to run on a single computer, on multiple computers at a single site, or distributed across multiple sites and interconnected by a communication network.

[0086] A file can be a digital file stored on, for example, a hard drive, SSD, CD, or other tangible, non-temporary medium. A file can be transmitted from one device to another over a network (for example, from a server to a client, as a packet transmitted via, for example, a network interface card, modem, wireless card, or similar).

[0087] Writing a file according to the present invention involves transforming a tangible, non-temporary, computer-readable medium by adding, removing, or rearranging particles (e.g., using net charge or dipole moment on a magnetization pattern by a read / write head), where the pattern represents a new collocation of information about a physical phenomenon of interest to the user and useful to the user. In some embodiments, writing involves a physical transformation of a material in a tangible, non-temporary, computer-readable medium (e.g., having certain optical properties so that an optical read / write device can then read the new useful collocation of information (e.g., burn it to a CD-ROM)). In some embodiments, writing a file involves transforming a physical flash memory device, such as a NAND flash memory device, and storing information by transforming physical elements in an array of memory cells consisting of floating-gate transistors. Methods for writing files are well known in the art and can be invoked manually or automatically, for example, by a program, by a save command from software, or by a write command from a programming language.

[0088] A suitable computing device typically includes mass memory, at least one graphical user interface, at least one display device, and typically includes communication between devices. Mass memory refers to a type of computer-readable medium, i.e., computer storage medium. Computer storage medium may include volatile, non-volatile, removable, and non-removable media implemented in any way or technique for storing information such as computer-readable instructions, data structures, program modules, or other data. Examples of computer storage mediums include RAM, ROM, EEPROM, flash memory, or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, radio frequency identification tags or chips, or any other medium that can be used to store desired information and can be accessed by a computing device.

[0089] As those skilled in the art will recognize as necessary or most suitable for carrying out the methods of the present invention, the computer system or machine of the present invention includes one or more processors (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both) communicating with each other via a bus, main memory, and static memory.

[0090] The method of the present invention may utilize a machine learning system. For example, the machine learning system may be trained using supervised, unsupervised, semi-supervised, or reinforcement learning methods.

[0091] In unsupervised or autonomous models, the machine learning system is given only input training data without paired output data for autonomously identifying patterns. Unsupervised models identify underlying patterns or structures in the training data to make predictions on test data. Unsupervised models are advantageous for clustering data, detecting anomalies, and independently discovering rules in data. The accuracy of unsupervised models is more difficult to evaluate because there are no predetermined output variables for the system to optimize. Autonomous models may employ periods of both supervised and unsupervised learning to optimize predictions. Unsupervised models are advantageous for training machine learning systems to cluster data when labeled training data is unavailable. Unsupervised models may use principal component analysis (PCA), Uniform Manifold Approximation and Projection (UMAP). Discriminant analysis may also be used when groups in the training and test data are already known. Discriminant analysis may include linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA).

[0092] In a semi-supervised model, the machine learning system is given training data containing input variables, and output variable pairs are available only for a limited pool of input variables. The model learns patterns using the input variables with output variable pairs and the remaining input training data, and performs inference to generate predictions about test data that have not been seen before. A semi-supervised model may, advantageously, query the user for additional paired output data based on unpaired data. Semi-supervised models are advantageous for training machine learning systems when only incomplete training datasets are available.

[0093] In reinforcement learning models, the machine learning system is not given either input or output variables. Rather, the model provides a "reward" condition and then attempts to maximize the cumulative reward condition through trial and error. Reinforcement learning models are Markov decision processes. Supervised, unsupervised, semi-supervised, and reinforcement models are incorporated by reference in Jordan and Mirchell, 2015, Machine learning, Trends, perspectives, and prospects, Science 349(6245):255-260.

[0094] One example of a supervised learning model is a "decision tree." A decision tree is a nonparametric supervised learning model that uses simple decision rules to infer the classification of test data from features within the test data. In a classification tree, the test data can be discrete values ​​or a finite set of classes, while in a regression tree, the test data can be continuous values ​​such as real numbers. Decision trees have several advantages in that they are easy to understand and can be visualized as a tree that starts with a root (usually a single node) and repeatedly branches into leaves (multiple nodes) associated with classification. See Criminisi, 2012, Decision Forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning, Foundations and Trends in Computer Graphics and Vision 7(2-3):81-227, which is incorporated by reference.

[0095] Another supervised learning model is the “Support Vector Machine” (SVM), “Support Vector Network” (SVN), or “Support Vector Classifier” (SVC), which is a supervised learning model for classification and regression problems. When used to classify new data into one of two categories, an SVM creates a hyperplane in a multidimensional space that separates data points into one category or the other. The original problem can be expressed in terms that require only a finite-dimensional space, but linear separation of data between categories may not be possible in a finite-dimensional space. As a result, a multidimensional space is chosen to allow the construction of a hyperplane that results in a clear separation of data points. See Press, WH et al., Section 16.5. Support Vector Machines. Numerical Recipes: The Art of Scientific Computing (3rd ed.). New York: Cambridge University (2007), incorporated herein by reference. If the output variable pair is not available for the input variables in the training data, an SVM can be designed as an unsupervised or semi-supervised learning model using support vector clustering. See Ben-Hur, 2001, Support Vector Clustering, J Mach Learning Res 2:125-137, which is incorporated by reference. SVM models may be advantageous for machine learning systems where the test data falls into a limited number of possible categories. Furthermore, SVM models may be advantageous when only a limited set of training data is available to the machine learning system.

[0096] Logistic regression analysis is another statistical process that can be used by machine learning systems to find patterns in training and test data for making predictions. It involves techniques for modeling and analyzing relationships between multiple variables. Specifically, regression analysis focuses on the change in a dependent variable in response to a change in a single independent variable. Using regression analysis, it is possible to estimate the conditional expected value of the dependent variable, taking the independent variable into account. The variation in the dependent variable can be characterized around a regression function and described by a probability distribution. The parameters of the regression model can be estimated using, for example, least squares, Bayesian methods, percentile regression, least absolute deviation, nonparametric regression, or distance metric learning. Regression models also offer the advantage that they can be effectively implemented by a variety of tools, and the models can be easily updated to identify new particles.

[0097] SVM and logistic regression systems can use stochastic gradient descent (SGD) techniques to fit data. SGD is advantageous for optimizing machine learning systems using this technique.

[0098] Bayesian algorithms can also be used to discover patterns in training and test data to make predictions. A Bayesian network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). A DAG has nodes that represent observable quantities, latent variables, node unknown parameters, or random variables that may be hypothetical. Edges represent conditional dependencies; unconnected nodes represent variables that are conditionally independent of each other. Each is associated with a probability function that takes a specific set of values ​​of the node's parent variable as input and gives (as output) the probability (or probability distribution, if applicable) of the variable represented by the node. Bayesian models generally offer the advantage of requiring less training data than other models.

[0099] Some models may rely on clustered training and test data to find patterns and make predictions. The "k-nearest neighbors" (k-NN) model is a supervised nonparametric learning model for classification and regression problems. The k-nearest neighbors model assumes that similar data exist in close proximity and assigns a category or value to each data point based on its k nearest neighbors. k-NN models can be advantageous when the data has few outliers and can be defined by uniform features. Furthermore, k-NN models offer the advantage of continuously learning from test data, eliminating the need for a training period before identifying material from the training data.

[0100] One example of an unsupervised learning model using clustering is the "k-means" clustering model. The k-means model aims to discover clusters of data in the input and test data. The k-means model is advantageous when a defined number of clusters are known to exist in the data, and also when the test data has few outliers and uniform features can be defined. Additional models for clustering training data include, for example, far-neighbor, centroid, sum of squares, fuzzy k-means, and Jarvis-Patrick clustering. The k-means and other unsupervised clustering models are advantageous when training data is unavailable or limited.

[0101] A trained machine learning model can be a "stable learner." A stable learner is a model that is less sensitive to perturbations in its predictions based on new training data. Stable learners may be advantageous when the test data is stable, but they may be less advantageous when the system needs to continuously improve its performance to accurately predict new test data that may be less stable. Therefore, stable learned models may be advantageous for use in machine learning systems when the type of data that may be introduced is known and unlikely to change.

[0102] Several machine learning system types can be combined into a final predictive model known as an ensemble. Ensembles can be divided into two types: homogeneous ensembles and heterogeneous ensembles. A homogeneous ensemble combines multiple machine learning models of the same type. A heterogeneous ensemble combines multiple machine learning models of different types. Ensembles can offer advantages because they may be more accurate than any of the individual basic member models ("members") within the ensemble. The number of members combined in an ensemble can affect the accuracy of the final prediction. Therefore, it is advantageous to determine the optimal number of members when designing an ensemble system to be used by a machine learning system.

[0103] Ensembles used by machine learning systems can combine or aggregate the outputs from individual members by using "voting" methods for classification systems and "averaging" methods for regression systems. In "majority voting," each member makes a prediction about the test data, and the prediction that receives a majority of the votes is the final output of the ensemble. If no prediction receives a majority of the votes, it may be determined that the ensemble cannot make stable predictions. In "plurality voting," the most voted prediction may be considered the final output of the ensemble, even if it receives less than half of the votes. In "weighted voting," the votes of the more accurate members are multiplied by a weight assigned to each member based on their accuracy. In "simple averaging," each member makes a prediction about the test data, and the average of the outputs is calculated. This method may be advantageous in reducing overfitting and creating smoother regression models. In the weighted averaging method, each member's prediction output is multiplied by a weight assigned to each member based on its accuracy. By combining voting methods, averaging methods, and weighting methods, the accuracy of the ensemble used by the machine learning system can be improved.

[0104] Members within an ensemble used by a machine learning system can be trained independently, or new members can be trained by utilizing information from previously trained members. In a "parallel ensemble," the ensemble attempts to provide higher accuracy than individual members by leveraging the independence of its members, for example, by training multiple members simultaneously to identify and aggregate their outputs. In a "sequential ensemble system," the ensemble attempts to provide higher accuracy than individual members by leveraging the dependencies between members, for example, by using information from a first member regarding data identification to improve the training of a second member to identify data and weight the outputs from that member.

[0105] The overall accuracy of an ensemble used by a machine learning system can be optimized by using ensemble meta-algorithms, such as "bagging" algorithms to reduce variance, "boosting" algorithms to reduce bias, or "stacking" algorithms to improve predictions.

[0106] Boosting algorithms can be used to reduce bias and improve less accurate or "weakly learned" models. A member may be considered a "weakly learned" model if it has a substantial error rate but its performance is non-random. The algorithm is boosted to gradually build an ensemble by sequentially training each member on the same training dataset, examining the prediction error on the test data, and assigning weights to the training data based on the difficulty of the member making accurate predictions. For each sequentially trained member, the algorithm highlights the training data that previous members found difficult. Members are then weighted based on the accuracy of their prediction outputs, taking into account the weights applied to the training data. Predictions from each member may be combined using a weighted voting or weighted averaging method. Boosting algorithms are advantageous when combining multiple weakly learned models. However, boosting algorithms can overfit the test data to the training data. Examples of boosting algorithms include AdaBoost, gradient boosting, and eXtreme Gradient Boost (XGBoost). See Freund, 1997, A decision-theoretic generalization of on-line learning and an application to boosting, J Comp Sys Sci 55:119; and Chen, 2016, XGBoost: A Scalable Tree Boosting System, arXiv:1603.02754, which are incorporated by reference.

[0107] The bagging algorithm, or "bootstrap aggregation" algorithm, reduces variance by averaging multiple estimates from members together. The bagging algorithm provides each member with a random subsample of the full training dataset, each random subsample known as a "bootstrap" sample. In the bootstrap samples, some data from the training dataset may appear multiple times, while some data from the training dataset may be absent. Since the subsamples can be generated independently of each other, training can be performed in parallel. The test data predictions from each member are then aggregated using methods such as voting or averaging.

[0108] One example of a bagging algorithm that can be used by machine learning systems is the "random forest." In a random forest, an ensemble combines multiple randomized decision tree models. Each decision tree model is trained on bootstrap samples from a training set for test data. The training set itself may be a random subset of features from an even larger training set. By providing a random subset of a larger training set at each split in the learning process, spurious correlations that may arise from the presence of individual features that are strong predictors for the output variables are reduced. By averaging the predictions for the test data, the variance of the ensemble is reduced, and the predictions for the test data are improved. A random forest can be an autonomous model and may include periods of both supervised and unsupervised learning. Bagging may not be very advantageous when optimizing an ensemble of stable learning systems, as stable learning systems tend to provide generalized outputs with less variability across bootstrap samples. Random forests are advantageous for use by machine learning systems for data identification by providing a high degree of versatility in identifying test data by machine learning systems and in reducing spurious identification by machine learning systems. See Breiman, 2001, Random Forests, Machine Learning 45:5-32, which is incorporated by reference.

[0109] Stacking algorithms, or "stacked generalization" algorithms, improve predictions by combining and building ensembles using meta-machine learning models. In stacking algorithms, the base member models are trained on a training dataset, generating a new dataset as output. This new dataset is then used as the training dataset for the meta-machine learning models to build the ensemble. Stacking algorithms are generally favored by machine learning systems to identify test data when building heterogeneous ensembles. Ensembles are incorporated by reference in Villaverde et al., 2019, On the adaptability of ensemble methods for distribution classification systems: A comparative analysis, International Journal of Distributed Sensor Networks 15(7); and Heitor et al., 2017, A Survey of Ensemble Learning for Data Stream Classification, 50(2):Art. 23, respectively.

[0110] Neural networks, modeled after the human brain, enable information processing and machine learning. A neural network includes nodes that mimic the function of individual neurons, and these nodes are organized into layers. A neural network includes an input layer, an output layer, and one or more hidden layers that define connections from the input layer to the output layer. The systems and methods of the present invention may include any neural network that facilitates machine learning. The system may include known neural network architectures such as GoogLeNet (Szegedy, et al. Going deeper with convolutions, in CVPR 2015, 2015); AlexNet (Krizhevsky, et al. Imagenet classification with deep convolutional neural networks, in Pereira, et al. Eds., Advances in Neural Information Processing Systems 25, pages 1097-3105, Curran Associates, Inc., 2012); VGG16 (Simonyan & Zisserman, Very deep convolutional networks for large-scale image recognition, CoRR, abs / 3409.1556, 2014); or FaceNet (Wang et al., Face Search at Scale: 80 Million Gallery, 2015) (each of the above references is incorporated by reference). The advantage of using machine learning systems based on neural network architectures is that neural networks can learn patterns and correlations on their own and produce outputs that are not limited by the training data provided to them.

[0111] Deep learning neural networks (also known as deep structured learning, hierarchical learning, or deep machine learning) encompass a class of machine learning behaviors that can be used by classifiers that use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer takes the output from the previous layer as input. The algorithm may be supervised or unsupervised, and applications include pattern analysis (unsupervised) and classification (supervised). Certain embodiments are based on unsupervised learning of multi-level features or representations of data. Higher-level features are derived from lower-level features to form a hierarchical representation. Deep learning with neural networks involves learning multi-level representations corresponding to different levels of abstraction, where the levels form a hierarchy of concepts. In some embodiments, the neural network includes at least five, preferably more than ten, hidden layers. Many layers between input and output allow the system to operate through multiple processing layers.

[0112] In a neural network that can be used by machine learning systems, nodes are connected in layers, and signals travel from the input layer to the output layer. Each node in the input layer may correspond to a feature from the training data. The nodes in the hidden layer are computed as a function of a bias term and a weighted sum of the input layer nodes, with each connection between the input layer nodes and the hidden layer nodes assigned a respective weight. The bias term and the weights between the input and hidden layers are learned autonomously to an advantage during the training of the neural network. The network may contain thousands or millions of nodes and connections. Typically, the signals and states of artificial neurons are real numbers, usually between 0 and 1. If necessary, each connection and unit itself may have a threshold function or limit function so that the signal must exceed a limit before it propagates. Backpropagation is the use of forward stimuli to correct connection weights and may be performed to train the network with known correct outputs. See WO2016 / 182551, U.S. Patent Application Publication No. 2016 / 0174902, U.S. Patent No. 8,639,043, and U.S. Patent Application Publication No. 2017 / 0053398 (each incorporated by reference).

[0113] Features from test or training data can be represented by a deep learning network in many forms, such as a vector of intensity values ​​for each pixel in an image, or in a more abstract form, such as a set of edges or a region of a specific shape. These features are represented by nodes in the network. Preferably, each feature is configured as a numerical feature or vector representing an image feature. Such representations facilitate processing and statistical analysis, for example, providing a numerical representation of an object from an image. Numerical features are often combined with weights using a dot product to construct a linear predictor function used to determine a score for making predictions.

[0114] The vector space associated with these feature vectors is sometimes called the feature space. To reduce the dimensionality of the feature space, dimensionality reduction may be employed by the network used by the classifier. Higher-level features may be acquired from already available features and added to the feature vector in a process called feature building. Feature building is the application of a set of build operators to an existing set of features that results in the construction of new features. For example, a machine learning system based on a neural network architecture may be provided with image data from an image sensor. The early layers of the neural network may identify horizontal and vertical lines in the image data. Later layers in the network may then use the identified lines to acquire edges, which are higher-level features about particles in the image.

[0115] A deep learning neural network may be a multilayer perceptron (MLP), a convolutional neural network (CNN), or a recurrent neural network (RNN).

[0116] Assay for obtaining genetic data The identification or analysis of one or more genetic modifiers of LRRK2 may involve performing an assay on a sample obtained from a subject. The sample may be any type of sample containing genetic material such as DNA or RNA. For example, and not limited to, the sample may be from amniotic fluid, biopsy material, blood, body fluids, cells, cerebrospinal fluid, lymph, mouthwash, needle aspiration biopsy material, hair, phlegm, plasma, pus, saliva, semen, serum, sputum, feces, swabs, sweat, synovial fluid, tears, tissue, urine, or any combination of the above samples. For example, and not limited to, tissue samples may be derived from bone marrow tissue, CNS tissue, eye tissue, gastrointestinal tissue, genitourinary tissue, hair, kidney tissue, liver tissue, mammary gland tissue, musculoskeletal tissue, nails, nasal passage tissue, nerve tissue, placental tissue, or skin tissue.

[0117] The subjects may be of any type. The subjects may be human. The subjects may exhibit one or more symptoms of Parkinson's disease, or they may be asymptomatic. The patients may be associated with PD patients. The subjects may be pediatric patients, newborns, neonates, infants, children, adolescents, preteens, teenagers, adults, or elderly individuals. The subjects may exhibit one or more symptoms of Parkinson's disease, or they may be asymptomatic. The patients may be associated with PD patients.

[0118] Methods for gene analysis are well known in the art. In certain embodiments, known single nucleotide polymorphisms at specific locations can be detected, for example, by single-nucleotide extension of a primer that binds to the adjacent sample DNA, as described in U.S. Patent No. 6,566,101, the entire contents of which are incorporated herein by reference. In other embodiments, hybridization probes can be used that overlap with the SNP of interest and selectively hybridize to a sample nucleic acid containing a specific nucleotide at that location, as described in U.S. Patents No. 6,214,558 and No. 6,300,077, the entire contents of which are incorporated herein by reference.

[0119] In certain embodiments, nucleic acids are sequenced to detect variants (i.e., mutations) in the nucleic acid compared to wild-type and / or non-mutant forms of the sequence. The nucleic acid may comprise multiple nucleic acids derived from multiple gene elements. Methods for detecting sequence variants are known in the art, and sequence variants can be detected by any sequencing method known in the art, such as ensemble sequencing or single-molecule sequencing.

[0120] Sequencing may be performed by any method known in the art. DNA sequencing techniques include classical dideoxy sequencing (Sanger method) using labeled terminators or primers and gel separation on slabs or capillaries, synthetic sequencing using reversibly terminated labeled nucleotides, pyrosequencing, 454 sequencing, synthetic sequencing using allele-specific hybridization of labeled oligonucleotide probes into a library, synthetic sequencing using allele-specific hybridization of labeled clones followed by ligation into a library, real-time monitoring of the incorporation of labeled nucleotides during the polymerization step, Polony sequencing, and SOLiD sequencing. Sequencing of isolated molecules has recently been demonstrated by sequential or single extension reactions using polymerases or ligases, as well as single or sequential differential hybridization with libraries of probes.

[0121] One conventional method for performing sequencing is by strand termination and gel separation, as described, for example, in Sanger et al., Proc. Natl. Acad. Sci. USA, 74(12): 5463 67 (1977). Another conventional sequencing method involves the chemical degradation of nucleic acid fragments, as described, for example, in Maxam et al., Proc. Natl. Acad. Sci., 74: 560 564 (1977). Finally, a method based on sequencing by hybridization has been developed, as described, for example, in U.S. Patent Application Publication No. 2009 / 0156412. The contents of each reference are incorporated herein by reference in their entirety.

[0122] Examples of sequencing techniques that may be used in the methods of the present invention provided include, for example, Harris TD et al., Single-Molecule DNA Sequencing of a Viral Genome, (2008) Science 320:106-109. In true single-molecule sequencing (tSMS) techniques, a DNA sample is cut into strands of approximately 100-200 nucleotides, and a poly(A) sequence is added to the 3' end of each DNA strand. Each strand is labeled by the addition of a fluorescently labeled adenosine nucleotide. The DNA strands are then hybridized to a flow cell containing millions of oligo-T capture sites immobilized on the flow cell surface. The template consists of approximately 100 million templates / cm². 2 The density can be as follows. The flow cell is then loaded into an instrument, such as a HeliScope® sequencer, and a laser illuminates the surface of the flow cell to reveal the position of each template. A CCD camera can map the positions of the templates on the flow cell surface. The fluorescent labels of the templates are then cut and washed away. The sequencing reaction is initiated by introducing DNA polymerase and fluorescently labeled nucleotides. Oligo-T nucleic acids act as primers. The polymerase incorporates the labeled nucleotides into the primers in a template-directed manner. The polymerase and unincorporated nucleotides are removed. Templates with directional incorporation of fluorescently labeled nucleotides are detected by imaging the flow cell surface. After imaging, the process is repeated with other fluorescently labeled nucleotides until the cutting step removes the fluorescent label and the desired read length is achieved. Sequence information is collected after each nucleotide addition step. Further descriptions of tSMS are found, for example, in U.S. Patent Nos. 7,169,560; 6,818,395; and 7,282,337; U.S. Patent Application Publication Nos. 2009 / 0191565 and 2002 / 0164629; and in Braslavsky, et al., PNAS (USA), 100: 3960-3964 (2003), the contents of each of these are incorporated herein by reference in their entirety. Another example of a DNA sequencing technique that may be used in the methods of the present invention provided is 454 sequencing (Roche), as described, for example, in Margulies, M et al. 2005, Nature, 437, 376-380. 454 sequencing comprises two steps. In the first step, the DNA is sheared into fragments of approximately 300-800 base pairs and the fragments are blunt-ended. Oligonucleotide adapters are then ligated to the ends of the fragments. The adapters serve as primers for amplification and sequencing of the fragments. The fragments can be bound to DNA capture beads, such as streptavidin-coated beads, using, for example, adapter B containing a 5'-biotin tag. The fragments bound to the beads are PCR-amplified in droplets of oil-water emulsion. The result is multiple copies of the clone-amplified DNA fragment on each bead. In the second step, the beads are captured in wells (picoliter size). Pyrosequencing is performed in parallel for each DNA fragment. The addition of one or more nucleotides generates a light signal that is recorded by a CCD camera in a sequencing instrument. The signal intensity is proportional to the number of nucleotides incorporated. Pyrosequencing utilizes pyrophosphate (PPi) released during nucleotide addition. PPi is converted to ATP by ATP sulfurylase in the presence of adenosine 5' phosphosulfate. Luciferase uses ATP to convert luciferin to oxyluciferin, and this reaction generates light that is detected and analyzed.

[0123] Another example of a DNA sequencing technique that may be used in the methods of the present invention provided is SOLiD (Applied Biosystems) technology. In SOLiD sequencing, genomic DNA is sheared into fragments, and adapters are attached to the 5' and 3' ends of the fragments to produce a fragment library. Alternatively, internal adapters can be introduced by ligating the adapters to the 5' and 3' ends of the fragments, circularizing the fragments, digesting the circularized fragments to produce internal adapters, and attaching the adapters to the 5' and 3' ends of the resulting fragments to produce a mate-pair library. Next, a population of cloned beads is prepared in a microreactor containing beads, primers, templates, and PCR components. After PCR, the templates are denatured, the beads are enriched, and beads with the extended templates are separated. The templates on the selected beads are subjected to 3' modification to enable binding to a glass slide. The sequence can be determined by sequential hybridization and ligation of partially random oligonucleotides with a central determined base (or base pair) identified by a specific fluorophore. After recording the color, the ligated oligonucleotides are cleaved and removed, and then the process is repeated.

[0124] Another example of DNA sequencing techniques that may be used in the methods of the present invention provided is Ion Torrent sequencing, as described in U.S. Patent Publications 2009 / 0026082, 2009 / 0127589, 2010 / 0035252, 2010 / 0137143, 2010 / 0188073, 2010 / 0197507, 2010 / 0282617, 2010 / 0300559, 2010 / 0300895, 2010 / 0301398, and 2010 / 0304982, the contents of each of these are incorporated herein by reference in their entirety. In ion torrent sequencing, DNA is sheared into fragments of approximately 300–800 base pairs, and the fragments are blunt-ended. Oligonucleotide adapters are then ligated to the ends of the fragments. The adapters function as primers for fragment amplification and sequencing. The fragments are bound at a resolution such that they can be ligated to the surface and the fragments can be individually separated. The addition of one or more nucleotides is followed by the proton (H) + This releases a signal, which is detected and recorded by sequencing instruments. The signal intensity is proportional to the number of nucleotides incorporated. Another example of sequencing techniques that may be used in the methods of the present invention provided is Illumina sequencing. Illumina sequencing is based on the amplification of DNA on a solid surface using folded PCR and fixed primers. Genomic DNA is fragmented, and adapters are added to the 5' and 3' ends of the fragments. The DNA fragments bound to the surface of the flow cell channel are extended and crosslinked for amplification. The fragments become double-stranded, and the double-stranded molecules are denatured. Multiple cycles of solid-phase amplification and subsequent denaturation can create millions of clusters of approximately 1,000 copies of single-stranded DNA molecules of the same template in each channel of the flow cell. Sequential sequencing is performed using primers, DNA polymerase, and four fluorophore-labeled, reversibly terminated nucleotides. After nucleotide incorporation, a laser is used to excite the fluorophores, acquire an image, and record the identity of the first base. The 3' terminator and fluorophores are removed from each incorporated base, and the incorporation, detection, and identification steps are repeated.

[0125] Another example of sequencing techniques that may be used in the methods of the present invention provided is Pacific Biosciences' Single-Molecule Real-Time (SMRT) technology. In SMRT, each of the four DNA bases is bound to one of four different fluorescent dyes. These dyes are phospholinked. A single DNA polymerase is immobilized at the bottom of a zero-mode waveguide (ZMW) with a single molecule of template single-stranded DNA. The ZMW is a confinement structure that allows observation of the incorporation of a single nucleotide by the DNA polymerase against a background of fluorescent nucleotides rapidly diffusing outside the ZMW (microseconds). It takes several milliseconds to incorporate the nucleotide into the growing strand. During this time, the fluorescent label is excited, generating a fluorescent signal, and the fluorescent tag is cleaved. Detection of the corresponding fluorescence of the dye indicates which base has been incorporated. This process is repeated. Another example of sequencing techniques that may be used in the methods of the present invention provided is nanopore sequencing, as described, for example, in Soni GV and Meller A. (2007) Clin Chem 53: 1996-2001. A nanopore is a small pore with a diameter of about 1 nanometer. When a nanopore is immersed in a conductive fluid and a potential is applied across it, a small current is generated due to the conduction of ions through the nanopore. The amount of current that flows is sensitive to the size of the nanopore. As a DNA molecule passes through the nanopore, each nucleotide on the DNA molecule interferes with the nanopore to a different degree. Therefore, the change in the current passing through the nanopore as a DNA molecule passes through it represents the reading of the DNA sequence.

[0126] Another example of a sequencing technique that can be used in the methods of the present invention provided involves sequencing DNA using a chemical-sensitive field-effect transistor (chemFET) array, as described, for example, in U.S. Patent Application Publication No. 20090026082. In one example of this technique, a DNA molecule can be placed in a reaction chamber and hybridized with a sequencing primer conjugated to polymerase, which is a template molecule. The incorporation of one or more triphosphates into a new nucleic acid chain at the 3' end of the sequencing primer can be detected by a change in current by the chemFET. The array may have multiple chemFET sensors. In another example, a single nucleic acid can be bound to beads, the nucleic acid can be amplified on the beads, and the individual beads can be transferred to individual reaction chambers on a chemFET array, each chamber having a chemFET sensor, which can sequence the nucleic acid. Another example of a sequencing technique that may be used in the method of the present invention provided involves using an electron microscope, as described, for example, in Modrianakis EN and Beer M. Proc Natl Acad Sci USA. 1965 March; 53:564-71. In this example of the technique, individual DNA molecules are labeled with identifiable metal labels using an electron microscope. These molecules are then spread on a flat surface and imaged using an electron microscope to measure their sequences.

[0127] If nucleic acids derived from a sample are degraded, or if only a minimal amount of nucleic acid can be obtained from the sample, PCR can be performed on the nucleic acid to obtain a sufficient amount of nucleic acid for sequencing, for example, as described in U.S. Patent No. 4,683,195 (the contents of which are incorporated herein by reference in their entirety).

[0128] Methods for detecting the levels of gene products (e.g., RNA or protein) are well known in this field.

[0129] Commonly known methods in the art for quantifying mRNA expression in a sample include, for example, Northern blotting and in situ hybridization as described in Parker & Barnes, Methods in Molecular Biology 106:247-283 (1999) (the entire content of which is incorporated herein by reference); RNAse protection assay, Hod, Biotechniques 13:852-854 (1992) (the entire content of which is incorporated herein by reference); and PCR-based methods such as reverse transcription polymerase chain reaction (RT-PCR), Weis et al., Trends in Genetics 8:263-264 (1992) (the entire content of which is incorporated herein by reference). Alternatively, antibodies capable of recognizing specific double helixes, including RNA double helixes, DNA-RNA hybrid double helixes, or DNA-protein double helixes, may be used. Other methods known in the art for measuring gene expression (e.g., RNA levels or protein levels) are described, for example, in U.S. Patent Application Publication No. 2006 / 0195269, the contents of which are incorporated herein by reference in their entirety.

[0130] Differentially or abnormally expressed genes refer to genes whose expression is activated at higher or lower levels in subjects suffering from disorders such as Parkinson's disease (PD) compared to their expression in normal or control subjects. This term also includes genes whose expression is activated at higher or lower levels at different stages of the same disorder. Differentially expressed genes are also understood to be activated or inhibited at the nucleic acid or protein level, or to be subjected to alternative splicing resulting in different polypeptide products. Such differences may be demonstrated, for example, by changes in polypeptide mRNA levels, surface expression, secretion, or other distribution.

[0131] Differential gene expression can include comparisons of expression between two or more genes or their gene products, comparisons of expression ratios between two or more genes or their gene products, or even comparisons of two differently processed products of the same gene, which differ between normal subjects and subjects with disorders such as PD, or between different stages of the same disorder. Differential expression includes both quantitative and qualitative differences in temporal or cellular expression patterns in a gene or its expression product. Differential gene expression (increases and decreases in expression) is based on a percentage or fold change relative to expression in normal cells. An increase may be 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, or 200% compared to the expression level in normal cells. Alternatively, a multiplicative increase may be a multiplicative increase of 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, or 10 times compared to the expression level in normal cells. A decrease may be a decrease of 1, 5, 10, 20, 30, 40, 50, 55, 60, 65, 70, 75, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 99, or 100% compared to the expression level in normal cells.

[0132] In certain embodiments, reverse transcriptase PCR (RT-PCR) is used to measure gene expression. RT-PCR is a quantitative method that can be used to characterize gene expression patterns, identify closely related mRNAs, and analyze RNA structure, as well as to compare mRNA levels in different sample populations.

[0133] The first step is the isolation of mRNA from the target sample. The starting material is typically total RNA isolated from human tissue or bodily fluids.

[0134] General methods for mRNA extraction are well known in this field and are disclosed in standard molecular biology textbooks, including Ausubel et al., *Current Protocols of Molecular Biology*, John Wiley and Sons (1997). Methods for RNA extraction from paraffin-embedded tissues are disclosed, for example, in Rupp and Locker, *Lab Invest. 56:A67* (1987) and De Andres et al., *BioTechniques 18:42044* (1995). The contents of each of these references are incorporated herein by reference in their entirety. In particular, RNA isolation can be performed using purification kits, buffer sets, and proteases from commercial manufacturers such as Qiagen, according to the manufacturer's instructions. For example, total RNA from cells in culture can be isolated using a Qiagen RNeasy mini-column. Other commercially available RNA isolation kits include the MASTERPURE Complete DNA and RNA Purification Kit (EPICENTRE, Madison, Wis.) and the Paraffin Block RNA Isolation Kit (Ambion, Inc.). Total RNA from tissue samples can be isolated using RNA Stat-60 (Tel-Test). RNA prepared from tumors can be isolated, for example, by cesium chloride density gradient centrifugation.

[0135] The first step in gene expression profiling by RT-PCR is the reverse transcription of an RNA template into cDNA, followed by exponential amplification in a PCR reaction. The two most commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney mouse leukemia virus reverse transcriptase (MMLV-RT). The reverse transcription step is typically primed using specific primers, random hexamers, or oligo-dT primers, depending on the context and the purpose of expression profiling. For example, extracted RNA can be reverse transcribed using the GeneAmp RNA PCR kit (Perkin Elmer, Calif., USA) according to the manufacturer's instructions. The resulting cDNA can then be used as a template in the subsequent PCR reaction.

[0136] The PCR step can use various thermostable DNA-dependent DNA polymerases, but typically uses Taq DNA polymerase, which has 5'-3' nuclease activity but lacks 3'-5' proofreading endonuclease activity. Therefore, TaqMan® PCR typically utilizes the 5' nuclease activity of Taq polymerase to hydrolyze the hybridization probe bound to its target amplicon, but any enzyme with equivalent 5' nuclease activity can be used. Two oligonucleotide primers are used to generate an amplicon typical of the PCR reaction. A third oligonucleotide or probe is designed to detect a nucleotide sequence located between the two PCR primers. The probe is not extendable by the Taq DNA polymerase enzyme and is labeled with a reporter fluorescent dye and a quencher fluorescent dye. Any laser-induced emission from the reporter dye is quenched by the quenching dye when the two dyes are located close to each other on the probe. During the amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The obtained probe fragments dissociate in solution, and the signal from the released reporter dye is unquenched by the second fluorophore. One molecule of reporter dye is released for each newly synthesized molecule, and the detection of the unquenched reporter dye provides a basis for quantitative interpretation of the data.

[0137] TaqMan® RT-PCR can be performed using commercially available instruments, such as the ABI PRISM 7700® Sequence Detection System® (Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA) or Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In certain embodiments, the 5' nuclease procedure is performed using a real-time quantitative PCR device such as the ABI PRISM 7700® Sequence Detection System®. This system consists of a thermocycler, laser, charge-coupled device (CCD), camera, and computer. The system amplifies the sample in a 96-well format in the thermocycler. During amplification, laser-induced fluorescence signals are collected in real time for all 96 wells via fiber optic cables and detected by the CCD. The system includes software for operating the instrument and analyzing the data.

[0138] The 5'-nuclease assay data is initially expressed as Ct, or threshold cycle. As described above, the fluorescence value is recorded during each cycle and represents the amount of product amplified up to that point in the amplification reaction. The threshold cycle (Ct) is the point in time when the fluorescence signal is first recorded as statistically significant.

[0139] To minimize errors and inter-sample variability, RT-PCR is typically performed using an internal standard. An ideal internal standard is one that is represented at a consistent level across different tissues and is unaffected by experimental procedures. The RNAs most frequently used to normalize gene expression patterns are the housekeeping genes glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and actin-beta (ACTB) mRNA. For performing analyses on pre-implantation embryos and oocytes, the conserved helix-loop-helix ubiquitous kinase (CHUK) is the gene used for normalization.

[0140]

[0141] A more recent variation of the RT-PCR technique is real-time quantitative PCR, which measures PCR product accumulation via a double-labeled fluorescence-generating probe (i.e., a TaqMan® probe). Real-time PCR can be described as both quantitative competitive PCR, which uses internal competitors for each target sequence for normalization, and quantitative comparative PCR, which uses normalization genes or housekeeping genes for RT-PCR present in the sample. For further details, see, for example, Held et al., Genome Research 6:986 994 (1996), the contents of which are incorporated herein by reference in their entirety.

[0142] In another embodiment, gene expression is measured using a MassARRAY-based gene expression profiling method. Developed by Sequenom, Inc. (San Diego, Calif.), the MassARRAY-based gene expression profiling method involves spiking the resulting cDNA with a synthetic DNA molecule (competitor) after RNA isolation and reverse transcription. This synthetic DNA molecule coincides with the target cDNA region at all positions except a single base and serves as an internal standard. The cDNA / competitor mixture is amplified by PCR and subjected to post-PCR alkaline phosphatase (SAP) enzymatic treatment, resulting in dephosphorylation of the remaining nucleotides. After alkaline phosphatase inactivation, the PCR products from the competitor and cDNA are subjected to primer extension, resulting in different mass signals for the competitor and cDNA-derived PCR products. After purification, these products are dispensed onto a pre-loaded chip array containing the components necessary for analysis by matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF MS). Next, the cDNA present during the reaction is quantified by analyzing the ratio of peak areas in the generated mass spectra. For further details, see, for example, Ding and Cantor, Proc. Natl. Acad. Sci. USA 100:3059 3064 (2003).

[0143] Further PCR-based techniques include, for example, differential display (Liang and Pardee, Science 257:967 971 (1992)); amplified fragment length polymorphism (iAFLP) (Kawamoto et al., Genome Res. 12:1305 1312 (1999)); BeadArray™ technology (Illumina, San Diego, Calif.; Oliphant et al., Discovery of Markers for Disease (Supplement to Biotechniques), June 2002; Ferguson et al., Analytical Chemistry 72:5618 (2000)); and BeadsArray (BADGE) for gene expression detection using the commercially available Luminex100 LabMAP system and multiple color-coded microspheres (Luminex Corp., Austin, Tex.) in rapid gene expression assays (Yang et al., Genome Res. Examples include 11:1888 1898 (2001)); and high-coverage expression profiling (HiCEP) analysis (Fukumura et al., Nucl. Acids. Res. 31(16) e94 (2003)). The contents of each of these are incorporated herein by reference in their entirety.

[0144] In certain embodiments, differential gene expression can be identified or confirmed using microarray techniques. In this method, a polynucleotide sequence of interest (including cDNA and oligonucleotides) is plated or arrayed onto a microchip substrate. The arrayed sequence is then hybridized with a specific DNA probe derived from the cell or tissue of interest. The method for constructing the microarray and determining gene product expression (e.g., RNA or protein) is described in U.S. Patent Application Publication No. 2006 / 0195269, which is incorporated herein by reference in its entirety.

[0145] In a specific embodiment of the microarray technique, a PCR-amplified insert of a cDNA clone is applied to a substrate in a dense array, for example, applying at least 10,000 nucleotide sequences to the substrate. Microarrayed genes, each immobilized on a microchip with 10,000 elements, are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes can be generated by incorporating fluorescent nucleotides through reverse transcription of RNA extracted from the tissue of interest. The labeled cDNA probes applied to the chip specifically hybridize to each spot of DNA on the array. After thorough washing to remove non-specifically bound probes, the chip is scanned by another detection method such as confocal laser microscopy or a CCD camera. Quantification of the hybridization of each placed element allows for the assessment of the corresponding mRNA abundance. Dichromatic fluorescence is used to hybridize separately labeled cDNA probes generated from two RNA sources into the array in a pairwise manner. Thus, the relative abundances of transcripts from the two sources corresponding to each particular gene are determined simultaneously. By reducing the scale of hybridization, the expression patterns of numerous genes can be evaluated simply and rapidly. Such methods have been shown to have the sensitivity necessary to detect rare transcripts expressed at a few copies per cell and to reproducibly detect differences in expression levels of at least approximately twofold, as described, for example, in Schena et al., Proc. Natl. Acad. Sci. USA 93(2):106 149 (1996) (the contents of which are incorporated herein by reference in their entirety). Microarray analysis can be performed using commercially available instruments according to the manufacturer's protocol, such as by using Affymetrix GenChip technology or Incyte microarray technology.

[0146] Alternatively, protein levels can be determined by constructing an antibody microarray containing immobilized, preferably monoclonal, antibodies whose binding sites are specific to multiple protein species encoded by the cell genome. Preferably, the antibody is present against a substantial portion of the protein of interest. Methods for producing monoclonal antibodies are well known (see, for example, Harlow and Lane, 1988, ANTIBODIES: A LABORATORY MANUAL, Cold Spring Harbor, NY, whose entirety is incorporated for all purposes). In one embodiment, the monoclonal antibody is produced against a synthetic peptide fragment designed based on the cell's genome sequence. Using such an antibody array, proteins from cells are brought into contact with the array, and their binding is assayed using assays known in the art. Generally, the expression and expression levels of proteins of interest for diagnostic or prognostic purposes can be detected by immunohistochemical staining of tissue slices or sections.

[0147] Finally, the levels of marker gene transcripts in several tissue samples can be characterized using a “tissue array,” such as the one described, for example, in Kononen et al., Nat. Med 4(7):844-7 (1998). In a tissue array, multiple tissue samples are evaluated on the same microarray. The array allows for in situ detection at the RNA and protein levels, and serial sections allow for simultaneous analysis of multiple samples.

[0148] In other embodiments, gene expression is measured using gene expression linkage analysis (SAGE). SAGE is a method that enables simultaneous quantitative analysis of multiple gene transcripts without the need to provide individual hybridization probes for each transcript. First, short sequence tags (approximately 10–14 bp) are generated that contain enough information to uniquely identify the transcripts, as long as the tags are obtained from unique locations within each transcript. Then, many transcripts are ligated together to form a long continuous molecule that can be sequenced, simultaneously revealing the identity of multiple tags. By determining the abundance of individual tags and identifying the genes corresponding to each tag, the expression pattern of any population of transcripts can be quantitatively evaluated. For further details, see, for example, Velculescu et al., Science 270:484 487 (1995); and Velculescu et al., Cell 88:243 51 (1997) (the contents of each of these are incorporated herein by reference in their entirety).

[0149] In other embodiments, gene expression is measured using large-scale parallel signature sequencing (MPSS). This method, described by Brenner et al., Nature Biotechnology 18:630 634 (2000), is a sequencing approach that combines non-gel-based signature sequencing with in vitro cloning of millions of templates on separate 5 μm diameter microbeads. First, a microbead library of DNA templates is constructed by in vitro cloning. Subsequently, a planar array of template-containing microbeads in a flow cell is constructed at high density (typically 3 × 10⁶). 6 Microbeads / cm 2 The microbeads are assembled using a fluorescence-based signature sequencing method that does not require DNA fragment separation, allowing simultaneous analysis of the free ends of the cloned templates on each microbead. This method has been shown to provide hundreds of thousands of gene signature sequences from a yeast cDNA library simultaneously and accurately in a single operation.

[0150] Immunohistochemical methods are also suitable for detecting the expression levels of the gene products of the present invention. Therefore, expression is detected using antibodies (monoclonal or polyclonal) or antisera such as polyclonal antiserum that are specific to each marker. Antibodies can be detected, for example, by radiolabeling, fluorescent labeling, hapten labeling such as biotin, or direct labeling of the antibody itself with enzymes such as horseradish peroxidase or alkaline phosphatase. Alternatively, an unlabeled primary antibody is used in combination with a labeled secondary antibody containing an antiserum, polyclonal antiserum, or monoclonal antibody specific to the primary antibody. Immunohistochemical protocols and kits are well known in the art and are commercially available.

[0151] In certain embodiments, a proteomics approach is used to measure gene expression. The proteome refers to the entirety of proteins present in a sample (e.g., tissue, organism, or cell culture) at a specific point in time. Proteomics, among other things, includes the study of the overall changes in protein expression in a sample (also called expression proteomics). Proteomics typically includes the following steps: (1) separation of individual proteins in the sample by 2-D gel electrophoresis (2-D PAGE); (2) identification of individual proteins recovered from the gel, for example by mass spectrometry or N-terminal sequencing; and (3) analysis of the data using bioinformatics. Proteomics is a useful complement to other methods of gene expression profiling and can be used alone or in combination with other methods to detect the products of the diagnostic markers of the present invention.

[0152] In some embodiments, mass spectrometry (MS) analysis can be used alone or in combination with other methods (e.g., immunoassays or RNA measurement assays) to determine the presence and / or amount of one or more biomarkers disclosed herein in a biological sample. In some embodiments, the MS analysis includes matrix-assisted laser desorption / ionization (MALDI) time-of-flight (TOF) MS analysis, such as direct spot MALDI-TOF or liquid chromatography-MALDI-TOF mass spectrometry. In some embodiments, the MS analysis includes electrospray ionization (ESI) MS (e.g., liquid chromatography (LC)-ESI-MS). Mass spectrometry can be achieved using commercially available spectrometers. Methods utilizing MS analysis, including MALDI-TOF MS and ESI-MS, to detect the presence and amount of biomarker peptides in a biological sample are known in the art. See, for example, U.S. Patents 6,925,389; 6,989,100 and 6,890,763 (each of which is incorporated herein by reference in whole).

[0153] Report on genetic modifiers of LRRK2 The method of the present invention may include providing a report concerning a subject. The report may identify one or more genetic modifiers of LRRK2 in genetic data from the subject. The report may include additional information concerning the subject, such as age, sex, weight, height, genetic data, genomic data, or other health or medical information. The report may also include other information concerning PD. For example, and not limited to, the report may include information concerning symptoms of PD or genes associated with PD, such as the symptoms and genes described above.

[0154] The report may be provided in any preferred format. For example, and not limited to, the report may be provided on paper or on a display device such as a computer monitor, telephone, or portable electronic device.

[0155] The report may be provided to healthcare providers, such as physicians or nurses. This report may provide healthcare provider guidance on whether treatment of the subject with an LRRK2 inhibitor is appropriate. The report may provide healthcare providers with instructions or recommendations for treating the subject with an LRRK2 inhibitor. The report may recommend that healthcare providers prescribe or provide an LRRK2 inhibitor to the subject, or instruct the subject to obtain and take an LRRK2 inhibitor.

[0156] The report may include guidance on whether to use a second agent in addition to an LRRK2 inhibitor to treat the subject. The second agent could be a known therapeutic agent for the treatment of PD, for example, one of the above.

[0157] LRRK2 inhibitors The methods of the present invention may include providing one or more LRRK2 inhibitors to subjects, or recommending that subjects take one or more LRRK2 inhibitors. LRRK2 inhibitors are known in the art, for example, by International Patent Publication Nos. WO2012 / 028629, WO2012 / 058193, WO2012 / 118679, WO2012 / 143143, WO2012 / 143144, WO2014 / 001973, WO2014 / 060112, WO2014 / 060113, WO2014 / 145909, WO2014 / 160430, WO2014 / 170248, WO2015 / 092592, WO2015 / 113451, WO2015 / 113452, WO2016 / 130920, WO2017 / 012576, WO2017 / 046675, WO2017 / 087905, WO201 7 / 106771, WO2017 / 156493, WO2017 / 218843, WO2018 / 137573, WO2018 / 137593, WO2018 / 137618, WO2018 / 137619, WO20 18 / 163030, WO2018 / 163066, WO2018 / 217946, WO2019 / 012093, WO2019 / 104086, WO2019 / 112269, WO2019 / 126383, WO2020 / 149723, WO2020 / 170205, and WO2020 / 210684; U.S. Patent No. 9,499,535; concurrently pending U.S. Patent Applications No. 63 / 050,385 and No. 63 / 133,523 This is described in PCT / IB22020 / 000727, PCT / IB2020 / 000730, PCT / US2021 / 041270, and PCT / US2021 / 041271, the contents of each of these are incorporated herein by reference in their entirety. Any LRRK2 disclosed in any of the above references may be used in the method of the present invention.

[0158] For example, and not limited to, LRRK2 inhibitors may be CZC-25146, CZC-54252, DNL151, DNL201, GNE-7915, GSK2578215A, HG-10-102-01, JH-II-127, K252A, K252B, LRRK2-IN-1, MLi-2, PF-06447475, or staurosporine.

[0159] In some methods of the present invention, the LRRK2 inhibitor is one compound from formulas (I), (II), (III), and (IV): [ka] (In the formula: A is NH, O, S, C=O, NR 3 or CR 4 R 5 And, X is an arylene, heteroarylene, cycloalkylene, heterocycloalkylene, alkylcycloalkylene, heteroalkylcycloalkylene, aralkylene, or heteroaralkylene group, which may be substituted as needed. R 1 These are alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl, or heteroaralkyl groups, which are substituted as needed. R 2 This is a hydrogen atom, halogen atom, NO2, N3, OH, SH, NH2 or alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl group. R 3 These are alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkyl-cycloalkyl, heterocycloalkyl, aralkyl, or heteroaralkyl groups. R 4is a hydrogen atom, NO2, N3, OH, SH, NH2, or alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl, or heteroaralkyl group. R 5 is a hydrogen atom, NO2, N3, OH, SH, NH2, or alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl, or heteroaralkyl group. B is NH, O, S, C=O, NR 14 or CR 15 R 16 And, R 11 These are alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl, or heteroaralkyl groups. R 12 R is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl, or heteroaralkyl group, 12 It is bonded to the pyrimidine ring of formula (II) via a carbon-carbon bond, R 13 This is a hydrogen atom, halogen atom, NO2, N3, OH, SH, NH2 or alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl group. R 14 These are alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkyl-cycloalkyl, heterocycloalkyl, aralkyl, or heteroaralkyl groups. R15 is a hydrogen atom, NO2, N3, OH, SH, NH2, or alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl, or heteroaralkyl group. R 16 is a hydrogen atom, NO2, N3, OH, SH, NH2, or alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl, or heteroaralkyl group. R 21 These are aryl or heteroaryl compounds, which are substituted as needed. R 22 H, Halo, OH, CN, CF3, C 1~6 Alkyl, C 1~6 Alkoxy, C 1~6 Haloalkyl, C 1~6 Thioalkyl, C 3~8 Cycloalkyl, C 2~8 It is a heterocycloalkyl, aryl, or heteroaryl, Y is an aryl or a 5-membered or 6-membered heteroaryl, C 1~6 Alkyl, C 1~6 Alkoxy, C 1~6 Haloalkyl, C 1~6 Thioalkyl, C 3~8 Cycloalkyl, C 2~8 Heterocycloalkyl, aryl, and heteroaryl are, respectively, halo, OH, CN, CF3, NH2, NO2, and C. 1~6 Alkyl, C 1~6 Haloalkyl, C 1~6 Thioalkyl, C 3~8 Cycloalkyl, C 2~8 Heterocycloalkyl, C 2~8 Heterocycloalkenyl, C 2~6 Alkenil, C 2~6 Alkinyl, C 1~6 Alkoxy, C 1~6 Haloalkoxy, C 1~6Alkylamino, C 2~6 Dialkylamino, C 7~12 Aralkil, C 1~12 It is optionally replaced by one or more parts selected from the group consisting of heteroaralkyl, aryl, heteroaryl, -C(O)R, -C(O)OR, -C(O)NRR', -C(O)NRS(O)2R', -C(O)NRS(O)2NR'R'', -OR, -OC(O)NRR', -NRR', -NRC(O)R', -NRC(O)NR'R'', -NRS(O)2R', -NRS(O)2NR'R'', -S(O)2R, and -S(O)2NRR'. R, R', and R'' are, independently, H, H, OH, and C, respectively. 1~6 Alkyl, C 1~6 Haloalkyl, C 1~6 Alkoxy, C 3~8 Cycloalkyl, C 2~8 Heterocycloalkyl, aryl, or heteroaryl, or R and R' or R' and R'' together with the nitrogen they are bonded to, C 2~8 Forms heterocycloalkyl groups, R 31 is C(O)CH2R 33 , optionally substituted cycloalkyl, optionally substituted cycloheteralkyl, optionally substituted cycloalkenyl, optionally substituted cycloheteralkenyl, optionally substituted aryl, or optionally substituted heteroaryl, R 32 Each example is independently a halo, haloalkyl, optionally substituted alkoxyl, optionally substituted alkyl, optionally substituted heteroalkyl, optionally substituted alkenyl, or optionally substituted heteroalkenyl. R 33These are optionally substituted cycloalkyls, optionally substituted cycloheteralkyls, optionally substituted cycloalkenyls, optionally substituted cycloheteralkenyls, optionally substituted aryls, or optionally substituted heteroaryls. Z is a cycloalkyl, cycloheteroalkyl, cycloalkenyl, cycloheteroalkenyl, aryl, or heteroaryl, and Z may be an aryl substituted with two or three examples of R2. Z may be a phenyl substituted with two or three examples of R2. Z may be a heteroaryl substituted with two or three examples of R2. Z may be a six-membered heteroaryl substituted with two or three examples of R2. n is between 0 and 5. Alternatively, it is a pharmaceutically acceptable salt of any of the above compounds.

[0160] In a particular embodiment, an LRRK2 inhibitor is presented in WO2021 / 048620, which is incorporated in whole by reference.

[0161] In a particular embodiment, the LRRK2 inhibitor is one of the compounds of formula (V): [ka] and its pharmaceutically acceptable salts. (In the formula: G1 is CF3, CHF2, CH2F, halogen, or cyclopropyl, ethyl, or isopropyl. G2 is H, substituted or unsubstituted C1-C6 alkyl, C3-C6 cycloalkyl, C1-C6 haloalkyl, C3-C6 halocycloalkyl, 1-6 membered heterocycle, heteroaryl, -CH2-cycloalkyl, -CF2-cycloalkyl, -CH(CH3)-cycloalkyl, -CH2-aryl, -CH2-heterocyclic, -CH2-heteroaryl, -CF2-aryl, and -CH(-CH3)-aryl. A and B are independently selected from 5-membered or 6-membered cycloalkyl or cycloaryl rings containing one or more heteroatoms and one or more substitutions. Both A and B contain at least two nitrogen heteroatoms cumulatively, A and B are condensed at two positions. One or more substitutions on rings A and B include H, halo, C1-C6-alkyl, branched alkyl, haloalkyl, substituted or unsubstituted alkenyl, substituted or unsubstituted alkynyl, alkoxy, cycloalkoxy, haloalkoxy, alkoxyalkoxy, cyano, -CH2-cycloalkyl, -CF2-cycloalkyl, -(CH2) 0~2 C(CH3)2-CN, -(CH2) 0~4 -CN, -(CH2) 0~4 SO2R', -CH(CH3)-cycloalkyl, -CH2-aryl, -CF2-aryl, -CH(-CH3)-aryl, -C(=O)-alkyl, -C(=O)-cycloalkyl, -C(=O)-NR'R'', -C(=O)-NH-alkyl, -C(=O)NH2, hydroxy, -COOH (and its esters), alkylsulfonyl, cycloalkylsulfonyl, arylsulfonyl, -S(=O)2-NR'R'', -NH2, -NR'R'', -NR'-S(=O)2R'', cyanoalkyl, haloalkyl, substituted or unsubstituted alkylsulfonyl, arylsulfonyl, -C(=O)-morpholine, -C(=O)-heterocycle, -C(H) 0~1 (-CH3) 1~2 -OH, -CH2-C(=O)-NH2; independently selected from 3- to 6-membered heterocycles, each of which may have one or more substituents, and the 3- to 6-membered heterocycle contains at least one heteroatom independently selected from O, S, and N. R' and R'' are independently selected from the group consisting of H, alkyl, substituted or unsubstituted aryl, and heterocycles.

[0162] In a particular embodiment, the LRRK2 inhibitor is a compound of formula (VI): [ka] (In the formula, X1, X2, X3, and X4 are C or N, and X1, X2, and X3 contain either one N, two Ns, or three Ns. R1 is CF3, CHF2, CH2F, halogen, cyclopropyl, ethyl, isopropyl. R2 is H, substituted or unsubstituted C1-C6 alkyl, C3-C6-cycloalkyl, C3-C6-halocycloalkyl; branched alkyl, alkenyl, alkynyl, haloalkyl, alkoxy, cycloalkoxy, haloalkoxy, -CH2-cycloalkyl, -CF2-cycloalkyl, tetrahydrofuran, -CH(-CH3)-cycloalkyl, -CH2-aryl, -CF2-aryl, -CH(-CH3)-aryl, -C(=O)-alkyl, -C(=O)-cycloalkyl, -C(=O)-NH-alkyl, R3, R4, and R5 are independently selected from the group consisting of H, substituted or unsubstituted C1-C6 alkyl, C3-C6-cycloalkyl, C3-C6-halocycloalkyl; branched alkyl, alkenyl, alkynyl, haloalkyl, alkoxy, cycloalkoxy, haloalkoxy, nitro, cyano, alkylcyano, -CH2-cycloalkyl, -CF2-cycloalkyl, -CH(CH3)-cycloalkyl, -CH2-aryl, -CF2-aryl, -CH(-CH3)-aryl, -C(=O)-R', -C(=O)-NR'R'', -S=(O)2-R', -CH2CN, -C-(CH3)2-CN, -C(-CH3)2-OH, and -CH2-C(=O)-NH2. R6, R7 and R8 are H, halo, C1-C6-alkyl, branched alkyl, alkenyl, alkynyl, haloalkyl, alkoxy, cycloalkoxy, haloalkoxy, nitro, cyano, -CH2-cycloalkyl, -CF2-cycloalkyl, -CH(CH3)-cycloalkyl, -CH2-aryl, -CF2-aryl, -CH(-CH3)-aryl, -C(=O)-alkyl, -C(=O)-cycloalkyl, -C(=O)-NH-alkyl, -C(=O)NH2, hydroxy, -COOH (and its esters), -C(=O)-morpholine, -C(=O)-heterocyclic, -S=(O)2-R', ami -NR'R'', -NR'S=(O)2-R', cyanoalkyl, haloalkyl, -C(-CH3)2-OH, -CH2-C(=O)-NH2; 3-6 membered heterocycles (each of which may have one or more substituents, and the 3-6 membered heterocycle contains at least one heteroatom independently selected from O, S, and N); -S=(O)2-cyclopropyl, -C(=O)-morpholine, -C(-CH3)2-OH, -CH2-C(=O)-NH2, -CF3, -OCF3, tetrahydropyran, 3H-pyran, 2H-pyran, piperidine, alkyl-morpholine, independently selected from the group. R' and R'' are independently selected from the group consisting of H, unsubstituted or substituted C1-C8 alkyl, substituted or unsubstituted C1-C8 aryl, substituted or unsubstituted C1-C8 cycloalkyl or heterocycloalkyl. Substitutions R3, R4, R5, and R6 are present only if their valence allows. R3 and R6 optionally form 5- to 7-membered heteroalkyl rings. It is possible.

[0163] In a particular embodiment, the LRRK2 inhibitor is a compound of formula (VII): [ka] (In the formula, X5 is selected from C or N, R9, R 10 or R 11This is independently selected from the group consisting of H, substituted or unsubstituted C1-C6 alkyl, C1-C6-cycloalkyl, C3-C6-halocycloalkyl; C1-C6 alkyl, branched alkyl, alkenyl, alkynyl, haloalkyl, alkoxy, cycloalkoxy, haloalkoxy, nitro, cyano, -CH2-cycloalkyl, -CF2-cycloalkyl, -CH(CH3)-cycloalkyl, -CH2-aryl, -CF2-aryl, -CH(-CH3)-aryl, C(=O)-alkyl, -C(=O)-cycloalkyl, -C(=O)-NH-alkyl, -S=(O)2-CH3, -C-(CH3)2-CN, -S=(O)2-cyclopropyl, -C(=O)-morpholine, -C(-CH3)2-OH, and -CH2-C(=O)-NH2. Replacement R9,R 10 and R 11 (It exists only if the valence allows it.) It is possible.

[0164] In a particular embodiment, the LRRK2 inhibitor is a compound of formula (VIII): [ka] (In the formula, X6 and X7 are selected from C or N, and at least one of X6 and X7 is N, R 12 , R 13 and R 14 This is independently selected from the group consisting of H, substituted or unsubstituted C1-C6 alkyl, C3-C6-cycloalkyl, C3-C6-halocycloalkyl; C1-C6 branched alkyl, C1-C6-alkenyl, C1-C6-alkynyl, C1-C6-haloalkyl, C1-C6-alkoxy, cycloalkoxy, haloalkoxy, halo, nitro, cyano, -CH2-cycloalkyl, -CF2-cycloalkyl, -CH(CH3)-cycloalkyl, -CH2-aryl, -CF2-aryl, -CH(-CH3)-aryl, C(=O)-alkyl, -C(=O) heterocycloalkyl and -C(=O)-morpholine. R 15 and R 16The following are independently selected from the group consisting of H, halo, substituted or unsubstituted C1-C6 alkyl, alkyloxy, cyanoalkyl, C1-C6-branched alkyl, alkenyl, alkynyl, haloalkyl, alkoxy, cycloalkoxy, haloalkoxy, halo, nitro, cyano, -CH2-cycloalkyl, -CF2-cycloalkyl, -CH(CH3)-cycloalkyl, -C(=O) heterocycloalkyl, and -C(=O)-morpholine. replacement R 12 , R 13 and R 14 (It exists only if the valence allows it.) It is possible.

[0165] In certain embodiments, LRRK2 inhibitors are presented in WO2024 / 073073, which are incorporated in their entirety by reference.

[0166] LRRK2 inhibitors may be provided to subjects in the form of pharmaceutical compositions. These pharmaceutical compositions may contain a therapeutically effective dose of the LRRK2 inhibitor. A therapeutically effective dose means an amount effective in preventing, alleviating, or improving the symptoms of a disease such as PD, or in extending the survival of the subject being treated. Determining the therapeutically effective dose is within the scope of the art. The therapeutically effective dose or dosage of an LRRK2 inhibitor may vary within a broad limit and may be determined in methods known in the art. Such dosages may be adjusted for the individual requirements of each specific case, including the particular compound administered, the route of administration, the condition being treated, and the patient being treated.

[0167] For oral administration, such therapeutically useful drugs may be administered by one of the following routes: orally, intravenously, intramuscularly, and subcutaneously, including parenterally, including injectable solutions or suspensions, rectally as suppositories, by inhalation or insufflation, for example, as powder formulations, as microcrystals, or as sprays (e.g., liquid aerosols), transdermally, via transdermal delivery systems (TDS) such as plasters containing the active ingredient, or intranasally. For the manufacture of such tablets, pills, semi-solids, coated tablets, sugar-coated tablets, and hard capsules, such as gelatin, therapeutically useful products can be mixed with pharmaceutically inert inorganic or organic additives such as lactose, sucrose, glucose, gelatin, malt, silica gel, starch or its derivatives, talc, stearic acid or salts thereof, dried skim milk, etc. For the manufacture of soft capsules, additives such as vegetable oils, petroleum, animal oils or synthetic oils, waxes, fats, polyols, etc. may be used. For the manufacture of liquid preparations, emulsions or suspensions or syrups, additives such as water, alcohol, saline solution, aqueous dextrose, polyols, glycerin, lipids, phospholipids, cyclodextrins, vegetable oils, petroleum, animal oils or synthetic oils may be used. Lipids such as phospholipids (e.g., of natural origin and / or having a particle size between 300 and 350 nm) in phosphate-buffered saline (pH = 7 to 8, e.g., 7.4) are particularly useful. In the case of suppositories, additives such as vegetable oils, petroleum, animal or synthetic oils, waxes, fats, and polyols may be used. In the case of aerosol formulations, compressed gases suitable for this purpose (e.g., oxygen, nitrogen, and carbon dioxide) may be used. Pharmaceutically useful agents may also contain additives for preservation and stabilization, such as UV stabilizers, emulsifiers, sweeteners, fragrances, salts to alter osmotic pressure, buffers, coating additives, and antioxidants.

[0168] Provision of LRRK2 inhibitors to target populations The method of the present invention may include providing an LRRK2 inhibitor. The LRRK2 inhibitor may be provided by any suitable route of administration or form of administration. For example and without limitation, the compound may be administered orally, dermally, enterally, intra-arterially, intramuscularly, intraocularly, intravenously, nasally, orally, parenterally, to the lungs, rectally, subcutaneously, topically, transdermally, by injection, or together with an implantable medical device (e.g., a stent or drug-eluting stent or balloon equivalent) or on an implantable medical device.

[0169] The LRRK2 inhibitor may be provided according to a dosing regimen. The dosing regimen may include the dosage, dosing frequency, or both.

[0170] The dosages may be provided at any suitable interval. For example and without limitation, the dosages may be provided once a day, twice a day, three times a day, four times a day, five times a day, six times a day, eight times a day, once every 48 hours, once every 36 hours, once every 24 hours, once every 12 hours, once every 8 hours, once every 6 hours, once every 4 hours, once every 3 hours, once every two days, once every three days, once every four days, once every five days, once a week, twice a week, three times a week, four times a week, or five times a week.

[0171] The dosage may be provided as a single dose, i.e., the dosage may be provided as a single tablet, capsule, pill, etc. Alternatively, the dosage may be provided as a divided dose, i.e., the dosage may be provided as multiple tablets, capsules, pills, etc.

[0172] The administration may be continued for a defined period. For example and without limitation, the dosages may be provided for at least one week, at least two weeks, at least three weeks, at least four weeks, at least six weeks, at least eight weeks, at least ten weeks, at least twelve weeks or longer.

[0173] The subject may be any type of subject, such as any of the above subjects related to an assay for obtaining genetic data.

[0174] The present invention includes a combination therapy in which an LRRK2 inhibitor is provided to a subject in combination with a second agent, such as any of the drugs described above in the section on PD. The LRRK2 inhibitor and the second agent may be provided in a single composition or in separate compositions. The LRRK2 inhibitor and the second agent may be provided according to the same dosing regimen or according to different dosing regimens.

[0175] Incorporation by reference Throughout this disclosure, references and citations have been made to other documents such as patents, patent applications, patent publications, journals, books, papers, web content, etc. All such documents are hereby incorporated by reference in their entirety for all purposes.

[0176] Equivalents Various modifications of the present invention and numerous additional embodiments thereof will be apparent to those skilled in the art from the entire contents of this specification, including references to scientific and patent documents cited herein in addition to those shown and described herein. The subject matter herein includes important information, examples and guidance applicable to the practice of the present invention in various embodiments thereof and equivalents thereof.

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