Predictive biomarkers and their use for treatment of Parkinson's disease

Abstract

The present invention provides a method of treating a patient suffering from Parkinson's disease (PD) associated with wild type LRRK2, and a method of treating a patient suffering from Parkinson's disease (PD) associated with wild type LRRK2. The present invention recognizes that analysis of biomarkers of such patients allows for identification of patients who will respond to LRRK2 inhibitors. Accordingly, the invention provides methods of identifying PD patients who will respond to LRRK2 inhibitors and methods of treating such patients.

Description

Technical Field

[0001] The present invention relates to methods for treating and diagnosing patients suffering from Parkinson's disease associated with wild-type leucine-rich repeat kinase 2 (LRRK2). Background Art

[0002] Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects over six million people worldwide. PD is typically initially recognized by movement disorders, where the main symptoms are tremors, stiffness, slowness of movement, and difficulty walking. In advanced stages, PD also gives rise to neuropsychiatric disorders, including dementia, depression, and anxiety. More than 1% of people over 60 years old suffer from PD, and it causes over 100,000 deaths per year.

[0003] PD is thought to result from the combined action of genetic and environmental factors. Many mutations associated with familial PD have been identified, but 85 - 90% of PD cases are idiopathic. Among PD cases associated with known genetic factors, mutations in the LRRK2 gene are the most common cause of both familial and idiopathic PD. LRRK2 encodes a protein kinase that is expressed in multiple tissues, including brain regions associated with PD, such as the basal ganglia, and the pathogenic mutations enhance kinase activity. However, recent evidence suggests that some PD cases are associated with increased activity of wild-type (i.e., non-mutated) LRRK2.

[0004] Since there is no cure for PD, current treatments focus on alleviating symptoms, particularly movement disorders. The main approach for decades has been to use dopamine precursors such as levodopa, dopamine agonists, or monoamine oxidase inhibitors to enhance dopaminergic function. However, as the disease progresses, this medication loses its effectiveness, and ultimately its side effects outweigh its benefits. Summary of the Invention

[0005] Recently, LRRK2 inhibitors have been investigated for treating PD cases associated with mutant forms of the LRRK2 kinase. However, in the vast majority of PD cases, no mutations in LRRK2 have been identified. Unfortunately, for PD patients with wild-type LRRK2, there is no way to identify the subset of patients whose disease is associated with elevated LRRK2 activity, and due to the risk of harm to patients without pathological LRRK2 activity, LRRK2 inhibitors cannot be administered to PD patients indiscriminately. Thus, the current treatments for most PD patients are inadequate, and millions of people continue to suffer from the progressive and debilitating effects of the disease.

[0006] The present invention addresses this problem by using biomarkers to determine whether PD patients with wild-type LRRK2 are likely to benefit from LRRK2 inhibitor therapy. The present invention provides methods for using predictive biomarkers to determine whether PD patients with wild-type LRRK2 are more likely to respond to LRRK2 inhibitors. The present invention recognizes that biomarkers including bis(monoacylglycerol)phosphate (BMP) and / or semi-bis(monoacylglycerol)phosphate (semi-BMP) can serve as biomarkers for determining whether LRRK2 inhibitor therapy is effective in the treatment of PD. The present invention recognizes that the levels of BMP and / or semi-BMP can serve as indicators of LRRK2 kinase levels and activity. Thus, the present invention recognizes that BMP and / or semi-BMP levels can be used as an indicator for determining whether LRRK2 inhibitor therapy is suitable for a given individual. The methods of the present invention can be used both to identify PD patients as candidates for LRRK2 inhibitor therapy and to treat such patients.

[0007] On the one hand, the present invention provides a method for treating a patient suffering from LRRK2 - related Parkinson's disease, the method comprising: providing to the patient exhibiting PD one or more LRRK2 inhibitors, the patient having wild - type LRRK2, and having elevated BMP and / or semi - BMP levels compared to a subject without a neurological disease and having wild - type LRRK2, thereby treating PD related to wild - type LRRK2. In certain embodiments, BMP can be selected from the group consisting of: (i) di - 22:6 BMP, (ii) di - 18:1 BMP, (iii) 16:0 / 18:1 BMP, (iv) di - 20:4 BMP, (v) 18:0 / 20:4 BMP, (vi) 2,2' - di - 22:6 BMP, and (vii) any combination thereof. In certain embodiments, semi - BMP can be selected from the group consisting of: (i) semi - BMP(18:1 / 18:1)_16:0, (ii) semi - BMP(14:0 / 14:0)_14:0, (iii) semi - BMP(18:1 / 18:1)_18:0, (iv) semi - BMP(18:1 / 18:1)_18:1, and (iv) any combination thereof. These BMP levels are measured in any biological fluid from the patient. In certain embodiments, the biological fluid used to measure BMP and / or semi - BMP levels is urine, blood, cerebrospinal fluid (CSF), bile, or saliva. In certain preferred embodiments, the biological fluid used to measure BMP and / or semi - BMP is urine or CSF. In certain embodiments, the elevated BMP and / or semi - BMP levels in the patient are at a concentration indicative that the patient will be responsive to the one or more LRRK2 inhibitors. In certain embodiments, the one or more LRRK2 inhibitors for the therapy are selected from the group consisting of: 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, and staurosporine. In certain embodiments, these LRRK2 inhibitors are selected from the group consisting of formula (I), (II), (III), and (IV):

[0008]

[0009] Wherein:

[0010] A is NH, O, S, C = O, NR 3 or CR 4 R 5 ;

[0011] X is an optionally substituted arylene, heteroarylene, cycloalkylene, hetero cycloalkylene, alkyl cycloalkylene, heteroalkyl cycloalkylene, aralkyl or heteroaralkyl;

[0012] R 1 is an optionally substituted alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl cycloalkyl, hetero cycloalkyl, aralkyl or heteroaralkyl;

[0013] R 2 is a hydrogen atom, a halogen atom, NO 2 、N 3 、OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl cycloalkyl, hetero cycloalkyl, aralkyl or heteroaralkyl;

[0014] R 3 is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl - cycloalkyl, hetero cycloalkyl, aralkyl or heteroaralkyl;

[0015] R 4 is a hydrogen atom, NO 2 、N 3 、OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl cycloalkyl, hetero cycloalkyl, aralkyl or heteroaralkyl; and

[0016] R 5 is a hydrogen atom, NO 2 、N 3 、OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl cycloalkyl, hetero cycloalkyl, aralkyl or heteroaralkyl;

[0017] B is NH, O, S, C = O, NR 14 or CR 15 R 16 ;

[0018] R 11 is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl cycloalkyl, hetero cycloalkyl, aralkyl or heteroaralkyl;

[0019] R 12 is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl cycloalkyl, hetero cycloalkyl, aralkyl or heteroaralkyl, wherein R 12 is bonded to the pyrimidine ring of formula (II) through a carbon - carbon bond;

[0020] R 13 is a hydrogen atom, a halogen atom, NO 2 、N 3 、OH, SH, NH 2 or an alkyl group, an alkenyl group, an alkynyl group, a heteroalkyl group, an aryl group, a heteroaryl group, a cycloalkyl group, an alkylcycloalkyl group, a heteroalkylcycloalkyl group, a heterocycloalkyl group, an aralkyl group or a heteroaralkyl group;

[0021] R 14 is an alkyl group, an alkenyl group, an alkynyl group, a heteroalkyl group, an aryl group, a heteroaryl group, a cycloalkyl group, an alkylcycloalkyl group, a heteroalkyl - cycloalkyl group, a heterocycloalkyl group, an aralkyl group or a heteroaralkyl group;

[0022] R 15 is a hydrogen atom, NO 2 、N 3 、OH, SH, NH 2 or an alkyl group, an alkenyl group, an alkynyl group, a heteroalkyl group, an aryl group, a heteroaryl group, a cycloalkyl group, an alkylcycloalkyl group, a heteroalkylcycloalkyl group, a heterocycloalkyl group, an aralkyl group or a heteroaralkyl group;

[0023] R 16 is a hydrogen atom, NO 2 、N 3 、OH, SH, NH 2 or an alkyl group, an alkenyl group, an alkynyl group, a heteroalkyl group, an aryl group, a heteroaryl group, a cycloalkyl group, an alkylcycloalkyl group, a heteroalkylcycloalkyl group, a heterocycloalkyl group, an aralkyl group or a heteroaralkyl group;

[0024] R 21 is an aryl group or a heteroaryl group, each of which is optionally substituted;

[0025] R 22 is H, a halogen group, OH, CN, CF 3 、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 or heteroaryl; and

[0026] Y is an aryl group or a 5 - or 6 - membered heteroaryl group; wherein the C 1-6 alkyl, the C 1-6 alkoxy, the C 1-6 haloalkyl, the C 1-6 thioalkyl, the C 3-8 cycloalkyl, the C 2-8Each of the heterocycloalkyl, the aryl, and the heteroaryl is optionally substituted with one or more moieties selected from the group consisting of: halogen, OH, CN, CF 3 、NH 2 、NO 2 、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 alkenyl, C 2-6 alkynyl, C 1-6 alkoxy, C 1-6 haloalkoxy, C 1-6 alkylamino, C 2-6 dialkylamino, C 7-12 aralkyl, C 1-12 heteroaralkyl, aryl, heteroaryl, -C(O)R, -C(O)OR, -C(O)NRR', -C(O)NRS(O) 2 R', -C(O)NRS(O) 2 NR'R”, -OR, -OC(O)NRR', -NRR', -NRC(O)R', -NRC(O)NR'R”, -NRS(O) 2 R', -NRS(O) 2 NR'R”, -S(O) 2 R and -S(O) 2 NRR',

[0027] wherein each of R, R', and R” is independently H, halogen, OH, C 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 to which they are attached form C 2-8 heterocycloalkyl;

[0028] R 31 is C(O)CH 2 R 33 、optionally substituted cycloalkyl, optionally substituted cycloheteroalkyl, optionally substituted cycloalkenyl, optionally substituted cycloheteroalkenyl, optionally substituted aryl, or optionally substituted heteroaryl;

[0029] R 32Each instance independently is a halogen group, haloalkyl group, optionally substituted alkoxy group, optionally substituted alkyl group, optionally substituted heteroalkyl group, optionally substituted alkenyl group, or optionally substituted heteroalkenyl group;

[0030] R 33 is an optionally substituted cycloalkyl group, optionally substituted cycloheteroalkyl group, optionally substituted cycloalkenyl group, optionally substituted cycloheteroalkenyl group, optionally substituted aryl group, or optionally substituted heteroaryl group;

[0031] Z is a cycloalkyl group, cycloheteroalkyl group, cycloalkenyl group, cycloheteroalkenyl group, aryl group, or heteroaryl group; Z can be an aryl group substituted with two or three instances of R 2 ; Z can be a phenyl group substituted with two or three instances of R 2 ; Z can be a heteroaryl group substituted with two or three instances of R 2 ; Z can be a six-membered heteroaryl group substituted with two or three instances of R 2 ; and

[0032] n is 0 - 5,

[0033] or a pharmaceutically acceptable salt of any of the compounds described above.

[0034] On the other hand, the present invention provides a method for determining whether a patient suffering from PD associated with wild-type LRRK2 will respond to an LRRK2 inhibitor, the method comprising: generating a report that identifies the BMP and / or semi-BMP levels of the patient compared to those of a subject without PD and having wild-type LRRK2; providing the report to a physician such that if the report indicates an elevation in the BMP and / or semi-BMP levels in the patient compared to the subject, the physician prescribes or provides one or more LRRK2 inhibitors to the patient. In certain embodiments, the BMP can be selected from the group consisting of: (i) di-22:6 BMP, (ii) di-18:1 BMP, (iii) 16:0 / 18:1 BMP, (iv) di-20:4 BMP, (v) 18:0 / 20:4 BMP, (vi) 2,2'-di-22:6 BMP, and (vii) any combination thereof. In certain embodiments, the semi-BMP can be selected from the group consisting of: (i) semi-BMP(18:1 / 18:1)_16:0, (ii) semi-BMP(14:0 / 14:0)_14:0, (iii) semi-BMP(18:1 / 18:1)_18:0, (iv) semi-BMP(18:1 / 18:1)_18:1, and (iv) any combination thereof. These BMP and / or semi-BMP levels are measured in any biological fluid from the patient. In certain embodiments, the biological fluid used to measure the BMP and / or semi-BMP levels is urine, blood, cerebrospinal fluid (CSF), bile, or saliva. In certain preferred embodiments, the biological fluid used to measure the BMP and / or semi-BMP is urine or CSF. In certain embodiments, the elevated BMP and / or semi-BMP levels in the patient are at a concentration indicating that the patient will be responsive to the one or more LRRK2 inhibitors. The LRRK2 inhibitor can be any of the inhibitors described above.

[0035] On the other hand, the present invention provides a method for treating a patient suffering from PD associated with wild-type LRRK2, the method comprising: receiving data identifying the BMP and / or semi-BMP levels of the patient; comparing the data with the BMP and / or semi-BMP levels of a subject without PD and having wild-type LRRK2; and if the BMP and / or semi-BMP levels of the patient are elevated compared to the subject, prescribing or providing to the patient one or more LRRK2 inhibitors. In certain embodiments, the BMP can be selected from the group consisting of: (i) di-22:6 BMP, (ii) di-18:1 BMP, (iii) 16:0 / 18:1 BMP, (iv) di-20:4 BMP, (v) 18:0 / 20:4 BMP, (vi) 2,2'-di-22:6 BMP, and (vii) any combination thereof. In certain embodiments, the semi-BMP can be selected from the group consisting of: (i) semi-BMP(18:1 / 18:1)_16:0, (ii) semi-BMP(14:0 / 14:0)_14:0, (iii) semi-BMP(18:1 / 18:1)_18:0, (iv) semi-BMP(18:1 / 18:1)_18:1, and (iv) any combination thereof. These BMP and / or semi-BMP levels are measured in any biological fluid from the patient. In certain embodiments, the biological fluid used to measure BMP and / or semi-BMP levels is urine, blood, cerebrospinal fluid (CSF), bile, or saliva. In certain preferred embodiments, the biological fluid used to measure BMP and / or semi-BMP is urine or CSF. In certain embodiments, the elevated BMP and / or semi-BMP levels in the patient are at a concentration indicative that the patient will be responsive to the one or more LRRK2 inhibitors. The LRRK2 inhibitor can be any of the inhibitors described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Total urinary di-18:1-BMP levels are provided for different groups of patient populations.

[0037] Figure 2 Total urinary di-22:6-BMP levels are provided for different groups of patient populations.

[0038] Figure 3 Total urinary 2,2'-di-22:6-BMP levels are provided for different groups of patient populations.

[0039] Figure 4 Classification of the population with Parkinson's disease and the status of LRRK2 are provided.

[0040] Figure 5 Urinary BMP (uBMP) levels are provided for patients with LRRK2 activity and LRRK2-normal PD.

[0041] Figure 6A and 6B provides that the LRRK2 活性 score is stable across different AMP - PD cohorts. Detailed implementation mode

[0042] Parkinson's disease (PD) is a progressive neurodegenerative disease caused by both genetic and environmental factors. A gene that plays a role in the development of some PD cases is LRRK2, which encodes a kinase expressed in multiple tissues, including brain regions associated with PD such as the basal ganglia. LRRK2 mutations are the most common known genetic cause of PD, but patients with LRRK2 mutations account for only a small fraction of the total number of PD cases. Nevertheless, the pathology of some patients with wild - type (i.e., non - mutant) LRRK2 seems similar to that of patients with mutant LRRK2. Specifically, pathogenic mutations in LRRK2 lead to increased LRRK2 kinase activity, and recent studies have shown that LRRK2 activity is elevated in some PD patients with wild - type LRRK2.

[0043] Currently, various LRRK2 inhibitors are being studied as PD therapeutics. Such drugs offer hope for PD patients with LRRK2 mutations. However, due to the different etiologies of the disease, treating PD patients with wild - type LRRK2 with LRRK2 inhibitors is problematic. Although patients with enhanced wild - type LRRK2 activity will benefit from LRRK2 inhibitors, for PD patients with normal LRRK2 activity levels and whose disease pathology can be attributed to changes in other molecular pathways, inhibition of LRRK2 may be ineffective. Since neurons expressing LRRK2 are located in the midbrain and are extremely difficult to access, kinase activity cannot be evaluated in living patients. Thus, to date, there has been no method for identifying a subset of PD patients with wild - type LRRK2 who may still benefit from LRRK2 inhibition.

[0044] The present invention solves this problem by using a biomarker to determine whether PD patients with wild - type LRRK2 are likely to benefit from LRRK2 inhibitors. Thus, the method of the present invention allows candidates for LRRK2 drug therapy to be identified based on genetic data that can be easily obtained from patients. Thus, for a subset of PD patients, the present invention unlocks the therapeutic potential of a class of drugs that were previously not recommended for these patients.

[0045] Parkinson's disease and its treatment

[0046] Parkinson's disease (PD) is a progressive neurodegenerative disease of the central nervous system. In the early stage, the disease affects the motor system, and the main symptoms are tremors, stiffness, slow movement, and difficulty walking. Cognitive and behavioral symptoms, such as dementia, depression, and anxiety, usually appear in the late stage of PD. PD usually occurs in people over 60 years old, and about 1% of them are affected, but the so-called early-onset PD may occur before the age of 50.

[0047] PD is characterized by the death of cells in the basal ganglia, which contain dopamine-secreting neurons, astrocytes, and substantia nigra microglial cells. Five mechanisms of neuronal death in PD have been proposed. First, the oligomerization of proteins such as α-synuclein into aggregates called Lewy bodies may directly cause cell death. The second proposed cause is the dysregulation of autophagy, specifically the degradation of mitochondria. Another proposed mechanism is mitochondrial dysfunction leading to reduced energy production and increased reactive oxygen species. The fourth proposed mechanism is neuroinflammation due to the secretion of pro-inflammatory factors by microglial cells. Finally, it has been proposed that the disruption of the blood-brain barrier allows plasma proteins to leak into the substantia nigra and promotes apoptosis.

[0048] PD is thought to be the result of a combination of genetic and environmental factors. In some cases, the gene mutations that increase the risk of PD are inheritable, and approximately 10-15% of individuals with PD have a first-degree relative with the disease. However, the majority of PD is 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, the most common known cause is a mutation in LRRK2. Pathogenic mutations in LRRK2 result in forms of the kinase with enhanced activity. Enhanced activity of wild-type LRRK2 has also recently been implicated in idiopathic PD. The role of LRRK2 in PD is described in, for example, 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 Apr 27, 2018; Di Maio et al., LRRK2 activation in idiopathic Parkinson's disease, Sci Transl Med. Jul 25, 2018;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. 2016;14(3):214-25, doi:10.2174 / 1570159x13666151030102847, the content of each of these references is incorporated herein by reference.

[0049] A number of behaviors and environmental conditions are known to increase the risk of having 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 concentrations of urate in the blood are associated with an increased risk of PD.

[0050] The management of PD generally requires 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, piribedil, cabergoline, apomorphine, and lisuride, can also be used to treat PD. The third class of drugs used to treat PD includes monoamine oxidase inhibitors, such as selegiline and rasagiline.

[0051] BMP and hemibMP

[0052] BMP and its isoforms, including hemibMP, are located within the inner membranes of late endosomes (multivesicular bodies) and lysosomes, where they contribute to the multivesicular / lamellar morphology of the lysosomal network. BMP is a structural isomer of phosphatidylglycerol (PG) and is synthesized through a series of acylation and deacylation steps involving transacylases that reorient the glycerol backbone. It has a unique sn-1-glycerophospho-sn-1'-glycerol stereoconformation that renders it highly resistant to phospholipase degradation in acidic organelles. Because it is negatively charged at lysosomal pH, it can act as a docking platform to recruit positively charged lipolytic enzymes to ILVs, thereby facilitating the degradation of lipid cargo. Additionally, BMP is an important cofactor in lysosomal cholesterol and sphingolipid metabolism through its interactions with cholesterol transporters and sphingolipid activator proteins. Based on these multifunctional roles, BMP is considered a key activator of lipid sorting and digestion.

[0053] Bis(acylglycerol) phosphates can contain multiple acyl chains: two (BMP), three (referred to as hemibMP), or four (bis(diacylglycerol) phosphate, BDP). There are multiple species of BMP and hemibMP. Examples of BMP and hemibMP species are provided in Showalter et al. (Int. J. Mol. Sci. 2020, 21, 8067), which is incorporated herein by reference in its entirety.

[0054] Exemplary BMP and semi-BMP species of the present invention are di-22:6 BMP, di-18:1 BMP, 16:0 / 18:1 BMP, di-20:4 BMP, 18:0 / 20:4 BMP, 2,2'-di-22:6 BMP, semi-BMP(18:1 / 18:1)_16:0, semi-BMP(14:0 / 14:0)_14:0, semi-BMP(18:1 / 18:1)_18:0 and semi-BMP(18:1 / 18:1)_18:1.

[0055] The present invention also provides that BMPs closely related to lipids can also be used as biomarkers for predicting responsiveness to LRRK2 inhibitor therapy.

[0056] Measurement of BMP levels

[0057] The present invention provides that BMP levels can be measured in any biological fluid from a patient. BMP levels can be measured in any biological fluid from a patient. Biological fluids from a patient include urine, blood, cerebrospinal fluid (CSF), bile, or saliva. Specifically, the present invention provides for measuring BMP levels in CSF or urine. In another preferred embodiment, BMP levels are measured in urine. A variety of techniques can be used to measure BMP levels. For example, techniques for measuring BMP and / or semi-BMP include high-pressure liquid chromatography (HPLC) and mass spectrometry (MS). A review of techniques for measuring BMP and / or semi-BMP is provided in: Luquain et al., High-Performance Liquid Chromatography Determination of Bis(monoacylglycerol) Phosphate and Other Lysophospholipids, 2001, 296(1), 41-48 and Pan et al., Quantitative Analysis of Polyphosphoinositide, Bis(monoacylglycero)phosphate and Phosphatidylglycerol Species by Shotgun Lipidomics after Methylation, 2021, 2306:77-91, which are incorporated herein by reference in their entirety.

[0058] BMP as a biomarker for LRRK2 inhibitor therapy

[0059] The present invention provides methods for using predictive biomarkers to determine whether PD patients with wild-type LRRK2 are more likely to respond to LRRK2 inhibitors. The present invention recognizes that biomarkers including bis(monoacylglycerol)phosphate (BMP) and / or semi-bis(monoacylglycerol)phosphate (semi-BMP) can serve as biomarkers for determining whether LRRK2 inhibitor therapy is effective for the treatment of PD. The present invention recognizes that the levels of BMP and / or semi-BMP can serve as indicators of LRRK2 kinase levels and activity. Thus, BMP and / or semi-BMP levels can be used as indicators for determining whether LRRK2 inhibitor therapy is suitable for a given individual. The methods of the present invention can be used both to identify PD patients as candidates for LRRK2 inhibitor therapy and to treat such patients.

[0060] On the one hand, the present invention provides a method for treating a patient suffering from Parkinson's disease associated with LRRK2, the method comprising: providing to the patient exhibiting PD one or more LRRK2 inhibitors, the patient having wild-type LRRK2, and having elevated BMP and / or semi-BMP levels compared to those of a subject without a neurological disease and having wild-type LRRK2, thereby treating PD associated with wild-type LRRK2. In certain embodiments, the BMP may be selected from the group consisting of: (i) di-22:6 BMP, (ii) di-18:1 BMP, (iii) 16:0 / 18:1 BMP, (iv) di-20:4 BMP, (v) 18:0 / 20:4 BMP, (vi) 2,2'-di-22:6 BMP, and (vii) any combination thereof. In certain embodiments, the semi-BMP may be selected from the group consisting of: (i) semi-BMP(18:1 / 18:1)_16:0, (ii) semi-BMP(14:0 / 14:0)_14:0, (iii) semi-BMP(18:1 / 18:1)_18:0, (iv) semi-BMP(18:1 / 18:1)_18:1, and (iv) any combination thereof. These BMP levels are measured in any biological fluid from the patient. In certain embodiments, the biological fluid used to measure BMP and / or semi-BMP levels is urine, blood, cerebrospinal fluid (CSF), bile, or saliva. In certain preferred embodiments, the biological fluid used to measure BMP and / or semi-BMP is urine or CSF. In certain embodiments, the elevated BMP and / or semi-BMP levels in the patient are at a concentration indicating that the patient will be responsive to the one or more LRRK2 inhibitors. The LRRK2 inhibitor of the present invention can be any LRRK2 inhibitor. Exemplary LRRK2 inhibitors useful in the present invention may be selected from the group consisting of: CZC-25146, CZC-54252, DNL151, DNL201, GNE-7915, GSK2578215A, HG-10-102-01, JH-II-127, K252A, K252B, LRRK2-TN-1, MLi-2, PF-06447475, and staurosporine.

[0061] In certain embodiments, these LRRK2 inhibitors are selected from the group consisting of: formula (I), (II), (III), and (IV):

[0062]

[0063] Wherein:

[0064] A is NH, O, S, C═O, NR 3 or CR 4 R5 ;

[0065] X is an optionally substituted arylene, heteroarylene, cycloalkylene, hetero cycloalkylene, alkyl cycloalkylene, heteroalkyl cycloalkylene, aralkylidene or heteroaralkylidene;

[0066] R 1 is an optionally substituted alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl cycloalkyl, hetero cycloalkyl, aralkyl or heteroaralkyl;

[0067] R 2 is a hydrogen atom, a halogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl cycloalkyl, hetero cycloalkyl, aralkyl or heteroaralkyl;

[0068] R 3 is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl - cycloalkyl, hetero cycloalkyl, aralkyl or heteroaralkyl;

[0069] R 4 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl cycloalkyl, hetero cycloalkyl, aralkyl or heteroaralkyl; and

[0070] R 5 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl cycloalkyl, hetero cycloalkyl, aralkyl or heteroaralkyl;

[0071] B is NH, O, S, C = O, NR 14 or CR 15 R 16 ;

[0072] R 11 is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl cycloalkyl, hetero cycloalkyl, aralkyl or heteroaralkyl;

[0073] R 12is alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl, where R 12 is bonded to the pyrimidine ring of formula (II) via a carbon-carbon bond;

[0074] R 13 is a hydrogen atom, a halogen atom, NO 2 、N 3 、OH, SH, NH 2 or alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl;

[0075] R 14 is alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkyl - cycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl;

[0076] R 15 is a hydrogen atom, NO 2 、N 3 、OH, SH, NH 2 or alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl;

[0077] R 16 is a hydrogen atom, NO 2 、N 3 、OH, SH, NH 2 or alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl;

[0078] R 21 is aryl or heteroaryl, each of which is optionally substituted;

[0079] R 22 is H, halo, OH, CN, CF 3 、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 or heteroaryl; and

[0080] Y is aryl or a 5 - or 6 - membered heteroaryl; where the C 1-6 alkyl, the C 1-6 alkoxy, the C 1-6 haloalkyl, the C 1-6Thioalkyl, the C 3-8 Cycloalkyl, the C 2-8 Each of the heterocycloalkyl, the aryl and the heteroaryl is optionally substituted by one or more moieties selected from the group consisting of: halo, OH, CN, CF 3 , NH 2 , NO 2 , 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 Alkenyl, C 2-6 Alkynyl, C 1-6 Alkoxy, C 1-6 Haloalkoxy, C 1-6 Alkylamino, C 2-6 Dialkylamino, C 7-12 Aralkyl, C 1-12 Heteroaralkyl, aryl, heteroaryl, -C(O)R, -C(O)OR, -C(O)NRR', -C(O)NRS(O) 2 R', -C(O)NRS(O) 2 NR'R”, -OR, -OC(O)NRR', -NRR', -NRC(O)R', -NRC(O)NR'R”, -NRS(O) 2 R', -NRS(O) 2 NR'R”, -S(O) 2 R and -S(O) 2 NRR',

[0081] wherein each of R, R' and R” is independently H, halo, OH, C 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 to which they are attached form a C 2-8 Heterocycloalkyl;

[0082] R 31 is C(O)CH 2 R 33 , optionally substituted cycloalkyl, optionally substituted cycloheteroalkyl, optionally substituted cycloalkenyl, optionally substituted cycloheteroalkenyl, optionally substituted aryl or optionally substituted heteroaryl;

[0083] R 32Each instance independently is a halogen group, haloalkyl group, optionally substituted alkoxy group, optionally substituted alkyl group, optionally substituted heteroalkyl group, optionally substituted alkenyl group, or optionally substituted heteroalkenyl group;

[0084] R 33 is an optionally substituted cycloalkyl group, optionally substituted cycloheteroalkyl group, optionally substituted cycloalkenyl group, optionally substituted cycloheteroalkenyl group, optionally substituted aryl group, or optionally substituted heteroaryl group;

[0085] Z is a cycloalkyl group, cycloheteroalkyl group, cycloalkenyl group, cycloheteroalkenyl group, aryl group, or heteroaryl group; Z can be an aryl group substituted with two or three instances of R 2 ; Z can be a phenyl group substituted with two or three instances of R 2 ; Z can be a heteroaryl group substituted with two or three instances of R 2 ; Z can be a six-membered heteroaryl group substituted with two or three instances of R 2 ; and

[0086] n is 0 - 5,

[0087] or a pharmaceutically acceptable salt of any of the compounds described above.

[0088] On the other hand, the present invention provides a method for determining whether a patient with PD associated with wild-type LRRK2 will respond to an LRRK2 inhibitor, the method comprising: generating a report that identifies the BMP and / or semi-BMP levels in the patient compared to those in a subject without PD and having wild-type LRRK2; providing the report to a physician such that if the report indicates that the BMP and / or semi-BMP levels in the patient are elevated compared to the subject, the physician prescribes or provides one or more LRRK2 inhibitors to the patient. In certain embodiments, the BMP can be selected from the group consisting of: (i) di-22:6 BMP, (ii) di-18:1 BMP, (iii) 16:0 / 18:1 BMP, (iv) di-20:4 BMP, (v) 18:0 / 20:4 BMP, (vi) 2,2'-di-22:6 BMP, and (vii) any combination thereof. In certain embodiments, the semi-BMP can be selected from the group consisting of: (i) semi-BMP(18:1 / 18:1)_16:0, (ii) semi-BMP(14:0 / 14:0)_14:0, (iii) semi-BMP(18:1 / 18:1)_18:0, (iv) semi-BMP(18:1 / 18:1)_18:1, and (iv) any combination thereof. These BMP and / or semi-BMP levels are measured in any biological fluid from the patient. In certain embodiments, the biological fluid used to measure the BMP and / or semi-BMP levels is urine, blood, cerebrospinal fluid (CSF), bile, or saliva. In certain preferred embodiments, the biological fluid used to measure the BMP and / or semi-BMP is urine or CSF. In certain embodiments, the elevated BMP and / or semi-BMP levels in the patient are at a concentration indicative that the patient will be responsive to the one or more LRRK2 inhibitors. The LRRK2 inhibitor can be any of the inhibitors described above.

[0089] On the other hand, the present invention provides a method for treating a patient suffering from PD associated with wild-type LRRK2, the method comprising: receiving data identifying the BMP and / or semi-BMP levels of the patient; comparing the data with the BMP and / or semi-BMP levels of a subject without PD and having wild-type LRRK2; and if the BMP and / or semi-BMP levels of the patient are elevated compared to the subject, prescribing or providing to the patient one or more LRRK2 inhibitors. In certain embodiments, the BMP can be selected from the group consisting of: (i) di-22:6 BMP, (ii) di-18:1 BMP, (iii) 16:0 / 18:1 BMP, (iv) di-20:4 BMP, (v) 18:0 / 20:4 BMP, (vi) 2,2'-di-22:6 BMP, and (vii) any combination thereof. In certain embodiments, the semi-BMP can be selected from the group consisting of: (i) semi-BMP(18:1 / 18:1)_16:0, (ii) semi-BMP(14:0 / 14:0)_14:0, (iii) semi-BMP(18:1 / 18:1)_18:0, (iv) semi-BMP(18:1 / 18:1)_18:1, and (iv) any combination thereof. These BMP and / or semi-BMP levels are measured in any biological fluid from the patient. In certain embodiments, the biological fluid used to measure the BMP and / or semi-BMP levels is urine, blood, cerebrospinal fluid (CSF), bile, or saliva. In certain preferred embodiments, the biological fluid used to measure the BMP and / or semi-BMP is urine or CSF. In certain embodiments, the elevated BMP and / or semi-BMP levels in the patient are at a concentration indicative that the patient will be responsive to the one or more LRRK2 inhibitors. The LRRK2 inhibitor can be any of the inhibitors described above.

[0090] The present invention provides methods and systems for predicting a subject's responsiveness to an LRRK2 inhibitor based on the subject's BMP and / or semi-BMP levels. In some embodiments, the methods and systems of the present invention use a diagnostic signature to predict responsiveness.

[0091] A diagnostic predictor can be based on any suitable pattern recognition method that receives input data representing BMP levels in PD patients who are carriers of LRRK2 deleterious variants, (2) PD of an apparently unknown mechanism, and (3) suitable controls, and provides an output indicative of the probability that a subject will respond to an LRRK2 inhibitor. The diagnostic predictor can be trained with data from multiple individuals whose BMP and / or semi-BMP levels, medical interventions, and LRRK2 inhibitor response outcomes are known. The multiple individuals used to train the diagnostic predictor are also referred to as the training population. For each individual in the training population, the training data includes: (a) data representing the patient's BMP and / or semi-BMP levels; (b) the medical intervention; and (c) the LRRK2 inhibitor response information. The LRRK2 inhibitor response outcome may not be required to generate a diagnostic signature. The LRRK2 inhibitor response can be evaluated in a prospectively selected patient population. Various diagnostic predictors that can be used in conjunction with the present invention are described below. In some embodiments, additional individuals with known trait profiles and LRRK2 response outcomes can be used to test the accuracy of the diagnostic predictor obtained using the training population. Such additional patients are referred to as the test population.

[0092] In certain embodiments, the methods of the present invention use a diagnostic predictor (also referred to as a classifier) to determine the probability of response to LRRK2 inhibition. As described above, the diagnostic predictor can 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 indicative of whether a patient is more or less likely to respond to an LRRK2 inhibitor and may include the possible risks and benefits of treatment with such an inhibitor. The profile can be obtained by completing a questionnaire containing questions about certain phenotypic traits or by collecting a biological sample to obtain genotype data or a combination thereof. The diagnostic predictor is trained with training data from an individual training population whose phenotypic traits, drug interventions, and LRRK2 inhibitor response outcomes are known.

[0093] The profile and diagnostic data of the training patients can be used to construct a diagnostic predictor based on any such method. Then, such a diagnostic predictor can be used to predict the LRRK2 inhibitor response of a subject based on the profile of the subject's phenotypic traits, genotype traits, or both. These methods can also be used to identify traits that distinguish responders from non-responders to LRRK2 inhibition using the trait profiles and diagnostic data of the training population.

[0094] In one embodiment, a diagnostic predictor can be prepared by: (a) generating a reference set of individuals with known phenotypic traits, drug interventions, and LRRK2 response outcomes; (b) determining, for each trait, a correlation measure between the trait and the LRRK2 response outcome among a plurality of individuals having known LRRK2 response outcomes at a predetermined time; (c) selecting one or more traits based on the level of association; (d) training a diagnostic predictor, wherein the diagnostic predictor receives data representative of the traits selected in the previous step and provides an output indicative of the probability of responding to LRRK2 inhibition, wherein the training data is from a reference set of subjects and includes an assessment of the traits taken from the individuals.

[0095] 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 selected markers of the present invention. In such an embodiment, the coefficients of the regression model are calculated using, for example, the maximum likelihood method.

[0096] Cox proportional hazards regression also includes coefficients for each of the selected markers of the present invention. Cox proportional hazards regression incorporates censored data (individuals in the reference set who did not return for treatment). In such an embodiment, the coefficients of the regression model are calculated using, for example, the maximum partial likelihood method.

[0097] Some embodiments of the present invention provide a generalization of the logistic regression model for handling multi-class (multinomial) responses. Such embodiments can be used to classify organisms into one or three or more diagnostic groups. Such a regression model uses a multinomial logit model, referring to all pairs of classes simultaneously, and describes the probability of responding in one class rather than another. Once the model has specified the logic for a certain (J - 1) pair of classes, the rest is redundant. See, for example, Agresti, An Introduction to Categorical Data Analysis, John Wiley & Sons, Inc., 1996, New York, Chapter 8, which is hereby incorporated by reference. Linear discriminant analysis (LDA) attempts to classify subjects into one of two classes based on certain object attributes. In other words, LDA tests whether the object attributes measured in an experiment predict the classification of the object. LDA generally requires continuous independent variables and a binary classification dependent variable. In the present invention, the selected phenotypic traits are used as the required continuous independent variables. The diagnostic group classification of each member of the training population is used as the binary classification dependent variable.

[0098] LDA finds a linear combination of variables that maximizes the ratio of between - group variance to within - group variance by using grouping information. Implicitly, the linear weights used by LDA depend on how the selected phenotypic traits behave in two groups (e.g., the group that responds to LRRK2 inhibition and the group that does not), and how the selected traits are related to the behavior of other traits. For example, LDA can be applied to the data matrix of N members in a training sample by K genes in the gene combination described by the present invention. Then, the linear discriminants of each member in the training population are plotted. Ideally, those members of the training population representing the first subgroup (e.g., those subjects who do not respond to LRRK2 inhibition) will cluster into one range of linear discriminant values (e.g., negative), and those members of the training population representing the second subgroup (e.g., those subjects who respond to LRRK2 inhibition) will cluster into a second range of linear discriminant values (e.g., positive). LDA is considered more successful when the gap between the clusters of discriminant values is large. For more information on linear discriminant analysis, see Duda, Pattern Classification, 2nd Edition, 2001, John Wiley & Sons; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York; Venables and Ripley, 1997, Modern Applied Statistics with s - plus, Springer, New York.

[0099] Quadratic discriminant analysis (QDA) uses the same input parameters as LDA and returns the same results. QDA uses quadratic equations, rather than linear equations, to produce the results. LDA and QDA are interchangeable, and which one to use depends on the preference and / or availability of the software supporting the analysis. Logistic regression uses the same input parameters and also returns the same results as LDA and QDA.

[0100] In some embodiments of the present invention, using the expression data of a set of selected molecular markers of the present invention, decision trees are used to classify patients. The decision tree algorithm belongs to a class of supervised learning algorithms. The purpose of a decision tree is to introduce a classifier (tree) from real - world instance data. This tree can be used to classify unseen instances that have not been used to derive the decision tree.

[0101] Decision trees are derived from training data. Instances contain values of different attributes and the class to which the instance belongs. In one embodiment, the training data is data representing multiple phenotypic traits, medical interventions, and the results of LRRK2 inhibition responses.

[0102] The following algorithm describes the derivation of a decision tree:

[0103] tree(instance, class, attribute)

[0104] Create the root node

[0105] If all instances have the same class value, assign this label to the root

[0106] Otherwise, if the attributes are empty, label the root according to the most

[0107] common value

[0108] Otherwise begin

[0109] Calculate the information gain for each attribute

[0110] Select the attribute A with the highest information gain and make

[0111] it the root attribute

[0112] For each possible value v of this attribute

[0113] Add a new branch below the root corresponding to A = v

[0114] Let instance(v) be those instances for which A = v

[0115] If instance(v) is empty, make the new branch a leaf node labeled with the most

[0116] common value in the instances

[0117] Otherwise, let the new branch be the tree created by

[0118] tree(instance(v), class, attribute - {A})

[0119] End

[0120] A more detailed description of the information gain calculation is shown below. If the possible classes vi of the instances have probabilities P(vi), the information content I of the actual answer is given by:

[0121] I(P(v 1 ),...,P(v n )) = ni = 1 - P(vi)log 2 P(v i )

[0122] The I value shows how much information is needed to describe the classification result of the particular data set used. Suppose the data set contains p positive examples (e.g., responders) and n negative examples (e.g., non - responders). The information contained in the correct answer is:

[0123] I(p / (p + n), n / (p + n)) = -p / (p + n) log 2 p / (p + n) - n / (p + n) log 2 n / (p + n)

[0124] where log 2 is the logarithm to the base 2. By testing a single attribute, the amount of information required for correct classification can be reduced. The remainder for a particular attribute A (e.g., a trait) shows the amount by which the required information can be reduced.

[0125] Remainder(A) = ∑i = 1vp i + n i / (p + n) nI(p i / pi + n i , n i / p i + n i )

[0126] "v" is the number of unique attribute values of attribute A in a given dataset, "i" is an attribute value, "p i " is the number of instances for which attribute A is classified as positive (e.g., responders), "n i " is the number of instances for which attribute A is classified as negative (e.g., non - responders).

[0127] The information gain for a particular attribute A is calculated as the difference between the information content of the class and the remainder part of attribute A:

[0128] Gain(A) = I(p / (p + n), n / (p + n)) - Remainder(A)

[0129] Information gain is used to evaluate the importance of different attributes for classification (the degree to which these attributes split the instances), and the attribute with the highest information.

[0130] Typically, there are many different decision tree algorithms, many of which are described in Duda, "Pattern Classification", 2nd Edition, 2001, John Wiley & Sons. Decision tree algorithms generally need to consider feature handling, impurity measurement, 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.

[0131] In one method, when using an exemplary embodiment of a decision tree, data representing multiple phenotypic traits in a training population is normalized 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. Expression values of selected combinations of traits are used to construct the decision tree. Then, the ability of the decision tree to correctly classify members of 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, 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 to be the average of each such iteration of the decision tree calculation.

[0132] In some embodiments, phenotypic traits and / or genotype data are used to cluster the training set. For example, consider the case of using the ten genes described in the present invention. Each member m in the training population will have an expression value for each of the ten genes. Such values from member m in the training population define a vector:

[0133] X 1m X 2m X 3m X 4m X 5m X 6m X 7m X 8m X 9m X 10m

[0134] where X im is the expression level of the i-th gene in organism m. If there are m organisms in the training set, selecting i genes will define m vectors. Note that the method of the present invention does not require that each expression value of each individual trait used in the vector be represented in each individual vector m. In other words, data from a subject in whom one of the i-th traits is not found can still be used for clustering. In such cases, the missing expression value is given a "zero" or some other normalized value. In some embodiments, prior to clustering, the trait expression values are normalized to have a mean of zero and a unit variance.

[0135] Those members of the training population that exhibit similar expression patterns throughout the training group will tend to cluster together. When the vectors are clustered into groups of traits found in the training population, a particular combination of the traits of the present invention is considered to be a good classifier in this aspect of the present invention. For example, if the training population includes patients with good or poor prognoses, the clustering classifier clusters the population into two groups, where each group uniquely represents good or poor prognosis.

[0136] Clustering is described in the following literature: Duda and Hart, pages 211-256 of *Pattern Classification and Scene Analysis*, 1973, John Wiley & Sons, New York. As described in Section 6.7 of Duda, the clustering problem is described as the problem of finding natural groupings in a dataset. To identify natural groupings, two problems are solved. First, a way to measure the similarity (or dissimilarity) between two samples is determined. Using this metric (similarity metric) ensures that samples within one cluster are more similar to each other than samples in other clusters. Second, a mechanism for partitioning the data into clusters using the similarity metric is determined.

[0137] The similarity metric is discussed in Section 6.7 of Duda, which states 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 the dataset.

[0138] If distance is a good measure of similarity, then the distances between samples within the same cluster will be significantly less than the distances between samples in different clusters. However, as stated on page 215 of Duda, clustering does not require the use of a distance metric. For example, a non-metric similarity function s(x,x') can be used to compare two vectors x and x'. Generally, s(x,x') is a symmetric function with a larger value when x and x' are "similar" to some extent. Page 216 of Duda provides an example of the non-metric similarity function s(x,x').

[0139] Once a method for measuring "similarity" or "dissimilarity" between points in the dataset has been selected, clustering requires a criterion function for measuring the quality of a clustering of any partition of the data. The partition of the dataset that extremizes the criterion function is used to cluster the data. See page 217 of Duda. The criterion function is discussed in Section 6.8 of Duda.

[0140] Recently, John Wiley & Sons, Inc. of New York has published the second edition of "Pattern Classification" by Duda et al. Clustering is described in detail on pages 537 - 563. More information on clustering techniques can be found in the following references: Kaufman and Rousseeuw, 1990, "Finding Groups in Data: An Introduction to Cluster Analysis", Wiley Press, New York, NY; Everitt, 1993, "Cluster analysis" (3rd edition), Wiley Press, New York, NY; 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, but are not limited to, 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.

[0141] The nearest - neighbor classifier is memory - based and does not require model fitting. Given a query point x 0 , identify the k training points x( 0 ) that are closest to x r , r,..., k and then use the k nearest neighbors to classify the point x 0 . Ties can be broken arbitrarily. In some embodiments, the Euclidean distance in the feature space is used to determine the distance according to the following formula:

[0142] d (i) = ||x( i)- x o ||.

[0143] Typically, when using the nearest neighbor algorithm, the expression data used to calculate the linear discriminant is normalized to have a mean of zero and a variance of 1. In the present invention, the 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 feature space to which the members of the test set are to be plotted is outlined. Next, the ability of the training set to correctly characterize the members of the test set is calculated. In some embodiments, for a given combination of phenotypic traits, the nearest neighbor calculation is performed a number of times. 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 trait combination is considered to be the average of each such iteration of the nearest neighbor calculation.

[0144] The nearest neighbor rule can be refined to handle problems of unequal class priors, differential misclassification costs, and feature selection. Many of these refinements involve some form of weighted voting on the neighbors. For more information on nearest neighbor analysis, see Duda, Pattern Classification, 2nd Edition, 2001, John Wiley & Sons; and Hastie, The Elements of Statistical Learning, 2001, Springer, New York.

[0145] The pattern classification and statistical techniques described above are merely examples of the types of models that can be used to build classification models. It should be understood that any statistical method can be used in accordance with the present invention. In addition, combinations of those described above can also be used. More 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.

[0146] It should be understood that during the course of treatment, individuals comprising the reference set may drop out before their LRRK2 inhibition response is determined. It is unclear whether these individuals will ultimately respond to LRRK2 inhibition. Omitting only those individuals from the reference set will bias the reference data set by omitting the characteristics of individuals with a poor response prognosis. This bias will result in an overly optimistic reported probability of response to treatment with an LRRK2 inhibitor.

[0147] Using the systems and methods of the present invention, the present invention utilizes certain statistical analysis methods to address the dropout problem rather than completely omitting those large groups of subjects. For example, the Kaplan-Meier method can be used to examine or exclude data of individuals in the reference set who did not return for treatment. According to the present invention, other forms of statistical analysis can be used to compile data of the reference set. For example, logistic regression, ordinal logistic regression, Cox proportional hazards regression, and other methods can all be used to compile data within the reference set. Additionally, it is contemplated that the reference set can examine or consider dropouts based on the characteristics of the individuals rather than making blanket assumptions about the responsiveness of dropouts. For example, rather than simply assuming that dropouts have the same chance of response as individuals who continue treatment or assuming that dropouts have no chance of response, the present invention can evaluate the characteristics of dropouts and informatively examine dropouts based on such information. In this way, over-optimistic estimates (due to assuming that all dropouts have the same chance of response) or over-conservative estimates (due to assuming that dropouts have no chance of response) are avoided.

[0148] In some aspects, the present invention incorporates the use of manual review to address dropouts. In manual review, when participants meet predefined study criteria, such as exposure to an intervention, non-compliance with the treatment protocol, or the occurrence of competing outcomes, these participants will be reviewed. Additional analysis methods, such as inverse probability of censoring weights (IPCW), can be used to determine the survival experience of participants who are manually reviewed if these participants had never been exposed to the intervention, complied, or developed competing outcomes. In some embodiments, methods that cover the use of manual screening and further cover the use of IPCW to address dropouts in the reference set are covered in the present invention. Additional details regarding the use of manual review and the use of IPCW are described in: 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 hereby incorporated by reference in its entirety.

[0149] Aspects of the present invention described herein can be performed using any type of computing, such as a computer, that includes a processor, such as a central processing unit, or any combination of computing devices, where each device performs at least a portion of a process or method. In some embodiments, the systems and methods described herein can be performed using a handheld device, such as a smart tablet computer, or a smartphone, or a dedicated device produced for the system.

[0150] The method of the present invention can be implemented using software, hardware, firmware, hardwiring, or any combination thereof. The features implementing the functions can also be physically located at different positions, including being distributed such that parts of the functions are implemented at different physical locations (e.g., an imaging device in one room and a host workstation in another room, or in separate buildings, e.g., with a wireless or wired connection).

[0151] By way of example, processors suitable for executing computer programs include both general and special purpose microprocessors, as well as any one or more processors of any type of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The basic elements of a computer are a processor for executing the instructions and one or more memory devices for storing the instructions and data. Generally, a computer will also include one or more mass storage devices for storing data (e.g., magnetic disks, magneto - optical disks, or optical disks) or be operatively coupled to receive data from or transfer data to a mass storage device or both. Information carriers suitable for embodying computer program instructions and data include all forms of non - volatile memory, including, by way of example, semiconductor memory devices (e.g., EPROM, EEPROM, solid state drives (SSD), and flash memory devices); magnetic disks (e.g., internal hard disks or removable disks); magneto - optical disks; and optical disks (e.g., CD and DVD disks). The processor and the memory can be supplemented by, or incorporated in, special logic circuitry.

[0152] In order to provide interaction with a user, the subject matter of the invention described herein can be implemented on a computer having I / O devices such as a CRT, LCD, LED, or a projection device for displaying information to the user and input or output devices such as a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user. 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, speech, or tactile input.

[0153] The subject matter described herein can be implemented in a computing system that includes: 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 a web browser through which a user can interact with an implementation of the inventive subject matter described herein), or any combination of such backend, middleware, and frontend components. The components of the system can be interconnected by any form or medium of digital data communication over a network (e.g., a communication network). For example, a reference dataset can be stored at a remote location and a computer communicates over the network to access the reference set to compare data derived from a 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 within the CPU to compare the subject data with the reference set. Examples of communication networks include cellular networks (e.g., 3G or 4G), local area networks (LANs), and wide area networks (WANs), such as the Internet.

[0154] The inventive subject matter described herein can be implemented as one or more computer program products, such as computer programs tangibly embodied in an information carrier (e.g., in a non-transitory computer-readable medium) for performing operations or controlling the operation of a data processing apparatus (e.g., a programmable processor, a computer, or multiple computers). A computer program (also referred to as a program, software, software application, application program, macro, or code) can be written in any form of programming language, including compiled or interpreted languages (e.g., C, C++, Perl), and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. The systems and methods of the present invention can include instructions written in any suitable programming language known in the art, including but not limited to C, C++, Perl, Java, ActiveX, HTML5, Visual Basic, or JavaScript.

[0155] A computer program does not necessarily correspond to a file. A program can be stored in a file or a portion of a file that holds other programs or data, in a single file dedicated to the relevant program, or in multiple coordinated files (e.g., files that store one or more modules, subroutines, or portions of code). A computer program can be deployed to execute on one computer or on multiple computers distributed at one site or across multiple sites and interconnected by a communication network.

[0156] The file can be a digital file, for example, stored on a hard drive, SSD, CD, or other tangible non-transitory medium. The file can be sent from one device to another over a network (e.g., from a server to a client as data packets, e.g., via a network interface card, modem, wireless card, or similar device).

[0157] Writing to a file according to the present invention involves, for example, transforming a tangible non-transitory computer-readable medium by adding, removing, or rearranging particles (e.g., converting a tangible non-transitory computer-readable medium having a net charge or dipole moment to a magnetization pattern via a read / write head), and then these patterns represent a new information configuration about an objective physical phenomenon that is desired by and useful to the user. In some embodiments, writing involves a physical transformation of the material in the tangible non-transitory computer-readable medium (e.g., having certain optical properties so that an optical read / write device can then read the new and useful information configuration, e.g., burning 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 made of floating-gate transistors. Methods of writing files are well known in the art and can be invoked, for example, manually or automatically by a program or by a save command from software or a write command from a programming language.

[0158] Suitable computing devices typically include mass storage, at least one graphical user interface, at least one display device, and typically include communication between devices. The mass storage represents a computer-readable medium, i.e., a computer storage medium. The computer storage medium can include volatile, non-volatile media, and removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include RAM, ROM, EEPROM, flash memory, or other memory technologies, CD-ROM, digital versatile disk (DVD), or other optical disk storage devices, magnetic cassettes, tapes, magnetic disk storage devices, or other magnetic storage devices, radio frequency identification tags or chips, or any other medium that can be used to store the desired information and can be accessed by a computing device.

[0159] As recognized by those skilled in the art, 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), a main memory, and a static memory, which communicate with each other via a bus, that are necessary or most suitable for the execution of the methods of the present invention.

[0160] The method of the present invention can utilize a machine learning system. For example, the machine learning system can learn in a supervised manner, an unsupervised manner, a semi-supervised manner, or through reinforcement learning.

[0161] In an unsupervised model or an autonomous model, the machine learning system is only given input training data without paired output data, and autonomously identifies patterns from these output data. The unsupervised model identifies potential patterns or structures in the training data to make predictions on test data. The unsupervised model is beneficial for clustering data, detecting anomalies, and independently discovering data rules. The accuracy of the unsupervised model is more difficult to evaluate because there is no predefined output variable for system optimization. The autonomous model can adopt epochs of both supervised and unsupervised learning in order to optimize predictions. When labeled training data is not available, the unsupervised model is beneficial for training the machine learning system to cluster data into clusters. The unsupervised model can use principal component analysis (PCA), uniform manifold approximation and projection (UMAP). When the groups in the training and test data are known, discriminant analysis can also be used. Discriminant analysis can include linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA).

[0162] In a semi-supervised model, the machine learning system is given training data including input variables, where output variable pairs are only available for a limited pool of input variables. The model uses the input variables with output variable pairs and the remaining input training data to learn patterns and make inferences in order to produce predictions on previously unseen test data. The semi-supervised model can advantageously query the user for additional paired output data based on unpaired data. When only an incomplete training data set is available, the semi-supervised model is beneficial for training the machine learning system.

[0163] In a reinforcement learning model, the machine learning system is given neither input variables nor output variables. Instead, the model provides "reward" conditions and then seeks to maximize the cumulative reward conditions through trial and error. The reinforcement learning model is a Markov Decision Process. The supervised model, the unsupervised model, the semi-supervised model, and the reinforcement model are described in the following: Jordan and Mitchell, 2015, Machine learning, Trends, perspectives, and prospects, Science 349(6245):255 - 260, which is incorporated by reference.

[0164] An example of a supervised learning model is a "decision tree". A decision tree is a non-parametric supervised learning model that uses simple decision rules to infer the classification of test data from features in the test data. In a classification tree, the test data takes on a finite set of discrete values or classes, while in a regression tree, the test data can take on continuous values, such as real numbers. Decision trees have several advantages because they are easy to understand and can be visualized as a tree that starts from a root (usually a single node) and repeatedly branches to leaves (multiple nodes) associated with the 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.

[0165] Another supervised learning model is the "Support Vector Machine" (SVM), "Support Vector Network" (SVN), or Support Vector Classifier (SVC), which are supervised learning models for classification and regression problems. When used to classify new data into one of two classes, the SVM creates a hyperplane in a multi-dimensional space that separates the data points into one class or the other. Although the original problem can be expressed in terms that require only a finite-dimensional space, in a finite-dimensional space, a linear separation of the data between the classes may be impossible. Therefore, a multi-dimensional space is chosen to allow the construction of a hyperplane that provides a clear separation of the data points. See Press, W.H. et al., §16.5, Support Vector Machines, Numerical Recipes: The Art of Scientific Computing (3rd ed.) New York: Cambridge University Press (2007), which is incorporated by reference. In cases where the output variable pairs are not available for the input variables in the training data, support vector clustering can be used to design the SVM as an unsupervised or semi-supervised learning model. See Ben-Hur, 2001, Support Vector Clustering, J Mach Learning Res 2:125-137, which is incorporated by reference. The SVM model can be advantageous for machine learning systems in which the test data falls into a finite number of possible classes. Additionally, the SVM model can be advantageous in cases where only a finite set of training data is available for the machine learning system.

[0166] Logistic regression analysis is another statistical process that can be used by a machine learning system to discover patterns in training and test data for prediction. This logistic regression analysis encompasses techniques for modeling and analyzing the relationships between multiple variables. Specifically, regression analysis focuses on the response of the change in the dependent variable to the change in a single independent variable. Given the independent variable, regression analysis can be used to estimate the conditional expectation of the dependent variable. The change in the dependent variable can be characterized by a regression function and described by a probability distribution. Techniques such as least squares, Bayesian methods, percentage regression, least absolute deviation, nonparametric regression, or distance metric learning can be used to estimate the parameters of the regression model. The regression model also offers the advantage of being effectively implemented through various tools, and the model can be easily updated to identify new particles.

[0167] SVM and logistic regression systems can use the Stochastic Gradient Descent (SGD) method to fit data. SGD is advantageous in optimizing machine learning systems using this method.

[0168] Bayesian algorithms can also be used to find patterns in training and test data for prediction. A Bayesian network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies through a directed acyclic graph (DAG). A DAG has nodes representing random variables, which can be observables, latent variables, unknown parameters of the nodes, or hypotheses. The edges represent conditional dependencies; unconnected nodes represent variables that are conditionally independent of each other. Each is associated with a probability function that takes as input a particular set of values of the parent variables of the node and gives (as output) the probability (or probability distribution, if applicable) of the variable represented by the node. The advantage provided by Bayesian models is that they generally require less training data than other models.

[0169] Some models may rely on clustering training and test data to find patterns and make predictions. The "k-nearest neighbor" (k-NN) model is a supervised non-parametric learning model for classification and regression problems. The k-NN model assumes that similar data exists nearby and assigns a class or value to each data point based on the k nearest data points. The k-NN model can be advantageous when the data has few outliers and can be defined by homogeneous features. Additionally, the k-NN model offers the advantage of continuous learning from test data and does not require a training period before identifying materials from the training data.

[0170] An example of an unsupervised learning model using clustering is the "k-means" clustering model. The k-means model finds clusters of data in the input data and test data. The k-means model is advantageous when a defined number of clusters are known to exist in the data and when the test data has few outliers and can be defined by homogeneous features. Additional models for clustering the training data include, for example, farthest neighbor, centroid, sum of squares, fuzzy k-means, and Jarvis-Patrick clustering. The k-means and other unsupervised clustering models are advantageous when the training data is unavailable or limited.

[0171] A trained machine learning model can become a "stable learner". A stable learner is a model that is less sensitive to prediction perturbations based on new training data. In cases where the test data is stable, a stable learner may be beneficial, but in cases where the system needs to continuously improve its performance to accurately predict new test data that may be less stable, a stable learner may be less beneficial. Therefore, when the types of data that can be introduced are known and cannot be changed, a stable learning model may be beneficial for the use of a machine learning system.

[0172] Several types of machine learning system types can be combined into a final prediction model, called 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 provide advantages because they can be more accurate than any individual base member model ("member") in the ensemble. The number of members combined in an ensemble can affect the accuracy of the final prediction. Therefore, when designing an ensemble system for use in a machine learning system, it is beneficial to determine the optimal number of members.

[0173] An ensemble used by a machine learning system can combine or aggregate the outputs from individual members by using a "voting" type of method for classification systems and an "averaging" type of method for regression systems. In the "majority voting" method, each member makes a prediction on the test data, and the prediction that receives more than half of the votes is the final output of the ensemble. If no prediction receives more than half of the votes, it can be determined that the ensemble cannot make a stable prediction. In the "majority voting" method, the prediction with the most votes, even if it receives less than half of the votes, can be considered the final output of the ensemble. In the "weighted voting" method, the votes of more accurate members are multiplied by weights assigned to each member based on their accuracy. In the "simple averaging" method, each member makes a prediction on the test data, and the average of the outputs is calculated. This method reduces overfitting and can be beneficial for creating a smoother regression model. In the "weighted averaging" method, the predicted output of each member is multiplied by a weight assigned to each member based on their accuracy. Voting methods, averaging methods, and weighted methods can be combined to improve the accuracy of an ensemble used by a machine learning system.

[0174] Members in an ensemble used by a machine learning system can be trained independently of each other, or new members can be trained using information from previously trained members. In a "parallel ensemble", the ensemble attempts to provide higher accuracy than individual members by exploiting the independence among the members, e.g., by training multiple members simultaneously to identify and aggregate outputs from the members. In a "sequential ensemble system", the ensemble attempts to provide higher accuracy than individual members by exploiting the correlation among the members, e.g., by using information about data identification from a first member to improve the training of a second member to identify data and weight the outputs from the members.

[0175] The overall accuracy of an ensemble used by a machine learning system can be optimized by using an ensemble meta-algorithm, e.g., the "bagging" algorithm for reducing variance, the "boosting" algorithm for reducing bias, or the "stacking" algorithm for improving prediction.

[0176] The boosting algorithm reduces bias and can be used to improve less accurate or "weak learning" models. A member can be considered a "weak learning" model if it has a relatively high error rate, but its performance is non-random. The boosting algorithm progressively builds the ensemble by training each member sequentially with the same training data set, examining the prediction error of the test data, and assigning weights to the training data based on the difficulty for the member to make accurate predictions. In each successive member trained, the algorithm emphasizes the training data that was difficult for the previous member. Then, considering the weights applied to the training data, the members are weighted based on the accuracy of their predicted outputs. The predictions from each member can be combined by a weighted voting-type or weighted averaging-type method. The boosting algorithm is advantageous when combining multiple weak learning models. However, the boosting algorithm may lead to overfitting of the test data to the training data. Examples of the boosting algorithm include AdaBoost, gradient boosting, and Extreme Gradient Boosting (XGBoost). See Freund, 1997, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences 55:119; and Chen, 2016, XGBoost: A Scalable Tree Boosting System, arXiv:1603.02754, both incorporated by reference.

[0177] The bagging algorithm, or "bootstrap aggregation" algorithm, reduces variance by averaging multiple estimates of the members. The bagging algorithm provides each member with a random subsample of the complete training data set, where each random subsample is called a "bootstrap" sample. In a bootstrap sample, some data from the training data set may occur more than once, and some data from the training data set may not be present. Since the subsamples can be generated independently of each other, training can be performed in parallel. Then, the predictions of the test data from each member are aggregated, such as by a voting-type or averaging-type method.

[0178] An instance of the bagging algorithm that can be used by a machine learning system is "random forest". In a random forest, the ensemble combines multiple random decision tree models. Each decision tree model is trained from a bootstrap sample in the training set of the test data. The training set itself can be a random subset of the features from an even larger training set. By providing a random subset of the larger training set at each split during the learning process, the spurious correlation due to the presence of individual features that are strong predictors of the output variable can be reduced. By averaging the predictions of the test data, the variance of the ensemble is reduced, resulting in an improvement in the test data predictions. A random forest can be an autonomous model and can include epochs of both supervised and unsupervised learning. Bagging may be less beneficial in optimizing the ensemble of a stable learning system because a stable learning system tends to provide a generalized output with less variability on the bootstrap samples. A random forest benefits the use of a machine learning system in identifying data by providing a high degree of generality in identifying test data and reducing false identifications of the machine learning system. See Breiman, 2001, Random Forests, Machine Learning 45:5-32, which is incorporated by reference.

[0179] Stacking algorithms, or "stacked generalization" algorithms, improve predictions by using meta machine learning models to combine and build an ensemble. In stacking algorithms, base member models are trained with a training data set, and a new data set is produced as output. This new data set is then used as the training data set for a meta machine learning model to build the ensemble. Stacking algorithms are generally beneficial for use by machine learning systems in identifying test data when building heterogeneous ensembles. The ensembles are described 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): Chapter 23, each incorporated by reference.

[0180] Neural networks modeled after the human brain allow for information processing and machine learning. Neural networks contain nodes that mimic the functions of individual neurons, and these nodes are organized into layers. Neural networks contain an input layer, an output layer, and one or more hidden layers that define the connections from the input layer to the output layer.

[0181] The systems and methods of the present invention can include any neural network that facilitates machine learning. The system can include known neural network architectures such as GoogLeNet (Szegedy et al., Going deeper with convolutions, CVPR 2015, 2015); AlexNet (Krizhevsky et al., Imagenet classification with deep convolutional neural networks, edited by Pereira et al., Advances in Neural Information Processing Systems 25, pp. 1097 - 3105, Curran Associates, Inc., 2012); VGG16 (Simonyan and 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 a machine learning system based on a neural network architecture is that the neural network can learn patterns and correlations on its own and produce outputs that are not limited by the training data provided to them.

[0182] Deep learning neural networks (also known as deep structured learning, hierarchical learning, or deep machine learning) include a class of machine learning operations that can be used by a classifier, which use a cascade of many non-linear processing unit layers for feature extraction and transformation. Each subsequent layer uses the output of the previous layer as input. The algorithms can be supervised or unsupervised, and the applications include pattern analysis (unsupervised) and classification (supervised). Some embodiments are based on unsupervised learning of multiple levels of features or data representations. Higher-level features are derived from lower-level features to form a hierarchical representation. Deep learning in a neural network involves learning multiple representation levels corresponding to different levels of abstraction; these levels form a hierarchy of concepts. In some embodiments, the neural network includes at least 5, and preferably more than ten hidden layers. The many layers between the input and output allow the system to operate through multiple processing layers.

[0183] In a neural network that can be used by a machine learning system, nodes are connected into layers, and signals travel from an input layer to an output layer. Each node in the input layer can correspond to a respective feature from training data. The nodes of a hidden layer are calculated as a function of a weighted sum of bias terms and the nodes of the input layer, where respective weights are assigned to each connection between the nodes of the input layer and the nodes in the hidden layer. The bias terms and weights between the input layer and the hidden layer are advantageously learned autonomously during the training of the neural network. The network can contain thousands or millions of nodes and connections. Typically, the signals and states of artificial neurons are real numbers, usually between 0 and 1. Optionally, there can be a threshold function or a limiting function at each connection and on the unit itself such that the signal must exceed a limit before it propagates. Backpropagation is used to modify the connection weights using forward excitation and sometimes to train the network using known correct outputs. See WO 2016 / 182551, U.S. Publication No. 2016 / 0174902, U.S. Patent No. 8,639,043, and U.S. Publication No. 2017 / 0053398, each of which is incorporated herein by reference.

[0184] Features from test or training data can be represented in a variety of ways by a deep learning network, such as a vector of intensity values for each pixel in an image, or in a more abstract way as a set of edges, regions of a particular shape, etc. These features are represented at the nodes in the network. Preferably, each feature is constructed as a digital feature or vector representing an image feature. This provides, for example, a digital representation of an object from an image, as such a representation facilitates processing and statistical analysis. Digital features are typically combined with weights using a dot product to construct a linear prediction function for determining a score for making a prediction.

[0185] The vector space associated with these feature vectors can be referred to as a feature space. To reduce the dimensionality of the feature space, the network used by a classifier can employ dimensionality reduction. In a process called feature construction, higher-level features can be obtained from the features that are already available and added to the feature vector. Feature construction is the construction of applying a set of constructive operators to a set of existing features, thereby producing new features. For example, image data can be provided from an image sensor to a machine learning system based on a neural network architecture. Early layers in the neural network can identify horizontal and vertical lines in the image data. Then, subsequent layers in the network can use the identified lines to obtain the edges of particles in the image, which is a higher-level feature.

[0186] A deep learning neural network can be a multi-layer perceptron (MLP), a convolutional neural network (CNN), or a recurrent neural network (RNN).

[0187] Figure 1 Total urinary di-18:1-BMP levels are provided for different groups of patient populations.

[0188] Figure 2 Total urinary di-22:6-BMP levels in different groups of patient populations are provided.

[0189] Figure 3 Total urinary 2,2'-di-22:6-BMP levels in different groups of patient populations are provided. It is evident from the data that the total BMP level is a good indicator of the efficacy of LRRK2 inhibitor therapy.

[0190] Figure 4 The distribution of PD patients and their LRRK2 activity is provided. Figure 4 The data provided demonstrate that LRRK2 in PD patients can be classified as LRRK2 activity, LRRK2 rare variants, and LRRK2 normal.

[0191] Figure 5 Data related to the total urinary di-22:6-BMP concentrations in patients with LRRK2 normal, LRRK2 predicted, and LRRK2 rare variants are provided. This data demonstrates that the urinary di-22:6-BMP level is a good representative for predicting LRRK2 pathway activity. The urinary BMP levels of LRRK2 predicted patients identified by the method provided herein are between those of LRRK2 罕见变体 and LRRK2 正常 patients. Therefore, patients with a certain urinary BMP level may be more responsive to treatment with LRRK2 inhibitors.

[0192] Figure 6A and 6B demonstrate that the LRRK2 活性 scores in idiopathic Parkinson's disease are stable across different groups of the AMP-PD (Accelerating Medicines Partnership - PD) patient cohort. Therefore, the method provided herein for separating patient populations is important for identifying patients who are more responsive to LRRK2 inhibitor therapy.

[0193] Identifying genetic modifiers from genetic data

[0194] The present invention provides a method for using genetic modifiers of LRRK2 in a patient's genome as an indicator to determine whether a PD patient with wild-type LRRK2 is more likely to respond to an LRRK2 inhibitor. The present invention recognizes that genetic modifiers of LRRK2 can cause changes in the level or activity of LRRK2 kinase, such as an increase or decrease, or can otherwise alter the LRRK2 signaling pathway through upstream or downstream regulators, and thus contribute to PD etiology. Therefore, a PD patient having one or more such modifiers may benefit from drug treatment with an LRRK2 inhibitor, despite having an LRRK2 allele that produces the normal form of the kinase. Thus, genetic modifiers of LRRK2 activity can be used as an indicator for determining whether LRRK2 inhibitor therapy is suitable for a given individual. The methods of the present invention can be used both to identify PD patients as candidates for LRRK2 inhibitor therapy and to treat such patients.

[0195] In one aspect, the present invention provides a method of treating a subject having Parkinson's disease associated with wild-type LRRK2 by: providing an LRRK2 inhibitor to a subject having Parkinson's disease and having wild-type LRRK2 and a genetic modifier of wild-type LRRK2 such that the subject will respond to the LRRK2 inhibitor, thereby treating the subject's Parkinson's disease associated with wild-type LRRK2.

[0196] Genetic data can include any type of data regarding the composition and / or expression of one or more genes of a subject. Genetic data can comprise one or more of exogenous, genomic, genotype, proteomic, sequence, and transcriptomic data.

[0197] A genetic modifier can be any genetic element that modifies or is associated with a change in the activity of LRRK2 expression or activity, or causes a change (either an increase or a decrease) in the protein level associated with disease burden. A genetic modifier can increase or decrease the expression and / or activity of LRRK2; a genetic modifier can also increase or decrease the degradation of LRRK2. A genetic modifier can be an amplification, deletion, duplication, fusion, insertion, inversion, rearrangement, single nucleotide polymorphism (SNP), substitution, or translocation. A genetic modifier can be located within the coding or non-coding region of a subject's genome. A genetic modifier may be associated with family history and genetically determined Ashkenazi Jewish identity.

[0198] In certain embodiments, the genetic modifier can be any genetic modifier provided in PCT / US2021 / 056443, which is incorporated herein by reference in its entirety. In certain embodiments, the SNP can be any SNP listed in PCT / US2021 / 056443, which is incorporated herein by reference in its entirety.

[0199] The SNP can be rs10784722, rs10877877, rs10879122, rs11181542, rs113111234, rs113736300, rs12230765, rs12816484, rs12829831, rs13377670, rs141551396, rs144377852, rs149173058, rs17580794, rs17621741, rs1838354, rs184120094, rs188535877, rs188583486, rs188604552, rs189517205, rs200611801, rs200907772, rs201889643, rs201944175, rs2406426, rs2406860, rs285561, rs34566033, rs368141132, rs369084695, rs371700002, rs371905892, rs373439540, rs376468815, rs377104202, rs377627337, rs384234, rs61920964, rs6581941, rs6650226, rs71078241, rs7304080, rs73088926, rs74434364, rs74842215, rs75043969, rs78468120, rs7960429, rs7979420, rs76904798, rs57025360, rs112515153, rs10877877, rs10784722, rs4272849, rs2404832, rs117534366, rs1838343, rs10880342, rs11177660, rs183028452, rs116912628, rs147755361, rs11584630, rs3793397, rs111794893, rs4931640, rs526507, rs79307177, rs187116363, rs71609573, rs74390551, rs144665441, rs1718880, rs1991401, rs11052225, rs145801597, rs72907976, rs147286120, rs378690, rs73188365, rs610037, rs75479531, rs1112191556, rs308303, rs10790282, rs3729912, rs4326638, rs4414548,rs13009437, rs56045011, rs6858566, rs4425, rs11052253, or any other SNP that is in linkage disequilibrium (LD) with these SNPs and suitable as a proxy for these SNPs. The LRRK2 inhibitor can be any inhibitor listed in this application.

[0200] In certain embodiments, the SNP can be rs33939927, rs35801418, rs34805604, rs34637584, rs35870237, rs34995376, rs34778348, rs11611119, rs6581439, rs12296462, rs549790, rs12423473, rs555740, rs2242367, rs7295598, rs2253736, rs11564274, rs2708419, rs17519419, rs76904798, rs17519573, rs11175847, rs10878452, rs17444612, rs117929583, rs11564235, rs17128233, rs7960976, rs1918942, rs611829, rs7531501, rs9793102, rs3755541, rs7578955, rs17738103, rs4676776, rs1259475, rs10477505, rs7380062, rs4636028, rs12704998, rs2768282, rs10283642, rs3750779, rs1362993, rs7089200, rs9783486, rs4763946, rs16920645, rs12312400, rs2708494, rs12813279, rs11613339, rs7300813, rs166806, rs3794253, rs7960429, rs7955116, rs10491998, rs12814145, rs2708078, rs1922761, rs1373422, rs17691793, rs7301498, rs7967809, rs1866074, rs10507535, rs9300705, rs1642819, rs8048361, rs1152838, rs2331796, rs10515969, rs6566942, or rs2422956.

[0201] On the other hand, the present invention provides methods for determining whether a subject having Parkinson's disease associated with wild-type LRRK2 will respond to an LRRK2 inhibitor. These methods comprise: assaying a sample from a subject having Parkinson's disease associated with wild-type LRRK2 to obtain genetic data of the subject; generating a report identifying one or more genetic modifiers of LRRK2 in the genetic data, wherein the one or more genetic modifiers in the LRRK2 network indicate that the subject having Parkinson's disease associated with wild-type LRRK2 will be responsive to an LRRK2 inhibitor; and providing the report to a physician such that the physician prescribes or provides an LRRK2 inhibitor to the subject. The genetic data can be any type of genetic data described above. The genetic modifier can be a genetic modifier of LRRK2 described above. The genetic modifier can be any SNP listed above. The LRRK2 inhibitor can be any inhibitor listed in the present application.

[0202] On the other hand, the present invention provides methods for treating a subject having PD associated with wild-type LRRK2. These methods comprise: receiving genetic data identifying one or more genetic modifiers of LRRK2, wherein the one or more genetic modifiers indicate that a subject having Parkinson's disease associated with wild-type LRRK2 will be responsive to an LRRK2 inhibitor; and prescribing or providing an LRRK2 inhibitor to the subject. The genetic data can be any type of genetic data described above. The genetic modifier can be a genetic modifier of LRRK2 described above. The genetic modifier can be any SNP listed above.

[0203] The present invention also recognizes that genetic modifiers of LRRK2 can be used as an indicator of whether PD patients with wild-type LRRK2 may benefit from drug therapy using one or more LRRK2 inhibitors. A genetic modifier of LRRK2 can be one or more genetic elements (e.g., a single genetic element alone or any combination of genetic elements) that operably modify LRRK2 (e.g., wild-type LRRK2), such that, for example, the genetic element alters the expression, degradation, localization (e.g., intracellularly or across cell types), binding, or activity of LRRK2 in a subject, including the LRRK2 gene, transcripts of the LRRK2 gene, and polypeptide products of the LRRK2 gene. By way of example and not limitation, a genetic modifier can alter, e.g., increase or decrease, the expression, activity, stability, binding, localization, degradation, transcription, or translation of LRRK2, including the LRRK2 gene, transcripts of the LRRK2 gene, and polypeptide products of the LRRK2 gene. In certain embodiments, a genetic modifier of LRRK2 can be a structural variant in the genome of a subject. By way of example and not limitation, a genetic modifier can be an amplification, deletion, duplication, fusion, insertion, inversion, rearrangement, single nucleotide polymorphism (SNP), substitution, or translocation. SNPs that can be genetic modifiers of LRRK2 are listed in Example 1. Additionally, any other SNP that is in linkage disequilibrium (LD) with the SNPs listed in Example 1 can be used as a genetic modifier. A genetic modifier can be a cis-regulatory element, such as a promoter, enhancer, silencer, or operator. A cis-regulatory element can regulate the binding of one or more proteins to DNA near LRRK2. A cis-regulatory element can affect the binding of histones, transcription factors, initiation factors, helicases, polymerases, or components of any of the foregoing proteins. A genetic modifier can be a trans-acting factor. A trans-acting factor can affect the transcription or translation of LRRK2. A genetic modifier can be located in any region of the genome of a subject. A genetic modifier can be located within the coding or non-coding regions of the genome of a subject. The coding region can be located in LRRK2 or another gene. A genetic modifier can be located within the LRRK2 coding region but not alter the sequence of the LRRK2 polypeptide, the size of the LRRK2 polypeptide, or both.

[0204] The methods of the present invention can include identifying or analyzing one or more genetic modifiers of LRRK2 from genetic data obtained from a subject. Genetic data can include any type of data regarding the composition and / or expression of one or more genes of a subject. Genetic data can include one or more of exogenous, genomic, genotype, proteomic, sequence, and transcriptomic data. Genetic data can include data regarding one or more genes known to be associated with PD, such as any of the genes described above.

[0205] Any suitable method can be used to identify gene modifiers from genetic data. In some embodiments, genetic data collected from a subject is compared to a data reference set to provide a probability of responsiveness to an LRRK2 inhibitor. The reference set can include data collected from individuals who have never had PD. Phenotypic data from the subject and reference individuals can also be used. The phenotypic data can contain traits associated with PD, including PD symptoms or PD risk factors, such as those described above. The data can include outcomes such as whether an individual responds to LRRK2 inhibitor treatment.

[0206] The present invention provides methods and systems 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 methods and systems of the present invention use 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) the LRRK2-like manifestation of PD observed in carriers of LRRK2 deleterious variants, (2) PD of apparently unknown mechanism, and (3) suitable controls, 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 whom the phenotypic traits, medical interventions, and LRRK2 inhibitor response outcomes are known. The multiple individuals used to train the diagnostic predictor are also referred to 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 the diagnostic signature. LRRK2 inhibitor responses can be evaluated in a prospectively selected patient population. Various diagnostic predictors that can be used in conjunction with the present invention are described below. In some embodiments, additional individuals with known trait profiles and LRRK2 response outcomes can be used to test the accuracy of the diagnostic predictor obtained using the training population. Such additional patients are referred to as the test population.

[0207] In certain embodiments, the methods of the invention use a diagnostic predictor (also referred to as a classifier) to determine the probability of response to LRRK2 inhibition. As described above, the diagnostic predictor can 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 a greater or lesser likelihood that a patient will respond to an LRRK2 inhibitor and may include the possible risks and benefits of treatment with such an inhibitor. The profile can be obtained by completing a questionnaire containing questions about certain phenotypic traits or by collecting a biological sample to obtain genotype data or a combination thereof. The diagnostic predictor is trained with training data from an individual training population for which the phenotypic traits, drug intervention, and LRRK2 inhibitor response results are known.

[0208] The profile and diagnostic data of the training patients can be used to construct a diagnostic predictor based on any such method. Then, such a diagnostic predictor can be used to predict an LRRK2 inhibitor response of a subject based on the profile of the subject's phenotypic traits, genotype traits, or both. These methods can also be used to identify traits that distinguish responders from non-responders to LRRK2 inhibition using the trait profiles and diagnostic data of the training population.

[0209] In one embodiment, a diagnostic predictor can be prepared by: (a) generating a reference set of individuals with known phenotypic traits, drug intervention, and LRRK2 response results; (b) determining, for each trait, a correlation measure between the trait and the LRRK2 response results in a plurality of individuals with known LRRK2 response results at a predetermined time; (c) selecting one or more traits based on the level of association; (d) training a diagnostic predictor, wherein the diagnostic predictor receives data representative of the traits selected in the previous step and provides an output indicative of the probability of response to LRRK2 inhibition, wherein the training data is from the reference set of subjects and includes an assessment of the traits taken from the individuals.

[0210] A variety of known statistical pattern recognition methods can be used in conjunction with the present invention. Statistical pattern recognition methods are described in detail above.

[0211] Assays for obtaining genetic data

[0212] The identification or analysis of one or more genetic modifiers of LRRK2 can include assays on samples obtained from a subject. The sample can be any type of sample containing genetic material such as DNA or RNA. For example but not limited to, the sample can be from amniotic fluid, biopsy, blood, body fluid, cell, cerebrospinal fluid, lymph fluid, mouthwash, needle biopsy, hair, sputum, plasma, pus, saliva, semen, serum, sputum, feces, swab, sweat, synovial fluid, tears, tissue, urine or any combination of the foregoing. For example but not limited to, the tissue sample can be from bone marrow tissue, CNS tissue, eye tissue, gastrointestinal tissue, urogenital tissue, hair, kidney tissue, liver tissue, mammary tissue, mammary tissue, musculoskeletal tissue, nail, nasal tissue, nerve tissue, placental tissue, placental tissue or skin tissue.

[0213] The subject can be any type of subject. The subject can be a human. The subject may exhibit one or more symptoms of Parkinson's disease, or the subject may be asymptomatic. The patient can be related to a PD patient. The subject can be a pediatric patient, neonate, infant, toddler, child, adolescent, preteen, young adult, adult or elderly subject. The subject may exhibit one or more symptoms of Parkinson's disease, or the subject may be asymptomatic. The patient can be related to a PD patient.

[0214] Methods of genetic analysis are known in the art. In certain embodiments, a known single nucleotide polymorphism at a particular location can be detected by single base extension of a primer that binds to the sample DNA adjacent to that location, as described, for example, in U.S. Patent No. 6,566,101, the contents of which are incorporated herein by reference in their entirety. In other embodiments, hybridization probes can be employed that overlap the SNP of interest and selectively hybridize to sample nucleic acids containing a particular nucleotide at that location, as described, for example, in U.S. Patents Nos. 6,214,558 and 6,300,077, the contents of which are incorporated herein by reference in their entirety.

[0215] In certain embodiments, the nucleic acid is sequenced to detect variants (i.e., mutations) in the nucleic acid compared to the sequence of the wild-type and / or non-mutated form. The nucleic acid can comprise multiple nucleic acids from multiple genetic elements. Methods of detecting sequence variants are known in the art, and sequence variants can be detected by any sequencing method known in the art, such as pooled sequencing or single molecule sequencing.

[0216] Sequencing can be by any method known in the art. DNA sequencing techniques include: classical dideoxy sequencing reactions (Sanger method) using labeled terminators or primers and gel separation in a slab or capillary; sequencing-by-synthesis using reversibly terminating labeled nucleotides; pyrosequencing; 454 sequencing; allele-specific hybridization with a labeled oligonucleotide probe library; sequencing-by-synthesis using allele-specific hybridization with a labeled clone library and subsequent real-time monitoring of ligation and incorporation of labeled nucleotides during a polymerization step; polony sequencing; and SOLiD sequencing. More recently, sequencing of isolated molecules has been demonstrated by using successive or single extension reactions of a polymerase or ligase, and by single or successive differential hybridization with a probe library.

[0217] One conventional method for performing sequencing is by chain termination and gel separation, as described, for example, in Sanger et al., Proc Natl. Acad. Sci. USA, 74(12):5463-5467 (1977). Another conventional sequencing method involves chemical degradation of nucleic acid fragments, as described, for example, in Maxam et al., Proc Natl. Acad. Sci., 74:560-564 (1977). Finally, methods based on sequencing by hybridization have been developed, as described, for example, in U.S. Patent Publication No. 2009 / 0156412. The content of each reference is hereby incorporated by reference in its entirety.

[0218] Sequencing techniques that can be used in the methods of the present invention include, for example, Harris T.D. et al., Single-Molecule DNA Sequencing of a Viral Genome, (2008) Science 320:106-109. In true single molecule sequencing (tSMS) technology, DNA samples are fragmented into strands of approximately 100 to 200 nucleotides, and a polyA sequence is added to the 3' end of each DNA strand. Each strand is labeled by addition of a fluorescently labeled adenosine nucleotide. The DNA strands are then hybridized to a flow cell that contains millions of oligonucleotide T capture sites immobilized to the flow cell surface. The density of the templates can be about 100 million templates / cm 2。The flow cell is then loaded into an instrument, such as a HeliScope.TM. sequencer, and the surface of the flow cell is irradiated with a laser to reveal the location of each template. A CCD camera can map the location of the templates on the surface of the flow cell. The template fluorescent labels are then cleaved and washed away. The sequencing reaction begins with the introduction of DNA polymerase and fluorescently labeled nucleotides. Oligonucleotide T nucleic acid is used as a primer. The polymerase incorporates the labeled nucleotides into the primer in a template-directed manner. The polymerase and unincorporated nucleotides are removed. Templates that have incorporated fluorescently labeled nucleotides in an oriented manner are detected by imaging the surface of the flow cell. After imaging, the cleavage step removes the fluorescent labels, and the process is repeated with other fluorescently labeled nucleotides until the desired read length is achieved. Sequence information is collected at each nucleotide addition step. Additional descriptions of tSMS are shown, for example, in U.S. Patent Nos. 7,169,560; 6,818,395; and 7,282,337; U.S. Patent Publications 2009 / 0191565 and 2002 / 0164629; and Braslavsky et al., Proceedings of the National Academy of Sciences of the United States of America (PNAS(USA)), 100:3960-3964 (2003), the contents of each of which are incorporated herein by reference in their entirety.

[0219] Another example of a DNA sequencing technology that can 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 involves two steps. In the first step, DNA is fragmented into pieces 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 the amplification and sequencing of the fragments. For example, adapters B containing a 5'-biotin tag can be used to ligate the fragments to DNA capture beads, such as streptavidin-coated beads. The fragments ligated to the beads are PCR amplified within droplets of an oil-water emulsion. The result is multiple copies of the DNA fragment cloned and amplified on each bead. In the second step, the beads are captured in wells (picoliter-sized). Pyrosequencing is performed in parallel on each DNA fragment. The addition of one or more nucleotides generates a light signal that is recorded by a CCD camera in the sequencing instrument. The signal intensity is proportional to the number of nucleotides incorporated. Pyrosequencing utilizes the pyrophosphate (PPi) released upon the addition of nucleotides. In the presence of adenosine 5'-phosphosulfate, PPi is converted to ATP by ATP sulfurylase. Luciferase utilizes ATP to convert luciferin to oxyluciferin, and the light generated by this reaction can be detected and analyzed.

[0220] Another example of a DNA sequencing technology that can be used in the methods of the present invention provided herein is the SOLiD technology (Life Technologies Corporation (Applied Biosystems)). In SOLiD sequencing, genomic DNA is fragmented and adapters are ligated to the 5' and 3' ends of the fragments to produce a fragment library. Alternatively, internal adapters can be introduced by ligating adapters to the 5' and 3' ends of the fragments, circularizing the fragments, digesting the circularized fragments to produce internal adapters, and ligating adapters to the 5' and 3' ends of the resulting fragments to produce a paired library. Next, a population of clonal beads is prepared in a microreactor containing beads, primers, template, and PCR components. After PCR, the template is denatured and the beads are enriched to isolate beads with extended templates. The templates on the selected beads are 3'-modified to allow binding to a slide. The sequence can be determined by hybridization and ligation of a partially random oligonucleotide to the central determining base (or base pair) recognized by a specific fluorophore. After recording the color, the ligated oligonucleotide is cleaved and removed, and then the process is repeated.

[0221] Another example of a DNA sequencing technology that can be used in the methods of the present invention provided herein is Ion Torrent sequencing, as described in U.S. Patent Publications Nos. 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 which are incorporated herein by reference in their entirety. In Ion Torrent sequencing, DNA is fragmented into fragments of approximately 300 - 800 base pairs and the fragments are blunt-ended. Then oligonucleotide adapters are ligated to the ends of the fragments. The adapters serve as primers for amplification and sequencing of the fragments. The fragments can be attached to a surface and the resolution of the attachment is such that the fragments can be resolved individually. The addition of one or more nucleotides releases a proton (H + ) which is detected and recorded in a sequencing instrument. The signal intensity is proportional to the number of nucleotides incorporated.

[0222] Another example of a sequencing technique that can be used in the methods of the present invention provided herein is Illumina sequencing. Illumina sequencing is based on the amplification of DNA on a solid surface using bridge PCR and anchored primers. Genomic DNA is fragmented, and adapters are added to the 5' and 3' ends of the fragments. The DNA fragments attached to the surface of the flow cell channels are extended and bridge amplified. The fragments become double-stranded, and the double-stranded molecules are denatured. Multiple cycles of solid-phase amplification followed by denaturation can generate clusters of approximately 1,000 copies of millions of single-stranded DNA molecules with the same template in each channel of the flow cell. Primers, DNA polymerase, and four fluorophore-labeled reversible terminator nucleotides are used to perform sequential sequencing. After nucleotide incorporation, the fluorophores are excited with a laser, an image is captured, and the identity of the first base is recorded. The 3' terminator and fluorophore are removed from each incorporated base, and the incorporation, detection, and identification steps are repeated.

[0223] Another example of a sequencing technique that can be used in the methods of the present invention provided herein includes the single molecule real-time (SMRT) technology of Pacific Biosciences. In SMRT, each of the four DNA bases is linked to one of four different fluorescent dyes. These dyes are phosphate-linked. A single DNA polymerase is immobilized at the bottom of a zero-mode waveguide (ZMW) together with a single molecule of template single-stranded DNA. The ZMW is a confinement structure capable of observing the incorporation of individual nucleotides by the DNA polymerase against a background of fluorescent nucleotides that diffuse rapidly in and out of the ZMW (in microseconds). It takes several milliseconds for a nucleotide to be incorporated into the growing strand. During this time, the fluorescent label is excited and emits a fluorescent signal, and the fluorescent tag is excised. Detection of the corresponding fluorescence of the dye indicates which base has been incorporated. This process is repeated.

[0224] Another example of a sequencing technique that can be used in the methods of the present invention provided herein is nanopore sequencing, as described, for example, in Soni G V and Meller A. (2007) Clin Chem 53:1996-2001. A nanopore is a small hole with a diameter of approximately 1 nanometer. The nanopore is immersed in a conductive fluid and an electric potential is applied across it, resulting in a small current due to the conduction of ions through the nanopore. The amount of current flowing through is sensitive to the size of the nanopore. When a DNA molecule passes through the nanopore, each nucleotide on the DNA molecule obstructs the nanopore to a different extent. Thus, as the DNA molecule passes through the nanopore, changes in the current passing through the nanopore represent a reading of the DNA sequence.

[0225] Another example of a sequencing technology that can be used in the provided methods of the present invention involves sequencing DNA using a Chemically Sensitive Field Effect Transistor (chemFET) array, e.g., as described in U.S. Patent Publication No. 20090026082. In one example of this technology, DNA molecules can be placed in a reaction chamber, and the template molecules can hybridize with sequencing primers that are bound to polymerase. Incorporation of one or more triphosphates into a new nucleic acid strand at the 3'-end of the sequencing primer can be detected by a change in current of the chemFET. The array can have multiple chemFET sensors. In another example, individual nucleic acids can be attached to beads, the nucleic acids can be amplified on the beads, and the individual beads can be transferred to separate reaction chambers on a chemFET array, where each reaction chamber has a chemFET sensor, and the nucleic acids can be sequenced.

[0226] Another example of a sequencing technology that can be used in the provided methods of the present invention involves using an electron microscope, as described, e.g., in Moudrianakis E.N. and Beer M., Proceedings of the National Academy of Sciences of the United States of America, March 1965; 53:564 - 71. In one example of this technology, individual DNA molecules are labeled with metal labels that are distinguishable using an electron microscope. The molecules are then stretched on a plane and imaged using an electron microscope to measure the sequence.

[0227] If the nucleic acids in the sample are degraded or only a very small amount of nucleic acids can be obtained from the sample, the nucleic acids can be subjected to PCR to obtain a sufficient amount of nucleic acids for sequencing, as described, e.g., in U.S. Patent No. 4,683,195 (the content of which is incorporated herein by reference in its entirety).

[0228] Methods for detecting the levels of gene products (e.g., RNA or protein) are known in the art. Commonly used methods known in the art for quantifying mRNA expression in a sample include Western blotting and in situ hybridization, as described, for example, in: Parker and Barnes, Methods in Molecular Biology 106:247-283 (1999), the content of which is incorporated herein by reference in its entirety; ribonuclease protection assay, Hod, Biotechniques 13:852 854 (1992), the content of which is incorporated herein by reference in its entirety; and PCR-based methods, such as reverse transcription polymerase chain reaction (RT-PCR), Weis et al., Trends in Genetics 8:263 264 (1992), the content of which is incorporated herein by reference in its entirety. Alternatively, antibodies that can recognize specific duplexes can be employed, including RNA duplexes, DNA-RNA hybrid duplexes, or DNA-protein duplexes. Other methods known in the art for measuring gene expression (e.g., RNA or protein levels) are shown, for example, in U.S. Patent Publication No. 2006 / 0195269, the content of which is incorporated herein by reference in its entirety.

[0229] Differentially or abnormally expressed genes refer to genes whose expression is activated to a higher or lower level in a subject with a disorder such as PD, relative to its expression in a normal or control subject. These terms also include genes whose expression is activated to a higher or lower level at different stages of the same disorder. It should also be understood that differentially expressed genes can be activated or inhibited at the nucleic acid level or protein level, or can be alternatively spliced to produce different polypeptide products. Such differences can be demonstrated, for example, by changes in the mRNA level, surface expression, secretion, or other partitioning of the polypeptide.

[0230] Differential gene expression can involve a comparison of expression between two or more genes or their gene products, or a comparison of expression ratios between two or more genes or their gene products, or even a comparison of two different processed products of the same gene, which is different between normal subjects and subjects suffering from a disorder such as PD, or between different stages of the same disorder. Differential expression encompasses both quantitative and qualitative differences in the temporal or cellular expression patterns in the gene or its expression products. Differential gene expression (increases and decreases in expression) is based on the percentage or fold change of expression in normal cells. An increase can be 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, or 200% relative to the expression level in normal cells. Alternatively, the fold increase can be 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-fold relative to the expression level in normal cells. A decrease can be 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% relative to the expression level in normal cells.

[0231] 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 compare mRNA levels in different sample populations to characterize gene expression patterns, distinguish closely related mRNAs, and analyze RNA structure.

[0232] The first step is to isolate mRNA from the target sample. The starting material is typically total RNA isolated from human tissue or body fluid.

[0233] General methods for mRNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, which include Ausubel et al., Current Protocols of Molecular Biology, John Wiley & Sons (1997). Methods for RNA extraction from paraffin-embedded tissues are disclosed, for example, in Rupp and Locker, Lab Invest. 56:A67 (1987) and DeAndres et al., Biotechnology 18:420-44 (1995). The content of each of these references is incorporated herein by reference in its entirety. Specifically, RNA isolation can be performed using purification kits, buffers, and proteases from commercial manufacturers such as Qiagen. For example, total RNA can be isolated from cultured cells 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 can be isolated from tissue samples using RNA Stat-60 (Tel-Test). RNA prepared from tumors can be isolated, for example, by cesium chloride density gradient centrifugation.

[0234] The first step in gene expression profiling by RT-PCR is to reverse transcribe the RNA template into cDNA, followed by its exponential amplification in a PCR reaction. The two most commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine 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 circumstances and the goals of the profiling. For example, the extracted RNA can be reverse transcribed using the GeneAmp RNA PCR Kit (Perkin Elmer, Calif., USA) according to the manufacturer's instructions. The derived cDNA can then be used as a template in subsequent PCR reactions.

[0235] Although the PCR step can use a variety of thermostable DNA-dependent DNA polymerases, this step typically employs Taq DNA polymerase, which has 5'-3' nuclease activity but lacks 3'-5' proofreading endonuclease activity. Therefore, PCR typically utilizes the 5'-nuclease activity of Taq polymerase to hydrolyze hybridization probes that are bound to their target amplicons, but any enzyme with equivalent 5'-nuclease activity can be used. Two oligonucleotide primers are used to generate the typical amplicon for the PCR reaction. A third oligonucleotide or probe is designed to detect a nucleotide sequence that is located between the two PCR primers. The probe cannot be extended by Taq DNA polymerase and is labeled with a reporter fluorophore and a quencher fluorophore. When the two dyes are positioned close together on the probe, any laser-induced emission from the reporter dye is quenched by the quencher dye. During the amplification reaction, Taq DNA polymerase cleaves the probe in a template-dependent manner. The resulting probe fragments dissociate in solution, and the signal from the released reporter dye is not affected by the quenching effect of the second fluorophore. One reporter dye molecule is released for every new molecule synthesized, and detection of the unquenched reporter dye provides the basis for the quantitative interpretation of the data.

[0236] RT-PCR can be performed using commercially available equipment such as, for example, the ABIPRISM 7700TM Sequence Detection SystemTM (Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA) or the Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In certain embodiments, the 5'-nuclease assay is run on a real-time quantitative PCR instrument such as the ABIPRISM 7700TM Sequence Detection SystemTM. The system consists of a thermal cycler, a laser, a charge-coupled device (CCD), a camera, and a computer. The system amplifies samples in a 96-well format on the thermal cycler. During amplification, laser-induced fluorescence signals are collected in real time through fiber optic cables for all 96 wells and detected at the CCD. The system includes software for running the instrument and for analyzing the data.

[0237] 5'-Nuclease assay data are initially expressed as Ct, or threshold cycle. As discussed above, fluorescence values are recorded during each cycle and represent the amount of product amplified to that point in the amplification reaction. The threshold cycle (Ct) is the point at which the fluorescence signal is first recorded as being statistically significant.

[0238] To minimize the effects of errors and sample - to - sample variations, RT - PCR is usually performed using an internal standard. An ideal internal standard is expressed at a constant level in different tissues and is not affected by experimental treatments. The RNAs most commonly used to normalize gene expression patterns are the mRNAs of the housekeeping genes glyceraldehyde - 3 - phosphate dehydrogenase (GAPDH) and actin beta (ACTB). For the analysis of pre - implantation embryos and oocytes, the conserved helix - loop - helix ubiquitous kinase (CHUK) is the gene used for normalization.

[0239] An updated variant of the RT - PCR technique is real - time quantitative PCR, which measures the accumulation of PCR products through dual - labeled fluorescent probes (i.e., probes). Real - time PCR is compatible both with quantitative competitive PCR, in which an internal competitor for each target sequence is used for normalization, and with quantitative comparative PCR, which uses a normalization gene included in the sample or a housekeeping gene for RT - PCR. For additional details, see, e.g., Held et al., Genome Research 6:986 - 994 (1996), the contents of which are incorporated herein by reference in their entirety.

[0240] In another embodiment, a MassARRAY - based gene expression profiling method is used to measure gene expression. In the MassARRAY - based gene expression profiling method developed by Sequenom, Inc. (San Diego, California), after RNA isolation and reverse transcription, the resulting cDNA is incorporated with synthetic DNA molecules (competitors) that match the targeted cDNA region in all positions except for a single base and serve as internal standards. The cDNA / competitor mixture is PCR - amplified and subjected to post - PCR shrimp alkaline phosphatase (SAP) enzyme treatment, which results in the dephosphorylation of remaining nucleotides. After alkaline phosphatase inactivation, the PCR products from the competitors and cDNA are subjected to primer extension, which generates different mass signals for the PCR products derived from the competitors and cDNA. After purification, these products are dispensed on a chip array that is pre - loaded with the components required for matrix - assisted laser desorption / ionization time - of - flight mass spectrometry (MALDI - TOF MS) analysis. The cDNA present in the reaction is then quantified by analyzing the peak area ratio in the resulting mass spectrum. For further details, see, e.g., Ding and Cantor, Proceedings of the National Academy of Sciences of the United States of America 100:3059 - 3064 (2003).

[0241] Additional 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 Research 12:1305-1312 (1999)); BeadArrayTM technology (Illumina, Inc., San Diego, Calif.; Oliphant et al., Discovery of Markers for Disease (Supplement to Biotechniques), June 2002; Ferguson et al., Analytical Chemistry 72:5618 (2000)); BeadsArray for Detection of Gene Expression (BADGE), using a commercially available Luminex 100 LabMAP system and multiple color-coded microspheres (Luminex Corp., Austin, Tex.) in a rapid assay for gene expression (Yang et al., Genome Research 11:1888-1898 (2001)); and high-coverage gene expression profiling (HiCEP) analysis (Fukumura et al., Nucl. Acids Res. 31(16)e94 (2003)). The content of each of these references is incorporated herein by reference in its entirety.

[0242] In certain embodiments, differential gene expression can also be identified or confirmed by microarray technology. In this method, polynucleotide sequences of interest (including cDNA and oligonucleotides) are plated or arrayed on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes from the cells or tissues of interest. Methods for making microarrays and determining gene product expression (e.g., RNA or protein) are shown in U.S. Patent Publication No. 2006 / 0195269, the content of which is incorporated herein by reference in its entirety.

[0243] In a specific embodiment of microarray technology, PCR-amplified cDNA clone inserts are applied to a substrate in a dense array, e.g., at least 10,000 nucleotide sequences are applied to the substrate. Microarray genes immobilized on a microchip in respective 10,000-element arrays are suitable for hybridization under stringent conditions. RNA extracted from the tissue of interest by reverse transcription can be used to generate fluorescently labeled cDNA probes by incorporation of fluorescent nucleotides. The labeled cDNA probes applied to the chip hybridize specifically to each DNA spot on the array. After stringent washing to remove non-specifically bound probes, the chip is scanned with a confocal laser microscope or another detection method (such as a CCD camera). Quantification of the hybridization of each arrayed element allows assessment of the corresponding mRNA abundance. Using two-color fluorescence, separately labeled cDNA probes generated from RNA from two sources are hybridized in pairs to the array. Thus, the relative abundances of transcripts from two sources corresponding to each specific gene are determined simultaneously. The miniaturized scale of hybridization provides a convenient and rapid assessment of the expression patterns of a large number of genes. This method has been shown to have the sensitivity required to detect rare transcripts expressed at a few copies per cell and to reproducibly detect at least approximately two-fold differences in expression levels, as described, for example, in Schena et al., Proceedings of the National Academy of Sciences of the United States of America 93(2):106 149 (1996), the contents of which are incorporated herein by reference in their entirety. Microarray analysis can be performed by commercially available equipment according to the manufacturer's protocol, e.g., by using Affymetrix GenChip technology or Incyte's microarray technology.

[0244] Alternatively, protein levels can be determined by constructing an antibody microarray in which the binding sites comprise immobilized monoclonal antibodies that are preferably specific for a plurality of protein species encoded by the cell genome. Preferably, antibodies exist for a large portion of the proteins of interest. Methods for preparing monoclonal antibodies are well known (see, for example, Harlow and Lane, 1988, Antibodies: A Laboratory Manual, Cold Spring Harbor, N.Y., which is incorporated herein by reference in its entirety for all purposes). In one embodiment, monoclonal antibodies are generated against synthetic peptide fragments designed based on the cell's genomic sequence. Using such an antibody array, proteins from the cell are contacted with the array and their binding is detected using assays well known in the art. Typically, the expression and expression levels of diagnosis- or prognosis-related proteins can be detected by immunohistochemical staining of tissue sections or slices.

[0245] Finally, a "tissue array" as described, for example, in Kononen et al., Nat. Med. 4(7):844-7(1998) can be used to characterize the transcript levels of marker genes in multiple tissue samples. In a tissue array, multiple tissue samples are evaluated on the same microarray. The array allows in situ detection of RNA and protein levels; serial sections allow simultaneous analysis of multiple samples.

[0246] In other embodiments, serial analysis of gene expression (SAGE) is used to measure gene expression. Serial analysis of gene expression (SAGE) is a method that allows quantitative analysis of large numbers of gene transcripts simultaneously without the need for a separate hybridization probe for each transcript. First, short sequence tags (about 10-14 bp) containing information sufficient to uniquely identify a transcript are generated, provided that the tag is obtained from a unique position in each transcript. Then, many transcripts are ligated together to form a long sequence molecule that can be sequenced, revealing the identities of multiple tags simultaneously. By determining the abundance of individual tags and identifying the genes corresponding to each tag, the expression pattern of any transcript population can be quantitatively evaluated. For additional 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 which are incorporated herein by reference in their entirety.

[0247] In other embodiments, massively parallel signature sequencing (MPSS) is used to measure gene expression. This method, described by Brenner et al., Nature Biotechnology 18:630-634(2000), is a sequencing method that combines non-gel-based signature sequencing with in vitro cloning of millions of templates on separate 5 μm diameter microspheres. First, a DNA template bead library is constructed by in vitro cloning. Subsequently, a planar array of beads containing the templates is assembled at high density (usually greater than 3×10 6 beads / cm 2 2) in a flow cell. The free ends of the cloned templates on each bead are analyzed simultaneously using a fluorescence-based signature sequencing method that does not require DNA fragment separation. This method has been shown to provide hundreds of thousands of gene signature sequences simultaneously and accurately from a yeast cDNA library in a single operation.

[0248] Immunohistochemical methods are also applicable to detecting the expression level of the gene products of the present invention. Thus, antibodies (monoclonal or polyclonal) or antisera specific for each marker, such as polyclonal antisera, are used to detect the expression. The antibody can be detected by directly labeling the antibody itself, for example, with a radioactive label, a fluorescent label, a hapten label (such as biotin), or an enzyme (such as horseradish peroxidase or alkaline phosphatase). Alternatively, an unlabeled primary antibody is used in combination with a labeled secondary antibody, which includes an antiserum, a polyclonal antiserum, or a monoclonal antibody specific for the primary antibody. Immunohistochemical protocols and kits are well known in the art and are commercially available.

[0249] In certain embodiments, proteomic methods are used to measure gene expression. The proteome refers to all the proteins present in a sample (e.g., tissue, organism, or cell culture) at a given point in time. Proteomics involves studying the global changes in protein expression in a sample (also known as expression proteomics). Proteomics generally includes the following steps: (1) separating individual proteins in the sample by two-dimensional gel electrophoresis (2-D PAGE); (2) identifying the individual proteins recovered from the gel, for example, by mass spectrometry or N-terminal sequencing; and (3) analyzing the data using bioinformatics. Proteomic methods are a valuable complement to other gene expression profiling methods and can be used alone or in combination with other methods to detect the products of the diagnostic markers of the present invention.

[0250] 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 quantity of one or more biomarkers disclosed herein in a biological sample. In some embodiments, 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 analysis. In some embodiments, MS analysis includes electrospray ionization (ESI) MS, such as liquid chromatography (LC) ESI-MS. Mass analysis can be accomplished using commercially available spectrometers. Methods for detecting the presence and quantity of biomarker peptides in biological samples using MS analysis (including MALDI-TOF MS and ESI-MS) are known in the art. See, for example, U.S. Patent Nos. 6,925,389, 6,989,100, and 6,890,763; each of these patents is incorporated herein by reference in its entirety.

[0251] Research Report on Gene Modifiers of LRRK2

[0252] The method of the present invention may include providing a report on a subject. The report may identify one or more genetic modifiers of LRRK2 in genetic data from the subject. The report may contain additional information about the subject, such as age, gender, weight, height, genetic data, genomic data, or other health or medical information. The report may include other information related to PD. For example, but not limited to, the report may contain information about the symptoms of PD or genes related to PD, such as the symptoms and genes described above.

[0253] The report gene may be provided in any suitable form. For example, but not limited to, the report may be provided on paper or on a display device such as a computer monitor, phone, portable electronic device, etc.

[0254] The report may be provided to a healthcare provider, such as a doctor or nurse. The report may provide guidance to the healthcare provider on whether it is appropriate to treat the subject with an LRRK2 inhibitor. The report may provide instructions or recommendations to the healthcare provider for treating the subject with an LRRK2 inhibitor. The report may recommend that the healthcare provider prescribe or provide an LRRK2 inhibitor to the subject, or otherwise direct the subject to obtain and take an LRRK2 inhibitor.

[0255] 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 may be a therapeutic agent known to be used to treat PD, such as any one of the agents described above.

[0256] LRRK2 inhibitor

[0257] The methods of the invention can comprise administering to a subject one or more LRRK2 inhibitors or advising a subject to take one or more LRRK2 inhibitors. LRRK2 inhibitors are known in the art and are described, for example, in International Patent Publications WO 2012 / 028629, WO 2012 / 058193, WO 2012 / 118679, WO 2012 / 143143, WO 2012 / 143144, WO 2014 / 001973, WO 2014 / 060112, WO 2014 / 060113, WO 2014 / 145909, WO 2014 / 160430, WO 2014 / 170248, WO 2015 / 092592, WO 2015 / 113451, WO 2015 / 113452, WO 2016 / 130920, WO 2017 / 012576, WO 2017 / 046675, WO 2017 / 087905, WO 2017 / 106771, WO 2017 / 156493, WO 2017 / 218843, WO 2018 / 137573, WO 2018 / 137593, WO 2018 / 137618, WO 2018 / 137619, WO 2018 / 163030, WO 2018 / 163066, WO 2018 / 217946, WO 2019 / 012093, WO 2019 / 104086, WO 2019 / 112269, WO 2019 / 126383, WO 2020 / 149723, WO 2020 / 170205, and WO 2020 / 210684; U.S. Patent No. 9,499,535; co-pending U.S. applications 63 / 050,385, 63 / 133,523, 63 / 113,533, 63 / 137,814, 63 / 137816, and 63 / 142009; and co-pending International applications PCT / IB2020 / 000727, PCT / IB2020 / 000730, PCT / US2021 / 041270, and PCT / US2021 / 041271, the contents of each of which are incorporated herein by reference in their entirety. Any LRRK2 disclosed in any of the foregoing references can be used in the methods of the invention.

[0258] For example, but not limited to, the LRRK2 inhibitor can 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.

[0259] In some embodiments of the present invention, the LRRK2 inhibitor is a compound having one of the formulas (I), (II), (III) and (IV):

[0260]

[0261]

[0262] Wherein:

[0263] A is NH, O, S, C═O, NR 3 or CR 4 R 5 ;

[0264] X is an optionally substituted arylene, heteroarylene, cycloalkylene, hetero cycloalkylene, alkyl cycloalkylene, heteroalkyl cycloalkylene, arylalkylene or heteroarylalkylene;

[0265] R 1 is an optionally substituted alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl cycloalkyl, hetero cycloalkyl, arylalkyl or heteroarylalkyl;

[0266] R 2 is a hydrogen atom, a halogen atom, NO 2 、N 3 、OH、SH、NH 2 or alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl cycloalkyl, hetero cycloalkyl, arylalkyl or heteroarylalkyl;

[0267] R 3 is alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl-cycloalkyl, hetero cycloalkyl, arylalkyl or heteroarylalkyl;

[0268] R 4 is a hydrogen atom, NO 2 、N 3 、OH、SH、NH 2 or alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkyl cycloalkyl, heteroalkyl cycloalkyl, hetero cycloalkyl, arylalkyl or heteroarylalkyl; and

[0269] R 5 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl;

[0270] B is NH, O, S, C═O, NR 14 or CR 15 R 16 ;

[0271] R 11 is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl;

[0272] R 12 is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl, wherein R 12 is bonded to the pyrimidine ring of formula (II) via a carbon-carbon bond;

[0273] R 13 is a hydrogen atom, a halogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl;

[0274] R 14 is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkyl - cycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl;

[0275] R 15 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl;

[0276] R 16 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl;

[0277] R 21 is an aryl or heteroaryl, each of which is optionally substituted;

[0278] R 22 is H, halogen, OH, CN, CF 3 , 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 or heteroaryl; and

[0279] Y is an aryl or a 5- or 6-membered heteroaryl; wherein the C 1-6 alkyl, the C 1-6 alkoxy, the C 1-6 haloalkyl, the C 1-6 thioalkyl, the C 3-8 cycloalkyl, the C 2-8 heterocycloalkyl, the aryl and the heteroaryl are each optionally substituted by one or more moieties selected from the group consisting of: halogen, OH, CN, CF 3 , NH 2 , NO 2 , 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 alkenyl, C 2-6 alkynyl, C 1-6 alkoxy, C 1-6 haloalkoxy, C 1-6 alkylamino, C 2-6 dialkylamino, C 7-12 aralkyl, C 1-12 heteroaralkyl, aryl, heteroaryl, -C(O)R, -C(O)OR, -C(O)NRR', -C(O)NRS(O) 2 R', -C(O)NRS(O) 2 NR'R”, -OR, -OC(O)NRR', -NRR', -NRC(O)R', -NRC(O)NR'R”, -NRS(O) 2 R', -NRS(O) 2 NR'R”, -S(O) 2 R and -S(O) 2 NRR',

[0280] wherein each of R, R' and R” is independently H, halogen, OH, C 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 to which they are attached form a C 2-8 heterocycloalkyl;

[0281] R 31 is C(O)CH 2 R 33 , optionally substituted cycloalkyl, optionally substituted cycloheteroalkyl, optionally substituted cycloalkenyl, optionally substituted cycloheteroalkenyl, optionally substituted aryl or optionally substituted heteroaryl;

[0282] R 32 each instance of which is independently halogen, haloalkyl, optionally substituted alkoxy, optionally substituted alkyl, optionally substituted heteroalkyl, optionally substituted alkenyl, optionally substituted heteroalkenyl;

[0283] R 33 is optionally substituted cycloalkyl, optionally substituted cycloheteroalkyl, optionally substituted cycloalkenyl, optionally substituted cycloheteroalkenyl, optionally substituted aryl or optionally substituted heteroaryl;

[0284] Z is cycloalkyl, cycloheteroalkyl, cycloalkenyl, cycloheteroalkenyl, aryl or heteroaryl; Z may be aryl substituted with 2 or 3 instances of R 2 ; Z may be phenyl substituted with 2 or 3 instances of R 2 ; Z may be heteroaryl substituted with 2 or 3 instances of R 2 ; Z may be six-membered heteroaryl substituted with 2 or 3 instances of R 2 ; and

[0285] n is 0 - 5,

[0286] or a pharmaceutically acceptable salt of any of the compounds described above.

[0287] LRRK2 inhibitors can be provided to a subject in the form of a pharmaceutical composition. The pharmaceutical composition can contain a therapeutically effective amount of an LRRK2 inhibitor. A therapeutically effective amount means an amount effective to prevent, alleviate or improve the symptoms of a disease such as PD or to extend the survival rate of the subject being treated. Determination of a therapeutically effective amount is within the skill of the art. The therapeutically effective amount or dose of an LRRK2 inhibitor can vary within wide limits and can be determined in a manner known in the art. Such doses can be adjusted to the individual needs in each particular case, which includes the specific compound being administered, the route of administration, the condition being treated and the patient being treated.

[0288] For oral administration, such therapeutically useful agents can be administered by one of the following routes: oral administration, such as as tablets, dragees, coated tablets, pills, semisolids, soft or hard capsules, such as soft and hard gelatin capsules, aqueous or oily solutions, emulsions, suspensions or syrups; parenteral administration, including intravenous, intramuscular and subcutaneous injection, such as as injectable solutions or suspensions; rectal administration, as suppositories; by inhalation or insufflation, for example, as powder formulations, as microcrystals or as sprays (e.g., liquid aerosols); transdermal administration, for example, by a transdermal delivery system (TDS), such as a plaster containing the active ingredient; or intranasal administration. For the production of such tablets, pills, semisolids, coated tablets, dragees and hard capsules, such as gelatin, the therapeutically useful product can be mixed with pharmaceutically inert inorganic or organic excipients, such as lactose, sucrose, glucose, gelatin, malt, silica gel, starch or its derivatives, talc, stearic acid or its salts, skimmed milk powder, etc. For the production of soft capsules, excipients such as vegetable oils, petroleum oils, animal oils or synthetic oils, waxes, fats, polyols can be used. For the production of liquid solutions, emulsions or suspensions or syrups, excipients such as water, alcohols, saline solutions, aqueous glucose solutions, polyols, glycerol, lipids, phospholipids, cyclodextrins, vegetable oils, petroleum oils, animal oils or synthetic oils can be used. Particularly useful are lipids, such as phospholipids (e.g., of natural origin and / or with a particle size between 300 and 350 nm) in phosphate buffered saline (pH = 7 to 8, e.g., 7.4). For suppositories, excipients such as vegetable oils, petroleum oils, animal oils or synthetic oils, waxes, fats and polyols can be used. For aerosol formulations, compressed gases suitable for this purpose, such as oxygen, nitrogen and carbon dioxide, can be used. Pharmaceutically useful agents can also contain additives for preservation, stabilization, such as UV stabilizers, emulsifiers, sweeteners, flavorants, salts for altering the osmotic pressure, buffers, coating additives and antioxidants.

[0289] Providing an LRRK2 inhibitor to a subject

[0290] The method of the present invention may comprise providing a LRRK2 inhibitor to a subject. The LRRK2 inhibitor may be provided by any suitable route or mode of administration. For example but not limited to, the compound may be provided orally, dermally, enterally, intra-arterially, intramuscularly, intraocularly, intravenously, nasally, orally, parenterally, pulmonary, rectally, subcutaneously, topically, transdermally, by injection or using an implantable medical device (e.g., a stent, or a drug-eluting stent or balloon equivalent) or provided thereon.

[0291] The LRRK2 inhibitor may be provided according to a dosing regimen. The dosing regimen may comprise a dose, a dosing frequency, or both.

[0292] The dose may be provided at any suitable interval. For example but not limited to, the dose 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.

[0293] The dose may be provided as a single dose, i.e., the dose may be provided as a single tablet, capsule, pill, etc. Alternatively, the dose may be provided as separate doses, i.e., the dose may be provided as multiple tablets, capsules, pills, etc.

[0294] The administration may continue for a defined period of time. For example but not limited to, the dose 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.

[0295] The subject may be any type of subject, such as any subject described above with respect to the assays for obtaining genetic data.

[0296] The present invention encompasses combination therapies in which a LRRK2 inhibitor is provided to a subject in combination with a second agent such as any of the drugs described above in the section regarding PD. The LRRK2 inhibitor and the second agent may be provided in a single composition, or they may be provided in separate compositions. The LRRK2 inhibitor and the second agent may be provided according to the same dosing regimen, or they may be provided according to different dosing regimens.

[0297] Example

[0298] Example 1

[0299] The likelihood of responsiveness to LRRK2 inhibitors was analyzed in a human subject population. The dataset included the complete dataset from the Accelerating Medicines Partnership - Parkinson's Disease (AMP - PD). The input data was quality - controlled data for Parkinson's disease cases, focusing on clinical, demographic, RNA, and DNA sequencing data at baseline in samples available as of June 1, 2020. Whole - genome sequencing and RNA sequencing were processed using the standard pipelines described on the AMP - PD website. The analysis was limited to samples with a data - missing rate < 15% after consistent quality control. The analysis was also repeated, adjusting for population substructure among Europeans, yielding nearly identical results in the same group of > 1000 cases.

[0300] To identify potential modifiers, the open - source automated machine - learning package GenoML was used. This package performed feature selection / weighting and normalization, and then competing algorithms in a randomly determined 70% training set and 30% test set. Then, the algorithm that performed best in terms of balanced accuracy was selected for further tuning and cross - validation. Then, the best - performing algorithm was hyperparameter - tuned using a random grid - search method and 10 - fold cross - validation, with the tuning process focusing on maximizing balanced accuracy. The results were encoded as 0 / 1, where 1 indicated carrying a known LRRK2 pathogenic variant. A probability matrix for WT LRRK2 cases was output, where these probabilities indicated the degree of "LRRK2 - like" at the molecular / clinical / demographic levels for these cases. In all iterations of the model, the most important features were used as potential modifiers.

[0301] The results are provided in Table 1.

[0302] Table 1.

[0303]

[0304]

[0305]

[0306] Example 2

[0307] The likelihood of responsiveness to LRRK2 inhibitors was analyzed in a human subject population. Figure 1 、 2 Total urinary di - 18:1 BMP, total urinary di - 22:6 BMP, and 2,2' - di - 22:6 BMP for different patient populations are provided in 3, respectively.

[0308] Incorporated by reference

[0309] Throughout this invention, other documents have been referenced and cited, such as patents, patent applications, patent publications, magazines, books, papers, and web content. All such documents are hereby incorporated by reference in their entirety for all purposes.

[0310] Equivalent

[0311] Based on the entire content of this document, including references to scientific and patent literature cited herein, various modifications of the invention and many additional embodiments thereof will become apparent to those skilled in the art in addition to those shown and described herein. The subject matter herein contains important information, exemplification, and guidance that can be adapted to practice the invention in various embodiments of the invention and their equivalents.

Claims

1. A method of treating a patient suffering from Parkinson's disease associated with wild-type leucine-rich repeat kinase 2 (LRRK2), the method comprising: administering to the patient presenting with Parkinson's disease one or more LRRK2 inhibitors, the patient having wild-type LRRK2, and having elevated levels of bis(monoacylglycerol) phosphate (BMP) and / or semi-bis(monoacylglycerol) phosphate (semi-BMP) compared to a subject without Parkinson's disease and having wild-type LRRK2, thereby treating Parkinson's disease associated with wild-type LRRK2.

2. The method according to claim 1, wherein BMP is selected from the group consisting of: (i) di-22:6BMP, (ii) di-18:1BMP, (iii) 16:0 / 18:1BMP, (iv) di-20:4BMP, (v) 18:0 / 20:4BMP, (vi) 2,2'-di-22:6BMP, and (vii) any combination thereof; semi-BMP is selected from the group consisting of: (i) semi-BMP(18:1 / 18:1)_16:0, (ii) semi-BMP(14:0 / 14:0)_14:0, (iii) semi-BMP(18:1 / 18:1)_18:0, (iv) semi-BMP(18:1 / 18:1)_18:1, and (iv) any combination thereof.

3. The method according to claim 1, wherein the BMP level is measured in a biological fluid of the patient.

4. The method according to claim 3, wherein the biological fluid is urine, blood, cerebrospinal fluid (CSF), bile or saliva.

5. The method according to claim 1, wherein the elevated BMP and / or semi-BMP levels are at a concentration indicative of the patient being responsive to the one or more LRRK2 inhibitors.

6. The method according to claim 1, wherein the one or more LRRK2 inhibitors are selected from the group consisting of: 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 and staurosporine.

7. The method according to claim 1, wherein the one or more LRRK2 inhibitors are selected from the group consisting of formula (I), (II), (III) and (IV): wherein: A is NH, O, S, C=O, NR 3 or CR 4 R 5 ; X is an optionally substituted arylene, heteroarylene, cycloalkylene, hetero cycloalkylene, alkyl cycloalkylene, heteroalkyl cycloalkylene, aralkyl or heteroaralkyl; R 1 is an optionally substituted alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 2 is a hydrogen atom, a halogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl group, an alkenyl group, an alkynyl group, a heteroalkyl group, an aryl group, a heteroaryl group, a cycloalkyl group, an alkylcycloalkyl group, a heteroalkylcycloalkyl group, a heterocycloalkyl group, an aralkyl group or a heteroaralkyl group; R 3 is alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkyl-cycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 4 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; and R 5 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; B is NH, O, S, C=O, NR 14 or CR 15 R 16 ; R 11 is alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 12 is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl, wherein R 12 is bonded to the pyrimidine ring of formula (II) via a carbon-carbon bond; R 13 is a hydrogen atom, a halogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl group, an alkenyl group, an alkynyl group, a heteroalkyl group, an aryl group, a heteroaryl group, a cycloalkyl group, an alkylcycloalkyl group, a heteroalkylcycloalkyl group, a heterocycloalkyl group, an aralkyl group or a heteroaralkyl group; R 14 is alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkyl-cycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 15 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 16 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 21 is an aryl or a heteroaryl, each of which is optionally substituted; R 22 is H, a halogen group, OH, CN, CF 3 , 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 or heteroaryl; and Y is an aryl or a 5- or 6-membered heteroaryl; wherein said C 1-6 alkyl, said C 1-6 alkoxy, said C 1-6 haloalkyl, said C 1-6 thioalkyl, said C 3-8 cycloalkyl, said C 2-8 heterocycloalkyl, each of said aryl and said heteroaryl is optionally substituted by one or more moieties selected from the group consisting of: halo, OH, CN, CF 3 , NH 2 , NO 2 , 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 alkenyl, C 2-6 alkynyl, C 1-6 alkoxy, C 1-6 haloalkoxy, C 1-6 alkylamino, C 2-6 dialkylamino, C 7-12 aralkyl, C 1-12 heteroaralkyl, aryl, heteroaryl, -C(O)R, -C(O)OR, -C(O)NRR', -C(O)NRS(O) 2 R', -C(O)NRS(O) 2 NR'R”, -OR, -OC(O)NRR', -NRR', -NRC(O)R', -NRC(O)NR'R”, -NRS(O) 2 R', -NRS(O) 2 NR'R”, -S(O) 2 R and -S(O) 2 NRR', where each of R, R' and R” is independently H, a halogen group, OH, C 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 to which they are attached form a C 2-8 heterocycloalkyl; R 31 is C(O)CH 2 R 33 、an optionally substituted cycloalkyl, an optionally substituted cycloheteroalkyl, an optionally substituted cycloalkenyl, an optionally substituted cycloheteroalkenyl, an optionally substituted aryl or an optionally substituted heteroaryl; R 32 Each instance of which is independently a halogen group, haloalkyl group, optionally substituted alkoxy group, optionally substituted alkyl group, optionally substituted heteroalkyl group, optionally substituted alkenyl group, or optionally substituted heteroalkenyl group; R 33 is an optionally substituted cycloalkyl, an optionally substituted cycloheteroalkyl, an optionally substituted cycloalkenyl, an optionally substituted cycloheteroalkenyl, an optionally substituted aryl or an optionally substituted heteroaryl; Z is cycloalkyl, cycloheteroalkyl, cycloalkenyl, cycloheteroalkenyl, aryl or heteroaryl; Z may be aryl substituted with two or three instances of R 2 ; Z may be phenyl substituted with two or three instances of R 2 ; Z may be heteroaryl substituted with two or three instances of R 2 ; Z may be six-membered heteroaryl substituted with two or three instances of R 2 ; and n is 0-5, or a pharmaceutically acceptable salt of any of the compounds described above.

8. A method for determining whether a patient suffering from Parkinson's disease associated with wild-type LRRK2 will respond to an LRRK2 inhibitor, the method comprising: performing an assay to measure the level of bis(monoacylglycerol) phosphate (BMP) and / or semi-bis(monoacylglycerol) phosphate (semi-BMP) in the patient; generating a report that identifies the BMP and / or semi-BMP level of the patient compared to the BMP and / or semi-BMP level of a subject without Parkinson's disease and having wild-type LRRK2; providing the report to a physician such that if the report indicates an elevated BMP and / or semi-BMP level in the patient compared to the subject, the physician prescribes or provides one or more LRRK2 inhibitors to the patient.

9. The method according to claim 8, wherein BMP is selected from the group consisting of: (i) di-22:6BMP, (ii) di-18:1BMP, (iii) 16:0 / 18:1BMP, (iv) di-20:4BMP, (v) 18:0 / 20:4BMP, (vi) 2,2'-di-22:6BMP, and (vii) any combination thereof; semi-BMP is selected from the group consisting of: (i) semi-BMP(18:1 / 18:1)_16:0, (ii) semi-BMP(14:0 / 14:0)_14:0, (iii) semi-BMP(18:1 / 18:1)_18:0, (iv) semi-BMP(18:1 / 18:1)_18:1, and (iv) any combination thereof.

10. The method according to claim 8, wherein the BMP level is measured in a biological fluid of the patient.

11. The method according to claim 10, wherein the biological fluid is urine, blood, cerebrospinal fluid (CSF), bile, or saliva.

12. The method according to claim 8, wherein an elevated BMP and / or semi-BMP level in the patient indicates that the patient will be responsive to one or more LRRK2 inhibitors.

13. The method according to claim 8, wherein one or more LRRK2 inhibitors are selected from the group consisting of: 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, and staurosporine.

14. The method according to claim 8, wherein one or more LRRK2 inhibitors are selected from the group consisting of formula (I), (II), (III), and (IV): wherein: A is NH, O, S, C=O, NR 3 or CR 4 R 5 ; X is an optionally substituted arylene, heteroarylene, cycloalkylene, hetero cycloalkylene, alkyl cycloalkylene, heteroalkyl cycloalkylene, arylalkylene, or heteroarylalkylene; R 1 is an optionally substituted alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 2 is a hydrogen atom, a halogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 3 is an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkyl-cycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 4 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; and R 5 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl group, alkenyl group, alkynyl group, heteroalkyl group, aryl group, heteroaryl group, cycloalkyl group, alkylcycloalkyl group, heteroalkylcycloalkyl group, heterocycloalkyl group, aralkyl group or heteroaralkyl group; B is NH, O, S, C=O, NR 14 or CR 15 R 16 ; R 11 is alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 12 is alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl, wherein R 12 is attached to the pyrimidine ring of formula (II) via a carbon-carbon bond; R 13 is a hydrogen atom, a halogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl group, an alkenyl group, an alkynyl group, a heteroalkyl group, an aryl group, a heteroaryl group, a cycloalkyl group, an alkylcycloalkyl group, a heteroalkylcycloalkyl group, a heterocycloalkyl group, an aralkyl group or a heteroaralkyl group; R 14 is alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkyl-cycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 15 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 16 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 21 is an aryl or a heteroaryl, each of which is optionally substituted; R 22 is H, a halogen group, OH, CN, CF 3 , 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 or heteroaryl; and Y is an aryl or a 5- or 6-membered heteroaryl; wherein said C 1-6 alkyl, said C 1-6 alkoxy, said C 1-6 haloalkyl, said C 1-6 thioalkyl, said C 3-8 cycloalkyl, said C 2-8 heterocycloalkyl, each of said aryl and said heteroaryl is optionally substituted by one or more moieties selected from the group consisting of: halo, OH, CN, CF 3 , NH 2 , NO 2 , 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 alkenyl, C 2-6 alkynyl, C 1-6 alkoxy, C 1-6 haloalkoxy, C 1-6 alkylamino, C 2-6 dialkylamino, C 7-12 aralkyl, C 1-12 heteroaralkyl, aryl, heteroaryl, -C(O)R, -C(O)OR, -C(O)NRR', -C(O)NRS(O) 2 R', -C(O)NRS(O) 2 NR'R”, -OR, -OC(O)NRR', -NRR', -NRC(O)R', -NRC(O)NR'R”, -NRS(O) 2 R', -NRS(O) 2 NR'R”, -S(O) 2 R and -S(O) 2 NRR', where each of R, R' and R” is independently H, halogen, OH, C 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 to which they are attached form C 2-8 heterocycloalkyl; R 31 is C(O)CH 2 R 33 、an optionally substituted cycloalkyl, an optionally substituted cycloheteroalkyl, an optionally substituted cycloalkenyl, an optionally substituted cycloheteroalkenyl, an optionally substituted aryl or an optionally substituted heteroaryl; R 32 Each instance of which is independently a halogen group, haloalkyl group, optionally substituted alkoxy group, optionally substituted alkyl group, optionally substituted heteroalkyl group, optionally substituted alkenyl group, or optionally substituted heteroalkenyl group; R 33 is an optionally substituted cycloalkyl, an optionally substituted cycloheteroalkyl, an optionally substituted cycloalkenyl, an optionally substituted cycloheteroalkenyl, an optionally substituted aryl or an optionally substituted heteroaryl; Z is cycloalkyl, cycloheteroalkyl, cycloalkenyl, cycloheteroalkenyl, aryl or heteroaryl; Z may be aryl substituted with two or three instances of R 2 ; Z may be phenyl substituted with two or three instances of R 2 ; Z may be heteroaryl substituted with two or three instances of R 2 ; Z may be six-membered heteroaryl substituted with two or three instances of R 2 ; and n is 0 - 5, or a pharmaceutically acceptable salt of any of the compounds described above.

15. A method of treating a patient suffering from Parkinson's disease associated with wild-type LRRK2, the method comprising: receiving data identifying the levels of bis(monoacylglycerol) phosphate (BMP) and / or hemibis(monoacylglycerol) phosphate (hemibMP) in the patient; comparing the data with the BMP and / or hemibMP levels from a subject without neurological conditions and having wild-type LRRK2; if the levels of BMP and / or hemibMP in the patient are elevated compared to the subject, prescribing or providing to the patient one or more LRRK2 inhibitors.

16. The method according to claim 15, wherein the BMP levels are measured in a biological fluid of the patient and the subject.

17. The method according to claim 16, wherein the biological fluid is urine, blood, cerebrospinal fluid (CSF), bile or saliva.

18. The method according to claim 15, wherein BMP is selected from the group consisting of: (i) di-22:6BMP, (ii) di-18:1BMP, (iii) 16:0 / 18:1BMP, (iv) di-20:4BMP, (v) 18:0 / 20:4BMP, (vi) 2,2'-di-22:6BMP, and (vii) any combination thereof; hemibMP is selected from the group consisting of: (i) hemibMP(18:1 / 18:1)_16:0, (ii) hemibMP(14:0 / 14:0)_14:0, (iii) hemibMP(18:1 / 18:1)_18:0, (iv) hemibMP(18:1 / 18:1)_18:1, and (iv) any combination thereof.

19. The method according to claim 15, wherein the elevated BMP and / or hemibMP levels in the patient indicate that the patient will be responsive to one or more LRRK2 inhibitors.

20. The method according to claim 15, wherein one or more LRRK2 inhibitors are selected from the group consisting of: 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 and staurosporine.

21. The method according to claim 15, wherein one or more LRRK2 inhibitors are selected from the group consisting of formula (I), (II), (III) and (IV): wherein: A is NH, O, S, C=O, NR 3 or CR 4 R 5 ; X is an optionally substituted arylene, heteroarylene, cycloalkylene, hetero cycloalkylene, alkyl cycloalkylene, heteroalkyl cycloalkylene, arylalkylene or heteroarylalkylene; R 1 is an optionally substituted alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 2 is a hydrogen atom, a halogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl group, an alkenyl group, an alkynyl group, a heteroalkyl group, an aryl group, a heteroaryl group, a cycloalkyl group, an alkylcycloalkyl group, a heteroalkylcycloalkyl group, a heterocycloalkyl group, an aralkyl group or a heteroaralkyl group; R 3 is alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkyl-cycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 4 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; and R 5 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; B is NH, O, S, C=O, NR 14 or CR 15 R 16 ; R 11 is alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 12 is alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl, wherein R 12 is attached to the pyrimidine ring of formula (II) by a carbon-carbon bond; R 13 is a hydrogen atom, a halogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl group, an alkenyl group, an alkynyl group, a heteroalkyl group, an aryl group, a heteroaryl group, a cycloalkyl group, an alkylcycloalkyl group, a heteroalkylcycloalkyl group, a heterocycloalkyl group, an aralkyl group or a heteroaralkyl group; R 14 is alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkyl-cycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 15 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 16 is a hydrogen atom, NO 2 , N 3 , OH, SH, NH 2 or an alkyl, alkenyl, alkynyl, heteroalkyl, aryl, heteroaryl, cycloalkyl, alkylcycloalkyl, heteroalkylcycloalkyl, heterocycloalkyl, aralkyl or heteroaralkyl; R 21 is an aryl or heteroaryl, each of which is optionally substituted; R 22 is H, a halogen group, OH, CN, CF 3 , 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 or heteroaryl; and Y is an aryl or a 5- or 6-membered heteroaryl; wherein said C 1-6 alkyl, said C 1-6 alkoxy, said C 1-6 haloalkyl, said C 1-6 thioalkyl, said C 3-8 cycloalkyl, said C 2-8 heterocycloalkyl, each of said aryl and said heteroaryl is optionally substituted by one or more moieties selected from the group consisting of: halo, OH, CN, CF 3 , NH 2 , NO 2 , 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 alkenyl, C 2-6 alkynyl, C 1-6 alkoxy, C 1-6 haloalkoxy, C 1-6 alkylamino, C 2-6 dialkylamino, C 7-12 aralkyl, C 1-12 heteroaralkyl, aryl, heteroaryl, -C(O)R, -C(O)OR, -C(O)NRR', -C(O)NRS(O) 2 R', -C(O)NRS(O) 2 NR'R”, -OR, -OC(O)NRR', -NRR', -NRC(O)R', -NRC(O)NR'R”, -NRS(O) 2 R', -NRS(O) 2 NR'R”, -S(O) 2 R and -S(O) 2 NRR', where each of R, R' and R” is independently H, a halogen group, OH, C 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 to which they are attached form C 2-8 heterocycloalkyl; R 31 is C(O)CH 2 R 33 、optionally substituted cycloalkyl, optionally substituted cycloheteroalkyl, optionally substituted cycloalkenyl, optionally substituted cycloheteroalkenyl, optionally substituted aryl or optionally substituted heteroaryl; R 32 Each instance of which is independently a halogen group, haloalkyl group, optionally substituted alkoxy group, optionally substituted alkyl group, optionally substituted heteroalkyl group, optionally substituted alkenyl group, or optionally substituted heteroalkenyl group; R 33 is an optionally substituted cycloalkyl, an optionally substituted cycloheteroalkyl, an optionally substituted cycloalkenyl, an optionally substituted cycloheteroalkenyl, an optionally substituted aryl or an optionally substituted heteroaryl; Z is cycloalkyl, cycloheteroalkyl, cycloalkenyl, cycloheteroalkenyl, aryl or heteroaryl; Z may be aryl substituted with two or three instances of R 2 ; Z may be phenyl substituted with two or three instances of R 2 ; Z may be heteroaryl substituted with two or three instances of R 2 ; Z may be six-membered heteroaryl substituted with two or three instances of R 2 ; and n is 0-5, or a pharmaceutically acceptable salt of any of the compounds described above.

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