WO2020072004A2 - Method of predicting treatment response - Google Patents

Description

METHOD OF PREDICTING TREATMENT RESPONSE

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority of Singapore provisional application no. 10201808749 Y, filed 03 October 2018, the contents of it being hereby incorporated by reference in its entirety for all purposes.

FIELD OF THE INVENTION

[0002] The present invention relates generally to the field of molecular biology. In particular, the present invention relates to the use of biomarkers for the prediction and/or determination of treatment response.

BACKGROUND OF THE INVENTION

[0003] Age-related macular degeneration (AMD) is the most common cause of blindness in the elderly population. This progressive macular disease can result in the profound loss of central vision if left untreated. Choroidal neovascularization (CNV) is the hallmark of neovascular age-related macular degeneration (nAMD), the latter of which is characterized by abnormal vessel leakage and/or bleeding resulting in the formation of fibrovascular tissue, thus severely affecting the function of neural tissues of the central retina. Intravitreal injection of anti -vascular endothelial growth factor (anti-VEGF) is the current standard of treatment for nAMD, and has shown favourable response in terms of visual acuity gains in several large randomized controlled trials.

[0004] However, there remains a broad range of responses to anti-VEGF treatment despite its overall efficacy. It has been estimated that 50% of patients with nAMD are not dry after initial 3 months of treatment with anti-VEGF therapy. Current methods used to identify“good” and“poor” responders include stratifying disease status by markers of structure or functional measures, such as optical coherence tomography (OCT), fluorescein angiography for lesion type, and visual acuity tests. Some imaging biomarkers, such as the presence of intra retina fluid, and clinical signs, such as poor starting vision, are associated with long-term poor prognosis, but none of these measurements have been able to predict treatment response and do not offer any novel insight into pre -functional biological and pathogenic changes in nAMD. Also, none of these biomarkers are capable of predicting or determining subject response to anti-VEGF treatment.

[0005] There is therefore an unmet need for a method of determining patient response to treatment. SUMMARY OF THE INVENTION

[0006] In one aspect, the present invention refers to a method of predicting treatment response of a subject suffering, or thought to suffer from, age-related macular degeneration, the method comprising obtaining a sample from the subject; determining the level of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4); comparing the level of the biomarkers from step b. with the level of the same biomarkers in a control sample; wherein a decrease in level of the biomarkers indicates that the subject is unlikely/will not to respond to treatment.

[0007] In another aspect, the present invention refers to a method of treating age-related macular degeneration in a subject, the method comprising obtaining a sample from the subject; determining the level of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4); comparing the level of the biomarkers from step b. with the level of the same biomarkers in a control sample; wherein the subject is to be treated with an anti-VEGF treatment if an increase of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4) is detected.

[0008] In yet another aspect, the present invention refers to a kit for use according to the method according to any one of the preceding claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The invention will be better understood with reference to the detailed description when considered in conjunction with the non-limiting examples and the accompanying drawings, in which:

[0010] Fig, 1 shows principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLD-DA) score plots of the untargeted metabolomics analysis of serum samples. A) shows PCA score plot of responders, non-responders and quality control (QC) samples; B) shows principal component analysis (PCA) score plot of responders and non-responders; C) orthogonal partial least squares discriminant analysis (OPLD-DA) score plot of responders and non responders. Circles (·) -responders; squares (□) - non-responders; triangles (D) - QC.

[0011] Fig, 2 shows a receiver-operating characteristic curve (ROC) for validation of metabolomics classification of responders and non-responders. In short, a receiver-operating characteristic (ROC) curve is a probability curve, and area under curve (AUC) represents degree or measure of separability. This infers how much a model is capable of distinguishing between classes, or in this case between responders and non-responders of anti-VEGF treatment. [0012] Fig. 3 shows estimation plots of altered metabolites in responders and non-responders of AMD patients. The mean difference is depicted as a dot and the 95% confidence interval is indicated by the ends of the vertical error bar.

[0013] Fig. 4 shows box plots of altered metabolites in responders and non-responders of age- related macular degeneration (AMD) patients (p<0.05).

[0014] Fig. 5 shows graphs illustrating the pathway analysis performed based on metabolites associated with differentiation between responders and non-responders of age-related macular degeneration patients (p<0.05). -log(p) = minus logarithm of the p value. The node colour is based on its p value and the node radius is determined based on their pathway impact values.

[0015] Fig. 6 shows the chemical structures of the compounds glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2), and phosphatidylserine (PS; 18:0/20:4), whereby R, Ri and R₂ are variable fatty acid chains.

[0016] Fig. 7 shows the tandem mass spectrometry spectra of glycerophosphocholine from a metabolomics analysis (A) verified with standard (B).

DETAILED DESCRIPTION OF THE PRESENT INVENTION

[0017] Age-related macular degeneration (AMD) is a progressive chronic macular disease of the central retina. Intravitreal injection of anti-vascular endothelial growth factor (anti-VEGF) is the current standard of treatment for neo vascular age-related macular degeneration (nAMD), showing visual acuity gains in large randomized controlled trials. Age-related macular degeneration is also a common cause of blindness in elderly people. The neovascular form of age-related macular degeneration (nAMD) is characterized by abnormal vessel leakage and/or bleeding resulting in the formation of fibrovascular tissue which leads to poor vision without treatment.

[0018] Intravitreal injection of anti-vascular endothelial growth factor (anti-VEGF) is the current standard of treatment for choroidal neovascularization (CNV) secondary to neovascular age-related macular degeneration (nAMD), but there are no diagnostic tools to predict response of these therapies. Without being bound by theory, it is thought that differences in baseline metabolic profiles of patients with neovascular age-related macular degeneration may influence responsiveness to anti- VEGF therapy, and thus provide prognosticating information for these patients. A study was performed on 100 patients with neovascular age-related macular degeneration treated with anti- VEGF therapy. The patients were classified into two groups: responders (n=54) and non-responders (n=46). The expression levels of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4) were increased in non-responders. These findings were verified in the validation cohort, implicating that reductions in these three metabolites can be used as predictors for responsiveness to anti-VEGF therapy, for example, during the initial loading phase, for patients with neovascular age-related macular degeneration. This study also provided further insights into the pathophysiological changes and molecular mechanism of anti-VEGF therapy for neovascular age- related macular degeneration patients. The aim was to examine the baseline serum metabolic profile in patients with neovascular age-related macular degeneration and to relate this neovascular age- related macular degeneration to the anatomical response from anti-VEGF therapy during the initial treatment phase over 3 months (typically referred to as the“loading” dose phase).

[0019] Metabolomics, the global quantitative assessment of endogenous metabolites within a biological system, can identify metabolites responsible for differentiation between individuals despite intra-individual variations. This method may provide metabolite information from environmental and lifestyle factors, as well as individual characteristics, such as dietary response and disease history. The metabolic profiling of a biological system can reflect the phenotype of the study subject and provide information that is complementary to genomics, transcriptomics or proteomics studies.

[0020] It was thought that there is a difference between baseline metabolic profiles of neovascular age-related macular degeneration patients who respond well to anti-VEGF therapy, compared to those with poor response. To this end, a study of 50 patients with neovascular age- related macular degeneration, who had been treated with anti-VEGF therapy, was performed. Responders were defined as eyes with no sub- or intra-retinal fluid at month 3. Non-responders were defined by persistent sub-or intra-retinal fluid at month 3. Baseline serum metabolomics profiles from responders (n = 29) non-responders (n = 21) were investigated using a combination of ultra performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UPLC- QTOFMS). Multivariate analysis and univariate analysis were used to analyse metabolomics data. A total of 86 metabolite features were evaluated as potential metabolites responsible for the differentiation of responders and non-responders to anti-VEGF therapy. 56 of the metabolite features were identified, 6 of which were confirmed with reference compounds. Elevated levels of glycerophosphocholine, L-carnitine, creatinine and L-palmitoylcarnitine and reduced levels of 3- carboxy-4-methyl-5-propyl-2-furanpropionic acid (CMPF) and 4-pyridoxic acid were observed in non-responders. Three pathways, including glycerophospholipid metabolism, vitamin B6 and glycine, serine and threonine metabolism, were identified as significantly different between the two patient groups (p<0.05).

[0021] Patients with neovascular age-related macular degeneration have been known to have systemic risk factors that are different from age-matched controls, suggesting generalized alterations. Previous metabolomics studies have shown that patients with neovascular age-related macular degeneration differ in metabolic profiles from similarly aged persons without neovascular age-related macular degeneration in pathways that include, but are not limited to, tyrosine metabolism, sulphur amino acid metabolism, amino acids related to urea metabolism and enrichment of glycerophospholipid pathway. Differences in metabolites including peptides, bile acids and vitamin D had been found in patients with neovascular age-related macular degeneration compared to age matched controls, and summarised that tyrosine and urea metabolisms may be important in age- related macular degeneration pathophysiology. Another metabolomics study investigating age- related macular degeneration patients revealed that glycerophospholipid pathway is associated with significantly altered metabolites between control group without any vitreoretinal disease and age- related macular degeneration group. It has been found higher serum level of glycerophospholipids, covalently modified amino acids and di/tri-peptides, fatty acids and carnitines in patients with choroidal neovascularization and polypoidal choroidal vasculopathy compared to healthy controls. Small changes were also detected in the levels of some amino acids, organic acids, dimethyl sulfone and specific moieties when investigating the plasma metabolomics profiles of patients with age- related macular degeneration. The intestinal microbiomes of neovascular age-related macular degeneration patients were shown to be enriched in genes of the L-alanine fermentation, glutamate degradation and arginine biosynthesis pathways and decreased in genes of the fatty acid elongation pathways. Disclosed herein are the differences in baseline metabolomics signatures in neovascular age-related macular degeneration patients and their use in predicting subject responses to the initial treatment (3 monthly anti-VEGF injections during the“loading phase”).

[0022] Thus, there is disclosed herein a method of predicting treatment response of a subject suffering, or thought to suffer from, age-related macular degeneration. In one example, the method comprises obtaining a sample from the subject; determining the level of at least one or more biomarkers, wherein the biomarkers are, but are not limited to, 3-carboxy-4-methyl-5-propyl-2- furanpropionic acid (CMPF), 4-pyridoxic acid, glycerophosphocholine, lysophosphatidylcholine (FysoP; 18:2), phosphatidylserine (PS; 18:0/20:4), F-carnitine, creatinine and F-palmitoylcarnitine; comparing the level of the one or more biomarkers disclosed herein with the level of the same biomarker(s) in a control sample.

[0023] As used herein, the term“sample” includes, but is not limited to, any quantity of a substance from a living thing or formerly living thing. Such living things include, but are not limited to, humans, mice, monkeys, rats, rabbits, and other animals. Such substances include, but are not limited to, blood and serum. In one example, the sample is a blood sample. In another example, the sample is a serum sample.

[0024] Also disclosed herein is a method of predicting treatment response of a subject suffering, or thought to suffer from, age-related macular degeneration. In one example, the method comprises obtaining a sample from the subject; determining the level of at least one or more biomarkers, wherein the biomarkers are, but are not limited to, glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2), and phosphatidylserine (18:0/20:4); comparing the level of the one or more biomarkers disclosed herein with the level of the same biomarker(s) in a control sample.

[0025] In one example, the biomarkers are, but are not limited to, any one or more of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (18:0/20:4). In another example, the biomarker is glycerophosphocholine. In yet another example, the biomarker is lysophosphatidylcholine (LysoP; 18:2). In a further example, the biomarker is phosphatidylserine (18:0/20:4). In a further example, the biomarkers are a combination of biomarkers as disclosed herein. In yet another example, the biomarkers are glycerophosphocholine and lysophosphatidylcholine (LysoP; 18:2). In a further example, the biomarkers are glycerophosphocholine and phosphatidylserine (18:0/20:4). In another example, the biomarkers are lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (18:0/20:4).

[0026] In another example, the method disclosed herein can be performed using at least one of the biomarkers disclosed herein. In another example, the method is performed with at least one, at least two, at least three, at least four, or at least five biomarkers disclosed herein. In another example, the method disclosed herein is performed with one biomarker. In yet another example, the method disclosed herein is performed with two biomarkers. In another example, the method disclosed herein is performed with three biomarkers.

[0027] It was found that the semm level of glycerophosphocholine (GPC) was higher in non responders compared to responders. Glycerophosphocholine has been recognized as a degradation product of phosphatidylcholine, which is one of the most important glycerophospholipids in mammalian cells. The breakdown of phosphatidylcholine may be reflected in elevated concentrations of glycerophosphocholine in semm since glycerophosphocholine is a specific indicator of phosphatidylcholine degradation. Increased level of glycerophosphocholine has been detected in cerebrospinal fluid of Alzheimer patients, and a favourable response to neoadjuvant chemotherapy is associated with a reduction in glycerophosphocholine concentration during the treatment for patients with breast cancer. These results implicate that high concentration of glycerophosphocholine can have harmful effect on age-related macular degeneration recovery. Therefore, a reduction in glycerophosphocholine levels can be predictive of response to anti-VEGF therapy.

[0028] As used herein, the term“non-responder” refers to a subject who is unlikely to, or will not, respond to treatment. Non-responders are defined by persistent sub- or intra-retinal fluid at month 3.

[0029] As used herein, the term“responder” refers to a subject who is likely to, or will, respond to treatment. Responders are defined as subjects with eyes with no sub- or intra-retinal fluid at month 3. [0030] Elevated levels of lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4) were also detected and validated in non-responders. Lysophosphatidylcholine (LPC) is a breakdown product of phosphatidylcholine and higher levels of lysophosphatidylcholine have been linked to the cardiovascular complications associated with atherosclerosis, ischemia and diabetes. It has also been shown that higher level of lysophosphatidylcholine in aged aorta from rats is likely responsible for reactive species generation, and thus enhances oxidative stress in old rat aorta, suggesting that increased level of lysophosphatidylcholine may play a significant role on redox balance during the vascular aging process. Without being bound by theory, it is thought that lysophosphatidylcholines are likely to be degraded from glycerophospholipids by the activity of phospholipase enzymes (sPLA2). Glycerophospholipids are important for maintaining structural stability and membrane fluidity and have been implicated in initiation and promulgation of oxidative stress in neurological disorders. Accumulation of lysophosphatidylcholine (18:2) in serum is thought to have damaging effect, and thus results in poor response to anti-VEGF therapy.

[0031] Thus, using the method disclosed herein, in one example, an increase in any one of the biomarkers disclosed herein, and defined as such, indicates the presence of a subject that is unlikely to, or will not, respond to treatment. In another example, a decrease in any one of the biomarkers disclosed herein, and defined as such, indicates the presence of a subject that is unlikely to, or will not, respond to treatment. In other words, the method disclosed herein predicts whether a subject is likely to, or unlikely to, respond to treatment.

[0032] Phosphatidylserine (PS) is predominately localized in the inner membrane leaflet and this asymmetry is actively maintained by ATP-dependent lipid transporters regulations. The loss of asymmetric distribution of phospholipid can result in changes of membrane biochemical properties. Dysregulation of phosphatidylserine has been found in tumour microenvironment and antagonises tumour immunity development by acting as a global immunosuppressive signal in efferocytosis, infectious disease and cancer. Based on this evidence, agents targeting phosphatidylserine are thought to be of value in cancer and infectious disease therapeutics. It had been previously reported that phosphatidylserine is exposed in choroidal neovascularization (CNV) endothelium, suggesting that antibodies targeting exposed phosphatidylserine may have therapeutic value in choroidal neovascularization (CNV). Therefore, up-regulation of phosphatidylserine (18:0/20:4) is thought to have side effects on age-related macular degeneration recovery. Progressive Bruch’s membrane thickening and deposition of extracellular deposits with abundant lysophospholipid and free fatty acids (deposited as drusen) have been noted on histological sections of eyes with age-related macular degeneration, suggesting the role of phosphatidylcholine hydrolysis as potential pathogenic mechanism in age-related macular degeneration. The exact role of serum lipid levels in age-related macular degeneration is not yet clear and studies on the association of serum lipid and age-related macular degeneration risk have been inconsistent. In a previous study, little difference in lipoprotein (a) concentrations was observed between age-related macular degeneration patients with control groups, and there was no significant difference in total cholesterol, triglycerides, phospholipids, high and low density lipoprotein-cholesterol concentration when compared age-related macular degeneration patients with controls. On the other hand, it has been shown that higher total cholesterol and low density lipoprotein were associated with increased risk whereas higher high density lipoprotein levels tended to reduce age-related macular degeneration risk. These controversial results are possibly due to high variability of lipid and fatty acid levels and the use of medication and/or dietary intake. It has also been previously shown that elevated serum level of glycerophospholipids had been detected in choroidal neovascularization and polypoidal choroidal vasculopathy group compared to healthy controls in an untargeted metabolomics study. The results shown herein further supports phosphatidylcholine hydrolysis being more prominent in non-responders.

[0033] In one example, an increase in the level of glycerophosphocholine (GPC) indicates the presence of a subject that is unlikely to, or will not, respond to treatment (that is, a non-responder). In another one example, an increase in the level of lysophosphatidylcholine (18:2) indicates (predicts) the presence of a subject that is unlikely to, or will not, respond to treatment (that is, a non responder). In one example, an increase of phosphatidylserine (PS; 18:0/20:4) indicates (predicts) the presence of a subject that is unlikely to, or will not, respond to treatment (that is, a non -responder).

[0034] Thus, in one example, the method disclosed herein comprises obtaining a sample from the subject; determining the level of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4); comparing the level of the biomarkers with the level of the same biomarkers in a control sample; wherein a decrease in level of the biomarkers indicates (predicts) that the subject is unlikely/will not to respond to treatment.

[0035] Serum metabolomics profile for responders and non-responders to anti-VEGF therapy during the initial 3 monthly“loading” phase of treatment among a cohort of neovascular age-related macular degeneration patients, which was validated in an independent dataset. Increased levels of glycerophosphocholine (GPC), lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4) were found in non-responders, implicating significant impairment to glycerophospholipid metabolism. These biomarkers can therefore be used as predictive responses to initial anti-VEGF therapy.

[0036] Also disclosed herein is a method for treating age-related macular degeneration in a subject. The method as disclosed herein comprises obtaining a sample from a subject; determining the level of at least one or more biomarkers as disclosed herein, and comparing the level of the one or more biomarkers with the level of the same biomarker(s) in a control sample. In another example, the subject is to be treated with an anti-VEGF treatment if an increase in level of 3-carboxy-4-methyl-5- propyl-2-furanpropionic acid (CMPF) and/or 4-pyridoxic acid is detected; and/or a decrease in level of glycerophosphocholine, L-carnitine, creatinine and/or L-palmitoylcarnitine is detected. In yet another example, the subject is to be treated with an anti-VEGF treatment if an increase in the level of glycerophosphocholine (GPC), lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4) is detected. In yet another example, the method comprises obtaining a sample from the subject; determining the level of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4); comparing the level of the biomarkers from step b. with the level of the same biomarkers in a control sample; wherein the subject is to be treated with an anti- VEGF treatment if an increase of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4) is detected. In another example, the method disclosed herein comprises obtaining a sample from the subject; determining the level of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4); comparing the level of the biomarkers from step b. with the level of the same biomarkers in a control sample; d. treating the subject having an increased level of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4) as previously determined (under step b) with an anti- VEGF treatment; wherein the subject is to be treated with an anti-VEGF treatment if an increase of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4) is detected. In yet another example, the method disclosed herein comprises : a. obtaining a sample from the subject; b. determining the level of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4); c. comparing the level of the biomarkers from step b. with the level of the same biomarkers in a control sample, wherein the subject is to be treated with an anti-VEGF treatment if an increase of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4) is detected: and d. treating the subject having an increased level of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4) as determined under step c. with an anti-VEGF treatment.

[0037] In one example, the anti-VEGF treatment is, but is not limited to, bevacizumab, ranibizumab, and/or aflibercept.

[0038] As disclosed herein, the person skilled in the art would be able to work the invention using methods know in the art. For example, the levels of the biomarkers disclosed herein can be measured and/or identified using quantitative methods such as, but not limited to, liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometer (GC-MS), tandem mass spectroscopy (MSMS), time-of-flight mass spectroscopy (TOFMS) and nuclear magnetic resonance spectroscopy (NMR). These methods also include, but are not limited to, reverse phase (RP) chromatography, hydrophilic interaction chromatography (HILIC), as well as other methods of column chromatography known in the art. Thus, in one example, the method disclosed herein is performed using liquid chromatography-mass spectrometry (LC-MS).

[0039] As used herein, the term“ethnic” or“ethnicity” relates to large groups of people classed according to common racial, national, tribal, religious, linguistic, or cultural origin or background. The term“race” is a term that was once commonly used in physical anthropology to denote a division of humankind possessing traits that are transmissible by descent and sufficient to characterize it as a distinct human type. Ethnic factors are used by the International Conference on Harmonization in a document (ICH E5 : Ethnic Factors in the Acceptability of Foreign Clinical Data) that makes recommendations for strategies to permit clinical data collected in one region to be used to support drug and biologic registrations in another region while allowing for the influence of ethnic factors. Common ethnic populations are, but are not limited to Asian, Black and Caucasian. It is of note that the study disclosed herein was performed on subjects of Asian ethnicity.

[0040] Also disclosed herein is a kit for use according to the method according to any one of the preceding claims. In one example, the kit comprises buffers, standards and comparison samples in order to perform the methods as disclosed herein. As such, standards can be, but are not limited to, chemically synthesised versions of the markers disclosed herein. Thus, in one example, the kit comprises chemically synthesised versions of glycerophosphocholine, lysoPC(l8:2) and PS(l8:0/20:4). Comparison samples can refer to, for example, control samples and/or samples that are used as a comparison basis for any of the markers disclosed herein. Control samples can include positive and/or negative controls, depending on the type of analysis that is to be performed.

[0041] The invention illustratively described herein may suitably be practiced in the absence of any element or elements, limitation or limitations, not specifically disclosed herein. Thus, for example, the terms "comprising", "including", "containing", etc. shall be read expansively and without limitation. Additionally, the terms and expressions employed herein have been used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present invention has been specifically disclosed by preferred embodiments and optional features, modification and variation of the inventions embodied therein herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention.

[0042] As used in this application, the singular form“a,” “an,” and“the” include plural references unless the context clearly dictates otherwise. For example, the term“a genetic marker” includes a plurality of genetic markers, including mixtures and combinations thereof. [0043] As used herein, the term“about”, in the context of concentrations of components of the formulations, typically means +/- 5% of the stated value, more typically +/- 4% of the stated value, more typically +/- 3% of the stated value, more typically, +/- 2% of the stated value, even more typically +/- 1% of the stated value, and even more typically +/- 0.5% of the stated value.

[0044] Throughout this disclosure, certain embodiments may be disclosed in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosed ranges. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub ranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed sub -ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

[0045] Certain embodiments may also be described broadly and generically herein. Each of the narrower species and sub-generic groupings falling within the generic disclosure also form part of the disclosure. This includes the generic description of the embodiments with a proviso or negative limitation removing any subject matter from the genus, regardless of whether or not the excised material is specifically recited herein.

[0046] The invention has been described broadly and generically herein. Each of the narrower species and sub-generic groupings falling within the generic disclosure also form part of the invention. This includes the generic description of the invention with a proviso or negative limitation removing any subject matter from the genus, regardless of whether or not the excised material is specifically recited herein.

[0047] Other embodiments are within the following claims and non- limiting examples. In addition, where features or aspects of the invention are described in terms of Markush groups, those skilled in the art will recognize that the invention is also thereby described in terms of any individual member or subgroup of members of the Markush group.

EXPERIMENTAL SECTION

[0048] Baseline and month 3 characteristics of responders and non -responders are summarized in Table 1. The serum metabolomics profiles of a cohort of age-related macular degeneration patients from 29 responders and 21 non-responders were assessed. Samples were assigned to training set and validation set. There was no significant difference in the age, gender, or proportion with ischemic heart disease, stroke, diabetes, hyperlipidaemia, hypertension, chronic kidney disease or smoking (Table 1). Clinical characteristics and response after administration of 3 monthly anti-VEGF treatments

[0049] There was no difference in baseline visual acuity (VA) (0.89[0.68-l.l0] versus 0.88[0.89- 1.12], p=0.94 in training set; 0.89[0.66-l.l2] versus 0.63[0.48-0.78], p=0.07 in validation set) and central retinal thickness (CRT) (488 pm [407-569] versus 488 pm [402-574], p=0.99 in training set; 426 pm [367-485] versus 476 pm [427-525], p=0.2l in validation set) between responders and non responders. At month 3 mean visual acuity was better in the responder group compared to non responders, although the difference was not statistically significant (0.60[0.43-0.77] versus 0.7l[0.50-0.92], p=0.32 in training set; 0.55[0.4l-0.82] versus 0.65[0.45-0.88], p=0.7l in validation set). Responders had significantly thinner central retinal thickness than non-responder (291 pm [252- 330] versus 515 pm [399-631], p<0.0l in training set; 275 pm [257-293] versus 364 pm [321-407], p<0.0l in validation set).

High-resolution mass spectral data

[0050] Principal Component Analysis (PCA) model constructed from aligned peak data from responders and non-responders in training set was optimized at 7 principal components (PC), with R2 and Q2 value at 0.692 and 0.539, respectively. The first component explained 33.6% of the variance as shown in Fig. 1B. Most samples from responders are located toward the negative scores while non-responders are located toward the positive scores along the first principal component.

[0051] Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify the m/z features responsible for the differentiation between age-related macular degeneration responders and non-responders observed in Principal Component Analysis score plot. After removal of the first orthogonal component (20.1% of variation), the first predictive component (20.4% of variation) could obviously separate responders from non-responders (Fig. 1C, R2=0.656, Q2=0.378, cross validation analysis of variance (CV-ANOVA) p value < 0.0005). The 999 times permutation test Q2 intercept was -0.394, demonstrating the stability and non-randomness of this model. The score plots of OPLS-DA model showed clear separation between responder group and non-responder group, implicating that this model could explain the differentiation between these two groups. S-plot and variable importance for the projection (VIP) plot were used to identify the m/z features responsible for the separation m/z features with high contribution to the variation and correlation within the dataset (top and bottom 10% values of p[l] and p(corr) [1] in S plot and VIP >1) were selected as potential biomarkers.

[0052] The general metabolomics signature diagnostic for anti-VEGF responses in patients with neovascular age-related macular degeneration was then subjected to validation in an independent dataset consisting of 25 responders and 25 non-responders. The diagnostic signature had a sensitivity of 66.6% and a specificity of 82.7%. Overall the precision of the model (positive predictive value) was 73.7%. The area under the receiver-operating characteristic (AUROC) was 0.874 (95% Cl, 0.766-0.971) (Fig. 2).

Interpretation of metabolic differences between responders and non-responders

[0053] An analysis of the LC-MS spectra was conducted to identify which metabolites were contributing to the metabolic profile differentiation between responders and non-responders. Compared with profiles from non-responders, serum profiles from responders had significantly lower level of glycerophosphocholine, LysoPC (18:2) and PS (18:0/20:4) in training set (p=0.023, p=0.020 and p=0.032, respectively). These results were confirmed in the validation set (LysoPC (18:2) p=0.03l; PS (18:0/20:4) p=0.038). Similar trend, although not reaching statistical significance was also observed for glycerophosphocholine (p=0.087) (Fig. 3). Glycerophosphocholine was also verified by pure standards (FIG. 7).

Materials and Methods

Study design and participants

[0054] A prospective case-control study was performed using baseline serum from a total of 100 participants with neovascular age-related macular degeneration who participated in a prospective clinical cohort study, the Asian AMD Phenotyping Study as described previously. Briefly, the study prospectively recruited consecutive treatment-naive participants with neovascular age-related macular degeneration from the retinal clinic of the Singapore National Eye Centre from March 2010 and is still ongoing. The study was approved by the SingHealth Institutional Review Board (IRB Approval number: 2009/788/A) and was conducted in accordance with the Declaration of Helsinki (protocol number R697/47/2009 and R498/47/2006). Informed consent was obtained from all participants.

Demographic and medical history

[0055] Baseline socio-demographic and medical history was collected using an interviewer- administered questionnaire which was previously validated. Data included information on participants’ lifestyle factors, history of smoking, current medications, systemic medical and surgical history.

Clinical measurement variables

[0056] At the baseline visit, all patients underwent a full ophthalmic examination, colour fundus photography, fluorescein and indocyanine green angiography and optical coherence tomography (OCT) (Heidelberg Engineering GmbH, Dossenheim, Germany). Baseline measure of best corrected visual acuity (VA) recorded as whichever reading was best: uncorrected, corrected or pinhole, was expressed as the logarithm of the minimum angle of resolution (logMAR). Central retinal thickness (CRT) was obtained using the in-built software where an automated segmentation algorithm was used to produce retinal thickness map of the central lmm zone. [0057] All patients received three injections at monthly intervals of intravitreal anti-VEGF. The choice of agent type (aflibercept, bevacizumab or ranibizumab) was decided by the treating physician.

[0058] Patients were evaluated at month 3 and categorized into treatment responders (responder group, n = 54) or treatment non responders (non-responder group, n = 46). Treatment response was based on OCT findings of disease activity. Responders were defined as eyes with no sub or intra retinal fluid at month 3. Non-responders were defined by persistent sub or intra retinal fluid at month 3. All OCT scans were qualitatively analysed by 2 graders blinded to each other’s decision (KYCT, CMGC). Any grading disagreement was openly arbitrated and the final decision was made by the senior grader (CMGC).

LC-MS based metabolic profiling analysis

[0059] The recruited samples were randomly divided into two independent cohorts, i.e. a training set and a validation set. The training set, including 29 responders and 21 non -responders, was used to establish if serum metabolomics profiles could distinguish between patients with nAMD regarding their response to anti-VEGF injections. The validation set, comprising 25 responders and 25 non responders, was used to independently validate the metabolite biomarkers and assess the effect of anti-VEGF on nAMD patients.

[0060] After enrolment, blood was extracted from the cubital vein of each participant. The blood was then immediately transferred to the collection tube and kept at room temperature for 30 minutes to allow clotting. The clotted blood samples were centrifuged at 3000 g at 4 °C for 20 minutes to eliminate the supernatant serum and then quickly stored at -80°C prior to metabolomics detection.

[0061] Metabolites were extracted from 200 pl serum samples using 800 mΐ ice cold 1: 1:1 methanol/acetone/acetonitrile, incubated at -20°C for 30 minutes, and centrifuged at l6,000g for 15 minutes (4°C) to remove protein. Each sample extract was divided into two equal aliquots and dried in a vacuum concentrator before liquid chromatography-mass spectrometry (LC-MS) analysis.

[0062] Each sample was analysed both on reverse phase (RP) column and hydrophilic interaction chromatography (HILIC) column in positive and negative ionization modes. Aliquots for RP injection were reconstituted in 25 mΐ 2% acetonitrile and aliquots for HILIC column injections were reconstituted in 25 mΐ 80% acetonitrile. Metabolites separation was performed on an ACQUITY I- class UPLC system (Waters, Milford, Massachusetts, US). The injection volume was 10 mΐ and flow rate was 0.6 ml/min. The column and auto-sampler were maintained at 40 °C and l0°C, respectively. Table 2 lists the columns, mobile phases and gradients for RP and HILIC. Quality control (QC) samples were prepared by pooling equal volume of all serum samples in this study to monitor the stability and repeatability during LC-MS analysis. The pre-treatment of QC samples was the same as that of tested samples, and were injected after every ten samples. [0063] Mass detection was achieved on a TripleTOF 5600 fitted with a DuoSpray ion source (SCIEX, Foster, California, US). Mass calibration was automatically performed after every 20 injections by the automated calibration delivery system. The source voltage was set to 5500 V for positive ionization and 4500 V for negative ionization mode. The declustering potential was 80 V and source temperature was 500 °C for both polarities. The curtain gas flow, nebulizer and heater gas were set to 30, 55 and 60 arbitrary units, respectively. Information dependent acquisition (IDA) was used to collect full scan MS and MSMS information simultaneous with an m/z mass range of 100-1000. The instrument performed a TOFMS survey with 160 milliseconds accumulation time, followed by five MSMS scans with 18 milliseconds (ms) accumulation time. The collision energy was linearly ramped from 20 to 40 V. The following parameters were also applied to data acquisition: dynamic background subtraction, charger monitoring to exclude multiple charged ions and dynamic exclusion of former target ions for 1 second.

[0064] Peak extraction and quantification of ion intensities were performed using both XCMS online and Markerview (SCIEX), which provide lists containing m/z values, retention time and integrated ion intensity for each m/z features.

Statistical analysis

[0065] Descriptive data are presented as mean (confidence interval) or number (percentage). Statistical tests such as Student’s t-test, and chi squared test were used where appropriate to compare demographic and clinical characteristics between the responder and non-responder groups. Analyses for demographic and clinical characteristics were calculated using R V3.3.1.

[0066] A combination of analysis of the variance (ANOVA) and multivariate analysis methods including principle component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) using SMICA (Umertrics, Umea, Sweden) were used to select potential metabolites which are the most responsible for the differentiation between groups. Student’s t-test was used for statistical comparison of pairs of groups and a p value <0.05 was considered a priori to be statically significant. The peak lists from both positive and negative mode were normalized by total ion intensity and Pareto scaled first. A principle component analysis was first performed to show a trend of intergroup separation on the score plots. The tight cluster of QC samples in PCA score plot indicated robustness of our metabolic profiling platform (Fig. 1A). R2Y and Q2Y scores were used for assessment of variance coverage by predictive component and model predictability in a seven times cross-validation, respectively. A 999-time permutation test was carried out to confirm the stability and robustness of OPLS-DA model. A Q2 intercept of zero or below from permutation test demonstrates the stability and non-randomness of the model and thus strongly supports the validity of the model. Metabolite annotation and pathway analysis

[0067] Metabolites identification was achieved by database search against accurate m/z and MS/MS spectra with METLIN and HMDB. Metabo Analyst was used for pathway analysis. Selected metabolites were further validated by commercially available pure standards. GPC was purchased from Sigma-Aldrich (St. Louis, Missouri, US).

Data availability

[0068] All the metabolomics datasets described in our study can be accessed at MetaboLights (https://www.ebi.ac.uk/metabolights/) (Project ID: MTBLS950).

[0069] Table 1. Comparison of characteristics of responders and non -responders at baseline and after 3 monthly administrations of anti-vascular endothelial growth factor (VEGF) therapy.

Abbreviations: IHD, Ischemic heart disease; VA, visual acuity; logMAR, logarithmic of the minimum angle of resolution; Cl, confidence interval; CRT, central retinal thickness. [0070] Table 2: ACCUITY LC parameter settings

Analysis

Column Mobile Phases Gradient mode

0- 1 min. 2% B: 1-3 5

6.5 mM ammonium

min, 2%-20% B: 3.5-9

ACQUITY UPLC bicarbonate in water (A)

RP min, 20%-100% B: 9- BEH€18 (2.1 x 100 and 6 5 mM ammonium

negative 10.5 min. 100% B;

mnr 1.7 mhi) bicarbonate in 95%

10.5-11 min, 100% to acetonitrile (B)

6.5 mM ammonium min. 95%-60% B; 8-8.5

ACQUITY UPLC

HILIC bicarbonate in water (A) and min, 60%-20% B; 8.5- BEH HILIC (2.1 x

negative 10 mM ammonium 9.5 min, 20% B; 9.5-10

100 mm. 1 7 mih )

bicarbonate in 95% acetonitrile min, 20% to 95% B; 10- 17 min. 95%B.

Claims

1. A method of predicting treatment response of a subject suffering, or thought to suffer from, age-related macular degeneration, the method comprising:

a. obtaining a sample from the subject;

b. determining the level of glycerophosphocholine, lysophosphatidylcholine (LysoP;

18:2) and phosphatidylserine (PS; 18:0/20:4);

c. comparing the level of the biomarkers from step b. with the level of the same biomarkers in a control sample;

wherein a decrease in level of the biomarkers indicates that the subject is unlikely/will not respond to treatment.

2. A method of treating age-related macular degeneration in a subject, the method comprising: a. obtaining a sample from the subject;

b. determining the level of glycerophosphocholine, lysophosphatidylcholine (LysoP;

18:2) and phosphatidylserine (PS; 18:0/20:4);

c. comparing the level of the biomarkers from step b. with the level of the same biomarkers in a control sample;

wherein the subject is to be treated with an anti-VEGF treatment if an increase of glycerophosphocholine, lysophosphatidylcholine (LysoP; 18:2) and phosphatidylserine (PS; 18:0/20:4) is detected.

3. The method of any of the preceding claims, wherein the control sample is obtained from a subject who responded to anti-VEGF treatment.

4. The method of any of the preceding claims, wherein the anti-VEGF treatment is selected from the group consisting of bevacizumab, ranibizumab, and aflibercept.

5. A kit for use according to the method according to any one of the preceding claims.