METHODS OF TREATMENT

FIELD

The specification relates to methods for stratifying an individual as a likely therapy responder or likely therapy non-responder to HMG-CoA reductase inhibitors wherein a therapy responder displays a reduced risk of a cardiovascular event and a therapy non-responder does not, using a lipid profiling approach. The description enables stratification of individuals into treatment categories and in part facilitates clinical management of individuals identified as at risk for future cardiovascular events. Small molecule and lipid chemistry and lipidomics are employed to characterise lipid classes and species in biological samples. In a further aspect, the specification relates to methods of treatment including methods of reducing risk of a cardiovascular event.

BACKGROUND

Bibliographic details of references in the subject specification are also listed at the end of the specification.

Reference to any prior art in this specification is not, and should not be taken as, acknowledgement or any form of suggestion that this prior art forms part of the common general knowledge in any country.

Lipids are among the least studied molecules of the metabalome. Levels of certain plasma lipid species have been associated with future cardiovascular events but lipid profiling on the whole is in its infancy. It is certainly not known to select a profile of plasma lipid species before or after HMG-CoA reductase inhibitor treatment that is predictive of future cardiovascular events.

There is a need to identify individuals who are likely therapy responders or -likely therapy non-responders to statin treatment unrelated to enable, for example, targeted therapy based upon relative risk reduction rather than cholesterol lowering.

SUMMARY

In one embodiment, the present disclosure enables a method for determining the likelihood that a HMG-CoA reductase inhibitor treatment will reduce the risk of a cardiovascular event in an individual, the method comprising (i) detecting a level of one or two or more non steroidal lipid species in a biological sample obtained from the individual at a time point before or after treatment; (ii) comparing the level one or two or more lipid species, or a ratio of the level of two lipid species, from step (i) in the sample to a reference level or ratio for the lipid species; and (iii) determining whether the treatment is likely or not to reduce the risk of the cardiovascular event in the individual on the basis of the comparison. In one embodiment, the cardiovascular event is selected from one or more of the group consisting of coronary heart disease, atherosclerosis, stroke, unstable angina, cardiomyopathy, cardiovascular disease death, myocardial infarction, ischaemic heart disease, heart failure, hypertensive heart disease, inflammatory heart disease and valvular heart disease.

In one embodiment, the lipid species is/are selected from a lipid class or subclass selected from the group consisting of dihydroceramide (Cer(dl8:0)), ceramide (Cer(dl8: l)), monohexocylceramide (HexCer), dihexosylceramide (Hex2Cer), trihexosylceramide (Hex3Cer), GM3 ganglioside (GM3), sphingomyelin (SM), phosphatidylcholine (PC), alkylphosphatidylcholine (PC(O)), alkenylphosphatidylcholine (PC(P)), lysophosphatidylcholine (LPC), lysoalkylphosphatidylcholine (LPC(O)), phosphatidylethanolamine (PE), alkylphosphatidylethanolamine (PE(O)), alkenylphosphatidylethanolamine (PE(P)), lysophosphatidylethanolamine (LPE), phosphatidylinositol (PI), lysophosphatidylinositol (LPI), phosphatidylserine (PS), phosphatidylglycerol (PG), diacylglycerol (DG), and triacylglycerol (EG).

In one embodiment the lipid species is a phospholipid selected from the group consisting of lysophosphatidylcholine, sphingomyelin, phosphatidylinositol, phosphatidylglycerol, phosphatidylcholine, phosphatidylserine, phosphatidylethanolamine, and ly sophosphati dy 1 ethal ol amine .

As described herein, the present assay methods include methods the employ statin administration and determining the change in lipid species after administration. The present methods also include assay methods that do not employ statin administration and/or determining the change in lipid species as a result of statin administration, and instead predict a risk reduction based on the current lipid profile of the individual who is not taking statins.

In one embodiment therefore, the present description provides a method as described above, wherein step (i) is conducted before or without a HMG-CoA reductase inhibitor treatment.

In one embodiment, the one, two or more lipid species in step (ii) are selected from Hex2Cer(l6:0), LPC(l6: l), LPC(20: l), PC(3l : l), PC(32:2), PC(32:3), PC(33:2), PC(33:3), PC(34:0), PC(34: l), PC(35:3), PC(36:3), LPC(0-22: l), LPC(0-24: l), PC(0-34:3), PI(34:0), PI(36:4), PI(38:2), PI(38:6), SM(34: l), SM(35:2), SM(36: l), SM(36:2), SM(36:3), SM(37:2), and TG(l8:0/l8:0/l8:0).

In another embodiment, a lipid species or the lipid species in at least one ratio of the level of two lipid species in step (ii) are selected from Hex2Cer(l6:0), LPC(l6: l), LPC(20: l), PC(3 l : l), PC(32:2), PC(32:3), PC(33:2), PC(33:3), PC(34:0), PC(34: l), PC(35:3), PC(36:3), LPC(0-22: l), LPC(0-24:l), PC(0-34:3), PI(34:0), PI(36:4), PI(38:2), PI(38:6), SM(34: l), SM(35:2), SM(36: l), SM(36:2), SM(36:3), SM(37:2), and TG(l8:0/l8:0/l8:0). In one embodiment, the one, two or more lipid species in step (ii) are selected from LPC(l6: l), LPC(20: l), PC(32:2), LPC(0-22: l), PI(36:4), PI(38:2), PI(38:6), SM(35:2), SM(36: l), SM(36:2), SM(37:2), and TG(l8:0/l8:0/l8:0).

In one embodiment a lipid species or the lipid species in at least one ratio of the level of two lipid species in step (ii) are selected from LPC(l6: l), LPC(20: l), PC(32:2), LPC(0-22: l), PI(36:4), PI(38:2), PI(38:6), SM(35:2), SM(36: l), SM(36:2), SM(37:2), and TG(l8:0/l8:0/l8:0).

In another embodiment therefore, step (i) is conducted after a HMG-CoA reductase inhibitor treatment. In one embodiment, this allows a lipid profile to be determined after a HMG- CoA reductase inhibitor treatment and compared to that before HMG-CoA reductase inhibitor treatment. Thus the present description provides a method for determining the likelihood that a HMG-CoA reductase inhibitor treatment will reduce the risk of a cardiovascular event in an individual, the method comprising (i) detecting a level of one or two or more non-steroidal lipid species in a biological sample obtained from the individual at a time point after treatment; (ii) comparing the level one or two or more lipid species, or a ratio of the level of two lipid species, from step (i) in the sample to a reference level or ratio for the lipid species; and (iii) determining whether the treatment is likely or not to reduce the risk of the cardiovascular event in the individual on the basis of the comparison.

In one embodiment, at least one lipid species is a lipid species such as a phospholipid species, or a phosphatidylinositol lipid species, that displays a reduced level after the treatment in reference individuals who also show a reduced risk of a cardiovascular event as a result of the treatment.

In one embodiment, at least one lipid species is a phosphatidylcholine species or a lipid comprising an omega 6 fatty acid that displays an elevated level as a result of the treatment in reference individuals who also show a reduced risk of a cardiovascular event as a result of the treatment.

In one embodiment, the omega 6 fatty acid is selected from 18:3, 20:3, 20:4, 22:4, 24:4, 24:5 and 22:5. In one embodiment the lipid species is PC(38.4).

In one embodiment, the method comprises detecting the level of two or more lipid species in the sample.

In one embodiment, the lipid species are selected from two or more different lipid classes or subclasses.

In one embodiment, a ratio in step (ii) is between the level of two lipid species selected from Figure 8.

In one embodiment, a ratio in step (ii) is between the level of two lipid species selected from a lipid classes or sub classes selected from Figure 9.

In one embodiment, a ratio in step (ii) is between (a) the level of a phosphatidylinositol species that displays reduced levels as a result of the treatment in reference individuals who also show a reduced the risk of a cardiovascular event as a result of the treatment and (b) a phosphatidylcholine species that displays an elevated level as a result of the treatment in reference individuals who also show a reduced risk of a cardiovascular event as a result of the treatment. In one embodiment, species in a) is PI(36:3) or PI(38:4). In one embodiment, the species in b) is PC(38:4).

In one embodiment, a ratio in step (ii) is between the level of PI(36:3) and PC(38:4) and/or between the level of PI(36:2) and PC(38:4).

In another aspect, the disclosure provides a method for stratifying an individual as a likely statin therapy responder or likely therapy non-responder with respect to a HMG-CoA reductase inhibitor treatment regimen comprising ascertaining the likelihood that the HMG-CoA reductase inhibitor treatment regimen will reduce the risk of a cardiovascular event in the individual according to the methods described herein.

In one embodiment, the stratifying method comprises (i) detecting a level of one or two or more non-steroidal lipid species in a biological sample obtained from the individual at a time point before or after treatment; (ii) comparing the level one or two or more lipid species, or a ratio of the level of two lipid species, from step (i) in the sample to a reference level or ratio for the lipid species; and (iii) determining whether the treatment is likely or not to reduce the risk of the cardiovascular event in the individual on the basis of the comparison.

In another embodiment, there is provided a method of screening for an agent that reduces the risk of a cardiovascular event, the method comprising screening an agent in a test subject for an ability to develop a therapy responder profile according to the assay method as described herein.

In one embodiment the method of screening for an agent that reduces the risk of a cardiovascular event, that comprises screening an agent for an ability to reduce the level of a phophosphatidylinositol lipid species in a test subject.

In another aspect, the description enables a method of treatment or prophylaxis of an individual comprising administering an HMG-CoA reductase inhibitor treatment regimen to reduce the risk of a cardiovascular event, the method comprising administering an HMG-CoA reductase inhibitor treatment regimen to the individual after ascertaining the likelihood that the HMG-CoA reductase inhibitor treatment regimen will reduce the risk of a cardiovascular event in the individual according to the assay method described herein.

In one embodiment, the description enables a method of treatment or prophylaxis of an individual to reduce the/their risk of a cardiovascular event, the method comprising administering an HMG-CoA reductase inhibitor treatment regimen in combination with fatty acid supplementation such with a lipid comprising an omega-6 fatty acid or arachidonic acid.

In one embodiment, the method comprises administering fatty acid supplementation with a lipid comprising an omega-6 fatty acid or arachidonic acid to an individual at risk of a cardiovascular event, who has previously been administered an HMG-CoA reductase inhibitor. In one embodiment, the omega 6 fatty acid is selected from Cl8:2, 18:3, 20:3, 20:4, 22:4, 24:4, 24:5 and 22:5.

In one embodiment, the omega 6 fatty acid is arachidonic acid.

In another embodiment, the description provides a method of treating an individual at risk of a cardiovascular event, the method comprising administering an agent that lowers plasma levels of phosphatidylinositol in the individual.

In one embodiment, the agent inhibits phosphatidylinositol synthase.

In one embodiment, the agent is selected from one or more of: an inositol analogue, an anti-CDIPT antibody or antigen binding part thereof that is a phosphatidylinositol synthase inhibitor, an aptamer, small molecule, an antisense, sgRNA or iRNA, zinc in the case of zinc deficiency, and sorbitol or glucose. Illustrative inhibitors include inostamycin and 8- hexachl orocy cl ohexane .

The summary is not an exhaustive recitation of all embodiments of the present disclosure.

BRIEF DESCRIPTION OF THE FIGURES

Figure 1: Consort diagram for patient flow in treatment analysis. From the 9014 participants that were randomized to receive pravastatin treatment, baseline samples were available for 5991 participants and 1 year follow-up samples were available for 5782 participants. 4991 participants for whom both baseline and follow-up samples were available were used in this study for subsequent analysis. Of the 4991 participants, 91 experienced a CVE within the first year and were removed from the CVE analysis (resulting in n=4900, with 944 CVE). All 4991 samples were used in the analysis of CVD death (498 CVD deaths) as there were no CVD deaths within the first year in this subcohort.

Figure 2: Association of change in lipid concentration with future cardiovascular events within treatment group. Hazard Ratio denotes hazard ratio per unit interquartile range, and 95% Cl denotes 95% of confidence interval. Models were adjusted for age, BMI, cholesterol at baseline, high density lipoprotein at baseline, triglycerides at baseline, current smoking, systolic blood pressure, fasting glucose, atrial fibrillation, gender, stroke history, diabetes history, history of hypertension, nature of prior acute coronary syndrome, revascularization, eGFR, dyspnea grade, angina grade, white blood cell count, peripheral vascular disease, aspirin at baseline, change in LDL-C, and baseline level of a given lipid species. P-values were corrected for multiple comparisons using Benjamini-Hochberg method. Lipid species with P-value<0.0l are presented.

Figure 3: Association of change in lipid concentration with future cardiovascular death within treatment group. Hazard Ratio denotes hazard ratio per unit interquartile range, and 95% Cl denotes 95% of confidence interval. Models were adjusted for age, BMI, cholesterol at baseline, high density lipoprotein at baseline, triglycerides at baseline, current smoking, systolic blood pressure, fasting glucose, atrial fibrillation, gender, stroke history, diabetes history, history of hypertension, nature of prior acute coronary syndrome, revascularization, eGFR, dyspnea grade, angina grade, white blood cell count, peripheral vascular disease, aspirin at baseline, change in LDL-C, and baseline level of a given lipid specie. P-values were corrected for multiple comparisons using Benjamini-Hochberg method. Lipid species with P-value<0.0l are presented.

Figure 4: Association of treatment with future cardiovascular events after adjustment for changes in LDL-C and lipid concentration. The base model was adjusted for age, BMI, cholesterol at baseline, high density lipoprotein at baseline, triglycerides at baseline, current smoking, systolic blood pressure, fasting glucose, atrial fibrillation, gender, stroke history, diabetes history, history of hypertension, nature of prior acute coronary syndrome, revascularization, eGFR, dyspnea grade, angina grade, white blood cell count, peripheral vascular disease and aspirin at baseline. * denotes treatment effect relative to the base + change in LDL-C model; ** denotes treatment effect relative to the base model. Randomized treatment effect represents the median over 1000 randomizations and the 5th and 95th percentiles.

Figure 5: Association of treatment with future cardiovascular death after adjustment for changes in LDL-C and lipid concentration. The base model was adjusted for age, BMI, cholesterol at baseline, high density lipoprotein at baseline, triglycerides at baseline, current smoking, systolic blood pressure, fasting glucose, atrial fibrillation, gender, stroke history, diabetes history, history of hypertension, nature of prior acute coronary syndrome, revascularization, eGFR, dyspnea grade, angina grade, white blood cell count, peripheral vascular disease and aspirin at baseline. * denotes treatment effect relative to the base + change in LDL-C model; ** denotes treatment effect relative to the base model. Randomized treatment effect represents the median over 1000 randomizations and the 5th and 95th percentiles.

Figure 6: Change in the lipid ratio and LDL-C in quartiles of the treatment group stratified on the change in the lipid ratio. The treatment group was stratified into quartiles using the change in the lipid ratio PI(36:2)/PC(38:4). Panel A shows the median change of LDL-C in each quartile with the interquartile range shown by the whiskers. The median change and interquartile range of the control group is also shown for reference (open squares). Panel B shows the median change of the lipid ratio in each quartile with the interquartile range shown by the whiskers. The median change and interquartile range of LDL-C in the control group is also shown for reference (open squares).

Figure 7: Association of treatment with future cardiovascular events and cardiovascular death in quartiles of change in lipid ratio (PI(36:2)/PC(38:4)) in the statin treatment group. The samples in each quartile of the delta lipid ratio in statin treatment were combined with all the samples in the placebo group and a cox regression analysis was performed to find the hazard ratio of treatment for cardiovascular events and death. Base models included the variables: age, BMI, cholesterol at baseline, high density lipoprotein at baseline, triglycerides at baseline, current smoking, systolic blood pressure, fasting glucose, atrial fibrillation, gender, stroke history, diabetes history, history of hypertension, nature of prior acute coronary syndrome, revascularization, eGFR, dyspnea grade, angina grade, white blood cell count, peripheral vascular disease and aspirin at baseline.

Figure 8 tabulates the results showing the mean percentage change of lipid species levels in treatment and control groups. The index for superscripted numbers in column descriptions are as follows: ¹ Median concentration and Inter-quartile range (mM); ² Standard-deviation; ³ Uncorrected P-value; ⁴ P-values are corrected for multiple comparisons using the Benjamini- Hochberg approach. P-values are color coded as follows: P-value<0.00l red (grey); 0.00l<P- value<0.0l yellow (palest grey); 0.0l<P-value<0.05 green (paler grey).

Figure 9 tabulates the results showing the association of treatment with percentage change in lipid species. The index for superscripted numbers in column descriptions are as follows: ¹ Model 1 was adjusted for: age, sex, body mass index; ² Model 2 was adjusted for: age, sex, body mass index and the percentage changes in total cholestrol, HDL-C and triglicerides;

³ Beta coefficient denotes the difference in percentage changes (from baseline to follow-up) between treatment and placebo groups; ⁴ 95% Cl denotes 95% of confidence interval (Lower and Upper limits); ⁵ P-values are corrected for multiple comparisons using the Benjamini-Hochberg approach. P-values are color coded as follows: P-value<0.00l red (grey) ; 0.00l<P-value<0.0l yellow (palest grey); and 0.0l<P-value<0.05 green (paler grey).

Figure 10 tabulates the results showing the risk reduction for lipid species ratios all LIPID individuals (statin treated and untreated), females, T2D and non-T2D in stroke (Figure 10A), all CVE (Figure 10B), CVD death (Figure 10C) and MI (Figure 10D) and to determine lipid biomarkers of therapy responders and therapy non-responders in the absence of treatment.

Figure 11 is a schematic representation showing the omega 3 and omega 6 fatty acids and their relationships as described in Example 5.

BRIEF DESCRIPTION OF THE TABLES

Table 1 provides details of conditions for tandem mass spectrometry of lipid species.

Table 2 provides baseline characteristics of the LIPID cohort.

DESCRIPTION OF CERTAIN EMBODIMENTS

Throughout this specification, unless the context requires otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or integer or group of elements or integers but not the exclusion of any other element or integer or group of elements or integers. By "consisting of is meant including, and limited to, whatever follows the phrase "consisting of. Thus, the phrase "consisting of indicates that the listed elements are required or mandatory, and that no other elements may be present. By "consisting essentially of is meant including any elements listed after the phrase, and limited to other elements that do not interfere with or contribute to the activity or action specified in the disclosure for the listed elements. As used herein the singular forms "a", "an" and "the" include plural aspects unless the context clearly dictates otherwise. Thus, for example, reference to "a lipid species" includes a single lipid species, as well as two or more lipid species; reference to "an agent" includes one agent, as well as two or more agents; reference to "the disclosure" includes single and multiple aspects of the disclosure and so forth.

Reduction in low-density lipoproteins cholesterol (LDL-C) is thought to be the major mechanism by which 3 -hydroxy-3 -methyl -glutary 1 coenzyme A (HMG-CoA) reductase inhibitors such as the statins reduce risk of cardiovascular events. However, in some groups of patients, statin therapy has not produced the desired reduction in cardiovascular events even after reducing the LDL-C levels, (Olsson et ah, Eur. Heart J: 26:890-896, 2005, Puri et ah, Circulation·.128:2395-2403, 2013). This suggests additional mechanisms other than change in LDL-C are needed to explain the statin treatment effect and underpin new methods of treatment.

Determining the responder status of an individual

The present description enables a lipidomic approach for determining the statin therapy responder status of an individual. The approach is predicated in part on the determination that non-steroidal plasma lipid profiles can be used to stratify individuals as therapy responders or non-responders to statin treatment. By "therapy responder or responder" is meant that the individual is likely to respond to a HMG-CoA reductase inhibitor treatment by displaying or benefiting from a relative risk reduction in a future cardiovascular event. By "therapy non responder or non-responder" is meant that the individual is not likely to respond to HMG-CoA reductase inhibitor treatment by displaying or benefiting from a relative risk reduction in a future cardiovascular event.

In one embodiment of the method, the statin therapy responder status of an individual is determined based upon the "baseline" or current lipid profile without directly assessing the individual's lipid profile change in response to statin treatment as part of the method.

In another embodiment of the method, the statin therapy responder status of an individual is determined based upon the "baseline" lipid profile and the profile obtained after directly assessing the individual's lipid profile in response to a HMG-CoA reductase inhibitor treatment.

An individual's lipid profile may be assessed after statin treatment such as within days, weeks or months of treatment. In one embodiment assessment is within 2 to 6, 1 to 10, 3 to 4 weeks.

Accordingly, in one embodiment, there is provided a method for determining the likelihood that a HMG-CoA reductase inhibitor treatment will reduce the risk of a cardiovascular event in an individual, the method comprising: (i) detecting a level of one or two or more non steroidal lipid species in a biological sample obtained from the individual at a time point before or after treatment; (ii) comparing the level one or two or more lipid species, or a ratio of the level of two lipid species, from step (i) in the sample to a reference level or ratio for the lipid species; and (iii) determining whether the treatment is likely or not to reduce the risk of the cardiovascular event in the individual on the basis of the comparison.

In one embodiment, by the determining step (iii) the individual can be stratified as a statin therapy responder or non-responder.

The term "level" is used herein to include a concentration, relative amount, or other signature indicative of an amount or relative amount of a lipid species present in a blood plasma sample obtained from an individual at given time point. The level of a lipid species may be determined directly such as by liquid chromatography electrospray ionization and tandem mass spectrometry using internal standards or indirectly, such as by assessing the level of a reporter molecule that is bound to a lipid species.

"Reference to "non-steroidal" means a lipid species other than a lipid species characterized by a fused ring system, such as cholesterol, the sex hormones and adrenocorticoid hormones such as cortisol.

Reference to a "biological sample" includes a biological material such as whole blood, serum, plasma or other biological fluid, or a solid or semi-solid sample. Samples may be diluted or concentrated, treated or untreated.

The term "reference ratio or level" includes data or a control a skilled person would use to facilitate the accurate interpretation of technical data. In one embodiment reference ratios or levels are obtained from an individual at an earlier or later time point than for test ratios or levels. Thus reference levels may be pre-determined or pre-selected. Reference levels may be expressed as a median, mode or mean level or range from an individual or a cohort of subjects or a mean together with standard deviation to determine suitable threshold or cut off levels. Reference levels may be expressed in any form such as by concentration or molality.

Reference to a "subject" or "individual" includes any human, primate, mammalian or other species of veterinary importance, or test organism known to the skilled person.

In the work leading to the present disclosure detailed plasma lipidomic measurements incorporating 345 lipid species were performed using electrospray ionisation tandem mass spectrometry on baseline and on one year follow up samples from an unbiased LIPID study sub cohort (n=499l). The relationship between the statin treatment and changes in lipids was investigated using linear regression. Cox regression was used to identify associations between changes in lipid species and cardiovascular outcomes within the treatment group, adjusting for appropriate covariates (see Figure 9 and the Examples). The statin treatment effect on future cardiovascular events and cardiovascular deaths was examined by estimating the relative risk reduction (RRR) while adjusting for appropriate covariates including changes in individual lipid species.

The associations of lipid classes/subclasses and species with statin treatment identified several classes of glycosphingolipid as negatively associated with statin treatment independent of the changes in clinical lipids. Ceramide itself was also negatively associated (with a smaller effect size), while the biosynthetic precursor of ceramide, dihydroceramide, was positively associated with pravastatin treatment. These observations indicate that HMG-CoA reductase inhibitor treatment is modulating the glycosphingolipid biosynthetic pathway independently of the reduction in total cholesterol and LDL cholesterol.

In one aspect of the disclosure and method, the level of one or two or more non-steroidal lipid species is determined in a biological sample obtained from the individual at a time point before or after treatment.

In one embodiment, the lipid species are non-steroidal lipid species selected from those set out in Figure 8.

In one embodiment, the lipid species comparing step is an iterative process wherein different lipid species are compared sequentially according to a prognostic algorithm.

In one embodiment, a cardiovascular event (CVE) is selected from the group consisting of coronary heart disease, atherosclerosis, stroke, cardiomyopathy, cardiovascular disease, ischaemic heart disease, heart failure, hypertensive heart disease, inflammatory heart disease and valvular heart disease. In one embodiment, reference to CVE includes an associated event such as one or more forms of stroke.

In one embodiment, the individual has previously experienced a cardiovascular event.

In one embodiment, the individual has been previously identified as at risk for a CVE.

In one embodiment, the lipid species is/are selected from a lipid class or subclass selected from the group consisting of dihydroceramide (Cer(dl8:0)), ceramide (Cer(dl8: l)), monohexocylceramide (HexCer), dihexosylceramide (Hex2Cer), trihexosylceramide (Hex3Cer), GM3 ganglioside (GM3), sphingomyelin (SM), phosphatidylcholine (PC), alkylphosphatidylcholine (PC(O)), alkenylphosphatidylcholine (PC(P)), lysophosphatidylcholine (LPC), lysoalkylphosphatidylcholine (LPC(O)), phosphatidylethanolamine (PE), alkylphosphatidylethanolamine (PE(O)), alkenylphosphatidylethanolamine (PE(P)), lysophosphatidylethanolamine (LPE), phosphatidylinositol (PI), lysophosphatidylinositol (LPI), phosphatidylserine (PS), phosphatidylglycerol (PG), cholesteryl ester (CE), diacylglycerol (DG), and triacylglycerol (TG).

Depending upon the analysis format elected, as few as one or two lipid species may be assessed and its/their level determined for the method. Alternatively, multiple lipid species may be analysed. In one embodiment, the method comprises determining or determining and comparing the level of at least 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30 or more lipid species including 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 ,

52, 53, 54, 55, 56, 57, 58, 59, 60, 61 , 62, 63, 64, 65, 66, 67, 68, 69, 70, 71 , 72, .73, 74, 75, 76,

77, 78, 79, 80, 81 , 82, 83, 84, 85, 86, 87, 88, 89, 90, 91 , 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,

121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151 , 152, 153, 154, 155, 156, 157, 158, 159, 160, 161 , 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196,

197, 198, 199, 200, 201 , 202, 203, 204, 205, 206, 207, 208, 209, 210, 21 1, 212, 213, 214, 215,

216, 217, 218, 219, 220, 221 , 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,

235, 236, 237, 238, 239, 240, 241 , 242, 243, 244, 245, 246, 247, 248, 249, 250, 251 , 252, 253,

254, 255, 256, 257, 258, 259, 260, 261 , 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291 , 292, 293, 294, 295, 296, 297, 298, 299, 300, 301 , 302, 303, 304, 305, 306, 307, 308, 309, 310, 31 1, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344 or 345 lipid species.

In one embodiment the number of ratios determined or determined and compared is between one and one hundred such as 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30 or more lipid species including 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 , 62, 63, 64, 65, 66, 67, 68, 69, 70, 71 , 72, .73, 74, 75, 76, 77, 78, 79, 80, 81 , 82, 83, 84, 85, 86, 87, 88, 89, 90, 91 , 92, 93, 94, 95, 96, 97, 98, or 99 ratios.

Illustrative methods capable of analysing multiple lipid species include classical lipid extraction methods, mass spectrometry together with electrospray ionization and matrix-assisted laser desorption ionisation, with mass analysis such as quadruple and/or TOF (e.g,. Quadrapole/TOF) or orbitrap mass analysers. Chromatographic methods are used for the separation of lipid mixtures such as gas chromatography, high pressure liquid chromatography (HPLC), ultra-high pressure liquid chromatography (UHPLC), capillary electrophoresis (CE). These may be used with mass spectrometry based detection systems or other detectors including optical detectors. Clinical mass spectrometry systems are used by clinical laboratories to provide lipid profiles and ratios upon request. Another suitable technique for quantitative lipid analysis is one or two dimensional nuclear magnetic resonance (NMR). Two dimensional techniques such as heteronuclear single quantum coherence (HSQC) are suitable for lipid profiling through the ability to elucidate C-H bonds within a structure.

Lipid level data may be processed to produce a report of levels and/or ratios. In one embodiment lipid data are processed as described herein to identify and/or report the therapy responder or non-responder status of an individual.

The methods enabled herein permit integration into pathology architecture or platform systems.

For example, the method described herein allows a user or client to determine the statin therapy responder status of an individual, the method including: (a) receiving data in the form of lipid levels, relative lipid levels or signature profiles developed from an individual's plasma sample before or after treatment from the user via a communications network; (b) processing the individuals data via an algorithm which provides therapy responder value (risk reduction) by comparing levels and/or ratios of lipid levels to those from one or more reference levels or ratios.

In some embodiments, an indication of the responder status of the individual to the user is transferred via a communications network. It will also be appreciated that in one example, the end stations can be hand-held devices, such as PDAs, mobile phones, or the like, which are capable of transferring the subject data to the base station via a communications network such as the Internet, and receiving the reports. When a server is used, it is generally a client server or more particularly a simple object application protocol (SOAP).

In one embodiment, the method is suitable to be practised as a point-of-care method typically employing a device suitable for point of care.

Biosensor technologies that permit less expensive equipment or trained personnel are available for developing devices for lipid species analysis that may be used at point of care. Biosensors which recognise a target molecule and produce a measurable or observable signal may be for example, optical, electrochemical or mechanical biosensors. Assays that use a label indirectly measure the binding of an analyte lipid to a target molecule using a reporter molecule as an indication of binding and amount. Label free assays measure signal changes directly associated with target binding or cellular processes. Examples of label free optical sensors include surface plasmon resonance sensing (SPR), Interferometry (such as backscattering inferometry (BSI), ellipsometry, and assays based on UV absorption of lipid-functionalized gold nanorods. In optical assays using labels, the target lipid molecule is immobilized on the surface of a biosensor and then probed with a binding agent, such as an antibody couples to a label (many labels are known to the skilled person such as a flurophore, quantum dot, radioisotope, enzyme). Reference may be made to Sakamuri et al. "Detection of stealthy small amphiphilic biomarkers Journal of Microbiological Methods 103: 112-117, 2014. These authors have used a waveguide based biosensor measuring only surface attached fluorescence antibody signals to detect lipids and amphiphilic targets in biological samples. Electrochemical sensors use an electrode to directly detect a reaction, typically a current from electron transfer during binding of an analyte and a chemically functionalized surface. Potentiometric sensors usefully measure charge accumulation to detect lipid antigens such as amphiphilic cholesterol using lipid films. Mechanical sensors are ideal for clinical applications and include cantilever and quartz crystal microbalances (QCM). The later detects changes in resonance frequency on the sensor surface from increased mass due to analyte binding.

In one embodiment the method is an enzyme-linked immunosorbent (ELISA)-type, flow cytometry, bead array, lateral flow, cartridge, microfluidic or immunochromatographic based method or the like. Typically, such methods employ binding agents such as an antibody or an antigen-binding fragment thereof. Other suitable binding agents are known in the art and include antigen binding constructs such as affimers, aptamers, or suitable ligands or parts thereof.

Antibodies, such as monoclonal antibodies, or derivatives or analogs thereof, include without limitation: Fv fragments; single chain Fv (scFv) fragments; Fab' fragments; F(ab')2 fragments; humanized antibodies and antibody fragments; camelized antibodies and antibody fragments, and multivalent versions of the foregoing. Multivalent binding reagents also may be used, as appropriate, including without limitation: monospecific or bispecific antibodies; such as disulfide stabilized Fv fragments, scFv tandems (scFv) fragments, diabodies, tribodies or tetrabodies, which typically are covalently linked or otherwise stabilized (i.e. leucine zipper or helix stabilized) scFv fragments.

Methods of making antigen-specific binding agents, including antibodies and their derivatives and analogs and aptamers, are well-known in the art. Polyclonal antibodies can be generated by immunization of an animal. Monoclonal antibodies can be prepared according to standard (hybridoma) methodology. Antibody derivatives and analogs, including humanized antibodies can be prepared recombinantly by isolating a DNA fragment from DNA encoding a monoclonal antibody and subcloning the appropriate V regions into an appropriate expression vector according to standard methods. Phage display and aptamer technology is described in the literature and permit in vitro clonal amplification of antigen-specific binding reagents with very affinity low cross-reactivity. Phage display reagents and systems are available commercially, and include the Recombinant Phage Antibody System (RPAS), commercially available from Amersham Pharmacia Biotech, Inc. of Piscataway, New Jersey and the pSKAN Phagemid Display System, commercially available from MoBiTec, LLC of Marco Island, Florida. Aptamer technology is described for example and without limitation in US Patent Nos. 5,270,163; 5,475,096; 5,840,867 and 6,544,776.

Determining the therapy responder status of an individual before treatment

In one embodiment, the method comprises determining lipid species levels in a plasma sample taken from an individual before treatment. In one embodiment, the individual is HMG- CoA reductase inhibitor treatment naive. Lipid level data may be subjected to an algorithm to integrate the new data with reference levels of corresponding lipid species from reference individuals or the individual at an earlier time point, and with any relative risk reduction for future a cardiovascular event.

In one particularly useful embodiment of the method, baseline lipid species are used to predict the response of an individual to statin treatment before treatment, that is, in the absence of specific HMG-CoA reductase inhibitor administration. Accordingly, in one embodiment, there is provided a method for determining the likelihood that a HMG-CoA reductase inhibitor treatment will reduce the risk of a cardiovascular event in an individual, the method comprising: (i) detecting a level of one or two or more non steroidal lipid species in a biological sample obtained from the individual at a time point before treatment; (ii) comparing the level one or two or more lipid species, or a ratio of the level of two lipid species, from step (i) in the sample to a reference level or ratio for the lipid species; and (iii) determining whether the treatment is likely or not to reduce the risk of the cardiovascular event in the individual on the basis of the comparison.

As described in Example 4, within a LIPID cohort four sub groups were examined to assess relative risk reduction (RRR): all individuals (those receiving treatment and untreated individuals), females, non-type 2 diabetes individuals, and type 2 diabetes individuals. Each group was stratified into quartiles based on the lipid ratios and the difference in the relative risk reduction (RRR) allocated between Ql and Q4. This allows determination where a change in the level of a single lipid species is associated with a reduced risk of a cardiovascular event. Lipid ratios are selected if the difference in RRR between Ql and Q4 is greater than about 50% in all groups (for CVD death), greater than 35% in all groups (for CVE), greater than 40% in all groups (for MI), greater than 60% in all groups (for stroke). Lipid species within all selected lipid ratios are examined and those lipid species that appear more than once are considered treatment biomarkers and included in the final lipid list.

In one embodiment, lipid biomarkers used to predict the RRR response to statin treatment include predominantly species of phosphatidylcholine (including lyso and ether species), phosphatidylinositol, and sphingomyelin. Lipid species of dihexosylceramide and triacylglycerol were also identified as useful.

In one embodiment, biomarker lipid species are selected from or include one or more of namely Hex2Cer(l6:0), LPC(l6: l), LPC(20:l), PC(3 l : l), PC(32:2), PC(32:3), PC(33:2), PC(33:3), PC(34:0), PC(34: l), PC(35:3), PC(36:3), LPC(0-22: l), LPC(0-24: l), PC(0-34:3), PI(34:0), PI(36:4), PI(38:2), PI(38:6), SM(34: l), SM(35:2), SM(36: l), SM(36:2), SM(36:3), SM(37:2), and TG(l8:0/l8:0/l8:0).

In one embodiment, reference to CVE includes an associated event such as one or more forms of stroke. Lipid species can be categorised as those that are elevated or those that are reduced in statin therapy responders or statin non-responders. In one embodiment, these lipid biomarkers are selected when considering statin response and risk reduction in terms of mycardial infarction (MI), cardiovascular disease (CVD) death, stoke and all cardiovascular events (CVE).

In one embodiment, lipid species are selected from or include one or more of namely LPC(l6: l), LPC(20: l), PC(32:2), LPC(0-22: l), PI(36:4), PI(38:2), PI(38:6), SM(35:2), SM(36: l), SM(36:2), SM(37:2), and TG(l8:0/l8:0/l8:0). Lipid species can be categorised as those that are elevated or those that are reduced in statin therapy responders or statin non- responders. In one embodiment, these lipid biomarkers are selected when considering statin response and risk reduction in terms of mycardial infarction (MI), cardiovascular disease (CVD) death, and all cardiovascular events (CVE).

In one embodiment, lipid species for ratio calculations are selected one from each category (elevated or reduced) to maximise the diagnostic potential of the assessment.

In one embodiment, one or two or more different classes or sub classes are selected in step (ii).

In one embodiment, the class or subclass is selected in step (ii) from a phosphatidylcholine (including lyso and ether species), a phosphatidylinositol, a sphingomyelin, a dihexosylceramide and a triacylglycerol.

In one embodiment, the lipid species is a phospholipid selected from the group consisting of lysophosphatidylcholine, sphingomyelin, phosphatidylinositol, phosphatidylglycerol, phosphatidylcholine, phosphatidylserine, phosphatidylethanolamine, ly sophosphati dy 1 ethal ol amine .

As described herein, in one version the method step (i) is conducted before a HMG-CoA reductase inhibitor treatment. Accordingly, the lipidomic profile of one, two or more lipid species or at least one ratio of the level of two lipid species is assessed in the absence of a HMG- CoA reductase inhibitor treatment to determine whether statin treatment is likely or not to reduce the risk of the cardiovascular event in the individual on the basis of the comparison between the baseline level or ratio and a reference level or profile such as a predetermined threshold, ratio, level or cut off.

In accordance with one embodiment, step (i) is conducted before a HMG-CoA reductase inhibitor treatment and wherein the lipid species in at least one ratio of the level of two lipid species in step (ii) are selected from Hex2Cer(l6:0), LPC(l6: l), LPC(20: l), PC(3 l :l), PC(32:2), PC(32:3), PC(33:2), PC(33:3), PC(34:0), PC(34: l), PC(35:3), PC(36:3), LPC(0-22: l), LPC(0- 24: 1), PC(0-34:3), PI(34:0), PI(36:4), PI(38:2), PI(38:6), SM(34: l), SM(35:2), SM(36: l), SM(36:2), SM(36:3), SM(37:2), and TG(l8:0/l8:0/l8:0).

In accordance with one embodiment, step (i) is conducted before a HMG-CoA reductase inhibitor treatment and wherein the one, two or more lipid species in step (ii) are selected from Hex2Cer(l6:0), LPC(l6: l), LPC(20: l), PC(3l : l), PC(32:2), PC(32:3), PC(33:2), PC(33:3), PC(34:0), PC(34: l), PC(35:3), PC(36:3), LPC(0-22: l), LPC(0-24: l), PC(0-34:3), PI(34:0), PI(36:4), PI(38:2), PI(38:6), SM(34: l), SM(35:2), SM(36: l), SM(36:2), SM(36:3), SM(37:2), and TG(l8:0/l8:0/l8:0).

In accordance with one embodiment, step (i) is conducted before a HMG-CoA reductase inhibitor treatment and wherein the one, two or more lipid species in step (ii) are selected from LPC(l6: l), LPC(20: l), PC(32:2), LPC(0-22: l), PI(36:4), PI(38:2), PI(38:6), SM(35:2), SM(36: l), SM(36:2), SM(37:2), and TG(l8:0/l8:0/l8:0). In accordance with one embodiment, step (i) is conducted before a HMG-CoA reductase inhibitor treatment and wherein the lipid species in at least one ratio of the level of two lipid species in step (ii) are selected from LPC(l6: l), LPC(20: l), PC(32:2), LPC(0-22: l), PI(36:4), PI(38:2), PI(38:6), SM(35:2), SM(36: l), SM(36:2), SM(37:2), and TG(l8:0/l8:0/l8:0).

In one embodiment of the method steps (i) to (iii) are repeated after a treatment.

Determining therapy responder status of an individual after HMG-CoA reductase inhibitor administration/treatment

In another embodiment of the method disclosed herein, step (i) is conducted after a HMG- CoA reductase inhibitor treatment. Such a treatment may be a "test" treatment conducted for example prior to deciding whether or not to proceed with a HMG-CoA reductase inhibitor treatment of an individual. Alternatively, the administered treatment may be part of a current or recent treatment regimen, the effects of which are sought to be determined. Accordingly, the individual may be naive to HMG-CoA reductase inhibitor treatment or the individual may have a history of treatment or a current or on-going experience of HMG-CoA reductase inhibitor treatment.

Accordingly, in one embodiment, there is provided a method for determining the likelihood that a HMG-CoA reductase inhibitor treatment will reduce the risk of a cardiovascular event in an individual, the method comprising: (i) detecting a level of one or two or more non steroidal lipid species in a biological sample obtained from the individual after the treatment; (ii) comparing the level one or two or more lipid species, or a ratio of the level of two lipid species, from step (i) in the sample to a reference level or ratio for the lipid species; and (iii) determining whether the treatment is likely or not to reduce the risk of the cardiovascular event in the individual on the basis of the comparison.

As described in the example, HMG-CoA reductase inhibitor treatment was associated with changes in 191 lipid species independent of changes in clinical lipids (total cholesterol, HDL-C and triglycerides). Species containing arachidonic acid showed a positive association while species of phosphatidylinositol were negatively associated. A larger decrease in the phosphatidylinositol species (PI(36:2)) and a larger increase in the phosphatidylcholine species (PC(38:4)) were associated with fewer future cardiovascular events after adjustment for several risk factors including change in LDL-C. The relative risk reduction from pravastatin treatment for cardiovascular events was reduced from 22.6% to 12.7% after adjustment for change in LDL- C, this was further reduced to 3.9% and 6.1% after further adjustment for the change in PI(36:2) and PC(38:4) respectively. Stratification of the treatment group into quartiles based on the change in the lipid ratio PI(36:2)/PC(38:4) resulted in a 33.6% RRR in Ql and a -5.6% RRR in Q4. Similar results are observed when the models were also adjusted for change in LDL-C. Similar effects are observed when the cardiovascular event is cardiovascular death. Reference to an HMG-CoA reductase inhibitor treatment regimen includes administration of compositions that may vary for example in terms of a particular formulation or species of HMG-CoA reductase inhibitor, or administration of a composition at a particular dose or frequency. Once the results of a particular treatment are assessed using the instant method, different compositions and treatments regimen such as using an increased or a decreased dose may be indicated. A composition may be administered alone or in combination with other treatments, either simultaneously or sequentially dependent upon the condition to be treated.

Specific treatments include 40mg pravastatin daily but multiple dosing regimens are known in the art for statins.

In one embodiment, the HMG-CoA reductase inhibitor is a statin selected from synthetic or naturally occurring inhibitors and may be selected from the group consisting of atorvastatin, fluvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin and simvastatin.

In an embodiment, a reference level is from a sample from the same individual taken at an earlier time point.

In one embodiment step (i) is conducted after a HMG-CoA reductase inhibitor treatment and wherein at least one lipid species is a lipid species such as a phospholipid species or a phosphatidylinositol lipid species that displays a reduced level as a result of the treatment in reference individuals who also show a reduced the risk of a cardiovascular event as a result of the treatment.

In one embodiment, exemplary species are set out in Figure 8 or 9.

In one embodiment step (i) is conducted after a HMG-CoA reductase inhibitor treatment and wherein at least one lipid species is a phosphatidylcholine species or a lipid species comprising arachidonic acid (20:4) or a precursor (20:3) that displays an elevated level as a result of the treatment in reference individuals who also show a reduced risk of a cardiovascular event as a result of the treatment.

In one embodiment, exemplary species are set out in Figure 8 or 9.

In accordance with one embodiment of the method wherein step (i) is conducted after a HMG-CoA reductase inhibitor treatment, the method comprises detecting the level of two or more lipid species in the sample.

In one embodiment, lipid species are selected from two or more different lipid classes or subclasses.

In one embodiment, a ratio in step (iii) is between the level of two lipid species selected from Figure 9.

In one embodiment of the method, a ratio in step (iii) is between the level of two lipid species selected from a lipid classes or sub classes selected from Figure 9.

In one embodiment of the method wherein step (i) is conducted after a HMG-CoA reductase inhibitor treatment, a ratio in step (iii) is between (a) the level of a phosphatidylinositol species that displays reduced levels as a result of the treatment in reference individuals who also show a reduced the risk of a cardiovascular event as a result of the treatment and (b) a phosphatidylcholine species that displays an elevated level as a result of the treatment in reference individuals who also show a reduced risk of a cardiovascular event as a result of the treatment.

In one embodiment, the change in the PLPC lipid ratio of selected lipid species after test treatment is used to predict the relative risk reduction an individual is likely to receive from the treatment.

In one embodiment of the method wherein step (i) is conducted after a HMG-CoA reductase inhibitor treatment wherein a ratio in step (iii) is between the level of PI(36:3) and PC(38:4) and/or between the level of PI(36:2) and PC(38:4).

In one embodiment, the present description provides a method for stratifying an individual as a likely therapy responder or likely non-responder with respect to a HMG-CoA reductase inhibitor treatment regimen, the method comprising ascertaining the likelihood that the HMG-CoA reductase inhibitor treatment regimen will reduce the risk of a cardiovascular event in the individual according to the herein described method comprising: (i) detecting a level of one or two or more non-steroidal lipid species in a biological sample obtained from the individual at a time point before or after treatment; (ii) comparing the level one or two or more lipid species, or a ratio of the level of two lipid species, from step (i) in the sample to a reference level or ratio for the lipid species; and (iii) determining whether the treatment is likely or not to reduce the risk of the cardiovascular event in the individual on the basis of the comparison.

The present methods will find broad application in clinical studies for subject stratification and surrogate endpoint determinations for clinical trials and drug screening protocols.

In another embodiment, the present description provides method of screening for an agent that reduces the risk of a cardiovascular event, the method comprising screening agent/s for an ability to: (i) reduce the level of a phophosphatidylinositol lipid species in a test subject or (ii) establish a statin therapy responder or non-responder lipid profile as identified herein.

In one embodiment, an agent is pharmaceutical agents include small organic molecules such as drug compounds (e.g., compounds approved for human or veterinary use), peptides, proteins, carbohydrates, monosaccharides, oligosaccharides, polysaccharides, nucleoproteins, mucoproteins, lipoproteins, synthetic polypeptides or proteins, small molecules linked to proteins, glycoproteins, steroids, nucleic acids, DNAs, RNAs, nucleotides, nucleosides, oligonucleotides, antisense oligonucleotides, lipids, hormones, vitamins, and cells.

Methods of treatment

In another embodiment, the present description enables a method of treatment or prophylaxis comprising administering an HMG-CoA reductase inhibitor treatment regimen to reduce the risk of a cardiovascular event, the method comprising administering an HMG-CoA reductase inhibitor treatment regimen after ascertaining the likelihood that the HMG-CoA reductase inhibitor treatment regimen will reduce the risk of a cardiovascular event in the individual according to the herein described method.

Reference to an HMG-CoA reductase inhibitor treatment regimen includes administration of compositions that may vary for example in terms of a particular formulation or species of HMG-CoA reductase inhibitor, or administration of a composition at a particular dose or frequency. Once the results of a particular treatment are assessed using the instant method, different compositions and treatments regimen such as using an increased or a decreased dose may be indicated. A composition may be administered alone or in combination with other treatments, either simultaneously or sequentially dependent upon the condition to be treated.

In one embodiment, the HMG-CoA reductase inhibitor is a statin selected from synthetic or naturally occurring inhibitors and may be selected from the group consisting of atorvastatin, fluvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin and simvastatin.

Accordingly, there is provided a method of treatment or prophylaxis comprising administering an HMG-CoA reductase inhibitor to reduce the risk of a cardiovascular event, the method comprising administering an HMG-CoA reductase inhibitor after ascertaining the likelihood that the HMG-CoA reductase inhibitor treatment will reduce the risk of a cardiovascular event in the individual, the method of ascertaining comprising: (i) detecting a level of one or two or more non-steroidal lipid species in a biological sample obtained from the individual at a time point before or after treatment; (ii) comparing the level one or two or more lipid species, or a ratio of the level of two lipid species, from step (i) in the sample to a reference level or ratio for the lipid species; and (iii) determining whether the treatment is likely or not to reduce the risk of the cardiovascular event in the individual on the basis of the comparison.

Once an individual is stratified as a therapy responder or a non-responder at any given time point, a decision can be made whether to increase or reduce the dose and re-test, to treat with a statin or to treat with an alternative agent.

In one non-limiting embodiment, an alternative agent may be selected from a PCSK9 inhibitor, - Bile acid sequestrants, Ezetimibe (or NPC1L1 blockers), niacin, phytosterol, a behavioural or dietary modification (including physical activity) and the like.

In one embodiment of the method steps (i) to (iii) are repeated after a treatment.

Co-administration of PC and/or arachidonic acid

In another embodiment, the disclosure enables a method of treatment or prophylaxis including co-administration of a HMG-CoA reductase inhibitor treatment and a fatty acid selected from an omega-6 fatty acid or arachidonic acid. As described herein, in one embodiment HMG-CoA reductase inhibitor treatment in combination with fatty acid supplementation enhances responsiveness to statins and/or provides treatment or prophylaxis of CVE and/or stoke.

In one embodiment, the description enables a method of treatment or prophylaxis to reduce the risk of a cardiovascular event comprising administering an HMG-CoA reductase inhibitor agent in combination with a fatty acid agent such as omega-6 fatty acid or arachidonic acid.

In one embodiment the combination treatment is advanced based on the results of testing according to the methods described herein. Thus in one embodiment, the description enables a method of treatment or prophylaxis to reduce the risk of a cardiovascular event comprising administering an HMG-CoA reductase inhibitor agent in combination with a fatty acid agent such as omega-6 fatty acid or arachidonic acid after ascertaining the likelihood that the HMG- CoA reductase inhibitor treatment regimen will reduce the risk of a cardiovascular event in the individual according to the herein described method.

Reference to "in combination" and "co-administration" includes simultaneous or sequential administration of an HMG-CoA reductase inhibitor and fatty acid supplementation such as omega-6 fatty acid or arachidonic acid, and administration of a formulation comprising one or more of both agents. Regimes may be selected based upon the individual and/or the desired therapeutic and/or prophylactic effect to be achieved, and the exigencies of the situation.

In one embodiment the omega 6 fatty acid is selected from species within the metabolicpathway 08:2, 18:3, 20:3, 20:4, 22:4, 24:4, 24:5 and 22:5.

The additional agents may also be administered together with each other and/or with the HMG-CoA reductase inhibitor in a single dose or administered separately in different doses. The particular combination to employ in a regimen will take into account compatibility of the HMG- CoA reductase inhibitor and fatty acid. In general, it is expected that the agent(s) utilized in combination are utilized at levels that do not exceed the levels at which they are utilized individually. In some embodiments, the levels utilized in combination will be lower than those utilized individually.

Also encompassed by the disclosure are kits (e.g., pharmaceutical packs). The kits provided may comprise of the HMG-CoA reductase inhibitor and fatty acid supplement and a container (e.g., a vial and/or dispenser package, or other suitable container). In some embodiments, provided kits may optionally further include a second container comprising a pharmaceutical excipient for dilution or suspension of an active agent. In some embodiments, agents provided in a first container and a second container is combined to form one unit dosage form. The kits may contain separate agents in separate containers together with instructions for use in combination such as simultaneously or sequentially. Phosphatidylinositol inhibitors

In one embodiment, the present description provides method of treating a subject at risk of a cardiovascular event, the method comprising administering an agent that lowers the plasma levels of phosphatidylinositol or phosphatidylinositol synthase in the subject.

Down regulation of phophosphatidylinositol may be achieved in a subject by administration of phosphatidylinositol synthase (CIDPT/PISI) inhibitor or of another enzyme in the biosynthetic pathway.

In one embodiment, CIDPT inhibitors are selected from inositol analogues (such as chlorinated analogues of inositol, 3-substituted lD-myo inositol), an anti-CDIPT antibody or antigen binding part thereof that is a phosphatidylinositol synthase inhibitor, an aptamer, small molecule, an RNA such as an editing, ribozyme, antisense or inhibitory RNA, zinc such as zinc oxide in the case of zinc deficiency.

Antibody or natural product inhibitory agents are known in the art and are clearly contemplated herein.

Other agents are based on nucleic acids and include for example antisense oligonucleotides, ribozymes and small inhibitory RNAs. There molecules act to inhibit expression of a gene encoding a target enzyme whose activity facilitates phophosphatidylinositol production.

Nucleic acids may be single stranded or double stranded, or may contain portions of both double stranded and single stranded sequences. The nucleic acid may be DNA, both genomic and cDNA, RNA, or a hybrid, where the nucleic acid may contain combinations of deoxyribo- and ribo-nucleotides, and combinations of bases including uracil, adenine, thymine, cytosine, guanine, inosine, xanthine hypoxanthine, isocytosine and isoguanine. Nucleic acids may be synthesized as a single stranded molecule or expressed in a cell using a synthetic gene. Nucleic acids may be obtained by chemical synthesis methods or by recombinant methods.

The nucleic acid may also be a RNA such as a mRNA, tRNA, short hairpin RNA (shRNA), short interfering RNA (siRNA), double-stranded RNA (dsRNA), transcriptional gene silencing RNA (ptgsRNA), Piwi-interacting RNA, pri-miRNA, pre-miRNA, micro-RNA (miRNA), or anti-miRNA, as known in the art.

The nucleic acid may also be an aptamer which refers to a nucleic acid or oligonucleotide molecule that binds to a specific molecular target. Aptamers are derived from an in vitro evolutionary process (e.g., SELEX (Systematic Evolution of Ligands by Exponential Enrichment), disclosed in ET.S. Pat. No. 5,270,163), which selects for target-specific aptamer sequences from large combinatorial libraries. Aptamer compositions may be double-stranded or single-stranded, and may include deoxyribonucleotides, ribonucleotides, nucleotide derivatives, or other nucleotide-like molecules. The nucleotide components of an aptamer may have modified sugar groups (e.g., the 2'— OH group of a ribonucleotide may be replaced by 2'-F or 2'-NH.sub.2), which may improve a desired property, e.g., resistance to nucleases or longer lifetime in blood. Aptamers may be conjugated to other molecules, e.g., a high molecular weight carrier to slow clearance of the aptamer from the circulatory system. Intramers and spegelmers are also contemplated. There refer to an aptamer which is expressed in vivo. For example, a vaccinia virus-based RNA expression system has been used to express specific RNA aptamers at high levels in the cytoplasm of leukocytes.

Spiegelmer are also aptamer which includes L-DNA, L-RNA, or other left-handed nucleotide derivatives or nucleotide-like molecules. Aptamers containing left-handed nucleotides are resistant to degradation by naturally occurring enzymes, which normally act on substrates containing right-handed nucleotides.

A nucleic acid will generally contain phosphodiester bonds, although nucleic acid analogs may be included that may have at least one different linkage, e.g., phosphoramidate, phosphorothioate, phosphorodithioate, or O-methylphosphoroamidite linkages and peptide nucleic acid backbones and linkages. Other analog nucleic acids include those with positive backbones; non-ionic backbones, and non-ribose backbones, including those disclosed in U.S. Pat. Nos. 5,235,033 and 5,034,506. Nucleic acids containing one or more non-naturally occurring or modified nucleotides are also included within the definition of nucleic acid. The modified nucleotide analog may be located for example at the 5'-end and/or the 3'-end of the nucleic acid molecule.

Antisense oligonucleotides, which may be RNA or DNA, would act to directly block the translation of CIDPT RNA by binding thereto and thereby preventing protein translation or increasing mRNA degradation, thus decreasing the level of CIDPT. For example, antisense oligonucleotides of at least about 15 bases and complementary to unique regions of the mRNA transcript sequence encoding CIDPT can be synthesized, e.g., by conventional phosphodiester techniques and administered by e.g., intravenous injection or infusion. Methods for using antisense techniques for specifically inhibiting gene expression of genes whose sequence is known are well known in the art. It should be further recalled that antisense oligonucleotides may be modified with phosphorothioate to prevent their in vivo hydrolysis by nucleases. Such modifications are well known in the art.

Small inhibitory RNAs (siRNAs) can also function as inhibitors of CIDPT expression for use in the instant methods. CIDPT gene expression can be reduced by contacting the individual with a small double stranded RNA (dsRNA), or a vector or construct causing the production of a small double stranded RNA, such that CIDPT gene expression is specifically inhibited (i.e. RNA interference or RNAi). Methods for selecting an appropriate dsRNA or dsRNA-encoding vector are well known in the art for genes whose sequence is known.

Ribozymes can also function as inhibitors of CIDPT gene expression for use in the present methods. Ribozymes are enzymatic RNA molecules capable of catalyzing the specific cleavage of RNA. The mechanism of ribozyme action involves sequence specific hybridization of the ribozyme molecule to complementary target RNA, followed by endonucleolytic cleavage. Engineered hairpin or hammerhead motif ribozyme molecules that specifically and efficiently catalyze endonucleolytic cleavage of CIDPT mRNA sequences are thereby useful within the scope of the present invention. Specific ribozyme cleavage sites within any potential RNA target are initially identified by scanning the target molecule for ribozyme cleavage sites, which typically include the following sequences, GUA, GUU, and GUC. Once identified, short RNA sequences of between about 15 and 20 ribonucleotides corresponding to the region of the target gene containing the cleavage site can be evaluated for predicted structural features, such as secondary structure, that can render the oligonucleotide sequence unsuitable. The suitability of candidate targets can also be evaluated by testing their accessibility to hybridization with complementary oligonucleotides, using, e.g., ribonuclease protection assays.

Pharmaceutical compositions are conveniently prepared according to conventional compounding techniques. See for example Remington's Pharmaceutical Sciences 18* Edition Mack Publishing Company Easton PA ETSA 1009. Compositions may comprise one or more active agents/sub stances, together with acceptable excipient, carrier, diluents, buffer, stabilizer, or other materials well known in the art. The carrier may take a wide variety of forms depending upon the form of preparation desired for administration, e.g., intravenous, oral or parenteral.

The terms "treatment" or "prophylaxis" are used in their broadest context and include any measurable or statistically significant risk reduction or change in a surrogate marker therefore such as development towards a therapy responder profile or a particular lipid level or ratio as described herein. In accordance with these embodiments, a composition or combination of compositions is generally administered for a time and under conditions sufficient to elicit a minimum beneficial response and then continued optionally with monitoring as described herein. Accordingly, compositions may be administered once, or twice, or 5X, or 10X or more times and, similarly administration may be daily, weekly, monthly or less frequently. A broad range of doses may be applicable and the instant methods may be employed to determine the most effective dose/s.

In one embodiment of the determining method, the level of non-steroidal lipid species in the individual further characterises the individual’s risk of a comorbid event. A comorbid event may be diabetes, for example.

Accordingly, the description further enables a method of treatment or prophylaxis comprising administering an HMG-CoA reductase inhibitor treatment regimen to reduce the risk of a cardiovascular event, the method comprising administering an HMG-CoA reductase inhibitor treatment regimen after ascertaining the likelihood that the HMG-CoA reductase inhibitor treatment regimen will reduce the risk of a cardiovascular event in the individual according to the methods described herein, and administering a second agent to treat or prevent a comorbid event. In one embodiment, the second agent is an anti-inflammatory agent, an antithrombotic agent, an anti-platelet agent, a fibrinolytic agent, a thrombin inhibitor, a calcium channel blocker, a beta-adrenergic receptor blocker, a cyclooxygenase-2 inhibitor, an angiotensin system inhibitor, or a peptide hormone.

In one example the comorbid event is diabetes and the second agent is insulin.

The following methods and procedures are illustrative of the methods described herein.

Lipid analysis methodology

The lipidomic methodology used may be an advance upon earlier targeted methodology developed on an Agilent 1200 liquid chromatography system combined with an Applied Biosystems API 4000 Q/TRAP mass spectrometer (Alshehry el al., Metabolites. 20l5;5:389- 403) (Alshehry et al., Circulation. 2016;134: 1637-1650) (Weir et al. J. Lipid Res. 20l3;54:2898- 2908). Lipidomic analysis may be performed by liquid chromatography electrospray ionisation tandem mass spectrometry on an Agilent 1290 liquid chromatography system combined with an Agilent 6490 triple quadrupole mass spectrometer, utilizing Mass Hunter software. Liquid chromatography may be performed on a Zorbax Eclipse Plus 1.8 pm Cl 8, 50 x 2.1 mm column (Agilent Technologies). Solvents A and B consist of tetrahydrofuran: methanol: water in the ratio (20:20:60) and (75:20:5) respectively, both containing 10 mM ammonium formate. Columns are heated to 50°C and the auto-sampler regulated to 25°C. Lipid species (1 pL injection) are separated under gradient conditions at a flow rate of 400 pL/min. The gradient is as follows; 0% solvent B to 40% solvent B over 2.0 min, 40% solvent B to 100% solvent B over 6.5 min, 0.5 min at 100% solvent B, a return to 0% solvent B over 0.5 min then 0.5 min at 0% solvent B prior to the next injection (total run time of 10 min).

The mass spectrometer is operated in dynamic/scheduled multiple reaction monitoring (dMRM) mode. 345 unique lipid species were measured together with 16 stable isotope or non- physiological lipid standards (test lipid species are listed in Figure 8). Mass spectrometer voltages used for the acquisition of data were; fragmentor voltage, 380 V and cell accelerator voltage, 5 V. The collision energy voltage was set individually for each lipid class and subclass and is listed in Table 1. Acquisition windows were set to between 0.7 and 1.76 min depending on the chromatographic properties of the lipid. Further, there were several sets of isobaric lipids which shared the same nominal parent ion mass and also give rise to the same product ions. Specifically, for isobaric species of phosphatidylcholine, alkylphosphatidylcholine and alkenylphosphatidylcholine the parent and product ions (m/z 184) were the same. As a result a single MRM transition was used to measure the corresponding species within each subclass, using an increased MRM window time (21 combinations). Additionally there was one further occurrence of isobaric phosphatidylethanolamine and alkylphosphatidylethanolamine lipid species, representing the neutral loss of 141 Da, which were similarly combined into a single dMRM transition. While most lipid classes and subclasses have similar response factors for lipid species within the class, some classes show greater variation in response factors between species. Consequently, correction factors were applied for some lipid classes as we have described earlier (Weir et al. J. Lipid Res. 2013;54:2898-2908) but now adjusted for the Agilent mass spectrometer.

Diacyl- and triacylglycerol: Fragmentation of the ammoniated adducts of diacyl- and triacylglycerol leads to the loss of ammonia and a fatty acid. In this context it is important to recognize that for species which contain more than one of the same fatty acid, the loss of that fatty acid will result in an enhanced signal, as it is the end product from two competing pathways. Consequently, for an MRM transition that corresponded to the loss of a fatty acid that was present more than once, this was divided by the number of times that fatty acid was present. While the response factor for different species of triacylglycerol varied substantially, the lack of suitable standards precluded the determination of suitable response factors for each triacylglycerol species.

Cholesteryl ester: Response factors were determined with seven commercially available species and used to create a formula to extrapolate for all cholesteryl ester chain lengths and double bonds. Saturated species were characterized by the following relationship: y = 0.1486c - 1.5917, where y is the response factor relative to the CE 18:0 d 6 internal standard and x is the carbon chain length. For monounsaturated species, the response factor was multiplied by 1.84 and for polyunsaturated species by 6.0.

Phosphatidylinositol: A single response factor was calculated for all phosphatidylinositol species to account for the use of the phosphatidylethanolamine (PE(l 7:0/17:0)) as the internal standard for this lipid class. A nine point standard curve was created using commercially available phosphatidylinositol (PI(32:0)) and subsequently spiked into solvent containing a fixed concentration of RE(17:0/17:0). The standard curve resulted in a linear response and indicated a response factor of 1.44 for phosphatidylinositol species relative to phosphatidylethanolamine standard. Other lipid species were not corrected.

Quality control samples

Two types of quality control samples were utilized in this study. Plasma from six healthy volunteers was pooled and split into multiple aliquots. We refer to these samples as plasma quality control (PQC) samples. These samples are then subjected to extraction and LC-MS analysis alongside samples from the study to provide a measure of analytical variability across the study as a whole.

Additionally identical lipid extracts were utilized, which were prepared by pooling the lipid extracts from multiple PQC samples using this mixture to prepare multiple aliquots which were referred to as technical quality control (TQC) samples. Analysis of these samples captures only the variation associated with the LC-MS performance. Within the analytical process every twenty plasma samples a PQC and TQC were included.

Pre-processing of data

In one study, samples were run in multiple batches. An extraction batch consisted 448 plasma samples, 24 PQC, 24 TQC and 12 blank samples (resulting in 27 batches). Two batches were run consecutively between cleaning of the mass spectrometer. A median centering approach was used for correction of the batch effect. The median PQC concentration of each lipid for each batch was used as a reference point to align the samples with the entire cohort. The alignment was performed by calculating a correction factor to adjust the concentration of each PQC lipid in each batch to the median value for all batches.

The following non-limiting examples are provided:

EXAMPLES

Example 1: Lipid analysis, statistical analysis and Study Population

The LIPID Study design has been published in detail elsewhere (The Long-Term Intervention with Pravastatin in Ischaemic Disease (LIPID) Study Group. Prevention of cardiovascular events and death with pravastatin in patients with coronary heart disease and a broad range of initial cholesterol levels. . N. Engl. J. Med. 1998;339: 1349-1357.). Study subjects with a previous history of cardiovascular disease (myocardial infarction (MI) or hospital admission for unstable angina) were between 31 to 75 years of age and had plasma total cholesterol level between 4.0 to 7.0 mmol/L and fasting triglycerides <5.0 mmol/L. Patients who experienced heart failure were excluded. Before the actual trial, a single-blind placebo run-in phase was conducted for 8 weeks. After this, 9014 patients (7498 men and 1516 women) were randomly allocated to receive pravastatin (40 mg daily) or matching placebo. The median follow-up period was 6 years. In the surviving patients, plasma samples and clinical lipid measurements were also collected at the first year follow up.

Detailed lipidomic profiling was conducted on 4,991 participants (49.7% on pravastatin treatment) using both baseline and one-year follow up plasma samples. Within this cohort, 944 patients (out of 4900 who did not experience events within the first year) experienced cardiovascular events (non-fatal MI, non-fatal stroke and cardiovascular death) in the follow up period, with 498 cardiovascular deaths. Major CVD events and deaths were adjudicated by Expert Committees blinded to treatment allocation.

Lipid Extraction and Profiling

The extraction and lipidomic analysis method used in this study has been previously described (Alshehry et al., 2016, 2015 and Weir et al, 2013). Briefly, lipids were extracted from lOpL of human plasma using a single phase butanol/methanol extraction. Extractions were performed in batches of 486 which consisted of plasma samples, pooled plasma controls (every 20 samples) and water blanks (every 40 samples). To each sample, lOOpL of the butanol/methanol mix containing internal standards was added and the samples were vortexed and sonicated on a water bath for 1 hour at l8-22°C. Extracts were centrifuged (l6,000xg, 10 min) and the supernatant transferred to 0.2mL micro-inserts in sample vials for analysis.

Lipidomic analysis was performed by liquid chromatography, electrospray ionisation- tandem mass spectrometry using a Agilent 1290 liquid chromatography system with a 50mm Zorbax Eclipse Plus 1.8 pm Cl 8 column, combined with an Agilent 6490 triple quadrupole mass spectrometer. The relative concentration of each lipid species was calculated from the area of the resultant chromatograms for the lipid species and the corresponding internal standards.

Statistical Analysis

To facilitate the interpretation of the results from Cox regression models (hazard ratios), the concentration values for lipid species were log transformed and normalised to the interquartile range (IQR), prior to statistical analysis. The p-values were corrected for multiple comparisons using Benjamini-Hochberg approach (Benjamini el ah, Journal of Royal Statistical Society. Series B. 1995;57:289-300). Analyses were performed using MATLAB software (R20l3a), Stata version 13.1 ( Stata Corp. Stata statistical software: Release 13. 2013 and R version 3.3.2 (R Core Team. R: A language and environment for statistical computing. 2016).

To assess the effect of pravastatin on individual lipid classes, subclasses and species, the mean percentage change of each lipid species from baseline to one-year follow-up, was compared between placebo and pravastatin treatment groups using the Student’s t-test. To further assess the relationship between treatment allocation and the percentage change in lipid concentration from baseline to one-year follow-up independent of the changes in clinical lipid measures, linear regression models were used. These models were adjusted for either baseline age, gender and body mass index (BMI) or for the same covariates and the percentage changes in total cholesterol, high density lipoprotein cholesterol (HDL-C) and triglycerides.

To assess the association between change in each lipid species from baseline to the one- year follow-up and future cardiovascular events and cardiovascular death, Cox regression models were used in a landmark analysis of either the treatment group only or the whole cohort. The models were adjusted for covariates previously identified as associated with cardiovascular outcomes (Marschner et al. , J. Am. Coll. Cardiol. 2001;38:56-63). (age, gender, systolic blood pressure, BMI, total cholesterol, HDL-C, triglycerides, atrial fibrillation, current smoking, fasting glucose, prior history of stroke, diabetes mellitus, hypertension, nature of prior acute coronary syndromes (MI, yes or no), estimated glomerular filtration rate, dyspnea class, angina grade, white blood cell count, peripheral vascular disease, use of aspirin at baseline and randomized treatment allocation (for the whole cohort analysis). The models were also adjusted for baseline levels of each individual lipid species and the change in LDL-C. The relative risk reduction resulting from pravastatin treatment was determined by a Cox regression model using treatment as a predictor, adjusted for the baseline risk factors indicated above. The analysis was then repeated adjusting for changes in each lipid species, changes in LDL-C or both in addition to the baseline lipid levels and the interaction between change in lipid species and treatment. The relative risk reduction from pravastatin in these models was used to determine the extent to which the pravastatin treatment effect on subsequent cardiovascular events/deaths was accounted for by changes in each lipid species. To assess the validity of the treatment effect obtained from these models, the above analysis was repeated for 1000 randomizations of the treatment allocation within each group (pravastatin treatment and placebo) and the percentiles were determined for the treatment effect.

Based on the analyses described above the lipid ratio PI(36:2)/PC(38:4) was identified as a sensitive marker of the relative risk reduction afforded by statin treatment. To assess this, the above analyses were repeated on the lipid ratio PI(36:2)/PC(38:4). The LDL-C lowering and RRR by pravastatin treatment were also explored in the quartiles of the lipid ratio in the statin treatment group compared to placebo group. In this analysis, the samples in each quartile of the lipid ratio within the treatment group were pooled together with the entire placebo group and a Cox regression model was fitted to find the relative risk reduction by pravastatin treatment. RRR was also calculated based on the relative risk in the pravastatin group and the placebo group, without using a modelling approach (no adjustments for covariates).

Example 2: Results -

Baseline Characteristics

The LIPID cohort analysed in this study consisted of 4991 participants with a median (Q1-Q3) age of 64 (57-69) years (details of the study are described in "The Long-Term Intervention with Pravastatin in Ischaemic Disease (LIPID) Study Group. Prevention of cardiovascular events and death with pravastatin in patients with coronary heart disease and a broad range of initial cholesterol levels". . N. Engl. J. Med. 1998; 339: 1349-1357). Plasma samples were collected at both baseline and the one year follow-up. In this cohort, 944 patients experienced cardiovascular events after the one year follow-up and there were 498 cardiovascular deaths (Figure 1). Details of baseline characteristics stratified by outcome in the present sub-cohort are shown in Table 2. Individuals who experienced cardiovascular events in the follow up period were older, had higher blood pressure and were more likely to have a history of diabetes, stroke, atrial fibrillation compared to individuals who did not have events. Importantly, with the exception of HDL-C, the clinical lipid measures (LDL-C and triglycerides) were not significantly different between the groups that did or did not experience cardiovascular events or cardiovascular death. Change in Lipid Concentration upon Pravastatin Treatment

The statistical significance of the difference in mean percentage change in lipid classes/subclasses and species in the first year of pravastatin treatment between placebo and pravastatin treatment arms as determined by the Student’s t-test and results for species are shown in Figure 8. As expected, with the exception of phosphatidylserine, all lipid classes/subclasses, were significantly reduced by pravastatin treatment, and the majority of lipid species similarly, were significantly reduced in the pravastatin group relative to the placebo group. There were only 10 lipid species that showed a significant increase in the pravastatin group relative to the placebo group, these were predominately characterised by the presence of arachidonic acid (20:4) or its precursor dihomo-gamma-linolenic acid (20:3) in eight out of 10 of these lipid species (see Figure 8).

In assessing the relationship between pravastatin treatment and percentage change in lipid classes, subclasses and species independent of age, gender, BMI and the percentage changes in total cholesterol, HDL-C and triglycerides, seventeen of the twenty four lipid classes/subclasses were significantly associated with treatment after correction for multiple comparisons (Data not shown). Most of the significantly associated lipid classes/subclasses showed a negative association with treatment, indicating that these classes were decreased by pravastatin independently of the changes in clinical lipids (total cholesterol, HDL-C and triglycerides). Dihydroceramide and lysophosphatidylinositol showed a positive association with treatment after adjustment for changes in clinical lipids, but still showed a decrease in absolute levels (Data not shown). Of the 345 lipid species examined, 191 were significantly associated with treatment after correction for multiple comparisons (Figure 9). There were 129 lipid species that showed a negative association with statin treatment independent of changes in clinical lipids. Eleven of the 16 phosphatidylinositol species were negatively associated with pravastatin treatment, 2 species were positively associated and three species were not significantly associated. Of the 50 phosphatidylcholine species measured, 10 were negatively associated, while 18 were positively associated with treatment.

Association of Change in Lipid Concentration with Future Events within Treatment Group

Cox regression analyses, adjusted for baseline risk factors including change in LDL-C, and the baseline values of each lipid species, show that change in 34 and 29 lipid species were associated with future cardiovascular events and cardiovascular death respectively (p-value < 0.05, uncorrected). However, these were not significant after correction for multiple comparisons (summarised in Figures 2 and 3). The majority of the change in phosphatidylinositol species was positively associated with future events, indicating that a larger decrease in these lipids was associated with fewer cardiovascular events and deaths. In contrast, the change in phospholipid species containing arachidonic acid (20:4) were negatively associated indicating that a smaller decrease (or an increase) in these lipids was associated with fewer cardiovascular outcomes. The associations were weaker when the analyses were done in the whole cohort adjusting for treatment allocation (results not shown).

Treatment Effect Explained by Change in Lipid Species

The effect of pravastatin treatment on reduction in cardiovascular events and cardiovascular deaths before and after adjustment for changes in LDL-C and/or changes in concentration of lipid species are shown in Figure 4 and 5. After adjusting for 21 baseline risk factors (indicated above), pravastatin was associated with a 22.6% reduction in cardiovascular events, and 19.7% reduction in cardiovascular deaths. After adjustment for change in LDL-C, the estimated decrease in cardiovascular events and cardiovascular deaths by pravastatin (Relative Risk Reduction (RRR)) were reduced to 12.7% and 9.4%, indicating that change in LDL-C accounted for 43.7% and 52.1% of the treatment effect respectively.

When examining cardiovascular events as the outcome and further adjusting for the changes in individual lipid species, baseline lipid values and the interaction between changes in lipid species and treatment, RRR was reduced to 3.9-8.7%, thus accounting for a further 16-43% of the treatment effect (32-69% of the remaining treatment effect). Several phosphatidylinositol species accounted for a large proportion of this treatment effect, with PI(36:2) and PI(36:3) reporting the highest proportions (69% and 55% of the remaining treatment effect respectively). Without adjusting for change in LDL-C, the estimated decrease in cardiovascular events by pravastatin was reduced to 10.5% and 13.7% for phosphatidylinositol PI(36:2) and PI(36:3) species respectively (equating to 53.5% and 39.5% of the treatment effect accounted for by the change in these species). PC(38:4) also accounted for a large proportion of the treatment effect (52.1% of the remaining treatment effect after LDL-C was considered). The median treatment effect of the randomized data remained close to 0% for all lipid species, with the 95 ^th percentile typically falling below the treatment effect calculated for that lipid species (Figure 4).

Similar trends, although with stronger treatment effects, were observed with cardiovascular death (Figure 5). All of the remaining treatment effect after adjustment for change in LDL-C was accounted for by change in PI(36:2), with PC(38:4), SM(33: l), PI(36:3) and PI(38:6) also accounting for large proportions (90.8%, 83.3%, 69.9% and 69.9% respectively) of the remaining treatment effect. Results for all the lipid species are shown in (Data not shown).

Landmark analysis using the lipid ratio

The changes in the lipid species PI(36:2) and PC(38:4) showed significant associations with the treatment (negative and positive associations respectively), and in the opposite directions with future cardiovascular outcomes (positive and negative associations respectively). These species also explain large proportions of the treatment effect for both cardiovascular events and death. The effect of the ratio of PI(36:2)/PC(38:4) in the landmark analysis was also investigated. The percentage change in the ratio of PI(36:2)/PC(38:4) was significantly associated with pravastatin treatment independent of age, gender, BMI and the changes in total cholesterol, HDL-C and triglycerides (Figure 9). While the individual lipid species PI(36:2) and PC(38:4) showed negative and positive associations with pravastatin treatment respectively, the ratio of these lipid species showed a stronger negative association, indicating that larger decrease in the level of this lipid ratio was associated with pravastatin treatment.

The change in the lipid ratio was significantly associated with future cardiovascular events and cardiovascular death, independent of baseline risk factors including change in LDL-C and baseline values of the lipid ratio as indicated by cox regression analysis (Data not given). This association was positive indicating that larger decrease in this lipid ratio was associated with fewer cardiovascular events and deaths.

After adjustment for change in LDL-C and 21 baseline risk factors and the change in the lipid ratio and baseline ratio, there was no risk reduction from pravastatin treatment for cardiovascular events (RRR = -0.83%, Figure 4) indicating that all of the remaining treatment effect was accounted for by the lipid ratio. Without the adjustment for change in LDL-C, RRR was reduced to 7.56% (Base model RRR=22.59%) with 66.53% of the remaining treatment effect accounted for by the lipid ratio. For cardiovascular death (Figure 5), these proportions of treatment effect explained were higher, with lower RRR compared to the base models (RRR less than or equal to 0%), such that the change in the lipid ratio accounted for 100% of the treatment effect compared to only 52% accounted for by the change in LDL-C. The proportions of treatment effect explained by adjusting for the lipid ratio was also greater than that of the individual lipid species PI(36:2) and PC(38:4) for both cardiovascular events and death.

When the treatment group was stratified into quartiles of change in the lipid ratio PI(36:2)/PC(38:4) the median change of each quartile ranged from -1.57 to 0.01 with the median change in quartile 4 (0.01) being close to zero and similar to the median change of the placebo group (0.02). In contrast, all four quartiles of the treatment group showed a significant decrease in the median change of LDL-C (-1.29 to -0.95 mM) compared to the median change in the placebo group which was close to zero (-0.02mM) (Figure 6). The risk of cardiovascular events and cardiovascular death increased in higher quartiles of the lipid ratio compared to lower quartiles (Figure 7, full data not shown). Importantly, quartile 4 of the change in the lipid ratio showed an increased risk, relative to the placebo group, for both cardiovascular events and cardiovascular death, which was independent of the change in LDL-C (Figure 7).

Example 3: Discussion

Statins change levels of several plasma lipid classes and individual species and the inventors initially hypothesized that some of the benefits of pravastatin could be explained by this effect and that changes in these same lipid species will identify those who benefit from statin treatment (i.e. receive a relative risk reduction) from those who do not. In the work leading to the present description, in the largest single lipidomic study to date; 345 lipid species in 4,991 participants were analysed at two time points. Using the resulting dataset, the relationship between pravastatin treatment and the change in lipid species from baseline to l-year follow-up was determined. In a landmark analysis association of the change in lipid species with future cardiovascular events and cardiovascular deaths independent of changes in LDL-C levels was assessed. The change in relative risk reduction (RRR) from pravastatin treatment explained by changes in lipids over a period of one year was also assessed. These analyses identify a lipid ratio that, based on change over 12 month, stratifies the population into those that receive a RRR from pravastatin treatment from those that do not. In this cohort, at least 25% received no pravastatin treatment benefit in terms of reduced risk of cardiovascular events or death.

Sphingolipid metabolism is impacted by pravastatin treatment

The associations of lipid classes/subclasses and species with pravastatin treatment identified several classes of glycosphingolipid as negatively associated with statin treatment independent of the changes in clinical lipids. Ceramide itself was also negatively associated (with a smaller effect size), while the biosynthetic precursor of ceramide, dihydroceramide, was positively associated with pravastatin treatment. These observations suggest that pravastatin is modulating the glycosphingolipid biosynthetic pathway independently of the reduction in total cholesterol and LDL cholesterol.

Pravastatin has differential effects on arachidonic acid and phosphatidylinositol metabolism

Although the phospholipid classes/subclasses, for the most part, showed only weak (small effect size) associations with statin treatment, at a species level two major effects of pravastatin were observed. Firstly lipid species from multiple classes containing either arachidonic acid (20:4) or its biosynthetic precursor dihomo-gamma-linolenic acid (20:3) were positively associated with pravastatin. Coupled with this was the observation that the same species containing linoleic acid (18:2) were typically negatively associated with pravastatin treatment, suggesting that the effect was driven by an increase in the conversion of linoleic acid to arachidonic acid. This was most obvious when the adjusted beta-coefficients of the association of pravastatin with change in the diacylglycerol species DG(l8:0_l8:2) (-6.058, p=l.87E-06) and DG(l8:0_20:4 (7.046, p=6.04E-06, Figure 9) were observed which demonstrates the opposing effects on the change in 18:2 and 20:4 fatty acids. Since this effect is evident in multiple lipid classes including cholesteryl esters and several phospholipid species it is likely that this is controlled at the level of fatty acid metabolism and specifically the conversion of linoleic acid to arachidonic acid which appears to be upregulated in response to statin treatment.

In addition, 11 of 16 species of phosphatidylinositol were negatively associated with treatment, although these were offset by two abundant species (PI(38:3) and PI(38:4)) that were positively associated such that as a class there was a only a weak negative association. Phosphatidylinositol is synthesized by the enzyme CDP-diacylglycerol-inositol 3- phosphatidyltransferase which transfers the phosphodiacylglycerol from the CDP-diacylglycerol onto a myoinositol. This is in contrast to other phospholipids such as phosphatidylcholine which is synthesised by the action of diacylglycerol cholinephosphotransferase, which transfers the phosphocholine group from CDP-choline onto a diacylglycerol. Thus it appears that in addition to altering the synthesis of arachidonic acid from linoleic acid, pravastatin down regulates the production of CDP-diacylglycerol leading to an overall downregulation of phosphatidylinositol synthesis. This effect is compounded in the phosphatidylinositol species PI(36:2) which is primarily RI(18:0_18:2) with a small amount of PI(l8: 1/18: 1) as the decrease in the 18:2, resulting from the increased conversion to arachidonic acid, is combined with the decrease in PI species.

Changes in lipid species predict cardiovascular outcomes and explain the pravastatin treatment effect

When the changes in these lipid species were related to future cardiovascular events, it was observed that changes in PI(36:2), PI(36:3) and PC(38:4) reported the lowest RRR (thus accounting for over 50% of the remaining treatment effect) after adjusting for change in LDL-C, which itself accounted for only 44% of the treatment effect (13% RRR compared to 23% in the base model). Without the adjustment for change in LDL-C, PI(36:2) and PI(36:3) accounted for 54% and 40% of the treatment effect respectively, while PC(38:4) accounted for only 14%. Stronger effects were observed for cardiovascular death.

The positive association of the change in phosphatidylinositol species with cardiovascular outcomes, independent of traditional risk factors and change in LDL-C, indicates that a larger reduction in these lipid species lowers the risk of cardiovascular outcomes. However, those species containing arachidonic acid such as PC(38:4) that also explained a large proportion of the treatment effect (52% and 91% of the remaining treatment effect after adjusting for change in LDL-C in cardiovascular events and death respectively), this relationship was negative, thus associating larger reductions in these lipid species with increased risk of cardiovascular outcomes.

Given that the change in these lipid species is associated with future cardiovascular events independently of the change in clinical lipids these lipid species represent therapeutic targets.

The lipid ratio identified those who do not receive a risk reduction from pravastatin treatment. Association analysis of pravastatin treatment with the change in lipid species (adjusted for change in clinical lipid measures) identified multiple species that were altered independently of the changes in clinical lipid measures. Two species that showed the strongest negative and positive associations (PI(36:2) and PC(38:4)) were also observed to be associated with cardiovascular outcomes and to explain the largest proportion of the pravastatin treatment effect on outcomes. Since each of these two lipid species appear to reflect multiple metabolic pathways and they showed opposite associations with cardiovascular outcomes, we reasoned that the ratio of these lipids may provide a stronger signal relating to pravastatin treatment and change in cardiovascular risk, not dissimilar to the total cholesterol :HDL cholesterol ratio currently used in cardiovascular risk assessment. The change in the lipid ratio (PI(36:2)/PC(38:4)) showed a stronger association with pravastatin treatment than the change in either individual species, independent of the change in clinical lipids (beta coefficient = -26.2 compared to 14.7 and 13.6 in PI(36:2) and PC(38:4) respectively). The change in the lipid ratio was also a better predictor of both cardiovascular events and death than the individual species and explained all of the treatment effect unaccounted for by the change in LDL-C.

In light of these observations the inventors sought to assess whether this lipid ratio could predict who would benefit (or not) from pravastatin treatment. Stratification of the treatment arm into quartiles of change in the lipid ratio (PI(36:2)/PC(38:4)) demonstrated firstly that Q4 showed no change in the lipid ratio upon pravastatin treatment, but did show a significant (albeit slightly smaller) decrease in LDL-C (0.95 mM in Q4 compared to 1.29 mM in Ql). Importantly, those individuals in Q4 showed no risk reduction for future cardiovascular events or cardiovascular death but rather showed a slight (non-significant) increase in risk for these outcomes, independent of the change in LDL-C. In contrast Ql and Q2 showed much greater risk reduction (up to 40% for cardiovascular death) than the overall risk reduction in the treatment arm. The same effect was observed by calculation of the RRR based on event numbers (with no adjustment) thereby demonstrating the potential for translation of this lipid ratio as a clinical test to monitor statin therapy.

These results demonstrate that pravastatin treatment has clinically important effects on lipid metabolism beyond LDL cholesterol reduction and that these effects influence risk of future cardiovascular events. The ability to identify a subset of patients who do not receive a risk reduction from pravastatin treatment despite showing a reduction in LDL-C provides insights into how these patients might best be managed to minimise risk of future events. Further lowering of LDL-C with higher doses or alternate therapies such as PCSK9 inhibitors will lead to both changes in the lipid ratio and reduced risk. In addition, the description provides new treatment strategies aimed at modulating either or both of the lipid species within the ratio that will have beneficial effects in this group of patients.

The description provides the results of landmark longitudinal analyses of the effects of statins on plasma lipid species and the subsequent influence on cardiovascular risk. As determined herein the change in phosphatidylinositol and phosphatidylcholine species can explain a large proportion of the pravastatin treatment effect independent of changes in LDL-C. The change in the ratio of two lipid species PI(36:2)/PC(38:4) upon pravastatin treatment quantitates the relative risk reduction from treatment and identified at least 25% of the treatment group that does not receive a reduction in risk of future cardiovascular events. Accordingly, this lipid ratio represents a useful biomarker to monitor statin treatment and reduce the risk of future cardiovascular events.

Example 4 - Identification of lipid levels that predict an individual's response to a HMG-CoA reductase inhibitor treatment regimen in the absence of HMG-CoA reductase inhibitor administration

Baseline lipid ratios were identified that predict response to statin treatment. Within the LIPID cohort four sub groups were examined to assess relative risk reduction (RRR): all individuals, females, non-type 2 diabetes individuals, and type 2 diabetes individuals. Each group was stratified into quartiles based on the lipid ratios and the difference in the relative risk reduction allocated between Ql and Q4. Lipid ratios were selected if the difference in RRR between Ql and Q4 was greater than 50% in all groups (for CVD death), greater than 35% in all groups (for CVE), greater than 40% in all groups (for MI), greater than 60% in all groups (for stroke). Lipid species within all selected lipid ratios were examined and those lipid species that appeared more than once were considered treatment biomarkers and included in the final lipid list. Lipid biomarkers able to predict RRR response to statin treatment include predominantly species of phosphatidylcholine (including lyso and ether species), phosphatidylinositol, and sphingomyelin. Lipid species of dihexosylceramide and triacylglycerol were also identified. Considering MI, CVD death, stoke and all CVE, 26 lipid species were identified as optimum biomarkers, namely Hex2Cer(l6:0), LPC(l6: l), LPC(20: l), PC(3 l : l), PC(32:2), PC(32:3), PC(33:2), PC(33:3), PC(34:0), PC(34: l), PC(35:3), PC(36:3), LPC(0-22: l), LPC(0-24: l), PC(0-34:3), PI(34:0), PI(36:4), PI(38:2), PI(38:6), SM(34: l), SM(35:2), SM(36: l), SM(36:2), SM(36:3), SM(37:2), and TG(l8:0/l8:0/l8:0).When cardiovascular events of MI, CVD death and all CVE were considered 12 lipid species were identified as optimum, namely LPC(l6: l), LPC(20: l), PC(32:2), LPC(0-22: l), PI(36:4), PI(38:2), PI(38:6), SM(35:2), SM(36:l), SM(36:2), SM(37:2), and TG(l8:0/l8:0/l8:0). Lipid species can be categorised as those that are elevated or those that are reduced in statin therapy responders or statin non-responders. In one embodiment, lipid species for ratio calculations are selected one from each category to maximise the diagnostic potential of the assessment. The results are set out in Figure 10 and lipid species can be selected based upon these results.

Example 5 -Co-administration of a HMG-CoA reductase inhibitor treatment and PC and/or arachidonic acid supplementation enhances responsiveness to statins and/or provides treatment or prophylaxis of CVE and/or stoke

As determined herein, omega 6 fatty acid supplementation in combination with a statin is required in a sizable proportion of the population to achieve a risk reduction from statin treatment. Individuals who do not get an upregulation of omega 6 fatty acid production do not benefit from the statin treatment and so supplementation of omega 6 fatty acids in people using a statin will improve the effect of the statin to reduce their risk of having a cardiovascular event. In one embodiment, treatment is proposed for those individuals who are non-responders (i.e., they do not have a natural increase in omega 6 on treatment statins. In the present study at least 25% of the population fell into this category.

Omega 6 supplementation includes administration of omega 6 fatty acids including 18:2, 18:3 20:3, 20:4, 22:4, 24:4, 24:5 and 22:5 lipid species. Arachidonic acid (20:4) is biologically active for example, however one or more additional or alternative lipid species along that pathway can also be administered. The complete list of omega-6 fatty acids is set out in Figure 11

Example 6 - CIDPT inhibitors or other phosphatidylinositol inhibitors enhances responsiveness to statins and/or provides treatment or prophylaxis of CVE and/or stoke

As determined herein down-regulation of phosphatidylinositol (PI) in response to statin treatment is associated with a reduced risk of a cardiovascular event. Downregulation of phosphatidylinositol may be produced by the inhibition of the enzyme phosphatidylinositol synthase (also known as CDP-Diacylglycerol— Inositol 3-Phosphatidyltransferase (CIDPT)) or other enzymes in the biosynthetic pathway. Several reports of inhibitors of CIDPT have been made using analogues of inositol (see for example, Nakamura et al Am J physiol. 262(4 Pt 1):E417-26, 1992, Tomaru et al. Biochem. Biophys. Res. Commun. 310{ 2):667-74, 2003, Johnson SC et al, J. Med. Chem. 3<5(23):3628-35, 1993.). 8-hexachlorocyclohexane and inostamycin are CIDPT inhibitors.

Thus, individuals may be treated by treating a subject at risk of a cardiovascular event, by administering an agent that lowers the plasma levels of a phosphatidylinositol in the subject. Agents that inhibit phosphatidylinositol synthase are known in the art and include inositol analogues. An anti-CDIPT antibody or antigen binding part thereof that is a phosphatidylinositol synthase inhibitor is also contemplated. Agents may also be selected from one or more of the following agents using technology that forms part of the common knowledge of the skilled addressee: an aptamer, small molecule, an antisense, sgRNA or iRNA, zinc in the case of zinc deficiency and/or sorbitol or glucose.

It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the above-described embodiments, without departing from the broad general scope of the present disclosure. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive Table 1: Conditions for tandem mass s ectrometr anal sis of li id s ecies.

Table 2: Baseline Characteristics

¹ p-values were calculated using Mann-Whitney U-test for continuous variables and chi-square for dichotomous variables.

² denotes dichotomous variables. The number and the percentage of cases are presented for these variables.

O

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