Simplified method of analytical verification of biochemicals detected using non-targeted mass spectrometry platforms

Abstract

A method of evaluating the analytical performance of a biochemical measured using a multi-analyte assay is described. The method includes measuring a value for the level of a first biochemical in an analytical verification sample, wherein the first biochemical has been previously analytically verified against three or more analytical verification conditions; measuring the level of a second biochemical in a sample, wherein the second biochemical is structurally or biochemically related to the first biochemical; and comparing a verification parameter for the first biochemical and a verification parameter for the second biochemical to determine whether the performance of the second biochemical is acceptable based on the comparison.

Description

[0001] BACKGROUND

[0002] Metabolomics analysis (global biochemical profiling) is a large-scale semi-quantitative method that examines disturbances associated with biochemical abnormalities, such as disturbances in amino acid, carbohydrate, organic acid, lipid, and nucleotide metabolism. This test uses a combination of chromatographic and mass spectrometric (e.g., GC-MS and LC-MS / MS) techniques to simultaneously analyze thousands of compounds. Metabolomics analysis can be used as a screening tool for individuals with, for example, undifferentiated phenotypes, or as supporting evidence in individuals with suspected mutations in genes related to metabolic processes.

[0003] For example, biochemical screening studies in the field of inborn errors of metabolism (IEM) have shown the clinical significance and utility of metabolomics analysis methods (Miller, MJ, et al. J Inherit Metab Dis. 2015 Nov;38(6): 1029-39). Screening of the human metabolome in sample types such as plasma, serum, urine, and cerebrospinal fluid has shown novel metabolic signatures that provide informative information compared to the limited biochemical analysis provided by clinical diagnostic kits. The ability to generate biochemical phenotypes using metabolomics analysis methods provides additional analytical tools to detect abnormal levels of metabolites that can be used in conjunction with targeted quantitative biochemical assays. Importantly, global biochemical profiling identifies and measures levels of biochemicals not currently monitored in clinical diagnostic kits, which has highlighted the utility of global metabolomics profiling as a tool to monitor various clinically relevant biochemicals.

[0004] Prior to being able to use a test or assay in a clinical setting, certain regulatory requirements must be met. In addition to clinical validation and clinical utility, analytical validation must also be demonstrated. Traditional methods for validating biochemicals for clinical use require full analytical validation of each biochemical by assessing a variety of conditions including, for example, single-day precision (intra-day precision), multi-day precision (inter-day precision), limit of detection (LOD), linearity, stability, carryover effect, matrix effect / biochemical recovery, interferences, and comparison to standard clinical tests currently in use by correlation analysis.

[0005] Experiments have demonstrated that global metabolomics analysis is a useful method for assessing health and diagnosing disease. However, the process of analytical verification of all biochemicals in metabolomics analysis is very time consuming and resource intensive for all the validation conditions that are verified for biochemical analytes, which limits the use of metabolomics analysis methods in a clinical setting due to the need to assess and meet sufficient analytical parameters to meet regulatory requirements for analytical verification. A quantitative method has been proposed that allows measurement of the level of one compound based on the level of an internal standard of a compound with similar chemical properties as the first compound (CLSI. Mass Spectrometry in the Clinical Laboratory: General Principles and Guidance; Approved Guideline. CLSI document C50-A. Wayne, PA: Clinical and Laboratory Standards Institute; 2007). However, this method requires calibration standards and internal standards to accurately quantify the compound. Therefore, its use in analytical verification of global metabolomics analysis is limited by the number of internal standards that can be analyzed simultaneously with the large number of compounds measured using metabolomics techniques.

[0006] SUMMARY

[0007] To fully meet the defined validation conditions, analytical verification of biochemical assays is conventionally performed on quantitative assays of a single analyte or a multi-analyte panel consisting of a limited number of analytes. Typically, the multi-analyte panel consists of less than 50 analytes. However, this full analytical verification method is not feasible for semi-quantitative multi-analyte panels that contain tens to hundreds or thousands of analytes. To enable semi-quantitative global metabolomics assays to be used for assessing human health, a simplified method is needed to analytically verify the large number of analytes measured in these assays without the need for full analytical verification of each analyte.

[0008] In the method, a simplified set of analytical verification conditions is used to assess and verify the analytical performance of biochemicals in the assay. In some embodiments, the biochemical is structurally related to another metabolite that has been fully analytically verified. In some embodiments, the biochemical is biochemically related to another metabolite that has been fully analytically verified.

[0009] In one aspect of the application, a method of evaluating the analytical performance of a biochemical measured using a multi-analyte assay includes: performing analytical verification of a measured value of a level of a first biochemical in a sample, wherein the first biochemical has been analytically verified against a plurality of analytical verification conditions selected from the group consisting of: within-run precision, between-run precision, linearity, limit of detection (or limit of quantitation), matrix effect, exogenous interference, recovery, stability, carryover effect, and comparison to (i.e., correlation with) measurements obtained using a standard clinical assay; measuring a level of a second biochemical in the sample, wherein the second biochemical is structurally or biochemically related to the first biochemical; selecting one or more analytical verification conditions; determining or calculating a performance value for the selected one or more analytical verification conditions for the second biochemical based on the measured level of the second biochemical; comparing the determined or calculated performance value for the analytical verification condition for the first biochemical to the determined or calculated performance value for the analytical verification condition for the second biochemical; determining that the analytical performance of the second biochemical is acceptable if the calculated performance value for the second biochemical meets the acceptance criteria for the analytical verification condition; and determining that the analytical performance of the second biochemical is not acceptable if the calculated performance value for the second biochemical does not meet the acceptance criteria for the analytical verification condition.

[0010] In one embodiment of the first aspect, the analytical performance of the second biochemical is determined to be acceptable if the calculated performance value for the second biochemical is within 50% of the value for the first biochemical; and the analytical performance of the second biochemical is determined to be not acceptable if the calculated performance value for the second biochemical is not within 50% of the value for the first biochemical. In another embodiment, the analytical performance of the second biochemical is determined to be acceptable if the calculated performance value for the second biochemical is within 70% of the value for the first biochemical; and the analytical performance of the second biochemical is determined to be not acceptable if the calculated performance value for the second biochemical is not within 70% of the value for the first biochemical.

[0011] In some embodiments, the first biochemical is analytically verified against two or more analytical verification conditions. In other embodiments, the first biochemical is analytically verified against three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or ten or more, or all analytical verification conditions.

[0012] In yet another embodiment, two analytical validation conditions are selected for the second biochemical. In a feature of this embodiment, the two conditions can be intra-day (single day) precision and inter-day (multiple day) precision. In another aspect of this embodiment, the performance values calculated for the performance acceptance criteria include percent fill and percent CV.

[0013] In a second aspect of the application, a method of evaluating performance of a biochemical measured using a multi-analyte assay is provided. The method includes: measuring a level of a biochemical in a sample, wherein the biochemical is structurally or biochemically related to one or more biochemicals that have been fully analytically validated (i.e., meet acceptance criteria for all analytical validation conditions); selecting one or more analytical validation conditions from single day precision (intra-day precision), multiple day precision (inter-day precision), limit of detection or limit of quantitation (LOD, LOQ), linearity, stability, carryover, matrix effect, biochemical recovery, interference, and correlation with a standard clinical assay; calculating one or more performance values for the selected one or more analytical validation conditions of the biochemical based on the measured level of the biochemical in the sample; comparing the one or more performance values calculated for the one or more analytical validation conditions of the biochemical to one or more acceptance criteria for the one or more analytical validation conditions; and determining that the analytical performance of the biochemical is acceptable for the corresponding analytical validation condition if the acceptance criteria are met, or determining that the analytical performance of the biochemical is not acceptable for the corresponding analytical validation condition if the acceptance criteria are not met.

[0014] In a third aspect of the application, a method of evaluating performance of a biochemical measured using an assay includes: measuring a level of a biochemical in a sample, wherein the biochemical is structurally or biochemically related to one or more biochemicals listed in Table 1 or Table 2; selecting one or more analytical validation conditions from single day precision (intra-day precision), multiple day precision (inter-day precision), limit of detection or limit of quantitation (LOD, LOQ), linearity, stability, carryover, matrix effect, biochemical recovery, interference, and comparison to a standard clinical assay (correlation therewith); determining or calculating a performance value for the selected one or more analytical validation conditions of the biochemical based on the measured level of the biochemical in the sample; comparing the one or more performance values calculated for the one or more analytical validation conditions of the biochemical to performance values for acceptance criteria for the one or more analytical validation conditions; and determining that the analytical performance of the biochemical is acceptable for the corresponding analytical validation condition if the acceptance criteria are met or exceeded, or determining that the analytical performance of the biochemical is not acceptable for the corresponding analytical validation condition if the acceptance criteria are not met.

[0015] In one embodiment of this aspect, the analytical performance of at least 50 biochemicals is evaluated in a single multi-analyte assay. In another embodiment, the analytical performance of at least 100 biochemicals is evaluated in a single multi-analyte assay. In yet another embodiment, the analytical performance of at least 150 biochemicals is evaluated in a single multi-analyte assay. In another embodiment, the analytical performance of at least 200 biochemicals is evaluated in a single multi-analyte assay. In another embodiment, the analytical performance of at least 500 biochemicals is evaluated in a single multi-analyte assay. In yet another embodiment, the analytical performance of at least 1000 biochemicals is evaluated in a single multi-analyte assay.

[0016] In one embodiment of these aspects, the multi-analyte assay consists of at least 50 biochemicals. In another embodiment, the multi-analyte assay consists of at least 100 biochemicals. In yet another embodiment, the multi-analyte assay consists of at least 150 biochemicals. In another embodiment, the multi-analyte assay consists of at least 200 biochemicals. In another embodiment, the multi-analyte assay consists of at least 500 biochemicals. In yet another embodiment, the multi-analyte assay consists of at least 1000 biochemicals.

[0017] In another embodiment, the performance acceptance criteria for the analytical verification condition is selected from the group comprising: a correlation analysis (R 2 ), a percent fill, a systematic error (SE) percent, a bias percent, a difference percent, and a coefficient of variation (CV) percent. In a feature of this embodiment, when the analytical verification condition is percent fill, at least 80% of the performance values meet the acceptance criteria.

[0018] In one feature of these aspects, one of the one or more selected analytical verification conditions is intra-day precision. In another feature of these aspects, one of the one or more selected analytical verification conditions is inter-day precision. In these features, a performance value of 30% CV or less meets the acceptance criteria. Further, in these features, a performance value of 25% CV or less meets the acceptance criteria.

[0019] In embodiments of the second and third aspects, two analytical validation conditions are selected. In features of this embodiment, the two analytical validation conditions are intra-day (single day) precision and inter-day (multi-day) precision. In another feature of this embodiment, the analytical validation condition is intra-day precision, and a calculated value of 30% CV or less of the biochemical is determined to be acceptable performance. In another feature, the analytical validation condition is intra-day precision, and a calculated value of 25% CV or less of the biochemical is determined to be acceptable performance. In yet another feature, the analytical validation condition is inter-day precision, and a calculated value of 30% CV or less of the biochemical is determined to be acceptable performance. In yet another feature, the analytical validation condition is inter-day precision, and a calculated value of 25% CV or less of the biochemical is determined to be acceptable performance.

[0020] In one feature of these aspects, the assay comprises mass spectrometry. In another feature, the assay comprises liquid chromatography and mass spectrometry. In yet another feature, the sample comprises a plasma, serum, urine, or CSF sample.

[0021] Definitions

[0022] As used herein, "biochemical," "compound," "small molecule," "metabolite," "analyte" refers to organic and inorganic molecules that are present in a cell. The term does not include macromolecules, such as large proteins (e.g., proteins having a molecular weight of more than 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000), large nucleic acids (e.g., nucleic acids having a molecular weight of more than 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000), or large polysaccharides (e.g., polysaccharides having a molecular weight of more than 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000). Small molecules in a cell are generally found free in solution in the cytoplasm or other organelles (e.g., mitochondria), where they form a pool of intermediates that can be further metabolized or used to make larger molecules, called macromolecules. The term "small molecule" includes signaling molecules and intermediates in chemical reactions that convert energy derived from food into forms that can be used. Non-limiting examples of small molecules include sugars, fatty acids, amino acids, nucleotides, intermediates formed in cellular processes, and other small molecules found within cells.

[0023] A "pathway" is a term often used to define a series of steps or reactions that are linked to one another. For example, for a biochemical pathway, the product of one reaction is the substrate for the subsequent reaction. Biochemical reactions are not necessarily linear. Rather, the term biochemical pathway is understood to include networks of interlinked biochemical reactions associated with metabolism, including biosynthetic reactions and catabolic reactions. "Pathway" without modifier can refer to "superpathway" and / or "subpathway". A "superpathway" refers to a broad metabolic category. A "subpathway" refers to any subset of a more general pathway. For example, glutamate metabolism is a subpathway of the biochemical superpathway of amino acid metabolism. Metabolites in the same biochemical pathway are said to be "biochemically related".

[0024] A "global metabolomics analysis" or "global biochemical analysis" refers to a method of determining the levels of biochemicals in a sample. The method measures the levels of hundreds of biochemicals in a sample, thereby providing a biochemical screen. The method can also be referred to as an "assay" in general.

[0025] A "test sample" refers to a sample to be analyzed.

[0026] A "reference sample" refers to a sample used to determine a standard range of levels of a small molecule. A "reference sample" can refer to an individual sample. The sample can be from an individual reference subject (e.g., a normal (healthy) reference subject or a disease reference subject), which can be selected to be very similar in age and gender to the test subject. A "reference sample" can also refer to a sample comprising a pooled aliquot of reference samples from individual reference subjects.

[0027] "Accuracy" refers to the ability of a measured value to match the actual biochemical identity and relative level of the quantity being measured.

[0028] "Precision" refers to the ability of a measured value to be consistently reproduced.

[0029] "Analytical validation" refers to the evaluation process that an analytical procedure or assay goes through to demonstrate its suitability for its intended purpose by meeting or exceeding accepted standards of performance characteristics (e.g., single-day precision (intra-day precision), multi-day precision (inter-day precision), limit of detection or limit of quantitation (LOD, LOQ), linearity, stability, carryover, matrix effects, biochemical recovery, interference, and correlation with standard clinical assays). Performance characteristics are also referred to herein as performance conditions. An acceptance standard is associated with a target value and an acceptance limit around the target value. The reference value for the acceptance standard is a range determined by the acceptance limit around the target value. For example, the target value can be 12, the standard deviation (SD) is 0.5, and the acceptance limit is + / - 1.5. In this example, the range of reference values is 10.5 to 13.5, and values within this range will satisfy the acceptance standard.

[0030] “Coefficient of variation” or “CV” refers to the ratio of the standard deviation of a biochemical measured in a plurality of samples to the mean of the biochemical in the plurality of samples. The ratio is typically expressed as a percentage (CV %).

[0031] “Bias” refers to the difference between the expected value of a test result and the accepted reference value (note that in the case of an interference test, the “accepted value” would be the result from the same measurement procedure in the absence of interference).

[0032] As used herein, “limit of detection” or “LOD” or “limit of quantitation” or “LOQ” refers to the lowest amount of an analyte in a sample that can be detected with a specified probability, but is not necessarily quantitated to an accurate value. This is also referred to as “minimum detectable concentration” and is sometimes used to indicate the functional sensitivity of a test. These terms can be used interchangeably herein.

[0033] “Recovery” refers to the amount of a substance present in a sample that can be detected by an analytical system. Typically, this is referred to as a percent recovery. A system with 100% recovery is perfectly accurate.

[0034] DETAILED DESCRIPTION

[0035] Described herein are methods for performing a simplified analytical validation of a plurality of biochemicals. The method includes evaluating the performance of biochemicals using a multi-analyte assay (e.g., a global metabolomics assay). In such a global metabolomics method, a selected subset of biochemicals representing a plurality of biochemical pathways are analytically validated (i.e., fully analytically validated) against the analytical validation conditions required by a regulatory agency (e.g., the U.S. Food and Drug Administration or the European Medicines Agency (EMA)). Using a simplified set of analytical validation conditions, the performance of other biochemicals that are not directly tested in the full analytical validation protocol can be evaluated and analytically validated. Typically, the biochemicals evaluated using the simplified performance conditions are structurally or biochemically related to one or more of the biochemicals that are fully analytically validated.

[0036] The global metabolomics analytical assay method described herein identifies small molecules with molecular weights between about 50 Daltons (Da) and about 1,500 Da. The identity of the small molecules is determined by comparing them to a library of biochemicals. For each compound analyzed using the LC-MS / MS method, a library is established using purified authentic chemical standards (currently containing over 4,000 biochemicals). The library contains features unique to each molecule’s compound that are used to identify the compound in future samples. The library contains the molecular weight / mass and analytical features (properties) of each molecule, including but not limited to, for example, information about adducts, in-source fragmentation, polymerization, chromatographic retention time, and mass spectrometry fragmentation patterns.

[0037] Biochemicals can be categorized into superpathways, including, for example, amino acids; peptides; carbohydrates; energy; lipids; complex lipids; nucleotides; cofactors and vitamins; and xenobiotics. Biochemicals can also be categorized into one or more biochemical subpathways, including, for example: glycine, serine, and threonine metabolism; alanine and aspartate metabolism; glutamate metabolism; histidine metabolism; lysine metabolism; phenylalanine and tyrosine metabolism; tryptophan metabolism; leucine, isoleucine, and valine metabolism; methionine, cysteine, SAM, and taurine metabolism; urea cycle; arginine and proline metabolism; creatine metabolism; polyamine metabolism; guanidinium and acetamide metabolism; glutathione metabolism; kynurenine metabolism; gamma-glutamyl amino acids; dipeptide derivatives; dipeptides; polypeptides; fibrinogen cleavage peptides; glycolysis, gluconeogenesis, and pyruvate metabolism; glycolysis, gluconeogenesis, and pyruvate metabolism; pentose phosphate pathway; pentose metabolism; glycogen metabolism; disaccharides and oligosaccharides; fructose, mannose, and galactose metabolism; nucleotide sugars; amino sugar metabolism; advanced glycation end products; TCA cycle; oxidative phosphorylation; short chain fatty acids; medium chain fatty acids; long chain fatty acids; polyunsaturated fatty acids (n3 and n6); quantified free fatty acids; fatty acids, branched-chain; fatty acids, dicarboxylic acids; fatty acids, methyl esters; fatty acids, esters; fatty acids, amides; fatty acids, ketones; fatty alcohols, long-chain; fatty acid synthesis; fatty acid metabolism; fatty acid metabolism (also known as BCAA metabolism); fatty acid metabolism (acyl glycine); fatty acid metabolism (acyl carnitine); carnitine metabolism; ketone bodies; neurotransmitters; fatty acids, monohydroxy; fatty acids, dihydroxy; fatty acids, oxidized; eicosanoids; endocannabinoids; inositol metabolism; phospholipid metabolism; lysolipids; glycerolipid metabolism; monoacylglycerol; diacylglycerol; sphingolipid metabolism; mevalonate metabolism; sterols; steroids; primary bile acid metabolism; secondary bile acid metabolism; diacylglycerol; triacylglycerol; lysophosphatidylcholine; phosphatidylcholine; phosphatidylethanolamine; phosphatidylserine; sphingomyelin; sphingolipid metabolism; cardiolipin; cholesterol esters; phospholipids; purine metabolism, containing (low) xanthine / inosine; purine metabolism, containing adenine; purine metabolism, containing guanine; pyrimidine metabolism, containing orotic acid; pyrimidine metabolism, containing uracil; pyrimidine metabolism, containing cytidine; pyrimidine metabolism, containing thymine; purine and pyrimidine metabolism; nicotinate and nicotinamide metabolism; riboflavin metabolism; pantothenate and CoA metabolism; ascorbate and aldo / keto reductics metabolism; tocopherol metabolism; biotin metabolism; folate metabolism; tetrahydrobiopterin metabolism; pteridine metabolism; hemoglobin and heme metabolism; lipoic acid metabolism; thiamine metabolism; vitamin K metabolism; vitamin A metabolism; vitamin B12 metabolism; vitamin B6 metabolism; benzoate metabolism; xanthine metabolism; tobacco metabolites; food ingredients / plants; bacteria; drugs; phthalates; and chemicals.

[0038] In exemplary embodiments of the method, samples are analyzed (referred to herein as "run") on a mass spectrometer system (referred to herein as a platform) using, for example, an LC-MS / MS assay method. Biochemicals in the samples are identified by comparison to a biochemical library of authentic standards. Identification is based on properties, such properties including, for example, retention time, retention index, accurate mass, and biochemical fragmentation pattern. The signal intensity of a selected ion fragment (quantifier ion) of each identified biochemical in a test sample can be compared to, for example, signal intensity obtained from other test samples or one or more reference samples. This method enables relative quantitation of each biochemical in a sample or set of samples. Samples can be test samples or reference samples. Based on the levels of biochemicals and / or levels of biochemicals in biochemical pathways, relative biochemical quantitation can be used to compare individual samples or a set of samples. Relative biochemical quantitation can be used to compare the same molecules between samples.

[0039] Biochemicals measured in assays intended for clinical use (e.g., as a laboratory developed test, LDT) must be analytically validated to meet regulatory requirements.

[0040] The performance of the analytical validation of a biochemical can be assessed using a number of analytical validation conditions, including but not limited to single-day precision (intra-day precision), multi-day precision (inter-day precision), limit of detection (LOD), linearity, stability, carryover effect, matrix effect, biochemical recovery, interference, and / or comparison / correlation to standard clinical assays. A biochemical is determined to be analytically validated if the performance value for one or more analytical validation conditions meets or exceeds the acceptance criteria set for that condition (i.e., the performance value is within the reference value range). Non-limiting exemplary acceptable performance values for the performance acceptance criteria can include: percent fill > 80%; CV% < 35%; R 2 > 0.8. In addition, the conditions can be assessed on a multi-instrument system (i.e., platform) to assess inter-platform precision to demonstrate consistency and robustness of the validated analysis.

[0041] In metabolomics analysis assays, hundreds of biochemicals are measured. Due to resource requirements and time intensive, it is not practical to fully analytically validate all of these biochemicals. Described herein is a method to fully analytically validate a subset of biochemicals using a plurality of validation conditions. The analytical validation conditions and acceptance criteria necessary for the analysis validation will depend on how the assay is used. One of ordinary skill in the art will know and understand the conditions and acceptance criteria necessary based on the end use of the assay. Biochemicals that meet the analytical validation criteria for the analytical validation conditions necessary for the evaluation to meet regulatory requirements are determined to be and are referred to as "fully analytically validated" and are thus able to be used to analytically validate structurally or biochemically related biochemicals by using the simplified analytical validation method.

[0042] In one embodiment, the acceptance criteria for intra-day precision and inter-day precision of a biochemical can be based on the number of samples in which the biochemical is detected (“% fill”). In one example, a biochemical is considered to meet the acceptance criteria for intra-day precision or inter-day precision if the biochemical is detected in at least 80% of the samples. In another example, a biochemical is considered to meet the acceptance criteria for intra-day precision or inter-day precision if the biochemical is detected in 100% of the samples.

[0043] In another embodiment, the acceptance criteria for intra-day precision, inter-day precision, and inter-platform precision of a biochemical can be based on coefficient of variation (CV). For example, a biochemical is considered to meet the acceptance criteria for intra-day precision, inter-day precision, or inter-platform precision if the CV for the biochemical is less than 40%. In other examples, a biochemical is considered to meet the acceptance criteria for intra-day precision, inter-day precision, or inter-platform precision if the CV for the biochemical is less than 35%, less than 30%, less than 25%, or less than 20%. Examples

[0044] I. General Methods.

[0045] Generation of a sample small molecule profile requires analysis of the constituent biochemical small molecules. This analysis can include extraction of at least some of the plurality of small molecules from the sample. Analysis can be performed using one or more different analytical techniques known in the art, for example, liquid chromatography (LC), high performance liquid chromatography (HPLC) (see Kristal et al., Anal. Biochem. 263: 18-25 (1998)), gas chromatography (GC), thin layer chromatography (TLC), electrochemical separation techniques (see WO 99 / 27361, WO 92 / 13273, U.S. 5,290,420, U.S. 5,284,567, U.S. 5,104,639, U.S. 4,863,873, and US RE 32,920), refractive index spectroscopy (RI), ultraviolet spectroscopy (UV), fluorescence analysis, radiochemical analysis, near infrared spectroscopy (Near-IR), nuclear magnetic resonance spectroscopy (NMR), light scattering analysis (LS), mass spectrometry (MS), tandem mass spectrometry (MS / MS2), and combined methods such as gas chromatography / mass spectrometry (GC-MS), liquid chromatography / mass spectrometry (LC-MS), ultra-high liquid chromatography / tandem mass spectrometry (UHLC / MS / MS2), or gas chromatography / tandem mass spectrometry (GC / MS / MS2).

[0046] Global biochemical analysis methods can be composed of one or more assays, including, for example, liquid chromatography (LC), gas chromatography (GC), mass spectrometry (MS), or combinations thereof. Assays can include, for example: LC positive ion polar UHPLC-RP (reverse phase) / MS / MSn; and LC positive ion lipid UHPLC-RP / MS / MSn; LC negative ion UHPLC-RP / MS / MSn; LC negative ion UHPLC-HILIC (hydrophilic interaction liquid chromatography) / MS / MSn, GC-MS, or combinations thereof.

[0047] Biochemicals can be categorized into biochemical pathways (superpathways and subpathways). Biochemicals in the same pathway often have similar chemical structures and are considered structurally related. Biochemicals with similar chemical structures often have similar performance in assays. The performance of selected biochemicals was evaluated using a variety of conditions (Tables 1 and 2): single-day precision (intra-day precision), multi-day precision (inter-day precision), limit of detection (LOD), linearity, stability, carryover, matrix effects, biochemical recovery, interference, and correlation with standard clinical assays. A simplified set of conditions was used to evaluate the performance of other biochemicals, enabling the performance of over 4,000 biochemicals to be evaluated while consuming only a fraction of the time and expense of a full evaluation.

[0048] A. Global biochemical analysis.

[0049] In the exemplary embodiments described herein, the global biochemical analysis method includes four separate liquid chromatography (LC) mass spectrometry (MS) methods: LC positive ion polar UHPLC-RP (reverse phase) / MS / MSn n , LC positive ion lipid UHPLC-RP / MS / MSn n , LC negative ion UHPLC-RP / MS / MSn n , and LC negative ion UHPLC-HILIC (hydrophilic interaction liquid chromatography) / MS / MSn n .

[0050] B. UPLC methods.

[0051] Extracted samples were reconstituted in solvent containing an internal standard. All reconstituted aliquots analyzed by LC-MS were separated using a Waters Acquity UPLC (Waters Corp., Milford, MA). Aliquots reconstituted in 0.1% formic acid used mobile phase solvents consisting of 0.1% formic acid in water (A) and 0.1% formic acid in methanol (B). Aliquots reconstituted in 6.5 mM ammonium bicarbonate used mobile phase solvents consisting of 6.5 mM ammonium bicarbonate, pH 8 in water (A) and 6.5 mM ammonium bicarbonate in methanol and water. The gradient profile used for both formic acid reconstituted extracts and ammonium bicarbonate reconstituted extracts was performed under initial conditions of 0.5% B and a flow rate of 350 μL / min. Total run time was less than 6 minutes. Flow rate was 350 μL / min. Sample injection volume was 5 μL, and a 2x needle loop overfill was used. Liquid chromatography separations were performed on separate acid or base dedicated 2.1 mm x 100 mm Waters BEH C18 1.7 μm particle size columns at 40 °C.

[0052] C. UPLC-MS Method.

[0053] An Orbitrap Elite (OrbiElite Thermo Scientific, Waltham, MA) mass spectrometer was used. The OrbiElite mass spectrometer used a HESI-II source with a sheath gas setting of 80, an auxiliary gas setting of 12, and a voltage setting of 4.2 kV in positive mode. The settings for negative mode were a sheath gas of 75, an auxiliary gas of 15, and a voltage of 2.75 kV. The source heater temperature was 430 °C for both modes, and the capillary temperature was 350 °C. The mass range was 99-1000 m / z with a scan speed of 4.6 total scans per second, with one full scan and one MS / MS scan alternated, and a resolution setting of 30,000. The Fourier transform mass spectrometry (FTMS) full scan automatic gain control (AGC) target was set to 5 x 10 5 with a 500 ms cutoff time. The ion trap MS / MS AGC target was 3 x 10 3 with a 100 ms maximum fill time. The normalized collision energy was set to 32 arbitrary units in positive mode and 30 in negative mode. The activation Q was 0.35, and the activation time was 30 ms, again using a 3 m / z isolation mass window. A dynamic exclusion setting of 3.5 second duration was enabled. Calibration was performed weekly using an infused Pierce TM LTQ Velos electrospray ionization (ESI) positive ion calibration solution or Pierce TM ESI negative ion calibration solution.

[0054] D. Data Processing and Analysis.

[0055] For each biological matrix data set on each instrument, the relative standard deviation (RSD) of the peak area of each internal standard was calculated to confirm extraction efficiency, instrument performance, column integrity, chromatography, and mass calibration. Several of these internal standards were used as retention index (RI) markers and were checked for retention time and alignment. Internal standards were used for QC purposes and not for quantitation of biochemicals in the assay. The modified version of software accompanying the UPLC-MS system was used for peak detection and integration. The output of this process generated a list of m / z ratios, retention times, and area under the curve values. The peak detection criteria specified by the software included thresholds for signal-to-noise, height, and width.

[0056] Chromatographic alignment of biological data sets, including QC samples, was based on retention indices assigned to internal standards with fixed RI values. The RI of an experimental peak was determined by a linear fit between flanking RI markers with the assumption that the values were constant. The benefit of RI is that it corrects for retention time drift due to systematic errors such as sample pH and column age. The RI of each biochemical was assigned based on its elution relationship to two flanking retention markers. The integrated, aligned peaks were matched against a chemical library of authentic standards, and unknown biochemicals were routinely detected, which was specific to the data collection method employed. The match was based on the retention index value, and the experimental precursor mass was matched to the library authentic standard. The experimental MS / MS was compared to library spectra for authentic standards and assigned forward and reverse scores. A perfect forward score indicates that all ions in the experimental spectrum were found in the library of authentic standards in the correct ratios, and a perfect reverse score indicates that all authentic standard library ions were present in the experimental spectrum in the correct ratios. The forward and reverse scores were compared, and a score for the MS / MS fragment spectrum was given for the proposed match.

[0057] Details regarding the chemical library, method for matching integrated, aligned peaks to identify named compounds and routinely detected unknown compounds, and computer readable code for identifying small molecules in a sample can be found in U.S. Patent No. 7,561,975, which is incorporated herein by reference.

[0058] Example 1: Full analysis validation of representative metabolites

[0059] The analytical performance of a representative set of metabolites was evaluated and fully validated using currently accepted or required routine analytical verification techniques. Ten (10) analytical verification conditions were evaluated: single day precision (intra-day precision), multi-day precision (inter-day precision), and limit of detection / quantification (LOD / LOQ), linearity, stability, carryover, matrix effect, biochemical recovery, interference, and correlation with standard clinical assays. A performance value was calculated for each analytical verification condition for each biochemical evaluated. The analytical verification and performance evaluation was performed on plasma, serum, urine, and CSF samples using four independent LC-MS / MS instrument systems, each of which is referred to herein as a platform (i.e., platforms Q, R, S, and T). As described below, the analytical performance of 276 metabolites in plasma and serum samples and 176 metabolites in urine and CSF samples was evaluated and fully analytically validated according to currently accepted practices and standards.

[0060] Intra-day precision Intra-day precision was evaluated using two independent test samples (i.e., Test Sample 1 and Test Sample 2). Each test sample was produced by pooling six (6) different EDTA plasma samples from healthy adult volunteers (i.e., six healthy adult plasma samples were pooled to produce Test Sample 1, and six healthy adult plasma samples different from those used to produce Test Sample 1 were pooled to produce Test Sample 2). Certain metabolites are only found in patient samples with disease and are not found in samples from healthy individuals; these biochemicals are referred to as rare metabolites. Since these rare metabolites are not present in Test Sample 1 and Test Sample 2, these metabolites were spiked into the pooled test samples. On each of the 4 LC-MS / MS platforms (Q, R, S, and T), each test sample was analyzed with 5 technical replicates over 5 days to determine intra-day precision based on CV%. The CV% of raw counts was determined for each metabolite listed in Tables 1 and 2 in each technical replicate sample on all 4 platforms. The best intra-day precision is CV < 25%, and metabolites with CV < 25% meet the CV% acceptance criteria; however, metabolites with CV of 25-30% meet the CV% acceptance criteria provided that no more than 3 samples have CV within this range; metabolites with CV of 30-40% meet the CV% acceptance criteria provided that no more than 1 sample has CV% within this range; metabolites with intra-day precision analysis with CV > 40% are not precise and do not meet the acceptance criteria.

[0061] Inter-day precision: Two unique test samples were analyzed for each test sample with 5 technical replicates within 5 days to determine intra-day precision. A pooled reference sample was included for standardization over several days (days 1-5). Nine reference sample pools were analyzed with each sample batch and replicate test samples. CV% was used to calculate the performance value for inter-day precision. The CV% for each biochemical in Tables 1 and 2 was determined using the average raw counts for each sample, which was then normalized (i.e., divided by) the average raw counts for the 9 pooled reference samples. The best inter-day precision is a CV of < 25%, and metabolites with a CV < 25% meet the CV% acceptance criteria; however, metabolites with a CV of 25-35% for one replicate test meet the CV% acceptance criteria provided all other replicate tests are < 25% CV.

[0062] Linearity : To assess linearity, the biochemicals listed in Tables 1 and 2 were spiked into solvent in amounts covering 6 orders of magnitude, and then extracted. Samples were run in triplicate on each of 4 LC-MS / MS analysis platforms. Each biochemical was analyzed in a six-step dilution series at the following concentrations: 0.0404 ng / mL, 0.482 ng / mL, 5.79 ng / mL, 69.4 ng / mL, 833 ng / mL, and 10,000 ng / mL. The average signal intensity for each biochemical in triplicate sample analysis at each dilution was plotted against the known concentration of the biochemical. Only data points equal to or above the limit of detection were included in the linear calculation. The dilution series included a 12-fold dilution in six steps. The experiment was performed on each of the four analysis platforms. For each platform, a full standard curve was calculated for each biochemical in Tables 1 and 2, including the minimum and maximum concentrations of the linear range, and a linear plot was generated. The performance value for linearity was calculated using R 2 and system error (SE%). The calculated performance value was then compared to the linearity acceptance criteria to determine if the biochemical met or exceeded the linearity acceptance criteria. The acceptance criteria for linearity validation are as follows: the R 2 value for the biochemical must be > 0.95, and the system error (SE) % at the lowest concentration point should be within 20%, or within 15% at other concentrations, for the data point to meet the acceptance criteria for a linear curve.

[0063] Limit of detectionLimit of detection (LOD) for metabolites was defined as: 1) all replicates at the dilution level and all subsequent levels were observed (i.e., 100% fill), and 2) the average raw ion intensity at the dilution level and all subsequent dilution levels was at least 2-fold greater than the average intensity of any previous level. Each biochemical in Tables 1 and 2 was spiked into surrogate matrix and then extracted. Metabolites were spiked at a series of dilutions covering 6 orders of magnitude. Samples were prepared in triplicate on each of the four instrument platforms. Each metabolite (except hexanoate and phenylpyruvic glycine - described below) was analyzed in a 6-step serial dilution series at the following concentrations: 0.0404 ng / mL, 0.482 ng / mL, 5.79 ng / mL, 69.4 ng / mL, 833 ng / mL, and 10,000 ng / mL. Hexanoate was analyzed in a 6-step serial dilution series at the following concentrations: 2.01 ng / mL, 24.1 ng / mL, 289 ng / mL, 3,470 ng / mL, 41,700 ng / mL, 500,000 ng / mL. Phenylpyruvic glycine was analyzed in a 6-step serial dilution series at the following concentrations: 2.01 ng / mL, 24.1 ng / mL, 289 ng / mL, 3,470 ng / mL, 41,700 ng / mL, 500,000 ng / mL. The LOD was determined as the lowest value observed across all four instrument platforms. To meet the acceptance criteria for LOD / LOQ, the measured level of the metabolite must be above the LOD / LOQ.

[0064] Matrix effect / recovery To evaluate matrix effects / recovery, each biochemical listed in Tables 1 and 2 was spiked into surrogate matrix and then extracted. Biochemicals were spiked at 1) low and 2) high concentrations into the following solutions: (A) neat solution; (B) MTRX QC spiked after extraction; (C) MTRX QC spiked before extraction; (D) unspiked MTRX QC. Samples were analyzed on platform R and raw ion intensity was used to calculate matrix effect (ME), percent recovery (REC), and overall process efficiency (OPE) as shown below:

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[0067] Overall process efficiency:

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[0069] The acceptance criteria for matrix effect / recovery are based on performance values ​​of ME%, REC%, or OPE%. The acceptance criteria for matrix effect / recovery are as follows: biochemicals with ME%, REC%, and OPE% close to 100% and those meeting or exceeding the minimum acceptance criteria are considered to have optimal performance. To meet the recovery acceptance criteria, the calculated average concentration should be within ±15% of the concentration in the QC control. To meet the matrix effect acceptance criteria, the effect on quantification should not exceed ±15%.

[0070] Exogenous interference Historical data captured using global metabolomics analysis indicate that exogenous interferences can be measured independently of other biochemical characteristics. In other words, data from historical studies show that molecules from exogenous interfering agents (such as adhesives and diaper materials) do not interfere with the chromatographic separation and identification of biochemical substances on this platform. Furthermore, it has been established that biochemical identification of drugs / molecules (e.g., statins, nonsteroidal anti-inflammatory drugs, analgesics, antibiotics, antihistamines, and diabetes medications) does not interfere with the chromatographic separation and biochemical identification of other small molecules on this platform. To meet the acceptance criteria for exogenous interference from exogenous interfering agents or drugs, the percentage difference between levels measured in the presence of interfering agents should be within ±15% of the levels measured in the absence of interfering agents.

[0071] Carry-over effect The flow-carrying effect of each biochemical substance listed in Table 1 was evaluated on each of the four platforms and analyzed as part of the linearity and limit of detection (LOD) analysis of the biochemical standards. Specifically, for each analytical run, two process blanks were included after the highest standard curve sample in the linear serial dilution. The first blank served as the syringe flow-carrying effect sample, and the second blank served as the column flow-carrying effect sample. The original ionic strength of the sample with the highest concentration (i.e., 10,000 ng / mL) of the spiked biochemical substance in the LOD / linear dilution series, syringe blank, and flow-carrying effect blank was determined. The performance value of the flow-carrying effect was calculated using the total flow-carrying effect percentage. The total flow-carrying effect percentage was defined by summing the ionic strengths of the two blanks and calculating the flow-carrying effect percentage of the highest concentration in the dilution series. In this embodiment, the flow-carrying effect percentage was calculated using the following formula: (analyte area in the process blank) / (analyte area in the final replicate sample in the dilution series) × 100. If no biochemical substance was detected in the flow-carrying effect sample, it was reported as 0, indicating no flow-carrying effect.

[0072] Due to the instrument's dual-column configuration, the first flow-carrying effect blank (INJ_CO) reports the syringe flow-carrying effect, and the second flow-carrying effect blank (COLUMN_CO) reports the column flow-carrying effect. The total flow-carrying effect is the sum of the two.

[0073] The calculated performance values ​​obtained for the flow-carrying effect were then compared with acceptance criteria to determine the acceptability of the flow-carrying effect of the biochemical substance. The acceptance criteria for the flow-carrying effect (described as the compound flow-carrying effect limit) are as follows: a compound flow-carrying effect limit (i.e., the Cmpd flow-carrying effect limit) was set according to clinically acceptable guidelines stating that the LOD concentration of a compound with a dynamic range of 200-fold should not be affected by >20% (i.e., have a total flow-carrying effect percentage >20%). Based on this guideline and based on the dynamic range of each compound (determined by multiple batches of testing in multiple patients), the Cmpd flow-carrying effect limit was established.

[0074] Comparison to standard clinical assays (accuracy) If available, compare the measured values ​​of the biochemical substance with a standard CAP / CLIA-certified kit used for measuring the analyte in clinical / diagnostic laboratories. This standard kit produces a quantitative measurement of the analyte. These values ​​are correlated with semi-quantitative values ​​obtained using global metabolomics analysis. Calculate the performance value compared to the standard clinical assay using correlation analysis. Perform correlation analysis between the measurements from the global metabolomics analysis and those obtained from the standard kit. Calculate and report the correlation for each biochemical substance. Then compare the calculated performance value to acceptance criteria to determine the acceptability of the biochemical substance for comparison with the standard clinical assay (accuracy). The acceptance criterion for comparison with the standard clinical assay (accuracy) is a correlation of 0.8 or higher; biochemical substances meeting or exceeding this performance value are considered acceptable.

[0075] The performance of 276 compounds in plasma and serum samples was evaluated and validated based on the aforementioned traditional and currently accepted practices. Table 1 shows a list of the 276 compounds that were evaluated and fully analyzed in plasma and serum. Table 1 also includes the biochemical pathways or subfamilies associated with each compound.

[0076] Table 1: Biochemical substances that have been fully analyzed and validated in plasma, serum, and related biochemical pathways.

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[0084] A number of studies were performed to evaluate the performance of a total of 176 compounds in urine and CSF samples. In these studies, the analytical performance of the analytes was evaluated and validated according to the traditional, currently accepted practices described above. Table 2 lists a representative list of 132 compounds that were evaluated and fully analytically validated upon initial analysis in CSF and urine. Table 2 also includes the biochemical pathway associated with each compound.

[0085] Table 2: Biochemicals and associated biochemical pathways that were fully analytically validated (urine and CSF)

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[0090] In addition, fully validated analyses were performed on multiple platforms to determine inter-platform precision and to assess the reproducibility of the validation across multiple instrument systems. The acceptance criteria for inter-platform precision was based on the calculated CV%. Inter-platform precision (CV%) varied by metabolite and ranged from a low of 2.7% to a high of 297%. However, the inter- platform precision for the vast majority of metabolites was less than 40% (242 of 276) with the majority of metabolites being less than 20% (234 of 276).

[0091] Table 3 summarizes the exemplary analytical validation conditions, the analyses performed for each condition, the acceptance criteria, and the results (pass / fail) obtained for the analytes listed in Tables 1 and 2. Analytical validation analyses were performed on blood (plasma, serum), urine, and CSF samples to determine matrix effects.

[0092] Table 3. Analytical validation conditions, analyses performed, acceptance criteria, and results

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[0096] Example 2: Simplified evaluation of biochemical performance

[0097] Following the full analytical validation described in Example 1, a simplified analytical validation analysis was performed to assess inter-day precision and intra-day precision. The performance of the compounds that passed the full analytical validation (Tables 1 and 2) was used to assess the performance of approximately 4,000 biochemicals. Two analytical validation conditions were used: intra-day precision and inter-day precision to assess the performance of approximately 4,000 biochemicals. The performance values for intra-day and inter-day precision were calculated based on the percent fill (Fill%) and CV for each biochemical. The acceptance criteria for intra-day precision of Fill% was based on the detection of the biochemical in all or a majority (e.g., 70% or more, preferably 80% or more) of the technical replicate samples run in a day (referred to herein as a run day). The acceptance criteria for intra-day precision of CV was less than or equal to a CV of 30%. The acceptance criteria for inter-day precision of Fill% was based on the detection of the biochemical in all or a majority (e.g., 70% or more, preferably 80% or more) of the replicate samples run over multiple days. The acceptance criteria for inter-day precision of CV was determined to be a CV of 25% or less.

[0098] Intra-day precision analysis For intra-day precision analysis, 4 technical replicate samples using pooled reference samples were used for each of plasma, urine, and CSF in ten independent run days.

[0099] The acceptance criteria for inter-day precision analysis of biochemicals was based on the detection of the biochemical in the technical replicate samples run in a day. Biochemicals detected in all 4 (100% fill) technical replicate samples run in a day were determined to meet the Fill% acceptance criteria for intra-day precision. CV% was also used to assess performance. Biochemicals determined to have a CV of 30% or less were determined to meet the CV% acceptance criteria and have acceptable performance for intra-day precision.

[0100] As described in the General Methods section, the intra-day precision of biochemicals in plasma samples (N=40) analyzed by LC-MS was assessed. 670 unique biochemicals were detected in 100% of the technical replicate samples (i.e., 100% fill) in a day and were determined to meet the Fill% acceptance criteria and have acceptable performance for intra-day precision. The average CV% for these 670 biochemicals was 10.1% with a median of 7.2%.

[0101] Using a subset of samples, the performance of the in-run precision of biochemicals was evaluated based on the acceptance criteria for CV% and fill%. In this subset of samples, 580 biochemicals were detected in 100% of the technical replicate samples in one day. Of these 580 analytes, 166 had been previously analytically validated (Table 1), while 414 had not. Of the 414 not previously analytically validated, 387 had a CV% of <30%. It was determined that these 387 biochemicals met the acceptance criteria and had acceptable performance for in-run precision (100% fill, CV% <30), indicating that the simplified performance evaluation using in-run precision was able to expand the number of analytically validated molecules in serum and plasma in a simplified, efficient manner.

[0102] For example, isovalerate was not fully analytically validated in the initial full evaluation. In the simplified evaluation, the fill% of isovalerate in plasma samples was 100% and the CV% was 0.9% to 15%, with a mean of 5.1% and a median of 3.1%. Using these performance metrics, it was determined that the analytical validation of isovalerate was acceptable using the simplified evaluation method. In the same samples, isovalerylcarnitine (a molecule related to isovalerate biochemistry and previously fully analytically validated using the full set of conditions) had a fill% of 100% and a CV of 3% to 14.4%, with a mean of 7.5% and a median of 7.1%.

[0103] Other exemplary biochemicals detected in all or a majority of the samples evaluated (fill%) include: 1) Pipecolate had a CV% of 0.9% to 11.7%, with a mean of 6% and a median of 5.9%, 2) 4 molecules related to tyrosine metabolism were fully analytically validated and 10 related analytes evaluated in the simplified analysis were detected in the in-run precision analysis and all 10 showed low CV; 3) related to urea cycle; arginine and proline metabolism - 8 molecules were evaluated and validated in the full analysis and 4 additional molecules were detected in the simplified analysis and all 4 showed low CV; 4) related to carbohydrate metabolism - 9 molecules in 4 subfamilies were validated using the full evaluation; 10 additional molecules were validated using the simplified analysis.

[0104] As described above in the General Methods section, the in-run precision of biochemicals in urine samples (N=40) analyzed by LC-MS was determined. 568 unique biochemicals detected in 100% of the technical replicate samples in one day (i.e., 100% fill) were determined to have acceptable performance for in-run precision. The mean CV% of these 568 biochemicals was 8.1% with a median of 5.9%.

[0105] The performance of the intra-day precision of biochemicals was evaluated based on the acceptance criteria of CV% and fill% using a subset of samples. In this subset of samples, 442 biochemicals were detected in 100% of the technical replicate samples within one day. Of these 442 analytes, 138 were validated using the full analysis method described in Example 1, while 304 biochemicals were not previously validated. Of the 304 not previously analytically validated, 296 had a CV% of <30%. It was determined that these 296 biochemicals met the acceptance criteria of fill% and CV% and had acceptable performance for intra-day precision, indicating that the simplified performance evaluation using intra-day precision could be used to expand the number of analytically validated molecules in urine in a simplified and efficient manner.

[0106] For example, dihydrobiopterin was not evaluated in the previous full analysis validation study. It was detected in all samples (100% fill) and the CV% of dihydrobiopterin in urine samples ranged from 2.2% to 20.8% with a mean of 7.9% and a median of 7.1%. Using these performance metrics, it was determined that dihydrobiopterin met the acceptance criteria and was acceptable for analytically validation using the simplified evaluation method. In the same samples, the CV of glucose (a molecule related to the dihydrobiopterin biochemistry and previously validated using the full set of conditions) ranged from 2.2% to 14.7% with a mean of 6.3% and a median of 4.7%.

[0107] Other examples include: 1) Glutamate Metabolism Subfamily: glutamine and 2-pyrrolidone were validated using full evaluation. Biochemically related molecules N-acetyl glutamine, pyroglutamine, glutamic acid, gamma-carboxyglutamate, N-acetyl- aspartylglutamate (NAAG), carboxyethyl-GABA, N-acetylglutamate, N-methyl-GABA, and 4- hydroxyglutamate were also measured in the samples and all had a CV of <30% when evaluated using the simplified method; 2) Another superfamily with expanded biochemical coverage is the lipid superfamily, which includes several subfamilies such as primary and secondary bile acids, dicarboxylic acids, and nucleotide superfamily.

[0108] As described above in the General Methods section, the intra-day precision of biochemicals in CSF samples (N=40) analyzed by LC-MS was determined. A total of 346 unique biochemicals detected in 100% of the technical replicate samples within one day were determined to meet the acceptance criteria and have acceptable performance for intra-day precision. The mean CV% of these 346 biochemicals was 11.8% with a median of 8.6%.

[0109] Using a subset of samples, the intra-day precision of biochemicals was evaluated. In this subset of samples, 286 biochemicals were detected in 100% of technical replicate samples over the course of a day. Of these 286 analytes, 94 were fully analytically validated, while 192 were not validated in the full evaluation. Of the 192 not fully validated, 182 had a CV% of <30%. It was determined that these 182 biochemicals met the acceptance criteria for fill % and CV% and had acceptable performance for intra-day precision, indicating that the simplified performance evaluation using intra-day precision could be used to expand the number of analytically validated molecules in the CSF in a simplified, efficient manner.

[0110] For example, acetylcarnitine was not evaluated in the full analytical validation study. It was detected in all samples, and the CV% of acetylcarnitine in CSF samples ranged from 2.1% to 25.8%, with a mean of 13% and a median of 10.5%. Using these performance metrics, it was determined that acetylcarnitine met the acceptance criteria and was acceptable for analytical validation using the simplified evaluation method. In the same samples, the CV of glutamine (a molecule that is biochemically related to acetylcarnitine and that had previously been validated using the full set of conditions) ranged from 2% to 7.8%, with a mean of 5.3% and a median of 5.5%.

[0111] Other examples of metabolites evaluated and analytically validated using the simplified validation method include: 1) biochemicals in the glycine, serine, and threonine metabolic subfamily: betaine, dimethylglycine, glycine, N-acetylglycine, N-acetylserine, N-acetylthreonine, serine, and threonine; 2) biochemicals in the tyrosine metabolic subfamily: 3-(4-hydroxyphenyl)lactate, 3-methoxytyramine sulfate, 3-methoxytyrosine, dopamine 3-O-sulfate, homovanillic acid (HVA), phenol sulfate, and tyrosine. Tyrosine and 3-(4-hydroxyphenyl)lactate were evaluated in the full analysis and passed the full analytical validation study.

[0112] Inter-day precision analysis For inter-day precision analysis, 30 plasma samples, 44 urine samples, and 32 CSF samples were analyzed in two independent analyses.

[0113] The acceptance criteria for biochemical inter-day precision analysis was based on the detection of biochemicals in technical replicate samples over fifteen sample run days. Biochemicals that were detected in at least 80% of technical replicate samples (i.e., 80% fill) over all 15 sample days were determined to meet the fill % acceptance criteria and have acceptable performance for inter-day precision. An alternative acceptance criteria based on CV% was also used. Biochemicals that had a CV of less than 25% were determined to meet the CV% acceptance criteria and have acceptable performance for inter-day precision.

[0114] As described above in the General Methods section, the inter-day precision of biochemicals in plasma samples analyzed by LC-MS was evaluated. In one example, using 30 plasma samples, 523 biochemicals met the % fill acceptance criteria of being detected in at least 80% of samples analyzed over multiple days. The performance of the inter-day precision of all of these biochemicals was further evaluated using CV. In this example, 443 of the 523 biochemicals had a CV of less than 25% and were determined to meet the CV% acceptance criteria and have acceptable performance for inter-day precision; 163 of the 443 represent fully validated molecules (Table 1). Using the described simplified performance evaluation method, 280 biochemicals that were not fully validated were determined to meet the acceptance criteria and have acceptable performance for inter-day precision.

[0115] In another example, using a separate set of 30 plasma samples, 507 biochemicals met the % fill criteria of being detected in at least 80% of samples analyzed over multiple days. The performance of the inter-day precision of all of these biochemicals was further evaluated using CV. In this example, 410 of the 507 biochemicals had a CV of less than 25% and were determined to meet the CV% acceptance criteria and have acceptable performance for inter-day precision; 148 of the 410 represent fully validated molecules (Table 1). Using the described simplified performance evaluation method, 262 biochemicals that were not fully validated were determined to meet the acceptance criteria and have acceptable performance for inter-day precision.

[0116] As described above in the General Methods section, the inter-day precision of biochemicals in urine samples analyzed by LC-MS was evaluated. In one example, using 44 urine samples, 457 biochemicals met the % fill acceptance criteria of being detected in at least 80% of samples analyzed over multiple days. The performance of the inter-day precision of all of these biochemicals was further evaluated using CV. In this example, 408 of the 457 biochemicals had a CV of less than 25% and were determined to meet the acceptance criteria and have acceptable performance for inter-day precision; 162 of the 408 represent fully validated molecules. Using the described simplified performance evaluation method, 246 biochemicals that were not fully validated were determined to meet the acceptance criteria and have acceptable performance for inter-day precision.

[0117] In another example, using a set of 44 independent urine samples, 445 biochemicals met the % fill criteria of being detected in at least 80% of samples analyzed over multiple days. The performance of the inter-day precision of all of these biochemicals was further evaluated using CV. In this example, 370 of the 445 biochemicals had a CV of less than 25% and were determined to meet the % CV acceptance criteria and have acceptable performance for inter-day precision; 147 of the 370 represent molecules that were fully analytically validated. Using the described simplified performance evaluation method, 223 non-fully validated biochemicals were determined to meet the acceptance criteria and have acceptable performance for inter-day precision.

[0118] As described above in the General Methods section, the inter-day precision of biochemicals in CSF samples analyzed by LC-MS was determined. In one example, using 32 CSF samples, 212 of the biochemicals detected in at least 80% of samples analyzed over multiple days had a CV of less than 25%. These biochemicals were determined to meet the acceptance criteria and have acceptable performance for inter-day precision; 86 represent molecules that were fully analytically validated. Using the described simplified performance evaluation method, 126 non-fully validated biochemicals were determined to meet the acceptance criteria and have acceptable performance for inter-day precision.

[0119] In another example, using a set of 44 independent urine samples, 445 biochemicals met the % fill criteria of being detected in at least 80% of samples analyzed over multiple days. The performance of the inter-day precision of all of these biochemicals was further evaluated using CV. In this example, 370 of the 445 biochemicals had a CV of less than 25% and were determined to meet the % CV acceptance criteria and have acceptable performance for inter-day precision; 147 of the 370 represent molecules that were fully analytically validated. Using the described simplified performance evaluation method, 223 non-fully validated biochemicals were determined to meet the acceptance criteria and have acceptable performance for inter-day precision.

[0120] Inter-platform precision Two pooled human EDTA plasma samples (referred to as Test Sample 1 (T1) and Test Sample 2 (T2), respectively) were used to evaluate precision. Each test sample was created by pooling the six (6) different EDTA plasma samples described above (i.e., six healthy adult plasma samples were pooled to produce Test Sample 1, and six healthy adult plasma samples different from those used to produce Test Sample 1 were pooled to produce Test Sample 2). Certain metabolites (referred to as rare metabolites) are only seen in patient samples that are diseased, and are not seen in samples of healthy individuals. Since these rare metabolites are not present in Test Samples 1 and 2, they were spiked into these pooled samples to evaluate performance on the platform. These two test samples were run on all four platforms (Q, R, S, and T) with 5 replicates each over 5 days.

[0121] To determine the inter-platform (instrument system to instrument system) precision, embedded pools of plasma samples collected from healthy volunteers (i.e., standardized matrices) were included and used to standardize test samples over five days (Days 1-5). Nine sample pools of aliquots of plasma samples from 26 healthy adult volunteers (approximately half female and half male) were run with each sample batch and test sample replicate. The inter-platform precision for each compound was assessed by calculating the % CV of the standardized raw intensity values (i.e., raw ion intensity of each compound / average raw ion intensity of the compound in the embedded pool sample) for each replicate sample over all five days (i.e., 5 replicates x 5 days) and on four independent instrument platforms.

[0122] The inter-assay precision of technical replicates of 206 (T1) / 207 (T2) compounds on platform R was calculated using the standardized matrix samples to determine the average CV% of test sample 1 (T1) and test sample 2 (T2) were 8.7% and 11.5%, respectively. In addition, the precision of 173 (T1) / 174 (T2) compounds measured over all five days and on all platforms showed an average inter-platform CV of 10.2% and 11.5%, respectively. Based on these results, it was determined that the metabolites met acceptance criteria and had acceptable performance.

Claims

1. A method for evaluating the analytical performance of biochemical substances measured using a multianalyte assay, the method comprising: a) Analyze and validate the measurement of the level of a first biochemical substance in a sample, wherein the first biochemical substance is analyzed and validated against three or more of the following analytical validation conditions: intra-day accuracy, inter-day accuracy, linearity, limit of detection / limit of quantitation, matrix effects, external interference, current carrying effect, recovery, stability, or correlation with standard clinical assays. b) Measure the level of a second biochemical substance in the sample, wherein the second biochemical substance is structurally or biochemically related to the first biochemical substance and originates from the same biochemical pathway as the first biochemical substance; c) Select one or more of the analytical verification conditions described above for the second biochemical substance; d) Based on the measured level of the second biochemical substance, calculate one or more performance values ​​for the second biochemical substance under one or more selected analytical validation conditions; e) Compare one or more performance values ​​calculated for one or more selected analytical validation conditions for the second biochemical substance with the acceptance criteria for three or more analytical validation conditions for the first biochemical substance. and f) If, for three or more analytical validation conditions of the first biochemical substance, one or more calculated performance values ​​of the second biochemical substance meet the acceptance criteria, then the analytical performance of the second biochemical substance is determined to be acceptable. and g) If, for three or more analytical validation conditions of the first biochemical substance, one or more calculated performance values ​​of the second biochemical substance do not meet the acceptance criteria, then the analytical performance of the second biochemical substance is determined to be unacceptable.

2. The method of claim 1, wherein the analytical performance of at least 50 biochemical substances is evaluated.

3. The method according to claim 1, wherein the determination method includes mass spectrometry.

4. The method according to claim 1, wherein the determination method includes liquid chromatography and mass spectrometry.

5. The method of claim 1, wherein correlation analysis (R) is used. 2 The percentage of fill, percentage of systematic error (SE), percentage of bias, percentage of difference, or percentage of coefficient of variation (CV) are used to calculate one or more performance values ​​for one or more analytical validation conditions, where the percentage of fill is based on the number of samples in which biochemical substances were detected.

6. The method of claim 5, wherein one or more analytical validation conditions are intra-day accuracy, and a performance value of 30% CV or less meets the acceptance criteria.

7. The method of claim 5, wherein one or more analytical validation conditions are daytime accuracy, and a performance value of 30% CV or less meets the acceptance criteria.

8. The method of claim 5, wherein at least 80% of the fill performance values ​​meet the acceptance criteria, wherein the fill percentage is based on the number of samples in which biochemical substances were detected.

9. The method of claim 1, wherein two analytical verification conditions are selected for the second biochemical substance.

10. The method according to claim 1, wherein the two analytical verification conditions are intraday (single-day) accuracy and interday (multi-day) accuracy.

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