• Analytical Science and Technology
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• v.19 no.1
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• pp.107-114
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• 2006

Early diagnosis and medical intervention are critical for the treatment of patients with metabolic disorders. A rapid analytical method was developed for simultaneous quantification of organic acids and amino acids in urine without labor-intensive pre-extraction procedure showing high sensitivity and specificity. A new method consisted of simple two-step trimethylsilyl (TMS)-trifluoroacetyl (TFA) derivatization using GC/MS-selective ion monitoring (SIM). Filter paper urine specimens were dried under nitrogen after being fortified with internal standard (tropate) in a mixture of distilled water and methanol. Methyl orange was added to the residue as indicator reagent. Silyl derivative of carboxylic functional group was followed by trifluoroacetyl derivative for amino functional group. N-methyl-N-(trimethylsilyl-trifluoroacetamide) and N-methyl-bistrifluoroacetamide were consecutively added and heated for 15-20 min at $65^{\circ}C-70^{\circ}C$, for TMS-TFA derivative, respectively. This reactant was analyzed by GC/MS-SIM. Linear dynamic range showed 0.001-50 mg with the detection limit of (S/N=3) 10-200 ng, and the quantification limit of 80-900 ng in urine. Correlation coefficient of regression line was 0.994-0.998. When the method was applied to the patients 'urine, it clearly differentiated the normal from the patient with metabolic disorder. The study showed that the developed method could be the method of choices in rapid and sensitive screening for organic aciduria and amino acidopathy.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.6 no.1
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• pp.40-51
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• 2006

Purpose: A personal computer-based system was developed for automated metabolic profiling of organic aciduria and aminoacidopathy by gas chromatography-mass spectrometry and data interpretation for the diagnosis of metabolic disorders Methods: For automatic data profiling and interpretation, we compiled retention time, two target ions and their intensity ratio for 77 organic acids and 13 amino acids metabolites. Metabolites above the cut-off values were flagged as abnormal compounds. The data interpretation was a based on combination of flagged metabolites. Diagnostic or index metabolites were categorized into three groups, "and", "or" and "NO" compiled for each disorder to improve the specificity of the diagnosis. Groups "and" and "or" comprised essential and optional compounds, respectively, to reach a specific diagnosis. Group "NO" comprised metabolites that must be absent to make a definite diagnosis. We tested this system by analyzing patients with confirmed Propionic aciduria and others. Results: In all cases, the diagnostic metabolites were identified and correct diagnosis was founded to be made among the possible disease suggested by the system. Conclusion: The study showed that the developed method could be the method of choices in rapid, sensitive and simultaneous screening for organic aciduria and amino acidopathy with this simplified automated system.