Metabolic kinetics of p-aminobenzoic acid in rabbits

Urinary Metabolic Distinction of Niemann-Pick Class 1 Disease through the Use of Subgroup Discovery

In this investigation, we outline the applications of a data mining technique known as Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based dataset. The SD technique utilized a supervised learning strategy, which lies midway between classificational and descriptive criteria, in which given the descriptive property of a dataset (i.e., the response target variable of interest), the primary objective was to discover subgroups with behaviours that are distinguishable from those of the complete set (albeit with a differential statistical distribution). These approaches have, for the first time, been successfully employed for the analysis of aromatic metabolite patterns within an NMR-based urinary dataset collected from a small cohort of patients with the lysosomal storage disorder Niemann-Pick class 1 (NPC1) disease (n = 12) and utilized to distinguish these from a larger number of heterozygous (parental) control participants. These subgroup discovery strategies discovered two different NPC1 disease-specific metabolically sequential rules which permitted the reliable identification of NPC1 patients; the first of these involved 'normal' (intermediate) urinary concentrations of xanthurenate, 4-aminobenzoate, hippurate and quinaldate, and disease-downregulated levels of nicotinate and trigonelline, whereas the second comprised 'normal' 4-aminobenzoate, indoxyl sulphate, hippurate, 3-methylhistidine and quinaldate concentrations, and again downregulated nicotinate and trigonelline levels. Correspondingly, a series of five subgroup rules were generated for the heterozygous carrier control group, and 'biomarkers' featured in these included low histidine, 1-methylnicotinamide and 4-aminobenzoate concentrations, together with 'normal' levels of hippurate, hypoxanthine, quinolinate and hypoxanthine. These significant disease group-specific rules were consistent with imbalances in the combined tryptophan-nicotinamide, tryptophan, kynurenine and tyrosine metabolic pathways, along with dysregulations in those featuring histidine, 3-methylhistidine and 4-hydroxybenzoate. In principle, the novel subgroup discovery approach employed here should also be readily applicable to solving metabolomics-type problems of this nature which feature rare disease classification groupings with only limited patient participant and sample sizes available.

11C-Para-aminobenzoic acid PET imaging of S. aureus and MRSA infection in preclinical models and humans

Tools for noninvasive detection of bacterial pathogens are needed but are not currently available for clinical use. We have previously shown that para-aminobenzoic acid (PABA) rapidly accumulates in a wide range of pathogenic bacteria, motivating the development of related PET radiotracers. In this study, ¹¹C-PABA PET imaging was used to accurately detect and monitor infections due to pyogenic bacteria in multiple clinically relevant animal models. ¹¹C-PABA PET imaging selectively detected infections in muscle, intervertebral discs, and methicillin-resistant Staphylococcus aureus–infected orthopedic implants. In what we believe to be first-in-human studies in healthy participants, ¹¹C-PABA was safe, well-tolerated, and had a favorable biodistribution, with low background activity in the lungs, muscles, and brain. ¹¹C-PABA has the potential for clinical translation to detect and localize a broad range of bacteria.