Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism

Abstract

We provide a demonstration in humans of the principle of pharmacometabonomics by showing a clear connection between an individual's metabolic phenotype, in the form of a predose urinary metabolite profile, and the metabolic fate of a standard dose of the widely used analgesic acetaminophen. Predose and postdose urinary metabolite profiles were determined by 1 H NMR spectroscopy. The predose spectra were statistically analyzed in relation to drug metabolite excretion to detect predose biomarkers of drug fate and a human-gut microbiome cometabolite predictor was identified. Thus, we found that individuals having high predose urinary levels of p-cresol sulfate had low postdose urinary ratios of acetaminophen sulfate to acetaminophen glucuronide. We conclude that, in individuals with high bacterially mediated p-cresol generation, competitive O-sulfonation of p-cresol reduces the effective systemic capacity to sulfonate acetaminophen. Given that acetaminophen is such a widely used and seemingly well-understood drug, this finding provides a clear demonstration of the immense potential and power of the pharmacometabonomic approach. However, we expect many other sulfonation reactions to be similarly affected by competition with p-cresol and our finding also has important implications for certain diseases as well as for the variable responses induced by many different drugs and xenobiotics. We propose that assessing the effects of microbiome activity should be an integral part of pharmaceutical development and of personalized health care. Furthermore, we envisage that gut bacterial populations might be deliberately manipulated to improve drug efficacy and to reduce adverse drug reactions.T he effects of drug treatments can vary greatly between different individuals, and pharmacogenomics has been widely advocated as a potential means of personalizing human drug treatments to increase drug efficacy and to decrease adverse reactions (1-6). However, environmental factors (such as nutritional status, gut bacterial activities, age, disease, and other drug use) are also important determinants of individual metabolic phenotypes, which modulate drug metabolism, efficacy, and toxicity. Such environmental complications, which may also alter gene expression, will tend to limit the usefulness of predictions of drug-induced responses that are based only on genomic differences (7,8). For instance, for many classes of compound, enzyme induction state, which is environmentally determined, influences drug metabolism and toxicity and this is not captured in genomic data. Recognizing this important limitation of pharmacogenomics, a different approach to personalized drug treatment has recently been proposed wherein predose metabolite profiling would instead be used to predict a subject's responses to potential drug interventions (9). This ''pharmacometabonomic'' approach has a number of major advantages, which include the ready availability and relative ease of analysis of biofluids, such as urine and blood plasma, as well as the fact that ...

Modulation of the gut microbiota by prebiotic fibres and bacteriocins

“…Individual differences in the microbiota composition can therefore have effects on how drugs [103] and food components [104] are metabolized, and consequently on how the health of the individual is affected.…”

Oxylipid Profile of Low‐Dose Aspirin Exposure: A Pharmacometabolomics Study

“…Metabolic profiles integrate genetic and environmental influences and provide unique information that can help explain the drug–response phenotype 20, 21. In the present investigation, we have used a robust, broad‐spectrum mass spectroscopy (MS)‐based metabolomics platform to measure the concentrations of multiple oxylipids in serum samples from 156 healthy volunteers of the Heredity and Phenotype Intervention (HAPI) Heart Study before and after 14 days of low‐dose (81 mg/day) aspirin treatment.…”

NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine

“…In addition, if the pre-dose levels of metabolite 4 normalised to creatinine were > 0.06, then the 3 -6 hour post-dose ratios of S/G were always < 0.6, a smaller value than that at 0 -3 hours, with p = 1.2 x 10 -4 , and also statistically significant ( Figure 5B). [48] Conversely, if the post-dose ratios of S/G were high, then the pre-dose levels of metabolite 4 normalised to creatinine were always low ( Figure 5 A and 5B). Corresponding plot for the 3-6 hour post-dose S/G ratios plotted against pre-dose ratios of metabolite 4 normalised to creatinine.…”

“…Reproduced with permission from PNAS. [48] Given the discovery of unknown metabolite 4 as a biomarker that could at least partially predict low S/G excreting volunteers from high S/G excreting volunteers, it became important to identify this metabolite. Metabolite 4 was characterised by a singlet methyl resonance at 2.348 ppm that was linked by statistical correlation spectroscopy (STOCSY) [49] to a pair of second order, aromatic pseudo-doublets at ca 7.210 and 7.285 ppm.…”