Metabolomics, while providing tangible advantages over traditional approaches, also poses significant challenges. Metabolomics aims at quantitation, either targeted or non-targeted, of a large number of analytes in the same sample. This task has inherent difficulties because many metabolites in the sample are unknowns, and, for many known metabolites, there is no authentic standard available. Targeted analysis requires availability of all the analytes of interest, which, depending on the study, could be in the range 50–500. Obtaining the pure compounds could be difficult, as some analytes may not be commercially available while others are very costly. Targeted analysis requires substantial method development and validation work. Stock solutions, working solutions and mixtures of all the analytes must be prepared in a short period of time (2–3 days) so that all the metabolites are simultaneously available for method development, validation or quality control. If some of the analytes are unstable, fresh solutions and mixtures need to be prepared periodically.
Careful selection of optimized chromatography conditions is necessary and may be time consuming. For example, analysis of acid, base and neutral molecules, as well as polar and non-polar molecules present in the sample, requires screening of a number of chromatography solvents, columns and gradients to select the most efficient combination of factors affecting separation. Hence, a compromise in terms of chromatography performance is needed, as all the analytes may not show acceptable chromatography.
Validation of the method can be complicated as all the analytes need to be validated simultaneously and the presence of reactive analytes could affect reproducibility. Multiple internal standards are needed to represent each class of analyte.
With a non-targeted approach, it is often difficult to identify the analytes, and it requires considerable complementary analytical approaches and, again, the availability of a large number of standard compounds.
Another challenge for metabolomics is the presence of unknowns in the sample, so a complementary research effort needs to focus on structural elucidation of the unknowns. With hundreds of analytes being analyzed in a single run, it is difficult, with current technology, to obtain faster separation times, so it is hard to achieve the high throughput required for large studies. This raises sample stability and storage issues. Manual data analysis can be complicated, and fast, proper data analysis requires novel, sophisticated computer algorithms.
Metabolomics is a promising and rapidly developing area of research. Despite current limitations, it still provides an opportunity to re-think current analytical methodology and offers researchers in pharmaceutical and environmental sciences an additional research platform that is robust and powerful.