A Rapid LC-MS-MS Method for the Quantitation of Antiepileptic Drugs in Urine.

Epilepsy is a common neurologic disease that requires treatment with one or more medications. Due to the polypharmaceutical treatments, potential side effects, and drug-drug interactions associated with these medications, therapeutic drug monitoring is important. Therapeutic drug monitoring is typically performed in blood due to established clinical ranges. While blood provides the benefit of determining clinical ranges, urine requires a less invasive collection method, which is attractive for medication monitoring. As urine does not typically have established clinical ranges, it has not become a preferred specimen for monitoring medication adherence. Thus, large urine clinical data sets are rarely published, making method development that addresses reasonable concentration ranges difficult. An initial method developed and validated in-house utilized a universal analytical range of 50-5,000 ng/mL for all antiepileptic drugs and metabolites of interest in this work, namely carbamazepine, carbamazepine-10,11-epoxide, eslicarbazepine, lamotrigine, levetiracetam, oxcarbazepine, phenytoin, 4-hydroxyphenytoin, and topiramate. This upper limit of the analytical range was too low leading to a repeat rate of 11.59% due to concentrations >5,000 ng/mL. Therefore, a new, fast liquid chromatography-tandem mass spectrometry (LC-MS-MS) method with a run time under 4 minutes was developed and validated for the simultaneous quantification of the previously mentioned nine antiepileptic drugs and their metabolites. Urine samples were prepared by solid-phase extraction and analyzed using a Phenomenex Phenyl-Hexyl column with an Agilent 6460 LC-MS-MS instrument system. During method development and validation, the analytical range was optimized for each drug to reduce repeat analysis due to concentrations above the linear range and for carryover. This reduced the average daily repeat rate for antiepileptic testing from 11.59% to 4.82%. After validation, this method was used to test and analyze patient specimens over the course of approximately one year. The resulting concentration data were curated to eliminate specimens that could indicate an individual was noncompliant with their therapy (i.e., positive for illicit drugs) and yielded between 20 and 1,700 concentration points from the patient specimens, depending on the analyte. The resulting raw quantitative urine data set is presented as preliminary reference ranges to assist with interpreting urine drug concentrations for the nine aforementioned antiepileptic medications and metabolites.

Plasma metabolomic profiles enhance precision medicine for volunteers of normal health.

Precision medicine, taking account of human individuality in genes, environment, and lifestyle for early disease diagnosis and individualized therapy, has shown great promise to transform medical care. Nontargeted metabolomics, with the ability to detect broad classes of biochemicals, can provide a comprehensive functional phenotype integrating clinical phenotypes with genetic and nongenetic factors. To test the application of metabolomics in individual diagnosis, we conducted a metabolomics analysis on plasma samples collected from 80 volunteers of normal health with complete medical records and three-generation pedigrees. Using a broad-spectrum metabolomics platform consisting of liquid chromatography and GC coupled with MS, we profiled nearly 600 metabolites covering 72 biochemical pathways in all major branches of biosynthesis, catabolism, gut microbiome activities, and xenobiotics. Statistical analysis revealed a considerable range of variation and potential metabolic abnormalities across the individuals in this cohort. Examination of the convergence of metabolomics profiles with whole-exon sequences (WESs) provided an effective approach to assess and interpret clinical significance of genetic mutations, as shown in a number of cases, including fructose intolerance, xanthinuria, and carnitine deficiency. Metabolic abnormalities consistent with early indications of diabetes, liver dysfunction, and disruption of gut microbiome homeostasis were identified in several volunteers. Additionally, diverse metabolic responses to medications among the volunteers may assist to identify therapeutic effects and sensitivity to toxicity. The results of this study demonstrate that metabolomics could be an effective approach to complement next generation sequencing (NGS) for disease risk analysis, disease monitoring, and drug management in our goal toward precision care.