Author Archives: Vera

There has been a lack of in vivo metabolite profiling update of hydrocodone since the original report on species differences was published in 1978. As such, the mechanism for its analgesic activity in different species has been ambiguous. To address safety concern from regulatory agencies, hydrocodone metabolite profiles in rats and dogs are updated herein aided by a newly developed software, Mass-MetaSite. 2. Samples collected from rats and dogs dosed orally with hydrocodone were analyzed with reversed phase liquid chromatography coupled with LTQ-Orbitrap. The exact mass measurement data collected with data-dependent acquisition methodology were analyzed both traditionally, using Xcalibur Qual Browser and MetWorks, and by Mass-MetaSite. 3. Profiling of hydrocodone metabolites in rat and dog plasma reflected previously reported species differences in circulating metabolites. While hydrocodone mainly underwent O-demethylation and ketone reduction in rats forming hydromorphone and reduced hydromorphone, which were then subsequently cleared via glucuronide conjugation, hydrocodone in dogs was cleared predominantly by N-demethylation and N-oxidation. 4. Given the success ratio of metabolite detection offered by Mass-MetaSite, the software will be able to aid chemists in early identification of drug metabolites from complex biomatrices.

Background: The ability to supplement high-throughput metabolic clearance data with structural information defining the site of metabolism should allow design teams to streamline their synthetic decisions. However, broad application of metabolite identification in early drug discovery has been limited, largely due to the time required for data review and structural assignment. The advent of mass defect filtering and its application toward metabolite scouting paved the way for the development of software automation tools capable of rapidly identifying drug-related material in complex biological matrices. Two semi-automated commercial software applications, MetabolitePilot™ and Mass-MetaSite™, were evaluated to assess the relative speed and accuracy of structural assignments using data generated on a high-resolution MS platform.

Results/conclusion: Review of these applications has demonstrated their utility in providing accurate results in a time-efficient manner, leading to acceleration of metabolite identification initiatives while highlighting the continued need for biotransformation expertise in the interpretation of more complex metabolic reactions.

One of the key factors in drug discovery is related to the metabolic properties of the lead compound, which may influence the bioavailability of the drug, its therapeutic window, and unwanted side-effects of its metabolites. Therefore, it is of critical importance to enable the fast translation of the experimentally determined metabolic information into design knowledge. The elucidation of the metabolite structure is the most structurally rich and informative end-point in the available range of metabolic assays. A methodology is presented to partially automate the analysis of this experimental information, making the process more efficient. The computer assisted method helps in the chromatographic peak selection and the metabolite structure assignment, enabling automatic data comparison for qualitative applications (kinetic analysis, cross species comparison). 

Rationale: Structural information on metabolites obtained in relevant biological systems can have considerable impact on the design of new drug candidates. However, with demanding turnaround times, the amount of available structural information may become rate limiting.

Methods: The workflow for metabolite identification used in our laboratory was compared to a workflow using a software tool built for computer-assisted metabolite identification. The present study covered the in vitro metabolism of a diverse set of 65 in-house compounds. The compounds were profiled across three liver-based systems, 17 compounds were tested in human liver microsomes (HLM), 12 in rat hepatocytes (RHEP), and 36 in human hepatocytes (HHEP).

Results: For 92% of the metabolites reported, the exact match or Markush representations were in agreement between the two workflows. The major specific biotransformations in hepatocytes which formed the metabolites were aromatic or aliphatic hydroxylations (33%), N-dealkylations (15%) and glucuronidations (12%).

Conclusions: The software was shown to perform well for structural elucidation of metabolites from both phase I and phase II metabolism where the focus was on quickly understanding the rate-limiting metabolic step(s).