Drug development is an expensive business. Current estimates put the cost of successfully bringing a drug to market at a staggering $2.6 billion. In part, that’s due to the high cost of biomedical research, preclinical and clinical trials, and commercialization. But it also stems from the fact that so many promising drug candidates fail during development.
One reason for drug failures is the development in humans or test animals of toxicity—a wayward drug/protein interaction, say, or the creation in vivo of unanticipated, toxic metabolites. Drug developers could be better prepared for such events if they had a better handle on drug biotransformation in the body, but to date, there are few efficient and truly representative in vitro models available for doing so.
Here, we review the key steps in drug metabolism, as well as a new system for addressing these challenges, the comprehensive metabolite identification, a one-stop solution from SCIEX and Hepregen Corp.
1. Metabolite generation
The first step in metabolite identification, of course, is metabolite generation. Typically, this is accomplished by delivering the candidate compound to cultured cells or fractions thereof—for instance, liver slices, primary hepatocytes, microsomes or S9 fractions—and monitoring how they metabolize the drug But none of these solutions efficiently recapitulates long-term liver enzyme activity in vitro, meaning they are less likely to produce more relevant metabolites such as Phase I, Phase II and GSH adducts—the metabolites of metabolites that often are overlooked.
HepatoPac® micro-liver technology, part of the Met ID Solution from SCIEX and Hepregen, represents an alternative approach. HepatoPac kits feature “micropatterned” islands of human, monkey, dog or rat hepatocytes growing on a uniform layer of stromal cells within the wells of microtiter plates. As such, they recapitulate in a tissue culture dish the architecture of natural structures called “hepatic plates,” and more closely approximate natural liver function than do, say, randomly co-cultured liver and stromal cells.
HepatoPac cells also remain viable and metabolically active for four weeks or longer, ensuring the ability to generate secondary metabolites over long incubation times.
2. Metabolite analysis
Once the biological assay has run its course, researchers must identify the resulting metabolites. This typically is accomplished using chromatographic separation and mass spectrometry analysis.
Triple-quadrupole mass spectrometers running in single- or multiple-reaction monitoring (SRM/MRM) modes are capable of sensitive detection of pre-determined compounds. But that strategy precludes the identification of molecules researchers aren’t expecting.
Capturing information on all metabolites, particularly low-level, in the early stages, is of particular importance to the selection of viable drug candidates. Missing a potentially toxic compound during the screening process due to incomplete data collection may cause valuable resources to be directed towards an unsuitable candidate that may later fail during clinical trials.
What has emerged as the new standard for metabolite studies is the coupling of high resolution, accurate mass instruments with effective computational strategies for filtering the large data sets from all-in-one fragmentation approaches, easing interpretation and increasing throughput.
The quantitative capacity of a triple quadrupole and the high-performance accurate mass analyzer of a high-resolution time-of-flight mass spectrometer are combined into a single, hybrid instrument, the TripleTOF® 5600 LC-MS/MS system—that performs both quantitative and qualitative analyses with one method.
Used with the TripleTOF system is an unbiased, “data-independent” spectral acquisition strategy: SWATH® Acquistion, which provides a kind of experimental “safety net,” enabling researchers to identify and quantify every metabolite in the sample, regardless of abundance, even if they weren’t aware of the molecule at the time they collected the data. And they can probe that dataset for both expected and unexpected molecular species, and even to retrospectively quantify compounds later determined to be important.
The use of TripleTOF sytems with SWATH Acquisition is described in detail in the SCIEX Drug Metabolism Compendium (linked at the end of this article).
3. Data analysis
The final step in the process is data analysis. The goal is to translate spectral data into metabolite identities, validate those identifications and quantify their abundance across different samples. The challenge with many available data analysis programs is, each step in this process typically requires a different piece of software.
SCIEX MetabolitePilot™ software, part of the Met ID Solution, represents an integrated alternative solution. Rather than requiring researchers to transfer data from tool to tool, MetabolitePilot software incorporates metabolite identification, confirmation and structure determination and cross-species comparison in a single interface, simplifying workflows and decision-making faster than ever.
4. Putting it all together
One study demonstrating the interplay of these various components investigated the metabolites produced by tolbutamide, ziprasidone and linezolid across four different species. Pharmaceutical companies typically test compounds in a variety of species as they progress through the clinical trials, and must be able to flag and evaluate new metabolites as they arise and mitigate risk early on.
To assess that capability, researchers at SCIEX and Hepregen used multispecies HepatoPac kits to test different concentrations of each compound in human, monkey, dog and rat hepatocytes over 7 days. The resulting material was then subjected to SWATH analysis on a SCIEX TripleTOF 6600 LC-MS/MS system and analyzed using MetabolitePilot software, which enables simultaneous metabolite identification and quantification all in single LC/MS analysis.
In each case, several metabolites were detected and quantified across the species range. But interspecies differences also were observed.
The significance of those findings remains to be determined. But such data should make drug developers more aware of potential pitfalls in their candidate pool. And that, in turn, will enable them to advance better candidates earlier and – hopefully – reduce development time and expense.
For more information:
Download the SCIEX-Hepregen Tech Note »
Download the SCIEX Drug Metabolism Compendium »
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