A Discovery Biotransformation Strategy: Combining In Silico Tools with High-Resolution Mass Spectrometry and Software-Assisted Data Analysis for High-Throughput Metabolism

Understanding compound metabolism in early drug discovery aids medicinal chemistry in designing molecules with improved safety and ADME properties. While advancements in metabolite prediction brings increasedconfidence, structural decisions require experimental data. In vitro metabolism studies using liquid chromatography and high-resolution mass spectrometry (LC-MS) are generally resource intensive and performed on very few compounds, limiting the chemical space that can be examined.Here, we describe a novel metabolism strategy increasing compound throughput using residual in vitro clearance samples conducted at drug concentrations of 0.5 µM. Analysis by robust UHPLC separation and accurate-mass MS detection ensures major metabolites are identified from a single injection. In silico prediction (parent cLogD) tailors chromatographic conditions, with data-dependent MS/MS targeting predicted metabolites. Software-assisted data mining, structure elucidation and automatic reporting are used.Confidence in the globally-aligned workflow is demonstrated with sixteen marketed drugs. The approach is now implemented routinely across our laboratories. To date, the success rate for identification of at least one major metabolite is 85%. The utility of these data has been demonstrated across multiple projects, allowing earlier medicinal chemistry decisions to increase efficiency and impact of the design-make-test cycle; thus improving the translatability of early in vitro metabolism data.

Generic approach for the discovery of drug metabolites in horses based on data-dependent acquisition by liquid chromatography high-resolution mass spectrometry and its applications to pharmacokinetic study of daprodustat

In drug metabolism studies in horses, non-targeted analysis by means of liquid chromatography coupled with high-resolution mass spectrometry with data-dependent acquisition (DDA) has recently become increasingly popular for rapid identification of potential biomarkers in post-administration biological samples. However, the most commonly encountered problem is the presence of highly abundant interfering components that co-elute with the target substances, especially if the concentrations of these substances are relatively low. In this study, we evaluated the possibility of expanding DDA coverage for the identification of drug metabolites by applying intelligently generated exclusion lists (ELs) consisting of a set of chemical backgrounds and endogenous substances. Daprodustat was used as a model compound because of its relatively lower administration dose (100 mg) compared to other hypoxia-inducible factor stabilizers and the high demand in the detection sensitivity of its metabolites at the anticipated lower concentrations. It was found that the entire DDA process could efficiently identify both major and minor metabolites (flagged beyond the pre-set DDA threshold) in a single run after applying the ELs to exclude 67.7-99.0% of the interfering peaks, resulting in a much higher chance of triggering DDA to cover the analytes of interest. This approach successfully identified 21 metabolites of daprodustat and then established the metabolic pathway. It was concluded that the use of this generic intelligent "DDA + EL" approach for non-targeted analysis is a powerful tool for the discovery of unknown metabolites, even in complex plasma and urine matrices in the context of doping control.

Chemicalome and metabolome profiling of Chai-Gui Decoction using an integrated strategy based on UHPLC-Q-TOF-MS/MS analysis

Traditional Chinese medicine prescriptions are widely believed to exert therapeutic benefits via a multiple-component and multiple-target mode. The systemic profiling of their in vitro chemicalome and in vivo metabolome is of great importance for further understanding their clinical value. Herein, an integrated strategy using ultra-performance liquid chromatography coupled with quadruple time-of-flight mass spectrometry was proposed to profile the chemicalome and metabolome of Chai-Gui Decoction. Particularly, an approach combined mass defect filter, characteristic product ion filter, and neutral loss filter was adopted to identify metabolites in plasma, urine, bile, and feces by MetabolitePilot. Consequently, a total of 174 constituents were identified or tentatively characterized and 70 metabolites that related to 21 representative structural components were matched in rat biofluids. Among them, 19 prototypes and 7 metabolites that contributed to flavonoids, monoterpenes, and phenylpropanoids were detected distribution in brain, heart, kidney, liver, lung or spleen. This study provided a generally applicable approach to comprehensive investigation on chemicalome and metabolome of traditional Chinese medicine prescriptions, and offered reasonable guidelines for further screening of quality control indicators of Chai-Gui Decoction.

Software-aided approach to investigate peptide structure and metabolic susceptibility of amide bonds in peptide drugs based on high resolution mass spectrometry

Interest in using peptide molecules as therapeutic agents due to high selectivity and efficacy is increasing within the pharmaceutical industry. However, most peptide-derived drugs cannot be administered orally because of low bioavailability and instability in the gastrointestinal tract due to protease activity. Therefore, structural modifications peptides are required to improve their stability. For this purpose, several in-silico software tools have been developed such as PeptideCutter or PoPS, which aim to predict peptide cleavage sites for different proteases. Moreover, several databases exist where this information is collected and stored from public sources such as MEROPS and ExPASy ENZYME databases. These tools can help design a peptide drug with increased stability against proteolysis, though they are limited to natural amino acids or cannot process cyclic peptides, for example. We worked to develop a new methodology to analyze peptide structure and amide bond metabolic stability based on the peptide structure (linear/cyclic, natural/unnatural amino acids). This approach used liquid chromatography / high resolution, mass spectrometry to obtain the analytical data from in vitro incubations. We collected experimental data for a set (linear/cyclic, natural/unnatural amino acids) of fourteen peptide drugs and four substrate peptides incubated with different proteolytic media: trypsin, chymotrypsin, pepsin, pancreatic elastase, dipeptidyl peptidase-4 and neprilysin. Mass spectrometry data was analyzed to find metabolites and determine their structures, then all the results were stored in a chemically aware manner, which allows us to compute the peptide bond susceptibility by using a frequency analysis of the metabolic-liable bonds. In total 132 metabolites were found from the various in vitro conditions tested resulting in 77 distinct cleavage sites. The most frequent observed cleavage sites agreed with those reported in the literature. The main advantages of the developed approach are the abilities to elucidate metabolite structure of cyclic peptides and those containing unnatural amino acids, store processed information in a searchable format within a database leading to frequency analysis of the labile sites for the analyzed peptides. The presented algorithm may be useful to optimize peptide drug properties with regards to cleavage sites, stability, metabolism and degradation products in drug discovery.

Opportunities and Challenges for Fatty Acid Mimetics in Drug Discovery

Fatty acids beyond their role as an endogenous energy source and storage are increasingly considered as signaling molecules regulating various physiological effects in metabolism and inflammation. Accordingly, the molecular targets involved in formation and physiological activities of fatty acids hold significant therapeutic potential. A number of these fatty acid targets are addressed by some of the oldest and most widely used drugs such as cyclooxygenase inhibiting NSAIDs, whereas others remain unexploited. Compounds orthosterically binding to proteins that endogenously bind fatty acids are considered as fatty acid mimetics. On the basis of their structural resemblance, fatty acid mimetics constitute a family of bioactive compounds showing specific binding thermodynamics and following similar pharmacokinetic mechanisms. This perspective systematically evaluates targets for fatty acid mimetics, investigates their common structural characteristics, and highlights demands in their discovery and design. In summary, fatty acid mimetics share particularly favorable characteristics justifying the conclusion that their therapeutic potential vastly outweighs the challenges in their design.

Development, optimization and implementation of a centralized metabolic soft spot assay

AIM

High clearance is a commonly encountered issue in drug discovery. Here we present a centralized metabolic soft spot identification assay with adequate capacity and turnaround time to support the metabolic optimization needs of an entire discovery organization.

METHODOLOGY

An integrated quan/qual approach utilizing both an orthogonal sample-pooling methodology and software-assisted structure elucidation was developed to enable the assay. Major metabolic soft spots in liver microsomes (rodent and human) were generated in a batch mode, along with kinetics of parent disappearance and metabolite formation, typically within 1 week of incubation.

RESULTS & CONCLUSION

A centralized metabolic soft spot identification assay has been developed and has successfully impacted discovery project teams in mitigating instability and establishing potential structure-metabolism relationships.

Advances in high-resolution MS and hepatocyte models solve a long-standing metabolism challenge: the loratadine story

BACKGROUND

Loratadine (LOR, Claritin(®)) is a long-acting antihistamine used to treat allergic rhinitis. The major active human metabolite, desloratadine (DL, Clarinex(®)), is extensively metabolized to 3-hydroxydesloratadine (3-OH-DL) (M40) and subsequently glucuronidated before elimination. This study revealed the ability of a novel, long-term hepatocyte micropatterned co-culture (MPCC) model to generate in vivo metabolites. Metabolites were detected and characterized using non-targeted MS/MS(ALL) with SWATH™ acquisition by a UHPLC-Q-TOF system. Results & methodology: Human MPCCs extensively metabolized LOR and formed 3-OH-DL-glucuronide (M13). Cross-species comparisons revealed monkey- and rat-specific metabolites with gender-specific DL-pyridine-N-oxide formation in male rats. These results demonstrate a first for an in vitro hepatocyte model to generate circulating metabolites of LOR and detect species-specific differences. Early focus on human metabolites could have spared characterization of nonhuman metabolites in preclinical species.

Recent developments in software tools for high-throughput in vitro ADME support with high-resolution MS

The last several years have seen the rapid adoption of the high-resolution MS (HRMS) for bioanalytical support of high throughput in vitro ADME profiling. Many capable software tools have been developed and refined to process quantitative HRMS bioanalysis data for ADME samples with excellent performance. Additionally, new software applications specifically designed for quan/qual soft spot identification workflows using HRMS have greatly enhanced the quality and efficiency of the structure elucidation process for high throughput metabolite ID in early in vitro ADME profiling. Finally, novel approaches in data acquisition and compression, as well as tools for transferring, archiving and retrieving HRMS data, are being continuously refined to tackle the issue of large data file size typical for HRMS analyses.

Software-aided cytochrome P450 reaction phenotyping and kinetic analysis in early drug discovery

RATIONALE

Cytochrome P450 (CYP450) reaction phenotyping (CRP) and kinetic studies are essential in early drug discovery to determine which metabolic enzymes react with new drug entities. A new semi-automated computer-assisted workflow for CRP is introduced in this work. This workflow provides not only information regarding parent disappearance, but also metabolite identification and relative metabolite formation rates for kinetic analysis.

METHODS

Time-course experiments based on incubating six probe substrates (dextromethorphan, imipramine, buspirone, midazolam, ethoxyresorufin and diclofenac) with recombinant human enzymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) and human liver microsomes (HLM) were performed. Liquid chromatography/high-resolution mass spectrometry (LC/HRMS) analysis was conducted with an internal standard to obtain high-resolution full-scan and MS/MS data. Data were analyzed using Mass-MetaSite software. A server application (WebMetabase) was used for data visualization and review.

RESULTS

CRP experiments were performed, and the data were analyzed using a software-aided approach. This automated-evaluation approach led to (1) the detection of the CYP450 enzymes responsible for both substrate depletion and metabolite formation, (2) the identification of specific biotransformations, (3) the elucidation of metabolite structures based on MS/MS fragment analysis, and (4) the determination of the initial relative formation rates of major metabolites by CYP450 enzymes.

CONCLUSIONS

This largely automated workflow enabled the efficient analysis of HRMS data, allowing rapid evaluation of the involvement of the main CYP450 enzymes in the metabolism of new molecules during drug discovery.

Software-aided structural elucidation in drug discovery

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).