High-Throughput Analysis of Drugs and Metabolites in Biological Fluids Using Quan–Qual Approaches

October 1, 2016
Ronald de Vries
Special Issues

Volume 29, Issue 10

Page Number: 26–30

The new generation of high-resolution mass spectrometry (HRMS) systems offers high sensitivity, dynamic range, resolution, accuracy, and scan-to-scan reproducibility, enabling high-throughput quantitative analyses in combination with information-rich qualitative data. The most recently released HRMS systems offer an alternative to triple quadrupole (TQ)-MS systems. This provides a huge opportunity to obtain quantitative and qualitative information from one analysis, but also requires a different mindset and expertise to make the right choices and compromises to get the most information from your sample.

Ronald de Vries, Rob J. Vreeken, and Filip Cuyckens, Pharmacokinetics, Dynamics & Metabolism, Janssen R&D, Beerse, Belgium.

The new generation of high-resolution mass spectrometry (HRMS) systems offers high sensitivity, dynamic range, resolution, accuracy, and scan-to-scan reproducibility, enabling high-throughput quantitative analyses in combination with information-rich qualitative data. The most recently released HRMS systems offer an alternative to triple quadrupole (TQ)-MS systems. This provides a huge opportunity to obtain quantitative and qualitative information from one analysis, but also requires a different mindset and expertise to make the right choices and compromises to get the most information from your sample.

Liquid chromatography coupled to tandem mass spectroscopy (LC–MS/MS) using triple quadrupole mass spectrometers (TQ-MS) has evolved into a mainstream approach for quantitative bioanalysis of drugs and metabolites over the past 20 years. The high selectivity of triple quadrupole instruments in the selected reaction monitoring (SRM) mode makes the technique very suitable for targeted analysis in a high-throughput mode with short run times. The disadvantage of this approach is that only information on the analytes for which SRM mass transitions are put into the method are obtained. Qualitative information on other analytes will not be obtained using this approach.

The sensitivity and dynamic range of the latest generation of HRMS instruments now approaches that of TQ-MS, but at a higher and still increasing resolution. When HRMS is used, no SRM transitions for the analytes of interest need to be entered into the method. The system is run in full-scan MS mode and selectivity is achieved by the high resolution of the system, instead of by selecting a specific precursor–product ion combination in SRM. As a result, a much richer dataset is obtained, containing not only quantitative information on the analytes of interest, but also qualitative information on the other analytes present in the sample.

An example of the different data types obtained by HRMS and TQ-MS is shown in Figure 1. For TQ-MS, only information on the parent drug is obtained, whereas with HRMS, information on adducts, in-source fragments, metabolites, and endogenous compounds, including biomarkers and background ions, is obtained.

It can be very useful to obtain both quantitative information on the drug and qualitative information on other analytes that are present in the sample, and not be limited by only the analytes for which SRM mass transitions were entered. In the literature, this approach is referred to as a quan–qual approach, and the terminology quan–qual is mainly used in the context of obtaining quantitative information on the drug and qualitative information on drug metabolites (1–3). For example, in metabolic stability studies, quantitative data on the drug, as well as qualitative data of the in vitro metabolites, can be relevant (4). In this way, not only the metabolic stability of the drug is assessed, but also information is obtained on the metabolic hotspot(s) of the molecule, which is relevant for compound optimization to improve metabolic stability.

Our group, however, prefers to use a broader definition for quan–qual, because there is much more potential in the use of the qualitative data besides looking for drug metabolites. Looking at the presence and regulation of endogenous metabolites can be of interest, although the number and type of biomarkers that can be followed will be limited by the more generic sample preparation and LC conditions applied for quan–qual analyses. The availability of all ion information can be beneficial for speeding up method development, or for the resolution of analytical or other issues. A few examples of this are given below. Certain compounds, like in formulations (for example, PEG 400 and polysorbate [tween]) or endogenous compounds, such as phospholipids, can cause ion suppression and influence the quantitative result if they are coeluted with the analyte. Therefore, it is useful to know what is coeluted with the analyte(s) of interest, so this information can be used for potential troubleshooting. If an in vitro experiment does not seem to provide the expected result, it might be useful to check whether the right cofactors were present and in which form (for example, to determine the ratio of glutathione [GSH] and oxidized GSH in a reactive metabolite screening), or to check if cells were present during the in vitro experiments by looking for the presence of phospholipids via their marker ions at m/z 104 and 184.


Quan–qual approaches have clear advantages compared to doing “quan” alone but there are some caveats to take into account. First, for quantitative targeted analysis, very short analytical run times (typically 0.5–1.5 min) are preferred to enable the analysis of many samples in a short timeframe. For qualitative analysis, significantly longer analytical runtimes are more beneficial. For drug metabolite identification, longer analytical runtimes (typically 10–20 min) are important to reduce the risk of coelution of isobaric metabolites and, in this way, to correctly identify metabolites. Therefore, we see quan–qual more as a quantitative approach providing additional qualitative information “for free”, rather than using the approach as a replacement for separate metabolite identification studies.

When metabolite identification is (one of) the main goal(s) of the study, it could be better to reanalyze a small selection of the samples using a longer analytical runtime and use these data for metabolite identification, rather than analyzing all samples with a longer analytical runtime (5). Also, if obtaining biomarker information is one of the main goals, it may be better to rerun samples using a method where sample preparation and chromatography have been optimized for the analysis of the required biomarkers.

Furthermore, the large amount of data produced by quan–qual analysis increases the risk of confusion. It is important to ensure that the qualitative data produced are used with a focus on answering specific key questions, rather than spending time trying to extract all available information on a routine basis simply because it is available. The availability of the qualitative data should be used to avoid repetition of experiments and analyses if specific questions come up later in the development programme. Data can be reevaluated at the moment that the specific questions arise, rather than directly after the data has been acquired. For example, if a critical in vivo experiment is performed, where only quantitative data on the drug are requested, it might be useful to perform the analysis in quan–qual mode but initially only use the quantitative data. If, later on, questions arise (for example, the potential involvement of a metabolite explaining a pharmacokinetic or pharmacodynamic [PK or PD] disconnect, or the presence of a human metabolite in animal species), it is easy to go back to the qualitative data months or years after analyses to find the answer.

In this paper, sample preparation methods and mass spectrometry approaches suitable for high throughput quan–qual analysis are presented. High performance liquid chromatography (HPLC) optimization for high throughput quan–qual analysis is also discussed. Furthermore, potential uses, challenges, and future perspectives for quan–qual analysis will be discussed.

Sample Preparation

Since the qualitative part of a quan–qual analysis is an untargeted analysis with a potential interest in molecules that are unidentified at the start of the analysis, the sample preparation should be kept to a minimum to avoid any potential degradation or recovery issues of the compounds of interest. This is in line with what is done in metabolite profiling studies and might be in contrast with quantitative assays that often use liquid–liquid extraction (LLE) and solid‑phase extraction (SPE) to improve the specificity and sensitivity of the assay. For quan–qual analyses, typically one or multiple volumes of an organic solvent (usually 3 or more volumes of acetonitrile) are added to the sample to stop any remaining enzymatic activity, break the analyte–protein binding, and precipitate the majority of the proteins. The samples are then centrifuged and the supernatant injected onto the LC–MS system.

Liquid Chromatography Optimization

Metabolites are often structurally related molecules. Therefore, longer LC gradients are typically used in metabolite profiling and metabolomics studies to reduce the risk of missing coeluting isobaric compounds. Quantitative LC–MS runs are usually very short, allowing a higher throughput of the analysis of large(r) batches of samples. A compromise needs to be made providing sufficient throughput with decent separation power. This is also a function of the batch size because there is no use in running short gradients if the instrument sits idle for a couple of hours because the run finishes in the middle of the night. Nevertheless, longer runs also result in larger file sizes and processing times, and a higher potential for MS drift.

Longer LC gradients do not necessarily give rise to the separation of more compounds in one run - expressed as peak capacity for gradient elution. The effect of a longer run time on separation power is often overestimated because an increase in gradient time gives a less than proportional increase in peak capacity. When chromatographic conditions are optimized using shorter LC columns packed with sub-2 µm particles in combination with higher flow rates and the extracolumn dead volume is reduced, it is perfectly feasible to achieve peak capacities similar to those obtained in traditional metabolite profiling studies in 2–3-fold shorter run times. This was demonstrated on a mix of pharmaceutical standards and an in vitro incubation of buspirone containing multiple isobaric metabolites (6). The latter is illustrated in Figure 2, which shows a slightly better separation between the major metabolites of buspirone in rat hepatocytes using a 2.5-fold shorter generic method, which applies a short (5 cm) ultrahigh‑pressure liquid chromatography (UHPLC) column packed with solid core 1.6-µm particles in combination with a higher flow rate.

While the peak capacity of the LC separation is worthwhile optimizing because metabolites are often isobaric compounds, the selectivity provided by mass spectrometry - high-resolution MS in particular - has a much bigger impact on the total peak capacity of the overall analytical assay.


Mass Spectrometry Methods

To take full benefit of the optimized chromatographic systems boosted for peak capacity, the mass spectrometry methods should provide high-quality quantification and a maximum of qualitative information in accordance with the short timeframe of high resolution chromatographic peaks. To obtain high-quality quantitative information with good reproducibility, linearity, and sensitivity, an adequate description of the chromatographic peak is essential. This requires typically >12 data points per chromatographic peak. Current attainable peak widths when using UHPLC are around 2–3 s and are distinctively smaller than traditional LC peak widths. Current HRMS systems are well equipped with respect to scan speed to obtain at least 12 data points across the peak in full scan mode, even with these narrow peak widths. From full scan HRMS data, relevant qualitative information can be obtained. Analytes can already be identified to a certain extent because information on the likely elemental composition can be obtained based on the accurate mass measurement and isotope ratios, or by using very high-resolution systems (Fourier-transform MS) one can even look at the isotopic fine structure. Scan routines to obtain MS/MS spectra can be added to the MS method to obtain additional qualitative information to aid in structure identification, for example, when coeluting isobaric metabolites show differences in fragmentation. However, when additional scan modes are added to the mass spectroscopy method, in addition to the full scan mode, challenges can arise with regards to the available acquisition time for these experiments. Optimizing both the “quan” and “qual” world will result in a “sweet spot” of settings where, on the one hand, an acceptable number of data points allows reliable quantitation and, on the other, MS/MS spectra of sufficient quality further aids identification.

The MS/MS scan routines can be divided into targeted (data-dependent [7,8]) and untargeted (data-independent [8,9]) strategies (see Figure 3). Targeted approaches relate to acquisition of product ion scans of preselected m/z values. A targeted approach can be (but does not need to be) part of a data-dependent strategy. A data‑independent approach is by definition untargeted.

Data-dependent strategies, such as Data Directed Analysis (DDA) or Information Dependent Analysis (IDA), have been known for more than a decade and were first used on triple quadrupole instruments. The system acquires data in full-scan mode and upon passing a threshold (either intensity [untargeted] or intensity at predefined m/z values [targeted]), the system automatically switches to one or more product-ion scans of that m/z value to obtain product ion spectra that can be used for identification purposes. Instead of using a predefined list of m/z values (targeted approach), MS/MS spectra can also be acquired through different parameters, such as the 10 most intense ions. This can be combined with dynamic inclusion and exclusion lists, isotope pattern filtering (if the analytes of interest contain chlorine, the isotope pattern filtering can be used to obtain MS/MS spectra of only chlorine-containing analytes), dynamic background subtraction, and on-the-fly mass defect filtering to maximize the number of MS/MS spectra obtained for relevant analytes, while reducing the acquisition of MS/MS spectra of irrelevant background ions. (Mass-defect filtering is often used in drug metabolism and filters data on the basis of the mass-defect of the parent compound. Unrelated compounds are then dynamically excluded from MS/MS acquisition.) The use of HRMS also allows for “thresholding” based on accurate mass instead of nominal mass, further reducing the number of product ion spectra of irrelevant background ions. Despite all these available filtering algorithms, the disadvantages of data‑dependent strategies remain. Taking individual MS/MS spectra requires substantial scanning time and hence some relevant, often lower abundant, analytes might be missed. Another disadvantage of a data-dependent strategy is that it relies heavily on prior knowledge of the sample and that it cannot be used as a generic method, especially for parameters such as mass defect filtering, isotope filtering, and using a predefined list of m/z values for each drug. Although MS/MS spectral quality is often superior to that obtained in data-independent strategies (discussed below), it is not usually the method of choice for quan–qual analyses, which ideally comprise the maximum amount of information to allow questions that come up long after the analyses have finished to be addressed.

In data-independent strategies, no precursor ions are selected for MS/MS as it is in data-dependent or targeted approaches. Instead, MS allows the passage of all precursor ions at the same time and no selection of ions on whatever criteria needs to be made. Both full scan and MS/MS spectra of all incoming ions are obtained via this so-called MSall (also called MSE) approach by alternately acquiring spectra at low-collision energy (full MS) and high-collision energy (MS/MS). If the UHPLC separation of the different analytes in the sample is adequate, good quality MS/MS spectra can be obtained via this approach. However, when the complexity of the sample increases, resulting in coelution of analytes, mixed MS/MS spectra are obtained that complicate their interpretation. One way to obtain cleaner MS/MS spectra in MSall mode, even for low-abundant analytes coeluting with high-abundance interfering analytes, is to transmit smaller m/z ranges (for example, 25 amu) instead of the whole m/z range to pass through the first MS analyzer and subsequently produce MS/MS spectra for each separate window (multiplexed MS/MS, MSX, SWATH). By consecutively stepping up the m/z range of ions passing through the MS system, the complete mass range of interest is “scanned” over. Data deconvolution by the vendor software relates the precursor and associated product ions. Parameters like scan speed, width of each window, number of windows selected, scan speed or dwell time for each window, and overlap between windows will affect the information obtained for each compound. The disadvantage of multiplexed MS/MS is that the required scan time is significantly longer than for MSall, and inversely proportional to the width of the m/z windows. Therefore, the multiplexed MS/MS approach is usually not compatible with quan–qual analysis where full MS or MSall is to be preferred, unless future developments in HRMS technology result in large improvements in scan speed. Another technique resulting in cleaner MS/MS spectra in MSall mode without affecting scan times is ion mobility spectrometry (IMS). In IMS, ions are separated based on size, shape, and charge rather than on m/z. The combination of retention time (LC) and drift time (IMS) alignment results in cleaner MS/MS spectra because precursor and product ions have identical drift times, while coeluting background ions and their product ions might be separated in the ion mobility device preceding the collision cell (10). In full MS, ion mobility separation can be beneficial, providing advanced selectivity and, thus, better detection limits. The IMS separation also has some disadvantages: it affects the dynamic range of the detector (earlier saturation), the resolving power of current IMS systems is often not adequate enough, and it has an impact on scan speed; however, in general, it is still compatible with average UHPLC peak widths.


Challenges and Future Perspectives

There are a lot of advantages to using HRMS systems for the acquisition of both quantitative and qualitative information in the same analyses. The number of applications is also gradually growing in different (mainly non-regulated) fields. Nevertheless, the majority of LC–MS quantification is still triple quadrupole-based since quan–qual approaches and high-resolution quantification, in general, also come with some challenges, as described in previous publications (11,12).

Historically, most laboratories focusing on LC–MS quantification are equipped with triple quadrupoles. The replacement of an MS system requires substantial investment, which can hamper a rapid shift to HRMS. In the last few years HRMS systems have become available offering high sensitivity, dynamic range, resolution, accuracy, and scan-to‑scan reproducibility at a price similar to modern triple quadrupole systems. Most of these are still tandem mass spectrometers (mainly quadrupole time-of-flight [QTOF]). However, the selectivity provided by the narrow extraction windows, based on accurate mass in full MS, is sufficient. Therefore, a growing choice in single-stage high‑resolution systems will make the switch easier because the investment will be lower than a high-end triple quadrupole or a tandem HRMS system. In addition, the ease of use will be improved over a tandem HRMS and TQ-MS system. HRMS systems were historically designed to be used primarily by more experienced MS users. The newly released TOF systems are focused on quantification and ease of use, rather than on flexibility in scan speed, resolution, and a myriad in MSn or MSall scanning options. As is the case in any application, any good hardware is only useful when appropriate application software is available. Many good uni- and multivariate analysis approaches and structure identification tools are available for metabolite profiling, typically done on HRMS systems. There is still room for improvement because identification of molecules based on mass spectrometry can be time-consuming and requires expertise. Another bottleneck in the qualitative part of the quan–qual analysis is that appropriate blank samples (for example, from vehicle dosed animals) are often lacking. This gives rise to false positives upon extraction of metabolites from the background.

Quantification tools based on narrow extraction windows are also available in most, if not all, vendor software. Processing times might be longer compared to triple quadrupole data processing because of the exponentially larger file sizes linked to the richness of the data, which also require much larger servers for file storage. However, a lot more can potentially be done with the available MS data besides looking at one accurate mass extracted ion chromatogram of the analyte(s) of interest, as is currently the standard for quantitative processing. Besides the [M + H]+ ion chromatogram, the ion chromatograms of the isotopes could be automatically processed to check their correspondence with the theoretical isotope distribution. This could serve as a peak purity check to highlight potential coeluting isobaric species or other coeluting compounds. Isotope peaks could be processed instead of the primary isotope in case of interference or summed to increase signal-to-noise ratios, especially in cases where the isotope peaks are equally abundant (bromine-containing compounds, multiple charged ions). Isotopes could also be used in detector saturation (and no saturation of the ionization) to increase the dynamic range by processing a less abundant isotope peak. The richness of the accurate mass data provides many more opportunities, such as automatically highlighting the presence of adducts and in-source fragments, automated assignment of the optimal narrow extraction window, and alerting the user for coelution with potentially interfering matrix compounds (phospholipids, PEG from the formulation) to name a few. These extra capabilities and improved ease-of-use of HRMS systems are key to convincing the majority of the LC–MS quantification user community to make the switch to high-resolution MS quantification and quan–qual analysis in those cases where additional information on metabolites or background ions might be useful in the future, and where there is no need for that extra 2–5-fold more sensitivity provided by the newest high-end triple quadrupole systems. Furthermore, increasing scan speed and the resolution of both the mass spectrometer and ion mobility separation in newer HRMS instruments will inevitably lead to more adaptation to quan–qual workflows. The pace with which the capabilities of modern high-resolution systems increase and the computer power and software tools improve will dictate the speed of implementation. However, the split between “quantitative” and “qualitative” bioanalytical departments, lack of experience with high-resolution MS, and often a more conservative approach in regulatory environments are some of the other hurdles to overcome for a widespread application.


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Ronald de Vries graduated in organic and analytical chemistry at the Free University of Amsterdam, in the Netherlands. After working in a contract laboratory for 7 years, he joined Janssen R&D in 1998. At Janssen R&D, he worked in the bioanalytical department supporting both clinical and non‑clinical bioanalysis. He provided the bioanalytical support for various drug development programmes and led the method establishment group responsible for development of new bioanalytical assays using LC–MS/MS. Since 2014, he has worked in the drug metabolism group, focusing on metabolite identification using high resolution mass spectrometry and radiodetection. He has (co-)authored more than 40 peer-reviewed scientific publications.

Rob J. Vreeken has over 25 years experience in quantitative and qualitative analysis by hyphenated MS techniques in a wide variety of applications. He has worked in academia, service providers, and in industry, and is currently employed at Janssen Pharma R&D. His team focuses on quantitative assays in early discovery and exploratory pharmacokinetics, dynamics, and metabolism. Next to semi-HT compound exposure analysis, the team implements metabolomics strategies to collect information on efficacy through semi‑quantitative PD-marker analysis. He is also an associate professor at M4I, Maastricht University in the Netherlands, where he focuses on quantitative mass spectrometry imaging techniques for pharmaceutical markers. He has (co-)authored close to 100 peer-reviewed scientific papers and is a frequent presenter at international symposia.

Filip Cuyckens is a Scientific Director & Fellow at Janssen R&D. He is responsible for analytical sciences in the pharmacokinetics, dynamics, and metabolism department, focusing on metabolite profiling and identification of drugs in discovery and development, and quantification of drug candidates, metabolites, and biomarkers in biological matrices. He earned a pharmacist degree in 1998, a degree in industrial pharmacy in 2002, and a Ph.D. in pharmaceutical sciences in 2003. He has (co-)authored more than 50 peer‑reviewed scientific publications, and is a member of the associate editorial board of Rapid Communications in Mass Spectrometry and board member of the Belgian Society for Mass Spectrometry.