The Application of Fully Automated Dried Blood Spot Analysis for Liquid Chromatography-Tandem Mass Spectrometry using the CAMAG DBS-MS 500 Autosampler

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S0009-9120(19)31368-2 https://doi.org/10.1016/j.clinbiochem.2020.02.007 CLB 10083

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Clinical Biochemistry

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Please cite this article as: M. Luginbühl, S. Gaugler, The Application of Fully Automated Dried Blood Spot Analysis for Liquid Chromatography-Tandem Mass Spectrometry using the CAMAG DBS-MS 500 Autosampler, Clinical Biochemistry (2020), doi: https://doi.org/10.1016/j.clinbiochem.2020.02.007

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The Application of Fully Automated Dried Blood Spot Analysis for Liquid Chromatography-Tandem Mass Spectrometry using the CAMAG DBS-MS 500 Autosampler Marc Luginbühl1, Stefan Gaugler1 1CAMAG,

Sonnenmattstrasse 11, 4132 Muttenz, Switzerland

Corresponding author: Marc Luginbühl Sonnenmattstrasse 11 4132 Mutttenz Switzerland [email protected] Highlights  How and why fully automated DBS analysis reached the routine laboratory environment  DBS methods for forensics, therapeutic drug monitoring, and mass drug administration  Advantages of DBS automation: speed, reliability, and user-friendliness Abbreviations 17-OHP, 17 hydroxyprogesterone; DBS, dried blood spots; ELISA, enzyme-linked immunosorbent assay; HRMS, high-resolution mass spectrometry; IS, internal standard; LC-MS/MS, liquid chromatography-tandem mass spectrometry; NBS, newborn screening; PEth, Phosphatidylethanol; SPE, solid-phase extraction; SRM, selected reaction monitoring; TDM, therapeutic drug monitoring; TOF, time-offlight

Abstract In the past decade, dried blood spots (DBS) have increasingly been applied for microsampling in various fields, driven mainly by the significant advantages DBS can offer regarding sample shipment and analyte stability, accompanied by the DBS sampling strategy. However, the manual handling of DBS samples is laborsome and prevents the use of a high-capacity bioanalytical workflow. The recent introduction of robotic DBS extraction systems in combination with liquid chromatographytandem mass spectrometry (LC-MS/MS) has enabled the full automation of the analytical process. This results in overall higher sample throughput, minimal user interaction, and a significant reduction in consumables. Different instrumental setups are currently available which differ with respect to the extraction process, extract processing strategy, and internal standard application. This review article provides an overview of fully automated DBS analysis for one of these instruments, the DBS-MS 500 autosampler from CAMAG. The automated processes are described in detail and various applications are presented. Emphasis is placed on the advantages that the use of DBS, in combination with automation, brings – such as speed, reliability, and user-friendliness. Discussing DBS solutions for newborn screening, workplace drug testing, forensic screening, direct alcohol marker analysis, antiretroviral drugs, anti-epileptic drugs, and mass drug administration, the versatility and applicability of DBS are demonstrated in detail. In conclusion, this article shows how and why fully automated DBS analysis has penetrated the routine laboratory environment. Keywords Dried Blood Spots, Automation, Minimally Invasive Sampling, LC-MS/MS

1. Introduction Microsampling capillary blood as dried blood spots (DBS) facilitates the collection of biological samples to monitor endogenous and exogenous substances (1). After a simple and minimally invasive fingerprick, a few drops of blood are sampled onto a filter paper card. In contrast to sampling venous blood, the simplified sampling, using the DBS technique, does not require a trained doctor, nurse, or phlebotomist (2). The sampling process can be performed by adequately instructed patients or scientists. Furthermore, the DBS technique is proven to have many other advantages, compared to conventional liquid blood sampling. It is less expensive to collect and transport DBS samples, it reduces storage space, and many samples have increased substance stability (slower degradation), which is extremely beneficial (3–5). Drawbacks for the use of DBS are the fact that reference values for the target analytes may only available for plasma or serum samples. Therefore, a comparison study between whole blood DBS and serum or plasma samples may be necessary. Furthermore, dependent on the analyte concentration and ionization properties, highly sensitive instrumentation and process optimization may be required in order to reach cut-off concentrations within the limited sample volume provided by DBS. Another frequently discussed aspect of DBS sampling is the fact that DBS samples are potentially affected by the patient's hematocrit level, which influences the spreading area of the DBS (6). However, there are nowadays many options available to address the hematocrit before or after the sampling of a DBS, such as the HemaApp, single-wavelength reflectance-based hematocrit prediction, or nearinfrared spectroscopy (7–9). The sometimes referred inhomogeneous sample distribution in DBS, also known as the coffee-stain effect, can be compensated by the extraction from the center of the spot (10). There is an increasing need for the automation of DBS analytical processes as these applications require a high sample throughput environment. For routine laboratory applications, certain commercial systems are available, which allow partial automation of the DBS analytical process such as automated punchers and liquid handling systems. For fully automated DBS analysis approaches, there are few manufacturers in the marketplace (11). In contrast to partial automation, the use of fully automated systems offers a seamless workflow; it only requires the manual insertion of a batch of DBS cards into the analytical system at the start of the run. This is followed by automated extraction, separation, and analysis - without the requirement of any further human interaction. Currently, there are only two major hardware setups available, which can be coupled with an LC-MS/MS system: the

DBS-MS 500 (CAMAG), and the DBS AutosamplerTM (DBSA, Spark Holland, also sold as original equipment manufacturer device). These systems differ in their sample capacity (500 cards vs multiples of individual 24 card racks), extraction process (horizontal extraction vs flow-through desorption), extract processing strategy (loop, online SPE, or SPE), and internal standard application (spray application vs mixing to the extraction solvent). Therefore, the individual systems are not directly comparable with each other. A PubMed literature search for “fully automated dried blood spot/s” and “DBS Autosampler” resulted in six publications for the DBS AutosamplerTM system (12–17), and ten articles for the DBS-MS 500 (18–27). In this article, we present a review of the current applications of fully automated DBS LC-MS/MS analysis which have been realized using the CAMAG DBS-MS 500 system (Muttenz, Switzerland). The main emphasis is therefore how and why the fully automated DBS analysis has arrived in routine laboratory environments. 2. Analytics 2.1.General Fully automated DBS analysis is realized by coupling a DBS autosampler with an LC-MS/MS system, see Figure 1. Generally, the DBS autosampler can be operated with any major LC-MS/MS brand. To physically connect the LC-MS/MS system with the DBS autosampler, a remote control connection and two capillary tubings need to be installed. The DBS autosampler serves as a modular system, which is responsible for the sample documentation, internal standard application, and sample extraction, see Figure 2. Sample extraction is possible into a sample loop of variable size or onto a trapping column to perform online-SPE (19, 23). Unlike manual extraction procedures, directly eluting samples from the DBS eliminates the need for consumables such as sampling tubes, pipette tips, and glass vials. Once the extraction is finished, the sample information and the remote start signal to trigger the LC-MS/MS run is transmitted from the autosampler to the LC-MS/MS system. Sample handling is based on standard cardboard cards with an embedded filter paper (e.g. Ahlstrom® AutoCollect® or Whatman® DMPK-A, -B, and –C) (28). These cards can contain up to four DBS and have the advantage of being a low-cost, singleuse, sample collection medium. Using a highly efficient and robust card gripper with suitable error handling and a capacity for up to 500 DBS cards within 5 racks, the DBS autosampler permits to perform high-throughput analysis. The automated processes built within the DBS autosampler are listed in section 2.2-2.4. They

include a camera module, an internal standard spray application module, and an extraction module.

Figure 1 Schematic setup of a DBS-MS 500 autosampler from CAMAG coupled to an LC-MS/MS system. Depicted in the upper figure is the process of DBS extraction into a sample loop. The vertically movable extraction plunger seals the DBS card inside the extraction cell. The extraction solvent is pumped from the extraction pump, through the amboss, then horizontally through the card, and exits the amboss through a second hole which is connected to the sample loop placed at the 10-port valve. Depicted in the lower figure are the extraction cell cleaning process and the sample introduction into the LC-MS/MS system. During the cleaning process, the extraction cell is flushed backward by pumping solvent from the rinsing pump, through the 10-port valve, into the extraction cell. From the extraction cell, the rinsing solvent is removed by applying a vacuum onto the rinsing module wash station.

Figure 2 Photographs of the CAMAG DBS-MS 500 instrument and a typical sample card handling process. A robotic arm moves the DBS card between the modules situated at the back of the instrument. The modules include a camera module, an internal standard spray application module, and an extraction module with a wash station.

2.2.Camera Module A visual DBS recognition system is embedded for the photographic documentation prior to, and following the extraction process. It permits the monitoring of each extraction retrospectively and allows the inclusion of an image of the sample card into each analysis report. Image processing determines the shape, size, and accurate location of each DBS. Acceptance criteria pertaining to the spot size and shape can be defined within the extraction method. If the extraction of a spot is rejected based on the DBS quality criteria, the system can autonomously check the suitability of the alternative spots on the card. The identification of the exact DBS location on the card permits the application of internal standard and the sample extraction at the center of the spot. Further features of the camera module include barcode and checkbox reading (box on the DBS card, situated above each DBS spot). This checkbox is for the originator of the sample to indicate whether they consider the spot to be invalid and therefore should not be analysed.

2.3.Internal Standard Spray Application Module A spraying module permits the fully automated application of 5-40 µL of internal standard (IS) on each DBS prior to extraction. The IS is sprayed in a fine stream of liquid over an area of one square centimeter around the center of the DBS, causing no flooding (or any physical mass flow of sample components) (29). The adoption of spraying to apply the IS onto the sample was defined as the gold standard for DBS analysis, as it was observed, that the spray addition of IS is sufficient to effectively nullify hematocrit based recovery bias (11, 29, 30). Spraying allows the integration of the IS into the sample, allowing binding to matrix components and the paper substrate. Mixing the IS to the extraction solvent rather than spraying it directly onto the sample, does not result in any binding of the matrix components and substrate prior to the extraction process, and is therefore not able to compensate for any variability during the extraction process. Until today, Abu-Rabie et al. was the only publication focusing on a proper evaluation of extraction bias in fully automated DBS systems. 2.4.Extraction Module The DBS extraction module permits the horizontal extraction of a 4 mm diameter sub punch from the center of the DBS. A plasma polished stainless steel extraction head is pressed on top of the DBS card, tightly sealing the extraction cell, with the

filter paper material of the DBS card serving as a seal. Subsequently, the pressurized extraction of the DBS card is realized by the introduction of the extraction solvent from the bottom. During and after the entire extraction process, the connection between the sub punch and the DBS card remains intact. The extraction volume and the extraction flow rate (10-200 µL/min) are fully adjustable. For the collection of the concentrated sample extract, a variable volume sample loop (e.g. Thermo Scientific™ Viper™ Capillary) or a trapping column to perform online-SPE-LCMS/MS can be mounted at the ten port valve of the DBS-MS 500 autosampler. After the DBS extraction, a specially designed wash station is moved into the extraction cell. The wash station permits the cleaning of the extraction cell by reverse flushing the entire extraction pathway. Different durations of extraction cell rinsing with up to four different solvents, simultaneously connected to the instrument is possible.

3. Applications of Fully Automated DBS Methods 3.1.New Born Screening Newborn screening (NBS) is a public health program provided by most of the countries around the world. It’s purpose is the is targeted screening of newborns for a range of serious genetic and metabolic disorders (31–33). Early diagnosis of such conditions can help improve their further development which, if left untreated, often results in brain damage, organ damage, and even death. Two applications have been transferred to automated platforms. The first method uses a standardized, commercial reagent kit for the assessment of amino acids and acylcarnitines (34). The analysis with the commercial kit only takes 2 minutes per sample and permits the analysis of more than 500 samples per day. The DBS is extracted with the commercial extraction buffer with a volume of 60 μL and a 200 μL/min flow rate into a 20 μl loop (the 40 μl upfront volume was directed to the waste). Afterwards, the extract is directly guided into the mass spectrometer without any column or filter. This is a well-known procedure in newborn screening and permits the maximum possible efficiency resulting in the highest sample throughput. The drawback of the direct injection technique is that all compounds elute at the same time and are only distinguished by their selected reaction monitoring (SRM) transitions. Since this method is only required to be semi-quantitative, the imperfect peak shape and fast switching time of the mass spectrometer between all SRMs is acceptable. Sample signals are compared against certified high and low control samples from the commercial kit and the individual sample signals are reported relative to those samples. Since the difference in concentration from a normal NBS sample (healthy) to a positive sample (metabolic disorder) varies significantly for most diseases, higher sensitivity and precision for the amino acids and acylcarnitines are not required. The second method is a new, extended screening for amino acids, acylcarnitines, and steroids, such as 17-hydroxyprogesterone (17OHP), cortisol, and androstenedione (35). This screening panel permits the exclusion of 17OHP from the immune assay panel, as conventional 17OHP enzyme-linked immunosorbent assay (ELISA), based on manually punched DBS discs, leads to a unacceptably high percentage of false positives. The reason is that cortisol increases when the babies are stressed, which leads to elevated 17OHP concentrations and secondly, the ELISA shows significant cross-reaction potential with steroid sulfate, which can be monitored with androstenedione. Both amino acid and acylcarnitine detection screening and the

integration of the steroid panel into the DBS-LC-MS/MS workflow were successfully achieved. The extraction solvent for the DBS was a mixture of methanol and water (70:30, v/v), whereby a volume of 20 µL was extracted at a flow rate of 200 µL/min. Unlike most currently used NBS protocols, a short analytical column was used as the steroids had to be separated to prevent coelution of compounds sharing similar SRM transitions. The method requires 4 minutes per sample and exhibits significantly improved robustness and sensitivity: The use of an analytical column introduces a separation between matrix components and analytes and acts as a filter, reducing the contamination of the ion source. The article contains a protocol on how to manually prepare the mobile phase, the rinsing and extraction solvent, and the internal standard mixture. This extended screening method presents a very simple and costeffective workflow and allows full flexibility for the laboratory as the calibration and quality control cards can be produced in-house. The automated DBS solutions for NBS have successfully been transferred to the routine laboratory environment (24, 26).

3.2.Workplace Drug Testing and Forensic Screening The use of DBS in combination with a highly sensitive mass spectrometer is ideally suited for the fully automated analysis of substances of abuse. Two publications detailing this approach have been published so far (23, 36). In contrast to drug screening in urine or in hair samples, blood is the matrix of choice to indicate if a subject is currently under the influence of an illicit substance. The use of minimally invasive sampling facilitates the general sampling process, which could lead to future increases in the number of samples being collected for workplace drug testing and forensic screenings. For the first publication focusing on drug screening, 15 µL DBS were extracted into a 20 µL sample loop with a mixture of methanol and water (70:30, v/v) and subsequently analyzed for 12 illicit drugs and their respective internal standards using compound information from a forensic database (Forensic Toxicology Database from Shimadzu with up to 1281 substances for targeted screening) (36). Using four calibration levels for each of the drugs allowed quantitative targeted screening. To demonstrate the applicability of the screening method in a real setting, two anonymized DBS samples were obtained from healthy donors using back pain medication and successfully analyzed for codeine. For the second publication, which focused on forensic routine DBS screening for workplace testing a fast 28 compound and a slower extended screening method using the whole range of the Forensic Toxicology Database from Shimadzu was developed and applied to real case samples (23). DBS samples were extracted into a 20 µL loop using a mixture of methanol and water (70:30, v/v) as extraction solvent. To perform a system suitability test of the screening method, each day high- and low-QC samples were measured before starting the sample analysis. This allowed ensuring the proper functioning of all the involved instrumentation. The QC cards were prepared by using the DBS-MS 500 spraying module. At first, all alkali target compounds were sprayed, followed by spraying of the acidic compounds. The methods described were developed in a research laboratory using a Shimadzu 8060 instrument and later transferred to a routine laboratory equipped with a Shimadzu 8040 instrument. Due to the lower sensitivity of the Shimadzu 8040 system, an adjustment of the method’s limit of detection and quantification is recommended when performing such a transfer. Generally, the use of an extensive database allows creating highly customized screening methods for the individual country's drug-testing requirements and programs (37). Based on the number of substances within the LC-MS/MS method and the required degree of confirmation, different targeted screening approaches are available. The simplest screening method is based on scanning for hundreds of SRM transitions, in combination with retention time database matching. However, this

method does not provide a high degree of confirmation regarding structural identification, requiring additional confirmation and quantification (38). The addition of specific calibration samples (analyte and internal standard) permits the quantification of drug concentrations within the individual DBS samples, increasing the degree of confirmation. Compound identification by merged spectrum is another possible methodology. To obtain such spectra, three MS/MS spectra obtained at different collision energies are merged. Subsequently, this merged spectra is compared with pre-installed library spectra, which results in the generation of a similarity index and higher confidence for the identification of the individual compound within the sample. One disadvantage of this procedure is that obtaining such spectral data increases the loop time of the mass spectrometer, reducing the total number of analytes that can be contained within a method. An alternative to targeted screening would be the use of high-resolution mass spectrometry (HRMS) to perform untargeted screening (39). This approach could be very beneficial, as the higher mass resolution could prove itself to be advantageous. However, the reduced sensitivity of the time-of-flight (TOF) detector could be disadvantageous. A DBSMS 500 – HRMS coupling study has been conducted, but at present, the potential of fully automated, untargeted substance of abuse DBS screening using LC-MS-TOF instrumentation has not been fully evaluated (22). 3.3.Direct Alcohol Marker Analysis Phosphatidylethanol (PEth) is the most promising direct alcohol marker for the application in driving aptitude assessments, the evaluation of alcohol misuse in organ donors, and the alcohol withdrawal and dishabituation therapy (40–42). As the molecular structure contains fatty acyl chains, PEth is not stable in liquid blood - unless it is stored at -80°C (3). This makes the use of liquid blood unsuitable for sample transportation. However, PEth has been shown to be stable in DBS, as the inactivation of enzymes during the drying process prevents degradation. Automated extraction procedures have been developed for the analysis of PEth in liquid blood, using a robot to perform isopropanol extraction into a 96-well plate (43). However, this procedure still involves a manual step, as it is necessary to seal the well plate and introduce it into the autosampler of the LC-MS/MS system. The development of a fully automated DBS online-SPE-LC-MS/MS method for the analysis of PEth resulted in a run time of just 5 minutes per sample (19). Following extraction with a mixture of water, acetonitrile, 2-propanol and formic acid (34.5:15:50:0.05, v/v/v/v), the sample extract is guided to a small trapping column to perform solid-phase extraction. From there, direct elution from the trap onto an analytical column takes place, where analyte separation occurs. The method is well suited for highthroughput analysis, as it has the potential to analyse PEth 16:0/18:1 and PEth

16:0/18:2 for up to 200 samples a day. Once the calibration and quality cards are prepared (which are stable for weeks), each daily batch run can be immediately started following the introduction of the sample cards into the system. Comparison studies using manual liquid blood extraction and manual whole spot DBS extraction proved that the CAMAG-DBS MS 500 autosampler provides reliable results without labor-intensive and costly sample preparation. This fully automated procedure has already been successfully implemented in numerous routine laboratories. Furthermore, a follow-up study focusing on alcohol withdrawal and dishabituation therapy received financial support from the Swiss Foundation for Alcohol Research (SSA/FSRA) and is currently conducted using fully automated PEth analysis (42). 3.4.Antiretroviral Drugs The use of antiretroviral drugs has been strongly correlated with an increase in survival and improved quality of life for people who are HIV positive, as it leads to viral suppression (44). However, due to genetic and nongenetic heterogeneity in drug disposition, the circulating antiretroviral drug concentration shows a high degree of variability (45, 46). Using DBS for therapeutic drug monitoring (TDM) of antiretroviral drugs is a well suited and practicable way to study therapy adherence and the circulating antiretroviral drug concentration, as it minimizes the high infection risk of HIV/AIDS samples and facilitates the mailing of samples (47–50). Duthaler et al. automated the high throughput analysis of antiretroviral drugs in DBS in 2017 (51). They developed and validated a polarity switching ESI-LC-MS/MS method for the automated extraction of nevirapine, efavirenz, and lopinavir from DBS. In addition to the method details, the publication contain a graph showing the optimization process for the extraction solvent, extraction volume, and extraction flow. Based on the optimization, the extraction was performed with a mixture of methanol and water (70:30, v/v) into a 20 μl sample loop using a volume of 25 μl and a 40 μL/min flow rate. As a 20-μl loop was installed, the first 5 μl of each extraction was discarded, as this was found to increase the method’s precision. Analytes were separated on a core-shell pentafluorophenyl column within a total runtime of 3.3 min. The results obtained with the fully automated system were comparable with the results obtained with manual DBS extraction. However, the sample processing time and method sensitivity were improved by the automated extraction procedure. A subsequent clinical study comparing DBS with plasma samples was performed during a field study with samples from 299 patients from Tanzania (25). Accuracy and precision data established during the study showed that the automated approach was reliable and time-efficient. Furthermore, the authors suggest that this type of automated DBS analysis holds promise to further DBS sampling for TDM.

3.5.Anti-Epileptic Drugs A fully automated TDM method for the determination and quantification of antiepileptic drugs in DBS was developed and fully validated at the Laboratory of Toxicology in Ghent, Belgium (18). The analysis of four anti-epileptic drugs (carbamazepine, valproic acid, phenobarbital, and phenytoin) together with one active metabolite (carbamazepine-10,11-epoxide) was accomplished with a total runtime of 8 min. The extraction of the DBS samples was performed with a mixture of acetonitrile and water (80/20, v/v) containing 5 mM ammonium acetate and the IS mixture (with all IS at 0.50 g/mL). During the method development, a decrease in the IS peak was observed with an increasing analyte concentration for Carbamazepine-d10 and Carbamazepine-10,11-epoxide-d10, possibly indicating saturation at the ESI source. To prevent this from happening an extraction volume exceeding the loop volume was used (60 µL extraction solution into a 20 µL sample loop, discarding the first 40 µL). Additionally, the authors decided to include the IS into the extraction solution, as one of the sprayed deuterated standards indicated a different extraction behavior when overfilling the sample loop with the chosen solvent combination (extraction solution and the IS spray solution). With the applied changes, all validation criteria were met, and the DBS samples were shown to be stable for at least one month when stored at room temperature. The successful application of the fully automated DBS method to capillary DBS patient samples originating from developing countries (sub-Saharan Africa) demonstrated the applicability of the developed procedure in a remote setting. The use of DBS has been shown to be beneficial for monitoring anti-epileptic drugs in children (52), as for this patient group, a simple fingerprick is beneficial; it is less painful, can be administered by the guardian and permits the collection of acceptable quality DBS at home. Such a study has been realized by the use of fully automated DBS analysis of samples from the Democratic Republic of Congo (27). 3.6.Mass Drug Administration To monitor treatment during a mass drug administration campaign for the control of parasitic diseases in the tropics, a method for the determination of ivermectin in DBS was developed by Duthaler et al. (20). The method is based on a Kinetex C8 (2.6 µm, 100 A, 50 × 2.1 mm) reversed-phase column, and with a total runtime under 4 minutes ideally suited for the determination of thousands of samples. DBS extraction was realized by using a mixture of methanol and an aqueous solution of 20 mM

ammonium formate supplemented with 0.1% formic acid (pH 3.5) (7:3, v/v). The achieved sensitivity of 1 ng/mL is suitable to analyze ivermectin for at least 72 h post-treatment with a single oral dose of 12 mg. DBS samples were found to be stable after one month of storage at room temperature, permitting the uncooled collection, transportation and storage of samples. By treating volunteers with 12 mg ivermectin, the use of DBS was clinically validated as an alternative to conventional venous blood sampling. Ivermectin levels in plasma, whole blood and in capillary DBS were compared. Ivermectin in venous and capillary blood agreed strongly, showing a mean difference of only -4.8%. Additionally, it was found that plasma concentrations can be extrapolated from DBS measurements. In a second study, the ivermectin DBS method was applied to a pharmacokinetic study with 12 healthy volunteers who each received a single oral dose of 12 mg ivermectin (21). During the course of this study, peripheral venous and capillary DBS samples were collected. This resulted in a model that accurately depicts population pharmacokinetics of plasma and DBS concentrations against time for the oral ingestion of ivermectin. This method is to be applied in a future study, involving more than 8000 DBS samples. Such large sample volumes are ideally suited for fully automated DBS solutions. 4. Challenges of Fully Automated DBS Implementations The implementation of fully automated DBS methods as stated in this review may pose a challenge. The use of a fully automated extraction system such as the CAMAG DBS-MS 500 autosampler adds additional complexity to the LC-MS/MS analysis and requires specific strategies regarding method validation, including novel approaches to assess the extraction efficiency, recovery, and matrix effects (51). Furthermore, parameters such as the internal standard application volume, the internal standard drying time, the extraction cell rinsing solutions, the extraction solvent, the extraction duration, the extraction volume, the extraction flow rate, and the extraction principle (loop or online SPE) need to be carefully addressed. Thereby, the most challenging step is finding a combination of extraction solvent and analytical column that match. Contrary to manual extraction procedures where the sample extract can be dried and subsequently reconstituted to fit LC-MS/MS starting conditions, this is not possible for fully automated systems. For the extraction with a fully automated system, the extraction solvents must be compatible with LC-MS/MS analysis. Furthermore, the target analytes need to be extractable with the selected extraction solvent and after being introduced into the LC circuit still retain on the analytical column. Using large volumes of methanol, acetonitrile

or 2-propanol to extract the DBS may be very effective considering the extraction efficiency, however, it may cause issues considering the focusing and retention with reversed-phase column material. A figure presenting detailed method optimization with respect to flow rate, extraction solvent composition, and extraction flow can be found in the publication of Duthaler et al. (51). Upon optimization of the method, they observed that methanol concentration exceeding 80% in the extraction solvent disturbs the peak symmetry using a Kinetex 2.6 μm F5 100 Å (50 × 2.1mm) analytical column. Velghe et al. on the other hand observed that a mixture of acetonitrile and water (80:20, v/v) containing 5 mM ammonium acetate worked best with their LC-MS setup, using a Chromolith® reversed-phase (RP)-18 end-capped column (100 x 4.60 mm ) (18). Extraction solvents with a high water content were evaluated as being disadvantageous by Duthaler et al., as they increased the risk of clogging the extraction head, presumably because more biomolecules and cellular components were removed from the DBS (51). Overall, the transfer from a manual DBS method onto a fully automated DBS system may need LC and automation expert knowledge and can take several weeks.

5. Conclusions The use of fully automated DBS analysis has reached the routine laboratory environment and is being applied for high-throughput applications. Once adopted and established, the use of fully automated DBS analysis simplifies the entire analytical process, making previously labor-intensive analytical procedures more accessible. Furthermore, analysis using a fully automated system is accompanied by a significant reduction in consumables and labor costs; as the DBS card is the site of extraction, there is no requirement for consumables such as sampling tubes, glass vials, and pipette tips to perform the extraction process. 6. Summary Declaration of Interest Statement Dr. Marc Luginbühl and Dr. Stefan Gaugler are part of the DBS research laboratory at CAMAG (Muttenz, Switzerland). This review did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

7. References 1. Spooner, N. (2013) A dried blood spot update: Still an important bioanalytical technique? Bioanalysis, 2013: 10.4155/bio.13.56. 2. Tanna, S. and Lawson, G. (2015) Self-sampling and quantitative analysis of DBS: Can it shift the balance in over-burdened healthcare systems? Bioanalysis, 2015: 10.4155/bio.15.110. 3. Faller, A., Richter, B., Kluge, M., Koenig, P., Seitz, H.K. and Skopp, G. (2013) Stability of phosphatidylethanol species in spiked and authentic whole blood and matching dried blood spots. International Journal of Legal Medicine, 2013: 10.1007/s00414-012-0799-y. 4. Moretti, M., Visonà, S.D., Freni, F., Tomaciello, I., Vignali, C., Groppi, A., et al. (2018) A liquid chromatography–tandem mass spectrometry method for the determination of cocaine and metabolites in blood and in dried blood spots collected from postmortem samples and evaluation of the stability over a 3month period. Drug Testing and Analysis, 2018: 10.1002/dta.2399. 5. D’Arienzo, C.J., Ji, Q.C., Discenza, L., Cornelius, G., Hynes, J., Cornelius, L., et al. (2010) DBS sampling can be used to stabilize prodrugs in drug discovery rodent studies without the addition of esterase inhibitors. Bioanalysis, 2010: 10.4155/bio.10.94. 6. Zakaria, R., Allen, K.J., Koplin, J.J., Roche, P. and Greaves, R.F. (2016) Advantages and Challenges of Dried Blood Spot Analysis by Mass Spectrometry Across the Total Testing Process. EJIFCC, 2016. 7. Wang, E.J., Li, W., Hawkins, D., Gernsheimer, T., Norby-Slycord, C. and Patel, S.N. (2016) HemaApp: Noninvasive blood screening of hemoglobin using smartphone cameras. In UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. . 8. Capiau, S., Wilk, L.S., De Kesel, P.M.M., Aalders, M.C.G. and Stove, C.P. (2018) Correction for the Hematocrit Bias in Dried Blood Spot Analysis Using a Nondestructive, Single-Wavelength Reflectance-Based Hematocrit Prediction Method. Analytical Chemistry, 2018: 10.1021/acs.analchem.7b03784. 9. Oostendorp, M., El Amrani, M., Diemel, E.C., Hekman, D. and Van Maarseveen, E.M. (2016) Measurement of hematocrit in dried blood spots using near-infrared spectroscopy: Robust, fast, and nondestructive. Clinical Chemistry, 2016: 10.1373/clinchem.2016.263053.

10. Lenk, G., Hansson, J., Beck, O. and Roxhed, N. (2015) The effect of drying on the homogeneity of DBS. Bioanalysis, 2015: 10.4155/bio.15.135. 11. Abu-Rabie, P. (2011) Direct analysis of DBS: Emerging and desirable technologies. Bioanalysis, 2011: 10.4155/bio.11.159. 12. Tretzel, L., Thomas, A., Piper, T., Hedeland, M., Geyer, H., Schänzer, W., et al. (2016) Fully automated determination of nicotine and its major metabolites in whole blood by means of a DBS online-SPE LC-HR-MS/MS approach for sports drug testing. Journal of Pharmaceutical and Biomedical Analysis, 2016: 10.1016/j.jpba.2016.02.009. 13. Verplaetse, R. and Henion, J. (2016) Hematocrit-Independent Quantitation of Stimulants in Dried Blood Spots: Pipet versus Microfluidic-Based Volumetric Sampling Coupled with Automated Flow-Through Desorption and Online Solid Phase Extraction-LC-MS/MS Bioanalysis. Analytical Chemistry, 2016: 10.1021/acs.analchem.6b01190. 14. Verplaetse, R. and Henion, J. (2016) Quantitative determination of opioids in whole blood using fully automated dried blood spot desorption coupled to online SPE-LC-MS/MS. Drug Testing and Analysis, 2016: 10.1002/dta.1927. 15. Ryona, I. and Henion, J. (2016) A book-type dried plasma spot card for automated flow-through elution coupled with online SPE-LC-MS/MS bioanalysis of opioids and stimulants in blood. Analytical Chemistry, 2016: 10.1021/acs.analchem.6b03691. 16. Martial, L.C., van den Hombergh, E., Tump, C., Halmingh, O., Burger, D.M., van Maarseveen, E.M., et al. (2018) Manual punch versus automated flowthrough sample desorption for dried blood spot LC-MS/MS analysis of voriconazole. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 2018: 10.1016/j.jchromb.2018.04.039. 17. Hempen, C.M., Maarten Koster, E.H. and Ooms, J.A. (2015) Hematocritindependent recovery of immunosuppressants from DBS using heated flowthrough desorption. Bioanalysis, 2015: 10.4155/bio.15.97. 18. Velghe, S., Deprez, S. and Stove, C.P. (2019) Fully automated therapeutic drug monitoring of anti-epileptic drugs making use of dried blood spots. Journal of Chromatography A, 2019: 10.1016/j.chroma.2019.06.022. 19. Luginbühl, M., Gaugler, S. and Weinmann, W. (2019) Fully Automated Determination of Phosphatidylethanol 16:0/18:1 and 16:0/18:2 in Dried Blood Spots. Journal of Analytical Toxicology, 43, 489–496.

https://academic.oup.com/jat/article/43/6/489/5486352 (15 November 2019). 20. Duthaler, U., Suenderhauf, C., Gaugler, S., Vetter, B., Krähenbühl, S. and Hammann, F. (2019) Development and validation of an LC-MS/MS method for the analysis of ivermectin in plasma, whole blood, and dried blood spots using a fully automatic extraction system. Journal of Pharmaceutical and Biomedical Analysis, 2019: 10.1016/j.jpba.2019.04.007. 21. Duthaler, U., Suenderhauf, C., Karlsson, M.O., Hussner, J., Meyer zu Schwabedissen, H., Krähenbühl, S., et al. (2019) Population pharmacokinetics of oral ivermectin in venous plasma and dried blood spots in healthy volunteers. British Journal of Clinical Pharmacology, 2019: 10.1111/bcp.13840. 22. Oliveira, R. V., Henion, J. and Wickremsinhe, E.R. (2014) Automated highcapacity on-line extraction and bioanalysis of dried blood spot samples using liquid chromatography/high-resolution accurate mass spectrometry. Rapid communications in mass spectrometry : RCM, 2014: 10.1002/rcm.7033. 23. Gaugler, S., Al-Mazroua, M.K., Issa, S.Y., Rykl, J., Grill, M., Qanair, A., et al. (2019) Fully Automated Forensic Routine Dried Blood Spot Screening for Workplace Testing. Journal of Analytical Toxicology, 2019: 10.1093/jat/bky074. 24. Mak, C.M., Law, E.C.Y., Lee, H.H.C., Siu, W.K., Chow, K.M., Au Yeung, S.K.C., et al. (2018) The first pilot study of expanded newborn screening for inborn errors of metabolism and survey of related knowledge and opinions of health care professionals in Hong Kong. Hong Kong Medical Journal, 2018: 10.12809/hkmj176939. 25. Duthaler, U., Berger, B., Erb, S., Battegay, M., Letang, E., Gaugler, S., et al. (2018) Using dried blood spots to facilitate therapeutic drug monitoring of antiretroviral drugs in resource-poor regions. Journal of Antimicrobial Chemotherapy, 2018: 10.1093/jac/dky254. 26. Fingerhut, R., Silva Polanco, M.L., Silva Arevalo, G.D.J. and Swiderska, M.A. (2014) First experience with a fully automated extraction system for simultaneous on-line direct tandem mass spectrometric analysis of amino acids and (acyl-)carnitines in a newborn screening setting. Rapid Communications in Mass Spectrometry, 2014: 10.1002/rcm.6856. 27. Velghe, S., Delahaye, L., Ogwang, R., Hotterbeekx, A., Colebunders, R., Mandro, M., et al. (2019) Dried blood microsampling-based therapeutic drug monitoring of anti-epileptic drugs in children with nodding syndrome and

epilepsy in Uganda and the Democratic Republic of the Congo. Therapeutic drug monitoring, December 2019: 10.1097/FTD.0000000000000720. 28. Min, K.L., Ryu, J.Y. and Chang, M.J. (2019) Development and clinical applications of the dried blood spot method for therapeutic drug monitoring of anti-epileptic drugs. Basic and Clinical Pharmacology and Toxicology, 2019: 10.1111/bcpt.13269. 29. Abu-Rabie, P., Denniff, P., Spooner, N., Chowdhry, B.Z. and Pullen, F.S. (2015) Investigation of different approaches to incorporating internal standard in DBS quantitative bioanalytical workflows and their effect on nullifying hematocrit-based assay bias. Analytical Chemistry, 2015: 10.1021/acs.analchem.5b00908. 30. Capiau, S., Veenhof, H., Koster, R.A., Bergqvist, Y., Boettcher, M., Halmingh, O., et al. (2019) Official International Association for Therapeutic Drug Monitoring and Clinical Toxicology Guideline: Development and Validation of Dried Blood Spot-Based Methods for Therapeutic Drug Monitoring. Therapeutic drug monitoring, 2019: 10.1097/FTD.0000000000000643. 31. Holen, T., Norheim, F., Gundersen, T.E., Mitry, P., Linseisen, J., Iversen, P.O., et al. (2016) Biomarkers for nutrient intake with focus on alternative sampling techniques. Genes and Nutrition, 11, 1–20. 32. Merritt, J.L., Brody, L.L., Pino, G. and Rinaldo, P. (2018) Newborn screening for proximal urea cycle disorders: Current evidence supporting recommendations for newborn screening. Molecular Genetics and Metabolism, 2018: 10.1016/j.ymgme.2018.04.006. 33. Therrell, B.L., Padilla, C.D., Loeber, J.G., Kneisser, I., Saadallah, A., Borrajo, G.J.C., et al. (2015) Current status of newborn screening worldwide: 2015. Seminars in Perinatology, 39, 171–187. 34. Gaugler, S., Rykl, J. and Cebolla, V.L. (2019) Validation of an Automated Extraction Procedure for Amino Acids and Acylcarnitines for Use with Tandem Mass Spectrometry for Newborn Screening. Endocrinology, Diabetes and Metabolism Journal, 3, 1–9. 35. Gaugler, S., Rykl, J., Wegner, I., von Däniken, T., Fingerhut, R. and Schlotterbeck, G. (2017) Extended and Fully Automated Newborn Screening Method for Mass Spectrometry Detection. International Journal of Neonatal Screening, 4, 2. 36. Gaugler, S., Rykl, J., Grill, M. and Cebolla, V.L. (2018) Fully automated drug

screening of dried blood spots using online LC-MS/MS analysis. Journal of Applied Bioanalysis, 2018: 10.17145/jab.18.003. 37. Thomas, K. V., Bijlsma, L., Castiglioni, S., Covaci, A., Emke, E., Grabic, R., et al. (2012) Comparing illicit drug use in 19 European cities through sewage analysis. Science of the Total Environment, 2012: 10.1016/j.scitotenv.2012.06.069. 38. Maurer, H.H. (2005) Multi-analyte procedures for screening for and quantification of drugs in blood, plasma, or serum by liquid chromatographysingle stage or tandem mass spectrometry (LC-MS or LC-MS/MS) relevant to clinical and forensic toxicology. Clinical Biochemistry, 2005: 10.1016/j.clinbiochem.2005.01.014. 39. Wu, A.H.B. and Colby, J. (2016) High-resolution mass spectrometry for untargeted drug screening. In Methods in Molecular Biology. . 40. Andresen-Streichert, H., Beres, Y., Weinmann, W., Schröck, A., Müller, A., Skopp, G., et al. (2017) Improved detection of alcohol consumption using the novel marker phosphatidylethanol in the transplant setting: results of a prospective study. Transplant International, 2017: 10.1111/tri.12949. 41. Schröck, A., Pfäffli, M., König, S. and Weinmann, W. (2016) Application of phosphatidylethanol (PEth) in whole blood in comparison to ethyl glucuronide in hair (hEtG) in driving aptitude assessment (DAA). International Journal of Legal Medicine, 2016: 10.1007/s00414-016-1394-4. 42. Luginbühl, M., Weinmann, W., Butzke, I. and Pfeifer, P. (2019) Monitoring of direct alcohol markers in alcohol use disorder patients during withdrawal treatment and successive rehabilitation. Drug Testing and Analysis, 2019: 10.1002/dta.2567. 43. Isaksson, A., Walther, L., Hansson, T., Andersson, A., Stenton, J. and Blomgren, A. (2018) High-Throughput LC-MS/MS Method for Determination of the Alcohol Use Biomarker Phosphatidylethanol in Clinical Samples by Use of a Simple Automated Extraction Procedure—Preanalytical and Analytical Conditions. The Journal of Applied Laboratory Medicine: An AACC Publication, 2018: 10.1373/jalm.2017.024828. 44. Bangsberg, D.R. (2006) Less Than 95% Adherence to Nonnucleoside Reverse‐Transcriptase Inhibitor Therapy Can Lead to Viral Suppression. Clinical Infectious Diseases, 2006: 10.1086/507526. 45. Fabbiani, M., Di Giambenedetto, S., Bracciale, L., Bacarelli, A., Ragazzoni, E.,

Cauda, R., et al. (2009) Pharmacokinetic variability of antiretroviral drugs and correlation with virological outcome: 2 years of experience in routine clinical practice. Journal of Antimicrobial Chemotherapy, 2009: 10.1093/jac/dkp132. 46. Loutfy, M.R., Walmsley, S.L., Klein, M.B., Raboud, J., Tseng, A.L. in, Blitz, S.L., et al. (2013) Factors affecting antiretroviral pharmacokinetics in HIVinfected women with virologic suppression on combination antiretroviral therapy: A cross-sectional study. BMC Infectious Diseases, 2013: 10.1186/1471-2334-13-256. 47. Koal, T., Burhenne, H., Römling, R., Svoboda, M., Resch, K. and Kaever, V. (2005) Quantification of antiretroviral drugs in dried blood spot samples by means of liquid chromatography/tandem mass spectrometry. Rapid Communications in Mass Spectrometry, 2005: 10.1002/rcm.2158. 48. ter Heine, R., Rosing, H., van Gorp, E.C.M., Mulder, J.W., van der Steeg, W.A., Beijnen, J.H., et al. (2008) Quantification of protease inhibitors and non-nucleoside reverse transcriptase inhibitors in dried blood spots by liquid chromatography-triple quadrupole mass spectrometry. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 2008: 10.1016/j.jchromb.2008.04.003. 49. Meesters, R.J.W., Van Kampen, J.J.A., Reedijk, M.L., Scheuer, R.D., Dekker, L.J.M., Burger, D.M., et al. (2010) Ultrafast and high-throughput mass spectrometric assay for therapeutic drug monitoring of antiretroviral drugs in pediatric HIV-1 infection applying dried blood spots. Analytical and Bioanalytical Chemistry, 2010: 10.1007/s00216-010-3952-9. 50. Kromdijk, W., Mulder, J.W., Rosing, H., Smit, P.M., Beijnen, J.H. and Huitema, A.D.R. (2012) Use of dried blood spots for the determination of plasma concentrations of nevirapine and efavirenz. Journal of Antimicrobial Chemotherapy, 2012: 10.1093/jac/dks011. 51. Duthaler, U., Berger, B., Erb, S., Battegay, M., Letang, E., Gaugler, S., et al. (2017) Automated high throughput analysis of antiretroviral drugs in dried blood spots. Journal of Mass Spectrometry, 2017: 10.1002/jms.3952. 52. Linder, C., Neideman, M., Wide, K., von Euler, M., Gustafsson, L.L. and Pohanka, A. (2019) Dried Blood Spot Self-Sampling by Guardians of Children With Epilepsy Is Feasible: Comparison With Plasma for Multiple Antiepileptic Drugs. Therapeutic drug monitoring, 2019: 10.1097/FTD.0000000000000605.

Highlights  How and why fully automated DBS analysis reached the routine laboratory environment  DBS methods for forensics, therapeutic drug monitoring, and mass drug administration  Advantages of DBS automation: speed, reliability, and user-friendliness