Classical Validation Parameters to Be Evaluated
Most of the validation parameters described in traditional bioanalytical method validation guidelines will have to be assessed for DBS-based methods as well.5,6 Therefore, those documents will need to be consulted too when performing a DBS method validation. However, the particular points of attention when evaluating those classical validation parameters in the context of a DBS method are given below. Furthermore, to assist the reader, a brief overview of these classical validation parameters is given in Table 3.
To assess the selectivity of the method, blank matrices of at least 6 different individuals should be analyzed without IS, as well as 2 zero samples (blank DBS extracted with extraction solvent containing IS). These blank samples should be obtained using the same sampling approach as the one that will be used to collect the patient samples. In addition, DBS prepared from blank blood spiked with common comedications, metabolites, and other potential interferences could be tested. At this stage, it may also be worthwhile to run a few authentic patient samples to ascertain there is no nonanticipated coelution of a metabolite that may not be available as a standard.
Calibration Model, Accuracy and Precision, Measurement Range
For the evaluation of the calibration model, the LLOQ and upper limit of quantitation (ULOQ), accuracy, and precision, all experiments should be performed in accordance with existing guidelines.5,6 The only difference is that all calibrators, blank, zero, and QC samples should be prepared in blood with the median HT of the target population and should have a volume representative of the patient samples.53 As with any bioanalytical method, the measurement range should be representative of the concentration range in patient samples. For the purpose of TDM, a calibration range minimally spanning from half of the lower end of the therapeutic interval to twice the upper end of the therapeutic interval should suffice. Furthermore, intracard and intercard variability do not need to be evaluated separately, as these variables will be inherently included throughout the method validation.9 For a method to be applied in a routine context, interbatch variability should be assessed. The latter can be performed by including cards from multiple batches in the validation experiments. However, if noncertified filter paper is used, a more elaborate evaluation of the filter paper may be warranted.
Contrary to traditional liquid blood samples, DBS cannot be diluted directly. Hence, to analyze samples with a concentration above the measurement range, DBS extracts are typically diluted with blank DBS extracts or extraction solvent. Furthermore, IS-tracked dilution can be performed.6,73 With this approach, a higher concentration of IS is added to the extraction solvent, with the exact amount of IS depending on the envisaged dilution factor. This approach renders the dilution a volume-noncritical step. In addition, for DBS, the donut punch approach can be used.74 With this approach, a small central punch (ie, smaller than the regular punch size for a given DBS method) is made from a DBS sample and is extracted simultaneously with a donut punch prepared from a blank DBS sample. This donut punch is a regular sized DBS punch from which a small central punch (with the same punch size as used for the actual DBS sample) has been removed. However, to use the latter approach successfully, DBS homogeneity should be adequate for the small punch size, and the extraction efficiency should not depend on the punch size.
Aside from classical carryover, in a DBS workflow, the punching step could be considered a potential source of contamination. Hence, we propose to include in the method validation, the processing of one or more blanks after the processing of the highest calibrator.9 To the authors' knowledge, however, no punch-mediated carryover has been described for (therapeutic) drugs, although it has been observed for PCR-based methods.75 In addition, physical carryover between cards should be avoided by storing the cards separately. However, if multiple cards will be stored together, potential carryover between cards requires evaluation.9 The same acceptance criteria as for classical carryover should be applied.5,6
Matrix Effect, Recovery, and Process Efficiency
ME, recovery, and process efficiency should be evaluated in line with the set-up proposed by Matuszewski et al65 (also see METHOD DEVELOPMENT: CONSIDERATIONS FOR SUCCESSFUL VALIDATION). For this experiment, blood from at least 6 different donors should be used, and 2 concentration levels should be evaluated (ie, low and high QC levels). In addition, since it is known that the HT may strongly impact the recovery—and possibly also the ME—it is essential to evaluate recovery and ME at different HT levels, prepared from the blood of at least one donor. These HT levels should encompass the anticipated HT range of the target population. Alternatively, this experiment could also be performed using 5 HT levels (0.20, 0.30, 0.40, 0.50, and 0.60). The latter set-up has the advantage that whenever the most extreme HT values do not yield acceptable results, a narrower, acceptable HT range (regarding recovery and ME) may still be determined, without having to repeat the experiment. This set-up is schematically depicted in Figure 4. As mentioned before, to accurately perform this experiment, a fixed volume of blank or spiked blood needs to be applied on prepunched filter paper discs.
Although MEs are preferably as small as possible, recovery and process efficiency as high as possible, the exact values are not that relevant. It is essential, however, that they are reproducible (ie, relative SD or %RSD within 15% after IS normalization). It is relevant to note that observations by Abu-Rabie et al49 suggest that extraction procedures with lower recoveries may be more subject to an impact of HT (see DBS-Specific Validation Parameters).
The stability assessments performed during method validation should be representative of the ambient conditions encountered during sample transport, storage, and processing. Therefore, stability should be evaluated at room temperature (the exact temperature depending on where the method will be applied) and the investigated time frame should cover the maximum expected time frame between sample collection, analysis, and potential reanalysis. Furthermore, because temperatures may be significantly higher during transport (eg, in a mail box in the sun during summer time), short-term stability at elevated temperatures (ie, 2 or 3 days at 50–60°C, or higher temperatures depending on the country) should also be tested.45,76 If stability under ambient conditions is only sufficient for a couple of days (but long enough to allow transport to the laboratory), it may be evaluated if storage at lower temperatures in the laboratory may help stabilize the DBS until (re)analysis.
Importantly, stability may also be affected by other parameters such as humidity and exposure to (sun)light, conditions which are harder to replicate in the laboratory. To evaluate the effect of actual sample transport, samples which are generated in the laboratory can be analyzed immediately after drying, after storage for a certain time under controlled conditions, and after sending them to the laboratory through mail service. Preferably, the samples are deposited in a mail box that is relatively far from the laboratory. Furthermore, it may be relevant to repeat this experiment under different weather conditions, to rule out any seasonal effects on the stability of the samples. Although stability is typically evaluated using spiked samples, it may be worthwhile to also evaluate the stability of incurred samples, as spiked samples may not always display the same stability profile as actual samples.77 In addition, postpreparative stability should be assessed.
DBS-Specific Validation Parameters
The analytical validation of DBS methods requires the evaluation of several additional parameters (Table 2): that is, the volume effect, the volcano effect (ie, DBS homogeneity), and the HT effect.1,9,71 It is essential that these parameters are assessed simultaneously because they may affect one another. These parameters can be evaluated in a single day experiment in which the obtained results are compared with those obtained from the reference condition (ie, central DBS punches generated from DBS of average or median volume and HT). Alternatively, this evaluation can be combined with the accuracy and precision experiments (ie, by measuring 2 series of DBS samples with different volumes, different HT levels, etc., on each of 3 days). The latter approach has the advantage that accuracy profiles can be established.78,79 Importantly, if a certain effect is observed (ie, a relevant volume, HT, or volcano effect), appropriate measures need to be taken to ensure patient samples are within the validated limits and patient results are reliable. Obviously, it should also be demonstrated that these measures are indeed adequate.
The volume range in which DBS-based results are still acceptable should be defined during method validation. Typical volume ranges to be evaluated are 10–50 µL for hanging-drop-collection and 20–70 µL for falling-drop-collection. The volume effect should also be evaluated at low (0.30), medium (0.40), and high (0.50) HT and at both the low and high QC level as shown in Figure 5. Whether a sufficient volume is collected from a patient should always be evaluated in the laboratory before DBS analysis. This evaluation should be performed based on the diameter of the DBS. More particularly, the diameter of the patient DBS should be between the diameter of the DBS prepared from the smallest validated volume at low HT and the diameter of the DBS prepared from the largest validated volume at high HT. To help patients to collect DBS of adequate volume, filter paper with 2 concentric circles may be used (Fig. 5).80 These circles should correspond to the minimally required volume and the maximally allowed volume (also taking into account different HT levels, as described above).80 It should be noted, however, that this type of filter paper is not commercially available. Furthermore, although these circles may be printed onto commercially available filter paper, it should be considered that the printing itself may affect the analysis (interferences from ink or toner, potential effect on blood flow, eg, caused by paper compression or wax-like materials present in toner). Therefore, the printed filter paper should be used during the entire method validation. Alternatively, equivalence between the in-house printed filter paper and the filter paper used during validation should be demonstrated at both low and high QC levels, and at low, medium, and high volume and HT. In addition, the volcano effect might have to be re-evaluated, depending on the DBS punch size. Another option is to use a phone app to assess whether the generated DBS are within the validated volume ranges.81 Again, correct performance of the app should be verified during method validation using samples of known volume, covering the entire validated volume and HT range.
Spot homogeneity should be evaluated when embarking upon partial spot analysis (also see part 2, prevalidation). If a relevant volcano effect is observed (eg, punches from the central part of the spot yield different analytical results then punches from edges of the spot), only central punches should be analyzed.
As mentioned before, it is important to actually determine the HT of the calibrators and the samples used during method validation. This will ensure the exact HT value and, consequently, the validated HT range. At least 3 HT levels should be evaluated, more particularly, a QC generated with blood that has the same HT as the blood that was used to generate the calibrators, bracketed by HT values that encompass the expected patient HT range. At each HT level, 2 concentrations should be tested. The HT range that needs to be evaluated depends on the target population (Fig. 6). For a quasiuniversal method, the range should span from 0.20 to 0.65, although a narrower range will suffice for most applications.80 The exact range will depend on the target population and should encompass at least 95% of the target population.53
Unless no relevant HT effect is observed over the entire HT range (both during analytical and clinical validation, see CLINICAL VALIDATION) or unless it is reasonable to assume that all patient HT values will be within the validated HT range, a method should be used to assess the HT of the patient samples. Besides confirming that the HT of the patient sample effectively lies within the validated HT range, this may also allow to perform an HT correction, to alleviate the HT bias.82,83 Other options are to use volumetric dried blood samples (if there is no HT effect on recovery or ME) or DPS (if there is no HT effect on DPS generation).36
Validation of Online DBS Analysis
Whether the sample preparation and analysis are performed online or not does not affect the validation parameters that need to be evaluated. The way in which certain parameters (more particularly, recovery, ME, and process efficiency) are evaluated, however, will need to be adapted.84–87
Recovery is typically evaluated by comparing the peak areas from blank matrix samples spiked before extraction with the peak areas from blank matrix samples spiked after extraction. However, with an online sample preparation procedure, there is no option to spike the samples after extraction. Instead, the analytes are introduced to the system during the extraction step. Depending on the type of system used, this can be performed through the IS loop or by spiking the extraction solvent. The results of the samples spiked during extraction are then compared with those of DBS samples containing the same absolute amount of analyte. This requires the entire DBS to be analyzed. When adding the analyte during extraction, the analyte passes through the filter paper and dried blank blood matrix, during which, theoretically, some analyte adsorption may occur. If such adsorption occurs, this will yield a falsely lowered “100% extracted” reference value, which in turn will result in an overestimation of the analyte's recovery. Alternatively, recovery may be evaluated by comparing the peak area resulting from a single extraction with the sum of peak areas resulting from, for example, 10 consecutive extractions. It needs to be considered that even after 10 extractions, not all the analyte may be extracted, again leading to an overestimation of the recovery. Moreover, these multiple extractions may technically not be possible because of filter paper deterioration (depending on the type of filter paper used).
For the evaluation of the ME, the peak areas resulting from the analysis of blank DBS samples and blank DBS cards can be compared. In both cases, the analyte will be introduced during extraction.
It is generally accepted that a DBS sampling method can only be implemented in the routine care for the purpose of TDM—and thereby (partly) replacing the standard venous whole blood sampling with blood, serum, or plasma analysis—after it has been successfully validated in a clinical validation study.1,88–91 In a clinical validation study, paired DBS and venous blood, plasma, and/or serum samples are obtained and analyzed. The analytical results are compared and statistically evaluated. The purpose of a clinical validation is to demonstrate that results from DBS are interchangeable with those obtained with the standard method used for TDM, that is, a blood, serum, or plasma analysis. The aim of this part of the guideline is to provide recommendations on how to clinically validate a DBS assay for TDM in daily practice. Current recommendations regarding clinical validation are largely based on published clinical validation studies that used genuine finger prick blood-derived DBS, paired DBS and traditional matrix samples from at least 20 patients, and appropriate statistical analysis to compare both methods.90–102
Concentration Range, Number of Clinical Samples, and Patients
The concentration range that needs to be covered during clinical validation depends on the sampling time points of interest (ie, trough and peak) and the shape of the pharmacokinetic time curve of a particular drug and the intraindividual and/or interindividual variability.2 The CLSI guideline states that at least 40 patient samples should be analyzed for a clinical validation, ideally covering the entire measuring interval of the measurement procedures.8 This sample size is based on linear regression described by Linnet et al.103 The sample size that is necessary mostly depends on the coefficient of variation (CV%) of the method and the range ratio (maximum value divided by minimum value). Because most DBS methods have a CV% >5% and a range ratio >25, the number of samples needed after Linnet's calculation will always be 36 or 45. Therefore, using fewer than 40 samples is only possible if the CV% of the method is <5% and/or the range ratio <25. Depending on the situation, these 40 samples could either be paired capillary DBS venous blood samples from at least 40 different patients collected at a single time point (ie, trough or peak), or paired samples taken at 2–3 time points and from a smaller cohort, covering the whole concentration range of interest.8,103 Ideally, a total of 80 samples obtained from at least 40 different patients should be acquired for validation. This allows using one set of 40 randomly selected samples for fitting a line between DBS and blood (or serum or plasma) concentrations using appropriate statistical tests (see next paragraphs). If required, this will derive a conversion formula or factor to convert, for example, capillary DBS concentrations into venous plasma concentrations. The other set of 40 samples can be used to validate this conversion.104 Despite the limitation of collecting multiple samples from the same patient, this approach does not require a new cohort of 40 subjects. If the amount of patients is limited and multiple samples from the same patient (eg, trough and peak) are acquired, it is our recommendation to have a minimum of 40 samples from at least 25 different patients to account for variation in MEs. In those cases where there is only a limited number of paired samples available, the conversion of a concentration in one matrix to that of another can also be checked for by a jackknife method. In this approach, the original set of n samples is resampled n times by systematically creating all possible subsets of n-1 samples. Each of these subsets is then used to set up a conversion equation, which is subsequently applied to the nth sample (ie, that sample which was not included in the subset that was used to set up the conversion equation).105 To assess the predictive performance of the conversion equation, the median percentage predictive error (MPPE) = median (corrected [analyte]test matrix − [analyte]reference matrix/[analyte]reference matrix) × 100% and median absolute percentage predictive error (MAPE) =
can be calculated. These provide a measure of bias and imprecision, respectively.106,107
Comparing DBS Concentrations With Plasma or Whole Blood Concentrations and Effects of HT
Peripherally, collected blood consists of a mixture of venous and arterial blood and interstitial fluids. Therefore, the drug concentration in peripherally collected blood may differ from venously collected blood. This effect is mostly present during the distribution phase of the drug. Although drugs are usually rapidly distributed throughout the body, this process sometimes can take up to several hours, leading to unreliable results when samples are collected during the distribution phase.2,108–110 To detect a potential capillary-venous difference (Fig. 7), the results obtained from a DBS collected from a finger prick (sample A) can be compared with those from a DBS prepared from venously collected blood (sample B). This venous blood (sample C) can be used to generate plasma (sample D). Both sample C and D can be compared with blood collected by finger prick (sample A). Alternatively, another blood sample needs to be collected at the same time point if serum (sample E) is to be prepared. Serum or plasma is typically used for routine TDM. It is essential that samples B and C should give the same result. If they do not, this points to an effect of the DBS approach in se.
In vivo, drugs can bind to components of plasma or accumulate in red blood cells, leading to differences between observed concentrations in whole blood (and hence DBS) and in plasma (or serum, depending on the matrix that is routinely used for an analyte).98,108 The difference in drug concentration between blood (DBS) and plasma can be explained by the fraction of drug in plasma relative to whole blood, the HT, and the drug's affinity for red blood cells. The study design may allow for the generation of this blood–plasma relationship. If a blood concentration has to be expressed as a plasma or serum concentration for easy interpretation by the clinician, HT values should ideally be measured, known, or calculated for each blood (DBS) sample. Furthermore, when acceptance limits for the HT have been set based on the analytical validation, one should actually know whether the HT of a given sample effectively lies within these limits. When comparing capillary DBS values with reference whole blood values, correction factors (sometimes based on HT) can be necessary and should be derived from clinical validation studies comparing whole blood values to finger prick (capillary) DBS values.89,91,92,95,97,111–115
If, for a specified HT range, the analytical validation has demonstrated that a DBS analytical method is independent of HT (or dependency is within acceptable analytical limits, see above), confirmation is required in a clinical validation study by plotting the differences between DBS results and reference method results versus the HT. The slope of the resulting curve should not be significantly different from zero.80 When this has been confirmed, plasma or serum concentrations can be calculated based on the equation derived from the Passing–Bablok or weighted Deming regression line.91,101,116–120 If an analytical method has proven to be dependent on HT values during analytical and clinical validation using appropriate statistical tests, a conversion formula should include a correction for HT.121,122 An example is the estimation of plasma values from DBS concentrations using the formula
.122 This will only be possible if there is a systematic effect from HT on estimated venous blood concentrations, which is fixed within the relevant clinical range.123 If this is not the case, the method might not be suitable for clinical application. If an HT-dependent method is to be used in routine care, the HT of the DBS should ideally be known. Procedures to derive HT from a DBS card include potassium measurements,80 noncontact diffuse reflectance spectroscopy,52,83 near-infrared spectroscopy,124 or the use of sulfolyser reagent.125 If, for an HT-dependent method, it is—because of technical or other reasons—not possible to know the HT of a DBS, clinical validation can be performed for a specific patient population, provided the HT range in that specific population is narrow and lies within the method's acceptance limits (Fig. 6).94,98 In many instances, the mean or median HT and range for a given patient population can be calculated from historical patient data.53 For a different patient population, it should be determined whether a new clinical validation should be performed.10,98,122 Another approach to cope with the HT effect is whole blood spot analysis using a fixed spot volume. A volumetric capillary or pipet can be used to apply a fixed volume of finger prick blood to the filter paper.14,126,127 In this situation, no conversion formula to correct for HT is needed. However, it should be clear from the analytical validation that the HT has no impact on recovery or MEs.89,91,95,97,115 Moreover, this can be at the expense of the simplicity of sampling and/or bring along additional costs.
Statistical Methods and Interpretation
Technically, a DBS clinical validation is a cross-validation study because a candidate method (DBS-based) is compared with a reference method (blood-, serum- or plasma-based). Although guidelines from the EMA, FDA, and CLSI include cross-validation and subsequent statistical analysis of results, this paragraph provides additional recommendations and guidance for the interpretation of results.1,5,6,8
As part of a clinical validation, the results obtained from DBS and the reference method should be compared using appropriate statistical tests. To compare 2 methods, regression analysis should be performed to measure the correlation, followed by an agreement and bias estimation test.8 As both the reference and the DBS method have some inherent variability, so that either Passing–Bablok or weighted Deming regression should be used instead of standard linear regression.8,128–130 Both approaches have been used in various clinical validation studies.91–102,131 Deming regression takes variability of both x and y into account; Passing–Bablok regression makes no assumptions about the distribution of data points and is more resistant toward outliers.8,129,132 Various clinical validation studies have shown that the absolute difference between results from a reference and a DBS method is proportional to the concentration, at least at higher concentrations. However, in these studies, sometimes only a few high concentration samples were available.91,96,120 Theoretically, an outlier in this region would impose an inflated or deflated estimate of proportional difference. In this case, a Passing–Bablok regression analysis is the preferred statistical method.8,133 After regression analysis, a Bland–Altman difference plot should be made to assess the agreement between both methods and estimate the bias.8 When using a (HT-dependent) conversion formula obtained from Passing–Bablok or weighted Deming regression, the Bland–Altman difference plot should be made using the (blood, plasma, or serum) concentrations that were calculated from the DBS concentrations.1,91
Most clinical validation studies show some level of bias when performing a Bland–Altman test. Although it may seem obvious that Bland–Altman graphs should be generated and interpreted in a correct manner, this is not always the case.133 Several things can be deduced from a Bland–Altman difference plot. First, it can be observed whether there is an average bias between both methods and whether the 95% CI of this bias contains zero. Importantly, if the latter is not the case, it should have been formally decided beforehand what a clinically relevant or acceptable bias and corresponding limits of agreement (LoA) should maximally be. For instance, for tacrolimus, where trough concentrations in blood are usually between 5 and 20 mcg/L, a bias of 0.28 mcg/L (LoA −0.45 to −0.12 mcg/L), which is at most a bias of 5.6% (LoA 9.0%–2.4%) would not impact clinical decision making, whereas a higher bias or LoA might.134 Second, the LoAs can be derived from the Bland–Altman plot. Here, the same holds true: preset criteria are needed to define what concentration or %difference span between the LoAs is still considered acceptable. This is a critical point that, in many instances, is lacking: for example, although, on average, there may be no bias between a DBS- and blood-based procedure, the span of the LoA's may be too wide (implying there is too much variation) to be acceptable. What is considered acceptable in terms of bias or LoA will largely depend on the clinical setting, the laboratory's internal policy, the availability of guidelines (eg, RCPA criteria)135 and the drug of interest. Acceptance criteria should be decided by a multidisciplinary team of experts based on both clinical and analytical acceptance criteria. In addition, during a clinical validation, it can be investigated for each measured pair of samples whether the clinical decision by the health care provider would differ, based on the DBS concentration versus the concentration in the reference sample.92,93,99,136 Again, acceptance criteria should be stated beforehand in the study protocol. The EMA guideline states for cross-validation study samples, “the difference between the 2 values obtained should be within 20% of the mean for at least 67% of the repeats.”5 It has been suggested that this guideline could also be applied to assess agreement between DBS-based analytical results and reference results.1 For example, a study, in which for 30% of the samples, a difference of more than 20% of the mean is observed, would theoretically fulfill the criteria put forward by the EMA guideline. However, this would likely be clinically unacceptable, and in this case, stricter LoA would be preferred. It is also possible that, at lower concentrations, a maximum absolute deviation may be tolerated, while at higher concentrations, a maximum allowable percentage deviation may be set.
Type of Card/Paper Used
In a clinical validation study, it should be stated which type of paper or DBS card is used. This type of paper should be the same as the one that was used during analytical validation.29
Sampling Method and Spot Quality
A major problem during clinical validation is that the provided DBS may be of insufficient quality for analysis due to incorrect sampling.42,137 Therefore, during clinical validation, the method of sampling and spot quality assessment by either an analyst or an automated quality assessment method should be mentioned in the study protocol.138,139 As drug concentrations are dynamic, it is important to collect all paired samples within 5–10 minutes of each other.91,116 Time-dependent changes in drug concentration are determined by pharmacokinetics and should be taken into account for the preparation of a sampling scheme. This is particularly relevant for drugs with a very short half-life or during the absorption and distribution phase of the drug.
The sampling method that is used during clinical validation should be the same as the sampling method that will be used in daily practice. For example, if the method is intended for home sampling by patient finger prick, the DBS samples obtained for clinical validation should also be obtained by finger prick. Spotting of venous blood on a DBS card is only appropriate if in clinical practice venous blood will be spotted on DBS cards. For instance, this may be the case when transport of tubes of whole blood is not possible due to instability of the compound or because of logistic difficulties (eg, in remote areas or in resource-limited settings).58 This is highly relevant as for some analytes venous capillary differences may, or are known to, be present.
If a method is designed for home sampling, patients should ideally perform a finger prick to collect a DBS sample themselves during clinical validation. However, in most clinical validation studies, a trained phlebotomist collects or helps to collect samples, to rule out variability due to inexperienced sampling by the patient.91,95,97,99,116,123 Alternatively, both approaches can be used successively during clinical validation.
Proper finger prick DBS sampling technique has been described earlier by the WHO, CLSI, and in several studies11,42,131,138,140,141 and is also shown in Supplemental Digital Content 1 (see Figure S-2, http://links.lww.com/TDM/A342). In short, sampling should be performed after disinfecting the finger without excessive “milking” or squeezing of the puncture site to avoid hemolysis or dilution by tissue fluid. When possible, finger prick blood should fall on the sampling paper instead of applying the droplet of blood to the sampling paper with the finger (without touching the sampling paper with the finger). Both patient and phlebotomist should be trained before samples can be obtained. This training should include practicing the whole sampling procedure under supervision of someone experienced in DBS sampling using either a test kit or a real finger prick aided by educational material such as a movie or a written instruction.25,131,137,138,140
All spots provided in a clinical validation study should be checked for quality by an experienced analyst or through a validated automated quality assessment method. Some requirements for a good quality spot depend on the analytical method and should be stated on beforehand, such as minimum spot size imposed by punching size. Other requirements are independent of the analytical method. Criteria are stated in Supplemental Digital Content 1 (see Figure S-3, http://links.lww.com/TDM/A342). In short, all spots should be round, dried, consisting of one droplet of blood, and not touching other droplets.
ISR, Duplicates, and Outliers
In their guideline, the FDA mentions ISR as a validation parameter for DBS methods.6 In a clinical validation, ideally at least 2 replicate spots are available for analysis, to allow ISR and/or duplo analysis. However, reanalysis of the same spot (through a second punch) will not be possible when the protocol involves the use of larger punching sizes (eg, 6 or 8 mm).64 During clinical validation, it is recommended to analyze 2 different spots per sample, when possible, to evaluate within-card precision, which can be calculated as the percentage difference
.5,24 The %difference between duplicates should not be greater than 20% of their mean for at least 67% of the samples.5,6 In addition, ISR of the same spot is recommended when decentral punches may be used, provided spot homogeneity is supported by the analytical validation, and small punch sizes (eg, 3 mm) are used.27
The presence of an outlier may be explained by several reasons such as contamination of the sample, errors in sampling, extreme drying, or storage conditions during transport or analytical errors.42 In a clinical validation study, most of the possible errors can be accounted for by, for instance, checking of spot quality of the sample upon arrival in the laboratory or checking and logging the drying time. When an outlier cannot be explained by such errors, the extreme studentized deviate technique8 or a standardized score test can be used to exclude outliers.121 However, outliers should be discussed in the context of clinical application of the DBS method. Therefore, outliers require an argumented discussion considering clinical setting and the aforementioned statistics tests.8
Clinical Validation of Automated Analysis Methods
Automation of a DBS assay could improve DBS sample and workflow efficiency and reproducibility. Several examples exist of automated (online or offline) DBS assays using techniques such as online extraction and solid phase extraction.87,142,143 If an automated method is designed without a previous manual DBS method, the same recommendations for clinical validation apply. If a manual DBS assay used in clinical practice is replaced by an automated DBS method which is fully analytically validated, it is recommended to perform a cross-validation including sample size of 40 samples from at least 25 different patients.5,6,8 Because of the nature of DBS, it will most likely be challenging in real practice to measure the same spot using both an online and offline method. Therefore, if during the clinical validation the within-card precision is found to be acceptable and 2 spots per finger prick DBS sample are provided, it is recommended to analyze one spot using the automated method and one spot using the manual method. Evaluation of agreement can again be performed by Passing–Bablok or Deming analysis and through a Bland–Altman plot, as described earlier.
Laboratories should participate in external QC programs if a DBS assay is implemented in routine care or provide objective evidence for determining the reliability of their results.2,38 Apart from a proficiency test pilot for the immunosuppressant tacrolimus, no external QC programs are currently available for DBS assays for drugs.144 There is an urgent need for DBS proficiency testing programs to facilitate the uptake of DBS in routine care. Although external QC materials developed for the evaluation of liquid blood-based methods may be used to evaluate the quality of a DBS-based method, it should be taken into account that these materials typically have a different viscosity than true blood samples and will therefore yield DBS of deviating sizes. Therefore, when using these materials, they should always be analyzed using a full-spot approach.145 Furthermore, the extraction efficiency of an artificial matrix may always differ from the extraction efficiency of an actual sample. Since most external QC materials are only available for plasma analysis and not for whole blood analysis, another option might be to remove part of the plasma of a blank whole blood sample and to replace it with the external QC material. The resulting blood can then be used to generate DBS, as was successfully applied for, for example, conventional antiepileptics.67
Once a DBS assay has been successfully applied in clinical practice, it is possible that changes have to be made to the sampling method, filter paper, or analytical method. For some of these changes, the standard guidelines for cross-validation are applicable.5,6 This part will focus on additional recommendations when DBS assays or sampling methods are altered.
Different Punch Size
As stated before (see METHOD DEVELOPMENT: CONSIDERATIONS FOR SUCCESSFUL VALIDATION), a punch size is preferably less than 4 mm because punching the sample in the laboratory will be easier, and patients do not need to produce large blood spots. When the desired LLOQ, accuracy, and precision can be met with a different punch (eg, smaller or “donut” punch)74 than currently used in practice, a cross-validation study should be performed. If during the clinical validation the within-card precision is within analytical limits and 2 spots per sample are provided, it is recommended to analyze 1 spot with the new punch size and 1 with the old punch size. In total, 40 samples of at least 25 different patients should be analyzed. In addition, extraction efficiency and DBS homogeneity should be re-evaluated. The extraction volume used with smaller punches can be downscaled accordingly. Although theoretically possible, we do not recommend to use a surface-based formula to convert a result from a small (eg, 3 mm) DBS punch to a theoretical bigger (eg, 6 mm) DBS equivalent.
Different Type of Filter Paper
In routine practice, several types of DBS filter paper are used such as the Whatman 903, Whatman FTA DMPK cards (type, A, B and C) (GE Healthcare, Chicago, IL), and Perkin Elmer 226 cards (Ahlstrom, Helsinki, Finland).29 Although performance of the FDA-approved Whatman 903 (GE Healthcare) and Perkin Elmer 226 paper is consistent and comparable in newborn screening,146 the influence of drug concentration and HT can lead to a difference in recovery of up to 20% between cards.29,147 This may be caused by the drugs' ability to form hydrogen bonds with the cellulose paper, leading to decreased recoveries,57 differences in spot homogeneity, or differences in background signal.27 Not only the recovery of the analyte may be altered, also matrix, volcano, volume, and HT effects may have changed, as well as the analyte's stability. These parameters should all be re-evaluated as discussed before. Furthermore, QC samples for the new filter paper should be made using the same method as was performed for the old filter paper.54 Both old and new QC samples should be analyzed, and the obtained mean accuracy should be within 15%.5 The equivalence between both filter papers should be confirmed using a minimum of 40 samples obtained from at least 25 different patients. If not all parameters prove to be similar for both types of filter paper, full analytical validation and clinical validation are required.
Different Sampling Method
Switching the sampling method will, most likely, be accompanied by some change in the method. For instance, it is likely that whole spot analysis rather than partial-punch analysis will be performed when a fixed volume of finger prick blood is deposited on a card instead of direct application of blood from the fingertip to the card. Moreover, it is possible that DBS-based assays are replaced by newer alternatives such as the earlier discussed VAMS technique because of the convenience of sampling and/or automation possibilities.25 Importantly, as stated earlier, volumetric sampling does not necessarily eliminate the effect of HT or aging on recovery, so this remains an important parameter to be studied.7,29,57,62,148 In addition, a new sampling technique might influence spot homogeneity, thereby introducing a possible unknown error in analytical results.27 Therefore, when changing sampling technique, sample vehicle, or changing to whole spot analysis, it is recommended to perform a full clinical validation study, comparing the new method to the reference method, provided this change has been appropriately analytically validated.25
To successfully incorporate DBS-based methods in routine practice, good quality methods are a prerequisite. Since the quality of a method starts with its design, a sound method set-up not only ensures the method is suitable for a given application, it also increases the chances of a successful method validation. The quality of a method needs to be assessed both during analytical and clinical validation and should be compared with preset acceptance criteria. This is the first guidance document discussing how to evaluate the quality of a DBS-based method. This guideline outlines which traditional and nontraditional validation parameters should be assessed for this type of method and provides suggestions on how to do this. Most importantly, each parameter should be evaluated in a way that reflects the real-life situation in which the method will eventually be applied. Furthermore, to ensure the method's quality on a day-to-day basis, the first QC programs for quantitative DBS-based methods have been established recently. It is important to keep in mind that DBS for TDM applications only has a future if the quality of the result can be guaranteed. A proper analytical validation and clinical validation are essential to achieve this.
The authors thank M. Volmer of the UMCG for his assistance in writing the statistical paragraph and Anoek Houben for preparing the art work.
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dried blood spots; guideline; validation; microsampling; VAMS
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