Research Article An Integrated Strategy for Implementation of Dried Blood Spots in Clinical Development Programs Prajakti A. Kothare, 1,2 Kevin P. Bateman, 1,2 Marissa Dockendorf, 1 Julie Stone, 1 Yang Xu, 1 Eric Woolf, 1 and Lisa A. Shipley 1 Received 1 September 2015; accepted 11 December 2015; published online 8 February 2016 Abstract. Dried blood spot (DBS) sample collection has gained increased interest across the pharmaceutical industry as a potential alternative to plasma for pharmacokinetic (PK) evaluations. However, regulatory guidelines and examples of late-stage clinical trial applications in the literature are lacking. This paper communicates Merck’s strategy for the implementation of DBS exemplified by experience on a late-stage program (MK-8931). In this program, DBS was proposed as the sole matrix for phase 3 studies to decrease logistical burden in an aging target patient population (Alzheimer’s disease). In vitro and bioanalytical tests demonstrated initial method feasibility and suitability for further evaluations in the clinic. An in vivo dataset was developed initially in healthy subjects (phase 1 study) and then in patients (phase 2/3 study) to establish a quantitative relationship between the blood and plasma concentrations (bridging dataset) using descriptive and population PK analyses. This allowed for PK conclusions to be seamlessly drawn across the clinical program without impact from the choice of matrix. This integrated information package (in vitro, bioanalytical and clinical) was presented to major regulatory agencies (FDA and EMA) for regulatory input. Based on this package, regulatory concurrence was gained on accepting DBS as the sole matrix in late-stage clinical trials. KEY WORDS: bridging; dried blood spots; MK-8931; population PK. INTRODUCTION Since its original application for the detection of phenylketonuria in neonates half a century ago, dried blood spots (DBS) have gained popularity as a screening tool for various diagnostic tests including metabolic disorders, thera- peutic drug monitoring, and HIV infection in neonates (1–3). The pharmaceutical industry and regulators have continued to explore the potential of DBS as a viable alternate matrix for pharmacokinetic analyses (4–6). DBS was initially evalu- ated at Merck in 2001 for discovery stage PK studies and subsequently implemented for the pediatric development program of an anti-HIV compound in 2009. Since then, a number of compounds have been evaluated for initial in vitro and bioanalytical feasibility to implement DBS. A subset of these has progressed to implementation of DBS in clinical trials, including late-stage clinical trials. Strategically, Merck has chosen to primarily focus the application of DBS towards late-stage patient studies (phase 2 and/or 3) where it has the potential to render greater impact. In our opinion, the value proposition for DBS from a clinical perspective is as follows: (a) To add flexibility in the collection of PK data in phases 2 and 3 studies: Typically, sparse PK samples in phases 2 and 3 studies are constrained to limited time windows during a clinic visit. DBS, particularly in an out-patient setting, expands the window for access to such data. This may be especially beneficial for drugs with long half-lives or long acting formu- lations to evaluate steady-state or time for washout, or where clinical endpoints are collected by patient- completed diaries or are episodic (e.g., migraine, asthma, or erectile dysfunction trials). However, sampling methods in an out-patient setting need to mature further to gain adequate precision for pharmacokinetic modeling. (b) Decreased patient burden (blood volume) in vulner- able populations: The smaller sample volumes typi- cally associated with DBS (three spots of ∼20–40 μL each) vs. typical plasma samples (∼200–1000 μL) are clinically attractive for vulnerable populations where blood volumes are a clinical or ethical concern (e.g., younger pediatric or elderly populations). However, DBS should not be considered an automatic choice for studies in such populations. Equal consideration should be given to liquid microsampling approaches. (c) Improved logistical feasibility: DBS sampling offers a number of logistical advantages (e.g., ambient tem- perature storage or shipping, no need for specialized equipment such as refrigerated centrifuges and 1 Pharmacokinetics, Pharmacodynamics and Drug Metabolism, Merck & Co., Inc., 770 Sumneytown Pike, West Point, Pennsylvania 19486, USA. 2 To whom correspondence should be addressed. (e-mail: [email protected]; [email protected]) The AAPS Journal, Vol. 18, No. 2, March 2016 ( # 2016) DOI: 10.1208/s12248-015-9860-3 519 1550-7416/16/0200-0519/0 # 2016 The Author(s). This article is published with open access at Springerlink.com
Transcript
Research Article
An Integrated Strategy for Implementation of Dried Blood Spots in ClinicalDevelopment Programs
Prajakti A. Kothare,1,2 Kevin P. Bateman,1,2 Marissa Dockendorf,1 Julie Stone,1 Yang Xu,1
Eric Woolf,1 and Lisa A. Shipley1
Received 1 September 2015; accepted 11 December 2015; published online 8 February 2016
Abstract. Dried blood spot (DBS) sample collection has gained increased interest across thepharmaceutical industry as a potential alternative to plasma for pharmacokinetic (PK) evaluations.However, regulatory guidelines and examples of late-stage clinical trial applications in the literature arelacking. This paper communicates Merck’s strategy for the implementation of DBS exemplified byexperience on a late-stage program (MK-8931). In this program, DBS was proposed as the sole matrix forphase 3 studies to decrease logistical burden in an aging target patient population (Alzheimer’s disease).In vitro and bioanalytical tests demonstrated initial method feasibility and suitability for furtherevaluations in the clinic. An in vivo dataset was developed initially in healthy subjects (phase 1 study)and then in patients (phase 2/3 study) to establish a quantitative relationship between the blood andplasma concentrations (bridging dataset) using descriptive and population PK analyses. This allowed forPK conclusions to be seamlessly drawn across the clinical program without impact from the choice ofmatrix. This integrated information package (in vitro, bioanalytical and clinical) was presented to majorregulatory agencies (FDA and EMA) for regulatory input. Based on this package, regulatoryconcurrence was gained on accepting DBS as the sole matrix in late-stage clinical trials.
KEY WORDS: bridging; dried blood spots; MK-8931; population PK.
INTRODUCTION
Since its original application for the detection ofphenylketonuria in neonates half a century ago, dried bloodspots (DBS) have gained popularity as a screening tool forvarious diagnostic tests including metabolic disorders, thera-peutic drug monitoring, and HIV infection in neonates (1–3).The pharmaceutical industry and regulators have continuedto explore the potential of DBS as a viable alternate matrixfor pharmacokinetic analyses (4–6). DBS was initially evalu-ated at Merck in 2001 for discovery stage PK studies andsubsequently implemented for the pediatric developmentprogram of an anti-HIV compound in 2009. Since then, anumber of compounds have been evaluated for initial in vitroand bioanalytical feasibility to implement DBS. A subset ofthese has progressed to implementation of DBS in clinicaltrials, including late-stage clinical trials. Strategically, Merckhas chosen to primarily focus the application of DBS towardslate-stage patient studies (phase 2 and/or 3) where it has thepotential to render greater impact. In our opinion, the valueproposition for DBS from a clinical perspective is as follows:
(a) To add flexibility in the collection of PK data inphases 2 and 3 studies: Typically, sparse PK samplesin phases 2 and 3 studies are constrained to limitedtime windows during a clinic visit. DBS, particularlyin an out-patient setting, expands the window foraccess to such data. This may be especially beneficialfor drugs with long half-lives or long acting formu-lations to evaluate steady-state or time for washout,or where clinical endpoints are collected by patient-completed diaries or are episodic (e.g., migraine,asthma, or erectile dysfunction trials). However,sampling methods in an out-patient setting need tomature further to gain adequate precision forpharmacokinetic modeling.
(b) Decreased patient burden (blood volume) in vulner-able populations: The smaller sample volumes typi-cally associated with DBS (three spots of ∼20–40 μLeach) vs. typical plasma samples (∼200–1000 μL) areclinically attractive for vulnerable populations whereblood volumes are a clinical or ethical concern (e.g.,younger pediatric or elderly populations). However,DBS should not be considered an automatic choicefor studies in such populations. Equal considerationshould be given to liquid microsampling approaches.
(c) Improved logistical feasibility: DBS sampling offers anumber of logistical advantages (e.g., ambient tem-perature storage or shipping, no need for specializedequipment such as refrigerated centrifuges and
1 Pharmacokinetics, Pharmacodynamics and Drug Metabolism,Merck & Co., Inc., 770 Sumneytown Pike, West Point, Pennsylvania19486, USA.
simplified sample preparation) that may reduceoperational burden associated with PK sampling inlarger multi-center patient trials and lead to potentialcost savings. A reduced operational burden mayencourage greater participation of clinical sites forPK evaluation in phases 2 or 3 trials, and therebyenrich the database for characterization of thepopulation pharmacokinetics and exposure-responserelationships. Prior to implementing ambient ship-ping for DBS, the extended stability of DBS samplesshould be established to ensure integrity of thesamples.
DBS has been used extensively for the measurement ofendogenous biomarkers of disease (7,8). Merck is exploringthe use of DBS for clinical laboratory tests and biomarkers.Continued development of these approaches and coordina-tion with PK measurements is anticipated to further enhancethe value proposition for DBS in clinical trials.
Despite the upside potential of DBS, companies andregulators have wrestled with its utility and regulatoryexpectations compared to traditional matrices. Recently, theEuropean Bioanalysis Forum (EBF) and IQ have publishedposition papers reflecting a range of opinions on DBS (9,10).However, industry regulatory consensus positions and regu-latory guidelines for DBS in clinical development programshave been slow to emerge. Further, with few exceptions, theliterature has focused on technological challenges andbioanalytical considerations. Rowland and Shepard providean overview of the requirements for the interpretation ofDBS data in development and addressed some of theregulatory considerations that would pave the way to gainingacceptance for DBS (11). A theoretical assessment ofpharmacokinetic considerations in the interpretation of DBSdata has been published by Emmons and Rowland (12).However, the literature lacks in-depth guidance on pharma-cokinetic analyses, modeling, or late-stage clinical consider-ations, especially when integrating across a clinical programwith studies that include both plasma and DBS sampling.
The use of DBS should be thoughtfully weighed on acase-by-case basis with consideration to pros/cons of its userelative to traditional matrices for a given program. Success-ful implementation hinges on prospective multi-disciplinary(e.g., Bioanalytical, pharmacokinetic-pharmacodynamic(PKPD), Clinical Pharmacology, regulatory) planning. Asmost clinical development programs employ plasma samplingfor phase 1 studies, a robust in vivo bridging strategy shouldbe developed that allows quantitative inter-conversion ofpharmacokinetic information between matrices. This allowspharmacokinetic conclusions to be drawn across studiesagnostic to matrix. The overarching PKPD objectives forthe program (e.g., evaluation of intrinsic/extrinsic factors,exposure-response) should remain a core consideration andnot be impacted by the choice of the bioanalytical matrix.
This paper presents Merck’s strategy for the applicationof DBS in clinical programs which has been shaped byexperience gained from implementation on clinical programs.This augments an earlier company position paper whereinitial experience, bioanalytical, and logistical considerationswere communicated (6). Of note, this summary is intended toinform the scientific community of an emerging area of
interest with limited industry or regulatory precedence andis not intended to represent a broader consensus position byregulators or industry. The strategy is composed of stagedimplementation steps where initial methodological feasibilityis established through a series of in vitro and bioanalyticalevaluations. DBS is introduced in a staged manner in theclinic whereby DBS samples are taken concurrently withplasma initially in a healthy subject study and then in patients.Descriptive and population PK analyses of these data areconducted in a Blearn and confirm^ paradigm. Regulatoryfeedback is sought on this comprehensive data package(Fig. 1). Implementation of this strategy is exemplifiedthrough experience gained from MK-8931, a clinical programwhere regulatory concurrence was gained for the acceptanceof DBS as the sole PK collection matrix for late phase trials.
CASE STUDY: MK-8931
Strategic Rationale. MK-8931 is currently in late-stage devel-opment for Alzheimer’s disease. DBS would allow forshipping at ambient temperature and render a number ofother logistical advantages that could encourage clinical siteparticipation and facilitate faster study enrollment for largemulti-site clinical trials. Additionally, the reduced bloodvolume associated with DBS collection was anticipated toreduce patient burden. The goal of implementation was topursue DBS as the sole matrix for late-stage clinical trials.The characterization of exposure-response relationships wasconsidered an important element for dose justification. Thus,the bridging package needed to be robust to allow clinicalstudies to be pooled across the clinical program for popula-tion PK and exposure-response analyses in anticipation ofregulatory submission and labeling.
Initial Feasibility Assessment
Initially, a set of in vitro and bioanalytical assessments wereconducted to ascertain methodological feasibility prior to furtherevaluations in the clinic. These tests were conducted over aclinically relevant concentration range and required good com-munication between the bioanalytical and PKPD scientists.
In Vitro Tests. As detailed in the publication of Rowland andEmmons (12), plasma protein binding, blood–cell to unboundplasma concentration ratio (ρ), and hematocrit are important invitro determinants of the blood to plasma ratio and the suitabilityof DBS as a pharmacokinetic matrix. Emmons and Rowlandhypothesize that compounds with a blood to plasma ratio rangingfrom 0.55 to 2, and non-concentration-dependent unboundfraction (fu) and ρ can be equally analyzed by blood or plasma(5,12).
The average MK-8931 in vitro blood to plasma ratiowas 1.22 at 1 μM based on radiolabelled experiments. Theblood to plasma ratio was concentration independent in therange of 0.03 to 10 μM (bracketing the clinical concentra-tion range). MK-8931 was modestly plasma protein bound(~65% in human plasma) and not concentration dependentin the range of 0.03 to 1 μm. The in vitro tests showed thatDBS or plasma would be equally valid as a matrix for MK-8931 PK assessments.
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Bioanalytical Tests. Prior disseminations summarize Merck’sstrategy for evaluating bioanalytical feasibility (6,13). Thesetests encompass assay sensitivity and range, DBS card typeand extraction methods, the impact of hematocrit on thebioanalytical method over a wide range, impact of spotvolume/homogeneity, stability (including relevant metabo-lites) at extremes of temperate and relative humidity (poten-tially encountered during shipping), and any considerationsspecific to the quantitation of the molecule (e.g.,concentration-dependent binding to target and/or plasma).There are currently no established regulatory guidances forbioanalytical method development or validation specific toDBS. Hence, following a decision to pursue DBS, regulatorybioanalytical guidelines, internal SOPs, and industry bestpractices for plasma assays are applied for bioanalyticalvalidation.
For MK-8931, venous blood was drawn into a singleEDTA collection vial and a small quantity spotted ontoa DBS card; the remaining blood sample was centri-fuged to extract plasma. MK-8931 concentrations wereanalyzed in both the DBS and the plasma samples. TheDBS analytical method was based on either directextraction or direct extraction followed by liquid–liquidextraction of MK-8931 from human dried blood spotson DMPK-A cards. The analyte from DBS samples andits stable isotope labeled internal standard contained inthe extraction solvent were analyzed by HPLC-MS/MS.The lower limit of quantitation (LLOQ) was 1 ng/mL
with a 3-mm punch size and was deemed adequate forclinical trials. Hematocrit was shown not to have ananalytically relevant effect on MK-8931 concentrationmeasurements in the range of 19.5 to 86.0%. Followingthe receipt of clinical study samples, incurred sample re-analysis (ISR) was completed to further validate thereproducibility of the assay methodology. Assay valida-tion and clinical performance are summarized inTables I and II. Bland-Altman plots comparing DBSand plasma concentrations showed a lack of biasbetween the methods (Fig. 2). The DBS analyticalmethod was considered to have robust performance foruse in a clinical setting.
In other instances, bioanalytical evaluations have ruledout DBS as a possible matrix. For example, one compoundexhibited high levels of a circulating glucuronide metaboliteand stability testing of DBS samples containing theglucuronide showed that it back-converted to parentcompound at both room temperature and low temperaturewith acidification. This would confound the accuratemeasurement of the parent compound. Therefore, traditionalplasma sampling with acidification to stabilize the metab-olite was considered a more viable option, and DBS was notfurther pursued. In other instances, DBS has been success-fully evaluated in early clinical studies, but the programfailed to advance, and therefore, no further DBS data werecollected (13).
In Vivo Bridging
General Considerations
As most clinical development programs are initiatedwith, and likely to retain phase 1 studies with plasma assays, aquantitative in vivo relationship between blood and plasmaconcentrations is established through Bbridging studies.^These typically encompass a staged evaluation commencingin a healthy subject study and then proceeding to a patientstudy. In each study, DBS and plasma samples are takenconcurrently at various PK sampling time points that span atherapeutically relevant concentration range. Depending onwhether the proposed future clinical trial (after bridging isestablished) would continue blood collection through veni-puncture or in an out-patient setting, DBS may be collectedvia venipuncture in the clinic or through finger sticks. Assample collection has a direct impact on the quality of DBSassessments, Merck has developed extensive training material
Fig. 1. Components of the integrated DBS strategy
Table I. Assay Validation Performance Summary for MK-8931
Assessment Samples/Conditions Assessed N Mean Accuracy (%)
Mean Precision(Coefficient ofVariation %)
Regression model analysis Replicate standard curves(linear, 1 / x2)
5 92.0–105 3.7–7.3
Intra-run accuracy and precision at the LLOQ 3 core runs 5 in each run 101–111 9.2–18.9Intra-run accuracy and precision at low, mid,
and high QC3 core runs 5 in each run 99.4–109 2.4–7.3
Inter-run accuracy and precision at LLOQ,low, mid, and high QC
Mean of 3 core runs in 3 days 3 101–107 0.8–4.9
521Integrated Strategy for Dried Blood Spots
(including videos and educational materials) for sites forguidance on sample handling, and shipping. The participationof PKPD and/or BA scientists in start-up meetings is encour-aged to ensure participating sites are appropriately educated onpractical considerations of sample collection and handling.
For the healthy subject evaluation, an existing clinicalpharmacology study (e.g., multiple ascending dose, controlarm of a DDI, or special population study) should beleveraged. No a priori sample size is recommended; atypically sized clinical pharmacology study (e.g., 12–16subjects) is sufficient. Subsequently, bridging data should becollected from an early patient study (e.g., phase 1b or POC).
Merck utilizes a Bweight of evidence^ approach thatincludes a series of graphical/descriptive evaluations and withequal or greater weightage given to population pharmacoki-netic analyses. These analyses should follow a learn andconfirm paradigm. Analyses should commence with thehealthy subject study in an exploratory or Blearning^ modeand be confirmed in the patient study. Based on inputprovided for MK-8931 during the EMA oral hearing, anexternal qualification of the blood–plasma relationship ishighly desirable. Establishing and qualifying this relationshipenables pharmacometric analyses of concentration dataacross studies in a clinical program in support of thesubmission package and labeling.
Healthy Study
For MK-8931, DBS was initially included along withplasma concentrations in healthy subjects in a clinicalpharmacology study (12 subjects contributing 11 sampleseach). DBS samples were collected via venipuncture in theclinic. A number of graphical and descriptive analyses wereconducted. Concurrently drawn DBS and plasma concentra-tions were plotted to explore data trends (Fig. 3a). The bloodto plasma slope (95% CI) estimated by regression was 1.29
(1.27, 1.31), which was in close agreement with the in vitroestimated blood to plasma (B:P) ratio of 1.22. Of note, whileregression fits may be applied to quantify the relationship, aprospective R2 cutoff should not be applied as a go/no gocriterion. Further, non-linear trends should not be inferred asa lack of utility of DBS. Observed mean (Fig. 3b) andindividual (Fig. 3c) concentrations were plotted by nominaltime. The plots include MK-8931 concentration data derivedfrom plasma, DBS, and BDBS-predicted plasmaconcentrations^ (DBS concentration divided by the slopefrom regression fit of 1.29). The measured and DBSconverted plasma concentrations were generally comparableand followed similar trends over time. These graphical anddescriptive analyses suggested that the in vivo B:P relation-ship was well characterized in healthy subjects.
In Merck’s experience, the aforementioned graphicalplots have been well received by regulators. Additionally, amixed-effects modeling approach could be applied to evalu-ate the blood and plasma concentration data. In thisapproach, time-matched DBS and plasma concentration datapairs are fi t ted to a mixed-effects model (e.g . ,ln(DBS) = slope * ln (plasma)) in a program such asNONMEM. Inter- and intra-individual variability terms aswell as the influence of covariates such as hematocrit may beevaluated using this approach.
Equal or greater weightage was applied to populationPK as a critical element of the overall bridging strategy.Implementation followed a learn and confirm paradigm asshown in Fig. 4. MK-8931, DBS, and plasma concentrationdata from the phase 1 study were used to update anexisting plasma population pharmacokinetic model. Bloodconcentrations were modeled as a separate compartmentwith an estimated population Bslope^ that related bloodand plasma concentrations (Fig. 5). Separate residualerror terms were applied for blood and plasma. Parameterestimates and errors were similar between a model thatincluded plasma data alone and one that included plasmaand DBS and were consistent with historical knowledge ofthe compound (Table III). The population slope (%relative standard error) estimated from the model was1.27 (4.61%) and similar to that obtained from theregression analyses and the in vitro blood to plasma ratio.
Table II. Assay Clinical Study Performance Summary for MK-8931
Clinical Protocol
Mean Accuracy (%) Mean Precision (Coefficient of Variation %)
Fig. 2. Bland–Altman plot comparing plasma and DBS concentrationsfor pharmacokinetic samples from the phase 1 study for MK-8931
Fig. 3. a Correlation of blood and plasma concentration data from aphase 1 bridging study of MK-8931. b Mean plasma and bloodconcentration-time data from a phase 1 bridging study of MK-8931.Note: DBS-predicted plasma concentrations were calculated asmeasured DBS divided by 1.29, the slope of the DBS-plasma linearregression line. c Plasma and blood concentration-time data forindividual subjects from a phase 1 bridging study of MK-8931. DBS-predicted plasma concentrations were calculated as measured DBSdivided by 1.29, the slope of the DBS-plasma linear regression line
The impact of inter-individual variability on slope wasexplored and found not to be significant. Existing covar-iate relationships from the plasma model were applied tothe plasma-DBS model; however, the impact of covariateson the slope was not evaluated at this stage. Randomeffects and residual errors for the parameters were withinreasonable limits. Other key modeling parameters (e.g.,clearance) were also well estimated which would be ofrelevance to support covariate analyses (a key objectivefor the phase 2/3 population PK evaluations).
Modeling best practices and diagnostics were applied toexplore the goodness of fit and the quantitative interchange-ability of exposure metrics derived from the two matrices.Standard diagnostics and goodness of the fit plots such as visualpredictive checks or CWRES (conditional weighted residuals)or IWRES (individual weighted residuals) vs. time plotsdifferentiated by matrix were used to rule out the patterns ofsystematic bias or model mis-specification. Another usefuldiagnostic was to compare the individual post hoc plasmaexposures from the plasma-DBS bridging study using (1) theplasma data from the bridging study and the plasma alonemodel and (2) the DBS data from the bridging study and theplasma+DBSmodel, using model-estimated slope to convert toa plasma exposure. DBS-based exposure estimates were similarand did not show any over- or under-prediction bias comparedto the plasma-based estimates (Fig. 6).
Of note, while the blood to plasma relationship of MK-8931 was linear, if the underlying relationship for a givencompound is non-linear, a more complex parametric descrip-tion could be developed. However, the increased
parameterization would need to be balanced relative to theoverall advantages conferred by DBS to the program.
Patient Study
Descriptive and population analyses in healthy subjectssupported the continued utility of DBS. Therefore a subse-quent bridging dataset was developed in patients to confirmthis relationship in a more heterogeneous and relevantpatient population. For MK-8931, this was obtained from anearly cohort of patients in a late-phase clinical trial. Concur-rent DBS and plasma samples by venipuncture in clinic weretaken pre-dose and as three sparse PK samples over a 13-week period. Additional DBS and plasma samples continuedto be collected from the study based on advice received at anEMA oral hearing to develop an external qualificationdataset.
Simulations using the phase 1 pop PK model were used todevelop prospective go/no go decision criteria and included inthe modeling analysis the plan for the patient study to supportthe decision of whether DBS could be used as the solematrix forfuture phase 3 studies. The modeling analysis plan wouldevaluate (i) comparability of model-estimated slope to regres-sion estimated (in vivo) and in vitro blood to plasma ratio, (ii)similarity and lack of bias in parameters and post hoc exposuresderived from plasma and DBS converted to plasma, and (iii)similar central tendency of plasma predicted vs.DBS convertedplasma predicted exposures. For MK-8931, two decision treeswere included in the modeling analysis plan (Fig. 7). Thesedecision trees represent program-specific criteria and need to beadjusted on a case-by-case basis. Based on discussions at theEMAoral hearing, themodeling plan was updated to include anexternal qualification step where the DBS-plasma populationPK model (including the population slope term) would also beused to estimate exposures (based on DBS data alone) for anadditional set of patients not utilized for the modeldevelopment.
A similar set of descriptive and graphical analyses asthose performed on data from healthy subjects was plannedfor data from the patients. Furthermore, the population PKmodel would be updated with patient-derived DBS data andassessed relative to the proposed modeling plan and decisiontrees. Additional patient data from this data, not included inmodel development, would be used for external qualification.In its totality, the modeling plan was designed to evaluatewhether DBS could achieve the broader PK objectives of theclinical program, such as application for exposure-responsemodeling, with a similar degree of fidelity as plasma. Thesepharmacometric assessments (as well as the precedingstrategic, in vitro and bioanalytical components) were acritical aspect of the package submitted to regulatory agenciesfor feedback on whether DBS would be suitable as the solematrix for late-stage studies. For blinding considerations,results from the patient study are not included in the currentdissemination. These will be the subject of a subsequentexternal communication upon unblinding of the study.
Regulatory Input
As clinical application of DBS is still emerging,regulatory guidance on DBS is yet to be established. Thus,
Fig. 4. Road map for application of population PK to establish aquantitative bridge between plasma and DBS concentrations
Fig. 5. A base population pharmacokinetic model structure thatrelates plasma and DBS concentration data by a population estimatedslope. See Appendix for example NONMEM code
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feedback from key regulatory agencies is recommended ina timely manner for each program. The briefing packageshould present an integrated assessment of in vitro data,bioanalytical feasibility assessments, and in vivo bridgingevaluations. The timing of such correspondence is programspecific and should enable subsequent finalization of late-stage clinical plans. We recommend that meeting requestsspecify input from pharmacometric and bioanalytical re-viewers. The inclusion of external expert opinions as partof the submitted dossier may be considered. It is Merck’sexperience that comprehensive data packages that includedthe elements mentioned above enabled more productiveregulatory interactions.
This approach was used for MK-8931. The focus of theinteractions was to gain regulatory concurrence that thesubmitted package supported the use of DBS (collected inthe clinic via venipuncture) as the sole method of PKsampling in ongoing and future MK-8931 late-stage clinicaltrials. For MK-8931, FDA interactions occurred at an earliertime frame when the DBS-associated pharmacometric
package was not as well developed. The submitted packageprimarily relied on in vitro and BA feasibility assessments anddescriptive/graphical analyses of phase 1 clinical data alongwith plans for the collection of plasma and DBS bridging datain patients, and the agency agreed that our proposal appearedreasonable. During correspondence, the agency recom-mended the use of the individual concentration time profilesas denoted in Fig. 3c. A comprehensive background docu-ment (inclusive of pharmacometric evaluations) was submit-ted for scientific advice to the CHMP/EMA. The agencyrequested an oral hearing as they indicated that this was theirfirst regulatory experience of DBS. The key input receivedwere the following:
& Merck has presented a comprehensive approachconsisting of bioanalytical feasibility considerationsand in vitro studies followed by an in vivo bridgingprogram in both healthy and patient populations.Overall, this approach was considered robust tosupport the use of DBS as the sole source of PK
Table III. Phase 1 MK-8931 Population PK Model Parameter Estimates (% Residual Standard Error) for Relevant Parameters
Parameter Parameter Description
Plasma-Only Modela Plasma +DBS Modelb
Estimate (%RSE) Estimate (%RSE)
Slope DBS/plasma ratio – 1.27 (4.61)σ2plasma Additive residual variability for plasma 0.142 (7) 0.144 (7.29)σ2DBS Additive residual variability for DBS – 0.186 (34.7)
aModel developed using phase 1 plasma databModel developed using Phase 1 plasma data as well as DBS data from a healthy volunteer bridging study
Fig. 6. Individual MK-8931 model-predicted exposures using plasma alone data and model vs. from DBS concentration dataconverted to plasma using the model-estimated population slope
525Integrated Strategy for Dried Blood Spots
data for the remainder of the MK-8931 phase 3program.
& While the broader strategy was endorsed, the agencycautioned that the implementation of DBS requiresunique considerations which may not be readilytranslatable to other development programs.
& There was a strong focus on trying to understand theimpact of inter-individual variability on the slope andto ensure that the blood to plasma relationship (i.e.,slope) could be applied to describe populations notincluded in the modeling dataset. The agency recom-mended that external qualification of the slope bedemonstrated to show its predictive value in a datasetnot used for model development.
& Of note, while the agency acknowledged thepotential future benefits of home sampling, theyrefrained from providing commentary as theyconsidered it to be technology in the early stagesof development.
As stated before, the dissemination of this regulatoryinteraction is intended to advance the field by sharing
knowledge in an area with relatively little regulatoryprecedence or guidance and as such is not intended toreflect a broader regulatory position statement.
DISCUSSION
The strategy for using DBS in clinical programs atMerck has been developed over several years and hasbeen a non-linear process. Merck arrived at its currentstate by looking at DBS holistically in the context ofindividual development programs, avoiding a one-size-fits-all approach. Early efforts mainly focused on analyticalaspects, and it was only after a whole program approachwas adopted, which the broader utility of DBS has startedto be realized. Merck’s strategy requires a prospective andmulti-study approach to build the data sets to enablesuccessful clinical implementation. As such, the alignmentof all groups (analytical, pharmacokinetics, clinical, oper-ations) involved is critical to the successful implementa-tion of DBS. The input from regulatory agencies has beencritical to the refinement of our strategy. The feedback on
Fig. 7. a MK-8931 DBS-plasma decision tree no. 1 (linear regression analysis based onpatient data only). b MK-8931 DBS-plasma decision tree no. 2 (population PK model-based analysis based on healthy volunteer and patient data)
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both the analytical and pharmacokinetic aspects demon-strated scientific insightfulness and curiosity, while em-bracing a forward-looking attitude. As with any newapproach, Merck anticipates unexpected hurdles duringthe development and implementation stages. Learningfrom the large-scale implementation of DBS in these trialsis likely to benefit the broader community and will be thesubject of future disseminations.
Future Perspective
Merck believes that the strategies described above willhelp transform DBS from a niche bioanalytical technology toa practical clinical strategy that can be leveraged forpopulation-based PKPD models suitable for regulatorysubmissions. Presentations of such data packages to regula-tors should build regulatory confidence in acceptance of DBSas a mainstream matrix and presumably influence futureregulatory guidance. Merck believes that investing in novelmethodologies such as these is an essential part of thesolution to addressing rising drug development costs, whilestill meeting regulatory agencies expectations to demonstratea robust understanding of the PKPD relationship of newtherapeutic agents and demands by payers to reduce costs.
ACKNOWLEDGMENTS
The authors would like to thank PPDM staff and externalcollaborators Malcolm Rowland, David Jaworowicz(Cognigen), and Julie Passerell (Cognigen) for their valuableinput over the years that has led to the development of a clinicalstrategy for the implementation of dried blood spots at Merck.
Open Access This article is distributed under the termsof the Creative Commons Attribution 4.0 InternationalLicense (http://creativecommons.org/licenses/by/4.0/), whichpermits unrestricted use, distribution, and reproduction inany medium, provided you give appropriate credit to theoriginal author(s) and the source, provide a link to theCreative Commons license, and indicate if changes weremade.
APPENDIX
Population Analyses of DBS and Plasma ConcentrationData: Excerpt from NONMEM Code within $ERROR Block
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