Prediction of intracellular exposure bridges the gapbetween target- and cell-based drug discoveryAndré Mateusa, Laurie J. Gordonb, Gareth J. Waynec, Helena Almqvistd,e, Hanna Axelssond,e, Brinton Seashore-Ludlowd,e,Andrea Treyera, Pär Matssona, Thomas Lundbäckd,e,1, Andy Westb, Michael M. Hannb, and Per Arturssona,f,2

aDepartment of Pharmacy, Uppsala University, SE-751 23 Uppsala, Sweden; bPlatform Technology and Science, GlaxoSmithKline, SG1 2NY Stevenage, UnitedKingdom; cDepartment of Target and Pathway Validation, GlaxoSmithKline, SG1 2NY Stevenage, United Kingdom; dDivision of Translational Medicine,Laboratories for Chemical Biology, Chemical Biology Consortium Sweden, Science for Life Laboratory, Karolinska Institute, SE-171 65 Solna, Sweden;eDepartment of Medical Biochemistry & Biophysics, Chemical Biology, Karolinska Institute, SE-171 65 Solna, Sweden; and fDepartment of Pharmacy,Uppsala University Drug Optimization and Pharmaceutical Profiling Platform, Uppsala University, SE-751 23 Uppsala, Sweden

Edited by Kevan M. Shokat, University of California, San Francisco, CA, and approved June 21, 2017 (received for review February 2, 2017)

Inadequate target exposure is a major cause of high attrition in drugdiscovery. Here, we show that a label-free method for quantifyingthe intracellular bioavailability (Fic) of drug molecules predicts drugaccess to intracellular targets and hence, pharmacological effect. Wedetermined Fic in multiple cellular assays and cell types representingdifferent targets from a number of therapeutic areas, including can-cer, inflammation, and dementia. Both cytosolic targets and targetslocalized in subcellular compartments were investigated. Fic givesinsights on membrane-permeable compounds in terms of cellularpotency and intracellular target engagement, compared with bio-chemical potency measurements alone. Knowledge of the amountof drug that is locally available to bind intracellular targets providesa powerful tool for compound selection in early drug discovery.

intracellular drug bioavailability | drug exposure | target engagement |published kinase inhibitor set | MAPK14

Most known drug targets are located in the cell interior (1,2). Inadequate cellular drug exposure is hypothesized to

lead to a lower “biochemical efficiency” (3), typified by com-pounds that bind to the isolated target protein with high affinitybut fail to perform in cellular assays, a phenomenon termed “celldrop off” (2). Furthermore, insufficient exposure at the target isan important contributor to failure in clinical drug developmentand the high attrition rate in drug discovery programs (4–6). In arecent analysis of the drug candidate pipeline of a major drugcompany, all programs in which target exposure was uncertain(18 of 44 programs) resulted in failure of progression to phase IIIclinical trials (4). Importantly, routine measurement of com-pound levels at intracellular sites in target tissues is hindered bysampling constraints in human subjects (5).Currently, indirect estimates of intracellular drug levels are

extrapolated from transcellular permeability experiments (7) orcellular target engagement data (8–17). Some of these approachesare limited to certain target classes, because they require probes thatare known to bind the target (13–16, 18) or target properties to allowdetection [e.g., the stability of the target needs to be affected by thecompound (8, 9)]. Furthermore, exposure can only be detected incell types that express the target, limiting their use when studying off-target effects and drug toxicity. Qualitative measurements of thecellular distribution of compounds can be obtained with variousimaging modalities (19–25); however, most of these techniques re-quire labeling of the compound of interest. Labeling often alters themolecular properties of the compound, thereby perturbing its dis-tribution and target affinity. Tissue-based methodologies allow ac-curate determinations of intracellular drug concentrations usingunlabeled compounds, but they are based on animal tissues and havelow throughput (26, 27). Therefore, we developed a cell-basedmethodology for determination of intracellular drug concentrationsin a high-throughput format, which is applicable to a wide variety ofcell systems, not only those directly relevant for the target pharma-cology (28–30). Our method does not require chemical labeling, hasa high sensitivity, and measures the unbound drug concentration.

Importantly, it is this unbound fraction of the dosed drug that isavailable for interactions in the intracellular environment [i.e., whichis intracellularly bioavailable (Fic)] (Fig. 1A).In a preliminary study with a single cytosolic target (thymi-

dylate synthase), we showed that intracellular concentrations ofactive hits and reference drugs correlate with intracellular targetengagement (31). We, therefore, hypothesized that Fic might bea practical metric for predicting compound access to intracellulartargets. Herein, we tested this hypothesis with multiple com-pound sets and in different cell types. The compounds werescreened in biochemical assays, where the compound can directlyaccess and inhibit the target protein, and cellular assays, wherethe compound has to enter the cell before binding the target andeliciting the cellular response (Fig. 1A).We first investigated inhibitors of Mitogen-activated protein

kinase 14 (MAPK14 or p38α), an intracellular kinase involved inautoimmune and inflammatory diseases (32). Next, we used thepublished kinase inhibitor set (PKIS) (33, 34) to select compoundswith the potential to inhibit cell growth (a far from target endpoint) in a collection of 60 cancer cell lines from the NationalCancer Institute, the NCI-60 cell panel (35). The selected com-pounds were inhibitors of cyclin-dependent kinase 2 (CDK2) (36)and polo-like kinase 1 (PLK1) (37), cytosolic kinases that aretranslocated to the nucleus where they have important roles in the

Significance

Exposure at the site of action has been identified as one of thethree most important factors for success in drug discovery andthe design of chemical probes. Modern drug discovery pro-grams have, to a great extent, shifted to intracellular targets,but methods to determine intracellular drug concentrationshave been lacking. Here, we use a methodology for predictingintracellular exposure of small-molecule drugs to understandtheir potency toward intracellular targets. We show that ourapproach is generally applicable to multiple targets, cell types,and therapeutic areas. We expect that routine measurementsof intracellular drug concentration will contribute to reducingthe high attrition observed in drug discovery and the design ofboth better chemical probes and medicines.

Author contributions: A.M., G.J.W., P.M., T.L., A.W., M.M.H., and P.A. designed research;A.M., L.J.G., G.J.W., H. Almqvist, H. Axelsson, B.S.-L., and A.T. performed research; A.M.,L.J.G., G.J.W., H. Almqvist, H. Axelsson, B.S.-L., A.T., P.M., T.L., A.W., M.M.H., and P.A.analyzed data; and A.M., P.M., and P.A. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Freely available online through the PNAS open access option.1Present address: Discovery Sciences, Innovative Medicines and Early Development Bio-tech Unit, AstraZeneca, 43183 Mölndal, Sweden.

2To whom correspondence should be addressed. Email: Per.Artursson@farmaci.uu.se.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1701848114/-/DCSupplemental.

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cell cycle, and therefore, have been considered as targets in cancertherapy (38, 39). Finally, we investigated inhibitors of β-secretase 1(BACE-1), a protease involved in the cleavage of the amyloidprecursor protein (APP) into amyloid-β peptides (Aβs) (40).Modulation of this protease is proposed as a potential treatmentof Alzheimer’s disease (41). This target posed an additionalchallenge for prediction of exposure, because it is only active inearly endosomes (42–44). Compound concentration in these or-ganelles often differs from that in the cytosol, complicating pre-dictions of subcellular pharmacology (45, 46).We found that compounds displaying high cellular potency

generally exhibited high Fic, whereas those with a lower potency inthe cell than toward the isolated target (i.e., compounds with amarked cell drop off) had a low Fic (Fig. 1A). Knowledge of Ficconsistently improved predictions of cellular drug potency com-pared with using biochemical target binding assays alone. Weconclude that Fic is a generally applicable parameter for predictingtarget exposure and ranking of compounds acting on intracellulartargets. With our technique (Fig. 1B), Fic can be measured in ahigh-throughput manner in a variety of cell types, including thosedirectly relevant for the disease (e.g., patient cells).

ResultsIntracellular Bioavailability (Fic) of p38α (MAPK14) Inhibitors. Weinvestigated the impact of intracellular bioavailability (Fic) (below andFig. 1B) on the cellular potency of a set of 35 inhibitors of MAPK14(p38α). These compounds were screened for inhibition of TNFαexpression in human peripheral blood mononuclear cells (PBMCs)(Fig. 2A and Dataset S1) (32, 47, 48). Although there was a goodcorrelation (rS = 0.83) between the biochemical negative logarithm ofthe half-maximal inhibitory concentration (pIC50) and the cellularpIC50 for these compounds (Fig. 2B), the compounds were, on

average, one order of magnitude more potent in the biochemicalassay (where all added compound can interact directly with thetarget) (Fig. 1A). For kinase inhibitors, this drop in potency iscommonly attributed to the high cellular ATP concentrations(compared with those used in biochemical assays) that can competewith the compound (49, 50), leading to lower biochemical efficiency(3). Here, we studied if, in addition to this mechanism, the access ofthese inhibitors to the intracellular target in the cellular assay waslimited. We therefore determined the Fic of these compounds bymeasuring the intracellular fraction of unbound compound (fu,cell)and the cellular compound accumulation (Kp) (details are inMethods and Fig. 1B) in PBMCs (i.e., the same cell type as in thecellular pharmacological assay) (SI Appendix, Table S3).In agreement with the observation of marked cell drop off, most

compounds displayed low Fic (median = 0.088, interquartilerange = 0.069–0.19) (Fig. 2C). Because Fic represents the fractionof extracellularly added compound that is able to reach in-tracellular targets, we set out to predict the cellular pIC50 by usingthe Fic to correct the biochemically determined potencies of thecompounds toward the isolated target (log Fic + biochemicalpIC50). A good correlation was still observed between the pre-dicted and measured cellular potencies (rS = 0.79), and impor-tantly, the predicted cellular potencies were now at the same levelas the experimental ones (Fig. 2D)—indicating that the use of Ficcan explain the observed cell drop off.A low Fic, commensurate with a significant cell drop off, has

been historically attributed to low membrane permeability. How-ever, we observed a poor correlation for these compounds be-tween Fic and membrane permeability as determined in a typicalartificial membrane system [parallel artificial membrane perme-ability assay (PAMPA)] routinely used in drug discovery (rS =0.03) (SI Appendix, Fig. S1A and Table S5). In contrast to Fic, the

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Fig. 1. A comparison of biochemical and cellular assays used in drug discovery and measurement of intracellular bioavailability (Fic). (A) In biochemical assays,the compound is directly available to engage the target. In cellular assays, only the fraction of compound that is not bound to proteins in cell culture medium(fu,medium) or cellular components (fu,cell) is available for binding to an intracellular target; this fraction is the intracellularly bioavailable fraction (Fic). Afterengaging the target, the compound elicits a functional response, leading, in turn, to a phenotypic response. (B) General protocol for measurement of in-tracellular bioavailability (Fic). Intracellular fraction of unbound compound (fu,cell) measured after dialysis and intracellular compound accumulation (Kp) arequantified using LC-MS/MS and combined to give Fic.

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permeability data did not explain the cell drop off (rS = 0.21) (SIAppendix, Fig. S2A). These results are in line with our previousobservations that Fic is the net result of membrane permeabilityand other mechanisms, including carrier-mediated transport, me-tabolism, and nonspecific cellular binding (51). It should also beremembered that the widely used artificial membrane perme-ability data are reported as a rate, whereas Fic is directly related tothe amount of unbound drug in the cells and not related to howfast it gets there.Interestingly, our dataset included one pair of enantiomers

(compounds 1 and 2) (Fig. 2E) with identical biochemical po-tencies (IC50 = 12 nM) but different cellular potencies (12 and4.5 nM, respectively) (Fig. 2F). The 2.5-fold higher potency ofcompound 2 accompanied a 2.3-fold higher Fic, meaning that Ficaccurately detected the increase in cellular potency caused bystructural differences (Fig. 2F). We speculated that the differencein cellular potency of these enantiomers was caused by selectiveactive transport across the cellular membrane (51). We, therefore,investigated the Fic of these compounds in the presence of cy-closporine A, a paninhibitor of active transport processes (52, 53).The Fic of compound 1 was threefold higher in the presence ofcyclosporine A (P = 0.05, Mann–Whitney U test), whereas therewas no change for compound 2 (P = 0.95) (SI Appendix, Fig. S3).This result suggested the involvement of carrier-mediated efflux inthe cellular disposition of compound 1 (but not of 2), which de-creased its bioavailability in the cell interior and therefore, limited

its cellular potency. These observations illustrate how Fic, whenmeasured in the relevant cell type, can provide information abouttarget exposure without prior knowledge of the underlying mecha-nisms of compound disposition (51).To further explore the versatility of Fic as a metric of com-

pound access to intracellular targets, we investigated a differentcell type (HL60) that also expresses p38α. As in PBMC, the Ficvalues of these compounds in this cell type were also low (me-dian = 0.11, interquartile range = 0.062–0.45) (Fig. 2C). Sub-sequently, we determined intracellular target engagement usingthe cellular thermal shift assay (CETSA) in the isothermal dose–response fingerprint (ITDRF) mode (Dataset S1) (8, 9). Aftercorrecting the target affinity (biochemical pIC50) for Fic in therelevant cell type, the extent of intracellular target engagementwas successfully predicted (rS = 0.76) (Fig. 2G). Thermal shiftassays depend on not only the affinity of the ligand and itsconcentration at the target but also, the thermodynamics of itsbinding and protein unfolding (54, 55). The latter two were notconsidered in our prediction and may, therefore, explain theminor offset observed (on average, 0.5 log units).

Intracellular Bioavailability (Fic) of Compounds from the PKIS. Next,we applied Fic on molecules with more complex cellular phar-macology by investigating the impact of Fic on the cellular po-tency of compounds from the PKIS (33, 34). We selected kinaseinhibitors that affect the growth of cell lines in the NCI-60 cell

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Fig. 2. Intracellular bioavailability (Fic) of MAPK14 (p38α) inhibitors. (A) p38α Inhibition leads to a reduction in TNFα expression via p38α-activated MAPK-activated protein kinase 2 (MK2) and MK3, which are responsible for the phosphorylation of tristetraprolin (TTP). TTP inhibits TNFα mRNA translation bybinding the AU-rich element in the 3′ UTR (32, 48, 70). (B) p38α Inhibitors were less potent in the cellular assay than in the biochemical assay (linear fit: y =1.6x − 5.6; P < 0.0001). Biochemical potency was determined using an assay based on inhibition of ATP consumption, and cellular potency was determined asthe capacity to inhibit production of TNFα in PBMCs. Both measurements were performed with technical triplicates at each concentration. (C) Frequencydistribution of intracellular bioavailability (Fic) of p38α inhibitors measured in PBMCs (blue line) and HL60 cells (pink line) in triplicate on two independentoccasions. (D) Prediction of cellular pIC50 of p38α inhibitors by combining their Fic in PBMCs with their biochemical pIC50 (linear fit: y = 1.2x − 1.4; P < 0.0001).(E) Structures of two enantiomers (compounds 1 and 2) that displayed similar biochemical pIC50 against p38α. (F) Compound 2 (purple) showed a higher Fic inPBMCs than compound 1 (green), which was accompanied by an increase of similar magnitude in cellular pIC50 (Lower). Bars represent geometrical mean ±SEM. (G) Prediction of intracellular target engagement pEC50 of p38α inhibitors by combining their Fic in HL60 cells with their target affinity (biochemicalpIC50; linear fit: y = 0.9x + 0.21; P < 0.0001). CETSA measurements were performed with technical triplicates at each concentration.

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panel (Fig. 3A) (34, 35). We grouped the compounds accordingto their biochemical affinity profiles in the Nanosyn kinase panel(www.nanosyn.com/) using hierarchical clustering with completelinkage (56) (SI Appendix, Fig. S4). This procedure providedclusters of compounds that inhibit the same kinases and there-fore, should have similar effects on the cell. To enable the studyof compounds with potential to impact cell growth, we then se-lected only the clusters that contained at least one compoundthat was active [negative logarithm of half-maximal growth inhibitionconcentration (pGI50) > 6] in the majority of the NCI-60 cell lines(SI Appendix, Fig. S4). The selection resulted in three clusters ofcompounds (Fig. 3A): one cluster (n = 39) containing compoundsoriginally targeting CDK2 or glycogen synthase kinase 3 (GSK3) andtwo clusters (n = 9 and n = 8) that included compounds initiallydeveloped as inhibitors of PLK1 (Dataset S2). Importantly, thepotency of these compounds toward their respective originalintracellular targets was a poor predictor of their pGI50 (rS =0.33) (SI Appendix, Fig. S5). To assess if the differences in pGI50were a result of different concentrations of compound availableat the target, we determined their Fic (SI Appendix, Table S2).The Fic of the selected compounds followed the cellular potency

(rs = 0.64) (Fig. 3B). In other words, compounds with a high Ficwere also highly active in cells. As for the p38α inhibitors, wepredicted the cellular pGI50 by correcting the biochemical potencywith the specific Fic of each compound. The predicted pGI50values were, on average, one log unit higher than the measuredones (rs = 0.65) (SI Appendix, Fig. S6). This difference is perhapsnot surprising given that cell growth inhibition is a readout fardownstream of the kinase inhibition event, with redundant path-ways in the cell attenuating the effect of the compound.For such cases where the relation between target inhibition and

pharmacological effect is not clearly defined (e.g., in phenotypic

screening assays), we established a classification model to visualizethe impact of the compound Fic on the cellular activity. The modelcomprises four classes (Fig. 3C). Class 1 compounds are active incellular assays, and their concentrations at the target are equal orhigher than the nominal concentration added to the cells. Class2 compounds are active in cells, despite restricted access to thetarget [compounds with high affinity to the target(s)]. Class 3 com-pounds are inactive in cells, although they have high Fic [compoundswith low affinity to the target(s)], and class 4 compounds are notactive in cells and have low Fic. For the PKIS dataset, we definedcompounds as active in the cellular assay if their pGI50 was ≥6 (thesame cutoff used for compound selection as described above). Mostcompounds (71%) in the dataset belonged to either class 1 (23%)or class 4 (48%) (Fig. 3C), where the cellular potency is explainedby Fic. Only 18% of compounds were in class 2 and 11% in class 3,with all compounds being close to the cutoffs of each category(Fig. 3C). Our observations thus suggested that Fic is a usefulpredictor of compound activity in cellular assays even when thereis only a modest pharmacological connection between the cel-lular readout and target inhibition [or if the target is completelyunknown (e.g., in phenotypic screening)].

Endosomal Bioavailability (Fendo) of BACE-1 Inhibitors. To explorethe limits of the Fic methodology, we selected an intracellulartarget (BACE-1) that is only active in the early endosomalsubcellular compartment (42–44). In other words, the com-pounds must reach this low pH compartment to exert their ef-fects (Fig. 4A). Because Fic reflects an average bioavailability inthe cell interior, we expected a lower predictive power than fortargets available in the cytoplasm (e.g., p38α).Thirty BACE-1 inhibitors were assayed both biochemically

and in SH-SY5Y cells for their capacity to inhibit the cleavage of

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Fig. 3. Intracellular bioavailability of compounds from the PKIS. (A) Adapted with permission from Macmillan Publishers Ltd: Nature Biotechnology, ref. 34,copyright 2015. Each row represents one compound, and each column represents one kinase of the Nanosyn kinase panel (www.nanosyn.com/) or one cellline of the NCI-60 panel. Colors represent the potency of the compounds (pIC50 for kinases and pGI50 for cell lines). In Left, compounds, kinases, and cell linesare each sorted alphabetically. In Right, compounds were clustered based on their biochemical data to identify groups of compounds with similar targetinteraction patterns (SI Appendix, Fig. S4). For a better visualization of the cell growth inhibition potential of each compound, cell lines were sorted by pGI50(from the most affected to the least affected cell line). Selected compounds are highlighted in blue, and their primary targets are in gray. (B) Relationshipbetween cellular potency (pGI50) and Fic. Cellular potency represents an average of the pGI50 in the NCI-60 panel; Fic was measured in HEK293 cells in triplicateand on two independent occasions (geometrical mean is shown). (C) Classification model based on pGI50 and Fic. Classes are defined by thresholds of in-tracellular bioavailability (Fic = 1, which corresponds to a concentration at the target equal to the concentration added to the cells) and cellular potency (inthis example, pGI50 = 6).

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an APP peptide containing the Swedish mutation (57) (DatasetS3). On average, potencies were one order of magnitude lower inthe cellular screen than in the biochemical screen (rS = 0.49)(Fig. 4B). In accordance, these inhibitors displayed low Fic in theSH-SY5Y cells (median = 0.27, interquartile range = 0.16–0.72)(SI Appendix, Table S4).As expected from the specific localization required, the bio-

chemical pIC50 corrected for the Fic was not a good predictor ofthe cellular pIC50 of these compounds (SI Appendix, Fig. S9). Incontrast, a much improved correlation with the cellular pIC50 (rS =0.82) was observed when the biochemical potencies were insteadcorrected using the predicted accumulation in early endosomes[Fendo; calculated from Fic assuming pH-dependent compoundpartitioning into subcellular compartments (45, 46)] (details are inMethods, SI Appendix, Table S4, and Dataset S3). Interestingly, all ofthese compounds were now predicted to be more potent than whatwas actually observed in the cellular assay (Fig. 4C). A contributingfactor to this discrepancy is likely that BACE-1 is not present in allearly endosomes [as evident from double-staining experiments withBACE-1 and endosomal markers (42, 58, 59)]. In contrast, thepositively charged BACE-1 inhibitors will be evenly distributed inthe entire endolysosomal space, and consequently, only a fraction ofthe estimated Fendo is colocalized with the target and can exert itseffect. Additional studies are warranted to confirm this hypothesis,but the overprediction of cellular potency by 1.7 orders of magni-tude suggests that the fraction of active and target-accessible com-pound is only 2% of the concentration added to the cells.

DiscussionKnowing the amount of compound available at the target has beenidentified as a key factor for reducing attrition rates in the drugdiscovery process (4, 5). Lack of high-throughput methodologieshas precluded routine measurement of this parameter for targetsin the cell interior. Instead, drug discovery efforts are commonlydirected at increasing membrane permeability in the hope that ittranslates to increased intracellular compound levels. However, ahigh permeability across a membrane layer does not always resultin high compound concentrations in the cell interior (as evident bythe lack of correlation between membrane permeability and Fic)(SI Appendix, Fig. S1). This discrepancy is because membranepermeability is only one of many factors that can affect intracellularcompound concentrations (51). Thus, although cell permeabilityassays are important tools in predicting oral bioavailability, they

are not ideal for the prediction of intracellular drug exposure. Incontrast, the intracellular bioavailability concept (Fic) (Fig. 1B)(28) provides a direct measure of intracellular drug exposure andconsiderably improves predictions of cellular drug response fortargets in a variety of subcellular compartments (including thecytosol, nucleus, and endosomes).By predicting Fic in different cell types and for compound sets with

diverse biological mechanisms, we have shown the general applica-bility of our approach. The Fic value compiles all ongoing processesthat influence target exposure, including cell-specific transport andelimination mechanisms (51). The aggregation of multiple parallelprocesses explains why Fic is in agreement with not only responsesclosely related to the target (such as p38α target engagement and theclose-downstream production of TNFα) but also, far from targetresponses that are only indirectly associated with target inhibition(such as cell growth inhibition elicited by kinase inhibitors in thePKIS). Furthermore, by accounting for pH-dependent distribution,predictions of BACE-1 inhibitory activity in SH-SY5Y cells wereconsiderably improved for the majority of the compounds tested;such predictions were not possible with a biochemical assay alone forthis target, which is located in a specific subcellular compartment.In our example of a close to target cellular response (p38α in-

hibition of TNFα production) (Fig. 2A), the inhibitors had a lowFic in both PBMCs and HL60 cells (Fig. 2C), which translated to alower potency in the cellular assay than in the biochemical assay(Figs. 1A and 2B). By simply correcting the biochemical pIC50 ofthese compounds with the bioavailable concentration at the target(i.e., Fic), we obtained good predictions of their cellular potency(Fig. 2D) and intracellular target engagement (Fig. 2G). For thecellular potency, 74% of the compounds were predicted withinone order of magnitude of their true potency. The high predictivityis remarkable considering that kinase inhibitors are generallypromiscuous (Fig. 3A) (60), with inhibition of other kinases pos-sibly affecting TNFα expression.Our PKIS kinase inhibition data showed that a high Fic is

required for pharmacological effect of compounds that act onintracellular targets (Fig. 3B). Where the connection betweenthe cellular activity and target inhibition is unknown (e.g., inphenotypic screening), we implemented a simple and flexibleclassification model for the interpretation of the cellular potencyresults in relation to the cellular drug exposure (Fig. 3C). In thisclassification system, the cellular potency cutoff (i.e., the potencyat which a compound is considered active in the cells) can be

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Fig. 4. Endosomal bioavailability (Fendo) of BACE-1 inhibitors. (A) BACE-1 inhibition leads to a reduction of Aβ1–42 formation from APP. Proteolysis of APP byBACE-1 occurs in early endosomes. (B) BACE-1 inhibitors are less potent in a cellular assay than in a biochemical assay (linear fit: y = 0.75x − 0.96; P = 0.004).Biochemical potency was determined as the capacity to inhibit the cleavage of a peptide based on the Swedish mutant APP sequence, and cellular potency wasdetermined as the capacity to inhibit formation of Aβ1–42 in SH-SY5Y cells. Both measurements were performed with technical triplicates at each concentration.(C) Prediction of cellular pIC50 of BACE-1 inhibitors by combining their endosomal bioavailability (Fendo) in SH-SY5Y cells with their biochemical pIC50 (linear fit: y =0.94x − 1.2; P < 0.0001). Fendo was calculated from Fic, which was measured in triplicate on two independent occasions (geometrical mean is shown).

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adjusted to reflect the desired cellular potency. For classes 1 and 4,the Fic explains why some compounds are active (class 1; high Fic,high cellular potency) and why some have low cellular potency(class 4; low Fic, low cellular potency). The class 2 compounds (lowFic, high cellular potency) will appear when the biochemical po-tency is very high (e.g., as a consequence of high lipophilicity) (61).These compounds may have safety issues, because Fic is cell type-specific (51); cells other than the one tested might be exposed tohigher than anticipated levels of drug. In contrast, class 3 com-pounds (high Fic, low cellular potency) will appear when thebiochemical potency is low or when the compound targets proteinsthat are irrelevant to the desired cellular response [i.e., off-targeteffects (62)] (SI Appendix, SI Results). Thus, by knowing Fic, anydiscrepancies between biochemical and cellular potencies can berationalized, and optimization efforts can be focused (e.g., on in-creasing target affinity vs. intracellular exposure).Our studies with BACE-1 inhibitors showed that Fic can provide

estimates of subcellular distribution of compounds after takinginto consideration the organelle pH and volume, and the localcompound charge. Importantly, biochemical assays do not providesuch information. Indeed, the biochemical BACE-1 assay did notpredict the cellular potency (Fig. 4B). When the biochemicalpIC50 was corrected for the endosomal bioavailability (Fendo), amuch better prediction of the cellular potency of these compoundswas obtained (rS = 0.82). Still, the cellular pIC50 was lower thanpredicted (Fig. 4C). We attribute this to the fact that the com-pound is evenly distributed across all acidic organelles, whereasonly 2% is located in endosomes that also contain the target andits substrate (APP) (42, 58, 59). Additional studies are needed toconfirm this hypothesis.Although Fic is broadly applicable for predicting intracellular

target exposure, its potential limitations should be identified andscrutinized. First, it relies on sensitive analytical MS for compoundquantification, which is considered a low-throughput technology.However, we recently showed that extensions of our methodologycan process >1,000 compounds per week (29, 30), which should besufficient for secondary screening of hits from biochemical assays.Second, for compounds that need to be activated in the cell(prodrugs), the Fic of the dosed parent molecule might not berelevant. However, metabolically activated species can easily bemonitored as well, such as we previously did for compounds tar-geting thymidylate synthase (31). Third, the method is designed togive information on exposure in the whole cell (i.e., an average ofcytosolic and organelle exposure), which limits the resolution fortargets located in specific subcellular compartments. The examplewith BACE-1 inhibitors shows that mathematical modeling can beused to deconvolute the accumulation of the compounds in earlyendosomes. This approach can be applied to other subcellularcompartments if their volumes, pH, and other relevant physicalproperties are considered (63). A powerful expansion of ourtechnology would be to combine it with high-content imagingtechniques (20–25) or proteome-wide target engagement ap-proaches (10, 11), allowing for a quantitative assessment of in-tracellular compound distribution.In conclusion, we show that our label-free Fic technology ac-

curately predicts cellular and subcellular drug exposure acrossmultiple different drug targets and cell types. Our methodologyprovides direct measurements of intracellularly available drugconcentrations in cells relevant to the pharmacological effect incontrast to indirect extrapolations from more demanding cellpermeability experiments in generic cell lines. Compared withimaging-based studies, which typically require labeling of com-pounds, our label-free approach ensures that compound locali-zation and target affinity are not altered by chemical modification.We believe that insight into the amount of drug that is locallyavailable to bind intracellular targets is a powerful tool for im-proving success rates in early drug discovery. Our method providesessential information about drug exposure at the site of action, the

first of the “three pillars” for successful clinical drug development(4), and we believe that it will contribute to improved translationof target potency. Thus, when combined with methodologies tomeasure target engagement and pharmacological response (theother pillars), it has great potential for contributing to reducingdrug attrition. Beyond drug discovery, Fic can be universally ap-plied in other fields where information of intracellular exposure isdesired, such as in toxicological and metabolomics research andany applied science where a clearer understanding of local in-tracellular concentrations would be beneficial.

MethodsMaterials. Reagents for cell culture were purchased from Thermo FisherScientific or Sigma-Aldrich. Cell lines used in this study were acquired fromATCC (HL60: catalog no. CCL-240; SH-SY5Y: catalog no. CRL-2266) or ThermoFisher Scientific (HEK293: catalog no. R75007) and regularly tested for my-coplasma contamination with the MycoAlert mycoplasma detection kit(Lonza) according to the manufacturer’s instructions. Drug-like compoundswere synthesized at GlaxoSmithKline or made available from their screen-ing collections. Compound purity was confirmed, and only compoundswith ≥95% purity were used. Compounds were dissolved in DMSO at aconcentration of 10 mM and stored at −80 °C.

Cell Culture. Cell cultures were kept at 37 °C in a humidified 5% CO2 at-mosphere. HL60 cells were cultured in Roswell Park Memorial Institute me-dium 1640 (RPMI 1640) with GlutaMAX, 10% FBS, penicillin (100 units/mL),and streptomycin (100 μg/mL). HEK293 cells were maintained in DMEMsupplemented with 10% FBS, 2 mM L-glutamine, and 75 μg/mL HygromycinB. SH-SY5Y cells were grown in a 1:1 mixture of DMEM and nutrientF12 medium with GlutaMAX and 10% FBS.

PBMCs were isolated from a buffy coat purchased from the UppsalaUniversity Hospital, which has ethical approval for blood collection from theUppsala Regional Ethics Committee. Volunteers gave informed consent, andall samples were anonymized immediately after collection of blood samples.The buffy coat was layered on top of Histopaque-1077 (Sigma-Aldrich) andcentrifuged for 30 min at 400 × g. Cells were collected from the interface andwashed three times by centrifugation (200 × g for 5 min) in CO2-in-dependent medium (Thermo Fisher Scientific) supplemented with 5% heat-inactivated FBS, penicillin (100 units/mL), and streptomycin (100 μg/mL). Cellswere resuspended in RPMI 1640 with GlutaMAX; supplemented with 10%FBS, penicillin (100 units/mL), and streptomycin (100 μg/mL); and used im-mediately after isolation.

Biochemical and Cellular Screens of p38α Inhibition. Inhibition potency towardpurified p38α (recombinantly expressed in Escherichia coli) was measured for aseries of 35 compounds. Compounds were incubated for 30 min with 5 mUp38α and 50 μM [33P-γ-ATP] in 25mM Tris·HCl, pH 7.5, 100 μM EGTA, 330 μg/mLmyelin basic protein, and 10 mM magnesium acetate. Assays were stopped byaddition of 5 μL of 500 mM orthophosphoric acid and then harvested onto P81Unifilter plates (Sigma-Aldrich) with a wash buffer of 50 mM orthophosphoricacid. After scintillation counting, pIC50 was calculated based on the intensity ofthe signal at different compound concentrations.

Inhibition of TNFα production (a proximal downstream target of p38α) wasmeasured for the same series of compounds. PMBCs (10,000 cells per well in384-well plates in RPMI 1640 supplemented with 10% FBS, 100 units/mLpenicillin G, 100 μg/mL streptomycin sulfate, 250 ng/mL amphotericin B) wereincubated with the compound and LPS (final concentration: 100 pg/mL) for 4 hat 37 °C and 5% CO2. At the end of the incubation, TNFα levels were de-termined using a pair of antibodies, one of which was labeled with europiumcryptate and the other was conjugated with XL665 (catalog no. 62TNFPEC;Cisbio). After 2 h of incubation in the dark, fluorescence was measured (320-nm excitation; 615- and 665-nm emission) on an EnVision plate reader (Per-kinElmer). pIC50 was calculated based on the fluorescence intensity ratio (665/615 nm) at different concentrations of compound.

Target engagement was measured using a high-throughput version of theCETSA (9). In a preliminary experiment, we determined the apparent tem-peratures of aggregation (Taggs) of p38α with and without 10 μM AMG-548(Tocris Biosciences), a known inhibitor of p38α (64), to be 58 °C and 47 °C,respectively (SI Appendix, Fig. S10). The p38α inhibitors were then screened inITDRF mode at 52 °C to maximize the response window in the presence ofinhibitors. Positive (10 μM AMG-548) and negative controls (DMSO) were in-cluded on each assay plate. Briefly, cells (16,000 cells per well in 384-wellplates) were incubated at 37 °C with the compounds diluted in RPMI 1640(supplemented with 10% FBS, 2 mM L-glutamine, 100 units/mL penicillin,

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100 μg/mL streptomycin) for 30 min before heating to 52 °C for 3 min and coolingto 20 °C with a ProFlex PCR System (Thermo Fisher Scientific). Cells were lysedwith Alpha SureFire Ultra lysis buffer (PerkinElmer) and kept at −80 °C untildetection. For detection of the remaining folded p38α, we added a detectionmix consisting of 10 μg/mL rabbit acceptor beads (catalog no. AL104C; Perki-nElmer), 10 μg/mL mouse donor beads (catalog no. AS104D; PerkinElmer),0.2 nM rabbit anti-p38α antibody (catalog no. ab170099; Abcam), 0.8 nMmouse anti-p38α antibody (catalog no. ab31828; Abcam), and 0.05% SDS inimmunoassay buffer (PerkinElmer). After overnight incubation at room tem-perature, luminescence was read on an EnVision plate reader, and the nega-tive logarithm of the half-maximal stabilization concentration (pEC50) wascalculated based on the fraction of stabilized protein [(signal – negative con-trol)/positive control] at different compound concentrations.

PKIS Data.Data from the PKIS screening for inhibition of kinases were collectedfrom ChEMBL (https://www.ebi.ac.uk/chembldb/extra/PKIS/). These data in-cluded percentages of inhibition of 200 kinases (Nanosyn kinase panel; www.nanosyn.com/) at two concentrations (0.1 and 1 μM), named biochemical dataherein. The pIC50 for each compound–kinase pair was estimated (SI Appendix,Fig. S11 and Dataset S2). Because of the sigmoidal nature of the IC50 curve, welimited the predictions of pIC50 to values of inhibition between 20% and 80%,because outside this range, experimental errors would considerably affectpIC50 predictions. Hence, the following rules were applied to the data in thisorder: (i) If inhibition was ≥80% at 0.1 μM, pIC50 was set to eight. (ii) If in-hibition was between 20% and 80% at 0.1 μM, pIC50 was estimated accordingto Eq. 1 ([I] = 10−7 M, and percentage inhibition [%inhibition] was the value atthat concentration). (iii) If inhibition was between 20% and 80% at 1 μM, pIC50was estimated according to Eq. 1 ([I] = 10−6 M, and percentage inhibition[%inhibition] was the value at that concentration). (iv) If inhibition was ≤20%at 1 μM, pIC50 was set to five:

pIC50 =−log�½I� ·

�100

%inhibition− 1

��. [1]

Data from the PKIS screening for growth inhibition of cell lines in the NCI-60 panel were collected from the work by Elkins et al. (34), named cellulardata herein.

Compounds with potential to inhibit cell growth were selected from thePKIS and grouped according to their biochemical affinity profiles using hi-erarchical clustering with complete linkage (56) (SI Appendix, Fig. S4).Clusters of compounds were selected that contained at least one compoundthat was active (pGI50 > 6) in more than one-half of the NCI-60 cell lines (SIAppendix, Fig. S4). This procedure resulted in three clusters containing atotal of 51 compounds (Dataset S2).

Biochemical and Cellular Screens of BACE-1 Inhibition. BACE-1 biochemical andcellular data were generated as part of a legacy drug discovery program un-dertaken within GlaxoSmithKline. Inhibition potency of a series of 30 com-pounds toward purified BACE-1 was measured using a quenched fluorescentpeptide based on the Swedish mutant APP sequence (FAM-SEVNLDAEFK-TAMRA). Typically, compounds were incubated with 5 μM substrate and∼1 nM purified BACE-1 at pH 4.5 for 240 min as described by Hussain et al.(57). Quantification of cleaved substrate was measured using an Analystfluorimeter (LJL Biosystems; 485-nm excitation, 535-nm emission). The pIC50

was calculated based on the intensity of the signal at different compoundconcentrations.

For the same series of compounds, inhibition of BACE-1 in SH-SY5Y cellsexpressing the Swedish mutant of APP was determined with an AlphaLISAassay (PerkinElmer) or bioequivalent MSD assay (catalog no. K151FUE; MesoScale Discovery). Typically, 10,000 cells per well in 384-well plates were in-cubated with compounds (in a 1:1 mixture of DMEM and nutrientF12 medium supplemented with 10% FBS) for 48 h. For the AlphaLISA assay,Aβ1–42 was quantified in the supernatant using a pair of antibodies, of whichone was biotinylated (catalog no. 6E10; Signet Laboratories) and the otherwas conjugated to acceptor beads (produced in house). Chemiluminescence(680-nm excitation, 615-nm emission) was measured after addition ofstreptavidin-coated donor beads on an EnVision plate reader. For the MSDassay, Aβ1–42 was quantified in the supernatant using a pair of antibodies, ofwhich one was adsorbed to an MSD plate and the other was conjugated to aruthenium MSD tag. Electrochemiluminescence was measured using an MSDsector Imager 6000 reader (Meso Scale Discovery). pIC50 was calculated basedon the fluorescence intensities at different concentrations of compound.

Measurement of Intracellular Compound Bioavailability (Fic). Intracellular com-pound bioavailability (Fic) was determined using the technique described in the

work by Mateus et al. (28, 29). This technique is made up of parallel mea-surement of the intracellular fraction of unbound compound (fu,cell) and thesteady-state cellular compound accumulation (Kp) as described below (Fig. 1B).

For p38α inhibitors, Fic was first evaluated in PBMCs for comparison withthe TNFα cellular screen and then evaluated in HL60 cells for comparisonwith the CETSA results. For compounds from the PKIS, Fic was determined inHEK293 cells. This cell line was not included in the NCI-60 panel, but its geneexpression profile was close to that of an average cell line from this panel (SIAppendix, Fig. S7), allowing the use of the average of pGI50 across 60 celllines as a measure of compound potency. For BACE-1 inhibitors, Fic wasmeasured in SH-SY5Y cells, the same as in the cellular potency screen.

Measurement of fu,cell. The fu,cell was measured in cell homogenates at aconcentration of 10 × 106 cells per 1 mL using a Rapid Equilibrium Dialysisdevice (Thermo Fisher Scientific). Groups of six substances, together withhigh and low binding controls (atorvastatin and lopinavir, respectively),were added to cell homogenates at a concentration of 0.5 μM (29). Dialysiswas performed against HBSS at 37 °C for 4 h; fu,cell was calculated as

fu,cell =1

D ·�

1fu,hom

− 1�+ 1

, [2]

where fu,hom is the ratio of compound concentrations in the buffer chamber andthe cell homogenate chamber, and D is used to correct for homogenate dilution.D is calculated as 1/(Phom.·Vcell), where Phom. is the protein concentration of thecell homogenate (in milligrams per microliter), and Vcell is the cellular volume[6.5 μL/mg protein (28)]. Each compoundwasmeasured in at least three differentrandomly assigned groups. Compounds with fu,cell < 0.01% were excluded be-cause of large variability in the determination of fu,cell below this value.

Measurement of Kp. Before Kp measurements, freshly isolated PBMCs weretransferred to a 96-well plate (500,000 cells per well) immediately before theexperiment. HL60 cells in exponential growth phase (<1 × 106 cells per 1 mLin the culture flask) were resuspended in RPMI 1640 with GlutaMAX and10% FBS and transferred to a 96-well plate (500,000 cells per well) imme-diately before the experiment. HEK293 cells were seeded for 48 h in 24-wellplates (600,000 cells per well) in DMEM with 10% FBS and 2 mM L-glutamine.SH-SY5Y cells were seeded in 24-well plates (500,000 cells per well) in a1:1 mixture of DMEM and nutrient F12 medium with GlutaMAX and 10%FBS immediately before the experiment.

Measurement of Kp was then performed by incubating cells at 37 °C with0.5 μM compound solutions (in HBSS for HEK293 cells or the culture mediumdescribed above for each of the other cell types). The incubation was stop-ped after 45 min for PBMCs, HL60 cells, and HEK293 cells and after 48 h forSH-SY5Y cells. The longer incubation time for SH-SY5Y reflected that used inthe corresponding cellular potency assay. At the end of the experiment, asample of the incubated solutions was collected, and cells were rapidlywashed with ice-cold PBS before compound extraction. Kp was calculated as

Kp=Acell

ðVcell · Pcell ÞCextracellular

, [3]

where Acell is the amount of substance in the cell fraction, Pcell is the proteinamount of the cell fraction (in milligrams), and Cextracellular is the compoundconcentration in the incubated solution at the end of the experiment. Eachcompound was measured in triplicate in at least two independent occasions.

Calculation of Fic. Intracellular bioavailability (Fic), which represents the ratioof intracellular unbound (Cu,cell) concentration to extracellular (Cextracellular)compound concentration, was determined as

Fic =Cu,cell

Cextracellular= fu,cell ·Kp. [4]

Estimation of Endosomal Bioavailability (Fendo). In analogy to intracellularbioavailability (Fic), we defined endosomal bioavailability (Fendo) as the ratiobetween endosomal unbound compound concentration (Cu,endo) and ex-tracellular compound concentration (Cextracellular) (Eq. 9). Fendo was estimatedusing a model based on pH partitioning theory (65). The model consisted ofthree compartments: the extracellular space (pH 7.4), the cytosol [pH 7.2 (66)],and early endosomes, where BACE-1 is active (42–44) [i.e., a pH of 6 (66) andvolume equal to 1% of the cytosolic volume (67)]. Accumulation of basiccompounds in the endolysosomal compartment produces significant increasesin the volume of these organelles (68, 69). However, Fendo estimates were in-sensitive to changes in the volume of the endosomal compartment (up to 5%

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of the cytosolic volume). Therefore, we used the baseline value of endosomalvolume, 1% of cytosolic volume (67). In the model, transfer between compart-ments was limited to the unbound uncharged molecular species (Cu,uncharged;i.e., at equilibrium, Cu,uncharged was assumed to be equal in three compart-ments). Cu,uncharged was estimated from Fic and the Henderson–Hasselbalchequation for basic compounds in the following manner:

Fic =Cu,cell

Cextracellular=

�Au,cyto +Au,endo

·Vcell

Cextracellular, [5]

where Au,cyto and Au,endo are the amounts of compound unbound in thecytosol and the endosomes, respectively, the sum of which was assumed tobe the total amount of unbound drug in the cell. Vcell is the volume of thecell, considered to be equal to the cytosolic volume (Vcyto). The amount ofthe unbound species in each compartment (Au,comp) was the sum of theamounts of uncharged (Au,uncharged) and charged (Au,charged) species:

Au,comp =�Cu,uncharged +Cu,charged

·Vcomp, [6]

where Vcomp is the volume of the relevant compartment. Cu,charged was cal-culated with the Henderson–Hasselbalch equation for basic compounds:

Cu,charged =Cu,uncharged

10pHcomp-pKa, [7]

where Cu,charged is the concentration of the unbound charged molecular spe-cies, pHcomp is the pH of the relevant compartment, and pKa was the estimatedbasic pKa from ADMET Predictor v7.0 (SimulationsPlus). Replacing Eq. 7 in Eq. 6and the subsequent equation into Eq. 5, Cu,uncharged was estimated to be

Cu,uncharged =Cextracellular · Fic

1+ 110pHcyto-pKa +

VendoVcyto

�1+ 1

10pHendo-pKa�. [8]

Fendo was then calculated as

Fendo =Au,endo

Vendo

Cextracellular=

Fic ·�1+ 1

10pHendo-pKa�

1+ 110pHcyto-pKa +

VendoVcyto

�1+ 1

10pHendo-pKa�. [9]

PAMPA. Permeability of p38α and BACE-1 inhibitors was measured in anartificial phosphatidylcholine/cholesterol membrane assay. The membraneswere prepared by adding 3.5 μL of a solution of 1.8% (wt/vol) phosphati-dylcholine and 1% (wt/vol) cholesterol in decane to a Millicell 96-well cultureplate (0.4 μm; MilliPore Corp.). The plate was then briefly shaken, and 250 μLof 50 mM phosphate buffer with 0.5% encapsin was added to the donor

side, and 100 μL of the same buffer was added to the receiver side. Aftershaking the plate for 45 min, compounds were added to a final concentra-tion of 100 μM. Samples from the receiver and donor sides were collectedafter 3 h, and permeability was calculated as

Papp =−ln�1−

CA

CD

�·

VA ·VD

ðVA +VDÞ ·a · t, [10]

where CA and CD are the compound concentrations at time t in the acceptorand donor compartments, respectively; VA and VD are the volumes of theacceptor and donor compartments, respectively; and a is the area of themembrane. Compounds were assayed in duplicate.

Analytical Procedures. Compound quantification was performed using liquidchromatography coupled to tandemMS (LC-MS/MS). The system consisted of aWaters Xevo TQ MS with electrospray ionization coupled to a Waters AcquityUPLC system. Compounds were separated on a reversed phase Waters BEHC18 column (2.1 × 50 mm; 1.7 μm) at 60 °C. Mobile phase A consisted of 5%acetonitrile and 0.1% formic acid in water, and mobile phase B consisted of0.1% formic acid in acetonitrile. Chromatographic separation comprised a2-min gradient with a flow rate of 0.5 mL/min: (i) 0–0.5 min, 5%mobile phase B;(ii) 0.5–1.2 min, linear gradient from 5 to 90% mobile phase B; (iii) 1.2–1.6 min,90% mobile phase B; and (iv) 1.6–1.7 min, return to initial conditions. Sampleswere kept at 10 °C until analysis. Sample injection volume was 5 μL. Internalstandard (warfarin) was added to all samples during sample preparation.Detection of the compounds was performed in a Waters Xevo TQ MS in-strument with electrospray ionization. Mass transitions and mass spectro-metric conditions (ionization mode, cone voltage, and collision energy) canbe found in SI Appendix, Table S1.

Protein quantification was performed with the BCA assay (Thermo FisherScientific) according to the manufacturer’s instructions.

ACKNOWLEDGMENTS. We thank Derek Poore and Hu Li for performing theTNFα cellular screen for p38α inhibitors and Terence Johnson and ShenazBunally for providing the AMPA assay data. We also thank SimulationsPlusfor access to the ADMET Predictor software and ChemAxon for access to theJChem Suite. This work was supported by Swedish Research Council Grant2822, the Swedish Fund for Research without Animal Experiments, Carl Tryggersstiftelse, Magnus Bergvalls stiftelse, and Åke Wibergs stiftelse. A.M. was sup-ported by Fundação para a Ciência e Tecnologia PhD Training Grant SFRH/BD/68304/2010. H. Almqvist, H. Axelsson, B.S.-L., and T.L. acknowledge the KarolinskaInstitute, Science for Life Laboratory, and the Swedish Research Council, whichfunds Chemical Biology Consortium Sweden. A.T. was supported by the EuropeanSeventh Framework Initial Training Network Program Grant 607517 ARIADME(Analytical Research in Absorption, Distribution, Metabolism, and Excretion).

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Mateus et al. PNAS | Published online July 12, 2017 | E6239

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