One-Step Identification of Antibody Degradation Pathways Using

Jul 14, 2017 - Therapeutic antibodies are prone to degradation via a variety of pathways during each stage of the manufacturing process. Hence, a low-...
0 downloads 7 Views 691KB Size
Download PDF
Subscriber access provided by UNIV OF DURHAM

Letter

One-step identification of antibody degradation pathways using fluorescence signatures generated by cross-reactive DNA-based arrays Shunsuke Tomita, Ayumi Matsuda, Suguru Nishinami, Ryoji Kurita, and Kentaro Shiraki Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b01264 • Publication Date (Web): 14 Jul 2017 Downloaded from http://pubs.acs.org on July 14, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Analytical Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

One-step identification of antibody degradation pathways using fluorescence signatures generated by cross-reactive DNA-based arrays Shunsuke Tomita,*† Ayumi Matsuda,‡ Suguru Nishinami,‡ Ryoji Kurita,† Kentaro Shiraki‡ †

Biomedical Research Institute, National Institute of Advanced Industrial Science and Technology, and DAILAB, 1-1-1 Higashi, Tsukuba, Ibaraki 305-8566, Japan. E-mail: [email protected]. Fax: +81-29-861-6177. ‡ Faculty of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan. ABSTRACT: Therapeutic antibodies are prone to degradation via a variety of pathways during each stage of the manufacturing process. Hence, a low-cost, rapid, and broadly applicable tool that is able to identify when and how antibodies degrade would be highly desirable to control the quality of therapeutic antibody products. With this goal in mind, we have developed signature-based sensing system to discriminate differently degraded therapeutic antibodies. The use of arrays consisting of conjugates between nano-graphene oxide and fluorophore-modified single-stranded DNAs under acidic pH conditions generated unique fluorescence signatures for each state of the antibodies. Multivariate analyses of the thus obtained signatures allowed identifying (i) common features of native, denatured, and visibly aggregated antibodies, (ii) complicated degradation pathways of therapeutic omalizumab upon time-course heat-treatment and (iii) the individual compositions of differently degraded omalizumab mixtures. As the signature-based sensing has the potential to identify a broad range of degraded antibodies formed by different kinds of realistic stress types, this system may serve as the basis for high-throughput assays for the screening of antibody manufacturing processes.

Therapeutic antibodies are of great interest, especially in the context of autoimmune and inflammatory diseases, as well as oncology.1 The high potency of antibodies is usually attributed to their target specificity and efficacy, which are derived from their complex higher-order structures. However, antibodies are generally unstable and prone to degradation via various pathways, e.g. upon exposure to chemical or physical stress such as acidification, heating, agitation, or freeze-thawing, all of which may occur during each stage of the manufacturing process.2-4 Antibody degradation causes not only lower efficacy, but also other adverse effects such as undesirable immunogenic responses.5,6 Therefore, the quality of therapeutic antibody products must be strictly controlled in order to ensure both efficacy and safety.1,7 Typical degradation pathways include denaturation and aggregation, but the resulting degraded antibodies are distinctly subdivided according to their conformational, morphological, and physicochemical features.8-10 Various analytical techniques have been developed in order to identify when and how antibodies degrade, and the information obtained has been used to improve antibody manufacturing processes. Conventional techniques are based on UV-vis, circular dichroism (CD), or Fourier-transform infrared (FTIR) spectroscopy, or on size-based separation techniques such as size exclusion chromatography (SEC) and dynamic light scattering (DLS).11 However, these methods usually require substantial expertise and suffer from low-throughput performance for routine screening. Probe molecules that specifically recognize exposed hydrophobic surfaces of non-native antibodies, such as small molecules,8,12 peptides,13 and proteins,14 have enabled a high-throughput assessment of antibodies. Still, these probes provide only partial information on the components of differently degraded antibodies. Due to these issues, a simultaneous

use of combined analytical techniques is necessary to provide a robust measure for antibodies. Therefore, a low-cost, rapid, and broadly applicable tool that offers accurate detection and quantification of differently degraded antibodies represents an attractive research target. Toward this goal, we have developed the first signaturebased sensing system for the identification of antibody degradation pathways. Signature-based sensing exploits the patternrecognition of unique optical signatures for individual analytes.15 Such signatures can be acquired using an array of “cross-reactive” molecules that are able to interact in different ways with target analytes. This sensing strategy has been used by our16-19 and other research groups20-26 to discriminate various proteins. Moreover, this strategy is potentially useful for the identification of changes in the state of the same protein. Examples include the use of conjugates between nanographene oxide (nGO) and single-stranded DNAs (ssDNAs);27,28 for example, Pei et al. have shown that crossreactive conjugates between nGO and ssDNAs modified with carboxyfluorescein (FAM) can discriminate between untreated and heat-treated proteins.29 Inspired by these previous approaches, we envisioned that fluorescence-quenched conjugates between nGO and fluorophore-modified ssDNAs should be well suited to obtain signatures reflecting unique physicochemical properties of differently degraded antibodies. Most antibodies are positively charged under acidic conditions, as the isoelectric points (pI) of antibodies are usually at pH = 6-9.30,31 We reasoned that negatively charged nGO/ssDNA conjugates should interact similarly with different antibodies in the native state through electrostatic interactions at pH = 4.0, which may cause partial recovery of the fluorescence of ssDNAs (Figure 1A). Denaturation increases the exposed hydrophobic areas, while aggrega-

ACS Paragon Plus Environment

Analytical Chemistry

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 1. Schematic representation of the signature-based identification of antibody degradation pathways. (A) 3’Carboxytetramethylrhodamine (TAMRA)-modified ssDNAs quenched by nGO can interact with each degraded antibody in a different way, (B) which results in the generation of fluorescence signatures that can be used for discrimination. (C) Sequences of designed ssDNAs.

tion alters their morphology and total surface area, whereby the affinities toward nGO/ssDNA conjugates are most likely affected. Beyond the detection of simply heat-stressed proteins, arrays of nGO/ssDNA conjugates were also expected to generate fluorescence signatures that are capable of identifying antibody degradation pathways (Figure 1B). To test these hypotheses, we selected two different human antibodies as target analytes: i) polyclonal immunoglobulin G (IgG, pI ≈ 7.3, a mixture of IgG subtypes), and ii) monoclonal omalizumab [Oma, pI ≈ 7.6, IgG1κ subtype with approval from the Food and Drug Administration (FDA) for the treatment of allergic asthma]. Irreversibly degraded antibodies were prepared upon thermal treatment at 75 °C (IgG) or 80 °C (Oma) in phosphate-buffered saline (PBS) at pH = 7.4 according to previously reported procedures.32-34 In the case of Oma, an increase in negative ellipticity at 216 nm (θ216), i.e., an increase in the proportion of β-sheet secondary structure, occurred within several minutes (Figure 2, red circles), although no substantial increase in optical density at 400 nm (OD400) was observed (Figure 2, black circles). These are typical signatures of the so-called ‘alternatively folded state (AFS)’.32 Further heat-treatment induced a gradual increase in optical density due to the formation of visible large aggregates. Thus, Oma solutions after incubation for 5 min and 15 min were used as denatured and visibly aggregated states, respectively. In addition, denatured and visibly aggregated states of IgG were prepared (cf. section 3 of the ESI). As sensing elements, ssDNAs (P1-P6) consisting of 15 to 23 bases (Figure 1C) were tagged with 3’carboxytetramethylrhodamine (TAMRA) (P1-P3: simple repeated nucleobases; P4: quadraplex; P5: hairpin; P6: quadraplex with specificity to amyloid oligomers35). Rhodaminetype TAMRA was selected, as it does not exhibit a fluorescence drop at pH < 7 in contrast to FAM.36 The fluorescence of all ssDNA-TAMRAs was quenched to a different extent upon addition of nGO at pH = 4.0 (cf. section 4 of the ESI). Once the binding ratio that provided high fluorescence quenching had been determined, we investigated the ability of the nGO/ssDNA-TAMRA array to identify different degradation states of the antibodies. On a 96-well plate, antibody solutions in PBS (40 µL) were added to each well, which con-

Page 2 of 5

Figure 2. Monitoring the degradation of Oma based on conventional spectroscopy techniques. Time-course plots of OD400 (black circles) and θ216 (red circles) for IgG in PBS buffer (0.5 mg/mL; pH = 7.4) at 80 °C.

tained solutions of nGO/ssDNA-TAMRA conjugate in 50 mM acetate buffer (pH = 4.0) and 140 mM NaCl (160 µL), to reach a final concentration of 0.1 mg/mL antibodies, 50 nM ssDNATAMRAs, and 100 µg/mL nGO. The fluorescence signals from each conjugate were recorded as (I-I0) at three different channels [λex/λem: 515/560 (Ch1), 535/590 (Ch2), and 570/610 (Ch3)]. These channels were chosen based on similar grounds as those in a recent report on signature-based sensing using a xanthene derivative.37 As shown in the heat plot in Figure 3, each state was attributed a subscript: N (native), D (denatured), or A (visibly aggregated). The addition of degraded antibodies resulted in a variety of fluorescence responses (for raw data, see Table S1). A linear discriminant analysis (LDA),15 which is a supervised pattern recognition method for dimensionality reduction and for the classification of multivariate data, was subsequently used to discriminate the fluorescence signatures. The LDA plot constructed based on selected conjugates (nGO with P1P4 and P6) resulted in a clear separation of six clusters (Figure 4A). In this conjugate combination, a high classification accuracy was obtained in the jack-knife classification38 (100% accuracy, Table S2) and the blind test (22 out of 24 samples, 92 % accuracy; Tables S2 and S3). In Figure 4A, native IgG and Oma are also well separated in

Figure 3. Heat plot of the fluorescence signatures of 0.1 mg/mL degraded IgG and Oma obtained from nGO/ssDNATAMRA conjugates. Six replicates are shown per analyte.

ACS Paragon Plus Environment

Page 3 of 5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

Figure 4. Identifying differently degraded antibodies: (A) Discriminant score plots for 0.1 mg/mL degraded IgG and Oma obtained from conjugates of nGO with P1-P4 and P6. (B) Re-labeled data from (A) according to antibody states obtained from using nGO with P3 and P4. Ellipses represent confidence intervals (± 1 SD) for the individual analytes.

the first discriminant score that provided the best discrimination among the classes, indicating that nGO/ssDNA-TAMRA conjugates exhibit different affinities toward IgG and Oma, despite the fact that both are positively charged. Notably, the analysis yielded very similar trajectories for IgG and Oma together with degradation transitions from the native to the denatured and the visibly aggregated state. It may therefore be possible that common alterations on the heat-induced degradation pathways exist. To confirm the presence of underlying similarities in the degradation pathways of antibodies, we re-labeled the six analytes in three different states (native, denatured, and visibly aggregated), and then applied an LDA. The nGO/P3 and nGO/P4 arrays afforded the best accuracy in the jack-knife classification (86%; Table S4), where antibody states were clustered with slight overlap between the denatured and visibly aggregated states (Figure 4B). This indicates a widespread utility for the signature-based sensing system as (i) polyclonal IgG consists of a variety of IgG subtypes and (ii) many approved therapeutic antibodies, including Oma, bear similar physicochemical properties due to their IgG1κ subtype structure, i.e., a γ1-heavy chain and κ-light chain.39 A preliminary experiment using IgG, Oma, and the therapeutic monoclonal antibodies panitumumab (Pan, pI ≈ 6.5, IgG2κ subtype) and rituximab (Rit, pI ≈ 8.7, IgG1κ subtype) demonstrated that native and visibly aggregated antibodies can be discriminated regardless of the antibody type (Figure S1; 94% accuracy in the Jack-knife classification). To investigate which sensing elements contribute to the discrimination, each element shown in Figure 4 was subjected to a hierarchical clustering analysis (HCA), which identifies clusters on the basis of the Euclidean distances between elements.15 A low correlation was observed between ssDNATAMRAs with repeated sequences (P1-P3) and the fluorescence responses against degraded IgG and Oma (Figure S2). Diverse cross-reactivity can therefore be obtained without considering specific functionality and higher-order structure for the ssDNA design (for a detailed interpretation, see section 5 of the ESI). Subsequently, time-course changes of Oma signatures during heat-treatment were examined in order to better understand the relationship between the degradation pathway and the gen-

Figure 5. Investigating the degradation pathway of Oma. Time-dependent discriminant score plots for 0.1 mg/mL Oma heated to 80 °C obtained from conjugates of nGO with all ssDNA-TAMRAs. The centers of the points that represent identical analytes (same color) were connected to facilitate a visual comparison between clusters.

erated signatures (Table S5). In the three-dimensional LDA plot (Figure 5), the cluster moved in positive direction with respect to the x- and y-axis for the initial 5 min, before moving in negative direction with respect to the x-axis and positive direction with respect to the z-axis for the following 5 min. After 10 min of heating, the cluster position on the x- and yaxis varied only slightly, while it changed substantially relative to the z-axis. Considering the intrinsic tryptophan fluorescence, the hydrophobic dye binding, and the size-exclusion chromatography (cf. section 6 of the ESI) in addition to the optical density and the CD (Figure 2), this complicated behavior may reflect different structural transitions occurring in Oma (see captions in Figure 5). As these results suggest that the obtained fluorescence signatures contain information on the complex degradation processes, the nGO/ssDNA system may be used to monitor and analyze the heat-induced degradation pathway of antibodies. The discriminatory power of the nGO/ssDNA system was subsequently used to detect mixtures of Oma in three different states. The quantification of individual degraded antibodies that coexist in solution should be difficult to accomplish using a single conventional analytical technique. To examine the efficacy of this system in this context, we prepared 1:1 mixtures of combinations of native, denatured, and visibly aggregated Oma with a total concentration of 0.1 mg/mL, and compared their signatures to those of pure Oma analytes (Table S6). Although a maximum accuracy of 94% (jack-knife classification) was achieved using a combination of three nGO/ssDNA-TAMRAs among all conjugate combinations (Table S7 and Figure S3), we found that, in the case of an array consisting of nGO with P1, P2, P5, and P6 (89% accuracy), 1:1 mixtures were most likely located between individual components in a two-dimensional LDA plot (Figure 6). Moreover, linear correlations were observed between the first discriminant scores and the composition ratios of degraded/native Oma (0, 25, 50, 75 and 100%; r = 0.94 and 0.98 for denatured/native and visibly aggregated/native, respectively; Figure S3). Thus, it was possible simply to use the scores for the quantification of the Oma composition. When using these as calibration curves, we detected limits of detection (LODs)

ACS Paragon Plus Environment

Analytical Chemistry

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 5

ACKNOWLEDGMENT We would like to thank S. Ishihara for the technical assistance with the experiments. This work was supported by JSPS KAKENHI grant JP16K14043.

REFERENCES (1) (2) (3)

Figure 6. Discriminant score plot for mixtures of Oma in three different states with a total concentration of 0.1 mg/mL obtained from conjugates of nGO with P1, P2, P5, and P6. Ellipses represent confidence intervals (± 1 SD) for the individual analytes.

of 31.6% (denatured antibodies) and 12.6% (aggregated antibodies). Thus, this system may potentially serve as a tool to analyze populations of degraded antibodies in drug products. In summary, we have presented a signature-based sensing method that allows a one-step identification of antibody degradation pathways using common laboratory equipment. Unique fluorescence signatures were generated in response to differently degraded antibodies by using arrays consisting of nGO/ssDNA-TAMRA conjugates. Multivariate analyses allow an effective discrimination of these signatures, even though differently degraded antibodies were added simultaneously to the analytes. Although increased temperature represents one of the most frequently encountered forms of stress, particularly during fermentation and storage,2,4 therapeutic antibodies are usually exposed to other types of stress in the manufacturing processes, resulting in a variety of discretely degraded antibodies.8-10 Signature-based sensing should have the potential to identify a wide range of degraded antibodies formed by a variety of realistic stress types. We are currently expanding the analytical scope of this strategy into different degradation pathways, and into the detection of small fractions of non-native antibodies. These studies should open new avenues for signature-based high-throughput assays for the screening of antibody manufacturing processes or the quality control of therapeutic antibodies.

ASSOCIATED CONTENT

(4) (5) (6)

(7)

(8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23)

Supporting Information The Supporting Information is available free of charge on the ACS Publications website.

(24) (25)

Experimental details, signature profiles, statistical analyses, preparation, and characterization of nGO/ssDNA conjugates and analytes, Figures S1-S11, and Tables S1-S9 (PDF)

(26)

AUTHOR INFORMATION

(27)

Corresponding Author

(28)

* E-mail: [email protected] (29)

Notes The authors declare no competing financial interest.

(30) (31)

Elgundi, Z.; Reslan, M.; Cruz, E.; Sifniotis, V.; Kayser, V. Adv. Drug Deliv. Rev. 2016, DOI: 10.1016/j.addr.2016.11.004 Vazquez-Rey, M.; Lang, D.A. Biotechnol. Bioeng. 2011, 108, 1494-1508. Dengl, S.; Wehmer, M.; Hesse, F.; Lipsmeier, F.; Popp, O.; Lang, K. Pharm. Res. 2013, 30, 1380-1399. Dorai, H.; Ganguly, S. Curr. Opin. Biotechnol. 2014, 30, 198204. Wang, W.; Singh, S.K.; Li, N.; Toler, M.R.; King, K.R.; Nema, S. Int. J. Pharm. 2012, 431, 1-11. Moussa, E.M.; Panchal, J.P.; Moorthy, B.S.; Blum, J.S.; Joubert, M.K.; Narhi, L.O.; Topp, E.M. J. Pharm. Sci. 2016, 105, 417-430. Guidance for Industry Immunogenicity Assessment for Therapeutic Protein Products [Internet]. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRe gulatoryInformation/Guidances/UCM338856.pdf. Accessed Feb 2017. Joubert, M.K.; Luo, Q.; Nashed-Samuel, Y.; Wypych, J.; Narhi, L.O. J. Biol. Chem. 2011, 286, 25118-25133. Luo, Q.; Joubert, M.K.; Stevenson, R.; Ketchem, R.R.; Narhi, L.O.; Wypych, J. J. Biol. Chem. 2011, 286, 25134-25144. Yageta, S.; Lauer, T.M.; Trout, B.L.; Honda, S. Mol. Pharm. 2015, 12, 1443-1455. Beck, A.; Wagner-Rousset, E.; Ayoub, D.; Van Dorsselaer, A.; Sanglier-Cianferani, S. Anal. Chem. 2013, 85, 715-736. Samra, H.S.; He, F. Mol. Pharm. 2012, 9, 696-707. Sakamoto, K.; Ito, Y.; Hatanaka, T.; Soni, P.B.; Mori, T.; Sugimura, K. J. Biol. Chem. 2009, 284, 9986-9993. Watanabe, H.; Yageta, S.; Imamura, H.; Honda, S. Anal. Chem. 2016, 88, 10095-10101. Askim, J.R.; Mahmoudi, M.; Suslick, K.S. Chem. Soc. Rev. 2013, 42, 8649-8682. Tomita, S.; Yoshimoto, K. Chem. Commun. 2013, 49, 1043010432. Tomita, S.; Soejima, T.; Shiraki, K.; Yoshimoto, K. Analyst 2014, 139, 6100-6103. Tomita, S.; Sakao, M.; Kurita, R.; Niwa, O.; Yoshimoto, K. Chem. Sci. 2015, 6, 5831-5836. Tomita, S.; Niwa, O.; Kurita, R. Anal. Chem. 2016, 88, 90799086. De, M.; Rana, S.; Akpinar, H.; Miranda, O.R.; Arvizo, R.R.; Bunz, U.H.F.; Rotello, V.M. Nat. Chem. 2009, 1, 461-465. Wu, P.; Miao, L.N.; Wang, H.F.; Shao, X.G.; Yan, X.P. Angew. Chem. Int. Ed. 2011, 50, 8118-8121. Tao, Y.; Ran, X.; Ren, J.; Qu, X. Small 2014, 10, 1667-3671. Ran, X.; Pu, F.; Ren, J.; Qu, X. Chem. Commun. 2015, 51, 2675-2678. Selvakumar, K.; Motiei, L.; Margulies, D. J. Am. Chem. Soc. 2015, 137, 4892-4895. Galpothdeniya, W.I.S.; Fronczek, F.R.; Cong, M.; Bhattarai, N.; Siraj, N.; Warner, I.M. J. Mater. Chem. B 2016, 4, 14141422. Xu, Q.; Zhang, Y.; Tang, B.; Zhang, C.Y. Anal. Chem. 2016, 88, 2051-2058. Lu, C.H.; Yang, H.H.; Zhu, C.L.; Chen, X.; Chen, G.N. Angew. Chem. Int. Ed. 2009, 48, 4785-4787. Zhang, M.; Yin, B.C.; Tan, W.; Ye, B.C. Biosens. Bioelectron. 2011, 26, 3260-3265. Pei, H.; Li, J.; Lv, M.; Wang, J.; Gao, J.; Lu, J.; Li, Y.; Huang, Q.; Hu, J.; Fan, C. J. Am. Chem. Soc. 2012, 134, 13843-13849. Farnan, D.; Moreno, G.T. Anal. Chem. 2009, 81, 8846-8857. Fekete, S.; Beck, A.; Fekete, J.; Guillarme, D. J. Pharm. Biomed. Anal. 2015, 102, 282-289.

ACS Paragon Plus Environment

Page 5 of 5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

(32) Hawe, A.; Kasper, J.C.; Friess, W.; Jiskoot, W. Eur. J. Pharm. Sci. 2009, 38, 79-87. (33) Andersen, C.B.; Manno, M.; Rischel, C.; Thorolfsson, M.; Martorana, V. Protein Sci. 2010, 19, 279-290. (34) Kanmert, D.; Brorsson, A.C.; Jonsson, B.H.; Enander, K. Biochemistry 2011, 50, 981-988. (35) Tsukakoshi, K.; Abe, K.; Sode, K.; Ikebukuro, K. Anal. Chem. 2012, 84, 5542-5547.

(36) Lee, M.H.; Han, J.H.; Lee, J.H.; Park, N.; Kumar, R.; Kang, C.; Kim, J.S. Angew. Chem. Int. Ed. 2013, 52, 6206-6209. (37) Liu, Y.; Bonizzoni, M. J. Am. Chem. Soc. 2014, 136, 1422314229. (38) Tabachnick, B.G.; Fidell, L.S. Using Multivariate Statistics; Allyn & Bacon, Needham Heights, MA, 4th edn, 2001. (39) Jiang, X.R.; Song, A.; Bergelson, S.; Arroll, T.; Parekh, B.; May, K.; Chung, S.; Strouse, R.; Mire-Sluis, A.; Schenerman, M. Nat. Rev. Drug Discov. 2011, 10, 101-111.

for TOC only

ACS Paragon Plus Environment