- Email: [email protected]
Kinetic screening of antibodies from crude hybridoma samples using Biacore
ANALYTICAL BIOCHEMISTRY Analytical Biochemistry 325 (2004) 301–307 www.elsevier.com/locate/yabio
Kinetic screening of antibodies from crude hybridoma samples using Biacore Gabriela A. Canziani,a Scott Klakamp,b and David G. Myszkaa,* a
Center for Biomolecular Interaction Analysis, University of Utah, Salt Lake City, UT 84132, USA b Abgenix, Inc., Fremont, CA 94555, USA Received 14 August 2003
Abstract Experimental and data analysis protocols were developed to screen antibodies from hybridoma culture supernatants using Biacore surface plasmon resonance biosensor platforms. The screening methods involved capturing antibodies from crude supernatants using Fc-specific antibody surfaces and monitoring antigen binding at a single concentration. After normalizing the antigen responses for the amount of antibody present, a simple interaction model was fit to all of the binding responses simultaneously. As a result, the kinetic rate constants (ka and kd ) and affinity (KD ) could be determined for each antibody interaction under identical conditions. Higher-resolution studies involving multiple concentrations of antigen were performed to validate the reliability of single-concentration measurements. The screening protocols can be used to characterize antigen binding kinetics to 200 antibody supernatants per day using automated Biacore 2000 and 3000 instruments. Ó 2003 Elsevier Inc. All rights reserved.
Monoclonal antibodies continue to be important agents for biochemical research, medical diagnostics, and therapeutics [1,2]. Advanced antibody production technology makes it possible to generate large batches of antibodies, creating a new bottleneck in characterizing antigen binding activity. One technology that has potential to improve the pace of antibody characterization is surface plasmon resonance (SPR)1. In fact, some of the earliest applications of SPR biosensors were as immunosensors to characterize antibody activity and map binding epitopes [3–5]. While the use of biosensor * Corresponding author. Fax: 1-801-585-2978. E-mail address: [email protected] (D.G. Myszka). 1 Abbreviations used: Ag, antigen; BSA, bovine serum albumin; CM-dextran, carboxymethyl dextran sodium salt; DMEM, DulbeccoÕs modified EagleÕs medium; EDC, 3-(N,N-dimethylamino)propyl-N ethylcarbodiimide; ELISA, enzyme-linked immunosorbent assay; FBS, fetal bovine serum; HBS, Hepes-buffered saline; Hepes, N-[2hydroxyethyl]piperazine-N0 [2-ethanesulfonic acid]; IL-6, interleukin 6; ka , association rate constant; kd , dissociation rate constant; KD , equilibrium dissociation constant; mAb, monoclonal antibody; NHS, N-hydroxysuccinimide; OPI, oxaloacetate, pyruvate, bovine insulin; P-20, polyoxyethylene [20] sorbitan monooleate; RIA, radioisotopic interaction analysis; RU, response unit; SPR, surface plasmon resonance.
0003-2697/$ - see front matter Ó 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.ab.2003.11.004
technology has now expanded into other areas, including small molecule analysis and membrane-associated receptors, we are witnessing a renaissance in its applications in antibody characterization [6–11]. SPR biosensors have a number of advantages over traditional immunoassays such as ELISA or RIA. First, SPR technology can be used to measure complex formation without labeling the reactants. Second, complex formation can be monitored in real time, providing detailed information about the reaction kinetics, and equilibrium dissociation constants (affinities). Third, samples from crude preparations may be analyzed. Of course there are limits to this technology, most of which can be minimized by careful assay design and experimental execution. For example, it is important to design the antibody assay properly to avoid misinterpretation of the reaction data. Historically, in screens of antibody activity crude samples were injected across antigen immobilized on the sensor surface [12–15]. There are two significant limitations with this approach: (i) The bivalent antibody can cross-link on the antigen surface, leading to a misinterpretation of the dissociation rate constant and, hence, the affinity. (ii) To determine the association rate constant of a reaction, the antibody
302
G.A. Canziani et al. / Analytical Biochemistry 325 (2004) 301–307
concentration in the crude sample needs to be determined, which is rarely done. Given our general interest in SPR applications development, we focused our efforts on improving the methodology related to antibody screening with biosensors. We designed an assay that uses an anti-Fcspecific monoclonal antibody to capture antibodies of interest on the sensor surface. This allows us to purify and quantitate the amount of antibody present in any given preparation without knowing the antibody concentration a priori. We also avoid avidity effects by presenting independent antigen binding sites. In addition, the anti-Fc antibody surfaces can be recycled by a simple regeneration step. Therefore, using fully automated SPR biosensors such as Biacore 2000 or 3000, it is possible to screen hundreds of hybridoma samples using the same sensor surface. Batch screening of antibodies requires appropriate data processing and analysis methods to increase the accuracy of the antigen binding constants. We routinely normalize the antigen response data for the amount of antibody captured on the surface and globally analyze the entire data set. To evaluate the reliability of the screening results we ran a full-concentration series on a subset of the antibody samples. The results obtained from the single-concentration screen agree with those obtained from the full-concentration series, illustrating that accurate binding constants can be obtained from a single antigen concentration.
Materials and methods Binding experiments were performed using Biacore 2000 and 3000 optical biosensors equipped with research-grade CM5 sensor chips (Biacore AB, Uppsala, Sweden). Amine-coupling reagents (EDC, NHS; and sodium ethanolamine HCl, pH 8.5) were obtained from Biacore AB. Goat anti-human-Fc purified IgG antibody (2 mg/mL, 0.1% sodium azide) was purchased from Caltag Laboratories (No. H10500; Burlingame, CA), carboxymethyl dextran sodium salt (No. 27560) was purchased from Fluka Chemical Corp. (Milwaukee, WI), and BSA fraction V (No. BP1605-100) was purchased from Fisher Scientific (Pittsburgh, PA) for the kinetic screening experiments. Alum (No. 1452-250) was purchased from Superfos Biosector A/S (Vedbaek, Denmark), DMEM culture medium (No. 51444) was obtained from JRH Biosciences (Lenexa, KS), FBS (No. SH30070.03) was purchased from Hyclone (Logan, UT), the penicillin–steptomycin (No. 400-109) was obtained from Gemini Bio-Products (Woodland, CA), IL-6 (No. 1131567) was purchased from Roche (Mannheim, Germany), while Titermax (No. T-2684), OPI media supplement (No. O-5003), and other general reagents used in the generation of human monoclonal
antibodies were purchased from Sigma Chemical Co. (St. Louis, MO). Generation of fully human monoclonal antibodies from XenoMouse strains XenoMouse strains were produced as described earlier by Mendez et al. [16]. Animals aged 8–10 weeks were immunized in the footpad with 10 lg of soluble antigen A (MW 65.5 KDa) emulsified in Titermax (25 ll/mouse) for the first injection. The animals were boosted twice per week with 10 lg antigen A emulsified with alum (100 lg/mouse). Lymphocytes from lymph nodes of the animals were fused with P3 myeloma cells. The selected hybridoma lines were grown in T75 flasks with 15% FBS, penicillin/streptomycin (100 U/ml, 100 lg/ml), OPI media supplement (0.15 g oxaloacetate, 0.05 g pyruvate, 8.2 mg bovine insulin), and 10 U/ml of IL-6 in DMEM medium until 95% cell death to produce a hybridoma exhaust supernatant. These exhaust supernatants were used in the biosensor screening experiments. Immobilization of Fc-specific IgG A standard coupling protocol was employed to immobilize the Fc-specific IgG via exposed primary amines [17]. The stock anti-Fc IgG solution (15 ll) was first flowed through a fast desalting column (Pharmacia) equilibrated with 10 mM sodium acetate, pH 5.0, to remove azide preservative. The immobilization was performed at 25 °C using HBS-P (10 mM Hepes, 150 mM NaCl, pH 7.4, 0.005% P-20) as the running buffer. The four flow cellsÕ CM-dextran surfaces were activated simultaneously by a 7-min injection (20 ll/min) of freshly prepared 1:1 50 mM NHS:200 mM EDC [18]. Then, 70 ll IgG solution (pH 5.0) was injected for 7 min at a flow of 10 ll/min. This coupling was followed by a 7-min injection (20 ll/min) of 1 M ethanolamine, which deactivated residual reactive sites. The surfaces were immediately conditioned by three 6-s injections of 100 mM H3 PO4 . Typically, 10,000 RU of anti-Fc IgG was coupled using this method. Antibody screening The screening analysis was performed at 25 °C and a data collection rate of 2.5 Hz. The screening buffer (HBS-P + 12 mg/ml BSA + 12 mg/ml carboxymethyl– dextran sodium salt) was prepared, vacuum filtered, and degassed immediately prior to use. Supernatants and antigens were diluted in screening buffer and centrifuged at 14,000 rpm for 5 min at 4 °C. Three antibodies were captured on individual flow cells per binding cycle; the fourth IgG surface was left free to serve as a control. Each sample was injected for 2 min at a flow rate of 5 ll/ min over the Fc-specific IgG surface using QUICKIN-
G.A. Canziani et al. / Analytical Biochemistry 325 (2004) 301–307
JECT to capture the antibodies. Then, buffer and antigen were injected serially (1-min association, 5-min dissociation) over the antibody and reference surfaces using KINJECT at a flow rate of 50 ll/min. Processing antigen binding data Both buffer and antigen binding responses were zeroed on the y axis and aligned on the x axis. Data from the reference flow cell were subtracted to remove systematic artifacts that occurred in all four flow cells. Each antigen response was then double-referenced [19] by subtracting the buffer response collected in the same binding cycle. Finally, the panel of antigen binding data was normalized by dividing each antigen response by the corresponding amount of antibody captured from the supernatant. Kinetic analysis of antigen binding data Binding of antigen at one concentration to all antibodies from the panel was analyzed globally using a 1:1 binding model in CLAMP [20] but a similar modeling scheme may be applied in BIAevaluation. Each antibody was permitted its own set of rate constants, while one global maximum capacity (Rmax ) was maintained for the entire set of antibodies. Affinities were then calculated from the rate constants (kd =ka ). Multiple-concentration kinetic analysis of selected supernatants Supernatants selected for the high-resolution kinetic analysis were analyzed in batches of three. Each supernatant was diluted 1/20 and antibody capture levels were optimized to target maximum antigen binding responses of 60 RU. Antigen samples, spanning 1.2 to 100 nM in concentration, were injected in duplicate and in random order at a flow rate of 50 ll/min over the four surfaces. Buffer injections identical to the antigen injections were randomly interspersed for the purpose of double-referencing. Association and dissociation phases were monitored for 1 and 10 min, respectively. The surfaces were regenerated with two 12-s injection of 100 mM H3 PO4 . To determine the kinetic parameters of the interactions, each data set was double-referenced and fit globally to a 1:1 interaction model using CLAMP.
303
immobilized in all flow cells. Second, each of three supernatants from the panel was flowed across an IgG surface to capture antibody and create a stable, homogeneous surface. Third, antigen binding and dissociation were monitored over each captured antibody. The antibody/antigen complexes were completely stripped from the surface to assay another supernatant sample. Each cycle was repeated eight times to screen a panel of 24 supernatants. Antibody capture and regeneration Aliquots of three of the hybridoma supernatants were diluted 1/20 and injected over the anti-IgG surfaces for 2 min. As shown in Fig. 1, the capture levels for supernatants a, b, and c were 570, 600, and 720 RU, respectively, because of the different antibody expression levels. To establish the proper surface regeneration conditions, all the surfaces were simultaneously washed with two 12-s injections of 100 mM H3 PO4 , followed by a buffer rinse. This regeneration step removed the bound antibodies and returned the response signal to baseline. The three supernatants were then reinjected over the capturing surface to demonstrate that the anti-Fc antibody surfaces retained their full capture capacity. An overlay of three repeat injections of each supernatant sample revealed a variation of less than 5% in the capture levels. Antigen response optimization Since the complex formation is a second order binding process, to extract information about the association rate constant, there needs to be curvature in the response data. The concentration of antigen required to obtain curved binding responses was determined empirically. To describe the ideal binding profile, Fig. 2A
Results Antibody screening assay A capture method was used to monitor antigen binding to monoclonal antibodies isolated from crude supernatants. First, the Fc-specific anti-human IgG was
Fig. 1. Exploring supernatant capture and regeneration conditions. Three different supernatants, labeled A, B, and C, were diluted 1/20 in screening buffer and injected for 2 min over anti-IgG surfaces. After regenerating the IgG surfaces with two 12-s injections of H3 PO4 , each dilute supernatant was injected again using the same conditions. Three overlaid captures of antibodies are shown. No samples were injected over the fourth anti-IgG surface.
304
G.A. Canziani et al. / Analytical Biochemistry 325 (2004) 301–307
Fig. 2. Antigen response test. (A) Antigen binding responses were simulated at a wide range of antigen concentrations (in this example: a, 900 nM; b, 100 nM; c, 11.1 nM). (B) Antigen was injected at 100 nM over three captured antibodies (indicated by d, e, and f antigen binding responses). The antibody capture levels (not shown) ranged from 400 to 600 RU.
shows simulated responses of antigen binding antibody at three different antigen concentrations. At a low antigen concentration (Fig. 2A, curve c) the binding response is linear and therefore lacks detailed information about the reaction kinetics. At a high antigen concentration, on the other hand, the association response is too rapid and has only a few data points during the kinetic phase (Fig. 2A, curve a). Fig. 2A, curve b shows an antigen binding response with enough curvature to provide sufficient information about the kinetics of reaction of antigen with antibody. With the ideal association profile in mind, antigen was injected at different concentrations over three randomly selected antibodies. An example of the binding responses obtained by injecting 100 nM antigen is shown in Fig. 2B. These data display the desired curvature to enable us to calculate the interaction parameters. Kinetic screening The overlay plot in Fig. 3, which is composed of eight cycles, shows the antibody screening data for 24 crude hybridoma samples. Each cycle consisted of a capture step at a flow rate of 5 ll/min, followed by identical buffer and antigen injections at a higher flow rate (50 ll/ min). From the preliminary binding tests, the antigen/ antibody complexes appeared very stable. Thus, the
Fig. 3. Antibody screening data. Twenty-four supernatants diluted 1/20 were injected for 2 min over each flow cell (fc1, fc3, fc4), followed by an injection of screening buffer and then an injection of antigen at a concentration of 100 nM over all surfaces (Fc1–4). The IgG surfaces were finally regenerated and the signal returned to baseline. Box at the antigen injection start highlights the region used to determine the average antibody capture response level.
association phases were monitored for 1 min and the dissociation phases of the buffer and antigen injections were monitored for 5 min to obtain estimates of the rate of complex decay. Flow cell 2 was used as a reference to subtract instrument artifacts. The antibody capture levels for the panel (1/20 dilution) spanned 100 to 700 RU. Antigen responses ranged from 0 to 150 RU. To extract binding constants for the antigen/antibody reactions, the data were further processed and normalized as described below. Data processing The antigen binding data obtained after double-referencing [19] were normalized by dividing each antigen response curve by the corresponding antibody capture level. The normalization makes it possible to globally fit the antigen binding kinetics of antibodies from variable surface densities. The antibody capture levels were determined for each interaction by measuring the baseline signal immediately before the antigen injection (see box in Fig. 3). The antigen response data were then divided by the antibody capture level. The data shown in Figs. 4A and B show the antigen responses before and after normalization, respectively. To determine the affinity of each interaction, the normalized responses for each antigen were analyzed simultaneously. Simultaneous kinetic analysis The normalized antigen binding data were fit globally to a simple interaction model (A + B ¼ AB) [20]. Each
G.A. Canziani et al. / Analytical Biochemistry 325 (2004) 301–307
305
Fig. 5. Global analysis of antigen response data. Antigen responses (black lines) and their fits (red lines) were overlaid in groups of six interactions per plot to facilitate viewing the whole data set. All responses were fit simultaneously to a 1:1 interaction model using a global Rmax . The asterisks mark the responses with lower-than-expected Rmax .
Fig. 4. Processed antigen binding data. Each double-referenced response shown in (A) was divided by the capture level of the corresponding antibody to generate the normalized profiles shown in (B).
antibody/antigen interaction was defined in the analysis software as a different species. Accordingly, the kinetics of antigen binding to each antibody were mathematically described in the model by a set of local kinetic parameters and a global maximum binding capacity or Rmax . To fit the simple 1:1 model, an approximate Rmax value was set while the association and dissociation rate constants were manually adjusted to simulate as closely as possible the binding profiles. The model was then fit by adjusting the rate constants to minimize the deviation of the model from the data automatically. The Rmax for the normalized responses of the antibody panel was fit globally to a single value. In this system the global Rmax equaled 0.4, but this value will vary with different antigen systems since it depends on the mass ratio of antigen to antibody. The analyzed data and fits for the supernatant panel are shown in Fig. 5. Approximate fits of the model were obtained when the normalized responses were of lower magnitude and highly curved (see asterisks, Fig. 5), suggesting that only a fraction of the captured antibodies bound antigen. In these cases, the Rmax was floated locally to better analyze the kinetic rates of these interactions. Fig. 6 shows a two-dimensional kinetic plot of the association and dissociation rate constants for the 24 antibody samples. The diagonal lines represent equilib-
Fig. 6. Kinetic distribution plot. The affinities of the antibody/antigen complexes from the panel, represented by the kinetic constants, were distributed in the 0.5–500 nM range. The highest affinity complexes are clustered in the upper right-hand corner of the plot. Selected antibodies (filled circles) were analyzed in higher-resolution experiments.
rium isotherms to help visualize the affinity distribution of these antibodies. Higher-affinity complexes appear in the upper right-hand corner of the plot. About half of the supernatants displayed an affinity for antigen equal or higher than 5 nM. The association rate constants for antibody/antigen complex formation spanned 100-fold and the dissociation rates were distributed over a 1000fold interval. The binding constants determined for the six highest affinity antibodies are shown in Table 1.
306
G.A. Canziani et al. / Analytical Biochemistry 325 (2004) 301–307
Table 1 Kinetic and equilibrium dissociation constants for antibody/antigen complex formation Supernatant
ka (M1 s1 ) single [Ag]
H3 A1 B1 C1 D1 B2
a
kd (s1 ) six [Ag]
5
3.9[1] 10 4.54[3] 105 3.79[3] 105 4.37[5] 105 4.12[3] 105 2.18[4] 105
b
KD (nM)
single [Ag] 5
3.21[1] 10 2.71[2] 105 3.453[4] 105 3.01[1] 105 3.63[1] 105 1.87[1] 105
c
six [Ag] 4
1.5[1] 10 2.1[1] 104 4.0[1] 104 4.5[1] 104 7.5[1] 104 2.4[1] 104
c
single [Ag] 4
1.7[1] 10 1.73[3] 104 1.73[1] 104 2.11[2] 104 7.89[4] 104 2.45[4] 104
d
0.39[2] 0.45[1] 1.06[3] 1.04[2] 1.82[2] 1.10[4]
six [Ag] 0.51[5]d 0.64[2] 0.50[1] 0.70[1] 2.17[1] 1.37[2]
a
The number in brackets represents the standard error in the last significant digit of the ka value from each fit to the antigen response. The number in brackets represents the standard error in the last significant digit of the ka value from each experiment (consisting of randomized injections repeated at least twice). c The number in brackets represents the standard error in the last significant digit of the kd value from each experiment. d The number in brackets gives the calculated standard error in the last significant digit of the KD value (KD ¼ kd =ka ). b
Multiple-concentration kinetic analysis
Discussion
Six antibodies, having antigen binding affinities higher than 2 nM, were reanalyzed using a concentration series of antigen. The study consisted of duplicate random injections of a three-fold dilution series of antigen (spanning 1.2 to 100 nM) and multiple buffer injections over three different antibodies (dilution 1/20, 2-min injections) per run. The double-referenced data and their fits are shown in Fig. 7. As a basis for comparison, Table 1 shows the binding constants calculated for the single concentration and multiple concentration experiments. In most cases, the rate constants ranked in a very similar manner in the two assays. This provides evidence that the information collected from a single antigen concentration from the screening assay can be accurate.
We demonstrated a method to determine binding kinetics for antibody/antigen interactions in a screening mode on Biacore instruments. A key to the method is the ability to immobilize and quantitate the amount of antibody from crude samples using specific capturing systems. Immobilizing the bivalent antibodies (instead of the antigen) avoided avidity effects, which have led to misinterpretations of antibody/antigen binding kinetics in the past [10,11]. The use of a specific IgG recognition molecule allowed all of the monoclonal antibodies to be captured in a similar manner. The availability of four separate binding surfaces on Biacore 2000 and 3000 systems allowed three antibodies to be analyzed at one time (including one surface as a control). The capturing molecule was robust, allowing over 100 capture cycles
Fig. 7. Kinetic analysis of selected antibodies from the panel. Antigen was injected at 0, 1.2, 3.7, 11, 33, and 100 nM over antibodies captured from supernatants H3 (A), A1 (B), B1 (C), C1 (D), D1 (E), and B2 (F). Associations were monitored for 1 min and dissociations for 10 min. The responses (black lines) were analyzed using a 1:1 interaction model. The red lines represent the global fits of the data.
G.A. Canziani et al. / Analytical Biochemistry 325 (2004) 301–307
on each sensor chip surface. Finally, the artifact of nonspecific binding that is often encountered when working with crude samples was minimized by including BSA and soluble carboxymethyl dextran in the buffer. To set up the screening assay for a panel of hybridoma supernatants a few precautions had to be taken. The antibody capture conditions for the panel had to be determined empirically, since each panel showed differences in antibody expression level. However, wide differences in the antibody expression levels (perhaps up to 50-fold) are not a concern since the antigen binding responses can be normalized based on capture levels. The preliminary capture tests also involved verifying the amount of antigen that could bind to the antibodies as a measure of their activity. To collect accurate kinetic binding constants, it was essential to choose an antigen concentration that yielded visible curvature in the response data. To extract binding constants from a single concentration of antigen used in the screening assay, data from the entire antibody panel were fit globally to a common binding capacity but with independent reaction kinetics. This method assumed that all captured antibodies were active and their antigen binding sites were freely accessible to antigen. For a few samples, in which this was not the case, a separate maximum capacity was used. In general, the method allowed us to rapidly rank the activity of the antibodies based on the association or dissociation kinetics and the overall affinity. It is worth noting that not all antibodies with slow dissociation rates displayed high-affinity antigen binding. These results illustrate how the association rate also plays an important role in defining the affinity of a complex. We showed that the association and dissociation rate constants calculated using a single antigen concentration were comparable to those determined from data collected in separate binding studies using multiple antigen concentrations. These results validate that the kinetic information gathered from the rapid screening approach was accurate. Using this single antigen concentration approach it is possible to assay between 100 and 200 antibody samples per day with an automated Biacore 2000 or 3000 system. In addition, the screening protocols are not specific to antibodies and may be applied to other systems where fusion-tagged ligands such as GST, FLAG, and 6-His are available.
[4]
[5] [6] [7] [8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
References [1] C. Milstein, The hybridoma revolution: an offshoot of basic research, Biossays 21 (1999) 966–973. [2] C. Souriau, P.J. Hudson, Recombinant antibodies for cancer diagnosis and therapy, Expert Opin. Biol. Ther. 3 (2003) 305–318. [3] R. Karlsson, A. Michaelsson, L. Mattsson, Kinetic analysis of monoclonal antibody–antigen interactions with a new biosensor
[18]
[19] [20]
307
based analytical system, J. Immunol. Methods 145 (1991) 229–240. R. Karlsson, L. F€agerstam, H. Nilshans, B. Persson, Analysis of active antibody concentration. Separation of affinity and concentration parameters, J. Immunol. Methods 166 (1993) 75–84. J.L. Daiss, E.R. Scalice, Epitope Mapping on BIAcore: theoretical and practical considerations, Methods 6 (1994) 143–156. R.L. Rich, D.G. Myszka, Survey of the year 2000 commercial optical biosensor literature, J. Mol. Recognit. 14 (2001) 273–294. R.L. Rich, D.G. Myszka, Survey of the year 2001 commercial optical biosensor literature, J. Mol. Recognit. 15 (2002) 352–376. L. Laricchia Robbio, P. Uboldi, S. Marcovina, R.P. Revoltella, A.L. Catapano, Epitope mapping of apolipoprotein B-100 using surface plasmon resonance-based biosensor, Biosensors and Bioelectronics 16 (2001) 963–969. D.W. Frank, A. Vallis, J.P. Wiener-Kronish, A. Roy-Burman, E.G. Spack, B.P. Mullaney, M. Megdoud, J.D. Marks, R. Fritz, T. Sawa, Generation and characterization of a protective monoclonal antibody to Pseudomonas aeruginosa PcrV, J. Infect. Dis. 186 (2002) 64–73. D.W. Jone, P.J. Nicholls, S. Donohoe, M.J. Gallimore, M. Winter, Antibodies to factor XII are distinct from antibodies to prothrombin in patients with the anti-phospholipid syndrome, Thromb. Haemost. 87 (2002) 426–430. Z. Zhu, K. Hattori, H. Zhang, X. Jimenez, D.L. Ludwig, S. Dias, P. Kussie, H. Koo, H.J. Kim, D. Lu, M. Liu, R. Tejada, M. Friedrich, P. Bohlen, L. Witte, S. Rafii, Inhibition of human leukemia in an animal model with human antibodies directed against vascular endothelial growth factor receptor 2. Correlation between antibody affinity and biological activity, Leukemia 17 (2003) 604–611. M.A. Takacs, S.J. Jacobs, R.M. Bordens, S.J. Swanson, Detection and characterization of antibodies to PEG-IFN-alpha2b using surface plasmon resonance, J. Interferon Cytokine Res. 19 (1999) 781–789. N. Ohkura, G. Soe, I. Kohno, K. Kumeda, H. Wada, Y. Kamikubo, H. Shiku, H. Kato, Monoclonal antibody specific for tissue factor pathway inhibitor-factor Xa complex: its characterization and application to plasmas from patients with disseminated intravascular coagulation and predisseminated intravascular coagulation, Blood Coagul. Fibrinolysis 10 (1999) 309–319. S.H. Spalter, S.V. Kaveri, E. Bonnin, J.C. Mani, J.P. Cartron, M.D. Kazatchkine, Normal human serum contains natural antibodies reactive with autologous ABO blood group antigens, Blood 93 (1999) 4418–4424. R. Pei, X. Cui, X. Yang, E. Wang, Real-time immunoassay of antibody activity in serum by surface plasmon resonance, Talanta 53 (2000) 481–488. M.J. Mendez, L.L. Green, J.R. Corvalan, X.C. Jia, C.E. Maynard-Currie, X.D. Yang, M.L. Gallo, D.M. Louie, D.V. Lee, K.L. Erickson, J. Luna, C.M. Roy, H. Abderrahim, F. Kirschenbaum, M. Noguchi, D.H. Smith, A. Fukushima, J.F. Hales, S. Klapholz, M.H. Finer, C.G. Davis, K.M. Zsebo, A. Jakobovits, Functional transplant of megabase human immunoglobulin loci recapitulates human antibody response in mice, Nat. Genet. 15 (1997) 146–156. B. Johnsson, S. L€ of as, G. Lindquist, Immobilization of proteins to a carboxymethyldextran-modified gold surface for biospecific interaction analysis in surface plasmon resonance sensors, Anal. Biochem. 198 (1991) 268–277. U. J€ onsson, L. F€agerstam, B. Ivarsson, B. Johnsson, R. Karlsson, K. Lundh, S. L€ of as, B. Persson, H. Roos, I. R€ onnberg, et al., Real-time biospecific interaction analysis using surface plasmon resonance and a sensor chip technology, Biotechniques 11 (1991) 620–627. D.G. Myszka, Improving biosensor analysis, J. Mol. Recognit. 12 (1999) 279–284. D.G. Myszka, T.A. Morton, CLAMP: a biosensor kinetic data analysis program, Trends Biochem. Sci. 23 (1998) 149–150.
Kinetic screening of antibodies from crude hybridoma samples using Biacore Gabriela A. Canziani,a Scott Klakamp,b and David G. Myszkaa,* a
Center for Biomolecular Interaction Analysis, University of Utah, Salt Lake City, UT 84132, USA b Abgenix, Inc., Fremont, CA 94555, USA Received 14 August 2003
Abstract Experimental and data analysis protocols were developed to screen antibodies from hybridoma culture supernatants using Biacore surface plasmon resonance biosensor platforms. The screening methods involved capturing antibodies from crude supernatants using Fc-specific antibody surfaces and monitoring antigen binding at a single concentration. After normalizing the antigen responses for the amount of antibody present, a simple interaction model was fit to all of the binding responses simultaneously. As a result, the kinetic rate constants (ka and kd ) and affinity (KD ) could be determined for each antibody interaction under identical conditions. Higher-resolution studies involving multiple concentrations of antigen were performed to validate the reliability of single-concentration measurements. The screening protocols can be used to characterize antigen binding kinetics to 200 antibody supernatants per day using automated Biacore 2000 and 3000 instruments. Ó 2003 Elsevier Inc. All rights reserved.
Monoclonal antibodies continue to be important agents for biochemical research, medical diagnostics, and therapeutics [1,2]. Advanced antibody production technology makes it possible to generate large batches of antibodies, creating a new bottleneck in characterizing antigen binding activity. One technology that has potential to improve the pace of antibody characterization is surface plasmon resonance (SPR)1. In fact, some of the earliest applications of SPR biosensors were as immunosensors to characterize antibody activity and map binding epitopes [3–5]. While the use of biosensor * Corresponding author. Fax: 1-801-585-2978. E-mail address: [email protected] (D.G. Myszka). 1 Abbreviations used: Ag, antigen; BSA, bovine serum albumin; CM-dextran, carboxymethyl dextran sodium salt; DMEM, DulbeccoÕs modified EagleÕs medium; EDC, 3-(N,N-dimethylamino)propyl-N ethylcarbodiimide; ELISA, enzyme-linked immunosorbent assay; FBS, fetal bovine serum; HBS, Hepes-buffered saline; Hepes, N-[2hydroxyethyl]piperazine-N0 [2-ethanesulfonic acid]; IL-6, interleukin 6; ka , association rate constant; kd , dissociation rate constant; KD , equilibrium dissociation constant; mAb, monoclonal antibody; NHS, N-hydroxysuccinimide; OPI, oxaloacetate, pyruvate, bovine insulin; P-20, polyoxyethylene [20] sorbitan monooleate; RIA, radioisotopic interaction analysis; RU, response unit; SPR, surface plasmon resonance.
0003-2697/$ - see front matter Ó 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.ab.2003.11.004
technology has now expanded into other areas, including small molecule analysis and membrane-associated receptors, we are witnessing a renaissance in its applications in antibody characterization [6–11]. SPR biosensors have a number of advantages over traditional immunoassays such as ELISA or RIA. First, SPR technology can be used to measure complex formation without labeling the reactants. Second, complex formation can be monitored in real time, providing detailed information about the reaction kinetics, and equilibrium dissociation constants (affinities). Third, samples from crude preparations may be analyzed. Of course there are limits to this technology, most of which can be minimized by careful assay design and experimental execution. For example, it is important to design the antibody assay properly to avoid misinterpretation of the reaction data. Historically, in screens of antibody activity crude samples were injected across antigen immobilized on the sensor surface [12–15]. There are two significant limitations with this approach: (i) The bivalent antibody can cross-link on the antigen surface, leading to a misinterpretation of the dissociation rate constant and, hence, the affinity. (ii) To determine the association rate constant of a reaction, the antibody
302
G.A. Canziani et al. / Analytical Biochemistry 325 (2004) 301–307
concentration in the crude sample needs to be determined, which is rarely done. Given our general interest in SPR applications development, we focused our efforts on improving the methodology related to antibody screening with biosensors. We designed an assay that uses an anti-Fcspecific monoclonal antibody to capture antibodies of interest on the sensor surface. This allows us to purify and quantitate the amount of antibody present in any given preparation without knowing the antibody concentration a priori. We also avoid avidity effects by presenting independent antigen binding sites. In addition, the anti-Fc antibody surfaces can be recycled by a simple regeneration step. Therefore, using fully automated SPR biosensors such as Biacore 2000 or 3000, it is possible to screen hundreds of hybridoma samples using the same sensor surface. Batch screening of antibodies requires appropriate data processing and analysis methods to increase the accuracy of the antigen binding constants. We routinely normalize the antigen response data for the amount of antibody captured on the surface and globally analyze the entire data set. To evaluate the reliability of the screening results we ran a full-concentration series on a subset of the antibody samples. The results obtained from the single-concentration screen agree with those obtained from the full-concentration series, illustrating that accurate binding constants can be obtained from a single antigen concentration.
Materials and methods Binding experiments were performed using Biacore 2000 and 3000 optical biosensors equipped with research-grade CM5 sensor chips (Biacore AB, Uppsala, Sweden). Amine-coupling reagents (EDC, NHS; and sodium ethanolamine HCl, pH 8.5) were obtained from Biacore AB. Goat anti-human-Fc purified IgG antibody (2 mg/mL, 0.1% sodium azide) was purchased from Caltag Laboratories (No. H10500; Burlingame, CA), carboxymethyl dextran sodium salt (No. 27560) was purchased from Fluka Chemical Corp. (Milwaukee, WI), and BSA fraction V (No. BP1605-100) was purchased from Fisher Scientific (Pittsburgh, PA) for the kinetic screening experiments. Alum (No. 1452-250) was purchased from Superfos Biosector A/S (Vedbaek, Denmark), DMEM culture medium (No. 51444) was obtained from JRH Biosciences (Lenexa, KS), FBS (No. SH30070.03) was purchased from Hyclone (Logan, UT), the penicillin–steptomycin (No. 400-109) was obtained from Gemini Bio-Products (Woodland, CA), IL-6 (No. 1131567) was purchased from Roche (Mannheim, Germany), while Titermax (No. T-2684), OPI media supplement (No. O-5003), and other general reagents used in the generation of human monoclonal
antibodies were purchased from Sigma Chemical Co. (St. Louis, MO). Generation of fully human monoclonal antibodies from XenoMouse strains XenoMouse strains were produced as described earlier by Mendez et al. [16]. Animals aged 8–10 weeks were immunized in the footpad with 10 lg of soluble antigen A (MW 65.5 KDa) emulsified in Titermax (25 ll/mouse) for the first injection. The animals were boosted twice per week with 10 lg antigen A emulsified with alum (100 lg/mouse). Lymphocytes from lymph nodes of the animals were fused with P3 myeloma cells. The selected hybridoma lines were grown in T75 flasks with 15% FBS, penicillin/streptomycin (100 U/ml, 100 lg/ml), OPI media supplement (0.15 g oxaloacetate, 0.05 g pyruvate, 8.2 mg bovine insulin), and 10 U/ml of IL-6 in DMEM medium until 95% cell death to produce a hybridoma exhaust supernatant. These exhaust supernatants were used in the biosensor screening experiments. Immobilization of Fc-specific IgG A standard coupling protocol was employed to immobilize the Fc-specific IgG via exposed primary amines [17]. The stock anti-Fc IgG solution (15 ll) was first flowed through a fast desalting column (Pharmacia) equilibrated with 10 mM sodium acetate, pH 5.0, to remove azide preservative. The immobilization was performed at 25 °C using HBS-P (10 mM Hepes, 150 mM NaCl, pH 7.4, 0.005% P-20) as the running buffer. The four flow cellsÕ CM-dextran surfaces were activated simultaneously by a 7-min injection (20 ll/min) of freshly prepared 1:1 50 mM NHS:200 mM EDC [18]. Then, 70 ll IgG solution (pH 5.0) was injected for 7 min at a flow of 10 ll/min. This coupling was followed by a 7-min injection (20 ll/min) of 1 M ethanolamine, which deactivated residual reactive sites. The surfaces were immediately conditioned by three 6-s injections of 100 mM H3 PO4 . Typically, 10,000 RU of anti-Fc IgG was coupled using this method. Antibody screening The screening analysis was performed at 25 °C and a data collection rate of 2.5 Hz. The screening buffer (HBS-P + 12 mg/ml BSA + 12 mg/ml carboxymethyl– dextran sodium salt) was prepared, vacuum filtered, and degassed immediately prior to use. Supernatants and antigens were diluted in screening buffer and centrifuged at 14,000 rpm for 5 min at 4 °C. Three antibodies were captured on individual flow cells per binding cycle; the fourth IgG surface was left free to serve as a control. Each sample was injected for 2 min at a flow rate of 5 ll/ min over the Fc-specific IgG surface using QUICKIN-
G.A. Canziani et al. / Analytical Biochemistry 325 (2004) 301–307
JECT to capture the antibodies. Then, buffer and antigen were injected serially (1-min association, 5-min dissociation) over the antibody and reference surfaces using KINJECT at a flow rate of 50 ll/min. Processing antigen binding data Both buffer and antigen binding responses were zeroed on the y axis and aligned on the x axis. Data from the reference flow cell were subtracted to remove systematic artifacts that occurred in all four flow cells. Each antigen response was then double-referenced [19] by subtracting the buffer response collected in the same binding cycle. Finally, the panel of antigen binding data was normalized by dividing each antigen response by the corresponding amount of antibody captured from the supernatant. Kinetic analysis of antigen binding data Binding of antigen at one concentration to all antibodies from the panel was analyzed globally using a 1:1 binding model in CLAMP [20] but a similar modeling scheme may be applied in BIAevaluation. Each antibody was permitted its own set of rate constants, while one global maximum capacity (Rmax ) was maintained for the entire set of antibodies. Affinities were then calculated from the rate constants (kd =ka ). Multiple-concentration kinetic analysis of selected supernatants Supernatants selected for the high-resolution kinetic analysis were analyzed in batches of three. Each supernatant was diluted 1/20 and antibody capture levels were optimized to target maximum antigen binding responses of 60 RU. Antigen samples, spanning 1.2 to 100 nM in concentration, were injected in duplicate and in random order at a flow rate of 50 ll/min over the four surfaces. Buffer injections identical to the antigen injections were randomly interspersed for the purpose of double-referencing. Association and dissociation phases were monitored for 1 and 10 min, respectively. The surfaces were regenerated with two 12-s injection of 100 mM H3 PO4 . To determine the kinetic parameters of the interactions, each data set was double-referenced and fit globally to a 1:1 interaction model using CLAMP.
303
immobilized in all flow cells. Second, each of three supernatants from the panel was flowed across an IgG surface to capture antibody and create a stable, homogeneous surface. Third, antigen binding and dissociation were monitored over each captured antibody. The antibody/antigen complexes were completely stripped from the surface to assay another supernatant sample. Each cycle was repeated eight times to screen a panel of 24 supernatants. Antibody capture and regeneration Aliquots of three of the hybridoma supernatants were diluted 1/20 and injected over the anti-IgG surfaces for 2 min. As shown in Fig. 1, the capture levels for supernatants a, b, and c were 570, 600, and 720 RU, respectively, because of the different antibody expression levels. To establish the proper surface regeneration conditions, all the surfaces were simultaneously washed with two 12-s injections of 100 mM H3 PO4 , followed by a buffer rinse. This regeneration step removed the bound antibodies and returned the response signal to baseline. The three supernatants were then reinjected over the capturing surface to demonstrate that the anti-Fc antibody surfaces retained their full capture capacity. An overlay of three repeat injections of each supernatant sample revealed a variation of less than 5% in the capture levels. Antigen response optimization Since the complex formation is a second order binding process, to extract information about the association rate constant, there needs to be curvature in the response data. The concentration of antigen required to obtain curved binding responses was determined empirically. To describe the ideal binding profile, Fig. 2A
Results Antibody screening assay A capture method was used to monitor antigen binding to monoclonal antibodies isolated from crude supernatants. First, the Fc-specific anti-human IgG was
Fig. 1. Exploring supernatant capture and regeneration conditions. Three different supernatants, labeled A, B, and C, were diluted 1/20 in screening buffer and injected for 2 min over anti-IgG surfaces. After regenerating the IgG surfaces with two 12-s injections of H3 PO4 , each dilute supernatant was injected again using the same conditions. Three overlaid captures of antibodies are shown. No samples were injected over the fourth anti-IgG surface.
304
G.A. Canziani et al. / Analytical Biochemistry 325 (2004) 301–307
Fig. 2. Antigen response test. (A) Antigen binding responses were simulated at a wide range of antigen concentrations (in this example: a, 900 nM; b, 100 nM; c, 11.1 nM). (B) Antigen was injected at 100 nM over three captured antibodies (indicated by d, e, and f antigen binding responses). The antibody capture levels (not shown) ranged from 400 to 600 RU.
shows simulated responses of antigen binding antibody at three different antigen concentrations. At a low antigen concentration (Fig. 2A, curve c) the binding response is linear and therefore lacks detailed information about the reaction kinetics. At a high antigen concentration, on the other hand, the association response is too rapid and has only a few data points during the kinetic phase (Fig. 2A, curve a). Fig. 2A, curve b shows an antigen binding response with enough curvature to provide sufficient information about the kinetics of reaction of antigen with antibody. With the ideal association profile in mind, antigen was injected at different concentrations over three randomly selected antibodies. An example of the binding responses obtained by injecting 100 nM antigen is shown in Fig. 2B. These data display the desired curvature to enable us to calculate the interaction parameters. Kinetic screening The overlay plot in Fig. 3, which is composed of eight cycles, shows the antibody screening data for 24 crude hybridoma samples. Each cycle consisted of a capture step at a flow rate of 5 ll/min, followed by identical buffer and antigen injections at a higher flow rate (50 ll/ min). From the preliminary binding tests, the antigen/ antibody complexes appeared very stable. Thus, the
Fig. 3. Antibody screening data. Twenty-four supernatants diluted 1/20 were injected for 2 min over each flow cell (fc1, fc3, fc4), followed by an injection of screening buffer and then an injection of antigen at a concentration of 100 nM over all surfaces (Fc1–4). The IgG surfaces were finally regenerated and the signal returned to baseline. Box at the antigen injection start highlights the region used to determine the average antibody capture response level.
association phases were monitored for 1 min and the dissociation phases of the buffer and antigen injections were monitored for 5 min to obtain estimates of the rate of complex decay. Flow cell 2 was used as a reference to subtract instrument artifacts. The antibody capture levels for the panel (1/20 dilution) spanned 100 to 700 RU. Antigen responses ranged from 0 to 150 RU. To extract binding constants for the antigen/antibody reactions, the data were further processed and normalized as described below. Data processing The antigen binding data obtained after double-referencing [19] were normalized by dividing each antigen response curve by the corresponding antibody capture level. The normalization makes it possible to globally fit the antigen binding kinetics of antibodies from variable surface densities. The antibody capture levels were determined for each interaction by measuring the baseline signal immediately before the antigen injection (see box in Fig. 3). The antigen response data were then divided by the antibody capture level. The data shown in Figs. 4A and B show the antigen responses before and after normalization, respectively. To determine the affinity of each interaction, the normalized responses for each antigen were analyzed simultaneously. Simultaneous kinetic analysis The normalized antigen binding data were fit globally to a simple interaction model (A + B ¼ AB) [20]. Each
G.A. Canziani et al. / Analytical Biochemistry 325 (2004) 301–307
305
Fig. 5. Global analysis of antigen response data. Antigen responses (black lines) and their fits (red lines) were overlaid in groups of six interactions per plot to facilitate viewing the whole data set. All responses were fit simultaneously to a 1:1 interaction model using a global Rmax . The asterisks mark the responses with lower-than-expected Rmax .
Fig. 4. Processed antigen binding data. Each double-referenced response shown in (A) was divided by the capture level of the corresponding antibody to generate the normalized profiles shown in (B).
antibody/antigen interaction was defined in the analysis software as a different species. Accordingly, the kinetics of antigen binding to each antibody were mathematically described in the model by a set of local kinetic parameters and a global maximum binding capacity or Rmax . To fit the simple 1:1 model, an approximate Rmax value was set while the association and dissociation rate constants were manually adjusted to simulate as closely as possible the binding profiles. The model was then fit by adjusting the rate constants to minimize the deviation of the model from the data automatically. The Rmax for the normalized responses of the antibody panel was fit globally to a single value. In this system the global Rmax equaled 0.4, but this value will vary with different antigen systems since it depends on the mass ratio of antigen to antibody. The analyzed data and fits for the supernatant panel are shown in Fig. 5. Approximate fits of the model were obtained when the normalized responses were of lower magnitude and highly curved (see asterisks, Fig. 5), suggesting that only a fraction of the captured antibodies bound antigen. In these cases, the Rmax was floated locally to better analyze the kinetic rates of these interactions. Fig. 6 shows a two-dimensional kinetic plot of the association and dissociation rate constants for the 24 antibody samples. The diagonal lines represent equilib-
Fig. 6. Kinetic distribution plot. The affinities of the antibody/antigen complexes from the panel, represented by the kinetic constants, were distributed in the 0.5–500 nM range. The highest affinity complexes are clustered in the upper right-hand corner of the plot. Selected antibodies (filled circles) were analyzed in higher-resolution experiments.
rium isotherms to help visualize the affinity distribution of these antibodies. Higher-affinity complexes appear in the upper right-hand corner of the plot. About half of the supernatants displayed an affinity for antigen equal or higher than 5 nM. The association rate constants for antibody/antigen complex formation spanned 100-fold and the dissociation rates were distributed over a 1000fold interval. The binding constants determined for the six highest affinity antibodies are shown in Table 1.
306
G.A. Canziani et al. / Analytical Biochemistry 325 (2004) 301–307
Table 1 Kinetic and equilibrium dissociation constants for antibody/antigen complex formation Supernatant
ka (M1 s1 ) single [Ag]
H3 A1 B1 C1 D1 B2
a
kd (s1 ) six [Ag]
5
3.9[1] 10 4.54[3] 105 3.79[3] 105 4.37[5] 105 4.12[3] 105 2.18[4] 105
b
KD (nM)
single [Ag] 5
3.21[1] 10 2.71[2] 105 3.453[4] 105 3.01[1] 105 3.63[1] 105 1.87[1] 105
c
six [Ag] 4
1.5[1] 10 2.1[1] 104 4.0[1] 104 4.5[1] 104 7.5[1] 104 2.4[1] 104
c
single [Ag] 4
1.7[1] 10 1.73[3] 104 1.73[1] 104 2.11[2] 104 7.89[4] 104 2.45[4] 104
d
0.39[2] 0.45[1] 1.06[3] 1.04[2] 1.82[2] 1.10[4]
six [Ag] 0.51[5]d 0.64[2] 0.50[1] 0.70[1] 2.17[1] 1.37[2]
a
The number in brackets represents the standard error in the last significant digit of the ka value from each fit to the antigen response. The number in brackets represents the standard error in the last significant digit of the ka value from each experiment (consisting of randomized injections repeated at least twice). c The number in brackets represents the standard error in the last significant digit of the kd value from each experiment. d The number in brackets gives the calculated standard error in the last significant digit of the KD value (KD ¼ kd =ka ). b
Multiple-concentration kinetic analysis
Discussion
Six antibodies, having antigen binding affinities higher than 2 nM, were reanalyzed using a concentration series of antigen. The study consisted of duplicate random injections of a three-fold dilution series of antigen (spanning 1.2 to 100 nM) and multiple buffer injections over three different antibodies (dilution 1/20, 2-min injections) per run. The double-referenced data and their fits are shown in Fig. 7. As a basis for comparison, Table 1 shows the binding constants calculated for the single concentration and multiple concentration experiments. In most cases, the rate constants ranked in a very similar manner in the two assays. This provides evidence that the information collected from a single antigen concentration from the screening assay can be accurate.
We demonstrated a method to determine binding kinetics for antibody/antigen interactions in a screening mode on Biacore instruments. A key to the method is the ability to immobilize and quantitate the amount of antibody from crude samples using specific capturing systems. Immobilizing the bivalent antibodies (instead of the antigen) avoided avidity effects, which have led to misinterpretations of antibody/antigen binding kinetics in the past [10,11]. The use of a specific IgG recognition molecule allowed all of the monoclonal antibodies to be captured in a similar manner. The availability of four separate binding surfaces on Biacore 2000 and 3000 systems allowed three antibodies to be analyzed at one time (including one surface as a control). The capturing molecule was robust, allowing over 100 capture cycles
Fig. 7. Kinetic analysis of selected antibodies from the panel. Antigen was injected at 0, 1.2, 3.7, 11, 33, and 100 nM over antibodies captured from supernatants H3 (A), A1 (B), B1 (C), C1 (D), D1 (E), and B2 (F). Associations were monitored for 1 min and dissociations for 10 min. The responses (black lines) were analyzed using a 1:1 interaction model. The red lines represent the global fits of the data.
G.A. Canziani et al. / Analytical Biochemistry 325 (2004) 301–307
on each sensor chip surface. Finally, the artifact of nonspecific binding that is often encountered when working with crude samples was minimized by including BSA and soluble carboxymethyl dextran in the buffer. To set up the screening assay for a panel of hybridoma supernatants a few precautions had to be taken. The antibody capture conditions for the panel had to be determined empirically, since each panel showed differences in antibody expression level. However, wide differences in the antibody expression levels (perhaps up to 50-fold) are not a concern since the antigen binding responses can be normalized based on capture levels. The preliminary capture tests also involved verifying the amount of antigen that could bind to the antibodies as a measure of their activity. To collect accurate kinetic binding constants, it was essential to choose an antigen concentration that yielded visible curvature in the response data. To extract binding constants from a single concentration of antigen used in the screening assay, data from the entire antibody panel were fit globally to a common binding capacity but with independent reaction kinetics. This method assumed that all captured antibodies were active and their antigen binding sites were freely accessible to antigen. For a few samples, in which this was not the case, a separate maximum capacity was used. In general, the method allowed us to rapidly rank the activity of the antibodies based on the association or dissociation kinetics and the overall affinity. It is worth noting that not all antibodies with slow dissociation rates displayed high-affinity antigen binding. These results illustrate how the association rate also plays an important role in defining the affinity of a complex. We showed that the association and dissociation rate constants calculated using a single antigen concentration were comparable to those determined from data collected in separate binding studies using multiple antigen concentrations. These results validate that the kinetic information gathered from the rapid screening approach was accurate. Using this single antigen concentration approach it is possible to assay between 100 and 200 antibody samples per day with an automated Biacore 2000 or 3000 system. In addition, the screening protocols are not specific to antibodies and may be applied to other systems where fusion-tagged ligands such as GST, FLAG, and 6-His are available.
[4]
[5] [6] [7] [8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
References [1] C. Milstein, The hybridoma revolution: an offshoot of basic research, Biossays 21 (1999) 966–973. [2] C. Souriau, P.J. Hudson, Recombinant antibodies for cancer diagnosis and therapy, Expert Opin. Biol. Ther. 3 (2003) 305–318. [3] R. Karlsson, A. Michaelsson, L. Mattsson, Kinetic analysis of monoclonal antibody–antigen interactions with a new biosensor
[18]
[19] [20]
307
based analytical system, J. Immunol. Methods 145 (1991) 229–240. R. Karlsson, L. F€agerstam, H. Nilshans, B. Persson, Analysis of active antibody concentration. Separation of affinity and concentration parameters, J. Immunol. Methods 166 (1993) 75–84. J.L. Daiss, E.R. Scalice, Epitope Mapping on BIAcore: theoretical and practical considerations, Methods 6 (1994) 143–156. R.L. Rich, D.G. Myszka, Survey of the year 2000 commercial optical biosensor literature, J. Mol. Recognit. 14 (2001) 273–294. R.L. Rich, D.G. Myszka, Survey of the year 2001 commercial optical biosensor literature, J. Mol. Recognit. 15 (2002) 352–376. L. Laricchia Robbio, P. Uboldi, S. Marcovina, R.P. Revoltella, A.L. Catapano, Epitope mapping of apolipoprotein B-100 using surface plasmon resonance-based biosensor, Biosensors and Bioelectronics 16 (2001) 963–969. D.W. Frank, A. Vallis, J.P. Wiener-Kronish, A. Roy-Burman, E.G. Spack, B.P. Mullaney, M. Megdoud, J.D. Marks, R. Fritz, T. Sawa, Generation and characterization of a protective monoclonal antibody to Pseudomonas aeruginosa PcrV, J. Infect. Dis. 186 (2002) 64–73. D.W. Jone, P.J. Nicholls, S. Donohoe, M.J. Gallimore, M. Winter, Antibodies to factor XII are distinct from antibodies to prothrombin in patients with the anti-phospholipid syndrome, Thromb. Haemost. 87 (2002) 426–430. Z. Zhu, K. Hattori, H. Zhang, X. Jimenez, D.L. Ludwig, S. Dias, P. Kussie, H. Koo, H.J. Kim, D. Lu, M. Liu, R. Tejada, M. Friedrich, P. Bohlen, L. Witte, S. Rafii, Inhibition of human leukemia in an animal model with human antibodies directed against vascular endothelial growth factor receptor 2. Correlation between antibody affinity and biological activity, Leukemia 17 (2003) 604–611. M.A. Takacs, S.J. Jacobs, R.M. Bordens, S.J. Swanson, Detection and characterization of antibodies to PEG-IFN-alpha2b using surface plasmon resonance, J. Interferon Cytokine Res. 19 (1999) 781–789. N. Ohkura, G. Soe, I. Kohno, K. Kumeda, H. Wada, Y. Kamikubo, H. Shiku, H. Kato, Monoclonal antibody specific for tissue factor pathway inhibitor-factor Xa complex: its characterization and application to plasmas from patients with disseminated intravascular coagulation and predisseminated intravascular coagulation, Blood Coagul. Fibrinolysis 10 (1999) 309–319. S.H. Spalter, S.V. Kaveri, E. Bonnin, J.C. Mani, J.P. Cartron, M.D. Kazatchkine, Normal human serum contains natural antibodies reactive with autologous ABO blood group antigens, Blood 93 (1999) 4418–4424. R. Pei, X. Cui, X. Yang, E. Wang, Real-time immunoassay of antibody activity in serum by surface plasmon resonance, Talanta 53 (2000) 481–488. M.J. Mendez, L.L. Green, J.R. Corvalan, X.C. Jia, C.E. Maynard-Currie, X.D. Yang, M.L. Gallo, D.M. Louie, D.V. Lee, K.L. Erickson, J. Luna, C.M. Roy, H. Abderrahim, F. Kirschenbaum, M. Noguchi, D.H. Smith, A. Fukushima, J.F. Hales, S. Klapholz, M.H. Finer, C.G. Davis, K.M. Zsebo, A. Jakobovits, Functional transplant of megabase human immunoglobulin loci recapitulates human antibody response in mice, Nat. Genet. 15 (1997) 146–156. B. Johnsson, S. L€ of as, G. Lindquist, Immobilization of proteins to a carboxymethyldextran-modified gold surface for biospecific interaction analysis in surface plasmon resonance sensors, Anal. Biochem. 198 (1991) 268–277. U. J€ onsson, L. F€agerstam, B. Ivarsson, B. Johnsson, R. Karlsson, K. Lundh, S. L€ of as, B. Persson, H. Roos, I. R€ onnberg, et al., Real-time biospecific interaction analysis using surface plasmon resonance and a sensor chip technology, Biotechniques 11 (1991) 620–627. D.G. Myszka, Improving biosensor analysis, J. Mol. Recognit. 12 (1999) 279–284. D.G. Myszka, T.A. Morton, CLAMP: a biosensor kinetic data analysis program, Trends Biochem. Sci. 23 (1998) 149–150.