Methods of qualifying a subset of target binding biomolecules from a larger set of target binding biomolecules for analysis

Abstract

Methods of qualifying a subset of target binding biomolecules from a larger set of target binding biomolecules for analysis are disclosed. Interactions between 100 different pairs of target binding biomolecules are identified using a competitive immunoassay comprising target proteins and 200 interaction profiles are generated. Each target binding biomolecule is assigned 300 to a bin representing an epitope family and the identified bins are associated with the identified respective target binding biomolecules in a circular or semi-circular bin chart on a display. Based on the association 400 between the identified bins in the bin chart and the identified respective target binding molecules, a subset of target binding biomolecules is selected 500 for further analysis by selecting one or more target binding biomolecules of one or more bins.

Description

TECHNICAL FIELD

[0001] The present document relates to methods and systems for qualifying a subset of target-binding biomolecules from a larger set of target-binding biomolecules for analysis. BACKGROUND

[0002] Epitope binning can be used in the discovery and development of new therapeutics, vaccines, and diagnostics. Epitope binning is a competitive immunoassay used to characterize and then sort libraries of, for example, monoclonal antibodies against a target protein. Antibodies against a similar target can be tested against all other antibodies in the library in a pairwise fashion to see which antibodies block each other's binding to the antigen. Each antibody has a competitive blocking profile generated against all other antibodies in the library. Epitope binning defines topological epitopes on a target protein according to the ability of antibodies to bind to the same target protein simultaneously. If two different antibodies can bind simultaneously, they bind to topologically distinct epitopes on the target protein. If they interfere with each other's binding, they bind to the same or overlapping epitopes. Closely related binning profiles indicate that antibodies have the same or closely related epitopes and are "binned" together.

[0003] Epitope binning does not strictly answer the question of what specific epitope of a target protein, antigen molecule, or a certain antibody binds (i.e., epitope mapping), but rather groups a series of antibodies into different bins according to their binding to different epitopes. If the specific epitope of some antibodies is known in the experiment, other antibodies grouped in the same bin can also bind to that specific epitope. As a result, the line of demarcation between epitope binning and epitope mapping is blurry.

[0004] Epitope binning can be a key early screening technique in the biopharmaceutical discovery workflow. Engineering of monoclonal antibodies (mAbs) that target specific functional epitopes on a target antigen is often more important than discovering high affinity, tight binders, primarily because affinity maturation is a well-established and cost-effective protein engineering technique. Using epitope binning, the best antibodies from each bin can be tested, allowing for faster and more cost-effective identification of therapeutically relevant epitopes. Using epitope binning can reduce late-stage failures. Using epitope binning can increase the number of antibody candidates without increasing cost or cycle time; thus, epitope binning increases the overall probability of success. When affinity or other criteria are used as the primary selection tool, the results can be biased toward a small number of epitopes. This limits the likelihood of functional epitopes being represented in the selected set, or misses candidates with other desirable properties whose affinities can be matured. When all candidates are first grouped according to epitope, i.e., binned, epitope diversity is maintained, and then the best-performing antibodies in each bin can be selected.

[0005] The binned experimental data can be displayed to the user in a so-called heat map, which is a table displaying the binding response levels (or other parameters) of all analyzed antibody pairs and distinguishing antibodies competing for the same epitope and antibodies not competing for the same epitope using different colors / patterns. The heat map can contain a grid of squares (colored or patterned), which represents the interactions between the antibodies. Squares of a first color / pattern represent blocking interactions between two antibodies, while squares of a second color / pattern represent that they can bind to different locations on the antigen at the same time. A third color / pattern can be used to represent ambiguous interactions. Other colors / patterns can be used to represent e.g. "one-way" interactions, where blocking occurs when one antibody is attached to the antigen first.

[0006] An alternative way for displaying a heat map for sorting and displaying data from a binning experiment is shown in US20150269312A1. In this document, a node graph is displayed, in which nodes can be grouped together, representing antibodies in a common bin. Nodes can be grouped together for antibodies having the same blocking behavior in both ligand and analyte direction. Nodes can be grouped together by proximity between nodes, e.g. clustering nodes of a single bin closely together. Nodes in a single bin can be represented by displaying an envelope or contour around the nodes. Nodes can also be grouped by formatting the nodes themselves, e.g. by matching node color / shape / border etc. When observing the node graph, the observer can determine which antibodies belong to which bin. The node graph can also contain connections between nodes, e.g. lines, cords etc., which represent interactions between the antibodies.

[0007] However, both the heat map and the node graph table can be difficult to quickly understand and interpret the associations of the binned experimental data, especially for inexperienced users. Therefore, there is a need for improved binning methods and systems, which provide a simpler interpretation and extraction of the associations of the binned experimental data. SUMMARY

[0008] It is an object of the present disclosure to provide improved binning methods and systems, which provide a simpler interpretation and extraction of the associations of the binned experimental data.

[0009] The invention is defined by the appended independent patent claims. Non-limiting embodiments emerge from the dependent patent claims, the drawings and the following description.

[0010] According to a first aspect, there is provided a method of qualifying a subset of target binding biomolecules from a larger set of target binding biomolecules for analysis. The method comprises identifying interactions between pairs of different target binding biomolecules in a competitive immunoassay comprising a target protein. Using a processing unit, an interaction profile of the target binding biomolecules is generated from the identified interactions. Using a binning unit, each target binding biomolecule is assigned to a bin, wherein each bin represents an epitope family, and target binding biomolecules sharing a common interaction profile are assigned to a common bin, and each target binding biomolecule is assigned to only one bin. The identified bins are associated with the identified respective target binding biomolecules in a circular or semi-circular bin chart on a display, wherein the identified bins are represented as circular sectors in the bin chart. Based on the association between the identified bins and the identified respective target binding molecules in the bin chart, a subset of target binding biomolecules is selected for further analysis by selecting one or more of the target binding biomolecules of one or more of the bins.

[0011] The bin chart can be a circular or semi-circular chart, and the identified bins can be represented as circular sectors.

[0012] The competitive immunoassay can be performed on e.g. a Biacore instrument. The form of the competitive immunoassay used can be a sandwich format, a tandem format or a premix format.

[0013] The target protein can be an antigen, and the target binding biomolecules can be monoclonal antibodies binding to the same or different epitopes of the target. Alternatively, it can be a receptor-antibody system.

[0014] The processing unit can be any processor suitable for the task.

[0015] The binning unit can use a binning algorithm to assign the target binding molecules to different bins.

[0016] The display can be an electronic display, paper, etc.

[0017] The subset of target binding biomolecules can comprise one or more or all target binding molecules of one or more or all bins for further analysis.

[0018] The circular or semi-circular chart can be a pie chart or a ring chart.

[0019] The bin chart can be a sun chart displaying the hierarchy by different series of rings: e.g. bin number, antibody name and e.g. antibody class.

[0020] The circular sectors of a box plot can be distinguished from adjacent circular sectors in the box plot by a number, a name, a color, a pattern, a border line type, a border line color, or a colored or patterned label at the perimeter of the circular sector.

[0021] A box plot can contain one color for each circular sector / box, for example, or circular sectors / boxes that are not adjacent to each other in the plot can have the same color / pattern. Circular sectors / boxes can be distinguished from adjacent boxes by one, two or more deviating features, such as color and name.

[0022] Target binding biomolecules assigned to the same box can block binding to the target protein from each other by one-way blocking or two-way blocking, or interact with the target protein by displacement.

[0023] The type of interaction between a target binding biomolecule assigned to a first box and a target binding biomolecule assigned to a second box can be a blocking interaction selected from one-way blocking or two-way blocking, a non-blocking interaction, or an undefined type of interaction.

[0024] The type of interaction between a target binding biomolecule assigned to a first box and a target binding biomolecule assigned to a second box can be shown as an arrow or line between the first and second box in the box plot.

[0025] The direction of the arrow can indicate one-way binding. A dashed line can indicate uncertain binding.

[0026] The box plot can be a circular plot, and the arrows or lines can be arranged in the middle of the circular shape, connecting separate boxes to each other.

[0027] The target binding molecules can be monoclonal antibodies. The protein target can be a receptor. Such a receptor can be a cytokine receptor, a growth factor receptor, or an Fc receptor.

[0028] Boxes with connections can be grouped together in the box plot.

[0029] A connection between two boxes shows that the antibodies in these two boxes have overlapping interaction patterns. Such boxes are box clusters.

[0030] Boxes that are not connected to other boxes can be arranged with a spacing from other boxes in the box plot. The spacing can be a small gap between adjacent circular sectors.

[0031] The display can be an electronic display, and the display or underlying computing software can provide the user with the ability to modify the displayed box plot by changing one or more of the color, pattern, border line type, border line color, label pattern or color at the perimeter of the boxes.

[0032] The bins can be arranged separately in the bin chart based on the number of target binding biomolecules in the bin.

[0033] According to a second aspect, there is provided a system for qualifying a subset of target binding biomolecules from a larger group of target binding biomolecules for analysis. The system comprises: a competitive immunoassay with target proteins arranged to identify interactions between different pairs of target binding biomolecules; a processing unit arranged to generate an interaction profile of the target binding biomolecules from the identified interactions; a binning unit arranged to assign each target binding biomolecule to a bin, wherein each bin represents an epitope family, and to assign target binding biomolecules sharing a common interaction profile to a common bin, and each target binding biomolecule to only one bin; a display module arranged to associate the identified bins with the identified respective target binding biomolecules in a circular or semi-circular bin chart on a display, wherein the identified bins are represented as circular sectors in the bin chart; a selection unit arranged to select a subset of target binding biomolecules for further analysis by selecting one or more of the target binding biomolecules of one or more of the bins based on the association between the identified bins and the identified respective target binding biomolecules.

[0034] The selection unit can be a human, or a programmed unit trained to select a subset of biomolecules from a larger group of biomolecules based on the information in the bin chart. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 A detection curve from the interaction between a sample and a target molecule is shown using a Biacore instrument.

[0036] Figure 2 Steps for a method for qualifying a subset of target binding molecules from a larger group of molecules for analysis are illustrated.

[0037] Figure 3a , 3b And 3 illustrate different binding assay formats that can be used for epitope binning analysis.

[0038] Figure 4 A heatmap from epitope binning analysis is shown.

[0039] Figure 5a A bin chart for epitope binning analysis is shown. Figure 5b A bin chart of Figure 5a is shown, but with identified connections between the bins. Figure 5c A bin chart with labels for each antibody and grouping of bins with connections is shown.

[0040] Figure 6A raw box plot with a plurality of boxes and a second box plot with a lower number of boxes selected from the raw box plot are displayed, making it easier to understand the connections between specific boxes.

[0041] Figure 7 A box plot is displayed along with a corresponding heat map, where the antibodies are sorted in the same order in both the heat map and the box plot. Here, the box clusters are sorted according to the smallest antibody number within each box cluster. DETAILED DESCRIPTION

[0042] Analysis sensor systems arranged to monitor interactions between molecules, such as biomolecules, in real time can be based on label-free biosensor systems, such as optical biosensor systems. A representative such biosensor system is the Biacore ® instrument, which uses surface plasmon resonance (SPR) to detect interactions between molecules in a sample and molecular structures immobilized on a sensing surface. The sample is passed over the sensor surface, and the progress of binding directly reflects the rate at which interactions occur. Typical output from a Biacore ® system and similar biosensor systems is a response plot or detection curve, see Figure 1 , which describes the progress of molecular interactions over time, including both the association and dissociation phases.

[0043] Detection curves produced by biosensor systems based on other detection principles, such as other optical methods and electrochemical methods, will have a similar appearance.

[0044] Different high-throughput bioanalytical systems have been developed to enable efficient screening and characterization of biomolecular interactions. One example is the Biacore 8K instrument, where more than 1000 molecules can be screened in one day.

[0045] Such high-throughput systems can be valuable tools in the early screening of new therapies, vaccines, and epitope binning for diagnostics. Epitope binning is a competitive immunoassay used to characterize and sort libraries of, for example, monoclonal antibodies against a target protein. Antibodies against a similar target can be tested against all other antibodies in the library in a pairwise fashion to see if the antibodies block each other from binding to an epitope of the target protein (antigen). Each antibody has a competitive blocking profile generated against all other antibodies in the library. Epitope binning defines topological epitopes on an antigen according to the ability of antibodies to bind to the same antigen molecule at the same time. If two different antibodies can bind at the same time, they bind to topologically distinct epitopes. If they interfere with each other's binding, they bind to the same or overlapping epitopes. Closely related binning profiles indicate that the antibodies have the same or closely related epitopes, and are "binned" together.

[0046] Using epitope binning, the best antibodies from each bin can be tested, allowing for faster and more cost-effective identification of therapeutically relevant epitopes. Thus, the number of antibody candidates can be increased without increasing costs or cycle times, and thus epitope binning increases the overall probability of success.

[0047] In Figure 2 the method of qualifying a subset of target binding biomolecules (e.g. antibodies) from a larger set of target binding molecules for analysis is described. In a first step 100, the interactions between pairs of different target binding molecules are analyzed against a target protein (antigen) using a competitive immunoassay using a biosensor.

[0048] When using e.g. a Biacore instrument as a Biacore 8K instrument, different competitive immunoassay formats can be used: a sandwich format (see Figure 3a ), a tandem format (see Figure 3b ) or a premix assay format (see Figure 3c ) can be used. In the sandwich format, a first antibody is captured or immobilized on a sensor chip, and an antigen molecule is injected and allowed to bind to the first antibody. Thereafter, a second antibody is injected and binds or does not bind to the antigen molecule depending on whether it binds to the same (or overlapping) epitope as the first antibody. In the tandem format, an antigen molecule is immobilized on a sensor chip, and a first antibody is injected and allowed to bind to the antigen molecule. A second antibody is subsequently injected and binds or does not bind to the antigen molecule depending on whether it binds to the same (or overlapping) epitope as the first antibody. In the premix format, a first antibody is captured or immobilized on a sensor chip. An antigen molecule is mixed with a second antibody and subsequently injected and binds or does not bind to the first antibody depending on whether it binds to the same (or overlapping) epitope as the first antibody.

[0049] In a next step 200 of the method, an interaction profile of the tested target binding biomolecules is generated from the identified interactions using a processing unit.

[0050] Thereafter, a step 300 is performed in which each target binding biomolecule is assigned to one or more bins. This can be performed using a binning unit, which can utilize a binning algorithm. One bin represents one epitope family, wherein target binding biomolecules sharing a common interaction profile are assigned to a common bin, and each target binding biomolecule is assigned to only one bin. The identified bins can be associated 400 with the identified respective target binding biomolecules in a bin chart on a display (electronic display or on paper). Based on the association between the identified bins in the bin chart and the identified respective target binding biomolecules, a subset of target binding biomolecules can be selected 500 for analysis by selecting one or more target binding biomolecules of one or more bins.

[0051] In the evaluation, the response level of each step in the binding analysis of any of the above binding formats can be checked. This can help to compensate for variations in antibody concentration and also eliminate false negative responses where the lack of binding of the secondary antibody can be attributed to low binding or fast dissociation between the primary antibody and the antigen rather than interference between the first and second epitopes. Secondary antibodies classified as binding to independent epitopes can be ranked using the reporting dots based on the percentage of dissociation after a certain time period or in some cases characterized by the apparent dissociation rate constant with the antigen.

[0052] Data from binning experiments can be displayed to the user in so-called heat maps by using binning algorithms, see Figure 4 a. Heat maps are tables in which the binding response levels (or other parameters) of all analyzed antibody pairs are displayed and different colors / patterns are used to distinguish between antibodies competing for the same epitope and antibodies not competing for the same epitope. The heat map can comprise a grid of squares (colored or patterned) which represent the interactions between the antibodies. Squares of a first color / pattern represent blocking interactions between two antibodies, while squares of a second color / pattern represent that they can bind to different locations on the antigen at the same time. A third color / pattern can be used to represent ambiguous interactions. Other colors / patterns can be used to represent "one-way" interactions, where blocking occurs when one antibody is attached to the antigen first. In Figure 4 The heat map illustration 8*8 binning experiment shown in

[0053] The heat maps and node graphs described and illustrated in US20150269312A1 can be difficult to quickly understand and interpret the correlations of the experimental binning data therein, especially for inexperienced users. There is a need for a simpler way of sorting and displaying data from binning experiments that enables inexperienced users to quickly understand, interpret and extract the information given from binning experiments.

[0054] As illustrated in Figure 5a , the data from the heat map ( Figure 4 ) identifying the interaction types between antibody pairs is displayed in a box graph. The box graph can be a ring graph as shown in Figure 5a . Alternatively, the box graph can be a circular graph, a semi-circular graph, a sun graph, a bar graph or a line graph. The box graph illustrates how the heat map ( Figure 4) shown in Figure 1. In the example shown, eight different antibodies have been grouped into six bins. Bin 3 contains two antibodies, Ab 01 and Ab 08, meaning that these two antibodies have the same blocking / non-blocking pattern to all other antibodies tested in the epitope binning experiment. Antibodies with the same interaction pattern are grouped into the same bin. Antibodies in the other five bins deviate from this pattern in at least one interaction. In Figure 5a the bin chart shown in Figure 1, the antibodies are sorted in the same order as in the heat map shown in Figure 1, starting at 12 o'clock and continuing clockwise. The different bins in the bin chart can be numbered from 1 upwards, and / or they can be colored or patterned in different colors / patterns. Figure 4

[0055] The bin chart can comprise bin connections, i.e. an identification of the interaction type between antibodies assigned to a first bin and antibodies assigned to a second bin, see Figure 2. A connection between two bins shows that the antibodies in these two bins have overlapping interaction patterns. A bin with no connections to other bins means that no antibodies in this bin have interactions with any of the antibodies in the other bins. The bin connections can be lines connecting two bins and contain information about the interaction type (blocking / non-blocking / ambiguous) of the antibodies in these two bins. The interactions between bins can be represented as lines / arrows connecting two bins. In the example shown in Figure 2, the antibodies Ab 02 in bin 1 have overlapping interactions with the antibodies Ab 01 and Ab 08 of bin 3, and vice versa, as represented by the connecting lines between the two bins. The dashed line between bin 1 and bin 2 represents that the interaction type is ambiguous. The arrowed dashed line seen between bin 5 and bin 4 represents that the interaction type is ambiguous, but the antibodies in bin 4 can block the antibodies of bin 3 (one-way blocking). This is represented with the arrow on the connection. Figure 5b Figure 5b The bin chart can also be provided with labels for each antibody, see Figure 3. Such labels can for example be located on the bin chart perimeter of the circular (as shown), circular or semi-circular bin chart, and they can be colored / patterned to represent any type of data, e.g. antibody species. A legend explaining the colors / patterns can be displayed. In the bin chart, bins with connections can be grouped together, and bins with no connections to other bins can be represented with a space to the other bins, see Figure 4.

[0056] The bin chart of Figure 5 is an example sunburst chart displaying the hierarchy by three series of rings: bin number, antibody name and e.g. antibody species. Figure 5c Figure 5c Figure 5c

[0057] ​​​​​In addition to the clockwise order of the antibodies in the heat map, other sorting possibilities of the bins (not shown) can be e.g. according to size (number of antibodies in the bin). The bins can have names instead of just numbers. When direct bin connections of a bin can be highlighted and bin connections, and all unconnected bins are dimmed, it can be allowed to select a bin in the bin chart. This will simplify the analysis of bin connections for larger binning experiments. Instead of labels colored / patterned on the perimeter of the bin chart, metadata values related to the antibodies can be presented, e.g. their affinities, concentrations, etc. The metadata values can be presented on a colored / patterned background that automatically fades from light to dark (color / pattern gradient) to emphasize the magnitude of the metadata values.

[0058] When analyzing larger data sets, different parts of the bin chart can be zoomed in when displayed on the screen in order to study different bins and bin connections shown in the bin chart more closely. In one embodiment, the bins selected for closer inspection by the zoom-in function can be presented in a new bin chart containing only the selected bins, making the connections between the particular bins easier to understand, e.g. as schematically illustrated in Figure 6 .

[0059] In contrast to the node chart of US20150269312A1, the connections shown in the bin chart Figure 5b , 5c are between bins. Such bin connections are easier to observe and interpret since they are less numerous than presenting connections from each antibody.

[0060] In user tests it has been shown that most users can intuitively understand the interpretation of the basis of the bin chart, even inexperienced users. The bin chart in the shape of a circle, pie or sun can be seen as representing the antigen, and the separate bins arranged in the chart can be seen as binding to different epitopes at the surface of the antigen.

[0061] In one embodiment, the bin chart is displayed with the corresponding heat Figure 1 map, and as mentioned, the antibodies can be sorted in the same order in both the heat map and the bin chart. The binning algorithm can be arranged to sort the bin clusters based on the order of appearance in the experimental test. In Figure 7 , this is illustrated by sorting the bin clusters according to the smallest number of antibodies within each bin cluster. The binning algorithm can also be arranged to differently sort the bin clusters, e.g. by the order of size of the bin clusters. Here, a bin cluster refers to bins grouped together because the antibodies in those bins have overlapping interaction patterns.

[0062] The binning algorithm identifies antibodies that block each other in the same way from binding to the antigen at the same time. Such identification can for example use machine learning techniques, which can learn after training, where the blocking / non-blocking determination for each antibody pair is done by the user (setting the cutoff). The antibodies are defined as bins, and can be displayed as bins in a bin chart.

[0063] In various embodiments, the blocking / non-blocking determination for each antibody pair can be done by the user (setting the cutoff) with software, then helping to sort the antibodies into groups that block each other, and determining whether the groups contain antibodies with the same blocking partners.

[0064] For example, the groups (bins) of antibodies can be presented as sections in a bin chart. Subsequently the user (or trained algorithm) can change which antibody pairs are classified as blocking, and subsequently the sorting and grouping will automatically change accordingly.

[0065] For example, for antibodies A, B, C, and D: if A blocks B and C and B blocks A and C and C blocks A and B, then A, B, C can define one bin; but if B also blocks D, while A and C do not block D, then A and C are provided in a bin separate from B; then in a graphical display chart, lines in the bin chart can show whether the antibodies in the two bins block each other, so in this case the A, C bin will have a line to the B bin, and the B bin will have a line to the D bin. Then in the graphical display chart the bins with lines between them are kept together, while bins that are not blocked from other bins are sectioned apart. Thus, such a graphical display chart (not shown) or representative data thereof can provide a clear dynamic automatically updated visualization or display of the relationships between various antibody pairs, enabling faster user (or machine) interpretation thereof, and thus providing improved throughput for a system for qualifying a target binding biomolecule sub-group from a larger group of target binding biomolecules. Such techniques can also be used to provide input data, for example, for an automated robotic controlled processing device for screening a candidate sub-group of target binding biomolecules.

[0066] While the above description contains numerous specificities, these specificities should not be construed as limiting the scope of the concepts described herein but merely as providing illustrations of some exemplary embodiments of the described concepts. It should be appreciated that the scope of the presently described concepts fully encompasses other embodiments which can become apparent to those skilled in the art and should be intended to limit the scope of the presently described concepts. Unless explicitly stated otherwise, reference to elements or embodiments of the described concepts does not limit the number of such elements or embodiments that combinations of such elements or embodiments can include. All structural and functional equivalents to the elements of the aforementioned embodiments known or that can become ascertained by those skilled in the art are expressly incorporated herein and are intended to be encompassed by the scope of the described concepts. Moreover, no component or elements described herein should be deemed critical unless explicitly described as being essential.

Claims

1. A method for qualifying a subgroup of target-binding biomolecules from a larger group for analysis, the method comprising: In competitive immunoassays involving target proteins, the interactions between (100) different target-binding biomolecule pairs are identified. Using the processing unit, an interaction profile of the target-binding biomolecules is generated (200) from the identified interactions. Using binning units, each target-binding biomolecule is assigned (300) to a bin, where each bin represents an epitope family, and target-binding biomolecules sharing a common interaction profile are assigned to a common bin, with each target-binding biomolecule assigned to only one bin. The identified bins are associated with their respective target-binding biomolecules in a circular or semi-circular bin diagram on the display (400), wherein the identified bins are represented as circular sectors in the bin diagram, and Based on the association between the identified bins and their respective target-binding molecules in the bin diagram, a (500) target-binding biomolecule subgroup is selected for further analysis by selecting one or more of the target-binding biomolecules from one or more of the bins. In the aforementioned box diagram, boxes that are not connected to other boxes are arranged with intervals from other boxes. In the aforementioned box diagram, boxes connected by lines are kept together, while boxes that are not blocked from other boxes are separated into segments. The circular or semi-circular charts mentioned above are pie charts. The interaction type between the target-binding biomolecules assigned to the first bin and the target-binding biomolecules assigned to the second bin is shown as the arrows and lines between the first and second bins in the bin diagram, and The connection between two boxes indicates that the target-binding biomolecules in the two boxes have overlapping interaction patterns, while a box that is not connected to other boxes means that no target-binding biomolecules in that box interact with any target-binding biomolecules in other boxes.

2. The method of claim 1, wherein the circular sectors of the box chart are distinguished from adjacent circular sectors in the box chart by means of numbers, names, colors, patterns, boundary line types, boundary line colors, or colored or patterned labels at the perimeter of the circular sectors.

3. The method of claim 1, wherein the target-binding biomolecules allocated (300) to the same box block each other from binding to the target protein by unidirectional or bidirectional blocking, or interact with the target protein by substitution.

4. The method of claim 1, wherein the type of interaction between the target-binding biomolecules allocated to the first box and the target-binding biomolecules allocated to the second box is a blocking interaction selected from unidirectional or bidirectional blocking, non-blocking interaction, or an undefined type of interaction.

5. The method of claim 1, wherein the arrows and lines are arranged in the middle of the annular shape to connect the separate boxes to each other.

6. The method of claim 1, wherein the target binding molecule is a monoclonal antibody.

7. The method of claim 1, wherein the target protein is a receptor.

8. The method of claim 5, wherein connected boxes are grouped together in the box chart.

9. The method of claim 1, wherein the display is an electronic display, and the display or the underlying computing software provides a user with the ability to modify the displayed box chart by changing one or more of the following: color, pattern, border line type, border line color, label pattern or color at the perimeter of the box.

10. A product system for qualifying a subgroup of target-binding biomolecules from a larger group for analysis, said system comprising: A competitive immunoassay with target proteins, configured to identify (100) interactions between different target-binding biomolecule pairs. A processing unit, arranged to generate (200) an interaction profile of the target-binding biomolecules from the identified interactions. Binding units, arranged to assign each target-binding biomolecule (300) to a bin, wherein each bin represents an epitope family, and target-binding biomolecules sharing a common interaction profile are assigned to a common bin, and each target-binding biomolecule is assigned to only one bin. A display module arranged in a circular or semi-circular box diagram on the display to associate identified boxes with their respective identified target-binding biomolecules (400), wherein the identified boxes are represented as circular sectors in the box diagram, and The selection unit, arranged based on the association between identified bins and their respective identified target-binding biomolecules, selects (500) target-binding biomolecule subgroups for further analysis by selecting one or more of the target-binding biomolecules from one or more bins. In the aforementioned box diagram, boxes that are not connected to other boxes are arranged with intervals from other boxes. In the aforementioned box diagram, boxes connected by lines are kept together, while boxes that are not blocked from other boxes are separated into segments. The circular or semi-circular charts mentioned above are pie charts. The interaction type between the target-binding biomolecules assigned to the first bin and the target-binding biomolecules assigned to the second bin is shown as the arrows and lines between the first and second bins in the bin diagram, and The connection between two boxes indicates that the target-binding biomolecules in the two boxes have overlapping interaction patterns, while a box that is not connected to other boxes means that no target-binding biomolecules in that box interact with any target-binding biomolecules in other boxes.

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