Method for separating counter ligands

Abstract

The present invention relates to an improved method for isolating counter ligands having binding specificity of interest, in particular a method for isolating counter ligands from a library of counter ligands wherein these counter ligands are specific for differentially expressed ligands and / or non-normally expressed ligands.

Description

Technical Field

[0001] The present invention relates to improved methods for isolating counter-ligands having binding specificities of interest, and in particular methods for isolating counter-ligands from a library of counter-ligands, wherein the counter-ligands are specific for differentially expressed ligands and / or infrequently expressed ligands. Background Art

[0002] Protein or peptide based libraries are often used to select counter-ligand molecules that are specific to certain ligands. Such libraries are constructed so that the protein molecules are physically linked in some way to the genetic information encoding the specific protein molecules. Thus, the protein molecules are displayed together with their genes.

[0003] Commonly used display formats rely on cellular or viral host particles to present protein molecules; including bacterial display (Francisco et al., 1993) and phage display (Smith, 1985; Smith and Scott, 1993; Winter et al., 1994). Such systems display potential counterligand molecules on the surface of the host particles, while the genetic information of the displayed molecules is contained within the particles, and the methods have been successfully used to select counterligands based on specific proteins.

[0004] There are other display formats that rely on in vitro translation; including various forms of ribosome display (Mattheakis et al., 1994; Hanes and Pluckthun, 1997; He and Taussig, 1997), which rely on non-covalent bonding of the genetic information to the protein molecule; and other display formats that also rely on in vitro translation, whereby there is a covalent bond between the genetic information and the potential counter-ligand protein molecule, such as Profusion (Weng et al., 2002) or covalent display technology (Gao et al., 1997).

[0005] The displayed peptide or protein counter-ligand libraries can be completely randomized (eg, when using peptide libraries), or they can be based on a constant region scaffold structure combined with another structure that confers variability.

[0006] Commonly used scaffold structures are based on antibody heavy and light chain variable domains (McCafferty et al., 1990), but can also be based on other scaffolds such as fibronectin (Jacobsson and Frykberg, 1995; Koide et al., 1998), protein A domains (Stahl et al., 1989), or small stable protein domains such as BPTI (Markland et al., 1991).

[0007] Counterligands showing a certain binding specificity are usually selected from display libraries using so-called "biopanning" methods.

[0008] The target ligand can be immobilized on a solid surface, and specific counter-ligand members in the library are exposed to the immobilized target ligand, allowing the counter-ligand of interest to bind to the target ligand. Unbound library members are then washed away, and the counter-ligand of interest is retrieved and amplified.

[0009] In another selection method, the target ligand is bound to a specific counter-ligand library member in solution. The bound counter-ligand is then isolated using, for example, a retrievable tag attached to the target ligand. The most commonly used tag is biotin, which allows the complex between the target molecule and the displayed specific library member to be retrieved using avidin coupled to a solid support (e.g., magnetic beads) (Parmley and Smith, 1988).

[0010] These methods are used when the target ligand is well known and available in purified form. It is routine to make selections for a single target ligand at a time. Several defined target ligands can be selected simultaneously. The target ligand can be one or more of a small hapten, a protein, a carbohydrate, a DNA, and a lipid.

[0011] Selection can also be made for ligands expressed on the surface of cells. This can be done using endogenous cell lines, primary cells, or cells that overexpress the ligand of interest.

[0012] Protein particles that are not members of the counter-ligand library (e.g., phage expressing antibody fragments) may be "sticky", causing some binding and dissociation of non-target specific molecules. Non-specific binding can be minimized by adding certain compounds to the counter-ligand display construct / ligand mixture to act as blocking agents to reduce this background binding of non-specific counter-ligands, for example, milk, bovine serum albumin, serum (human / fetal calf serum), gelatin, and detergents for certain (non-cellular) applications.

[0013] Many washing procedures have been designed to reduce nonspecific binding of library members to cells and to help separate cells from contaminating and / or nonspecifically bound library members. Such methods include washing cells magnetically immobilized in a column (Siegel et al., 1997) to minimize shear forces and allow reassociation of dissociated phage. Another method of washing cells is centrifugation in a higher density medium (such as Ficoll or Percoll) to selectively remove nonspecific and low affinity counterligands and further spatially separate cells and cell-bound counterligands from free and nonspecifically bound counterligands (Carlsson et al., 1988; Williams and Sharon, 2002).

[0014] Depending on the efficiency of the selection process, several rounds of panning may be required to eliminate nonspecific counter-ligands or at least sufficiently reduce them to the desired level (Dower et al., 1991).

[0015] For many applications, specific counter-ligands for differentially expressed ligands are of interest. For example, proteins may be differentially expressed on cells, or present in different amounts in tissues and samples derived from disease patients when compared to proteins from healthy controls. Such diseases include microbial, viral or parasitic infections, asthma, chronic inflammatory and autoimmune disorders, cancer, nervous system diseases, cardiovascular diseases or gastrointestinal diseases. Similarly, differences may exist between the protein composition of body fluids (e.g., plasma, cerebrospinal fluid, urine, semen, saliva and mucus) of disease patients compared to healthy controls.

[0016] Therefore, in addition to their general applicability as research tools for identifying differentially expressed ligands, counter-ligands specific for differentially expressed ligands may also be used as tools for use in the diagnosis, prevention and / or treatment of disease.

[0017] Advances in the fields of genomics and proteomics have revealed the existence of a large number of as yet undefined differentially expressed molecules, which underscores the importance of methods for generating specific counterligands for these potential target ligands. Many of these differentially expressed molecules are expected to be present on the cell surface, constituting potential targets for targeted therapy using, for example, specific antibodies that can be conjugated to a biologically active (e.g., cytotoxic) agent.

[0018] Large and highly diverse counter-ligand display libraries provide a means to isolate counter-ligands specific for unknown cellular ligands acting on carbohydrates, proteins, lipids, or combinations thereof.

[0019] Currently available biopanning methods include whole cell, cell fraction and cell membrane based methods which in principle permit the isolation of displayed constructs that behave in their native configuration as counter-ligands specific for cell membrane ligands.

[0020] Human therapeutic antibodies and humanized therapeutic antibodies are increasingly used to treat a variety of diseases, including acute and chronic inflammatory disorders, immune and central nervous system disorders, and cancer. Human therapeutic antibodies are considered the most attractive way to treat human diseases due to their fully human nature and associated lack of immunogenicity, optimal ability to engage antibody Fc-dependent host immune effector mechanisms, and superior in vivo half-life compared to their murine, chimeric, and humanized counterparts. Today, human antibodies are typically produced by different technologies, including humanized mice and highly diverse phage antibody libraries.

[0021] Human antibody libraries are further considered to have advantages over transgenic mice carrying human immunoglobulin genes when selecting antibodies that bind to receptor epitopes that are structurally conserved between humans and mice, because such antibodies are negatively selected in vivo through self-tolerance mechanisms. Such conserved regions are of particular therapeutic interest because conserved regions are often functionally relevant (e.g., ligand binding domains necessary for binding to and conferring ligand / receptor-induced cellular responses), and antibodies targeting such conserved epitopes can be screened for in vivo therapeutic activity in syngeneic experimental disease model systems.

[0022] Large binder libraries (>10 10 Members of the phage display technology (Smith et al., 1985; McCafferty et al., 1990), which physically links the antibody genotype to the phenotype (biomolecule specificity) through phage (Smith et al., 1985; McCafferty et al., 1990), yeast (Boder and Wittrup, 1997) or ribosome (Ellington and Szostak, 1990) display technology, typically contain antibodies that bind to a number of clinically relevant target biomolecules with high (nM) affinity and selectivity (Hanes et al., 2000; Soderlind et al., 2000; Rothe et al., 2008). Therefore, by applying positive selection pressure for binding to a defined target biomolecule (e.g., a cell surface receptor) and negative selection pressure for binding to highly homologous non-target biomolecules (e.g., related receptors of the same superfamily), antibodies against a variety of different biomolecules can be isolated from such libraries (Winter et al., 1994). Similarly, by applying positive and negative selection pressures in the form of a complex population of biomolecules (e.g., diseased cells, blood, or tissue versus normal cells, blood, or tissue), antibodies against a priori unknown biomolecules that are differentially expressed between target and non-target samples can be isolated from an antibody library.

[0023] When combined with clinically predictive high-throughput functional screening (Frendeus, 2013; Ljungars et al., 2018) and target deconvolution (Mattsson et al., 2021), this allows for phenotypic drug discovery (PDD) of medically relevant antibodies, including first-in-class antibodies specific for novel targets (Ljungars et al., 2018; Waldmann et al., 1984; Veitonmaki et al., 2013; Roghanian et al., 2015; Williams et al., 2016), as well as best-in-class antibodies specific for unique functional epitopes on validated targets (Semmrich et al., 2022). Multiple selection strategies incorporating cells, tissues, and fluids to generate antibodies for PDD further demonstrate the broad applicability of PDD for biologics (Veitonmaki et al., 2013; Roghanian et al., 2015; Dyer et al., 1989). However, realizing the full potential of biologic PDD (i.e., functional screening of antibodies against all disease-relevant biomolecules) will require significant enhancements and outputs compared to current target-agnostic approaches that generate very few (101 to 103) antibodies specific for a limited number of highly expressed biomolecules (Ljungars et al., 2018; Williams et al., 2016; de Kruif et al., 1995; Ridgway et al., 1999; Qin et al., 2014; Egloff et al., 2019; Sandercock et al., 2015; Nixon et al., 2019).

[0024] Furthermore, therapeutic efficacy is not easily predicted from antibody receptor specificity; antibodies against the same target receptor may vary greatly in therapeutic efficacy regardless of their binding affinity (Beers et al., 2008; Cragg and Glennie, 2004), and antibodies against alternative molecular targets may show promising and sometimes unexpected therapeutic potential (Beck et al., 2010; Cheson and Leonard, 2008). For example, different CD20-specific antibody clones that bind to the CD20 antigen with similar affinity and carry the same mouse IgG2a constant region differ fundamentally in their ability to deplete B cells in vivo (Beers et al., 2008; Cragg and Glennie, 2004), and antibodies against tumor-associated cell surface receptors other than CD20 can have significant antitumor activity against B cell cancers (for review, see: Cheson and Leonard, 2008). Thus, within a highly diverse antibody library, the most therapeutically effective, potent, and best tolerated antibodies for any given type of cancer may be specific for any one of several different receptors, and identification of the therapeutically optimal antibody clone within a highly diverse library requires functional screening of multiple, ideally all, library members specific for different disease cell-associated receptors.

[0025] The applicant has previously developed two different screening techniques (biopanning methods) that enable the retrieval of antibody clones from human phage antibody libraries that bind to different surface receptors that are differentially expressed on one cell population (target cells) compared to another cell population (non-target cells) (hereinafter referred to as differential biopanning). The first of these methods is described in WO2004 / 023140 (and Fransson et al., 2006; Frendéus, 2006). The second of these methods is described in WO2013 / 041643. The disclosures of WO2004 / 023140 and WO2013 / 041643 (and all national phase applications derived therefrom) are incorporated herein by reference in their entirety.

[0026] The method of WO2004 / 023140 comprises the following steps in order:

[0027] 1) Differential bio-panning, followed by

[0028] 2) Screening for target specificity versus non-target specificity, followed by

[0029] 3) Routine sequencing of a small number of clones using the Sanger technique.

[0030] Using this technology, it is possible to generate antibody pools that display high specificity for differentially expressed surface receptors of target cells compared to differentially expressed surface receptors of non-target cells.

[0031] The method of WO2013 / 041643 comprises the following steps in the following order:

[0032] 1) Differential bio-panning, followed by

[0033] 2) High-throughput sequencing, followed by

[0034] 3) Perform confirmatory screening for the counter-ligand specificity of differentially expressed ligands.

[0035] Sanger sequencing, using the method of WO2004 / 023140, is an example of a technique currently used to identify unique binders in a "low throughput" manner. Other examples include running antibody gene DNA on gels before and after restriction enzyme digestion to reveal unique sizes and, indirectly, different sequences through different sensitivities to different restriction enzymes.

[0036] When applied to isolate antibodies targeting surface receptors differentially expressed on cancer B cells (target) versus T cells (non-target) (“BnonT” differential biopanning), this approach identified antibodies with specificity for surface receptors differentially expressed on different target cells, including HLA-DR, surface Ig, and ICAM-1 (Table 1).

[0037] Table 1. The number of genes identified by existing screening methods (e.g., sequence differential biopanning, binding screening and Sanger sequencing, targeted Frequency and specificity of antibodies isolated from cancer B cells versus T cells expressing surface receptors ("BnonT" differential biopanning select)

[0038]

[0039] However, the targeted receptors were all relatively well expressed, and the number of unique antibody sequences identified by this approach (only 8 out of 81 screened) was limited.

[0040] Although only a limited number of clones specific for differentially expressed surface receptors of target cells were sequenced, the high frequency of one antibody clone indicated limited antibody diversity in the retrieved "BnonT" antibody pool. Thus, although this technology provides a significant improvement over previous cell-based panning techniques in terms of identifying antibodies with therapeutic potential for several different differentially expressed receptors through limited screening efforts (Fransson et al., 2006), this observation suggests that further improvements are needed, because according to the prevailing general view, limited binding screening can only discover the most common antibodies, which have limited diversity and consist of antibodies against relatively highly expressed and strongly differentially expressed surface receptors (Hoogenboom, 2002) (Liu et al., 2004; Mutuberria et al., 1999; Osbourn et al., 1998).

[0041] Computer simulations following the teachings of early biopanning methods indicated that the differentially selected "BnonT" antibody pool should contain significantly greater numbers of antibodies against each of several differentially expressed surface receptors.

[0042] Sequencing capabilities at the time made it extremely difficult (and practically infeasible) to sequence a significantly larger number of antibody clones in the pool, so an indirect approach was used to test the hypothesis that the differentially selected antibody pool should be more diverse than that revealed by the initial screen. Thus, the differentially selected "BnonT" antibody pool was panned for the presence of additional ICAM-1 specific antibody clones using immunobeads conjugated to recombinant ICAM-1 protein (ICAM-1 is the cell surface receptor targeted by a single antibody clone from the initial 81 sequenced clones in the differentially selected antibody pool from Table 1). Screening of 1260 antibody clones, retrieved after panning the differentially selected antibody pool against recombinant ICAM-1, identified twenty-one (21) additional ICAM-1 specific antibody sequences / clones.

[0043] These observations suggest that the original differential biopanning approach of WO2004 / 023140 can identify antibody clones against differentially expressed antigens, but that the differentially selected antibody pools are more diverse than was apparent from these initial screens and significantly more numerous than identified by conventional screening methods.

[0044] The method of WO2013 / 041643 improves the accuracy of the original differential bio-panning method for detecting multiple different counter-ligands for a ligand of interest. This improved bio-panning method enables the retrieval of high-affinity counter-ligand pools from human antibody libraries (and other molecular libraries), such as human antibodies that are specific for different ligands (e.g., receptors) that are differentially expressed in target cell populations at low to high levels compared to other cell populations in their native cell surface configuration. The method combines the differential bio-panning method with next-generation deep sequencing and uses confirmatory screening of counter-ligand specificity for differentially expressed surface receptors of target cells, which is actually a "reverse screening."

[0045] Importantly, counterligands (such as antibody clones) identified by this approach are likely to have therapeutic potential, firstly based on their high affinity binding to receptors that are: a) differentially expressed on target cells relative to non-target cells, and b) expressed on target cells in their native cell surface configuration; and secondly based on the documented ability of antibodies with these properties to mediate therapeutic effects in relevant in vitro and in vivo experimental disease model systems (Beck et al., 2010; Fransson et al., 2006).

[0046] Despite the progress made in Applicants' prior methods, there remains a need for further improved methods to identify antibodies specific for ligands that are differentially expressed between two or more samples, cell types, tissues, fluids, etc. Such counter-ligands may have different therapeutic and / or diagnostic value depending on the relative and absolute expression levels of target and non-target complex biomolecules in the sample (as discussed above). Summary of the invention

[0047] The present application solves this problem through the various aspects of the present invention described below. Specifically, the present application relates to a method for preferentially identifying anti-ligands for ligands based on the ligand expression level in target and non-target complex biomolecules / ligand samples by generating predicted antibody enrichment features and experimental antibody enrichment features. This allows the most promising clones to be identified, expressed and focused on analysis based on their targeted ligand expression in target and non-target complex biomolecules / ligand samples in the most appropriate functional assays, wherein these most appropriate functional assays are usually limited in terms of flux due to the lack of biological samples of interest (e.g., primary patient-derived cells). For example, diagnostic antibodies and therapeutic antibodies (e.g., anti-IL-6R (31)) that rely entirely on blocking ligand-receptor signaling may be specific for receptors expressed over a wide dynamic range. In contrast, Fc-dependent and enhanced therapeutic antibodies that mediate ADCC and CAR-T cell specificity require low (Fc-dependent) or no (enhanced) receptor expression on key normal cells and tissues due to their powerful cytolytic properties. By enabling the identification of counterligands specific for ligands expressed across a therapeutically and diagnostically relevant range, and by generating distinct predicted, in silico and / or experimentally derived antiligand display-selection enrichment profiles, the present invention enables not only the identification of an order of magnitude greater number of counterligands compared to existing target-agnostic methods, but also the preferential identification of counterligands with distinct therapeutic and / or diagnostic potential for prioritized and focused functional screening and target deconvolution in lower throughput downstream assays. Thus, the methods of the present invention enable the retrieval of multiple therapeutically useful antibodies enriched and identified in differential biopanning studies, and enable the efficient retrieval of rare antibody clones discovered in such studies.

[0048] In a first aspect of the present invention, a method for isolating at least one counter-ligand for at least one differentially expressed target ligand in a target cell, tissue or sample of interest from a counter-ligand library is provided, wherein the method comprises the following steps:

[0049] (a) providing one or more reference enrichment characteristics of the counter-ligand library used;

[0050] (b) performing one or more rounds of differential biopanning on the counter-ligand library to generate a counter-ligand pool;

[0051] (c) performing high-throughput sequencing on the counter-ligand pool generated during step (b) to generate a discovery enrichment signature for each counter-ligand in the counter-ligand pool.

[0052] In a preferred embodiment of the first aspect, the method further comprises:

[0053] (d) matching the one or more reference enrichment signatures provided in step (a) with the counter-ligand discovery enrichment signatures generated in step (c) to isolate at least one counter-ligand for at least one differentially expressed target ligand in the target cell, tissue, or sample of interest.

[0054] By "enrichment signature," we mean the pattern of change in frequency of a given counterligand in a population of counterligands during successive rounds of biopanning. The change in frequency of a given counterligand from one round to another is determined based on the complete population of counterligands from the previous round. Enrichment signatures provide a means by which the frequency of a certain counterligand in a given population of counterligands can be tracked and / or predicted during successive rounds of biopanning.

[0055] The so-called "reference enrichment signature" includes the meaning of a known enrichment signature of a known counter-ligand. The reference enrichment signature can be a reference enrichment signature of a counter-ligand with a known sequence, a counter-ligand against a known target ligand, and / or a counter-ligand against a target ligand with a known expression level in a cell, tissue or sample of interest. The reference enrichment signature can be derived from one or more previously performed counter-ligand library selection experiments (e.g., using one or more differential bio-panning steps), or generated using computer simulation calculations and modeling.

[0056] By "discovery enrichment signature," we mean an unknown enrichment signature of a previously unknown counter-ligand. A discovery enrichment signature can be a discovery enrichment signature of a counter-ligand with a previously unknown sequence, a counter-ligand for a previously unknown target ligand, and / or a counter-ligand for a target ligand with a previously unknown expression level in a cell, tissue, or sample of interest. A discovery enrichment signature can be obtained by performing a counter-ligand library selection experiment (e.g., using one or more differential bio-panning steps) followed by sequencing the generated counter-ligand pool.

[0057] The enrichment signature information can be used to identify counter-ligands specific for differentially expressed ligands by comparing and matching the discovery enrichment signatures with counter-ligands for known ligand targets and / or reference enrichment signatures for counter-ligands for ligand targets with known expression levels in cells, tissues or samples of interest. The enrichment signature information can also be used to optimize the parameters of biopanning experiments to obtain counter-ligands for specific different classes of ligands based on the absolute expression level of the ligand in the target cells, tissues or samples or the relative expression level of the ligand in the target cells, tissues or samples compared to non-target cells, tissues or samples.

[0058] By "matching" is meant pairing a reference enrichment signature with a discovery enrichment signature that has a similar counter-ligand frequency variation profile in a population of counter-ligands during successive rounds of biopanning.

[0059] The term "tissue" includes any of the different types of material that make up an animal, including cells, extracellular matrix and its components, and substances secreted by cells.

[0060] The term "sample" includes the meaning of a biological sample or a biological specimen. For example, a biological sample / specimen can be a sample / specimen taken from a larger entity (e.g., a sample / specimen of blood, urine, tissue, or saliva), a cell lysate, a microbial culture (e.g., a bacterial or fungal culture), or a sample / specimen of a viral particle population.

[0061] In some embodiments of the method of the first aspect, the method further comprises the step of screening for the counter-ligand specificity of the differentially expressed ligand, wherein the screening is performed by:

[0062] (i) performing a screening step prior to step (c) to identify target binding versus non-target binding; and / or

[0063] (ii) after step (c), performing a confirmatory screening step for the counter-ligand specificity of the differentially expressed ligand; and / or

[0064] (iii) After step (d), a confirmatory screening step is performed for the counter-ligand specificity of the differentially expressed ligands.

[0065] In some embodiments of the method of the first aspect, the confirmatory screening step is performed by binding analysis using methods such as flow cytometry, FMAT, ELISA, MSD or CBA.

[0066] In some embodiments of the method of the first aspect, the method of isolating at least one counter-ligand does not include a screening step.

[0067] By "the method for isolating at least one counter-ligand does not include a screening step" is meant that the method for isolating the counter-ligand does not include:

[0068] - Prior to step (b), a screening step for identifying target binding versus non-target binding; nor

[0069] - after step (b), a confirmatory step for screening the counter-ligand specificity of the differentially expressed ligands; nor

[0070] - after step (c), performing a confirmatory screening step for the counter-ligand specificity of the differentially expressed ligand; nor

[0071] - After step (d), a confirmatory screening step is performed for the counter-ligand specificity of the differentially expressed ligands.

[0072] In some embodiments of the method of the first aspect, the one or more reference enrichment signatures provided in step (a) are reference enrichment signatures obtained by computer simulation.

[0073] By "in silico reference enrichment signature" is meant a reference enrichment signature that has been generated using computational methods, such as in silico modeling of counter-ligand enrichment during successive rounds of biopanning.

[0074] In some embodiments of the method of the first aspect, the enrichment signature obtained by computer simulation is generated using an equation derived from the universal law of mass action. In some embodiments of the method of the first aspect, the enrichment signature obtained by computer simulation is generated using the following equation:

[0075]

[0076] in

[0077] FA T = Frequency of antibody recovery

[0078] A T =The amount of recovered antibodies

[0079]

[0080] HR = hit rate, the fraction of antibodies that are specific for the target cell (determined experimentally).

[0081] In some embodiments of the method of the first aspect, the reference enrichment signature obtained by computer simulation is a reference enrichment signature of: an anti-ligand for a highly expressed ligand of a target cell, tissue, or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue, or sample of interest; or an anti-ligand for a lowly expressed ligand of a target cell, tissue, or sample of interest.

[0082] In some embodiments of the method of the first aspect, the one or more reference enrichment signatures provided in step (a) are experimentally obtained reference enrichment signatures. In some embodiments of the method of the first aspect, the experimentally obtained reference enrichment signatures are generated from at least one biopanning experiment comprising at least one reference counter-ligand.

[0083] In some embodiments of the method of the first aspect, the reference enrichment signature obtained experimentally is a reference enrichment signature of: an anti-ligand for a highly expressed ligand of a target cell, tissue, or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue, or sample of interest; or an anti-ligand for a lowly expressed ligand of a target cell, tissue, or sample of interest.

[0084] The experimentally obtained reference enrichment signature can be generated from one or more previously performed counter-ligand library selection experiments (e.g., using one or more differential bio-panning steps). The experimentally obtained reference enrichment signature can relate to reference counter-ligands, which can be identified in and isolated from a counter-ligand library by a variety of methods. For example, reference counter-ligands can be identified in and isolated from a counter-ligand library using isolated reference ligands (e.g., purified recombinant proteins), using cells, tissues, or samples enriched for the ligand of interest (e.g., cell lines engineered to overexpress the protein of interest, or cell lines or tissues transformed / transfected with constructs expressing the protein of interest), or using differential bio-panning schemes in which the specific ligand target is not known in advance. Therefore, it is not always necessary to know the identity of the ligand targeted by the reference counter-ligand, and instead, it is usually sufficient to know only the expression level of the target ligand in the target cell / tissue / sample and non-target cells / tissues / samples.

[0085] By "reference counter-ligand," it is meant a counter-ligand with known sequence and / or specificity, the frequency of which can be tracked in the counter-ligand pool during successive rounds of bio-panning. The presence of such a reference counter-ligand in the counter-ligand pool can be confirmed using, for example, nucleic acid sequencing (e.g., next generation sequencing).

[0086] By "isolated reference ligand," it is meant an isolated ligand that has a known expression level (i.e., in terms of copy number per cell / tissue / sample) in a cell, tissue, or sample of interest. An isolated reference ligand is substantially pure and separated from its usual cellular and / or physiological environment. For example, an isolated reference ligand can be a purified recombinant protein of interest immobilized on affinity beads or a solid surface.

[0087] In some embodiments of the method of the first aspect, two or more reference enrichment signatures are provided in step (a), wherein one or more of the reference enrichment signatures are reference enrichment signatures obtained by computer simulation, and wherein one or more of the reference enrichment signatures are enrichment signatures obtained experimentally.

[0088] In some embodiments of the method of the first aspect, when two or more reference enrichment features are provided in step (a), one or more computer-derived enrichment features are generated using equations derived from the universal law of mass action. In some embodiments of the method of the first aspect, when two or more reference enrichment features are provided in step (a), one or more computer-derived enrichment features are generated using the following equations:

[0089]

[0090] in

[0091] FA T = Frequency of antibody recovery

[0092] A T =The amount of recovered antibodies

[0093]

[0094] HR = hit rate, the fraction of antibodies that are specific for the target cell (determined experimentally).

[0095] In some embodiments of the method of the first aspect, when two or more reference enrichment signatures are provided in step (a), one or more computer-simulated reference enrichment signatures are reference enrichment signatures of: anti-ligands for highly expressed ligands of target cells, tissues, or samples of interest, anti-ligands for moderately expressed ligands of target cells, tissues, or samples of interest; or anti-ligands for lowly expressed ligands of target cells, tissues, or samples of interest.

[0096] In some embodiments of the method of the first aspect, when two or more reference enrichment signatures are provided in step (a), the experimentally obtained reference enrichment signatures are generated from at least one biopanning experiment comprising at least one reference counter-ligand.

[0097] In some embodiments of the method of the first aspect, when two or more reference enrichment signatures are provided in step (a), one or more experimentally obtained reference enrichment signatures are reference enrichment signatures of: anti-ligands for highly expressed ligands of target cells, tissues, or samples of interest, anti-ligands for moderately expressed ligands of target cells, tissues, or samples of interest; or anti-ligands for lowly expressed ligands of target cells, tissues, or samples of interest.

[0098] In some embodiments of the method of the first aspect, the biopanning step (a) comprises the following sub-steps:

[0099] (i) providing a counter-ligand library;

[0100] (ii) providing a first ligand population comprising ligands immobilized to or incorporated into a subtractor ligand construct;

[0101] (iii) providing a second ligand population comprising ligands immobilized to or incorporated into the target ligand construct;

[0102] (iv) determining the amount of subtractor ligand construct and target ligand construct in these populations using one or more equations derived from the following universal law of mass action:

[0103]

[0104] in:

[0105] A, B, C, and D = the participants in the reaction (reactants and products)

[0106] a, b, c, and d = coefficients required to balance the chemical equation in order to permit separation of counter-ligands to the differentially expressed target ligand;

[0107] (v) providing said amount of subtractor ligand construct determined in step (iv);

[0108] (vi) providing the amount of target ligand construct determined in step (iv);

[0109] (vii) providing a separation means for separating the counter-ligand bound to the target ligand construct from the counter-ligand bound to the subtractor ligand construct;

[0110] (viii) exposing the library of (i) to the ligand constructs provided by (v) and (vi) to permit binding of the counter-ligand to the ligand; and

[0111] (ix) separating the counter-ligand bound to the ligand immobilized to or incorporated into the target ligand construct using a separation means.

[0112] This does not imply that the steps of the present invention must be performed in any particular order.

[0113] By "providing a defined amount" is included the meaning of providing a known amount of a ligand, such that the equation of the invention has been used to verify that the provided known amount is suitable for isolating the desired counter-ligand.

[0114] The reaction parameters for a given selection process can be optimized according to the present invention by calculation applying the law of mass action and the equations derived therefrom, and taking into account parameters such as molecular library diversity, counter-ligand copy number, desired upregulation detection limit, desired counter-ligand affinity and ligand concentration.

[0115] In some embodiments of the method of the first aspect, the equation in sub-step (iv) is:

[0116]

[0117] in

[0118] bA = amount of counterligand bound to the receptor

[0119] A = total number of antiligands A

[0120] B = total number of target receptors B

[0121] K d = Anti-ligand affinity (M)

[0122] V = reaction volume (dm 3 )

[0123] N A = Avogadro constant (6.022 × 10 per mole 23 particles)

[0124] or

[0125]

[0126] in

[0127] bA = amount of counterligand bound to the receptor

[0128] A = total number of antiligands A

[0129] B = total number of target receptors B

[0130] K d = Anti-ligand affinity (M)

[0131] V = reaction volume (dm 3 )

[0132] N A = Avogadro constant (6.022 × 10 per mole 23 particles)

[0133] C T = Number of target cells

[0134] C N =Number of non-target cells

[0135] B T =C T The number of receptors

[0136] B N =C N The number of receptors.

[0137] In some embodiments of the method of the first aspect, step (b) of the method comprises performing two or more rounds of bio-panning, three or more rounds of bio-panning, or four or more rounds of bio-panning. Preferably, step (b) comprises performing two or more rounds of bio-panning, more preferably performing three or more rounds of bio-panning, and most preferably performing four or more rounds of bio-panning.

[0138] In some embodiments of the method of the first aspect, at least one differentially expressed ligand is:

[0139] - Highly expressed ligand in target cells, tissues or samples of interest;

[0140] - a ligand moderately expressed by the target cell, tissue or sample of interest; or

[0141] - ligands that are under-expressed by the target cell, tissue or sample of interest;

[0142] In some embodiments of the method of the first aspect, the highly expressed ligand is expressed at more than 1,000,000 copies per target cell of interest, the moderately expressed ligand is expressed at between 100,000 and 1,000,000 copies per target cell of interest, and the lowly expressed ligand is expressed at less than 100,000 copies per target cell of interest.

[0143] In some embodiments of the method of the first aspect, the ligand is not expressed on one of the target construct or the subtractor construct.

[0144] In some embodiments of the method of the first aspect, the method may further comprise the step of releasing the counter-ligand from the ligand.

[0145] In some embodiments of the method of the first aspect, sub-steps (ii) to (ix) are performed in parallel to separate multiple counter-ligands for multiple different ligands. In some embodiments, sub-steps (ii) to (ix) are repeated one or more times.

[0146] In some embodiments of the method of the first aspect, the amount of one of the subtractor construct or the target construct provided exceeds the amount of the other of the subtractor construct or the target construct. In certain embodiments, the ligand excess is between 10-fold and 1000-fold, or between 2-fold and 10-fold, or between 1000-fold and 1,000,000-fold.

[0147] The degree of excess of the subtractor ligand population determines the highest possible "resolution" that will be detected (i.e., how strong is the ability to distinguish between counter-ligands specific for lowly upregulated, moderately upregulated, highly upregulated, or uniquely expressed ligands), as well as how strong is the ability to distinguish between differently expressed ligands. For example, if a library of 100 target ligand-specific counter-ligands is being used and a sufficiently large concentration of positive ligand is added so that all counter-ligands will bind to the ligand at equilibrium, a 10-fold excess of the subtractor ligand population will allow the operator to reduce the frequency of counter-ligands specific for commonly expressed ligands by 90%, while a 200-fold excess (twice the number of counter-ligand-specific binders) will allow the operator to remove common binders (see WO2004 / 023140 for details). Figure 5 , and the data in the last paragraph of Example 4 in the document used to confirm this).

[0148] In some embodiments of the method of the first aspect, the high-throughput sequencing step (c) is performed using 454 sequencing, Illumina, SOLiD method or Helicos system, or those from Complete Genomics and Pacific Biosciences.

[0149] The advent of next generation sequencing has enabled the sequencing of large numbers (1,000s to 1,000,000s) of candidate genes in a high-throughput manner (referred to as "deep sequencing" from here on out).

[0150] 454 sequencing is described by Margulies et al. (2005) (and incorporated herein by reference). In the 454 method, the DNA to be sequenced is fractionated and provided with adapters, or the DNA fragments can be PCR amplified using primers containing adapters. The adapters are 25-mers of nucleotides required for binding to DNA capture beads and annealing of emulsion PCR amplification primers and sequencing primers. The DNA fragments are made single-stranded and attached to the DNA capture beads in a manner that only allows one DNA fragment to attach to one bead. Next, the beads containing the DNA are emulsified in an oil-in-water mixture to obtain a microreactor containing only one bead.

[0151] In this microreactor, the fragments are amplified by PCR to obtain millions of copies of each bead. After PCR, the emulsion is broken and the captured beads are loaded onto a microtiter plate. Each well of the microtiter plate can accommodate only one bead. Sequencerase is added to the wells so that nucleotides flow through these wells in a fixed order. Incorporation of nucleotides causes pyrophosphate to be released, which catalyzes a reaction that produces a chemiluminescent signal. The signal is recorded by a CCD camera and translated into a DNA sequence using software.

[0152] In the Illumina method (Bentley (2008)), single-stranded, adapter-providing fragments are attached to an optically transparent surface and subjected to "bridge amplification". This process produces millions of clusters, each containing a copy of a unique DNA fragment. DNA polymerase, primers, and four labeled reversible terminator nucleotides are added, and the surface is imaged by laser fluorescence to determine the location and nature of these labels. The protecting groups are then removed and the process is repeated for several cycles.

[0153] The SOLiD method (Shendure (2005)) is similar to 454 sequencing, in that DNA fragments are amplified on the surface of beads. Sequencing involves cycles of ligation and detection of labeled probes.

[0154] Several other high-throughput sequencing technologies are currently under development. Examples of such technologies are the Helicos system (Harris (2008)), Complete Genomics (Drmanac (2010)), and Pacific Biosciences (Lundquist (2008)). As this is an extremely rapidly developing field of technology, the applicability of high-throughput sequencing methods to the present invention will be apparent to those skilled in the art.

[0155] In some embodiments of the method of the first aspect, the separation means is selected from at least one of the following: a solid support, a cell membrane and / or a portion thereof, a synthetic membrane, a bead, a chemical tag and a free ligand, or fluorescence activated cell sorting.

[0156] In some embodiments of the method of the first aspect, substep (ix) can be performed by at least one of the following: density centrifugation (Williams and Sharon, 2002), solid support chelation, magnetic bead chelation using beads specific for receptors expressed on a cell population or biotin-specific beads after biotinylation of a cell population (Siegel et al., 1997), chemical tag binding and aqueous phase partitioning, fluorescence activated cell sorting.

[0157] In some embodiments of the method of the first aspect, the counter-ligand library is a display library comprising a plurality of library members displaying counter-ligands. In some embodiments of the method of the first aspect, the counter-ligand display library comprising a plurality of library members displaying counter-ligands is a phage display library, an RNA display library, a ribosome display library, a yeast display library, or a mammalian display library. Preferably, the counter-ligand library is a phage display library.

[0158] In some embodiments of the method of the first aspect, the anti-ligand library is constructed from at least one of the following: antibodies, antigen-binding variants, derivatives and / or fragments thereof; scaffold molecules with engineered variable surfaces; receptors and enzymes. Preferably, the anti-ligand library is constructed from antibodies, antigen-binding variants, derivatives and / or fragments thereof.

[0159] In some embodiments of the method of the first aspect, the library of counter-ligands comprises at least one counter-ligand having a known copy number. In some embodiments of the method of the first aspect, at least one counter-ligand having a known copy number is added to the library of counter-ligands at a known copy number. For example, the library of counter-ligands may comprise sequences encoding commercially available counter-ligands (e.g., commercially available antibodies) that have been added to the library at a known copy number and can be used to generate an enrichment signature obtained experimentally.

[0160] In some embodiments of the method of the first aspect, the counter-ligand library is a pool of counter-ligands generated by previously performed counter-ligand library selection experiments.

[0161] The display of proteins and polypeptides fused to one of the phage coat proteins on the surface of phage provides a powerful tool for selecting specific ligands. This "phage display" technology was first used by Smith in 1985 to create large antibody libraries with the goal of selecting antibodies with high affinity for a specific antigen. More recently, this method has been used to present peptides, protein domains, and complete proteins on the surface of phage in order to identify ligands with desired properties.

[0162] The principle behind phage display technology is as follows:

[0163] (i) cloning a nucleic acid encoding a protein or polypeptide for display into a bacteriophage;

[0164] (ii) the cloned nucleic acid is expressed as a fusion with the coat anchor portion of one of the phage coat proteins (usually the p3 or p8 coat protein in the case of filamentous phages), so that the foreign protein or polypeptide is displayed on the phage surface;

[0165] (iii) Phage displaying proteins or polypeptides with desired properties are then selected (e.g., by affinity chromatography), thereby providing a genotype (linked to a phenotype) that can be sequenced, multiplied, and transferred to other expression systems.

[0166] Alternatively, a phagemid vector (i.e., a vector containing replication origins derived from a phage and a plasmid) can be used to express a foreign protein or polypeptide, which can be packaged as a single-stranded nucleic acid in a phage coat. When a phagemid vector is used, a "helper phage" is used to provide replication and packaging functions for the phagemid nucleic acid. The resulting phage will express both the wild-type coat protein (encoded by the helper phage) and the modified coat protein (encoded by the phagemid), while only the modified coat protein is expressed when a phage vector is used.

[0167] The use of phage display to isolate ligands that bind biologically relevant molecules has been reviewed by Felici et al. (1995), Katz (1997), and Hoogenboom et al. (1998). Several randomized combinatorial peptide libraries have been constructed to select polypeptides that bind to different targets (e.g., cell surface receptors or DNA) (Kay and Paul, (1996)).

[0168] Proteins and multimeric proteins have been successfully displayed as functional molecules by phage (see Chiswell and McCafferty (1992)). In addition, functional antibody fragments (e.g., Fab, single-chain Fv [scFv]) have been expressed (McCafferty et al., (1990); Barbas et al. (1991); Clackson et al. (1991)), and some of the shortcomings of human monoclonal antibody technology have been overcome since the isolation of human high-affinity antibody fragments (Marks et al. (1991), and Hoogenboom and Winter (1992)).

[0169] Further information on the principles and practice of phage display is provided in Phage display of peptides and proteins: a laboratory manual, ed. Kay, Winter and McCafferty (1996), the disclosure of which is incorporated herein by reference.

[0170] The counter-ligand library may be constructed from at least one selected from the group consisting of: antibodies and antigen-binding variants, derivatives or fragments thereof; scaffold molecules with engineered variable surfaces; receptors and enzymes.

[0171] In some embodiments of the method of the first aspect, the ligand is selected from at least one of an antigen, a receptor ligand, and an enzyme target, including at least one of a carbohydrate, a protein, a peptide, a lipid, a polynucleotide, an inorganic molecule, and a conjugated molecule. Preferably, the ligand is a cell surface receptor. More preferably, the cell surface receptor is in its native form.

[0172] In some embodiments of the method of the first aspect, the method further comprises the step of exposing the ligand and the means for separating it to a stimulus that affects the expression of the target ligand on the ligand construct.

[0173] In a second aspect of the present invention, there is provided a method for isolating at least one counter-ligand for at least one differentially expressed target ligand in a target cell, tissue or sample of interest from a counter-ligand library, wherein the method comprises the following steps:

[0174] (a) providing one or more reference enrichment characteristics of counter-ligands present in the counter-ligand library used; and

[0175] (b) providing one or more discovery enrichment characteristics of the counter-ligands present in the counter-ligand library used.

[0176] In a preferred embodiment of the second aspect, the method further comprises:

[0177] (c) matching the one or more reference enrichment signatures provided in step (a) with the one or more discovery enrichment signatures provided in step (b) to isolate at least one counter-ligand for at least one differentially expressed target ligand in a target cell, tissue, or sample of interest.

[0178] In some embodiments of the method of the second aspect, the method further comprises the step of screening for the counter-ligand specificity of the differentially expressed ligand, wherein the screening is performed by:

[0179] (i) after step (b), performing a confirmatory screening step for the counter-ligand specificity of the differentially expressed ligand; and / or

[0180] (ii) After step (c), a confirmatory screening step is performed for the counter-ligand specificity of the differentially expressed ligands.

[0181] In some embodiments of the method of the second aspect, the method of isolating at least one counter-ligand does not include a screening step.

[0182] By "the method for isolating at least one counter-ligand does not include a screening step" is meant that the method for isolating the counter-ligand does not include:

[0183] - Prior to step (b), a screening step for identifying target binding versus non-target binding; nor

[0184] - after step (b), a confirmatory step for screening the counter-ligand specificity of the differentially expressed ligands; nor

[0185] - after step (c), performing a confirmatory screening step for the counter-ligand specificity of the differentially expressed ligand; nor

[0186] - After step (d), a confirmatory screening step is performed for the counter-ligand specificity of the differentially expressed ligands.

[0187] In some embodiments of the method of the second aspect, the one or more reference enrichment signatures provided in step (a) are reference enrichment signatures obtained by computer simulation.

[0188] In some embodiments of the method of the second aspect, the enrichment signature obtained by computer simulation is generated using an equation derived from the universal law of mass action. In some embodiments of the method of the second aspect, the enrichment signature obtained by computer simulation is generated using the following equation:

[0189]

[0190] in

[0191] FA T = Frequency of antibody recovery

[0192] A T =The amount of recovered antibodies

[0193]

[0194] HR = hit rate, the fraction of antibodies that are specific for the target cell (determined experimentally).

[0195] In some embodiments of the method of the second aspect, the reference enrichment signature obtained by computer simulation is a reference enrichment signature of: an anti-ligand for a highly expressed ligand of a target cell, tissue, or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue, or sample of interest; or an anti-ligand for a lowly expressed ligand of a target cell, tissue, or sample of interest.

[0196] In some embodiments of the method of the second aspect, the one or more reference enrichment signatures provided in step (a) are experimentally obtained reference enrichment signatures. In some embodiments of the method of the second aspect, the experimentally obtained enrichment signature is generated from at least one biopanning experiment comprising at least one reference ligand.

[0197] In some embodiments of the method of the second aspect, the reference enrichment signature obtained experimentally is a reference enrichment signature of: an anti-ligand for a highly expressed ligand of a target cell, tissue, or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue, or sample of interest; or an anti-ligand for a lowly expressed ligand of a target cell, tissue, or sample of interest.

[0198] The experimentally obtained reference enrichment signature can be generated from one or more previously performed counter-ligand library selection experiments (e.g., using one or more differential bio-panning steps). The experimentally obtained reference enrichment signature can relate to reference counter-ligands, which can be identified in and isolated from a counter-ligand library by a variety of methods. For example, reference counter-ligands can be identified in and isolated from a counter-ligand library using isolated reference ligands (e.g., purified recombinant proteins), using cells, tissues, or samples enriched for the ligand of interest (e.g., cell lines engineered to overexpress the protein of interest, or cell lines or tissues transformed / transfected with constructs expressing the protein of interest), or using differential bio-panning schemes in which the specific ligand target is not known in advance. Therefore, it is not always necessary to know the identity of the ligand targeted by the reference counter-ligand, and instead, it is usually sufficient to know only the expression level of the target ligand in the target cell / tissue / sample and non-target cells / tissues / samples.

[0199] In some embodiments of the method of the second aspect, two or more reference enrichment signatures are provided in step (a), wherein one or more of the reference enrichment signatures are reference enrichment signatures obtained by computer simulation, and wherein one or more of the reference enrichment signatures are enrichment signatures obtained experimentally.

[0200] In some embodiments of the method of the second aspect, when two or more reference enrichment features are provided in step (a), one or more computer-simulated enrichment features are generated using equations derived from the universal law of mass action. In some embodiments of the method of the first aspect, when two or more reference enrichment features are provided in step (a), the computer-simulated enrichment features are generated using the following equations:

[0201]

[0202] in

[0203] FA T = Frequency of antibody recovery

[0204] A T =The amount of recovered antibodies

[0205]

[0206] HR = hit rate, the fraction of antibodies that are specific for the target cell (determined experimentally).

[0207] In some embodiments of the method of the second aspect, when two or more reference enrichment signatures are provided in step (a), one or more computer-simulated reference enrichment signatures are reference enrichment signatures of: anti-ligands for highly expressed ligands of target cells, tissues, or samples of interest, anti-ligands for moderately expressed ligands of target cells, tissues, or samples of interest; or anti-ligands for lowly expressed ligands of target cells, tissues, or samples of interest.

[0208] In some embodiments of the method of the second aspect, when two or more reference enrichment signatures are provided in step (a), the experimentally obtained enrichment signatures are generated from at least one biopanning experiment comprising at least one reference ligand.

[0209] In some embodiments of the method of the second aspect, when two or more reference enrichment signatures are provided in step (a), the reference enrichment signature obtained experimentally is a reference enrichment signature of: an anti-ligand for a highly expressed ligand of a target cell, tissue, or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue, or sample of interest; or an anti-ligand for a lowly expressed ligand of a target cell, tissue, or sample of interest.

[0210] In some embodiments of the method of the second aspect, the one or more discovery enrichment features provided in step (b) are derived from previously performed counter-ligand library selection experiments.

[0211] In some embodiments of the method of the second aspect, at least one differentially expressed ligand is:

[0212] - Highly expressed ligand in target cells, tissues or samples of interest;

[0213] - a ligand moderately expressed by the target cell, tissue or sample of interest; or

[0214] - ligands that are under-expressed by the target cell, tissue or sample of interest;

[0215] In some embodiments of the method of the second aspect, the highly expressed ligand is expressed at more than 1,000,000 copies per target cell of interest, the moderately expressed ligand is expressed at between 100,000 and 1,000,000 copies per target cell of interest, and the lowly expressed ligand is expressed at less than 100,000 copies per target cell of interest.

[0216] In some embodiments of the method of the second aspect, the counter-ligand library is a display library comprising a plurality of library members displaying counter-ligands. In some embodiments of the method of the first aspect, the counter-ligand display library comprising a plurality of library members displaying counter-ligands is a phage display library, an RNA display library, a ribosome display library, a yeast display library, or a mammalian display library. Preferably, the counter-ligand library is a phage display library.

[0217] In some embodiments of the method of the second aspect, the anti-ligand library is constructed from at least one of the following: antibodies, antigen-binding variants, derivatives and / or fragments thereof; scaffold molecules with engineered variable surfaces; receptors and enzymes. Preferably, the anti-ligand library is constructed from antibodies, antigen-binding variants, derivatives and / or fragments thereof.

[0218] In some embodiments of the method of the first aspect, the library of counter-ligands comprises at least one counter-ligand having a known copy number. In some embodiments of the method of the second aspect, at least one counter-ligand having a known copy number is added to the library of counter-ligands at a known copy number. For example, the library of counter-ligands may comprise sequences encoding commercially available counter-ligands (e.g., commercially available antibodies) that have been added to the library at a known copy number and can be used to generate an enrichment signature obtained experimentally.

[0219] In some embodiments of the method of the second aspect, the counter-ligand library is a pool of counter-ligands generated by previously performed counter-ligand library selection experiments.

[0220] In some embodiments of the method of the second aspect, the ligand is selected from at least one of an antigen, a receptor ligand, and an enzyme target, including at least one of a carbohydrate, a protein, a peptide, a lipid, a polynucleotide, an inorganic molecule, and a conjugated molecule. Preferably, the ligand is a cell surface receptor. More preferably, the cell surface receptor is in its native form.

[0221] In a third aspect of the present invention, there is provided a counter-ligand enrichment signature, wherein the enrichment signature is generated by step (c) of the method according to the first aspect.

[0222] Selected counterligands identified by the method of the first aspect of the invention may then be used to manufacture a pharmaceutical composition for use in medicaments for the treatment, imaging, diagnosis or prognosis of disease.Antiligands based on antibodies, and most importantly on human antibodies, have enormous therapeutic potential.

[0223] Thus, in a fourth aspect of the invention there is provided a method of preparing a pharmaceutical composition comprising, after identifying a counter-ligand having desired properties by a method according to the first or second aspect, adding the counter-ligand to a pharmaceutically acceptable carrier.

[0224] In the fifth aspect of the present invention, a pharmaceutical composition prepared by the method according to the fourth aspect is provided.

[0225] In the sixth aspect of the present invention, a pharmaceutical composition prepared by the method of the fourth aspect is provided, which is used in medicine. The pharmaceutical composition can also be used to manufacture drugs for preventing, treating, imaging, diagnosing or prognosing diseases.

[0226] Other definitions

[0227] By "biopanning" is meant a process for selecting one member from a desired counter-ligand-ligand binding pair based on the ability of one member to bind to the other member with high affinity.

[0228] By "differential biopanning" we mean a biopanning method used to select one member from a desired counter-ligand-ligand binding pair expressed in different amounts in or on two different sources (e.g., a subtractor / control and a target) based on the ability of one member to bind with high affinity to the other.

[0229] The terms "high-throughput sequencing" and "deep sequencing" include the meaning of a sequencing process in which large numbers of sequences are sequenced in parallel (up to millions), making sequencing large numbers of molecules practically feasible and significantly faster and cheaper.

[0230] By "confirmatory screening" we include the meaning of testing isolated counter-ligand pools and / or individual counter-ligand clones for specific ligand binding to the target construct versus the subtractor construct using any assay for ligand / counter-ligand binding (e.g., flow cytometry, FMAT, ELISA, MSD, and CBA). The term also includes the meaning that once a counter-ligand is identified as binding to a differentially expressed ligand, the nature and identity of the ligand, as well as the binding interaction between the counter-ligand and the ligand, is investigated.

[0231] The term "ligand" includes the meaning of a member of a ligand / anti-ligand binding pair. A ligand can be, for example, one nucleic acid strand in a complementary, hybridized nucleic acid duplex binding pair; an effector molecule in an effector / receptor binding pair; or an antigen in an antigen / antibody or antigen / antibody fragment binding pair.

[0232] The term "counterligand" includes the meaning of the opposite member of a ligand / counterligand binding pair. The counterligand can be the other nucleic acid strand in a complementary, hybridized nucleic acid duplex binding pair; a receptor molecule in an effector / receptor binding pair; or an antibody or antibody fragment molecule in an antigen / antibody or antigen / antibody fragment binding pair, respectively.

[0233] The term "antigen" includes molecules or chemical compounds that can interact with antibodies but do not necessarily produce an immune response. Such antigens include, but are not limited to, molecules of proteins, peptides, nucleotides, carbohydrates, lipids, or conjugates thereof.

[0234] By "differentially expressed ligands" is meant those ligands that are expressed at different levels between the target source and the subtractor source, including those ligands that are expressed only under certain conditions / locations and not under other conditions / locations; or where the target ligand or the subtractor ligand is a modified form of the other of the target ligand and the subtractor ligand. For example, some antigens are highly expressed on the cell surface of diseased cells (e.g., cancer cells) but expressed at low levels or not at all on corresponding healthy cells (e.g., non-cancerous cells).

[0235] By "lowly expressed ligand", it is meant a ligand that is expressed at relatively low levels (ie, less than 100,000 copies per cell / tissue / sample), or that occurs at a frequency less than 1% of any other more highly expressed ligand in a sample of a positive ligand population.

[0236] By "moderately expressed ligand," it is meant a ligand that is expressed at a relatively moderate level (ie, from 100,000 copies per cell / tissue / sample to 1,000,000 copies per cell / tissue / sample).

[0237] By "highly expressed ligand", it is meant a ligand that is expressed at relatively high levels (ie, greater than 1,000,000 copies per cell / tissue / sample).

[0238] By "ligand construct" is meant a system comprising a target ligand and / or a subtractor ligand associated with a separation means.

[0239] The term "antibody variant" should be taken to refer to any synthetic antibody, recombinant antibody or antibody hybrid, such as but not limited to: a single chain antibody molecule produced by phage display of immunoglobulin light chain and / or heavy chain variable and / or constant regions, or other immunointeractive molecules known to those skilled in the art that are capable of binding to an antigen in an immunoassay format.

[0240] The term "antibody derivative" refers to any modified antibody molecule known to those skilled in the art that is capable of binding to an antigen in an immunoassay format, such as an antibody fragment (e.g., a Fab or Fv fragment), or by adding one or more amino acids or other molecules to facilitate the binding of the antibody to another peptide or polypeptide, a large carrier protein or a solid support (e.g., the amino acids tyrosine, lysine, glutamic acid, aspartic acid, cysteine ​​and its derivatives, NH 2 The term "antibody derivative" also refers to bispecific antibodies and cells expressing antibodies on their surface (such as chimeric antigen receptor T cells).

[0241] By "density centrifugation" is meant the separation of substances (e.g., cells, organelles, and macromolecules) based on differences in density. This separation is achieved by centrifugation using a density gradient of an appropriate solution, through which the substances being separated move based on their density.

[0242] The "Law of Mass Action" is a universal law of nature that applies in all circumstances. It states that for this reaction:

[0243] aA+bB→cC+dD

[0244] And if the system is in equilibrium at a given temperature, then the following ratios are constants:

[0245]

[0246] in:

[0247] A, B, C, and D = the participants in the reaction (reactants and products)

[0248] a, b, c, and d = coefficients needed to balance the chemical equation

[0249] And where the constant is calculated based on the concentration (indicated by []), the unit of K is M c+d-(a+b) . BRIEF DESCRIPTION OF THE DRAWINGS

[0250] Examples embodying certain aspects of the invention will now be described with reference to the following drawings.

[0251] Figure 1 A schematic overview of the method of the present invention is shown. First, the target biomolecule class of interest is defined by its absolute expression level and relative expression level in the target sample versus non-target sample. The selection is subjected to computer simulation modeling for optimizing the selection reaction parameters, so that antibodies can be enriched according to the affinity (KD) driven binding of the differentially expressed target biomolecules of the defined class, and antibodies are consumed for the small biomolecules expressed at similar levels in the target sample and non-target sample. Second, the optimized reaction parameters are used to enrich antibodies by experiment in four consecutive selections. The resulting phage pool is analyzed by massive parallel sequencing to provide antibody enrichment features, and a small subset of the enriched antibodies is analyzed to determine the score (hit rate) of the target reactive binder after each selection. Third, the hit rate is incorporated into the computer simulation modeling to generate the predicted enrichment features of antibodies for the differentially expressed target biomolecules of the defined class. Finally, the enrichment features of the individual antibody clones obtained by the experiment are matched with the enrichment features of the computer simulation modeling to identify antibodies for the differentially expressed biomolecule class being sought.

[0252] Figure 2 Binding of antibodies to DU145 target cells in flow cytometry is shown. The calibration curve shows the log MFI at saturating antibody concentration versus the log number of receptors quantified using ABC beads.

[0253] Figure 3 Computer simulations to optimize selection reaction parameters using the methods of the invention are shown. The recovery of 10 nM conjugates targeting a receptor expressed at 5,000, 10,000, 50,000, 200,000, or 1,000,000 receptors / target cell and not expressed on non-target cells, or a receptor upregulated 10x, 5x, 2x, or 1x on target cells compared to non-target cells using optimized conditions was calculated. (A) Number of conjugates recovered in selections with non-target cell competition. (B) Number of conjugates recovered in selections without non-target cell competition.

[0254] Figure 4Determined hit rates are shown. Flow cytometric binding analysis shows binding to target and non-target cells. (A) Analysis of clones from selections 1, 2, 3, and 4 with non-target cell competition. (B) Analysis of clones from selections 2, 3, and 4 without non-target cell competition. For selection 1 without competition, the estimated hit rate was 0.09%, the same as selection 1 with competition.

[0255] Figure 5 Show how the prediction feature simulates antibody enrichment based on the absolute and relative expression levels of the targeted receptors. (A) Overview of ten reference receptors used to evaluate the accuracy of the prediction feature, showing the number of receptors on target cells and non-target cells. The selection conditions used in this study were optimized for finding antibodies targeting receptors in the gray area. (B) Prediction features and experimental results. The black line shows the enrichment feature calculated by computer simulation of antibodies targeting reference receptors. The violin plots show the enrichment of antibodies targeting these receptors in the selection with non-target cell competition (violin plots in the upper figure) and in the selection without non-target cell competition (violin plots in the lower figure). The area shown in the "Correlation" section indicates the range of diagnostic and therapeutically relevant receptors, as well as the therapeutic applicability of antibodies targeting these receptors. The area shown in the "Discovery Strategy" section indicates whether antibodies targeting these receptors can be found using conventional screening techniques and / or the methods of the present invention.

[0256] Figure 6 The enrichment characteristics of ten reference receptors in the selection with non-target cell competition calculated by computer simulation are shown. The shaded area indicates the changes of different parameters. (A) Changes in antibody affinity. The dotted line shows the predicted enrichment characteristics of 10nM antibody affinity, and the gray area indicates the predicted enrichment characteristics of 10 times or 1 / 10 concentration antibody affinity (1nM to 100nM). (B) Changes in the number of cell surface receptors. The dotted line shows the predicted enrichment characteristics of the number of cell surface receptors measured according to Table 3, and the shaded area indicates the predicted enrichment characteristics of 2 times or 1 / 2 the number of receptors.

[0257] Figure 7The enrichment of antibodies in four consecutive selections on target cells with non-target cell competition monitored by massive parallel sequencing is shown. (A) Enrichment features show the frequency of antibody sequences after selections 1 to 4. The black line shows the computer simulation prediction features of antibodies targeting receptors expressed at 1,000,000 and 100,000 copies / target cells but not expressed on non-target cells. The antibodies are divided into three groups, targeting receptors expressed at >1,000,000 copies / cell, expressed at 100,000 to 1,000,000 copies / cell, or expressed at <100,000 copies / cell or upregulated receptors. n represents the total number of unique sequences in each group. (B) The frequency of antibodies for different receptor classes after selections 1 to 4. Antibodies classified as "non-enrichment" are predicted not to bind to target cells, or to bind to receptors expressed at similar levels on target cells and non-target cells. (C) The number of unique antibodies for different receptor classes after each selection.

[0258] Figure 8 Figure 2 shows the enriched features in selections with and without non-target cell competition. (A) Low frequency regions in selections with non-target cell competition ( Figure 7 The lower group in A) represents antibodies targeting receptors expressed at <100,000 copies / target cell, but also represents antibodies targeting receptors that are upregulated on target cells compared to non-target cells, as shown by the in silico enrichment signature (solid line). The in silico signature from the selection without non-target cell competition (dashed line) suggests that these antibodies can be further classified under the guidance of comparative analysis of the enrichment signatures from the selections with and without non-target cell competition. The lower graph shows the direction of change in binders to receptors with the indicated expression levels in the selection with non-target cell competition compared to the selection without non-target cell competition. (B) Antibodies classified as targeting receptors expressed at <100,000 copies / target cell ( Figure 7 The lower group in A) has been further divided based on features from selections without non-target cell competition. The violin plots in the upper panel show selections with competition, and the violin plots in the lower panel show selections without competition. Antibodies have been classified as binders of upregulated receptors (decreased in frequency in selections with competition), binders of low-expressing target cell-restricted receptors (increased in frequency in selections with competition), or unclassified (not significantly affected by selections with competition). (C) Antibodies against target cell-restricted receptors with similar expression levels have been classified based on their EC in flow cytometry. 50The values ​​are divided into two groups. The frequency of antibodies in the selected phage pools after selections 1 to 4 is plotted as mean frequency (ppm) ± SEM. The upper graph shows the frequency of antibodies targeting receptors expressed at levels of 1 million to 4 million copies / cell, and the lower graph shows the frequency of antibodies targeting receptors expressed at levels of 0.3 million to 1 million copies / cell. EC 50 Antibodies with EC 50 values ​​< 3 nM (circle data points) occur more frequently than antibodies with EC 50 values ​​> 3 nM (square data points). This difference is significant for antibodies targeting receptors expressed at 1 to 4 million copies / cell (n=22). * indicates p<0.05 by Mann Whitney test (GraphPad Prism-Select 2: p=0.046522; Select 3: p=0.026175; Select 4: p=0.033252).

[0259] Fig. 9 .A subset of antibodies from each enrichment signature group was generated (top panel) and tested for binding to target and non-target cells (bottom panel). The crosses in the bottom panel represent the geometric mean of receptor expression on target and non-target cells. Inset: Antibodies in the <100,000 or upregulated group were further classified as binding to upregulated expressed receptors or restricted to lowly expressed receptors based on comparative analysis of selections with (solid line) and without (dashed line) non-target cell competition. DETAILED DESCRIPTION

[0260] Example

[0261] Example 1 - Derivation of the equations used in the experiments of the examples

[0262] According to the law of mass action, the interaction between the counterligand (A), its target receptor (B) and their complex (AB) is given by the following equilibrium interaction:

[0263]

[0264] The equilibrium dissociation constant or affinity

[0265]

[0266] The equilibrium interaction between (A) and (B) can be described as

[0267] Combined

[0268] in

[0269]

[0270] Total A or B ([A] or [B]) is the sum of free A or B and bound A or B, i.e.

[0271] [A]=[fA]+[bA], and [B]=[fB]+[bA]

[0272] Therefore, in (1), replacing [fA] with [A]-[bA] and [fB] with [B]-[bA] yields

[0273]

[0274] Rearrange it to form

[0275] (K d [bA])=([A][B]-[A][bA])-([B][bA]-[bA] 2 )

[0276] 0=[bA] 2 -([A]+[B]+K d )[bA]+[A][B]

[0277] The solution to this equation is

[0278]

[0279] where negative roots are related roots

[0280]

[0281] The concentration is used instead of the number of antibodies / number of particles per mole (N A ) / volume (V), we get

[0282]

[0283] Or simplified to

[0284]

[0285] in

[0286] bA = amount of counterligand bound to the receptor

[0287] A = total number of antiligands A

[0288] B = total number of target receptors B

[0289] K d = Antibody affinity (M)

[0290] V = reaction volume (dm 3 )

[0291] N A= Avogadro constant (6.022 × 10 per mole 23 particles)

[0292] If target and non-target cells are mixed, the total number of receptors (B) will be:

[0293] B=(B T C T +B N C N )

[0294] in

[0295] C T = Number of target cells

[0296] C N =Number of non-target cells

[0297] B T =C T The number of receptors

[0298] B N =C N The number of receptors.

[0299] The number of counterligands A bound to receptors B on target cells at equilibrium will be equal to the total number of bound counterligands on target and non-target cells multiplied by the ratio between receptors on target cells and the total number of receptors (receptors on both target and non-target cells):

[0300]

[0301] Furthermore, the combination of equations (3) and (4) yields

[0302]

[0303] Since not all antibodies are recovered after selection, the number of recovered antibodies (rA T ) is given by:

[0304]

[0305] in

[0306] E = fraction of counterligand eluted from target cells

[0307] Y = fraction of recovered target cells

[0308] In n- The average copy number of each counterligand in the library was 2,000 and the display level was 10%. Therefore, A in selection 1 was set to 200. In subsequent selections, A was calculated as the number of counterligands recovered in the previous selection (rA T) multiplied by the amplification factor. The amplification factor is determined experimentally (or set to 10,000, 100,000, and 10,000 between selections 1-2, 2-3, and 3-4, respectively, during selection optimization). E and Y are determined experimentally (or set to 0.5 during selection optimization).

[0309] The frequency of recovered counterligands (FA) in the selected phage pool T ) is calculated as follows:

[0310]

[0311] in

[0312] FA T = frequency of recovery of counterligand

[0313] A T = the amount of recovered counterligand

[0314]

[0315] HR = hit rate, the fraction of counterligands that are specific for the target cell (determined experimentally)

[0316] is equal to the total number of counterligands bound to the receptor considered to be relevant.

[0317] Example 2 - Overview of Materials and Methods for the Study

[0318] Research Overview

[0319] The results of this study are presented in Examples 3 to 6 below. This study used a new prediction-based approach that combined in silico modeling of antibody enrichment with iterative experimental antibody display selection and massively parallel sequencing analysis to rationally identify tens of thousands of sequences encoding antibodies to a priori unknown differentially expressed biomolecules that are present at unknown levels in complex biomolecule samples. When applied to a naive phage-displayed human scFv antibody library to select antibodies against surface receptors differentially expressed on cancer cells compared to immune cells, 10 5 The enriched antibody sequences bind to differentially expressed biomolecules. After sampling the subsets and subsequently expressing them in full-length human IgG format, 90% of these antibodies were confirmed to bind to cancer cell-associated receptors that are expressed in a therapeutically and diagnostically relevant range (from a few thousand to millions of receptors per cell). The methods of the present invention are widely applicable to macromolecular libraries that link genotype to phenotype during screening of complex biomolecule populations (e.g., body fluids, immune cells or cancer cells, or pandemic microorganisms).

[0320] The present invention integrates computational modeling with experimental (phage, yeast or ribosome display) antibody selection and massively parallel sequencing to rationally enrich and identify antibodies against a priori unknown biomolecules that are differentially expressed between two samples from large antibody libraries that link genotype to phenotype ( Figure 1 ).

[0321] First, the specificity of the model for the differentially expressed target biomolecule was defined by the hypothetical absolute and relative expression levels of the biomolecule, increasing approximately 10-fold in a continuous manner from the lowest estimate to the highest estimate in both target and non-target samples. The selection reaction parameters were identified by computer simulation modeling of the selection, which enabled the selection reaction parameters to be determined based on the affinity (K) of the antibody to the biomolecule of interest. D ) drives binding to enrich antibodies and, in the case of competitive selection, to deplete tiny antibodies specific for biomolecules expressed at similar levels in target and non-target samples ( Figure 3 ).

[0322] Second, the antibody library is experimentally selected and the retrieved antibody pools are subjected to parallel sequence analysis to provide: (1) the antibody sequences enriched experimentally, (2) their relative enrichment signatures, and (3) the fraction (hit rate) of displayed antibodies that have been enriched in an antibody- and biomolecule-dependent manner.

[0323] Third, the hit rates are incorporated into in silico modeling to generate predicted / reference enrichment signatures for antibodies against the sought class of differentially expressed target biomolecules. The predicted enrichment signatures can be validated experimentally using reference binder sequences of molecules with known expression in target and non-target antigen populations (optional).

[0324] Finally, the experimentally obtained enrichment signatures of individual antibody clones are matched to the predicted enrichment signatures in order to identify antibody sequences (genotypes) encoding specificity (phenotypes) for the sought class of differentially expressed target biomolecules ( Figure 1 ).

[0325] Materials and Methods for Examples 3 to 6

[0326] cell

[0327] Cell lines DU145 (prostate cancer) and Jurkat (clone E6-1, acute T-cell leukemia) were obtained from ATCC and cultured according to the supplier's instructions. DU145 cells were stimulated with 27 ng / mL INF-γ (R&D Systems) for 16 to 24 hours before use.

[0328] Computer simulation optimization selection conditions

[0329] The number of target and non-target cells required to recover and enrich for 10 nM of antibodies directed against (1) a receptor that has ≥5,000 or more copies per target cell and is not expressed on non-target cells, and (2) a receptor that is at least 5-fold upregulated on target cells compared to non-target cells and has ≥200,000 copies was calculated using Equation 6 described in Example 1. The total number of antibodies A in Option 1 was set to 200 because at a 10% display level, n- The average copy number of every kind of antibody in the library is 2,000.In the subsequent selection, A is calculated as the number (rAT) of the antibody recovered in the previous selection multiplied by the amplification factor.Use amplification factors 10,000, 100,000 and 10,000 to calculate respectively between selection 1-2, 2-3 and 3-4, these amplification factors show that can promote the enrichment of the specificity sought, and reduce negligible specificity.The fraction E of the antibody eluted from the target cell and the fraction Y of the target cell recovered are set to 0.5.

[0330] Prediction-guided cell selection

[0331] For selections with non-target cell competition, DU145 target cells were harvested, washed, biotinylated with EZ-Link™ Sulfo-NHS-SS-Biotin (Thermo Fisher Scientific), and subsequently labeled with anti-biotin microbeads (Miltenyi Biotec) according to the manufacturer's instructions. Labeled target cells (10 million, 2.5 million, 5 million, or 5 million cells in selections 1, 2, 3, and 4, respectively) were mixed with approximately 1,000-fold excess of Jurkat non-target cells and then placed on a rocking platform with n- The cells were incubated overnight at +4°C with the scFv phage display library 1 (BioInvent International). The cell-phage mixture was loaded onto a MACS column and then washed extensively. After eluting the target cells from the column, the phage bound to the target cells were recovered and amplified for continuous selection. The phagemid DNA was purified and the gene encoding the scFv was used to produce soluble scFv as previously described (Ljungars et al., 2019) and then subjected to Illumina sequencing.

[0332] In selections without non-target cell competition, phage were incubated with 10 million, 2.5 million, 5 million, or 5 million DU145 cells at +4°C for 4 hours on a rocking platform in selections 1, 2, 3, and 4, respectively. Cells were washed four times with phosphate-buffered saline (PBS) before phage recovery and processing as described above.

[0333] Determine the hit rate

[0334] Supernatants containing 300 to 1,100 ScFvs from each selection were filtered (0.45 μm Millipore) and added at 25 μL / well to a 1:1 mixture of DU145 cells and CellTraceTM CSFE-labeled (Thermo Fisher Scientific) Jurkat cells (50,000 cells in 25 μL PBS + 0.5% BSA per well) and allowed to bind for 1 hour at +4°C. After washing, scFvs bound to live cells were detected using anti-His-AF647 (R&D Systems) (eBioscience TM Fixable Viability Dye eFluor TM 780, Thermo Fisher Scientific) and analyzed by flow cytometry (iQue, IntellicytSartorius FortCyt v 8.0). The hit rate was determined as the fraction of analyzed clones with a mean fluorescence intensity on target cells at least three times that of the isotype control.

[0335] In silico prediction of enrichment features

[0336] The in silico predicted enrichment signature, defined as the expected antibody frequency observed in four consecutive rounds of selections targeting receptors with different expression profiles at 10 nM, was calculated using equations 6 and 7 described in Example 1. The total number of antibody A in selection 1 was set to 200 because at a 10% display level, n- The average copy number of each antibody in the library is 2,000. In the continuous selection, A is calculated as the number of antibodies recovered in the previous selection (rAT) multiplied by the amplification factor (determined experimentally). The fraction E of antibodies eluted from target cells and the fraction Y of target cells recovered are determined experimentally. The binding of phage antibodies to biomolecules expressed in the entire expression range determined experimentally (5×103 to 4×106 copies / target cell) is modeled, assuming that the same median affinity (KD=10nM) exists in the unselected original antibody library and the same number of antibodies specific for different classes of biomolecules.

[0337] Sequence library preparation and Illumina sequencing

[0338] Phagemid DNA was purified from the enriched phage pool using the QIAprep Spin Miniprep kit (Qiagen). One-step PCR was performed using PfuUltra II Fusion HSDNA polymerase (Agilent) to amplify scFv encoding genes from phagemid DNA and attach Illumina adapters and indexes to the samples. The reaction volume was 50 μl / sample, containing 50 ng template and 0.2 μM of each primer. The following two different primer pairs were used to amplify samples for MiSeq sequencing; the first pair covered CDR-H1, CDR-H2, and CDR-H3, and the second pair covered CDR-L1, CDR-L2, CDR-L3, and CDR-H3. The primer pair covering CDR-H3 was used to amplify samples for NextSeq sequencing. The reverse primer included a 10 bp index sequence to facilitate multiplexing. The primer sequences used are provided in Table 2. PCR amplification was performed under the following conditions: 95°C / 2 min; 95°C / 20 s, 62°C / 30 s, 72°C / 30 s, 12 cycles; followed by 72°C / 3 min. PCR products were purified from 2% agarose gel (MinElute Gel Extraction Kit, Qiagen) and quantified (Qubit TM dsDNA HS Assay Kit, Thermo Fisher Scientific), purity and size were analyzed on an Agilent 2100 Bioanalyzer using the Agilent 1000 DNA Kit and Agilent 2100 Expert Software (Version B.02.08.SI648 (SRI)). The concentration of the pooled sequence library was measured using the KAPA Library Quantification Kit Universal qPCR Mix (Roche, KK4824).

[0339] Table 2: Primer sequences

[0340]

[0341] Upper case letters = sequencing adaptors; lower case letters = scFv primers.

[0342] Samples for MiSeq were mixed with 10% PhiX added on a flow cell (Illumina MiSeq Reagent Kit v3 (600 cycles)) and sequenced on MiSeq to a median depth of 8.5 million usable reads / sample using paired-end reads. Samples for NextSeq were mixed with 25% PhiX added on four flow cells (Illumina NextSeq 500 / 550 High Output Reagent Kit v2.5 (300 cycles)) and sequenced on Illumina NextSeq 500 to a median depth of 25 million usable reads / sample using single reads. Base calling and multiplex demultiplexing were performed using bcl2fastq version 2.20.0.422, and the quality of the resulting fastq files was checked using fastQC version 0.11.8 with default settings. For each sequence, the number of reads was normalized to the total number of reads in the corresponding pooled library. Sequences with fewer than two reads were omitted from further analysis.

[0343] Identification of reference receptors covering the expression range of diagnostically and therapeutically relevant targets

[0344] Ten cell surface receptors with different gene expression profiles on target cells (DU145) and non-target cells (Jurkat) were identified by searching the Encyclopedia of Cancer Cell Lines (Broad Institute, Encyclopedia of Cancer Cell Lines, 2019) and literature research (Liu et al., 2000) (see Table 3). Cell surface expression of these cell surface receptors was measured by flow cytometry using labeled antibodies (see Table 4) and quantitative beads (Bang Laboratories, 815B) according to the manufacturer's instructions.

[0345] Table 3: Number of receptors expressed on target and non-target cells experimentally determined using flow cytometry .

[0346] Receptors Target cell expression (receptor / cell) Non-target cell expression (receptor / cell) ICAM-1 4,700,000 Below detection limit CD44 400,000 Below detection limit EGFR 200,000 Below detection limit HER2 50,000 Below detection limit ROR1 30,000 Below detection limit CD40 10,000 Below detection limit CD130 8,000 Below detection limit CD55 200,000 20,000 CD71 200,000 100,000 CD59 400,000 100,000

[0347] Table 4: Labeled Antibodies / Recombinant Reference Receptors

[0348]

[0349]

[0350] Generation of antibodies targeting reference receptors

[0351] n from cell selection 2 with non-target cell competition Libraries or amplified phage were used for selection against recombinant reference receptors (Table 4) using polystyrene beads (Polysciences, 17175) coated with 25 pmole / bead, 4 beads / receptor as described previously (Ljungars et al., 2019). The library was selected at the beginning, and the second selection was performed on the recombinant protein, followed by the third selection on DU145 cells. The phage-bound antibodies were converted into soluble scFvs, expressed, and then analyzed by flow cytometry as described above to determine the hit rate. The binding of ScFvs bound to DU145 cells to the corresponding receptors was analyzed in ELISA. The reference receptors (Table 4) were coated on the plates overnight at +4°C. The next day, the scFv supernatant was diluted 1:4 in PBS containing 0.05% Tween 20 and 0.45% fish gelatin (both from Sigma-Aldrich) and allowed to bind to the washed ELISA plates for 1 hour at room temperature. The bound scFv was detected using an AP-conjugated anti-FLAG M2 antibody (Sigma-Aldrich), followed by the addition of a luminescent substrate (CDP StarEmerald II, Thermo Fisher Scientific) and the plate was read in a plate reader (TecanUltra using Tecan Magellan v.3.0). All receptor binding clones were selected, grown overnight in 96-well microtiter plates, and then subjected to Sanger sequencing.

[0352] Prediction-guided antibody classification

[0353] The antibody frequencies (F) from the beginning to the end of these sequential selections were obtained from the NextSeq data. S1 、F S2 、F S3 、F S4 ). Antibodies are classified in three steps.

[0354] First, if F S2 >F S1 And F S2 and F S3 >0, the antibody was classified as a binder using features from selection with non-target cell competition. Antibodies that did not meet these criteria were classified as non-enriched antibodies.

[0355] Second, the antibodies in the binder panel were divided into three binder types, again based on selection with non-target cell competition. S4 Comparisons to the predicted signatures of binders with 10 nM affinity for a receptor expressed at 100,000 and 1,000,000 copies / target cell but not on non-target cells (1, 3 ppm and 750 ppm, respectively) were used to guide classification.

[0356] 1) >1,000,000 (antibodies with frequency features higher than 1,000,000 predicted features) - Inclusion criteria:

[0357] FS4 >750ppm

[0358] 2) 100,000-1,000,000 (antibodies with frequency features between 100,000 and 1,000,000 predicted features) - inclusion criteria: 1,32ppm <F S4 <750ppm

[0359] 3) <100,000 (antibodies with frequency features below 100,000 predicted features) - Inclusion criteria:

[0360] F S4 <1,32ppm

[0361] Third, antibodies classified as <100,000 were further classified by comparing features from selections with and without non-target cell competition.

[0362] 1) Upgrading the inclusion criteria

[0363] Or, if there is a competing F S4 =0; no competition F S4 >0 and

[0364] 2) Low expression, limited target cells - inclusion criteria F S1 ≠0 and F S2 、F S3 、F S4 =0(all

[0365] No non-target cell competition)

[0366] 3) Unclassified – does not meet any of the above criteria.

[0367] Preparation of IgG

[0368] The complete scFv sequences required for clonal synthesis were obtained from Miseq data. Briefly, VH and VL + CDR-H3 sequences were combined based on CDR-H3 sequences. In cases where one CDR-H3 was associated with more than one set of VH and / or VL sequences, the frequencies in both libraries were used to join the correct VH / VL pair. Synthetic antibody genes (Twist Bioscience) were ligated into a vector containing genes encoding the heavy and light chain constant regions of human IgG1 antibodies, produced in HEK293 cells, and then purified as previously described (Ljungars et al., 2018).

[0369] Confirmatory binding assays for prediction-guided antibodies

[0370] Purified IgG was diluted to 100 μg / mL, titrated in 25 μL PBS+0.5% BSA, and then added to 50,000 DU145 (target) and Jurkat (non-target) cells / well, and allowed to bind at +4°C for 1 hour. After washing, the anti-human Fc antibody (Jackson Immunoresearch, 109-136-098) conjugated with APC was used to detect the IgG bound to live cells together with a live / dead cell marker (SYTOXgreen, Thermo Fisher Scientific), and then analyzed by flow cytometry (iQue, Intellicyt Sartorius, using FortCyt v 8.0). In order to generate a calibration curve to convert the MFI during saturated binding into the number of receptors, a subset of IgG with different signal intensities at saturated cell binding concentration was selected for determining the number of receptors. Alexa Fluor 647 carboxylic acid succinimidyl ester (ThermoFisher Scientific) was used to label purified IgG according to the manufacturer's instructions with AF647. The labeled antibodies were used to determine the number of receptors ( Figure 2 The assay detection limit (number of receptors for isotype control) was 1,000 receptors / cell.

[0371] Quantitative and statistical analysis

[0372] Statistical analysis was performed using GraphPad Prism 9 and by Mann-Whitney test, with *P ≤ 0.05 ( Figure 8 C).

[0373] Example 3 - Based on the absolute and relative expression levels of the targeted receptors, the predicted enrichment signatures accurately Enrichment of mock binders .

[0374] The method of the present invention was used to screen a large (>1010 members) naive human antibody library (Soderlind et al., 2000) for antibodies against cell surface receptors that were differentially expressed between two cell types - DU145 prostate cancer (target) cells and Jurkat T (non-target) cells.

[0375] In the first step, classes of differentially expressed surface receptors covering a wide dynamic expression range were defined, spanning thousands (several 1,000s) to millions (several 1,000,000s) of receptors per cell. Any receptor upregulated 5-fold or more on target cells compared to non-target cells was considered to have potential therapeutic or diagnostic value. Therefore, selective response parameters were sought that would enrich for the relevant antibody binders to receptors upregulated by more than 5-fold, and remove or deselect the minor binders on target cells compared to non-target cells to receptors upregulated by less than 5-fold. Computer simulation modeling identified the responses that should elicit responses against receptors within this range (10 3 to 10 6 receptors / cell) and is upregulated at least 5-fold on target cells relative to non-target cells. D Selection scheme for enrichment of antibodies ≤10 nM ( Figure 3 A). The resulting scheme involves applying positive and negative selection pressures in the form of target cells and excess non-target cells, respectively. Modeling suggests that in the absence of negative selection pressure, selection will fail to reduce tiny antibodies specific for receptors that are either equally expressed or upregulated <5-fold on target cells compared to non-target cells, and will bias enrichment toward highly expressed rather than differentially expressed receptors ( Figure 3 B).

[0376] Four rounds of experimental selection (i.e., selection with or without competition) were performed according to the above-mentioned computer simulation optimization scheme. It is believed that the enrichment features based on four (compared to fewer) data points generated using selection with or without competition will help distinguish antibodies against different types of differentially expressed receptors, for example, differentially expressed receptors whose expression is restricted to target cells but whose expression levels are different (restricted receptors), and differentially expressed receptors that are upregulated on target cells, i.e., also expressed on non-target cells (upregulated receptors). After converting the phage antibodies into scFv antibody format, the fraction (i.e., hit rate) of phage antibodies enriched in an antibody- and target cell receptor-dependent manner in each round of selection was determined by flow cytometry ( Figure 4 ).

[0377] Using the experimentally determined hit rate and a fixed K equal to 10 nM D Value, according to antibody K DIn silico modeling of cell receptor-specific phage antibody enrichment was performed based on equilibrium binding driven by the classified receptors. To experimentally validate the predicted enrichment signatures, candidate reference cell surface receptors with expression profiles matching the classified receptors were identified by probing the genes encoding cell surface receptors differentially expressed in target cells compared to non-target cells in the Broad Institute Cancer Cell Line Encyclopedia (CCLE). Using commercially available antibodies and ABC quantitative beads, flow cytometry-based analysis confirmed that ICAM-1, CD44, EGFR, HER2, ROR1, CD40, CD130, and CD55 were differentially expressed on target cells compared to non-target cells, while CD59 and CD71 represented receptors that were upregulated <5-fold on target cells relative to non-target cells ( Figure 5 A, Table 3). The number of molecules of restricted receptors per target cell spans 10 per DU145 target cell. 3 receptors (CD130), 10 4 receptors (CD40, ROR1, HER2), 10 5 receptors (CD44, EGFR), up to 10 6 The upregulated receptors CD55, CD59, and CD71 were similarly expressed on target cells (2 to 4 × 10 5 receptors / cell), but upregulated >5-fold (CD55, 10-fold) and <5-fold (CD59, 4-fold; CD71, 2-fold) on target cells compared with non-target cells ( Figure 5 A, Table 3). Antibodies against each reference receptor and their coding sequences (n=4 to 69) were identified by using a selection of recombinant extracellular domains of each reference receptor (see Example 2), enabling the assessment of the enrichment profile of antibodies against biomolecules with defined expression differences between target and non-target cells.

[0378] The experimental enrichment of antibodies specific for reference receptors was monitored by massively parallel sequencing of antibody pools obtained by experimental cell-based selection and compared with results predicted by in silico modeling ( Figure 5 B) When according to K D = 10 nM When modeling the antibody-receptor equilibrium, a strong correlation was observed between the in silico predicted median frequencies of antibodies against differentially expressed reference receptors and the experimentally determined median frequencies ( Figure 5 B) This value corresponds well to the n- The median affinities of antibodies isolated from the antibody library for a variety of different cell surface receptors of different types (Roghanian et al., 2015; Fransson et al., 2006; Schiopu et al., 2007). A similarly strong fit between the frequencies predicted by computer simulations and the frequencies determined experimentally was observed for the selection reactions with and without non-target cell competition ( Figure 5 B). Notably, the characteristics of antibodies against lowly expressed target cell restricted receptors (CD40 and CD130) and an upregulated receptor (CD55) were very similar in selections with non-target cell competition but very different in selections without competition.

[0379] Since selecting antibodies from a highly diverse library will generate receptor-specific antibodies of varying affinities, antibody enrichment was additionally modeled based on 10-fold affinity (1 nM) or 1 / 10 affinity (100 nM). Although the enrichment profiles were similar overall, the modeled antibody frequencies increased with increasing affinity and decreased with decreasing affinity ( Figure 6 A). The lower the expression of the targeted receptor, the more sensitive the enrichment signature is to antibody affinity. This correlates with the larger frequency changes observed for antibodies targeting low expressed receptors (CD130, CD40, ROR1, HER2) compared to antibodies targeting high expressed receptors (ICAM-1, CD44, EGFR) ( Figure 5 B). To assess how possible errors in receptor quantification might affect antibody enrichment profiles, enrichment was modeled based on 2-fold or 1 / 2 the amount compared to the experimentally determined amount. This mainly affected the upregulated receptors (CD71, CD59, CD55; Figure 6 B) Characteristics of the antibodies.

[0380] Overall, in silico modeling of target receptor-specific antibody enrichment closely mirrored experimental antibody enrichment, which was driven by both the absolute and relative expression levels of the targeted receptor on target and non-target cells ( Figure 5 B).

[0381] Example 4: Enrichment profiles show that tens of thousands of antibodies against receptors are differentially expressed over a wide dynamic range .

[0382] The fact that this approach accurately mimics the enrichment of reference antibodies against receptors expressed over a wide dynamic range supports its potential utility as a discovery tool for identifying antibodies against targets associated with diverse therapeutic and diagnostic potentials ( Figure 5 B). The value of such a discovery tool will depend largely on the number of unique binders to differentially expressed targets generated by the technology. To assess this, the in silico optimized and selected antibody pools were queried for antibody sequences whose enrichment profiles matched the in silico predicted signatures of antibodies specific for different classes of therapeutically or diagnostically relevant receptors ( Figure 5 B). Binders developed for diagnostic purposes do not rely on functional modulatory properties and can therefore be specific for any differentially expressed receptor, regardless of expression level. Similarly, antibodies that rely entirely on blocking ligand-receptor signaling (e.g., anti-IL-6R) (Sebba, 2008) are likely to be specific for receptors expressed over a wide dynamic range. In contrast, Fc-dependent and enhanced therapeutic antibodies that mediate, for example, ADCC and CAR-T cell specificity require low (Fc-dependent) or no (enhanced) receptor expression on key normal cells and tissues due to their potent cytolytic properties. Therefore, antibodies with the following enrichment features were quantified by analyzing the frequency of individual antibody sequences in the eluted phage-antibody pools in each of the four rounds of experimental selection: These enrichment feature matches were predicted to represent specificity for (1) > 10 6 molecules / target cell, (2) 10 5 to 10 6 molecules / target cell or (3) <10 5 Restricted receptors that were expressed by 1 molecule / target cell or were upregulated >5-fold on target cells compared to non-target cells had specific enrichment signatures.

[0383] Analysis of complete sequence datasets indicates that there are tens of thousands of antibodies targeting any one receptor class ( Figure 7 A). Feature-guided analysis showed that antibodies specific for the most highly expressed receptors dominated the binder pool after two or more rounds of panning (10 2 ppm to 10 5 ppm / sequence), whereas antibodies specific for lower expressed target cell restricted or upregulated receptors are scarce (10 -2 ppm to 10ppm / sequence)( Figure 7 A to Figure 7 B) Interestingly, in contrast to their low frequencies, the same analysis showed that the number of low-expressing antibody clones specific for upregulated or target cell restricted receptors exceeded 10 4 indivual( Figure 7 C).

[0384] The data presented above support the view that the methods of the present invention enable the discovery of antibodies against receptors within a medically relevant expression range, including both under-expressed receptors and up-regulated receptors. Figure 7Importantly, the observation that antibodies against lowly expressed and upregulated receptors are present at very low frequencies in the selected pools (only one or less than one clone per million) is consistent with the observed shortcomings of existing methods for generating such antibodies. In the absence of comprehensive computational modeling and reliable enrichment signatures identified in data generated by massively parallel sequencing, robust identification of these specificities would require the production of millions of antibody clones and labor-intensive cell-based screening of them.

[0385] Example 5: Prediction enables the prioritized discovery of binders with different therapeutic and diagnostic potentials .

[0386] For the group with the lowest frequency but the largest number ( Figure 7 A, lower group) showed that, in addition to antibodies against low-expressing (10 3 to 10 5 In addition to antibodies to moderately to highly expressed (10 5 to 10 6 receptors / cell) up-regulates a receptor (e.g., CD55) Figure 5 B). As discussed above, these two target expression profiles are associated with the therapeutic potential of enhancing and function-blocking antibodies, respectively. Therefore, depending on the intended therapeutic modality, it makes sense to pay special attention to either type of antibody specificity.

[0387] The observations in Example 4 above indicate that the enrichment profiles of antibodies against low-expressing constrained and upregulated receptors are similar in selections with non-target cell competition, but are significantly different in selections without competition ( Figure 5 B). This suggests that comparative analysis of enrichment signatures generated from selections with and without competition could potentially be used to distinguish sequences encoding antibodies with therapeutic potential as either enhancing antibodies (directed against restricted receptors) or function-blocking antibodies (directed against upregulated receptors). Supporting this notion, in silico modeled enrichment signatures showed that in selections with off-target cell competition, the frequency of antibodies directed against upregulated receptors decreased compared to selections without off-target cell competition, and that the frequency of antibodies directed against restricted receptors increased or remained unchanged, depending on the expression level ( Figure 8 A). These observations are consistent with increased negative selection pressure in the form of non-target cells that promote binder retrieval, increase selectivity for target cells compared with non-target cells, and reduce binders specific for commonly expressed weakly upregulated receptors on target cells, as shown in computer simulation modeling ( Figure 3 ) and verified by flow cytometry analysis ( Figure 4 ).

[0388] The comparative analysis was extended to include a total of 94,429 sequences indicated by enriched features to encode antibodies against lowly expressed, restricted or upregulated receptors ( Figure 7 A, lower group), which allowed a subset of these antibodies to be classified into either category. Thus, 13,709 antibodies specific for low-expressed, restricted receptors (increasing in frequency) and 753 antibodies specific for upregulated receptors (decreasing in frequency) were shown ( Figure 8 The remaining antibodies were less significantly affected by competitive selection ( Figure 8 B; n = 79,967).

[0389] Example 6: Predictive discovery of an unparalleled number of antibodies with diverse therapeutic and diagnostic potential .

[0390] Thus, the collective data demonstrate that prediction-guided selection has generated a highly diverse pool of antibodies targeting a priori unknown, differentially expressed, surface receptors expressed over a wide dynamic range, and that enrichment signatures can be used to identify an unparalleled number (approximately 10 5 , compared to about 10 3 ) against therapeutically and diagnostically relevant receptors. To address its relevance to such antibody discovery, a subset of antibody sequences indicated by the enrichment signature (n=102) was selected to encode the following antibodies for production in full-length IgG format: 6 molecules / cell (n=8), (Group 2) 10 5 to 10 6 molecules / cell (n=16), (group 3) <10 5 molecule / cell expressed or upregulated on target cells (n=67), or (Group 4) antibodies directed against receptors upregulated <5-fold on target cells, or antibodies lacking specificity for target cell receptors at all (n=11) ( Fig. 9 , upper panel). After expression and purification, the number of antibody epitopes (i.e., expression of the targeted receptor on target and non-target cells) was quantified by flow cytometry using ABC beads ( Fig. 9 , lower panel). Consistent with the ability of the methods of the invention to generate an unparalleled number of antibodies against differentially expressed surface receptors, 93% (85 / 91) of the antibodies in Groups 1 to 3 were confirmed to be specific for differentially expressed receptors on target cells. Equally important and demonstrating the value of the prediction-guided approach, 9 / 11 (82%) antibodies were significantly enriched between rounds 1 and 2 of selection, but the enrichment profile indicated a lack of specificity for medically relevant receptors, and these antibodies were confirmed to be negative for binding to target cells.

[0391] Furthermore, consistent with the enrichment features informing antibody specificity, the experimentally determined number of receptors targeted by the antibodies on target cells correlated highly with the predicted number. 6 The target cell expression was determined to be 1.4 × 10 6 (7.3×10 5 to 2.6×10 6 )(geometric mean (95% confidence interval)). Group 2 is predicted to have an expression level between 10 5 with 10 6 The number of receptors between 10 and 20 was determined experimentally to be 3.6 × 10 5 (1.0×10 5 to 1.3×10 6 ). Finally, prediction group 3 combined with expression level <10 5 of restricted receptors, or upregulated receptors.

[0392] To help separate these different classes of antibodies, we performed a comparative analysis of group 3 enrichment features from selections with and without off-target cell competition. This dataset contains sequences that showed an increase (28 / 67) or decrease (7 / 67) in frequency in the presence of competitive selection compared to the absence of competitive selection ( Fig. 9 , inset). Consistent with these respective enrichment signatures informing antibody specificity against upregulated and restricted, low expressed receptors, respectively, the number of epitopes experimentally determined on target and non-target cells for antibodies with predicted specificity for upregulated receptors was 5.4 × 105 (1.4 × 105 to 2.2 × 106) and 1.3 × 104 (2.3 × 103 to 7.9 × 104), while the number of epitopes for antibodies with predicted specificity for restricted, low expressed receptors was 3.0 × 104 (1.3 × 104 to 7.1 × 104), as well as undetermined because 22 / 28 antibodies in the panel bound to receptors expressed below the limit of detection (limit of detection = 1 × 103 receptors / cell) on non-target cells ( Fig. 9 ,illustration).

[0393] However, overall, antibody specificity can be predicted by the enriched features, while the specificity of some individual antibodies deviates from the prediction ( Fig. 9). For example, only Group 1 (left panel) was predicted to contain antibodies specific for a receptor expressed at >1 million copies / target cell and not expressed on non-target cells. However, all groups contained fractions of antibodies with this expression profile. Since the modeling data indicated that antibodies with different affinities for the same receptor (or receptors with the same expression level) were differentially enriched, and the naive antibody library contained antibodies with different affinities, we next analyzed how antibody affinity affects the antibody enrichment signature. Antibodies with similar defined epitopes (i.e., similar receptor expression levels on target cells) were enriched according to their EC binding to endogenously expressed receptors. 50 Values ​​>3 nM or <3 nM were divided into two groups and their enrichment during selection was compared. Figure 6 Consistent with the modeling shown in A, we found that for both monitored target expression levels (1 million to 4 million copies / cell, n=22, and 0.3 million to 1 million copies / cell, n=10), the EC 50 The frequency of antibodies with values ​​< 3 nM was higher than that of EC 50 For antibodies with 1 to 4 million copies of the receptor per cell, this difference was statistically significant (p<0.05) after selections 2, 3, and 4 ( Figure 8 C).

[0394] Overall, the methods of the invention enable the discovery of an unparalleled number of antibodies directed against differentially expressed surface receptors of varying potential therapeutic or diagnostic value.

[0395] Discussion of the results in Examples 3 to 6

[0396] This study describes the use of a novel selection and screening method that enables the rational identification of antibodies against a priori unknown differentially expressed cell surface receptors. Compared to previously described techniques, this method identifies an order of magnitude greater number of antibodies that have been shown to bind receptors expressed over a wide dynamic range, including low-expressing, restricted receptors. Thus, prediction-based discovery overcomes several limitations of selection and screening methods in the art.

[0397] The methods of the present invention are particularly suitable for phenotypic discovery (PD) of biologics (for a review of PD, see Moffat et al., 2017). In PDD, small molecules or antibodies from a macromolecular library are screened for functional activity (e.g., inhibition of proinflammatory cytokine release or induction of tumor cell death) without prior knowledge of their molecular targets. PDD is therefore able to discover the most functional molecules and antibodies against specific disease-associated pathways across multiple receptors and epitopes. PDD is a well-validated strategy for first-in-class small molecule drug discovery (Swinney, 2013; Swinney and Lee, 2020) and has been used to identify several first-in-class antibodies and their associated targets currently in clinical development, such as CD52 (Waldmann et al., 1984), ICAM-1 (Veitonmaki et al., 2013), CD32b (Roghanian et al., 2015; Ljungars et al., 2018), TNFR2 (Williams et al., 2016). By combining the ability to identify large numbers of antibodies against a broad range of surface receptors through prediction-based discovery with appropriate functional screening (Ljungars et al., 2018; Chandrasekaran et al., 2021) and recently described high-throughput CRISPR-based approaches for target deconvolution (Mattsson et al., 2021), biologics PDD can be taken to the next level, directly comparable to small molecule PDD.

[0398] The key feature of the method of the present invention related to these two types of applications of diagnosis and treatment is its ability to identify antibodies against low-expression disease-related molecules. Antibodies against low-expression tumor-restricted antigens and rare disease-related conformational epitopes may have significant therapeutic potential when developed as enhanced biological preparations. Regarding diagnosis, liquid biopsies are easy to sample, but usually contain only ultra-low concentrations of biomarkers. Therefore, the technology that helps identify and quantify rare disease-related biomarkers helps to find early diagnosis and personalized medicine. The observation that antibodies against low-expression receptors and up-regulated receptors in the selected pool exist at very low frequencies (only one or less than one clone per million) in this study is consistent with the shortcomings of the existing methods for generating such antibodies observed. In the absence of comprehensive computational modeling and reliable enrichment features identified in the data generated by massive parallel sequencing, robust identification of these specificities will require the production of millions of antibody clones and labor-intensive cell-based screening. Antibodies against low-expression receptors are also valuable in diagnosis.

[0399] Although in this study, prediction-based discovery was applied to identify antibodies that distinguish one cell type from another, it is broadly applicable to other complex antigenic systems, for example, blood, urine, cerebrospinal fluid (Cortese et al., 1996), tissues (Larsen et al., 2015), bacteria (DiGiandomenico et al., 2012), or viruses (van der Brink et al., 2005) associated with a variety of different inflammatory diseases, immune diseases, neurological diseases, infectious diseases, and cancers. Another highly relevant application of the present invention is the identification of antibodies against key virulence factors, for example, adhesion glycoproteins of pandemic microorganisms (Bertoglio et al., 2021) and their receptors on host cells. The identification of such antibodies and their associated molecular targets has the potential to generate passive (antibody-based) and active (vaccine) immunotherapies to help treat and prevent infections with drug-resistant microorganisms.

[0400] Another advantage of prediction-based discovery relative to other discovery approaches (e.g., gene expression-based or proteomics-based approaches) is the parallel discovery of target molecules and candidate therapeutic or diagnostic antibodies against these target molecules, which can be complexes or have processed properties, such as oxidized low-density lipoprotein (Schiopu et al., 2007; Lehrer-Graiwer et al., 2015; Li et al., 2013), and can include disease-associated epitopes and conformations.

[0401] The key application of the ability to support the discovery of prediction-based generation of a comprehensive set of binders for differentially expressed molecules involves integrating computer simulation modeling with experimental selection. First, computer simulation modeling is used to establish the selection reaction parameters required for achieving robust enrichment of related binders and deselection of minor binders without knowing the actual binder specificity or abundance of target molecules. This is achieved by simulating the enrichment of hypothetical binders, which are defined by their specificity for molecules with different absolute expression levels and relative expression levels in two complex antigen populations (e.g., sick blood, serum or cells compared to healthy ones). As modeled in this study and verified by experiments, insufficient positive selection pressure will lead to a reduction in the number of binders retained, and there is a risk of exhausting binders to reduce molecular expression (data not shown). On the other hand, insufficient reduction of selection pressure will generate the highest expressed binders, rather than the strongest differentially expressed binders (i.e., disease-associated molecules) ( Figure 2 Retrospective application of in silico modeling of the previously described differential selection regime was consistent with insufficient positive and subtractive selection pressures underlying the poor quality and low number of observed binders (data not shown).

[0402] Second, the method of the present invention is to integrate computer simulation modeling with experimental difference selection, so that it is possible to find an unparalleled number of binding substances with different treatment and diagnosis potentials. Specifically, the score (that is, the binding substance hit rate) of the binding substance enriched in an antigen-specific manner is determined by experiment. Then, by carrying out computer simulation modeling to the binding substance score of antigen-specific enrichment according to the presence or absence of target molecule abundance and competitive selection, the enrichment characteristics of the classification binding substances are generated, as defined above by their specificity for molecules with different absolute expression levels and relative expression levels in two antigen populations. By matching the enrichment characteristics of each binding substance sequence identified by experiment with the reference enrichment characteristics of computer simulation modeling, it is possible to identify the binding substances for these molecules with high accuracy (90%) in some differentially expressed molecules of an unparalleled number (about 100,000). This same method allows binding substances to be reliably prioritized according to indicated treatment and diagnostic purposes (such as naked IgG or enhancing IgG).

[0403] In this study, the equations presented are used to simulate in silico optimization of selection reactions and to generate predicted reference enrichment features for identifying (experimentally enriched) antibody sequences encoding specificity for therapeutic and diagnostic related cell surface receptors. However, modeling can also be used to simulate antibody enrichment for selections that have already been performed. Retrospective application of in silico modeling of our differential selection schemes and other previously described differential selection schemes is consistent with the lack of positive selection pressure and subtractive selection pressure that constitute the basis for the observed limited quality and quantity of antibodies. Therefore, by reanalyzing the binding enrichment features, and / or by indicating the benefits of additional (optimized) selection rounds in the presence or absence of competition, predictions can help "save" existing antibody pools generated using precious patient-derived materials.

[0404] In summary, and with particular reference to PDD, prediction-based discovery shifts the current bottleneck from identifying large numbers of antibodies to summing differentially expressed disease-associated biomolecules to developing efficient antibody production and high-throughput clinically predictive functional assays that allow screening of thousands (tens of thousands) of clones.

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[0484] Embodiments of the present invention will now be described in the paragraphs numbered below:

[0485] 1. A method for isolating at least one counter-ligand against at least one differentially expressed target ligand in a target cell, tissue or sample of interest from a counter-ligand library, wherein the method comprises the following steps:

[0486] (a) providing one or more reference enrichment characteristics of the counter-ligand library used;

[0487] (b) performing one or more rounds of differential biopanning on the counter-ligand library to generate a counter-ligand pool;

[0488] (c) performing high-throughput sequencing on the counter-ligand pool generated during step (b) to generate a discovery enrichment signature for each counter-ligand in the counter-ligand pool.

[0489] 2. The method according to paragraph 1, wherein the method further comprises:

[0490] (d) matching the one or more reference enrichment signatures provided in step (a) with the counter-ligand discovery enrichment signatures generated in step (c) to isolate at least one counter-ligand for at least one differentially expressed target ligand in the target cell, tissue, or sample of interest.

[0491] 3. The method according to paragraph 1 or paragraph 2, wherein the one or more reference enrichment features provided in step (a) are reference enrichment features obtained by computer simulation.

[0492] 4. The method according to paragraph 3, wherein the reference enrichment signature obtained by computer simulation is generated using an equation derived from the universal law of mass action, optionally wherein

[0493] The computer simulated characteristics were generated using the following equation:

[0494]

[0495] in

[0496] FA T = Frequency of antibody recovery

[0497] A T =The amount of recovered antibodies

[0498]

[0499] HR = hit rate, the fraction of antibodies (determined experimentally) that are specific for the target cell, tissue or sample.

[0500] 5. The method according to paragraph 3 or paragraph 4, wherein the reference enrichment feature obtained by computer simulation is a reference enrichment feature of: an anti-ligand for a highly expressed ligand of a target cell, tissue or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue or sample of interest; or an anti-ligand for a lowly expressed ligand of a target cell, tissue or sample of interest.

[0501] 6. The method according to paragraph 1 or paragraph 2, wherein the one or more reference enrichment features provided in step (a) are reference enrichment features obtained experimentally.

[0502] 7. The method of paragraph 6, wherein the experimentally obtained reference enrichment signature is generated from at least one biopanning experiment comprising at least one reference counter-ligand.

[0503] 8. The method of paragraph 6 or paragraph 7, wherein the reference enrichment signature obtained experimentally is a reference enrichment signature of: an anti-ligand for a highly expressed ligand of a target cell, tissue or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue or sample of interest; or an anti-ligand for a lowly expressed ligand of a target cell, tissue or sample of interest.

[0504] 9. A method according to paragraph 1 or paragraph 2, wherein two or more reference enrichment features are provided in step (a), wherein one or more of the reference enrichment features are reference enrichment features obtained by computer simulation, and wherein one or more of the reference enrichment features are enrichment features obtained by experiments.

[0505] 10. The method of paragraph 9, wherein the computer-simulated reference enrichment signature is generated using an equation derived from the universal law of mass action, optionally wherein the computer-simulated enrichment signature is generated using the following equation:

[0506]

[0507] in

[0508] FA T = Frequency of antibody recovery

[0509] A T =The amount of recovered antibodies

[0510]

[0511] HR = hit rate, the fraction of antibodies (determined experimentally) that are specific for the target cell, tissue or sample.

[0512] 11. The method of paragraph 9 or paragraph 10, wherein the experimentally obtained reference enrichment signature is generated from at least one biopanning experiment comprising at least one reference counter-ligand.

[0513] 12. The method according to any one of paragraphs 9 to 11, wherein the reference enrichment signature obtained by computer simulation is a reference enrichment signature of: an anti-ligand for a highly expressed ligand of a target cell, tissue or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue or sample of interest; or an anti-ligand for a lowly expressed ligand of a target cell, tissue or sample of interest, and / or

[0514] The reference enrichment signature obtained by the experiment is a reference enrichment signature of: an anti-ligand for a highly expressed ligand of a target cell, tissue or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue or sample of interest; or an anti-ligand for a lowly expressed ligand of a target cell, tissue or sample of interest.

[0515] 13. The method according to any one of paragraphs 1 to 12, wherein the biopanning step (a) comprises the following sub-steps:

[0516] (i) providing a counter-ligand library;

[0517] (ii) providing a first ligand population comprising ligands immobilized to or bound within a subtractor ligand construct;

[0518] (iii) providing a second ligand population comprising ligands immobilized to or bound within the target ligand construct;

[0519] (iv) determining the amount of said subtractor ligand construct and said target ligand construct in said population using one or more equations derived from the following universal law of mass action:

[0520] in:

[0521] A, B, C, and D = the participants in the reaction (reactants and products)

[0522] a, b, c, and d = coefficients needed to balance the chemical equation

[0523] so as to permit isolation of counter-ligands to differentially expressed target ligands;

[0524] (v) providing said amount of subtractor ligand construct determined in step (iv);

[0525] (vi) providing the amount of target ligand construct determined in step (iv);

[0526] (vii) providing separation means for separating the counter-ligand bound to the target ligand construct from the counter-ligand bound to the subtractor ligand construct;

[0527] (viii) exposing the library of (i) to the ligand constructs provided by (v) and (vi) to permit binding of the counter-ligand to the ligand; and

[0528] (ix) separating the counter-ligand bound to the ligand immobilized to or incorporated into the target ligand construct using the separation means.

[0529] 14. The method according to paragraph 13, wherein the equation in step (iv) is:

[0530]

[0531] in

[0532] bA = amount of counterligand bound to the receptor

[0533] A = total number of antiligands A

[0534] B = total number of target receptors B

[0535] K d = Anti-ligand affinity (M)

[0536] V = reaction volume (dm 3 )

[0537] N A = Avogadro constant (6.022 × 10 per mole 23 particles)

[0538] or

[0539]

[0540] in

[0541] bA = amount of counterligand bound to the receptor

[0542] A = total number of antiligands A

[0543] B = total number of target receptors B

[0544] K d = Anti-ligand affinity (M)

[0545] V = reaction volume (dm 3 )

[0546] N A = Avogadro constant (6.022 × 10 per mole23 particles)

[0547] C T = Number of target cells

[0548] C N =Number of non-target cells

[0549] B T =C T The number of receptors

[0550] B N =C N The number of receptors.

[0551] 15. The method of any of paragraphs 1 to 14, wherein step (b) comprises performing two or more rounds of biopanning, three or more rounds of biopanning, or four or more rounds of biopanning.

[0552] 16. The method according to any one of paragraphs 1 to 15, wherein the differentially expressed ligand is:

[0553] - Highly expressed ligand in target cells, tissues or samples of interest;

[0554] - a target cell, tissue or sample of interest that moderately expresses the ligand; or

[0555] - Low expression of the ligand by the target cell, tissue or sample of interest.

[0556] 17. The method of any one of paragraphs 13 to 16, wherein the ligand is not expressed on one of the target construct or the subtractor construct.

[0557] 18. The method according to any one of paragraphs 1 to 17, wherein the method further comprises the step of releasing the counter-ligand from the ligand.

[0558] 19. A method according to any of paragraphs 13 to 18, wherein steps (ii) to (ix) are performed in parallel to separate multiple counter-ligands for multiple different ligands; and / or wherein steps (ii) to (ix) are repeated one or more times.

[0559] 20. The method of any of paragraphs 13 to 19, wherein the amount of one of the subtractor construct or the target construct provided exceeds the amount of the other of the subtractor construct or the target construct; and / or wherein the ligand excess is between 10-fold and 1000-fold, or between 2-fold and 10-fold, or between 1000-fold and 1,000,000-fold.

[0560] 21. The method according to any one of paragraphs 1 to 20, wherein the high-throughput sequencing in step (c) is performed using 454 sequencing, Illumina, SOLiD method or Helicos system.

[0561] 22. The method according to any one of paragraphs 13 to 21, wherein the separation means is selected from at least one of the following: a solid support, a cell membrane and / or a portion thereof, a synthetic membrane, a bead, a chemical tag and a free ligand, or fluorescence activated cell sorting.

[0562] 23. The method according to any one of paragraphs 13 to 22, wherein step (ix) is performed by at least one of density centrifugation, solid support chelation, magnetic bead chelation, chemical tag binding and aqueous phase partitioning, and fluorescence activated cell sorting.

[0563] 24. A method according to any of paragraphs 1 to 23, wherein the counter-ligand library is a display library comprising multiple library members displaying counter-ligands; optionally, wherein the library is a phage display library and / or wherein the counter-ligand library is constructed from at least one of the following: antibodies, antigen-binding variants, derivatives and / or fragments thereof; scaffold molecules with engineered variable surfaces; receptors and enzymes.

[0564] 25. The method of any one of paragraphs 1 to 24, wherein the counter-ligand library is a pool of counter-ligands generated by a previously performed counter-ligand library selection experiment.

[0565] 26. A method according to any of paragraphs 1 to 25, wherein the counter-ligand library comprises at least one counter-ligand with a known copy number, optionally wherein the at least one counter-ligand with a known copy number is added to the counter-ligand library in a known copy number.

[0566] 27. A method according to any one of paragraphs 1 to 26, wherein the ligand is selected from at least one of the following: an antigen, a receptor ligand, and an enzyme target, which includes at least one of the following: a carbohydrate, a protein, a peptide, a lipid, a polynucleotide, an inorganic molecule, and a conjugated molecule; optionally wherein the ligand is a cell surface receptor, optionally wherein the cell surface receptor is in its native form.

[0567] 28. A method for isolating from a library of counter-ligands at least one counter-ligand against at least one differentially expressed target ligand in a target cell, tissue or sample of interest, wherein the method

[0568] The following steps are involved:

[0569] (a) providing one or more reference enrichment characteristics of counter-ligands present in the counter-ligand library used; and,

[0570] (b) providing one or more discovery enrichment characteristics of the counter-ligands present in said counter-ligand library used.

[0571] 29. The method according to paragraph 28, wherein the method further comprises:

[0572] (c) matching the one or more reference enrichment signatures provided in step (a) with the one or more discovery enrichment signatures provided in step (b) to isolate at least one counter-ligand for at least one differentially expressed target ligand in a target cell, tissue, or sample of interest.

[0573] 30. The method of paragraph 28 or paragraph 29, wherein the one or more reference enrichment signatures provided in step (a) are reference enrichment signatures obtained by computer simulation.

[0574] 31. The method of paragraph 30, wherein the computer-derived reference enrichment signature is generated using an equation derived from the universal law of mass action, optionally wherein the computer-derived enrichment signature is generated using the following equation:

[0575]

[0576] in

[0577] FA T = Frequency of antibody recovery

[0578] A T =The amount of recovered antibodies

[0579]

[0580] HR = hit rate, the fraction of antibodies that are specific for the target cell, tissue or sample (determined experimentally),

[0581] Optionally, the reference enrichment signature obtained by computer simulation is a reference enrichment signature of: an anti-ligand for a highly expressed ligand of a target cell, tissue or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue or sample of interest; or an anti-ligand for a lowly expressed ligand of a target cell, tissue or sample of interest.

[0582] 32. The method of paragraph 28 or paragraph 29, wherein the one or more reference enrichment signatures provided in step (a) are experimentally obtained reference enrichment signatures.

[0583] 33. The method of paragraph 32, wherein the experimentally derived reference enrichment signature is generated from at least one biopanning experiment comprising at least one reference counter-ligand.

[0584] 34. A method according to paragraph 33, wherein the reference enrichment signature obtained experimentally is a reference enrichment signature of: an anti-ligand for a highly expressed ligand of a target cell, tissue or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue or sample of interest; or an anti-ligand for a lowly expressed ligand of a target cell, tissue or sample of interest.

[0585] 35. A method according to paragraph 28 or paragraph 29, wherein two or more reference enrichment features are provided in step (a), wherein one or more of the reference enrichment features are reference enrichment features obtained by computer simulation, and wherein one or more of the reference enrichment features are enrichment features obtained by experiments.

[0586] 36. The method of paragraph 35, wherein the in silico derived reference enrichment signature is generated using an equation derived from the universal law of mass action, optionally wherein the in silico derived enrichment signature is generated using the following equation:

[0587]

[0588] in

[0589] FA T = Frequency of antibody recovery

[0590] A T =The amount of recovered antibodies

[0591]

[0592] HR = hit rate, the fraction of antibodies (determined experimentally) that are specific for the target cell, tissue or sample.

[0593] 37. The method of paragraph 35 or paragraph 36, wherein the experimentally derived reference enrichment signature is generated from at least one biopanning experiment comprising at least one reference counter-ligand.

[0594] 38. The method according to any one of paragraphs 35 to 37, wherein the reference enrichment signature obtained by computer simulation is a reference enrichment signature of: an anti-ligand for a highly expressed ligand of a target cell, tissue or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue or sample of interest; or an anti-ligand for a lowly expressed ligand of a target cell, tissue or sample of interest, and / or

[0595] The reference enrichment signature obtained by the experiment is a reference enrichment signature of: an anti-ligand for a highly expressed ligand of a target cell, tissue or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue or sample of interest; or an anti-ligand for a lowly expressed ligand of a target cell, tissue or sample of interest.

[0596] 39. The method according to any of paragraphs 28 to 38, wherein the one or more discovery enrichment features provided in step (b) are derived from a previously performed counter-ligand library selection experiment.

[0597] 40. The method according to any one of paragraphs 28 to 39, wherein the at least one differentially expressed ligand is:

[0598] - Highly expressed ligand in target cells, tissues or samples of interest;

[0599] - a target cell, tissue or sample of interest that moderately expresses the ligand; or

[0600] - ligands that are under-expressed by the target cell, tissue or sample of interest;

[0601] 41. A method according to any of paragraphs 28 to 40, wherein the counter-ligand library is a display library comprising multiple library members displaying counter-ligands; optionally, wherein the library is a phage display library and / or wherein the library is constructed from at least one of the following: antibodies, antigen-binding variants, derivatives and / or fragments thereof; scaffold molecules with engineered variable surfaces; receptors and enzymes.

[0602] 42. The method of any of paragraphs 28 to 41, wherein the counter-ligand library is a pool of counter-ligands generated by a previously performed counter-ligand library selection experiment.

[0603] 43. A method according to any of paragraphs 28 to 42, wherein the counter-ligand library comprises at least one counter-ligand with a known copy number, optionally wherein the at least one counter-ligand with a known copy number is added to the counter-ligand library in a known copy number.

[0604] 44. A method according to any of paragraphs 28 to 43, wherein the ligand is selected from at least one of the following: an antigen, a receptor ligand, and an enzyme target, which includes at least one of the following: a carbohydrate, a protein, a peptide, a lipid, a polynucleotide, an inorganic molecule, and a conjugated molecule; optionally wherein the ligand is a cell surface receptor, optionally wherein the cell surface receptor is in its native form.

[0605] 45. A counter-ligand enrichment signature, wherein the enrichment signature is generated by step (c) of the method according to any one of paragraphs 1 to 27.

[0606] 46. ​​A method for preparing a pharmaceutical composition, the method comprising the step of adding a counter-ligand isolated by the method according to any one of paragraphs 1 to 44 to a pharmaceutically acceptable carrier.

[0607] 47. A pharmaceutical composition prepared by the method according to paragraph 46.

[0608] 48. A pharmaceutical composition prepared by the method according to paragraph 47 for use in medicine.

Claims

1. A method for isolating at least one counter-ligand against at least one differentially expressed target ligand in a target cell, tissue or sample of interest from a counter-ligand library, wherein the method comprises the following steps: (a) providing one or more reference enrichment characteristics of the counter-ligand library used; (b) performing one or more rounds of differential biopanning on the counter-ligand library to generate a counter-ligand pool; (c) performing high-throughput sequencing on the counter-ligand pool generated during step (b) to generate a discovery enrichment signature for each counter-ligand in the counter-ligand pool; (d) matching the one or more reference enrichment signatures provided in step (a) with the counter-ligand discovery enrichment signatures generated in step (c) to isolate at least one counter-ligand for at least one differentially expressed target ligand in the target cell, tissue, or sample of interest.

2. The method of claim 1, wherein the one or more reference enrichment signatures provided in step (a) are computer-derived reference enrichment signatures, optionally wherein the computer-derived reference enrichment signatures are generated using equations derived from the universal law of mass action, optionally wherein the computer-derived signatures are generated using the following equations: in FA T = Frequency of antibody recovery A T = the amount of recovered antibodies HR = hit rate, the fraction of antibodies that are specific for the target cell, tissue or sample (determined experimentally), Optionally, the computer-derived reference enrichment signature is a reference enrichment signature of: anti-ligands for highly expressed ligands of target cells, tissues or samples of interest, anti-ligands for moderately expressed ligands of target cells, tissues or samples of interest; or a counterligand against a low-expressing ligand in the target cell, tissue, or sample of interest.

3. The method of claim 1, wherein the one or more reference enrichment signatures provided in step (a) are experimentally derived reference enrichment signatures, optionally wherein the experimentally derived reference enrichment signatures are generated from at least one biopanning experiment comprising at least one reference counter-ligand, optionally wherein the experimentally derived reference enrichment signature is a reference enrichment signature of: anti-ligands for highly expressed ligands of target cells, tissues or samples of interest, anti-ligands for moderately expressed ligands of target cells, tissues or samples of interest; or a counterligand against a low-expressing ligand in the target cell, tissue, or sample of interest.

4. The method of claim 1, wherein in step (a) two or more reference enrichment signatures are provided, wherein one or more of the reference enrichment signatures are computer-simulated reference enrichment signatures, and wherein one or more of the reference enrichment signatures are experimentally obtained enrichment signatures, optionally wherein the computer-simulated reference enrichment signatures are generated using an equation derived from the universal law of mass action, optionally wherein the computer-simulated enrichment signatures are generated using the following equation: in FA T = Frequency of antibody recovery A T = the amount of recovered antibodies HR = hit rate, the fraction of antibodies that are specific for the target cell, tissue or sample (determined experimentally), And / or optionally, wherein the experimentally obtained reference enrichment signature is generated from at least one biopanning experiment comprising at least one reference counter-ligand.

5. The method of claim 4, wherein the reference enrichment signature obtained by computer simulation is a reference enrichment signature of: An anti-ligand for a highly expressed ligand of a target cell, tissue or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue or sample of interest; or an anti-ligand for a lowly expressed ligand of a target cell, tissue or sample of interest, and / or wherein the experimentally obtained reference enrichment signature is a reference enrichment signature of: an anti-ligand for a highly expressed ligand of a target cell, tissue or sample of interest, an anti-ligand for a moderately expressed ligand of a target cell, tissue or sample of interest; or a counterligand against a low-expressing ligand in the target cell, tissue, or sample of interest.

6. The method according to any one of claims 1 to 5, wherein the biopanning step (a) comprises the following sub-steps: (i) providing a counter-ligand library; (ii) providing a first ligand population comprising ligands immobilized to or bound within a subtractor ligand construct; (iii) providing a second ligand population comprising ligands immobilized to or bound within the target ligand construct; (iv) determining the amount of said subtractor ligand construct and said target ligand construct in said population using one or more equations derived from the following universal law of mass action: in: A, B, C, and D = the participants in the reaction (reactants and products) a, b, c, and d = coefficients needed to balance the chemical equation so as to permit isolation of counter-ligands to differentially expressed target ligands; (v) providing said amount of subtractor ligand construct determined in step (iv); (vi) providing the amount of target ligand construct determined in step (iv); (vii) providing separation means for separating the counter-ligand bound to the target ligand construct from the counter-ligand bound to the subtractor ligand construct; (viii) exposing the library of (i) to the ligand constructs provided by (v) and (vi) to permit binding of the counter-ligand to the ligand; as well as (ix) separating the counter-ligand bound to the ligand immobilized to or incorporated into the target ligand construct using the separation means, Optionally, wherein the equation in step (iv) is: in bA = amount of counterligand bound to the receptor A = total number of antiligands A B = total number of target receptors B K d = Anti-ligand affinity (M) V = reaction volume (dm 3 ) N A = Avogadro constant (6.022 × 10 per mole 23 particles) or in bA = amount of counterligand bound to the receptor A = total number of antiligands A B = total number of target receptors B K d = Anti-ligand affinity (M) V = reaction volume (dm 3 ) N A = Avogadro constant (6.022 × 10 per mole 23 particles) C T = Number of target cells C N =Number of non-target cells B T =C T The number of receptors B N =C N The number of receptors.

7. The method of any one of claims 1 to 6, wherein step (b) comprises performing two or more rounds of biopanning, three or more rounds of biopanning, or four or more rounds of biopanning.

8. The method according to any one of claims 1 to 7, wherein the differentially expressed ligand is: - Highly expressed ligand in target cells, tissues or samples of interest; - a ligand moderately expressed by the target cell, tissue or sample of interest; or - Low expression of the ligand by the target cell, tissue or sample of interest.

9. The method of any one of claims 6 to 8, wherein the ligand is not expressed on one of the target construct or the subtractor construct.

10. The method according to any one of claims 1 to 9, wherein the method further comprises the step of releasing the counter-ligand from the ligand.

11. A method according to any one of claims 6 to 10, wherein steps (ii) to (ix) are performed in parallel to separate multiple counter-ligands for multiple different ligands; and / or wherein steps (ii) to (ix) are repeated one or more times.

12. The method of any one of claims 6 to 11, wherein the amount of one of the subtractor construct or the target construct provided exceeds the amount of the other of the subtractor construct or the target construct; and / or wherein the ligand excess is between 10-fold and 1000-fold, or between 2-fold and 10-fold, or between 1000-fold and 1,000,000-fold.

13. The method according to any one of claims 1 to 12, wherein the high-throughput sequencing in step (c) is performed using 454 sequencing, Illumina, SOLiD method or Helicos system.

14. The method according to any one of claims 6 to 13, wherein the separation means is selected from at least one of the following: a solid support, a cell membrane and / or a portion thereof, a synthetic membrane, a bead, a chemical tag and a free ligand, or fluorescence activated cell sorting.

15. The method according to any one of claims 6 to 14, wherein step (ix) is performed by at least one of density centrifugation, solid support chelation, magnetic bead chelation, chemical tag binding and aqueous phase partitioning, and fluorescence activated cell sorting.

16. A method according to any one of claims 1 to 15, wherein the anti-ligand library is a display library comprising multiple library members displaying anti-ligands; optionally, wherein the library is a phage display library and / or wherein the anti-ligand library is constructed from at least one of the following: antibodies, antigen-binding variants, derivatives and / or fragments thereof; scaffold molecules with engineered variable surfaces; receptors and enzymes, optionally wherein the anti-ligand library is a pool of anti-ligands generated by a previously performed anti-ligand library selection experiment.

17. A method according to any one of claims 1 to 16, wherein the counter-ligand library comprises at least one counter-ligand with a known copy number, optionally wherein the at least one counter-ligand with a known copy number is added to the counter-ligand library in a known copy number.

18. The method of any one of claims 1 to 17, wherein the ligand is at least one selected from the group consisting of an antigen, a receptor ligand, and an enzyme target, including at least one of the following: a carbohydrate, a protein, a peptide, a lipid, a polynucleotide, an inorganic molecule, and a conjugated molecule; optionally wherein the ligand is a cell surface receptor, optionally wherein the cell surface receptor is in its native form.

19. A method for isolating at least one counter-ligand from a counter-ligand library for at least one differentially expressed target ligand in a target cell, tissue or sample of interest, wherein the method comprises the following steps: (a) providing one or more reference enrichment characteristics of counter-ligands present in the counter-ligand library used; (b) providing one or more discovery enrichment characteristics of the counter-ligands present in the counter-ligand library used; (c) matching the one or more reference enrichment signatures provided in step (a) with the one or more discovery enrichment signatures provided in step (b) to isolate at least one counter-ligand for at least one differentially expressed target ligand in a target cell, tissue, or sample of interest.

20. The method of claim 19, wherein the one or more reference enrichment signatures provided in step (a) are computer-derived reference enrichment signatures, optionally wherein the computer-derived reference enrichment signatures are generated using an equation derived from the universal law of mass action, optionally wherein the computer-derived enrichment signatures are generated using the following equation: in FA T = Frequency of antibody recovery A T = the amount of recovered antibodies HR = hit rate, the fraction of antibodies that are specific for the target cell, tissue or sample (determined experimentally), Optionally, the computer-derived reference enrichment signature is a reference enrichment signature of: anti-ligands for highly expressed ligands of target cells, tissues or samples of interest, anti-ligands for moderately expressed ligands of target cells, tissues or samples of interest; or a counterligand against a low-expressing ligand in the target cell, tissue, or sample of interest.

21. The method of claim 19, wherein the one or more reference enrichment signatures provided in step (a) are experimentally derived reference enrichment signatures, optionally wherein the experimentally derived reference enrichment signatures are generated from at least one biopanning experiment comprising at least one reference counter-ligand, optionally wherein the experimentally derived reference enrichment signature is a reference enrichment signature of: anti-ligands for highly expressed ligands of target cells, tissues or samples of interest, anti-ligands for moderately expressed ligands of target cells, tissues or samples of interest; or a counterligand against a low-expressing ligand in the target cell, tissue, or sample of interest.

22. The method of claim 19, wherein in step (a) two or more reference enrichment signatures are provided, wherein one or more of the reference enrichment signatures are computer-derived reference enrichment signatures, and wherein one or more of the reference enrichment signatures are experimentally derived enrichment signatures, optionally wherein according to the method of claim 35, wherein the computer-derived reference enrichment signatures are generated using equations derived from the universal law of mass action, optionally wherein the computer-derived enrichment signatures are generated using the following equations: in FA T = Frequency of antibody recovery A T = the amount of recovered antibodies HR = hit rate, the fraction of antibodies that are specific for the target cell, tissue or sample (determined experimentally) And / or optionally, wherein the experimentally obtained reference enrichment signature is generated from at least one biopanning experiment comprising at least one reference counter-ligand.

23. The method of claim 22, wherein the reference enrichment signature obtained by computer simulation is a reference enrichment signature of: an anti-ligand for a highly expressed ligand in a target cell, tissue or sample of interest, an anti-ligand for a moderately expressed ligand in a target cell, tissue or sample of interest; or an anti-ligand for a lowly expressed ligand in a target cell, tissue or sample of interest, and / or wherein the experimentally obtained reference enrichment signature is a reference enrichment signature of: a counter-ligand for a highly expressed ligand of a target cell, tissue or sample of interest, and a counter-ligand for a moderately expressed ligand of a target cell, tissue or sample of interest; or a counterligand against a low-expressing ligand in the target cell, tissue, or sample of interest.

24. The method according to any one of claims 19 to 23, wherein the one or more discovery enrichment features provided in step (b) are derived from a previously performed counter-ligand library selection experiment.

25. The method according to any one of claims 19 to 24, wherein the at least one differentially expressed ligand is: - Highly expressed ligand in target cells, tissues or samples of interest; - a ligand moderately expressed by the target cell, tissue or sample of interest; or - Low expression of the ligand by the target cell, tissue or sample of interest.

26. A method according to any one of claims 19 to 25, wherein the anti-ligand library is a display library comprising multiple library members displaying anti-ligands; optionally, wherein the library is a phage display library and / or wherein the library is constructed from at least one of the following: antibodies, antigen-binding variants, derivatives and / or fragments thereof; scaffold molecules with engineered variable surfaces; receptors and enzymes, optionally wherein the anti-ligand library is a pool of anti-ligands generated by a previously performed anti-ligand library selection experiment.

27. A method according to any one of claims 19 to 26, wherein the counter-ligand library comprises at least one counter-ligand with a known copy number, optionally wherein the at least one counter-ligand with a known copy number is added to the counter-ligand library in a known copy number.

28. The method of any one of claims 19 to 27, wherein the ligand is at least one selected from the group consisting of an antigen, a receptor ligand, and an enzyme target, including at least one of the following: a carbohydrate, a protein, a peptide, a lipid, a polynucleotide, an inorganic molecule, and a conjugated molecule; optionally wherein the ligand is a cell surface receptor, optionally wherein the cell surface receptor is in its native form.

29. A counter-ligand enrichment signature, wherein the enrichment signature is generated by step (c) of the method according to any one of claims 1 to 18.

30. A method for preparing a pharmaceutical composition, the method comprising the step of adding a counter-ligand isolated by the method according to any one of claims 1 to 28 to a pharmaceutically acceptable carrier.

31. A pharmaceutical composition prepared by the method according to claim 30.

32. A pharmaceutical composition prepared by the process according to claim 31 for use in medicine.

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