Two-color fluorescence cross-correlation spectroscopy

Abstract

A method for determining the hydrodynamic radius of a sample, comprising: a) contacting the sample with at least one specific fluorophore capable of binding to a protein; b) measuring the correlation time of the sample using a confocal microscope; and c) determining the hydrodynamic radius of the sample based on the correlation time.

Description

[Technical Field]

[0001] This application relates to methods for detecting and quantifying hydrodynamic radius. This application also relates to methods for estimating ligand-drug stoichiometry. [Background technology]

[0002] Information regarding the protein-ligand stoichiometry of a therapeutic protein can provide information about the safety and efficacy of a therapeutic protein-based treatment. Drug and dose-switching studies can determine whether switching the therapeutic protein and / or dose affects the safety and efficacy of a therapeutic protein-based treatment. For example, incorporating an additional, non-competing therapeutic monoclonal antibody (mAb) into a treatment already using a therapeutic monoclonal antibody can lead to the formation of large protein-ligand complexes (e.g., "paper-dolling") that can affect immunogenicity. Identifying off-target proteins and elucidating the mechanism of action of a therapeutic protein can also provide information about the safety and efficacy of a therapeutic protein-based treatment. Therefore, it can be appreciated that there is a need for sensitive in situ methods and systems for detecting and measuring the protein-ligand stoichiometry of therapeutic proteins. Summary of the Invention

[0003] The present disclosure provides a method for determining hydrodynamic radius and protein-ligand stoichiometry. In an exemplary embodiment, the method includes (a) contacting a sample with at least one specific fluorophore capable of binding to a protein, (b) measuring a correlation time of the sample using a confocal microscope, and (c) determining a hydrodynamic radius of the sample based on the correlation time.

[0004] In one embodiment, the correlation time is determined using fluorescence correlation spectroscopy (FCS). In one embodiment, the method is used to estimate protein-ligand stoichiometry in the sample.

[0005] In another aspect, a fitted model is used to determine the correlation time. In yet another aspect, the fitting model is selected from the group consisting of a triplet fitting model, a translation fitting model, or a combination thereof.

[0006] In one embodiment, the protein is an antibody. In another embodiment, the protein is a monoclonal antibody. In one embodiment, the two unique fluorophores exhibit non-overlapping emission spectra.

[0007] In another embodiment, the two unique fluorophores include Alexa Fluor® 488, Alexa Fluor® 647, or both. In one aspect, the correlation time is selected from the group consisting of a cross-correlation time, an auto-correlation time, or a combination thereof.

[0008] In one embodiment, the sample is serum. In another embodiment, the sample comprises a biological system. In another exemplary embodiment, a method for determining protein-ligand stoichiometry or hydrodynamic radius includes (a) contacting a sample with at least one unique fluorophore capable of binding to a protein, (b) measuring cross-correlation and / or autocorrelation times of fluorophores in the sample using a confocal microscope capable of FCS, and (c) determining a hydrodynamic radius for the sample based on the correlation times.

[0009] In one embodiment, the cross-correlation time and / or autocorrelation time are determined using fluorescence correlation spectroscopy (FCS). In one embodiment, the method is used to estimate protein-ligand stoichiometry in the sample.

[0010] In another embodiment, a fitted model is used to determine the cross-correlation time and / or auto-correlation time. In yet another aspect, the fitting model is selected from the group consisting of a triplet fitting model, a transformation fitting model, or a combination thereof.

[0011] In one embodiment, the protein is an antibody. In another embodiment, the protein is a monoclonal antibody. In one embodiment, the two unique fluorophores exhibit non-overlapping emission spectra.

[0012] In another embodiment, the two unique fluorophores include Alexa Fluor® 488, Alexa Fluor® 647, or both. In one embodiment, the sample is serum.

[0013] In another embodiment, the sample comprises a biological system. In another exemplary embodiment, a method for determining protein-ligand stoichiometry or hydrodynamic radius includes (a) contacting a sample with at least one unique fluorophore capable of binding to a protein, (b) measuring cross-correlation and / or autocorrelation times of fluorophores in the sample using a confocal microscope capable of FCS, and (c) estimating protein-ligand stoichiometry in the sample based on the correlation times.

[0014] In one aspect, a fitted model is used to determine the cross-correlation time and / or auto-correlation time. In another aspect, the fitting model is selected from the group consisting of a triplet fitting model, a transformation fitting model, or a combination thereof.

[0015] In one embodiment, the protein is an antibody. In another embodiment, the protein is a monoclonal antibody. In one embodiment, the two unique fluorophores exhibit non-overlapping emission spectra.

[0016] In another embodiment, the two unique fluorophores include Alexa Fluor® 488, Alexa Fluor® 647, or both. In one embodiment, the sample is serum.

[0017] In another embodiment, the sample comprises a biological system. In another exemplary embodiment, a method for determining protein-ligand stoichiometry or hydrodynamic radius includes (a) labeling a protein with a first fluorophore, (b) labeling a ligand of the protein with a second fluorophore, (c) combining the labeled protein and labeled ligand in a sample, (d) measuring a correlation time of the sample using an FCS-capable confocal microscope, and (e) determining a hydrodynamic radius of the sample based on the correlation time.

[0018] In one embodiment, the correlation time is determined using fluorescence correlation spectroscopy (FCS). In one embodiment, the method is used to estimate protein-ligand stoichiometry in the sample.

[0019] In another aspect, a fitted model is used to determine the correlation time. In yet another aspect, the fitting model is selected from the group consisting of a triplet fitting model, a transformation fitting model, or a combination thereof.

[0020] In one embodiment, the protein is an antibody. In another embodiment, the protein is a monoclonal antibody. In one embodiment, the first and second fluorophores exhibit non-overlapping emission spectra.

[0021] In another embodiment, the first and second fluorophores comprise Alexa Fluor® 488, Alexa Fluor® 647, or both. In one aspect, the correlation time is selected from the group consisting of a cross-correlation time, an auto-correlation time, or a combination thereof.

[0022] In one embodiment, the sample is serum. In another embodiment, the sample comprises a biological system. In another exemplary embodiment, a method for determining protein-ligand stoichiometry or hydrodynamic radius includes (a) labeling a protein with a first fluorophore; (b) labeling a ligand of the protein with a second fluorophore; (c) combining the labeled protein and labeled ligand in a sample; (d) measuring cross-correlation and / or autocorrelation times of the sample using an FCS-capable confocal microscope; and (e) estimating protein-ligand stoichiometry in the sample based on the correlation times.

[0023] In one embodiment, the cross-correlation and autocorrelation times are determined using fluorescence correlation spectroscopy (FCS). In another embodiment, a fitted model is used to determine the cross-correlation time and / or auto-correlation time.

[0024] In yet another aspect, the fitting model is selected from the group consisting of a triplet fitting model, a transformation fitting model, or a combination thereof. In one embodiment, the protein is an antibody.

[0025] In another embodiment, the protein is a monoclonal antibody. In one embodiment, the first and second fluorophores exhibit non-overlapping emission spectra. In another embodiment, the first and second fluorophores comprise Alexa Fluor® 488, Alexa Fluor® 647, or both.

[0026] In one embodiment, the sample is serum. In another embodiment, the sample comprises a biological system. In another exemplary embodiment, a method for determining protein-ligand stoichiometry or hydrodynamic radius includes (a) labeling a protein with a first fluorophore; (b) labeling a secondary labeled reporter with a second fluorophore; (c) combining the labeled protein and the secondary labeled reporter in a sample; (d) measuring the cross-correlation time and / or autocorrelation time of the sample using an FCS-capable confocal microscope; and (e) determining a hydrodynamic radius for the sample based on the correlation time.

[0027] In one aspect, a fitted model is used to determine the cross-correlation time and / or auto-correlation time. In one embodiment, the method is used to estimate protein-ligand stoichiometry in the sample.

[0028] In another aspect, the fitting model is selected from the group consisting of a triplet fitting model, a transformation fitting model, or a combination thereof. In one embodiment, the protein is an antibody.

[0029] In another embodiment, the protein is a monoclonal antibody. In one embodiment, the first and second fluorophores exhibit non-overlapping emission spectra. In another embodiment, the first and second fluorophores comprise Alexa Fluor® 488, Alexa Fluor® 647, or both.

[0030] In one embodiment, the sample is serum. In another embodiment, the sample comprises a biological system. [Brief explanation of the drawings]

[0031] [Figure 1]1 illustrates the path light travels from a laser to a detector in a fluorescence correlation spectroscopy (FCS) device, according to an exemplary embodiment. [Figure 2] 1 illustrates components of a fluorescence correlation spectroscopy apparatus according to an exemplary embodiment. [Figure 3] 1 shows an example of a fluorescence signal (middle panel) and autocorrelation curve (bottom panel) of a fluorescent particle in a focal volume (top panel) using a fluorescence correlation spectroscopy device according to an exemplary embodiment. [Figure 4] 1 shows an example of correlation (e.g., "autocorrelation") between a signal and its delayed copies as a function of delay, according to an exemplary embodiment. [Figure 5] 1 shows an example of a comparison of two data series from different spectra, where the degree of similarity can be quantified (eg, "cross-correlated") as a function of the displacement of one relative to the other, according to an exemplary embodiment. [Figure 6] 1 shows cross-correlation and auto-correlation curves according to an exemplary embodiment. On the left, two source signals are correlated in time and have different magnitudes. On the right, two source signals are perfectly anti-correlated in time and have different magnitudes. [Figure 7] 1 shows that the use of two non-competing therapeutic monoclonal antibodies can result in paper doring, according to an exemplary embodiment. [Figure 8] 1 shows Alexa Fluor® 488 (top panel), Alexa Fluor® 647 (middle panel), and the excitation and emission spectra of Alexa Fluor® 488 and Alexa Fluor® 647 (bottom panel), according to an exemplary embodiment. [Figure 9] An IgG4 antibody (top panel) composed of two heavy chains (cyan) and a light chain (yellow-green) and a dimeric ligand of IgG4, nerve growth factor (bottom panel), are shown according to an exemplary embodiment. [Figure 10]1 shows fluorescence correlation spectroscopy autocorrelation curves of free dye, dye-conjugated mAb1, and mAb1 complex generated using 1:5 dye-conjugated mAb1:target, according to an exemplary embodiment. [Figure 11-1] 1 shows fluorescence correlation spectroscopy autocorrelation times of free dye, dye-conjugated mAb1, and mAb1 complexes generated using 1:5 dye-conjugated mAb1:target, according to an exemplary embodiment. [Figure 11-2] Same as above. [Figure 12] Fluorescence correlation spectroscopy autocorrelation curves of free dye, dye-conjugated mAb1, dye-conjugated mAb1 mixture, and mAb1 complexes generated using 1:5 dye-conjugated mAb1:target, 0.5:0.5:5 mAb1-A488:mAb1-A647:target, or 2.5:2.5:1 mAb1-A488:mAb1-A647:target, according to exemplary embodiments. [Figure 13] 1 shows fluorescence correlation spectroscopy cross-correlation curves of dye-conjugated mAb1 mixtures and mAb1 complexes generated using 0.5:0.5:5 mAb1-A488:mAb1-A647:target or 2.5:2.5:1 mAb1-A488:mAb1-A647:target, according to an exemplary embodiment. [Figure 14] 1 shows autocorrelation and cross-correlation curves of fluorescence correlation spectroscopy of dye-conjugated mAb1 using 0.5:0.5:5 mAb1-A488:mAb1-A647:target or 2.5:2.5:1 mAb1-A488:mAb1-A647:target, according to an exemplary embodiment. [Figure 15-1] 1 shows the autocorrelation times of fluorescence correlation spectroscopy of free dye, dye-conjugated mAb1, and mAb1 complexes generated using 1:5 dye-conjugated mAb1:target, 0.5:0.5:5 mAb1-A488:mAb1-A647:target, or 2.5:2.5:1 mAb1-A488:mAb1-A647:target, according to exemplary embodiments. [Figure 15-2] Same as above. [Figure 16] 1 shows FCS / FCCS results for mAb1:NGF samples, according to an exemplary embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0032] Detailed Description In an exemplary embodiment, fluorescence correlation spectroscopy (FCS) can be used to detect and determine hydrodynamic radius. FCS is similar to dynamic light scattering spectroscopy (DLS). Both FCS and DLS can measure particle size distribution based on intensity fluctuations of scattered light (e.g., DLS) or fluorescence (e.g., FCS). FCS utilizes the physical phenomenon of fluorescence, while DLS utilizes the physical phenomenon of light scattering. In an exemplary embodiment, a laser can pass through a dichroic mirror that filters the correct wavelength of light and reflect off the sample to excite the fluorophores of interest. The emitted light can then be reflected from outside the sample volume through a pinhole that can filter the wavelength of light to a detector. In an exemplary embodiment, the detector can convert the captured light into an electrical signal and transmit the electrical signal via a cable to a computer. In one aspect, the cable is an optical fiber. In yet another aspect, the computer can record the intensity fluctuations and apply an autocorrelation Fourier transform. Figures 1 and 2 show an exemplary embodiment of fluorescence correlation spectroscopy in which a laser passes through a polarizer, reflects off the sample, and is converted into an electrical signal by a photomultiplier. FIG. 3 shows how the fluorescent signal and correlation curve of a fluorescent particle typically appears in an exemplary embodiment.

[0033] In an exemplary embodiment, a confocal microscope capable of FCS is used to measure the diffusion coefficient. The diffusion coefficient can be used to calculate the hydrodynamic radius using the Stokes-Einstein equation. From these calculations, the protein-ligand stoichiometry can be estimated.

[0034] In an exemplary embodiment, an advantage of FCS is the ability to observe only the species of interest with high sensitivity (e.g., nanomolar concentrations). In another aspect, measurements of protein-ligand stoichiometry can be measured in situ. In yet another aspect, measurements can involve one or more cellular systems.

[0035] In an exemplary embodiment, autocorrelation can be the correlation between a signal and its delayed copies as a function of delay. FIG. 4 illustrates how an autocorrelation curve according to one embodiment can be determined. Good autocorrelation data suggests that the data was relatively constant throughout a given delay period. In an exemplary embodiment, protein size affects the diffusion rate of the molecule, which can change the shape of the autocorrelation function. In an exemplary embodiment, the diffusion rate can be inversely proportional to the size of the protein, as smaller proteins penetrate liquids more easily and diffuse faster. FIG. 5 illustrates an exemplary embodiment in which smaller proteins have lower signal intensity and faster diffusion rates than larger proteins.

[0036] In an exemplary embodiment, cross-correlation allows for the comparison of two or more data series from different spectra and the quantification of the similarity of one to the other as a function of the displacement of one relative to the other. Figure 6 illustrates the correlation of the data, with the source signals on the left correlated in time and the source signals on the right perfectly anti-correlated in time.

[0037] In an exemplary embodiment, when a therapeutic mAb binds to a soluble multimeric target, a large heterogeneous complex, or "paper doring," can form. Paper doring is a common phenomenon when the hydrodynamic radius is large due to the presence of an excess of ligand. When an excess of antibody is present, paper doring is less likely to occur. Paper doring occurs when two antibodies bind to opposite sides of the same ligand / target and then join together to form a large chain. As more ligands are added, more antibodies bind, and the newly bound antibodies then bind even more ligands, creating a feedback loop. In an exemplary embodiment, a system capable of paper doring is ideal for maximizing the difference in correlation delay times observed when a ligand is added. Figure 7 shows an exemplary embodiment in which the antibodies and ligands used are capable of paper doring.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing, specific methods and materials are described herein.

[0039] The term "one" should be understood to mean "at least one," and the terms "about" and "approximately" should be understood to allow for standard variations understood by one of ordinary skill in the art, and when ranges are provided, the endpoints are included. As used herein, the term "comprising" is intended to be open-ended and is understood to mean "including."

[0040] As used herein, the term "composition" refers to a pharmaceutical agent formulated together with one or more pharmaceutically acceptable vehicles. As used herein, the terms "medicine" and "pharmaceutical product" may include biologically active ingredients of a formulation. Medicaments and pharmaceutical products refer to any substance or combination of substances used in a formulation to produce pharmacological activity or to have a direct or indirect effect in the diagnosis, cure, mitigation, treatment, or prevention of disease, or to have a direct or indirect effect in the restoration, correction, or modification of an animal's physiological function. Non-limiting examples of medicaments or pharmaceutical products include drugs, compounds, nucleic acids, nucleotides, nucleosides, oligonucleotides, toxins, peptides, proteins, fusion proteins, antibodies, antibody fragments, Fab regions of antibodies, scFvs, monoclonal antibodies, bispecific antibodies, multispecific antibodies, antibody-drug conjugates, or pharmaceutical protein products, or combinations thereof. Non-limiting examples of processes or elements that can be used in the manufacture of medicaments or pharmaceutical products include fermentation processes, recombinant DNA, isolation and recovery from natural sources, chemical synthesis, biosynthesis, polymerase chain reaction, or combinations thereof.

[0041] As used herein, the terms "protein" and "pharmaceutical protein product" may include any amino acid polymer containing covalently linked amide bonds. Proteins contain one or more amino acid polymer chains, commonly known in the art as "polypeptides." A "polypeptide" refers to a polymer composed of amino acid residues, related naturally occurring structural variants, and their synthetic non-naturally occurring analogs, linked via peptide bonds. A "synthetic peptide or polypeptide" refers to a peptide or polypeptide that does not occur in nature. Synthetic peptides or polypeptides can be synthesized, for example, using an automated polypeptide synthesizer. Various solid-phase peptide synthesis methods are known to those skilled in the art. A protein may comprise one or more polypeptides that form a single functional biomolecule. Proteins may include antibody fragments, nanobodies, recombinant antibody chimeras, cytokines, chemokines, peptide hormones, etc. Proteins of interest include biotherapeutic proteins, recombinant proteins used in research and therapy, trap proteins and other chimeric receptor-Fc fusion proteins, chimeric proteins, antibodies, monoclonal antibodies, polyclonal antibodies, human antibodies, and bispecific antibodies. Proteins can be produced using recombinant cell-based production systems such as insect baculovirus systems, yeast systems (e.g., Pichia), mammalian systems (e.g., CHO cells and CHO derivatives such as CHO-K1 cells).For a recent review of biotherapeutic proteins and their production, see Ghaderi et al., "Production platforms for biotherapeutic glycoproteins. Occurrence, impact, and challenges of non-human sialylation" (Darius Ghaderi et al., "Production platforms for biotherapeutic glycoproteins. Occurrence, impact, and challenges of non-human sialylation," 28 Biotechnology and Genetic Engineering Reviews 147-176 (2012), the entire contents of which are incorporated herein). Proteins can be classified based on their composition and solubility and thus include simple proteins, such as globular proteins and fibrous proteins; complex proteins, such as nucleoproteins, glycoproteins, mucoproteins, chromoproteins, phosphoproteins, metalloproteins, and lipoproteins; and derived proteins, such as primary derived proteins and secondary derived proteins. Non-limiting examples of proteins or pharmaceutical protein products include recombinant proteins, antibodies, bispecific antibodies, multispecific antibodies, antibody fragments, monoclonal antibodies, fusion proteins, scFvs, and combinations thereof.

[0042] As used herein, the term "recombinant protein" refers to a protein produced as a result of transcription and translation of a gene carried in a recombinant expression vector introduced into a suitable host cell. In certain exemplary embodiments, the recombinant protein may be an antibody, such as a chimeric antibody, a humanized antibody, or a fully human antibody. In certain exemplary embodiments, the recombinant protein may be an antibody of an isotype selected from the group consisting of IgG (e.g., IgG1, IgG2, IgG3, IgG4), IgM, IgA1, IgA2, IgD, or IgE. In certain exemplary embodiments, the antibody molecule is a full-length antibody (e.g., an IgG1 or IgG4 immunoglobulin), or the antibody may be a fragment (e.g., an Fc fragment or a Fab fragment).

[0043] The term "antibody" as used herein includes immunoglobulin molecules and multimers thereof (e.g., IgM) that contain four polypeptide chains: two heavy (H) chains and two light (L) chains linked together by disulfide bonds. Each heavy chain contains a heavy chain variable region (abbreviated herein as HCVR or VH) and a heavy chain constant region. The heavy chain constant region contains three domains: CH1, CH2, and CH3. Each light chain contains a light chain variable region (abbreviated herein as LCVR or VL) and a light chain constant region. The light chain constant region contains one domain (CL1). The VH and VL regions are further subdivided into regions of hypervariability called complementarity-determining regions (CDRs) interspersed with more conserved regions called framework regions (FRs). Each VH and VL is composed of three complementarity-determining regions and four framework regions, arranged from the amino terminus to the carboxy terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4. In different embodiments of the present invention, the framework regions of an anti-big ET-1 antibody (or antigen-binding portion thereof) may be identical to human germline sequences or may be naturally or artificially modified. An amino acid consensus sequence can be defined based on a parallel analysis of two or more complementarity-determining regions. As used herein, the term "antibody" also includes antigen-binding fragments of an intact antibody molecule. As used herein, the terms "antigen-binding portion" of an antibody, "antigen-binding fragment" of an antibody, and the like include naturally occurring, enzymatically derived, synthetic, or genetically engineered polypeptides or glycoproteins that specifically bind to an antigen to form a complex. Antigen-binding fragments of antibodies can be derived from intact antibody molecules using any suitable standard technique, such as proteolytic digestion or recombinant genetic engineering techniques involving the manipulation and expression of DNA encoding the variable and optional constant domains of the antibody. Such DNA is known and / or readily available, for example, from commercial sources, DNA libraries (including, for example, phage antibody libraries), or can be synthesized.The DNA can be sequenced and manipulated chemically or using molecular biology techniques to, for example, place one or more variable and / or constant domains in the appropriate configuration, introduce codons, create cysteine ​​residues, or change, add, or delete amino acids.

[0044] As used herein, the term "antibody fragment" includes a portion of an intact antibody, such as the antigen-binding or variable region of the antibody. Examples of antibody fragments include, but are not limited to, Fab fragments, Fab' fragments, F(ab')2 fragments, scFv fragments, Fv fragments, dsFv diabodies, dAb fragments, Fd' fragments, Fd fragments, and isolated complementarity-determining regions, as well as triabodies, tetrabodies, linear antibodies, single-chain antibody molecules, and multispecific antibodies formed from antibody fragments. An Fv fragment is a combination of the variable regions of an immunoglobulin heavy and light chains, and an ScFv protein is a recombinant single-chain polypeptide molecule in which an immunoglobulin light chain variable region and a heavy chain variable region are linked by a peptide linker. In some exemplary embodiments, an antibody fragment contains the full amino acid sequence of a parent antibody and binds to the same antigen as the parent antibody. In some exemplary embodiments, the fragment binds to an antigen with an affinity comparable to that of the parent antibody and / or competes with the parent antibody for binding to the antigen. Antibody fragments can be produced by any means. For example, antibody fragments may be enzymatically or chemically produced by fragmentation of an intact antibody and / or recombinantly produced from a gene encoding a partial antibody sequence. Alternatively, or in addition, antibody fragments may be wholly or partially synthetically produced. Antibody fragments may optionally include single-chain antibody fragments. Alternatively, or in addition, antibody fragments may include multiple chains linked together, for example, by disulfide bonds. Antibody fragments may optionally include multimolecular complexes. Functional antibody fragments typically contain at least about 50 amino acids, more typically at least about 200 amino acids.

[0045] The term "bispecific antibody" includes antibodies capable of selectively binding to two or more epitopes. Bispecific antibodies typically contain two different heavy chains, each of which specifically binds to a different epitope, either on two different molecules (such as antigens) or on the same molecule (e.g., the same antigen). When a bispecific antibody can selectively bind to two different epitopes (a first epitope and a second epitope), the affinity of the first heavy chain for the first epitope is typically at least one to two, three, or four orders of magnitude lower than the affinity of the first heavy chain for the second epitope, or vice versa. The epitopes recognized by a bispecific antibody can be on the same or different targets (e.g., the same or different proteins). Bispecific antibodies can be generated, for example, by combining heavy chains that recognize different epitopes of the same antigen. For example, nucleic acid sequences encoding heavy chain variable sequences that recognize different epitopes of the same antigen can be fused to nucleic acid sequences encoding different heavy chain constant regions, and such sequences can be expressed in cells that express immunoglobulin light chains.

[0046] A typical bispecific antibody has two heavy chains, each with three heavy chain complementarity-determining regions, followed by a CH1 domain, hinge, CH2 domain, and CH3 domain, and either an immunoglobulin light chain that does not confer antigen-binding specificity but can bind to each heavy chain, or an immunoglobulin light chain that can bind to each heavy chain and to one or more of the epitopes bound by the heavy chain antigen-binding region, or an immunoglobulin light chain that can bind to each heavy chain and enable one or both heavy chains to bind to one or both epitopes. BsAbs can be divided into two major classes: those that have an Fc region (IgG-like) and those that lack an Fc region. The latter are usually smaller than IgG and IgG-like bispecific molecules that contain an Fc region. IgG-like bispecific antibodies (bsAbs) can have various formats, including but not limited to Triomab®, knobs-into-holes IgG (KiH IgG), crossMab, orth-Fab IgG, dual variable domain Ig (DVD-Ig), two-in-one or dual action Fab (DAF), IgG single chain Fv (IgG-scFv), or κλ body. Different non-IgG-like formats include tandem scFvs, diabody formats, single-chain diabodies, tandem diabodies (TandAbs), dual affinity retargeting molecules (DARTs), DART-Fcs, nanobodies, or antibodies generated by the dock-and-lock (DNL) method (Gaowei Fan, Zujian Wang and Mingju Hao, Bispecific Antibodies and Their Applications, 8 Journal of Hematology & Oncology 130; Dafne Mueller and Roland E. Kontermann, Bispecific Antibodies, Handbook of Therapeutic Antibodies 265-310 (2014), the entire teachings of which are incorporated herein).

[0047] As used herein, the term "multispecific antibody" refers to an antibody that has binding specificities for at least two different antigens. Such molecules typically bind only two antigens (e.g., bispecific antibodies / bsAbs), although antibodies with additional specificities, such as trispecific antibodies and KiH trispecific antibodies, can also be addressed by the systems and methods disclosed herein.

[0048] The term "monoclonal antibody" as used herein is not limited to antibodies produced through hybridoma technology. Monoclonal antibodies can be derived from a single clone, including eukaryotic, prokaryotic, or phage clones, by any means available or known in the art. Monoclonal antibodies useful in the present disclosure can be prepared using a wide variety of techniques known in the art, including the use of hybridoma, recombinant, and phage display technologies, or a combination thereof.

[0049] As used herein, the term "secondary labeled reporter" refers to anything that binds to the complex of interest, including, but not limited to, a secondary mAb (capable of binding to the human Fc region of the drug-antibody complex), a Fab, or another ligand.

[0050] In some exemplary embodiments, proteins and pharmaceutical protein products can be produced from mammalian cells. Mammalian cells can be of human or non-human origin and include primary epithelial cells (e.g., keratinocytes, cervical epithelial cells, bronchial epithelial cells, tracheal epithelial cells, renal epithelial cells, retinal epithelial cells), established cell lines and their strains (e.g., 293 embryonic kidney cells, BHK cells, HeLa cervical epithelial cells and PER-C6 retinal cells, MDBK (NBL-1) cells, 911 cells, CRFK cells, MDCK cells, CHO cells, BeWo cells, Chang cells, Detroit562 cells, HeLa229 cells, HeLaS3 cells, etc.). cells, Hep-2 cells, KB cells, LSI80 cells, LS174T cells, NCI-H-548 cells, RPMI2650 cells, SW-13 cells, T24 cells, WI-28VA13,2RA cells, WISH cells, BS-CI cells, LLC-MK2 cells, clones M-3 cells, 1-10 cells, RAG cells, TCMK-1 cells, Yl cells, LLC-PKi cells, PK(15) cells, GHi cells, GH3 cells, L2 cells, LLC-RC256 cells, MHiCi cells, XC cells, MDOK cells, VSW cells, and TH-I,B1 cells, BSC-1 cells, RAf cells, RK cells, PK-15 cells or derivatives thereof), fibroblasts derived from any tissue or organ (including, but not limited to, heart, liver, kidney, colon, intestine, esophagus, stomach, nervous tissue (brain, spinal cord), lung, vascular tissue (arteries, veins, capillaries), lymphatic tissue (lymph glands, adenoids, tonsils, bone marrow, and blood), spleen), and fibroblasts and fibroblast-like cells. Lines (e.g., CHO cells, TRG-2 cells, IMR-33 cells, Don cells, GHK-21 cells, citrullinemia cells, Dempsey cells, Detroit 551 cells, Detroit 510 cells, Detroit 525 cells, Detroit 529 cells, Detroit 532 cells, Detroit 539 cells, Detroit 548 cells, Detroit 573 cells, HEL299 cells, IMR-90 cells, MRC-5 cells) , WI-38 cells, WI-26 cells, Midi cells, CHO cells, CV-1 cells, COS-1 cells, COS-3 cells, COS-7 cells, Vero cells, DBS-FrhL-2 cells, BALB / 3 T3 cells, F9 cells, SV-T2 cells, M-MSV-BALB / 3T3 cells, K-BALB cells, BLO-11 cells, NOR-10 cells, C3H / IOTI / 2 cells, HSDMiC3 cells, KLN 205 cells, McCoy cells, mouse L cells, line 2071 (mouse L) cells, LM line (mouse L) cells, L-MTK' (mouse L) cells, NCTC clones 2472 and 2555, SCC-PSA1 cells, Swiss / 3T3 cells, Indian muntjac cells, SIRC cells, Cn cells, and Jensen cells, Sp2 / 0, NS0, NS1 cells, or derivatives thereof.

[0051] The compositions can be used for the treatment, prevention, and / or amelioration of diseases or disorders. Exemplary, non-limiting diseases and disorders that can be treated and / or prevented by administration of the pharmaceutical formulations of the present invention include infectious diseases, respiratory diseases, pain resulting from any condition associated with neurological, neuropathic, or nociceptive pain, genetic diseases, congenital diseases, cancer, herpetiformis, chronic idiopathic urticaria, scleroderma, hypertrophic scars, Whipple's disease, benign prostatic hyperplasia, pulmonary diseases such as mild, moderate, or severe asthma, allergic reactions, Kawasaki disease, sickle cell disease, Churg-Strauss syndrome, Graves' disease, pre-eclampsia, Sjogren's syndrome, and autoimmune diseases. Autoimmune lymphoproliferative syndrome; autoimmune hemolytic anemia; Barrett's esophagus; autoimmune uveitis; tuberculosis; nephrosis; arthritis, including rheumatoid arthritis; inflammatory bowel disease, including Crohn's disease and ulcerative colitis; systemic lupus erythematosus; inflammatory diseases; HIV infection; AIDS; LDL apheresis; diseases caused by PCSK9 activating mutations (gain-of-function mutations, "GOF"), diseases caused by heterozygous familial hypercholesterolemia (heFH); primary hypercholesterolemia; dyslipidemia; cholestatic liver disease; nephrotic syndrome; Hypothyroidism; Obesity; Atherosclerosis; Cardiovascular Disease; Neurodegenerative Disease; Neonatal-Onset Multisystem Inflammatory Disease (NOMID / CINCA); Muckle-Wells Syndrome (MWS); Familial Cold Autoinflammatory Syndrome (FCAS); Familial Mediterranean Fever (FMF); Tumor Necrosis Factor Receptor-Associated Periodic Fever Syndrome (TRAPS); Systemic-Onset Juvenile Idiopathic Arthritis (Still's Disease); Type 1 and Type 2 Diabetes; Autoimmune Disease; Motor Neuron Disease; Eye Disease; Sexually Transmitted Diseases; Tuberculosis; Diseases or Conditions that are Improved, Inhibited, or Relieved by VEGF Antagonists conditions; diseases or symptoms that are ameliorated, inhibited, or alleviated by PD-1 inhibitors; diseases or symptoms that are ameliorated, inhibited, or alleviated by interleukin antibodies; diseases or symptoms that are ameliorated, inhibited, or alleviated by NGF antibodies; diseases or symptoms that are ameliorated, inhibited, or alleviated by PCSK9 antibodies; diseases or symptoms that are ameliorated, inhibited, or alleviated by ANGPTL antibodies; diseases or symptoms that are ameliorated, inhibited, or alleviated by activin antibodies; diseases or conditions, disorders, or symptoms that are ameliorated, inhibited, or alleviated by GDF antibodies; diseases or symptoms that are ameliorated, inhibited, or alleviated by Fel d1 antibodies;Diseases or conditions that are ameliorated, inhibited, or alleviated by CD antibodies; diseases or conditions that are ameliorated, inhibited, or alleviated by C5 antibodies, or a combination thereof;

[0052] The composition can be administered to a patient. Administration can be by any route acceptable to those skilled in the art. Routes of administration include, but are not limited to, oral, topical, or parenteral. A specific parenteral route of administration can involve introducing the formulation of the present invention into the patient's body through a needle or catheter and propelling it through a sterile syringe or other mechanical device, such as a continuous infusion system. The composition can be administered using a syringe, infuser, pump, or any other device recognized in the art for parenteral administration. The composition can also be administered as an aerosol to be absorbed in the lungs or nasal cavity. A solution can also be administered to be absorbed through the mucous membrane, such as by oral administration.

[0053] The formulation may further comprise excipients including, but not limited to, buffers, bulking agents, tonicity agents, solubilizers, preservatives, etc. Other additional excipients may also be selected based on function and compatibility with the formulation. These excipients are described, for example, in Remington: The Science and Practice of Pharmacy, (2005); US Pharmacopeia: National formulary; Louis Sanford Goodman et al., Goodman and Gilmans The Pharmacological Basis of Therapeutics (2001); Kenneth E. Avis, Herbert A. Lieberman and Leon Lachman, Pharmaceutical Dosage Forms: Parenteral Medications(1992);Praful Agrawala,Pharmaceutical Dosage Forms:Tablets.Volume 1,79 Journal of Pharmaceutical Sciences 188(1990);Herbert A.Lieberman,Martin M.Rieger and Gilbert S.Banker,Pharmaceutical Dosage Forms:Disperse Systems(1996);Myra L.Weiner and Lois A.Kotkoskie,Excipient Toxicity and Safety (2000), which is incorporated herein by reference in its entirety.

[0054] It is understood that the present invention is not limited to any of the aforementioned solutions, compositions, medicaments, pharmaceutical products, proteins, pharmaceutical protein products, proteins, polypeptides, synthetic polypeptides, recombinant proteins, antibodies, antigen-binding portions, antigen-binding fragments, antibody fragments, bispecific antibodies, multispecific antibodies, formulations, excipients, or cells, and that the solutions, compositions, medicaments, pharmaceutical products, proteins, polypeptides, synthetic polypeptides, recombinant proteins, antibodies, antigen-binding portions, antigen-binding fragments, antibody fragments, bispecific antibodies, multispecific antibodies, formulations, excipients, or cells can be selected by any suitable means.

[0055] Example Phosphors with non-overlapping emission spectra Cross-correlation requires two samples with different emission spectra containing different fluorophores. Figure 8 shows the excitation spectra of Alexa Fluor® 488 (A488) (light blue) and Alexa Fluor® 647 (A647) (light pink), the emission spectra of Alexa Fluor® 488 (blue) and Alexa Fluor® 647 (pink), and the overlap of the excitation and emission spectra of Alexa Fluor® 488 with the excitation and emission spectra of Alexa Fluor® 647 (green).

[0056] Example 1 Figure 9 shows that FCS and three different reagents were used in one study: Alexa Fluor 488-labeled IgG4 (mAb1-A488), Alexa Fluor® 647-labeled IgG4 (mAb1-647), and nerve growth factor (NGF) (unlabeled target). IgG4 and nerve growth factor were used in the study because the IgG4 epitope is located on both ends of nerve growth factor, which allows for paper docking. Table 1 shows the study design and sample set used for single-channel FCS. An excess of mAb1 over ligand was predicted to form larger complexes and result in longer correlation times than an excess of ligand over mAb1.

[0057] [Table 1]

[0058] Figure 10 shows that the FCS autocorrelation curves for free dye, dye-bound mAb1, and mAb1 complex showed the expected trend in diffusion rates. mAb1-A488 (purple line) and mAb1-A647 (red line) exhibit slower diffusion than free Alexa Fluor® 488 (blue dashed line) and free Alexa Fluor® 647 (red dashed line). The FCS autocorrelation curves show two transitions: the first transition represents the triplet state, an intrinsic property of the fluorophore, and the second transition is due to diffusion of the labeled mAb particle. The mAb1-target complex exhibits the slowest decay, indicating the formation of a large complex. The purple line in the Alexa Fluor® 647 channel is slightly less shifted than the red line in the Alexa Fluor® 488 channel, which may indicate that Alexa Fluor® 647 interferes with complex formation in the mAb1-A647 sample.

[0059] Figure 11 shows that the Alexa Fluor® 488 control and the Alexa Fluor® 647 control diffused rapidly and were only observed in their respective channels. The labeled antibodies exhibited a long correlation time of approximately 400 μs, consistent with a hydrodynamic radius of approximately 4 to 6 nm. mAb1-A647 complexed with the target exhibited a slightly longer correlation time than the mAb-only control, suggesting the formation of a 1:1 or 1:2 complex. No species representing mAb dimers or 2:2 complexes were observed. The Alexa Fluor® 488 control and the Alexa Fluor® 647 control were only observed in their respective channels. mAb1-A488 complexed with the target exhibited the longest correlation time, consistent with a larger complex, and possibly a paper-doping complex. Figure 12 shows that the mAb1 complex formed a higher complex between mAbs labeled with two different dyes and a smaller complex than mAbs conjugated using a single dye. This may indicate that each label interferes with the formation of the complex.

[0060] Figure 13 shows that the cross-correlation of mixed antibodies (e.g., mAb1-A488 and mAb1-A647) negative control samples did not cross-correlate. Figures 13, 14, and Table 1 show that the excess antibody sample (e.g., 2.5:2.5:1 mAb1-A488:mAb1-647:target) showed the strongest cross-correlation.

[0061] [Table 2]

[0062] Figure 15 shows that the cross-correlation (e.g., XC) times were consistently longer than the auto-correlation times, indicating that co-localized (e.g., interacting) particles were detected because the cross-correlation time does not account for non-interacting species.

[0063] Figure 16 shows the FCS / FCCS data from the mAb1:NGF sample. The data demonstrate that the mAb1:NGF sample exhibits similar hydrodynamic radii in both PBS and serum across all channels.

Claims

1. 1. A method for determining the hydrodynamic radius of a sample, comprising: a) contacting said sample with at least one specific fluorophore capable of binding to proteins; b) measuring the correlation time of said sample using a confocal microscope; and c) determining a hydrodynamic radius for the sample based on the correlation time. A method comprising:

2. The method of claim 1 , wherein the correlation time is determined using fluorescence correlation spectroscopy (FCS).

3. The method of claim 1, wherein the method is used to estimate protein-ligand stoichiometry in the sample.

4. The method of claim 2 , wherein a fitted model is used to determine the correlation time.

5. The method of claim 4 , wherein the fitting model is selected from the group consisting of a triplet fitting model, a translation fitting model, or a combination thereof.

6. The method of claim 1 , wherein the protein is an antibody.

7. The method of claim 1 , wherein the protein is a monoclonal antibody.

8. The method of claim 1 , wherein the two unique fluorophores exhibit non-overlapping emission spectra.

9. 10. The method of claim 1, wherein the two unique fluorophores include Alexa Fluor® 488, Alexa Fluor® 647, or both.

10. The method of claim 1 , wherein the correlation time is selected from the group consisting of a cross-correlation time, an auto-correlation time, or a combination thereof.

11. The method of claim 1 , wherein the sample is serum.

12. The method of claim 1 , wherein the sample comprises a biological system.

13. 1. A method for determining the hydrodynamic radius of a sample, comprising: a) contacting said sample with at least one specific fluorophore capable of binding to proteins; b) measuring the cross-correlation and / or autocorrelation times of said fluorophores in said sample using a confocal microscope capable of fluorescence correlation spectroscopy (FCS); and c) determining a hydrodynamic radius for the sample based on the correlation time. A method comprising:

14. 14. The method of claim 13, wherein the cross-correlation time and / or autocorrelation time are determined using fluorescence correlation spectroscopy (FCS).

15. The method of claim 13, wherein the method is used to estimate protein-ligand stoichiometry in the sample.

16. The method of claim 14 , wherein a fitted model is used to determine the cross-correlation time and / or the auto-correlation time.

17. 17. The method of claim 16, wherein the fitting model is selected from the group consisting of a triplet fitting model, a translation fitting model, or a combination thereof.

18. The method of claim 13 , wherein the protein is an antibody.

19. The method of claim 13 , wherein the protein is a monoclonal antibody.

20. 14. The method of claim 13, wherein the two unique fluorophores exhibit non-overlapping emission spectra.

21. 14. The method of claim 13, wherein the two unique fluorophores include Alexa Fluor® 488, Alexa Fluor® 647, or both.

22. The method of claim 13, wherein the sample is serum.

23. The method of claim 13 , wherein the sample comprises a biological system.

24. 1. A method for estimating protein-ligand stoichiometry in a sample, comprising: a) contacting said sample with at least one specific fluorophore capable of binding to proteins; b) measuring the cross-correlation time and / or autocorrelation time of said fluorophores in said sample using a confocal microscope capable of fluorescence correlation spectroscopy (FCS); and c) estimating the protein-ligand stoichiometry in the sample based on the correlation time. A method comprising:

25. 25. The method of claim 24, wherein a fitted model is used to determine the cross-correlation time and / or the autocorrelation time.

26. 26. The method of claim 25, wherein the fitting model is selected from the group consisting of a triplet fitting model, a translation fitting model, or a combination thereof.

27. 25. The method of claim 24, wherein the protein is an antibody.

28. 25. The method of claim 24, wherein the protein is a monoclonal antibody.

29. 25. The method of claim 24, wherein the two unique fluorophores exhibit non-overlapping emission spectra.

30. 25. The method of claim 24, wherein the two unique fluorophores include Alexa Fluor® 488, Alexa Fluor® 647, or both.

31. 23. The method of claim 22, wherein the sample is serum.

32. 23. The method of claim 22, wherein the sample comprises a biological system.

33. 1. A method for determining the hydrodynamic radius of a sample, comprising: a) labeling a protein with a first fluorophore; b) labeling the ligand of said protein with a second fluorophore; c) combining the labeled protein and the labeled ligand in the sample; d) measuring the correlation time of said sample using a confocal microscope capable of FCS; and e) determining a hydrodynamic radius for the sample based on the correlation time. A method comprising:

34. 34. The method of claim 33, wherein the correlation time is determined using fluorescence correlation spectroscopy (FCS).

35. 34. The method of claim 33, wherein the method is used to estimate protein-ligand stoichiometry in the sample.

36. 34. The method of claim 33, wherein a fitted model is used to determine the correlation time.

37. 37. The method of claim 36, wherein the fitting model is selected from the group consisting of a triplet fitting model, a translation fitting model, or a combination thereof.

38. 34. The method of claim 33, wherein the protein is an antibody.

39. 34. The method of claim 33, wherein the protein is a monoclonal antibody.

40. 34. The method of claim 33, wherein the first and second phosphors exhibit non-overlapping emission spectra.

41. 34. The method of claim 33, wherein the first and second fluorophores comprise Alexa Fluor® 488, Alexa Fluor® 647, or both.

42. 34. The method of claim 33, wherein the correlation time is selected from the group consisting of a cross-correlation time, an auto-correlation time, or a combination thereof.

43. 34. The method of claim 33, wherein the sample is serum.

44. 34. The method of claim 33, wherein the sample comprises a biological system.

45. 1. A method for estimating protein-ligand stoichiometry in a sample, comprising: a) labeling a protein with a first fluorophore; b) labeling the ligand of said protein with a second fluorophore; c) combining the labeled protein and the labeled ligand in the sample; d) measuring the cross-correlation time and / or autocorrelation time of said sample using a confocal microscope capable of FCS; and e) estimating the protein-ligand stoichiometry in the sample based on the correlation time. A method comprising:

46. 46. ​​The method of claim 45, wherein the cross-correlation and auto-correlation times are determined using fluorescence correlation spectroscopy (FCS).

47. 46. ​​The method of claim 45, wherein a fitted model is used to determine the cross-correlation time and / or the auto-correlation time.

48. 48. The method of claim 47, wherein the fitting model is selected from the group consisting of a triplet fitting model, a translation fitting model, or a combination thereof.

49. 46. ​​The method of claim 45, wherein the protein is an antibody.

50. 46. ​​The method of claim 45, wherein the protein is a monoclonal antibody.

51. 46. ​​The method of claim 45, wherein the first and second fluorophores exhibit non-overlapping emission spectra.

52. 46. ​​The method of claim 45, wherein the first and second fluorophores comprise Alexa Fluor® 488, Alexa Fluor® 647, or both.

53. 46. ​​The method of claim 45, wherein the sample is serum.

54. 46. ​​The method of claim 45, wherein the sample comprises a biological system.

55. 1. A method for determining the hydrodynamic radius of a sample, comprising: a) labeling a protein with a first fluorophore; b) labeling the secondary labeled reporter with a second fluorophore; c) combining the labeled protein and a secondary labeled reporter in said sample; d) measuring the cross-correlation time and / or autocorrelation time of said sample using a confocal microscope capable of fluorescence correlation spectroscopy (FCS); and e) determining a hydrodynamic radius for the sample based on the correlation time. A method comprising:

56. 56. The method of claim 55, wherein said method is used to estimate protein-ligand stoichiometry in said sample.

57. 56. The method of claim 55, wherein a fitted model is used to determine the cross-correlation time and / or the auto-correlation time.

58. 58. The method of claim 57, wherein the fitting model is selected from the group consisting of a triplet fitting model, a translation fitting model, or a combination thereof.

59. 58. The method of claim 57, wherein the protein is an antibody.

60. 58. The method of claim 57, wherein the protein is a monoclonal antibody.

61. 58. The method of claim 57, wherein the first and second fluorophores exhibit non-overlapping emission spectra.

62. 58. The method of claim 57, wherein the first and second fluorophores comprise Alexa Fluor® 488, Alexa Fluor® 647, or both.

63. 58. The method of claim 57, wherein the sample is serum.

64. 58. The method of claim 57, wherein the sample comprises a biological system.

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