Method and compositions for interferon type i decoys as a therapeutic for interferon autoantibodies

Abstract

Disclosed herein are Methods and Compositions for Interferon-α2 and Interferon-ω decoys as a therapeutic for interferon autoantibodies.

Description

Docket No. MIS-110-PCT TITLE Method and Compositions for Interferon type I decoys as a therapeutic for interferon autoantibodies BACKGROUND

[0001] The present disclosure relates generally to interferons and therapeutics.

[0002] Interferons are a family of proteins used by the human immune system to activate cell intrinsic programs that enhance resistance to viral infection. Three families of interferons exist, designated as: type I, type II and type III. These three families are described by 21 family members: sixteen type I IFNs (12 IFNα’s, 1 IFNβ, 1 IFNε, 1 IFNκ, 1 IFNω), one type II IFN (IFNγ), and four type III IFNs (IFNλ1, IFNλ2, IFNλ3, IFNλ4)1.

[0003] Autoantibodies (autoAbs) against type I IFNs exist in humans and make those individuals susceptible to viral infections. Some patient populations with rare genetic mutations harbor type I IFN autoAbs, such as individuals with mutations in: AIRE2,3, FoxP34,5, RAG1 / RAG26, and genes involved in alternative NFκβ signaling7-9. Type I IFN autoAbs are not restricted to those with rare genetic mutations; 4% of the elderly (defined by being >70 years old) in the general population harbor autoantibodies against type I IFN8. Type I IFN autoAbs are not passive entities; studies indicate that these autoAbs are particularly potent inhibitors of antiviral immunity in their human hosts. Indeed, type I autoAbs drive advanced disease presentation in individuals challenged by any of the following viruses: SARS-CoV-28,10-12and MERS- CoV13coronaviruses, influenza14, West Nile Virus15, herpesvirus16, cytomegalovirus16, tick-borne encephalitis virus17, Powassan, Usutu and Ross River viruses18. Furthermore, type I IFN autoAbs interfere with antiviral immunity that is usually established through vaccination. This notion is supported by adverse events occurring in type I IFN autoantibody carriers when administered live viral vaccines, particularly live-attenuated yellow fever vaccine19. Furthermore, breakthrough infections are observed more frequently in carriers despite receiving and COVID19 mRNA vaccines20. In summary, individuals who possess type IDocket No. MIS-110-PCT IFNs autoAbs are atypically susceptible to viral infection. As such, these patients would benefit from a therapeutic that addresses these autoAbs.

[0004] Interestingly, type I IFN autoAbs from patients preferentially bind to a subset of type I IFN family members. Reactivities autoAbs have been restricted to IFNα’s and IFNω over other type I IFNs; these autoAbs also do not bind type II and type III IFNs2,3,5,7-10,14,19. Therefore, a therapeutic for autoAbs need only to address the autoAbs’s ability to bind IFNα’s and IFNω.

[0005] Treatments for autoimmune disorders currently alter patient biology by broad brush. Treatments such as steroids, whole IgG depletion and plasmapheresis are non-specific. Although they may elicit a therapeutic effect, their non-specific nature causes many undesirable off-target effects.

[0006] The present invention attempts to solve these problems, as well as others. SUMMARY OF THE INVENTION

[0007] Provided herein are composition and methods for Interferon-α2 and Interferon-ω decoys as a therapeutic for interferon autoantibodies.

[0008] The composition and methods are set forth in part in the description which follows, and in part will be obvious from the description, or can be learned by practice of the composition and methods. The advantages of the composition and methods will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the composition and methods, as claimed.

[0009] Accordingly, it is an object of the invention not to encompass within the invention any previously known product, process of making the product, or method of using the product such that Applicants reserve the right and hereby disclose a disclaimer of any previously known product, process, or method. It is further noted that the invention does not intend to encompass within the scope of the invention any product, process, or making of the product or method of using the product, which does not meet the written description and enablement requirements of the USPTO (35 U.S.C. § 112, first paragraph) or the EPO (Article 83 of the EPC), such that Applicants reserve the right and hereby disclose a disclaimer of anyDocket No. MIS-110-PCT previously described product, process of making the product, or method of using the product. It may be advantageous in the practice of the invention to be in compliance with Art. 53(c) EPC and Rule 28(b) and (c) EPC. All rights to explicitly disclaim any embodiments that are the subject of any granted patent(s) of applicant in the lineage of this application or in any other lineage or any prior filed application of any third party is explicitly reserved. Nothing herein is to be construed as a promise. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In the accompanying figures, like elements are identified by like reference numerals among the several preferred embodiments of the present invention.

[0011] FIGS. 1A-1C are schematics showing three therapeutic strategies to address type I IFN autoAbs using an IFN decoy.

[0012] FIGS. 2A-2B are schematics showing the advantages of using a decoy IFN (FIG. 2B) over wild- type (FIG.2A) IFN as a therapeutic for anti-IFN autoantibodies.

[0013] FIG. 3 is a schematic workflow to determine target residues to mutate to produce potential decoy IFNs incapable of activating the IFNAR receptor.

[0014] FIGS. 4A-4B are graphs showing the most contributing amino acids residues on IFNα2(YNS), FIG.4A, and IFNω interface, FIG.4B, against IFNAR1 and IFNAR2 in two body docking analysis.

[0015] FIG. 5 is a graph showing the most contributing IFNα2 amino acids residues in the interface with IFNAR1 and IFNAR2 in a three-body docking analysis.

[0016] FIG. 6 is a graph showing the most contributing IFNω amino acid residues in the interface with IFNAR1 and IFNAR2 in a three-body docking analysis.

[0017] FIGS. 7A-7B are data that shows a coincidence of IFN autoAb binding preferences with protein sequence similarity trends within the type I IFN family. FIG.7A is a graph showing reactivities of different patient autoAbs against all members of the type I IFN family as evaluated by ELISA. Each row represents serum from a different patient while each column represents reactivity against any given type I IFN family member. FIG.7B is a phylogenetic tree that represents evolutionary relationships between all human typeDocket No. MIS-110-PCT I IFN family members. The scale bar indicates evolutionary distance as per the number of substitutions per amino acid site.

[0018] FIGS.8A-8B is a filtered multiple sequence alignment of the type I IFN family which showcases hypothesized autoAb epitopes on IFNα2. Epitopes were identified through dissimilarity analysis that designated the ‘In-group’ as IFN(α’s, ω) and the ‘Out-group’ as IFN(β, κ, ε). Surface or buried residues were obtained from accessible surface area analysis carried out on IFNα2.

[0019] FIGS.9A-9B is a filtered multiple sequence alignment of the type I IFN family which showcases hypothesized autoAb epitopes on IFNω. Epitopes were identified through dissimilarity analysis that designated the ‘In-group’ as IFN(α’s, ω) and the ‘Out-group’ as IFN(β, κ, ε). Surface or buried residues were obtained from accessible surface area analysis carried out on IFNω.

[0020] FIGS.10A-10B is a filtered multiple sequence alignment of the type I IFN family which showcases hypothesized autoAb epitopes specific to IFNα’s. Epitopes were identified through dissimilarity analysis that designated the ‘In-group’ as IFNα’s and the ‘Out-group’ as IFN(ω, β, κ, ε). Surface or buried residues obtained from accessible surface area analysis carried out on IFNα2.

[0021] FIGS. 11A-11B are representative SDS-PAGE examining the purification of FIG. 11A) a well- behaved IFNα2a mutant and FIG. 11B) a poorly-behaved IFNα2a mutant from bacteria. Lane legend: NiNTA1 = all protein isolated after nickel-affinity chromatography of clarified bacterial lysate. NiNTA2 = all untagged IFNα2a isolated after using nickel-affinity chromatography to remove his-tagged SUMO.

[0022] FIG. 12 is an SDS-PAGE examining a time-course of expression of secreted, glycosylated His- IFN from Expi293F cells. Culture supernatants were sampled at each day of expression. The ‘ctrl’ lane is loaded with 1 μg of untagged IFNα2a purified from bacteria.

[0023] FIG. 13: Cell viability of A549 cells treated with different doses of IFN-WT and Decoy IFNα2a- R33D for 24 hours. Puromycin and a combination of PolyI:C and lipofectamine were included in this experiment as positive controls for toxicity. High doses of Decoy IFNα2a-R33D show a lack of toxicity in vitro.Docket No. MIS-110-PCT

[0024] FIG.14 is a graph showing IFNAR activation as measured by luciferase assay. Wild type IFNα2a was titrated from 10-3to 105ng / mL. The blue datapoints represent the assay done under transient transfection of reporter DNA. The red datapoints represent the assay using a reporter cell line created through lentiviral transduction and antibiotic selection.

[0025] FIGS. 15A-15C: IFN Decoys exhibit IFNAR inactivity. FIG. 15A) is a schematic detailing how the Inactivity Index is calculated as a quantitative representation of the inability of an IFN Decoy to activate IFNAR. FIG. 15B) is a representative experiment examining IFNα2a Decoys R33A, R33E, and R33D. FIG.15C) is a compilation of Inactivity Indices of all IFNα2a Decoys tested. WT = wild-type, * = an IFN decoy with an inactivity difference compared that wild-type that is statistically significant (unpaired t-test, p < 0.05).

[0026] FIG.16: MX1 mRNA levels in A549 cells treated with different doses of IFN-WT, Decoy IFNα2a- R33D, Decoy IFNα2a-R120E and Decoy IFNα2a-R149E. MX1 mRNA levels are assessed as an indicator of ISG activation. The lack of MX1 induction in cells treated with Decoy IFNα2a-R120E and Decoy IFNα2a-R149E suggests a lack of activation of the IFNAR receptors.

[0027] FIG. 17: Heatmap of differentially expressed ISGs in A549 cells treated with different doses of IFN-WT, Decoy IFNα2a-R120E and Decoy IFNα2a-R149E by RNAseq. The cluster of upregulated genes is unique to IFN-WT and not Decoy IFNα2a-R120E and Decoy IFNα2a-R149E, suggesting a lack of IFN activity by those mutants.

[0028] FIGS. 18A-18C: IFNAR competition assays. FIG. 18A) is a schematic detailing how the Competition Index is calculated as a quantitative representation of how much an IFN Decoy competes with IFN-WT for IFNAR. FIG. 18B) is a representative competition experiment examining Decoy IFNα2a- R120E. FIG. 18C) is a compilation of Competition Indices of measured to-date. The Decoys evaluated here are IFNα2a-R33D, -R33E, R149E, R120E. Positive (anti-IFNAR2 antibody) and negative (an isotype antibody) controls are also evaluated. The identity of the competitor used in the experiment is in the title of the corresponding graph. A zone of ‘no competition’ was determined by taking the average of EC50’s fromDocket No. MIS-110-PCT technical replicates of IFNα2a-WT alone, dividing each individual IFNα2a EC50by the average IFNα2a- WT EC50, then plotting the average + / - one standard deviation of the aforementioned quotient.

[0029] FIG. 19 MX1 mRNA levels in A549 cells treated first with poly:IC followed by a treatment with different doses of IFN-WT, Decoy IFNα2a-R33D, Decoy IFNα2a-R120E and Decoy IFNα2a-R149E. MX1 mRNA levels are assessed as an indicator of ISG activation. MX1 is induced to a basal level as a consequence of endogenous IFNs produced by the cells in the presence of poly:IC. Treatment with IFN- WT shows an exacerbated production of MX1 mRNA; cells treated with Decoy IFNα2a-R120E and Decoy IFNα2a-R149E do not show changes in MX1 expression, suggesting a lack of activity of the decoys and lack of antagonistic function towards the endogenous IFNs.

[0030] FIG. 20: IFN decoy affinity and neutralization of autoAb in patient sera observed using surface plasmon resonance (SPR). FIG. 20A) IFN decoy (R33D, R33E, R33A, and L30A) and IFN-WT immobilized on the SPR chip, and autoAb positive and negative patient sera were introduced. AutoAb positive patient sera showed strong affinity to IFN decoys similar to the affinity to IFN-WT while showing no affinity to autoAb negative patient sera. FIG. 20B) IFN-WT was immobilized on the SPR chip and solutions of autoAb positive patient sera and IFN (100 nM) were flown over the chip. AutoAb positive patient sera and neutralizing anti-IFNα2a Ab showed strong affinity, but the mixtures with IFN decoy and IFN-WT showed poor affinity indicating interception of the autoAb in the sera and preventing interaction with the immobilized IFN-WT. FIG.20C) Degree of neutralization of the autoAb in pooled patient sera by the INF decoys (R33D, R33E, L30A, R149E, and R120E) and IFN-WT. IFN decoys show significantly high neutralization of the autoAb comparable to IFN-WT.

[0031] FIG. 21: Reconstitution of IFNAR, IFN-WT, Decoy IFNα2a-R120E, and patient autoAbs in the luciferase assay. Patient autoAbs used here is a mixture of sera from three patients that were hospitalized with COVID and shown to have a high levels of type I IFN autoantibodies by ELISA8(data not shown). In this experiment, patient sera is diluted by 1000-fold after mixture with all other assay components. DETAILED DESCRIPTIONDocket No. MIS-110-PCT

[0032] Embodiments of the invention will now be described with reference to the Figures, wherein like numerals reflect like elements throughout. The terminology used in the description presented herein is not intended to be interpreted in any limited or restrictive way, simply because it is being utilized in conjunction with detailed description of certain specific embodiments of the invention. Furthermore, embodiments of the invention may include several novel features, no single one of which is solely responsible for its desirable attributes, or which is essential to practicing the invention described herein.

[0033] Generally speaking, the invention is a method or a composition comprising three therapeutic embodiments to neutralize or nullify type I IFN autoAbs (Figs. 1A-1C), wherein the therapeutic methods and compositions comprise administering a therapeutically effective amount of a protein referred to herein as an “IFN decoy”. In the first embodiment, the therapeutic method or composition comprises nullifying circulating autoAbs in situ (Fig. 1A) by injecting the IFN decoy at concentrations much higher than endogenous IFN. By having [IFN decoy] > [endogenous IFN], anti-IFN autoAbs are compelled to bind to the decoy, leaving the endogenous IFN available to initiate antiviral immunity. In the second embodiment, the therapeutic comprises removing autoAbs from circulation (Fig.1B) by conjugating the IFN decoy to a clearance molecule that leverages existing mechanisms in humans (such as CR1-mediated liver disposal21, or FcRn-mediated IgG recycling22,23) to remove anti-IFN autoAbs from circulation. By having anti-IFN autoAbs bind to the decoy-fused clearance molecule, the resulting binary complex is depleted from circulation. In the third embodiment, the therapeutic comprises cutting off the source of the autoAbs by targeting the B-cell lymphocytes that produce them (Fig.1C), and conjugating the IFN decoy to a cytotoxic molecule that leverages existing cytotoxic mechanisms in humans (such as antibody-mediated cellular cytotoxicity24, or death via CAR T-Cells25). The anti-IFN B-cells contain a membrane bound B-cell receptor that retains a binding specificity for IFNs seen in the autoAbs. By allowing the anti-IFN B-cells to bind to the decoy, the B-cell is brought within proximity of the cytotoxic molecule to result in death of the B-cell. In this entire scheme (Fig.1), the IFN decoy is the proposed invention.Docket No. MIS-110-PCT

[0034] A natural choice for the IFN decoy would be to use the wild-type IFN (IFNα’s and / or IFNω). Wild- type IFN retains an ability to bind autoAbs2,3,5,7-10,14,19. However, wild-type IFN also retains the ability to bind to its cognate interferon-α / β receptor (IFNAR) receptor26(Fig. 2A). Although physiological concentrations of type I IFN are responsible for initiating antiviral responses through IFNAR, therapeutic efficacy may require decoy IFN concentrations much higher than physiological concentrations, which will lead to unwanted side effects through overactivation of IFNAR (Fig. 2A). This is supported by the observation that clinically approved IFNα’s (Roferon-A, Intron A, PEGINTRON, PEGASYS) are associated with a wide range of side effects that impart flu-like symptoms and toxicities associated with the gastrointestinal tract, liver, central nervous system and immune system27-30. The present invention is an improved IFN decoy comprising an engineered mutation (Fig.2B) which abolishes the IFNAR interaction to allow higher doses of decoy to be administered with diminished side effects (Fig. 2B). The invention comprises decoy mutated IFNs that cannot activate IFNAR but retain an ability to bind a majority of autoAbs (Fig.2B).

[0035] While cytokine decoys have been used in other therapeutic contexts, this invention is the first application of cytokine decoys to address autoAbs.

[0036] Treatments for autoimmune disorders currently alter patient biology by broad brush. Treatments such as steroids, whole IgG depletion and plasmapheresis are non-specific. Although they may elicit a therapeutic effect, their non-specific nature causes many undesirable off-target effects. The IFN decoys are specific to the offending agent (IFN autoAbs), and as a result, are anticipated maintain their on-target therapeutic effect with reduced off-target effects.

[0037] In one embodiment, the method or composition comprises mutation of following candidate IFNα2a amino acids: (Arg12, Arg13, Leu15, Gln20, Arg22, Lys23, Leu30, Lys31, Arg33, Asp35, Phe64, Lys70, Glu78, Asp82, Lys83, Tyr85, Tyr89, Glu96, Arg120, Lys121, Lys131, Lys134, Arg144, Ala145, Met148, Arg149, Ser152, Leu153, Asn156) are candidate residues for mutation to modulate affinity for binding IFNAR. All but one of these residues have been examined by mutation for their effect on binding IFNAR. Mutation of Lys83 has not been tested for its affinity against IFNAR in the prior art. (Thr6, Leu9, Arg12,Docket No. MIS-110-PCT Arg13, Met16, Lys23, Asp35, Ala74, Gln101, Val105, Asp114, Thr127, Leu128) are candidate residues for mutation to modulate affinity for binding patient autoAbs. None of these residues have been examined by mutation in published literature for their effect on binding patient autoAbs.

[0038] This method and composition comprise mutation of the following candidate IFNω amino acids: (Asp4, Gln7, Arg14, Arg24, Leu32, Arg35, Asp37, Arg39, Lys52, Phe67, His71, Glu73, Arg74, Asp85, Gln96, Glu99, Gln104, Arg123, Arg124, Arg130, Lys134, Lys152, Asn159) are candidate residues for mutation to modulate affinity for binding IFNAR. The following IFNω residues have not been tested through mutation for their effect on binding IFNAR in published literature: Asp4, Gln7, Arg14, Arg24, Arg35, Asp37, Arg39, Lys52, Arg74, Gln96, Glu99Ala, Gln104, Arg124, Arg130, Lys134, Asn159. All other listed residues have been tested. (Asn8, Leu11, Arg14, Val18, Arg25, Asp37, Ala77, Gln104, Ala119, Val131) are candidate residues for mutation to modulate affinity for binding patient autoAbs. None of these residues have been examined by mutation in published literature for their effect on binding patient autoAbs.

[0039] An alternative embodiment of this invention is a method or composition that uses a different modality than an IFN mutation to disrupt its interaction with the IFNAR receptor. Other methods include using small organic molecules or targeted peptides that bind to areas on IFN that are critical for IFNAR interaction.

[0040] An alternative embodiment of this invention is a method or composition that uses a different entity than an IFN decoy to inhibit the autoAbs. Other methods or compositions include using small molecule or targeted peptides to bind to autoAb complementarity determining regions.

[0041] An alternative embodiment of this invention is an IFN that evades binding to autoAbs but still retains an ability to activate IFNAR. In contrast to the primary embodiment of this disclosure, which seeks to mutate IFNAR binding residues and leave autoAb epitopes intact, this alternative embodiment would seek to abolish autoAb epitopes through mutation but leave IFNAR binding residues intact. Such an IFN would be able to activate antiviral responses if exogenously administered and would not rely on endogenous IFN to elicit an antiviral response. This alternative embodiment is termed to be a “Stealth IFN” because itDocket No. MIS-110-PCT would utilize its mutations to avoid molecular interception by autoantibodies but still be able to activate IFNAR.

[0042] Definitions

[0043] “Binding” or “affinity” refers to the strength of the sum total of non-covalent interactions between a single binding site of a molecule (e.g., a receptor) and its binding partner (e.g., a ligand). Unless indicated otherwise, as used herein, “binding” or “affinity” refers to intrinsic binding affinity which reflects a 1:1 interaction between members of a binding pair (e.g., an antigen binding moiety and an antigen and / or a receptor and its ligand). The affinity of a molecule X for its partner Y can generally be represented by the dissociation constant (KD), which is the ratio of dissociation and association rate constants (koffand kon, respectively). Thus, equivalent affinities may comprise different rate constants, as long as the ratio of the rate constants remains the same. Affinity can be measured by well-established methods known in the art, including those described herein. A preferred method for measuring affinity is Surface Plasmon Resonance (SPR) and a preferred temperature for the measurement is 25° C.

[0044] The term “amino acid” refers to naturally occurring and synthetic amino acids, as well as amino acid analogs and amino acid mimetics that function in a manner similar to the naturally occurring amino acids. Naturally occurring amino acids are those encoded by the genetic code, as well as those amino acids that are later modified, e.g. hydroxyproline, γ-carboxyglutamate, and O-phosphoserine. Amino acid analogs refer to compounds that have the same basic chemical structure as a naturally occurring amino acid, i.e., an α carbon that is bound to a hydrogen, a carboxyl group, an amino group, and an R group, e.g., homoserine, norleucine, methionine sulfoxide, methionine methyl sulfonium. Such analogs have modified R groups (e.g., norleucine) or modified peptide backbones, but retain the same basic chemical structure as a naturally occurring amino acid. Amino acid mimetics refers to chemical compounds that have a structure that is different from the general chemical structure of an amino acid, but that function in a manner similar to a naturally occurring amino acid. Amino acids may be referred to herein by either their commonly known three letter symbols or by the one-letter symbols recommended by the IUPAC-IUB Biochemical Nomenclature Commission.Docket No. MIS-110-PCT

[0045] The term “amino acid mutation” as used herein is meant to encompass amino acid substitutions, deletions, insertions, and modifications. Any combination of substitution, deletion, insertion, and modification can be made to arrive at the final construct, provided that the final construct possesses the desired characteristics, e.g., reduced binding to an Fc receptor. Amino acid sequence deletions and insertions include amino- and / or carboxy-terminal deletions and insertions of amino acids. Particular amino acid mutations are amino acid substitutions. For the purpose of altering e.g., the binding characteristics of an Fc region, non-conservative amino acid substitutions, i.e. replacing one amino acid with another amino acid having different structural and / or chemical properties, are particularly preferred. Amino acid substitutions include replacement by non-naturally occurring amino acids or by naturally occurring amino acid derivatives of the twenty standard amino acids (e.g., 4-hydroxyproline, 3-methylhistidine, ornithine, homoserine, 5-hydroxylysine). Amino acid mutations can be generated using genetic or chemical methods well known in the art. Genetic methods may include site-directed mutagenesis, PCR, gene synthesis and the like. It is contemplated that methods of altering the side chain group of an amino acid by methods other than genetic engineering, such as chemical modification, may also be useful. Various designations may be used herein to indicate the same amino acid mutation.

[0046] As will be recognized it is possible to make conservative amino acid substitutions within the sequences of the current invention. By “conservative substitution” means amino acids having similar properties. As used in this specification the following groups of amino acids are to be seen as conservative substitutions: H, R and K; D, E, N and Q; V, I and L; C and M; S, T, P, A and G; and F, Y and W. D and E – anionic residues; S, T, N, and Q – polar uncharged residues; A, V, I, L, P and M – hydrophobic aliphatic residues. It is not intended, however, that substitutions other than those specifically recited are made at the sites of attenuation and / or glycosylation.

[0047] The term “autoantibodies” refers to antibodies that specifically target self-antigens, causing an autoimmune response. AutoAbs are generated as a result of the loss of tolerance response against self- antigens and can be pathogenic.Docket No. MIS-110-PCT

[0048] As used herein, an “antibody” refers to a protein comprising one or more polypeptides substantially or partially encoded by immunoglobulin genes or fragments of immunoglobulin genes and having specificity to a tumor antigen or specificity to a molecule overexpressed in a pathological state. The recognized immunoglobulin genes include the kappa, lambda, alpha, gamma, delta, epsilon and mu constant region genes, as well as subtypes of these genes and myriad of immunoglobulin variable region genes. Light chains are classified as either kappa or lambda. Heavy chains are classified as gamma, mu, alpha, delta, or epsilon, which in turn define the immunoglobulin classes, IgG, IgM, IgA, IgD and IgE, respectively. A typical immunoglobulin (e.g., antibody) structural unit comprises a tetramer. Each tetramer is composed of two identical pairs of polypeptide chains, each pair having one “light” (about 25 kD) and one “heavy” chain (about 50-70 kD). The N-terminus of each chain defines a variable region of about 100 to 110 or more amino acids primarily responsible for antigen recognition. The terms variable light chain (VL) and variable heavy chain (VH) refer to these light and heavy chains, respectively.

[0049] “Mutated” or “variant” of a protein or polypeptide comprises an amino acid sequence wherein one or more amino acid residues are inserted into, deleted from and / or substituted into the amino acid sequence relative to another polypeptide sequence. Variants of the invention include fusion proteins.

[0050] The term “nullifying” or “neutralizing” refers to effectively targeting autoAbs immunomodulatory proteins, reducing or stopping their intended functions.

[0051] A “therapeutically effective amount” of an agent, e.g., a pharmaceutical composition, refers to an amount effective, at dosages and for periods of time necessary, to achieve the desired therapeutic or prophylactic result. A therapeutically effective amount of an agent for example eliminates, decreases, delays, minimizes or prevents adverse effects of a disease.

[0052] Description of Embodiments

[0053] The challenge of designing mutations for an IFN decoy arises from the fact that wild-type IFN can do two things: 1) wild-type IFN can bind autoAbs and 2) wild-type IFN can bind the IFNAR receptor (Fig. 2A). This creates a tension that must be intelligently navigated; IFN mutations that perturb binding toDocket No. MIS-110-PCT IFNAR may also perturb binding to autoAbs. Thus, IFN mutations of the present invention are chosen to asymmetrically diminish its affinity to IFNAR over its affinity to autoAbs (Fig.2B).

[0054] A structural understanding of the IFN:IFNAR interaction and the IFN:autoAbs interaction aids the choice of IFN mutation. To model the IFN:IFNAR interaction, structures of IFNα2 or IFNω bound to IFNAR31are used to initialize molecular docking. This analysis identifies IFN surface amino acids that are high-contributing keystones for the IFN:IFNAR interaction. Here, single amino acid mutations of said keystones are anticipated to result in asymmetrically high perturbance of the IFN:IFNAR interaction.

[0055] To model the IFN:autoAbs interaction, the preference of patient autoAbs to bind certain members of the type I IFN family are used. Namely, the patient autoAbs bind to all IFNα subtypes and IFNω, but do not bind to IFNβ, IFNκ, and IFNε. Using structural and bioinformatic constraints, hypothesized epitopes that the autoAbs use to bind to IFNα’s and IFNω were derived.

[0056] In one embodiment, the methods and compositions of the present invention comprises IFNs (IFNα2 or IFNω) that contain the least number of mutations to abolish the IFNAR interaction while preserving a high degree of binding to IFN autoAbs.

[0057] Validation

[0058] IFN:IFNAR candidate interfacial residues to be mutated (Docking studies, structural analyses)

[0059] The native IFN and receptor crystal structure PDBs31are used to carry out docking studies between IFNAR1 and IFNAR2 against IFNα2(YNS) and IFNω. The docking was done in an ab initio computational model under solvated conditions. Fig. 3 outlines the workflow involved with the docking analysis. The initial analysis consisted of interactions between the native IFNAR and IFNs in two body and three body systems with explicit definition of Amino Acids (AA) on the surface at the interface. The computational data provided per residue energy contribution of defined AA which would be contributing most to the protein-protein interaction. The most contributing AA on the IFN are identified as target residues to mutate to disrupt the IFN-IFNAR interaction desired in the decoy IFN.

[0060] Two body analysis between IFNs and IFNARs identify the most energetically contributing residues for the interaction revealed several residues of interest (Fig. 4 and Table 1). IFNα2(YNS) and IFNω bothDocket No. MIS-110-PCT showed higher affinity towards the IFNAR2, which indicated the possibility to disrupt the IFN interaction with IFNAR2 which could subsequently lead to poor affinity of IFN to both biological IFNARs. IFNα2(YNS) is a mutated IFN that was developed to improve affinity towards IFNAR1 to aid in crystallization31. There were three AA mutated in IFNα2(YNS). Further investigation of the interactions of the native IFNα2 was carried out by converting mutated AAs back to their original moieties (IFNα2-wt).

[0061] Table 1. Per residue energy contribution of IFNs against the IFNARs Residue Energy (kcal / mol) btain per energycontribution of the IFN interface residues (Fig. 5). Table 2 highlights the 20 most contributing residues of IFNα2-wt. Computation showed that the wild type IFNα2 and the mutated IFNα2(YNS) both have someDocket No. MIS-110-PCT common residues that AA of interest to carry out mutations to produce decoy IFN. A similar three-body docking was carried out using IFNω-wt and the IFNAR1 / IFNAR2 receptors. The energetic contribution of IFNω interface residues are detailed in Fig.6. Table 3 highlights the 20 most contributing residues of IFNω- wt.

[0063] Table 2. Top 20 IFNα2-wt residues that contribute to the stability of the IFNα2-IFNAR complex in docking studies Residue Energy Residue Energy Residue Energy l l

[0064] es stability of the IFNω-IFNAR complex indocking studies Energy Energy Energy ) 435 75 38 98 5Docket No. MIS-110-PCT LYS 52 B -15.2382 GLN 104 B -9.12627 HIS 71 B -7.09381GLU 99 B -14.5253 LYS 152 B -8.61857

[0065] e oc ng s u es o e IF: n er ace a en e o over-represen y rop c residues and under-represent hydrophobic residues. This left open the possibility that the docking had missed important interfacial hydrophobic residues, which are known to be essential for protein:protein binding in other contexts32,33. Therefore, the IFN:IFNAR crystal structures were manually scrutinized31to identify important IFN interfacial residues that were not strongly represented in the docking studies. Important residues identified from this search are listed in Table 4. As suspected, most of these residues are hydrophobic in nature.

[0066] Table 4: Important interfacial IFN residues IFNα2 IFNωLEU15 LEU30 PHE64 ng studies and manual scrutiny of prior art,Table 5 contains the complete list of IFNα2 and IFNω residues that, if mutated, is expected to produce an IFN Decoy that is unable to engage with the IFNAR receptors.

[0068] Table 5. Target residues to mutate to disrupt the IFN:IFNAR interaction IFNα2 IFNω , 1,Docket No. MIS-110-PCT ARG120, LYS121, LYS131, ARG123, ARG124, ARG130, LY 1 4 AR 144 ALA14 LY 1 4 LY 1 2 A N1

[0069] Bioinformatic and structural constraints to obtain candidate IFN residues that are critical for binding patient autoAbs.

[0070] To determine which IFN residues were critical for binding patient autoAbs, preferential reactivities of patient autoAbs to select members of the type I IFN family were relied upon. As shown in Fig. 7A, patient sera from multiple patients tend to bind all IFNα subtypes and IFNω. However, the same sera shows substantially less affinity towards IFNβ, IFNκ, and IFNε. The preference of patient autoAbs to bind IFNα’s and IFNω, but not IFN(β / κ / ε), has been reproduced multiple times in published literature2,3,5,7-10,14,19. Members of the type I IFN family possessed a distinct evolutionary architecture. Fig. 7B shows a phylogenetic tree of the type I IFN family. The twelve subtypes of IFNα are closely related in sequence. IFNω is the most closely related member to the IFNα subtype cluster, while IFN(β,κ,ε) form a cluster that is divergent from all other IFN members. If the observed autoAb reactivities (Fig. 7A) are overlayed onto the type I IFN phylogenetic tree, the reactivity preferences of the autoAbs tracking with homologous sequence trends in the type I IFN family are shown (Fig. 7B). Namely, the preference of the autoAbs to bind only to IFN(α’s, ω) but not IFN(β,κ,ε) tracked with homologous sequence trends which show that IFN(α’s, ω) form a homologous sequence cluster while IFN(β,κ,ε) formed a divergent cluster. Because all type I IFNs adopt a similar conformation1, the amino acid differences between IFN(α’s, ω) and IFN(β,κ,ε) were hypothesized to be sufficient to explain the observed binding preferences of patient autoAbs. Therefore, IFN autoAb epitopes were designated to be IFN residues that 1) are located on the surface of IFN and 2) are chemically similar amongst IFN(α’s, ω) but chemically dissimilar to IFN(β, κ, ε).Docket No. MIS-110-PCT

[0071] To identify surface accessible amino acids of IFNα’s and IFNω, crystal structures of IFNα2 (PDB 4YPG34) and IFNω (PDB 3SE431) were selected to use as representative atomic structures. Structures were analyzed by the Define Secondary Structure of Proteins (DSSP) v2.2.1 tool35, which calculated the solvent accessible surface area (ASA) for any given residue. The per-residue solvent ASA was normalized to any given residue’s maximum allowable solvent accessibility to arrive at a new per-residue metric: relative solvent accessibility (RSA)36. IFN residues with an RSA greater than 25% were designated to be a surface accessible residue37to autoAbs. Following this automated analysis, atomic structures were manually curated in PyMOL to ensure that the selected residues were on the surface. During manual curation, a few amino acids had RSAs below the 25% cutoff, but their side chains could still be accessed in protein-protein binding and thus these were also added to the list of surface accessible amino acids.

[0072] To obtain IFN(α’s, ω) amino acids that were putative IFN autoAb epitopes, multiple sequence alignments of the type I IFN family were filtered to find residues that possessed the two aforementioned qualities: 1) located on the surface of IFN and 2) are chemically similar amongst IFN(α’s, ω) but chemically dissimilar to IFN(β, κ, ε). Fig. 8 and Fig. 9 contain the end result of filtering sequence alignments that showcase putative autoAb epitopes on IFNα2 and IFNω, respectively. Technical description of how the filtered sequence alignments were generated were as follows: Protein sequences of all 16 human type I IFNs (Uniprot ID P01563, P05000, P32881, P05014, P01571, P01566, P01567, P01568, P05015, P01570, P01569, P01562, P05013, Q9P0W0, P01574, Q86WN2) were subjected to a structure-based sequence alignment using PROMALS3D38. For each of the alignments, a column in the alignment was excluded from further analysis if it contained a buried amino acid from the protein of interest (IFNα2 for Fig.8, IFNω for Fig. 9), while columns containing a surface exposed amino acid from the protein of interest were retained for further analysis (Fig. 8 and 9, see “SASA: exposed” and “SASA: buried” in the legend). The criteria for determining whether an amino acid is buried or exposed is described in the section above. Of the retained columns in the alignments, every position in was assigned to one of seven properties (Fig. 8 and 9, see “Property Scale” in the legend): hydrophobic (alanine, valine, isoleucine, leucine, methionine), aromatic (phenylalanine, tyrosine, tryptophan), polar (serine, threonine, asparagine, glutamine), positiveDocket No. MIS-110-PCT (arginine, histidine, lysine), negative (aspartate, glutamate), ‘special case’ (cysteine, glycine, proline) or gap (if no amino acid present). Next, we defined two groups in the alignment: an ‘in-group’ that consisted of IFN(α’s, ω) and an ‘out-group’ that consisted of IFN(β,κ,ε). Columns where the in-group members did not share a consensus property were excluded from further analysis. For columns where the in-group members did share a consensus property, a “Dissimilar Count” is defined (Figs. 8 and 9, see “Dissimilar Count” in legend) as a metric to describe chemical dissimilarity between the in-group and the out-group. If all 3 members of the out-group contained a different property from the in-group’s consensus property, the column would be given a Dissimilar Count of 3. A column with Dissimilar Count: 3 is the strongest candidate epitope possible in this analysis, as it represents an amino acid that is chemically similar between IFN(α’s, ω) but chemically dissimilar in IFN(β,κ,ε). If none of the members of the out-group contained a different property from the in-group’s consensus property, the column would be given a Dissimilar Count of 0. A column with Dissimilar Count: 0 are not candidate epitopes for IFN autoAbs. Similar rules were used to derive Dissimilar Counts of 2 and 1, which are marginal candidate epitopes. Table 6 contains the Dissimilar Count 3 candidate epitope amino acids from this analysis. Table 6: ‘Dissimilar Count: 3’ amino acids from IFN that are candidate IFN autoAb epitopes. IFNα2 IFNωTHR LE AR 12 , bs tended to bind to IFNα’smore often than IFNω (Fig. 7A). This implied that there are autoAb epitopes that are specific to IFNα’s that are not found in IFNω. To find these IFNα-specific epitopes (Fig.10), the filtered sequence alignment of IFNα2 in Fig. 8 was modified to redefined the in-group to be IFNα subtypes only and the out-group to be IFN(ω,β,κ,ε). A dissimilarity analysis was carried out to find columns in the sequence alignment where the IFNα subtypes carried a consensus property that was not found in any of the 4 members of the out-Docket No. MIS-110-PCT group. These amino acids were given a Dissimilar Count of 4; these amino acids represent IFNα-specific autoAb epitopes. Table 7 contains candidate amino acids from this analysis.

[0074] Table 7: IFNα-specific candidate epitope amino acids IFNα2Residues

[0075] Table 8 combines amino acids from Table 6 and 7, which represents the complete list candidate IFN autoAb epitopes from this analysis.

[0076] Table 8: a comprehensive list of candidate IFN autoAb epitopes IFNα2 IFNω, ,

[0077] Expression and purification of IFN constructs

[0078] The method heterologously expresses IFNs (wild-type and mutants) in bacteria and purify them using NiNTA affinity that targets a protein-encoded polyhistidine tag. Using this procedure, the following IFNα2a mutants were expressed and purified: R12A, L30A, K31A, R33A, R33D, R33E, K70A, K83A, R120E, A145G, M148A, R149A, R149E, S152A, L153A. Fig. 11a is a representative SDS-PAGE of these mutants. Following NiNTA chromatography of clarified bacterial supernatant, a highly pure preparation of SUMO-IFNα2a is isolated (Fig 11a, NiNTA1 lane). The SUMO-IFNα2a fusion protein is cleaved by a substoichiometric amount of SUMO protease to yield histagged SUMO and untagged IFNα2a in the same mixture (Fig.11a, SUMO cleave lane). The mixture of histagged SUMO and untagged IFNα2a was then subjected to NiNTADocket No. MIS-110-PCT chromatography to purify untagged IFNα2a from the histagged SUMO (Fig.11a, NiNTA2 lane). All of the aforementioned mutants were considered to be biochemically well-behaved because they were purified with a high degree of purity and generous yields (40 mgs of purified untagged IFNα2a per 1L of bacterial culture). Other IFNα2a (such as R13A, R15A, R22A, R120A, R120D, R144A, R149D) were more poorly-behaved. Final protein yields were much lower and these poorly behaved mutants were seen co-purifying with bacterial chaperones DnaK, DnaJ and GrpE (Fig. 11b), indicating that these mutants were undergoing difficulties in protein folding during bacterial expression.

[0079] Some IFN autoAbs may be glycosylation specific15,39. IFN Decoys expressed in bacteria will not be able to nullify such autoAbs because bacteria cannot glycosylate proteins. To address this limitation, wild-type histagged IFN in human Expi293F cells were expressed (ThermoFisher) to obtain glycosylated IFN capable of nullifying glyco-specific autoAbs. Fig.12 shows successful expression of secreted wild-type IFNα2a in the culture supernatant of Expi293F cells. The secreted IFNα2a presents itself as two bands on SDS-PAGE (Fig.12), consistent with the presence a higher- molecular weight form that corresponds to glycosylated IFN. Although this patent only presents functional validations conducted on bacterially expressed wild-type and mutant IFNs, future validations conducted on IFNs expressed from mammalian sources are expected to yield validation results that are equal or superior to their bacterial analogs. Glycosylation on IFNs have no effect on their ability to activate IFNAR40,41 42. Therefore, an IFN Decoy bearing the same mutation expressed from bacterial and mammalian sources is expected to have the same IFNAR activity. Because literature suggests that some autoAbs may be glycosylation specific15,39, an IFN Decoy bearing the same mutation expressed is expected to capture more patient autoAbs if expressed glycosylated from a mammalian source than unglycosylated from a bacterial source.Docket No. MIS-110-PCT

[0080] IFN Decoys are not toxic to A549 cells in vitro

[0081] To test whether the IFN decoys are toxic to the cells in vitro, cell viability was evaluated using an Alamar blue assay following treatment with different compounds (Fig.13). A549 cells were treated with different doses of WT IFNα2a and an IFNα2a decoy (R33D) for 24 hours and then the viability of the cells was measured. Poly:IC + Lipofectamine and puromycin were included as controls since they are both toxic to the cells at high concentrations. IFN-WT has been used in the clinic for decades, and it hasn’t shown any toxicity in vitro. Therefore, no toxicity was anticipated in the decoy treated group. As expected, even with treatment at the highest doses no decrease in cell viability with either IFN-WT or the IFN decoy tested (R33D) was observed.

[0082] IFN Decoys do not activate the IFNAR receptor

[0083] A luciferase assay3,10,43was used to test the ability of IFNs (wild-type and mutant) to activate the IFNAR receptor. In HEK293T cells, activation of IFNAR by IFN is known to result in the translocation of a STAT1-STAT2-IRF9 transcription factor to the nucleus. STAT1-STAT2-IRF9 is known to bind specifically to promoters containing the ISRE sequence to drive gene expression. IFNAR activation is measured after transient transfection of the following reporter gene cassette: an inducible ISRE promoter that drives the expression of Firefly luciferase to report on IFNAR activation. Chemical substrates allow luminescence measurements from expressed Firefly luciferase to result in an assay readout that is expressed Firefly luminescence units. In an alternative embodiment, the same reporter cassette is stably introduced to the genome of HEK293T cells through lentiviral transduction.

[0084] Fig. 14 represents data gathered on this luciferase assay using bacterially expressed wild-type IFNα2a. The data indicates that Firefly luminescence was observed that in a dose-dependent manner to the concentration of IFNα2a. Although transient transfection yields higher peak luminescence, the stably transduced reporter line possesses similar response kinetics to transient transfection (EC50of 7.650 vs 3.129 ng / mL).Docket No. MIS-110-PCT

[0085] To measure the inactivity of any given Decoy, the experimental scheme is followed and as detailed in Fig 15a. Wild-type IFN or IFN Decoy are titrated in the luciferase assay to elicit luminescence responses from the reporter cells. The luminescence data are fit to a four-parameter logistic equation (GraphPad Prism) to extract an EC50for each titration. A metric called the Inactivity Index is defined to quantitively describe how much more inactive an IFN Decoy is compared to wild-type IFN. The Inactivity Index for a single IFN Decoy is a ratio of that Decoy’s EC50divided by the EC50of wild-type IFN that was run within the same experiment; a larger Inactivity Index indicates a more inactive Decoy.

[0086] Fig. 15b is an example experiment that examines the inactivity of IFNα2a Decoys R33A, R33D and R33E. Mutating Arg33 to alanine had a modest effect on activity, as indicated by an Inactivity Index of 39 (EC50shift from 12.6 to 488 ng / mL). By mutating Arg33 to charge inverted variants (aspartate and glutamate), these IFN decoys became so inactive that their luminescence curves were shifted to concentration ranges that were too high to be measured. An EC50could not be obtained because the data could no longer be fit to a logistic curve. As such, highly inactive IFN Decoys (such as IFNα2a-R33E and -R33D) have Inactivity Indices that cannot be calculated.

[0087] Fig. 15c is a compilation of Inactivity Indices of all IFNα2a Decoys tested to-date. Although all point mutations attempted were anticipated disrupt IFN:IFNAR binding (Fig. 5, Table 5), different Inactivity Indices amongst these point mutants were observed. Amongst the alanine point mutants, some have activities that are indistinguishable from wild-type IFNα2a (Fig.14c, K70A to K31A). However, the predictive power of the in silico IFN:IFNAR analysis is validated by alanine point mutants that have statistically significant Inactivity Index differences as compared to wild-type IFN (Fig. 15c, R12A to R120A). Some of the most inactive alanine point mutant candidates (R33A, R149A, R120A) were selected for a second round of structure-guided mutation design to produce second generation decoys (R33D, R33E, R149E, R120E) that were anticipated to be more disruptive to IFN:IFNAR binding than their alanine mutant counterparts. When measured in the luciferase assay, all second-generation decoys had Inactivity Indices that were too inactive to be measured (Fig. 15c, R33D to R149E), indicating that our second-generationDocket No. MIS-110-PCT mutation design was successful. These second generation IFNα2a Decoys represent our most IFNAR-inert proteins.

[0088] To further assess whether the IFN decoys are unable to activate IFN signaling due to their inability to bind the IFNARs, A549 cells were stimulated with different doses of IFN-WT, Decoy IFNα2a-R33D, Decoy IFNα2a-R120E and Decoy IFNα2a-R149E and measured the levels of MX1 mRNA by qPCR. MX1 is an interferon-stimulated gene (ISG) that gets upregulated as a result of the activation of the IFNARs. Therefore, MX1 mRNA levels are used as a readout for IFN-mediated activation of IFNAR. The lack of MX1 induction in cells treated with Decoy IFNα2a-R33D, Decoy IFNα2a-R120E and Decoy IFNα2a- R149E suggests a lack of activation of the IFNAR receptors (Fig.16).

[0089] To confirm the results obtained by qPCR and to investigate the comprehensive transcriptomic changes caused by the binding and activation of IFN-WT and IFN-Decoys to the IFNARs, we stimulated A549 cells with different doses of IFN-WT and Decoy IFNα2a-R120E and Decoy IFNα2a-R149E and evaluated the differential expression of ISGs by RNAseq. Cluster analysis showed an upregulation of ISGs in cells stimulated with IFN-WT but not with Decoy IFNα2a-R120E and Decoy IFNα2a-R149E. This clear IFN-WT-specific ISG signature clearly shows a lack of activation of the IFNAR by the IFN-decoys (Fig.17).

[0090] IFN Decoys do not compete with IFN-WT for binding to IFNAR

[0091] To activate the heterodimeric IFNAR receptor, a single type I IFN molecule engages both IFNAR1 and IFNAR2 subunits to bring the receptor proteins in close proximity to enable intracellular signaling44. While our second generation IFNα2a Decoys (R33D, R33E, R149E, R120E) exhibit a profound inability to activate the IFNAR receptor (Fig. 15c), a lack of IFNAR activity does not guarantee that the Decoy is wholly unable to engage IFNAR. It is possible that an IFNAR-inactive Decoy is unable to bind only one of the two IFNAR subunits, but retains a stable interaction with the other of the two IFNAR subunits. Such a Decoy would become a competitive inhibitor of IFNAR by competing with IFN-WT for binding to the IFNAR receptor. This invention endeavors to enable an endogenous IFN response in patients. An IFNDocket No. MIS-110-PCT Decoy that is a competitive inhibitor of IFNAR is anticipated to counterproductively block an endogenous IFN response. To screen out Decoys that are competitive inhibitors of IFNAR, the luciferase assay was adapted to measure competition between the IFN Decoy and IFN-WT for the IFNAR receptor.

[0092] To measure competition, the experimental scheme outlined in Fig. 18a is followed. IFN-WT is titrated in the presence and absence of a constant concentration of IFN Decoy. An EC50is derived from each titration and divided by each other to yield a Competition Index. A Competition Index of 1 indicates a non-competitive Decoy. A Competition Index greater than 1 indicates that the IFN Decoy is a competitive inhibitor of IFNAR. A Competition Index less than 1 indicates that an IFN Decoy is a positive modulator of IFNAR activity. However, given the biochemical constraints on the system, the mechanism underlying a <1 Competition Index is unclear. This process is repeated for multiple IFN-WT titrations in the presence of increasing constant concentrations of IFN Decoys, one constant IFN Decoy concentration per IFN-WT titration, to determine the Competition Index across a range of Decoy concentrations.

[0093] Fig. 18b shows a representative competition experiment examining IFNα2a-R120E. Inclusion of IFNα2a-R120E, up to 5.2 x 104pM, has no significant effect on the EC50of IFN-WT (Fig.18b, grey curves, Competition Index = 0.7 – 1.1). Inclusion of IFNα2a-R120E at the highest concentrations, at 5.2 x 105and 2.4 x 106pM, shifts the titration to the right and increases the Competition Index in a dose-dependent manner (Fig. 18c, purple curves, Competition Index = 2.2 – 5.5). Therefore, IFNα2a-R120E is interpreted to be a competitive inhibitor of IFNAR. These data are consistent with prior art, which have determined IFNα2a-R120E to be an IFNAR competitive inhibitor45.

[0094] Fig. 18c is a compilation of Competition Indices measured to-date. As controls, a commercially available neutralizing anti-IFNAR2 monoclonal antibody was selected (PBL Assay Bioscience, # 21385) as a positive control competitor that 1) binds only one of two the IFNAR subunits 2) is known to inhibit IFNAR activity. The anti-IFNAR antibody shows a dose-dependent increase of the Competition Index (Fig 18c, green curve), while a negative control isotype-matched antibody shows no concentration-dependent trend on the Competition Index (Fig.18c, orange curve). Of the four IFNα2a Decoys tested, -R120E is the only Decoy that shows an increasing Competition Index starting at 5.2 x 105pM, while the other threeDocket No. MIS-110-PCT Decoys do not show increases in Competition Index at any of the assay concentrations. This result is interpreted to state that IFNα2a Decoys R33D, R33E and R149E do not compete with IFN-WT for binding to IFNAR.

[0095] Additionally, the ability of IFN Decoys to compete with endogenously produced IFNs was evaluated. To test this hypothesis, a 2-step stimulation of Poly:IC + Lipofectamine followed by a stimulation of several doses of IFN-WT and IFN-Decoys was performed in A549 cells, followed by measurement of MX1 mRNA levels. MX1 mRNA levels are assessed as an indicator of ISG activation. MX1 is induced to a basal level as a consequence of endogenous IFNs produced by the cells in the presence of poly:IC. Treatment with IFN-WT shows an exacerbated production of MX1 mRNA; in contrast, cells treated with Decoy IFNα2a-R33D, Decoy IFNα2a-R120E and Decoy IFNα2a-R149E do not show changes in MX1 expression, suggesting a lack of activity of the decoys and lack of antagonistic function towards the endogenous IFNs (Fig.19).

[0096] IFN Decoys retain autoAb binding similar to wild type IFN

[0097] Surface plasmon resonance will test whether IFNs (wild-type and mutant) can interact with autoAbs. Surface plasmon resonance is a well-established assay method to probe antibody-antigen interactions. This assay uses an optical readout to detect the binding of a mobile analyte to a surface- immobilized probe. Purified IFNs will be immobilized using protein-encoded polyhistidine tags (probe) to NiNTA-functionalized surfaces, and flow over patient autoAbs (analyte) to test their interaction. The acquisition of patient autoAbs from scientific collaborators were accumulated. In one embodiment, commercially available monoclonal antibodies specific for type I IFNs are available to pilot and optimize initial setup of this assay.

[0098] Immobilized IFN Decoys show similar binding in SPR to autoAbs as compared to immobilized wild-type IFN. Clinical samples containing polyclonal sera against IFNα2a (autoAb positive patient sera) were flown over the immobilized IFN Decoys. Similar, assessments were done in parallel with immobilized IFNα2a-WT (Fig. 20a, left SPR sensogram). The autoAb affinity towards the decoys and WT remained similar qualitatively having near identical association and dissociation phases. A representative set ofDocket No. MIS-110-PCT decoys are shown in Fig. 16a and similar results were observed for the other decoys covered under this disclosure. The decoys were also tested with autoAb negative patient sera to observe any potential affinity towards other proteins in the sera media (Fig.20a, right SPR sensogram). The results showed that decoys and WT had no affinity to the autoAb negative patient sera, which indicate lack of off-target interactions with proteins in plasma.

[0099] IFN Decoys can block IFN autoAbs in flow from binding to immobilized wild-type IFN effectively neutralizing the action of autoAb. This neutralization activity was tested by introducing decoys (100 nM) into autoAb positive pooled patient sera and flowing the mixture over the immobilized IFN-WT while monitoring the SPR response. It was observed that decoys R33D, R33E, L30A, R149E, and R120E showed low response similar to the sera sample mixed with IFN-WT (Fig.20b). This showed that decoys have the same ability as the IFN-WT to intercept autoAb serving the therapeutic function of inactivating autoAb. The degree of neutralization of the decoys were compared based on the SPR response signal which indicated that there can be variations of neutralization among the prospective decoys (Fig.20c).

[0100] IFN Decoys relieve the suppression of IFNAR activity imparted by IFN autoAbs

[0101] To be an efficacious drug, an IFN Decoy must be able to rescue IFNAR activity from autoAb-mediated suppression. To address this experimentally, the entire system was reconstituted (IFNAR, IFNα2a-WT, Decoy IFNα2a-R120E, patient autoAbs) in the luciferase assay. As controls, 52 nM IFNα2a-R120E elicits no IFNAR activity above background while 0.52 nM IFNα2a-WT elicits a robust activity signal (Fig.21). Therefore, all observable IFNAR activity at these IFN concentrations are driven purely by IFNα2a-WT. Concurrent administration of 0.52 nM IFNα2a-WT and 52 nM IFNα2a-R120E to the experiment shows an IFNAR activity indistinguishable to 0.52 nM IFNα2a-WT alone (Fig.21), indicating that 52 nM IFNα2a-R120E does not compete with IFNα2a-WT for binding to the IFNAR receptor under the conditions of this assay. Although IFNα2a-R120E was shown to compete with IFNα2a-WT in competition assays, competition occurred at 520 nM and higher (Fig.18c). IFNα2a-R120E is 10 times more dilute inDocket No. MIS-110-PCT this reconstitution assay, which explains why no competitive effect was observed. Patient autoAbs suppress the activity of 0.52 nM IFN-WT to background levels (Fig. 18). By adding 52 nM IFNα2a-R120E to this autoAb suppressed condition, IFNAR activity is rescued back to levels that are indistinguishable from 0.52 nM IFNα2a-WT alone (Fig. 18). This result is interpreted as IFNα2a-R120E sequestering patient autoAbs from binding IFNα2a-WT, allowing IFNα2a-WT to drive IFNAR signaling unhindered. This data serves as therapeutic proof of concept for the IFN Decoys in vitro.

[0102] Sequences

[0103] Listed below are sequences of wild type (IFNα2a amino acid index according to PDB # 3SE3, IFNω amino acid index according to PDB # 3SE4).

[0104] Residues identified in this disclosure as being only important for binding IFNAR (IFNα2a: Leu15, Gln20, Arg22, Leu30, Lys31, Arg33, Phe64, Lys70, Glu78, Asp82, Lys83, Tyr85, Tyr89, Glu96, Arg120, Lys121, Lys131, Lys134, Arg144, Ala145, Met148, Arg149, Ser152, Leu153, Asn156 | IFNω: Asp4, Gln7, Arg24, Leu32, Arg35, Arg39, Lys52, Phe67, His71, Glu73, Arg74, Asp85, Gln96, Glu99, Arg123, Arg124, Arg130, Lys134, Lys152, Asn159) are highlighted in red underlined.

[0105] Residues identified in this disclosure as being only important for binding patient autoAbs (IFNα2a: Thr6, Leu9, Met16, Ala74, Gln101, Val105, Asp114, Thr127, Leu128 | IFNω: Asn8, Leu11, Val18, Arg25, Ala77, Ala119, Val131) are highlighted in blue bold.

[0106] Residues identified in thisas being important for binding both IFNAR and patient autoAbs (IFNα2a: Arg12, Arg13, Lys23, Asp35 | IFNω: Arg14, Asp37, Gln104) are highlighted in purple italics. SEQ ID NO: 1 - IFNalpha2a-wildtype

[0108] CDLPQTHSLGSRRTLMLLAQMRKISLFSCLKDRHDFGFPQEEFGNQFQKAETIPVLHEMI QQIFNLFSTKDSSAAWDETLLDKFYTELYQQLNDLEACVIQGVGVTETPLMKEDSILAVRKYFQ RITLYLKEKKYSPCAWEVVRAEIMRSFSLSTNLQESLRSKEDocket No. MIS-110-PCT

[0109] SEQ ID NO: 2 - IFNomega-wildtype

[0110] LGCDLPQNHGLLSRNTLVLLHQMRRISPFLCLKDRRDFRFPQEMVKGSQLQKAHVMSVL HEMLQQIFSLFHTERSSAAWNMTLLDQLHTGLHQQLQHLETCLLQVVGEGESAGAISSPALTLR RYFQGIRVYLKEKKYSDCAWEVVRMEIMKSLFLSTNMQERLRSKDRDLGSS

[0111] References: 1. Walter, M.R. (2020). The Role of Structure in the Biology of Interferon Signaling. Frontiers in Immunology 11.10.3389 / fimmu.2020.606489. 2. Meager, A., Visvalingam, K., Peterson, P., Möll, K., Murumägi, A., Krohn, K., Eskelin, P., Perheentupa, J., Husebye, E., Kadota, Y., and Willcox, N. (2006). Anti-Interferon Autoantibodies in Autoimmune Polyendocrinopathy Syndrome Type 1. PLoS Medicine 3, e289.10.1371 / journal.pmed.0030289. 3. Meyer, S., Woodward, M., Hertel, C., Vlaicu, P., Haque, Y., Kärner, J., Macagno, A., Onuoha, S.C., Fishman, D., Peterson, H., et al. (2016). AIRE-Deficient Patients Harbor Unique High-Affinity Disease-Ameliorating Autoantibodies. Cell 166, 582-595. 10.1016 / j.cell.2016.06.024. 4. Rosenberg, J.M., Maccari, M.E., Barzaghi, F., Allenspach, E.J., Pignata, C., Weber, G., Torgerson, T.R., Utz, P.J., and Bacchetta, R. (2018). Neutralizing Anti-Cytokine Autoantibodies Against Interferon-α in Immunodysregulation Polyendocrinopathy Enteropathy X-Linked. Frontiers in Immunology 9.10.3389 / fimmu.2018.00544. 5. Eriksson, D., Bacchetta, R., Gunnarsson, H.I., Chan, A., Barzaghi, F., Ehl, S., Hallgren, A., van Gool, F., Sardh, F., Lundqvist, C., et al. (2019). The autoimmune targets in IPEX are dominated by gut epithelial proteins. J Allergy Clin Immunol 144, 327-330 e328. 10.1016 / j.jaci.2019.02.031. 6. Walter, J.E., Rosen, L.B., Csomos, K., Rosenberg, J.M., Mathew, D., Keszei, M., Ujhazi, B., Chen, K., Lee, Y.N., Tirosh, I., et al. (2015). Broad-spectrum antibodies against self- antigens and cytokines in RAG deficiency. Journal of Clinical Investigation 125, 4135- 4148.10.1172 / jci80477. 7. Bastard, P., Lévy, R., Henriquez, S., Bodemer, C., Szwebel, T.-A., and Casanova, J.-L. (2021). Interferon-β Therapy in a Patient with Incontinentia Pigmenti and Autoantibodies against Type I IFNs Infected with SARS-CoV-2. Journal of Clinical Immunology 41, 931-933.10.1007 / s10875-021-01023-5. 8. Bastard, P., Rosen, L.B., Zhang, Q., Michailidis, E., Hoffmann, H.H., Zhang, Y., Dorgham, K., Philippot, Q., Rosain, J., Beziat, V., et al. (2020). Autoantibodies against type I IFNs in patients with life-threatening COVID-19. Science 370. 10.1126 / science.abd4585. 9. Le Voyer, T., Parent, A.V., Liu, X., Cederholm, A., Gervais, A., Rosain, J., Nguyen, T., Perez Lorenzo, M., Rackaityte, E., Rinchai, D., et al. (2023). Autoantibodies against type I IFNs in humans with alternative NF-κB pathway deficiency. Nature 623, 803-813. 10.1038 / s41586-023-06717-x. 10. Bastard, P., Gervais, A., Le Voyer, T., Rosain, J., Philippot, Q., Manry, J., Michailidis, E., Hoffmann, H.-H., Eto, S., Garcia-Prat, M., et al. (2021). Autoantibodies neutralizing type I IFNs are present in ~4% of uninfected individuals over 70 years old and accountDocket No. MIS-110-PCT for ~20% of COVID-19 deaths. Science Immunology 6, eabl4340. doi:10.1126 / sciimmunol.abl4340. 11. Bastard, P., Orlova, E., Sozaeva, L., Levy, R., James, A., Schmitt, M.M., Ochoa, S., Kareva, M., Rodina, Y., Gervais, A., et al. (2021). Preexisting autoantibodies to type I IFNs underlie critical COVID-19 pneumonia in patients with APS-1. J Exp Med 218. 10.1084 / jem.20210554. 12. Beccuti, G., Ghizzoni, L., Cambria, V., Codullo, V., Sacchi, P., Lovati, E., Mongodi, S., Iotti, G.A., and Mojoli, F. (2020). A COVID-19 pneumonia case report of autoimmune polyendocrine syndrome type 1 in Lombardy, Italy: letter to the editor. Journal of Endocrinological Investigation 43, 1175-1177.10.1007 / s40618-020-01323-4. 13. Alotaibi, F., Alharbi, N.K., Rosen, L.B., Asiri, A.Y., Assiri, A.M., Balkhy, H.H., Al Jeraisy, M., Mandourah, Y., Aljohani, S., Al Harbi, S., et al. (2023). Type I interferon autoantibodies in hospitalized patients with Middle East respiratory syndrome and association with outcomes and treatment effect of interferon beta‐1b in MIRACLE clinical trial. Influenza and Other Respiratory Viruses 17.10.1111 / irv.13116. 14. Zhang, Q., Pizzorno, A., Miorin, L., Bastard, P., Gervais, A., Le Voyer, T., Bizien, L., Manry, J., Rosain, J., Philippot, Q., et al. (2022). Autoantibodies against type I IFNs in patients with critical influenza pneumonia. Journal of Experimental Medicine 219. 10.1084 / jem.20220514. 15. Gervais, A., Rovida, F., Avanzini, M.A., Croce, S., Marchal, A., Lin, S.-C., Ferrari, A., Thorball, C.W., Constant, O., Le Voyer, T., et al. (2023). Autoantibodies neutralizing type I IFNs underlie West Nile virus encephalitis in ∼40% of patients. Journal of Experimental Medicine 220.10.1084 / jem.20230661. 16. Busnadiego, I., Abela, I.A., Frey, P.M., Hofmaenner, D.A., Scheier, T.C., Schuepbach, R.A., Buehler, P.K., Brugger, S.D., and Hale, B.G. (2022). Critically ill COVID-19 patients with neutralizing autoantibodies against type I interferons have increased risk of herpesvirus disease. PLOS Biology 20, e3001709.10.1371 / journal.pbio.3001709. 17. Gervais, A., Marchal, A., Fortova, A., Berankova, M., Krbkova, L., Pychova, M., Salat, J., Zhao, S., Kerrouche, N., Le Voyer, T., et al. (2024). Autoantibodies neutralizing type I IFNs underlie severe tick-borne encephalitis in approximately 10% of patients. J Exp Med 221.10.1084 / jem.20240637. 18. Gervais, A., Bastard, P., Bizien, L., Delifer, C., Tiberghien, P., Rodrigo, C., Trespidi, F., Angelini, M., Rossini, G., Lazzarotto, T., et al. (2024). Auto-Abs neutralizing type I IFNs in patients with severe Powassan, Usutu, or Ross River virus disease. J Exp Med 221. 10.1084 / jem.20240942. 19. Bastard, P., Michailidis, E., Hoffmann, H.-H., Chbihi, M., Le Voyer, T., Rosain, J., Philippot, Q., Seeleuthner, Y., Gervais, A., Materna, M., et al. (2021). Auto-antibodies to type I IFNs can underlie adverse reactions to yellow fever live attenuated vaccine. Journal of Experimental Medicine 218.10.1084 / jem.20202486. 20. Bastard, P., Vazquez, S.E., Liu, J., Laurie, M.T., Wang, C.Y., Gervais, A., Le Voyer, T., Bizien, L., Zamecnik, C., Philippot, Q., et al. (2023). Vaccine breakthrough hypoxemic COVID-19 pneumonia in patients with auto-Abs neutralizing type I IFNs. Sci Immunol 8, eabp8966.10.1126 / sciimmunol.abp8966. 21. Taylor, R.P., Sutherland, W.M., Reist, C.J., Webb, D.J., Wright, E.L., and Labuguen, R.H. (1991). Use of heteropolymeric monoclonal antibodies to attach antigens to the C3bDocket No. MIS-110-PCT receptor of human erythrocytes: a potential therapeutic treatment. Proc Natl Acad Sci U S A 88, 3305-3309.10.1073 / pnas.88.8.3305. 22. Ward, E.S., and Ober, R.J. (2018). Targeting FcRn to Generate Antibody-Based Therapeutics. Trends in Pharmacological Sciences 39, 892-904. 10.1016 / j.tips.2018.07.007. 23. Devanaboyina, S.C., Khare, P., Challa, D.K., Ober, R.J., and Ward, E.S. (2017). Engineered clearing agents for the selective depletion of antigen-specific antibodies. Nat Commun 8, 15314.10.1038 / ncomms15314. 24. Pinto, S., Pahl, J., Schottelius, A., Carter, P.J., and Koch, J. (2022). Reimagining antibody-dependent cellular cytotoxicity in cancer: the potential of natural killer cell engagers. Trends in Immunology 43, 932-946.10.1016 / j.it.2022.09.007. 25. Kansal, R., Richardson, N., Neeli, I., Khawaja, S., Chamberlain, D., Ghani, M., Ghani, Q.U., Balazs, L., Beranova-Giorgianni, S., Giorgianni, F., et al. (2019). Sustained B cell depletion by CD19-targeted CAR T cells is a highly effective treatment for murine lupus. Sci Transl Med 11.10.1126 / scitranslmed.aav1648. 26. Pestka, S., Krause, C.D., and Walter, M.R. (2004). Interferons, interferon-like cytokines, and their receptors. Immunol Rev 202, 8-32.10.1111 / j.0105-2896.2004.00204.x. 27. Hauschild, A., Gogas, H., Tarhini, A., Middleton, M.R., Testori, A., Dréno, B., and Kirkwood, J.M. (2008). Practical guidelines for the management of interferon‐α‐2b side effects in patients receiving adjuvant treatment for melanoma. Cancer 112, 982-994. 10.1002 / cncr.23251. 28. Quesada, J.R., Talpaz, M., Rios, A., Kurzrock, R., and Gutterman, J.U. (1986). Clinical toxicity of interferons in cancer patients: a review. J Clin Oncol 4, 234-243. 10.1200 / JCO.1986.4.2.234. 29. Vial, T., and Descotes, J. (1994). Clinical toxicity of the interferons. Drug Saf 10, 115- 150.10.2165 / 00002018-199410020-00003. 30. Tarhini, A.A., Gogas, H., and Kirkwood, J.M. (2012). IFN-α in the treatment of melanoma. J Immunol 189, 3789-3793.10.4049 / jimmunol.1290060. 31. Thomas, C., Moraga, I., Levin, D., Peter, Podoplelova, Y., Trejo, A., Lee, C., Yarden, G., Susan, Jeffrey, et al. (2011). Structural Linkage between Ligand Discrimination and Receptor Activation by Type I Interferons. Cell 146, 621-632. 10.1016 / j.cell.2011.06.048. 32. Clackson, T., and Wells, J.A. (1995). A Hot Spot of Binding Energy in a Hormone- Receptor Interface. Science 267, 383-386. doi:10.1126 / science.7529940. 33. Guharoy, M., and Chakrabarti, P. (2010). Conserved residue clusters at protein-protein interfaces and their use in binding site identification. BMC Bioinformatics 11, 286. 10.1186 / 1471-2105-11-286. 34. Oganesyan, V., Peng, L., Woods, R.M., Wu, H., and Dall'Acqua, W.F. (2015). Structural Insights into the Neutralization Properties of the Fully Human, Anti-interferon Monoclonal Antibody Sifalimumab. J Biol Chem 290, 14979-14985. 10.1074 / jbc.M115.652156. 35. Touw, W.G., Baakman, C., Black, J., te Beek, T.A., Krieger, E., Joosten, R.P., and Vriend, G. (2015). A series of PDB-related databanks for everyday needs. Nucleic Acids Res 43, D364-368.10.1093 / nar / gku1028.Docket No. MIS-110-PCT 36. Tien, M.Z., Meyer, A.G., Sydykova, D.K., Spielman, S.J., and Wilke, C.O. (2013). Maximum allowed solvent accessibilites of residues in proteins. PLoS One 8, e80635. 10.1371 / journal.pone.0080635. 37. Wu, W., Wang, Z., Cong, P., and Li, T. (2017). Accurate prediction of protein relative solvent accessibility using a balanced model. BioData Min 10, 1.10.1186 / s13040-016- 0121-5. 38. Pei, J., Kim, B.-H., and Grishin, N.V. (2008). PROMALS3D: a tool for multiple protein sequence and structure alignments. Nucleic Acids Research 36, 2295-2300. 10.1093 / nar / gkn072. 39. Bastard, P., Gervais, A., Taniguchi, M., Saare, L., Särekannu, K., Le Voyer, T., Philippot, Q., Rosain, J., Bizien, L., Asano, T., et al. (2024). Higher COVID-19 pneumonia risk associated with anti-IFN-α than with anti-IFN-ω auto-Abs in children. Journal of Experimental Medicine 221.10.1084 / jem.20231353. 40. van Pesch, V., Lanaya, H., Renauld, J.C., and Michiels, T. (2004). Characterization of the murine alpha interferon gene family. J Virol 78, 8219-8228.10.1128 / JVI.78.15.8219- 8228.2004. 41. Bose, S., Gurari-Rotman, D., Ruegg, U.T., Corley, L., and Anfinsen, C.B. (1976). Apparent dispensability of the carbohydrate moiety of human interferon for antiviral activity. Journal of Biological Chemistry 251, 1659-1662.10.1016 / s0021- 9258(17)33699-2. 42. Goeddel, D.V., Shepard, H.M., Elizabeth, Y., Leung, D., Crea, R., Sloma, A., and Pestka, S. (1980). Synthesis of human fibroblast interferon by E. coli. Nucleic Acids Research 8, 4057-4074.10.1093 / nar / 8.18.4057. 43. Fanunza, E., Frau, A., Sgarbanti, M., Orsatti, R., Corona, A., and Tramontano, E. (2018). Development and Validation of a Novel Dual Luciferase Reporter Gene Assay to Quantify Ebola Virus VP24 Inhibition of IFN Signaling. Viruses 10, 98. 10.3390 / v10020098. 44. Zanin, N., Viaris de Lesegno, C., Lamaze, C., and Blouin, C.M. (2020). Interferon Receptor Trafficking and Signaling: Journey to the Cross Roads. Front Immunol 11, 615603.10.3389 / fimmu.2020.615603. 45. Pan, M., Kalie, E., Scaglione, B.J., Raveche, E.S., Schreiber, G., and Langer, J.A. (2008). Mutation of the IFNAR-1 Receptor Binding Site of Human IFN-α2 Generates Type I IFN Competitive Antagonists. Biochemistry 47, 12018-12027.10.1021 / bi801588g.

[0112] All publications and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference.

[0113] The use of the terms “a” and “an” and “the” and similar referents in the context of describing the invention are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. It will be further understood that the terms “comprises,” “comprising,”Docket No. MIS-110-PCT “includes,” and / or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0114] Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. The word “about,” when accompanying a numerical value, is to be construed as indicating a deviation of up to and inclusive of 10% from the stated numerical value. The use of any and all examples, or exemplary language (“e.g.” or “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any nonclaimed element as essential to the practice of the invention.

[0115] References to “one embodiment,” “an embodiment,” “example embodiment,” “various embodiments,” etc., may indicate that the embodiment(s) of the invention so described may include a particular feature, structure, or characteristic, but not every embodiment necessarily includes the particular feature, structure, or characteristic. Further, repeated use of the phrase “in one embodiment,” or “in an exemplary embodiment,” do not necessarily refer to the same embodiment, although they may.

[0116] As used herein the term “method” refers to manners, means, techniques and procedures for accomplishing a given task including, but not limited to, those manners, means, techniques and procedures either known to, or readily developed from known manners, means, techniques and procedures by practitioners of the chemical, pharmacological, biological, biochemical and medical arts. Unless otherwise expressly stated, it is in no way intended that any method or aspect set forth herein be construed as requiring that its steps be performed in a specific order. Accordingly, where a method claim does not specifically state in the claims or descriptions that the steps are to be limited to a specific order, it is no way intended that an order be inferred, in any respect. This holds for any possible non-express basis for interpretation, including matters of logic with respect to arrangement of steps or operational flow, plain meaning derivedDocket No. MIS-110-PCT from grammatical organization or punctuation, or the number or type of aspects described in the specification.

Claims

Docket No. MIS-110-PCT CLAIMS What is claimed is:

1. A method of neutralizing type I Interferon (IFN) autoantibodies, comprising administering a therapeutically effective amount of an IFN decoy to bind to IFN autoantibodies in a subject.

2. The method of Claim 1, further comprising administering the IFN decoy at concentrations much higher than endogenous IFN in the subject, and providing the endogenous IFN not bound to the IFN decoy available to initiate antiviral immunity.

3. The method of Claim 1, further comprising removing IFN autoantibodies from circulation by conjugating the IFN decoy to a clearance molecule, wherein the clearance molecules is selected from the group consisting of: CR1-mediated liver disposal, and FcRn-mediated IgG recycling; and removing anti-IFN autoantibodies from circulation of the subject.

4. The method of Claim 1, further comprising conjugating the IFN decoy to a cytotoxic molecule, wherein the cytotoxic molecule is selected from the group of antibody-mediated cellular cytotoxicity, or death via CAR T-Cells; wherein the IFN decoy binds to a B-cell lymphocyte and the death of the B-cell is due to proximity of the cytotoxic molecule.

5. The method of Claim 1, wherein the IFN decoy comprises an engineered mutation that abolishes the interferon-α / β receptor (IFNAR) interaction to allow higher doses of IFN decoy to be administered with diminished side effects.

6. The method of Claim 1, further comprising binding type I IFNs to IFNAR with an amino acid selected from the group consisting of: Arg12, Arg13, Leu15, Gln20, Arg22, Lys23, Leu30, Lys31, Arg33, Asp35, Phe64, Lys70, Glu78, Asp82, Lys83, Tyr85, Tyr89, Glu96, Arg120, Lys121, Lys131, Lys134, Arg144, Ala145, Met148, Arg149, Ser152, Leu153, Asn156 for IFNα2a and Asp4, Gln7, Arg14, Arg24, Leu32, Arg35, Asp37, Arg39, Lys52,Docket No. MIS-110-PCT Phe67, His71, Glu73, Arg74, Asp85, Gln96, Glu99, Gln104, Arg123, Arg124, Arg130, Lys134, Lys152, and Asn159 for IFNω.

7. The method of Claim 1, further comprising binding patient autoantibodies to the type I IFNs with an amino acid selected from the group consisting of: Thr6, Leu9, Arg12, Arg13, Met16, Lys23, Asp35, Ala74, Gln101, Val105, Asp114, Thr127, Leu128 for IFNα2a and Asn8, Leu11, Arg14, Val18, Arg25, Asp37, Ala77, Gln104, Ala119, Val131 for IFNω.

8. An IFN Decoy comprising at least one mutation of the amino acid in Claim 6 to disrupt IFNAR binding, and the IFN Decoy does not include a mutation of the amino acid in Claim 7 to retain binding affinity to patient autoantibodies.

9. The IFN Decoy of Claim 8, further comprising an IFNα2a-R33D mutant is no more toxic to A549 cells in culture than wild-type IFNα2a.

10. The IFN Decoy of Claim 8, further comprising IFNα2a mutants that are IFN Decoys that activate the IFNAR receptor less than wild-type IFNα2a: R12A, L30A, R33A, R33D, R33E, R120A, R120E, A145G, M148A, R149A, R149E and L153A.

11. The IFN Decoy of Claim 8, further comprising IFNα2a mutants that are IFN Decoys that do not compete with wild-type IFNα2a for binding to IFNAR: R33D, R33E and R149E.

12. The IFN Decoy of Claim 8, further comprising IFNα2a mutants that are IFN Decoys that bind patient autoAbs with similar affinity to wild-type IFNα2a: L30A, R33A, R33D, R33E, R120E and R149E.

13. The IFN Decoy of Claim 8, further comprising IFNα2a-R120E mutant that is an IFN Decoy that can relieve the suppression of IFNAR activity imparted by patient IFN autoantibodies.

Images