BISPECIFIC IN TANDEM RECEPTOR CAR AND METHOD FOR MODULATING THE TUMORAL MICROENVIRONMENT

Abstract

The present invention refers to a bispecific in tandem receptor CAR, named RfuCAR, which includes a scFv that recognizes and ligates surface molecules on tumoral cells (CD33, CD123 or another tumoral target) and the IL-1 receptor type 2 (IL-1R2). According to this, the IL1-R2 was chosen as the ideal receptor to compose the RfuCAR construction, being able to capture the IL-103 with high affinity and specificity. These proprieties indicate it as a good candidate to reduce the neurotoxicity and CRS effects of CAR-T therapies. Additionally, the present invention deals with a method for modulating the tumoral microenvironment, for example, in case of acute myeloid leukemia, or other cancer type like but not restricted to acute lymbloblastic leukemia, pancreatic, lung and ovarian cancer.

Claims

1. Bispecific in tandem receptor CAR, characterized by comprising the SEQ ID. Nos. 1 to 6 for RfuCAR anti-CD33 and SEQ ID. Nos. 7 to 12 for RfuCAR anti-CD123.

2. Bispecific in tandem receptor CAR, according to claim 1, characterized by comprising preferably the SEQ. ID. No. 2 for RfuCAR anti-CD33.

3. Bispecific in tandem receptor CAR, according to claim 2, characterized in that the predicted binding sites and locations for the RfuCAR anti-CD33, mainly model 2 represented by SEQ. ID. No. 2, are positions 50, 227, 228, 229; 272 and 360.

4. Bispecific in tandem receptor CAR, according to claim 2, characterized in that the most likely ligands at each site are ILE, FUL; the centroid ligands at each site are SER657, FUL641; all ligands in clusters are GLY-1, TRP-1, GLU-2, ILE-3, PRO-3, SER-2, THR-2, TYR-3, ASP-1, LEU-1, ASN-1; FUL-1, FUC-1; and likely+centroid ligands at each site are ILE650, FUL641.

5. Bispecific in tandem receptor CAR, according to claim 1, characterized by comprising preferably the SEQ ID. No. 8 for RfuCAR anti-CD123.

6. Bispecific in tandem receptor CAR, according to claim 5, characterized in that the predicted binding sites and locations for the RfuCAR anti-CD123, mainly model 8 represented by SEQ. ID. No. 8, are positions 33, 235.

7. Bispecific in tandem receptor CAR, according to claim 5, characterized in that the most likely ligands at each site is TYR; the centroid ligands at each site is PR0656; all ligands in clusters are GLY-3, TRP-2, GLU-2, ILE-3, PRO-4, SER-3, ARG-1, THR-2, TYR-5, ASP-1, LEU-1, ASN-4, ALA-2, CYS-1; and likely+centroid ligands at each site is TYR672.

8. Bispecific in tandem receptor CAR, according to any one of claims 1 to 7, characterized by comprising a scFv that recognizes and ligates surface molecules on tumoral cells, like CD33, CD123 or another tumoral target, like CD19, Mesothelin, BCMA, and the IL-1 receptor type 2, like IL-1R2.

9. Method for modulating the tumoral microenvironment characterized in that the secreted IL-10 will be trapped binding in the IL-1R of RfuCAR inhibiting the binding to the IL-1R1 and IL-1 signaling transduction, wherein such inhibition decreases the IL-1 pathway activation leading to the modulation of tumoral cell proliferation.

10. Method, according to claim 9, characterized in that the regulation of IL-1 content in the microenvironment modulates other cytokines activation in the tumoral environment, putatively preventing the oversecretion of cytokines observed in CRS and neurotoxicity events.

11. Method, according to claim 9, characterized in that the RfuCAR cells action can be transiently switched-off and/or tuned by the administration of different peptides that link to IL-1R, IL-1-IL-1R and/or scFv epitopes in the case any of the toxic CAR-T effects are detected in the patient.

12. Method, according to any one of claims 9 to 11, characterized in that the tumoral microenvironment comprises any types of cancer, like acute myeloid leukemia, acute lymphoblastic leukemia, pancreatic, lung and ovarian cancers, mainly acute myeloid leukemia.

Description

BRIEF DESCRIPTION OF THE TIGURES

[0044] FIG. 1 presents the schematic of RfuCAR structure, wherein A is the vector scheme; and B is the Rfu scheme;

[0045] FIG. 2 presents the scheme demonstrating the switch and tuning of RfuCAR. The combination of peptides can switch-off or modulate the RfuCAR action. Peptides to IL-1R and Tumor Target, Peptides to IL-1R-IL-1 and Tumor Target and both three peptides can switch-off RfuCAR. The administration of only one of the peptides modulates the RfuCAR action;

[0046] FIG. 3 presents the schematic view of putative anti-CD33 RfuCAR;

[0047] FIG. 4 presents the predictive structures (RaptorX) for Anti-CD33 scFv domain, ILI-R2 extracellular domain and IgG4 Hinge-CH2CH3 domains;

[0048] FIG. 5 presents the tridimensional models for the tested sequences of extracellular domain of anti-CD33 RfuCAR. The numbers in the boxes corresponds to the model number 1 to 6. Are shown the best matched model for each construction in the two softwares: IntFold and RaptorX;

[0049] FIG. 6 presents the quality plots for all the best matches for each model for CD33-RfuCAR models (1-6);

[0050] FIG. 7 presents the disorder plots for the prediction models of anti-CD33 RfuCAR. Each plot represents the disorder prediction for each model 1 to 6;

[0051] FIG. 8 presents the predicted binding sites for RfuCAR model 2;

[0052] FIG. 9 presents the schematic view of putative anti-CD123 RfuCAR;

[0053] FIG. 10 presents the predictive structures (RaptorX) for Anti-CD123 scFv domain, ILi-R2 extracellular domain and IgG4 Hinge-CH2CH3 domains;

[0054] FIG. 11 presents the tridimensional models for the tested sequences of extracellular domain of anti-CD123 RfuCAR. The numbers in the boxes corresponds to the model number 7 to 12. Are shown the best matched model for each construction in the two softwares: IntFold and RaptorX;

[0055] FIG. 12 presents the quality plots for all the best matches for each model. for CD123-RfuCAR models (7-12);

[0056] FIG. 13 presents the disorder plots for the prediction models of anti-CD123 RfuCAR. Each plot represents the disorder prediction for each model 7 to 12;

[0057] FIG. 14 presents the predicted binding sites for RfuCAR model 8.

DETAILED DESCRIPTION OF THE INVENTION

[0058] The present invention describes a bispecific in tandem receptor CAR, named RfuCAR, which includes a scFv that recognizes and ligates surface molecules on tumoral cells (CD33, CD123 or another tumoral target, like but not restricted to CD19, Mesothelin, BCMA) and the IL-1 receptor type 2 (IL-1R2), as well as a method for modulating the tumoral microenvironment. Additionally, such mechanism is able to be switched-off by the administration of both receptor epitope peptides, constituting a regulatory safety switch (FIG. 1 A and B).

[0059] The scFv motif will ligate the tumoral cell and lead them to apoptosis exactly as a third generation CAR works. The secreted IL-1p will be trapped binding in the IL-1R of RfuCAR inhibiting the binding to the IL-1R1 and IL-1 signaling transduction. This inhibition will decrease the IL-1 pathway activation leading to the modulation of tumoral cell proliferation. Regulation of IL-1 content in the microenvironment will modulate other cytokines activation in the tumoral environment, putatively preventing the oversecretion of cytokines observed in CRS and neurotoxicity events.

[0060] If any of the toxic CAR-T effects are detected in the patient, the RfuCAR cells action can be transiently switched-off and also tuned by the administration of different peptides that link to IL-1R, IL-1-IL-1R and/or scFv epitopes (FIG. 2). These peptides will be able to link the two RfuCAR preventing the ligation to toumoral cells and/or IL-1 inhibiting or modulating RfuCAR activity. The RfuCAR cells will still be able to proliferate in the patient and get back to kill tumoral cells as soon as the administration of the peptides ceases.

[0061] RfuCAR putative structures

[0062] The extracellular putative structures of RfuCAR were tested in two structure prediction online features IntFold (MCGUFFIN, et al. 2010; MCGUFFIN, et al. 2018; MCGUFFIN, et al. 2015; BUENAVISTA, et al. 2012; ROCHE, et al. 2012) and RaptorX (KALLBERG, et al. 2012; MA, et al. 2012; PENG, et al. 2011; PENG, et al. 2011; MA, et a.. 2013). These features compare the submitted primary structure with characterized secondary and tertiary structures in the PDB database predicting the putative structure and given scores to indicate the similarity. Also, they provide disorder regions and putative binding sites.

[0063] For the data interpretation, the following points should be taken into account: [0064] RaptorX [0065] Score: is the alignment score falling between 0 and the (domain) sequence length, with 0 indicating the worst. in practice, Score may slightly go beyond the sequence length due to estimation error. [0066] uSeqID and SeqID: is the number of identical residues in the alignment. SeqID is uSeqID normalized by the protein (or domain) sequence length and multiplied by 100. The higher the uSeqiD (SeqID), the better. If the SeqiD>30% and the protein (or domain) has >200 residues, it usually indicates that the predicted model has a correct fold. [0067] uGDT and GDT: uGDT is the unnormalized GDT (Global Distance Test) score defined as 1*N(1)+0.75*N(2)+0.5*N(4)+0.25*N(8), where N(x) is the number of residues with estimated modeling error (in A) smaller than x. GDT is calculated as uGDT divided by the protein (or domain) length and multiplied by a 100. uGDT(GDT) measures the absolute model quality. For a protein with >100 residues, uGDT>50 is a good indicator. For a protein with <100 residues, GDT>50 is a good indicator. If a model has good uGDT (>50) but. bad GDT (<50), it indicates that only a small portion of the model may be good. [0068] P-value: is the likelihood of a predicted model being worse than the best of a set of randomly-generated models for this protein (or domain), so P-value evaluates the relative quality of a model. The smaller the P-value, the higher quality the model. For mainly alpha proteins, P-value less than 10.sup.?3 is a good indicator. For manly beta proteins, P-value less than 10-4 is a good indicator. [0069] IntFold

[0070] The results table is ranked according to decreasing global model quality score. The global model quality scores range between 0 and 1. In general scores less than 0.2 indicate there may be incorrectly modelled domains and scores greater than 0.4 generally indicate more complete and confident models, which are highly similar to the native structure. Each model is also assigned a color coded confidence level depending on the p-value:

TABLE-US-00001 P-value cut-off Confidence Description p < 0.001 CERT Less than a 1/1000 chance that the model is incorrect. p < 0.01 HIGH Less than a 1/100 chance that the model is incorrect, p < 0.05 MEDIUM Less than a 1/20 chance that the model is incorrect. p < 0.1 p Less than a 1/10 chance that the model 0 LOW is incorrect. P > 0.1 Poor Likely to be a poor model with little or no similarity to the native structure. [0071] The confidence scores should be considered in conjunction with the local model quality (per-residue scores) and the coverage of the target protein by the template/templates. The per-residue scores indicate the predicted distance (in Angstroms) between the CA atom of the residue in the model and the CA atom of the equivalent residue in the native structure.

[0072] The 3D cartoon view of the model that is color-coded with the residue error according to the RasMol temperature coloring scheme. [0073] Disorder predictionThe image shows a plot of the probability of disorder (on the y axis) for each numbered amino acid in the sequence (on the x axis). The disorder/order probability threshold is shown as a dashed line on the plot. Residues above the threshold could be considered as mostly disordered and below as mostly ordered, however this threshold serves only to guide the user. [0074] Domain boundary prediction The image shows the top predicted 3D model colored to indicate predicted domains [0075] a change in color indicates a likely domain boundary. [0076] Binding site prediction The image shows the top predicted 3D model annotated to indicate putative binding site residues. The cartoon view of the model is shown in green and the binding site resides are shown as blue sticks with labeled residues. A list of the binding residues is provided along with the most likely (numerous) ligand, the ligand identified at nearest to the center of the predicted binding pocket and a list of the likely interacting ligands and the number of each that were identified in related template structures.

[0077] RfuCAR anti-CD33

[0078] The tested models for RfuCAR anti-CD33 were described in the FIG. 3 and the sequences used are represented by SEQ. ID. Nos. I to 6. The schemes of colors are the same in the schematic view and in the sequences.

[0079] The tested sequences generated the following tridimensional predictive structures for each domain alone (FIG. 4) and for the putative constructions (FIG. 5).

[0080] Apparently, the structure of model 2 (represented by SEQ. ID. No. 2) is the one that maintains the adequate structure of the included components. It is corroborated by the technical data reported by each software compiled in the tables 1 (Domains alone) and table 2 (Anti-CD33 PfuCAR models). The model 2 has the best quality score (0.4465) in IntFold, when compared to the other models. This score is not high but sufficient to indicate a good structural prediction. Also, the analysis of the quality plots (FIG. 6) for all models indicate that the mismatches between the primary sequence of the model and the matched templates are minor in the model 2, indicating a more accurate prediction.

TABLE-US-00002 TABLE 1 Results for predictive tridimensional structures for each component of RfuCAR tested alone on RaptorX software. RaptorX Overall Disordered Templates uSecgd uGDT Positions Component Matched Position p-value uGDT (GDT) (SegId): Score (GDT): (numberA) IL1-R2 Extracellular 3o4o:C 1-330 2.41e?13 263 (80) 314 (95) 218 263 (79) 15 (4%) IgG4 (Hinge-CH2CH3) 5dk3:B 1-229 2.53e?10 182 (80) 212 (93) 156 182 (79) 24 (10%) Anti-CD33 51xaH 6ehyA 1-290 4.25e?11 194 (67) 150 (52) 69 193 (66) 29 (10%) 5aewA Anti-CD123 2gki:A 1-293 3.62e?12 193 (66) 158 (54) 171 192 (65) 39 (13%)

TABLE-US-00003 TABLE 2 Compiled results of predictive structure for models 1 to 6 anti-CD33 RfuCAR in the softwares IntFold and RaptorX. RaptorX Disordered IntFold Overall Positions Templates Confidence Quality Templates uGDT uSeclId GDT (number/ Model Matched p-value Score Domains Matched Position p-value (GDT) (SectId): Score (GDT): ) 1 , MEDIUM 0. 8 2 5dk3:B 1-4 6 3.13e?18 311 ( ) 572 ( ) dyfcB 2.01 e?2 3o4o:C 477-843 1.54e?1 68( ) 314 (84) 220 2 , 8.724E?4 0. 65 2 3o4o:C 267-632 1. ?13 264(72) 314 ( ) 217 462 (7 ) 21 ( ) 3o4oC 1-266 3.15e?11 199(75) 150 (56) 169 6 3 lm , MEDIUM 0. 614 2 5dk3:B 1-464 2. e?18 300 (6 ) 298 3 1.4 E?2 3o4o:C 465-837 1.72e?1 266 (71) 314 (84) 2 08, 3o4o:C 4 , 5.1 0.4055 2 5dk3:B 1-476 5.01e?16 306 ( ) 31 (66) 2 567 (70) 32 ( ) 3o4o:C 3o4o:C 2. 262 (80) 31 6) 220 5 A, 1.0 E?3 0.4412 2 3o4o:C 267- 3.27e?13 261(81) 314 (98) 220 466 (77) 22(3 ) 3o4oC H 87 3.53e?11 196(74) 148( ) 167 6 1-266 A 6 D, 5.677E?3 0.4032 2 5dk3:B 1-464 .19e?18 308(66) 302 (6 ) 298 569 (71) ) 4B 465-792 2.58e?13 261(80) 314 (96) 319 indicates data missing or illegible when filed

[0081] The model Z has the best results of predicted structure in both softwares. The templates matched in the database are also more similar to the function expected for RfuCAR. Model 2 has matched to the structure of IL-1 receptor complex and also to an scFv motif (Table 3 reports all of the matched templates), indicating that probably the structure of the anti-CD33 and the IL-1R2 receptor are maintained in this model. The spacer applied in this model is only the Hinge of IgG4 but considering the percentage arid regions of disorder (table 2, FIG. 7) it seems to allow the adequate mobility of the motifs. The most disordered regions, those regions without a regular secondary structure and more flexible, are in the IgG4 Hinge region and in the linker between the two chains on the scFv (anti-CD33) region. The other models present more disorder regions, that can indicate a less accurate predictive model or a protein with a tertiary structure that is not similar to the expected.

TABLE-US-00004 TABLE 3 Matched templates for all the predicted models of RfuCAR Template Description 3o4oC Interleukin-1 receptor complex - C chain 5dk3:B IgG4 Antibody - B chain 51xaH Adiponectin receptor 2 6ehyA scFv AbVance (Generic scFv) - A chain 6ehxB B-chain 5aawA Structure of a redesigned cross-reactive antibody to dengue virus - A chain 2qki:A Heavy and light chain variable single domains of an 2gkiB anti-DNA binding antibody - A chain Bchain 1hzhH Human igg b12 - Heavy chain 4yfcB Interleukin-1 receptor accessory protein-like 1. Chain: b. 5yd5A scFv antibody 4b08 with epitope peptid - A chain 2qhwB Sars spike protein receptor binding domain in complex with a neutralizing antibody - B chain 5b6fB Fab fragment of an anti-leukotriene c4 monoclonal antibody - B chain 116xA Fc fragment of rituximab (anti-CD20) - A-chain 3ay4A Nonfucosylated fc complexed with bis-glycosylated soluble form of fc gamma receptor iiia - A chain 4pp1D der p 1 allergen complexed with fab fragment of mab 5h8 - D chain ligtD Igg2a intact antibody - mab231. D chain ligtB B chain 1mcoH Human immunoglobulin with a hinge deletion- Heavy Chain 1r7OB Human iga2 (ml) light chain - B-chain

[0082] The predicted binding sites and locations for the Model 2 are positions: 50, 227, 228, 229; 272 and 360. Most likely ligands at each. site (Type): ILE; :UL. Centroid-Ligands at each site (TypeID):. SER 57; FU-1641. All ligands in clusters (Type-Frequency): GLY-1, TRIP-1, GLU-2, ILE-3, PRO-3, SER-2, THR-2, TYR-3, ASP-1, LEU-1, ASN-1; FUL-1, PUC-1. Likely+centroid ligands at each site: ILE650; FUL641. The predicted binding sites are shown in the FIG. 8.

[0083] RfuCAR Anti-CD123

[0084] The tested models for RfuCAR anti-CD123 were described in the FIG. 9 and the sequences used are represented by SEQ. ID. Nos. 7 to 12. The schemes of colors are the same in the schematic view and in the sequences.

[0085] The tested sequences generated the following tridimensional predictive structures for each domain alone (FIG. 10) and for the putative constructions (FIG. 11).

[0086] Apparently, the structure of model 8 (represented by SEQ. ID. No. 8) is the one that maintains the adequate structure of the included components. It is corroborated by the technical. data reported by each software compiled in the tables 1 (Domains alone) and 4 (Anti-CD123 RfuCAR models). This model. has the second best quality score on IntFold analysis (0.4404). The model 8 quality score is minor than the model 11 and the quality plots have very similar distribution (FIG. 12). Despite this, model 8 maintains the CD8 hinge near the cells membrane as constructed for the anti-CD3.sup.3 and anti-CD123 CAR-Ts, that have been already proven to be functional. The model 11 does not have the CD8 hinge region.

[0087] The model 8 has also one of the best results in RaptorX software. The templates matched in the database are more similar to the function expected for RfuCAR. Model 8 has matched to the structure of IL-1 receptor complex and also to an scFv motif (Table 3 reports all of the matched templates), indicating that probably the structure of the anti-CD123 and the IL-1R2 receptor are maintained in this model. The spacer applied in this model is only the Hinge of IgG4 but considering the percentage and regions of disorder (table 11, FIG. 13) it seems to allow the adequate mobility of the motifs. The most disordered regions, those regions without a regular secondary structure and more flexible, are in the IgG4 Hinge region and in the linker between the two chains on the scFv (anti-CD123) region. The models 7, 9, 10 and 12 have many highly disordered regions, indicating regions without good predictive fold.

TABLE-US-00005 TABLE 11 Compiled results of predictive structure for models 8 to 12 anti-CD123 RfuCAR in IntFold and RaptorX. RaptorX Disordered IntFold Overall Positions Templates Confidence Quality Do- Templates uGDT uSeclId uGDT (number/ Model Matched p-value Score mains Matched Position p-valve (GDT) (Secad): Score (GDT): ) 7 , MEDIUM 0.37 9 2 5dk3 1-480 1.0 e?18 17 (66) 30 ) 581 (6 ) (28) 1.5 ?2 3o4o:C 451-853 1.74e?13 260 ( 1) 14 (34) 2 0 8 , 1.137E?3 0. 04 2 3o4o:C 271-636 1.05e?13 266 (73) 14 (66) 223 48 (70) 24(3 ) , 6ehx 1- .05e?12 183 (68) 153( 7) 70 3o4oC, 5 A 27 9 5 B, MEDIUM 0. 762 2 1-46 1.0 e?18 306( ) 205(63) 307 57 ) 24(2 ) 116xA, 1. 08E?2 3o4o:C 469-841 1. e?1 2 6(71) 314 ( 4) 2 1 4 10 , 1. 3 0.3 49 2 5dk3:B 1-480 5.46e?19 318(66) 307 (64) 203 582 (7 ) 2 ) , 3o4o:C 481- 1.27e?15 365( ) 14 (96) 221 08 11 5yd5A, 3. 6E?4 0.4656 2 3o4o:C 371-591 1. e?13 263( ) 314 (98) 221 450 (76) 2 ) 3o4oC, 6 1-270 8 77e?12 1 7 (69) 153( 7) 168 12 9.326E?3 0.3900 2 1-468 1.70e?18 309(66) 2 5(63) 30 571 (71) 24(3 ) 3o4o:C 469-796 2.98e?13 263( ) 314 (96) 315 4 indicates data missing or illegible when filed

[0088] The predicted binding sites and locations for the Model 8 are positions: 33, 235. Most likely ligands at each site (Type): TYR. Centroid ligands at each site (TypeiD): PRO656. All ligands in clusters (Type-Frequency): GLY-3, TRP-2, GLJ-2, ILE-3, PRO-4, SER-3, ARG-1, THR-2, TYR-5, ASP-1, LE!-1, ASN-4, ALA-2, CYS-1. Likely+centroid ligands at each site: TYR672. The predicted binding sites are shown in the FIG. 14.

[0089] Therefore, according to the reported predictive structures the best choice of spacer, those which have the better chance to maintain in vivo the structure of the domains in RfuCAR are the model. 2 (SEQ. ID. No. 2) for anti-CD33 RfuCAR and model 8 (SEQ. ID. No. 8) for anti-CD123 RfucAR. Both include the anti-tumoral scFv, the 1Ll-R2 receptor with IgG4 Hinge as a spacer between receptors and CD8 hinge as a spacer from cell membrane.

[0090] Although the invention has been amply described, it is obvious to those skilled in the art that various changes and modifications may be made to improve the design without such changes being outside the scope of the invention.

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