Assessing Major Histocompatibility Complex-Associated Peptide Proteomics assay as a tool to predict immunogenicity potential of protein therapeutics and antigens

doi:10.21203/rs.3.rs-1944419/v1

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Assessing Major Histocompatibility Complex-Associated Peptide Proteomics assay as a tool to predict immunogenicity potential of protein therapeutics and antigens

https://doi.org/10.21203/rs.3.rs-1944419/v1

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Major histocompatibility complex-II (MHC-II)-Associated Peptide Proteomics (MAPPs) is a mass spectrometry-based approach, to identify and relatively quantitate naturally processed and presented MHC-II-associated peptides, that could potentially activate CD4 + T cells and elicit a patient immune response against protein therapeutics in the preclinical development phase. Methods to identify these peptide antigens are critical to the development of new vaccines. Conversely, it is critical to bring safer new biological entities (NBEs) as drug candidates into clinical trials, with a reduced risk of triggering an adaptive immune response that could hamper a therapeutic outcome. Here, we describe a robust protocol for the identification of MHCII-bound peptides from Peripheral Blood Mononuclear Cells (PBMCs) of healthy donors, using nano-ultra-performance liquid chromatography coupled to high-resolution mass spectrometry (nUHPLC–MS/MS) and recent improvements in methods for isolation of these peptides.

This manuscript demonstrated the crucial role of MAPPs assay as a tool to early screen NBE candidates in the discovery process, thus bringing the safest molecules in the clinical phase studies.

Immunogenicity is a term that is used in the pharmaceutical industry to describe the undesired immune responses to therapeutic proteins⁽¹⁾. It has been more than three decades since the first monoclonal antibody was approved by the United States Food and Drug Administration (US FDA) in 1986^(2,3), and over the years several health authorities have become more interested in how pharmaceutical companies deal with this issue⁽⁴⁾. Although protein therapeutics hold significant promise for improving human health, sometimes their repeated administration is highly immunogenic and can elicit an unwanted anti-drug antibody (ADA) response⁽⁵⁾. The formation of an ADA response can interfere with the efficacy of the drug candidate or alter the drug pharmacokinetic (PK) and pharmacodynamic (PD) properties, thereby potentially resulting in reduction of its efficacy⁽⁶⁾, and in addition to this can lead to severe adverse immune reactions in patients⁽⁷⁾. Many health authorities have issued guidelines on the assessment of immunogenicity because of the negative implications for patients by compromised drug efficacy and safety^(8–11). Immunogenicity has been suggested to be dependent on the interplay between drug-related factors (e.g., non-human sequence, glycosylation, impurities, aggregation, host cell proteins), patient-related factors (e.g., disease type, genetic factors, concomitant immunomodulators), or to the drug route and frequency of administration^(12–14).

Establishing in-silico and in-vitro systems to predict and assess T cell epitopes in amino acid sequences is therefore necessary for the development of therapeutic antibodies with low immunogenicity, which could be linked to the reduction in risk of clinical sequelae related to the potential generation of ADAs.

There is a plethora of different types of assays which can be used to investigate protein immunogenicity:

Firstly, there are different commercial and publicly available in-silico tools with their own proprietary algorithms that have been already implemented and described so far^(15–17). Nevertheless, it has been reported that in some cases in silico tools may be over-predictive and may not be able to predict immunodominant T cell epitopes properly^(18,19).

On the other hand, the use of several in-vitro tools to predict protein T cell epitopes have been described. These include the MHC II binding assay with synthetic potential immunogenic peptides⁽²⁰⁾ and a T cell assay to evaluate the potential for inducing T cell activation⁽²¹⁾. Alternatively, and perhaps more appropriately, another approach is to detect immunogenic peptide sequences by using liquid chromatography–mass spectrometry (LC-MS), a truly powerful analytical tool that finds various applications in proteomics and peptidomics, the so-called MAPPs assay^(22–24). As in this method the peptides are naturally processed and presented in-vitro on MHC II molecules by mimicking what also happens in-vivo, the MAPPs technique could be a more suitable tool to detect genuine T cell epitopes than in silico methods or binding assays using synthetic peptides (Fig. 1).

Compared to other previous published MAPPs⁽²⁵⁾, we developed a highly sensitive method for identifying the T cell epitopes of therapeutic antibodies with a new bead material for the immuno-enrichment of MHCII eluted ligands. In the MAPPs protocol for immunoprecipitation (IP) and purification of peptide–MHC II complexes, we compared Streptavidin-coated Sepharose magnetic beads with magnetic nanoparticle beads coated with a hydrophilic polymer, polyglycidyl methacrylate (pGMA) (FG magnetic beads)⁽²⁶⁾. We also evaluated the impact of cell medium composition in the immunophenotyping and morphology of Dendritic cells (DCs) that act as Antigen Presenting Cells (APCs) in MAPPs assay. As a model therapeutic antibody, we used Infliximab, an anti-TNF-α chimeric antibody, because it showed high frequency of ADA incidence in clinical practice. We then compared the peptides and sequence regions derived from the antibody when using each type of bead. Finally, we present MAPPs analysis of other two well-known immunogenic antigens (Birch pollen allergen, Bet v1, and Keyhole Limpet Hemocyanin, KLH) used as positive controls to follow antigen processing and presentation, demonstrating that the MAPPs platform could become a routine screening tool that can be taken into account for the preclinical and, potentially, clinical immunogenicity risk assessment process of protein biotherapeutics.

Ethics statement

Buffy coats from healthy human volunteers were supplied by the Azienda Ospedaliero – Universitaria Città della Salute e della Scienza di Torino, Centro di Produzione e Validazione Emocomponenti, Turin, Italy) according to current ethical practices and the samples were anonymized.

Antibodies and proteins evaluated

Model therapeutic antibodies were Infliximab originator (Creative Biolabs, TP-088CL. Shirley, NY), and Infliximab biosimilar (BioXcell, SIM0006, Lebanon, New Hampshire). Control positive proteins were KLH (Sigma Aldrich, H7017, St. Louis, Missouri) and BetV1 (Biomay, BET_1_1A, Vienna).

In-silico analysis of T-cell epitopes

In-silico analysis of the light chain (LC) and heavy chain (HC) of IFX was carried out on a public database, Immune Epitope Database (IEDB)⁽³⁴⁾, using TepiTool (http://tools.iedb.org/tepitool) interface⁽³⁵⁾ for T cell epitope predictions. This is designed as a step-by-step wizard combining both MHC class I and class II prediction methods. The tool provides recommended default values at each step for the prediction and selection of an optimal set of peptides for a given application (26 most frequent panel of alleles, IEDB recommended method). The median of the percentile ranks of the three methods involved was used as consensus percentile ranking (Cut-off guidelines: Percentile rank ≤ 10.0). For the assessment of most promiscuous peptides the cut-off was set at 20% as threshold percentile rank, as suggested by software developers. Summary of the results for IFX and positive controls is reported in the Supporting Information section.

Generation and maturation of Mo-DCs

Human Mo-DCs were generated from monocytes obtained from PBMCs of healthy volunteers. PBMCs were purified from buffy coats by density gradient centrifugation on Histopaque (Sigma-Aldrich, St. Louis, Missouri) and buffy coats were anonymously provided by the “Blood Components Production and Validation Center” (Azienda Ospedaliera Universitaria, Città della Salute e della Scienza, Turin, Italy) for research use only. The CD14⁺ monocytes were positively selected from PBMCs using anti-CD14-conjugated MicroBeads (Miltenyi Biotec, Bergisch Gladbach, Germany) according to the manufacturer’s instructions. To prepare immature Mo-DCs (imMo-DC), the enriched monocyte fraction was incubated in 6-well culture plates (6 × 10⁶ cells/5 ml/well) with two different culture media supplemented with 200 ng/mL of recombinant human granulocyte-macrophage colony-stimulating factor (GM-CSF) and 50 ng/mL of recombinant human interleukin 4 (IL-4) (both Miltenyi Biotec) for 6 days at 37°C in 5% CO2. The two culture media tested in this study were: serum free AIM-V medium (Gibco, Waltham, MA USA) and Roswell Park Memorial Institute Medium (RPMI) 1640 (Gibco, MA, USA) supplemented with 10% heat-inactivated fetal bovine serum (FBS), 100 U/ml penicillin, 100 µg/ml streptomycin, 2 mM glutamax, 1 mM sodium pyruvate, and 1× MEM non-essential amino acids (all from Gibco). On day 2, half culture medium was replaced by fresh medium containing twice concentration of both GM-CSF and IL-4 cytokines compared to the initial concentration. On day 6, imMo-DCs were resuspended in fresh medium supplemented or not with different concentrations of IFX, KLH and Bet v1 and incubated for 3 hours at 37°C in 5% CO2. To induce dendritic cell maturation, 1 ug/mL or 100ng/mL of Lipopolysaccharide (LPS) from Escherichia coli O55:B5 (Sigma-Aldrich, Merck) was added to the cell culture. After 24 hours, mature Mo-DCs (mMo-DCs) were harvested and analyzed.

Cell morphology, viability and Immunophenotypic analysis

Images of monocytes, imMo-DCs and mMo-DCs were captured by EVOS M5000 light microscope with phase-contrast (Thermo Scientific, CA, USA). Cell recovery and viability were analyzed by NucleoCounter NC200 automated cell counter (ChemoMetec, Allerod, Denmark). ImMo-DCs and mMo-DCs were labeled with fluorochrome-conjugated monoclonal antibodies for 30 min at 4°C, after blocking non-specific sites with Human BD Fc Block™ (BD Biosciences, Franklin Lakes, NJ) to detect expression of maturation markers and co-stimulatory molecules. The following mAbs were used: anti-CD14-PECy7, anti-CD80-PE, anti-CD83-BV786, anti-CD86-APC, anti-MHC class II antigen-BB515 (BD Biosciences), anti-CD1a-eFluor450 (Thermo Scientific). Non-specific antibody binding was assessed using appropriate isotype controls (mouse IgG2a PE-Cy7, mouse IgG2a PE, mouse IgG1 BV786, mouse IgG1 APC, mouse IgG2a BB515, mouse IgG1 eFluor450). Cell debris and dead cells were excluded from the analysis based on light-scatter properties and 7-AAD fluorescence signal (BD Biosciences). Immunophenotype (IPT) was assessed by BD FACSCelesta™ SORP (BD Biosciences) and data analysis was carried out by FlowJo™ software (BD Biosciences).

Cell pellet extraction procedure

Frozen pellets were thawed and mature DCs were lysed in ice-cold lysis buffer (20 mM Tris buffer pH 7.8) containing 5 mM MgCl₂, 1% Triton X-100 (Roche Diagnostics, 11332481001, Rotkreuz, Switzerland), and 1 tablet of cOmplete™ Mini protease inhibitors (Roche Diagnostics, 11836153001) for 1 hour at 4°C. The volume was scaled accordingly to the cell pellet number. After centrifugation, the lysate was incubated with two different immunoenrichment reagents: i) biotinylated anti-HLA-DR antibody (clone G46-6, Biolegend, 307614, San Diego) coupled to Streptavidin Sepharose magnetic beads (Cytiva, GE Healthcare, 28985738, Marlborough, MA), ii) anti-HLA-DR antibody (clone G46-6, Purified NA/LE Mouse Anti-Human HLA-DR, New England BioLabs GmbH, 555809, Ipswich, MA) coupled to polyglycidyl methacrylate (pGMA) FG NHS magnetic beads (Tamagawa Seiki Co, Ltd., TAS8848N1141, Tokyo, Japan). FG beads were prepared accordingly to vendor protocol E105⁽³⁶⁾. The comparability between the two bead preparations was ensured by the equimolar quantity of the capturing reagent present in the two preparations for the same amount of beads. After overnight incubation at 4°C, beads were washed several times with phosphate-buffered saline (PBS) and PBS containing 0.1% Zwittergent 3–12 (Merck KGaA, 693015, Darmstadt. Germany). After washing, peptides were eluted from the beads by adding 25µL of an aqueous solution containing 2% ACN 0.05% trifluoroacetic acid for two times at 37°C on a Thermomixer for 30 minutes. Eluates were pooled and injected into the LC-MS instrument.

LC-MS method

MHC-II peptide preparations obtained from matured monocyte-derived DCs (moDCs) samples were separated on a nanocapillary liquid chromatography system (UltiMate 3000 RSLC, Thermo Scientific, CA, USA) using self-packed fused-silica C18 reversed phase column (75 µm i.d.× 150 mm, Pep Map™ RSLC C18, 2µm, 100Å, set at 35°C, ThermoScientific, ES904) connected to a Q-Exactive Plus Orbitrap mass spectrometer (Thermo Scientific) via nanoelectrospray ionization (EasySpray source, ThermoScientific). Samples (20 µL volume dissolved in 0.05% (v/v) trifluoroacetic acid in 2% (v/v) acetonitrile/water) were loaded for 4 min at 10 µL/min onto an Acclaim PepMap C18 trap column (300 µm i.d. × 5 mm, Thermo Scientific, 160454). Peptides were then eluted at a flow rate of 400 nL/min using a nonlinear 50 min gradient of 2 − 40% B, followed by a 10 min column wash at 95%B, and re-equilibration for 19 min [buffer A: 0.1% (v/v) formic acid in water; buffer B: 0.1% (v/v) formic acid in acetonitrile]. MHC II peptides were analyzed by tandem MS using standard operating parameters. Survey scans (scanning range 266.7 − 4000m/z) were recorded in the Orbitrap mass analyzer at a resolution of 70,000 at 200 m/z, max injection time 100ms, AGC target 3x10⁶ ions, 1 microscan, without the lock mass option enabled. Data-dependent MS/MS spectra of the 10 most abundant ions from the survey scan were recorded in the HCD Orbitrap cell at a resolution of 17,500 at 200m/z, max injection time 100ms, AGC target 1x10⁵ ions, 1 microscan, isolation window 2 m/z. Target ions selected for MS/MS were excluded with a dynamic exclusion of 10 s.

K_D estimation from saturation binding experiments

We carried out equilibrium saturation experiments to study how the concentration of reaction products (pMHCII complexes) changed as a function of reactant concentrations (protein concentration) and/or reaction conditions. In our case, peptide processing and presentation involved different partners. For MHCII endocytic pathway we simplified and boiled them down to two main key players, namely the antigenic peptides and HLA-DR receptors exposed onto the membrane of mature DCs that have been made soluble after the cell lysis step. For a typical bimolecular equilibrium reaction such as

$$\left[P\right]\left[R\right]\underrightarrow{\leftarrow } \left[PR\right]$$

where P is a generic peptide, R is the MHCII receptor and PR is the peptide-receptor complex, increasing amounts of reactant [P] might be titrated against a fixed amount of the available receptors [R] and the equilibrium concentration of the product [PR] determined. For the bimolecular reaction from Eq. (1), we can define an equilibrium dissociation constant (K_D), as shown below in Eq. (2), whose value will be constant only for a given temperature:

$${K}_{D}= \frac{\left[P\right]\left[R\right]}{\left[PR\right]} \left(2\right)$$

The mathematical relationship between the fraction of [R] bound ([PR]/[R_TOT]) and the free concentration of [P] is the following:

$$\frac{\left[PR\right]}{\left[{R}_{TOT}\right]}=\frac{\left[P\right]}{{K}_{D}+\left[P\right]} \left(3\right)$$

that represents a rectangular hyperbola equation and bimolecular binding curves are often referred to as hyperbolic binding curves. We could assume that the precursor abundances of the peptides seen in MS1 spectra arise from a certain amount of peptide-MHCII complexes which have been isolated in our affinity capture step and eluted from the beads. Bearing this in mind, our results seemed to agree with a rectangular hyperbola model, as shown in Eq. (3) for the identified peptides from which K_D and the number of bound receptors could be estimated (see parameter “m” and results in Fig. 6B).

To make these parameters easier to visualize, it is also possible to linearize the hyperbola equation Eq. (3) by doing a little bit of rearranging and by making its double reciprocal Eq. (6) as following:

$$\left[PR\right]=R\text{TOT} \frac{\left[P\right]}{K\text{D}+ \left[P\right]} \left(4\right)$$

$$\frac{1}{\left[PR\right]}=\frac{1}{R\text{TOT}}\frac{K\text{D}+\left[P\right]}{\left[P\right]} \left(5\right)$$

$$\frac{1}{\left[PR\right]}=\frac{K\text{D}}{R\text{TOT}}\frac{1}{\left[P\right]}+\frac{1}{R\text{TOT}} \left(6\right)$$

from which it is possible to estimate R_TOT by doing the inverse of the y-axis intercept (1/R_TOT) of the linear regression and the K_D value by multiplying the slope of the regression K_D/R_TOT by the number of previously estimated R_TOT. K_D value in nM of each peptide is obtained by simple molar transformation from Infliximab to the i^th peptide. Curve fitting to derive K_D values from both hyperbola and linear models was performed using the nonlinear curve and linear fitting algorithm respectively in Origin 2021b.

Data Analysis

Peptides were identified with Sequest search algorithm against Swiss-Prot human database containing the sequence of the evaluated therapeutic antibody or antigen using Proteome Discoverer software ver. 2.5.0.400 (Thermo Fisher Scientific, Inc.). The search was performed with a mass tolerance of ± 10 ppm for precursor ions and ± 0.02 Da for fragment ions. Met-sulfoxide, Asn/Gln deamidation, and N-terminal pyroglutamylation were considered as variable modifications. Data were searched without enzyme specificity, and peptide results were reported at 1% FDR cutoff. Peptides showing more than 1.9 or 2.3 of cross-correlation value (Xcorr) for doubly or triply charged ions, respectively, and less than 0.1 of the delta cross-correlation (dCn) were considered as true hits.

Label Free Quantitation (LFQ) approach was used to statistically test differences among experimental conditions by Proteome Discoverer. Firstly, this was deployed to evaluate the intra-donor and intra-assay reproducibility. Secondly, it enabled us to select the optimal experimental settings to secure that the maximum number of MHCII ligands is reported by the method.

The Proteome Discoverer LFQ processing workflow contained additional nodes compared to the Search workflow, such as Minora Feature Detector node for identification and quantitation, Percolator node for Peptide Spectra matches (PSMs) validation. The consensus included Feature Mapper and Precursor Ion Quantifier from which the precursor abundances were based on peptide intensities.

Further statistical data were obtained by standard Proteome Discoverer tools (Box-Whiskers plots and Volcano plots and hierarchical clustering). In particular for the hierarchical clustering Euclidean distance (d_AB) was used as metric, see Eq. (7):

$${d}_{AB}=\sqrt{\sum _{i=1}^{n}{({e}_{Ai}-{e}_{Bi})}^{2}}$$

where d_AB is the distance between donors A and B, and the e_Ai and e_Bi are expression values of the ith peptide for donors A and B.

Comparison of different culture media in monocytes differentiation into iMo-DCs/mMo-DC

CD14⁺ monocytes were isolated from PBMCs with purity around 95% (± 3.8%), as determined by flow cytometry (data not shown). To optimize cell culture conditions, monocytes were cultured with GM-CSF and IL-4 in two different media for comparison: one serum-free medium (AIM-V) and one of the most widely used culture medium in research (FBS-supplemented RPMI). The culture media impact on dendritic cells differentiation and maturation was evaluated by analysis of cell morphology, cell viability/recovery and expression level of surface markers. On day 0, monocytes were rounded in shape, but they slowly changed during differentiation into dendritic cells. After 6 days, the cells showed cytoplasmic protrusions, which were even more pronounced after maturation on day 7, in parallel with the capacity to form cell clusters. Cells cultured in AIM-V appeared strongly adherent and elongated in shape, in contrast to DCs differentiated in RPMI that were more rounded and less adherent (Fig. 2A). The observed difference in morphology and stickiness shown by the two media might have negatively affected cell recovery and viability in AIM-V. In fact, mMo-DC viability, evaluated by excluding DAPI + dead cells, was quite different between cells cultured in the two media: viability was 71% (± 6.1) for cells cultured in AIM-V and 91% (± 4.4) with FBS-supplemented RPMI (Fig. 2B). The mMo-DC recovery, calculated based on the number of initially seeded monocytes, was higher for cells cultured in FBS-supplemented RPMI (29% ± 14) than in AIM-V (10% ± 3.6) (Fig. 2C).

Flow cytometry analysis of cell surface markers showed that in vitro generated Mo-DCs assumed the typical DC phenotype (Fig. 2D). Differentiation of monocytes toward an immature DC phenotype was observed for both tested media by flow cytometry evaluation: monocytes down-regulated the expression of CD14 and Mo-DCs expressed several DC markers involved in the formation of immunological synapse between DC and (naïve) T cells, including the costimulatory proteins CD80 and CD86, and the antigen-presenting molecule MHC II (HLA-DR). Mature Mo-DCs also expressed the DC activation marker, CD83, which was up-regulated accordingly. The main difference observed between the two DC culture media was the expression of CD1a: DC cultured in AIM-V presented lower levels of CD1a than DC cultured in FBS-supplemented RPMI⁽²⁷⁾.

Moreover, cells cultured in RPMI presented a significantly higher expression of CD80 when compared to AIM-V DCs (Fig. 2D). Considering all the obtained data and results, FBS-supplemented RPMI was selected as the most suitable medium for the MAPPs assay.

MAPPs results on donors

PBMC samples from two donors (BC01, BC02) were used to test the initial performance of the MAPPs assay in terms of intra-day and intra-donor reproducibility. This was done by carrying out an overnight immunoaffinity capture with streptavidin Sepharose magnetic beads coupled with a biotinylated antibody anti HLA-DR. For each investigated donor, we typically used LPS-only treatment as a negative control and KLH treatment as positive control. Experimentally, MHC-II peptides are typically identified in clusters represented by a core sequence (containing the four anchor residues) and nested sets with flanking N- and C-termini residues⁽²⁸⁾. The redundant characterization of related peptides across compounds increases the probability for a true positive hit in addition to the MS-driven classical peptide false discovery rate (FDR) approach. Results with Infliximab originator are reported in Fig. 3A and B. We were able to identify nearly all the same sequences in the same donors on each occasion: peptide_{31 − 45} (CDR1-LFR2 spanning peptide) and cluster_{167 − 184} (LC constant region) were present in both the sample replicates from donor BC02 and BC01 respectively. Regarding the heavy chain (HC), the cluster_{79 − 93} (HFR3, HC framework 3) was present in both the sample replicates from BC02. However, HC peptide_{48 − 63} (CDR2) and peptide_{258 − 275} (CH2) were present in one out of 2 replicates. Proteomics techniques generally do not possess good reproducibility, but the above findings suggest that our proposed technique was robust and was also capable of indicating relative amounts of MHC II-presented peptides in DC samples when comparing their amounts in the same cluster.

Beads Comparison

To refine the extraction method, a direct comparison of two different magnetic bead formats were performed on the same donor (BC05): Streptavidin Sepharose magnetic beads and FG NHS magnetic beads. This is the first report about the performance of the Streptavidin Sepharose-based beads format in a MAPPs assay. Indeed, the NHS activated beads, along with the CNBr activated beads, are the most used support in the immunoprecipitation step and represented the reference material. Streptavidin beads extracted samples showed clusters with a higher number of LC peptides (Fig. 3C and D) when compared to the FG counterparts. Moreover, some HC peptides were missing in the FG processed samples. Furthermore, the same findings hold true with KLH treated sample as a positive control (Fig. 3F).

For sample BC05-5, DCs maturation was performed at 0.1µg/mL instead of 1µg/mL of LPS (Fig. 3E) to investigate the impact of the LPS maturation step on the immunopeptidome, but there was not a significant impact both on the cell immunophenotype and the identified peptides (see Fig. 4D).

Statistical analysis of biological replicates and experimental conditions by LFQ

Evaluation of method robustness concerning the intra-donor / intra-day assay variability was assessed by comparing protein abundances with LFQ. This method evaluates the experimental variability on the whole MHCII proteome considering peak intensities without pooling samples.

In the BC01 biological replicates (n = 2) the protein content is not significantly different (Fig. 4A). The volcano plot further reinforces this conclusion showing in detail that out of 2506 identified peptides, 106 peptides had a log₂ fold change greater than 1.0 and 101 peptides lower than − 1.0 (Fig. 4B) in BC01-2. The same conclusion can be drawn for the BC02 donor (data not shown).

In the optimization phase we also investigated the impact of LPS concentration on the MHCII ligandome. BC05-5 sample was matured in the presence of LPS 0.1µg/mL instead of 1µg/mL in the case of BC05-3 and BC05-4. The log₁₀ of protein median abundance did not show a significant difference (Fig. 4C). Out of 1430 identified peptides, 91 peptides had a log₂ fold change greater than 1.0 and 134 peptides lower than − 1.0 in BC05-5 (Fig. 4D).

We also investigated the impact of bead types and chemistry on the number of identified MHCII peptides. We compared the reference bead (NHS activated chemistry) to the streptavidin beads coated with anti-non-biotinylated or biotinylated HLA-DR antibody capture reagent respectively. As shown in Fig. 4E, the streptavidin beads showed a better performance than the FG beads, with a yield increase up to tenfold the median values of IFX abundance. Regarding the whole MHCII ligandome, out of 1939 identified peptides, 525 peptides had a log₂ fold change greater than 1.6 and 74 peptides lower than − 1.6 for the streptavidin extracted samples. Peptide clustering, using normalized abundance, showed an eye-catching larger number of missing peptides (green areas) from FG extracted peptides (Fig. 4G). Similar results were obtained for KLH-treated samples (Fig. 4I and Fig. 4L).

Assessment of Remicade® and IFX biosimilar overall immunogenicity

To investigate significant differences between the IFX originator (Remicade®) and its biosimilar (by BioXcell) two donors and two concentrations (10µg/mL and 20µg/mL) were tested in the same day (Fig. 5A). The two antibodies showed the same set of peptides and the concentration had no or little impact on the types of peptides: HC peptide_197− 210 for donor BC08 and LC peptide_42− 60, for BC09. Only at 20µg/mL the MAPPs evaluation, for the biosimilar antibody in the BC08 donor sample, showed the unique peptide_293− 304 not present at lower concentration. This finding prompted us to test a broader range of concentrations to assess the impact of increasing concentrations of test molecule on peptide presentation. Furthermore, the immunophenotyping of mMo-DCs treated with the two antibodies did not show any significant difference in surface markers expression (Fig. 5B).

LC-MS analysis of Infliximab peptides K _D from saturation binding experiments on MHC II receptors by MAPPs

To further understand and classify the relevance of the presented peptides which may infer their immunodominance propensity, the MHCII peptide affinity constants can be extrapolated from MAPPs experiments. To this end six different concentrations of IFX biosimilar were tested on one donor. The method provided a good sensitivity since the HC peptide_{197 − 210} (GTQTYICNVNHKPS) and LC peptide_{43 − 60} (SPRLLIKYASESMSGIPS) were detected even at 1µg/mL. HC peptide_{331 − 346} (LPAPIEKTISKAKGQP) and LC peptide_{43 − 57} (SPRLLIKYASESMSG, that belongs to the same peptide cluster of the LC peptide_{43 − 60}) were detected from 5µg/mL and 10µg/mL respectively (Fig. 6A).

From either the hyperbolic binding curves or linear plots it is possible to estimate the number of total available receptors and the K_D of that specific MHCII ligand, expressed as molar peptide concentration (nM), as listed in Table 1.

Both the models provided robust results in estimating the K_D and number of available MHCII receptors for each peptide. The K_D spanned from 58 to 202nM range, confirming a medium affinity of these MHCII binders.

MAPPs results on HLA-genotyped donors

Since the donor HLA-genotype has a large impact on the MHC-II peptidome, we selected five different donors which contained alleles covering around 50% of the European population. On these 5 samples we then applied our MAPPs assay with IFX biosimilar at 50µg/mL (Fig. 7A). Among the tested donors, four out five showed FR3 peptides, spanning the HC sequence_{72 − 94}, and two out of five, the CDR3 HC sequence_{92 − 106}. The donor HLA-4 gave two peptides on the LC, peptide_{31 − 45} and peptide_{187 − 202}. Finally, the donor HLA-3 did not provide us any peptide. As expected, the genotype differences are reflected in the ligandome. From Fig. 7D, HLA-1 and HLA-2 donors, sharing allele HLA-DRB1*01:01:01, have a more similar ligandome pattern. The same is valid for HLA-4 and HLA-5 donors since they share the allele HLA-DRB1*04:01:01.

In parallel to IFX testing, we assayed two molecules as positive controls, i.e. KLH and Bet V1 proteins, both at 50µg/mL. This ensured that the entire procedure was working correctly for each and every donor, from DC maturation, antigen processing and presentation, down to immunoaffinity extraction, LC-MS analysis and data analysis.

KLH is a mixture of two different haemocyanins, which are called KLH1 and KLH2. The subunit of KLH1 is a 390 kDa polypeptide which consists of eight globular domains called 1a to 1h according to their sequence in the subunit. On the other hand, KLH2 has a molecular weight of 350 kDa and according to the most recent data also contains 8 domains, called 2a to 2h. In vivo, every type of subunit has been shown to form homo-oligomers, and no hetero-oligomers have been observed. The remarkable immunostimulatory properties of KLH result from high antigenicity derived from numerous carbohydrate and peptide epitopes⁽²⁹⁾.

The main feature of the Bet V1 structure is a seven stranded anti-parallel β-sheet that wraps around a 25 residue-long C-terminal α-helix. The β-sheet and the C-terminal part of the long helix are separated by two consecutive helices. The seven stranded anti-parallel β-sheet consists of five longer strands of residues 2◊11(I), 121⇓111(VII), 97◊107 (VI), 89⇓79(V), 66◊75(IV), and two shorter strands 57⇓52(III), and 40◊44(II). The C-terminal α-helix, (α3), consists of residues 130–154 and the consecutive helices consist of residues 15–23, (αl), and 25–34, (α2), respectively⁽³⁰⁾.

KLH results are reported in Fig. 7B. The high antigenicity of the KLH is confirmed by huge number of peptides and clusters in both isoforms spanning the entire sequence, from N- to C-terminus.

Bet V1 heatmap is shown in Fig. 7C. Bet V1 antigenicity is less prominent that that of KLH and in fact it is expected to have a prevalence of around 8 to 16% positive response in the general population⁽³¹⁾.

In HLA-3, HLA-4 and HLA-5 donors a consensus peptide_{19 − 37} was found, spanning the α1 and α2 helices (for HLA-4 we identified clusters of three peptides). In the HLA-3 donor, peptide_2 − 38 was found in the βI strand and in the α1 and α2 helices (a cluster composed by two peptides) and the HLA-4 donor’s peptide_{63 − 77} in the βIV strand. In the HLA-3 donor cluster_{134 − 160} was found in the C-terminus α3 helix composed by four peptides. Two donors out of five did not show any presented peptide, both sharing one out of two DRB1 alleles (DRB1*01:01:01).

Definition of the therapeutic window is a key objective in clinical development of drug candidates. The intimate interconnection between efficacious and toxic doses stems from the different molecular properties of the drug candidate such as its structure, distribution, and disposition. Following the principles of translational medicine, it is of paramount importance to project the preclinical therapeutic window to the clinical one, in order to reduce the attrition rate of drug candidates and biosimilars as well.

Assessment of nonclinical immunogenicity risk with in vitro assays to track the innate and adaptive immune systems is recommended by health authorities⁽⁸⁾. MAPPs assay is one of the key in vitro assays that can be inserted in pre-clinical development to test as early as possible one of the underlying processes involved in T-cell activation and responses, i.e. antigen processing and presentation of biotherapeutics to the immune system. MAPPs is not considered a high-throughput assay, therefore it is of crucial importance to try to balance the number of preclinical candidates and the associated workload. In our company, it was therefore decided to insert it immediately before a Lead candidate is identified, where the clinical discovery target profile starts to be defined. Thus, the lead discovery phase should in fact have one to two candidates selected and characterized, a front-runner and a backup candidate molecule to enter the next development phase.

The main strategy to assess immunogenicity by utilizing the MAPPs assay should involve a first step where the candidate molecules are screened on a set of HLA-genotyped PBMCs that represent 80% of the general population. In addition, exotic HLA alleles, that have been pointed out by the in-silico analysis, should be further assessed to get a complete MAPPs picture.

To gain some insight about the assay reliability, we examined literature data about IFX⁽³²⁾ and Bet V1⁽³³⁾. Our findings confirmed that the assay can recapitulate known T-cell epitopes (Fig. 8). Among those, IFX peptides L_6 − 20 and L_{46 − 60} generated the highest T-cell response. Similarly, the immunodominant epitope Bet V1_{142 − 156}, among 57 donors with birch pollen allergy, frequently showing a stimulation index greater than 5, was identified in our MAPPs assay too.

This manuscript represents the first report on the implementation of streptavidin beads in the MAPPs workflow compared to the standard NHS-activated beads. Our findings suggest that the streptavidin beads provide a more robust identification of the IFX epitopes due to a larger number of peptides found in clusters and likely owing to higher recovery of the former. Lastly, streptavidin beads have a less demanding handling complexity due to a shorter incubation time and number of steps to be applied in the coating procedure (e.g., 20h of quenching step is necessary in FG beads, as opposed to Streptavidin beads that do not require any additional quenching step.). Plus, the plausible crosslinking of lysines by N-Hydroxy-succinimide group in the variable region of HLA-DR antibody which is coated on FG beads might have hampered the recovery of the immunoaffinity reaction compared to Streptavidin beads.

In addition, two different cell media were evaluated and RPMI outperformed AIM-V both in terms of cell viability (91 vs 71%) and recovery (29 vs 10%). Moreover, we saw an impact of medium in the cell surface marker expression of dendritic cells. In particular, in RPMI we spotted higher expression of CD1a and CD80 compared to AIM-V.

A remarkable breakthrough is the capability of obtaining insights about MHCII ligand affinities from antigen titration experiments. This parameter determines the strength of interaction with naïve T-helper cells too, influencing T-cell activation and differentiation, and its experimental determination can further complement the assessment of immunogenicity risk associated with a particular amino acid sequence. Lastly, our results on HLA-genotyped donors have confirmed the crucial interplay between the inherited HLA genome and the associated MHCII ligandome pattern.

As expected, MAPPs assay detected far fewer peptides than in silico analysis tool (Tepitool): e.g., in the IFX LC it overpredicted peptide regions spanning positions L_{67 − 85} and L_{101 − 115} as top 1–2% (consensus % rank) and for IFX HC regions spanning positions H_{297 − 317} (top 2–5%) and H_{395 − 412} (top 1–2%), but they were not found in the MAPPs assay. Furthermore, in vitro MAPPs peptides H_{331 − 346} and H_{92 − 106} were not predicted at all at top 10% ranking cut-off. Eventually, even when in silico and MAPPs converged on the same peptide regions, there are some shifts in the actual peptide sequences. This is due to the different peptide lengths naturally occurring in the in vitro assay and not identified by the in silico tool since the analysis is conducted using 15mer as scanning sequence length. Same considerations hold true for the analysis of KLH and BetV1 (see supporting info section).

To sum up, while T-helper cell proliferation assays can perform an initial population-based screening tier, regarding the de-immunization process, MAPPs with the in-silico tools play a critical role to rank one or the other candidate and lead the re-engineering process when necessary.

This manuscript clearly demonstrates that the MAPPs platform could become a routine screening tool that can be taken into account for the preclinical and, potentially, clinical immunogenicity risk assessment process of protein biotherapeutics.

Acknowledgments: We would like to thank Valeria Castagna for cellular training and support to the early phase of the project and Vanita Sood and Klaus Urbahns for paper revision. The authors would also like to thank anonymous reviewers for thorough and insightful comments that helped improve the manuscript.

Funding: This research was funded by the healthcare business of Merck KGaA, Darmstadt, Germany. (CrossRef Funder ID: 10.13039/100009945)

Author contributions:

Conceptualization: ADI, LMB, FRS, KJC, TF, PB, VS

Methodology: ADI, LMB, TF, PB

Writing – original draft: ADI, LMB, TF, PB

Writing – review & editing: ADI, LMB, FRS, KJC, TF, PB, VS, KU

Competing interests: Authors declare that they have no competing interests.

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Assessing Major Histocompatibility Complex-Associated Peptide Proteomics assay as a tool to predict immunogenicity potential of protein therapeutics and antigens

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Abstract

Figures

One Sentence Summary

Introduction

Materials And Methods

Ethics statement

Antibodies and proteins evaluated

Generation and maturation of Mo-DCs

Cell morphology, viability and Immunophenotypic analysis

Cell pellet extraction procedure

LC-MS method

K_D estimation from saturation binding experiments

Data Analysis

Results

Comparison of different culture media in monocytes differentiation into iMo-DCs/mMo-DC

MAPPs results on donors

Beads Comparison

Statistical analysis of biological replicates and experimental conditions by LFQ

Assessment of Remicade® and IFX biosimilar overall immunogenicity

MAPPs results on HLA-genotyped donors

Discussion

Declarations

References

Table

Additional Declarations

Supplementary Files

Status:

Version 1

Assessing Major Histocompatibility Complex-Associated Peptide Proteomics assay as a tool to predict immunogenicity potential of protein therapeutics and antigens

Status:

Version 1

Abstract

Figures

One Sentence Summary

Introduction

Materials And Methods

Ethics statement

Antibodies and proteins evaluated

Generation and maturation of Mo-DCs

Cell morphology, viability and Immunophenotypic analysis

Cell pellet extraction procedure

LC-MS method

KD estimation from saturation binding experiments

Data Analysis

Results

Comparison of different culture media in monocytes differentiation into iMo-DCs/mMo-DC

MAPPs results on donors

Beads Comparison

Statistical analysis of biological replicates and experimental conditions by LFQ

Assessment of Remicade® and IFX biosimilar overall immunogenicity

MAPPs results on HLA-genotyped donors

Discussion

Declarations

References

Table

Additional Declarations

Supplementary Files

Status:

Version 1

K_D estimation from saturation binding experiments