MHC-II-associated peptide proteomics (MAPPs) is a mass spectrometry-based (MS) method to identify naturally presented MHC-II-associated peptides that could elicit CD4+T cell activation. MAPPs assay is considered one of the assays that better characterize the safety of biotherapeutics by driving the selection of the best candidates concerning their immunogenicity risk. However, there is little knowledge about the impact of bead material on the recovery of MHC-II MS-eluted ligands in MAPPs assays. Here, we firstly describe a robust MAPPs protocol by implementing streptavidin magnetic beads for the isolation of these peptides instead of commonly used NHS-activated beads. Moreover, we assessed the impact of the cell medium used for cell cultures on the morphology and recovery of the in vitro-generated APCs, and its potential implications in the amount of MHC-II isolated peptides. We also described an example of a MAPPs assay application to investigate drug-induced immunogenicity of two bispecific antibodies and compared them with monospecific trastuzumab IgG1 control. This work highlighted the importance of MAPPs in the preclinical in vitro strategy to mitigate the immunogenicity risk of biotherapeutics.

The complexity of therapeutic proteins like antibody–drug conjugates (ADCs) holds a tremendous analytical challenge. Complementary mass spectrometry approaches such as peptide mapping and intact mass analysis are required for the in-depth characterization of these bioconjugates. Cysteine-linked ADCs have shown a unique challenge for characterization, mainly when the conjugation is carried out on interchain cysteines, because their intact analysis requires native mass spectrometry conditions to preserve non-covalent binding between antibody chains. In this work, two different approaches were proposed. Specifically, a full scan data-independent all ion fragmentation (FS-AIF) and a full scan data-dependent targeted MS2 (FS-ddtMS2) were applied to generate complementary datasets for a cysteine-linked ADC characterization with a highly reactive payload. These two methods were applied to in vitro plasma stability and in vivo PK samples to calculate and refine mean drug-to-antibody ratio over time. Using this approach, we successfully characterized an ADC containing a hydrolysis-sensitive payload and refined the “active” drug-to-antibody ratio on in vitro stability and in vivo samples. These two methods allowed the confirmation of the different ADC species and potential metabolites of conjugated payload attached to the antibody backbone in a single analysis without needing a dedicated method for the conjugated payload metabolite identification.

The development of biotherapeutics, particularly multi-domain biotherapeutics (MDBs) and antibody drug conjugates (ADCs), presents unique challenges due to their complexity and increased immunogenicity risks. Bioanalytical approaches play a pivotal role in addressing these challenges. Robust bioanalytical methods are essential for effectively assessing the pharmacokinetics (PK), pharmacodynamics (PD), and immunogenic responses of novel biologics. Advanced techniques, such as multiplexed assays using ligand binding assays (LBAs) and Liquid Chromatography-Mass Spectrometry (LC-MS) methods, are employed to handle the complexity of MDBs and ADCs effectively. Additionally, high-resolution mass spectrometry can investigate the potential biotransformation of biologics directly in in vivo studies. Furthermore, the Context of Use (CoU) is critical for defining assay purposes across nonclinical and clinical settings, ensuring their relevance and efficiency. Regulatory compliance is paramount to ensure assays meet standards for drug approval. Practical considerations include assay sensitivity, specificity, and the method's ability to investigate the structural integrity of MDBs and ADCs in in vivo studies. This paper aims to review recent publications on the application of bioanalytical techniques, specifically LBA and LC-MS, in supporting the development of pioneering biotherapeutics. Additionally, it will discuss optimal strategies for bioanalytical methods and immunogenicity assessment.

Biotherapeutic immunogenicity remains a great challenge for researchers because multiple factors trigger immune responses. Predicting and assessing the potential human immune response against biological drugs could represent an impressive breakthrough toward generating potentially safer and more efficacious therapeutic proteins. This article describes an in vitro assay that can contribute to evaluating the potential immunogenicity of biotherapeutics by focusing on lysosomal proteolysis. We selected human liver lysosomes (hLLs) from four different donors as a surrogate in vitro model instead of APC lysosomes because they are a ready-to-use lysosomal source. To assess the biological comparability of this surrogate to APC lysosomal extract, we compared the proteome content of hLLs with literature data of lysosomal fractions extracted from murine bone marrow and human blood-derived dendritic cells. Then we tested infliximab (IFX; Remicade) under different proteolytic conditions using liquid chromatography and high-resolution and -accuracy mass spectrometry to better define the degradation kinetics inside the lysosomes. hLLs revealed similar enzymatic content compared with human and murine dendritic cell lysosomes. Degradation assays demonstrated that our liquid chromatography and high-resolution and -accuracy mass spectrometry method could identify both the intact protein and the peptides resulting from proteolysis with high specificity and resolution. The rapid and easy assay described in this article can be extremely useful for evaluating the immunogenic risk associated with therapeutic proteins. In addition, this method can complement information from MHC class II–associated peptide proteomics assays and other in vitro and in silico techniques.

: In silico prediction of peptide-HLAII (human leucocyte antigen class II) complexes has emerged as a crucial approach in bioinformatics for deciphering antigen presentation mechanisms. Several in silico tools have been developed to predict peptide binding to HLAII alleles, trying to deconvolute the intricate peptide-HLAII binding specificity. These approaches integrate bases from molecular modeling, machine learning, and bioinformatics to predict peptide-HLAII interactions. Initially, structure-based methods relying on molecular docking algorithms were widespread, utilizing structural data of HLAII molecules and peptides to infer plausible binding conformations. These methods often faced challenges in accuracy due to the dynamic nature of peptide-HLAII interactions. Besides, the high flexibility of peptide sidechains makes their placement into the HLA-binding site even more complex. In recent years, machine learning techniques have drawn attention to peptide-HLAII binding predictions. Supervised learning algorithms, such as support vector machines (SVMs), neural networks, and ensemble methods, have been considerably applied to discriminate patterns from large datasets of experimentally validated peptide-HLAII binding affinities (like Immune Epitope Data Base, IEDB) and more recently mass spectrometry- eluted ligands from MHC-associated peptide proteomics (MAPPs) assay. The role of experiment- assisted integrative modeling in aiding peptide-HLAII complexes prediction still needs to be clarified. In this work, we benchmarked the use of AlphaLink2 (AlphaFold2 + cross-links restraints) and compared it to AlphaFold2 Multimer in predicting correct peptide binding motifs. These results can pave the way to an integrated strategy for vaccine development and protein deimmunization or autoimmunity mitigation.