mAbs最新文献

In silico immunogenicity risk assessment has been an important step in the development path for many biologic therapeutics, including monoclonal antibodies. Even if the source of a given biologic is 'fully human', T cell epitopes that are contained in the sequences of the biologic may activate the immune system, enabling the development of anti-drug antibodies that can reduce drug efficacy and may contribute to adverse events. Computational tools that identify T cell epitopes from primary amino acid sequences have been used to assess the immunogenic potential of therapeutic candidates for several decades. To facilitate larger scale analyses and accelerate preclinical immunogenicity risk assessment, our group developed an integrated web-based platform called ISPRI, (Immunogenicity Screening and Protein Re-engineering Interface) that provides hands-on access through a secure web-based interface for scientists working in large and mid-sized biotech companies in the US, Europe, and Japan. This toolkit has evolved and now contains an array of algorithms that can be used individually and/or consecutively for immunogenicity assessment and protein engineering. Most analyses start with the advanced epitope mapping tool (EpiMatrix), then proceed to identify epitope clusters using ClustiMer, and then use a tool called JanusMatrix to define whether any of the T cell epitope clusters may generate a regulatory T cell response which may diminish or eliminate anti-drug antibody formation. Candidates can be compared to similar products on a normalized immunogenicity scale. Should modifications to the biologic sequence be an option, a tool for moderating putative immunogenicity by editing T cell epitopes out of the sequence is available (OptiMatrix). Although this perspective discusses the in-silico immunogenicity risk assessment for monoclonal antibodies, bi-specifics, multi-specifics, and antibody-drug conjugates, the analysis of additional therapeutic modalities such as enzyme replacement proteins, blood factor proteins, CAR-T, gene therapy products, and peptide drugs is also made available on the ISPRI platform.

Rabbits produce robust antibody responses and have unique features in their antibody repertoire that make them an attractive alternative to rodents for in vivo discovery. However, the frequent occurrence of a non-canonical disulfide bond between complementarity-determining region (CDR) H1 (C35a) and CDRH2 (C50) is often seen as a liability for therapeutic antibody development, despite limited reports of its effect on antibody binding, function, and stability. Here, we describe the discovery and humanization of a human-mouse cross-reactive anti-programmed cell death 1 (PD-1) monoclonal rabbit antibody, termed h1340.CC, which possesses this non-canonical disulfide bond. Initial removal of the non-canonical disulfide resulted in a loss of PD-1 affinity and cross-reactivity, which led us to explore protein engineering approaches to recover these. First, guided by the sequence of a related clone and the crystal structure of h1340.CC in complex with PD-1, we generated variant h1340.SA.LV with a potency and cross-reactivity similar to h1340.CC, but only partially recovered affinity. Side-by-side developability assessment of both h1340.CC and h1340.SA.LV indicate that they possess similar, favorable properties. Next, and prompted by recent developments in machine learning (ML)-guided protein engineering, we used an unbiased ML- and structure-guided approach to rapidly and efficiently generate a different variant with recovered affinity. Our case study thus indicates that, while the non-canonical inter-CDR disulfide bond found in rabbit antibodies does not necessarily constitute an obstacle to therapeutic antibody development, combining structure- and ML-guided approaches can provide a fast and efficient way to improve antibody properties and remove potential liabilities.

Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (T_onset, T_m, T_agg), affinity (K_D) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in T_m1), with most clones retaining the favorable developability profile of the parental antibody. Additional in silico tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of in silico antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.

During the development process of therapeutic monoclonal antibodies (mAbs), it is crucial to control (critical) quality attributes such as N-glycosylation influencing pharmacokinetics (PK) and Fc effector functions. Previous reports have shown that mAbs containing high-mannose N-glycans are cleared faster from blood circulation, leading to reduced half-lives. The high-mannose N-glycan content of mAbs can be influenced during the cell culture process by factors such as cell lines, process conditions, and media. Furthermore, mAbs have either one high mannose N-glycan (asymmetrical high-mannose glyco-pair) or two high mannose N-glycans (symmetrical high-mannose glyco-pair). The hypothesis that the mannose receptor (MR, CD206) accelerates clearance by facilitating their internalization and subsequent lysosomal degradation is widespread. However, the interaction between MR and mAbs has not been explicitly demonstrated. This study aimed to investigate this interaction, providing the first systematic demonstration of MR binding to the Fc region of mAbs with high-mannose N-glycans. Two novel analytical methods, MR surface plasmon resonance and MR affinity chromatography, were developed and applied to investigate the MR-mAb interaction. The interaction is found to be dependent on high-mannose content, but is independent of the mAb format or sequence. However, different glyco-pairs exhibited varying binding affinities to the MR, with the symmetrical high-mannose glyco-pair showing the strongest binding properties. These findings strengthen the hypothesis for the MR-mediated mAb interaction and contribute to a deeper understanding of the MR-mAb interaction, which could affect the criticality of high-mannose containing mAbs development strategies of IgG-based molecules and improve their PK profiles.

Despite the large number of existing bispecific antibody (bsAb) formats, the generation of novel bsAbs is still associated with development and bioprocessing challenges. Here, we present RUBY, a novel bispecific antibody format that allows rapid generation of bsAbs that fulfill key development criteria. The RUBY^TM format has a 2 + 2 geometry, where two Fab fragments are linked via their light chains to the C-termini of an IgG, and carries mutations for optimal chain pairing. The unique design enables generation of bsAbs with mAb-like attributes. Our data demonstrate that RUBY bsAbs are compatible with small-scale production systems for screening purposes and can be produced at high yields (>3 g/L) from stable cell lines. The bsAbs produced are shown to, in general, contain low amounts of aggregates and display favorable solubility and stress endurance profiles. Further, compatibility with various IgG isotypes is shown and tailored Fc gamma receptor binding confirmed. Also, retained interaction with FcRn is demonstrated to translate into a pharmacokinetic profile in mice and non-human primates that is comparable to mAb controls. Functionality of conditional active RUBY bsAbs is confirmed in vitro. Anti-tumor effects in vivo have previously been demonstrated, and shown to be superior to a comparable mAb, and here it is further shown that RUBY bsAbs penetrate and localize to tumor tissue in vivo. In all, the RUBY format has attractive mAb-like attributes and offers the possibility to mitigate many of the development challenges linked to other bsAb formats, facilitating both high functionality and developability.

In this work, we report the discovery of potent anti-epidermal growth factor receptor (EGFR) allosteric heavy-chain antibodies by combining camelid immunization and fluorescence-activated cell sorting (FACS). After immunization and yeast surface display library construction, allosteric clones were obtained by introducing the labeled EGF Fc fusion protein as an additional criterion for FACS. This sorting method enabled the identification of 11 heavy-chain antibodies that did not compete with the orthosteric ligand EGF for the binding to EGFR. These antibodies bind to a triple-negative breast cancer cell line expressing EGFR with affinities in the picomolar to nanomolar range. Those camelid-derived antibodies also exhibit interesting properties by modulating EGFR affinity for EGF. Moreover, they are also able to inhibit EGF-induced downstream signaling pathways. In particular, we identified one clone that is more potent than the approved blocking antibody cetuximab in inhibiting both PI3K/AKT and MAPK/ERK pathways. Our results suggest that allosteric antibodies may be potential new modalities for therapeutics.

High-concentration protein formulation is of paramount importance in patient-centric drug product development, but it also presents challenges due to the potential for enhanced aggregation and increased viscosity. The analysis of critical quality attributes often necessitates the transfer of samples from their primary containers together with sample dilution. Therefore, there is a demand for noninvasive, in situ biophysical methods to assess protein drug products directly in primary sterile containers, such as prefilled syringes, without dilution. In this study, we introduce a novel application of water proton nuclear magnetic resonance (wNMR) to evaluate the aggregation propensity of a high-concentration drug product, Dupixent® (dupilumab), under stress conditions. wNMR results demonstrate a concentration-dependent, reversible association of dupilumab in the commercial formulation, as well as irreversible aggregation when exposed to accelerated thermal stress, but gradually reversible aggregation when exposed to freeze and thaw cycles. Importantly, these results show a strong correlation with data obtained from established biophysical analytical tools widely used in the pharmaceutical industry. The application of wNMR represents a promising approach for in situ noninvasive analysis of high-concentration protein formulations directly in their primary containers, providing valuable insights for drug development and quality assessment.

Biologics have revolutionized disease management in many therapeutic areas by addressing unmet medical needs and overcoming resistance to standard-of-care treatment in numerous patients. However, the development of unwanted immune responses directed against these drugs, humoral and/or cellular, can hinder their efficacy and have safety consequences with various degrees of severity. Health authorities ask that a thorough immunogenicity risk assessment be conducted during drug development to incorporate an appropriate monitoring and mitigation plan in clinical studies. With the rapid diversification and complexification of biologics, which today include modalities such as multi-domain antibodies, cell-based products, AAV delivery vectors, and nucleic acids, developers are faced with the challenge of establishing a risk assessment strategy sometimes in the absence of specific regulatory guidelines. The European Immunogenicity Platform (EIP) Open Symposium on Immunogenicity of Biopharmaceuticals and its one-day training course gives experts and newcomers across academia, industry, and regulatory agencies an opportunity to share experience and knowledge to overcome these challenges. Here, we report the discussions that took place at the EIP's 14^th Symposium, held in April 2023. The topics covered included immunogenicity monitoring and clinical relevance, non-clinical immunogenicity risk assessment, regulatory aspects of immunogenicity assessment and reporting, and the challenges associated with new modalities, which were discussed in a dedicated session.

Rozanolixizumab is a humanized anti-neonatal Fc receptor (FcRn) monoclonal antibody (mAb) of the immunoglobulin G4 (IgG4) sub-class, currently in clinical development for the treatment of IgG autoantibody-driven diseases. This format is frequently used for therapeutic mAbs due to its intrinsic lower affinity for Fc gamma receptors (FcγR) and lack of C1q engagement. However, with growing evidence suggesting that no Fc-containing agent is truly "silent" in this respect, we explored the engagement of FcγRs and potential functional consequences with rozanolixizumab. In the study presented here, rozanolixizumab was shown to bind to FcγRs in both protein-protein and cell-based assays, and the kinetic data were broadly as expected based on published data for an IgG4 mAb. Rozanolixizumab was also able to mediate antibody bipolar bridging (ABB), a phenomenon that led to a reduction of labeled FcγRI from the surface of human macrophages in an FcRn-dependent manner. However, the presence of exogenous human IgG, even at low concentrations, was able to prevent both binding and ABB events. Furthermore, data from in vitro experiments using relevant human cell types that express both FcRn and FcγRI indicated no evidence for functional sequelae in relation to cellular activation events (e.g., intracellular signaling, cytokine production) upon either FcRn or FcγR binding of rozanolixizumab. These data raise important questions about whether therapeutic antagonistic mAbs like rozanolixizumab would necessarily engage FcγRs at doses typically administered to patients in the clinic, and hence challenge the relevance and interpretation of in vitro assays performed in the absence of competing IgG.

Subcutaneous injections are an increasingly prevalent route of administration for delivering biological therapies including monoclonal antibodies (mAbs). Compared with intravenous delivery, subcutaneous injections reduce administration costs, shorten the administration time, and are strongly preferred from a patient experience point of view. An understanding of the absorption process of a mAb from the injection site to the systemic circulation is critical to the process of subcutaneous mAb formulation development. In this study, we built a model to predict the absorption rate constant (k_a), which denotes how fast a mAb is absorbed from the site of administration. Once trained, our model (enabled by the XGBoost algorithm in machine learning) can predict the k_a of a mAb following a subcutaneous injection using in silico molecular properties alone (generated from the primary sequence). Our model does not need clinically observed plasma concentration-time data; this is a novel capability not previously achieved in predictive pharmacokinetic models. The model also showed improved performance when benchmarked against a recently reported mechanistic model that relied on clinical data to predict subcutaneous absorption of mAbs. We further interpreted the model to understand which molecular properties affect the absorption rate and showed that our findings are consistent with previous studies evaluating subcutaneous absorption through direct experimentation. Taken altogether, this study reports the development, validation, benchmarking, and interpretation of a model that can predict the clinical k_a of a mAb using its primary sequence as the only input.