mAbs最新文献

Biparatopic antibodies (bpAbs) bind distinct, non-overlapping epitopes on an antigen. This unique binding mode enables new mechanisms of action beyond monospecific and bispecific antibodies (bsAbs) that can make bpAbs effective therapeutics for various indications, including oncology and infectious diseases. Biparatopic binding can lead to superior affinity and specificity, promote antagonism, lock target conformation, and result in higher-order target clustering. Such antibody-target complexes can elicit strong agonism, increase immune effector function, or result in rapid target downregulation and lysosomal trafficking. These are not only attractive properties for therapeutic antibodies but are increasingly being explored for other modalities such as antibody-drug conjugates, T-cell engagers and chimeric antigen receptors. Recent advances in bpAb engineering have enabled the construction of ever more sophisticated formats that are starting to show promise in the clinic.

Monoclonal antibodies (mAb) and other biological drugs are affected by enzymatic polysorbate (PS) degradation that reduces product stability and jeopardizes the supply of innovative medicines. PS represents a critical surfactant stabilizing the active pharmaceutical ingredients, which are produced by recombinant Chinese hamster ovary (CHO) cell lines. While the list of potential PS-degrading CHO host cell proteins (HCPs) has grown over the years, tangible data on industrially relevant HCPs are still scarce. By means of a highly sensitive liquid chromatography-tandem mass spectrometry method, we investigated seven different mAb products, resulting in the identification of 12 potentially PS-degrading hydrolases, including the strongly PS-degrading lipoprotein lipase (LPL). Using an LPL knockout CHO host cell line, we were able to stably overexpress and purify the remaining candidate hydrolases through orthogonal affinity chromatography methods, enabling their detailed functional characterization. Applying a PS degradation assay, we found nine mostly secreted, PS-active hydrolases with varying hydrolytic activity. All active hydrolases showed a serine-histidine-aspartate/glutamate catalytical triad. Further, we subjected the active hydrolases to pH-screenings and revealed a diverse range of activity optima, which can facilitate the identification of residual hydrolases during bioprocess development. Ultimately, we compiled our dataset in a risk matrix identifying PAF-AH, LIPA, PPT1, and LPLA2 as highly critical hydrolases based on their cellular expression, detection in purified antibodies, active secretion, and PS degradation activity. With this work, we pave the way toward a comprehensive functional characterization of PS-degrading hydrolases and provide a basis for a future reduction of PS degradation in biopharmaceutical drug products.

Antibodies are one of the most important reagents used in biomedical and fundamental research, used to identify, and quantify proteins, contribute to knowledge of disease mechanisms, and validate drug targets. Yet many antibodies used in research do not recognize their intended target, or recognize additional molecules, compromising the integrity of research findings and leading to waste of resources, lack of reproducibility, failure of research projects, and delays in drug development. Researchers frequently use antibodies without confirming that they perform as intended in their application of interest. Here we argue that the determinants of end-user antibody choice and use are critical, and under-addressed, behavioral drivers of this problem. This interacts with the batch-to-batch variability of these biological reagents, and the paucity of available characterization data for most antibodies, making it more difficult for researchers to choose high quality reagents and perform necessary validation experiments. The open-science company YCharOS works with major antibody manufacturers and knockout cell line producers to characterize antibodies, identifying high-performing renewable antibodies for many targets in neuroscience. This shows the progress that can be made by stakeholders working together. However, their work so far applies to only a tiny fraction of available antibodies. Where characterization data exists, end-users need help to find and use it appropriately. While progress has been made in the context of technical solutions and antibody characterization, we argue that initiatives to make best practice behaviors by researchers more feasible, easy, and rewarding are needed. Global cooperation and coordination between multiple partners and stakeholders will be crucial to address the technical, policy, behavioral, and open data sharing challenges. We offer potential solutions by describing our Only Good Antibodies initiative, a community of researchers and partner organizations working toward the necessary change. We conclude with an open invitation for stakeholders, including researchers, to join our cause.

Antibody-mediated delivery of immunogenic viral CD8⁺ T-cell epitopes to redirect virus-specific T cells toward cancer cells is a promising new therapeutic avenue to increase the immunogenicity of tumors. Multiple strategies for viral epitope delivery have been shown to be effective. So far, most of these have relied on a free C-terminus of the immunogenic epitope for extracellular delivery. Here, we demonstrate that antibody-epitope conjugates (AECs) with genetically fused epitopes to the N-terminus of the antibody can also sensitize tumors for attack by virus-specific CD8⁺ T cells. AECs carrying epitopes genetically fused at the N-terminus of the light chains of cetuximab and trastuzumab demonstrate an even more efficient delivery of the T-cell epitopes compared to AECs with the epitope fused to the C-terminus of the heavy chain. We demonstrate that this increased efficiency is not caused by the shift in location of the cleavage site from the N- to the C-terminus, but by its increased proximity to the cell surface. We hypothesize that this facilitates more efficient epitope delivery. These findings not only provide additional insights into the mechanism of action of AECs but also broaden the possibilities for genetically fused AECs as an avenue for the redirection of multiple virus-specific T cells toward tumors.

Asparagine (Asn) deamidation and aspartic acid (Asp) isomerization are common degradation pathways that affect the stability of therapeutic antibodies. These modifications can pose a significant challenge in the development of biopharmaceuticals. As such, the early engineering and selection of chemically stable monoclonal antibodies (mAbs) can substantially mitigate the risk of subsequent failure. In this study, we introduce a novel in silico approach for predicting deamidation and isomerization sites in therapeutic antibodies by analyzing the structural environment surrounding asparagine and aspartate residues. The resulting quantitative structure-activity relationship (QSAR) model was trained using previously published forced degradation data from 57 clinical-stage mAbs. The predictive accuracy of the model was evaluated for four different states of the protein structure: (1) static homology models, (2) enhancing low-frequency vibrational modes during short molecular dynamics (MD) runs, (3) a combination of (2) with a protonation state reassignment, and (4) conventional full-atomistic MD simulations. The most effective QSAR model considered the accessible surface area (ASA) of the residue, the pKa value of the backbone amide, and the root mean square deviations of both the alpha carbon and the side chain. The accuracy was further enhanced by incorporating the QSAR model into a decision tree, which also includes empirical information about the sequential successor and the position in the protein. The resulting model has been implemented as a plugin named "Forecasting Reactivity of Isomerization and Deamidation in Antibodies" in MOE software, completed with a user-friendly graphical interface to facilitate its use.

The naïve human antibody repertoire has theoretical access to an estimated > 10¹⁵ antibodies. Identifying subsets of this prohibitively large space where therapeutically relevant antibodies may be found is useful for development of these agents. It was previously demonstrated that, despite the immense sequence space, different individuals can produce the same antibodies. It was also shown that therapeutic antibodies, which typically follow seemingly unnatural development processes, can arise independently naturally. To check for biases in how the sequence space is explored, we data mined public repositories to identify 220 bioprojects with a combined seven billion reads. Of these, we created a subset of human bioprojects that we make available as the AbNGS database (https://naturalantibody.com/ngs/). AbNGS contains 135 bioprojects with four billion productive human heavy variable region sequences and 385 million unique complementarity-determining region (CDR)-H3s. We find that 270,000 (0.07% of 385 million) unique CDR-H3s are highly public in that they occur in at least five of 135 bioprojects. Of 700 unique therapeutic CDR-H3, a total of 6% has direct matches in the small set of 270,000. This observation extends to a match between CDR-H3 and V-gene call as well. Thus, the subspace of shared ('public') CDR-H3s shows utility for serving as a starting point for therapeutic antibody design.

Therapeutic monoclonal antibody (mAb) development and the processes for manufacturing drug substance have evolved since the first approval of the mAb in 1986. As the past is often the prologue to the future, the history of these technologies has been classified here into three eras, leading to speculation about what the next era may hold with regard to development and manufacturing strategies, as well as the potential impacts to patients. The substantial increase in production culture titers and bioreactor production volumes and the availability of large-scale contract manufacturing facilities could translate into improved global access for these therapies and an expansion of indications for therapeutic antibodies.

Precise measurement of the binding activity changes of therapeutic antibodies is important to determine the potential critical quality attributes (CQAs) in developability assessment at the early stage of antibody development. Here, we report a surface plasmon resonance (SPR)-based relative binding activity method, which incorporates both binding affinity and binding response and allows us to determine relative binding activity of antibodies with high accuracy and precision. We applied the SPR-based relative binding activity method in multiple forced degradation studies of antibody developability assessment. The current developability assessment strategy provided comprehensive, precise characterization of antibody binding activity in the stability studies, enabling us to perform correlation analysis and establish the structure-function relationship between relative binding activity and quality attributes. The impact of a given quality attribute on binding activity could be confidently determined without isolating antibody variants. We identified several potential CQAs, including Asp isomerization, Asn deamidation, and fragmentation. Some potential CQAs affected binding affinity of antibody and resulted in a reduction of binding activity. Certain potential CQAs impaired antibody binding to antigen and led to a loss of binding activity. A few potential CQAs could influence both binding affinity and binding response and cause a substantial decrease in antibody binding activity. Specifically, we identified low abundance Asn33 deamidation in the light chain complementarity-determining region as a potential CQA, in which all the stressed antibody samples showed Asn33 deamidation abundances ranging from 4.2% to 27.5% and a mild binding affinity change from 1.76 nM to 2.16 nM.

In vitro assessments for the prediction of pharmacokinetic (PK) behavior of biotherapeutics can help identify corresponding liabilities significantly earlier in the discovery timeline. This can minimize the need for extensive early in vivo PK characterization, thereby reducing animal usage and optimizing resources. In this study, we recommend bolstering classical developability workflows with in vitro measures correlated with PK. In agreement with current literature, in vitro measures assessing nonspecific interactions, self-interaction, and FcRn interaction are demonstrated to have the highest correlations to clearance in hFcRn Tg32 mice. Crucially, the dataset used in this study has broad sequence diversity and a range of physicochemical properties, adding robustness to our recommendations. Finally, we demonstrate a computational approach that combines multiple in vitro measurements with a multivariate regression model to improve the correlation to PK compared to any individual assessment. Our work demonstrates that a judicious choice of high throughput in vitro measurements and computational predictions enables the prioritization of candidate molecules with desired PK properties.