mAbs最新文献

Proteins are an important class of therapeutics for combatting a wide variety of diseases. The increasing demand for convenient, patient-centric treatment options has propelled the development of subcutaneously delivered protein therapies and increased the interest in novel formulations and delivery methods. However, subcutaneous delivery of protein therapeutics remains a challenge due to the high protein concentrations ( >100 mg/mL) required to circumvent lower bioavailability and the smaller injection volumes required to enable the use of mature and cost-effective devices, such as standard prefilled syringes and autoinjectors. At high concentrations, protein solutions exhibit elevated viscosity, which poses injectability and manufacturing challenges. Here, we review the state of the art in experimental and computationally predictive formulation development approaches for viscosity mitigation of high-concentration protein solution therapeutics, and we suggest new directions for expanding the utility of these approaches beyond traditional monoclonal antibodies. Innovative approaches should leverage and combine advances in both experimental and computational methods, including machine learning and artificial intelligence, to rapidly identify formulation compositions for viscosity reduction, and subsequently facilitate the development of patient-centric biotherapeutics.

Since the approval of the first antibody drug in 1986, a total of 162 antibodies have been approved for a wide range of therapeutic areas, including cancer, autoimmune, infectious, or cardiovascular diseases. Despite advances in biotechnology that accelerated the development of antibody drugs, the drug discovery process for this modality remains lengthy and costly, requiring multiple rounds of optimizations before a drug candidate can progress to preclinical and clinical trials. This multi-optimization problem involves increasing the affinity of the antibody to the target antigen while refining additional biophysical properties that are essential to drug development such as solubility, thermostability or aggregation propensity. Additionally, antibodies that resemble natural human antibodies are particularly desirable, as they are likely to offer improved profiles in terms of safety, efficacy, and reduced immunogenicity, further supporting their therapeutic potential. In this article, we explore the use of energy-based generative models to optimize a candidate monoclonal antibody. We identify tradeoffs when optimizing for multiple properties, focusing on solubility, humanness and affinity and use the generative model we develop to generate candidate antibodies that lie on optimal Pareto fronts with respect to these properties.

Therapeutic monoclonal antibodies (mAbs) can be functionally enhanced via Fc engineering. To determine whether pairs of mAbs with different Fc modifications can be combined for functional complementarity, we investigated the in vitro activity of two HIV-1 mAb libraries, each equipped with 60 engineered Fc variants. Our findings demonstrate that the impact of Fc engineering on Fc functionality is dependent on the specific Fab clone. Notably, combinations of Fc variants of the same Fab specificity exhibited limited enhancement in functional breadth compared to combinations involving two distinct Fabs. This suggests that the strategic selection of complementary Fc modifications can enhance both functional activity and breadth. Furthermore, while some combinations of Fc variants displayed additive functional effects, others were detrimental, suggesting that the functional outcome of Fc mutations is not easily predicted. Collectively, these results provide preliminary evidence supporting the potential of complementary Fc modifications in mAb combinations. Future studies will be essential to identify the optimal Fc modifications that maximize in vivo efficacy.

Selection of lead therapeutic molecules is often driven predominantly by pharmacological efficacy and safety. Candidate developability, such as biophysical properties that affect the formulation of the molecule into a product, is usually evaluated only toward the end of the drug development pipeline. The ability to evaluate developability properties early in the process of antibody therapeutic development could accelerate the timeline from discovery to clinic and save considerable resources. In silico predictive approaches, such as machine learning models, which map molecular features to predictions of developability properties could offer a cost-effective and high-throughput alternative to experiments for antibody developability assessment. We developed a computational framework, PROPERMAB (PROPERties of Monoclonal AntiBodies), for large-scale and efficient in silico prediction of developability properties for monoclonal antibodies, using custom molecular features and machine learning modeling. We demonstrate the power of PROPERMAB by using it to develop models to predict antibody hydrophobic interaction chromatography retention time and high-concentration viscosity. We further show that structure-derived features can be rapidly and accurately predicted directly from sequences by pre-training simple models for molecular features, thus providing the ability to scale these approaches to repertoire-scale sequence datasets.

Identification of an optimal single protein sequence at the discovery stage for preclinical and clinical development is critical to the rapid development and overall success of a biologic drug. High throughput developability assessments at the discovery stage are used to rank potent molecules by their biophysical properties, deprioritize suboptimal molecules, or trigger additional rounds of protein engineering. Due to the amount of data acquired for these molecules, manual analysis methods to rank molecules are error prone and time-consuming. Here, we present applications of hierarchical clustering analysis for data-driven lead selection of biologics and preformulation screening using high throughput developability data. Hierarchical clustering analysis was applied here for prioritization of three different antibody modalities, including format and chain pairing of bispecific antibodies, sequence-optimized monoclonal antibodies from affinity maturation, preformulation screening of bispecific scFv-Fab fusion molecules, and monoclonal antibodies from an immunization campaign. This high-throughput method for ranking molecules by their developability characteristics and preformulation properties can substantially simplify, streamline, and accelerate biologics discovery and early development.

Therapeutic monoclonal antibodies, which are almost exclusively IgG isotypes, show significant promise but are prone to poor solution stability, including aggregation and elevated solution viscosity at dose-relevant concentrations. Recombinant IgE antibodies are emerging cancer immunotherapies. The first-in-class MOv18 IgE, recognizing the cancer-associated antigen folate receptor-alpha (FRα), completed a Phase 1 clinical trial in patients with solid tumors, showing early signs of efficacy at a low dose. The inaugural process development and scaled manufacture of MOv18 IgE for clinical testing were undertaken with little baseline knowledge about the solution phase behavior of recombinant IgE at dose-relevant concentrations. We evaluated MOv18 IgE physical stability in response to environmental and formulation stresses encountered throughout shelf life. We analyzed changes in physical stability using multiple orthogonal analytical techniques, including particle tracking analysis, size exclusion chromatography, and multidetector flow field flow fractionation hyphenated with UV. We used dynamic and multiangle light scattering to profile aggregation status. Formulation at pH 6.5, selected for use in the Phase 1 trial, resulted in high monomeric purity and no submicron proteinaceous particulates. Formulation at pH 5.5 and 7.5 induced significant submicron and sub-visible particle formation. IgE formulation was resistant to aggregation in response to freeze-thaw stress, retaining high monomeric purity. Exposure to thermal stress at elevated temperatures resulted in loss of monomeric purity and aggregation. Agitation stress-induced submicron and subvisible aggregation, but monomeric purity was not significantly affected. MOv18 IgE retains monomeric purity in response to formulation and stress conditions, confirming stability. Our results offer crucial guidance for future IgE-based drug development.

Highly concentrated antibody solutions are necessary for developing subcutaneous injections but often exhibit high viscosities, posing challenges in antibody-drug development, manufacturing, and administration. Previous computational models were only limited to a few dozen data points for training, a bottleneck for generalizability. In this study, we measured the viscosity of a panel of 229 monoclonal antibodies (mAbs) to develop predictive models for high concentration mAb screening. We developed DeepViscosity, consisting of 102 ensemble artificial neural network models to classify low-viscosity (≤20 cP) and high-viscosity (>20 cP) mAbs at 150 mg/mL, using 30 features from a sequence-based DeepSP model. Two independent test sets, comprising 16 and 38 mAbs with known experimental viscosity, were used to assess DeepViscosity's generalizability. The model exhibited an accuracy of 87.5% and 89.5% on both test sets, respectively, surpassing other predictive methods. DeepViscosity will facilitate early-stage antibody development to select low-viscosity antibodies for improved manufacturability and formulation properties, critical for subcutaneous drug delivery. The webserver-based application can be freely accessed via https://devpred.onrender.com/DeepViscosity.

Antibodies are extensively used in treating various diseases, with over 100 canonical monoclonal antibodies (mAbs) approved. Population pharmacokinetic (PK) models are typically developed for each individual mAb, despite their similarities in size, shape, and susceptibility to lysosomal degradation. However, sparse datasets with limited PK information pose challenges in deriving accurate parameter estimates. Here, we provide a comprehensive overview of 160 published models of 69 mAbs, administered either intravenously or subcutaneously, examining their structural, statistical, and covariate components. Median estimates for the base parameters are linear clearance (0.22 L/d), central volume (3.42 L), peripheral volume (2.68 L), intercompartmental clearance (0.54 L/d), absorption rate (0.25 L/d), and bioavailability (69%). Using these to simulate a 'generic' mAb results in plausible kinetics with a terminal half-life of 21 ds. We demonstrated that the median linear clearance was 26% lower in models that included nonlinear target-mediated kinetics, when compared to linear models (0.18 vs. 0.25 L/d). For chimeric mAbs median linear clearance was 50% higher compared to fully human and humanized mAbs. Variability in PK parameter estimates across models was comparable to the inter-individual variability, which have consistently shown to be large for mAbs PK (e.g. 55% vs. 43% for clearance and 25% vs. 30% for central volume, respectively). Our meta-analysis suggests that a priori parameter estimates derived from the large body of existing pharmacokinetic models for mAbs are representative for many mAbs and can facilitate the design of new and/or more complex pharmacokinetic models or assist in dose optimization models.

Monoclonal antibodies are well established as promising treatment options for a broad range of patients with severe diseases. In some cases, the formation of anti-drug antibodies (ADA) may limit their clinical use and potentially affect safety and efficacy for patients. Despite extensive research, some factors contributing to the immunogenicity of therapeutic antibodies remain poorly understood. In particular, the immunogenicity potential associated with multivalent antibody formats targeting oligomeric protein antigens has thus far received insufficient attention. Large, target-related immune complexes (TRICs) may be formed that can trigger Fc-mediated downstream effects and have the potential to contribute to the development of an ADA response. Here, we present experimental evidence highlighting the roles of epitope, paratope, and binding geometry in defining the composition and size distribution of TRICs formed by IL-17A, a homodimeric cytokine, with four clinical anti-IL-17 antibodies, secukinumab (Cosentyx^Ⓡ), ixekizumab (Taltz^Ⓡ), bimekizumab (Bimzelx^Ⓡ) and CJM112. Widely different ADA incidence rates have been reported for these antibodies. We found that all four antibodies formed closed-chain TRICs, each comprising two or more IgG molecules connected by an equivalent number of IL-17A homodimers. Secukinumab, the antibody with the lowest ADA incidence rate, uniquely exhibited primarily 2 + 2 closed-chain complexes. In contrast, CJM112 and bimekizumab showed higher amounts of 3 + 3 and 4 + 4 complexes. Additionally, CJM112, and to a greater extent, bimekizumab and ixekizumab, formed very high molecular weight TRICs. Our findings underscore the importance of conducting in-depth biophysical analyses of TRICs formed by therapeutic antibody candidates targeting multivalent protein antigens, to develop safer and more efficacious treatments.