In silico selection of therapeutic antibodies for development: viscosity, clearance, and chemical stability

Developability assessment at early-stage discovery to enable development of antibody-derived therapeutics

Developability refers to the likelihood that an antibody candidate will become a manufacturable, safe and efficacious drug. Although the safety and efficacy of a drug candidate will be well considered by sponsors and regulatory agencies, developability in the narrow sense can be defined as the likelihood that an antibody candidate will go smoothly through the chemistry, manufacturing and control (CMC) process at a reasonable cost and within a reasonable timeline. Developability in this sense is the focus of this review. To lower the risk that an antibody candidate with poor developability will move to the CMC stage, the candidate's developability-related properties should be screened, assessed and optimized as early as possible. Assessment of developability at the early discovery stage should be performed in a rapid and high-throughput manner while consuming small amounts of testing materials. In addition to monoclonal antibodies, bispecific antibodies, multispecific antibodies and antibody-drug conjugates, as the derivatives of monoclonal antibodies, should also be assessed for developability. Moreover, we propose that the criterion of developability is relative: expected clinical indication, and the dosage and administration route of the antibody could affect this criterion. We also recommend a general screening process during the early discovery stage of antibody-derived therapeutics. With the advance of artificial intelligence-aided prediction of protein structures and features, computational tools can be used to predict, screen and optimize the developability of antibody candidates and greatly reduce the risk of moving a suboptimal candidate to the development stage.

Antibody apparent solubility prediction from sequence by transfer learning

Developing therapeutic monoclonal antibodies (mAbs) for the subcutaneous administration requires identifying mAbs with superior solubility that are amenable for high-concentration formulation. However, experimental screening is often material and labor intensive. Here, we present a strategy (named solPredict) that employs the embeddings from pretrained protein language modeling to predict the apparent solubility of mAbs in histidine (pH 6.0) buffer. A dataset of 220 diverse, in-house mAbs were used for model training and hyperparameter tuning through 5-fold cross validation. solPredict achieves high correlation with experimental solubility on an independent test set of 40 mAbs. Importantly, solPredict performs well for both IgG1 and IgG4 subclasses despite the distinct solubility behaviors. This approach eliminates the need of 3D structure modeling of mAbs, descriptor computation, and expert-crafted input features. The minimal computational expense of solPredict enables rapid, large-scale, and high-throughput screening of mAbs using sequence information alone during early antibody discovery.

Nanoscale shape-dependent histone modifications

Recent observations suggest a role for complex nanoscale particulate shape in the regulation of specific immune-related cellular and in vivo processes. We suspect that cellular recognition of nanostructure architecture could involve nonmolecular inputs, including cellular transduction of nanoscale spatially resolved stresses induced by complex shape. Here, we report nanoscale shape-dependent control of the cellular epigenome. Interpretation of ChIP-Seq sequencing suggests that differential marking of H3K27me3 may be linked to sensory and synapse-recognition of nanoscale forces induced by complex shape. The observations raise significant questions on the role of particle-shape-induced immune regulation and memory, with potential consequences in both causes and treatment of immune-related disease.

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery

Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody-antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.

Utilizing cross-product prior knowledge to rapidly de-risk chemical liabilities in therapeutic antibody candidates

There is considerable pressure in the pharmaceutical industry to advance better molecules faster. One pervasive concern for protein-based therapeutics is the presence of potential chemical liabilities. We have developed a simple methodology for rapidly de-risking specific chemical concerns in antibody-based molecules using prior knowledge of each individual liability at a specific position in the molecule’s sequence. Our methodology hinges on the development of sequence-aligned chemical liability databases of molecules from different stages of commercialization and on sequence-aligned experimental data from prior molecules that have been developed at Amgen. This approach goes beyond the standard practice of simply flagging all instances of each motif that fall in a CDR. Instead, we de-risk motifs that are common at a specific site in commercial mAb-based molecules (and therefore did not previously pose an insurmountable barrier to commercialization) and motifs at specific sites for which we have prior experimental data indicating acceptably low levels of modification. We have used this approach successfully to identify candidates in a discovery phase program with exclusively very low risk potential chemical liabilities. Identifying these candidates in the discovery phase allowed us to bypass protein engineering and accelerate the program’s timeline by 6 months.

DeepSCM: An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity

Predicting high concentration antibody viscosity is essential for developing subcutaneous administration. Computer simulations provide promising tools to reach this aim. One such model is the spatial charge map (SCM) proposed by Agrawal and coworkers (mAbs. 2015, 8(1):43-48). SCM applies molecular dynamics simulations to calculate a score for the screening of antibody viscosity at high concentrations. However, molecular dynamics simulations are computationally costly and require structural information, a significant application bottleneck. In this work, high throughput computing was performed to calculate the SCM scores for 6596 nonredundant antibody variable regions. A convolutional neural network surrogate model, DeepSCM, requiring only sequence information, was then developed based on this dataset. The linear correlation coefficient of the DeepSCM and SCM scores achieved 0.9 on the test set (N = 1320). The DeepSCM model was applied to screen the viscosity of 38 therapeutic antibodies that SCM correctly classified and resulted in only one misclassification. The DeepSCM model will facilitate high concentration antibody viscosity screening. The code and parameters are freely available at https://github.com/Lailabcode/DeepSCM.

Computational models for studying physical instabilities in high concentration biotherapeutic formulations

Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.

A Descriptor set for Quantitative Structure-Property Relationship Prediction in Biologics

In addition to attaining the desired binding to their targets, a crucial aspect in the development of biotherapeutics is 'developability', which includes several desirable properties such as high solubility, low viscosity and aggregation, physico-chemical stability and low immunogenicity. The lack of any of these properties can lead to significant obstacles in advancing them to clinic; thus in silico methods capable of raising warning flags in earlier stages of development are highly beneficial. We have developed a computational framework based on a large and diverse set of protein specific descriptors ideal for making liability predictions using a machine-learning approach. This set offers a high degree of feature diversity classifiable by sequence, structure and surface patches. We assess the sensitivity and applicability of these descriptors in four dedicated case studies that are believed to be representative of biophysical characterizations commonly employed during the development process. In addition to data sets obtained from public sources, we have validated the descriptors on novel experimental data sets in order to address antibody developability and to generate prospective predictions on Adnectins. The results demonstrate that the descriptors are well suited to assist in the improvement of properties of systems that exhibit poor solubility or aggregation.

Antibodies with Weakly Basic Isoelectric Points Minimize Trade-offs between Formulation and Physiological Colloidal Properties

The widespread interest in antibody therapeutics has led to much focus on identifying antibody candidates with favorable developability properties. In particular, there is broad interest in identifying antibody candidates with highly repulsive self-interactions in standard formulations (e.g., low ionic strength buffers at pH 5-6) for high solubility and low viscosity. Likewise, there is also broad interest in identifying antibody candidates with low levels of non-specific interactions in physiological solution conditions (PBS, pH 7.4) to promote favorable pharmacokinetic properties. To what extent antibodies that possess both highly repulsive self-interactions in standard formulations and weak non-specific interactions in physiological solution conditions can be systematically identified remains unclear and is a potential impediment to successful therapeutic drug development. Here, we evaluate these two properties for 42 IgG1 variants based on the variable fragments (Fvs) from four clinical-stage antibodies and complementarity-determining regions from 10 clinical-stage antibodies. Interestingly, we find that antibodies with the strongest repulsive self-interactions in a standard formulation (pH 6 and 10 mM histidine) display the strongest non-specific interactions in physiological solution conditions. Conversely, antibodies with the weakest non-specific interactions under physiological conditions display the least repulsive self-interactions in standard formulations. This behavior can be largely explained by the antibody isoelectric point, as highly basic antibodies that are highly positively charged under standard formulation conditions (pH 5-6) promote repulsive self-interactions that mediate high colloidal stability but also mediate strong non-specific interactions with negatively charged biomolecules at physiological pH and vice versa for antibodies with negatively charged Fv regions. Therefore, IgG1s with weakly basic isoelectric points between 8 and 8.5 and Fv isoelectric points between 7.5 and 9 typically display the best combinations of strong repulsive self-interactions and weak non-specific interactions. We expect that these findings will improve the identification and engineering of antibody candidates with drug-like biophysical properties.

Minimal Physiologically-based Pharmacokinetic Model to Investigate the Effect of Charge on the Pharmacokinetics of Humanized anti-HCV-E2 IgG Antibodies in Sprague-Dawley Rats

PURPOSE

To develop a minimal physiologically-based pharmacokinetic (mPBPK) model in quantifying the relationships between the charge and pharmacokinetics (PK) of therapeutic monoclonal IgG antibody (TMAb).

METHODS

PK data used in this study were native IgG and five humanized anti-HCVE2-IgG antibodies in rats. Different models that related the effect of charge on interstitial distribution, transcapillary transport, and cellular uptake for FcRn-mediated metabolism were tested. External validation was conducted to assess if the charge-parameter relationships derived from rats could be used to predict the PK of TMAbs in mice. The final mPBPK model was used to construct the relationships between the FcRn binding and charge on the PK of TMAbs.

RESULTS

Increasing the isoelectric point (pI) of IgG was associated with higher interstitial space distribution and cellular uptake. The transcapillary transport of IgG from plasma to interstitial space remains constant with pI values below 7.96 and then increased linearly with pI. The model-based simulation results suggested that improving the FcRn binding affinity can overcome the problems of low plasma/interstitial space exposures associated with TMAbs with higher pI values by reducing the FcRn-mediated metabolism and hence increasing drug exposure in the interstitial space that has close contact with many solid tumors.

CONCLUSIONS

The final mPBPK model was developed and used to construct complex quantitative relationships between the pI/FcRn binding affinity and PK of TMAbs and such relationships are useful to select the discovery of a "sweet spot" of designing future generation of TMAbs with optimal PK properties to achieve desirable plasma and tissue drug exposures.

A universal in silico V(D)J recombination strategy for developing humanized monoclonal antibodies

BACKGROUND

Humanization of mouse monoclonal antibodies (mAbs) is crucial for reducing their immunogenicity in humans. However, humanized mAbs often lose their binding affinities. Therefore, an in silico humanization method that can prevent the loss of the binding affinity of mAbs is needed.

METHODS

We developed an in silico V(D)J recombination platform in which we used V(D)J human germline gene sequences to design five humanized candidates of anti-tumor necrosis factor (TNF)-α mAbs (C1-C5) by using different human germline templates. The candidates were subjected to molecular dynamics simulation. In addition, the structural similarities of their complementarity-determining regions (CDRs) to those of original mouse mAbs were estimated to derive the weighted interatomic root mean squared deviation (wRMSDi) value. Subsequently, the correlation of the derived wRMSDi value with the half maximal effective concentration (EC50) and the binding affinity (KD) of the humanized anti-TNF-α candidates was examined. To confirm whether our in silico estimation method can be used for other humanized mAbs, we tested our method using the anti-epidermal growth factor receptor (EGFR) a4.6.1, anti-glypican-3 (GPC3) YP9.1 and anti-α4β1 integrin HP1/2L mAbs.

RESULTS

The R2 value for the correlation between the wRMSDi and log(EC50) of the recombinant Remicade and those of the humanized anti-TNF-α candidates was 0.901, and the R2 value for the correlation between wRMSDi and log(KD) was 0.9921. The results indicated that our in silico V(D)J recombination platform could predict the binding affinity of humanized candidates and successfully identify the high-affinity humanized anti-TNF-α antibody (Ab) C1 with a binding affinity similar to that of the parental chimeric mAb (5.13 × 10-10). For the anti-EGFR a4.6.1, anti-GPC3 YP9.1, and anti-α4β1 integrin HP1/2L mAbs, the wRMSDi and log(EC50) exhibited strong correlations (R2 = 0.9908, 0.9999, and 0.8907, respectively).

CONCLUSIONS

Our in silico V(D)J recombination platform can facilitate the development of humanized mAbs with low immunogenicity and high binding affinities. This platform can directly transform numerous mAbs with therapeutic potential to humanized or even human therapeutic Abs for clinical use.

Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics

Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this study, we measured accelerated aggregation rates at 45°C and viscosity at 150 mg/ml for 20 preclinical and clinical-stage antibodies. Features obtained from molecular dynamics simulations of the full-length antibody and sequences were used for machine learning model construction. We found a k-nearest neighbors regression model with two features, spatial positive charge map on the CDRH2 and solvent-accessible surface area of hydrophobic residues on the variable fragment, gives the best performance for predicting antibody aggregation rates (r = 0.89). For the viscosity classification model, the model with the highest accuracy is a logistic regression model with two features, spatial negative charge map on the heavy chain variable region and spatial negative charge map on the light chain variable region. The accuracy and the area under precision recall curve of the classification model from validation tests are 0.86 and 0.70, respectively. In addition, we combined data from another 27 commercial mAbs to develop a viscosity predictive model. The best model is a logistic regression model with two features, number of hydrophobic residues on the light chain variable region and net charges on the light chain variable region. The accuracy and the area under precision recall curve of the classification model are 0.85 and 0.6, respectively. The aggregation rates and viscosity models can be used to predict antibody stability to facilitate pharmaceutical development.

In silico prediction of post-translational modifications in therapeutic antibodies

Monoclonal antibodies are susceptible to chemical and enzymatic modifications during manufacturing, storage, and shipping. Deamidation, isomerization, and oxidation can compromise the potency, efficacy, and safety of therapeutic antibodies. Recently, in silico tools have been used to identify liable residues and engineer antibodies with better chemical stability. Computational approaches for predicting deamidation, isomerization, oxidation, glycation, carbonylation, sulfation, and hydroxylation are reviewed here. Although liable motifs have been used to improve the chemical stability of antibodies, the accuracy of in silico predictions can be improved using machine learning and molecular dynamic simulations. In addition, there are opportunities to improve predictions for specific stress conditions, develop in silico prediction of novel modifications in antibodies, and predict the impact of modifications on physical stability and antigen-binding.

Fluid Drag Reduction by Magnetic Confinement

The frictional forces of a viscous liquid flow are a major energy loss issue and severely limit microfluidics practical use. Reducing this drag by more than a few tens of percent remain elusive. Here, we show how cylindrical liquid-in-liquid flow leads to drag reduction of 60-99% for sub-mm and mm-sized channels, regardless of whether the viscosity of the transported liquid is larger or smaller than that of the confining one. In contrast to lubrication or sheath flow, we do not require a continuous flow of the confining lubricant, here made of a ferrofluid held in place by magnetic forces. In a laminar flow model with appropriate boundary conditions, we introduce a modified Reynolds number with a scaling that depends on geometrical factors and viscosity ratio of the two liquids. It explains our whole range of data and reveals the key design parameters for optimizing the drag reduction values. Our approach promises a new route for microfluidics designs with pressure gradient reduced by orders of magnitude.

Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods

Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.

Transforming mutations in the development of pathogenic B cell clones and autoantibodies

Autoimmune diseases are characterized by serum autoantibodies, some of which are pathogenic, causing severe manifestations and organ injury. However, autoantibodies of the same antigenic reactivity are also present in the serum of asymptomatic people years before they develop any clinical signs of autoimmunity. Autoantibodies can arise during multiple stages of B cell development, and various genetic and environmental factors drive their production. However, what drives the development of pathogenic autoantibodies is poorly understood. Advances in single-cell technology have enabled the deep analysis of rare B cell clones producing pathogenic autoantibodies responsible for vasculitis in patients with primary Sjögren's syndrome complicated by mixed cryoglobulinaemia. These findings demonstrated a cascade of genetic events involving stereotypic immunoglobulin V(D)J recombination and transforming somatic mutations in lymphoma genes and V(D)J regions that disrupted antibody quality control mechanisms and decreased autoantibody solubility. Most studies consider V(D)J mutations that enhance autoantibody affinity to drive pathology; however, V(D)J mutations that increase autoantibody propensity to form insoluble complexes could be a major contributor to autoantibody pathogenicity. Defining the molecular characteristics of pathogenic autoantibodies and failed tolerance checkpoints driving their formation will improve prognostication, enabling early treatment to prevent escalating organ damage and B cell malignancy.

Development of QSAR models for in silico screening of antibody solubility

Although monoclonal antibodies (mAbs) have been shown to be extremely effective in treating a number of diseases, they often suffer from poor developability attributes, such as high viscosity and low solubility at elevated concentrations. Since experimental candidate screening is often materials and labor intensive, there is substantial interest in developing in silico tools for expediting mAb design. Here, we present a strategy using machine learning-based QSAR models for the a priori estimation of mAb solubility. The extrapolated protein solubilities of a set of 111 antibodies in a histidine buffer were determined using a high throughput PEG precipitation assay. 3D homology models of the antibodies were determined, and a large set of in house and commercially available molecular descriptors were then calculated. The resulting experimental and descriptor data were then used for the development of QSAR models of mAb solubilities. After feature selection and training with different machine learning algorithms, the models were evaluated with external test sets. The resulting regression models were able to estimate the solubility values of external test set data with R² of 0.81 and 0.85 for the two regression models developed. In addition, three class and binary classification models were developed and shown to be good estimators of mAb solubility behavior, with overall test set accuracies of 0.70 and 0.95, respectively. The analysis of the selected molecular descriptors in these models was also found to be informative and suggested that several charge-based descriptors and isotype may play important roles in mAb solubility. The combination of high throughput relative solubility experimental techniques in concert with efficient machine learning QSAR models offers an opportunity to rapidly screen potential mAb candidates and to design therapeutics with improved solubility characteristics.

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers

The propensity for some monoclonal antibodies (mAbs) to aggregate at physiological and manufacturing pH values can prevent their use as therapeutic molecules or delay time to market. Consequently, developability assessments are essential to select optimum candidates, or inform on mitigation strategies to avoid potential late-stage failures. These studies are typically performed in a range of buffer solutions because factors such as pH can dramatically alter the aggregation propensity of the test mAbs (up to 100-fold in extreme cases). A computational method capable of robustly predicting the aggregation propensity at the pH values of common storage buffers would have substantial value. Here, we describe a mAb aggregation prediction tool (MAPT) that builds on our previously published isotype-dependent, charge-based model of aggregation. We show that the addition of a homology model-derived hydrophobicity descriptor to our electrostatic aggregation model enabled the generation of a robust mAb developability indicator. To contextualize our aggregation scoring system, we analyzed 97 clinical-stage therapeutic mAbs. To further validate our approach, we focused on six mAbs (infliximab, tocilizumab, rituximab, CNTO607, MEDI1912 and MEDI1912_STT) which have been reported to cover a large range of aggregation propensities. The different aggregation propensities of the case study molecules at neutral and slightly acidic pH were correctly predicted, verifying the utility of our computational method.

The Therapeutic Antibody Profiler for Computational Developability Assessment

The need to consider an antibody's "developability" (immunogenicity, solubility, specificity, stability, manufacturability, and storability) is now well understood in therapeutic antibody design. Predicting these properties rapidly and inexpensively is critical to industrial workflows, to avoid devoting resources to non-productive candidates. Here, we describe a high-throughput computational developability assessment tool, the Therapeutic Antibody Profiler (TAP), which assesses the physicochemical "druglikeness" of an antibody candidate. Input variable domain sequences are converted to three-dimensional structural models, and then five developability-linked molecular surface descriptors are calculated and compared to advanced-stage clinical therapeutics. Values at the extremes of/outside of the distributions seen in therapeutics imply an increased risk of developability issues. Therefore, TAP, starting only from sequence information, provides a route to rapidly identifying drug candidate antibodies that are likely to have poor developability. Our web application ( opig.stats.ox.ac.uk/webapps/tap ) profiles input antibody sequences against a continually updated reference set of clinical therapeutics.

Next generation Fc scaffold for multispecific antibodies

Bispecific antibodies (Bispecifics) demonstrate exceptional clinical potential to address some of the most complex diseases. However, Bispecific production in a single cell often requires the correct pairing of multiple polypeptide chains for desired assembly. This is a considerable hurdle that hinders the development of many immunoglobulin G (IgG)-like bispecific formats. Our approach focuses on the rational engineering of charged residues to facilitate the chain pairing of distinct heavy chains (HC). Here, we deploy structure-guided protein design to engineer charge pair mutations (CPMs) placed in the CH3-CH3' interface of the fragment crystallizable (Fc) region of an antibody (Ab) to correctly steer heavy chain pairing. When used in combination with our stable effector functionless 2 (SEFL2.2) technology, we observed high pairing efficiency without significant losses in expression yields. Furthermore, we investigate the relationship between CPMs and the sequence diversity in the parental antibodies, proposing a rational strategy to deploy these engineering technologies.

Interaction with 2,4-dinitrophenol correlates with polyreactivity, self-binding, and stability of clinical-stage therapeutic antibodies

Therapeutic antibodies should cover particular physicochemical and functional requirements for successful entry into clinical practice. Numerous experimental and computational approaches have been developed for early identification of different unfavourable features of antibodies. Immune repertoires of healthy humans contain a fraction of antibodies that recognize nitroarenes. These antibodies have been demonstrated to manifest antigen-binding polyreactivity. Here we observed that >20 % of 112 clinical stage therapeutic antibodies show pronounced binding to 2,4-dinitrophenol conjugated to albumin. This interaction predicts a number of unfavourable functional and physicochemical features of antibodies such as polyreactivity, tendency for self-association, stability and expression yields. Based on these findings we proposed a simple approach that may add to the armamentarium of assays for early identification of developability liabilities of antibodies intended for therapeutic use.

Impact of IgG subclass on molecular properties of monoclonal antibodies

Immunoglobulin G-based monoclonal antibodies (mAbs) have become a dominant class of biotherapeutics in recent decades. Approved antibodies are mainly of the subclasses IgG1, IgG2, and IgG4, as well as their derivatives. Over the decades, the selection of IgG subclass has frequently been based on the needs of Fc gamma receptor engagement and effector functions for the desired mechanism of action, while the effect on drug product developability has been less thoroughly characterized. One of the major reasons is the lack of systematic understanding of the impact of IgG subclass on the molecular properties. Several efforts have been made recently to compare molecular property differences among these IgG subclasses, but the conclusions from these studies are sometimes obscured by the interference from variable regions. To further establish mechanistic understandings, we conducted a systematic study by grafting three independent variable regions onto human IgG1, an IgG1 variant, IgG2, and an IgG4 variant constant domains and evaluating the impact of subclass and variable regions on their molecular properties. Structural and computational analysis revealed specific molecular features that potentially account for the differential behavior of the IgG subclasses observed experimentally. Our data indicate that IgG subclass plays a significant role on molecular properties, either through direct effects or via the interplay with the variable region, the IgG1 mAbs tend to have higher solubility than either IgG2 or IgG4 mAbs in a common pH 6 buffer matrix, and solution behavior relies heavily on the charge status of the antibody at the desirable pH.

Stress-dependent flexibility of a full-length human monoclonal antibody: Insights from molecular dynamics to support biopharmaceutical development

After several decades of advancements in drug discovery, product development of biopharmaceuticals remains a time- and resource-consuming endeavor. One of the main reasons is associated to the lack of fundamental understanding of conformational dynamics of such biologic entities, and how they respond to various stresses encountered during manufacturing. In this work, we have studied the conformational dynamics of human IgG₁κ b12 monoclonal antibody (mAb) using molecular dynamics simulations. The hundreds of nanoseconds long trajectories reveal that b12 mAb is highly flexible. Its variable domains show greater conformational fluctuations than the constant domains. Additionally, it collapses towards a more globular shape in response to thermal stress, leading to decrease in the total solvent exposed surface area and radius of gyration. This behavior is more pronounced for the deglycosylated b12 mAb, and it appears to correlate with increase in inter-domain contacts between specific regions of the antibody. Conformational fluctuations also cause temporary formation and disruption of hydrophobic and charged patches on the antibody surface, which is particularly important for the prediction of CMC properties during development phases of antibody-based biotherapeutics. The insights gained through these simulations may help the development of biologic drugs, especially with regards to manufacturing processes where antibodies may undergo significant thermal stress.

Effect of variable domain charge on in vitro and in vivo disposition of monoclonal antibodies

A growing body of evidence supports the important role of molecular charge on antibody pharmacokinetics (PK), yet a quantitative description of the effect of charge on systemic and tissue disposition of antibodies is still lacking. Consequently, we have systematically engineered complementarity-determining regions (CDRs) of trastuzumab to create a series of variants with an isoelectric point (pI) range of 6.3–8.9 and a variable region (Fv) charge range of −8.9 to +10.9 (at pH 5.5), and have investigated in vitro and in vivo disposition of these molecules. These monoclonal antibodies (mAbs) exhibited incrementally enhanced binding to cell surfaces and cellular uptake with increased positive charge in antigen-negative cells. After single intravenous dosing in mice, a bell-shaped relationship between systemic exposure and Fv charge was observed, with both extended negative and positive charge patches leading to more rapid nonspecific clearance. Whole-body PK experiments revealed that, although overall exposures of most variants in the tissues were very similar, positive charge of mAbs led to significantly enhanced tissue:plasma concentration ratios for most tissues. In well-perfused organs such as liver, spleen, and kidney, the positive charge variants show superior accumulation. In tissues with continuous capillaries such as fat, muscle, skin, and bone, plasma concentrations governed tissue exposures. The in vitro and in vivo disposition data presented here facilitate better understanding of the impact of charge modifications on antibody PK, and suggest that alteration in the charge may help to improve tissue:plasma concentration ratios for mAbs in certain tissues. The data presented here also paves the way for the development of physiologically based pharmacokinetic models of mAbs that incorporate charge variations.

Intrinsic physicochemical profile of marketed antibody-based biotherapeutics

Successful biologic drug discovery and development involves finding functional as well as developable candidates. Once a candidate has been demonstrated to be functional, the next step is to determine whether it can be translated into a drug product. This requires that the candidate can withstand stresses encountered during manufacturing, shipping, and storage. Additionally, it must be safe, efficacious, and possess good pharmacology. In silico analyses of the variable regions of 77 marketed antibody-based biotherapeutics have revealed five nonredundant physicochemical descriptors. Distributions of these descriptors, observed for marketed biotherapeutics, can help prioritize a drug candidate for experimental testing at early discovery stages, guide engineering efforts to further optimize it, and help increase the productivity of biologic drug discovery and development.

Feeding biopharma pipelines with biotherapeutic candidates that possess desirable developability profiles can help improve the productivity of biologic drug discovery and development. Here, we have derived an in silico profile by analyzing computed physicochemical descriptors for the variable regions (Fv) found in 77 marketed antibody-based biotherapeutics. Fv regions of these biotherapeutics demonstrate significant diversities in their germlines, complementarity determining region loop lengths, hydrophobicity, and charge distributions. Furthermore, an analysis of 24 physicochemical descriptors, calculated using homology-based molecular models, has yielded five nonredundant descriptors whose distributions represent stability, isoelectric point, and molecular surface characteristics of their Fv regions. Fv regions of candidates from our internal discovery campaigns, human next-generation sequencing repertoires, and those in clinical-stages (CST) were assessed for similarity with the physicochemical profile derived here. The Fv regions in 33% of CST antibodies show physicochemical properties that are dissimilar to currently marketed biotherapeutics. In comparison, physicochemical characteristics of ∼29% of the Fv regions in human antibodies and ∼27% of our internal hits deviated significantly from those of marketed biotherapeutics. The early availability of this information can help guide hit selection, lead identification, and optimization of biotherapeutic candidates. Insights from this work can also help support portfolio risk assessment, in-licensing, and biopharma collaborations.

Effect of charged mutation on aggregation of a pentapeptide: Insights from molecular dynamics simulations

Aggregation of therapeutic monoclonal antibodies (mAbs) can negatively affect their chemistry, manufacturing, and control attributes and lead to undesirable immune responses in patients. Therefore, optimization of lead mAb drug candidates during discovery stages to mitigate aggregation is increasingly becoming an integral part of their developability assessments. The disruption of short sequence motifs called aggregation prone regions (APRs) found in amino acid sequences of mAb candidates can potentially mitigate their aggregation. In this work, we have performed molecular dynamics simulations to study the aggregation of an APR (VLVIY) found in λ light chains of human antibodies and its single point mutant KLVIY. Eighteen different multicopy peptide simulation systems of "VLVIY" and "KLVIY" were constructed by varying their concentrations, temperatures, termini capping, and flanking gate-keeper regions. Within 20 ns of the simulation, peptide "VLVIY" formed an aggregate of 100 peptides at ~0.1 M concentration with a 60% reduction in solvent accessible surface area (SASA). Furthermore, analysis of the SASA change, peptide cluster distribution, and water residence time demonstrated how Val ➔ Lys mutation resists aggregation and improves solubility. Presence of Lys slows down aggregation kinetics via charge-charge repulsions and by raising the kinetic barrier to formation of large oligomers. However, the effect of the Val ➔ Lys mutation is dependent on sequence and structural contexts around the APR. This mutation also alters the solvation shell around the peptide by favoring solute-solvent interactions, thereby increasing its solubility. This work has provided a detailed mechanistic explanation of how APR disruption can mitigate aggregation in biotherapeutics and improve their developability.

An accelerated surface-mediated stress assay of antibody instability for developability studies

High physical stability is required for the development of monoclonal antibodies (mAbs) into successful therapeutic products. Developability assays are used to predict physical stability issues such as high viscosity and poor conformational stability, but protein aggregation remains a challenging property to predict. Among different types of stresses, air–water and solid–liquid interfaces are well known to potentially trigger protein instability and induce aggregation. Yet, in contrast to the increasing number of developability assays to evaluate bulk properties, there is still a lack of experimental methods to evaluate antibody stability against interfaces. Here, we investigate the potential of a hydrophobic nanoparticle surface-mediated stress assay to assess the stability of mAbs during the early stages of development. We evaluate this surface-mediated accelerated stability assay on a rationally designed library of 14 variants of a humanized IgG4, featuring a broad span of solubility values and other developability properties. The assay could identify variants characterized by high instability against agitation in the presence of air–water interfaces. Remarkably, for the set of investigated molecules, we observe strong correlations between the extent of aggregation induced by the surface-mediated stress assay and other developability properties of the molecules, such as aggregation upon storage at 45°C, self-association (evaluated by affinity-capture self-interaction nanoparticle spectroscopy) and nonspecific interactions (estimated by cross-interaction chromatography, stand-up monolayer chromatography (SMAC), SMAC*). This highly controlled surface-mediated stress assay has the potential to complement and increase the ability of the current set of screening techniques to assess protein aggregation and developability potential of mAbs during the early stages of drug development.

Abbreviations:AC-SINS: Affinity-Capture Self-Interaction Nanoparticle Spectroscopy; AMS: Ammonium sulfate precipitation; ANS: 1-anilinonaphtalene-8-sulfonate; CIC: Cross-interaction chromatography; DLS: Dynamic light scattering; HIC: Hydrophobic interaction chromatography; HNSSA: Hydrophobic nanoparticles surface-stress assay; mAb: Monoclonal antibody; NP: Nanoparticle; SEC: Size exclusion chromatography; SMAC: Stand-up monolayer chromatography; WT: Wild type

Molecular computations of preferential interactions of proline, arginine.HCl, and NaCl with IgG1 antibodies and their impact on aggregation and viscosity

Preferential interactions of excipients with the antibody surface govern their effect on the stability of antibodies in solution. We probed the preferential interactions of proline, arginine.HCl (Arg.HCl), and NaCl with three therapeutically relevant IgG1 antibodies via experiment and simulation. With simulations, we examined how excipients interacted with different types of surface patches in the variable region (Fv). For example, proline interacted most strongly with aromatic surfaces, Arg.HCl was included near negative residues, and NaCl was excluded from negative residues and certain hydrophobic regions. The differences in interaction of different excipients with the same surface patch on an antibody may be responsible for variations in the antibody's aggregation, viscosity, and self-association behaviors in each excipient. Proline reduced self-association for all three antibodies and reduced aggregation for the antibody with an association-limited aggregation mechanism. The effects of Arg.HCl and NaCl on aggregation and viscosity were highly dependent on the surface charge distribution and the extent of exclusion from highly hydrophobic patches. At pH 5.5, both tended to increase the aggregation of an antibody with a strongly positive charge on the Fv, while only NaCl reduced the aggregation of the antibody with a large negative charge patch on the Fv. Arg.HCl reduced the viscosities of antibodies with either a hydrophobicity-driven mechanism or a charge-driven mechanism. Analysis of this data presents a framework for understanding how amino acid and ionic excipients interact with different protein surfaces, and how these interactions translate to the observed stability behavior.

Metal Ion Interactions with mAbs: Part 2. Zinc-Mediated Aggregation of IgG1 Monoclonal Antibodies

PURPOSE

To evaluate the physical and chemical degradation of monoclonal antibodies in the presence of Zn²⁺.

METHODS

A full length IgG1 monoclonal antibody (mAb1) was formulated with various amounts of Zn²⁺. The resulting mixture was incubated for several weeks at room temperature and analyzed using a variety of biochemical techniques to look for various physical (e.g. aggregation) and chemical (e.g. fragmentation) degradation pathways.

RESULTS

mAb1 of the IgG1 subclass undergoes aggregation in the presence of Zn²⁺ in a concentration dependent manner. Up to hexamers were characterized using SEC-MALS. No fragmentation was noticed in the presence of Zn²⁺ as opposed to that found in our previous report when IgG1 mAbs were incubated in the presence of Cu²⁺ ions. Site directed mutagenesis indicated the involvement of Fc histidine (His 310) in Zn²⁺ mediated aggregation.

CONCLUSIONS

A novel metal ion mediated isodesmic aggregation mechanism was found in IgG1 class of monoclonal antibodies. Histidine residues in the Fc region were determined to be the binding site and implicated in Zn²⁺ mediated aggregation.

A cell-based FcRn-dependent recycling assay for predictive pharmacokinetic assessment of therapeutic antibodies

Aim: Evaluation of suitable pharmacokinetic properties is critical for successful development of IgG-based biotherapeutics. The prolonged half-lives of IgGs depend on the intracellular trafficking function of neonatal Fc receptor, which rescues internalized IgGs from lysosomal degradation and recycles them back to circulation. Results: Here, we developed a novel cell-based assay to quantify recycling of monoclonal antibodies in a transwell culture system that uses a cell line that stably expresses human neonatal Fc receptor. We tested seven therapeutic antibodies and showed that the recycling output of the assay strongly correlated with the clearance in humans. Conclusion: This recycling assay has potential application as a pharmacokinetic prescreening tool to facilitate development and selection of IgG-based candidate therapeutic monoclonal antibodies.

Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties along the entire pharmaceutical process chain

The discovery of therapeutic monoclonal antibodies (mAbs) primarily focuses on their biological activity favoring the selection of highly potent drug candidates. These candidates, however, may have physical or chemical attributes that lead to unfavorable chemistry, manufacturing, and control (CMC) properties, such as low product titers, conformational and colloidal instabilities, or poor solubility, which can hamper or even prevent development and manufacturing. Hence, there is an urgent need to consider the developability of mAb candidates during lead identification and optimization. This work provides a comprehensive proof of concept study for the significantly improved developability of a mAb variant that was optimized with the help of sophisticated in silico tools relative to its difficult-to-develop parental counterpart. Interestingly, a single amino acid substitution in the variable domain of the light chain resulted in a three-fold increased product titer after stable expression in Chinese hamster ovary cells. Microscopic investigations revealed that wild type mAb-producing cells displayed potential antibody inclusions, while the in silico optimized variant-producing cells showed a rescued phenotype. Notably, the drug substance of the in silico optimized variant contained substantially reduced levels of aggregates and fragments after downstream process purification. Finally, formulation studies unraveled a significantly enhanced colloidal stability of the in silico optimized variant while its folding stability and potency were maintained. This study emphasizes that implementation of bioinformatics early in lead generation and optimization of biotherapeutics reduces failures during subsequent development activities and supports the reduction of project timelines and resources.

Caffeine as a Viscosity Reducer for Highly Concentrated Monoclonal Antibody Solutions

Many monoclonal antibody (mAb) solutions exhibit high viscosity at elevated concentrations, which prevents manufacturing and injecting of concentrated mAb drug products at the small volumes needed for subcutaneous (SC) administration. Addition of excipients that interrupt intermolecular interactions is a common approach to reduce viscosity of high concentration mAb formulations. However, in some cases widely used excipients can fail to lower viscosity. Here, using infliximab and ipilimumab as model proteins, we show that caffeine effectively lowers the viscosity of both mAb formulations, whereas other common viscosity-reducing excipients, sodium chloride and arginine, do not. Furthermore, stability studies under accelerated conditions show that caffeine has no impact on stability of lyophilized infliximab or liquid ipilimumab formulations. In addition, presence of caffeine in the formulations does not affect in vitro bioactivities of infliximab or ipilimumab. Results from this study suggest that caffeine could be a useful viscosity reducing agent that complements other traditional excipients and provides viscosity reduction to a wider range of mAb drug products.

Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies

Understanding the pharmacokinetic (PK) properties of a drug, such as clearance, is a crucial step for evaluating efficacy. The PK of therapeutic antibodies can be complex and is influenced by interactions with the target, Fc-receptors, anti-drug antibodies, and antibody intrinsic factors. A growing body of literature has linked biophysical properties of antibodies, particularly nonspecific-binding propensity, hydrophobicity and charged regions to rapid clearance in preclinical species and selected human PK studies. A clear understanding of the connection between biophysical properties and their impact on PK would allow for early selection and optimization of antibodies and reduce costly attrition during clinical trials due to sub-optimal human clearance. Due to the difficulty in obtaining large and unbiased human PK data, previous studies have focused mostly on preclinical PK. For this study, we obtained and curated the most comprehensive clinical PK dataset to date and calculated accurate estimates of linear clearance for 64 monoclonal antibodies ranging from investigational candidates in Phase 2 trials to marketed products. This allows for the first time a deep analysis of the influence of biophysical and sequence-based in silico properties directly on human clearance. We use statistical analysis and a Random Forest classifier to identify properties that have the greatest influence in our dataset. Our findings indicate that in vitro poly-specificity assay and in silico estimated isoelectric point can discriminate fast and slow clearing antibodies, extending previous observations on preclinical clearance. This provides a simple yet powerful approach to select antibodies with desirable PK during early-stage screening.

An antibody engineering platform using amino acid networks: A case study in development of antiviral therapeutics

We present here a case study of an antibody-engineering platform that selects, modifies, and assembles antibody parts to construct novel antibodies. A salient feature of this platform includes the role of amino acid networks in optimizing framework regions (FRs) and complementarity determining regions (CDRs) to engineer new antibodies with desired structure-function relationships. The details of this approach are described in the context of its utility in engineering ZAb_FLEP, a potent anti-Zika virus antibody. ZAb_FLEP comprises of distinct parts, including heavy chain and light chain FRs and CDRs, with engineered features such as loop lengths and optimal epitope-paratope contacts. We demonstrate, with different test antibodies derived from different FR-CDR combinations, that despite these test antibodies sharing high overall sequence similarity, they yield diverse functional readouts. Furthermore, we show that strategies relying on one dimensional sequence similarity-based analyses of antibodies miss important structural nuances of the FR-CDR relationship, which is effectively addressed by the amino acid networks approach of this platform.

Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning

The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 10³ variants) expressed in mammalian cells, which typically results in few optimized leads. Here we show that optimized antibody variants can be identified by predicting antigen specificity via deep learning from a massively diverse space of antibody sequences. To produce data for training deep neural networks, we deep-sequenced libraries of the therapeutic antibody trastuzumab (about 1 × 10⁴ variants), expressed in a mammalian cell line through site-directed mutagenesis via CRISPR-Cas9-mediated homology-directed repair, and screened the libraries for specificity to human epidermal growth factor receptor 2 (HER2). We then used the trained neural networks to screen a computational library of approximately 1 × 10⁸ trastuzumab variants and predict the HER2-specific subset (approximately 1 × 10⁶ variants), which can then be filtered for viscosity, clearance, solubility and immunogenicity to generate thousands of highly optimized lead candidates. Recombinant expression and experimental testing of 30 randomly selected variants from the unfiltered library showed that all 30 retained specificity for HER2. Deep learning may facilitate antibody engineering and optimization.

Pragmatic mAb lead molecule engineering from a developability perspective

As the number of antibody drugs being approved and marketed increases, our knowledge of what makes potential drug candidates a successful product has increased tremendously. One of the critical parameters that have become clear in the field is the importance of mAb "developability." Efforts are being increasingly focused on simultaneously selecting molecules that exhibit both desirable biological potencies and manufacturability attributes. In the current study mutations to improve the developability profile of a problematic antibody that inconsistently precipitates in a batch scale-dependent fashion using a standard platform purification process are described. Initial bioinformatic analysis showed the molecule has no obvious sequence or structural liabilities that might lead it to precipitate. Subsequent analysis of the molecule revealed the presence of two unusual positively charged mutations on the light chain at the interface of VH and VL domains, which were hypothesized to be the primary contributor to molecule precipitation during process development. To investigate this hypothesis, straightforward reversion to the germline of these residues was carried out. The resulting mutants have improved expression titers and recovered stability within a forced precipitation assay, without any change to biological activity. Given the time pressures of drug development in industry, process optimization of the lead molecule was carried out in parallel to the "retrospective" mutagenesis approach. Bespoke process optimization for large-scale manufacturing was successful. However, we propose that such context-dependent sequence liabilities should be included in the arsenal of in silico developability screening early in development; particularly since this specific issue can be efficiently mitigated without the requirement for extensive screening of lead molecule variants.

Complex formation of anti-VEGF-C with VEGF-C released during blood coagulation resulted in an artifact in its serum pharmacokinetics

A phage‐derived human monoclonal antibody against VEGF‐C was developed as a potential anti‐tumor therapeutic and exhibited fast clearance in preclinical species, with notably faster clearance in serum than in plasma. The purpose of this work was to understand the factors contributing to its fast clearance. In vitro incubations in animal and human blood, plasma, and serum were conducted with radiolabeled anti‐VEGF‐C to determine potential protein and cell‐based interactions with the antibody as well as any matrix‐dependent recovery dependent upon the matrix. A tissue distribution study was conducted in mice with and without heparin infusion in order to identify a tissue sink and determine whether heparin could affect antibody recovery from serum and/or plasma. Incubation of radiolabeled anti‐VEGF‐C in human and animal blood, plasma, or serum revealed that the antibody formed a complex with an endogenous protein, likely VEGF‐C. This complex was trapped within the blood clot during serum preparation from blood, but not within the blood cell pellet during plasma preparation. Low level heparin infusion in mice slowed down clot formation during serum preparation and allowed for better recovery of the radiolabeled antibody in serum. No tissue sink was found in mice. Thus, during this characterization, we determined that the blood sampling matrix greatly impacted the amount of antibody recovered in the samples, therefore, altering its derived pharmacokinetic parameters. Target biology should be considered when selecting appropriate sampling matrices for PK analysis.

Machine learning prediction of methionine and tryptophan photooxidation susceptibility

Photooxidation of methionine (Met) and tryptophan (Trp) residues is common and includes major degradation pathways that often pose a serious threat to the success of therapeutic proteins. Oxidation impacts all steps of protein production, manufacturing, and shelf life. Prediction of oxidation liability as early as possible in development is important because many more candidate drugs are discovered than can be tested experimentally. Undetected oxidation liabilities necessitate expensive and time-consuming remediation strategies in development and may lead to good drugs reaching patients slowly. Conversely, sites mischaracterized as oxidation liabilities could result in overengineering and lead to good drugs never reaching patients. To our knowledge, no predictive model for photooxidation of Met or Trp is currently available. We applied the random forest machine learning algorithm to in-house liquid chromatography-tandem mass spectrometry (LC-MS/MS) datasets (Met, n = 421; Trp, n = 342) of tryptic therapeutic protein peptides to create computational models for Met and Trp photooxidation. We show that our machine learning models predict Met and Trp photooxidation likelihood with 0.926 and 0.860 area under the curve (AUC), respectively, and Met photooxidation rate with a correlation coefficient (Q²) of 0.511 and root-mean-square error (RMSE) of 10.9%. We further identify important physical, chemical, and formulation parameters that influence photooxidation. Improvement of biopharmaceutical liability predictions will result in better, more stable drugs, increasing development throughput, product quality, and likelihood of clinical success.

Interaction of clinical-stage antibodies with heme predicts their physiochemical and binding qualities

Immunoglobulin repertoires contain a fraction of antibodies that recognize low molecular weight compounds, including some enzymes' cofactors, such as heme. Here, by using a set of 113 samples with variable region sequences matching clinical-stage antibodies, we demonstrated that a considerable number of these antibodies interact with heme. Antibodies that interact with heme possess specific sequence traits of their antigen-binding regions. Moreover they manifest particular physicochemical and functional qualities i.e. increased hydrophobicity, higher propensity of self-binding, higher intrinsic polyreactivity and reduced expression yields. Thus, interaction with heme is a strong predictor of different molecular and functional qualities of antibodies. Notably, these qualities are of high importance for therapeutic antibodies, as their presence was associated with failure of drug candidates to reach clinic. Our study reveled an important facet of information about relationship sequence-function in antibodies. It also offers a convenient tool for detection of liabilities of therapeutic antibodies.

Characterization and Modeling of Reversible Antibody Self-Association Provide Insights into Behavior, Prediction, and Correction

Reversible antibody self-association, while having major developability and therapeutic implications, is not fully understood or readily predictable and correctable. For a strongly self-associating humanized mAb variant, resulting in unacceptable viscosity, the monovalent affinity of self-interaction was measured in the low μM range, typical of many specific and biologically relevant protein-protein interactions. A face-to-face interaction model extending across both the heavy-chain (HC) and light-chain (LC) Complementary Determining Regions (CDRs) was apparent from biochemical and mutagenesis approaches as well as computational modeling. Light scattering experiments involving individual mAb, Fc, Fab, and Fab'2 domains revealed that Fabs self-interact to form dimers, while bivalent mAb/Fab'2 forms lead to significant oligomerization. Site-directed mutagenesis of aromatic residues identified by homology model patch analysis and self-docking dramatically affected self-association, demonstrating the utility of these predictive approaches, while revealing a highly specific and tunable nature of self-binding modulated by single point mutations. Mutagenesis at these same key HC/LC CDR positions that affect self-interaction also typically abolished target binding with notable exceptions, clearly demonstrating the difficulties yet possibility of correcting self-association through engineering. Clear correlations were also observed between different methods used to assess self-interaction, such as Dynamic Light Scattering (DLS) and Affinity-Capture Self-Interaction Nanoparticle Spectroscopy (AC-SINS). Our findings advance our understanding of therapeutic protein and antibody self-association and offer insights into its prediction, evaluation and corrective mitigation to aid therapeutic development.

Predicting Antibody Developability Profiles Through Early Stage Discovery Screening

Monoclonal antibodies play an increasingly important role for the development of new drugs across multiple therapy areas. The term 'developability' encompasses the feasibility of molecules to successfully progress from discovery to development via evaluation of their physicochemical properties. These properties include the tendency for self-interaction and aggregation, thermal stability, colloidal stability, and optimization of their properties through sequence engineering. Selection of the best antibody molecule based on biological function, efficacy, safety, and developability allows for a streamlined and successful CMC phase. An efficient and practical high-throughput developability workflow (100 s-1,000 s of molecules) implemented during early antibody generation and screening is crucial to select the best lead candidates. This involves careful assessment of critical developability parameters, combined with binding affinity and biological properties evaluation using small amounts of purified material (<1 mg), as well as an efficient data management and database system. Herein, a panel of 152 various human or humanized monoclonal antibodies was analyzed in biophysical property assays. Correlations between assays for different sets of properties were established. We demonstrated in two case studies that physicochemical properties and key assay endpoints correlate with key downstream process parameters. The workflow allows the elimination of antibodies with suboptimal properties and a rank ordering of molecules for further evaluation early in the candidate selection process. This enables any further engineering for problematic sequence attributes without affecting program timelines.

Combining random mutagenesis, structure-guided design and next-generation sequencing to mitigate polyreactivity of an anti-IL-21R antibody

Despite substantial technological advances in antibody library and display platform development, the number of approved biotherapeutics from displayed libraries remains limited. In vivo, 20–50% of peripheral B cells undergo a process of receptor editing, which modifies the variable and junctional regions of light chains to delete auto-reactive clones. However, in vitro antibody evolution relies primarily on interaction with antigen, with no in-built checkpoints to ensure that the selected antibodies have not acquired additional specificities or biophysical liabilities during the optimization process. We had previously observed an enrichment of positive charge in the complementarity-determining regions of an anti-IL-21 R antibody during affinity optimization, which correlated with more potent IL-21 neutralization, but poor in vivo pharmacokinetics (PK). There is an emerging body of data that has correlated antibody nonspecificity with poor PK in vivo, and established a series of screening assays that are predictive of this behavior. In this study we revisit the challenge of developing an anti-IL-21 R antibody that can effectively compete with IL-21 for its highly negatively charged paratope while maintaining favorable biophysical properties. In vitro deselection methods that included an excess of negatively charged membrane preparations, or deoxyribonucleic acid, during phage selection of optimization libraries were unsuccessful in avoiding enrichment of highly charged, nonspecific antibody variants. However, a combination of structure-guided rational library design, next-generation sequencing of library outputs and application of linear regression models resulted in the identification of an antibody that maintained high affinity for IL-21 R and exhibited a desirable stability and biophysical profile.

Machine Learning Applied to Determine the Molecular Descriptors Responsible for the Viscosity Behavior of Concentrated Therapeutic Antibodies

Predicting the solution viscosity of monoclonal antibody (mAb) drug products remains as one of the main challenges in antibody drug design, manufacturing, and delivery. In this work, the concentration-dependent solution viscosity of 27 FDA-approved mAbs was measured at pH 6.0 in 10 mM histidine-HCl. Six mAbs exhibited high viscosity (>30 cP) in solutions at 150 mg/mL mAb concentration. Combining molecular modeling and machine learning feature selection, we found that the net charge in the mAbs and the amino acid composition in the Fv region are key features which govern the viscosity behavior. For mAbs whose behavior was not dominated by charge effects, we observed that high viscosity is correlated with more hydrophilic and fewer hydrophobic residues in the Fv region. A predictive model based on the net charges of mAbs and a high viscosity index is presented as a fast screening tool for classifying low- and high-viscosity mAbs.

Heparin chromatography as an in vitro predictor for antibody clearance rate through pinocytosis

The pharmacokinetic (PK) properties of therapeutic antibodies directly affect efficacy, dose and dose intervals, application route and tissue penetration. In indications where health-care providers and patients can choose between several efficacious and safe therapeutic options, convenience (determined by dosing interval or route of application), which is mainly driven by PK properties, can affect drug selection. Therapeutic antibodies can have greatly different PK even if they have identical Fc domains and show no target-mediated drug disposition. Biophysical properties like surface charge or hydrophobicity, and binding to surrogates for high abundant off-targets (e.g., baculovirus particles, Chinese hamster ovary cell membrane proteins) were proposed to be responsible for these differences. Here, we used heparin chromatography to separate a polyclonal mix of endogenous human IgGs (IVIG) into fractions that differ in their PK properties. Heparin was chosen as a surrogate for highly negatively charged glycocalyx components on endothelial cells, which are among the main contributors to nonspecific clearance. By directly correlating heparin retention time with clearance, we identified heparin chromatography as a tool to assess differences in unspecific cell-surface interaction and the likelihood for increased pinocytotic uptake and degradation. Building on these results, we combined predictors for FcRn-mediated recycling and cell-surface interaction. The combination of heparin and FcRn chromatography allow identification of antibodies with abnormal PK by mimicking the major root causes for fast, non-target-mediated, clearance of therapeutic, Fc-containing proteins.

Structure-based charge calculations for predicting isoelectric point, viscosity, clearance, and profiling antibody therapeutics

The effect of hydrophobicity on antibody aggregation is well understood, and it has been shown that charge calculations can be useful for high-concentration viscosity and pharmacokinetic (PK) clearance predictions. In this work, structure-based charge descriptors are evaluated for their predictive performance on recently published antibody pI, viscosity, and clearance data. From this, we devised four rules for therapeutic antibody profiling which address developability issues arising from hydrophobicity and charged-based solution behavior, PK, and the ability to enrich for those that are approved by the U.S. Food and Drug Administration. Differences in strategy for optimizing the solution behavior of human IgG1 antibodies versus the IgG2 and IgG4 isotypes and the impact of pH alterations in formulation are discussed.

Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods

There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable in vitro and in vivo properties, including favorable antibody stability, specificity, solubility, pharmacokinetics, and immunogenicity profiles, that together encompass overall drug developability. In particular, we highlight impressive recent progress in developing computational methods for improving rational antibody design and prediction of drug-like behaviors that hold great promise for reducing the amount of required experimentation. We also discuss outstanding challenges that will need to be addressed in the future to fully realize the great potential of using such analysis for minimizing development times and improving the success rate of antibody candidates in the clinic.