A systemic approach to identifying sequence frameworks that decrease mAb production in a transient Chinese hamster ovary cell expression system

Monoclonal antibodies (mAbs) are often engineered at the sequence level for improved clinical performance yet are rarely evaluated prior to candidate selection for their "developability" characteristics, namely expression, which can necessitate additional resource investments to improve the manufacturing processes for problematic mAbs. A strong relationship between primary sequence and expression has emerged, with slight differences in amino acid sequence resulting in titers differing by up to an order of magnitude. Previous work on these "difficult-to-express" (DTE) mAbs has shown that these phenotypes are driven by post-translational bottlenecks in antibody folding, assembly, and secretion processes. However, it has been difficult to translate these findings across cell lines and products. This work presents a systematic approach to study the impact of sequence variation on mAb expression at a larger scale and under more industrially relevant conditions. The analysis found 91 mutations that decreased transient expression of an IgG1κ in Chinese hamster ovary (CHO) cells and revealed that mutations at inaccessible residues, especially those leading to decreases in residue hydrophobicity, are not favorable for high expression. This workflow can be used to better understand sequence determinants of mAb expression to improve candidate selection procedures and reduce process development timelines.

Designing Robust Monoclonal Antibody Drug Products: Pitfalls of Simplistic Approaches for Stability Prediction

Thermal stability attributes including unfolding onset (Tonset) and mid-point (Tm) are often utilized for efficient development of monoclonal antibody (mAb) products during lead selection and formulation screening workflows. An assumption of direct correlation between thermal and kinetic physical stability underpins this basic approach. While literature reports have substantiated this general approach under specific conditions, clear exceptions have been highlighted alongside. Herein, a set of mAbs formulated under diverse solution conditions to generate a broad array of thermal and kinetic stability profiles were systematically analyzed. Sequence modifications in the Fc region were purposefully engineered to generate a set of low-melting mAbs. A diverse set of excipients were subsequently utilized and shown to modulate the Tm over a wide range. While a general correlation between high Tm and low aggregation rate was observed under accelerated conditions, the predictive utility of Tm under relevant product storage conditions was inadequate at best. Critically, Tm data did not correlate with long-term aggregation rates under refrigerated or room temperature conditions. Even under accelerated conditions, Tm appeared to be a poor predictor of aggregation once it exceeded the solution storage temperature (40°C) by ∼15°C, similar to conditions routinely encountered in the development of canonical mAbs (Tm > 60°C). Pitfalls of simplistic correlative approaches are discussed in the context of practical biologics product development.

Predicting deamidation and isomerization sites in therapeutic antibodies using structure-based in silico approaches

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.

Matrixed CDR grafting: A neoclassical framework for antibody humanization and developability

Discovery and optimization of a biotherapeutic monoclonal antibody requires a careful balance of target engagement and physicochemical developability properties. To take full advantage of the sequence diversity provided by different antibody discovery platforms, a rapid and reliable process for humanization of antibodies from nonhuman sources is required. Canonically, maximizing homology of the human variable region (V-region) to the original germline was believed to result in preservation of binding, often without much consideration for inherent molecular properties. We expand on this approach by grafting the complementary determining regions (CDRs) of a mouse anti-LAG3 antibody into an extensive matrix of human variable heavy chain (VH) and variable light chain (VL) framework regions with substantially broader sequence homology to assess the impact on complementary determining region-framework compatibility through progressive evaluation of expression, affinity, biophysical developability, and function. Specific VH and VL framework sequences were associated with major expression and purification phenotypes. Greater VL sequence conservation was correlated with retained or improved affinity. Analysis of grafts that bound the target demonstrated that initial developability criteria were significantly impacted by VH, but not VL. In contrast, cell binding and functional characteristics were significantly impacted by VL, but not VH. Principal component analysis of all factors identified multiple grafts that exhibited more favorable antibody properties, notably with nonoptimal sequence conservation. Overall, this study demonstrates that modern throughput systems enable a more thorough, customizable, and systematic analysis of graft-framework combinations, resulting in humanized antibodies with improved global properties that may progress through development more quickly and with a greater probability of success.

Reduction of monoclonal antibody viscosity using interpretable machine learning

Early identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. Our model was trained on antibody viscosity data (>100 mg/mL mAb concentration) obtained at a common formulation pH (pH 5.2), and it identifies three key Fv features of antibodies linked to viscosity, namely their isoelectric points, hydrophobic patch sizes, and numbers of negatively charged patches. Of the three features, most predicted antibodies at risk for high viscosity, including antibodies with diverse antibody germlines in our study (79 mAbs) as well as clinical-stage IgG1s (94 mAbs), are those with low Fv isoelectric points (Fv pIs < 6.3). Our model identifies viscous antibodies with relatively high accuracy not only in our training and test sets, but also for previously reported data. Importantly, we show that the interpretable nature of the model enables the design of mutations that significantly reduce antibody viscosity, which we confirmed experimentally. We expect that this approach can be readily integrated into the drug development process to reduce the need for experimental viscosity screening and improve the identification of antibody candidates with drug-like properties.

Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning

Antibody development, delivery, and efficacy are influenced by antibody-antigen affinity interactions, off-target interactions that reduce antibody bioavailability and pharmacokinetics, and repulsive self-interactions that increase the stability of concentrated antibody formulations and reduce their corresponding viscosity. Yet identifying antibody variants with optimal combinations of these three types of interactions is challenging. Here we show that interpretable machine-learning classifiers, leveraging antibody structural features descriptive of their variable regions and trained on experimental data for a panel of 80 clinical-stage monoclonal antibodies, can identify antibodies with optimal combinations of low off-target binding in a common physiological-solution condition and low self-association in a common antibody-formulation condition. For three clinical-stage antibodies with suboptimal combinations of off-target binding and self-association, the classifiers predicted variable-region mutations that optimized non-affinity interactions while maintaining high-affinity antibody-antigen interactions. Interpretable machine-learning models may facilitate the optimization of antibody candidates for therapeutic applications.

Hyperstable Synthetic Mini-Proteins as Effective Ligand Scaffolds

Small, single-domain protein scaffolds are compelling sources of molecular binding ligands with the potential for efficient physiological transport, modularity, and manufacturing. Yet, mini-proteins require a balance between biophysical robustness and diversity to enable new functions. We tested the developability and evolvability of millions of variants of 43 designed libraries of synthetic 40-amino acid βαββ proteins with diversified sheet, loop, or helix paratopes. We discovered a scaffold library that yielded hundreds of binders to seven targets while exhibiting high stability and soluble expression. Binder discovery yielded 6-122 nM affinities without affinity maturation and Tms averaging ≥78 °C. Broader βαββ libraries exhibited varied developability and evolvability. Sheet paratopes were the most consistently developable, and framework 1 was the most evolvable. Paratope evolvability was dependent on target, though several libraries were evolvable across many targets while exhibiting high stability and soluble expression. Select βαββ proteins are strong starting points for engineering performant binders.

Predicting and Interpreting Protein Developability Via Transfer of Convolutional Sequence Representation

Engineered proteins have emerged as novel diagnostics, therapeutics, and catalysts. Often, poor protein developability─quantified by expression, solubility, and stability─hinders utility. The ability to predict protein developability from amino acid sequence would reduce the experimental burden when selecting candidates. Recent advances in screening technologies enabled a high-throughput (HT) developability dataset for 105 of 1020 possible variants of protein ligand scaffold Gp2. In this work, we evaluate the ability of neural networks to learn a developability representation from a HT dataset and transfer this knowledge to predict recombinant expression beyond observed sequences. The model convolves learned amino acid properties to predict expression levels 44% closer to the experimental variance compared to a non-embedded control. Analysis of learned amino acid embeddings highlights the uniqueness of cysteine, the importance of hydrophobicity and charge, and the unimportance of aromaticity, when aiming to improve the developability of small proteins. We identify clusters of similar sequences with increased recombinant expression through nonlinear dimensionality reduction and we explore the inferred expression landscape via nested sampling. The analysis enables the first direct visualization of the fitness landscape and highlights the existence of evolutionary bottlenecks in sequence space giving rise to competing subpopulations of sequences with different developability. The work advances applied protein engineering efforts by predicting and interpreting protein scaffold expression from a limited dataset. Furthermore, our statistical mechanical treatment of the problem advances foundational efforts to characterize the structure of the protein fitness landscape and the amino acid characteristics that influence protein developability.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains

Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of diseases.However, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine-based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the in silico evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://i.uestc.edu.cn/AB-Amy/.

A Rapid Solid Form Risk Assessment Workflow for Ophthalmic Drug Candidates

ObjectiveThis work introduces a material-sparing process that rapidly screens the solid form landscape for ophthalmic compound candidates.SignificanceCrystalline form of compound candidates generated by a Form Risk Assessment (FRA) can be used to reduce their downstream development risk.MethodsThis workflow evaluated nine model compounds with various molecular and polymorphic profiles by using less than 350 mg of drug substances. Kinetic solubility of the model compounds in a variety of solvents was screened to support the experimental design. The FRA workflow integrated several crystallization methods such as temperature-cycled slurrying (thermocycling), cooling, and evaporation. The FRA was also applied on ten ophthalmic compound candidates for verification. X-ray powder diffractometry (XRPD) was used for form identification.ResultsFor the nine model compounds studied, multiple crystalline forms were generated. This demonstrates the potential of the FRA workflow to reveal polymorphic tendency. In addition, thermocycling process was found to be the most effective technique to capture the thermodynamically most stable form. Satisfactory results were observed with the discovery compounds intended for ophthalmic formulations.ConclusionsThis work introduces a form risk assessment workflow by using sub-gram level of drug substances. The capability of this material-sparing workflow to discover polymorphs and capture the thermodynamically most stable forms within 2 - 3 weeks makes it suitable for discovery stage compounds, esp. for ophthalmic candidates.

Sequence-based prediction of pH-dependent protein solubility using CamSol

Solubility is a property of central importance for the use of proteins in research in molecular and cell biology and in applications in biotechnology and medicine. Since experimental methods for measuring protein solubility are material intensive and time consuming, computational methods have recently emerged to enable the rapid and inexpensive screening of solubility for large libraries of proteins, as it is routinely required in development pipelines. Here, we describe the development of one such method to include in the predictions the effect of the pH on solubility. We illustrate the resulting pH-dependent predictions on a variety of antibodies and other proteins to demonstrate that these predictions achieve an accuracy comparable with that of experimental methods. We make this method publicly available at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolph, as the version 3.0 of CamSol.

AF4 and PEG Precipitation as Predictive Assays for Antibody Self-Association

Monoclonal antibodies (mAbs) are often formulated as high-protein-concentration solutions, which in some cases can exhibit physical stability issues such as high viscosity and opalescence. To ensure that mAb-based drugs can meet their manufacturing, stability, and delivery requirements, it is advantageous to screen for and select mAbs during discovery that are not prone to such behaviors. It has been recently shown that both these macroscopic properties can be predicted to a certain extent from the diffusion interaction parameter (k_D), which is a measure of self-association under dilute conditions.¹ However, k_D can be challenging to measure at the early stage of discovery, where a relatively large amount of a high-purity material, which is required by traditional methods, is often not available. In this study, we demonstrate asymmetric field-flow fractionation (AF4) as a tool to measure self-association and therefore identify antibodies with problematic issues at high concentrations. The principle lies on the ability to concentrate the sample close to the membrane during the injection mode, which can reach formulation-relevant concentrations (>100 mg/mL).² By analyzing a well-characterized library of commercial antibodies, we show that the measured retention time of the antibodies allows us to pinpoint molecules that exhibit issues at high concentrations. Remarkably, our AF4 assay requires very little (30 μg) sample under dilute conditions and does not need extensive sample purification. Furthermore, we show that a polyethylene glycol (PEG) precipitation assay provides results consistent with AF4 and moreover can further differentiate molecules with issues of opalescence or high viscosity. Overall, our results delineate a two-step strategy for the identification of problematic variants at high concentrations, with AF4 for early developability screening, followed by a PEG assay to validate the problematic molecules and further discriminate between opalescence or high-viscosity issues. This two-step antibody selection strategy enables us to select antibodies early in the discovery process, which are compatible with high-concentration formulations.

Identifying developability risks for clinical progression of antibodies using high-throughput in vitro and in silico approaches

With the growing significance of antibodies as a therapeutic class, identifying developability risks early during development is of paramount importance. Several high-throughput in vitro assays and in silico approaches have been proposed to de-risk antibodies during early stages of the discovery process. In this review, we have compiled and collectively analyzed published experimental assessments and computational metrics for clinical antibodies. We show that flags assigned based on in vitro measurements of polyspecificity and hydrophobicity are more predictive of clinical progression than their in silico counterparts. Additionally, we assessed the performance of published models for developability predictions on molecules not used during model training. We find that generalization to data outside of those used for training remains a challenge for models. Finally, we highlight the challenges of reproducibility in computed metrics arising from differences in homology modeling, in vitro assessments relying on complex reagents, as well as curation of experimental data often used to assess the utility of high-throughput approaches. We end with a recommendation to enable assay reproducibility by inclusion of controls with disclosed sequences, as well as sharing of structural models to enable the critical assessment and improvement of in silico predictions.

Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties

Antibody drugs should exhibit not only high-binding affinity for their target antigens but also favorable physicochemical drug-like properties. Such drug-like biophysical properties are essential for the successful development of antibody drug products. The traditional approaches used in antibody drug development require significant experimentation to produce, optimize, and characterize many candidates. Therefore, it is attractive to integrate new methods that can optimize the process of selecting antibodies with both desired target-binding and drug-like biophysical properties. Here, we summarize a selection of techniques that can complement the conventional toolbox used to de-risk antibody drug development. These techniques can be integrated at different stages of the antibody development process to reduce the frequency of physicochemical liabilities in antibody libraries during initial discovery and to co-optimize multiple antibody features during early-stage antibody engineering and affinity maturation. Moreover, we highlight biophysical and computational approaches that can be used to predict physical degradation pathways relevant for long-term storage and in-use stability to reduce the need for extensive experimentation.

Impact of IgG subclass on monoclonal antibody developability

IgG-based monoclonal antibody therapeutics, which are mainly IgG1, IgG2, and IgG4 subclasses or related variants, have dominated the biotherapeutics field for decades. Multiple laboratories have reported that the IgG subclasses possess different molecular characteristics that can affect their developability. For example, IgG1, the most popular IgG subclass for therapeutics, is known to have a characteristic degradation pathway related to its hinge fragility. However, there remains a paucity of studies that systematically evaluate the IgG subclasses on manufacturability and long-term stability. We thus conducted a systematic study of 12 mAbs derived from three sets of unrelated variable regions, each cloned into IgG1, an IgG1 variant with diminished effector functions, IgG2, and a stabilized IgG4 variant with further reduced FcγR interaction, to evaluate the impact of IgG subclass on manufacturability and high concentration stability in a common formulation buffer matrix. Our evaluation included Chinese hamster ovary cell productivity, host cell protein removal efficiency, N-linked glycan structure at the conserved N297 Fc position, solution appearance at high concentration, and aggregate growth, fragmentation, charge variant profile change, and post-translational modification upon thermal stress conditions or long-term storage at refrigerated temperature. Our results elucidated molecular attributes that are common to all IgG subclasses, as well as those that are unique to certain Fc domains, providing new insight into the effects of IgG subclass on antibody manufacturability and stability. These learnings can be used to enable a balanced decision on IgG subclass selection for therapeutic antibodies and aid in acceleration of their product development process.

Assessing developability early in the discovery process for novel biologics

Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.

Trends in industrialization of biotherapeutics: a survey of product characteristics of 89 antibody-based biotherapeutics

There is considerable interest in the pharmaceutical industry toward development of antibody-based biotherapeutics because they can selectively bind diverse receptors and often possess desirable pharmacology. Here, we studied product characteristics of 89 marketed antibody-based biotherapeutics that were approved from 1986 to mid-2020 by gathering publicly available information. Our analyses revealed major trends in their emergence as the best-selling class of pharmaceuticals. Early on, most therapeutic monoclonal antibodies were developed to treat cancer, with CD20 being the most common target. Thanks to industrialization of antibody manufacturing technologies, their use has now blossomed to include 15 different therapeutic areas and nearly 60 targets, and the field is still growing! Drug manufacturers are solidifying their choices regarding types of antibodies and their molecular formats. IgG1 kappa continues to be the most common molecular format among marketed antibody-based biotherapeutics. Most antibody-based biotherapeutics approved since 2015 are either humanized or fully human, but the data we collected do not show a direct correlation between humanness and reported incidence of anti-drug antibodies. Furthermore, there have also been improvements in terms of drug product stability and high concentration liquid formulations suitable for subcutaneous route of administration, which are being approved more often in recent years. These improvements, however, have not been uniformly adopted across all therapeutic areas, suggesting that multiple options for drug product development are being used to serve diverse therapeutic purposes. Insights gained from this analysis may help us devise better end-to-end antibody-based biotherapeutic drug discovery and development strategies.

Developability profiling of a panel of Fc engineered SARS-CoV-2 neutralizing antibodies

To combat the COVID-19 pandemic, potential therapies have been developed and moved into clinical trials at an unprecedented pace. Some of the most promising therapies are neutralizing antibodies against SARS-CoV-2. In order to maximize the therapeutic effectiveness of such neutralizing antibodies, Fc engineering to modulate effector functions and to extend half-life is desirable. However, it is critical that Fc engineering does not negatively impact the developability properties of the antibodies, as these properties play a key role in ensuring rapid development, successful manufacturing, and improved overall chances of clinical success. In this study, we describe the biophysical characterization of a panel of Fc engineered ("TM-YTE") SARS-CoV-2 neutralizing antibodies, the same Fc modifications as those found in AstraZeneca's Evusheld (AZD7442; tixagevimab and cilgavimab), in which the TM modification (L234F/L235E/P331S) reduce binding to FcγR and C1q and the YTE modification (M252Y/S254T/T256E) extends serum half-life. We have previously shown that combining both the TM and YTE Fc modifications can reduce the thermal stability of the CH2 domain and possibly lead to developability challenges. Here we show, using a diverse panel of TM-YTE SARS-CoV-2 neutralizing antibodies, that despite lowering the thermal stability of the Fc CH2 domain, the TM-YTE platform does not have any inherent developability liabilities and shows an in vivo pharmacokinetic profile in human FcRn transgenic mice similar to the well-characterized YTE platform. The TM-YTE is therefore a developable, effector function reduced, half-life extended antibody platform.

Structure-based engineering of a novel CD3ε-targeting antibody for reduced polyreactivity

Bispecific antibodies continue to represent a growth area for antibody therapeutics, with roughly a third of molecules in clinical development being T-cell engagers that use an anti-CD3 binding arm. CD3 antibodies possessing cross-reactivity with cynomolgus monkey typically recognize a highly electronegative linear epitope at the extreme N-terminus of CD3 epsilon (CD3ε). Such antibodies have high isoelectric points and display problematic polyreactivity (correlated with poor pharmacokinetics for monospecific antibodies). Using insights from the crystal structure of anti-Hu/Cy CD3 antibody ADI-26906 in complex with CD3ε and antibody engineering using a yeast-based platform, we have derived high-affinity CD3 antibody variants with very low polyreactivity and significantly improved biophysical developability. Comparison of these variants with CD3 antibodies in the clinic (as part of bi- or multi-specifics) shows that affinity for CD3 is correlated with polyreactivity. Our engineered CD3 antibodies break this correlation, forming a broad affinity range with no to low polyreactivity. Such antibodies will enable bispecifics with improved pharmacokinetic and safety profiles and suggest engineering solutions that will benefit the large and growing sector of T-cell engagers.

A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties

Identification of favorable biophysical properties for protein therapeutics as part of developability assessment is a crucial part of the preclinical development process. Successful prediction of such properties and bioassay results from calculated in silico features has potential to reduce the time and cost of delivering clinical-grade material to patients, but nevertheless has remained an ongoing challenge to the field. Here, we demonstrate an automated and flexible machine learning workflow designed to compare and identify the most powerful features from computationally derived physiochemical feature sets, generated from popular commercial software packages. We implement this workflow with medium-sized datasets of human and humanized IgG molecules to generate predictive regression models for two key developability endpoints, hydrophobicity and poly-specificity. The most important features discovered through the automated workflow corroborate several previous literature reports, and newly discovered features suggest directions for further research and potential model improvement.

High concentration formulation developability approaches and considerations

The growing need for biologics to be administered subcutaneously and ocularly, coupled with certain indications requiring high doses, has resulted in an increase in drug substance (DS) and drug product (DP) protein concentrations. With this increase, more emphasis must be placed on identifying critical physico-chemical liabilities during drug development, including protein aggregation, precipitation, opalescence, particle formation, and high viscosity. Depending on the molecule, liabilities, and administration route, different formulation strategies can be used to overcome these challenges. However, due to the high material requirements, identifying optimal conditions can be slow, costly, and often prevent therapeutics from moving rapidly into the clinic/market. In order to accelerate and derisk development, new experimental and in-silico methods have emerged that can predict high concentration liabilities. Here, we review the challenges in developing high concentration formulations, the advances that have been made in establishing low mass and high-throughput predictive analytics, and advances in in-silico tools and algorithms aimed at identifying risks and understanding high concentration protein behavior.

Blueprint for antibody biologics developability

Large-molecule antibody biologics have revolutionized medicine owing to their superior target specificity, pharmacokinetic and pharmacodynamic properties, safety and toxicity profiles, and amenability to versatile engineering. In this review, we focus on preclinical antibody developability, including its definition, scope, and key activities from hit to lead optimization and selection. This includes generation, computational and in silico approaches, molecular engineering, production, analytical and biophysical characterization, stability and forced degradation studies, and process and formulation assessments. More recently, it is apparent these activities not only affect lead selection and manufacturability, but ultimately correlate with clinical progression and success. Emerging developability workflows and strategies are explored as part of a blueprint for developability success that includes an overview of the four major molecular properties that affect all developability outcomes: 1) conformational, 2) chemical, 3) colloidal, and 4) other interactions. We also examine risk assessment and mitigation strategies that increase the likelihood of success for moving the right candidate into the clinic.

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.

Computational counterselection identifies nonspecific therapeutic biologic candidates

Effective biologics require high specificity and limited off-target binding, but these properties are not guaranteed by current affinity-selection-based discovery methods. Molecular counterselection against off targets is a technique for identifying nonspecific sequences but is experimentally costly and can fail to eliminate a large fraction of nonspecific sequences. Here, we introduce computational counterselection, a framework for removing nonspecific sequences from pools of candidate biologics using machine learning models. We demonstrate the method using sequencing data from single-target affinity selection of antibodies, bypassing combinatorial experiments. We show that computational counterselection outperforms molecular counterselection by performing cross-target selection and individual binding assays to determine the performance of each method at retaining on-target, specific antibodies and identifying and eliminating off-target, nonspecific antibodies. Further, we show that one can identify generally polyspecific antibody sequences using a general model trained on affinity data from unrelated targets with potential affinity for a broad range of sequences.

SSH2.0: A Better Tool for Predicting the Hydrophobic Interaction Risk of Monoclonal Antibody

Therapeutic antibodies play a crucial role in the treatment of various diseases. However, the success rate of antibody drug development is low partially because of unfavourable biophysical properties of antibody drug candidates such as the high aggregation tendency, which is mainly driven by hydrophobic interactions of antibody molecules. Therefore, early screening of the risk of hydrophobic interaction of antibody drug candidates is crucial. Experimental screening is laborious, time-consuming, and costly, warranting the development of efficient and high-throughput computational tools for prediction of hydrophobic interactions of therapeutic antibodies. In the present study, 131 antibodies with hydrophobic interaction experiment data were used to train a new support vector machine-based ensemble model, termed SSH2.0, to predict the hydrophobic interactions of antibodies. Feature selection was performed against CKSAAGP by using the graph-based algorithm MRMD2.0. Based on the antibody sequence, SSH2.0 achieved the sensitivity and accuracy of 100.00 and 83.97%, respectively. This approach eliminates the need of three-dimensional structure of antibodies and enables rapid screening of therapeutic antibody candidates in the early developmental stage, thereby saving time and cost. In addition, a web server was constructed that is freely available at http://i.uestc.edu.cn/SSH2/.

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.

The evolution of bispecific antibodies

Not required for editorial.

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.

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.

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.

Investigating protein-excipient interactions of a multivalent VHH therapeutic protein using NMR spectroscopy

Multispecific therapeutic proteins come in a variety of formats, including bi- and tri-specific antibodies, dual-variable domain antibodies, and CrossMabs. These multivalent proteins are engineered to interact with multiple therapeutic target proteins with high specificity. Multi-domain proteins can be created by linking together a variety of high-affinity antibody fragments. The choice of protein domains and linkers not only affects the interactions of these molecules with therapeutic targets but also influences the intrinsic behavior in solution that affects their stability. The complexity of solution interactions may translate into developability and manufacturing challenges. Here, we use nuclear magnetic resonance (NMR) spectroscopy to study the solution behavior of a multivalent V_HH molecule composed of three flexibly linked heavy-chain-only domains that show dramatic stabilization against thermal degradation in the presence of sucrose. A collection of NMR fingerprinting and profiling methods were used to simultaneously monitor the protein solution behavior and capture details of protein–excipient interactions. We provide a framework to characterize and begin to understand the role of molecular flexibility in protein stabilization with potential applications in the design of novel therapeutic protein scaffolds that include multivalent proteins, fusion proteins, antibody-drug conjugates, and proteins modified with flexible lipids.

High-throughput profiling of antibody self-association in multiple formulation conditions by PEG stabilized self-interaction nanoparticle spectroscopy

Affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) is an assay developed to monitor the propensity of antibody self-association, hence assessing its colloidal stability. It has been widely used by pharmaceutical companies to screen antibodies at the early discovery stages, aiming to flag potential issues with high concentration formulation. However, the original assay format is not suitable for certain formulation conditions, in particular histidine buffer. In addition, the previous data extrapolation method is suboptimal and cumbersome for processing large amounts of data (100s of molecules) in a high-throughput fashion. To address these limitations, we developed an assay workflow with two major improvements: 1) use of a stabilizing reagent to enable screening of a broader range of formulation conditions beyond phosphate-buffered saline, pH 7.4; and 2) inclusion of a novel algorithm and robust data processing schema that empowers streamlined data analysis. The optimized assay format expands the screening applicability to a wider range of formulation conditions critical for downstream development. Such capability is enhanced by a custom data management workflow for optimal data extraction, analysis, and automation. Our protocol and the R/Shiny application for analysis are publicly available and open-source to benefit the broader scientific community.

Development of disulfide-stabilized Fabs for targeting of antibody-directed nanotherapeutics

Antibody-directed nanotherapeutics (ADNs) represent a promising delivery platform for selective delivery of an encapsulated drug payload to the site of disease that improves the therapeutic index. Although both single-chain Fv (scFv) and Fab antibody fragments have been used for targeting, no platform approach applicable to any target has emerged. scFv can suffer from intrinsic instability, and the Fabs are challenging to use due to native disulfide over-reduction and resulting impurities at the end of the conjugation process. This occurs because of the close proximity of the disulfide bond connecting the heavy and light chain to the free cysteine at the C-terminus, which is commonly used as the conjugation site. Here we show that by engineering an alternative heavy chain-light chain disulfide within the Fab, we can maintain efficient conjugation while eliminating the process impurities and retaining stability. We have demonstrated the utility of this technology for efficient ADN delivery and internalization for a series of targets, including EphA2, EGFR, and ErbB2. We expect that this technology will be broadly applicable for targeting of nanoparticle encapsulated payloads, including DNA, mRNA, and small molecules.

Reduction of therapeutic antibody self-association using yeast-display selections and machine learning

Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of immunoconjugate binding. Deep sequencing and machine learning analysis of the enriched bococizumab libraries, along with similar library analysis for antibody affinity, enabled identification of extremely rare variants with co-optimized levels of low self-association and high affinity. This analysis revealed that co-optimizing bococizumab is difficult because most high-affinity variants possess positively charged variable domains and most low self-association variants possess negatively charged variable domains. Moreover, negatively charged mutations in the heavy chain CDR2 of bococizumab, adjacent to its paratope, were effective at reducing self-association without reducing affinity. Interestingly, most of the bococizumab variants with reduced self-association also displayed improved folding stability and reduced nonspecific binding, revealing that this approach may be particularly useful for identifying antibody candidates with attractive combinations of drug-like properties.Abbreviations: AC-SINS: affinity-capture self-interaction nanoparticle spectroscopy; CDR: complementarity-determining region; CS-SINS: charge-stabilized self-interaction nanoparticle spectroscopy; FACS: fluorescence-activated cell sorting; Fab: fragment antigen binding; Fv: fragment variable; IgG: immunoglobulin; QD: quantum dot; PBS: phosphate-buffered saline; V_H: variable heavy; V_L: variable light.

Deciphering deamidation and isomerization in therapeutic proteins: Effect of neighboring residue

Deamidation of asparagine (Asn) and isomerization of aspartic acid (Asp) residues are among the most commonly observed spontaneous post-translational modifications (PTMs) in proteins. Understanding and predicting a protein sequence's propensity for such PTMs can help expedite protein therapeutic discovery and development. In this study, we used proton-affinity calculations with semi-empirical quantum mechanics and microsecond long equilibrium molecular dynamics simulations to investigate mechanistic roles of structural conformation and chemical environment in dictating spontaneous degradation of Asn and Asp residues in 131 clinical-stage therapeutic antibodies. Backbone secondary structure, side-chain rotamer conformation and solvent accessibility were found to be key molecular indicators of Asp isomerization and Asn deamidation. Comparative analysis of backbone dihedral angles along with N-H proton affinity calculations provides a mechanistic explanation for the strong influence of the identity of the n + 1 residue on the rate of Asn/Asp degradation. With these findings, we propose a minimalistic physics-based classification model that can be leveraged to predict deamidation and isomerization propensity of proteins.

An adapted consensus protein design strategy for identifying globally optimal biotherapeutics

Biotherapeutic optimization, whether to improve general properties or to engineer specific attributes, is a time-consuming process with uncertain outcomes. Conversely, Consensus Protein Design has been shown to be a viable approach to enhance protein stability while retaining function. In adapting this method for a more limited number of protein sequences, we studied 21 consensus single-point variants from eight publicly available CD3 binding sequences with high similarity but diverse biophysical and pharmacological properties. All single-point consensus variants retained CD3 binding and performed similarly in cell-based functional assays. Using Ridge regression analysis, we identified the variants and sequence positions with overall beneficial effects on developability attributes of the CD3 binders. A second round of sequence generation that combined these substitutions into a single molecule yielded a unique CD3 binder with globally optimized developability attributes. In this first application to therapeutic antibodies, adapted Consensus Protein Design was found to be highly beneficial within lead optimization, conserving resources and minimizing iterations. Future implementations of this general strategy may help accelerate drug discovery and improve success rates in bringing novel biotherapeutics to market.

Simultaneous affinity maturation and developability enhancement using natural liability-free CDRs

Affinity maturation is often a necessary step for the development of potent therapeutic molecules. Many different diversification strategies have been used for antibody affinity maturation, including error-prone PCR, chain shuffling, and targeted complementary-determining region (CDR) mutation. Although effective, they can negatively impact antibody stability or alter epitope recognition. Moreover, they do not address the presence of sequence liabilities, such as glycosylation, asparagine deamidation, aspartate isomerization, aggregation motifs, and others. Such liabilities, if present or inadvertently introduced, can potentially create the need for new rounds of engineering, or even abolish the value of the antibody as a therapeutic molecule. Here, we demonstrate a sequence agnostic method to improve antibody affinities, while simultaneously eliminating sequence liabilities and retaining the same epitope binding as the parental antibody. This was carried out using a defined collection of natural CDRs as the diversity source, purged of sequence liabilities, and matched to the antibody germline gene family. These CDRs were inserted into the lead molecule in one or two sites at a time (LCDR1-2, LCDR3, HCDR1-2) while retaining the HCDR3 and framework regions unchanged. The final analysis of 92 clones revealed 81 unique variants, with each of 24 tested variants having the same epitope specificity as the parental molecule. Of these, the average affinity improved by over 100 times (to 96 pM), and the best affinity improvement was 231-fold (to 32 pM).

Differences in human IgG1 and IgG4 S228P monoclonal antibodies viscosity and self-interactions: Experimental assessment and computational predictions of domain interactions

Human/humanized IgG4 antibodies have reduced effector function relative to IgG1 antibodies, which is desirable for certain therapeutic purposes. However, the developability and biophysical properties for IgG4 antibodies are not well understood. This work focuses on the head-to-head comparison of key biophysical properties, such as self-interaction and viscosity, for 14 human/humanized, and chimeric IgG1 and IgG4 S228P monoclonal antibody pairs that contain the identical variable regions. Experimental measurements showed that the IgG4 S228P antibodies have similar or higher self-interaction and viscosity than that of IgG1 antibodies in 20 mM sodium acetate, pH 5.5. We report sequence and structural drivers for the increased viscosity and self-interaction detected in IgG4 S228P antibodies through a combination of experimental data and computational models. Further, we applied and extended a previously established computational model for IgG1 antibodies to predict the self-interaction and viscosity behavior for each antibody pair, providing insight into the structural characteristics and differences of these two isotypes. Interestingly, we observed that the IgG4 S228P swapped variants, where the CH3 domain was swapped for that of an IgG1, showed reduced self-interaction behavior. These domain swapped IgG4 S228P molecules also showed reduced viscosity from experiment and coarse-grained simulations. We also observed that experimental diffusion interaction parameter (kD) values have a high correlation with computational diffusivity prediction for both IgG1 and IgG4 S228P isotypes.

Abbreviations: AHc, constant region Hamaker constant; AHv, variable region Hamaker constant; CDRs, Complementarity-determining regions; CG, Coarse-grained model; CH1, Constant heavy chain 1; CH2 Constant heavy chain 2; CH3 Constant heavy chain 3; chgCH3 Effective charge on the CH3 region; CL Constant light chain; cP, Centipoise; DLS, Dynamic light scattering; Fab, Fragment antigen-binding; Fc, Fragment crystallizable; Fv, Variable domaing; (r) Radial distribution function; H1 CDR1 of Heavy Chain; H2 CDR2 of Heavy Chain; H3 CDR3 of Heavy Chain; HVI, High viscosity index; IgG1 human immunoglobulin of IgG1 subclass; IgG4 human immunoglobulin of IgG4 subclass; kD, Diffusion interaction parameter; L1 CDR1 of Light Chain; L2 CDR2 of Light Chain; L3 CDR3 of Light Chain; mAb, Monoclonal antibody; MD, Molecular dynamics; PPI Protein–protein interactions; SCM, Spatial charge map; UP-SEC, Ultra-high-performance size-exclusion chromatography; VH, Variable domain of Heavy Chain; VL, Variable domain of Light Chain

Regulatory Approved Monoclonal Antibodies Contain Framework Mutations Predicted From Human Antibody Repertoires

Monoclonal antibodies (mAbs) are an important class of therapeutics used to treat cancer, inflammation, and infectious diseases. Identifying highly developable mAb sequences in silico could greatly reduce the time and cost required for therapeutic mAb development. Here, we present position-specific scoring matrices (PSSMs) for antibody framework mutations developed using baseline human antibody repertoire sequences. Our analysis shows that human antibody repertoire-based PSSMs are consistent across individuals and demonstrate high correlations between related germlines. We show that mutations in existing therapeutic antibodies can be accurately predicted solely from baseline human antibody sequence data. We find that mAbs developed using humanized mice had more human-like FR mutations than mAbs originally developed by hybridoma technology. A quantitative assessment of entire framework regions of therapeutic antibodies revealed that there may be potential for improving the properties of existing therapeutic antibodies by incorporating additional mutations of high frequency in baseline human antibody repertoires. In addition, high frequency mutations in baseline human antibody repertoires were predicted in silico to reduce immunogenicity in therapeutic mAbs due to the removal of T cell epitopes. Several therapeutic mAbs were identified to have common, universally high-scoring framework mutations, and molecular dynamics simulations revealed the mechanistic basis for the evolutionary selection of these mutations. Our results suggest that baseline human antibody repertoires may be useful as predictive tools to guide mAb development in the future.

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.

Design of Biopharmaceutical Formulations Accelerated by Machine Learning

In addition to activity, successful biological drugs must exhibit a series of suitable developability properties, which depend on both protein sequence and buffer composition. In the context of this high-dimensional optimization problem, advanced algorithms from the domain of machine learning are highly beneficial in complementing analytical screening and rational design. Here, we propose a Bayesian optimization algorithm to accelerate the design of biopharmaceutical formulations. We demonstrate the power of this approach by identifying the formulation that optimizes the thermal stability of three tandem single-chain Fv variants within 25 experiments, a number which is less than one-third of the experiments that would be required by a classical DoE method and several orders of magnitude smaller compared to detailed experimental analysis of full combinatorial space. We further show the advantage of this method over conventional approaches to efficiently transfer historical information as prior knowledge for the development of new biologics or when new buffer agents are available. Moreover, we highlight the benefit of our technique in engineering multiple biophysical properties by simultaneously optimizing both thermal and interface stabilities. This optimization minimizes the amount of surfactant in the formulation, which is important to decrease the risks associated with corresponding degradation processes. Overall, this method can provide high speed of converging to optimal conditions, the ability to transfer prior knowledge, and the identification of new nonlinear combinations of excipients. We envision that these features can lead to a considerable acceleration in formulation design and to parallelization of operations during drug development.

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.

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

An end-to-end automated platform process for high-throughput engineering of next-generation multi-specific antibody therapeutics

Next-generation multi-specific antibody therapeutics (MSATs) are engineered to combine several functional activities into one molecule to provide higher efficacy compared to conventional, mono-specific antibody therapeutics. However, highly engineered MSATs frequently display poor yields and less favorable drug-like properties (DLPs), which can adversely affect their development. Systematic screening of a large panel of MSAT variants in very high throughput (HT) is thus critical to identify potent molecule candidates with good yield and DLPs early in the discovery process. Here we report on the establishment of a novel, format-agnostic platform process for the fast generation and multiparametric screening of tens of thousands of MSAT variants. To this end, we have introduced full automation across the entire value chain for MSAT engineering. Specifically, we have automated the in-silico design of very large MSAT panels such that it reflects precisely the wet-lab processes for MSAT DNA library generation. This includes mass saturation mutagenesis or bulk modular cloning technologies while, concomitantly, enabling library deconvolution approaches using HT Sanger DNA sequencing. These DNA workflows are tightly linked to fully automated downstream processes for compartmentalized mammalian cell transfection expression, and screening of multiple parameters. All sub-processes are seamlessly integrated with tailored workflow supporting bioinformatics. As described here, we used this platform to perform multifactor optimization of a next-generation bispecific, cross-over dual variable domain-Ig (CODV-Ig). Screening of more than 25,000 individual protein variants in mono- and bispecific format led to the identification of CODV-Ig variants with over 1,000-fold increased potency and significantly optimized production titers, demonstrating the power and versatility of the platform.

Ultradilute Measurements of Self-Association for the Identification of Antibodies with Favorable High-Concentration Solution Properties

There is significant interest in formulating antibody therapeutics as concentrated liquid solutions, but early identification of developable antibodies with optimal manufacturability, stability, and delivery attributes remains challenging. Traditional methods of identifying developable mAbs with low self-association in common antibody formulations require relatively concentrated protein solutions (>1 mg/mL), and this single challenge has frustrated early-stage and large-scale identification of antibody candidates with drug-like colloidal properties. Here, we describe charge-stabilized self-interaction nanoparticle spectroscopy (CS-SINS), an affinity-capture nanoparticle assay that measures colloidal self-interactions at ultradilute antibody concentrations (0.01 mg/mL), and is predictive of antibody developability issues of high viscosity and opalescence that manifest at four orders of magnitude higher concentrations (>100 mg/mL). CS-SINS enables large-scale, high-throughput selection of developable antibodies during early discovery.

High-throughput developability assays enable library-scale identification of producible protein scaffold variants

Proteins require high developability-quantified by expression, solubility, and stability-for robust utility as therapeutics, diagnostics, and in other biotechnological applications. Measuring traditional developability metrics is low throughput in nature, often slowing the developmental pipeline. We evaluated the ability of 10 variations of three high-throughput developability assays to predict the bacterial recombinant expression of paratope variants of the protein scaffold Gp2. Enabled by a phenotype/genotype linkage, assay performance for 105 variants was calculated via deep sequencing of populations sorted by proxied developability. We identified the most informative assay combination via cross-validation accuracy and correlation feature selection and demonstrated the ability of machine learning models to exploit nonlinear mutual information to increase the assays' predictive utility. We trained a random forest model that predicts expression from assay performance that is 35% closer to the experimental variance and trains 80% more efficiently than a model predicting from sequence information alone. Utilizing the predicted expression, we performed a site-wise analysis and predicted mutations consistent with enhanced developability. The validated assays offer the ability to identify developable proteins at unprecedented scales, reducing the bottleneck of protein commercialization.

Assessment of Antibody Stability in a Novel Protein-Free Serum Model

Therapeutic proteins can degrade upon administration as they are subjected to a variety of stresses in human body compartments. In vivo degradation may cause undesirable pharmacokinetic/pharmacodynamic profiles. Pre-clinical in vitro models have gained scientific interest as they enable one to evaluate the in vivo stability of monoclonal antibodies (mAbs) and ultimately can improve patient safety. We used a novel approach by stripping serum of endogenous proteins, which interfere with analytical test methods. This enabled the direct analysis of the target protein without laborious sample work-up procedures. The developed model retained the osmolality, conductivity, temperature, and pH of serum. We compared the impact of human, bovine, and artificial serum to accelerated stability conditions in histidine buffer. Target mAbs were assessed in regard to visible and sub-visible particles, as well as protein aggregation and fragmentation. Both mAbs degraded to a higher extent under physiological conditions compared to accelerated stability conditions. No relevant stability differences between the tested mAbs were observed. Our results reinforced the importance of monitoring protein stability in biological fluids or fluids emulating these conditions closely. Models enabling analysis in fluids directly allow high throughput testing in early pre-clinical stages and help in selecting molecules with increased in vivo stability.