A strategy for risk mitigation of antibodies with fast clearance

Development of a [89Zr]Zr-labeled Human Antibody using a Novel Phage-displayed Human scFv Library

PURPOSE

Tax-interacting protein 1 (TIP1) is a cancer-specific radiation-inducible cell surface antigen that plays a role in cancer progression and resistance to therapy. This study aimed to develop a novel anti-TIP1 human antibody for noninvasive PET imaging in patients with cancer.

EXPERIMENTAL DESIGN

A phage-displayed single-chain variable fragment (scFv) library was created from healthy donors' blood. High-affinity anti-TIP1 scFvs were selected from the library and engineered to human IgG1. Purified Abs were characterized by size exclusion chromatography high-performance liquid chromatography (SEC-HPLC), native mass spectrometry (native MS), ELISA, BIAcore, and flow cytometry. The labeling of positron emitter [89Zr]Zr to the lead Ab, L111, was optimized using deferoxamine (DFO) chelator. The stability of [89Zr]Zr-DFO-L111 was assessed in human serum. Small animal PET studies were performed in lung cancer tumor models (A549 and H460).

RESULTS

We obtained 95% pure L111 by SEC-HPLC. Native MS confirmed the intact mass and glycosylation pattern of L111. Conjugation of three molar equivalents of DFO led to the optimal DFO-to-L111 ratio of 1.05. Radiochemical purity of 99.9% and specific activity of 0.37 MBq/μg was obtained for [89Zr]Zr-DFO-L111. [89Zr]Zr-DFO-L111 was stable in human serum over 7 days. The immunoreactive fraction in cell surface binding studies was 96%. In PET, preinjection with 4 mg/kg cold L111 before [89Zr]Zr-DFO-L111 (7.4 MBq; 20 μg) significantly (P < 0.01) enhanced the tumor-to-muscle standard uptake values (SUVmax) ratios on day 5 compared with day 2 postinjection.

CONCLUSIONS

L111 Ab targets lung cancer cells in vitro and in vivo. [89Zr]Zr-DFO-L111 is a human antibody that will be evaluated in the first in-human study of safety and PET imaging.

DOTAD: A Database of Therapeutic Antibody Developability

The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative. However, the antibody research community faces significant challenges due to the scarcity of readily accessible data on antibody developability, which is essential for training and validating computational models. To address this gap, DOTAD (Database Of Therapeutic Antibody Developability) has been built as the first database dedicated exclusively to the curation of therapeutic antibody developability information. DOTAD aggregates all available therapeutic antibody sequence data along with various developability metrics from the scientific literature, offering researchers a robust platform for data storage, retrieval, exploration, and downloading. In addition to serving as a comprehensive repository, DOTAD enhances its utility by integrating a web-based interface that features state-of-the-art tools for the assessment of antibody developability. This ensures that users not only have access to critical data but also have the convenience of analyzing and interpreting this information. The DOTAD database represents a valuable resource for the scientific community, facilitating the advancement of therapeutic antibody research. It is freely accessible at http://i.uestc.edu.cn/DOTAD/ , providing an open data platform that supports the continuous growth and evolution of computational methods in the field of antibody development.

Reducing Immunogenicity by Design: Approaches to Minimize Immunogenicity of Monoclonal Antibodies

Monoclonal antibodies (mAbs) have transformed therapeutic strategies for various diseases. Their high specificity to target antigens makes them ideal therapeutic agents for certain diseases. However, a challenge to their application in clinical practice is their potential risk to induce unwanted immune response, termed immunogenicity. This challenge drives the continued efforts to deimmunize these protein therapeutics while maintaining their pharmacokinetic properties and therapeutic efficacy. Because mAbs hold a central position in therapeutic strategies against an array of diseases, the importance of conducting comprehensive immunogenicity risk assessment during the drug development process cannot be overstated. Such assessment necessitates the employment of in silico, in vitro, and in vivo strategies to evaluate the immunogenicity risk of mAbs. Understanding the intricacies of the mechanisms that drive mAb immunogenicity is crucial to improving their therapeutic efficacy and safety and developing the most effective strategies to determine and mitigate their immunogenic risk. This review highlights recent advances in immunogenicity prediction strategies, with a focus on protein engineering strategies used throughout development to reduce immunogenicity.

177Lu-Labeled Iron Oxide Nanoparticles Functionalized with Doxorubicin and Bevacizumab as Nanobrachytherapy Agents against Breast Cancer

The use of conventional methods for the treatment of cancer, such as chemotherapy or radiotherapy, and approaches such as brachytherapy in conjunction with the unique properties of nanoparticles could enable the development of novel theranostic agents. The aim of our current study was to evaluate the potential of iron oxide nanoparticles, coated with alginic acid and polyethylene glycol, functionalized with the chemotherapeutic agent doxorubicin and the monoclonal antibody bevacizumab, to serve as a nanoradiopharmaceutical agent against breast cancer. Direct radiolabeling with the therapeutic isotope Lutetium-177 (177Lu) resulted in an additional therapeutic effect. Functionalization was accomplished at high percentages and radiolabeling was robust. The high cytotoxic effect of our radiolabeled and non-radiolabeled nanostructures was proven in vitro against five different breast cancer cell lines. The ex vivo biodistribution in tumor-bearing mice was investigated with three different ways of administration. The intratumoral administration of our functionalized radionanoconjugates showed high tumor accumulation and retention at the tumor site. Finally, our therapeutic efficacy study performed over a 50-day period against an aggressive triple-negative breast cancer cell line (4T1) demonstrated enhanced tumor growth retention, thus identifying the developed nanoparticles as a promising nanobrachytherapy agent against breast cancer.

i-shaped antibody engineering enables conformational tuning of biotherapeutic receptor agonists

The ability to leverage antibodies to agonize disease relevant biological pathways has tremendous potential for clinical investigation. Yet while antibodies have been successful as antagonists, immune mediators, and targeting agents, they are not readily effective at recapitulating the biology of natural ligands. Among the important determinants of antibody agonist activity is the geometry of target receptor engagement. Here, we describe an engineering approach inspired by a naturally occurring Fab-Fab homotypic interaction that constrains IgG in a unique i-shaped conformation. i-shaped antibody (iAb) engineering enables potent intrinsic agonism of five tumor necrosis factor receptor superfamily (TNFRSF) targets. When applied to bispecific antibodies against the heterodimeric IL-2 receptor pair, constrained bispecific IgG formats recapitulate IL-2 agonist activity. iAb engineering provides a tool to tune agonist antibody function and this work provides a framework for the development of intrinsic antibody agonists with the potential for generalization across broad receptor classes.

Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling

In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules.

Variable domain mutational analysis to probe the molecular mechanisms of high viscosity of an IgG1 antibody

Subcutaneous injection is the preferred route of administration for many antibody therapeutics for reasons that include its speed and convenience. However, the small volume limit (typically ≤ 2 mL) for subcutaneous delivery often necessitates antibody formulations at high concentrations (commonly ≥100 mg/mL), which may lead to physicochemical problems. For example, antibodies with large hydrophobic or charged patches can be prone to self-interaction giving rise to high viscosity. Here, we combined X-ray crystallography with computational modeling to predict regions of an anti-glucagon receptor (GCGR) IgG1 antibody prone to self-interaction. An extensive mutational analysis was undertaken of the complementarity-determining region residues residing in hydrophobic surface patches predicted by spatial aggregation propensity, in conjunction with residue-level solvent accessibility, averaged over conformational ensembles from molecular dynamics simulations. Dynamic light scattering (DLS) was used as a medium throughput screen for self-interaction of ~ 200 anti-GCGR IgG1 variants. A negative correlation was found between the viscosity determined at high concentration (180 mg/mL) and the DLS interaction parameter measured at low concentration (2-10 mg/mL). Additionally, anti-GCGR variants were readily identified with reduced viscosity and antigen-binding affinity within a few fold of the parent antibody, with no identified impact on overall developability. The methods described here may be useful in the optimization of other antibodies to facilitate their therapeutic administration at high concentration.

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.

The origins of nonideality exhibited by monoclonal antibodies and Fab fragments in human serum

The development of therapeutic antibodies remains challenging, time-consuming, and expensive. A key contributing factor is a lack of understanding of how proteins are affected by complex biological environments such as serum and plasma. Nonideality due to attractive or repulsive interactions with cosolutes can alter the stability, aggregation propensity, and binding interactions of proteins in solution. Fluorescence correlation spectroscopy (FCS) can be used to measure apparent second virial coefficient (B2,app ) values for therapeutic and model monoclonal antibodies (mAbs) that capture the nature and strength of interactions with cosolutes directly in undiluted serum and similar complex biological media. Here, we use FCS-derived B2,app measurements to identify the components of human serum responsible for nonideal interactions with mAbs and Fab fragments. Most mAbs exhibit neutral or slightly attractive interactions with intact serum. Generally, mAbs display repulsive interactions with albumin and mildly attractive interactions with IgGs in the context of whole serum. Crucially, however, these attractive interactions are much stronger with pooled IgGs isolated from other serum components, indicating that the effects of serum nonideality can only be understood by studying the intact medium (rather than isolated components). Moreover, Fab fragments universally exhibited more attractive interactions than their parental mAbs, potentially rendering them more susceptible to nonideality-driven perturbations. FCS-based B2,app measurements have the potential to advance our understanding of how physiological environments impact protein-based therapeutics in general. Furthermore, incorporating such assays into preclinical biologics development may help de-risk molecules and make for a faster and more efficient development process.

Utilizing PK and PD Biomarkers to Guide the First-in-Human Starting Dose Selection of MTBT1466A: A Novel Humanized Monoclonal Anti-TGFβ3 Antibody for the Treatment of Fibrotic Diseases

MTBT1466A is a high-affinity TGFβ3-specific humanized IgG1 monoclonal antibody with reduced Fc effector function, currently under investigation in clinical trials as a potential anti-fibrotic therapy. Here, we characterized the pharmacokinetics (PK) and pharmacodynamics (PD) of MTBT1466A in mice and monkeys and predicted the PK/PD of MTBT1466A in humans to guide the selection of the first-in-human (FIH) starting dose. MTBT1466A demonstrated a typical IgG1-like biphasic PK profile in monkeys, and the predicted human clearance of 2.69 mL/day/kg and t1/2 of 20.4 days are consistent with those expected for a human IgG1 antibody. In a mouse model of bleomycin-induced lung fibrosis, changes in expression of TGFβ3-related genes, serpine1, fibronectin-1, and collagen 1A1 were used as PD biomarkers to determine the minimum pharmacologically active dose of 1 mg/kg. Unlike in the fibrosis mouse model, evidence of target engagement in healthy monkeys was only observed at higher doses. Using a PKPD-guided approach, the recommended FIH dose of 50 mg, IV, provided exposures that were shown to be safe and well tolerated in healthy volunteers. MTBT1466A PK in healthy volunteers was predicted reasonably well using a PK model with allometric scaling of PK parameters from monkey data. Taken together, this work provides insights into the PK/PD behavior of MTBT1466A in preclinical species, and supports the translatability of the preclinical data into the clinic.

Defining and Studying B Cell Receptor and TCR Interactions

BCRs (Abs) and TCRs (or adaptive immune receptors [AIRs]) are the means by which the adaptive immune system recognizes foreign and self-antigens, playing an integral part in host defense, as well as the emergence of autoimmunity. Importantly, the interaction between AIRs and their cognate Ags defies a simple key-in-lock paradigm and is instead a complex many-to-many mapping between an individual's massively diverse AIR repertoire, and a similarly diverse antigenic space. Understanding how adaptive immunity balances specificity with epitopic coverage is a key challenge for the field, and terms such as broad specificity, cross-reactivity, and polyreactivity remain ill-defined and are used inconsistently. In this Immunology Notes and Resources article, a group of experimental, structural, and computational immunologists define commonly used terms associated with AIR binding, describe methodologies to study these binding modes, as well as highlight the implications of these different binding modes for therapeutic design.

A Proposed Global Medicines Agency (GMA) to Make Biological Drugs Accessible: Starting with the League of Arab States

Medical anthropology teaches us of historical disparity in the accessibility of medicines in the developing world due to their lack of availability and affordability, more particularly of biological drugs, including therapeutic proteins, gene therapy, CRISPR-Cas9, mRNA therapeutics, CART therapy, and many more. This challenge can be resolved by establishing an independent regulatory agency, proposed as the Global Medicines Agency (GMA), with a charter to allow originators from the Stringent Regulatory Agency (SRA) countries to receive immediate registrations applicable to all member states, expanding the market potential as an incentive. For non-SRA countries, it will be limited to biological drugs that are allowed their copies to be made, only biosimilars. A transparent approval process will involve using a rapporteur, a third-party product-related current Good Manufacturing Practice (cGMP), and assurance of the integrity of samples tested for analytical similarity and clinical pharmacology testing. GMA membership will be open to all countries. Still, it is suggested that the League of Arab States, representing 22 states with a population of 400 million, takes the lead due to their cultural and language homogeneity, which is likely to provide a concurrence among the member states. However, some states, like the Gulf Cooperative Council, are already accustomed to this approach, albeit with a different perspective. The target drugs are biotechnology and gene therapy pharmaceuticals, and their scope can be expanded to any drug.

A specialized integrin-binding motif enables proTGF-β2 activation by integrin αVβ6 but not αVβ8

Activation of latent transforming growth factor (TGF)-β2 is incompletely understood. Unlike TGF-β1 and β3, the TGF-β2 prodomain lacks a seven-residue RGDLXX (L/I) integrin-recognition motif and is thought not to be activated by integrins. Here, we report the surprising finding that TGF-β2 contains a related but divergent 13-residue integrin-recognition motif (YTSGDQKTIKSTR) that specializes it for activation by integrin αVβ6 but not αVβ8. Both classes of motifs compete for the same binding site in αVβ6. Multiple changes in the longer motif underlie its specificity. ProTGF-β2 structures define interesting differences from proTGF-β1 and the structural context for activation by αVβ6. Some integrin-independent activation is also seen for proTGF-β2 and even more so for proTGF-β3. Our findings have important implications for therapeutics to αVβ6 in clinical trials for fibrosis, in which inhibition of TGF-β2 activation has not been anticipated.

Physiologic Changes During Pregnancy and Impact on Small-Molecule Drugs, Biologic (Monoclonal Antibody) Disposition, and Response

Pregnancy is a unique physiological state that results in many changes in bodily function, including cellular, metabolic, and hormonal changes. These changes can have a significant impact on the way small-molecule drugs and monoclonal antibodies (biologics) function and are metabolized, including efficacy, safety, potency, and adverse effects. In this article, we review the various physiologic changes that occur during pregnancy and their effects on drug and biologic metabolism, including changes in the coagulation, gastrointestinal, renal, endocrine, hepatic, respiratory, and cardiovascular systems. Additionally, we discuss how these changes can affect the processes of drug and biologic absorption, distribution, metabolism, and elimination (pharmacokinetics), and how drugs and biologics interact with biological systems, including mechanisms of drug action and effect (pharmacodynamics) during pregnancy, as well as the potential for drug-induced toxicity and adverse effects in the mother and developing fetus. The article also examines the implications of these changes for the use of drugs and biologics during pregnancy, including consequences of suboptimal plasma drug concentrations, effect of pregnancy on the pharmacokinetics and pharmacodynamics of biologics, and the need for careful monitoring and individualized drug dosing. Overall, this article aims to provide a comprehensive understanding of the physiologic changes during pregnancy and their effects on drug and biologic metabolism to improve the safe and effective use of drugs.

Evolving strategies and application of proteins and peptide therapeutics in cancer treatment

Several proteins and peptides have therapeutic potential and can be used for cancer therapy. By binding to cell surface receptors and other indicators uniquely linked with or overexpressed on tumors compared to healthy tissue, protein biologics enhance the active targeting of cancer cells, as opposed to the passive targeting of cells by conventional small-molecule chemotherapeutics. This study focuses on peptide medications that exist to slow or stop tumor growth and the spread of cancer, demonstrating the therapeutic potential of peptides in cancer treatment. As an alternative to standard chemotherapy, peptides that selectively kill cancer cells while sparing healthy tissue are developing. A mountain of clinical evidence supports the efficacy of peptide-based cancer vaccines. Since a single treatment technique may not be sufficient to produce favourable results in the fight against cancer, combination therapy is emerging as an effective option to generate synergistic benefits. One example of this new area is the use of anticancer peptides in combination with nonpeptidic cytotoxic drugs or the combination of immunotherapy with conventional therapies like radiation and chemotherapy. This review focuses on the different natural and synthetic peptides obtained and researched. Discoveries, manufacture, and modifications of peptide drugs, as well as their contemporary applications, are summarized in this review. We also discuss the benefits and difficulties of potential advances in therapeutic peptides.

Physiologically Based Pharmacokinetic Modeling to Characterize the Effect of Molecular Charge on Whole-Body Disposition of Monoclonal Antibodies

Motivated by a series of work demonstrating the effect of molecular charge on antibody pharmacokinetics (PK), physiological-based pharmacokinetic (PBPK) models are emerging that relate in silico calculated charge or in vitro measures of polyspecificity to antibody PK parameters. However, only plasma data has been used for model development in these studies, leading to unvalidated assumptions. Here, we present an extended platform PBPK model for antibodies that incorporate charge-dependent endothelial cell pinocytosis rate and nonspecific off-target binding in the interstitial space and on circulating blood cells, to simultaneously characterize whole-body disposition of three antibody charge variants. Predictive potential of various charge metrics was also explored, and the difference between positive charge patches and negative charge patches (i.e., PPC-PNC) was used as the charge parameter to establish quantitative relationships with nonspecific binding affinities and endothelial cell uptake rate. Whole-body disposition of these charge variants was captured well by the model, with less than 2-fold predictive error in area under the curve of most plasma and tissue PK data. The model also predicted that with greater positive charge, nonspecific binding was more substantial, and pinocytosis rate increased especially in brain, heart, kidney, liver, lung, and spleen, but remained unchanged in adipose, bone, muscle, and skin. The presented PBPK model contributes to our understanding of the mechanisms governing the disposition of charged antibodies and can be used as a platform to guide charge engineering based on desired plasma and tissue exposures.

Complex PK-PD of an engineered IL-15/IL-15Rα-Fc fusion protein in cynomolgus monkeys: QSP modeling of lymphocyte dynamics

XmAb24306 is a lymphoproliferative interleukin (IL)-15/IL-15 receptor α (IL-15Rα) Fc-fusion protein currently under clinical investigation as an immunotherapeutic agent for cancer treatment. XmAb24306 contains mutations in IL-15 that attenuate its affinity to the heterodimeric IL-15 receptor βγ (IL-15R). We observe substantially prolonged pharmacokinetics (PK) (half-life ∼ 2.5 to 4.5 days) in single- and repeat-dose cynomolgus monkey (cyno) studies compared to wild-type IL-15 (half-life ∼ 1 hour), leading to increased exposure and enhanced and durable expansion of NK cells, CD8+ T cells and CD4-CD8- (double negative [DN]) T cells. Drug clearance varied with dose level and time post-dose, and PK exposure decreased upon repeated dosing, which we attribute to increased target-mediated drug disposition (TMDD) resulting from drug-induced lymphocyte expansion (i.e., pharmacodynamic (PD)-enhanced TMDD). We developed a quantitative systems pharmacology (QSP) model to quantify the complex PKPD behaviors due to the interactions of XmAb24306 with multiple cell types (CD8+, CD4+, DN T cells, and NK cells) in the peripheral blood (PB) and lymphoid tissues. The model, which includes nonspecific drug clearance, binding to and TMDD by IL15R differentially expressed on lymphocyte subsets, and resultant lymphocyte margination/migration out of PB, expansion in lymphoid tissues, and redistribution to the blood, successfully describes the systemic PK and lymphocyte kinetics observed in the cyno studies. Results suggest that after 3 doses of every-two-week (Q2W) doses up to 70 days, the relative contributions of each elimination pathway to XmAb24306 clearance are: DN T cells > NK cells > CD8+ T cells > nonspecific clearance > CD4+ T cells. Modeling suggests that observed cellular expansion in blood results from the influx of cells expanded by the drug in lymphoid tissues. The model is used to predict lymphoid tissue expansion and to simulate PK-PD for different dose regimens. Thus, the model provides insight into the mechanisms underlying the observed PK-PD behavior of an engineered cytokine and can serve as a framework for the rapid integration and analysis of data that emerges from ongoing clinical studies in cancer patients as single-agent or given in combination.

Intravital Microscopy Reveals Unforeseen Biodistribution Within the Liver and Kidney Mechanistically Connected to the Clearance of a Bifunctional Antibody

Bifunctional antibody (BfAb) therapeutics offer the potential for novel functionalities beyond those of the individual monospecific entities. However, combining these entities into a single molecule can have unpredictable effects, including changes in pharmacokinetics that limit the compound's therapeutic profile. A better understanding of how molecular modifications affect in vivo tissue interactions could help inform BfAb design. The present studies were predicated on the observation that a BfAb designed to have minimal off-target interactions cleared from the circulation twice as fast as the monoclonal antibody (mAb) from which it was derived. The present study leverages the spatial and temporal resolution of intravital microscopy (IVM) to identify cellular interactions that may explain the different pharmacokinetics of the two compounds. Disposition studies of mice demonstrated that radiolabeled compounds distributed similarly over the first 24 hours, except that BfAb accumulated approximately two- to -three times more than mAb in the liver. IVM studies of mice demonstrated that both distributed to endosomes of liver endothelia but with different kinetics. Whereas mAb accumulated rapidly within the first hour of administration, BfAb accumulated only modestly during the first hour but continued to accumulate over 24 hours, ultimately reaching levels similar to those of the mAb. Although neither compound was freely filtered by the mouse or rat kidney, BfAb, but not mAb, was found to accumulate over 24 hours in endosomes of proximal tubule cells. These studies demonstrate how IVM can be used as a tool in drug design, revealing unpredicted cellular interactions that are undetectable by conventional analyses. SIGNIFICANCE STATEMENT: Bifunctional antibodies offer novel therapeutic functionalities beyond those of the individual monospecific entities. However, combining these entities into a single molecule can have unpredictable effects, including undesirable changes in pharmacokinetics. Studies of the dynamic distribution of a bifunctional antibody and its parent monoclonal antibody presented here demonstrate how intravital microscopy can expand our understanding of the in vivo disposition of therapeutics, detecting off-target interactions that could not be detected by conventional pharmacokinetics approaches or predicted by conventional physicochemical analyses.

Protein Stability After Administration: A Physiologic Consideration

Regulatory authorities and the scientific community have identified the need to monitor the in vivo stability of therapeutic proteins (TPs). Due to the unique physiologic conditions in patients, the stability of TPs after administration can deviate largely from their stability under drug product (DP) conditions. TPs can degrade at substantial rates once immersed in the in vivo milieu. Changes in protein stability upon administration to patients are critical as they can have implications on patient safety and clinical effectiveness of DPs. Physiologic conditions are challenging to simulate and require dedicated in vitro models for specific routes of administration. Advancements of in vitro models enable to simulate the exposure to physiologic conditions prior to resource demanding pre-clinical and clinical studies. This enables to evaluate the in vivo stability and thus may allow to improve the safety/efficacy profile of DPs. While in vitro-in vivo correlations are challenging, benchmarking DP candidates enables to identify liabilities and optimize molecules. The in vivo stability should be an integral part of holistic stability assessments during early development. Such assessments can accelerate development timelines and lead to more stable DPs for patients.

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.

Preferential precipitation of acidic variants from monoclonal antibody pools

Microheterogeneity of monoclonal antibodies (mAbs) can impact their activity and stability. Formation of charge variants is considered as the most important source of the microheterogeneity. In particular, controlling the content of the acidic species is often of major importance for the production process and regulatory approval of therapeutic proteins. In this study, the preferential precipitation process was developed for reducing the content of acidic variants in mAb downstream pools. The process design was preceded by the determination of phase behavior of mAb variants in the presence of different precipitants. It was shown that the presence of polyethylene glycol (PEG) in protein solutions favored precipitation of acidic variants of mAbs. Precipitation yield was influenced by the variant composition in the mAb feed solutions, the concentration of the precipitant and the protein, and the ionic strength of the solutions. To improve yield, multistage precipitation was employed, where the precipitate was recycled to the precipitation process. The final product was a mixture of supernatants pooled together from the recycling steps. Such an approach can be potentially used either instead or in a combination with chromatography for adjusting the acidic variant content of mAbs, which can benefit in improvement in throughput and reduction in manufacturing costs.

SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters

To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity, and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.

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.

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies

Biologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration-time curve (AUC0-672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL.

Translational Approach for Predicting Human Pharmacokinetics of Engineered Therapeutic Monoclonal Antibodies with Increased FcRn-Binding Mutations

INTRODUCTION

Recently, increasing FcRn binding by Fc engineering has become a promising approach for prolonging the half-life of therapeutic monoclonal antibodies (mAbs). This study is the first to investigate the optimization of an allometric scaling approach for engineered mAbs based on cynomolgus monkey data to predict human pharmacokinetics.

METHODS

Linear two-compartmental model parameters (clearance [CL]; volume of distribution in the central compartment [V_c]; inter-compartmental clearance [Q]; volume of distribution in the peripheral compartment [V_p]) after the intravenous (IV) injection of engineered mAbs (M252Y/S254T/T256E or M428L/N434S mutations) in cynomolgus monkeys and humans were collected from published data. We explored the optimal exponent for allometric scaling to predict parameters in humans based on cynomolgus monkey data. Moreover, the plasma concentration-time profile of engineered mAbs after IV injection in humans was predicted using parameters estimated based on an optimized exponent.

RESULTS

For engineered mAbs, a significant positive correlation between cynomolgus monkeys and humans was observed for CL, but not for other parameters. Whereas conventional exponents (CL: 0.8, Q: 0.75, V_c: 1.0, V_p: 0.95) previously established for normal mAbs showed poor prediction accuracy for CL and Q of engineered mAbs, the newly optimized exponents (CL: 0.55, Q: 0.6, V_c: 0.95, V_p: 0.95) achieved superior predictability for engineered mAbs. Moreover, the optimized exponents accurately predicted plasma mAb concentration-time profiles after IV injection of engineered mAbs in humans.

CONCLUSIONS

We found that engineered mAbs require specially optimized exponents to accurately predict pharmacokinetic parameters and plasma concentration-time profiles after IV injections in humans based on cynomolgus monkey data. This optimized approach can contribute to a more accurate prediction of human pharmacokinetics in the development of engineered mAbs.

Utility of physiologically based pharmacokinetic modeling to predict inter-antibody variability in monoclonal antibody pharmacokinetics in mice

In this investigation, we tested the hypothesis that a physiologically based pharmacokinetic (PBPK) model incorporating measured in vitro metrics of off-target binding can largely explain the inter-antibody variability in monoclonal antibody (mAb) pharmacokinetics (PK). A diverse panel of 83 mAbs was evaluated for PK in wild-type mice and subjected to 10 in vitro assays to measure major physiochemical attributes. After excluding for target-mediated elimination and immunogenicity, 56 of the remaining mAbs with an eight-fold variability in the area under the curve (A U C 0 - 672 h : 1.74 × 106 -1.38 × 107 ng∙h/mL) and 10-fold difference in clearance (2.55-26.4 mL/day/kg) formed the training set for this investigation. Using a PBPK framework, mAb-dependent coefficients F1 and F2 modulating pinocytosis rate and convective transport, respectively, were estimated for each mAb with mostly good precision (coefficient of variation (CV%) <30%). F1 was estimated to be the mean and standard deviation of 0.961 ± 0.593, and F2 was estimated to be 2.13 ± 2.62. Using principal component analysis to correlate the regressed values of F1/F2 versus the multidimensional dataset composed of our panel of in vitro assays, we found that heparin chromatography retention time emerged as the predictive covariate to the mAb-specific F1, whereas F2 variability cannot be well explained by these assays. A sigmoidal relationship between F1 and the identified covariate was incorporated within the PBPK framework. A sensitivity analysis suggested plasma concentrations to be most sensitive to F1 when F1 > 1. The predictive utility of the developed PBPK model was evaluated against a separate panel of 14 mAbs biased toward high clearance, among which area under the curve of PK data of 12 mAbs was predicted within 2.5-fold error, and the positive and negative predictive values for clearance prediction were 85% and 100%, respectively. MAb heparin chromatography assay output allowed a priori identification of mAb candidates with unfavorable PK.

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.

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.

An in silico method to assess antibody fragment polyreactivity

Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naïve synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally test our models' performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the models allow us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its functional properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.

Prediction of polyreactive and nonspecific single-chain fragment variables through structural biochemical features and protein language-based descriptors

BACKGROUND

Monoclonal antibodies (mAbs) have been used as therapeutic agents, which must overcome many developability issues after the discovery from in vitro display libraries. Especially, polyreactive mAbs can strongly bind to a specific target and weakly bind to off-target proteins, which leads to poor antibody pharmacokinetics in clinical development. Although early assessment of polyreactive mAbs is important in the early discovery stage, experimental assessments are usually time-consuming and expensive. Therefore, computational approaches for predicting the polyreactivity of single-chain fragment variables (scFvs) in the early discovery stage would be promising for reducing experimental efforts.

RESULTS

Here, we made prediction models for the polyreactivity of scFvs with the known polyreactive antibody features and natural language model descriptors. We predicted 19,426 protein structures of scFvs with trRosetta to calculate the polyreactive antibody features and investigated the classifying performance of each factor for polyreactivity. In the known polyreactive features, the net charge of the CDR2 loop, the tryptophan and glycine residues in CDR-H3, and the lengths of the CDR1 and CDR2 loops, importantly contributed to the performance of the models. Additionally, the hydrodynamic features, such as partial specific volume, gyration radius, and isoelectric points of CDR loops and scFvs, were newly added to improve model performance. Finally, we made the prediction model with a robust performance ([Formula: see text]) with an ensemble learning of the top 3 best models.

CONCLUSION

The prediction models for polyreactivity would help assess polyreactive scFvs in the early discovery stage and our approaches would be promising to develop machine learning models with quantitative data from high throughput assays for antibody screening.

Non-specificity as the sticky problem in therapeutic antibody development

Antibodies are highly potent therapeutic scaffolds with more than a hundred different products approved on the market. Successful development of antibody-based drugs requires a trade-off between high target specificity and target binding affinity. In order to better understand this problem, we here review non-specific interactions and explore their fundamental physicochemical origins. We discuss the role of surface patches - clusters of surface-exposed amino acid residues with similar physicochemical properties - as inducers of non-specific interactions. These patches collectively drive interactions including dipole-dipole, π-stacking and hydrophobic interactions to complementary moieties. We elucidate links between these supramolecular assembly processes and macroscopic development issues, such as decreased physical stability and poor in vivo half-life. Finally, we highlight challenges and opportunities for optimizing protein binding specificity and minimizing non-specificity for future generations of therapeutics.

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.

The Fab region of IgG impairs the internalization pathway of FcRn upon Fc engagement

Binding to the neonatal Fc receptor (FcRn) extends serum half-life of IgG, and antagonizing this interaction is a promising therapeutic approach in IgG-mediated autoimmune diseases. Fc-MST-HN, designed for enhanced FcRn binding capacity, has not been evaluated in the context of a full-length antibody, and the structural properties of the attached Fab regions might affect the FcRn-mediated intracellular trafficking pathway. Here we present a comprehensive comparative analysis of the IgG salvage pathway between two full-size IgG1 variants, containing wild type and MST-HN Fc fragments, and their Fc-only counterparts. We find no evidence of Fab-regions affecting FcRn binding in cell-free assays, however, cellular assays show impaired binding of full-size IgG to FcRn, which translates into improved intracellular FcRn occupancy and intracellular accumulation of Fc-MST-HN compared to full size IgG1-MST-HN. The crystal structure of Fc-MST-HN in complex with FcRn provides a plausible explanation why the Fab disrupts the interaction only in the context of membrane-associated FcRn. Importantly, we find that Fc-MST-HN outperforms full-size IgG1-MST-HN in reducing IgG levels in cynomolgus monkeys. Collectively, our findings identify the cellular membrane context as a critical factor in FcRn biology and therapeutic targeting.

A naturally arising broad and potent CD4-binding site antibody with low somatic mutation

The induction of broadly neutralizing antibodies (bNAbs) is a potential strategy for a vaccine against HIV-1. However, most bNAbs exhibit features such as unusually high somatic hypermutation, including insertions and deletions, which make their induction challenging. VRC01-class bNAbs not only exhibit extraordinary breadth and potency but also rank among the most highly somatically mutated bNAbs. Here, we describe a VRC01-class antibody isolated from a viremic controller, BG24, that is much less mutated than most relatives of its class while achieving comparable breadth and potency. A 3.8-Å x-ray crystal structure of a BG24-BG505 Env trimer complex revealed conserved contacts at the gp120 interface characteristic of the VRC01-class Abs, despite lacking common CDR3 sequence motifs. The existence of moderately mutated CD4-binding site (CD4bs) bNAbs such as BG24 provides a simpler blueprint for CD4bs antibody induction by a vaccine, raising the prospect that such an induction might be feasible with a germline-targeting approach.

Learning what not to select for in antibody drug discovery

Identifying antibodies with high affinity and target specificity is crucial for drug discovery and development; however, filtering out antibody candidates with nonspecific or polyspecific binding profiles is also important. In this issue of Cell Reports Methods, Saksena et al. report a computational counterselection method combining deep sequencing and machine learning for identifying nonspecific antibody candidates and demonstrate that it has advantages over more established molecular counterselection methods.

Ultra-high-diversity factorizable libraries for efficient therapeutic discovery

The successful discovery of novel biological therapeutics by selection requires highly diverse libraries of candidate sequences that contain a high proportion of desirable candidates. Here we propose the use of computationally designed factorizable libraries made of concatenated segment libraries as a method of creating large libraries that meet an objective function at low cost. We show that factorizable libraries can be designed efficiently by representing objective functions that describe sequence optimality as an inner product of feature vectors, which we use to design an optimization method we call stochastically annealed product spaces (SAPS). We then use this approach to design diverse and efficient libraries of antibody CDR-H3 sequences with various optimized characteristics.

Nonclinical Pharmacokinetics and Pharmacodynamics Characterization of Anti-CD79b/CD3 T Cell-Dependent Bispecific Antibody Using a Surrogate Molecule: A Potential Therapeutic Agent for B Cell Malignancies

The T cell-dependent bispecific (TDB) antibody, anti-CD79b/CD3, targets CD79b and CD3 cell-surface receptors expressed on B cells and T cells, respectively. Since the anti-CD79b arm of this TDB binds only to human CD79b, a surrogate TDB that binds to cynomolgus monkey CD79b (cyCD79b) was used for preclinical characterization. To evaluate the impact of CD3 binding affinity on the TDB pharmacokinetics (PK), we utilized non-tumor-targeting bispecific anti-gD/CD3 antibodies composed of a low/high CD3 affinity arm along with a monospecific anti-gD arm as controls in monkeys and mice. An integrated PKPD model was developed to characterize PK and pharmacodynamics (PD). This study revealed the impact of CD3 binding affinity on anti-cyCD79b/CD3 PK. The surrogate anti-cyCD79b/CD3 TDB was highly effective in killing CD79b-expressing B cells and exhibited nonlinear PK in monkeys, consistent with target-mediated clearance. A dose-dependent decrease in B cell counts in peripheral blood was observed, as expected. Modeling indicated that anti-cyCD79b/CD3 TDB’s rapid and target-mediated clearance may be attributed to faster internalization of CD79b, in addition to enhanced CD3 binding. The model yielded unbiased and precise curve fits. These findings highlight the complex interaction between TDBs and their targets and may be applicable to the development of other biotherapeutics.

Recent Advances in Translational Pharmacokinetics and Pharmacodynamics Prediction of Therapeutic Antibodies Using Modeling and Simulation

Therapeutic monoclonal antibodies (mAbs) have been a promising therapeutic approach for several diseases and a wide variety of mAbs are being evaluated in clinical trials. To accelerate clinical development and improve the probability of success, pharmacokinetics and pharmacodynamics (PKPD) in humans must be predicted before clinical trials can begin. Traditionally, empirical-approach-based PKPD prediction has been applied for a long time. Recently, modeling and simulation (M&S) methods have also become valuable for quantitatively predicting PKPD in humans. Although several models (e.g., the compartment model, Michaelis–Menten model, target-mediated drug disposition model, and physiologically based pharmacokinetic model) have been established and used to predict the PKPD of mAbs in humans, more complex mechanistic models, such as the quantitative systemics pharmacology model, have been recently developed. This review summarizes the recent advances and future direction of M&S-based approaches to the quantitative prediction of human PKPD for mAbs.

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.

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.

Separating clinical antibodies from repertoire antibodies, a path to in silico developability assessment

Approaches for antibody discovery have seen substantial improvement and success in recent years. Yet, advancing antibodies into the clinic remains difficult because therapeutic developability concerns are challenging to predict. We developed a computational model to simplify antibody developability assessment and enable accelerated early-stage screening. To this end, we quantified the ability of hundreds of sequence- and structure-based descriptors to differentiate clinical antibodies that have undergone rigorous screening and characterization for drug-like properties from antibodies in the human repertoire that are not natively paired. This analysis identified 144 descriptors capable of distinguishing clinical from repertoire antibodies. Five descriptors were selected and combined based on performance and orthogonality into a single model referred to as the Therapeutic Antibody Developability Analysis (TA-DA). On a hold-out test set, this tool separated clinical antibodies from repertoire antibodies with an AUC = 0.8, demonstrating the ability to identify developability attributes unique to clinical antibodies. Based on our results, the TA-DA score may serve as an approach for selecting lead antibodies for further development.

Abbreviations: Affinity-Capture Self-Interaction Nanoparticle Spectroscopy (AC-SINS), Area Under the Curve (AUC), Complementary-Determining Region (CDR), Clinical-Stage Therapeutics (CST), Framework (FR), Monoclonal Antibodies (mAbs), Observed Antibody Space (OAS), Receiver Operating Characteristic (ROC), Size-Exclusion Chromatography (SEC), Structural Aggregation Propensity (SAP), Therapeutic Antibody Developability Analysis (TA-DA), Therapeutic Antibody Profiler (TAP), Therapeutic Structural Antibody Database (Thera-SAbDab), Variable Heavy (VH), Variable Light (VL).

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.

Non-human primates in the PKPD evaluation of biologics: Needs and options to reduce, refine, and replace. A BioSafe White Paper

Monoclonal antibodies (mAbs) deliver great benefits to patients with chronic and/or severe diseases thanks to their strong specificity to the therapeutic target. As a result of this specificity, non-human primates (NHP) are often the only preclinical species in which therapeutic antibodies cross-react with the target. Here, we highlight the value and limitations that NHP studies bring to the design of safe and efficient early clinical trials. Indeed, data generated in NHPs are integrated with in vitro information to predict the concentration/effect relationship in human, and therefore the doses to be tested in first-in-human trials. The similarities and differences in the systems defining the pharmacokinetics and pharmacodynamics (PKPD) of mAbs in NHP and human define the nature and the potential of the preclinical investigations performed in NHPs. Examples have been collated where the use of NHP was either pivotal to the design of the first-in-human trial or, inversely, led to the termination of a project prior to clinical development. The potential impact of immunogenicity on the results generated in NHPs is discussed. Strategies to optimize the use of NHPs for PKPD purposes include the addition of PD endpoints in safety assessment studies and the potential re-use of NHPs after non-terminal studies or cassette dosing several therapeutic agents of interest. Efforts are also made to reduce the use of NHPs in the industry through the use of in vitro systems, alternative in vivo models, and in silico approaches. In the case of prediction of ocular PK, the body of evidence gathered over the last two decades renders the use of NHPs obsolete. Expert perspectives, advantages, and pitfalls with these alternative approaches are shared in this review.

Pharmacokinetic Developability and Disposition Profiles of Bispecific Antibodies: A Case Study with Two Molecules

Bispecific antibodies (BsAb) that engage multiple pathways are a promising therapeutic strategy to improve and prolong the efficacy of biologics in complex diseases. In the early stages of discovery, BsAbs often exhibit a broad range of pharmacokinetic (PK) behavior. Optimization of the neonatal Fc receptor (FcRn) interactions and removal of undesirable physiochemical properties have been used to improve the 'pharmacokinetic developability' for various monoclonal antibody (mAb) therapeutics, yet there is a sparsity of such information for BsAbs. The present work evaluated the influence of FcRn interactions and inherent physiochemical properties on the PK of two related single chain variable fragment (scFv)-based BsAbs. Despite their close relation, the two BsAbs exhibit disparate PK in cynomolgus monkeys with BsAb-1 having an aberrant clearance of ~2 mL/h/kg and BsAb-2 displaying a an ~10-fold slower clearance (~0.2 mL/h/kg). Evaluation of the physiochemical characteristics of the molecules, including charge, non-specific binding, thermal stability, and hydrophobic properties, as well as FcRn interactions showed some differences. In-depth drug disposition results revealed that a substantial disparity in the complete release from FcRn at a neutral pH is a primary factor contributing to the rapid clearance of the BsAb-1 while other biophysical characteristics were largely comparable between molecules.

Novel In Vivo and In Vitro Pharmacokinetic/Pharmacodynamic-Based Human Starting Dose Selection for Glofitamab

We present a novel approach for first-in-human (FIH) dose selection of the CD20xCD3 bispecific antibody, glofitamab, based on pharmacokinetic/pharmacodynamic (PKPD) assessment in cynomolgus monkeys to select a high, safe starting dose, with cytokine release (CR) as the PD endpoint. Glofitamab pharmacokinetics were studied in mice and cynomolgus monkeys; PKPD of IL-6, TNF-α and interferon-γ release following glofitamab, with/without obinutuzumab pretreatment (Gpt) was studied in cynomolgus monkeys. Potency differences for CR between cynomolgus monkeys and humans were determined by glofitamab incubation in whole blood of both species. The PKPD model for CR was translated to humans to project a starting dose that did not induce CR exceeding a clinically-predefined threshold. In cynomolgus monkeys, glofitamab showed a species-specific atypical high clearance, with and without B-cell debulking by Gpt. CR was related to glofitamab serum levels and B-cell counts. B-cell reduction by Gpt led to a marked decrease in CR. FIH starting dose (5 µg) was selected based on IL-6 release considering the markedly higher glofitamab in vitro potency in human vs monkey blood. This is a novel PKPD-based approach for selection of FIH starting dose for a CD20xCD3 bispecific antibody in B-cell lymphoma, evidenced in the glofitamab study, NP30179 (NCT03075696).

Improving antibody drug development using bionanotechnology

Monoclonal antibodies are being used to treat a remarkable breadth of human disorders. Nevertheless, there are several key challenges at the earliest stages of antibody drug development that need to be addressed using simple and widely accessible methods, especially related to generating antibodies against membrane proteins and identifying antibody candidates with drug-like biophysical properties (high solubility and low viscosity). Here we highlight key bionanotechnologies for preparing functional and stable membrane proteins in diverse types of lipoparticles that are being used to improve antibody discovery and engineering efforts. We also highlight key bionanotechnologies for high-throughput and ultra-dilute screening of antibody biophysical properties during antibody discovery and optimization that are being used for identifying antibodies with superior combinations of in vitro (formulation) and in vivo (half-life) properties.

Polyreactivity and polyspecificity in therapeutic antibody development: risk factors for failure in preclinical and clinical development campaigns

Antibody-based drugs, which now represent the dominant biologic therapeutic modality, are used to modulate disparate signaling pathways across diverse disease indications. One fundamental premise that has driven this therapeutic antibody revolution is the belief that each monoclonal antibody exhibits exquisitely specific binding to a single-drug target. Herein, we review emerging evidence in antibody off-target binding and relate current key findings to the risk of failure in therapeutic development. We further summarize the current state of understanding of structural mechanisms underpining the different phenomena that may drive polyreactivity and polyspecificity, and highlight current thinking on how de-risking studies may be best implemented in the screening triage. We conclude with a summary of what we believe to be key observations in the field to date, and a call for the wider antibody research community to work together to build the tools needed to maximize our understanding in this nascent area.

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.

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.