Toward Drug-Like Multispecific Antibodies by Design

RUBY® - a tetravalent (2+2) bispecific antibody format with excellent functionality and IgG-like stability, pharmacology and developability properties

Despite the large number of existing bispecific antibody (bsAb) formats, the generation of novel bsAbs is still associated with development and bioprocessing challenges. Here, we present RUBY, a novel bispecific antibody format that allows rapid generation of bsAbs that fulfill key development criteria. The RUBYTM format has a 2 + 2 geometry, where two Fab fragments are linked via their light chains to the C-termini of an IgG, and carries mutations for optimal chain pairing. The unique design enables generation of bsAbs with mAb-like attributes. Our data demonstrate that RUBY bsAbs are compatible with small-scale production systems for screening purposes and can be produced at high yields (>3 g/L) from stable cell lines. The bsAbs produced are shown to, in general, contain low amounts of aggregates and display favorable solubility and stress endurance profiles. Further, compatibility with various IgG isotypes is shown and tailored Fc gamma receptor binding confirmed. Also, retained interaction with FcRn is demonstrated to translate into a pharmacokinetic profile in mice and non-human primates that is comparable to mAb controls. Functionality of conditional active RUBY bsAbs is confirmed in vitro. Anti-tumor effects in vivo have previously been demonstrated, and shown to be superior to a comparable mAb, and here it is further shown that RUBY bsAbs penetrate and localize to tumor tissue in vivo. In all, the RUBY format has attractive mAb-like attributes and offers the possibility to mitigate many of the development challenges linked to other bsAb formats, facilitating both high functionality and developability.

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.

Next generation of multispecific antibody engineering

Multispecific antibodies recognize two or more epitopes located on the same or distinct targets. This added capability through protein design allows these man-made molecules to address unmet medical needs that are no longer possible with single targeting such as with monoclonal antibodies or cytokines alone. However, the approach to the development of these multispecific molecules has been met with numerous road bumps, which suggests that a new workflow for multispecific molecules is required. The investigation of the molecular basis that mediates the successful assembly of the building blocks into non-native quaternary structures will lead to the writing of a playbook for multispecifics. This is a must do if we are to design workflows that we can control and in turn predict success. Here, we reflect on the current state-of-the-art of therapeutic biologics and look at the building blocks, in terms of proteins, and tools that can be used to build the foundations of such a next-generation workflow.

Fetal Secretory IgA Delivery via Transamniotic Fetal Immunotherapy (TRAFIT) in a Rodent Model

PURPOSE

We sought to determine the feasibility and routing kinetics of transamniotic fetal delivery of secretory immunoglobulin-A (SIgA), in a rodent model.

METHODS

Fetuses (n = 94) from seven time-dated pregnant dams received intra-amniotic injections on gestational day 17 (E17, term = E21-22) of either saline (n = 15) or a solution of 1 mg/mL of ≥95% homogeneous human SIgA (n = 79). Animals were euthanized daily at E18-E21 for quantification of the IgA component by ELISA at gestational membranes, placenta, and select fetal anatomical sites against saline controls procured at term. Statistical analysis was by Mann-Whitney U-test.

RESULTS

None of the saline-injected animals had detectable human IgA. SIgA-injected fetuses showed human IgA in the stomach aspirate, intestinal wall, lungs, liver, and serum at all time points. IgA levels were significantly higher in the gastric aspirate and in the intestine than in all other sites (p < 0.001 for both), with intestinal levels remaining stable through E18-E21 (p = 0.09-0.62 pairwise). Serum and placental levels were consistently low throughout, reaching near zero levels by E21.

CONCLUSIONS

The chronology of exogenous secretory-IgA kinetics after intra-amniotic injection is suggestive of fetal uptake by ingestion, leading to consistent levels in the gastrointestinal tract. Transamniotic fetal immunotherapy (TRAFIT) with secretory-IgA may become a novel strategy for enhancing early mucosal immunity.

LEVEL OF EVIDENCE

N/A (animal and laboratory study).

TYPE OF STUDY

Animal and laboratory study.

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.

Engineering of conserved residues near antibody heavy chain complementary determining region 3 (HCDR3) improves both affinity and stability

Affinity and stability are crucial parameters in antibody development and engineering approaches. Although improvement in both metrics is desirable, trade-offs are almost unavoidable. Heavy chain complementarity determining region 3 (HCDR3) is the best-known region for antibody affinity but its impact on stability is often neglected. Here, we present a mutagenesis study of conserved residues near HCDR3 to elicit the role of this region in the affinity-stability trade-off. These key residues are positioned around the conserved salt bridge between VH-K94 and VH-D101 which is crucial for HCDR3 integrity. We show that the additional salt bridge at the stem of HCDR3 (VH-K94:VH-D101:VH-D102) has an extensive impact on this loop's conformation, therefore simultaneous improvement in both affinity and stability. We find that the disruption of π-π stacking near HCDR3 (VH-Y100E:VL-Y49) at the VH-VL interface cause an irrecoverable loss in stability even if it improves the affinity. Molecular simulations of putative rescue mutants exhibit complex and often non-additive effects. We confirm that our experimental measurements agree with the molecular dynamic simulations providing detailed insights for the spatial orientation of HCDR3. VH-V102 right next to HCDR3 salt bridge might be an ideal candidate to overcome affinity-stability trade-off.

The Dawn of a New Era: Targeting the "Undruggables" with Antibody-Based Therapeutics

The high selectivity and affinity of antibodies toward their antigens have made them a highly valuable tool in disease therapy, diagnosis, and basic research. A plethora of chemical and genetic approaches have been devised to make antibodies accessible to more "undruggable" targets and equipped with new functions of illustrating or regulating biological processes more precisely. In this Review, in addition to introducing how naked antibodies and various antibody conjugates (such as antibody-drug conjugates, antibody-oligonucleotide conjugates, antibody-enzyme conjugates, etc.) work in therapeutic applications, special attention has been paid to how chemistry tools have helped to optimize the therapeutic outcome (i.e., with enhanced efficacy and reduced side effects) or facilitate the multifunctionalization of antibodies, with a focus on emerging fields such as targeted protein degradation, real-time live-cell imaging, catalytic labeling or decaging with spatiotemporal control as well as the engagement of antibodies inside cells. With advances in modern chemistry and biotechnology, well-designed antibodies and their derivatives via size miniaturization or multifunctionalization together with efficient delivery systems have emerged, which have gradually improved our understanding of important biological processes and paved the way to pursue novel targets for potential treatments of various diseases.

Current status and future perspectives of bispecific antibodies in the treatment of lung cancer

ABSTRACT

Monoclonal antibodies have been successfully incorporated into the current therapeutical landscape of lung cancer in the last decades. Recently, with technological advances, bispecific antibodies (bsAbs) have also shown robust efficacy in the treatment of malignant cancers, including lung cancer. These antibodies target two independent epitopes or antigens and have been extensively explored in translational and clinical studies in lung cancer. Here, we outline the mechanisms of action of bsAbs, related clinical data, ongoing clinical trials, and potent novel compounds of various types of bsAbs in clinical studies, especially in lung cancer. We also propose future directions for the clinical development of bsAbs, which might bring a new era of treatment for patients with lung cancer.

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.

Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features

Over the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics has been steadily increasing as evident with FDA's recent landmark approval of the 100th mAb. Unlike mAbs that bind to single targets, multispecific biologics (msAbs) have garnered particular interest owing to the advantage of engaging distinct targets. One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. In recent years, engineering antibody sequences to enhance their stability by mutations has gained considerable momentum. As experimental methods for antibody engineering are time-intensive, laborious and expensive, computational methods serve as a fast and inexpensive alternative to conventional routes. In this work, we show two machine learning approaches - one with pre-trained language models (PTLM) capturing functional effects of sequence variation, and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features - to better classify thermostable scFv variants from sequence. Both of these models are trained over temperature-specific data (TS50 measurements) derived from multiple libraries of scFv sequences. On out-of-distribution (refers to the fact that the out-of-distribution sequnes are blind to the algorithm) sequences, we show that a sufficiently simple CNN model performs better than general pre-trained language models trained on diverse protein sequences (average Spearman correlation coefficient, ρ , of 0.4 as opposed to 0.15). On the other hand, an antibody-specific language model performs comparatively better than the CNN model on the same task (ρ = 0.52). Further, we demonstrate that for an independent mAb with available thermal melting temperatures for 20 experimentally characterized thermostable mutations, these models trained on TS50 data could identify 18 residue positions and 5 identical amino-acid mutations showing remarkable generalizability. Our results suggest that such models can be broadly applicable for improving the biological characteristics of antibodies. Further, transferring such models for alternative physicochemical properties of scFvs can have potential applications in optimizing large-scale production and delivery of mAbs or bsAbs.

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.

Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics

Adequate stability, manufacturability, and safety are crucial to bringing an antibody-based biotherapeutic to the market. Following the concept of holistic in silico developability, we introduce a physicochemical description of 91 market-stage antibody-based biotherapeutics based on orthogonal molecular properties of variable regions (Fvs) embedded in different simulation environments, mimicking conditions experienced by antibodies during manufacturing, formulation, and in vivo. In this work, the evaluation of molecular properties includes conformational flexibility of the Fvs using molecular dynamics (MD) simulations. The comparison between static homology models and simulations shows that MD significantly affects certain molecular descriptors like surface molecular patches. Moreover, the structural stability of a subset of Fv regions is linked to changes in their specific molecular interactions with ions in different experimental conditions. This is supported by the observation of differences in protein melting temperatures upon addition of NaCl. A DEvelopability Navigator In Silico (DENIS) is proposed to compare mAb candidates for their similarity with market-stage biotherapeutics in terms of physicochemical properties and conformational stability. Expanding on our previous developability guidelines (Ahmed et al. Proc. Natl. Acad. Sci. 2021, 118 (37), e2020577118), the hydrodynamic radius and the protein strand ratio are introduced as two additional descriptors that enable a more comprehensive in silico characterization of biotherapeutic drug candidates. Test cases show how this approach can facilitate identification and optimization of intrinsically developable lead candidates. DENIS represents an advanced computational tool to progress biotherapeutic drug candidates from discovery into early development by predicting drug properties in different aqueous environments.

Considerations for design, manufacture, and delivery for effective and safe T-cell engager therapies

T-cell engager (TCE) molecules provide a targeted immunotherapy approach to treat hematologic malignancies and solid tumors. Since the approval of the CD19-targeted BiTE® (bispecific T-cell engager) molecule blinatumomab, multiple TCE molecules against different targets have been developed in several tumor types, with the approval of three additional TCE molecules in 2022. Some of the initial challenges, such as the need for continuous intravenous administration and low productivity, have been addressed in subsequent iterations of the platform by advancing half-life extended, Fc-based molecules. As clinical data from these molecules emerge, additional optimization of formats and manufacturability will be necessary. Ongoing efforts are focused on further improving TCE efficacy, safety, and convenience of administration.

Engineered bispecific antibodies targeting the interleukin-6 and -8 receptors potently inhibit cancer cell migration and tumor metastasis

Simultaneous inhibition of interleukin-6 (IL-6) and interleukin-8 (IL-8) signaling diminishes cancer cell migration, and combination therapy has recently been shown to synergistically reduce metastatic burden in a preclinical model of triple-negative breast cancer. Here, we have engineered two novel bispecific antibodies that target the IL-6 and IL-8 receptors to concurrently block the signaling activity of both ligands. We demonstrate that a first-in-class bispecific antibody design has promising therapeutic potential, with enhanced selectivity and potency compared with monoclonal antibody and small-molecule drug combinations in both cellular and animal models of metastatic triple-negative breast cancer. Mechanistic characterization revealed that our engineered bispecific antibodies have no impact on cell viability, but profoundly reduce the migratory potential of cancer cells; hence they constitute a true anti-metastatic treatment. Moreover, we demonstrate that our antibodies can be readily combined with standard-of-care anti-proliferative drugs to develop effective anti-cancer regimens. Collectively, our work establishes an innovative metastasis-focused direction for cancer drug development.

Advances toward transformative therapies for tendon diseases

Approved therapies for tendon diseases have not yet changed the clinical practice of symptomatic pain treatment and physiotherapy. This review article summarizes advances in the development of novel drugs, biologic products, and biomaterial therapies for tendon diseases with perspectives for translation of integrated therapies. Shifting from targeting symptom relief toward disease modification and prevention of disease progression may open new avenues for therapies. Deep evidence-based clinical, cellular, and molecular characterization of the underlying pathology of tendon diseases, as well as therapeutic delivery optimization and establishment of multidiscipline interorganizational collaboration platforms, may accelerate the discovery and translation of transformative therapies for tendon diseases.

A Review of Methodologies for the Detection, Quantitation, and Localization of Free Cysteine in Recombinant Proteins: A Focus on Therapeutic Monoclonal Antibodies

Free-cysteine residues in recombinant biotherapeutics such as monoclonal antibodies can arise from incorrect cellular processing of disulfide bonds during synthesis or by reduction of disulfide bonds during the harvest and purification stage of manufacture. Free cysteines can affect potency, induce aggregation, and decrease the stability of therapeutic proteins, and the levels and positions of free cysteines in proteins are closely monitored by both manufacturers and regulators to ensure safety and efficacy. This review summarizes the latest methodologies for the detection and quantification of free cysteines.

Agonist antibody discovery: Experimental, computational, and rational engineering approaches

Agonist antibodies that activate cellular signaling have emerged as promising therapeutics for treating myriad pathologies. Unfortunately, the discovery of rare antibodies with the desired agonist functions is a major bottleneck during drug development. Nevertheless, there has been important recent progress in discovering and optimizing agonist antibodies against a variety of therapeutic targets that are activated by diverse signaling mechanisms. Herein, we review emerging high-throughput experimental and computational methods for agonist antibody discovery as well as rational molecular engineering methods for optimizing their agonist activity.

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.

Computational Evolution Protocol for Peptide Design

Computational peptide design is useful for therapeutics, diagnostics, and vaccine development. To select the most promising peptide candidates, the key is describing accurately the peptide-target interactions at the molecular level. We here review a computational peptide design protocol whose key feature is the use of all-atom explicit solvent molecular dynamics for describing the different peptide-target complexes explored during the optimization. We describe the milestones behind the development of this protocol, which is now implemented in an open-source code called PARCE. We provide a basic tutorial to run the code for an antibody fragment design example. Finally, we describe three additional applications of the method to design peptides for different targets, illustrating the broad scope of the proposed approach.

Beyond bispecificity: Controlled Fab arm exchange for the generation of antibodies with multiple specificities

Controlled Fab arm exchange (cFAE) has proven to be a generic and versatile technology for the efficient generation of IgG-like bispecific antibodies (DuoBodies or DBs), with several in clinical development and one product, amivantamab, approved by the Food and Drug Administration. In this study, we expand the cFAE-toolbox by incorporating VHH-modules at the C-termini of DB-IgGs, termed DB-VHHs. This approach enables the combinatorial generation of tri- and tetraspecific molecules with flexible valencies in a straightforward fashion. Using cFAE, a variety of multispecific molecules was produced and assessed for manufacturability and physicochemical characteristics. In addition, we were able to generate DB-VHHs that efficiently triggered natural killer cell mediated lysis of tumor cells, demonstrating the utility of this format for potential therapeutic applications.

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.

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.

Application of Genetic Studies to Flow Cytometry Data and Its Impact on Therapeutic Intervention for Autoimmune Disease

In recent years, systematic genome-wide association studies of quantitative immune cell traits, represented by circulating levels of cell subtypes established by flow cytometry, have revealed numerous association signals, a large fraction of which overlap perfectly with genetic signals associated with autoimmune diseases. By identifying further overlaps with association signals influencing gene expression and cell surface protein levels, it has also been possible, in several cases, to identify causal genes and infer candidate proteins affecting immune cell traits linked to autoimmune disease risk. Overall, these results provide a more detailed picture of how genetic variation affects the human immune system and autoimmune disease risk. They also highlight druggable proteins in the pathogenesis of autoimmune diseases; predict the efficacy and side effects of existing therapies; provide new indications for use for some of them; and optimize the research and development of new, more effective and safer treatments for autoimmune diseases. Here we review the genetic-driven approach that couples systematic multi-parametric flow cytometry with high-resolution genetics and transcriptomics to identify endophenotypes of autoimmune diseases for the development of new therapies.

Re-Envisioning Pharmaceutical Manufacturing: Increasing Agility for Global Patient Access

The traditional paradigm for pharmaceutical manufacturing is focused primarily upon centralized facilities that enable mass production and distribution. While this system reliably maintains high product quality and reproducibility, its rigidity imposes limitations upon new manufacturing innovations that could improve efficiency and support supply chain resiliency. Agile manufacturing methodologies, which leverage flexibility through portability and decentralization, allow manufacturers to respond to patient needs on demand and present a potential solution to enable timely access to critical medicines. Agile approaches are particularly applicable to the production of small-batch, personalized therapies, which must be customized for each individual patient close to the point-of-care. However, despite significant progress in the advancement of agile-enabling technologies across several different industries, there are substantial global regulatory challenges that encumber the adoption of agile manufacturing techniques in the pharmaceutical industry. This review provides an overview of regulatory barriers as well as emerging opportunities to facilitate the use of agile manufacturing for the production of pharmaceutical products. Future-oriented approaches for incorporating agile methodologies within the global regulatory framework are also proposed. Collaboration between regulators and manufacturers to cohesively navigate the regulatory waters is ultimately needed to best serve patients in the rapidly-changing healthcare environment.

Targeting Human Papillomavirus-Associated Cancer by Oncoprotein-Specific Recombinant Antibodies

In recent decades, recombinant antibodies against specific antigens have shown great promise for the therapy of infectious diseases and cancer. Human papillomaviruses (HPVs) are involved in the development of around 5% of all human cancers and HPV16 is the high-risk genotype with the highest prevalence worldwide, playing a dominant role in all HPV-associated cancers. Here, we describe the main biological activities of the HPV16 E6, E7, and E5 oncoproteins, which are involved in the subversion of important regulatory pathways directly associated with all known hallmarks of cancer. We then review the state of art of the recombinant antibodies targeted to HPV oncoproteins developed so far in different formats, and outline their mechanisms of action. We describe the advantages of a possible antibody-based therapy against the HPV-associated lesions and discuss the critical issue of delivery to tumour cells, which must be addressed in order to achieve the desired translation of the antibodies from the laboratory to the clinic.

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.

Taking a BiTE out of the CAR T space race

Chimaeric antigen receptor T-cell (CAR T) therapy has evolved at an exponential pace and seeks to revolutionize the CAR T space with next-generation CARs and expanding indications in plasma cell dyscrasias. Recent developments in Bispecific T-cell engager therapy (BiTEs) may level the playing field with CAR T therapy, offering key advantages with off-the-shelf or on-demand treatment and a manageable toxicity profile to encompass a wider pool of eligible patients in the outpatient setting. The coexistence of both modalities will remain important in overall management and accelerate the next iteration of both cellular and BiTEs. This article summarises the current progress, potential future of both therapies for haematologic malignancies, and their economic implications on the healthcare system.

Combining Unfolding Reversibility Studies and Molecular Dynamics Simulations to Select Aggregation-Resistant Antibodies

The efficient development of new therapeutic antibodies relies on developability assessment with biophysical and computational methods to find molecules with drug-like properties such as resistance to aggregation. Despite the many novel approaches to select well-behaved proteins, antibody aggregation during storage is still challenging to predict. For this reason, there is a high demand for methods that can identify aggregation-resistant antibodies. Here, we show that three straightforward techniques can select the aggregation-resistant antibodies from a dataset with 13 molecules. The ReFOLD assay provided information about the ability of the antibodies to refold to monomers after unfolding with chemical denaturants. Modulated scanning fluorimetry (MSF) yielded the temperatures that start causing irreversible unfolding of the proteins. Aggregation was the main reason for poor unfolding reversibility in both ReFOLD and MSF experiments. We therefore performed temperature ramps in molecular dynamics (MD) simulations to obtain partially unfolded antibody domains in silico and used CamSol to assess their aggregation potential. We compared the information from ReFOLD, MSF, and MD to size-exclusion chromatography (SEC) data that shows whether the antibodies aggregated during storage at 4, 25, and 40 °C. Contrary to the aggregation-prone molecules, the antibodies that were resistant to aggregation during storage at 40 °C shared three common features: (i) higher tendency to refold to monomers after unfolding with chemical denaturants, (ii) higher onset temperature of nonreversible unfolding, and (iii) unfolding of regions containing aggregation-prone sequences at higher temperatures in MD simulations.

Recent Advances in Radiometals for Combined Imaging and Therapy in Cancer

Nuclear medicine is defined as the use of radionuclides for diagnostic and therapeutic applications. The imaging modalities positron emission tomography (PET) and single-photon emission computed tomography (SPECT) are based on γ-emissions of specific energies. The therapeutic technologies are based on β^- -particle-, α-particle-, and Auger electron emitters. In oncology, PET and SPECT are used to detect cancer lesions, to determine dosimetry, and to monitor therapy effectiveness. In contrast, radiotherapy is designed to irreparably damage tumor cells in order to eradicate or control the disease's progression. Radiometals are being explored for the development of diagnostic and therapeutic radiopharmaceuticals. Strategies that combine both modalities (diagnostic and therapeutic), referred to as theranostics, are promising candidates for clinical applications. This review provides an overview of the basic concepts behind therapeutic and diagnostic radiopharmaceuticals and their significance in contemporary oncology. Select radiometals that significantly impact current and upcoming cancer treatment strategies are grouped as clinically suitable theranostics pairs. The most important physical and chemical properties are discussed. Standard production methods and current radionuclide availability are provided to indicate whether a cost-efficient use in a clinical routine is feasible. Recent preclinical and clinical developments and outline perspectives for the radiometals are highlighted in each section.

Recent Advances in the Molecular Design and Applications of Multispecific Biotherapeutics

Recombinant protein-based biotherapeutics drugs have transformed clinical pipelines of the biopharmaceutical industry since the launch of recombinant insulin nearly four decades ago. These biologic drugs are structurally more complex than small molecules, and yet share a similar principle for rational drug discovery and development: That is to start with a pre-defined target and follow with the functional modulation with a therapeutic agent. Despite these tremendous successes, this "one target one drug" paradigm has been challenged by complex disease mechanisms that involve multiple pathways and demand new therapeutic routes. A rapidly evolving wave of multispecific biotherapeutics is coming into focus. These new therapeutic drugs are able to engage two or more protein targets via distinct binding interfaces with or without the chemical conjugation to large or small molecules. They possess the potential to not only address disease intricacy but also exploit new therapeutic mechanisms and assess undruggable targets for conventional monospecific biologics. This review focuses on the recent advances in molecular design and applications of major classes of multispecific biotherapeutics drugs, which include immune cells engagers, antibody-drug conjugates, multispecific tetherbodies, biologic matchmakers, and small-scaffold multispecific modalities. Challenges posed by the multispecific biotherapeutics drugs and their future outlooks are also discussed.