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

Understanding IgM Structure and Biology to Engineer New Antibody Therapeutics

Immunoglobulin M (IgM) antibodies are an essential and conserved part of adaptive immunity. IgMs assemble into pentamers and hexamers that bind to antigens with high avidity. Pentamers incorporate a small protein called J-chain (JC) that is important for their transcytosis via the poly-immunoglobulin receptor (pIgR). IgM antibodies can efficiently activate complement and interact with different Fc receptors (FcμR, Fcα/μR, pIgR) that trigger distinct effector functions and biodistribution. Even if these features have made the clinical use of IgM attractive over the past decades, there are currently no approved therapeutic IgMs on the market. In this review, we summarize the recent advances in the knowledge of IgM biogenesis and structure and discuss the therapeutic opportunities of IgM over IgG arising from high avidity, target clustering, binding to distinct IgM receptors, complement activation, transcytosis, and protein engineering opportunities. In addition, we summarize possibilities and outstanding challenges in the production of therapeutic IgM, including available technologies for IgM purification. Finally, we review recent preclinical and clinical data showing that IgM outperforms IgG in various in vitro assays but still fails to pass through clinical trials successfully. Challenges remain for IgM development, such as the need for a better understanding of IgM biology to facilitate a smoother transition from the preclinic to successful clinical trials.

De Novo Antibody Design with SE(3) Diffusion

We introduce IgDiff, an antibody variable domain diffusion model based on a general protein backbone diffusion framework, which was extended to handle multiple chains. Assessing the designability and novelty of the structures generated with our model, we find that IgDiff produces highly designable antibodies that can contain novel binding regions. The backbone dihedral angles of sampled structures show good agreement with a reference antibody distribution. We verify these designed antibodies experimentally and find that all express with high yield. Finally, we compare our model with a state-of-the-art generative backbone diffusion model on a range of antibody design tasks, such as the design of the complementarity determining regions or the pairing of a light chain to an existing heavy chain, and show improved properties and designability.

Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools

Antibodies play a crucial role in defending the human body against diseases, including life-threatening conditions like cancer. They mediate immune responses against foreign antigens and, in some cases, self-antigens. Over time, antibody-based technologies have evolved from monoclonal antibodies (mAbs) to chimeric antigen receptor T cells (CAR-T cells), significantly impacting biotechnology, diagnostics, and therapeutics. Although these advancements have enhanced therapeutic interventions, the integration of artificial intelligence (AI) is revolutionizing antibody design and optimization. This review explores recent AI advancements, including large language models (LLMs), diffusion models, and generative AI-based applications, which have transformed antibody discovery by accelerating de novo generation, enhancing immune response precision, and optimizing therapeutic efficacy. Through advanced data analysis, AI enables the prediction and design of antibody sequences, 3D structures, complementarity-determining regions (CDRs), paratopes, epitopes, and antigen-antibody interactions. These AI-powered innovations address longstanding challenges in antibody development, significantly improving speed, specificity, and accuracy in therapeutic design. By integrating computational advancements with biomedical applications, AI is driving next-generation cancer therapies, transforming precision medicine, and enhancing patient outcomes.

Intrinsic Differential Scanning Fluorimetry for Protein Stability Assessment in Microwell Plates

Intrinsic differential scanning fluorimetry (DSF) is essential for analyzing protein thermal stability. Until now, intrinsic DSF was characterized by medium throughput and high consumable costs. Here, we present a microplate-based intrinsic DSF approach that enables the measurement of up to 384 samples in parallel by consuming only 10 μL per sample. We systematically test and benchmark the new intrinsic DSF against gold-standard methods such as differential scanning microcalorimetry and circular dichroism. Using a range of model proteins and sample conditions, we demonstrate the robustness and versatility of the intrinsic DSF method for characterizing protein stability and ranking protein drug candidates. In addition, we demonstrate modulated scanning fluorimetry (MSF) capabilities on the intrinsic DSF hardware that enable simultaneous MSF measurements in 384-microwell plates. Overall, the presented technology is a powerful tool for the early stability analysis of various protein samples and drug candidates.

Rationalizing mAb Candidate Screening Using a Single Holistic Developability Parameter

A framework for the rational selection of a minimal suite of nondegenerate developability assays (DAs) that maximize insight into candidate developability or storage stability is lacking. To address this, we subjected nine formulation:mAbs to 12 mechanistically distinct DAs together with measurement of their accelerated and long-term storage stability. We show that it is possible to identify a reduced set of key variables from this suite of DAs by using orthogonal statistical methods. We exemplify our approach by predicting the rank formulation:mAb degradation rate at 25 °C (determined over 6 months) using just five DAs that can be measured in less than 1 day, spanning a range of physicochemical features. Implementing such approaches focuses on resources, thus increasing sustainability and decreasing development costs.

Biophysical Analysis of Therapeutic Antibodies in the Early Development Pipeline

The successful progression of therapeutic antibodies and other biologics from the laboratory to the clinic depends on their possession of "drug-like" biophysical properties. The techniques and the resultant biophysical and biochemical parameters used to characterize their ease of manufacture can be broadly defined as developability. Focusing on antibodies, this review firstly discusses established and emerging biophysical techniques used to probe the early-stage developability of biologics, aimed towards those new to the field. Secondly, we describe the inter-relationships and redundancies amongst developability assays and how in silico methods aid the efficient deployment of developability to bring a new generation of cost-effective therapeutic proteins from bench to bedside more quickly and sustainably.

Accurate and transferable drug-target interaction prediction with DrugLAMP

MOTIVATION

Accurate prediction of drug-target interactions (DTIs), especially for novel targets or drugs, is crucial for accelerating drug discovery. Recent advances in pretrained language models (PLMs) and multi-modal learning present new opportunities to enhance DTI prediction by leveraging vast unlabeled molecular data and integrating complementary information from multiple modalities.

RESULTS

We introduce DrugLAMP (PLM-assisted multi-modal prediction), a PLM-based multi-modal framework for accurate and transferable DTI prediction. DrugLAMP integrates molecular graph and protein sequence features extracted by PLMs and traditional feature extractors. We introduce two novel multi-modal fusion modules: (i) pocket-guided co-attention (PGCA), which uses protein pocket information to guide the attention mechanism on drug features, and (ii) paired multi-modal attention (PMMA), which enables effective cross-modal interactions between drug and protein features. These modules work together to enhance the model's ability to capture complex drug-protein interactions. Moreover, the contrastive compound-protein pre-training (2C2P) module enhances the model's generalization to real-world scenarios by aligning features across modalities and conditions. Comprehensive experiments demonstrate DrugLAMP's state-of-the-art performance on both standard benchmarks and challenging settings simulating real-world drug discovery, where test drugs/targets are unseen during training. Visualizations of attention maps and application to predict cryptic pockets and drug side effects further showcase DrugLAMP's strong interpretability and generalizability. Ablation studies confirm the contributions of the proposed modules.

AVAILABILITY AND IMPLEMENTATION

Source code and datasets are freely available at https://github.com/Lzcstan/DrugLAMP. All data originate from public sources.

Predictive Model Building for Aggregation Kinetics Based on Molecular Dynamics Simulations of an Antibody Fragment

Computational methods including machine learning and molecular dynamics simulations have strong potential to characterize, understand, and ultimately predict the properties of proteins relevant to their stability and function as therapeutics. Such methods would streamline the development pathway by minimizing the current experimental testing required for many protein variants and formulations. The molecular understanding of thermostability and aggregation propensity has advanced significantly along with predictive algorithms based on the sequence-level or structural-level information on a protein. However, these approaches focus largely on a comparison of protein sequence variations to correlate the properties of proteins to their stability, solubility, and aggregation propensity. For therapeutic protein development, it is of equal importance to take into account the impact of the formulation conditions to elucidate and predict the stability of the antibody drugs. At the macroscopic level, changing temperature, pH, ionic strength, and the addition of excipients can significantly alter the kinetics of protein aggregation. The mechanisms controlling aggregation kinetics have been traced back to a combination of molecular features, including conformational stability, partial unfolding to aggregation-prone states, and the colloidal stability governed by surface charges and hydrophobicity. However, very little has been done to evaluate these features in the context of protein dynamics in different formulations. In this work, we have combined a range of molecular features calculated from the Fab A33 protein sequence and molecular dynamics simulations. Using the power of advanced, yet interpretable, statistical tools, it has been possible to uncover greater insights into the mechanisms behind protein stability, validating previous findings, and also develop models that can predict the aggregation kinetics within a range of 49 different solution conditions.

Real-Time Observation of Protein Aggregation at Liquid-Liquid Interfaces in a Microfluidic Device

A droplet microfluidic device to capture in real-time protein aggregation at liquid-liquid interfaces is described. In contrast to conventional methods, typically characterized by a lag time between the application of interfacial stress and the measurement of protein aggregation, here protein adsorption, the formation of a viscoelastic protein layer, aggregation, and shedding of protein particles into solution is simultaneously monitored. The device is applied to analyze the stability of antibody formulations over a wide range of concentrations (1-180 mg mL-1) at the silicone oil (SO)-water interface under controlled mechanical deformation. The adsorption onto oil droplets induces the formation of a viscoelastic protein layer on a subsecond timescale, which progressively restricts the relaxation of the droplets within the chip. Upon mechanical rupture, the protein layer releases particles in solution. The rate of particle formation increases strongly with concentration, similar to the bulk viscosity. Concentrations above 120 mg mL-1 lead to aggregation in seconds and drastically decrease the mechanical perturbations required to shed protein particles in solution. These results are important for the development of formulations at high-protein concentrations (>100 mg mL-1) and indicate that particular attention should be given to interface-induced particle formation in this concentration range. In this context, low-volume microfluidic platforms allow the assessment of protein physical instabilities early in development and represent attractive tools to optimize antibody stability and formulation design consuming limited amounts of material.

Bispecific FpFs: a versatile tool for preclinical antibody development

We previously described FpFs 1̲ (Fab-PEG-Fab) as binding mimetics of IgGs. FpFs are prepared with di(bis-sulfone) conjugation reagents 3̲ that undergo disulfide rebridging conjugation with the accessible disulfide of each Fab (Scheme 1). We have now prepared bispecific FpFs 2̲ (bsFpF and Fab1-PEG-Fab2) as potential bispecific antibody mimetics with the intent that bsFpFs could be used in preclinical antibody development since sourcing bispecific antibodies may be challenging during preclinical research. The di(bis-sulfone) reagent 3̲ was first used to prepare a bsFpF 2̲ by the sequential conjugation of a first Fab and then a second Fab to another target (Scheme 2). Seeking to improve bsFpF synthesis, the asymmetric conjugation reagent, bis-sulfone bis-sulfide 1̲6̲, with different thiol conjugation reactivities at each terminus (Scheme 4) was examined and the bsFpFs appeared to be formed at similar conversion to the di(bis-sulfone) reagent 3̲. To explore the advantages of using common intermediates in the preparation of bsFpF families, we investigated bsFpF synthesis with a protein conjugation-ligation approach (Scheme 5). Reagents with a bis-sulfone moiety for conjugation on one PEG terminus and a ligation moiety on the other terminus were examined. Bis-sulfone PEG trans-cyclooctene (TCO) 2̲8̲ and bis-sulfone PEG tetrazine (Tz) 3̲0̲ were used to prepare several bsFpFs targeting various therapeutic targets (TNF-α, IL6R, IL17, and VEGF) and tissue affinity targets (hyaluronic acid and collagen II). Surface plasmon resonance (SPR) binding studies indicated that there was little difference between the dissociation rate constant (k d) for the unmodified Fab, mono-conjugated PEG-Fab and the corresponding Fab in a bsFpF. The Fab association rate (k a) in the bsFpF was slower than for PEG-Fab, which may be because of mass differences that influence SPR results. These observations suggest that each Fab will bind to its target independently of the other Fab and that bsFpF binding profiles can be estimated using the corresponding PEG-Fab conjugates.

Next-Generation Therapeutic Antibodies for Cancer Treatment: Advancements, Applications, and Challenges

The field of cancer treatment has evolved significantly over the last decade with the emergence of next-generation therapeutic antibodies. Conventional treatments like chemotherapy pose significant challenges, including adverse side effects. Monoclonal antibodies have paved the way for more targeted and effective interventions. The evolution from chimeric to humanized and fully human antibodies has led to a reduction in immunogenicity and enhanced tolerance in vivo. The advent of next-generation antibodies, including bispecific antibodies, nanobodies, antibody-drug conjugates, glyco-engineered antibodies, and antibody fragments, represents a leap forward in cancer therapy. These innovations offer increased potency, adaptability, and reduced drug resistance. Challenges such as target validation, immunogenicity, and high production costs exist. However, technological advancements in antibody engineering techniques provide optimism for addressing these issues. The future promises a paradigm shift, where ongoing research will propel these powerful antibodies to the forefront, revolutionizing the fight against cancer and creating new preventive and curative treatments. This review provides an overview of three next-generation antibody-based molecules, namely bispecific antibodies, antibody-drug conjugates, and nanobodies that have shown promising results in cancer treatment. It discusses the evolution of antibodies from conventional forms to next-generation molecules, along with their applications in cancer treatment, production methods, and associated challenges. The review aims to offer researchers insights into the evolving landscape of next-generation antibody-based cancer therapeutics and their potential to revolutionize treatment strategies.

Molecular properties and diagnostic potential of monoclonal antibodies targeting cytotoxic α-synuclein oligomers

α-Synuclein (α-syn) accumulates as insoluble amyloid but also forms soluble α-syn oligomers (αSOs), thought to be even more cytotoxic than fibrils. To detect and block the unwanted activities of these αSOs, we have raised 30 monoclonal antibodies (mAbs) against different forms of αSOs, ranging from unmodified αSOs to species stabilized by lipid peroxidation products and polyphenols, αSOs formed by C-terminally truncated α-syn, and multivalent display of α-syn on capsid virus-like particles (cVLPs). While the mAbs generally show a preference for αSOs, they also bind fibrils, but to variable extents. Overall, we observe great diversity in the mAbs' relative affinities for monomers and αSOs, varied requirements for the C-terminal extension of α-syn, and only a modest effect on α-syn fibrillation. Several mAbs show several orders of magnitude preference for αSOs over monomers in in-solution studies, while the commercial antibody MJF14 only bound 10-fold more strongly to αSOs than monomeric α-syn. Gratifyingly, seven mAbs almost completely block αSO permeabilization of membrane vesicles. Five selected mAbs identified α-syn-related pathologies like Lewy bodies (LBs) and Lewy Neurites, as well as Glial Cytoplasmic Inclusions in postmortem brains from people diagnosed for PD, dementia with LBs or multiple system atrophy, although to different extents. Three mAbs were particularly useful for pathological evaluation of postmortem brain human tissue, including early stages of PD. Although there was no straightforward connection between the mAbs' biophysical and immunohistochemical properties, it is encouraging that this comprehensive collection of mAbs able to recognize different aggregated α-syn species in vitro also holds diagnostic potential.

Design and engineering of bispecific antibodies: insights and practical considerations

Bispecific antibodies (bsAbs) have attracted significant attention due to their dual binding activity, which permits simultaneous targeting of antigens and synergistic binding effects beyond what can be obtained even with combinations of conventional monospecific antibodies. Despite the tremendous therapeutic potential, the design and construction of bsAbs are often hampered by practical issues arising from the increased structural complexity as compared to conventional monospecific antibodies. The issues are diverse in nature, spanning from decreased biophysical stability from fusion of exogenous antigen-binding domains to antibody chain mispairing leading to formation of antibody-related impurities that are very difficult to remove. The added complexity requires judicious design considerations as well as extensive molecular engineering to ensure formation of high quality bsAbs with the intended mode of action and favorable drug-like qualities. In this review, we highlight and summarize some of the key considerations in design of bsAbs as well as state-of-the-art engineering principles that can be applied in efficient construction of bsAbs with diverse molecular formats.

A comparative study of the developability of full-length antibodies, fragments, and bispecific formats reveals higher stability risks for engineered constructs

Engineered antibody formats, such as antibody fragments and bispecifics, have the potential to offer improved therapeutic efficacy compared to traditional full-length monoclonal antibodies (mAbs). However, the translation of these non-natural molecules into successful therapeutics can be hampered by developability challenges. Here, we systematically analyzed 64 different antibody constructs targeting Tumor Necrosis Factor (TNF) which cover 8 distinct molecular format families, encompassing full-length antibodies, various types of single chain variable fragments, and bispecifics. We measured 15 biophysical properties related to activity, manufacturing, and stability, scoring variants with a flag-based risk approach and a recent in silico developability profiler. Our comparative assessment revealed that overall developability is higher for the natural full-length antibody format. Bispecific antibodies, antibodies with scFv fragments at the C-terminus of the light chain, and single-chain Fv antibody fragments (scFvs) have intermediate developability properties, while more complicated formats, such as scFv- scFv, bispecific mAbs with one Fab exchanged with a scFv, and diabody formats are collectively more challenging. In particular, our study highlights the propensity for fragmentation and aggregation, both in bulk and at interfaces, for many current engineered formats.

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

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

Facilitating high throughput bispecific antibody production and potential applications within biopharmaceutical discovery workflows

A major driver for the recent investment surge in bispecific antibody (bsAb) platforms and products is the multitude of distinct mechanisms of action that bsAbs offer compared to a combination of two monoclonal antibodies. Four bsAb products were granted first regulatory approvals in the US or EU during 2023 and the biopharmaceutical industry pipeline is brimming with bsAb candidates across a broad range of therapeutic applications. In previously reported bsAb discovery campaigns, following a hypothesis-based choice of two specific target proteins, selections and screening activities have often been performed in mono-specific formats. The conversion to bispecific modalities has usually been positioned toward the end of the discovery process and has involved small numbers of lead molecules, largely due to challenges in expressing, purifying, and analyzing large numbers of bsAbs. In this review, we discuss emerging strategies to facilitate the production of expanded bsAb panels, focusing particularly upon combinatorial methods to generate bsAb matrices. Such technologies will enable screening in. bispecific formats at earlier stages of discovery campaigns, not only widening the accessible protein space to maximize chances of success, but also advancing empirical bi-target validation activities to assess initial target selection hypotheses.

Comparison of the Protective Effect of Polysorbates, Poloxamer and Brij on Antibody Stability Against Different Interfaces

Therapeutic proteins and antibodies are exposed to a variety of interfaces during their lifecycle, which can compromise their stability. Formulations, including surfactants, must be carefully optimized to improve interfacial stability against all types of surfaces. Here we apply a nanoparticle-based approach to evaluate the instability of four antibody drugs against different solid-liquid interfaces characterized by different degrees of hydrophobicity. We considered a model hydrophobic material as well as cycloolefin-copolymer (COC) and cellulose, which represent some of the common solid-liquid interfaces encountered during drug production, storage, and delivery. We assess the protective effect of polysorbate 20, polysorbate 80, Poloxamer 188 and Brij 35 in our assay and in a traditional agitation study. While all nonionic surfactants stabilize antibodies against the air-water interface, none of them can protect against hydrophilic charged cellulose. Polysorbates and Brij increase antibody stability in the presence of COC and the model hydrophobic interface, although to a lesser extent compared to the air-water interface, while Poloxamer 188 has a negligible stabilizing effect against these interfaces. These results highlight the challenge of fully protecting antibodies against all types of solid-liquid interfaces with traditional surfactants. In this context, our high-throughput nanoparticle-based approach can complement traditional shaking assays and assist in formulation design to ensure protein stability not only at air-water interfaces, but also at relevant solid-liquid interfaces encountered during the product lifecycle.

Exploring a role for flow-induced aggregation assays in platform formulation optimisation for antibody-based proteins

The development time of therapeutic monoclonal antibodies (mAbs) has been shortened by formulation platforms and the assessment of 'protein stability' using 'developability' assays. A range of assays are used to measure stability to a variety of stresses, including forces induced by hydrodynamic flow. We have previously developed a low-volume Extensional Flow Device (EFD) which subjects proteins to defined fluid flow fields in the presence of glass interfaces and used it to identify robust candidate sequences. Here, we study the aggregation of mAbs and Fc-fusion proteins using the EFD and orbital shaking under different formulations, investigating the relationship between these assays and evaluating their potential in formulation optimisation. EFD experiments identified the least aggregation-prone molecule using a fraction of the material and time involved in traditional screening. We also show that the EFD can differentiate between different formulations and that protective formulations containing polysorbate 80 stabilised poorly developable Fc-fusion proteins against EFD-induced aggregation up to two-fold. Our work highlights common platform formulation additives that affect the extent of aggregation under EFD-stress, as well as identifying factors that modulate the underlying aggregation mechanism. Together, our data could aid the choice of platform formulations early in development for next-generation therapeutics including fusion proteins.

AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification of de novo humanized and sequence-optimized single domain antibodies: a prospective case study

Introduction: In this study, we demonstrate the feasibility of yeast surface display (YSD) and nextgeneration sequencing (NGS) in combination with artificial intelligence and machine learning methods (AI/ML) for the identification of de novo humanized single domain antibodies (sdAbs) with favorable early developability profiles. Methods: The display library was derived from a novel approach, in which VHH-based CDR3 regions obtained from a llama (Lama glama), immunized against NKp46, were grafted onto a humanized VHH backbone library that was diversified in CDR1 and CDR2. Following NGS analysis of sequence pools from two rounds of fluorescence-activated cell sorting we focused on four sequence clusters based on NGS frequency and enrichment analysis as well as in silico developability assessment. For each cluster, long short-term memory (LSTM) based deep generative models were trained and used for the in silico sampling of new sequences. Sequences were subjected to sequence- and structure-based in silico developability assessment to select a set of less than 10 sequences per cluster for production. Results: As demonstrated by binding kinetics and early developability assessment, this procedure represents a general strategy for the rapid and efficient design of potent and automatically humanized sdAb hits from screening selections with favorable early developability profiles.

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.

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.