Clinical drug development remains associated with a high failure rate, with approximately 90% of candidates failing after entering clinical trials. Analyses of clinical outcomes attribute these failures primarily to lack of efficacy (40-50%), unmanageable toxicity (~30%), and suboptimal drug-like properties (10-15%). These data indicate that, despite extensive optimization workflows, current strategies do not fully capture all determinants of clinical success.
Drug discovery processes have traditionally emphasized target validation, high-throughput screening, and optimization of potency and specificity through structure–activity relationships (SAR). While these approaches are well established, they do not consistently translate into clinical efficacy. One limitation identified is the insufficient consideration of additional parameters that influence in vivo performance, including tissue exposure, physicochemical stability, and manufacturability.
Although the analysis by Sun et al. is not specific to antibodies, the underlying principle, that early-stage optimization criteria may be incomplete, applies to biologics.1 For monoclonal antibodies (mAbs), analogous gaps are observed when candidate selection is driven primarily by binding affinity without systematic evaluation of developability attributes.
Current drug optimization frameworks prioritize potency and specificity but may underrepresent parameters that determine the balance between efficacy and safety. The structure-tissue exposure/selectivity-activity relationship (STAR) framework proposes integrating potency with tissue exposure and dose requirements to better predict clinical outcomes. This highlights that efficacy is not solely determined by target engagement in vitro but also by the distribution and behavior of the molecule in vivo.
For antibody therapeutics, a comparable issue arises when high-affinity binders are advanced without sufficient evaluation of their biophysical properties. Antibodies with favorable binding characteristics may still exhibit liabilities such as aggregation, instability, or poor manufacturability, which can compromise downstream development. Therefore, expanding candidate selection criteria to include developability metrics represents a parallel to the broader need for multi-parameter optimization described in small-molecule drug development.
Developability is defined as the likelihood that a candidate can be advanced into a manufacturable, safe, and efficacious drug. This encompasses multiple factors, including expression yield, stability, and suitability for large-scale production. Formulatability, a related concept, refers more specifically to the ability to identify formulation conditions that ensure stability throughout manufacturing, storage, and administration.
Formulatability assessment functions as an intermediate step between discovery and preclinical formulation development.2 It provides a structured approach to evaluate physicochemical properties and identify risks prior to committing to extensive formulation development. This positioning reflects an increasing trend toward integrating developability considerations earlier in antibody pipelines.
Within a Quality by Design (QbD) framework, developability data contribute to defining the Quality Target Product Profile (QTPP), which guides formulation and process decisions. This integration supports a more systematic evaluation of candidate suitability beyond target binding.
Colloidal stability describes the tendency of antibody molecules to interact with each other in solution. Protein–protein interactions are commonly quantified using parameters such as the second osmotic virial coefficient (A₂) or the diffusion interaction parameter (kD). Positive values indicate net repulsive interactions, which are generally associated with reduced aggregation and improved stability.
Unfavorable interaction profiles can lead to aggregation, increased viscosity, or opalescence, particularly at high concentrations required for subcutaneous administration. Thresholds for kD and related parameters can be used to identify candidates with higher risk of problematic high-concentration behavior.
Conformational stability refers to the structural integrity of the antibody under thermal or environmental stress. Metrics such as melting temperature (Tm), onset temperature of unfolding (Ton), and non-reversibility onset temperature (Tnr) are commonly measured using techniques such as nano differential scanning fluorimetry (nanoDSF) and modulated scanning fluorimetry (MSF).
Higher thermal stability is generally associated with reduced propensity for unfolding and aggregation. Conversely, candidates with low thermal stability are more likely to be eliminated during lead selection due to increased development risk.
Chemical degradation pathways, including deamidation, oxidation, and isomerization, can impact antibody stability and product quality. In silico methods are frequently used to identify sequence liabilities and solvent-exposed residues prone to modification. These predictions can guide early-stage screening by highlighting potential risks before conducting long-term stability studies.
Other physicochemical properties contribute to developability assessment. The isoelectric point (pI), for example, influences both colloidal stability and non-specific interactions. For IgG1 antibodies, weakly basic pI values (approximately 8-8.5) have been associated with a balance between repulsive self-interactions and acceptable pharmacokinetic behavior.
Assays such as the ReFOLD assay, which measures relative monomer yield after denaturation, provide additional insight into long-term stability. Similarly, aggregation onset temperature (Tagg) offers an indicator of aggregation propensity under thermal stress.
Formulatability assessment is typically conducted after narrowing the candidate pool to a manageable number of molecules, often fewer than 20. At this stage, detailed biophysical characterization enables identification of candidates with unfavorable stability profiles.
Benchmarking candidate properties against datasets of marketed antibodies allows identification of deviations from favorable ranges. Parameters falling outside these ranges can be interpreted as risk indicators rather than definitive predictors of failure.
Standardized screening conditions, such as slightly acidic pH and low ionic strength, are often used to enable comparison across candidates. Expanding these conditions to include variations in pH and ionic strength can provide additional insight into formulation sensitivity and the available formulation space.
It is important to note that formulatability assessment cannot identify all potential liabilities. The predictive power of current assays is limited, and unexpected stability issues may still arise during later stages. However, early identification of risk factors can inform decisions such as candidate selection, sequence optimization, or the need for more extensive formulation development.
Platform formulation approaches are widely used in antibody development to accelerate timelines. These approaches apply formulation conditions that have been successful for previously developed antibodies, commonly involving histidine buffers at pH 5-6.5, surfactants such as polysorbates, and stabilizing excipients.
The advantage of platform formulations lies in reduced development time and earlier progression to clinical studies. However, this strategy carries inherent risks. Platform conditions may not provide adequate stability for all molecules, particularly those with atypical physicochemical properties.
Challenges become more pronounced at higher protein concentrations (>150 mg/mL), where issues such as aggregation, viscosity, and phase separation may occur. Additionally, reliance on platform formulations can limit understanding of the formulation space, potentially complicating troubleshooting in later stages.
Formulatability assessment provides data to support the decision between adopting a platform formulation and conducting a more extensive pre-formulation screening. Candidates with favorable biophysical profiles may be suitable for platform approaches, whereas those with identified risks may require tailored formulation strategies.
Pre-formulation screening aims to define a formulation space that supports stability across relevant conditions. Two general approaches are described.
The first approach relies on predictive biophysical measurements, such as thermal stability (Tm), protein-protein interactions (kD), and solubility. These measurements allow rapid evaluation of multiple formulation conditions but do not directly assess stability under stress.
The second approach involves subjecting candidates to stress conditions, including elevated temperature, freeze–thaw cycles, mechanical stress, and oxidative environments. Analytical methods such as size-exclusion chromatography (SEC) and ion-exchange chromatography (IEX) are used to quantify degradation and aggregation.
High-throughput approaches combining Design of Experiments (DoE) and one-factor-at-a-time strategies enable systematic exploration of formulation variables, including pH, buffer type, and excipients. These methods generate data that inform selection of formulations for subsequent stability studies.
Developability and formulatability assessments influence multiple downstream processes. Antibodies with suboptimal stability profiles may present challenges during cell line development, manufacturing, and formulation.
High-concentration formulations required for subcutaneous delivery are particularly sensitive to colloidal instability, which can result in increased viscosity and aggregation. Additionally, long-term storage stability may be affected by factors such as excipient degradation. For example, polysorbate hydrolysis can lead to particle formation and reduced stability over time.
Early identification of these risks allows for mitigation strategies, including sequence optimization, formulation adjustments, or alternative storage conditions. Conversely, failure to identify such issues may result in delays or reformulation efforts in later stages of development.
The integration of developability assessment into antibody discovery workflows supports a more systematic selection of candidates. Decision points are introduced after initial candidate selection, where biophysical data guide progression toward either platform formulation or more extensive development pathways.
This approach reflects a shift from linear development models toward iterative processes that incorporate feedback from developability data. Such integration can reduce the likelihood of advancing candidates with unfavorable properties, although it does not eliminate development risk.
Implementing comprehensive developability assessment requires access to multiple analytical techniques, standardized workflows, and reference datasets. These requirements can present challenges in terms of throughput, reproducibility, and data interpretation.
Specialized service providers can support these activities by offering integrated platforms for biophysical characterization and formulation screening. Such services may include combinations of in vitro assays (e.g., DLS, SEC, thermal stability measurements) and in silico analyses to identify potential liabilities.
Within this context, antibody developability assessment services can contribute to early-stage decision-making by generating data that inform candidate selection and risk evaluation. This approach aligns with the broader objective of reducing downstream development challenges through early identification of physicochemical and formulation-related risks.
Antibody Developability Assessment →
Sun, D., Gao, W., Hu, H., & Zhou, S. (2022). Why 90% of clinical drug development fails and how to improve it? Acta Pharmaceutica Sinica B, 12(7), 3049-3062. https://doi.org/10.1016/j.apsb.2022.02.002
Menzen, T., Le Vay, K., Arsiccio, A., Helbig, C., Hausmann, K., Pabstmann, T., & Hawe, A. (2026). To platform or not to platform: Strategic considerations for antibody formulation in early clinical development. Journal of Pharmaceutical Sciences, 115(1), 104054. https://doi.org/10.1016/j.xphs.2025.104054
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