Therapeutic monoclonal antibodies (mAbs) continue to drive innovation across a wide range of disease areas, including oncology, autoimmune diseases, infectious diseases, and neurological disorders. The pace of clinical development is accelerating, with over 570 antibody therapeutics in the clinical pipeline and dozens reaching late-stage development or approval each year. Despite this growth, the overall failure rate in drug development remains a significant challenge. According to published analyses, approximately 90% of drug candidates fail during clinical development, and this figure may exceed 99% when preclinical stages are included.1
Many of these failures are attributable not to target binding or mechanism of action, but to issues in manufacturability, stability, or formulation, which are factors directly related to the developability of the molecule. Increasingly, antibody researchers are recognizing the importance of assessing these attributes during early discovery, rather than waiting until costly downstream stages. Modern antibody discovery programs are increasingly incorporating developability assessments alongside functional screening to identify high-quality antibody candidates earlier.
Developability refers to a set of physicochemical and biochemical properties that influence whether a biologic candidate can be efficiently manufactured, formulated, stored, and delivered as a drug product. These biophysical properties and physicochemical properties collectively contribute to the overall developability profile of an antibody candidate and can significantly impact its progression through development.
For antibodies, these properties include:
Aggregation propensity
Thermal stability and conformational stability
Self-interaction and colloidal stability
Charge heterogeneity and isoelectric point (pI)
Solubility and hydrophobicity
Chemical liabilities (e.g., deamidation, oxidation)
Non-specific binding
While antibodies are typically selected for high affinity, specificity, and desired effector functions, candidates that perform well in these categories may still fail if they exhibit poor developability. Common liabilities include poor expression yield, aggregation under stress, viscosity at high concentrations, or incompatibility with purification and formulation processes. Successful therapeutic development requires balancing antigen binding performance, binding affinity, effector functions, and manufacturability-related characteristics.
Lead selection in many discovery programs still focuses primarily on functional and binding data. However, early-stage assays that evaluate developability have been shown to provide predictive value for downstream manufacturability and formulation performance.
In a study evaluating 152 human or humanized monoclonal antibodies, researchers demonstrated that key biophysical properties measured early in the discovery process correlated with downstream process parameters. Antibodies with high melting temperatures (Tm) and aggregation onset temperatures (Tagg) performed better under low pH stress and exhibited lower viscosity at high concentrations. Self-interaction assays such as AC-SINS and hydrophobicity profiles (via HIC) provided early indicators of colloidal instability, which were later reflected in formulation challenges.2 These findings highlight how developability studies can establish a more comprehensive developability profile for each antibody candidate before significant resources are committed to downstream development.
These results support the integration of developability screening during early antibody generation and optimization stages. Doing so allows developers to:
Eliminate candidates with poor manufacturability profiles
Prioritize sequences with strong biophysical robustness
Guide engineering efforts without extending timelines
Streamline downstream purification and formulation development
By focusing on developability during early lead selection, teams can avoid investing in unstable or hard-to-manufacture molecules and accelerate the path to IND-enabling studies.
The ability to compare multiple developability profiles simultaneously enables teams to make more informed decisions when advancing an antibody candidate.
Aggregation is a common failure mode during purification, storage, and formulation. Early detection of aggregation-prone molecules can be achieved using high-throughput assays such as size-exclusion chromatography (SEC-HPLC), dynamic light scattering (DLS), and cross-interaction chromatography (CIC), as well as AC-SINS. These assays provide insight into both soluble aggregate content and self-association tendencies at relevant concentrations.
Importantly, molecules showing strong aggregation behavior under stress, such as low pH, freeze-thaw, or elevated temperature, can be deprioritized before significant investment is made.
Together, size-exclusion chromatography, dynamic light scattering, and cross-interaction chromatography provide complementary insights into aggregation behavior, self-association, and critical protein interactions that may affect developability.
Differential scanning fluorimetry (DSF) and nano-DSF allow rapid determination of thermal melting points (Tm) and aggregation onset temperatures (Tonset, Tagg). These metrics are predictive of a molecule’s stability during storage, purification, and viral inactivation steps.
Thermal stability is one of the most important developability properties because it can directly influence manufacturing robustness and product shelf life. Assessing thermal stability early helps researchers identify molecules with favorable biophysical properties and reduced risk during manufacturing, formulation, and storage.
A Tm above 65°C is generally considered desirable for therapeutic mAbs. In the 152-antibody study, molecules with high Tm and Tagg also showed reduced aggregation after low pH exposure, reinforcing the predictive power of thermal stability data.
Surface hydrophobicity contributes to aggregation and non-specific binding. Hydrophobic interaction chromatography (HIC) is used to assess this property, with longer retention times indicating more hydrophobic surface regions. Molecules with high HIC retention often require sequence modifications to improve solubility and reduce aggregation risks. The presence of hydrophobic patches on antibody surfaces can increase aggregation risk and negatively affect colloidal stability. Identifying hydrophobic patches early can support more effective sequence engineering strategies.
Charge heterogeneity affects purification and formulation behavior. Methods such as ion exchange chromatography (IEX-HPLC), imaged capillary isoelectric focusing (iCIEF), and heparin HPLC are used to characterize these variants. Molecules with extreme pI values or large charge patches in the CDRs may experience losses during purification or fail viral inactivation steps.
Common liabilities include oxidation (methionine, tryptophan), deamidation (asparagine, glutamine), isomerization, and glycation. In silico sequence analysis followed by peptide mapping can identify and confirm these risks. In the referenced study, problematic residues in CDRs were corrected through engineering, leading to improved aggregation resistance and expression yields.
Analysis of antibody sequences can reveal liabilities within complementarity-determining regions (CDRs) that may affect stability, antigen binding, and manufacturability. Strategic sequence engineering can often mitigate these risks while preserving target specificity.
Non-specific interactions are detected using assays such as PSR ELISA, BVP ELISA, and binding to unrelated targets (e.g., DNA, insulin). Molecules with strong off-target binding profiles often exhibit poor pharmacokinetics and are more likely to trigger immune responses.
Understanding how antibody sequences influence non-specific binding behavior is essential for generating favorable antibody developability profiles and improving clinical success potential.
Related: What is Antibody Developability?
To assess large candidate pools efficiently, developability screening must be compatible with high-throughput expression and purification. Emerging machine learning approaches are increasingly being integrated into antibody discovery workflows to predict developability profiles, prioritize antibody candidates, and analyze large antibody libraries more efficiently. Machine learning models can also help identify patterns across antibody sequences that may influence developability outcomes. As machine learning technologies continue to advance, they are becoming increasingly valuable for analyzing antibody sequences, ranking candidates from large antibody libraries, and predicting developability outcomes prior to experimental validation.
Assays need to operate with low material requirements (typically <1 mg per antibody) and short turnaround times.
In the 152-molecule study, each antibody was expressed in CHO-EXPI systems and screened using a battery of HT assays that closely resemble those used in preformulation development. The study confirmed that results obtained during discovery (using minimal material) could accurately predict performance during CMC activities.
Biointron’s Antibody Developability Assessment Platform is specifically designed to deliver early, high-throughput characterization of antibody candidates. Supporting over 3,000 antibodies per batch, Biointron enables rapid screening of large panels using <1 mg per assay and delivers results within 3-5 days.
The platform evaluates critical biophysical properties, physicochemical properties, thermal stability, and other developability-related metrics to support antibody drug development programs. In addition, data generated through these workflows can help optimize candidate selection while maintaining desirable antigen binding characteristics and manufacturability profiles.
Biointron also provides expert data interpretation, identifying high-risk candidates and recommending optimization strategies, such as sequence modifications or reformatting. This helps teams make faster, better-informed decisions about which molecules to advance and which to deprioritize.
By integrating developability screening with expression, affinity, and epitope binning capabilities, Biointron enables a fully aligned lead selection strategy that supports CMC readiness and de-risks early development.
Antibody Developability Assessment →
Early-stage developability assessment is a natural extension of Quality by Design (QbD) principles applied during discovery. It establishes a foundational understanding of each molecule’s strengths and liabilities, which is information that informs not only candidate ranking but also downstream process development.
Rather than applying strict cutoffs, developability data is best used to rank molecules across multiple critical attributes like aggregation, stability, viscosity, and chemical liabilities. This is then used to guide selection in context of the therapeutic indication and delivery route. For example, a molecule with moderate viscosity may still be viable for intravenous administration if it demonstrates high potency and low immunogenicity risk.
The key is integrating multiple sources of data from sequence-level insights, empirical HT screening, and biological performance, into a strategy that moves the best candidates forward.
Developability is a critical success factor in antibody drug development. Early identification of biophysical liabilities enables better lead selection, reduces downstream risk, and shortens the path to clinic. Data from high-throughput screening workflows now supports the predictive power of early assays for key manufacturing and formulation endpoints.
Biointron’s developability platform offers a practical, efficient way to screen large candidate panels and identify the most robust antibodies before committing to costly downstream work. By combining data-driven analysis with expert interpretation, Biointron empowers drug developers to move forward with confidence.
Antibody Developability Assessment →
Fogel, D. B. (2018). Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemporary Clinical Trials Communications, 11, 156-164. https://doi.org/10.1016/j.conctc.2018.08.001
Bailly, M., Mieczkowski, C., Juan, V., Metwally, E., Tomazela, D., Baker, J., Uchida, M., Kofman, E., Raoufi, F., Motlagh, S., Yu, Y., Park, J., Raghava, S., Welsh, J., Rauscher, M., Raghunathan, G., Hsieh, M., Chen, L., Nguyen, H. T., . . . Fayadat-Dilman, L. (2020). Predicting Antibody Developability Profiles Through Early Stage Discovery Screening. MAbs, 12(1), 1743053. https://doi.org/10.1080/19420862.2020.1743053
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