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Developability or immunogenicity issues can derail antibody development late in the process, resulting in potentially high cost, late-stage attrition. MindWalk’s LensAI platform supports early in silico screening of developability and immunogenicity-related risk indicators, enabling teams to flag or deprioritize potentially problematic clones as soon as sequence information becomes available. Furthermore, the scalability of the in silico processes is well-suited to seamlessly integrate into the screening stage of MindWalk’s proprietary B cell Select® antibody discovery platform during hit selection to support identification of comparatively lower-risk leads.
Why early screening matters in antibody discovery
In a typical antibody discovery and development process that follows a pre-defined screening funnel, evaluation for developability associated with manufacturability and stability as well as potential clinical risk associated with immunogenicity is conducted at later stages in development. If issues are identified, the drug developer may down-select the molecule (a costly, late-stage attrition), attempt to engineer out the hot spots (a time-consuming and labor-intensive process with uncertain outcome), or proceed with caution (and simply delay the difficult decision-making until even later).
With sophisticated and easy-to-use analytical tools, starting from just the sequences of the antibody clones, one can analyze hundreds to thousands of sequences within hours, depending on dataset size and computational configuration. What was typically reserved for late-stage analyses that only checked off a list of parameters for a handful of molecules can now be available at the hit screening stage. This enables the drug developer to make down-selection decisions for high-risk hits much earlier as well as introduce multi-parametric engineering to optimize molecules as needed. The information gained from analyses at the hit identification stage hence provides active guidance for proactive measures instead of after-the-fact observations.
The sequence features that would surface in late-stage drug development include:
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Charge distribution: Uneven structure-based charge distribution increases the potential for intermolecular interactions, increasing the likelihood of antibody aggregation. It can also impact the viscosity of the antibody solution and antibody solubility, risking precipitation1 and precluding indications that would require high-concentration formulation. As such, uneven charge distribution can impede antibody manufacturing, storage stability/conditions, formulation, and potentially efficacy.
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High-risk physicochemical liability hotspots: Physicochemical liabilities, when manifested particularly in CDRs, can significantly reduce antibody potency. Stress, such as temperature and pH changes, long-term storage under unfavorable or even standard storage conditions can result in chemical modifications of susceptible residues, which has the potential to impact target-binding.
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Aggregation propensity: Aggregated proteins can trigger innate and adaptive immune responses thus rendering an otherwise non-immunogenic antibody to become immunogenic. Various antibody features contribute to their propensity to aggregate, including aggregation-prone motifs rich in glutamine and asparagine residues and repeated motifs.
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Immunogenicity: Immunogenicity remains one of the most important and often one of the most costly factors in biologics development. The elicitation of an anti-drug antibody (ADA) response in the clinic is a phenomenon observed at the latest stage of drug development and can derail even the most promising candidates, since ADAs can reduce a drug's effectiveness, alter its pharmacokinetics, or introduce safety risks that can complicate regulatory review.
Traditional workflows tend to treat developability and immunogenicity as downstream filters rather than upstream design constraints. Consequently, by the time such potential issues surface, significant amounts of time and budget have already been invested in the potential candidate.
How in silico screening helps identify antibody developability and immunogenicity risk
To avoid costly late-stage attrition and delays due to iterative processes to correct such issues, it is often valuable to carry out developability and immunogenicity assessments at an early stage of antibody discovery. Thanks to recent advancements in computational biology and artificial intelligence, researchers are increasingly turning to in silico predictions to detect developability and immunogenicity issues early in the developmental process. In silico modelling can be carried out as soon as primary hit sequences become available, enabling the identification of any issues before a large amount of effort is invested into advancing potentially suboptimal candidates. Perhaps most importantly, such in silico analyses may reduce downstream wet lab burden by prioritizing candidates earlier in the process.
Why early in silico screening works best alongside B cell discovery
Case study: How MindWalk combines B cell discovery with LensAI screening
In silico screening is most useful when it informs choices early, not when it assesses failure after the fact.
LensAI supports developability and immunogenicity assessment as soon as primary B cell sequences are available. It analyzes both sequence- and structure-derived features and provides multi-level insight into potential liabilities without requiring physical material.
What is notable in MindWalk workflows is that LensAI is not used in isolation. It is embedded directly into MindWalk’s B cell discovery flow.
When MindWalk’s B cell discovery workflows generate a diverse panel of antibodies with desirable properties, early in silico screening enables meaningful comparison, prioritization, and optimization across candidates rather than validating a narrow set of options.
A typical B-cell discovery workflow involves the following steps:
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Immunization of host species with genetic, protein, or small molecule immunogen to develop a specific humoral immune response.
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Antigen-specific reactive B cell populations are enriched.
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Antibodies secreted from the enriched B cells are screened for binding to target molecules.
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The top hits are selected for downstream in silico screening and further characterization.
Hits generated through MindWalk’s B cell Select platform are screened early and flagged if potential risks are identified.
LensAI Developability assessment scans for potential biochemical/physicochemical and structural issues to assess a range of parameters including aggregation propensity, charge distribution patterns, stability indicators, and potential PTM sites. The score for each parameter is shown relative to the scores of post-Phase I clinical stage antibodies as reference.
LensAI Immunogenicity Screening generates an immunogenicity composite score consisting of MHCII epitope analysis and divergence from the total human proteome.
In a benchmarking analysis comparing LensAI Immunogenicity composite score to historical clinical ADA outcome, the analysis indicated an association with observed clinical ADA incidence and supporting its use as an early-stage risk stratification tool for distinguishing comparatively higher- versus lower-ADA incidence categories. In a study covering 217 therapeutic antibodies, LensAI correctly classified 197 out of 217 (91%) antibodies as having an ADA incidence risk >30%
LensAI can flag antibodies associated with elevated developability and immunogenicity risk, supporting earlier consideration of optimization or reprioritization during development. It is a risk indicator that allows teams to compare candidates, understand trade-offs, and decide how much caution or additional engineering is warranted.
How early risk assessment supports antibody optimization
When immunogenicity and developability data are available at the same time as functional data, engineering becomes proactive instead of reactive.
In MindWalk workflows:
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B cell platforms generate diverse, functionally validated hits with desirable binding characteristics
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LensAI screens those hits early for developability and immunogenicity risk
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Risk signals guide prioritization or targeted optimization
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Refined candidates can be re-evaluated
This approach supports earlier risk identification and candidate prioritisation.
Conclusion: early screening can reduce downstream antibody development risk
Late-stage attrition is costly, and approaches to derisk early are intended to minimize the likelihood of such failures and instead enable concentration of valuable downstream resources on high value candidates.
By integrating early in silico immunogenicity and developability assessment into B cell discovery, MindWalk combines high-quality B cell workflow and high-throughput, large-scale LensAI screening to support workflow efficiency, and informed decision-making, with the goal of improving development timelines.
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