Protein engineers boost antibody developability scores by 51%

The company set out to upgrade its antibody engineering platform with ML-driven developability prediction and optimization—improving biophysics, reducing immunogenicity risk, and accelerating lead optimization without compromising CMC readiness. Their goals were to predict and optimize key biophysical properties (solubility, stability, aggregation), reduce immunogenicity risk early via in-silico screening, shorten lead-optimization timelines while lifting CMC success rates, and increase expression/manufacturability to de-risk tech transfer.

Late failures and underperforming candidates were inflating cycle time and cost. Aggregation, poor expression, and late immunogenicity flags forced re-engineering, while earlier computational attempts lacked experimental concordance:

• High failure rate: 43% failure rate in developability assessment for lead antibodies
• Aggregation issues: Aggregation in 61% of candidates during stability studies
• Computational gaps: Prior computational design validated experimentally only 38% of the time
• Immunogenicity risks: Late immunogenicity risks forced re-engineering in 52% of programs
• Manufacturing yields: Manufacturing yields averaged 1.2 g/L vs 3–5 g/L benchmarks
• CMC failures: Late CMC failures cost $8.5M per program in delays

Standard protein engineering approaches couldn't reliably predict and optimize the complex biophysical properties needed for successful antibody therapeutics. The company needed a systematic approach that combined computational prediction with experimental validation to reduce late-stage failures.

CleverX assembled a cross-functional bench of antibody engineers, structural biologists, and formulation scientists to pair ML prediction with structure-guided design and high-throughput validation—anchored in QbD and IND/CMC expectations.

Expert recruitment:

• 17 specialists: 7 antibody engineers, 5 structural biologists, 5 formulation scientists
• Average 9 years in biologics with 30+ development candidates collectively
• Expertise in stability, immunogenicity, CHO expression, IND-enabling CMC
• Deep experience in biophysical characterization and formulation development
• Specialists in structure-based design and high-throughput screening
• Quality-by-design (QbD) experience for regulatory submissions

Technical framework:

• ML models predicting 12 developability parameters with high accuracy
• Structure-based design pipeline for stability/aggregation hotspots
• Immunogenicity prediction via T-cell epitope mapping
• High-throughput biophysical characterization at scale

Quality protocols:

• QbD-aligned developability criteria and design space definition
• Automated screening cascades across 500 variants for validation
• Stability protocols per ICH Q5C guidelines
• Manufacturability gate for tech transfer readiness

The program progressed from data foundation to validated platform deployment, ensuring model gains translated to benchtop performance and CMC outcomes.

Week 1: Platform assessment and data integration

• Analyzed 150 historical antibodies identifying failure patterns
• Integrated structural and sequence data for ML training
• Quantified $12M in avoidable late-stage failures
• Established baseline performance metrics across all programs

Weeks 2-4: Model development and validation

• Built developability prediction achieving 87% accuracy
• Developed optimization algorithms improving 8 properties
• Created automated design pipeline generating 50 variants daily
• Integrated structure-based design with biophysical prediction

Weeks 5-6: Experimental validation and optimization

• Tested 200 designed variants validating predictions
• Optimized 5 clinical candidates improving all metrics
• Achieved 2.8 g/L expression for previously low-yielding antibody
• Demonstrated consistent improvement across different antibody classes

Week 7: Platform deployment and integration

• Cloud platform deployed to 50 users across research teams
• Integrated with LIMS and structural databases
• SOPs established for end-to-end development workflow
• Training completed for all discovery and development scientists

A tight loop between modeling and wet-lab teams aligned targets, thresholds, and assays—trading volume for precision so only the best variants advanced.

CleverX's protein engineering platform transformation delivered comprehensive improvements across discovery efficiency, molecular quality, and business impact.

Efficiency gains:

Faster discovery-to-candidate cycles with fewer experiments. Lead optimization time was cut from 9 to 5 months, variants tested were reduced by 62% with smarter triage, formulation development was accelerated by 45%, and tech-transfer success improved from 65% to 91%.

Quality improvements:

More robust molecules with better manufacturability. The platform achieved 51% improvement in composite developability scores, average expression increased from 1.2 to 2.6 g/L, aggregation rates decreased by 68% at 40°C storage, and thermal stability (Tm) improved by +8.3°C on average.

Business impact:

Lower risk, lower cost, and faster path to clinic. The company saved $5.2M per program from fewer CMC failures, accelerated two programs to IND by 6 months, reduced manufacturing costs by 41% via higher yields, and enabled a $15M partnership on improved platform data.

Strategic advantages:

A repeatable engine for complex biologics. The initiative built a developability database of 500+ characterized antibodies, created a platform supporting multiple modalities including bispecifics, established competitive edge in bispecific design/optimization, and licensed computational tools to 2 partners.

The company's protein engineering capabilities received recognition from a biologics development society for excellence in computational antibody design and optimization.