Biologics

Accelerate Your Biologics Discovery with Arctoris’ Integrated and High-Precision Solutions.

Capabilities Overview 

Arctoris brings unparalleled expertise and cutting-edge technology to biologics drug discovery. Our comprehensive services span from antibody generation and engineering to functional characterization and optimization. Utilizing our advanced Ulysses® platform, we deliver high-throughput, high-quality data that drives the development of next-generation biologic therapeutics. Tailored solutions and AI/ML-ready datasets empower you to make informed decisions faster, propelling your biologics projects toward clinical success.

Key Features and Value

Expertise Across Modalities: Extensive experience with antibodies, bispecifics, peptides, and RNA therapeutics.

High-Throughput Precision: Accelerate screening and optimization with automated platforms and analytics.

Tailored Solutions: Whether starting from scratch or optimizing an existing assay, we customize our approach to meet your specific needs.

Comprehensive Characterization: Deep functional insights through advanced assays and structural biology.

Seamless Integration: Unified workflows from target validation to lead optimization.

Key Capabilities

Antibody discovery and engineering (humanization, affinity maturation)

Functional assays for biologic efficacy and mechanism of action

High-throughput screening of biologic libraries

Structural analysis for rational design and optimization

Advanced cell-based models including patient-derived systems

Pharmacokinetic and pharmacodynamic studies via partners

Automated data analysis for rapid decision-making

Validation Data

Clinical Success: Guided over 12 biologic candidates through IND into clinical trials.

Time Efficiency: Reduced lead optimization timelines by up to 30%.

Data Quality: Delivered high-quality data enhancing lead selection decisions.

Resource Optimization: Enabled rapid project progression with minimal in-house investment.

Predictive Accuracy: Provided datasets improving computational model predictions.