Selectivity engineering
Peptide Selectivity Optimization — Reducing Off-Target Activity
Selectivity is a critical determinant of therapeutic safety and tolerability. Peptide candidates with potent on-target activity can fail in development due to off-target binding, cross-reactivity with related receptors, or unintended biological effects. We apply computational methods to systematically identify and address selectivity liabilities in peptide candidates.
The Selectivity Challenge
Peptide selectivity failures arise from multiple sources: cross-reactivity with structurally related receptors, off-target binding to plasma proteins or membrane components, and unintended biological activities arising from sequence similarity to endogenous peptides. Identifying and addressing these liabilities early in development is critical to avoiding late-stage failures.
Computational selectivity analysis — profiling candidates against panels of related targets, predicting off-target binding, and identifying sequence-based liabilities — can identify selectivity risks before they become development-stage problems.
Our Selectivity Optimization Approach
Off-target profiling
Computational screening of candidates against panels of related receptors and potential off-targets to identify selectivity liabilities.
Structural selectivity analysis
Analysis of binding site differences between the target and related off-targets to identify selectivity-determining positions.
Sequence-based liability identification
Identification of sequence motifs associated with off-target activity, immunogenicity, or unintended biological effects.
Selectivity-preserving optimization
Design of modifications that improve selectivity while maintaining on-target potency.
Selectivity Optimization Workflow
Selectivity liability assessment
Identification of known and predicted off-target risks for the candidate.
Off-target panel analysis
Computational profiling against related receptors and potential off-targets.
Selectivity determinant identification
Analysis of structural and sequence features driving selectivity differences.
Analog design
Design of modified analogs with improved selectivity profiles.
Experimental selectivity profiling
Validation of selectivity improvements across the target panel.
Iterative refinement
Further optimization based on experimental selectivity data.
What Can Be Evaluated
What You May Receive
Selectivity-optimized analog series
Modified candidates with improved selectivity profiles and maintained on-target potency.
Off-target risk report
Computational assessment of selectivity liabilities and their structural basis.
Selectivity determinant analysis
Identification of key positions and modifications that drive selectivity improvement.
Experimental profiling plan
Recommended assay panel for experimental selectivity validation.
Deliverables depend on project scope and target family characteristics. Selectivity outcomes require experimental confirmation.
Who This Is For
Drug discovery teams
Programs where selectivity liabilities have been identified in lead candidates and require systematic resolution.
Safety pharmacology groups
Teams conducting early selectivity profiling to identify and address off-target risks before IND.
Biotech companies
Companies developing peptide therapeutics where receptor family selectivity is a key development criterion.
Frequently Asked Questions
Optimize Peptide Selectivity and Reduce Off-Target Risk
Submit your peptide candidate and selectivity objectives. We will assess the selectivity liability profile and outline a computational approach to improve specificity.
Confidential project discussions available under NDA.