Accelerated Identification of Cell Active KRAS Inhibitory Macrocyclic Peptides using Mixture Libraries and Automated Ligand Identification System (ALIS) Technology

Macrocyclic
peptides can disrupt previously intractable protein–protein
interactions (PPIs) relevant to oncology targets such as KRAS. Early
hits often lack cellular activity and require meticulous improvement
of affinity, permeability, and metabolic stability to become viable
leads. We have validated the use of the Automated Ligand Identification
System (ALIS) to screen oncogenic KRASG12D (GDP) against
mass-encoded mini-libraries of macrocyclic peptides and accelerate
our structure–activity relationship (SAR) exploration. These
mixture libraries were generated by premixing various unnatural amino
acids without the need for the laborious purification of individual
peptides. The affinity ranking of the peptide sequences provided SAR-rich
data sets that led to the selection of novel potency-enhancing substitutions
in our subsequent designs. Additional stability and permeability optimization
resulted in the identification of peptide 7 that inhibited
pERK activity in a pancreatic cancer cell line. More broadly, this
methodology offers an efficient alternative to accelerate the fastidious
hit-to-lead optimization of PPI peptide inhibitors.