We have been investigating the membrane permeability of large cyclic peptide scaffold model systems. In our efforts to apply the insights gained from these systems to find macrocycles that are both permeable and bioactive, we have designed mRNA-display and DNA-encoded libraries with enhanced permeability, using simple design criteria such as avoiding charged groups and hydrogen bond donors in the side chains, and limiting backbone NH groups via N-methylation. We now report lead compounds from these libraries selected against two intracellular proteins, their permeabilities, as well as their biochemical and cellular activities.

The development of macrocyclic peptides (MPs) able to access intracellular protein targets is of keen interest. Here we describe new types of MPs in which the ring is closed through formation of a moiety in which a permanent positive charged is embedded within a hydrophobic heterocyclic ring system. We show that this strategy increases the passive membrane permeability of any MP, often dramatically. We also describe the synthesis and screening of combinatorial libraries of these novel MPs.

Macrocyclic peptides promise access to intracellular “undruggable” targets but suffer poor permeability. We developed a microfluidic, liposome-based permeation screen using click chemistry and a 19.6K-member thioether-cyclized DNA-encoded macrocycle library. Using DOPC liposomes, we identified, resynthesized, and plate-validated permeant hits, demonstrating scalable, permeability-driven selection. Next, we’ll diversify scaffolds and deploy bacterial and mammalian-derived membranes to better mimic barriers, sharpening structure–permeability insight in the bRo5 space and enabling predictive PK modeling.

Peptidic conjugates offer a promising strategy for targeting intracellular proteins, yet limited cell permeability remains a major barrier. This work introduces strategies to enhance and validate the cellular uptake of moderately permeable peptides. By integrating an innovative assay system with structure–permeability design principles beyond PAMPA, MDCK, and Caco-2 models, the study provides insights to advance peptide-based modalities for intracellular drug discovery.

I present a targeted delivery approach based on conjugating peptide ligands of the EphA2 receptor to chemotherapeutic drugs. This strategy selectively delivers cytotoxic agents or radioligands to cancer cells, while sparing normal cells. In mice xenograft models, this approach increases the therapeutic window of chemotherapy by increasing efficacy while reducing side effects. We are applying this conjugation approach to first line cancer chemotherapeutic agents such as taxol, gemcitabine, and others.

Cyclic peptide conjugation is a powerful method for re-engineering classic chemotherapeutics to achieve higher efficacy and reduced side effects. I present our work on conjugating a cyclic peptide, [(WR)4WK]ßA, to epirubicin. The resulting conjugate was tested across multiple cancer cell lines, including triple-negative breast cancer and multidrug-resistant uterine sarcoma, with heart cells serving as a control for toxicity. The conjugate showed enhanced tumor uptake and potency while significantly reducing cardiotoxicity.

• Expanding peptide–drug conjugates (PDCs) as a platform for precision-guided oncology therapeutics
• Advantages versus ADCs
• Challenges in designing PDCs

My lab is working on the long-standing goal of developing cell membrane–permeable peptides for modulating intracellular targets and for oral delivery. We have developed nanoscale synthesis methods to generate and screen tens of thousands of sub-kDa synthetic peptides. My talk will highlight this platform and examples of successful ligand discovery, including membrane-permeable protein-protein inhibitors (unpublished) and orally available cyclic peptides.

In this presentation, we introduce how Koliber’s machine-learning technology, integrated with Robust Diagnostics' peptide-array technology, overcomes these limitations. We demonstrate that large libraries are unnecessary, as Koliber’s machine learning can optimize initial hits to achieve improved binding affinity. We also present visualization techniques for detecting binding modes, offering new insights into peptide-array applications for therapeutic peptide discovery.

Cyclic peptides have long been considered attractive as a drug modality due to their medium size and combining the advantages of small molecules and biologics. However, membrane permeability remains a significant challenge. Recently, physics-based Generative AI has emerged as a promising technology to design cyclic peptides with specific properties in mind. Here we focus on applying this method to design de novo cyclic peptides with drug-like oral bioavailability.