Souffle Therapeutics is focused on translating a novel platform for discovery of new cell and tissue specific ligands into a broad pipeline of targeted genetic medicines.  Souffle has demonstrated stronger, safer, more durable delivery to myocytes of the heart, muscle, or both with no concomitant silencing in other tissues screened, even at supratherapeutic doses. Souffle is pursuing several programs in genetically defined as well as broader indications enabled by this platform.

Human genetics has provided powerful validation for novel therapeutic targets, but translating these insights into effective medicines requires innovation in modality and delivery. I will describe the discovery and development of multiple platform approaches—spanning peptides, antibodies, and oligonucleotides—built around genetically validated targets. Together, these case studies illustrate how genetically informed targets, combined with tailored payload and delivery strategies, can accelerate translation from mechanism to medicine.

Zelenirstat is the first N-myristoyltransferase (NMT) inhibitor tested in patients. NMT inhibitors work by multiple mechanisms to kill cancer cells. Phase 1 results with oral zelenirstat showed good safety and extended survival in solid tumor patients. As an ADC payload, zelenirstat was significantly more potent against solid tumor cancer cells using several different targeting antibodies and linkers. Dozens of related NMTis may also serve as ADC payloads.

Dyne's FORCE Platform leverages a transferrin receptor-1 targeting Fab to enable targeted intracellular delivery of therapeutic payloads and is advancing in clinical development for neuromuscular disorders, including Duchenne muscular dystrophy and myotonic dystrophy type 1. In mouse models, the platform demonstrated the ability to deliver oligonucleotide therapeutics to the central nervous system with high efficiency. It also enables robust delivery of oligonucleotides to the primate brain, with widespread tissue distribution.

Antibody–oligonucleotide conjugates (AOCs) and peptide–drug conjugates (PDCs) are novel therapies that combine targeted delivery with strong intracellular effects but introduce unique safety risks. AOCs may cause immunogenicity from new conjugation sites, activate innate immunity via toll-like receptors, or induce unintended gene modulation through off-target hybridization.  For PDCs, risks include proteolytic breakdown, fast renal clearance, off-target binding, and payload leakage leading to toxicity. Both face aggregation, heterogeneity, and narrow therapeutic windows.

I will present novel protein conjugates that can be activated by light. One example is photoimmunotherapy (PIT), in which the photosensitizer payloads generate reactive species (ROS) that kills cells with light and oxygen. Another example is photo-caging or photo-activation. The precise spatial and temporal control offered by photomedicine not only significantly minimizes off-site toxicities and enhances the therapeutic index, but also markedly expand the range of druggable targets.

Despite nearly a hundred years of concerted efforts, drugs comparable to antibiotics remain elusive for the treatment of cancer. One of the key impediments has been that at the cellular level cancer cells remain similar to their environment and the often subtle changes have proved hard to target and vary widely by stage and type of cancer. Many properties common to tumors have remained untargeted primarily because they are difficult to modulate via small molecules. Transformative changes in peptide technology are now paving the way to target more complex collective cellular properties. This opens up a new route towards treatment of evolving and age related diseases such as cancer and dementia, with remarkable results.

Peptide-based therapeutics are gaining attention as they can combine the benefits of both small molecule and antibody drugs. The Gao group at Boston College contributes to peptide drug discovery by introducing new chemistries into peptide design and by developing novel display platforms. Specifically, we have been developing chemically “enhanced” phage libraries that incorporate multicyclic structures as well as designer covalent warheads.  This presentation will discuss the novel chemistries we recently developed for covalent binding of amines and for cyclizing peptides on phage. Case studies will also be presented to illustrate the scope of applications for the chemically enhanced phage libraries.

We present ring-closing amino acids bearing linkers with functional groups that enable further derivatization and efficient peptide macrocyclization. This strategy introduces a point of diversification, expanding the structural and chemical diversity of cyclic peptides. We are applying this approach to develop ligands selective for the D538G mutation in the ligand-binding domain of estrogen receptor alpha, a clinically relevant variant associated with endocrine therapy resistance. The method supports the generation of cyclic peptide libraries from a common sequence, offering a versatile platform for targeting wild-type and mutant estrogen receptors and other therapeutically important proteins.

Disease associated molecular profiles reveals new targets together with their molecular partners, interfaces, mutations, and therapeutic objectives. Some of the interfaces are not suitable for small molecules and larger oligomers, with unusual monomers and attachments needed. Constrained backbone helps to make sampling in both chemical space and conformational space feasible. We present benchmarks and optimized structure prediction algorithms to enable optimization of these binders and some success stories.

The Chatterjee Lab at Penn develops generative AI methods for designing functional biologics. We first built language models that create peptides to bind and control undruggable disease targets, with validation across neurodegeneration, pediatric cancer, and viral infection. We then developed discrete diffusion and flow-matching models to generate peptides, proteins, and mRNAs that balance binding with drug-like properties such as solubility, stability, and safety. More recently, we introduced Schrödinger Bridge models that learn biological trajectories, enabling context-aware design across protein folding, cell-state transitions, and therapeutic intervention. These tools also extend beyond medicine to applications such as heavy metal bioremediation.

In this talk we will discuss the development of predictive models to evaluate peptide target binding and passive diffusion across cell membranes. Application of AI-driven multi-objective optimization strategies to enhance both affinity and permeability simultaneously and case examples demonstrating how these approaches accelerate peptide drug discovery will also be highlighted.

Peptide drugs are potent but often short-lived. We introduce antibody-peptide fusion for selective GPCR agonism. Screening leverages machine-learning–designed soluble GPCR analogs that present native-like epitopes in stable, screening-ready surrogates. These designs are structurally validated and show specific ligand binding, providing reliable tools for discovery and characterization. We then apply this workflow to discover selective amylin receptor agonists.

2K015 is a novel Nectin-4-targeting peptide radioligand with pM binding affinity, demonstrating strong tumor uptake and an improved biodistribution profile versus 68Ga-BT8009 from Bicycle Therapeutics in SW780-Nectin4 CDX models. Importantly, in a human IIT (n=3), 68Ga-2K015 achieved clear tumor imaging with low liver and kidney uptake, supporting strong translational potential for clinical development.