Soren Ostergaard is a senior principal scientist at Novo Nordisk A/S where he has been working in various peptide projects including peptide synthesis technologies for the last +30 years. He has a long track record of successful peptide drug projects that have resulted in marketed products or compounds in clinical trials. He has been optimizing novel half-life extension methodologies of peptides in order to be able to dose once-weekly or beyond of peptide drugs. Another focus area has been improving the potency/selectivity profile e.g., endogenous peptides or de novo design of dual acting peptides for subcutaneous or oral dosing. In addition, he has also worked with challenges in the bRo5 space, immunogenicity of peptides and proteins (B- and T- cell epitope mapping) and integrating these technologies into the drug discovery process
More than 20 years ago he introduced high throughput array and plate synthesis and have been an early key driver in the implementation of these robotic technologies to successfully speed and improve the peptide drug discovery process at Novo Nordisk.
He is collaborating with academia and has through the years served as supervisor for PhD students. He attends career events representing Novo Nordisk as talent ambassador, giving educational lectures at various courses at Universities in Denmark and Sweden and from time to time invited as PhD assessor. He is the author or co-author of +90 publications and patents.

Semaglutide and tirzepatide approved for type 2 diabetes and obesity are redefining peptide therapeutics. A molecular engineering technology, fatty acid derivatization, initially applied to insulin, then semaglutide, has now enabled otherwise short-lived peptides to be dosed once weekly and moreover, this technology has unlocked unprecedented efficacy in terms of biological activity. Central to this advancement is the derivatization with a fatty acid, which facilitates the binding of the peptide or protein of interest to albumin (1). Due to the well-defined chemical nature of fatty acids, inherent safety, ease of synthesis and upscale, this technology offers a unique versatile approach to peptide and protein drug discovery.
In this presentation, the focus will be on early and current inventions, illustrating how this technology has been applied to address the challenges in designing long-acting peptide-based therapeutics. Additionally, how these endeavors might pave the way for future advancements in peptide drug design will be discussed.
(1) P. Kurtzhals, S. Østergaard, E. Nishimura, T. Kjeldsen, 2022, Nature Rev. Drug Disc, 22, 59-80, doi.org/10.1038/s41573-022-00529

César de la Fuente is a Presidential Associate Professor at the University of Pennsylvania, where he leads the Machine Biology Group. He completed postdoctoral research at the Massachusetts Institute of Technology (MIT) and earned a PhD from the University of British Columbia (UBC). He is best known for pioneering computational and artificial intelligence approaches to antibiotic discovery, which have drastically accelerated the time needed to identify preclinical candidates, from years to hours. These candidates show promise for therapeutic intervention against currently untreatable infections. Moreover, he spearheaded the discovery of therapeutic molecules from extinct organisms, and his lab has uncovered a myriad of novel peptide molecules across the tree of life, revealing a previously unrecognized branch of host immunity. Professor de la Fuente has received numerous awards for his contributions, including the Princess of Girona Prize and the Fleming Prize. He is a Fellow of the American Institute for Medical and Biological Engineering, a Sloan Fellow, and a National Academy of Medicine Emerging Leader. De la Fuente has authored more than 170 publications and holds multiple patents.

Traditional antibiotic discovery has long relied on physically exploring nature, collecting soil and water samples to isolate active compounds through a painstaking trial-and-error process. This approach has become increasingly unsustainable in the face of rising antibiotic resistance and the urgent need for new therapeutics. In this talk, I will discuss how our lab has moved beyond this paradigm over the past decade by leveraging artificial intelligence (AI) to digitally mine the world’s biological information—genomes, proteomes, and metagenomes. Computers excel at pattern recognition in images and text, but their application in biology and medicine is still nascent. In this talk, I will discuss our decade-long advances accelerating antibiotic discovery. We pioneered designing antibiotics using artificial intelligence (AI), achieving efficacy in preclinical animal models and demonstrating that machines could create therapeutic molecules. For the first time, we mined the human proteome to identify antibiotic candidates. Building on this work, we hypothesized that similar compounds exist throughout evolution. We expanded our efforts to extinct species, where our AI-driven approach led to the discovery of the first therapeutic molecules from organisms like Neanderthals and woolly mammoths. This work launched the field of molecular de-extinction and yielded preclinical candidates such as neanderthalin, mammuthusin, and elephasin. Furthermore, we expanded our antibiotic discovery efforts to explore other branches of the tree of life beyond eukaryotes. By computationally analyzing microbial dark matter, we identified nearly one million new antibiotic molecules, which have been made freely available to encourage global researchers to synthesize and develop them. Additionally, through computational exploration of thousands of human microbiomes, we discovered new antimicrobial agents, including prevotellin-2 from the gut microbe Prevotella copri. Collectively, our efforts have dramatically accelerated antibiotic discovery, reducing the time to identify preclinical candidates from years to just a few hours. We are on the cusp of a new era where AI advances will help control antibiotic resistance, infectious disease outbreaks, and pandemics.

Dr. Akshay Shah is an experienced medicinal chemists with 10 years of combined drug discovery experience across modalities that include small molecules and peptides. He has authored 15+ peer-reviewed publications and hold inventorship on 10+ patents while frequently providing peer-reviews to top journals. Upon completion of PhD at indiana University, Bloomington in 2014 focusing on natural product synthesis and subsequent postdoctoral stint with renowned chemist Prof. K. C. Nicolaou at Rice University, Dr. Shah joined medicinal chemistry department at Pfizer as a senior Scientist and focused on development of novel, high-throughput parallel chemistries for SAR expansions. In 2017, Dr. Shah moved to Merck Research Labs in West Point, PA where he made key contributions to identification of small molecule clinical candidate. His interest in peptide discovery led him to Johnson & johnson in 2021 as a principal Scientist where Dr. Shah led medicinal chemistry efforts on multiple programs delivering on key discovery milestones. Dr. Shah is currently the Director of Chemistry at Pinnacle Medicines where his efforts build and advance a deep discovery chemistry portfolio focused on cyclic peptides.

Late-stage-enabling, high-throughput chemistries have been gaining momentum in the peptide drug discovery campaigns as chemists strive for fuller, shorter and fewer design-test-make cycles to optimize chemicals leads into clinical candidates. However, one must strike a balance between the high throughput and the late-stage of diversity introduction as they often run counter to one another. It is up to the ultimate objective one hopes to achieve that may tilt the balance in the favor of one over the other. The presentation will highlight: 1) photoredox-catalyzed decarboxylative Giese additions to dehydroalanine derivatives; and 2) Suzuki cross-couplings with 4-Br phenylalanine derivatives and draw focus on chemistry entablements that maximize the late-stage versus high throughput diversity introduction.