The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to rapidly enable new therapeutically relevant molecules currently impractical to manufacture with conventional techniques. Peptides represent a growing subset of the therapeutics market: there are about 68 currently approved peptide treatments; ~140 peptide treatments in clinical trials and another 500 peptide treatments in the preclinical stage, representing a $21 B market growing to $44 B by 2025. While peptides therapeutics are known for indications such as oncology, cardiovascular disease, and metabolic disease, recent interest in personalized peptide therapeutics has grown. Currently, peptides take weeks to synthesize and are tremendously resource-intensive, costing hundreds to thousands of dollars for a few milligrams of product. These high costs and long lead times severely limit the number of molecules a researcher can test in a given period of time, representing the primary bottleneck in peptide therapeutic innovation. Faster access to these potentially therapeutic relevant molecules will accelerate discovery of new therapeutics.
This Small Business Innovation Research (SBIR) Phase I project overcomes the major bottlenecks of peptide manufacturing. The Rapid Automated Computation, Coupling, Cleavage, and Chromatography Execution (RAC4E) Platform produces high purity custom peptides. However, to further extend the ability to control process conditions and achieve real-time process optimization, we propose developing solid-phase slug flow (SPSF) coupling, using advanced engineering and machine learning solutions to generate purity in molecules during synthesis, enabling optimization of the peptide chain in real-time. Combining these solutions will allow the production of peptides both faster and at longer lengths, as well as dramatically reducing the need for downstream purification. Once fully implemented, the RAC4E platform with SPSF coupling will produce peptide libraries at a speed and purity that will significantly increase the density of biological screening data serving as machine learning inputs. The platform will be later be scaled into a cGMP manufacturing environment, accelerating the rate of entry for novel peptide therapeutics into the clinic.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
