Pharmaceutical researchers limited to today’s laborious, complex methodologies in the search for peptide drugs know that they require a way to leverage AI as a much more efficient and speedier process for candidate discovery and lead generation. In fact, more than 85% of the costs incurred in pharmaceutical projects come from failed discovery and development costs. The very first discovery phase of a drug’s development takes 2-3 years before the drug can potentially get to trial, so when development fails – especially toward the end of a process that can last up to ten years – it represents a painful loss of a substantial investment. Reducing the initial discovery process to months would transform the industry.
Peptides are being turned to more and more frequently because of their inherent properties of high selectivity and low toxicity. In addition, therapeutic indications for peptides are not limited to certain areas, but can be applied across a wide range of medical issues, from Oncology, Metabolic and Cardiovascular disease to Infectious diseases, Gastroenterology, Allergies and Pain. Analysts expect market growth in the upcoming years due to the rising incidences of cancer, lifestyle diseases and generic formulation of expired patent drugs.
Peptides offer an incredibly complex chemical diversity; discovering a new potential peptide drug is thus extremely difficult due to the immense number of possible solutions to be considered (1030 ). Current technologies are only capable of screening 1013 solutions at best, which means they actually screen fewer than one billionth of the possible solutions/drug candidates.
In addition, currently used peptide discovery methods (“brute force” technologies such as phage display and derivatives thereof) have limited capabilities in discovering novel peptide drug candidates. Consequently, most marketed peptide drugs are modified versions or derivatives of a natural peptide.
At the end of the day, traditional methods lead to a lengthy discovery process and even more innovative techniques for peptide discovery are still pricey, cumbersome and do not provide 3D structure resolution and binding insight. Moreover, during the lengthy process competitors are likely to emerge, lowering overall project profitability. All this has led to increased interest in computational solutions by traditional drug discovery platform companies.
Our technology enables the discovery of the most advanced peptide-based drug candidates:
this is a key advantage, as it prevents developers from optimistically spending time and money going down the wrong path.
The bottom line: Pepticom’s approach slashes the speed at which peptide therapeutics can be brought to market, and this speed is the primary concern in the financial viability and profitability of a drug for its developer.
See our Research and Collaborations page to learn more about Pepticom and how our paradigm-shifting, industry-transforming AI platform can help your company accelerate time-to-market for your peptide drug therapeutics.