PeptiMOL · Modeling the pharmacokinetics profiles of therapeutic peptides by chemoinformatics methods

Modeling the pharmacokinetics profiles of therapeutic peptides by chemoinformatics methods

Peptides are defined as macromolecules composed by 2-50 amino acids. Peptides possess multiple therapeutic applications, such as antivirals, antifungals, antibiotics, modulators of the immune, cardiovascular and nervous systems, in addition to their utility in diagnosis. Peptide drug discovery (PDD) has experienced renewed interest and momentum over the two last decades, thanks to the greater appreciation of the interesting advantages of peptides as an alternative to small molecules: high specificity and activity, easy degradation, do not yield toxic metabolites, and may be reutilized by the organism instead of being converted into waste products. Nevertheless, therapeutically relevant peptides generally exhibit limited capacity to diffuse across biomembranes such as the human gastrointestinal epithelium, in addition to their low stability. Moreover, due the short plasmatic half-life and low stability of these peptides, they are administered through injections, often several times a day. For those reasons, it is essential to develop methods for modelling the bioactivity of peptides, predict their pharmacokinetic profiles and ultimately allow for the design of novel peptide chains adapted to predetermined bioactivity profiles. Such modelling systems will allow the design of peptides with favourable therapeutic efficacy, and above all, ensure their adequate bioavailability and (preferably oral) administration. Based on the previous background, the objectives of PeptiMOL were the following: 1. Define parameters (numerical molecular descriptors) to characterize the structural, compositional and physicochemical properties of peptides and develop a user-friendly Java-based tool for their computation. 2. Build mathematical models to predict the PK properties of peptides using the state of the art on statistical and machine learning techniques. 3. Implement the developed models in a Java-based chemoinformatic platform which will enable potential end-users to virtually screen peptide libraries or design novel peptide structures with desirable physicochemical and PK profiles.

Data: CORDIS, © European Union