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PeptiMOL

Financiado

Modeling the pharmacokinetics profiles of therapeutic peptides by chemoinformati...

Peptides have been acclaimed as the drugs of the future, thanks to their high specificity and activity, as well as their easy degradation. This implies that they generally possess reduced toxicity, few secondary effects, and are t... ver más

30/04/2022

MOLDRUG AI SYSTEMS

161K€

Presupuesto del proyecto: 161K€

Líder del proyecto

MOLDRUG AI SYSTEMS Investigación y desarrollo experimental en biotecnología

TRL 3-4 | 130K€

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-04-30

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

MSCA-IF-2019: Individual Fellowships

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MOLDRUG AI SYSTEMS

160.93K€ | Lider

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27-11-2024: DGIPYME En las últimas 48 horas el Organismo DGIPYME ha otorgado 1 concesiones

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Duración del proyecto: 24 meses Fecha Inicio: 2020-04-15
Fecha Fin: 2022-04-30

Líder del proyecto

MOLDRUG AI SYSTEMS

TRL 3-4 | 130K€

Presupuesto del proyecto 161K€

Descripción del proyecto Peptides have been acclaimed as the drugs of the future, thanks to their high specificity and activity, as well as their easy degradation. This implies that they generally possess reduced toxicity, few secondary effects, and are thus administered in small doses. Peptides possess multiple therapeutic applications, which include: antivirals, antifungals, antibiotics, modulators of the immune, cardiovascular and nervous systems, etc. However, it has been demonstrated that 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. It is essential to develop methods for modeling the bioactivity of peptides, predict their pharmacokinetic profiles and ultimately allow for the design of novel peptide chains adapted to predetermined bioactivity profiles. Such modeling systems will allow for the design of peptides with favorable therapeutic efficacy, and above all, ensure their adequate bioavailability and (preferably oral) administration. Based on this background, the objectives of PeptiMOL are: • Define parameters (numerical molecular descriptors) for characterizing the structural, compositional and physicochemical properties of peptides and develop a user-friendly Java-based tool for their computation. • Construct mathematical models to predict the PK properties of peptides using the state of the art statistical and machine learning techniques. • 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.

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