H2020Индивидуална стипендия2020–2022

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

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2020-05-01 → 2022-04-30
Финансиране от ЕС: 160 932 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Терапевтичните пептиди, които действат като антибиотици или противовирусни средства, се анализират чрез математически модели и компютърни инструменти. Това помага за създаването на лекарства с по-добра стабилност, които да се усвояват по-лесно от организма, например чрез прием през устата.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

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.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.

Оригинален текст от CORDIS (на английски).

Участници

MOLDRUG AI SYSTEMS SL · ValenciaКоординаторИспания