Executive Summary: Disorders in melanogenesis, hyperpigmentation, age spots, as well as some forms of skin cancer are associated with overproduction of melanin in the basal layers of the skin. Melanin production is catalyzed in the first steps of the biochemical pathway by the enzyme tyrosinase (EC.1.14.18.1), which makes possible the enzymatic oxidation of the amino acid tyrosine to dopa-quinone. For all this and due to its wide distribution in the phylogenetic scale, this enzyme has attracted the interest of the international scientific community in the search for powerful inhibitors that make possible its use in the food industry as a food additive to retard its decomposition process. , as is the case of vegetables and wines, in the pharmaceutical industry for skin diseases, in the cosmetics industry as a skin depigmenting agent and in the agrochemical industry as an alternative means for effective control of insects that are harmful to crops. .
However, the compounds currently available, both for therapeutic-cosmetic and industrial applications, lack a good pharmacokinetic/toxicological profile and/or have effectiveness problems. Taking all these factors into consideration, it can be concluded that increasing the speed in the discovery of new tyrosinase inhibitors (TIs), and their introduction to the market, has a great impact on the national/international community. However, the discovery of such active compounds can be a financially risky and scientifically complex process. The use of computational techniques (DRY) of 'rational' design constitutes a useful tool to reduce costs and decrease the research time required to obtain the proposed objectives, increasing the possibility of success in the discovery of new chemotypes (new chemical skeletons bases, known as seeding). That is why in the present investigation we intend to combine computational techniques of proven effectiveness with others that are developed in the research itself so that, once new "series heads (leader compounds)" active against the tyrosinase enzyme have been identified, carry out the biosilico design. of new, more potent and less toxic agents. These compounds will be synthesized/obtained and evaluated experimentally (WET). Therefore, the present investigation aims to discover (select/identify or design/optimize) new families of chemical compounds with marked action against the tyrosinase enzyme that have potential utility in the medical-pharmaceutical, agrochemical, and cosmetic industries. In addition, all the theoretical aspects that are discovered/developed during this research will be computationally implemented, generating a multiplatform free software that will allow the automation of the discovery process of this important type of compound (new expert system).
General Objective: Obtain new mathematical models (ADME/Tox filters, Molecular Similarity, QSAR, Molecular Docking, Analysis of Activity Cliffs and Chemical Systems) and propose a more complete computational screening strategy that makes the identification of powerful ITs more effective through of virtual database screening (and/or de novo design) and that allows performing biochemical (Enzymatic) and in vitro tests of the most promising compounds in a more rational way.
Specific objectives
- Carry out an intensive search that allows expanding the classification database (Set of chemical compounds) including compounds that have been reported experimentally to be active against the tyrosinase enzyme and other inactive ones, then create another one to estimate the power of new TIs from of quantitative information on the activity.
- Develop QSAR models using machine learning techniques to estimate the passage of biological membrane molecules, eg, BBB (CNS), Skin, etc.
- Obtain QSAR models that allow estimating the environmental impact of discovered substances using terrestrial, airborne and aquatic endpoints.
- Extend and theoretically generalize the QuBILs-MAS indices using 2D multi-metrics (N-linear algebraic forms) and new aggregation operators, as well as implement their use in the TOMOCOMD-CARDD platform.
- Generate multi-reference molecular similarity search models using generalized aggregation operators and different molecular representations, computationally implementing the best configurations that allow focusing chemical libraries.
- Obtain QSAR mathematical models, using TOMOCOMD-CARDD molecular descriptors (Extended and generalized QuBILs-MAS Method as well as QuBILs-MIDAS) and various statistical, machine learning and artificial intelligence techniques available in WEKA, which allow classifying tyrosinase inhibitors and estimate the potency of the active compounds.
- Obtain assembled systems (meta-classifiers of different types, eg, selection, fusion, trained, etc.) of virtual screening that allow the identification/discovery of powerful TIs in a more precise way than using individual models.
Participating Institutions:
USFQ, UC, PUCESE, UEA, IKIAM.
Participants:
Director of the project Yovani Marrero.
- Yovani Marrero
- Maria Elena Hunt
- Cesar Garcia
- Xavier Quinonez
- Dagoberto Acosta
- Karel Dieguez
- Roldan Torres
Awarded budget: $59880
Project status: Signing of agreements.
