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Drug Mechanism: A bioinformatic update.
Cirinciani, Martina; Da Pozzo, Eleonora; Trincavelli, Maria Letizia; Milazzo, Paolo; Martini, Claudia.
Afiliação
  • Cirinciani M; Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy.
  • Da Pozzo E; Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy.
  • Trincavelli ML; Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy.
  • Milazzo P; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy; Department of Computer Science, University of Pisa, Largo Pontecorvo, 3, 56127 Pisa, Italy.
  • Martini C; Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy. Electronic address: claudia.martini@unipi.it.
Biochem Pharmacol ; 228: 116078, 2024 Oct.
Article em En | MEDLINE | ID: mdl-38402909
ABSTRACT
A drug Mechanism of Action (MoA) is a complex biological phenomenon that describes how a bioactive compound produces a pharmacological effect. The complete knowledge of MoA is fundamental to fully understanding the drug activity. Over the years, many experimental methods have been developed and a huge quantity of data has been produced. Nowadays, considering the increasing omics data availability and the improvement of the accessible computational resources, the study of a drug MoA is conducted by integrating experimental and bioinformatics approaches. The development of new in silico solutions for this type of analysis is continuously ongoing; herein, an updating review on such bioinformatic methods is presented. The methodologies cited are based on multi-omics data integration in biochemical networks and Machine Learning (ML). The multiple types of usable input data and the advantages and disadvantages of each method have been analyzed, with a focus on their applications. Three specific research areas (i.e. cancer drug development, antibiotics discovery, and drug repurposing) have been chosen for their importance in the drug discovery fields in which the study of drug MoA, through novel bioinformatics approaches, is particularly productive.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biologia Computacional / Reposicionamento de Medicamentos Limite: Animals / Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biologia Computacional / Reposicionamento de Medicamentos Limite: Animals / Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article