Making the most effective use of available computational methods for drug repositioning

PRADA GORI DENIS N.; ALBERCA, LUCAS N; TALEVI ALAN

EXPERT OPINION ON DRUG DISCOVERY

INFORMA HEALTHCARE

Lugar: London; Año: 2023

1746-0441

Introduction: A substantial debate has been given in the last decades around theapparent fall in productivity in the pharmaceutical sector. The development of secondor further medical uses for known drugs is a possible answer to expedite thedevelopment of new therapeutic solutions. Computational methods are among themain strategies for exploring drug repurposing opportunities in a systematic manner.Areas covered: This article reviews three general approximations to systematicallydiscover new therapeutic uses for existing drugs: disease-, target-, and drug-centricapproaches, along with some recently reported computational methods associatedwith them.Expert opinion: Computational methods are essential for organizing and analyzingthe large volume of available biomedical data, which has grown exponentially in theera of big data. The clearest trend in the field involves the use of integrativeapproaches where different types of data are combined into multipartite networks.Every aspect of computer-guided drug repositioning has currently incorporatedstate-of-the-art machine learning tools to boost their pattern recognition andpredictive capabilities. Remarkably, a majority of the recently reported platforms arepublicly available as web apps or open-source software. The introduction ofnationwide electronic health records provides invaluable real-world data to detectunknown relationships between approved drug treatments and diseases