Machine Learning and drug repositioning

Abstract

Machine learning has been proved to be useful on a pharmacological scale, too. Drugrepositioning is a very well-known method for big pharma, by which the use of an alreadyapproved drug (whose purpose is the treatment of a given disease) is extended to a differentdisorder against which its efficacity has been proved.In the most part of cases where drug response is predicted through machine learning models,it is necessary to perform a first step where information is selected and filtered, in whichresearchers must take into consideration that there is always a set of patients who will showan extreme response, which makes almost compulsory to use a wider range of simples or celllines. When it comes to classifying and selecting information, three of the most used methodsare elastic net regression models, random forest ones and specifically-designed algorithms.A training phase must always be incorporated to the process in order to callibrate the model.Following this stage, an independent evaluation must be carried out by performing multipletests. This process is conducted in order to ensure that the putative model is accurate in itspredictions. Lastly, it is necesary to test the model by using clinical-resembling data.• The evaluation can be performed via two processes: K-Fold or Leave-one-out. The firstone divides the “raw” dataset in two parts, using the first as a training dataset and thesecond one as a testing dataset. Leave-one-out, however, works similarly but it leavesonly a single sample from the “raw” dataset as a test, making it compulsory to repeatthis stage many times.• Nevertheless, general machine learning techniques can be divided in two types:supervised machine learning, which uses already-created gruoups whithin the traningdata, or unsupervised one, which creates these groups from the trainig data.On behalf of the building process for drug repositioning approaches, it comprises several steps,as well. When it comes to seeking for relationships between drugs and diseases, networkbased methods can be used in any of its forms: clustering (searching relationships betweendrugs and targets among clusters of these) or propagation approach. The last one can eximinea network in a sigle region or in its entirety. Anyways, networks can include homogeneous orheterogeneous data.• One example of this is the Zhao and So essay, where they used several algorithms ontranscriptomic data to examine the effects on protein synthesis and expression ofvarious drugs and examine other potential applications for them.Nevertheless, until today, only a few machine learning approaches have been applied onclinical trials. This is mainly due to the difficulties that must be faced when filtering the hugeamounts of data that are used. Moreover, data-filtering procedures are sometimes notsystematic, which limits its possible uses. However, machine learning offers very interestingbenefits compared to clinical trials, as it can save researchers much time and money.Extract from:1. Rethinking Drug Repositioning and Development with Artificial Intelligence, MachineLearning, and Omics [Internet]. [citado 17 de noviembre de 2021]. Disponible en:https://www.liebertpub.com/doi/epdf/10.1089/omi.2019.0151