Biology subjects

Otero-Carrasco, B.

Publications and source records attributed to Otero-Carrasco, B..

4 recordsLinked to original sources

bioRxiv2025

Accelerating Drug Repurposing with AI: The Role of Large Language Models in Hypothesis Validation

Drug repurposing accelerates drug discovery by identifying new therapeutic uses for existing drugs, but validating computational predictions remains a challenge. Large Language Models (LLMs) offer a potential solution by analyzing biomedical literature to assess drug-disease associations. This study evaluates four LLMs (GPT-4o, Claude-3, Gemini-2, and DeepSeek) using ten prompt strategies to validate repurposing hypotheses. The best-performing prompts and models were tested on 30 pathway-based cases and 10 benchmark cases. Results show that structured prompts enhance LLM accuracy, with GPT-4o and DeepSeek emerging as the most reliable models. Benchmark cases achieved significantly higher accuracy, precision, and F1-score (p < 0.001), while recall remained consistent across datasets. These findings highlight LLMs potential in drug repurposing validation while emphasizing the need for structured prompts and human oversight.

bioinformatics↗

bioRxiv2023

Identifying patterns to uncover the importance of biological pathways on known drug repurposing scenarios

BackgroundDrug repurposing plays a significant role in bringing effective treatments for certain diseases faster and more cost-effectively. Successful repurposing cases are mostly supported by a classical paradigm that stems from de novo drug development. This paradigm is based on the "one-drug-one-target-one-disease" idea. It consists of designing drugs specifically for a single disease and its drugs gene target. In this article, we investigated the use of biological pathways as potential elements to achieve effective drug repurposing. MethodsConsidering a total of 4.214 successful cases of drug repurposing, we identified cases in which biological pathways serve as the underlying basis for successful repurposing, referred to as DREBIOP. Once the repurposing cases based on pathways were identified, we studied the inherent patterns within them by considering the different biological elements associated with this dataset, as well as the pathways involved in these cases. Furthermore, we obtained gene-disease association values to demonstrate the diminished significance of the drugs gene target in these repurposing cases. To achieve this, we compared the values obtained for the DREBIOP set with the overall association values found in DISNET, as well as with the drugs target gene (DREGE) based repurposing cases using the Mann-Whitney U Test. ResultsA collection of drug repurposing cases, known as DREBIOP, was identified as a result. DREBIOP cases exhibit distinct characteristics when compared to DREGE cases. Notably, DREBIOP cases are associated with a higher number of biological pathways, with Vitamin D Metabolism and ACE inhibitor being the most prominent pathways involved. Additionally, it was observed that the association values of GDAs in DREBIOP cases are significantly lower than those in DREGE cases (p-value < 0.05). ConclusionsBiological pathways assume a pivotal role in drug repurposing cases. This investigation successfully revealed patterns that distinguish drug repurposing instances associated with biological pathways. These identified patterns can be applied to any known repurposing case, enabling the detection of pathway-based repurposing scenarios or the classical paradigm. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/572035v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@187d2ceorg.highwire.dtl.DTLVardef@210b96org.highwire.dtl.DTLVardef@17fc4b7org.highwire.dtl.DTLVardef@14c0184_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Orphan Drugs and Rare Diseases: Unveiling Biological Patterns through Drug Repurposing

Rare diseases are a collection of unusual pathologies that afflict millions of individuals globally. However, the creation of treatments for these conditions is frequently limited due to the high expenses and lack of profitability associated with drug development. Orphan drugs, which are medications specifically designed for rare diseases, have played a pivotal role in treating these diseases over the past several years. Nevertheless, their creation remains challenging, and many rare diseases lack approved therapies. Therefore, drug repurposing has emerged as a viable strategy for identifying potential new treatments for these pathologies, a technique consists in using existing drugs to treat a new disease different from the one that they were developed for. This approach can significantly reduce the time and cost of drug development while increasing the likelihood of success. In this paper, we examined the temporal progression of orphan drugs since their introduction and assess the impact of drug repositioning on treatments for rare diseases. Additionally, we aim to identify biological patterns that may be unique to rare diseases treated with repurposed orphan drugs. To this end, we analyzed various biological components associated with these diseases, categorized linked diseases, and obtained the type of orphan drug associated with them. Lastly, we evaluated the phenotypic similarity between diseases treated with an orphan drug through repurposing. Through these findings, we have gained insight into the evolution of orphan drug development in recent years and identified specific patterns that characterize rare diseases associated with them.

bioinformatics↗

bioRxiv2023

Enhancing drug repurposing on graphs by integrating drug molecular structure as feature

Drug repurposing has become increasingly important, particularly in light of the COVID-19 pandemic. This process involves identifying new therapeutic uses for existing drugs, which can significantly reduce the cost, risk, and time associated with developing new drugs, de novo development. A previous conducted study proved that Deep Learning can be used to streamline this process by identifying drug repurposing hypotheses. The study presented a model called REDIRECTION, which utilized the rich biomedical information available in graph form and combined it with Geometric Deep Learning to find new indications for existing drugs. The reported metrics for this model were 0.87 for AUROC and 0.83 for AUPRC. In this current study, the importance of node features in GNNs is explored. Specifically, the study used GNNs to embed two-dimensional drug molecular structures and obtain corresponding features. These features were incorporated into the drug repurposing graph, along with some other enhancements, resulting in an improved model called DMSR. Performance score for the reported metrics values raised by 0.0448 in AUROC and 0.0919 in AUPRC. Based on these findings, we believe that the method used for embedding drug molecular structures is interesting and captures valuable information about drugs. Its incorporation in the graph for drug repurposing can significantly benefit the process, leading to improved performance evaluation metrics.

bioinformatics↗