Discovering 6 applications of bioinformatics in drug repurposing
Bioinformatics plays a critical role across pharmaceutical innovation, with many applications in drug discovery and repurposing.
Northeastern University defines bioinformatics as a combination of computer programming, big data, and biology that can be applied to identify and understand sequences or patterns in healthcare. According to the National Human Genome Research Institute, bioinformatics’ role in biological research is comparable to data analysis in the information age.
The University of Melbourne notes that bioinformatics has been used for multiple healthcare applications, including precise cancer care, pathogen control, and personalized medicine. It has also been used for agricultural purposes.
“The goal of bioinformatics is to leverage all of the new technologies that we have — which would include advances in computational capacities, new graphics cards, new algorithms — and applying that to big data generated from biological systems to answer questions previously not answerable,” says Stefan Kaluziak, an assistant professor of bioinformatics at Northeastern University in a blog post.
While bioinformatics can be applied across various healthcare-related research and innovation, the pharmaceutical industry, in particular, can benefit from these applications.
Proventa International notes that bioinformatics can be critical in drug discovery and pharmaceutical research. For example, it can be a vital target identification and validation tool. Analyzing genomic and proteomic data can help researchers identify disease targets, such as genes or proteins, to focus on during the drug development process.
Another application is computer-aided drug design, which uses computational modeling to accelerate the drug development process by predicting drug and target molecules.
Drug repurposing is another area of the pharmaceutical industry that can benefit from bioinformatics.
Bioinformatics is crucial in drug repurposing and identifying new therapeutic uses for existing drugs. Traditionally, drug development involves identifying a specific target or mechanism of action and designing a drug to interact with that target. However, this process is time-consuming and expensive.
In contrast, drug repurposing offers a faster and more cost-effective approach by leveraging existing drugs that have already undergone extensive testing for safety and efficacy. Bioinformatics, with its ability to analyze and interpret vast amounts of biological data, plays a central role in several key aspects of drug repurposing.
Proventa International states, “With the help of bioinformatics, scientists can explore existing drugs and identify new therapeutic applications for known compounds. Drug repurposing offers a cost-effective and time-saving strategy to find potential treatments for different diseases.”
In this article, PharmaNewsIntelligence will explore six bioinformatics applications in drug repurposing, in no particular order.
1. Data Integration
Data integration is a critical application of bioinformatics across the pharmaceutical industry. Beyond overlaying diverse data sets, including genomics, proteomics, transcriptomics, and other omics data, with clinical insights for drug discovery, bioinformatics can also integrate these data sets for drug repurposing.
An article in Integrative Bioinformatics identifies multiple kinds of data integration, including the following:
- Vertical integration (VI) includes datasets carried out on the same samples. According to the journal, these include “separate bulk experiments with matched samples in different modalities or single-cells measured through joint assays.”
- Horizontal Integration (HI) “describes the complementary task where several datasets have been acquired in the same biological modality, allowing multiple batches to be expressed within a common features space.”
- Mosaic Integration “allows pairs of datasets to be measured in overlapping modalities.”
Drug repurposing approaches heavily rely on data availability and analysis to identify targets for existing drugs. Integrating datasets provides additional data visibility, offering a broader view that may facilitate improved target identification.
2. Computational Target Identification
Computational target identification is also a critical application for bioinformatics in drug repurposing. Bioinformatics tools can analyze large-scale genomic and proteomic datasets to identify disease-associated targets. By comparing healthy and diseased individuals' genetic and proteomic profiles, researchers can identify specific genes, proteins, or pathways that are dysregulated in a disease. These targets may be targeted by existing drugs.
An article in Translational and Clinical Pharmacology notes that machine learning, network models, text mining, and semantic inference are computation tools that can be used in drug repurposing.
3. Drug–Target Interaction Prediction
Bioinformatics methods help predict the interactions between drugs and their potential targets. By integrating information about the chemical properties of drugs and the structural features of target proteins, researchers can computationally evaluate the likelihood of a drug binding to a specific target. This information is essential for identifying potential drug-target interactions and prioritizing drug candidates for experimental validation.
While there are traditional laboratory methods for drug–target interaction predictions, these approaches are expensive and time-consuming. However, since drug repurposing uses approved medications, researchers can apply bioinformatics to existing datasets to understand drug-target interactions without additional testing.
4. Network Analysis
Bioinformatics enables the construction and analysis of complex biological networks, such as protein–protein or drug–target interaction networks.
Creative Proteomics defines network analysis as an approach that uses data analysis or generated graphs to simplify biological data, allowing for a measure of “the statistical characteristics of the structure and behavior of the network system.”
Beyond protein–protein and drug–target networks, Briefings in Bioinformatics identifies multiple other networks, including gene regulatory, metabolic, and drug–drug interaction networks.
Network-based approaches can identify indirect relationships between drugs, targets, and diseases, uncovering potential repurposing opportunities. For example, if two drugs target proteins that interact in a disease-related network, it suggests that one drug could be repurposed to treat the other drug's indication.
5. In Silico Screening
Bioinformatics tools facilitate the virtual screening of large chemical libraries against a target of interest. By employing molecular docking simulations and other computational methods, researchers can rapidly evaluate the potential of thousands or even millions of compounds to interact with a target. This approach helps prioritize drug candidates for further experimental testing, saving time and resources.
Translational and Clinical Pharmacology notes that in silico screening for drug, repurposing utilizes two technological trends. One is the availability of high-throuput data sets from various sources. The other is advances in computational and data sciences that lay the foundation for developing repurposing algorithms.
6. Adverse Event Prediction
Bioinformatics can also predict potential adverse effects of repurposed drugs. Researchers can estimate the likelihood of adverse events associated with a specific repurposed use by analyzing known drug-target interactions, side effect profiles, and genetic data.
Bioinformatics is critical for analyzing complex biological data, identifying potential drug targets, predicting drug-target interactions, and prioritizing drug candidates for repurposing. By leveraging bioinformatics approaches, researchers can accelerate drug discovery and uncover new therapeutic uses for existing drugs, improving patient outcomes and reducing costs.
