The pharmaceutical industry is currently challenged to accelerate the development of new drugs while reducing the cost of these efforts. Drug repositioning is an effective approach for achieving these goals. Evaluating existing compounds that have been proven safe—whether approved, still in clinical trials, or previously found to be ineffective for the diseases they were originally targeting—takes less time and money. The challenge is to identify the right existing compounds to investigate for a given disease.
Many basic strategies
Drug repositioning today is currently pursued using one of five key approaches, according to Aris Persidis, co-founder and president of Biovista, which is developing a pipeline of repositioned drug candidates in neurodegenerative diseases, epilepsy, oncology, and orphan diseases. Phenotypic screening is the one method that involves laboratory testing. In this approach, a drug substance is tested in parallel against a small set (10–50) of animal models or cell lines. “This approach is neither comprehensive nor systematic by any means, but if one is lucky and gets a hit, then a potential new use of a drug in a specific new disease has been identified with some initial experimental proof,” Persidis says.
To achieve systematic or comprehensive repositioning, computer-based approaches must be used. There is no single preferred digital tool or technology for accomplishing drug repositioning because different tools are optimally suited for different strategies, according to Hermann Mucke, CEO of H.M. Pharma Consultancy, a firm that provides drug development services, including support for drug repositioning activities. “Many pharmaceutical companies use their own in-house algorithmic solutions or leverage technology platforms developed by specialists in repositioning, such as Biovista, Excelra, etc.,” he notes.
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In addition to in-silico modeling and target docking using algorithmical solutions beyond medicinal chemistry, Mucke points to data mining of side effects and drug interactions as a way to find valuable clues for APIs formulated into marketed drugs. “Crucial information can also be extracted from the peer-reviewed and patent literature by creative pharmacologists who can think out of the box and ‘connect the dots’,” he adds.
The other four approaches identified by Persidis overlap somewhat with these strategies and include literature mining, pathway mining, adverse-event matching, and gene-regulation mining. “Pathway mining involves the use of pathway keywords to search the literature and other gene/protein pathway databases where a specific pathway is linked to different diseases, and then match drugs known to hit that pathway across the different diseases,” he explains.
Similarly, in adverse-event matching, lists of adverse events are used to mine the labels of drugs for the same adverse events. “If different drugs that work in different diseases have the same adverse events, then maybe they can work in the same diseases,” Persidis observes. Gene regulation mining uses data from microarrays to find genes that may be up/down-regulated in a disease microarray, and then the literature is searched to find drugs known to have the opposite effect.
The emergence of AI
Unfortunately, there are few data sources that are specifically tuned to the needs of drug repurposers, according to Mucke. In addition, analysis tools are not yet sufficiently advanced to assist without extensive training in pathway and systems medicine.
The existing approaches have all yielded some successes and failures, adds Persidis. They fall into the “quick/easy/cheap category,” however, and are limited in their applicability. “To do repositioning is easy, to do it well is hard. The key limitation of these existing computational approaches is that they are focused on reducing repositioning to one parameter (gene up/down, common side effects, etc). As a result, they do not take into account the complexity of diseases and their mechanisms of action, or the complexity of patient populations,” he said.
Going forward, both Persidis and Mucke believe that the application of artificial intelligence to drug repositioning will take the field much further than it can go today. “AI is emerging as the best tool to do repositioning well. New AI-driven technology shows the best promise, because AI is able to integrate and use many different types of data much more effectively than before,” Persidis explains.
Adds Mucke: “Advanced AI technologies that can look for connections they have not been specifically trained to identify will revolutionize drug repositioning within the next few years, taking us beyond mere expert systems.”
Date: September 11, 2018
Source: PharmaTech