Repositioning existing drug substances for the treatment of different indications can significantly reduce the cost and time required for the development of new medicines. While the field has graduated from discovery to purposeful evaluation using a range of software tools, advances in artificial intelligence are expected to dramatically improve predictive abilities.
Importance of Drug Repositioning
Initially informal and generally serendipitous (considering side effects and off-label uses), drug repositioning (repurposing, recycling, etc.) is much more systematic than it was in the past and is achieved using a broad array of tools, technologies and methods. This shift toward repurposing was driven by recognition across the pharmaceutical industry that leveraging existing drug substances for different usages — whether approved and marketed, currently in clinical trials or failed and on the shelf — offers many benefits.
Cost and time savings are the most influential factors driving repositioning. Development times for repositioned drugs can be 30–60% lower than those for novel compounds.1 Costs can also be reduced by as much as 60% of those for new chemical entities (NCEs).2 Approved drugs have already been subjected to regulatory review and have known pharmacological and safety profiles. As a result, expedited approval is possible via the 505(b)2 approach in the United States or the hybrid approach in the European Union.3 Risk may also be lowered for repurposed projects. Notably, phase III trials for products developed via 505(b) 2–type approaches have higher success rates,3 and the approval rates for repurposed drugs are close to 30%.2
Other benefits of drug repurposing include the potential to achieve longer life cycles and improved investment returns because of shorter development times. Rescuing previously failed compounds can lead to recovered R&D expenses and also contribute to improved returns. Patent-life extension for still-protected drugs and the introduction of new patent protections for off-patent drugs are also possible.
Leveraging the thousands of approved drugs and more than 4,000 compounds that have been discontinued at phase II development in new drug development efforts is particularly valuable when targeting rare diseases.4 As many existing anticancer drugs cause significant negative side effects, the repurposing of non-cancer agents with minimal side effects into cancer medicines is also quite attractive.5 Advances in drug repurposing methods and access to genomic data is also making it possible to develop personalized, repurposed options in a systematic manner.6
In 2015, repositioned drugs were estimated to account for 25% of pharmaceutical industry revenues.7 The global market for drug repurposing is estimated to reach $31.3 billion in 2020, growing at a compound annual growth rate of 5.1% from $24.4 billion, according to BCC Research.8
Current Strategies
In addition to the “serendipitous” or knowledge-based drug repositioning5 approaches, researchers have developed many other methods and tools to assist with the identification of new indications that can be treated by existing drug substances.
The next level above serendipity is activity-based drug repositioning, which involves physical experimentation and drug testing.5 This approach requires access to large libraries of compounds, which are currently limited. Phenotypic screening is the most common method and involves testing many drugs in parallel against a small set (10–50) of animal models or cell lines. It is generally not systematic or comprehensive, but can occasionally be successful in identifying lead compounds.9
In silico drug repositioning includes a variety of methods involving computational biology and chemistry methods using data from a wide variety of sources.3,5,10 Systems biology approaches to drug repositioning consider the pathophysiological maps of diseases to identify targets for modifying them and potential compounds that can hit those targets.5
Computer-based approaches are essential for processing the large quantities of information needed to enable systematic drug repositioning efforts. A wide variety of tools are currently available to support different strategies. In silico modeling and
target docking are often combined with data mining of clinical reports, peer-reviewed and patent literature and genomic data; pathway mining; adverse-event matching; and gene-regulation mining.8,11 Big data analytics, advanced modeling software and high-throughput screening techniques are all employed.12
Why Artificial Intelligence is Different
While these computational approaches have led to the identification of many indications for existing drug substances, they remain limited in capability. Most focus on only one aspect of the problem, such as common side effects or adverse events, and do not consider the complexity of diseases and their mechanisms of action or the complexity of patient populations, 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.9
The next step, therefore, is the application of artificial intelligence (AI) to drug repositioning. Drug repositioning cannot really be done well without AI, because it enables the effective integration of many different types of data and, if designed correctly, has the ability to make connections that are not likely to be seen by researchers on their own.9
Potential for Real Change
Some early examples of the application of AI programs to drug repositioning have been very promising. Biovista’s Project Prodigy AI has been successfully used to identify candidates in a number of disease classes. Repositioning even without AI can be more efficient than traditional drug development approaches.3 Current software tools provide significant assistance to researchers, but much of the success of drug repositioning depends on the ability of those scientists to understand and use those tools and interpret the results they generate. Applying AI to drug repositioning greatly increases the efficiency of the process. It has the potential to significantly reduce the cost and time required for drug development.9
References
- Dudley, Joel T., Tarangini Deshpande, Atul J. Butte. “Exploiting drug-disease relationships for computational drug repositioning.” Brief. Bioinforma. 12:303–311 (2011).
- Van Arnum, Patricia. “Drug Repurposing and Repositioning: Making New Out of Old.” DCAT Value Chain Insights. 26 Jul. 2016. Web.
- Challener, Cynthia A. “Expediting the Discovery and Development of Drugs.” Pharmaceutical Technology. 41: 24–25 (2017).
- Walker, Nigel. “Accelerating Drug Development through Repurposing, Repositioning and Rescue.” Pharmaceutical Outsourcing. 7 Dec. 2017. Web.
- Turanli, Beste, et al. “Drug Repositioning for Effective Prostate Cancer Treatment.” Front. Physiol. 15 May 2018. Web.
- Talevi, Alan. “Drug repositioning: current approaches and their implications in the precision medicine era.” Expert Review of Precision Medicine and Drug Development. 3: 49–61 (2018).
- Persidis, Aris. “Myths and Realities of Repositioning.” Arrowhead 4th Annual Drug Repositioning, Repurposing & Rescue Conference. Chicago, IL. 27–28 May 2015.
- Global Markets for Drug Repurposing. Rep. BCC Research. Jan. 2016. Web.
- Challener, Cynthia A. “Can Artificial Intelligence Take the Next Step for Drug Repositioning?” Pharmaceutical Technology. 42: 22–26 (2018).
- Vanhaelen, Quentin, et al. “Design of efficient computational workflows for in silico drug repurposing.” Drug Discov. Today. 22: 210–222 (2017).
- Nosengo, Nicola. “Can you teach old drugs new tricks?” Nature. 14 Jun. 2016. Web.
- Yella, Jaswanth K., Suryanarayana Yaddanapudi, Yunguan Wang, Anil G. Jegga. “Changing Trends in Computational Drug Repositioning.” Pharmaceuticals. 11: 57 (2018).