The promising technique of drug repurposing is gaining attention from across the pharma industry, not only for reducing time and cost, but also lowering risk in the process of developing drugs for cancers and other rare illnesses — ultimately getting life-saving treatments to patients faster.

Traditional drug discovery and development is expensive and involves several stages that can last years for a new drug to obtain FDA approval. It also comes with a high risk of failure. Only about 10% of new drug applications gain market approval. ⁽¹⁾ Comparatively, 30% of repurposed drugs are approved, giving companies a market-driven incentive to repurpose existing assets. By reducing this drug discovery time frame and failure rate, drug repurposing has become a more attractive, productive approach in identifying new therapeutic uses for already-available drugs.

Drug repurposing, often referred to as drug repositioning, identifies new indications for approved or clinically failed/investigational drugs that have not been approved. Approved or failed compounds are then developed for alternative uses.

Systematic Approaches to Drug Repurposing

There are three systematic approaches to repositioning old drugs: disease-centric, target-centric and drug-centric. The disease-centric approach identifies close relationships between a new and an old indication. A target-centric approach links a known target and its established drug to a new indication, and a drug-centric approach connects a known drug to a new target and its associated indication.

Drug repurposing strategies are expanding rapidly, especially in the area of rare and neglected diseases.

There are four main types:

• Drug Repurposing– Where an existing licensed drug is reused for a different indication than what it is currently used for in order to obtain a new-use patent.
• Drug Repositioning (target-centric)– Involves using the same drug for an indication extension or an adjacent indication in the same therapeutic area.
• Drug Rescue (drug-centric) – When new uses for chemical and biological entities that previously were investigated in clinical studies but not further developed nor submitted for regulatory approval, or had to be removed from the market, are developed.
• Combination Therapies (disease-centric) – Involves combining compounds from two or more existing drugs, which, when used together, make a positive impact on a pressing medical need.

Data-driven approaches maximize the value of existing drugs

Artificial intelligence (AI) is advancing drug discovery by extracting hidden patterns and evidence from biomedical data. IQVIA’s innovative AI and machine learning (ML) techniques are utilized in drug repurposing to align existing drugs with specific indications or combine drugs to address pressing medical needs. Deploying AI/ML methods speeds up the process of developing new drugs and giving a second chance to withdrawn or failed drugs.

AI approaches can include literature extraction, machine learning from genomics and bioinformatics data, and mining of electronic medical records (EMR) and claims data. They can be deployed to cover everything from drug to protein interactions at the molecular level to sifting through millions of records to find drugs used to treat other conditions.

Electronic health records are routinely collected patient clinical data, such as demographics, diagnoses, medications, procedures, and laboratory test results. The real-world data is stored in digital form, which can then be exchanged and accessed securely.

Different approaches used to improve drug development success include:

• Bioinformatics - Determine the degree of similarity between drugs that share molecular features using AI.
• Claims data/EMR - Learn how people are currently using an existing approved drug, then mine data for off-label usage to find a different indication it benefits,
• Natural Language Processing (NLP) - Assess associations between compounds, target proteins, and disease pathways, by mining text data from scientific literature. NLP would not be used alone but paired with one or both of the other two approaches to help validate findings.

AI sifts through massive amounts of records to find existing drugs used to treat other conditions to develop a drug treatment for an underserved disease or for a specific indication. Drug asset values can be maximized through gaining an understanding of drug target interactions at the indication level.

Because the safety of drugs being has already been tested in clinical trials for other applications, repurposing known drugs can bring medications to patients much faster and with a much lower cost than developing new drugs.

Challenges in drug repurposing

With traditional drug development, knowledge of failed assets is often limited and unpublished, impeding the insights needed to identify successful targets. Despite all the benefits, drug repurposing has a number of issues to consider. It is sometimes impossible to get all the necessary data to properly analyze older drugs, as many trials did not optimize the drug's clinical benefits and biological questions because of their expedient design and lack of clinical endpoints. Oftentimes it is not even evident in reporting the exact reason why a drug failed. Also, some trials only had a small number of patients enrolled, therefore lacking much statistical power. ⁽²⁾

Drug Repurposing continues to grow in popularity as advanced AI opens the door to new insights into disease drug targets and increases the odds that clinical development trials will be successful. Ultimately, this process will allow patients access to new treatments faster, offering answers for rare disease symptoms and potentially saving lives.

IQVIA is uniquely suited to meet any type of drug repurposing needs with a Drug Discovery and Development Services team of world-class data scientists, cutting-edge AI technology, and consultants with years of domain expertise.

(1) Source: www.ncbi.nlm.nih.gov/pmc/articles/PMC5694537/

(2) Source: Artificial intelligence in COVID-19 drug repurposing - PMC (nih.gov)