The promising technology of drug repurposing not only saves time and cost, but also reduces risk in the process of developing drugs to treat cancer and other rare diseases, ultimately bringing life-saving treatments to patients faster. It is attracting attention from the entire pharmaceutical industry because it can provide
Traditional drug discovery and development is expensive and requires several steps that can take years for a new drug to receive FDA approval. The risk of failure is also high. Only about 10% of new drug applications receive market approval. (1) Comparatively, 30% of repurposed medicines are approved, giving companies a market-driven incentive to reuse existing assets. By reducing this drug discovery time and failure rate, drug repurposing has become a more attractive and productive approach in identifying new therapeutic uses for existing drugs.
Drug repurposing, often referred to as drug repositioning, identifies new indications for approved or clinically failed/unapproved investigational drugs. Compounds that are approved or fail are developed for alternative uses.
A systematic approach to drug repurposing
There are three systematic approaches to repositioning old medications: disease-centered, target-centered and drug-centered. A disease-centric approach identifies close relationships between new and old indications. Target-centric approaches link known targets and their established drugs to new indications, while drug-centric approaches link known drugs to new targets and their associated indications.
Drug repurposing strategies are rapidly expanding, especially in the field of rare and neglected diseases.
There are four main types:
- drug reuse– In order to obtain a new use patent, an existing licensed drug is repurposed for an indication different from the one for which it is currently used.
- Drug repositioning (target centric)– When using the same drug for extended or adjacent indications in the same therapeutic area.
- Drug rescue (drug-centered) – New uses are developed for chemicals and biological substances that were previously investigated in clinical studies but were not further developed, regulatory approval was not submitted, or had to be removed from the market. If
- Combination therapy (disease-centered) – Involves combining two or more existing drug compounds that, when used together, have a positive impact on an immediate medical need.
Maximize the value of existing drugs with a data-driven approach
Artificial intelligence (AI) is advancing drug discovery by extracting hidden patterns and evidence from biomedical data. IQVIA's innovative AI and machine learning (ML) technologies are used for drug repurposing, tailoring existing medicines to specific indications or combining medicines to address pressing medical needs. Introducing AI/ML techniques can speed up the new drug development process and give discontinued or failed drugs a second chance.
AI approaches include literature extraction, machine learning from genomics and bioinformatics data, and mining of electronic medical records (EMR) and claims data. These can cover everything from drug-protein interactions at the molecular level to sifting through millions of records to find drugs used to treat other conditions.
Electronic medical records routinely collect patient clinical data such as demographics, diagnoses, medications, treatments, and laboratory test results. Real-world data is stored in digital format and can be securely exchanged and accessed.
Various approaches used to improve drug development success include:
- bioinformatics – Use AI to determine similarities between drugs that share molecular features.
- Insurance claims data/EMR – Learn how people are currently using existing approved drugs and mine data on off-label use to find other indications where the drug could be beneficial.
- Natural language processing (NLP) – Assess associations between compounds, target proteins, and disease pathways by mining text data from scientific literature. NLP is not used alone, but in combination with one or both of the other two approaches to help validate the results.
AI can sift through vast amounts of records to find existing drugs used to treat other conditions and develop drug treatments for undertreated diseases or specific indications. Understanding drug-target interactions at the adaptive level can maximize the value of drug assets.
Because the safety of the drugs being used has already been tested in clinical trials for other uses, repurposing known drugs allows drug development to occur much faster and at a much lower cost than developing new drugs. can be delivered to patients.
Challenges in drug reuse
In traditional drug development, knowledge of failed assets is often limited and unpublished, hindering the insights needed to identify successful targets. Despite all its benefits, drug repurposing has a number of issues to consider. Many trials do not optimize the drug's clinical benefits and biological questions due to opportunistic designs and lack of clinical endpoints, and therefore do not provide the data needed to properly analyze older drugs. It may not be possible to obtain them all. In many cases, it is not clear when reporting the exact reason why a drug fails. Also, some trials enrolled only a small number of patients and had low 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 likelihood of successful clinical development trials. Ultimately, this process could give patients faster access to new treatments, provide answers to the symptoms of rare diseases, and potentially save lives.
With a drug discovery and development services team of world-class data scientists, cutting-edge AI technology, and consultants with years of expertise, IQVIA is uniquely suited to meet all types of drug reuse needs.
(1) Source: www.ncbi.nlm.nih.gov/pmc/articles/PMC5694537/
(2) Source: Artificial Intelligence in Drug Repurposing for COVID-19 – PMC (nih.gov)
