4 drivers of success in drug repurposing for rare diseases
按 Anton Yuryev, PhD
For Elsevier’s Year of the Zebra initiative, and in the 40th anniversary year of the Orphan Drug Act, an Elsevier expert looks at challenges and successes in repurposing drugs for rare diseases.
2023 marks the 40th anniversary of the Orphan Drug Act(打開新的分頁／視窗) in the US, which catalyzed the development of hundreds of therapies for rare disorders, over half of which were approved in the last decade. Despite this progress, there are more than 300 million people worldwide living with a rare condition, half of whom are children, and only 5%(打開新的分頁／視窗) of 8,000 known rare diseases have licensed treatments.
This is why Elsevier has launched its Year of the Zebra initiative(打開新的分頁／視窗), aiming to improve education about rare diseases — patients of which are known as zebras in medical education as they are less common than horses. One strategy that’s emerged as a pragmatic and potentially life-changing solution to the rare disease challenge is drug repurposing — the exploration of new uses for approved, discontinued and investigational drugs.
A complex path
For drug repurposing, researchers take one of three approaches: drug-centric, disease-centric or target-centric. The most common method for rare diseases is disease-centric repurposing; this involves gaining an understanding of the underlying biological mechanisms of a disease, then identifying existing treatments that act on molecular targets involved in the mechanisms, such as interactions with specific proteins, enzymes or signaling pathways. Rare disease challenges that complicate this process include often poorly understood biology of conditions, small patient populations, and more stringent regulations when it comes to childhood medications.
Two use cases where these challenges were successfully mitigated to uncover drug repurposing candidates include diffuse intrinsic pontine glioma (DIPG) and Friedreich’s ataxia. Analyzing the similarities between them, we identify four driversto make rare disease repurposing projects successful.
Using AI to uncover therapies for childhood brain cancer
A collaborative project between Elsevier and the Sinergia Consortium(打開新的分頁／視窗) brought together data and disease experts to create in silico molecular pathway models for DIPG, a rare form of childhood brain cancer. The complex and poorly understood maturation pathways of human neuron precursors had limited treatment discovery for DIPG. Researchers used AI techniques to better understand these pathways and identify drug repurposing candidates. Using Elsevier’s Biology Knowledge Graph, the team analyzed omics data from real-world DIPG patients to identify the most active genes in DIPG. They focused on TP53, a cancer suppressor, and one of the most frequently mutated genes in cancer cells that drives radio resistance.
The team text-mined PubMed abstracts and full-text journal articles for reports of drugs inhibiting “mutant TP53.” They compiled a list of 144 FDA-approved drugs known to target this mutation, scoring candidate drugs according to GSEA enrichment score(打開新的分頁／視窗) and by the number of inhibited regulators. They also predicted an additional 322 drugs to inhibit mutant TP53 based on the mechanisms for known mutant TP53 inhibitors. The intersection of known and predicted mutant TP53 inhibitors with drugs predicted to inhibit DIPG disease model and with drugs predicted to inhibit DIPG tumor expression yielded a list of 284 drugs; the top-ranking drugs were vorinostat, aspirin, dasatinib, niclosamide and sirolimus.
Repurposing for Friedreich’s ataxia in under an hour
In a demonstration that took just 40 minutes, a SciBite and Stardog(打開新的分頁／視窗) partnership uncovered drug repurposing candidates to treat Friedreich’s ataxia, a rare genetic disorder caused by a deficiency of frataxin — a small nuclear-encoded mitochondrial protein. A schema was built in SciBite’s ontology management platform CENtree(打開新的分頁／視窗), and the ontologies were deployed within TERMite(打開新的分頁／視窗), an entity recognition engine. TERMite automatically expanded synonyms and used these to identify entities within unstructured text. Entities were mapped back to a common identifier — turning unstructured documents into structured semantic data, rapidly extracting terms from 4,000 documents to create a knowledge graph.
The resulting knowledge graph connected data in a way that was not possible manually or in siloed systems. The graph collated data and metadata, enabling a simple way for users and machines to query the disease. It pinpointed four Phase IV drugs that increased the expression of frataxin, including tranylcypromine and etravirine.
What are the key drivers underpinning the success of these initiatives?
Gather a range of voices and expertise. Both studies prove that drug repurposing projects require input from a range of AI technology, data science and disease experts. Science acumen provides context for a disease so research questions can be parsed, while skilled data scientists can standardize and harmonize data and make it searchable. The voice of the patient is also important in rare diseases, where literature is often limited. Patient groups can shed light on disease symptoms and progression and offer access to the right patient cohort for trials.
Access to multiple FAIR data sources. The studies demonstrate the need to incorporate multiple sources and formats of data. Crucially, all data must be FAIR(打開新的分頁／視窗) to facilitate computational analyses by different users that will pinpoint suitable repurposing candidates. Specific examples of data sources include disease pathology information (e.g., pharmacokinetic, efficacy, and metabolizing enzyme and transporter data), drug compound libraries and databases, clinical trials data, omics data (e.g., genomics, proteomics, phenomics insights) and regulatory and approval documentation (e.g., FDA/EMA documents).
Use the latest computational research methods. Both examples utilize the latest computational research methods, including knowledge graphs, in silico modeling and automated literature mining. Knowledge graphs enabled researchers to better visualize biological relationships in DIPG and Friedreich’s ataxia — specifically how TP53 relates to DIPG tumor formation, or how certain drugs increase the expression of frataxin in Friedreich’s ataxia. In silico modeling uses real-world patient data to predict pharmacokinetic properties; this is critical in rare diseases like DIPG where most patients are children, given the complex regulatory rules for pediatric medication. Finally, automated literature mining accelerated both cases by very quickly searching large bodies of text to produce shortlists.
Anticipate regulatory challenges. Anticipating regulatory issues when repurposing investigational or licensed drugs that were abandoned because of poor efficacy or safety issues is vital. Safety is paramount; drugs proven safe for humans have a known safety profile, but problems may emerge with altered dosages, preparations or in combination therapies. In rare diseases, finding enough patients for a statistically significant trial to demonstrate efficacy and safety is difficult. To maximize chances of regulatory approval, researchers must accurately predict off-target effects. In both cited examples, access to complete data and metadata enabled researchers to be confident when shortlisting candidates with the highest likelihood of approval.
Deepen your understanding of drug repurposing strategies by visiting Elsevier’s Drug repurposing: approaches, methods and considerations page.