Drug repurposing
The process of de-novo drug discovery and development pipelines is both time-consuming and costly. It typically spans around 17 years and incurs costs exceeding $2 billion from the initial drug discovery until it gets launched to the market. Additionally, drug candidates have a high rate of failure: only 10% of de-novo drugs put through clinical trials finally receive market approval, with the highest attrition occurring at phase I and II of those trials.
Drug repurposing, or drug repositioning, aims to identify existing drugs—either those already on the market or those that have passed human safety assessments—that can be used to treat conditions other than those they were originally developed for.
One can look at drug repurposing from multiple angles:
- Disease-centric approaches focus on an indication of interest and aim to find suitable drug targets through studying similar underlying biomolecular mechanisms across indications.
- Target-centric approaches focus on trying to find new therapeutic applications for a known drug target (indication expansion or indication repositioning). These drug targets of interest may have been deemed safe but failed to demonstrate effectiveness in their original studied indications.
The Discovery and Translational Science (DTS) consulting team at Clarivate leverages in-silico techniques to prioritize and select either drug targets or indications to be repurposed.
Our AI-based solutions systematically screen an asset’s Mechanism of Action (MoA) against numerous indications, using an updated collection of disease signatures and biological networks, ensuring a thorough and objective exploration of potential indications, that avoids subjective biases that may favor front-of-mind indications. We recently explored the methods, challenges and perspectives of drug repurposing in a dedicated webinar and blog post.
Drug repurposing strategies are particularly relevant in rare diseases, which pose significant challenges due to their often poorly understood biology, small patient populations, and stringent regulations.
Oncology is another field in which drug repurposing approaches can prove particularly relevant: systematic screenings and advanced computational methods can identify new drugs, their targets or even drug combinations that could enhance treatment efficacy. This is crucial for overcoming drug resistance, especially in rare cancers with limited treatment options.
Indication prioritization in oncology
Despite substantial investment in oncology research and development (R&D), the success rates in clinical trials remain low. Oncology compounds face an uphill battle, with only 3.4% eventually receiving regulatory approval. In that context, prioritizing cancer types most likely to benefit from a drug is key to improving the likelihood of approval.
At Clarivate, we boast an interdisciplinary team of scientists who are experienced in a broad spectrum of OMIC data and analysis techniques. Our expertise spans bioinformatics, data management workflows, systems biology, and statistical modeling, with a strong foundation in machine learning dating back to 2014. This diverse skill set enables us to seamlessly integrate various data sources, providing our clients with robust insights to effectively prioritize indications for their drugs.
Our work typically starts with unravelling drug’s Mechanism Of Action (MOA). To do so, we leverage information from MetaBaseTM, a Cortellis solution, which is manually annotated database of molecular interactions, pathways, and disease biomarkers. We employ a suite of network-based algorithms, implemented within the Computational Biology Methods for Drug Discovery (CBDD) consortium, to understand which proteins, genes and pathways are involved in drug response.
Our team includes OMICS data analysis experts (i.e. proteomics, transcriptomics, single-cell). By integrating different data sources (multi-cancer databases and clinical data), we can assess the association between the expression of the target and cancer subtype, disease stage and other clinical parameters to produce a ranked list of cancer types that are more likely to benefit from modulating target activity.
Machine learning techniques can also be powerful tools for indication prioritization:
- Machine learning algorithms can analyze large datasets to identify patterns and relationships based on observed features such as drug-disease pairs or disease OMICs data.
- Deep learning can model high-level abstractions in data, providing even more profound insights, when the amount of data available is large.
- Matrix factorization can be used to predict the potential effectiveness of a drug for a particular disease by decomposing the drug-disease interaction matrix into lower-dimensional matrices.
These techniques enable us to sift through vast amounts of biomedical data, predict drug-target interactions, and ultimately identify promising drug repurposing opportunities. To enhance transparency, we use explainable drivers linking assets to disease biology. Additionally, detailed disease annotations allow for flexible filtering strategies. This approach improves both the clarity and adaptability of indication prioritization workflows.
These computational methods can also be used to predict drug synergies, which is the interaction of two or more drugs that results in a therapeutic effect greater than the sum of their individual effects. Different types of data can be leveraged for that purpose:
- OMICs data: Synergies can be predicted between drugs that target complementary pathways using systems biology approaches.
- High-throughput cell line experiments, such as the DREAM challenge dataset shared by AstraZeneca. Machine Learning and Deep Learning can learn patterns from these experiments and make predictions on combinations that have not been tested yet.
- Knowledge graphs are a powerful data structure used in artificial intelligence and semantic web technology. They model a network of real-world entities and their interrelationships, such as known drug-drug or drug-target interactions.
Finding effective drug combinations is crucial in oncology due to the heterogeneous nature of tumors, which may lead to differential responses to treatment, with some cells being susceptible to a particular drug while others may be resistant. A combination of drugs can target multiple vulnerabilities in the tumor, increasing the overall effectiveness of the treatment. Moreover, the use of drug combinations can help prevent or delay the development of drug resistance.
Incorporating Real World Data (RWD)
In recent years, there has been growing interest in incorporating Real World Data (RWD) into drug development. RWD refers to data obtained from sources other than clinical trials: electronic health records (EHRs), claims and billing data, registries, surveys, etc.
In the field of oncology, the utilization of RWD is not a novel concept. In fact, since as early as 2015, researchers have been leveraging this resource to enhance their studies. For instance, in an article in 2015, Xu et al. employed Natural Language Processing (NLP) to analyze EHRs. They predicted that metformin, a drug used to manage diabetes could decrease mortality in cancer patients.
RWD can provide valuable insights to shape systematic indication prioritization strategies. Epidemiology data can be of great value in prioritizing indications with high prevalence amongst the indications with high predicted repurposing power. Epidemiological prioritization can be made according to several criteria:
- Population size (incidence/prevalence) worldwide or in a region of interest.
- Incidence/prevalence projections: some cancer types such as breast or prostate cancer are predicted to increase in prevalence due to the aging population and lifestyle changes, while some types of liver or cervical cancer are predicted to decrease due to a better treatment of viral infections causing them.
- Other epidemiological data such as comorbidities, mortality, or cancer recurrence.
Clarivate epidemiological data is sourced from various places including peer-reviewed journals, U.S. claims-based and EHR data and disease-specific forecasts. This diverse data collection covers over 800 diseases. The data is expertly curated and includes full citations from the original sources.
Biomedical and epidemiological prioritization can be complemented with an indication of competitive prioritization, based on already patented drugs for the indications of interest, drug development stages and clinical trials discontinued due to toxicity or safety alerts. This data can be pulled from Clarivate databases such as Cortellis Drug Discovery Intelligence or OFF-X Preclinical and Clinical Safety Intelligence.
The use of RWD in the drug discovery process spans many stages. At Clarivate, we aim to support our customers at each phase of the journey. Whether you’re repurposing your drug to find suitable targets for your population of interest or looking to expand into new therapeutic areas, a well-developed strategy for the planning and execution of your clinical trials is critical for the success of your drug. In an industry where over 53% of all protocols require amendments due to sub-optimal trial performance, we understand the critical importance of getting it right the first time.
Using a combination of RWD, historical and current clinical trial data, industry benchmarks and regulatory intelligence, we can provide insight and guidance for trial protocol optimization and implementation. The power of our clinical protocol optimization analysis starts with our robust datasets which are expertly and granularly curated allowing for an in-depth, data-driven approach. We can learn from the success and failure of similar trials and break down the trial design to understand what components ultimately played a key role. We can combine that data with RWD, to see how your trial design criteria impacts the available patient population you are targeting. We can also use RWD to examine the patient journey, standard of care, and unmet need to ensure your protocol meets necessary criteria to prove the value needed for approval. Lastly, RWD, along with previous trial experience, can be used to select the sites/investigators who will be best fit for your trial and who have highest likelihood for success based on availability and access to targeted patients. Ultimately, Clarivate can assess gaps and provide recommendations based on intricate trial design criteria tailored to your specific needs.
To summarize, we offer a one-stop comprehensive set of consulting services that range from a systematic biomedical screen of thousands of indications to a detailed commercial and patient impact assessment of the most promising indication candidates.
Our team of experts in the Discovery and Translational Science Consulting services stands ready to guide you in identifying the most suitable indications for your drug, integrating different data sources (OMICs data, Clarivate manually annotated biomedical databases and RWD) and using cutting-edge technology. Leveraging their vast knowledge and experience, they can also assist in determining the optimal drug combinations, thereby maximizing efficacy and safety.
Clarivate Commercial strategy consulting services can assist in maximizing patient and commercial impact.
Finally, Clinical and Regulatory consulting team can provide additional assistance and guidance in optimizing your clinical trial design to reduce the risk of failure when considering putting a new or repurposed drug on the market.
Take the next step towards maximizing the potential of your drug. Visit our Consulting Services page and learn more about how we can assist you in your journey.
Article written by:
- Cecilia Klein – Interim Director Discovery and Translational Science Consulting Services
- Verónica Miró – Consultant, Discovery and Translational Science
- Samantha Chesney – Lead Consultant, Clinical and Regulatory
- Sarah Bonnin – Senior Consultant, Discovery and Translational Science
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