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How can AI help in Transforming the Drug Development Cycle?
Artificial intelligence (AI) is transforming the pharmaceutical industry with extraordinary innovations that are automating processes at every stage of drug development. Pharma companies are investing in AI-driven applications for transforming healthcare, reducing R&D spends, and expanding the scope of drug discovery. By expediting research efforts, AI is turning hype into use cases and increasing ROI opportunities for pharma.
AI initiatives adopted by the pharmaceutical industry are focused on supporting potential candidates for drug development and pinpointing hidden connections for improving drug repurposing by leveraging big data in life sciences. Machine learning algorithms, neural networks, and deep learning techniques have enabled access to statistics and insights that can be leveraged for better-informed decision-making. The evolution of AI-based startups for drug discovery is expanding the reach of the pharmaceutical industry into the life sciences data ocean. Data, along with real-world evidence, is being used for predicting risks, potentials, and outcomes, and for generating real-time, relevant insights.
mHealth apps leveraging AI are enabling the biopharmaceutical industry to monitor real-time data collection and improve patient outcomes, accelerate drug development, expand the network of connections with key opinion leaders, predict clinical trial outcomes, identify optimal molecules, predict toxicity, discover previously unknown connections between biological entities, and sharpen innovation. Moreover, AI is making personalized medicine possible by evaluating patient databases and recognizing therapies most suitable to them.
AI through the drug development cycle
Understanding unmet needs
Enabling a comprehensive overview of therapeutic areas and identifying related trends can help in streamlining development processes. Discovering underserved therapeutic areas for drugs and pathways of interest can help move pharma companies in the right direction. Not only do cutting-edge AI technologies help in commercializing, they also support medical affairs, specifically in understanding unmet needs. Real-time updates in trends for various therapeutic areas can enable pharmaceutical companies to advance successfully in the drug development process.
Insights into competitor strategy
Pharma companies can generate relevant insights for competitive advantage using artificial intelligence technologies to understand patent-based dynamics. Network analysis technology can help in gaining information on desired markets, mapping out competitor activities, monitoring patent trends, staying up to date on marketed drugs, and finding top researchers for the preferred geography, Companies can use these capabilities to transform their R&D game and establish themselves as leaders in the therapeutic area. Additionally, AI enables biopharmaceutical companies to identify emerging research spheres by finding ongoing clinical trials.
Identifying connections between biological entities is like finding a needle in a haystack. It is not just challenging but also time-consuming. Research graphs developed using network analysis can facilitate the discovery of associations and possible interactions between closely and distantly related entities, such as drugs, pathways, genes, and targets. AI can provide modelling of protein-protein interactions in silico and assess binding affinities by analyzing life sciences big data. Data can also help in identifying and verifying the protein sequence, function, structure, classification, active site, and druggability.
Opportunities for drug repurposing
By accelerating drug repurposing, the pharmaceutical industry can generate ROI, which would take more than 12 years for a new drug. Artificial intelligence can help identify unmet needs and the potential uses of available drugs. In addition to understanding patient needs and making insight-driven decisions, an overview of the patent landscape is also necessary. Companies can pinpoint repurposing opportunities for off-patent drugs or for those that will go off-patent in the near future. This is also essential for expanding the life and marketability of a drug. A tried and tested drug candidate can receive FDA approval much faster than new candidates, which are bound to fail almost 90% of the time.1 Patent insights may even open up prospects for mergers and acquisitions.
Predicting success of clinical trials
Estimating the success of clinical trials is essential for focusing time, money, and efforts on potential candidates. Pharmaceutical companies can leverage artificial intelligence by aligning key assumptions about an indication. With custom scoring (based on competitors’ trial size, geographic site distribution, duration, phase, indication, principal investigators, and sample size) and scaled models, estimation time for clinical trials can be reduced. This will allow companies to pinpoint potential candidates early in the clinical trial phase and reduce their spends. In addition, it will also help pharma identify untapped patient centers and key opinion leaders.
In short, artificial intelligence helps in centralizing disparate sets of information in order to enable faster decision-making and accelerate drug development.
Reference: 1. Sullivan T. A tough road: cost to develop one new drug is $2.6 billion; approval rate for drugs entering market is less than 12%. Policy & Medicine. https://www.policymed.com/2014/12/a-tough-road-cost-to-develop-one-new-drug-is-26-billion-approval-rate-for-drugs-entering-clinical-de.html. Updated March 21, 2019. Accessed April 24, 2019.
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