Boosting pharma efficiency and innovation with AI
Learn how AI is poised to transform drug discovery and development, including helping reduce the costs of drug development and get life-saving treatments to patients faster.
Accelerating drug discovery and development with AI
Learn about the advantages and challenges of deploying AI solutions across the pharmaceutical value chain.
Key takeaways
- The pharmaceutical industry is undergoing a significant transformation made possible by AI.
- To better support healthcare professionals and their patients, pharma companies must accelerate drug discovery and development processes while controlling escalating costs.
- AI can assist in key steps of the drug discovery process, including identifying disease proteins and analyzing which compounds will most effectively interact with the proteins.
- AI also streamlines the drug development process by analyzing vast amounts of data, identifying patterns, and predicting outcomes.
- Pharma companies that adoption AI must overcome challenges such as maintaining data quality and finding workers with the right combination of technical and scientific skills.Learn how AI agents are transforming business operations.
- Looking to the future, the pharma industry will see more partnerships between pharma and tech companies and greater investments in medtech and AI startups.
The rising costs of new drugs
The way healthcare professionals treat patients is rapidly changing. They increasingly use personalized patient information to guide medical decisions. This approach, known as precision medicine, is becoming more common as researchers tackle complex health issues and pharma strives to cut the time and money it takes to develop life-saving drugs.
Currently, bringing a new drug to market is an extremely long and expensive process for pharma companies. According to recent data from Taconic Biosciences, a single drug takes about $2.6 billion and over 10 years to develop. And once developed, 88% of new drug candidates fail to gain FDA approval.
The good news is that AI has incredible potential to accelerate the drug discovery and development process.
Currently, bringing a new drug to market is an extremely long and expensive process for pharma companies. According to recent data from Taconic Biosciences, a single drug takes about $2.6 billion and over 10 years to develop. And once developed, 88% of new drug candidates fail to gain FDA approval.
The good news is that AI has incredible potential to accelerate the drug discovery and development process.
How AI streamlines drug discovery
The first step in creating most drugs is synthesizing a compound that can bind to and modulate a target molecule—usually a protein—involved in a disease. To find the right compound, researchers review thousands of potential candidates. Once a target is identified, researchers then screen huge libraries of similar compounds to find the optimal interaction with the disease’s protein.
Right now, it takes over a decade and hundreds of millions of dollars to get to this point. But medtech that uses AI and machine learning (ML) can streamline the process, cutting the time and money it takes pharma to release new drugs. For instance, these technologies can:
Right now, it takes over a decade and hundreds of millions of dollars to get to this point. But medtech that uses AI and machine learning (ML) can streamline the process, cutting the time and money it takes pharma to release new drugs. For instance, these technologies can:
Comb molecule libraries
The libraries screened for molecule candidates are so massive that it’s almost impossible for human researchers to review everything themselves. AI, on the other hand, can quickly identify potential target compounds in huge datasets, saving researchers hundreds of hours in the lab.
Predict compound properties
The traditional drug discovery process involves time-consuming trial and error. Medtech solutions combined with AI and ML can help speed the process by predicting the properties of potential compounds, ensuring that only those with the desired makeup are chosen for synthesis. This saves researchers from working on compounds that are unlikely to be effective.
Invent new compounds
When screening yields few promising results, AI can even brainstorm ideas for brand-new compounds that fit the desired parameters and have a higher chance of success.
How AI assists researchers
Consider how researchers can use AI solutions to support key steps of the drug discovery process.
Researchers perform the following steps:
Step 1: Pinpoint a molecule that is “druggable” to target.
Step 2: Conduct experiments to confirm the target is involved in disease progression.
Step 3: Identify or create a compound that interacts with the target.
Step 4: Optimize the chosen compound for efficacy and safety.
Step 2: Conduct experiments to confirm the target is involved in disease progression.
Step 3: Identify or create a compound that interacts with the target.
Step 4: Optimize the chosen compound for efficacy and safety.
With the help of AI, they can:
Step 1: Find more potential targets.
Step 2: Raise the success rate of experiments.
Step 3: Rule out compounds unlikely to interact.
Step 4: Speed up the process with intelligent analysis.
Step 2: Raise the success rate of experiments.
Step 3: Rule out compounds unlikely to interact.
Step 4: Speed up the process with intelligent analysis.
How AI is used in clinical trials
According to Taconic Biosciences, only 12% of drug candidates that enter the clinical trial stage are approved by regulatory bodies. The longest and most-expensive stage of the drug creation process, clinical trials involve multiple phases of human testing, and each phase involves hundreds or thousands of participants.
The traditional linear process of randomized controlled trials (RCTs) hasn’t changed in decades. It lacks the flexibility, speed, and analytical power necessary for the precision medicine model to thrive. Companies struggle to find the right participants, not to mention recruit, retain, and manage them effectively. This process inefficiency greatly contributes to the rising costs of drug discovery and development, as well as low approval rates. It also thwarts innovation.
Pharma companies can use predictive AI models throughout the clinical trial stage of drug development, from design all the way to data analysis, to help:
The traditional linear process of randomized controlled trials (RCTs) hasn’t changed in decades. It lacks the flexibility, speed, and analytical power necessary for the precision medicine model to thrive. Companies struggle to find the right participants, not to mention recruit, retain, and manage them effectively. This process inefficiency greatly contributes to the rising costs of drug discovery and development, as well as low approval rates. It also thwarts innovation.
Pharma companies can use predictive AI models throughout the clinical trial stage of drug development, from design all the way to data analysis, to help:
- Identify suitable patients by mining publicly available content.
- Assess trial site performance in real time.
- Automate data sharing across platforms.
- Provide data for final reports.
Barriers to AI adoption in pharma
Although the use of AI is becoming more widespread in drug discovery and development processes, barriers to adoption still exist. Here are some challenges that companies commonly face:
Data quality
An oft-cited challenge in many industries is that poor data can quickly diminish the helpfulness of AI and ML. For drug researchers, low-quality data makes medtech unreliable and, ultimately, no more accurate, helpful, or timesaving than traditional methods.
Apprehension
There’s a misconception across most industries that technology will eventually replace human workers entirely. The pharmaceutical industry is no exception. And while it’s true that AI can analyze large datasets more quickly humans, it’s not a replacement for skilled researchers and clinicians.
Skills shortage
Implementing medtech in the drug discovery process requires a niche set of skills. To keep the data clean and the AI effective, companies need workers with not only technical skills but also an understanding of the scientific side of the process, such as drug design, biology, and chemistry. It’s a tall order that companies have a tough time filling.
The future of AI in pharma
With the help of AI, researchers can be more innovative, clinicians can better meet the demands of precision medicine, and pharma companies can get life-changing drugs to market faster.
Current trends indicate that the AI transformation occurring in the pharmaceutical industry today will continue well into the future. For example, every year partnerships between pharmaceutical and tech companies rise in number, as do huge investments in medtech and AI startups.
Major pharma companies are also increasingly sharing data. The Machine Learning Ledger Orchestration for Drug Discovery, or MELLODDY, is a consortium that facilitates data sharing between its dozens of members. MELLODDY uses a blockchain-based system that allows companies to share proprietary data while maintaining confidentiality. Researchers can utilize existing data to jumpstart their drug discovery process and shave years off development.
Current trends indicate that the AI transformation occurring in the pharmaceutical industry today will continue well into the future. For example, every year partnerships between pharmaceutical and tech companies rise in number, as do huge investments in medtech and AI startups.
Major pharma companies are also increasingly sharing data. The Machine Learning Ledger Orchestration for Drug Discovery, or MELLODDY, is a consortium that facilitates data sharing between its dozens of members. MELLODDY uses a blockchain-based system that allows companies to share proprietary data while maintaining confidentiality. Researchers can utilize existing data to jumpstart their drug discovery process and shave years off development.
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