Two of the biggest challenges facing healthcare R&D are the sheer volume of data and the unique characteristics of every person’s health. Artificial intelligence (AI) and other technologies offer a way forward. AI for drug development is rapidly streamlining R&D processes, speeding up time to market and providing a better experience for patients and providers.
Learn more about the role of AI in drug development and how healthcare researchers are using it to shake up the field. Explore the opportunities and benefits of AI, and learn how you can improve your drug development process.
How AI Is used in drug discovery and development
AI is transforming drug development in several significant ways as it evolves from futuristic concept to today’s reality. With the power of AI, healthcare organizations are streamlining pharma R&D, revolutionizing patient care and improving business operations.
Here are some of the most valuable ways AI drug development is revolutionizing healthcare.
Identifying new drug targets
Identifying new drug targets is one of the most critical applications of AI. By analyzing vast amounts of data, AI identifies patterns that may point to the presence of diseases or the effectiveness of certain treatments.
Machine learning algorithms and other AI techniques analyze large volumes of biomedical data and identify potential drug targets and candidates. This information informs new drug development or improvements on existing applications. AI-driven processes are often more efficient and cost-effective than traditional research methods, which take years or even decades to produce successful treatments.
Developing safe and effective drugs
Pharma researchers are using AI in the earliest stages. They’re also deploying AI to predict how new drugs will interact with the human body — and what side effects might arise. By using AI, drug designers can more accurately predict which compounds will be most effective and have fewer side effects, which speeds up the development process.
Biotech companies have been especially successful in using AI for the design and testing of small-molecule drugs. These drugs are typically less expensive and easier to manufacture than biologics, making them a more accessible option for patients.
Accelerating clinical trials
Clinical trials are another necessary step in the drug development process, but they can be time-consuming and expensive. In fact the average cost of bringing a new drug to market is $2.6 billion USD, AI can filter a group of patients for those most likely to respond positively to a new treatment, which can speed up this stage of development. Faster, more efficient clinical trials reduce the time and cost involved while increasing the probability of successful outcomes.
AI also improves patient outcomes by analyzing data from past patients. By identifying trends and predicting how patients will respond to specific treatments, AI informs treatment decisions that are individualized. This approach ensures that patients receive the right treatment for their specific needs and medical conditions. When healthcare professionals offer personalized care, they can create better outcomes and feel more connected to their patients. And the knowledge gained from these personalized treatment plans can inform future decisions.
AI-powered chatbots that use natural language processing (NLP) are another way AI is transforming healthcare. These chatbots provide personalized support based on existing patient data and offer guidance throughout the person’s treatment journey. This approach improves patient engagement and satisfaction while freeing healthcare professionals’ time.
Improving knowledge sharing
AI for drug development isn’t just about replacing outdated practices with automation and computing. AI also improves your teams’ capabilities, collective intelligence and ability to work quickly, efficiently and effectively.
For example, AI can support knowledge management for healthcare by identifying expert knowledge and surfacing undocumented knowledge. By acting as an comprehensive knowledge base, AI empowers employees and organizations to quickly connect to experts outside of their immediate network and avoid duplicate knowledge intensive work using a platform like Starmind.
Benefits of AI in drug development
AI in drug development has the potential to be revolutionary. Here are some of the benefits.
Saving time and money
One of the main benefits of using AI in healthcare is the potential to save time and money. Developing a drug is a complex, costly and uncertain process, often taking many years with no guarantee of success. Using AI could speed up this process by identifying new targets and predicting how well they’re likely to work.
Drug discovery and development can also benefit from AI-powered tools such as Starmind. The platform helps teams easily access knowledge from anywhere in the organization, reducing the time wasted searching for answers that are inaccessible or undocumented. Such knowledge management platforms provide access to high-quality, up-to-date information and encourage human to human collaboration with subject matter experts inside your organization.
Improved drug safety and effectiveness
AI contributes to safety and effectiveness throughout the drug-development life cycle. For example, AI can screen vast amounts of compounds for their potential activity against a disease target. Conducting such analysis quickly can reduce the time spent on dead ends or ultimately ineffective compounds. By identifying the best candidates against a particular disease, researchers improve their odds of success and reduce the number of clinical trials needed.
AI’s ability to analyze data quickly also contributes to safety, including during clinical trials. AI can also detect patterns in patient data that suggest who’s more or less likely to respond positively to a particular drug. This knowledge helps developers use their resources wisely.
Faster approval process
By using AI to predict the safety and efficacy of new drugs, pharma companies submit better regulatory applications — shortening review times and generating faster approvals for new drugs.
Drug developers can also identify and address potential issues with regulatory compliance earlier in the development process — preventing costly delays or even rejections later on.
AI can identify patterns and trends that may not be immediately apparent to human researchers. This helps drug developers make more informed decisions about which compounds to pursue, which clinical trials to conduct and which drugs to bring to market.
With the right knowledge-sharing tools and AI applications, drug development teams always have the latest research and insights available.
The challenges of using AI for drug development
As with any new technology, there are problems to be addressed when integrating AI. Here are some of the challenges associated with using AI for drug development.
Data quality and availability
AI needs to train on high-quality data that’s accurate, consistent and complete. Unfortunately, healthcare data is often scarce or subject to restrictions. The available data is often unstructured, incomplete or scattered across sources. Regulatory restrictions and patient privacy concerns can further limit data access, making it difficult to obtain the large and diverse data sets needed to train AI models.
Healthcare requires high levels of trust and accountability because of the life-and-death decisions made every day. Healthcare professionals need to understand how AI made a particular decision — and why. While AI is great at identifying patterns and making predictions, understanding the algorithm’s reasoning is often less clear-cut. This creates issues of trust and, potentially, concerns around compliance and privacy.
If AI and machine learning models aren’t transparent and explicable, healthcare R&D teams will have trouble using them.
Bias can occur if the data used to train the AI isn’t representative of the population it’ll be used on. Bias can also arise from algorithmic design or selection, leading to unequal representation and unjust decision-making. When bias is present, you get skewed or inaccurate results that are, at best, applicable to a smaller slice of the patient population.
Patient safety must always be the top priority. AI systems must be thoroughly tested to ensure their decisions and recommendations aren’t jeopardizing safety. Even if the AI isn’t causing obvious issues, there could be inadvertent safety issues or second-order effects. AI algorithms need to be thoroughly and regularly tested to ensure they’re safe, reliable and accurate.
Any use of AI that affects patient care must meet ethical standards of transparency and accountability. Solutions like Starmind can help by providing secure and private knowledge-sharing AI platforms that empower employees to share expertise while maintaining the confidentiality of personal health information.
Ethics also applies to other uses of AI, such as ensuring that patients are sufficiently informed of and able to consent to any experimental methods of treatment.
Leveraging the power of AI in R&D
It’s an exciting time for healthcare. AI is already improving pharmaceutical drug development and will continue to push the boundaries of innovation and invention. With the help of machine learning and other AI techniques, pharma companies can improve their knowledge, develop groundbreaking treatment options and improve the patient and provider experience.
When your team is ready to harness the power of AI for drug development, look for a knowledgeable partner like Starmind that can help you maximize AI’s potential.
Starmind is an AI-powered knowledge-sharing platform that helps organizations break down information silos and promote knowledge sharing across teams. With Starmind, R&D teams can easily access the collective knowledge of their organization, regardless of location or department.
Healthcare organizations around the world rely on Starmind to connect their teams with the knowledge and resources they need to thrive. Learn how Roche partnered with Starmind to improve time to market by making it easier for a team of 100,000 to share knowledge and collaborate effectively.