How AI speeds up the drug development lifecycle

Being first with powerful medical treatments can save countless lives and generate millions or even billions in revenue. The pharmaceutical industry is constantly looking to accelerate the drug development lifecycle without sacrificing safety or reliability. Innovations such as high-throughput screening have made drug discovery quicker and more accurate, but we’ve never seen anything like the potential of artificial intelligence (AI) in drug development.

Pharma companies already use AI in research and development (R&D), with 60% expecting better productivity or lower costs in 2024 from generative AI alone. These firms are moving forward even while still figuring out the best ways to use these technologies.

Learn more about the role of AI in drug development and how healthcare R&D teams are using it to create faster and better drug development pipelines.

5 stages of drug development where AI can assist

Drug development is complex and expensive, averaging $2.3 billion per therapy brought to market. The promise of AI in drug discovery and development for companies is massive — getting drugs to market faster and at less cost, all without sacrificing safety or efficacy. Here are five ways pharma companies are using AI to improve different stages of development.

Streamlining discovery 

In the early stages of the drug discovery process, R&D teams can use AI to analyze massive datasets of chemical structures and biological interactions. Using deep learning algorithms, researchers can predict the therapeutic potential of molecules, including those most likely to succeed as effective drugs. 

AI can help identify novel pathways for drug action in cancers, cardiovascular disease, Alzheimer’s and more. By predicting how different compounds interact with targets, AI narrows the pool of potential drug candidates. 

Starmind’s AI-powered expertise directory is a hub for your organization’s knowledge, connecting researchers with the expertise they need. For instance, in the drug discovery phase, scientists can quickly find peers with relevant experience in drug design or genetics to inform their exploratory efforts.

Improving pre-clinical research

Patient trials are time-consuming and highly regulated because of the risks involved. But long before this stage, AI can help pharma researchers analyze drugs or compounds, predicting how they might interact with humans. 

Using machine learning models to analyze historical data on drug performance, AI can forecast a compound's pharmacokinetics (how it's absorbed, distributed, metabolized and excreted), pharmacodynamics (effects on the body) and toxicity. 

These predictions help in refining drug formulations and doses early, greatly reducing the time and resources spent on experimentation and human trials. This approach can also help R&D teams identify potential interactions between different types of drugs. Since individuals often take multiple medications, understanding these interactions can influence drug design, not to mention the instructions given to clinicians and patients. 

Speeding up clinical research

AI in clinical trials is transformational, particularly during the recruitment phase, where finding the right participants‌ at each trial phase ‌has traditionally been slow and challenging. R&D teams can use AI-powered tools to analyze electronic health records (EHRs) and identify potential candidates who precisely fit the study's inclusion criteria.

This tailored approach accelerates the process and aligns trial participants with the clinical trial's objectives. During a phase 2 trial, AI tools can monitor a treatment’s effects, including safety concerns, spotting problems and documenting trends over time faster than prior methods. AI-powered findings can help researchers react quickly and adjust study protocols as needed.

AI-powered tools like Starmind amplify R&D teams’ effectiveness across all stages and disciplines, including during testing. Such tools prove invaluable for identifying specialists in trial design or participant recruitment. This streamlined approach improves research outcomes while simplifying tasks such as compliance and study coordination.

Simplifying regulatory review

With technology like generative AI and natural language processing, R&D teams can transform raw data into clear insights — for example, highlighting safety and efficacy patterns or pinpointing anomalies. This kind of AI-driven analysis makes the entire dataset more accessible for reviewers when determining a drug's safety and efficacy. 

Pharmaceutical companies can also incorporate AI to automate much of the data preparation and preliminary analysis. This reduces the risk of human error and ensures that critical information isn't overlooked. AI-prepared submissions can offer substantial time savings for regulators who are reviewing drug applications. When presented with clear, concise and thorough trial results, reviewers can quickly grasp the essential findings and implications, potentially speeding up the review timeline.

Optimizing post-market safety monitoring 

After drugs come to market, AI tools can continue monitoring safety and effectiveness by analyzing real-world data from EHRs, insurance claims, patient registries, social media and more. This helps companies stay vigilant against any adverse drug reactions or other complications, especially those not detected during clinical trials. 

Even after a drug is released, your R&D teams need quick access to experts to respond to feedback, manage safety concerns, and adjust to regulatory changes. Starmind gives your teams unprecedented access to real-time insights for all your team needs.

Benefits of AI in drug development 

AI is already revolutionizing drug development, even in these early days of exploration and initial use cases. Here are some of the benefits pharma R&D teams are seeing.

Reduced costs

One of the main benefits of AI in healthcare is the potential to combat escalating costs. Developing a drug is a complex, costly and uncertain process with no guarantee of success. Productivity is a challenge, and the vast amounts of data available only make a researcher’s job more complicated. 

When AI tools help R&D teams identify promising drug candidates faster, there’s less time spent on fruitless quests. That saves money and shows ROI, which can also help justify budget requests. There’s less reputational and legal risk because drug development is more effective and safer, which saves money in the long run by reducing harm. 

Time saved

AI-powered tools can save time at every stage of drug development, starting with discovery. This is essential, as bringing a drug candidate from idea to fruition takes an average of 12 years, according to McKinsey estimates. Biolexis Therapeutics, for example, is hoping to use AI to develop drugs for weight loss and diabetes that will enter clinical trials within 1.5 years.

Roche saved nearly 100,000 working hours by using Starmind to create an enterprise expertise directory that empowers its teams to share knowledge and find experts more effectively and efficiently. The system accommodates different languages and integrates with other systems used by Roche, further saving time and improving productivity.

Expanded pipelines

When R&D teams use AI to thoroughly explore drug formulations, combinations and alternative uses, they're more likely to discover promising candidates. And by using AI throughout the development cycle — including in pre-clinical research and patient trials — they reduce the likelihood of false starts. 

All of this means your pharma company has a stronger drug development pipeline and a greater success of each candidate getting to market.

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.

AI’s ability to analyze data quickly 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.

AI also helps pharma companies make decisions about tailored treatments based on historical patient data. By identifying trends and predicting how patients will respond to specific treatments, AI helps R&D teams create individualized medications and treatments. This improves safety while creating business use cases that are harder for competitors to copy or combat.

Better decision-making

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. And with the right knowledge-sharing tools and AI applications, drug development teams always have the latest research and insights available. 

An AI-powered expertise directory like Starmind helps teams easily access insights from anywhere in the organization, reducing time wasted searching for answers that are inaccessible or undocumented. The platform provides access to high-quality, up-to-date information and encourages human-to-human collaboration with subject matter experts inside your organization. Roche saved 

This type of expertise can help R&D teams learn what research has already been done, collaborate with other scientists internally and contribute their findings to an ever-expanding database of expertise.

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 can be scarce, with available data often unstructured, incomplete or scattered. Regulatory restrictions and patient privacy concerns can further limit data access, making it difficult to obtain data sets that are optimal for training AI models.

Explainability

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. Patients and regulators also demand this kind of accountability. 

While AI is great at identifying patterns and making predictions, an algorithm’s reasoning is often unclear. This creates issues of trust and, potentially, concerns with compliance and privacy. If AI and machine learning models aren’t transparent and explicable, healthcare R&D teams will have trouble using them — or defending them.

Bias

Bias can occur when data used to train the AI isn't representative of the population it’s 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. At worst, they can lead to treatments that are ineffective and have unexpected side effects.

Safety 

Patient safety must always be the 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. 

Like any safety measure, AI safety is an ongoing task. Algorithms need to be thoroughly and regularly tested to ensure they're safe, reliable and accurate. Whenever pharma R&D teams adopt new AI-powered software or apply a new use case, they must ensure that the algorithms are unbiased, effective and safe.

Ethics

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 AI-powered expertise directories that unlock employee collaboration while maintaining patient confidentiality.

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 the drug development life cycle and will continue pushing the boundaries of innovation and invention. 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 enterprise-wide expertise directory that helps organizations streamline drug discovery, accelerate clinical trials and eliminate duplication of efforts. With Starmind, R&D teams can easily access the right person with the right expertise, 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 Novartis partnered with Starmind to improve time to market by making it easier for a team of 20,000 to share knowledge and collaborate effectively.