Revolutionizing Drug Development: How AI Can Transform Pharmaceutical Innovation

Artificial intelligence (AI) refers to the capability of machines to perform tasks typically requiring human intelligence, such as reasoning, learning from experience, and problem-solving; the aim is to simulate human cognitive processes in machines, enabling them to perform tasks autonomously. In health care, AI applications began in the 1970s, initially focusing on simple rule-based systems like MYCIN, which was an early AI designed for diagnosing bacterial infections. In the late 1990s, the application of artificial neural networks in drug development was first explored.^1,2 Since then, AI has rapidly evolved, driven by advances in computational power and the availability of large datasets.

Drug development faces many challenges, with failure rates for new drugs remaining high. It is estimated that more than 50% of resources in drug development are wasted due to inefficiencies in data utilization, resulting in prolonged timelines and higher costs.³ In health care overall, up to 25% of total spending is wasted, much of which could be rescued through better data management.⁴

Data-Driven Transformation in Drug Development

Today, the availability of data from various sources—including wearable technologies, electronic health records (EHRs), and sophisticated medical devices—offers an unprecedented opportunity for AI to streamline drug development. AI technology has matured significantly, enabling its application across the pharmaceutical industry.

Wearable technologies, such as smartwatches and fitness trackers, provide real-time data on a patient’s physiological parameters, including heart rate, activity levels, and sleep patterns. This continuous flow of data allows for more personalized monitoring during clinical trials. For instance, wearable devices can track patient adherence to medication or detect early signs of adverse reactions, giving researchers insights that would otherwise require time-consuming and invasive methods. By integrating these data into AI algorithms, researchers can make more informed decisions about patient safety and treatment efficacy in real-time, leading to faster and more accurate trial adjustments.

Utilizing EHRs With AI

EHRs store vast amounts of patient data, including medical history, lab results, treatment outcomes, and demographic information. AI can analyze EHR data to identify patterns and correlations that are not immediately evident to human researchers. For example, AI can flag potential drug-drug interactions or identify subpopulations of patients who are more likely to benefit from a particular therapy based on their genetic profile and clinical history. This allows pharmaceutical companies to better design and target clinical trials, improve patient selection, and make more precise predictions about a drug’s performance, ultimately reducing the risk of trial failure.

Sophisticated medical devices, such as imaging technologies (eg, MRI, CT scans) and diagnostic tools, produce high-resolution data that can be used to assess a drug’s impact on disease progression. AI-driven image analysis can automate the detection of biomarkers or disease-specific changes in these images, reducing the time required for manual interpretation by radiologists. This enables more accurate and timely assessments of drug efficacy. For example, AI can track tumor shrinkage in patients with cancer or monitor disease progression in neurodegenerative conditions, providing decision-makers with critical insights about a drug’s therapeutic potential earlier in the development process.

The maturation of AI technology itself—through improvements in algorithms, computing power, and data storage—has made it feasible to integrate and process these diverse data sources at unprecedented speed and scale. This capability empowers pharmaceutical companies to optimize decision-making at every stage of drug development, from preclinical research to post-marketing surveillance, leading to better risk assessment, faster drug approvals, and ultimately more effective therapies reaching patients sooner.

AI Investment and Workforce Development in Health Care

According to a report by Research and Markets, the pharmaceutical sector is projected to invest over $208 billion in AI by 2030.⁵ Furthermore, the number of hospitals adopting AI technologies has tripled since 2020, reflecting the increasing integration of AI in health care.⁵ Despite this growth, nearly 50% of pharmaceutical executives report a shortage of skilled professionals in AI, emphasizing the need for education and workforce development in AI-related fields.⁵

While AI has the potential to automate various aspects of drug development, humans must retain ultimate decision-making authority, particularly in scientific and ethical matters. AI can assist in data processing and analysis, but the interpretation of findings, risk-benefit assessments, and regulatory decisions should remain the responsibility of experts. This balance between human expertise and AI-driven automation is critical to ensuring safe and effective drug development.

AI’s Role in Regulatory Processes and Scientific Documentation

AI can automate many routine operations in drug development, such as data entry, validation, and analysis. By processing large datasets faster than humans can, AI reduces human error and increases efficiency. For instance, automating laboratory data management or standardizing patient record processing allows researchers to focus on higher-level tasks, reducing the time spent on manual, repetitive work. In regulatory submissions and scientific documentation, medical writing is crucial. AI can assist in generating regulatory reports, clinical study summaries, and other essential documents by extracting key information from raw data and structuring it according to regulatory standards. AI systems such as natural language processing tools are increasingly used to streamline medical writing, thus ensuring compliance and reducing the workload for regulatory affairs teams.

Regulatory Intelligence

About the Author

Joga Gobburu, PhD, MBA, is a professor of practice, sciences, and health outcomes research and director of the Center for Translational Medicine at the University of Maryland School of Pharmacy.

Regulatory intelligence involves gathering, processing, and interpreting data related to regulatory policies, precedents, and guidelines across different regions. AI can help automate the collection of regulatory information and identify trends in regulatory approvals, facilitating more informed decision-making. AI-driven tools allow drug developers to stay updated on regulatory changes and navigate complex global regulatory landscapes more efficiently.

The amount of data and documentation generated during drug development is enormous. AI can assist in organizing and managing these large volumes of data by categorizing documents, automating version control, and indexing information for easy retrieval. Document management systems driven by AI can significantly reduce the administrative burden of handling regulatory submissions and other documentation.

AI also plays a crucial role in monitoring drug safety post-approval. By analyzing vast amounts of data from adverse event reports and EHRs, AI systems can detect potential safety signals faster than traditional methods. This enables early detection of drug safety issues, ensuring that adverse reactions are identified and addressed before they affect large populations.

The Role of AI in Precision Medicine

AI is being used to enrich clinical trials by identifying patient populations most likely to respond favorably to new therapies. This personalized approach reduces the number of trial participants required, increases the likelihood of success, and accelerates drug development timelines. By analyzing preclinical and clinical data, AI can predict the safety and efficacy of drug candidates before they enter large-scale trials. This allows researchers to prioritize the most promising drug candidates and reduce the likelihood of late-stage trial failures.

AI can also assist in delivering precision medicine by tailoring therapies to individual patients based on their genetic profiles, environmental factors, and lifestyle. This approach increases the effectiveness of treatments and minimizes the risk of adverse reactions, improving patient outcomes.

AI can analyze large-scale genomic and proteomic data to identify biomarkers that predict disease progression or response to treatment. This allows for the development of targeted therapies and companion diagnostics, which can be used to monitor treatment effectiveness.

AI-powered diagnostic tools can also analyze medical images, laboratory results, and patient records to provide more accurate and timely diagnoses. This reduces diagnostic errors and ensures that patients receive appropriate treatments earlier in the disease process.

AI is transforming drug discovery by rapidly screening large chemical libraries to identify promising compounds. AI-driven models can predict how different compounds will interact with biological targets, accelerating the discovery process and reducing the need for costly and time-consuming laboratory experiments. In pharmaceutical manufacturing, AI can optimize production processes by monitoring real-time data and making adjustments to ensure quality and consistency. AI can also reduce waste, prevent equipment failures, and ensure that production meets regulatory standards. AI is reshaping clinical care by providing decision-support tools for health care providers. AI-driven systems can analyze patient data to offer real-time treatment recommendations, predict disease outcomes, and guide therapeutic interventions. This integration of AI in clinical care enables more personalized and effective treatments, improving patient care outcomes.

Regulatory Authorities and AI-Enhanced Operations

Regulatory authorities play a vital role in drug development innovation. Therefore, discussing potential ways to leverage AI is warranted. They can also leverage the power of AI to streamline several of their operations.^6,7 The FDA is exploring AI technologies to enhance internal operations and regulatory processes, aiming to streamline workflows and expedite the availability of novel medical products. AI can improve productivity and efficiency by processing complex data faster and automating repetitive tasks, allowing staff to focus on complex decision-making and better communication with the public and medical professionals. This approach aims to ensure high-quality products reach patients more swiftly.

Among several operational areas, regulatory authorities might focus on regulatory review, prioritization of site inspections, supply-chain management, and review drafting. By prioritizing these areas, regulatory bodies can effectively safeguard public health while streamlining processes to adapt to the evolving landscape of pharmaceutical innovation.

There are 2 specific areas under regulatory review where AI can enhance the efficiency of regulatory authorities’ work: rapid and reliable access to internal regulatory documents and generating first draft of regulatory reviews. Although regulatory authorities possess extensive information on virtually every development program, there are currently no tools available to assist the review staff in searching through previous thinking and decision-making for a regulatory decision. Finding precedents currently relies on the individual recollection of review staff, making the process vulnerable to the impacts of staff turnover. By utilizing AI-powered tools, regulatory reviewers can access previous decisions made under similar circumstances, ensuring consistency in the advice provided to sponsors. They can also gain insights into submission trends, which can guide and inform policy making.

The FDA uses a risk-based evaluation to select foreign and domestic medical product manufacturing facilities for inspection. The agency prioritizes medical product surveillance inspections deemed high-risk based on a variety of criteria, such as facility’s compliance history and inherent product risks. Regulatory bodies can utilize decades worth of site inspection data to build machine learning models to prioritize site inspections either before market approval or for life-cycle management.

The FDA is mandated to keep US medical product and food supply chains secure, robust, and resilient as an essential part of health and national security, and economic prosperity of the United States, including during emergencies like the COVID-19 pandemic.⁸ A vast amount of data are generated across the pharmaceutical supply chain in such instances, but they have historically been underutilized. Leveraging AI technologies to process these data will be crucial for supporting real-time decision-making, enhancing operational efficiency, and ultimately creating a cost-effective and thriving supply chain.

Conclusion: The Future of AI in Health Care

The integration of AI into drug development represents a seismic shift in the pharmaceutical industry. AI has the potential to revolutionize the process by automating routine tasks, enriching clinical trials, and accelerating drug discovery. However, while AI can offer significant efficiencies, human expertise will remain essential in interpreting AI-driven results and making critical decisions.

For pharmacists, the increasing role of AI in health care opens up new opportunities to engage with cutting-edge technologies and contribute to innovative solutions. AI cannot substitute a pharmacist’s knowledge and experience. Rather pharmacists have the power to direct the prudent use of AI to solving important challenges. While dispensing pharmacy operations can be assisted with AI, the greater value-add would be in precision pharmacotherapy.⁹ By combining clinical expertise with AI-driven analysis of EHR data, pharmacists can lead the development of personalized therapeutic guidelines.

As AI continues to evolve, pharmacists will play a pivotal role in navigating the complex ethical and regulatory challenges that accompany its use. With AI as a powerful tool, the future of drug development is brighter than ever, promising faster, safer, and more personalized treatments for patients worldwide.

Revolutionizing Drug Development: How AI Can Transform Pharmaceutical Innovation

Key Takeaways

Data-Driven Transformation in Drug Development

Utilizing EHRs With AI

AI Investment and Workforce Development in Health Care

AI’s Role in Regulatory Processes and Scientific Documentation

Regulatory Intelligence

About the Author

The Role of AI in Precision Medicine

Regulatory Authorities and AI-Enhanced Operations

Conclusion: The Future of AI in Health Care

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Artificial Intelligence In Drug Discovery Market Size, Share & Trends Analysis Report. Accessed on October 4, 2024. https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-drug-discovery-market#:~:text=What%20is%20artificial%20intelligence%20in,USD%209.1%20billion%20by%202030

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National Academies of Sciences, Engineering, and Medicine. Innovations in pharmaceutical manufacturing on the horizon: Technical Challenges, regulatory issues, and recommendations. The National Academies Press. 2021. Accessed July 16, 2024. https://doi.org/10.17226/26009

Naik K, Goyal RK, Foschini L, et al. Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision Medicine. Clin Pharmacol Ther. 2024 Apr;115(4):673-686. doi:10.1002/cpt.3152