The Transformative Potential of AI in the Pharmaceutical Industry: Opportunities and Cautions

Robert Paradiso
Partner
Life sciences group
Lowenstein Sandler

Artificial intelligence (AI) is set to revolutionize the pharmaceutical industry, offering unprecedented efficiencies in product development and manufacturing processes. By manipulating and processing vast amounts of data, AI can significantly enhance the development of new products and streamline the processes that manufacture them. This technology has already been implemented in various supporting functions such as supply chain management, information technology, finance, human resources, legal services, and compliance matters. These advancements are critical for pharmaceutical companies, which are now at the forefront of using AI to boost both profits and efficiencies in production, manufacturing, and shipment.

However, unlike many other industries, pharmaceutical, biotechnology, and medical device companies must navigate a complex landscape of legal, ethical, regulatory, and product development hurdles to ensure the safety and efficacy of new products aimed at diagnosing, treating, or preventing human diseases and disorders. This is where AI can be particularly beneficial—not only for the companies utilizing this technology and their investors but also for society at large. AI has the potential to identify and improve pharmaceutical and medical products during the development process, thereby reducing approval times and bringing life-saving products to those in need more quickly.

Drug Discovery

High-throughput screening is a standard process for identifying active pharmaceutical agents, involving the screening of large libraries of compounds to find promising candidates for further design and testing. AI can adopt an iterative approach to analyze the results of a portion of the library, feeding this data into a machine learning model to select the next portion to be tested. This method allows AI to identify the most active candidates while screening only a small portion of the library, significantly reducing the time and resources needed to move a product towards market.

A prime example of AI's effectiveness in drug development is the work of researchers at MIT, who have developed the first new class of antibiotics in over 60 years using a deep learning model. The developmental molecule, known as halicin, shows activity against drug-resistant staphylococcus infections and has the potential to be a crucial therapy in addressing the antimicrobial resistance crisis.

Clinical Trials

Conducting clinical trials is often the most limiting factor in bringing a life-saving medicine to the public in a timely manner. AI can be a valuable tool in improving and expediting the necessary protocol design, patient recruitment, and data analysis associated with clinical trials.

Protocol Design

The design of a clinical protocol relies on the expertise of the medical team conducting the trial. It sets forth the study's objectives, how it will be conducted, and methodologies for data collection and analysis. A poorly designed clinical trial could result in the denial of regulatory approval for a drug candidate that might otherwise have had high potential for success. AI can analyze large amounts of data from previous trials to identify the proper dosing, number of patients, and endpoints to determine if the therapy is beneficial. Given the high costs of clinical trials, repeating a trial is usually not an option. AI has the potential to increase the chances of success in both the design and implementation of clinical protocols.

Patient Recruitment

Successful patient recruitment is critical to the success of a clinical trial. The failure to include a sufficient number of qualified patients can lead to delays or termination, resulting in the loss of investment and potentially life-altering treatments. AI can analyze previous trials and existing data to adjust recruitment criteria, increasing the pool of potential patients and removing much of the guesswork. AI can also quickly review vast amounts of medical records to identify suitable participants more efficiently and accurately. Additionally, AI can help patient candidates find clinical trials that match their clinical requirements.

An exciting development in patient recruitment is the use of "Digital Twins"—virtual representations of physical objects that can simulate patient outcomes in response to drug treatments. Digital twins can reduce the number of real patients needed for a study, shorten the trial duration, minimize patient exposure to side effects, and address privacy issues related to clinical data sharing.

Data Analysis

Retrieving and analyzing data, and incorporating it into regulatory submissions, is another time-consuming aspect of conducting standard clinical trials. AI can extract data from raw reports, structuring it for easy review by clinicians. With AI, data can be retrieved and analyzed in real time to identify whether endpoints have been met or if sub-populations of patients have responded positively to the dosing regimen. AI can also incorporate this data into regulatory submissions.

Conclusion

While AI has the potential to transform the pharmaceutical industry, it must be implemented with caution. Potential risks include AI's tendency to learn from biased data, lack of transparency regarding personal data collection and sharing, and the need for proper validation to ensure patient safety during clinical trials and post-approval marketing. These issues should not deter the use of AI in drug research and development. Instead, the technology should be embraced by pharmaceutical companies and their investors, provided it is implemented with proper oversight and accountability. AI holds significant promise for advancing drug research and development, ultimately benefiting both the industry and society at large.