AI and Drug Development

Usage of Artificial Intelligence/Machine Learning, shortened to AI/ML, has accelerated in drug development during the past few years. There are even more potential AI drug development applications forthcoming. Want to learn more about the future of AI and drug development? Read our blog post below.

AI and Drug Development: Recent and Upcoming Activity

In 2021 alone, the US Food and Drug Administration, FDA, received over 100 applications on biologics and drugs using AI/ML. In 2023, the agency released its perspectives in a discussion paper. This paper was part of a multifaceted initiative to enhance learning and obtain feedback from the industry and other stakeholders. During the same year, the EMA published their AI draft reflection paper entitled “Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle.” This paper initiated a public consultation process and subsequent workshops to interact with external stakeholders on applications of AI for human and veterinarian medicines, including Life Sciences translation services and Life Sciences content solutions. In December 2023, the EMA and the Heads of Medicines Agencies, HMAs, published their AI workplan to 2028. Its purpose is maximizing the benefits and managing the risks of AI. This is a plan with more tracks, including:

Balancing Risk and Trust with AI and Drug Development

AI/ML are transforming the drug development landscape by providing innovative approaches to streamline and enhance the research process. These tools can improve clinical trial conduct by optimizing trial participant selection, enhancing trial monitoring, and improving data collection, management, and analysis. AI usage may also help design non-traditional trials, such as decentralized clinical trials (DCTs), and trials incorporating real-world data (RWD) extracted from electronic health records (EHRs), medical claims, or other sources. These applications of AI/ML not only enhance the efficiency of trials, but also create opportunities for more personalized patient experiences, bringing the clinical trial industry closer to an era of precision medicine.

AI/ML’s prospects are already widely recognized in drug development. However, their implementation is not. Using AI to replace or enhance human tasks in medicine development requires trust and managing risks. This is just as critical as training machines. Regulators will expect the industry to implement a risk-based approach to developing, deploying, and monitoring AI/ML technologies. The ultimate goal will be to proactively help implement the proper controls for the specific context of use and influence of AI/ML. To regulators, the core of medicine development and evaluation is their benefit-risk profile and general protection and advancement of public health. These priorities apply to the application of AI/ML, just as they did to other technologies that have penetrated drug development (such as electronic data capture). Notably, the complexity and uncertainty of AI/ML is unprecedented. Regulators have clearly realized that the computing abilities of AI/ML are already transforming and challenging drug development, prompting them to seek mutual learning and exploration in this fast-evolving field.

Language Explorations for AI and Drug Development

Language is a prerequisite for global research results and medical intervention marketing, and AI/ML will deeply enhance language services. These technologies have the potential to optimize language workflows and assets. They can also generate and process new content across languages, audiences, and intended uses. Language service providers, like Lionbridge, are rapidly exploring and developing AI/ML use cases in parallel and partnership with the industry. The volume of information and content in the life sciences industry is huge, with content types ranging from regulated to non-regulated content and medical to plain language styles. AI/ML is transforming the language services industry and how it may support future health outcomes by using Large Language Models in fusion with other language resources. AI/ML has the potential for generating or “remixing” new content intended for specific audiences or markets, with or without traditional source file dependency. With the right instructions and input, a Large Language Model (LLM) can produce different content types in various styles adjusted to specific audiences or media. However, since we process business-critical and sensitive content for our customers, language service providers must also manage risks and build trust around our solutions. To achieve this goal, Lionbridge continuously seeks a deep understanding of our customers’ content and products, regulatory requirements, and intended uses. We also encourage exploratory conversations about AI-powered solutions with our customers.  

Get in touch

Turn to Lionbridge for expert-led, AI-enabled Life Sciences translation services and Life Sciences content solutions. We have decades of experience assisting customers with clinical trial translation and language solutions. Rely on us to help you meet language compliance requirements and prepare and plan for multi-lingual clinical trials. Get in touch to discuss how we can assist your team.