Purpose: The Tufts Center for the Study of Drug Development (CSDD) and the Drug Information Association (DIA) in collaboration with 8 pharmaceutical and biotechnology companies conducted a study examining the adoption and effect of artificial intelligence (AI), such as machine learning, on drug development. The study was conducted to clarify and understand AI adoption across the industry and to gather detailed insights into the spectrum of activities included in the definition of AI. The study investigated and identified analytical platforms and innovations across pharmaceutical and biotechnology companies currently being used or planned for in the future.Methods: A 2-part method was used that comprised in-depth interviews with AI industry experts and a global survey conducted across pharmaceutical and biotechnology organizations. Eleven in-depth interviews focused on use and implementation of AI across drug development. The survey assessed use of AI and included perceptions about current and future use. The survey also examined technology definitions, assessment of organizational and personal AI expertise, and use of partnerships. A total of 402 responses, including data from 217 unique organizations, were analyzed.Findings: Although 7 in 10 respondents reported using AI in some capacity, a wide range of use was reported by AI type. Patient selection and recruitment for clinical studies was the most commonly reported AI activity, with 34 respondents currently using AI for this activity. In addition, identification of medicinal products data gathering was the top activity being piloted or in the planning stages, reported by 49 respondents. The study also revealed that the most significant challenges to AI implementation included staff skills (55%), data structure (52%), and budgets (49%). Nearly 60% of respondents noted planned increases in staff within 1e2 years to support AI use or implementation.Implications: Despite the challenges to AI implementation, the survey revealed that most organizations use AI in some capacity and that it is important to the success of an organization's workforce. Many organizations reported expectations for increasing staff as implementation of AI expands. Further research should examine the changing development landscape as the role of AI evolves.
The results of the study suggest that organizations have a varied approach to the adoption and implementation of patient-centric initiatives, with more activities occurring in the planning stages than are being piloted or implemented. Many factors affect implementation and adoption, including buy-in by senior management, organizational vision, resources, and level of investment.
The study start-up phase of a trial is an area that pharmaceutical and biotechnology companies are focusing on in order to reduce delays and improve efficiency. To better understand and examine metrics within study start-up, the Tufts Center for the Study of Drug Development, in collaboration with 11 pharmaceutical and biotechnology companies, examined a comprehensive set of metrics and analyzed study data from 105 global clinical trials. The results indicate that the early stages of the site initiation process are areas that accounted for the majority of cycle time. An examination of cycle time to the first patient in by therapeutic area also reveals variation. Variations in cycle time to the first patient occur by site type as well as by region. Academic institutions and government-funded sites were longest to the first patient in, while physician practices were fastest.
Expanding the use of RWE in regulatory decision making and increasing uses of real-world data by sponsors will fill the gaps that are critically needed for drug development and safety.
The objectives of this study were to benchmark patient recruitment and retention practices across recently completed global clinical trials from a working group of biopharmaceutical companies. The data collection focused on recruitment and retention tactics used by companies as well as detailed information about the size and scope of the global trials conducted. In-depth organizational information regarding patient recruitment and retention structure and functions was collected. Despite numerous tactics available, participating companies indicated using a small number of patient recruitment and retention tactics. In addition, companies reported that 32% of studies did not implement any tactics. Traditional tactics were most widely used, including physician referrals (16%), newspaper advertisements (16%), and radio advertisements (13%). The relationship between use of recruitment tactics and enrollment data was explored and a positive association was found between use of nontraditional recruitment tactics and enrollment rates.
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