Characterizing Pain Points in Clinical Data Management and Assessing the Impact of Mid-Study Updates.

BACKGROUND: The causes, degree and disruptive nature of mid-study database updates and other pain points were evaluated to understand if and how the clinical data management function is managing rapid growth in data volume and diversity. METHODS: Tufts Center for the Study of Drug Development (Tufts CSDD)-in collaboration with IBM Watson Health-conducted an online global survey between September and October 2020. RESULTS: One hundred ninety four verified responses were analyzed. Planned and unplanned mid-study updates were the top challenges mentioned and their management was time intensive. Respondents reported an average of 4.1 planned and 3.7 unplanned mid-study updates per clinical trial. CONCLUSION: Mid-study database updates are disruptive and present a major opportunity to accelerate cycle times and improve efficiency, particularly as protocol designs become more flexible and the diversity of data, most notably unstructured data, increases.

Evolving Clinical Data Strategies and Tactics in Response to Digital Transformation.

BACKGROUND: Contending with a continuously expanding volume and variety of clinical data poses challenges and opportunities for the industry and clinical data management organizations. METHODS: Tufts CSDD conducted an online survey aimed at further quantifying and understanding the magnitude and impact that expanded data volume, sources and diversity are having on clinical trials. The survey was distributed between October and December 2019. Responses from a total of 149 individuals were included in the final analysis. RESULTS: The survey found that companies use or pilot from one to six different data sources with the majority of respondents using or piloting 3-4 different sources of data in their clinical trials. The results showed that average times to database lock have increased an average 5 days compared to a 2017 study, possibly as a result of managing an even larger number of data sources. Finally, three key mitigation strategies surfaced as techniques respondents used to tackle expanding data volume, sources, and diversity: the creation of a formalized data strategy, investment in new analytics tools and more sophisticated data technology infrastructures, and the development of new data science disciplines. CONCLUSION: Without further investments into infrastructure and developments of additional mitigation techniques in this area, database lock cycle times are likely to continue to increase as more and more data supporting a clinical trial are coming from nontraditional, CRF sources. Further research must be done into organizations who are handling these challenges appropriately.

Assessing Outsourcing Oversight Practices and Performance.

BACKGROUND: The Tufts Center for the Study of Drug Development conducted a study updating benchmarks on outsourcing model adoption and assessing oversight practices and experience. METHODS: An online survey examining organizational use of clinical development outsourcing was distributed between February and April 2018. Responses from a total of 88 individuals were included in the final analysis. RESULTS: More than half of individuals responding reported using 3 or more models simultaneously, mixing and matching approaches to meet individual project needs. Outsourcing practices among small, medium, and large sponsor companies remain inconsistent and deliver mixed levels of satisfaction and performance. Full-service models are the most commonly used. Biopharmaceutical companies report that the primary purpose of their oversight mechanisms is to minimize risks and regulatory missteps. Oversight mechanisms are generally supported by middle management personnel focusing on more reactive and tactical issues. Executive-level involvement in outsourcing oversight is minimal and highly variable. CONCLUSION: Several opportunities to improve oversight practices were identified in the study, including increasing executive-level involvement and leveraging technologies to monitor performance, enhance communication, and expand collaboration capabilities.

Using Patient Advisory Boards to Solicit Input Into Clinical Trial Design and Execution.

Academic institutions, pharmaceutical and biotechnology companies, foundations, and associations are routinely implementing patient advisory boards (PABs) to solicit patients' voices and perspectives on a variety of clinical research-related areas, including protocol design, clinical trial execution, informed-consent form design, clinical trial medicine kit design, wearable devices and mobile technologies, and patient-communication materials. Based on experience conducting >50 PABs during the past several years, the authors provide insights into how to best plan and execute PABs and their value in informing improvement in patient engagement.

A Study on the Application and Use of Artificial Intelligence to Support Drug Development.

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 1-2 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.

Assessing the adoption of clinical trial results summary disclosure to patients and the public.

Introduction: There is a broad global acknowledgment that the timely and effective communication of clinical trial results is not only essential to the development, diagnosis, and treatment of medical conditions but also meets an ethical obligation to inform patients and the public. Areas covered: At this time, less than 2% of all clinical trials completed or terminated within the past three years returned plain language summaries to study volunteers. This estimate is far below our forecast made 10 years ago when we evaluated a pilot effort to demonstrate a feasible and efficient process for communicating summary results to patients. At that time, we anticipated that research sponsors would embrace the obligation and in so doing would improve their relationship with and trust among their study volunteers and patient communities. This article discusses why adoption remains low and suggests that the absence of clear regulatory requirements and their enforcement are the primary cause. Expert opinion: The authors anticipate that the regulatory environment will tighten and that public, patient and patient advocate appetite and expectation for the disclosure of clinical trial results summaries in plain language will intensify during the next 18 months. These pressures will compel research sponsors to accelerate adoption.

Baseline Assessment of the Evolving 2017 eClinical Landscape.

The volume and diversity of data collected to support each clinical study has increased dramatically in response to the rising scope and complexity of global drug development programs. The Tufts Center for the Study of Drug Development conducted an online survey of 257 unique global companies-77% drug development sponsors and 23% contract service providers-to assess clinical data management practices and experiences. Study results indicate that companies are using an average of 6 different applications to support each clinical study and that companies are collecting a range of data types including that from case report forms, lab procedures, pharmacokinetics, biomarker, outcomes assessment, mobile health, and social media. Companies report that the primary electronic data capture (EDC) is capturing traditional data types but not many of the newer ones. Respondents report spending an average of 68.3 days to build and release a study database, 8.1 days between the patient visit and when that patient's data are entered into the EDC system, and 36.3 days on average to lock the database following the last patient last visit. Average cycle time durations are longer and more variable than those observed ten years ago. Subgroup differences (eg, by company size and company type) and factors contributing to data management cycle time and experience are discussed.

Assessing Study Start-up Practices, Performance, and Perceptions Among Sponsors and Contract Research Organizations.

BACKGROUND: Site identification, site selection, and study start-up have become the focus of improvement by organizations conducting clinical trials. METHODS: To examine and measure the process from site identification through site activation, Tufts Center for the Study of Drug Development (CSDD) conducted a comprehensive survey among pharmaceutical organizations, biotech companies, and contract research organizations (CROs). Responses from over 400 unique companies were gathered and analyzed. RESULTS: The results indicate that the start-up process is on average 5 to 6 months in total duration, and cycle times across all activities, including site identification, site selection, and study start-up, are faster for repeat sites than for new sites. Comparisons between sponsor and CROs indicate that CROs completed all site-related activities 6 to 11 weeks faster than sponsors. Other areas impacting cycle times were examined, including centralized versus decentralized functions, investment in technology, and organizational strategies that improve cycle time efficiency and performance. CONCLUSION: Tufts CSDD will explore this area in future research to gather additional insights into other factors that may be associated with speed and efficiency.

The Use of Real-World Evidence and Data in Clinical Research and Postapproval Safety Studies.

BACKGROUND: The adoption and use of real-world evidence (RWE) is becoming increasingly important to drug development and patient safety. METHODS: The Tufts Center for the Study of Drug Development (CSDD) conducted a benchmark survey of pharmaceutical and biotechnology companies and contract research organizations in a number of areas that support real-world data (RWD) and evidence, including operations and performance areas. Data were gathered on organizational functions, staff, roles and responsibilities, and skill sets required. Also, current and future allocation of budgets and spending were examined as well as return on investment measures. A total of 30 unique companies responded to the survey. RESULTS: Nearly all respondents (29/30 companies) reported that their organizations had an RWE function and most companies indicated that their RWE functions were increasing in size (21 companies). From a postapproval regulatory and labeling perspective, there were two primary areas for company use of RWD to generate evidence: one for postapproval safety studies, including decreasing the severity of a label warning or to support risk evaluation and mitigation strategies (REMS) (12/22 companies; 55%), which allows for real-world patient population data to inform safety decisions; and the other for postmarketing studies (13/23 companies; 57%). Developing greater insight into therapeutic area needs, gaining market access, and greater understanding of drug effectiveness were the top measures identified for return on investment for use of RWE. CONCLUSIONS: 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.

Benchmarking the Study Initiation Process.

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.

Straight talk with...Ken Getz. [Interviewed by Cassandra Willyard].

How should clinical trials be improved? Ken Getz, a senior fellow at the Tufts Center for the Study of Drug Development, has been thinking up answers to this question for two decades. In 2003, Getz took on a new challenge when he launched the Center for Information & Study on Clinical Research, a nonprofit focused on providing education and outreach on clinical research to the public. Cassandra Willyard asked Getz for his thoughts on trial recruitment, financial conflicts of interest and keeping trial participants safe.

Variability in the costs of institutional review board oversight.

BACKGROUND: Previous studies have shown wide differences between institutions in economies of scale with regard to the costs of institutional review board (IRB) oversight of research. In this study, the authors explored variability among IRB costs, taking into account organizational size, components of the costs of oversight, and protocol type. METHOD: The authors conducted a survey of academic medical centers to collect information on resource utilization associated with IRB oversight in 2002. They used national cost weights to assign a cost to each type of resource used, and summed weighted resource utilization for IRB costs. Descriptive statistics were generated for costs over all, tertile of protocol volume, cost component, and type of review. They also determined where the greatest cost variability is found. RESULTS: IRB costs per protocol reviewed are highly variable both overall and within tertiles of volume. Higher-volume institutions have lower costs, which is indicative of economies of scale. However, not all components of IRB costs (e.g., board time) are subject to economies of scale. Expedited reviews of protocols are not less expensive at low-volume institutions. CONCLUSIONS: IRB costs for oversight are highly variable, and only some of the variation may be attributable to economies of scale. Given such wide variation in costs, the authors conclude that some institutions are conducting reviews in a manner that is inefficient or of low quality. Future work is needed to determine specific practices in reviews, and what leads to the best quality and most efficient oversight and review system.