IQVIA Institute for Human Data Science has reported findings from a new study that analyses past and future clinical trial productivity trends.
Titled ‘The Changing Landscape of Research and Development: Innovation, Drivers of Change, and Evolution of Clinical Trial Productivity’, the new report is based on information from IQVIA databases and/or third-parties.
Go deeper with GlobalData
It examines trial productivity trends using the firm’s Clinical Development Productivity Index, which is designed to reflect changes in trial complexity, success and timeframe.
According to the study, trial productivity during the coming five years will be primarily influenced by key trends such as biomarkers use, pre-screened patient pools, regulatory shifts and artificial intelligence and predictive analytics.
IQVIA Institute for Human Data Science executive director Murray Aitken said: “As advances in science, technology and data gradually find application within clinical development, the length of time that trials take to complete, the resources required due to trial complexity and likelihood of trial success are all shifting, with impacts varying by therapy area.”
The company identified multiple trends that would drive clinical development at therapy-area level.
How well do you really know your competitors?
Access the most comprehensive Company Profiles on the market, powered by GlobalData. Save hours of research. Gain competitive edge.
Thank you!
Your download email will arrive shortly
Not ready to buy yet? Download a free sample
We are confident about the unique quality of our Company Profiles. However, we want you to make the most beneficial decision for your business, so we offer a free sample that you can download by submitting the below form
By GlobalDataDigital health technologies are set to allow remote collection of clinical data, and thus facilitate virtual trials, while patient-reported outcomes are expected to provide information on drug use outside clinical setting and speed-up trial durations.
IQVIA noted that real-world data would help optimise trial design and accelerate investigator and site selection. It could also aid in new trial designs through virtual control arms.
In addition, predictive analytics and artificial intelligence are expected to enable identification of new clinical hypotheses, cut trial design risks and speed-up enrolment via detection of appropriate patients.
The report further noted that changes in types of drugs assessed, such as targeted treatments, have fast-tracked development timelines but involve longer follow-up durations.
Use of biomarkers to identify patients who are more likely to respond is said to have led to improvements in efficacy, safety and success.
Moreover, trial productivity is being influenced by regulatory system changes and pre-screened patient pools and direct-to-patient recruitment.
Also, the study revealed increased investment in research and development last year.
