Goal 3: Accelerate Translational Science by Breaking Barriers and Boosting Efficiency
Turning discoveries into health solutions takes too long. NCATS aims to improve both scientific and operational processes to make translation more effective and efficient, ultimately reducing the time it takes for treatments to reach patients.
By streamlining scientific and operational processes and integrating data science more effectively, we can reveal new knowledge and spur advances more broadly. Making processes easier and faster will allow teams to focus on the scientific goals. Our approach includes expanding and disseminating templated agreements to enable faster study start-up and providing resources like the Toolkit for Patient-Focused Therapy Development to involve patients and communities in research. We will automate routine tasks to save time and reduce errors, as well as invest in advanced collaboration tools to enhance communication and coordination among research teams. Additionally, we will optimize data management processes to ensure high-quality, FAIR (findable, accessible, interoperable, reusable) data and develop standardized protocols to maintain consistency and efficiency across studies. Working closely with regulatory bodies will further streamline approval processes and reduce delays.
Objective 3-1: Innovate to address scientific and operational issues that slow translation.
Numerous scientific and operational challenges can slow translation. They include inefficient processes, technologies, and inflexible clinical trial designs that cannot meet the challenges of the clinical trials of today, much less tomorrow.
We will streamline and improve research operations to speed translation to many diseases and conditions at a time and enhance rigor and reproducibility. Expanding collaborations and public-private partnerships with research, manufacturing, and regulatory scientists will reduce roadblocks associated with translating findings to health solutions. For example, public-private partnerships and collaborations are critical for certain aspects of gene- and cell-targeted therapy research, such as finding solutions for manufacturing challenges and regulatory issues. We will also continue to work closely with the rare diseases and CTSA Program research communities to harmonize and streamline operations, develop standardized master protocols to enhance clinical trial comparability and rigor, and enable faster activation of clinical trials and reporting to deliver clinically relevant information to health care providers sooner than traditional methods. Together, we will develop and implement novel clinical trial designs and demonstrate network-wide readiness to address emerging public health needs.
Objective 3-2: Apply data science approaches to speed translation.
Traditional methods for accumulating and accessing data are siloed and inefficient, which can slow translation. Leveraging data science approaches is key to speeding translation.
Our data science approaches support more rapid data aggregation, exploration, reuse, linkage, and interpretation, and we apply them across the translational research spectrum. For example, in the rare diseases community, broader data sharing enables translation of learnings from one rare disease to another. It also enables the ability to aggregate knowledge from many small communities to maximize insights and knowledge. These insights include learning about the genetic and cellular mechanisms that are the same in different diseases, improving care for patients with different diseases that have similar characteristics, and identifying potential drug repurposing opportunities. In addition, expanding the use of real-world data, such as electronic health records (EHRs), will inform different aspects of clinical research, including biomarker identification, trial design, and participant recruitment. Expanding informatics and data strategies that make data sources more interoperable allow researchers to harness real-world evidence to speed the understanding of disease onset and progression and possible prevention and treatment opportunities.
Objective 3-3: Develop innovative technologies and models in translation to achieve faster diagnosis and treatment.
Generating data, tools, and technologies needed for treatments to get regulatory approval is critical and timeconsuming. Harnessing new scientific discoveries and innovation can help streamline translation and shorten the time it takes to get a diagnosis, find the right patient cohorts, or identify a potential treatment, thereby preventing or reducing the impact of rare and chronic diseases on patients. This will also enable the application of platform technologies to many diseases and conditions.
We consistently work to develop and automate technologies that can speed the creation, analysis, screening, or testing of different compounds or drugs for multiple diseases. By applying innovative statistical and computational methods to link data from EHRs, digital and mobile technologies, and other sources, researchers can discover new, previously unexplored, causal relationships that enable the identification of prevention or treatment approaches. By combining clinical consultation with AI/ML and -omics analyses, we will shorten the diagnostic odyssey of hard-to-diagnose, often rare, diseases. Also, by using human cell–based models as predictive tools and working with the FDA and others during the development and validation process, we can streamline regulatory acceptance of new approaches and new treatments. In line with Goal 1, we can also speed up prevention, diagnosis, and access to effective treatments by identifying commonalities among diseases.