Data-Driven Tools to Accelerate the Clinical Translation of Novel Dental, Oral, and Craniofacial Biomaterials

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  1. Goal
  2. Background
  3. Gaps and Opportunities
  4. Specific Areas of Interest
  5. Impact
  6. Current Portfolio
  7. References

September 2021

Division of Extramural Research

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Goal

The goal of this initiative is to accelerate the clinical translation of novel biomaterials for dental, oral, and craniofacial (DOC) applications through implementations of advanced data-driven tools specifically designed to overcome critical problems affecting research and development (R&D) cycles that might contribute to delays in regulatory approval and human use. Empowering R&D cycles of novel, emerging, and/or repurposed biomaterial formulations with unique functional properties that are optimized for the restoration, repair, or replacement of DOC tissues is a primary focus of this initiative. The framework of the FDA-CDRH’s Medical Device Development Tools (MDDT) Program will be leveraged in this initiative as an innovative and important pathway to ensure that outcomes from this initiative yield practical and validated tools that are qualified by FDA to support a particular context-of-use. The overall objectives are to: 1) accelerate R&D cycles of biomaterials for DOC applications by de-risking potential safety and effectiveness uncertainties through robust and predictive preclinical characterization; 2) support technical developments and validation activities needed to satisfy the FDA MDDT qualification process for proposed data-driven tools; 3) promote multi-domain collaborations and empower workforce development at the intersection of data-driven technologies and biomaterial science in dentistry; and 4) build confidence in the use of data-driven technologies in biomaterial’s innovation by establishing a pipeline of robust and validated tools that are qualified by FDA.

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Background

Data-driven technologies such as Artificial Intelligence (AI)/Machine Learning (ML) are catalyzing transformational advancements in scientific, clinical, and industrial domains through process automation, extraction of patterns and inferences from complex datasets to enhance decision-making and problem-solving. Existing implementation of data-driven tools in biomedical sciences can range in scope, technical approaches, and context-of-use, such as assisting identification of biomarkers in drug development, genomics, and protein structure prediction, powering clinical treatment planning with decision support systems, enhancing the performance of digital pathology with intelligent image processing algorithms, and many others. The R&D cycle of DOC biomaterials requires consolidation and interpretation of different types of data to guide preclinical development, performance characterization, and upscale manufacturing based on existing knowledge and empirical data. However, despite many advancements in data-driven technologies fueling new innovations in life sciences, their adoption remains to be fully embraced in R&D of DOC biomaterials. Ensuring the safety and effectiveness of biomaterials is central to the successful clinical translation of countless emerging devices in dentistry and other clinical specialties. This initiative will set the stage for NIDCR to collaborate with FDA’s MDDT program to leverage a dedicated regulatory pathway to qualify proposed data-driven tools that innovators and manufacturers can use to streamline preclinical development and evaluation of new DOC biomaterials. The MDDT qualification asserts that FDA has evaluated the tool and concurs with the supporting scientific evidence that demonstrates the proposed tool works as intended within the specified context-of-use.

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Gaps and Opportunities

While biomaterials have dramatically improved in functionality and complexity, significant opportunities remain to enhance current R&D methods to ensure regulatory and clinical requirements are adequately satisfied. Major gaps in biomaterials R&D are attributed to complexities and inefficiencies in critical steps throughout their initial synthesis and formulation, the lack in predictiveness of preclinical characterization for safety and effectiveness on clinical performance, and challenges with upscale manufacturing. Only a limited number of developments in the biomaterials domain currently involve the use of data- and computational-science tools to enhance formulation discovery and complex preclinical evaluations. Data-driven tools specifically designed and validated to support R&D of novel DOC biomaterials have the potential to drive breakthrough designs of high-quality biomaterials by empowering the aggregation, analysis and interpretation of complex datasets that drive solutions to pressing bottlenecks in product development, streamline regulatory approvals and bridge gaps to clinical translation.

The convergence of emerging data-driven technologies, such as AI/ML, data science, and computational modeling and simulation, is gaining rapid acceptance and adoption in biomedical research and clinical settings to enhance efficiency, quality, and accuracy of decision-making and complex processes. These successful advancements suggest the timing is right to foster similar innovation in the R&D of biomaterials in dentistry. Furthermore, several data repositories and resources promoting the use of computational sciences for separate industrial materials development could be potentially leveraged for available data.

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Specific Areas of Interest

This initiative seeks to leverage the power of data-driven technologies to accelerate the clinical translation of novel DOC biomaterials. Effective utilization of large volumes of existing data and resources is a priority in the biomaterials R&D domain. Implementations of data-driven technologies have potential to address important regulatory concerns to enhance the efficiency to clinical translation of new safe and effective DOC biomaterials. This includes, but not limited to:

  • Develop predictive models for: Elution of substances (e.g., fluoride, calcium, potassium, etc.) from dental materials; antimicrobial activity of dental materials as a function of time; durability of dental materials in an oral environment; shelf-life based on chemical structure and use environment.
  • Develop predictive tools to assess: Breakdown of biomaterials to help design better in vitro evaluations; device failure modes; early biocompatibility risks and toxicity degradation byproducts; potential issues for reprocessing and reuse; characterize surface modifications of implanted or reusable devices (e.g., salt layers, sugars, diamond coating); overall process optimization and standardization; establish design and structural-functional relationships of biological and mechanical properties; degradation of biomaterial integrity as function of biofilm challenge.
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Impact

This initiative is expected to support the development of new tools to improve and accelerate product development cycles, the clinical safety and effectiveness of new biomaterials and optimize their pathway for regulatory approval. Leveraging the established framework of FDA’s MDDT program will ensure that tools developed under this program are fit-for-purpose and validated to address specific preclinical development bottlenecks. This initiative has potential impact to:

  • Bring advanced data-driven analytics and computational material science to the forefront in R&D of DOC biomaterials
  • Yield significant reductions in cost and time of regulatory approvals and in the clinical translation of safe and effective DOC biomaterials with improved clinical performance.
  • Drive multi-domain collaborations and empower workforce development at the intersection of emerging data-driven technologies and biomaterial science in dentistry.
  • Minimize use of laboratory animals and model organisms.
  • Lead to better science and new innovations in DOC biomaterials.
  • Enhance the quality and efficiency of device evaluation and the FDA regulatory review process.
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Current Portfolio

NIDCR supports multiple projects on R&D of new biomaterials for dental and craniofacial applications. However, no project has resulted in successful clinical translation or commercialization of a new biomaterial in the past 8 years. While many applications are received for new biomaterials development, very few seek to leverage AI/ML, data and computational sciences. Additionally, no specific Funding Opportunity Announcement currently exists to promote the use of leading digital technologies to support preclinical R&D of biomaterials across NIH. Therefore, developing a new initiative that specifically encourages multi-domain investigators to collaborate in the development, customization, and validation of data-driven technologies to support the acceleration of R&D in novel DOC biomaterials will provide an opportunity to help bridge knowledge and technology gaps in this space, enhance capacity building and workforce development in digital technologies applied to biomaterials. This initiative is aligned with HHS and NIH Office of Data Science Strategy released strategic plans for AI/ML and data science, respectively (HHS AI Strategy, NIH-ODSS).

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References

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Last Reviewed
April 2024