| Literature DB >> 34109196 |
Judith C Macdonald1, David C Isom2, Daniel D Evans3, Katy J Page4.
Abstract
The pace of scientific progress over the past several decades within the biological, drug development, and the digital realm has been remarkable. The'omics revolution has enabled a better understanding of the biological basis of disease, unlocking the possibility of new products such as gene and cell therapies which offer novel patient centric solutions. Innovative approaches to clinical trial designs promise greater efficiency, and in recent years, scientific collaborations, and consortia have been developing novel approaches to leverage new sources of evidence such as real-world data, patient experience data, and biomarker data. Alongside this there have been great strides in digital innovation. Cloud computing has become mainstream and the internet of things and blockchain technology have become a reality. These examples of transformation stand in sharp contrast to the current inefficient approach for regulatory submission, review, and approval of medicinal products. This process has not fundamentally changed since the beginning of medicine regulation in the late 1960s. Fortunately, progressive initiatives are emerging that will enrich and streamline regulatory decision making and deliver patient centric therapies, if they are successful in transforming the current transactional construct and harnessing scientific and technological advances. Such a radical transformation will not be simple for both regulatory authorities and company sponsors, nor will progress be linear. We examine the shortcomings of the current system with its entrenched and variable business processes, offer examples of progress as catalysts for change, and make the case for a new cloud based model. To optimize navigation toward this reality we identify implications and regulatory design questions which must be addressed. We conclude that a new model is possible and is slowly emerging through cumulative change initiatives that question, challenge, and redesign best practices, roles, and responsibilities, and that this must be combined with adaptation of behaviors and acquisition of new skills.Entities:
Keywords: artificial intelligence (AI); cloud; digital; dynamic; ecosystem; machine based learning; regulatory; review
Year: 2021 PMID: 34109196 PMCID: PMC8183468 DOI: 10.3389/fmed.2021.660808
Source DB: PubMed Journal: Front Med (Lausanne) ISSN: 2296-858X
Figure 1Simplified schematic of drug development and review. *Timing of HTA process varies according to national procedures—in some countries HTA review may start in parallel to regulatory view.
Examples of platforms/initiatives advancing data standardization, knowledge application, data sharing, and utilizing new forms of evidence.
| Pharmaceutical Quality/Chemistry manufacturing and Controls, (PQ/CMC, A HL7 | Informed decision making through structured data exchange between FDA and sponsors, enhancing understanding of context and precedence through use of internal tools for structured review, automated workflow, and knowledge management such as KASA |
| International Organization for Standardization (ISO), comprising national standards bodies in 165 countries, has developed | Common and unique identifiers for pharmaceutical products and substance information through data standardization. Applications in pharmacovigilance, clinical trials, regulatory submissions, and GMP inspections ( |
| Vulcan, launched by Health Level Seven® International (HL7®), seeks to use its widely recognized data exchange standards to facilitate collaboration among diverse stakeholders in the translational and clinical research community to define a common set of standards that can be implemented internationally ( | Effective acquisition, exchange, and use of data in translational and clinical research using data exchange standards to promote interoperability across healthcare and clinical development |
| Knowledge Aided Assessment and Structured Application program (KASA), used by US FDA in Generic drugs, mining data to recognize patterns and trends across different applications. Potential for broader FDA adoption with added risk assessment support ( | Enhanced internal workflow and learning through knowledge sharing across applications and efficiency of review through data mining |
| TransCelerate is a not-for-profit biopharmaceutical organization that has pioneered improvements in clinical research and development, specifically collaboration and data sharing. Examples include Common clinical trial protocol template; and DataCelerate® a global cloud-based data sharing platform that allows for deidentified, anonymized pre-clinical, and clinical data types to be requested and voluntarily shared in a secure and data compliant way ( | Reusable content, “cloud” collaboration and data sharing through structured reusable content to streamline clinical development data operations |
| Innovative Medicines Initiative (IMI), offers two examples of projects focusing on novel healthcare evidence sources such as EHDEN for electronic health records (European Health Data and Evidence Network) ( | Secure Data network using common data models for healthcare data to inform clinical practice, and promote clinical research |
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Figure 2Three horizons for transformative change.
Figure 3Hype cycle (20).