Emerging technologies are technologies that are in the early stages of adoption in drug substance and drug product development and manufacturing in the biopharmaceutical industry. In some cases, the technology may have been known for several decades; however, its adoption in drug substance and drug product development and manufacturing may still be in the nascent stage. I would include these technologies under the term “emerging technologies” in the context of drug substance and drug product development and manufacturing.
I think machine learning, robotics, continuous manufacturing, additive manufacturing, new catalysts (organic catalysts, photocatalysts, and enzyme catalysts), click and flow chemistry, drug bioavailability enhancement, drug delivery technologies, and blockchain are emerging technologies that hold a lot of promise in the next 5-10 years.
The typical challenge with regulatory compliance and validation and adoption of emerging technologies is the uncertainty of regulatory acceptance. This lack of guidance can hinder the integration of emerging technologies in the highly regulated environment of the biopharmaceutical industry, as any questions with the emerging technology can extend the timeline for regulatory review and approval of a marketing application.
There are several ways to address such challenges: The firms that promote the technology can form a consortium and engage directly with the regulatory agencies with proposed standards and seek guidance for a process/pathway for implementing their technology in the biopharmaceutical industry in a compliant manner as part of drug development and manufacturing.
From the perspective of someone leading drug development in the biopharmaceutical industry, early engagement with the regulatory agencies through meetings to propose a plan and seek agreement on implementing the new technologies can help avoid surprises. For some new technologies, there may not be an official position or guidance from the agency, and the lack of guidance can delay the implementation of those technologies.
In recent years, regulatory agencies have supported innovation and adoption of emerging technologies. Examples of this support are the new FDA discussion paper and request for feedback on Artificial Intelligence/Machine Learning titled “Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML) - Based Software as a Medical Device”. The draft guidance “Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions.”
Workforce readiness and skill development are critical issues for complex emerging technologies and some solutions include:
Some questions to consider when selecting an emerging technology include:
The quality impact depends on the technology, the area, and the stage of drug development in which the technology is adopted. For instance, the quality impact in the early stages of drug development is lesser than in later stages. To manage and mitigate risk, a change control process is used, and phased implementation is used, with feasibility testing, use testing, protocol-driven qualification and validation, and comparison of release and stability data for impacted drug substance and drug product batches before and after the change.
There are various regulatory information sources that one can use to stay updated, including Health authority websites (FDA, EMA, etc.), societies such as Regulatory Affairs Professionals Society RAPS, regulatory news publishers, blogs, software vendors, and newsletters such as Dickinson’s FDA review and FDA Aware. The above description is not an endorsement of any of the vendors or services; it is shared as an example of external sources of regulatory information that are available to keep oneself updated on the evolving regulatory guidelines.
It's very important to choose the right technology partners and collaborators. Some factors that can help in choosing the right partner/collaborator research institution or technology provider are
In the next 5 to 10 years, I expect the pharmaceutical landscape to evolve to become more automated, with more environment-friendly processes and more patient-friendly products,
Greater access to large volumes of more reliable curated data sets, the exponentially increasing computing power to process the data, and the sophistication of the machine learning algorithms will drive the increased use of machine learning in drug development and manufacturing. Advances in hardware and software will increasingly drive automation and robotics in manufacturing.
Greater adoption of flow chemistry, click chemistry, organic catalysts, photochemical catalysts, and enzyme catalysts will drive more environmentally friendly processes for drug substance manufacture. Increasing adoption of continuous manufacturing can lead to more environmentally friendly drug product manufacture closer to the markets where those drugs are needed and the adoption of additive manufacturing can lead to more custom drugs for patient-stratified medicines with distinct release profiles.
Novel drug bioavailability enhancement technologies will allow for the development of more chemical space in drug development, and novel drug delivery technologies will allow for the development of unique delivery of drugs allowing for patient comfort with extended release over a period of time supporting patient medication adherence, Greater adoption of blockchain can allow for more robust drug product supply chains and near real time update to patient drug labels
Three important lessons learned on the integration of an emerging technology include,