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Dear Readers, Welcome to the latest issue of Micr
For over 100 years, the major public health advances have been the result of major scientific discoveries. The ways in which new medicines are discovered and manufactured is undergoing as major a change as occurred during the discovery and development of the first medicines.
The increased use of automation, artificial intelligence, big data and the digitalization of laboratory sciences and of pharmaceutical manufacturing will change the pharmaceutical industry as much as have the various scientific discoveries on which the pharmaceutical industry has been built.
The pharmaceutical industry is rapidly adopting integrated systems, both within the laboratory and the manufacturing facilities.The goal of all of these innovations is to enhance the quality and integrity of pharmaceutical products while increasing the speed with which new medicines can be developed and produced. This is far more than the digitization of pharmaceutical industry.
From intelligent laboratories and AI-driven discovery to connected manufacturing and advanced analytics, the pharmaceutical industry is entering an era where data, automation and human expertise are converging to redefine how medicines are discovered, developed and manufactured.
This is the pharmaceutical industry’s next revolution. Traditionally, pharmaceutical manufacturing has been driven by a sequential flow. The flow has been from research to the development of active pharmaceutical ingredients (APIs) to the manufacture of a pharmaceutical product. Each of these has relied on the input of a different team or work group.
Now the focus of innovation has shifted to use the full potential of automated, data driven and integrated systems. The goal is to obtain actionable intelligence from data. This enables what has been coined, the Intelligent Pharma Enterprise.
What was once a relatively obscure area of research has now arrived in the pharmaceutical industry. AI-based innovations are helping to transform the pharmaceutical industry across the entire drug life-cycle, from target identification to clinical development. And the impacts of AI-based innovations extend well beyond drug development.
AI is helping to optimize formulation and manufacturing processes. It is also assisting with the analysis of complex types of real-time data for quality control and investigations into deviations.
AI is even finding applications in environmental monitoring and supply chain management. AI-based innovations are helping to digitalize and transform various areas of the pharmaceutical industry. In many cases, the focus of pharmaceutical companies has shifted from replacing workers with AI to integrating AI in order to enhance workers’ skills.
Digital transformation is now impacting the laboratory more than any other part of a pharmaceutical company.
The integration of advanced digital and analytics technologies in laboratories is shifting them from an isolated, fragmented state to a connected and integrated platform. This evolution from Lab 2.0 to Lab 3.0 and, ultimately, to Lab 4.0 is well underway.
With the aforementioned technologies, lab automation can be expanded and processes can be monitored in real time.
With recent advancements in automation, lab systems have evolved to a level where numerous processes can be fully automated. These systems can perform tasks including sample preparation, liquid handling, and high throughput assays.
Simulating lab processes can help evaluate and improve the efficiency of lab workflows. Digital twin technologies can also be used to improve and optimize lab processes before capital expenditures are made.
A digital twin is a virtual representation of a physical process or product that is augmented with real world data. Digital twins have the potential to revolutionize pharmaceutical manufacturing and provide significant benefits to the industry.
Pharmaceutical manufacturers are now able to digitally test various change scenarios before they are physically implemented. This testing may help to minimize the number of changeover events required for optimization, improve throughput, identify bottlenecks, and support decision making.
This capability may provide a significant competitive edge to pharmaceutical companies. The data available to pharmaceutical manufacturers is considerable. The challenge is interpreting this data in a useful and timely manner.
The use of advanced analytics can help pharmaceutical manufacturers and other related service providers to shift their focus from purely describing and reporting business activities to helping inform and make decisions on future business actions.
Actions to be taken to achieve a particular business outcome can be informed by the intersection of multiple data sets. It is used for predicting future events.
Flexible and data-rich pharmaceutical manufacturing is evolving with continuous manufacturing. Continuous pharma manufacturing focuses on processes where the active pharmaceutical ingredients (APIs) and intermediates are continuously flowed, mixed and/or transformed.
PAT, real-time process control and/or automation, Artificial Intelligence, Digital Twin Technology, and/or automated quality systems, continuous manufacturing can help pharma achieve advanced and intelligent real-time process and/or quality control.
Continuous manufacturing also enables the removal of the final and/or intermediary quality controls. From a pharma manufacturing perspective, it’s a shift from focus on end-of-the-line quality control (or assessment) to real-time control and/or assessment of quality throughout the manufacturing process.
Similar to other areas of pharma, laboratory instruments are also taking a shift toward connectivity and intelligence. Instrument manufacturers are incorporating advanced electronics, computing, networking and software in their instruments.
Data and analytics are at the core of the next evolution of instruments. The focus of future instrumentation will be on capturing data, performing analytics, and helping in making scientific or medical decisions.
Traditionally, a decision was made after performing an analysis and interpreting the results.
Digital transformations in the pharmaceutical industry increasingly rely on cloud computing technology.
Rather than work within the confines of closed, disconnected systems, companies can link data from multiple locations (e.g. laboratories and manufacturing sites) and cross-functional business processes (e.g. quality).
In global pharmaceutical companies with multiple facilities, cloud computing can optimize and integrate a variety of business processes. Integrating the cloud to facilitate business operations raises legal and ethical concerns including protection of data, cybersecurity, and IT system validation.
There is a need to strike a balance between improving the company’s business operations and processes and protecting legal and ethical concerns.
There are numerous advantages to integrating a company’s business operations. Each integration creates new cybersecurity risks. One of the most significant competitive advantages is protecting data. Data can include patient information and data generated during the course of research and/or clinical studies as well as manufacturing and/or quality data.
It is imperative to protect data and cyber systems to ensure the security of supply chains. Risks associated with protecting a company’s cyber systems should be integrated into business operations and processes.
While an increase in automation may decrease the need for human labor in a scientific laboratory, scientists will always be necessary. In fact, they will be at the heart of scientific operations.
Technology can quickly analyze and find patterns in data. It can perform activities that require little to no creativity and critical thinking. There are many skills in pharmaceutical sciences that cannot be replaced by technology.
Pharmaceutical sciences will always integrate skills that technology cannot perform. The focus will always be on the integration of technology and human skills. The organizations that will thrive in the future will possess state-of-the-art technology and people that can question and interpret findings generated by the technology.
Automation is widely implemented in industries. The next phase will include systems that can think and act on their own. These systems will remain within predefined limits and constraints. An automated laboratory will perform a series of tasks to arrive at a result.
An intelligent laboratory of the future will be able to arriving at a different series of result based on the constraints of a given situation.
The goal of an organization should not be to adopt the most recently available technology. It should identify opportunities where technology can generate scientific and business outcomes.
The quality of data will determine the limits of AI. This will determine the outcomes of digital transformations.
Data integrity, standardization, and interoperability are essential for pharmaceutical businesses. This is especially true for metadata, traceability, and governance. These elements help improve the accessibility of pharmaceutical data while maintaining security. Pharmaceutical businesses need to effectively integrate AI to better utilize large datasets.
The foundation for pharmaceutical AI is data. The focus on AI has obscured this reality. Data needs to be trusted for it to be useful. The intersection of sustainability and automation and the connection of pharmaceutical systems provides greater visibility into resource utilization.
This, coupled with improved analytics, can foster greater facility resource utilization. This improves sustainability while maintaining quality. Pharmaceutical businesses can improve their competitiveness and strengthen their positions by focusing on the rate of improvement and learning.
This is in addition to infrastructure, IP, and other traditional pharmaceutical business foci. Data is a catalyst for organizations to realize their greatest potential for improvement. The intersection of Data and Pharma 5.0 can positively transform the pharmaceutical industry.
More broadly, the change that is taking place can be described as part of Industry 5.0, in which the integration of various technologies enables human sensitivity and resilience in an efficient and sustainable manner.
Pharmaceutical companies may wish to pursue the above in their efforts to transform their businesses. One of the consequences of these developments will be the emergence of a digital pharmaceutical ecosystem.
We refer to this as Building the Pharmaceutical Enterprise of Tomorrow. In summary, a variety of technology building blocks have been developed.
Each can be connected to other technology building blocks, as well as to scientists.
Because of these developments, scientists can focus more on discovery and less on routine tasks. Of particular interest is the fact that numerous innovations in pharmaceutical and life science technology have reached a degree of maturity that allows for their convergence and integration.
These enable laboratories to become more connected, and manufacturing to become more responsive, intelligent and data-driven.
SMARTER SCIENCE
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ADVANCED TECHNOLOGY
human experience
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Innovations are already transforming the field. Leaders in the pharmaceutical industry have stopped debating whether different forms of technology would change the ways drugs are discovered and developed.
The more constructive debate is about how various technologies will be integrated into drug discovery.
The most important element will be the integration of human expertise and scientific knowledge in order to leverage technology to create innovative, safe and effective pharmaceutical products. The future of the pharmaceutical industry will be defined by the integration of knowledge, and the combination of various technologies. This is the race that will determine the industry’s future.
