Industry 4.0. This term is becoming more common when used to describe a company’s vision for how they look to optimize operations, increase the quality of their product and services, deliver a superior quality experience, and enable more cost efficient outcomes. We see this this term used in various industries ranging from manufacturing, to telecommunications, to agriculture. The world around us is becoming more connected and more productive than ever before due to new technologies being integrated into these industries that effect our personal and professional lives.

However, there are some industries that have taken a back seat with regards to the quick adoption and implementation of newer technologies. One such industry that we all have, or will eventually rely on at some point in our lives is, the pharmaceutical and biotech industry. In the 1980’s, the average time to research, develop, and approve a therapeutic was 13-15 years and cost $100M to $300M. In 2020, the time required to research, develop, and approve a therapeutic hasn’t changed. The average cost to get a therapeutic to market has grown exponentially and now averages between $1B and $3B. The average cost double every 8-10 years.

The same stagnant timelines from the 1980’s and exploding R&D costs have not gone unnoticed by the industry or by the general public. In fact, there is an unofficial name given to this trend – “Eroom’s Law”, which is Moore’s Law backwards. Eroom’s law is the observation that drug discovery and development is slowing and becoming more expensive over time despite the significant improvements in robotics, automation, instrumentation, and computational and data sciences.

Very little has been done to disrupt this trend. Newer, yet disparate, standalone software and/or hardware solutions in the R&D space have ultimately delivered very little to no noticeable value over the years. Operations and technology in today’s pharmaceutical labs lack any real integration or cohesion leading to subpar research outcomes, hampered decision-making, regulatory issues, and escalating drug development costs.

This trend is seen across the pharmaceutical landscape and includes the pharmaceutical companies themselves and their partners like contract research organizations (CROs), contract manufacturing organizations (CMO/CDMOs), and clinical trial site management organizations (SMOs).

Contract research organizations (CROs) are an integral partner to pharmaceutical companies. In fact, over the past decade, a growing number of R&D functions and workflows have been outsourced to CROs. A trend that is currently increasing and predicted to increase significantly over the next 10 years. Eroom’s Law is only going to become a greater impediment with pharmaceutical R&D progress.

Laboratories specifically are undergoing many shifting and transforming trends along with ongoing challenges. There is a pressure to significantly reduce the costs associated with bring a new drug to market. At the macro level, lab must contain costs by implementing concepts to permit smarter data faster so that decisions can be made at earlier points in time, thereby generating significant value.

Innovators in the CRO industry are needed who clearly understand these issues and want to address them by bringing new ideas, concepts and technology to the space. They need to understand that a better 21st century approach is needed to significantly enhance the research quality, speed, transparency, and cost efficiency that the pharmaceutical industry wants and desperately needs to get lifesaving therapies to the patients that need them. Embedding the principals of industry 4.0 into the foundation of operations would accomplish this by utilizing seamlessly integrated robotic systems, artificial intelligence (AI) and analytics to deliver superior quality and timeline outcomes.

Unlike today’s CROs, a new fully automated approach would be unique and designed to radically improve laboratory operations and ensuring superior quality research outcomes for customers. Excellent precision, traceability, accountability, reproducibility, and minimization of adverse human impacts are ensured in a more fully automated environment. This ultimately means quality, quantity, speed, and cost efficiency are mutually inclusive, not mutually exclusive. And that research results should be optimal, not just acceptable. This “lab of the future” places much less burden on needing so many scientists to be on site and instead leverages integrated operational robotics, machine learning, integrated systems and remote control of these laboratory systems.

Another game changing advancement opportunity for the Pharmaceutical industry is in the area of digital transformation. The next horizon is for companies to expand upon the use of robotics and AI, as described above, and combine this with Blockchain technology (BCT), the Internet of Things (IoT), and analytics in the laboratory to provide improved workflow compliance and operational efficiency. Greatly improved data reliability and security is a compelling driving factor. This will have great impact on labs, as the regulatory functions of the FDA are particularly focused on data integrity. They require documented evidence that current computer systems and databases cannot be manipulated for commercial gain. A system that inherently detects and records manipulation of data in a database will provide superior detection of data adulteration. The change is visible in many computers in the network. Thus, one can detect additional tests, which would indicate the prohibited “repeated assays to find a data that support the desired outcome.” BCT is expected to improve testing rigor and decision objectivity.

Another important BCT use case can be for biological sample management which will provide a solution to addressing these challenges in clinical trials. Today’s approach to clinical trial management operations involve a complex network of systems and data sources. Each data source may independently hold similar views of patient and investigator site information, including patient sample, storage location, and laboratory results. Yet clinical operations leaders in pharmaceutical companies often rely on manual data collection, such as compiled spreadsheets of data derived from multiple clinical and laboratory systems, to assemble a complete picture of information related to the clinical trials. Manual compilation of data leads to process complexity and potential for data errors. Additional issues include data volume, data quality issues, assessment of data anomalies in real time, and out-of-date study metrics.

A blockchain technology (BCT) approach brings improved visibility, transparency and tracking to the chain of custody in clinical trials. The solution will provide end-to-end sample tracking and real-time status updates to the entire chain of custody which will provide the level of data integrity and traceability required by regulatory authorities. The chain of custody spans multiple stakeholders and disparate data systems, augmented by extensive processes at clinical sites. During clinical trials, multiple biological samples are collected at various clinical sites over an extended period of time, this results in excessive manual data entry, the need for coordination between external and internal teams, frequent opportunities for error and difficult end-of-trial reconciliation. Blockchain provides immutable records through the process, which aids in compliance and audits. With a single source of truth that reduces errors and the need for reconciliation, it addresses patient privacy concerns and population health data requirements.

Operational data can be used to drive AI-enabled clinical trial analytics. Clinical trials generate immense operational data, but functional data silos and numerous disparate applications often do not provide biopharma scientists and executives the ability to see a comprehensive view of their trials portfolio over multiple global sites to make informed decisions. As a result, many hours are lost collecting and analyzing diverse data sets to optimize trial operations, as well as to improve cost and resource efficiencies. By consolidating operational data on a clinical trial analytics platform with predictive capabilities, biopharma companies can improve their ability to discern whether a data is an anomaly or a true risk. Consolidating all data – whatever the source – on a company’s shared analytics platform can foster collaboration and integration, and provide insights across vital metrics ranging from enrolment rate and screening failure rates, to protocol deviations. An effective integrated platform incorporates advanced analytics, including predictive analytics, at every stage of the process to uncover actionable insights that were previously difficult or even impossible to attain. Incorporating a self-learning system, designed to improve predictions and prescriptions over time, as well as data visualization tools, can proactively deliver reliable analytic insights to users. Automation, The Internet of Things (IoT), artificial, intelligence (AI), and blockchain represent an unprecedented opportunity for the pharma industry. Every company capable of leveraging these technologies will have a chance to radically streamline and enhance existing processes, create entirely new business models, and develop innovative products and services for a new generation of consumers.

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