Rachna Malik

	The year 2020 will be remembered as a watershed moment in world history due to the unprecedented and widespread outbreak of COVID-19. In most countries across the world, industries, institutions and establishments of all kinds, are in lockdown. To prevent spread and transmission of the disease, people worldwide have been forced to observe physical distancing, an idea so inconceivable that if anyone had even hinted at its possibility previously, we would have laughed.  

	The last few weeks have reinforced our long-held belief that technology is one of the key element that can hold the world together and keep operations running, even in times of a large-scale crisis. Technology has enabled banks to operate seamlessly, facilitated students to remotely connect with tutors, provided a platform for the government and judicial machinery to operate and has also helped physicians to remotely consult patients and administer necessary care. Essentially, technology has demonstrated the core Business 4.0 tenets of resilience and adaptability to help diverse industries to continue operations without disruption.

	Owing to the current worldwide pandemic situation, people are fearing for theirs and their family health and well-being and are holding out for a vaccine or a treatment for the novel coronavirus. While their concerns are justified, so are the frustrations and challenges faced by life sciences companies, who are working hard to accelerate the discovery of an effective drug that can help overcome the current pandemic. 

	In the current scenario, the wait for a new drug, seems inordinately long. Understandably so given that a large section of the life sciences industry too is impacted due to the extreme situation in North America and Europe. Going by the status of vaccine development underway, the earliest possible time for a vaccine to be made available would be in the second half of 2021. Many life sciences companies, however, are also working on re-testing existing drugs for treating coronavirus. In order to accelerate clinical R&D and bring drugs faster to the market, it is extremely imperative that science is complemented by new-age technology such as artificial intelligence and data-driven smart analytics. 

	There is, however, a transformational shift that is being seen. Interestingly, a new normal is emerging for us in our pursuit to expedite the clinical trials process. This new normal, we believe, will see an expeditious adoption of process automation, AI, data sciences and mHealth solutions in the life sciences industry. Technology will now play a far integral role than before, and in some ways, form a symbiotic relationship with life sciences, to speed up its R&D. Adopting a machine-led approach, where technology augments human capabilities to vastly enhance the efficiency and safety potential of clinical trials, would be the default.

	Agile and precise clinical trials process 

	During the entire clinical R&D value chain, life sciences organizations have to work with a tsunami of data that tends to be a mix of pre-clinical, clinical, supply, medical, safety and regulatory data. The challenges with such diverse data types is that they are ever-growing, fragmented, difficult to interpret, non-integrated and lack actionable insight.

	In addition, the existing work paradigm for life sciences organizations apportions a higher proportion of time, effort and resources on performing administrative and operational tasks of reviewing, comparing and correcting the trial data. Thus, the workload allocated to innovative, creative and patient-care tasks is significantly smaller. Leveraging the technology-first approach, organizations can enable automated collection, analysis, review, writing, etc. of data using artificial intelligence, machine learning, natural language processing and other technologies. This also necessitates the need to have an integrated approach towards making the clinical trials process more efficient and agile.

	To understand this better, let us see a few scenarios where technology can augment rapid clinical trials:

	Use of mobile and smart devices that effectively report patient outcome data more accurately and frequently should be implemented. Cognitive technology can be leveraged to take up bulk of the repetitive tasks, like reading safety case report form or deciphering adverse events. This will not only cut down the manual effort substantially but also improve the accuracy of case processing. Natural Language Generation (NLG) can be effectively leveraged to write scientific narratives. Technologies are available today to automate generation of SDTM files without the need for human intervention to write SAS programs. 

	In regulatory, AI can enable interpretation of large data sets and user queries to store relevant information. Technology powered smart search engines coupled with historical data-rich regulatory intelligence hubs, can help life sciences organizations to draft quick responses and share with health authorities for faster regulatory submissions.

	All the above scenarios can be realized while maintaining adherence to compliance, data safety, data privacy and data quality requirements. Agility and automation in clinical development can be driven only by digital technologies, more so in an era in which self-isolation coupled with physical distancing might become a standard. 

	At-home clinical trials will become a norm, rather than an exception. We have already seen the ‘at-home’ concept disrupt the banking and retail industries. Life sciences, similarly, will undergo a radical shift. An increasing number of patients can now enroll from home and participate in trials. Activities such as consent, medication dispensing and administration, remote monitoring can be easily accomplished under the at-home setting. All this while maintaining the highest quality levels of safety and patient experience. Mindsets will change for the good, not just in the life sciences fraternity, but also amongst providers and patients participating in trials.

	Data driven analytics and insights and AI based predictive tools can effectively monitor clinical trials and also assess and mitigate future risks by generating actionable and meaningful insights. This would not only help make the trial more efficient, but also facilitate early decision-making in cases of opt-out.

	AI-led predictive models for data analysis will lay down the future roadmap of clinical trials. Risk management can be done through sophisticated risk monitoring and mitigation tools. Early detection of risks will enable life sciences companies to take timely and informed decisions. There are tools that will help drug manufacturers to predict patterns among trial volunteers as far as trial enrolment is concerned. This will significantly benefit the future models of clinical trial protocols enabling life sciences organizations to apportion more bandwidth for activities such as research and patient care. Technologies that facilitate interoperability can enable a desired amendment in protocol and downstream processes very easily. 

	As advancements are taking place in the field of clinical trials, digital technologies are aligning at an equally rapid pace to support and bring speed to innovations across the life sciences value chain and bring safe and high-quality products into the market. The times ahead will see game changing high-end technology driven innovative solutions steering growth in the industry and enabling life sciences organizations to become more agile. These solutions will usher a new era in the world of science and medicine. Perspectives are already changing among all stakeholders i.e. volunteers, researchers, clinicians, doctors, pharmacologists, technologists, and health authorities. The new collective perspective will be an embodiment of the new normal we just talked about.

 is Global Head, TCS ADD platform suite, with the Life Sciences unit at Tata Consultancy Services (TCS).

Articles

In order to accelerate clinical R&D and bring drugs to the market quicker, it is imperative that science is complemented by new-age technology such as artificial intelligence and data-driven smart analytics. 

Rachna Malik

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