Top five digital technologies set to transform pharma R&D

Although earlier waves of digital disruption, such as social media and smartphones, were highly disruptive in many industry sectors, they were much less so for pharma. However, the current wave of emerging digital technologies offers an opportunity to significantly disrupt pharma business operating models in a variety of ways.

Robotic Process Automation, for example, will streamline or eliminate many costly, time-consuming and error-prone manual steps. Big Data techniques will aggregate and scrub massive, disparate new data sets, making them available for efficient use. Artificial intelligence (AI) will filter and process Big Data far faster than any human, generating insights supporting early decision-making, with increasingly powerful predictive analytics and statistical models.  

This digital transformation is already underway and likely to accelerate, according to an ICON survey of more than 300 executives, managers and professionals in biopharma and medical device development firms. Nearly 80 percent of respondents said their firms plan to use, or is using, AI or Big Data approaches to improve R&D performance. This is a significant increase from an earlier survey in November 2017 conducted for our whitepaper 'Improving Pharma R&D Efficiency,' in which 36 percent of respondents cited the benefits of Big Data and AI to improve clinical trial efficiency

Moreover, our survey respondents were optimistic that such developments will significantly increase R&D returns. Two-thirds said they have the potential to increase productivity by 26 percent or more, with 22 percent expecting 51 to 99 percent and 5.5 percent expecting 100 percent or more. 

With such an enormous opportunity for transformation, it is critical that pharmaceutical companies and device manufacturers alike understand emerging digital technologies. Here we discuss the top five digital technologies set to transform pharma R&D, and the role they can play in improving clinical development.

 

Big Data 

Big Data takes considerable effort to evaluate, normalise and structure, so that it can be reliably used for analysis. However, if managed effectively, Big Data can have a significant impact on clinical trial designs. 

For example, structured clinical data – which consists of data from current and past clinical trials, real-world evidence from registries and peer-reviewed studies – can improve clinical study efficiency in several ways, such as enabling go/no-go decisions and helping close out studies faster. This data can also be helpful in streamlining current trial protocols by predicting potentially high-performing study sites, further shortening study timelines.

Another example of a useful Big Data source is data from wearables and sensors. These sources range from commercial devices, such as Fitbits and cell phone accelerometers, to medical-grade heart, blood pressure and glucose monitors. The volume and granularity of data from mobile devices can increase the statistical power of subject data, allowing shorter periods to establish efficacy. 

Finally, data from genomic, proteomic and imaging studies can be used for diagnosis, monitoring and therapy development. These technologies have the potential to dramatically increase approval rates, multiplying R&D efficiency.

 

Artificial Intelligence (AI)

Artificial intelligence (AI) has gained increasing attention due to its potential to optimise trial efficiency. For example, AI techniques can be used for monitoring and encouraging patient compliance with study protocols. If a patient is failing to adhere to a treatment regimen because a study is not compatible with his or her personal life, AI can be used to detect this early, so that the sponsor can prevent the patient from dropping out by tailoring the trial accordingly (1). Improved patient compliance may reduce the need to over-recruit to offset projected subject or data losses, saving time and effort.  

Despite the potential of AI, the term itself covers a wide range of software and hardware which can lead to confusion, making it critically important that sponsors understand these types and their uses. Expert systems, for example, are one of the earliest and most widely used applications of AI. These use rules-based algorithms to mimic specific human expertise, which can be used to diagnose various conditions more efficiently and therefore help identify the appropriate treatment.

Machine learning is another application of AI. Machine learning is more flexible than expert systems because it allows the computer to improve its performance based on “learning” over time, as opposed to rules set by programmers. This can be used, for example, to analyse electronic health records and clinical trial eligibility databases to identify potentially viable patients. Ensuring that the right patients are recruited for the right studies is particularly valuable because it can prevent costly delays down the road. Moreover, machine learning is being leveraged by tech giants and startups alike to enable cheaper and more efficient drug discovery (2).

Finally, another useful application of AI in trials is robotic process automation (RBA), which can be used to design trial processes that allow machines to do anything that a machine can reliably do. These processes can include a range of things from testing data to flag safety issues, to detecting protocol deviations. RBA can cut days or weeks off of trial timelines simply by reducing human error and delays due to business hours, weekends and time off. RBA can also be leveraged to link trial stages. By automatically linking study requirements from end-to-end, it is possible to reduce the delays and manual effort required to fully implement a protocol amendment. 

 

Organ-on-a-chip 

Organ-on-a-chip and body-on-a-chip are in development to address a lack of robust preclinical models for gauging the potential efficacy and toxicity of drug candidates. The technology uses micromanufacturing techniques, such as photolithography, to create a microfluidics environment on a silicon chip that mimics in vivo conditions. These chips are then populated with differentiated human cells in physiologic arrangement. 

For example, an artificial liver has been developed that features three-dimensional scaffolds in a cell culture chamber perfused at physiological oxygen levels and stress. This promotes growth of hepatocellular aggregates that structurally and functionally resemble hepatic acini that remain viable for up to two weeks. Such a system would be valuable for testing the way drug candidates affect the liver, which is the organ most often responsible for drug metabolisation.

Organs-on-chips have been developed for lungs, kidneys and gut tissues. Similarly, a body-on-a-chip, including several organs, has been developed to assess how drugs might interact across organ systems. While the technology may one day dramatically reduce the cost of pre-clinical development and reduce the risk of human trials, it requires additional development and validation before it can be practically used. This will require significant collaboration among engineers, biologists and clinicians.

 

Blockchain 

Data integrity and transparency are essential to maintaining trust in clinical R&D and ensuring data are properly interpreted and analysed. At the same time, maintaining patient confidentiality is an ethical and legal requirement. Within clinical trials, patient data is the most notable item of transactional nature between networks such as healthcare institutions, patients and regulators.

Blockchain technology – which is essentially a decentralised ledger system that is fully transparent and immutable – has been shown to provide a web-based framework that allows patients and researchers access to their own data. It allows for user confidentiality, protecting patient privacy during the exchange of data between parties. 

Blockchain technology also allows for complete transparency of data, which has immense potential within clinical trials. With blockchain, there is an audit trail built into transaction of data, which allows for verification of the original source of the information being transacted, as well as the ability to detect any attempts to tamper with it. Despite the potential benefits, further functionality will need to be added to incorporate blockchain into trials. 

 

Quantum computing 

Led by several governments, and big technology companies such as IBM, Microsoft, Google, Alibaba and Intel, there has been significant investment into developing quantum computing technology over the last several years.

Quantum computers perform calculations using linear algebra to manipulate matrices of complex numbers (‘qubits’) – effectively connecting in multiple dimensions. This enables quantum computers to conduct vast numbers of computing calculations simultaneously, compared to conventional computers that must work through calculations linearly, one at a time. This makes quantum computers much more capable of solving complex problems, involving multiple connections among multiple data points, much faster (3). 

Although some applications have begun to emerge, currently the hardware and software to support quantum computing will require years of development before it is widely available for application in clinical trials. However, given its potential to revolutionise computing, industry executives should monitor this technology. 

 

Conclusion

The potential benefits of incorporating Big Data, AI, organ-on-a-chip, blockchain and quantum computing in clinical trials include automating routine data entry functions, accelerating site and patient recruitment, and much more

But how can these technologies be integrated into clinical trial designs, while adhering to the rigorous standards required to demonstrate drug safety and efficacy? To discover the answers, read our latest white paper:

References

1. Harrar, S. et al. Artificial Intelligence for Clinical Trial Design. Trends in Pharmacological Science. CellPress, 2019. https://www.cell.com/trends/pharmacological-sciences/fulltext/S0165-6147(19)30130-0
2. Fleming, N. How artificial intelligence is changing drug discovery. Nature, 2018. https://www.nature.com/articles/d41586-018-05267-x
3. Schtsky D and Puliyakodil R. From fantasy to reality: Quantum computing is coming to the marketplace. Deloitte University Press, 2017.