World Clinical Trials Day, May 20, marks the date of the first randomised clinical trial, held aboard a ship in 1747. Today, as part of biopharmacetuical R&D, clinical trials is the area where the spend is the highest and provides the most opportunity for automation and adoption of artificial intelligence (AI). Sowmyanarayan Srinivasan, Managing Director – Life Sciences, Accenture Advanced Technology Centers in India (ATCI) predicts that the future of clinical trials will be fully AI-enabled, starting right from protocol to clinical operations around site and investigator management and eventually pivoting to patients and making it easier for the patients to be part of clinical trials
The global biopharma industry is undergoing significant transformation across the spectrum of drugs, drug delivery and business model. There is an increasing focus on patient at the core. Some of the key drivers of the transformation are –
- End of blockbuster era and beginning of niche buster era. The drugs are getting more personalised and relevant for much smaller populations. This also means that the number of drug launches are increasing
- Focus on new science driving more companion and digital solutions as approved drugs, genomics is driving personalised drugs
- Healthcare consumerisation is being driven by the digital revolution. Patients do not want to be held back by disease and want to be treated more like consumers. They are looking for same experiences as in their daily lives, for example retail
- In North America and Europe, the focus on outcomes has increased and reimbursements are tied to defined and agreed outcomes
According to a recent report (1), while the transformation is driving a change in pricing models and patient engagement, the costs of bringing a drug to the market continues to increase and is projected to be worth $2 billion. This situation has created a perfect storm to reimagine the entire biopharma R&D process which is the main cost contributor in getting the drug to the market.
As biopharma organisations focus on reimagining the R&D process, one of the key enablers of this change is Artificial Intelligence (AI). Though AI has existed for several years (in fact decades), the adoption in the biopharma industry was slow and has increased significantly in the last six to eight years. This adoption can be directly attributed to increased generation and availability of data, ‘big data.’ There has been a tremendous increase in both volume and variety of data – genomics data becoming mainstream and cost effective, imaging data and the ability to store and analyse such data, patient data from digital solutions and patient medical data with higher adoption of EMR and EHR. In this evolution, the bottleneck moved from quantity of data to quality of data and curation, though the fact that data is available for leveraging artificial intelligence techniques is a critical factor for increase in adoption.
If you look at the history (2) of clinical trials, the first clinical trial of a novel therapy was accidentally conducted in 1537 and it took over 200 years (year 1747) to the first controlled clinical trials and another 200 years (year 1946) to the first double-blinded-controlled trials, which has been the norm since that time. It clearly shows us that scientific establishments take a long time to change and modernise processes. However, if you consider technology evolution in the same timeframe, for instance, it has grown by leaps and bounds. Today, we are at a point where there is a need to get medicines to the market faster and there are several technologies that are available to help the overall R&D, specifically clinical trials to leapfrog many challenges.
While clinical trials is an obvious place to start given the disproportionate amount of spends, the future of R&D will not only have a different approach for research, but to safety and regulatory as well. Let us start by looking at potential use cases in R&D and how they can drive transformation in R&D.
Potential use cases in R&D
The first step to reimagine the R&D process is to identify use cases which are ready and mature to be adopted for AI. The primary drivers for this would be quality and quantity of data and predictability of the use case. Some of the most common use cases across research, clinical, safety and regulatory are outlined in the next sections.
Summary view of potential use cases
Science is evolving rapidly, and it is humanly impossible to track these changes. Research is hypothesis-driven and is amenable to using AI. In fact, use of AI in research has existed for a very long time. The earliest set of databases, be it for compound, target or pathways, used AI and curation to build and deliver at scale.
Some of the use cases in research that are already in play are as follows:
1. Next Generation Sequencing or genetics and genomics data in general – This has been at the heart of big data for biopharma. Requires multiple steps to run the analysis to get to relevant insights around disease target or biomarkers.
2. Pharmacokinetics (PK)/ Pharmacodynamics (PD) Modeling – It has been in existence for very long. There are well-known products in the market that support this and is commonly used and adopted.
3. Target, compound profiling – There are companies that are focussed on building this as a product by leveraging AI and then curating to ensure high-quality data.
4. Pathways – AI plays a key role in extracting entity interactions from publications / other data sources which become the base for pathway building.
There are industry-leading solutions in this area which have been around for long and are well-accepted solutions. The continuing challenge in research has been around bringing data together, drive standardisation and leverage analytics to enable business decisions. The next horizon in research is data platforms that will enable broader AI and analytics to reduce overall cost of lab experiments and support rapid hypothesis testing.
Clinical trials reimagined
Clinical trials and research is the area where the spend is the highest and provides the most opportunity for automation and adoption of AI. Some of the main challenges in clinical trials are around patients, site, investigators and collaboration.
Patient identification and recruitment continues to be a challenge as the number of trials and studies continue to increase. Several of them are in the same therapeutic area with similar disease and target focus. This means that the target population for several companies is the same and success in getting to patients is limited. This is compounded by the fact that less than five per cent of the patients opt to participate in clinical trials.
While recruitment is key, patient engagement is also important. Although the first steps are to identify and recruit patients, the industry also struggles to retain patients for the duration of the trials. Patient drop out is a big ongoing challenge in clinical trials. On the other side, site or investigator selection is a critical part of trials success. As drugs become more specialised and targeted, the expertise for managing trials have also increased. Finally, lack of clinical stakeholder collaboration is also a challenge. It is important to bring together the various stakeholders on a common platform which is a critical success factor for trials completion on time. There are several opportunities that are fit for driving transformation in clinical trials.
The clinical process starts with a protocol document which outlines the approach and intent of the clinical study. The key content of this protocol document then needs to be used to set up an electronic data capture system that will help capture defined data of patients as outlined in the protocol during the various visits to the site. Today, the process of setting up the electronic data capture system from the protocol takes anywhere between 12-16 weeks since the process is completely manual. The data entry and quality-check iteration time take the bulk of this total time. The future of this process will be AI-driven, where AI will learn from past protocols and provide suggestions as new protocols are drafted. It will support data extraction from the protocol to load into the electronic data capture system and increase productivity by 80-90 per cent which will provide significant competitive advantage.
Today, there are some opportunities especially in the clinical operations area that have adopted AI to varying degree while there are several others that are ripe for disruption. Identifying the right patients and retaining them along with right sites and investigators is predominantly AI-driven and will continue to be dominated by AI, given the large pool of data available.
- Patient recruitment helps identify the right set of patients for a given study by using data across past studies, EHR/EMR, other data sources like patient communities based on data access
- Patient adherence and drop out is based on patient behaviours like medication adherence, diet,