As costs continue to rise with no end in sight, what can be done to return ROI to sustainable levels?
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$1.1 billon
In 2010 -
$2.1 billion
In 2018
The mean cost of bringing a new pharma product to market exploded to $2.1 billion in 2018 from $1.1 billion in 2010 – with clinical trials making up a large and growing share (1).
Transforming how clinical trials are conceived, designed and conducted will rely on harnessing the power of digital technologies. The current wave of emerging innovations offers opportunities to improve R&D productivity through automating processes, making efficient use of massive data sets, and supporting early decision-making with predictive analytics and statistical models, among others.
According to an ICON survey, artificial intelligence (AI) was considered the digital technology with the most potential to improve R&D productivity. Moreover, nearly 80 percent of respondents said their firm plans to use or is using AI or Big Data approaches to improve R&D performance.
Additionally, as the use of AI rapidly grows, regulatory support for such technologies will increase. For example, last year, the FDA quickly approved an AI-powered device for detecting diabetic retinopathy in primary care offices (2).
ICON survey respondents were optimistic that developments such as these will significantly increase R&D returns. Two-thirds reported 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. Less than one percent expects no improvement.
Given that an increasing number of sponsors and developers are looking to incorporate AI into their R&D processes, here we discuss different approaches to AI and how to best deploy them.
Expert systems
Expert systems use rules-based algorithms to mimic specific human expertise. One example includes decision-support trees for routine diagnostic tasks, such as differentiating between bacterial and viral respiratory infections for prescribing antibiotics, which are built into every electronic health record (EHR) drug-ordering module.
Robotic process automation
Robotic process automation (RPA) are specialised computer programs that automate and standardise processes based on pre-defined rules. Of itself, RPA has no ‘intelligence’ – however, increasingly it is typically integrated with other AI technologies to create faster automation, and it’s organisational impact is proving to be significant. When applied to clinical trials this includes:
- Capturing routine clinical data, such as patient vital signs
- Collecting operational data, such as drug administration dose and time
- Testing data to flag safety issues, such as an out-of-range lab result
- Assessing potential data entry errors, such as duplicated or missing data points
- Detecting potential protocol deviations, such as the emergence of a non-random variation trend
- Forwarding clean data to the trial master file and alerting trial monitors to anomalies
Benefits of robotic process automation include eliminating the need for manually transferring data from clinical sites to trial master files, reducing errors and delays, and reducing data loss by detecting anomalies more quickly and reliably than manual review.
Not only does robotic process automation yield immediate efficiency benefits, but also, it lays the groundwork for incorporating massive data sets from EHRs, mobile devices, automated image scanning, and individual patient genomic and molecular data.
Linking trial stages
Leveraging robotic process automation includes linking processes across study stages. This involves considering the final outputs — which include data supporting regulatory approval and commercial payment — in the design of every step of a study and automatically adjusting those steps when a change occurs. Automatically linking study requirements from end-to-end can significantly reduce delays and the manual effort required to fully implement a protocol amendment.
Also, adopting a linked process automation approach facilitates portfolio management decisions by allowing sponsors to model how specific changes in study protocols might affect development timelines.
Machine learning
Machine learning is defined as algorithms and statistical models that computers use to perform tasks. This allows for more flexibility than rules-based expert systems because it allows the computer to improve its performance, or “learning,” based on training, instead of relying on programmers to provide a fully worked out set of rules.
Moreover, the greater processing power of modern computers is now enabling deep machine learning, in which the device, itself, extracts features from a raw data set and has multiple layers of optimisation processing modelled on how neurons process information. This allows the machine to discover patterns in the data that do not depend on the insight or expertise of a human programmer, making deep machine learning more powerful for assessing images and complex data sets. For example, AI deep learning machine techniques have improved the formulae for predicting the power of intraocular lenses needed to reach uncorrected 20/20 vision after cataract surgery (3).
Deploying AI and its challenges
The power of AI for revolutionising pharma R&D is evident. For instance, Alphabet’s Deep Mind program can predict the risk of heart attack and stroke from retinal images. Additionally, image analysis is used to assess oncology pathology and heart rhythm with accuracy that rivals or exceeds experienced clinicians. AI has many applications for improving clinical research returns, including:
- Patient identification
- Site selection
- Patient monitoring and support
- Cohort composition
Yet, AI must be handled with care to ensure it is producing valid, reliable results. Its unstructured nature can lead to results that are not useful or defy logic. In addition, the cost and complexity of developing AI solutions can prevent the adoption of its implementation. Harnessing digital technology to transform clinical trials will require sponsors to develop or acquire a range of capabilities. To successfully deploy AI techniques, collaboration with outside experts, including clinical study process experts, will be critical. Steps for moving forward include:
- Identifying and developing operational and IT expertise and capacity
- Developing statistical expertise
- Developing global reach
- Managing change
AI has the potential to fundamentally improve pharma R&D. Overcoming the obstacles of integrating AI, while ensuring trial data and process integrity, requires an understanding of study processes. Therefore, adopting a strategic partnership to acquire insights from CROs and others with extensive experience will be critical to designing and testing AI processes to avoid potential pitfalls, and maximise benefits.
Digital Disruption in Biopharma
References:
(1) Terry C, Lesser N. Unlocking R&D productivity: Measuring the return from pharmaceutical innovation 2018. Deloitte Centre for Health Solutions, 2018. https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Life-Sciences-Health-Care/deloitte-uk-measuring-return-on-pharma-innovation-report-2018.pdf
(2) Lee KJ. AI device for detecting diabetic retinopathy earns swift FDA approval. American Academy of Ophthalmology, April 12, 2018. https://www.aao.org/headline/first-ai-screen-diabetic-retinopathy-approved-by-f
(3) Hill, W. American Society of Cataract and Refractive Surgery, San Diego, 2019.
In this section
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Digital Disruption
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Digital Disruption whitepaper
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Clinical trial data anonymisation and data sharing
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Clinical Trial Tokenisation
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Closing the evidence gap: The value of digital health technologies in supporting drug reimbursement decisions
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Digital disruption in biopharma
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Disruptive Innovation
- Remote Patient Monitoring
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Personalising Digital Health
- Real World Data
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The triad of trust: Navigating real-world healthcare data integration
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Digital Disruption whitepaper
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Patient Centricity
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Agile Clinical Monitoring
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Capturing the voice of the patient in clinical trials
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Charting the Managed Access Program Landscape
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Developing Nurse-Centric Medical Communications
- Diversity and inclusion in clinical trials
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Exploring the patient perspective from different angles
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Patient safety and pharmacovigilance
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A guide to safety data migrations
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Taking safety reporting to the next level with automation
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Outsourced Pharmacovigilance Affiliate Solution
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The evolution of the Pharmacovigilance System Master File: Benefits, challenges, and opportunities
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Sponsor and CRO pharmacovigilance and safety alliances
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Understanding the Periodic Benefit-Risk Evaluation Report
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A guide to safety data migrations
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Patient voice survey
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Patient Voice Survey - Decentralised and Hybrid Trials
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Reimagining Patient-Centricity with the Internet of Medical Things (IoMT)
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Using longitudinal qualitative research to capture the patient voice
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Agile Clinical Monitoring
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Regulatory Intelligence
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An innovative approach to rare disease clinical development
- EU Clinical Trials Regulation
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Using innovative tools and lean writing processes to accelerate regulatory document writing
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Current overview of data sharing within clinical trial transparency
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Global Agency Meetings: A collaborative approach to drug development
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Keeping the end in mind: key considerations for creating plain language summaries
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Navigating orphan drug development from early phase to marketing authorisation
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Procedural and regulatory know-how for China biotechs in the EU
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RACE for Children Act
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Early engagement and regulatory considerations for biotech
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Regulatory Intelligence Newsletter
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Requirements & strategy considerations within clinical trial transparency
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Spotlight on regulatory reforms in China
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Demystifying EU CTR, MDR and IVDR
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Transfer of marketing authorisation
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An innovative approach to rare disease clinical development
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Therapeutics insights
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- Cardiovascular
- Cell and Gene Therapies
- Central Nervous System
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Glycomics
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Respiratory
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Rare and orphan diseases
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Advanced therapies for rare diseases
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Cross-border enrollment of rare disease patients
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Crossing the finish line: Why effective participation support strategy is critical to trial efficiency and success in rare diseases
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Diversity, equity and inclusion in rare disease clinical trials
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Identify and mitigate risks to rare disease clinical programmes
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Leveraging historical data for use in rare disease trials
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Natural history studies to improve drug development in rare diseases
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Patient Centricity in Orphan Drug Development
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The key to remarkable rare disease registries
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Therapeutic spotlight: Precision medicine considerations in rare diseases
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Advanced therapies for rare diseases
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Transforming Trials
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Accelerating biotech innovation from discovery to commercialisation
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Ensuring the validity of clinical outcomes assessment (COA) data: The value of rater training
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Linguistic validation of Clinical Outcomes Assessments
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Best practices to increase engagement with medical and scientific poster content
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Decentralised clinical trials
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Biopharma perspective: the promise of decentralised models and diversity in clinical trials
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Decentralised and Hybrid clinical trials
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Practical considerations in transitioning to hybrid or decentralised clinical trials
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Navigating the regulatory labyrinth of technology in decentralised clinical trials
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Biopharma perspective: the promise of decentralised models and diversity in clinical trials
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eCOA implementation
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Implications of COVID-19 on statistical design and analyses of clinical studies
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Improving pharma R&D efficiency
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Increasing Complexity and Declining ROI in Drug Development
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Innovation in Clinical Trial Methodologies
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Risk Based Quality Management
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Transforming the R&D Model to Sustain Growth
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Accelerating biotech innovation from discovery to commercialisation
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Value Based Healthcare
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Strategies for commercialising oncology treatments for young adults
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US payers and PROs
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Accelerated early clinical manufacturing
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Cardiovascular Medical Devices
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CMS Part D Price Negotiations: Is your drug on the list?
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Ensuring scientific rigor in external control arms
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Evidence Synthesis: A solution to sparse evidence, heterogeneous studies, and disconnected networks
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Global Outcomes Benchmarking
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Perspectives from US payers
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Making Sense of the Biosimilars Market
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Navigating the Challenges and Opportunities of Value Based Healthcare
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Payer Reliance on ICER and Perceptions on Value Based Pricing
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Payers Perspectives on Digital Therapeutics
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Precision Medicine
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