There has been no shortage of excitement over 2023 with a huge number of impactful stories.
We have seen big funding rounds investments going into pharmaceutical players, such as a $51 million Series B round raised by the Parkinson’s disease player Cerevance in February and an impressive $210 million round raised in September to power Nimbus Therapeutics’ computational drug discovery.
Additionally, the pharmaceutical supply chain attracted major cash injections with one being a $57 million investment into Swiss-based tech firm SkyCell by M&G Investments in October. Meanwhile, venture capital (VC) funders also received big attention, with one notable example being Carlyle’s Abingworth, which capped its Clinical Co-Development Co-Investment Fund (CCD-CIF) at $356 million in November.
This year also saw major acquisitions, with some of the biggest being Danaher’s takeover of Abcam for $5.7 billion in August and the acquisition of Iveric Bio by Astellas Pharma for $5.9 billion in May.
No less important were the numerous regulatory approvals, such as the US and EU’s green light for Astellas’ fezolinetant for the treatment of hot flashes associated with menopause. And more approvals could be incoming, with promising trial results from compounds such as AstraZeneca’s osimertinib (Tagrisso) for the treatment of specific forms of early-stage non-small cell lung cancer (NSCLC).
There are also many key trends and challenges that 2023 presented. We turned to five key opinion leaders in the industry for their takes on the progress of the pharmaceutical industry in 2023, its challenges and their perspectives of 2024.
Where have you seen the biggest progress in 2023?
Vincent Keunen, founder and CEO of Andaman7: Over the last year, there has been huge progress in integrating data sources for the benefit of clinical research. It is now possible, for example, to combine electronic health records data from hospitals with clinical research tools, including patient reported outcomes. This allows for the seamless collection of a wide variety of rich real-world data that more accurately reflects the lives of patients than traditional clinical trial data.
Patrick Hughes, CCO and co-founder of CluePoints: Artificial intelligence (AI) techniques such as machine learning, deep learning, and large language models (LLMs) have really started to transform clinical data management this year, allowing us to tackle a range of complex use cases, from writing protocols and risk assessments, to streamlining medical coding review processes.
Data anomaly detection is a good example of AI-driven progress over the last year. Traditionally, data managers have written exhaustive data management plans, containing hundreds of edit checks at the start of each study. Yet bespoke ML models can predict data anomalies that should be queried with a greater degree of precision, saving huge amounts of time and resources whilst improving the quality and integrity of the data and never compromising on safety.
Graham Clark, CEO of Phastar: Trends in the pharma and biotech landscape have heavily impacted the CRO environment, which is now more diverse than ever before. On one hand, ‘big pharma’ is bringing more research activity back in house, to retain control and regulatory oversight over increasingly complex trial designs and spiralling number of datapoints. Yet the share of the industry-wide pipeline owned by these companies has shrunk, and a significant proportion of advanced molecules, including cell and gene therapies, monoclonal antibodies, and DNA and RNA therapeutics, are being developed by smaller biotech organizations.
Such companies do not necessarily have the therapy area-specific expertise or connections needed to drive their research programmes and have been turning to specialized or boutique CROs as much for their sector-specific expertise and strategic advice as their data capabilities and cost-effective solutions.
Jason Dong, co-CEO and co-founder of Mural Health: We have seen willingness from some technology providers to adopt a coordinated approach to decreasing site burden, as we move towards a more patient-centric model. As regulators increasingly ask sponsors to demonstrate how they have included the patient voice in their studies, we have also seen growing recognition of the importance of patient preference in trial design.
However, there is still work to do to overcome complexity of the vendor ecosystem and to ensure a complex landscape does not undermine steps to increase patient centricity.
What are the biggest challenges facing your sector now?
Keunen: Recruitment and retention remains the biggest challenge facing the clinical research sector. Despite much industry-wide discussion and numerous initiatives aimed at tackling the issue, elongated enrollment periods remain a common cause of study delay, and failure to meet sample size a common cause of cancellation.
Taking part in a clinical study can be extremely burdensome, especially for people in ill health, so arguments that focus on the common good or improving the chances of others are often insufficient.
Rather, we should be promoting the idea of Clinical Research as a Care Option (CRaaCO), which aims to integrate clinical research into routine healthcare. That means presenting clinical trials as a treatment option, as well as integrating clinical and research tools, such as data collection apps, rather than asking them to interact with numerous solutions and platforms. In short, CRaaCO answers the all-important question of “what’s in it for me?” and takes every step to reduce the burden of study participation.
Hughes: We’ve been reporting for years that the clinical trial data environment is becoming increasingly complex, with ever more data points being collected from ever more varied data sources which ultimately result in data quality issues and submission delays.
Traditional analysis methods like source data verification have been struggling to keep up, leading to the adoption of advanced statistical tools to reduce or, better still, eradicate this archaic process. However, with the adoption of machine and deep learning, and generative AI, such as LLMs, we now have the tools to attack other manual processes that need significant overhaul.
However, AI is at risk of becoming an umbrella term which covers everything and means very little with lots of hypothetical examples and little real use. At the broadest level, it has thousands of applications, not all of which will be relevant or useful for clinical operations and data management.
To unlock the potential for the long awaited ‘paradigm shift’, we need to understand the context of clinical data, the problem we are trying to solve with new technologies, and how those technologies fit into existing and new processes. We also need to identify the challenges, and work, as an industry, to develop the guidelines and regulations needed to embed AI safely and effectively into clinical research.
Clark: With clinical trials becoming ever more complex and specialized, the biggest challenge facing sponsors is accessing the necessary skills and expertise. Data science, for example, is a rapidly growing discipline, with life sciences being just one of the sectors vying for candidates. It means that many companies, particularly smaller biotechs, lack the in-house statisticians needed to conduct modern clinical trials.
At the same time, there is growing awareness that each research area has its own complexities, challenges, and regulatory considerations. Together, these factors are challenging the traditional paradigm of how sponsors work with CROs.
Specialist CROs can capitalize on this shift in the industry as they can often provide therapeutic area expertise that more general CROs cannot. They can provide specialized knowledge and valuable experience in specific disease areas. This ensures clinical trials can be designed and executed with a deep understanding of the context of a therapeutic area and the regulatory and compliance landscape, contributing to faster, more cost-effective studies.
Marc Buyse, founder of the International Institute for Drug Development (IDDI): Currently, clinical studies collect vast volumes of data while narrowly focusing on meeting only one or two primary endpoints. If unmet, all these resources are wasted.
Even when successful, many of the potential insights from the study go unexplored, often leaving valuable information untapped. Moreover, these few primary endpoints only partially reflect what matters to patients.
For instance, the quality of life is often not included in oncology, neurology, or rare disease primary endpoints. In some cases, this traditional model represents a waste of resources at a time when drug development costs are increasing, and medical needs are continuing to go unmet.
Future patient-informed trial designs should allow for multiple efficacies and/or safety criteria to be combined into an aggregated measure estimating the net benefit of a treatment. This will help sponsors shift to more patient-centric endpoints, thereby leveraging most of all the collected data and helping reduce the required sample size by more than 20%.
Dong: Despite all the sector’s advances in recent years, drug development remains a lengthy, costly process beset with systemic diversity issues.
Sponsors have pledged to make research more inclusive, but this is not possible until we remove the barriers to participation that disproportionately affect people from lower socioeconomic and minority groups.
For example, existing US tax rules unintentionally deter individuals on public assistance from participating in clinical trials, because taxing their clinical trial payments threatens to disqualify them from social welfare programs.
Many of these people represent communities that are grossly underrepresented in clinical research. Earlier this year, we launched an effort to work with Congress to make clinical trial payments tax free. This relatively simple measure, we argue, would help expand access to trials for the 90 million-plus Americans on public assistance and increase cohort diversity.
What do you think will be the most significant trend in 2024?
Keunen: Throughout 2024, we believe that drug developers will turn their focus to patient-mediated research.
Decentralized clinical trials (DCTs) and an increasing ability to access and analyze real-world data sources like electronic health records from hospitals and patient-reported outcomes have given sponsors a direct line to research participants. This presents them with an opportunity to collect richer real-world data than ever before.
Personal health records maintained by patients themselves, for example, can provide information on over-the-counter medicines, and patient-reported outcomes give a previously unmined patient-focused perspective and ensure they are working to the same goals as the people they seek to serve.
Much of this work is being driven by advances in AI, which is now able to extract meaningful information from the huge volumes of unstructured text contained in electronic health records. Over the next year, we expect many of the same techniques to be put to work improving the accessibility and quality of health data, as well as clinical trial recruitment capabilities.
Hughes: I think we will continue to unlock the opportunities of AI in 2024.
New approaches to overhaul traditional and inefficient processes will be introduced in 2024 and see widespread adoption over the next few years. These include areas like automated medical coding and query detection as well as more and more companies using LLMs, whether that be to design their protocols or evaluate the use of synthetic control arms. This technology already exists, meaning our focus is now shifting from developing AI applications, to developing AI implementation infrastructure.
Discussions on how we overcome challenges, including IP and data privacy, ensuring equity of access to data, and maintaining ‘human in the loop’ control, have already started. We expect to see the fruits of this labor, in the form of guidelines and regulation, to be a significant trend of 2024.
Clark: The use of AI in clinical trials has been increasing steadily in recent years, but in 2024 we expect talk to turn away from the technology itself, and on to how it will impact the way we run our companies.
The value of these technologies is undisputed, but we also need to recognize how they will change the way we work and the way we interact with each other, both internally and externally.
Over the next year we expect much discussion around questions such as the role and progression of analysts in a world where entry-level work and data sorting is performed by a machine, and the appropriate balance between computer and human input during decision-making.
Buyse: Over the last year, we have seen a growing awareness of the need to change the way we “do” clinical research.
The clear shift towards more clinically relevant, multi-criteria endpoints is being carried by improvements in computer power and advanced analytic applications. It is driven by a desire to ensure that the patient’s voice is embedded in the drug development pathway.
The biotech R&D industry invests heavily in software and new methodologies needed to enable more patient-centric trial design, including patients’ preferences. These innovative solutions will reduce trial sample size and consequently overall patient recruitment timelines. This will significantly accelerate time to market and result in treatments that better meet the holistic patient needs.
I see this being 2024’s most disruptive trend.
Dong: Generative AI will be the big topic of 2024.
Since ChatGPT burst onto the scene, everyone has been talking about how this kind of technology could help in the design and conduct of clinical trials. We see the debate now moving to how we, in the research space, can apply it safely, securely, and in ways that regulators will be comfortable with.