Life Sciences 2024ward
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Predictions for the Life Sciences 2024: Science and Technology

e asked members of DIA’s scientific and publications communities to share their predictions for the life sciences in the coming year. All responded creatively, although some responded anonymously. We hope you enjoy reading these predictions now AND revisiting them later to see how many came true in 2024.

AI in Patient and Professional Engagement

AI for patient and healthcare professional engagement is booming, but expect stricter rules in 2024:

Federal, state, and private watchdogs will check AI not only for accuracy, but also for fairness and bias.
Data privacy as applied to AI will get serious attention from regulators.

It will become more important to be able to explain how AI works, source the results, and explain what the AI finds.

Lawsuits will begin to be filed over AI mistakes. Some of these lawsuits may result from patients and healthcare professionals feeling targeted, and then worried about data privacy. This will result in a loss of trust.

If AI is done legally and compliantly, especially by sales and medical information personnel, trust can be built into the system. While FDA has focused on Quality by Design, AI lawyers will need to work with sales and medical affairs teams to build trust by design into the systems. — Darshan Kulkarni, Kulkarni Law Firm.

The Competitive Pharmaceutical Landscape

The competitive pharmaceutical marketplace will compel increased trial innovation: The pharmaceutical marketplace is more competitive than ever, particularly in the race to be first-to-market. For instance, the time between the first FDA approval for a vaccine for the respiratory ailment RSV to the fourth FDA approval for a similar vaccine was less than six weeks. The first-mover advantage is dramatic, so clinical trial sponsors need to be focused on maximizing every second and not wasting time. To that end, large pharmaceutical companies are setting lofty goals of 50 percent reductions in cycle time next year, which means that the status quo won’t allow them to reach such significant new goals. Sponsors will need to make commensurate changes to accelerate drug development. Decentralized clinical trial (DCT) methodologies will become part of the standard toolkit, embedded into most trial processes and operational decisions, because sponsors need new ways to collect data reliably and engage with patients in a competitive marketplace. In 2024, the shift to DCT methodologies will be transformative and wholesale, bringing DCT elements into the organization at the portfolio level rather than trial by trial. DCT methodologies allow for higher fidelity data collection and greater objectivity in signal detection, removing skews and biases. This enables a more informed understanding of an investigational medication’s safety and efficacy, which allows companies to reduce the sample size—and fewer participants means less patient recruitment time and lower costs. One major multinational pharmaceutical company reduced its sample size by 70 percent this way, helping to speed drug development and time to market. — Alison Holland, Medable.

Data Sharing and Cross-Industry Collaboration

Data sharing and cross-industry collaboration will power the 2024 AI revolution in clinical research. No one company has enough data to drive accurate productions around a single disease or use case, which is why cross-industry collaboration like we witnessed during the pandemic will be reinvigorated. During COVID-19, pharmaceutical companies, clinicians, researchers, technology companies, and regulators worked together in harmony. The MELLODDY trial provides a current example of how this can work: MELLODDY is using federated learning, a data-sharing model that protects companies’ proprietary information while still sharing important research data, to provide much-needed, high-quality protein data to help AI/ML models design proteins faster. Protein drug development is notoriously long, arduous, and costly. However, organizations contributing to the MELLODDY trial can use AI to adopt generative biology and are experiencing greater efficiency than any individual organization could alone. Without this level of collaboration, AI won’t work, because it won’t provide tangible ROI and so investment (and adoption) will slow to a trickle. — Rohit Nambisan, Lokavant.

Market Access Models for Digital Therapeutics (DTx)

Digital therapeutics (DTx) companies will continue to pivot away from prescription models. In 2023, one major DTx company filed for bankruptcy and another abandoned the prescription business model. Both DTx companies raised hundreds of millions of dollars by going public in special-purpose acquisition company (SPAC) transactions. While each company successfully gained FDA approvals, current market acceptance for prescription digital therapeutics is low and improvement may take years. Given this, many DTx companies will pivot away from the prescription model and go straight to consumers in 2024. They will continue to run clinical trials to confirm their claims and value proposition but will not seek FDA approvals. — Joel Morse, Curavit Clinical Research.

DCTs: Evidence versus Hypotheses and New Focus on “How”

Evidence will replace hypotheses in DCTs: By the end of 2024, the life sciences industry will replace hypothetical surveys about the impact of decentralized methodologies in clinical research with hard evidence. The Tufts Center for the Study of Drug Development has formed a pre-competitive consortium of more than 50 companies that will collate metrics to understand how decentralization impacts protocol performance such as cycle times, patient recruitment, and retention rates, with the goal to publish its first data set by spring 2024. The consortium will conduct a granular analysis of actual data to gain a better understanding of the impact of specific clinical trial innovations to better inform future protocol design. With this information, industry can take a more fit-for-purpose approach for determining how to incorporate decentralized elements into more trials. With more evidence, we can get better at matching decentralized innovations to populations, diseases, and specific study designs. This could have the add-on effect of decreasing complexity: For instance, trying to implement electronic clinical outcome assessments (eCOAs), wearables, and home-health nurses in the same trial may be overkill; instead, we can carefully pre-plan to use just the technologies that are most likely to improve outcomes. As we continue to capture evidence of DCT value, we will increasingly adopt a nuanced approach, but this progression will move at the pace of maturity of each DCT innovation. — Pamela Tenaerts, Medable.

We will stop focusing on the “what” of decentralized trials and focus on the “how”: Industry has been spending a lot of effort trying to define DCTs and debating what a DCT consists of; now, we focus on their outcomes. DCT methodologies give us more options around evidence collection and more opportunities for data capture in real time—all with greater integrity and less friction for the patient. In 2024, we will simply work to decide which elements should be decentralized to get the desired outcome. With more choices about how to frame and package a clinical trial ecosystem, we can make research a more consumer-friendly process. — Alison Holland, Medable.

The acronym “DCT” will disappear as decentralization becomes the norm: By the end of 2024, the life sciences industry won’t distinguish DCTs from “simple” or “traditional” clinical trials anymore. As noted by the FDA, the modernization of clinical trials is ongoing. In the US, it will require an act of Congress to change the definition of clinical trials because the term DCT is used in provisions published by the Food and Drug Omnibus Reform Act of 2022 (FDORA), which amended the Federal Food, Drug, and Cosmetic Act and the Public Health Service Act. Other regulators are already talking about trials with decentralized elements (i.e., EMA’s recommendations paper) instead of DCTs. Even with existing regulatory nomenclature, the acronym DCT will become less of a callout as a distinct type of unique clinical trial, which is a step in the right direction. The term suggests that a decentralized trial is uncommon and requires special extra adjustments, but that is not true. Recent FDA and other global regulatory agency guidances explicitly note that decentralized trials simply need to comply with existing regulations for clinical trials—nothing extraordinary. There may be some data quality/privacy/security considerations, but the existing playbook still applies to trials leveraging decentralized innovations. By the end of 2024, decentralized clinical trials will simply be clinical trials (with certain parts appropriately decentralized) and the impact of this model will be much more prevalent. — Pamela Tenaerts, Medable.

Generative AI Coupled with Quantum Computing

Generative AI coupled with quantum computing applied to the sciences: Whether it be novel drug candidates to bioprocess improvement, the impact is now just beginning. When AI is powered by quantum computing, it will accelerate the sciences in ways that are hard to imagine. — Clark Golestani, C Sensei Group LLC.

Health Economics and Outcomes Research (HEOR) in DCTs

Health economics and outcomes research (HEOR) will become more common in DCTs. In 2023, we saw the maturation of Claims and Health Information Exchanges, which enable a cost-effective and straightforward process for researchers to gain access to identified patients’ data. Given the improvements in costs, trial sponsors can now take advantage of these exchanges at scale and leverage the data for both pre-screening and health economic analysis. As DCTs and virtual site acceptance accelerate, a knock-on effect will be that more of these trials will include HEOR analysis. — Joel Morse, Curavit Clinical Research.

Improving Local Production Capacity and Access in Latin America

In Latin America, with new governments taking office in key countries, the private sector faces changes in the healthcare agenda and policy priorities, as well as budgeting maneuvers—all complicating market access. This might bring more focus to the discussion on alternative and sustainable models for health financing. Brazil might provide a good example which offers some degree of stability, policy continuity, and many interesting opportunities for the private sector including the Waitlist Reduction Program, the Growth Acceleration Program, the Health Economic-Industrial Complex, and other government initiatives. The 2023 debate around improving capacities for local production of pharmaceutical products will continue in the 2024 agenda. — Anonymous.

Pharmacovigilance Guidelines: Remote Audits and Inspections

Most audits and inspections in the pandemic had transitioned from onsite to remote, and the transition back to onsite versions began in early 2023. With global armed conflicts and geopolitical unrest continuing or even intensifying in 2024, it is likely that regulatory inspections and routine audits will continue in remote mode. The only difference will be the addition of newer technologies to support remote assessments, and more countries would develop new pharmacovigilance guidelines or revise existing ones to reflect this remote mode. — Manoj Swaminathan, Biorasi.

Diversity in Clinical Research Participation

Participant diversity in clinical research will become a bigger priority: For 2024 and beyond, the focus on patient diversity in clinical trials will increase. The difficulties of a lack of patient representation in clinical research surfaced during the pandemic’s vaccine trials, and that issue isn’t going away. Some of our approaches may change based on what we have learned—for instance, there is far more capacity now to connect with broader patient populations using new digital technologies. Given the convenience of these tools, more patients who become increasingly comfortable with technology will take advantage of digital opportunities to connect to a trial and remain engaged through to the end. This will also help ensure patient compliance for the full duration of a trial even when patience and perseverance start to wane in the later weeks and make it harder to capture consistent data. — Alison Holland, Medable.

While industry is moving in the right direction, racial and ethnic minorities still only account for 2 percent to 16 percent of clinical trial participants while comprising 39 percent of the US population. One of the biggest obstacles is patient recruitment and retention from historically underrepresented populations. That’s why some sponsors are starting to collaborate with partners that specialize in diverse patient recruitment to carefully plan and execute new strategies that reduce some of the traditional barriers to study participation and are more conducive to reaching a broader demographic. Actionable solutions that will flourish in 2024 include modern research models such as DCTs which use virtual sites to meet participants where they are; partnerships with community organizations, leaders, and medical providers; diverse research teams who can better understand participants from these communities; real-time monitoring to expeditiously rectify any gaps in representation early; and a focus on cultural linguistic barriers by translating study materials into different languages, using culturally appropriate messaging, and providing interpretation services as needed. — Rachel Rangel, Curavit Clinical Research.

Personalized Medicine Development

Personalized medicine development will continue its fast pace in 2024, which will lead to a proliferation in data types (including an increased focus on genomic data) and more adaptive trial designs. Data collection and (hopefully) data sharing will take center stage in 2024, enabling us to pursue additional research questions and perform meta-analyses. The key to success for AI and Large Language Models is our ability to access volumes of well-harmonized, governed, real-world data. With more personalized approaches, clinical trials grow more complex—again requiring better modeling and data collection plus a reliance on modern-day data engineering and data scientists to identify trends and understand the causality of the therapeutic interventions in question.

Side effects of the pandemic will continue to unfold in 2024, as well. We will see more remote monitoring of patients and adoption of more digital health technologies, including mobile apps and specialized devices. Collecting and gaining insights from these data sources, coupled with the continuing trend of distributed clinical trials, will require data strategies from both sponsors and CROs that leverage cloud computing and data governance at a much different scale than today. — Andreas Matern, Lokavant.

Zero-Knowledge Proofs

Zero-knowledge proof (ZKP) technologies allow the independent verification of the computational integrity of any transaction or operation without any additional information. This transformational technology is exploding in Web3 and is rapidly aligning to use cases across global industries. While this cryptography isn’t new, recent research advancements have enabled “Turing complete zero knowledge proofs” to open a new world of opportunities. ZKPs enable trust-minimized applications that cryptographically enable privacy-preserving, multiparty supply and asset tracking, private verifiable machine learning models, and zero-leak data privacy to enable regulatory use cases. The biopharmaceutical industry has been advancing thought leadership and investing in distributed and multiparty operations use cases. However, the problems of interoperability, privacy, and scalability have limited their progress and impact. Zero-knowledge proofs provide a direct solution to these issues by providing all stakeholders the ability to securely validate transactions, authenticate identities, and seamlessly collaborate and automate across networks at scale, all while maintaining control and privacy of intellectual property.

In connecting the pharmaceutical supply chain, consortium-based solutions have largely failed. ZKPs can simplify interoperability between private manufacturers, distributors, and pharmacies to eliminate the risk of interparty fraud or process errors. This end-to-end traceability and verifiable process compliance can be aggregated and provided as proof to counterparties, regulators, and consumers without giving them access to private software systems and data. In clinical research, ZKPs can streamline trials by preserving patient privacy while expanding secure access to data between sites, outcomes which serve to increase trust in sharing health information, study participation, and data exchange—all accelerating pharmaceutical research progress. Zero-knowledge proof innovations from PharmaLedger and Toposware are poised to have a major impact across the biopharmaceutical industry research, commercial operations, and supply chains for forward-thinking organizations. — Clark Golestani, C Sensei Group LLC.