Trendspotting: Predictions for Bio-IT World in 2025

Aman Wasan, CEO, ArisGlobal 

Life sciences is becoming the industry to watch for its application of generative AI (GenAI) and large language models (LLMs): Here is a major risk-averse industry that clearly sees something in the technology that helps solve an otherwise impossible resource and process efficiency problem. As one of the most safety- and risk-conscious industries there is, it's important that pharma/biopharma gets AI right. Where patient safety is concerned, there can be no tolerance for “black box” mystery; nor data security/privacy breaches. Results must be reliable, robust, explainable, consistent, and trust-inspiring. It’s why the sector is pushing hard not only to harness trailblazing applications but also to test the boundaries of AI explainability, optimized human sampling, and transparency (e.g. through the combination of LLMs and retrieval-augmented generation or RAG), so that regulators can see and assess outcomes for their reliability and consistency. 

Niven R. Narain, Ph.D., President and CEO, BPGBio 

2025 will be a year of technology “truth-bombs” as the biopharma industry starts to see the realities with hyped AI drug discovery companies in the past five years: In 2025, sophisticated biopharma leaders will emphasize the importance of understanding patient biology upfront to AI, prioritizing patient-derived, longitudinal data over static, and siloed datasets. This paradigm shift will highlight AI's role as an integrative tool, enhancing the drug development process rather than replacing it. Biology-first AI will be paramount in modeling complex systems like immune-tumor interactions, enabling smarter patient stratification, therapeutic combinations, and clinical trial designs. Oncology will continue to see groundbreaking advancements as this approach uncovers novel targets and pathways for aggressive cancers. By aligning AI insights with robust biological validation, the industry will move closer to addressing unmet medical needs and redefining innovation in medicine. 

Nick Armstrong, Head of AI and Digital Twin Strategy, CAI 

In 2024, the adoption and incorporation of in-house AI practices have grown exponentially within the pharma and biotech space: So much so, that more companies have created personalized large language models (LLMs) to ensure quick, easy access to crucial documentation in order to minimize deviations. However, the creation of personalized LLMs is a costly investment for small to medium-sized companies. Alternatively, the next, more cost-effective advancement for in-house large language models will be combining knowledge graphs with retrieval-augmented generation (RAG) LLMs. This technology helps map relationships between documents, concepts, and entities, making it easier to retrieve contextually relevant information. As the FDA ramps up audits to ensure compliance, this combination will help employees find accurate information in seconds, improving productivity and reducing errors in decision-making. 

Peter Ellman, President and CEO, Certis Oncology Solutions 

Virtually every pharmaceutical company is now using AI to identify new biomarkers, find new targets, design novel compounds, or look for new uses for existing medicines: But as AI has taken center stage in corporate narratives, a new challenge has emerged; namely, validating these predictions. As we see continued adoption of AI in drug discovery in 2024, we also expect to see an increase in demand for pharmacology studies using more clinically relevant animal models such as orthotopic patient-derived xenografts (O-PDX), and humanized O-PDX models will instill confidence in AI predictions and accelerate machine learning. 

Erez Podoly, VP of R&D, Cleveland Diagnostics 

I anticipate 2025 to bring advancements in spatial biology, which will transform our understanding of disease mechanisms by enabling the analysis of tissue samples at the cellular and subcellular level, in their native spatial context: These advanced methods, such as spatial transcriptomics and immunohistochemistry, allow researchers to map protein distribution and cellular interactions within intact tissue, preserving the tissue's architecture. By identifying how cells communicate and behave within their microenvironment, spatial biology can reveal critical insights into the progression of diseases like cancer, neurodegenerative disorders, and cardiovascular diseases. Spatial biology not only impacts our understanding of disease mechanisms but will also lay the groundwork for developing early diagnostic biomarkers. By detecting subtle cellular changes before symptoms manifest, these technologies could lead to the creation of highly sensitive, targeted diagnostic tools, enabling earlier interventions and personalized treatment strategies for a range of conditions. 

Christian Olsen, Associate VP, Dotmatics 

Multimodal will become a reality: Pharmaceutical and biotech companies are shifting from single-mode discovery toward a multimodal approach for research and development (R&D) in pursuit of new drug targets and therapies. Leading-edge players in drug discovery are increasingly advancing toward an AI-enabled, multimodal future. At its core, multimodal drug discovery enables scientists to identify the most effective therapy—or combination of therapies—for a specific target or combination of targets. This approach integrates research and testing across diverse scientific domains to discover new pharmaceutical, biological, or combination therapies. 

Mirit Eldor, Managing Director, Life Sciences Solutions, Elsevier 

2024 has seen the pharmaceuticals industry take significant steps toward using AI more systematically and effectively in drug research: In 2025, access to ever-more powerful AI systems—including life sciences domain-specific Generative AI tools—will allow researchers to leverage AI to design novel drugs. This is backed by research conducted earlier this year that showed 94% of researchers expect believe AI will help accelerate knowledge discovery. However, for AI to be used effectively and safely in R&D, it will be vitally important that AI systems are built on a foundation of trusted, reliable and comprehensive data sources that are domain-specific for drug discovery & development. General-purpose AI models are unable to meet the needs of the scientific community, which cannot run the risk of hallucinations or out-of-context predictions which can be simply wrong, and lead to expensive mistakes, such as launching clinical trials with a compound where the likelihood of success has been wrongly predicted. To counteract this risk, retrieval augmented generation architectures (RAG) will become the norm for AI use in sensitive industries. By limiting the data Generative AI tools can draw from to context and domain-specific sources, researchers will be able to ensure outputs generated by AI are accurate and trustworthy. 

Jesse Mendelsohn, SVP, Model N 

As we move into 2025, the full impact of Medicare price negotiations will become increasingly apparent, fundamentally reshaping pharmaceutical industry strategies: With 15 more drugs slated for addition to the Medicare price negotiation list, manufacturers will be making unprecedented efforts to retain revenue and rethink their approach to drug development. Manufacturers will make decisions about launch sequencing and investment based on whether they're likely to be on a future negotiation list. For instance, injectable products have a longer period before they're subject to negotiation compared to pills. You can be sure manufacturers will be pivoting towards injectables in their research decisions. What’s more, there's now a disincentive to pursue multiple indications for a drug, as this could subject the medication to negotiation sooner. Some manufacturers might choose not to pursue additional indications to avoid price controls. Companies are already doing the math to predict if they'll be on a list and preparing strategies to maintain profitability once they are. We'll see more proactive measures, including changes in launch strategies and investment decisions. The industry will be closely watching how the Centers for Medicare & Medicaid Services apply their pricing logic, as their methodology will significantly impact everyone's thinking and strategy going forward. 

Kimberly Powell, Vice President of Healthcare, NVIDIA 

Drug discovery and design AI factories: Just as ChatGPT can generate an email or a poem without putting a pen to paper for trial and error, generative AI models in drug discovery can liberate scientific thinking and exploration. Techbio and biopharma companies have begun combining models that generate, predict and optimize molecules to explore the near-infinite possible target drug combinations before going into time-consuming and expensive wet lab experiments. The drug discovery and design AI factories will consume all wet lab data, refine AI models and redeploy those models—improving each experiment by learning from the previous one. These AI factories will shift the industry from a discovery process to a design and engineering one. 

Christian Henry, President and CEO, PacBio 

The enhanced capabilities and declining costs of long-read sequencing, combined with its unique ability to accurately decode regions of the genome that remain 'invisible' to short-read technologies, will drive its widespread adoption in 2025: As the integration of multiomics and advanced analytics powered by AI and machine learning accelerates, long-read sequencing will play a critical role in answering complex translational and clinical research questions. The demand for richer, more comprehensive datasets will render short-read exome sequencing insufficient for many applications, prompting more labs to adopt long-read platforms that offer superior coverage, quality, and data insights. 

Graham Clark, CEO, Phastar 

An evolving drug development and clinical trials landscape requires new skills to manage detailed data and work with rapidly evolving technology: However, the talent to fill specialized roles is in short supply. In 2025, we will continue to face a critical shortage of skilled professionals. This shortage is likely to be felt most strongly in specialized roles like artificial intelligence (AI) experts, where recruitment competition from other industries is high, as well as data scientists and bioinformaticians. Big data, AI, and emerging digital health technologies present new opportunities to personalize treatments and improve trial oversight, but we need to be willing to adapt. Statisticians and SAS programmers will need to adjust to new ways of working, integrating AI, automation, and advanced tools for data management, analysis, and sharing into their processes. If we fail to evolve our skillsets alongside technology, we run the risk of missing out on key opportunities. 

Gen Li, Founder and President, Phesi 

The pharma R&D model has remained static for decades and is in need of rejuvenation: Costs keep going up, but productivity isn’t getting any better. This is in a dramatic contrast to our ability to bring innovative and exciting medical products to patients. This status quo has an impact on both big pharma, biotechs, and start-ups, with the need to demonstrate ROI becoming ever more critical. Big pharma continues to spend more money on R&D and many small firms lack the funds to continue investing when the first milestones have been met. 2025 is the perfect time to move away from this “traditional” model. The big focus over the next 12 months throughout the clinical development industry will be improving ROI by driving down operational costs, reducing trial cycle times, and making trials more precise. For many companies, this means embracing predictive analytics, big data and AI to make trials more precise and efficient, reducing development timelines and costs and significantly lowering burden on patients and investigators. 

Becky Upton, President, Pistoia Alliance 

2025 will see pharma companies and regulators working together to simplify AI legislation, addressing widespread global uncertainty: Pistoia Alliance research conducted this year found only 9% of life sciences professionals know US and EU AI regulations well, with many struggling to navigate complex and ambiguous requirements, such as the risk categories in the EU AI Act. New conformity assessments from bodies like the FDA and EMA are also adding to the already heavy compliance burden. In 2025, coming together to harmonize AI standards and regulatory requirements will be key to fostering a consistent, ethical AI landscape that allows companies to keep innovating safely. 

Miguel Tam, Director of Strategic Marketing, BioLegend (part of Revvity) 

One of the applications that will benefit the most from genomics looking ahead is the emerging field of Spatial Biology: The term Spatial Biology is used to describe the study of cells in the context of the surrounding tissue, how they are located respective to each other, how they position to build the tissue and the study of their functionality and interaction with their microenvironment. Under the umbrella of Spatial Biology, researchers have developed methods to quantify and characterize different molecules, including spatial transcriptomics, spatial proteomics, and spatial genomics, with some technologies capable of integrating multiple modalities. There are several methods to detect these molecules, one of them being next-generation sequencing. As technologies evolve and improve, this multiomics approach will become even more accessible and accurate. In addition, with improved resolution down to single cells and even single molecules, the genomic component will play a central role, and over the next several years will bring important discoveries in translational medicine and other very relevant areas of research and diagnostics. 

Pedro Echave, Senior Manager, Global Business Segment, Revvity 

The decreasing cost of sequencing combined with gene editing and modulation technologies, like CRISPR, will advance our understanding of non-protein-coding RNAs, which have regulatory functions in the cell: RNA-sequencing is a well-known set of tools for assessing gene expression in any sample type including bulk tissues, body fluids, and cell samples. Multiple applications for RNA profiling have been developed, including the determination of gene expression at single-cell resolution and spatial transcriptomics. Additionally, a variety of non-coding RNAs such as microRNA and long non-coding RNA are also now routinely studied. However, most of these techniques focus on protein-coding RNAs. Understanding how non-coding RNAs regulate disease can lead to the implementation of non-invasive, NGS-based tests to monitor or diagnose several diseases. Recently, profiling RNA in plasma (mRNA and microRNA) can detect early changes associated with breast cancer, melanoma, and cardiac arrhythmias. Additionally, a salivary microRNA signature has been recently shown to diagnose endometriosis in women with high accuracy. With thousands of published peer-reviewed articles showing correlation between RNA profiles and disease, and several clinical trials ongoing, we expect an increase in the number of RNA signatures that can be used in clinical practice. Analyzing samples such as blood, urine or saliva will make disease detection and monitoring accessible, repeatable, and more patient-friendly. 

Alyssa Farrell, Director, Global Health Care & Life Sciences Industry Marketing, SAS 

Health care organizations and pharmaceutical companies will explore new ways to implement AI-driven insights at every level, from patient care personalization to faster drug development cycles, with a focus on expanded use of AI in targeted areas of the ecosystem: As AI proves its own value in a variety of ecosystem-specific settings, we expect to see increased governance and directives for use of AI from CIOs, CTOs, regulators and industry leaders in the form of company-specific AI playbooks. 

Gail Stephens, Vice President, Health and Life Sciences, SAS 

Pharma and health care are working more closely than ever, using data and shared insights to drive innovation in patient care and treatment development: In 2025, this convergence is no longer experimental—it will be foundational to how these industries operate. However, data interoperability will remain the primary challenge for these traditionally siloed industries. Ensuring that data flows freely and securely across systems will be a critical focus in the year ahead to move toward tangible convergence. For patients, this means more cohesive health experiences where care delivery and medical advancements are intertwined. 

Giovanna Prout, President & CEO, Scale Bio 

As we saw through the success of our 100 Million Cell Challenge, drawing projects totaling nearly one billion cells, there remains tremendous pent-up demand to expand single cell profiling to more samples and studies: With the introduction of new single cell analysis tools capable of processing millions of cells at breakthrough prices (as low as 1 cent per cell), 2025 will be a year of unprecedented cellular profiling and data analysis, powering basic research, translational research, and drug discovery programs. The adoption of new technology will power a dramatic acceleration in the pace of discovery, and along with it, the development of foundation models to better understand and model cell biology. 

Andre Cerbe, CEO, Schlafender Hase 

2025 will be the year of 'End-to-End Digital Compliance.' Companies need cohesive, integrated compliance platforms that handle everything from data management to proofing. Pharma and biotech firms will likely lead the way, advancing their workflows with holistic solutions. 

Michelle Bridenbaker, COO, Unbiased Science (a Vital Statistics Consulting company) 

Freeing up creatives’ time will be important in 2025, as the industry strives to be heard and get across credible, evidence-based scientific insights amid an ocean of public health disinformation being distributed and shared by non-experts including social media influencers and their followers: Disinformation is continuously growing online and health communicators inside and outside of industry need to be more effective and relevant than ever. The other big theme for 2025 from a communications point of view will be the need to overcome organizational silos—for example, between clinical, commercialization, marketing, and medical affairs functions. This is so that the company’s messaging and interactions with healthcare professionals and patients become more consistent and joined up (e.g. in terms of presentation of the latest disease state information or treatment guidance), whether at a clinical trials stage or preceding/during real-world treatment; and individuals’ engagement tracked across the product lifecycle. The encouraging development is that companies are more aware now of the need to deliver a well-coordinated multi-channel experience; now they just need to understand how.