Scrip Asks... What Does 2025 Hold For Biopharma? Part 4: Artificial Intelligence and Data Science

From Sci-Fi To Table Stakes

More than 50 executives across industry share their expectations for the impact of AI on the biopharma industry over the coming year. While target identification and drug discovery featured highly, the opportunities to engage with patients and healthcare providers more effectively and the need for suitable regulatory frameworks were also flagged up.

Scrip Asks Part 4
(Shutterstock)

Pharma executives no longer ask whether AI is important in their industry – that much is certain, and it is already ubiquitous. As Lamine Mbow, global head of discovery research at Boehringer Ingelheim declared, “The pharmaceutical industry is undergoing a significant transformation, driven by the integration of AI and data science.”

For Mbow, “This trend will continue to gain stronger momentum in 2025 and beyond by enabling enhanced precision, increased efficiency, cost reduction, acceleration and ultimately the higher probability of successful clinical development.”

Mairi Dillon, associate director, Science Ecosystem, at London, UK-based real estate company Canary Wharf Group, agreed.

“The importance of AI and data science will only grow and accelerate in 2025 and beyond,” she said. “Amongst our customer base, we are seeing the impact that new models and more powerful screening and automation tools can have, truly transforming the way some companies operate.”

Dillon highlighted how AI is changing the fundamental skills upon which drug development is based: “This technology will not only impact operations but also the culture of our industry. It has the potential to bridge the gap between tech and biotech, building and realizing significant advantages as a result of new skillsets and partnerships.”

Ann Beliën, CEO of Rejuvenate Biomed, which develops drugs to promote healthy aging, also thought AI and data science would “become increasingly integrated into the drug development process from discovery to market, positively impacting speed to market, cost and associated risk.”

Beliën noted that “the integration of in vitro, preclinical and clinical datasets results in a more advanced understanding of the full activity of specific compounds. Additionally, AI and machine learning algorithms can model molecular interactions and identify hidden biological networks. Deeper understanding of diseases and the interplay within the involved pathways creates the potential to identify complex relationships between these drugs and diseases that traditional research methods might overlook. This elucidation can lead to the identification of promising new targets and identify drug classes to impact these. This is particularly valuable in addressing multifactorial conditions, such as complex age-related diseases. AI will significantly shorten development timelines, helping to bring safer and more effective drugs to patients faster.”

Mat Davis, Jazz Pharmaceuticals’ vice president, data science, evidence and value generation, and global and scientific medical affairs, also acknowledged the broad scope of potential applicability of AI and pinned it down to the fundamental outcome of benefits for patients.

“AI will remain a key trend for the biopharma industry in 2025, impacting everything from precision medicine to the whole drug discovery and development process as we know it. Whilst this seems incredibly large scale, if we consider it another way we can see that AI is simply allowing companies to become ever more focused on patients,” he said. “The more data that can be reviewed, both in quantity and detail, the better the outcomes will be for patients through new, advanced medications.”

Nathan McBride, senior vice president, information technology, at immuno-oncology development firm Xilio Therapeutics, shared the vision of AI having a huge effect on the full gamut of the sector’s activities.

“In 2025, the biopharma industry will accelerate the transition from merely envisioning Pharma 4.0 to implementing it at scale,” he said. “‘Innovation-first’ startups will emerge, seeking to achieve commercial launch readiness with remarkably lean teams of people leveraging AI-driven automation across all stages of clinical development. Similarly, drug discovery efficiency will improve with AI models vastly increasing candidate screening capabilities, while predictive analytics will help drive strategic M&A activity across the industry.”

McBride predicted: “The traditional biopharma model will evolve as AI-native companies pursue unprecedented efficiency.”

Many expressed optimism and highlighted specific areas for likely evolution in 2025. But to reap these benefits, AI must be used effectively, and with experience comes greater insight into the specific ways in which things can go wrong. This is reflected in comments from some of those we surveyed around the importance of planning and execution, and on how to differentiate from the competition.

 

Drug Discovery

Michelle Hoffmann, executive director of Chicago Biomedical Consortium (CBC), which works to stimulate collaboration between scientists in Chicago-area universities and institutions and translate research into biomedical applications, identified the 2024 Nobel Prize in Chemistry awarded to David Baker, Demis Hassabis and John Jumper for breakthroughs in protein prediction and design as “a pivotal moment for AI in therapeutics.”

But, she noted, “AI’s potential extends far beyond proteins. The same generative AI innovations now advancing protein design are also transforming the creation of nucleic acids, protein assemblies, vesicles, capsids, and complex cell consortia. By predicting and modeling interactions across biological scales, AI will enable the development of synthetic systems that push beyond natural evolution. Additionally, AI-driven simulations will accelerate progress by reducing design-build-test cycles and the amount of needed empirical data. This innovation will pave the way for biotherapeutics, diagnostics, and sustainable solutions that could revolutionize science and industry.”

“The future of AI in drug discovery lies in its ability to integrate and analyze data from multiple modalities – ranging from genomic and proteomic data to structural and clinical datasets,” said Jennifer Bath, CEO of IPA Therapeutics, a contract research organization (CRO) that specializes in antibody discovery services and AI technologies.

“This multimodal approach enables a more comprehensive understanding of complex biological systems and accelerates breakthroughs that were previously out of reach. While AI excels at identifying patterns across diverse data types, it works in tandem with wet-lab research, which remains critical for validating and enriching AI-driven insights,” Bath went on. “This dynamic interplay between modalities, AI, and laboratory science will redefine the landscape of drug discovery, allowing researchers to tackle previously insurmountable challenges and unlock new therapeutic opportunities. As we move into 2025, the industry’s ability to harmonize these modalities will be a key driver of innovation.”

Christian Olsen, associate vice president at R&D software developer Dotmatics, also highlighted multimodality. “Pharmaceutical and biotech companies are shifting from single-mode discovery toward a multimodal approach for 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.”

Margo Georgiadis
Margo Georgiadis

“AI will enable small-molecule developers to consistently break boundaries to solve complex biology once only the domain of the biologics,” declared Margo Georgiadis, CEO-partner at Flagship Pioneering and CEO of its portfolio company Montai Therapeutics, which uses computational chemistry and AI to predict the therapeutic potential in chronic disease of small molecules that humans already safely consume in foods, supplements and herbal medicines. “For the last 5-10 years, the biopharma industry has explored a growing range of new molecular modalities to solve hard-to-drug biology with small molecules – from covalent inhibitors to degraders to molecular glues to macrocycles and beyond.”

Georgiadis predicted: “In 2025, AI will fully unlock the power across these diverse modalities to consistently solve complex biology once only the domain of the biologics. The combination of unprecedented, scaled access to high-fidelity multimodal biological and chemical data and far more powerful computational tools will enable innovators to far more rapidly identify the optimal applications for each of these modalities to solve hard-to-drug targets with precision and selectivity.”

The combination of unprecedented, scaled access to high-fidelity multimodal biological and chemical data and far more powerful computational tools will enable innovators to far more rapidly identify the optimal applications for each of these modalities to solve hard-to-drug targets with precision and selectivity.

Margo Georgiadis, Flagship Pioneering, Montai Therapeutics

Mehmood Khan is CEO of the Riyadh, Saudi Arabia-headquartered non-profit Hevolution Foundation, which offers grants and investments for R&D and entrepreneurship aimed at extending healthy human lifespan. “AI will play a role in helping us solve one of the greatest challenges for all of humanity – aging,” he said.

“AI will help advance aging research, analyzing complex biological data and accelerating the developments of therapeutics, making healthspan (healthy lifespan) science not only more innovative but more accessible,” Khan continued. “AI has changed the ability for large language models to absorb massive amounts of data and look for patterns. It can automate trillions of pieces of data, which is impossible for any human to do alone. And biology has progressed in an amazing way so that we understand the molecules within cells. When you bring the two worlds together, AI has the potential to help us understand and find solutions for aging. AI will not replace the biologist – the biologist needs the high-performance computing and the analytics. The beauty is these two disciplines are working together.”

“We are seeing rapid progress in the field [of AI-based drug discovery],” said Joonhyuk Choi, head of US operations at South Korean AI drug discovery firm Syntekabio. “The true test of these innovations will come when the first AI-discovered products receive regulatory approval and are used to treat patients – a milestone the industry is steadily approaching.”

Choi also foresaw the possibility that validation of such products will become more straightforward over time: “While AI-driven discoveries still require validation through traditional in vitro and in vivo testing, this may evolve over time as the technologies become more proven,” he said.

Jim Jenson
Jim Jenson

Olsen outlined the need to change traditional workflows: “For AI to make a meaningful impact in life sciences, the drug discovery process must evolve into a ‘lab-in-a-loop’ model,” he explained. “In this model, R&D and clinical data from various applications, databases, and lab equipment are ingested, centralized, and used to create predictive models for guiding the next set of experiments. While traditional workflows are often viewed as linear, this approach requires flexibility to integrate new insights as they emerge from ongoing experiments.”

Beyond workflows, the nature of the companies that make up the drug discovery sector is changing too. “The new year will bring growth for TechBio companies,” said Jim Jenson, CEO of one such firm, Morphoceuticals. “With the proliferation of AI usage across industries, companies leveraging tech-first approaches will continue to appeal to a new generation of researchers and rise in interest amongst investors. With this, companies can develop innovative pipeline programs unlike we’ve seen before, providing extraordinary opportunities, such as the ability to develop higher-level-omics, for novel discoveries beneficial to human health.”

With the proliferation of AI usage across industries, companies leveraging tech-first approaches will continue to appeal to a new generation of researchers and rise in interest amongst investors

Jim Jenson, Morphoceuticals

James Sheppard, international head of asset management, Kadans Science Partner, which operates laboratory and office buildings for innovative sectors, was less bullish than some, seeing slower progress on the path to AI adoption. “As the role of AI in drug development evolves and the sector matures (as we are seeing increasingly across our portfolio), I expect pharma will start making some more bets on AI in drug discovery as early proof points for the industry continue to emerge. Until that time comes, any remaining skepticism may prevent AI from fully realizing its potential in 2025,” he said.

For Syntekabio’s Choi, on the other hand, the rate-limiting factor is more likely to be on the supply than the demand side: “As AI continues to integrate into drug discovery, it will be crucial to provide accessible, tailored supercomputing resources and sustainable infrastructure to meet the increasing demand.”

Meanwhile, Yanay Ofran, CEO of AI antibody design specialist Biolojic Design, addressed the question of whether AI in drug discovery was living up to its promise. “At the beginning of the decade, our industry was enchanted by AI’s promise to fundamentally change drug discovery and solve intractable challenges of human health. Some believed AI could be a magical solution for many of drug development’s problems. Inevitably, the unattainable expectations led to disillusionment, with some now saying that it is all hype,” he said.

“But AI, of course, is not magic. It’s a set of tools. With a better understanding of exactly how these tools can be used in drug discovery, and what they can, and more importantly, cannot do, AI’s true potential will begin to reveal itself in 2025,” Ofran predicted. “We’ll see how the correct application of AI to solve well-defined problems, using the right data, can redefine what drugs do. We’ll see early data from AI-designed molecules in clinical studies, as well as a steady increase in AI-designed drugs entering the clinic.”

Back To Top

 

 

Target Identification

One area where data science is already proving revolutionary is target identification. As eXmoor Pharma’s chairman and head of consultancy Ian Rhodes and senior translational consultant Harvey Branton noted, industry’s effort “to combine scientific understanding with the power of AI algorithms and big data is creating new, cost-effective approaches, driving an increase in the number of therapeutic targets identified.”

Lamine Mbow
Lamine Mbow

“The broader access to advanced digital and in silico tools for the analysis of large datasets and intricate biological networks will play a growing role in modern drug target identification and validation,” predicted Boehringer Ingelheim’s Lamine Mbow. “The use of sophisticated ‘digital patients’ will enhance our understanding of complex disease pathomechanisms and strengthen the target-to-disease link.”

“As we enter 2025, artificial intelligence will be a transformative force in biopharma, moving beyond experimental applications to become a critical strategic tool for drug discovery and development. We anticipate AI will dramatically accelerate target identification and validation, particularly in complex therapeutic areas like oncology,” said Kate Yen, CEO of oncology drug developer Auron. “Machine learning models are increasingly capable of deciphering intricate cellular dynamics, enabling researchers to uncover novel therapeutic targets and design more precise interventions. The real breakthrough lies not just in speed, but in AI’s ability to reveal biological insights that traditional methods might miss, potentially revolutionizing how we approach personalized medicine and tackle challenging disease areas.”

The use of sophisticated ‘digital patients’ will enhance our understanding of complex disease pathomechanisms and strengthen the target-to-disease link

Lamine Mbow, Boehringer Ingelheim

“Discovering new drug targets remains critical to creating breakthrough therapies,” agreed Simon Brunner, head of platform at genomics-focused drug discovery firm Quotient Therapeutics. “In 2025 and beyond, the convergence of AI, omics datasets and high-throughput data generation will uncover drug targets that are informed by the true complexity of human biology.

“Genomics has had a tremendous impact on drug discovery. Over the past decade, two thirds of approved drugs had genetic support and were 2.5 times more likely to succeed in the clinic. Beyond traditional germline and cancer genetics, the latest methods have revealed that each cell is genetically distinct, heralding the era of somatic genomics. In human disease, some cells carry beneficial mutations, while others carry disease drivers. Such insights can pinpoint disease-modulating genes,” Brunner explained. “AI provides the missing link to truly de-risk target biology. Histology-trained models can spot subtle differences in cellular health. In combination with somatic genomics, AI models can reveal targets that truly modulate biology in the context of human tissue.”

Ronel Veksler, CEO of Promise Bio, which has a platform for precision medicine in autoimmune diseases, also highlighted AI’s “immense potential to revolutionize our understanding of complex diseases.”

Predicting that “in 2025, the biopharma industry is likely to see significant advancements in how AI models are applied to decipher intricate disease mechanisms,” he added: “The efficacy of these models hinges on the quality and granularity of the data they are fed. For dynamic and complex diseases, such as immune-mediated conditions, the focus should shift to exploring the most downstream elements of biology, including proteins and post-translational modifications (PTMs). These provide critical insights into disease processes and could open new pathways for therapeutic discovery and development.”

Ashley Zehnder
Ashley Zehnder

“As the industry heads into 2025, leveraging AI to better understand the complex interplay of biological data will play a pivotal role in driving precision medicine and uncovering breakthroughs that were previously out of reach,” concluded Veksler.

“By using AI-driven platforms, companies can rapidly identify new targets and biomarkers, enabling the development of highly targeted therapies. I expect to see more partnerships and investments in AI-driven drug discovery, driving the next wave of novel therapies. In 2025, the continued integration of AI will be critical to bring effective and innovative treatments to market faster,” said Ashley Zehnder, CEO of Fauna Bio, whose AI drug discovery platform is the focus of a $494m licensing deal with Eli Lilly to help identify novel targets in obesity. “At Fauna Bio, we’re harnessing the power of AI to analyze vast datasets of animal genomic data to uncover novel targets with unprecedented precision. This approach not only speeds up the process of drug discovery but also enhances the quality and specificity of new therapeutic candidates.”

I expect to see more partnerships and investments in AI-driven drug discovery, driving the next wave of novel therapies.

Ashley Zehnder, Fauna Bio

Back To Top

 

 

Antibody Discovery

Flagging up AI’s potential specifically for antibody development, Jane Osbourn, CEO of Alchemab, said: “The future of antibody development lies in our ability to harness the power of nature’s most effective search engine: adaptive immunity. By studying individuals resilient to disease, we can unlock the secrets of their immune systems and use this understanding to develop antibody therapeutics with novel mechanisms of action.

“We’re excited about the potential of AI and machine learning to revolutionize antibody discovery. These technologies can help us analyze vast amounts of data to identify novel targets and optimize antibody design.”

Wyatt McDonnell
Wyatt McDonnell

“I predict that more companies will leverage AI in antibody research to accelerate the discovery and development of novel therapeutics,” said Wyatt McDonnell, CEO of Infinimmune. “The integration of AI-driven platforms in this field is already demonstrating its power to analyze complex datasets, predict antibody interactions, and optimize drug design. This technology enables more precise targeting of disease mechanisms, leading to the identification of new therapeutic antibodies with greater efficacy and reduced side effects.”

McDonnell said that Infinimmune was using AI to streamline its antibody discovery process and accelerate the translation of research into clinical applications. It recently signed a partnership with Grid Therapeutics, focused on deep sequencing of B-cells for oncology drug discovery, which he said “exemplifies the power of combining advanced technology with cutting-edge biology. This collaboration highlights the growing trend of using AI to enhance antibody research, driving the next wave of personalized medicine and accelerating the development of innovative therapies in 2025.”

I predict that more companies will leverage AI in antibody research to accelerate the discovery and development of novel therapeutics.

Wyatt McDonnell, Infinimmune

Back To Top

 

 

Drug Development

“Generative AI tools landed with great excitement, and over the past two years, GenAI hype has swept through clinical development,” said Stephen Pyke, chief clinical data and digital officer, Parexel. “AI is not a new technology; it’s just been massively accelerated by GenAI and its potential in boosting productivity across manual processes in clinical trials.”

For Pyke, “the question now is not ‘if’ but ‘when’ AI will become a cornerstone of clinical development.”

“2025 is likely to see further rapid advancements, fueled by continuing large language model (LLM) progression and supporting new use cases and capabilities. These incremental advances will enhance productivity and decision-making. We’re on an irreversible path towards ubiquitous AI, learning to mitigate risks and weaknesses in our highly regulated environment. A well-trained human-in-the-loop will oversee every step, setting clear guardrails to ensure AI enhances, rather than replaces, human expertise. The future of clinical development is bright, with AI playing a pivotal role in making it more efficient and effective,” he concluded.

“Across drug development, the uptake of artificial intelligence continues to grow, offering new ways to streamline processes and improve efficiency,” said Christina Coughlin, CEO of Avacta Therapeutics. “In 2025, we expect to see AI further enhance drug design, trial planning and patient stratification by enabling teams to evaluate a vast number of possibilities in parallel. These applications share a common theme: creating a seamless, end-to-end development process.”

Shashi Shankar
Shashi Shankar

Coughlin went on: “AI’s ability to assess structure-activity relationships, predict outcomes, and identify relevant patient populations is set to evolve further, moving beyond empirical approaches to more data-driven strategies. As these tools become more sophisticated, they will enable the integration of predictive insights at every stage of drug development. This evolution promises not only faster timelines but also more precise and personalized therapeutic solutions for patients.”

“AI will revolutionize biopharma research through unprecedented access to and analysis of currently fragmented patient data,” said Shashi Shankar, CEO of digital health data platform company Novellia. “As transformative regulations unlock access to previously siloed EMR [electronic medical record] systems, AI-powered approaches will unify fragmented records across care settings to capture the full spectrum of patient experiences at unprecedented scale and speed. Automated data integration and sophisticated pattern recognition will accelerate therapeutic development while ensuring treatments are more personalized and reach optimal patient populations, fostering a virtuous cycle of both scientific innovation and patient empowerment, ultimately driving better health outcomes.”

Automated data integration and sophisticated pattern recognition will accelerate therapeutic development while ensuring treatments are more personalized and reach optimal patient populations.

Shashi Shankar, Novellia

“Rapid improvements in artificial intelligence will soon enable drug design and evaluation using in silico simulations that are better predictors of human clinical outcomes than today’s animal models and millions of times faster, yielding results in minutes rather than months,” predicted Kevin Caldwell, CEO of bioengineering firm Ossium Health. “This technological breakthrough will drive the drug development process to more closely resemble automobile development, a field in which companies can design, drive, and crash test their products in computer simulations that are highly predictive of real-world performance. AI-powered drug development will give patients earlier access to the most effective and novel biopharmaceuticals, improving treatment outcomes and saving lives,” he said, adding: “Google Deepmind’s AlphaFold is the frontrunner in this space and has solved the decades-old protein folding problem. We’re now on the cusp of an innovation explosion with groups like Anthropic and OpenAI piling on.”

“Industries – from healthcare to CPG [consumer packaged goods] – are at a tipping point,” said Bianca Anghelina, CEO of Aily Labs, which she founded as a provider of AI-powered decision intelligence tools for global companies after previously heading up global digital finance at Novartis. “Critical decisions that once took months can now be made instantly, thanks to AI agents. Imagine a pharmaceutical company selecting the best clinical trial sites for a breakthrough therapy. The old way? Endless delays, missed opportunities, and spiraling costs. The new way? AI agents can horizontally connect the dots to pinpoint the most promising trial sites instantly – accelerating recruitment, cutting costs, and driving trial success in real time. These proactive, hyper-personalized advisors simulate 100,000 ‘what-if’ scenarios in seconds – optimizing R&D clinical trials, predicting and mitigating supply chain risks, and dynamically reallocating capital resources to maximize ROI; revolutionizing enterprise decision-making and transforming complex challenges into real-time solutions. By 2025, enterprise leaders who adopt AI agents won’t just keep pace, they’ll set the standard. My challenge to leaders? Embrace AI agents to redefine your industry – or risk being left behind.”

Back To Top

 

 

Partnership

Harnessing the potential of AI means pharma needs to get up a speed in a complex field that is not part of its core traditional skillset. While companies can hire talent and build internally, well-selected partnership with AI specialists is seen as a key activity by most in the pharma industry. Meanwhile, pharma has a wealth of experience in developing drugs for real-life patients in a real-world setting that therapeutic-focused AI start-ups need to tap into.

Greg Meyers
Greg Meyers

Chris Mansi, CEO of, Viz.ai, which provides an AI-powered clinical decision support app for physicians, told Scrip: “I look forward to the growing collaboration between biopharma and AI companies, which will fuel patient-centric innovation and better patient outcomes.”

Greg Meyers, executive vice president, chief digital and technology Officer, Bristol Myers Squibb, thought “consolidation and collaboration in AI drug discovery” would be a key factor in the advancement of AI in pharma this year. “I foresee further consolidation among venture-backed startups offering point AI solutions, particularly in or adjacent to drug discovery,” he said. “At the same time, I expect tighter collaborations between pure-play in silico research companies and traditional drug developers, reflecting a growing acknowledgment of the vast chasm between what is possible in silico and what is achievable in vivo.”

I expect tighter collaborations between pure-play in silico research companies and traditional drug developers, reflecting a growing acknowledgment of the vast chasm between what is possible in silico and what is achievable in vivo.

Greg Meyers, Bristol Myers Squibb

“In 2025, AI will become an essential driver of efficiency and innovation across the biopharma industry. From leveraging synthetic data for smarter trial design to optimizing patient recruitment through predictive analytics, AI is poised to significantly reduce timelines and costs in drug development. As collaborations between traditional biopharma and AI innovators deepen, these partnerships will unlock new opportunities to personalize therapies and accelerate market access,” said Kim Perry, chief growth officer of emtelligent, which uses natural language processing (NLP) to structure unstructured medical data for its partners in healthcare and pharma. “With its ability to process vast datasets from genomics to socioeconomic factors, AI will not only transform drug development but also help create a more patient-centered and equitable healthcare landscape.”

Back To Top

 

 

Execution

It is not enough to apply artificial intelligence to processes: it is necessary to apply the right tools for the job in hand, and to ensure that the data being processed is appropriate.

“2025 will be the year the biopharma industry comes to terms with the fact that AI cannot solve problems in a vacuum. We’ll see more companies focusing on ensuring that their data is of the quality and quantity needed to be fit-for-purpose for AI,” Martin Stumpe, chief data and AI officer at diagnostics and life sciences company Danaher, said. “We’ll also see more thoughtful integration of the outputs into workflows, products, and human processes. That last category includes regulatory processes, reimbursement, and legacy systems, all of which need to evolve in order to catch up to the pace of the technology.”

“Initial adoption of AI in scientific research has been tainted by a lack of consistency and untrustworthy scientific results, more akin to asking a junior intern for help than a skilled scientist. In 2025, I anticipate adoption will evolve towards AI-powered scientific assistants that provide expert scientific results that are both deeply reliable and totally credible,” said Kevin Cramer, CEO and chief technology officer of laboratory informatics platform provider Sapio Sciences.

“These advancements will combine AI models trained on scientifically relevant tools and resources and then autonomously manage these tools and information to satisfy a scientist’s query. This approach will greatly enhance the efficiency, precision, and speed of performing scientifically relevant tasks to accelerate the R&D pipeline.

“The integration of autonomous AI into early-stage experimentation will reshape how researchers conduct, track, and manage experiments. Tasks traditionally handled using extensive scientific research, bench lab work, and tedious tracking in electronic lab notebooks (ELNs) will increasingly be managed by AI agents, freeing researchers to focus on strategic, high-value activities and reducing the reliance on ELNs and wet lab work in the lab,” Cramer added.

His optimism was shared by Scott Weiss, vice president product and strategy at IDBS, a specialized cloud software and services provider to biopharma organizations. “In 2025, the pharmaceutical industry will undergo a significant transformation driven by AI-powered integration of internal and external data sources,” said Weiss. “Companies will seamlessly merge internal project knowledge, terminology, and individual team expertise with publicly available scientific data. This harmonization will provide researchers and decision-makers with relevant, critical insights, minimizing duplication of efforts and enabling faster, more strategic decision-making. The enhanced access to pertinent information will lead to smarter experimental designs, greater research efficiency, and increased innovation, making the industry more responsive to emerging challenges and improving overall sustainability efforts.”

Andrew Trister, chief medical and scientific officer at Verily
Andrew Trister

But this is no small task. “Many large language models, even those that are tuned for medical use, are insufficient for broader healthcare use,” pointed out Andrew Trister, chief medical and scientific officer at Alphabet Inc.’s life science research arm Verily. “And many healthcare organizations don’t have the ability to leverage the data they need to validate these models. This makes it difficult to effectively implement them to impact care. The greatest opportunity I see for AI in 2025 is to build the infrastructure to stitch together the disparate data sets needed to develop and validate these new models and demonstrate the value of insights from these data to deliver more personalized care.”

Separately, eXmoor Pharma’s Ian Rhodes and Harvey Branton flagged up that “Increased use of cloud-based algorithms and service providers will accelerate the requirement to clearly define the data ownership rights.” They proposed that “the use of Trust Law frameworks to create ‘data trusts’ will help with complex governance, conditional consent and stewardship, helping to clarify IP positions, opening up new value streams for companies operating in this space.”

The greatest opportunity I see for AI in 2025 is to build the infrastructure to stitch together the disparate data sets needed to develop and validate these new models and demonstrate the value of insights from these data to deliver more personalized care.

Andrew Trister, Verily

Meanwhile, BMS’s Greg Meyers wants to see a “transition from prototyping to production in LLMs.” He said: “With the rapid leapfrogging of novel LLMs slowing, our industry has an opportunity to catch its breath and transition from prototyping to implementing LLMs in everyday workflows at scale. In a document-heavy industry like biopharma, leveraging generative AI to synthesize and analyze patterns across vast corpora of text should evolve from being an exception to becoming the standard practice.”

Meyers also said: “In 2025, I anticipate the release of the first generation of AI features embedded within large, incumbent software platforms commonly used in biopharma. While there will likely be a challenging period of setting realistic expectations about the value and costs of these features, integrating AI into core product stacks has become table stakes rather than an ‘upsell.’ Consequently, I expect a decline in the need for custom-built AI applications within large enterprises, which have dominated the landscape over the past three years.”

“The pharmaceutical industry is at an inflection point powered by the convergence of advanced analytics and artificial intelligence. This shift is transforming the way we do business, including how we do drug discovery, execute clinical trials, manage the supply chain, and commercialize products,” said Nick Eshkenazi, chief digital and transformation officer at Astellas. “Looking ahead, democratization of data science, digital, and AI skills – making data accessible and enabling it for the most effective use by anyone who needs it within an organization – will reshape how companies create first-party data, how they consume and enrich third-party data, and how they leverage data at scale for value creation. This will require investment in frictionless, personalized self-service platforms, continuous internal skills development, and a commitment to fostering a collaborative data-driven culture, where everyone within the organization has the skills and tools to access the right data at the right time with the right relevancy for their role.”

Edward Kliphuis, who is a partner in the digital medicine strategy of investment firm Sofinnova Partners, said that: “2024 marked AI’s shift from experimentation to execution, with healthcare emerging as a surprising leader in generative AI adoption, spending $500m on tools like note-taking assistants and billing automation. AlphaFold’s Nobel Prize highlighted AI’s transformative role in life sciences, while deals like the $688m Recursion-Exscientia merger and Sanofi’s partnership with OpenAI signaled incumbents beginning to embrace AI-driven drug discovery.

“Building on these foundations, we believe 2025 will be a defining year for Bio-AI, transitioning from research to industrialization and commercialization. By breaking down barriers that previously hindered niche markets – such as rare diseases or fragmented regulatory territories – Bio-AI will unlock transformative opportunities. While challenges remain, including data integration, regulatory hurdles, and delivering measurable outcomes, companies navigating these complexities are primed to capture immense value. Bio-AI isn’t just reshaping life sciences; it’s redefining what’s possible, creating a new paradigm to impact patients’ lives.”

John Castle
John Castle

“By 2025, leveraging AI and computational chemistry is table stakes in our industry,” said John Castle, chief data and information officer of molecular glue degrader medicine developer Monte Rosa Therapeutics. “It’s not a question of whether you use it, but how, and your ability to integrate successes with other functions. Generating data at scale in labs to model molecular interactions and discover promising compounds is great, but it’s nothing without disciplined coordination with the rest of the team. To rationally discover and engineer effective drug candidates, your AI engine needs to meld seamlessly with the teams responsible for target identification, highly optimized proteomics, structural biology, next-generation sequencing (NGS), chemistry, and high-throughput screening. This soup-to-nuts coordinated process enables you to rapidly go from the discovery of solid starting points to highly engineered, optimized drug-like molecules advancing toward the clinic.”

To rationally discover and engineer effective drug candidates, your AI engine needs to meld seamlessly with the teams responsible for target identification, highly optimized proteomics, structural biology, next-generation sequencing (NGS), chemistry, and high-throughput screening.

John Castle, Monte Rosa Therapeutics

Back To Top

 

 

Differentiation

Kevin Parker
Kevin Parker

Cartography Biosciences CEO Kevin Parker noted that a key challenge for those in the sector now was to go beyond adopting AI to being a front-runner: “Having seen the proliferation of AI tools in 2024, the industry is poised to realign with an emphasis on what distinguishes truly differentiated leaders in an increasingly crowded field. Simply being an AI-enabled biotech will no longer be enough. Instead, the focus will be on what goes into AI models and what is done with what comes out.”

In Parker’s view, “companies with proprietary datasets and differentiated ways of generating data will jump ahead of those hoping AI will manifest new answers in old data. Proprietary molecular workflows will offer real value from a crowded room of untested AI insights by finding the right answers and building the right drugs. In 2025, I envision continued proliferation of AI – but AI itself is not enough. Differentiation and impact come from proprietary datasets and unique molecular workflows that unlock patient impact.”

Companies with proprietary datasets and differentiated ways of generating data will jump ahead of those hoping AI will manifest new answers in old data.

Kevin Parker, Cartography Biosciences

Several other industry leaders referred to this need for companies in the space to differentiate both in terms of the data fed into AI, and also in the way in which AI is deployed.

“As we continue to expand the utilization of AI-tools, the scientific community will also gain a better appreciation of its current limitations, which will feed into the optimization process of the various platforms,” said Boehringer Ingelheim’s Lamine Mbow.

“Decision-making in healthcare has never been more complex. Leaders grapple with how to make well-informed choices in the early stages of development, how to accelerate drugs to market, and how to work with key external stakeholders to demonstrate the safety, effectiveness, and value of therapeutics,” said Jeremy Rassen, interim CEO at healthcare real-world evidence software provider Aetion. “This is an enterprise-wide endeavor driven by better evidence.”

For Rassen, “I believe that while we’re swimming in an ocean of data, we’re finally at a point where we can deploy time-tested analytics, sophisticated AI, and enterprise-grade technology to turn real-world data into real-world evidence, and real-world evidence into decisions that drive all parts of a modern healthcare business. Organizations that have made this strategic shift are reaping the benefits and powering decisions across clinical, regulatory, and commercial functions, driving collaboration and faster innovation cycles. I expect that organizations that haven’t will realize they have an incredible opportunity to unlock value through a thoughtful enterprise RWE [real-world evidence] program."

“AI is a powerful tool for mining data, accelerating target identification and drug repurposing, enabling the analysis of vast biological datasets and uncovering discoveries previously hidden, said Yochi Slonim, CEO of mRNA modulating small-molecule drug discovery and development specialist Anima Biotech. However, he pointed out that “AI also brings challenges. By commoditizing processes, it erodes the competitive advantages pharmaceutical companies have built over decades. Data is the differentiator. Proprietary data creates proprietary assets, discoveries, and sustainable advantages.”

Slonim noted, “Most companies rely on public datasets; however, this is limited, unprotectable, and often leads to identical outcomes, frequently lacking reproducibility and failing to provide the depth needed to uncover complex disease biology.”

Anima has addressed this by taking what Slonim calls “a biology-first approach.” To differentiate, he said, the company “generates large-scale experimental imaging data, visualizing cellular pathways from healthy and diseased cells. Neural networks identify dysregulated pathways from diseased cells, unlocking actionable targets validated experimentally and further explored to identify small molecules with the right mRNA biology modulation strategy. This de-risks drug discovery programs and delivers competitive advantages for pharmaceutical partners.”

Andy Surinach
Andy Surinach

Andy Surinach, executive director, RWE and data strategy at Genesis Research Group, a RWE, HEOR [health economics and outcomes research] and market access consultancy, also agreed that “artificial intelligence is transforming biopharma, with 2025 poised for further breakthroughs. Generative AI will continue driving novel molecule design, improving clinical trial success rates, advancing drug repurposing through enhanced target identification and predictive modelling.”

While noting that “progress in real-world evidence generation will deliver faster, more accurate insights, enhancing decision-making and patient outcomes,” he pointed out that “continued validation of AI tools to ensure reliability, trust, and accountability within drug development and commercialization will continue to be of utmost importance.”

Surinach said: “Addressing challenges such as data quality, bias, and explainability will be critical to responsible adoption. The integration of agile, task-specific AI workflows will streamline operations – from protocol development to economic modeling –fostering efficiency while maintaining accuracy. By tackling these challenges, biopharma can create an ecosystem where AI-powered technologies enable quicker delivery of therapies to patients in need.”

Continued validation of AI tools to ensure reliability, trust, and accountability within drug development and commercialization will continue to be of utmost importance.

Andy Surinach, Genesis Research Group

Back To Top

 

 

Personalization

“AI has and will continue to fundamentally transform drug discovery and development. We are entering a pivotal moment in 2025 where AI will shift from an analytical assistant to a decision-maker,” said Armen Mkrtchyan, head of Pioneering Intelligence and origination partner at Flagship Pioneering. “With its ability to operate at the intersection of biology and computation, AI will shape our responses to individual and planetary health challenges. We’re already using AI to uncover disease pathways, but we’ll see it applied to advance precision medicine by tailoring the creation of medicines to an individual patient’s molecule signatures.”

“The biggest opportunity for AI in healthcare isn’t in treating disease – it’s in extending your health span through personalized solutions, argued Nathan Price, chief scientific officer of health tech company Thorne. "For a long time, one of the biggest barriers to effective preventative care and overall wellness was the inability to personalize approaches. But now, AI is helping us interpret vast amounts of data – from genomics, blood measures, wearables, and the gut microbiome to imaging and more – allowing for precision like never before. The successful launch and growing popularity of biologics, such as GLP-1s, further enable this shift. AI-powered tools allow us to predict how an individual’s body might interact with these treatments, enhancing safety and efficacy. With the biologics market projected to reach $699.5bn by 2032 [according to IMARC Group data], personalized solutions are no longer a distant dream – they are an exciting reality. Beyond pharmaceuticals, precision nutrition and lifestyle changes are key to optimizing long-term health. The future of healthcare lies in prediction and prevention, and AI is at the heart of this transformation. This is just the beginning of what we can achieve to help everyone practice personalized, scientific wellness."

“AI and advanced data science have already demonstrated transformative potential in drug discovery, with the first AI-generated molecules advancing through development. However, in areas where effective drugs are already available, there is an opportunity to use AI-derived personalized dosing to enhance existing treatments,” observed Hakim Yadi, CEO of Closed Loop Medicine, which offers a platform that integrates drugs and devices with digital solutions to support patients with long-term conditions. “Ultimately, it is easier to make a drug work better than to discover a new one.”

He went on: “This approach focuses on tailoring drugs to the unique needs of the individual, and not just the disease. Innovations such as the FDA’s Prescription Drug Use-Related Software (PDURS) guidance provide clarity on how software applications can work alongside prescription drugs, thus bringing the vision of personalized, AI-enhanced medicine closer to reality.

“By combining software companions with drug therapies under a single prescription, we can finally bridge the gap between effective treatments and individualized care, redefining how therapies are developed and experienced. If anything should be tailor-made, surely it should be medicine?”

Kevin McRaith
Kevin McRaith

Digital health firm Welldoc’s CEO Kevin McRaith took a similar view.

“AI and sensor technologies are unlocking new possibilities, empowering us to reimagine the future of healthcare with scalable, personalized solutions. In 2025, there will be increased focus on tightly integrating AI and digital health into healthcare, driving further collaboration between biopharma, tech and healthcare,” he said. “There will also be increased investment in AI and novel sensor research, demonstrating the opportunities for individualized, efficient, and effective care. New predictive capabilities will emerge, particularly in obesity and cardiometabolic health, to address the needs of an aging population and rising chronic illnesses. Additionally, there will be a focus on operations and scale, optimizing the balance between human and digital care to personally and effectively support consumers throughout their health journeys.”

In 2025, there will be increased focus on tightly integrating AI and digital health into healthcare, driving further collaboration between biopharma, tech and healthcare.

Kevin McRaith, Welldoc

Back To Top

 

 

Preventive Healthcare

Geoff Cook
Geoff Cook

“The integration of AI and wearable data will play an instrumental role in fulfilling the promise of preventive healthcare,” predicted Geoff Cook, CEO of Noom, a digital health company.

“More clinicians will be empowered to make personalized interventions that leverage biologics, molecular therapies, and wearable data. As we look ahead, healthcare providers will become more comfortable utilizing these data-driven tools to guide treatment decisions. AI-driven technology will help the healthcare system move beyond ‘sick care’ toward a personalized, preventive approach to helping people stay healthy.”

Flagship’s Mkrtchyan agreed: “AI will also promote the shift from reactive, sick care to proactive, preemptive healthcare by making it easier to detect and diagnose diseases before symptoms have even begun,” he said.

AI-driven technology will help the healthcare system move beyond ‘sick care’ toward a personalized, preventive approach to helping people stay healthy.

Geoff Cook, Noom

Back To Top

 

 

Patient Engagement

“Artificial intelligence offers vast potential in the biopharma industry to accelerate advances for patients and improve their care experience,” said Justin Mattice, vice president and general manager, branded specialty at specialty pharma company Endo. “As AI and data science continue to evolve, we are applying these tools to identify pain points in customer engagement so we can more quickly define and implement solutions. For example, using AI to analyze data from patient call centers enables us to pinpoint issues early and create a seamless experience for the patients we serve. This time saved from engagement to insight is essential for our industry as we work every day to help people live their best life.”

“Advances in AI and machine learning are helping to democratize access to healthcare by improving diagnostics and making specialized care more accessible to underserved communities,” pointed out Don Crawford, CEO of CorVista Health, which markets a cardiac diagnostic system. “There is a growing focus on health equity and bringing non-invasive technologies to patients who face significant barriers to care. At the same time, precision and efficiency in regulatory processes are driving the need for reliable, data-driven solutions. Point-of-care medical devices, powered by machine learning, have the potential to streamline diagnostics, provide timely insights, and close critical gaps in care.”

For Paul Howard, senior director, public policy at rare metabolic disease drug developer Amicus Therapeutics, there is an opportunity “harness tech to ‘rehumanize our approach to rare disease.”

Paul Howard
Paul Howard

He told Scrip: “As our industry continues to embrace the integration of AI to augment human intelligence, it’s exciting to consider how these solutions can help overcome specific challenges and areas of health inequality, such as rare disease. While AI isn’t going to replace doctors anytime soon (and probably never will) when it comes to diagnosing and treating rare disease, these tools have the potential to speed up processes and achieve things that humans currently struggle with. If you’re looking for a match for a one in a million rare disease, human beings are never going to have the time to scan a million medical records or tens of thousands of journal articles looking for a digital needle in petabytes of data. Soon doctors will collaborate with AI tools to find the needle and even help them to understand why it’s there.”

He concluded: “Integrated with the right safeguards, and with human beings in the loop every step of the way, 2025 could see AI ‘rehumanize’ a healthcare system that currently feels deeply impersonal to those with rare disease.”

Integrated with the right safeguards, and with human beings in the loop every step of the way, 2025 could see AI ‘rehumanize’ a healthcare system that currently feels deeply impersonal to those with rare disease.

Paul Howard, Amicus Therapeutics

Back To Top

 

 

HCP Engagement

Chris Mansi
Chris Mansi

Viz.ai’s Chris Mansi raised the point that there is room for AI in enhancing biopharma companies’ interactions with healthcare providers (HCPs).

“In 2025, biopharma investments in AI will increase to look at more use cases across the entire value chain, with a big focus on commercial and HCP engagement, post launch, aiming to increase the efficiency and effectiveness of launches,” he said.

“Successful biopharma AI initiatives in healthcare will be those that can drive meaningful improvements to clinical workflows and increase patient access to life-saving treatments.”

And Justin Holko, senior vice president and head of the global oncology/hematology commercial business unit at Regeneron Pharmaceuticals, noted that “our commercial sales team is one of our highest priorities – the question always is, how can we continue to help them work smarter and maximize ever-limiting resources? That’s where AI can help with targeting, creating efficiencies, analyzing for opportunities. So while I don’t see AI leading conversations with healthcare professionals, I do see its value provided as part of the solution we’re addressing and humans are still at the wheel.”

In 2025, biopharma investments in AI will increase to look at more use cases across the entire value chain, with a big focus on commercial and HCP engagement, post launch, aiming to increase the efficiency and effectiveness of launches.

Chris Mansi, Viz.ai

Back To Top

 

 

HCP Decision-Making

Among the many areas in which AI can transform the field of therapeutics is that of supporting healthcare providers by reducing excess administrative tasks and helping them make informed decisions. “The healthcare industry has consistently aimed to empower doctors to focus on what they do best – addressing complex, high-acuity cases. The rise of AI and data analytics takes this effort a step further, automating routine and administrative tasks that often pull providers away from patient care,” said Beth Houck, CEO of SonarMD, which has developed a patient monitoring platform for chronic gastrointestinal conditions.

“Rather than replacing clinicians, these tools act as force multipliers, streamlining workflows so doctors can devote more time to delivering hands-on, personalized care. This shift ensures that every level of care delivery operates at peak efficiency, ultimately improving patient outcomes and preserving the human connection at the heart of medicine.”

But as Verily’s Andrew Trister, pointed out, a “challenge we need to solve is clinician adoption.”

He drew the analogy with “convincing a seasoned driver to take a quicker route proposed by Google Maps instead of taking the same route they’ve taken for years,” and argued that “we need to help educate clinicians on the opportunities for AI to help enhance both their workflow and patient care. But we need to do so in a way that keeps the clinician in control. Just like drivers are the mainstay of authority when it comes to operating the car, clinicians should always be the mainstay of authority when it comes to what’s best for the patient.”

Théophile Mohr Durdez is CEO of Volta Medical, which is developing AI software solutions to help cardiac electrophysiologists in the operating room. “Interventional medicine is at a pivotal juncture, with 2025 poised to build upon a series of AI-driven innovations that are reshaping the field,” he said. “Studies have now demonstrated how physician-guided AI can enhance procedural success and improve patient outcomes – key metrics driving adoption.

“For instance, the integration of AI into electrophysiology workflows has shown a potential increase in diagnostic accuracy, as evidenced by recent meta-analyses. These advancements reflect an industry-wide trend toward leveraging big data and machine learning to refine decision-making and personalize care. As we look ahead, the challenge for 2025 will be scaling these successes to ensure broader access and measurable improvements across the healthcare ecosystem.”

Back To Top

 

 

Manufacturing

Xilio Therapeutics’ Nathan McBride underscored the value of data science in manufacturing. “Manufacturing excellence will emerge as a critical competitive differentiator, with smart factories and real-time quality control enabling precision at scale,” he said.

Nabiha Saklayen
Nabiha Saklayen

“Efficient uses of AI and data must flow along the challenged supply chain to limit technology-driven roadblocks,” said eXmoor Pharma’s Ian Rhodes and Harvey Branton. “Examples include more automation, enabling multiple small batches to be manufactured and assessed. The approaches employed must be suitable for seamless transfer to clinical GMP manufacturing facilities to maximize the full potential.”

Some focused specifically on the potential of AI in the production of advanced, complex therapies. Nabiha Saklayen, CEO of personalized stem cell engineering specialist Cellino, predicted: “AI-driven manufacturing will revolutionize regenerative medicine, transforming how we create and scale personalized therapies. Traditional manufacturing methods often struggle to meet the precision and scalability required for advanced treatments like cell and gene therapies. More companies in 2025 will hopefully use AI to enable automation, optimization, and unprecedented quality control throughout the production process.”

AI-driven manufacturing will revolutionize regenerative medicine, transforming how we create and scale personalized therapies

Nabiha Saklayen, Cellino

In Cellino’s case, she said, “by integrating machine learning and automation, we can address challenges like variability in cell production and enhance batch consistency. This paradigm shift isn’t just about efficiency, it’s about improving access to therapies and empowering patients. Scalable, cost-effective manufacturing ensures that transformative therapies reach more individuals, breaking down barriers to accessibility. As AI continues to advance, its integration into manufacturing will unlock new possibilities, bringing the promise of regenerative medicine closer to reality, as manufacturing is often a major bottleneck.”

“In 2025, a key theme will be leveraging artificial intelligence and data science tools to tackle challenges in producing complex therapies like precision medicines, enabling real-time monitoring, predictive maintenance, and process optimization,” said Alexander Seyf, CEO of Autolomous, which helps cell and gene therapy manufacturers scale their operations. “These innovations enhance product consistency, reduce downtime, and accelerate timelines, supporting the delivery of patient-centric solutions. While adoption remains gradual due to the complexity of integrating AI into legacy systems and meeting regulatory requirements, more companies are expected to adopt these through pilot programs, focusing on areas like predictive maintenance and process control. Early adopters are already reporting improved scalability and efficiency, setting a blueprint for broader implementation.”

Seyf added: “The FDA’s trend of accelerating approvals for AI-enabled tools further signals growing regulatory support, paving the way for AI and data science to reshape manufacturing and reinforce biopharma’s mission to deliver high-quality, personalized therapies more efficiently.”

Back To Top

 

 

Regulation

Dominik Vahrenhorst, mRNA therapy developer CureVac’s associate director, bioinformatics, homed in on the importance of companies working with regulators to facilitate adoption of AI in the industry.

“There is huge potential for AI to benefit patients and improve health outcomes,” he said. “The use of AI in the pharmaceutical industry is increasing rapidly, so there is a growing need for regulatory authorities to bring together the use of disparate platforms and technologies into a common regulatory framework. As AI is currently embedded as a major part of our discovery platform, CureVac is joining industry peers to work with regulatory bodies, using our AI and machine learning expertise to help guide the development and implementation of a prospective framework.”

Jörg Schüttrumpf
Jörg Schüttrumpf

He went on: “Recent discussions in the industry have resulted in three areas we believe need to be addressed: implementing a cross-industry standard in AI; developing a clear path to a regulatory framework; and defining areas of interests to the regulatory authorities. Working together, we should be able to quickly build an industry standard where AI is used ethically and responsibly in the development of novel medicines of the future.”

“I think we’re going to increasingly see RWE, derived from rigorously sourced, structured, and validated real-world data (RWD), inform the benefits and risks of regulatory decisions regarding the design and execution of clinical trials,” said Jörg Schüttrumpf, chief scientific innovation officer at Spanish plasma-based products specialist Grifols. “The acceptance of RWE by health authorities will support the increasing innovation coming out of the broader pharmaceutical industry and cover everything from small-molecule drugs to biologics. The sector will work closely with regulators to establish a clear and consistent definition of regulatory-grade RWE that meets the highest standards. Fit-for-purpose applications of RWD, including patient recruitment, trial site selection, and the standardized and accepted use of RWE for safety, tolerability and efficacy endpoints, are destined to accelerate approvals of label extensions and new medicines, all for the benefit of patients.”

The acceptance of RWE by health authorities will support the increasing innovation coming out of the broader pharmaceutical industry and cover everything from small-molecule drugs to biologics.

Jörg Schüttrumpf, Grifols

Xilio Therapeutics’ Nathan McBride thought the arrival of a new US president would herald change on the regulatory side. “Amid the new administration’s push for AI deregulation and institutional reform, regulatory frameworks will evolve to accommodate AI-driven processes, revolutionizing clinical trial design and validation methodologies,” he predicted.

Back To Top

More from Scrip Asks

Scrip Asks... What Does 2025 Hold For Biopharma? Part 4: Artificial Intelligence and Data Science

 

More than 50 executives across industry share their expectations for the impact of AI on the biopharma industry over the coming year. While target identification and drug discovery featured highly, the opportunities to engage with patients and healthcare providers more effectively and the need for suitable regulatory frameworks were also flagged up.

Scrip Asks… What Does 2025 Hold For Biopharma? Part 3: Impacts Of Political Change In US And Beyond

 

What do industry leaders anticipate as the US installs president Trump once again? Beyond the biopharma sector's biggest market, geopolitical instability has increased elsewhere: how might this affect markets and companies?

Scrip Asks… What Does 2025 Hold For Biopharma? Part 2: Funding, M&A And Partnering

 

More than 30 biopharma executives, investors and industry experts shared their views on the environment for funding and deal-making in the year to come. With a patent cliff looming over big pharma at the same time as technology opens manifold possibilities for new approaches to drug discovery and development, the general view is that 2025 will be a busy year for partnering, with a trend towards earlier-stage deals and milestone-dependent payments.

Scrip Asks… What Does 2025 Hold For Biopharma? Part 1: The State Of The Biopharma Industry

 

More than 40 industry executives shared their views on where the biopharma industry stands as it enters the new year. Innovation remains the sector’s driving force, but after two years of capital constraint, due diligence is key.

More from R&D

Genmab Foundations Firm Amid Share Price Slumps

 
• By 

The Danish drugmaker's stock keeps falling as investors wait to see if Johnson & Johnson will opt in to co-develop a successor to the partners' multiple myeloma big earner Darzalex, but analysts have been heralding Genmab's bright prospects for assets such as Epkinly and Rina-S.

Pipeline Watch: Fifteen Approvals And Two Phase III Readouts

Pipeline Watch is a weekly snapshot of selected late-stage clinical trial events and approvals announced by pharmaceutical and biotech companies at medical and industry conferences, in financial and company presentations, and in company releases and statements.

Cambridge-To-Cambridge: Flagship Wants To Help Build The UK’s Biotech Culture

 

Flagship Pioneering has unveiled a new collaboration with Cambridge UK, to find synergies with academics and hospitals in the scientific hotspot, and create new biotech start-ups. Scrip spoke to Flagship about its plans.