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Survey responses indicate that bio/pharma faces tariff-driven rising costs and supply strain, with firms aiming to boost compliance and diversification and seeking stable trade and R&D support.

Bio/Pharma's Tariff Response: Key Insights from Industry Survey Results

Help us get a better picture of how new tariffs and trade policy have impacted you and your organization.

Survey: Impacts of Tariffs and Trade Policy on the Bio/pharmaceutical Industry

Lack of skills in the AI realm was a distant second among those surveyed about the biggest barrier to innovation while using the technology.

Agentic AI Growth Predicted, but More Than Half of Pharma Still Resisting Change: Report

Industry 4.0 is driving adoption in the pharma industry for smart equipment 
and tools that will advance manufacturing.

Accelerating Adoption of Smart Tools to Advance Manufacturing 

Manufacturers of five autologous or matched allogenic cell therapy products have selected TrakCel's  IT platform, OCELLOS, to orchestrate the administration of these therapies, which are approved or expected to be approved in 2024.

TrakCel’s OCELLOS Platform Selected to Support Treatment Management of Five Upcoming Cell Therapy Products

In an interview with Pharmaceutical Technology®, Michelle Bridenbaker from Unbiased Science discusses her thoughts on the key industry from 2024 and those she anticipates will impact the industry in 2025 and beyond.

Industry Outlook 2025: Continuing Innovation 

Looking at 2024, Michelle Bridenbaker, COO of Unbiased Science—a health science communications company—believes the most noteworthy trend shaping the bio/pharma industry has been the level of innovation that has been seen. “The first area that I really see that has been consequential has been the advances we’ve made in kind of personalizing medicine,” she says. “So, this is everything from our ability to do DNA sequencing, to RNA technology, CRISPR [clustered regularly interspaced short palindromic repeats], even things that are more lab and bench work in organoids.”

Of course, adding to this trend is artificial intelligence (AI), Bridenbaker continues, which is allowing for greater analysis of data. “There’s no question that AI is also impacting [personalized medicine], because we’re catching patients [that we previously missed],” she asserts. “I work in the rare disease space, and we missed patients before, but now, we can use AI to look at data that already exists and pick out that one patient that we may not have caught before, that needle in the haystack.”

Moving into 2025 and beyond, Bridenbaker anticipates a continuation in innovation and use of AI. “In 2025 and beyond, I really feel that we are going to have to spend a lot of time educating and upskilling our workforce,” she stresses. “And I think this is sometimes underestimated. It's like, the technology is great and it's wonderful, we're implementing it, but I think as well, we have to make sure we have the requisite skills to be able to keep up with the technology.”

Michelle Bridenbaker, BSN, MBA, MS, is COO of Unbiased Science—a health science communications company. She is also the Vice-President of the Medical Information Leaders in Europe, and she heads up the Global Medical Information and Communications function at Recordati Rare Diseases. Michelle has approximately 20 years’ experience within industry, working across a variety of roles. Most recently, she has been involved in trying to help shape the future and strategy for the pharmaceutical industry to be more effective communicators.

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Regeneron’s Bari Kowal gave insight into how the biopharma industry views the application of AI in R&D.

Exploring the use of AI in R&D at the US Pharma and Biotech Summit

® at the US Pharma and Biotech Summit, Regeneron's Bari Kowal, senior vice-president, head Development Operations & Portfolio Management, talked about the implementation of artificial intelligence (AI) in the R&D space. For example. Regeneron, Kowal explained, has been using AI for quite some time. Though not a new technology, she observed, AI has allowed the company to leverage many large language models and advanced searches for the past few years. Because of this, the company has built up collections of data in different areas, such as in genomics, protein databases, or for operational uses, including patient populations, study-level data, information about investigators, and quality of documents, among others.

“[Much] of our work is crossing over multiple facets of the drug development lifecycle early on in the discovery space, looking at how we manufacture and improving those processes, but also really looking at how we execute trials and reach out to the patients create diversity in the populations,” Kowal said. “We utilize some of the genomic data for finding the targets and more personalized medicine. [There are many] ways that we're leveraging AI.”

However, she emphasizes that the real value of AI is the ability to use it for existing data, especially in the search for optimizing operational efficiency. “Looking in the future, how do we use this to help our decision making and really better predict molecules that will have a tremendous effect? The biggest benefit that I can see, at least today, is that operational efficiency. If we can streamline drug development, from start to finish, our mission is to bring important medicines to patients at the end of the day, and so the time it takes today for drugs to be developed is too long. It's not measured in years, it's measured in decades,” she remarked. “If we can go from the research space to the development space in [a] few years, that is so much better and more beneficial to the patients that are waiting for those treatments.”

While speaking on a panel at the summit about diverging schools of thought on how AI should be used in R&D, Kowal observed that there were not necessarily divergent views by the panelists on how to use AI, but rather that the panelists had like-minded views on the topic. “I think we all believe that AI can be transformative. I think we believe that, ultimately, we're scratching the surface of what AI can do, but we also understand the human component and the context of human intelligence that you have to apply,” she stated.

 occurred on May 16, 2024 in New York, NY.

Bari Kowal, senior vice-president, head Development Operations & Portfolio Management, Regeneron, discussed the utility of AI and how it can benefit R&D work.

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A Discussion on Using AI in R&D at the US Pharma and Biotech Summit

The management and analysis of vast data generated by advanced analytical technologies are revolutionizing the drug discovery and development process.

Drug Digest: The Rush for Quality Data and the Means to Protect It

In this episode of Drug Digest, industry experts discuss why data is the biopharma industry’s most valuable asset today. The discussion also delves into the means by which the industry as a whole is learning to upgrade data integrity practices to better protect their data and the use of innovative technologies, such as advanced graph technologies, to better shape the drug discovery and development process.

Nathalie Batoux, Product Manager, Research Innovation, IDBS

Nathalie Batoux left the lab and joined IDBS in 2005, driven by her passion to help scientists with workflows and informatics tools to expedite their research. At IDBS, Batoux is a product manager for Research and Innovation. In her role, she regularly interacts with customers and researchers to gain an understanding of their data management needs and drives the development of platform capabilities to provide a solution. Batoux is an organic chemist by training and started her career as a post-doctoral researcher in chemistry working in the domain of nucleoside and dinucleotide analogues.

Thierry Dorval, Head of Data Science & Data Management, Servier

Thierry Dorval received a PhD in image analysis and artificial intelligence (AI) from University Pierre & Marie Curie and then joined the institut Pasteur Korea in 2005 as a group leader specializing in high content screening applied to cellular differentiation and toxicity prediction. In 2012, he joined AstraZeneca, where he focused on developing and advising on quantitative image and data analysis solutions in support of high content phenotypic screening. In 2015 he joined Servier, France, to lead the Data Sciences & Data Management pole of activities where he is in charge of optimizing the early stages of the drug discovery process by taking advantage of cutting-edge computational approaches. This includes usage of AI, knowledge graph database, system modeling and sequences design applied to multiple modalities including small molecules, antibodies, and oligonucleotides.

Jeremy Grignard, Data & Research Scientist, Servier

Jeremy Grignard holds a PhD in computer science, data, and artificial intelligence (AI) from École Polytechnique. He also earned a master’s degree in applied mathematics in Data Science and an engineering degree in computer science and big data. In 2019, he joined the Servier Research & Development Institute to complete his thesis with Inria Saclay, during which he developed computational methods using knowledge graphs, data science, and mathematical modeling to improve early drug discovery. Since 2022, he has been designing and implementing AI projects related to gene inference, explainable machine learning, heteromodality algorithms, biologically constrained deep learning models, knowledge graphs, and large language models.

This episode of Drug Digest is sponsored by:

Drug Digest is a tech talk video series with the 

® editors, who interview industry experts to discuss the emerging opportunities, obstacles, and advances in the pharmaceutical and biopharmaceutical industry for the research, development, formulation, analysis, upstream and downstream processing, manufacturing, supply chain, and packaging of drug products.

This discussion explores how the management and analysis of vast data generated by advanced analytical technologies are revolutionizing the drug discovery and development process within the biopharma industry.

Data and Artificial Intelligence

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