What emerging technologies or scientific breakthroughs do you believe will most reshape biopharma in the next 10 years?

Kalim Saliba, Chief Product Officer, Dotmatics

Digital Threads
In 2026 and moving forward, life science organizations will begin taking early but important steps toward creating a digital thread that spans the entire flow of how therapeutics are discovered, developed, and manufactured. As this vision begins to take shape, teams will start moving from fragmented data systems to more connected digital ecosystems that enable end-to-end traceability across the make–test–decide–analyze life cycle — from molecular design through large-scale production. This continuous data thread will unify hypotheses, designs, samples, methods, instruments, and results into a single source of truth, strengthening data integrity, reproducibility, and regulatory confidence. The ultimate goal is for seamless integration between wet-lab and dry-lab environments to accelerate innovation and reduce costly rework, while standardized ontologies and contextualized analytics unlock deeper insights. As biologics grow more complex, these connected workflows will increasingly serve as the digital backbone of discovery — enabling faster, safer, and more compliant delivery of new therapies that improve patient lives.

Agentic AI
2025 has marked the beginning of the agentic AI era — and in the future, we’ll see this technology evolve into a practical and trusted force across life sciences. Rather than replacing today’s co-pilots, agentic AI will build on them, extending their capabilities along a continuum of agency. We’ll see systems that are more adaptive to data and context, able to plan and reason through multi-step processes by choosing which tools, APIs, or models to use to reach an outcome. Importantly, these systems will still keep humans in the loop — ensuring that actions are transparent, verifiable, and trustworthy. In R&D, this shift will enable AI to move from assisting with predefined queries to actively orchestrating analyses, generating hypotheses, and optimizing experiments within well-defined boundaries. It’s not about full autonomy — it’s about smarter collaboration between scientists and AI that accelerates discovery while maintaining scientific rigor and oversight.

Amos Chungwon Lee, Ph.D., Co-Founder and Chief Executive Officer, Meteor Biotech Co Ltd.

Over the next decade, biopharma will be fundamentally reshaped by the convergence of spatial biology, single-cell multi-omics, and AI-driven data interpretation. While genomics has identified countless potential targets, the field is now realizing that where and in what cellular context genes are expressed is equally critical to understanding disease mechanisms.

Spatially resolved single-cell technologies will move beyond descriptive atlases and become functional tools for drug discovery — allowing researchers to interrogate cell–cell interactions, tumor microenvironments, and tissue-level heterogeneity with unprecedented precision. This will be particularly transformative in oncology, immunology, and neuroscience, where rare or spatially restricted cell populations often drive therapeutic response or resistance.

Equally important will be advances in AI-native biology platforms that integrate spatial, transcriptomic, proteomic, and phenotypic data into predictive models. These systems will not only accelerate target discovery but also reduce late-stage clinical failure by improving biological validation early in development.

Ultimately, the biggest breakthrough will not be a single technology, but the shift from bulk-averaged biology to context-aware, cell-resolved decision-making across the entire drug development pipeline.

Steven Quay, M.D., Chairman, President, and Chief Executive Officer, Atossa Therapeutics

Over the next decade, biopharma will be reshaped by the fusion of AI and programmable biology. Foundation models trained on sequences, structures, and longitudinal clinical data will compress discovery: rapid target nomination, de novo protein/antibody design, and credible prediction of ADME/tox, immunogenicity, and resistance pathways before synthesis. Coupled to automated design–build–test–learn platforms, robotics, microfluidics, high-throughput single-cell assays, and closed-loop optimization, biology becomes an engineering workflow.

On the therapeutic side, next-gen gene editing (base/prime editing, epigenetic editing, and safer, tissue-selective delivery) will expand from rare diseases into cardiometabolic, oncology, and immune disorders. Cell therapies will move from bespoke to scalable via allogeneic platforms, better manufacturing analytics, and programmable safety switches. RNA modalities (mRNA, siRNA, circRNA) paired with targeted nanoparticles will broaden the druggable proteome. Finally, multimodal biomarkers, wearables, digital phenotyping, spatial/omics, and real-world evidence will enable smaller, faster trials, adaptive dosing, and earlier proof-of-mechanism decisions. Expect AI to rewrite clinical operations too: smarter site selection, synthetic control arms, and Bayesian adaptive designs. And as manufacturing digitizes, continuous processing plus real-time release testing will cut cost and make personalized medicines routine globally.

Karsten Eastman, Ph.D., Chief Executive Officer, Sethera Therapeutics

Over the next decade, AI will continue to play an important and expanding role in biopharma, particularly in data integration, target identification, and optimization within well-charted chemical and biological spaces. That said, there remains a fundamental limitation: AI systems can only extrapolate from what has already been observed. Large areas of chemical space, especially higher-order, conformationally constrained architectures, remain effectively inaccessible because they cannot be readily synthesized or screened at scale. As a result, many biologically relevant interaction modes, particularly those that resemble natural protein–protein interfaces, remain underexplored despite major computational advances.

What I believe will be truly transformative is the expanding use of enzymes as programmable tools for molecular construction. Enzymatic processes now enable access to entirely new regions of chemical space that were previously unreachable by traditional chemistry, while remaining compatible with high-throughput library formats. This allows the generation and screening of vast numbers of structurally complex, three-dimensional architectures that combine biological-like binding with small-molecule-like properties. Importantly, these molecules are being created and tested in real space and real time, often before any meaningful computational representation exists. As these enzyme-enabled discovery systems scale, they will produce pharmacologically relevant molecules that current AI could not directly generate, because no training data exist for them. In this sense, enzymatic innovation and high-throughput screening will not compete with AI but instead redefine the universe of molecules that AI will one day be able to learn from.

Jill Makin, Ph.D., Chief Scientific Officer, Touchlight

Over the next decade, biopharma will be reshaped by a convergence of cell-free biology, programmable genetic medicines, and AI-driven design. Cell-free and enzymatic manufacturing platforms will decouple DNA and RNA production from living systems, dramatically accelerating development timelines while improving control, quality, and sustainability.

At the same time, advances in gene editing, multi-gene and large-payload constructs, and synthetic regulatory elements will enable more precise, durable, and personalized therapies. These innovations will expand what is biologically possible, particularly for complex diseases that require sophisticated genetic architectures.

Finally, AI-driven design of complex, highly precise and personalized genetic medicines is made possible by the employment of cell-free production — reducing trial risk and increasing success rates. Together, these breakthroughs will shift biopharma from incremental improvement to truly programmable, scalable medicine.

Tom Sellig, Chief Executive Officer, Adare Pharma Solutions

The next decade in biopharma will be defined by technologies that can keep pace with a rapidly evolving development landscape. As scientific advances drive more targeted therapies, personalized dosing, and higher expectations for patient experience, products are becoming more complex. The ability to deliver precision and speed without compromising scalability will be critical as this trajectory continues.

One area poised to have an outsized impact is additive manufacturing, particularly 3D screen printing. Unlike earlier 3D printing approaches that struggled with cost, scalability, or API stability, 3D screen printing enables precise, layer-by-layer construction of complex oral dosage forms at a scale suitable for commercial manufacturing. This opens the door to integrating immediate, extended, delayed, or sequential release mechanisms — and even multiple APIs — within a single tablet, creating delivery systems that simply weren’t feasible before.

Over the next decade, technological advances like these will continue to reinforce the value of fully integrated development and manufacturing models. As timelines compress and capital remains constrained, sponsors are already placing greater importance on CDMO partners that can align formulation, manufacturing, and packaging from the outset. That reliance will only deepen as programs move faster and grow more complex.

Cheryl Sturgis, Head of Marketing, Bionova Scientific

Over the next decade, the technologies that will most reshape biopharma are those that compress development timelines, improve process predictability, and make increasingly complex modalities manufacturable at scale. The industry is shifting from empirical development toward a design-to-manufacture approach.

Artificial intelligence (AI) and machine learning (ML)will play a central role, not simply as retrospective analytics tools, but as engines for prospective decision-making. The key breakthrough will be linking molecular design directly to manufacturability, stability, and quality risk early in discovery. Predictive models connecting sequence, structure, and process behavior will enable earlier chemistry, manufacturing, and controls (CMC) confidence and reduce costly late-stage rework.

In parallel, high-throughput and automated process development platforms will transform how process knowledge is generated. Robotics, miniaturized bioreactors, and advanced experimental design will allow for broader exploration of design spaces in less time, improving reproducibility across sites and partners.

Continuous and intensified bioprocessing will also become more mainstream. Perfusion-based upstream processes, coupled with more increasingly continuous downstream operations, offer tighter quality control, smaller facility footprints, and improved scalability.

Advanced analytics and real-time monitoring will further enable proactive control strategies, shifting the industry toward continuous verification and real-time release.

Together, these advances signal a future where biomanufacturing is no longer a downstream constraint, but a strategic driver of innovation, speed, and therapeutic impact.

Maximillian Yeh, Vice President, Sales (Advanced Therapy Medicinal Products), Cohance Lifesciences

Oligonucleotide therapeutics are moving from niche to platform. The maturation of siRNA, antisense, and next generation RNA modalities represents a major inflection point, analogous to the industry’s experience with peptides over the past two decades. Advances in oligonucleotide chemistry — including backbone modifications and conjugates such as GalNAc — combined with improved delivery technologies and robust GMP manufacturing, are enabling these modalities to scale beyond rare diseases into cardiometabolic, CNS, and oncology indications.

Therapeutic modalities are also converging. The industry is moving away from siloed drug classes toward multimodal therapeutics, including antibody–drug conjugates, dual payload ADCs, antibody–oligonucleotide conjugates, and targeted RNA delivery systems. The key enablers are not only new payloads but also innovations in linker chemistry, conjugation precision, and manufacturability at scale — areas where CDMOs with deep chemistry, containment, and process development expertise are well positioned to add value.

At the same time, AI is moving from discovery hype to delivering operational impact across CMC, process optimization, impurity control, and candidate selection. By shortening development timelines and reducing technical risk, AI is reshaping how early assets progress and how partnerships are formed.

Together, these trends favor integrated capabilities in complex chemical synthesis, GMP nucleic acid manufacturing, and hybrid biochemical workflows — areas where Cohance is deliberately building long term leadership.

Russell Miller, Vice President, Global Sales and Marketing, Enzene

While many compelling scientific advances could significantly impact biopharma, a breakthrough already demonstrating significant impact is fully-connected continuous manufacturing — a more advanced technology with advantages over traditional batch processing, as well as semi-continuous and hybrid platforms. As the need for complex biologics as new therapies increases, having a fully connected manufacturing platform will enable biopharma companies to significantly increase productivity and quality. And the innovation’s ability to reduce cost can improve access to and amplify the impact that new therapies have on more patients, especially those in markets with limited healthcare access.

Marc Hummersone, Ph.D., Senior Director of Research & Development, Astrea Bioseparations

The next decade in biopharma will be shaped by AI-driven drug discovery, the expansion of mRNA therapeutics beyond vaccines, and CRISPR-based gene editing reaching mainstream clinical use. These technologies will dramatically compress development timelines and enable personalized medicine at scale.

However, manufacturing efficiency remains the bottleneck. As biologics become more complex and personalized therapies proliferate, traditional chromatography can struggle to scale quickly enough or keep pace with rising throughput demands.

This is where Astrea Bioseparations’ proprietary nanofiber chromatography technology, AstreAdept®, becomes transformative. Unlike conventional bead-based resins, AstreAdept® uses open-pore fiber matrices creating a highly accessible surface area, supporting high capacity, fast flow rates, and strong selectivity for large or delicate modalities. This enables faster, continuous processing, reduced buffer consumption by up to 80%, and cuts purification time from days to hours — critical advantages as the industry shifts toward continuous manufacturing and demand for biologics booms.

Complementing these advances, synthetic biology, organ-on-chip platforms, and quantum computing will reshape drug development. But the organizations best positioned to succeed will be those that solve the fundamental challenges of manufacturing speed, scalability, and cost, making breakthrough technologies commercially viable. That’s where separation and purification innovations like Astrea Bioseparations’ create genuine competitive advantage across the pharmaceutical workflow.

Steven Bulera, Ph.D., Corporate Vice President, Chief Scientific Officer: Safety Assessment, Charles River Laboratories

Over the next decade, emerging technologies will continue to fundamentally transform biopharma by providing more predictive, human-relevant models and speeding up drug development. Advanced in vitro systems, such as 3D tissue models, organoids, organs-on-a-chip, and multi-organ systems, are progressing beyond proof-of-concept to become powerful tools for studying diseases and assessing human safety. These alternative approaches will be able mimic organ-level structure and function, allowing researchers to address important safety and efficacy questions earlier in the drug development process and in chemical testing. New approach methodologies (NAMs) are gaining popularity in pharmaceutical development and chemical testing, especially in Europe, with the United States increasingly adopting these same approaches, resulting in the creation of new opportunities for the development and adoption of alternative testing methods. For example, microfluidic systems and brain organoids capable of processing and responding to stimuli are opening new paths for modeling neurodevelopmental and degenerative disorders in vitro. Additionally, virtual control groups (VCGs), which replace live control animals with historical curated data sets, could reduce animal use in both efficacy and safety animal studies. Lastly, AI remains a top focus, with recent examples demonstrating that AI can predict chemical toxicity faster than wet laboratory assays. While alternative methods are becoming better at mimicking the complexity of in vivo systems, it doesn’t mean that animal models and animal testing will disappear overnight. These breakthroughs point to a promising future where one day, integrated, technology-driven in vitro approaches will reduce and may even replace the need for animals in research.

Nathaniel Youndt, Vice President of Business Strategy and Program Management, Arranta Bio

Faster and more cost-effective manufacturing of personalized medicines will drive new levels of access for patients with disease targets not typically attractive for traditional pharma investment. Advancements in establishing, manufacturing, and releasing advanced therapies in record time also brings these treatments to more hospitals, providers in more rural areas, and carves a new pathway for developers to continue pursuing rare and orphan disease. This space also stands to be reshaped by solving key cell/tissue-targeting challenges in drug delivery. Addressing targeted delivery reduces the systemic burden of therapeutics and vaccines on patients by precisely delivering drugs to desired cells, not only avoiding the effects of undesirable immune response but potentially reducing the total amount of drug required for an effective treatment.

Bob Hughes, Ph.D., R&D Research Fellow, Grace

AI/ML is poised to revolutionize biopharma drug development in the next decade. We’ve only begun to explore its potential. In early-stage development, AI/ML accelerates experimentation and process optimization by analyzing large data sets, reducing the number of experiments and identifying optimal synthetic routes, ultimately helping to fast-track drug candidates to clinical trials.

AI/ML also enhances insight generation, uncovering complex relationships in data that traditional methods might miss, and leading to a better understanding of factors affecting yield, impurity formation, and process performance. AI/ML also rapidly optimizes analytical methods that once took weeks or months to complete — such as completing the challenging peak separation of an isomer from the main component in a matter of days.

Resource utilization will also improve, with AI/ML identifying opportunities to conserve raw materials and energy consumption. During scale-up and technology transfer, ML models can potentially predict full-scale outcomes, streamlining technology transfer and manufacturing scale-up.

Beyond process development, AI/ML helps identify drug targets, predict compound properties, optimize clinical trials, and even find new purposes for drugs. While AI/ML won’t replace human expertise, it will transform the way we work, empowering scientists and engineers to focus on problem-solving and strategic decisions.

Catherine Bladen, Ph.D., Vice President, Regional Executive and Principal Advisor, Vector Laboratories

Over the next decade, biopharma will be reshaped by the convergence of advanced biology, AI, and engineering — driving a shift from symptom management toward more curative, personalized therapies.

AI is rapidly evolving from an exploratory tool into a core scientific infrastructure. AI-native drug discovery and generative design approaches are already accelerating target identification, molecular design, and candidate optimization and are expected to play a central role in a significant portion of new drug discoveries. In parallel, next-generation gene editing technologies and emerging therapeutic modalities — such as advanced biologics, conjugates, and programmable medicines — will expand what is considered “druggable.”

Equally transformative will be advances in multi-omics and spatial biology, which are enabling a more precise understanding of disease biology in its native context. These insights are informing both target validation and therapeutic design in ways that were previously impossible. Finally, smart manufacturing, digital twins, and data-driven process optimization will be critical to translating innovation into scalable, reproducible therapies.

Ultimately, success in biopharma will depend not just on strong pipelines, but on the ability to integrate AI with wet-lab science, optimize manufacturing at scale, and deliver durable, patient-specific outcomes.

Camille Segarra, Ph.D., Head of Strategy and Innovation, Integrated Biologics R&D, Lonza

While new technologies will influence biopharma, the next decade is more likely to be shaped by well-validated innovation than disruptive change. Biopharma’s focus on patient safety, product quality, and regulatory compliance strongly favors proven, reliable technologies that can be broadly adopted. As a result, targeted innovation can still drive meaningful progress, particularly for organizations that successfully balance novelty with reliability. Established and trusted CDMOs play a critical role in this landscape — bringing stable platforms, proven infrastructure, and flexible capabilities to accelerate the safe and effective introduction of new modalities and manufacturing approaches.

Advanced analytics and AI-driven predictive models are reshaping development, scale-up, and commercial manufacturing. Integrated tools, such as digital twins, offer potential to enhance process understanding, reduce variability, increase right-first-time performance, and shorten development timelines.

Protein semi-synthesis, combining recombinant expression with chemical synthesis, enables precise protein modifications that traditional biologics methods cannot easily achieve. While broad near-term adoption remains unlikely, it may become important for specialized applications, such as site-specific conjugation.

Ultimately, the next decade will favor pragmatic, targeted advances that deliver measurable value. Rather than replacing existing frameworks, these technologies will complement and strengthen them, particularly when supported by the expertise and reliability of experienced industry partners.

Marc Hedrick, M.D., Chief Executive Officer, Plus Therapeutics

The next decade of biopharma will be shaped by breakthrough technologies in precision medicine that allow us to observe and intervene in cancers, especially in cancers of the central nervous system, with a deeper understanding of metastatic cancer biology. Metastatic CNS cancers, such as leptomeningeal metastases, remain among the most lethal and least understood diseases, in large part because we lack the tools to detect and track them in real time.

Emerging advances in ultra-sensitive liquid biopsy platforms, including cerebrospinal fluid–based diagnostics, are beginning to illuminate how these cancers evolve, spread, and resist therapy. When combined with next-generation targeted radiotherapeutics and novel delivery systems capable of crossing biological barriers, these insights open the door to fundamentally new treatment paradigms for diseases once considered nearly untreatable.

Most importantly, this convergence gives us a real opportunity to improve the survival curve, not by incremental gains but by enabling earlier intervention, more precise targeting, and continuous adaptation of therapy as the disease changes. As molecular diagnostics, therapeutic delivery, and AI-driven analytics merge into closed-loop clinical systems, oncology can move beyond static protocols toward truly dynamic, learning-based care that meaningfully extends both the length and quality of patients’ lives.

Steve Sweeny, Vice President, Business Development and Strategy, Viz.ai

Over the next decade, the future of biopharma will be reshaped by the emergence of point-of-care AI that is closed-loop and agentic, connecting real-time HCP-level signals with unified clinical and customer context to support guideline-based, next-best actions. Embedded directly into clinical workflows, these AI capabilities will help orchestrate compliant engagement across reps, MSLs, and digital channels, while supporting clinicians in the moments that matter most.

This shift enables earlier education, faster activation, and more seamless patient access support, from initiating HUB referrals and benefits verification to navigating prior authorization and copay assistance. Ultimately, by linking clinical decision-making with immediate access support, point-of-care AI will significantly reduce patient drop-off between the decision to treat and the start of therapy.

Louis Cicchini, Ph.D., Senior Director of Scientific Affairs and Partnerships for Cell & Gene Therapy (CGT), Cencora

Advances in mRNA and non-viral delivery systems are transforming genetic medicine by addressing key limitations of viral vectors, including immunogenicity, payload constraints, and manufacturing challenges. RNA-based therapies enable transient, titratable gene delivery without genomic integration, enhancing safety and versatility. Often used together, non-viral platforms, such as lipid nanoparticles (LNPs) and next-generation polymer-based systems, are emerging as market disruptors, enabling efficient delivery of nucleic acids or gene-editing tools like CRISPR with minimal immune response, scalable manufacturing, and more precise tissue targeting. As these technologies introduce new development and commercialization models, they also require bespoke, flexible, end-to-end commercial strategies to support the evolving journeys of patients, providers, and other stakeholders.

Together, these technologies address major bottlenecks in current CGT programs by enabling repeat dosing, expanding the range of treatable diseases, and supporting in vivo gene editing and personalized medicine. By reducing manufacturing complexity and costs, mRNA and non-viral vector systems have the potential to democratize genetic medicines and improve global patient access to treatments for rare diseases, cancer, and beyond.

Chris Hall, Chief Executive Officer, Personalis

Among the emerging technologies poised to reshape biopharma over the next 10 years, few hold greater promise than our capacity to track disease evolution in real time at the molecular level. Cancer is not static; it mutates and adapts in response to therapeutic pressure. For generations, we have been constrained to retrospective inference, understanding tumor evolution only after treatment failure. Real-time variant tracking represents a reordering of how we understand and respond to disease. At Personalis, we are pursuing this vision through Real-Time Variant Tracker, a new extension to our NeXT Personal MRD test. Having pioneered the shift toward ultrasensitive MRD, we are taking another step forward with a capability that marks a new milestone in our mission to enable physicians to fight cancer with a personalized approach. This empowers clinicians to identify emerging resistance and therapeutically targetable mutations during routine monitoring, including ESR1 mutations in HR+/HER2– breast cancer and hundreds of other clinically relevant alterations. The ability to detect evolving, clinically relevant mutations in genes like ESR1 during MRD monitoring and surveillance gives physicians a powerful new tool to help optimize care. Over the next decade, this longitudinal understanding will redefine drug development, moving our industry from reactive trial-and-error toward predictive, biology-driven innovation.

Bhanu Jena, Ph.D., Co-Founder and Academic Chairman, Porosome Therapeutics

Over the next 10 years, biopharma will be reshaped less by incremental advances in individual modalities and more by a fundamental shift toward understanding disease as a disruption of interconnected systems. Many complex disorders, particularly in neurology, metabolism, and inflammatory disease, arise not from a single faulty target but from breakdowns in coordinated cellular processes that govern communication and interaction.

Emerging technologies that enable this systems-level view will be critical. Organoids are already changing how researchers interrogate disease pathology by preserving tissue architecture, cellular diversity, and functional interactions that are lost in traditional models. These platforms make it possible to study processes in ways that more closely reflect human physiology, improving the translational relevance of preclinical findings and strengthening our confidence in potential drug candidates.

From screening platforms to rich sources of novel training data for AI models, organoids will provide a foundation for breakthroughs in even the most intractable diseases.

Michalis Papadakis, Ph.D., Chief Executive Officer and Co-Founder, Brainomix

In medicine, data is an invaluable resource, whether generated from a clinical trial or in clinical practice. Yet many of the richest, most multidimensional datasets, including scan imaging data, remain underused because they are difficult to interpret.

AI is already reshaping biopharma and clinical care by extracting meaning from complex data and converting ambiguous interpretation into measurable, repeatable quantification. In trials, this enables cleaner patient stratification and responder signals, and fewer false negatives, supporting confident decisions and a better understanding of who benefits and why. At Brainomix, we’ve seen this firsthand through the quantification of digital imaging biomarkers that clarified treatment response in Argenica’s phase II trial of a neuroprotective agent for acute ischemic stroke. In hospitals, this same capability enables more reliable predictions from scans and biomarkers, supporting faster, informed treatment decisions.

Over the next decade, this impact will compound. As models mature and validation and workflow integration advance, disease will be defined more precisely, change will be detected earlier, treatments will be matched more reliably, and learning will accelerate across research and care. AI becomes the connective tissue between research and the clinic, translating complex signals into shared, scalable insight that expands what medicine can see and prove.

Mark Bertagnolli, Chief Operating Officer, ViroMissile

Over the next decade, oncolytic viruses are likely to mature from a niche modality into a more established component of the oncology treatment landscape, both as monotherapies and in combination regimens. Progress in viral engineering has enabled greater tumor selectivity, improved control over viral persistence, and more deliberate immune-modulating designs, helping address historical limitations related to safety, durability, and systemic reach.

As these platforms advance, the field is shifting from proof-of-concept toward practical clinical performance. Programs that succeed will be those able to demonstrate reliable access to metastatic and deep-seated tumors, sustained immune activation, and predictable integration with other therapeutic classes, such as checkpoint inhibitors or targeted agents. Importantly, greater attention is being paid to how viral therapies behave in circulation, interact with the host immune system, and maintain activity over time.

Looking ahead, oncolytic viruses may help bridge direct tumor cell killing across different cancers with immune-mediated control of disease, offering an approach that complements existing standards of care. Continued clinical validation will be critical to defining where these therapies can deliver the greatest benefit and how they can be deployed most effectively for patients with advanced cancer.

Jhong-Jhe You, Ph.D., Vice President of Antibody Discovery, AP Biosciences

Monoclonal antibodies reshaped medicine by turning hard-to-drug biology into reliable therapies, pairing precision with scale and repeatable clinical impact. Over the next decade, bispecific and multispecific antibodies are poised to be the next platform shift, moving the field from single target control toward coordinated biology.

These molecules can connect functions that used to require combinations and deliver more intentional control of immune engagement and signaling. In parallel, the emergence of multispecific antibody–drug conjugates, which pair bispecific or multispecific antibodies with small molecule payloads, further expands this concept by integrating targeted biology with precise cytotoxic or functional delivery within a single molecule. In complex diseases, heterogeneity and adaptive resistance erode single-mechanism approaches. Coordinated biology, enabled by multispecific targeting and, in some cases, integrated payload delivery, meaningfully raises the performance ceiling of a single therapeutic agent. As context dependent designs mature, selectivity will continue to sharpen and the therapeutic index will widen, extending clinical benefit while improving tolerability.

Advances in protein engineering and manufacturability, together with AI-driven acceleration of target selection, epitope pairing, payload integration, and developability optimization, are transforming bispecifics, multispecifics, and multispecific ADCs into a repeatable and scalable strategy for building safer, more potent medicines and delivering truly transformative therapies at scale.

Ketan Mehta, Chief Executive Officer and Co-Founder, Tris Pharma

One of the most transformative shifts in biopharma over the next decade will be the convergence of advanced drug delivery systems with precision medicine. For too long, we’ve seen breakthrough molecules’ potential limited by suboptimal delivery, through inconsistent absorption or inability to target specific populations.

The future lies in technologies that don’t just deliver drugs but deliver them intelligently and precisely. At Tris Pharma, we believe precision delivery platforms can change treatment paradigms, particularly in CNS disorders where therapeutic windows are narrow and patient needs are diverse. Advanced delivery technologies, leveraging enhanced absorption, extended-release systems that maintain therapeutic levels, and formulations that bypass metabolic pathways or cross the blood–brain barrier, can transform expected efficacy into meaningful outcomes. These approaches help promising molecules become effective medicines that improve patient lives without over-medication, minimize side effects, and lower healthcare costs.

Looking ahead, integrating novel polymers, conjugates, nano- and micron-sized particle technology, and biologics-compatible delivery systems will expand what’s possible. The question will shift from “can we develop this molecule?” to “can we deliver it in a way that improves outcomes and demonstrates value?” When delivery innovation meets molecular innovation, we’ll finally realize the promise of patient-centric therapy and precision medicine.

Jeff Glazier, J.D., Chief Executive Officer, General Oncology

More than once, I’ve witnessed medical breakthroughs from the stitching together of obscure facts in scientific literature. To succeed, you aggregate information across seemingly unrelated journal articles, sometimes even crossing disciplines, with logic and endless persistence. Each grain of knowledge is potentially a puzzle piece, and we sift through literally hundreds of thousands of them, selecting only those that appear to fit together. When you reflect on that, it’s both amazing and humbling what the human brain can accomplish. But I believe there is going to come a time, probably seven to 10 years from now, when AI will do that faster and more accurately than humans. And to me, that’s even more amazing and humbling. The catch, however, is — just like now — the results will only be as good as the accuracy of the underlying data that the AI is working with — and that, at least for the far foreseeable future, I am convinced will remain fully human-dependent.

Ben Hwang, Ph.D., Chairman and Chief Executive Officer, Profusa

Over the next decade, continuous biosensing and data-driven personalized medicine will be among the most transformative forces shaping biopharma. Historically, clinical decision-making and therapeutic development have relied on intermittent data snapshots like blood draws, clinic visits, and retrospective patient reporting. Emerging biosensing technologies are changing that paradigm to enable real-time, insight into human physiology.

At Profusa, we see continuous biosensing as a foundational technology for the future of medicine. Our implantable, minimally invasive sensors can provide dynamic biochemical data, such as tissue oxygenation and metabolic markers, over extended periods. This unlocks a far more precise understanding of how patients respond to therapies in real-world settings, rather than controlled moments in time.

When paired with advanced analytics and AI, these rich data streams will enable truly personalized treatment strategies, earlier detection of adverse events, and more adaptive clinical trials. Beyond improving patient outcomes, continuous biosensing has the potential to significantly reduce development timelines and costs by generating higher-quality, more actionable data.

Ultimately, the integration of continuous biosensing with digital health platforms will move biopharma from reactive care toward predictive, preventive, and personalized medicine, reshaping both how therapies are developed and how patients are treated.

Brad Evans, Ph.D., Senior Scientific Advisor, Disease State, BioIVT

Liquid biopsy technology is moving beyond single markers toward multi-omics approaches that combine ctDNA, cfRNA, proteins, and EV signals to give a much more complete picture of disease. As sensitivity improves, we’ll be able to detect changes earlier, monitor response and resistance in near real time, and make faster decisions in drug development. More patient-centric sampling, both in clinic and at home, will enable denser longitudinal data sets, which opens the door to adaptive trials and more personalized therapies. This coupled with advances in AI, digital health, and standardized workflows, allows liquid biopsy technology to become a continuous decision-making tool that reshapes everything from trial design to how targeted and immuno-oncology therapies are developed and used.

Venu Mallarapu, Executive Vice President, Global Operations, eClinical Solutions

Over the next decade, biopharma will be reshaped less by a single breakthrough and more by how intelligence is applied across the clinical and development life cycle. One of the most transformative shifts will be the rise of collaborative, domain-aware AI agents, which are systems designed not only to analyze data but to work alongside humans, understand study context, and operate within regulated environments.

Unlike general-purpose AI, purpose-built agent systems are encoded with clinical, operational, and regulatory knowledge. This allows them to reason across complex data ecosystems, automate high-value workflows, and provide explainable, trustworthy insights at scale. By orchestrating multiple specialized agents, each one focused on a task such as data quality, anomaly detection, or study oversight, biopharma teams can move faster while maintaining control and compliance.

Just as important is the shift from isolated analytics to collaborative intelligence, where humans and AI continuously learn from one another. This model has the potential to dramatically reduce cycle times, improve trial execution, and surface insights that would otherwise remain hidden in fragmented data.

Together, these advances will redefine how biopharma organizations operate by turning data into a living, intelligent asset and enabling smarter, faster decisions from discovery through submission.

Arushi Narang, Senior Business Planning Manager, Samsung Biologics

I will quote Demis Hassabis, the 2024 Nobel Laureate in Chemistry: “Maybe in the next decade or so, we can cure all diseases with the help of AI.”

Two powerful technologies, AI and gene sequencing, are progressing in parallel, and pharmaceutical companies are applying AI to genomic databases to find quality therapies faster and tackle diseases for which no treatment exists.

During the 2026 J.P. Morgan Healthcare Conference (JPMHC) Week, one of the most significant deals announced was Illumina’s partnership with AstraZeneca, Merck, and Eli Lilly to build a ‘cell atlas,’ or a billion-cell model that uses genomic technology. The only acquisition during JPMHC Week was Modella AI by AstraZeneca, and even more recently, Johnson & Johnson paired up with Demis Hassabis’ Isomorphic Labs to find new drug targets.

It has been exciting to have witnessed in my career the breakthroughs in Alzheimer’s, RSV, obesity, HIV, rare diseases like Huntington’s, and COVID-19, for some of which Samsung Biologics manufactured medicines or vaccines.

Contract development and manufacturing organizations (CDMOs) stand ready to support innovators. If AI discovers treatments in weeks instead of years, Samsung Biologics will manufacture at speed to deliver them to patients. Our human tumor-derived organoids services and development platforms accelerate clients’ IND filings. We are scaling up in service scope and strategic capacity, thanks to our engineering and technological heritage at Samsung. And our newly announced Bio Campus III will be truly modality-agnostic in the future as we play our part to “cure all diseases.”

Rashid Mijumbi, Ph.D., Global Vice President – Data, Analytics & AI, FUJIFILM Biotechnologies

AI will be one of the most transformative technologies in biopharmaceutical manufacturing over the next decade. In an industry driven by speed, consistency, and quality, AI offers the ability to automate complex workflows, optimize bioprocess conditions in real time, and materially de risk scale up. These capabilities will directly impact the availability, reliability, and affordability of biologic medicines.

Advances in machine learning and digital twins are enabling biopharma manufacturers to simulate bioprocess behavior with unprecedented precision. AI driven cell simulators can now evaluate millions of media and process parameter combinations and predict the impact of subtle variable shifts on yield, viability, or product quality attributes. For example, AI enabled media design has demonstrated the potential to increase antibody yields by up to 40% — with no change to equipment footprint. What historically required 12–18 months of iterative experimentation can now be compressed into as little as 8–12 weeks. This fundamentally changes how fast companies can move from development to manufacturing readiness.

Laia Elías Rius, Ph.D., R&D Director, Esteve

CDMOs are entering a pivotal new era in active pharmaceutical ingredient (API) development, driven by rapid advances in digitalization and process intensification technologies. Automation, AI, and machine learning are reshaping how processes are conceived, optimized, and scaled. By integrating predictive models with automated experimentation, development teams can dramatically shorten cycle times, enhance robustness, and achieve levels of precision that were not possible before. These tools are becoming fundamental in designing efficient and reliable manufacturing routes for increasingly complex molecules.

At the same time, sustainability is emerging as an equally powerful driver of change. Sustainability is no longer optional; it is both a moral responsibility and a scientific necessity. Forward-looking process development must incorporate green chemistry principles from the very beginning, reducing waste, improving energy efficiency, and selecting environmentally friendly solvents whenever possible. Such strategies not only reduce environmental impact but also lead to more resilient, cost effective, and scalable processes that benefit partners and patients alike.

Together, digital innovation and sustainable practices are redefining how APIs are developed and manufactured. The future of process chemistry will be smarter, cleaner, and more responsible, ultimately setting a new standard for excellence in pharmaceutical manufacturing.

David Horn, President and Chief Financial Officer, Seer

Over the next decade, unbiased proteomics will fundamentally reshape how we understand and treat disease. Medicine is shifting from a reactive, symptoms-based model to one that identifies and intervenes years before symptoms appear and when biology is most modifiable. Within 10 years, clinicians will be able to see disease trajectories unfolding in patients who appear healthy and act before irreversible damage occurs. This becomes possible when we can measure human biology without preconceptions with millions of proteins and protein variants, across millions of individuals, measured longitudinally in a hypothesis-free manner. Most proteomic approaches today rely on fixed panels, which constrain discovery to known pathways. Unbiased proteomics removes that limitation, revealing biology at unprecedented resolution and exposing early molecular signals and shared disease mechanisms. For clinicians, this enables a new generation of diagnostics that detect risk, stratify patients, and track disease progression long before changes appear in traditional biomarkers or imaging. Diagnosis shifts from late-stage confirmation to early, biologically grounded prediction. For drug developers, this reframes discovery. Targets emerge from real-world human data rather than hypothesis alone, increasing the probability of success and opening therapeutic areas that do not fit neatly into today’s organ-based categories. This shift also moves AI upstream. Machine learning is trained on primary biological truth from high-resolution, longitudinal proteomic data and evolves from pattern recognition into causal insight. As a result, today’s therapeutic silos will begin to dissolve. The companies that lead will be those that generate and own unbiased biological datasets, shaping clinical practice and defining the targets the industry follows.

Source : www.pharmasalmanac.com