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A team at Stanford recently posted a preprint describing a system they called “Virtual Biotech”: a coordinated squad of AI agents organized to mirror a real drug discovery company, complete with a virtual Chief Scientific Officer, specialized scientist agents, and over 100 tools for querying biomedical databases.

For their headline demonstration, they deployed over 37,000 agents in parallel, each one tasked with annotating a single clinical trial, linking therapeutic targets to genomic and single-cell transcriptomic features. The resulting dataset spans 55,984 trials. The analysis turned up what the authors call previously unreported associations: drugs targeting cell-type-specific genes were 40% more likely to advance from Phase I to Phase II, 48% more likely to reach market, and showed 32% lower adverse event rates.

In another case study, the system pulled together genetics, transcriptomics, and clinical data on B7-H3 in lung cancer and landed on an antibody-drug conjugate strategy, the same bet several pharma companies are already running in the clinic. It also flagged liabilities and differentiation angles. The whole thing reportedly cost $46 in API credits and took less than a day.

The Virtual Biotech is one lab’s preprint, but it lands in what we recently likened to a “gold rush”—agents are being deployed across clinical operations, translational biology, antibody design, and regulatory workflows. Major pharma companies are in an apparent compute arms race, stacking GPU clusters and billion-dollar AI partnerships within months of each other. Startups backed by hundreds of millions are launching agent-focused platforms. NVIDIA’s Jensen Huang even went so far as to declare agentic AI “the new computer” at this year’s GTC.

Whether the implementations match is another question. In a recent experiment, researcher Liang Chang asked—”Can AI make better decisions than pharma executives?” and sent AI agent teams back to a pivotal 2012 decision in oncology, the BMS vs. Merck biomarker strategy that ultimately decided the Keytruda-Opdivo war, and found that both Claude and GPT independently recommended the same path BMS took. The path that lost.

The agents produced rigorous analysis, identified the exact competitive threat, and still followed the consensus. As Chang put it: “AI can give you the best possible analysis. It can’t give you the courage to go against it.”

What can AI agents do today, where are they falling short, and why is everyone building them?

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🤖 Why agents, and why now?

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⚠️ The most immediate one is patient recruitment. Far back in 1979, the father of clinical pharmacology Louis Lasagna observed that the pool of eligible patients shrinks by 90% the moment a trial opens, only to reappear once it closes. Lasagna’s Law remains as relevant as ever: according to a 2022 article, 11% of trial sites enrol zero participants and nearly 90% of trials face meaningful delays. With Phase II and III trials costing roughly $40,000 per day, the financial toll is brutal.

⚠️ Another issue is clinical attrition. Research from VU Amsterdam found that between 2012 and 2019, the share of trials successfully completing each phase declined steadily—particularly at Phase II. In the first half of 2024, nearly a third (32%) of trials were discontinued at Phase II—a 56% rise compared to pre-pandemic levels. Combined with stagnant rates of Phase III initiation over that same decade, the picture is one of a systemic bottleneck: trials that begin are increasingly unlikely to see the finish line.

⚠️ Rare disease research presents its own distinct challenge. As the FDA’s Rare Disease Evidence Principles note, shrinking patient populations make it progressively harder to generate reliable efficacy data through conventional designs—especially placebo-controlled trials, where enrolling enough participants to reach statistical significance can be close to impossible.

⚠️ Apart from operational intricacies, there is the ethical dilemma. Randomized controlled trials remain the gold standard for evaluating new therapies, but randomization isn’t always defensible. When an effective treatment already exists, assigning patients to a placebo raises serious moral questions, e.g. HIV cure trials with the antiretroviral treatment interruption.

The question, then, is whether parts of the control process can be simulated rather than physically recruited.

Digital twins are emerging as a compelling response. Last October, Sanofi Ventures invested in a digital twin platform developer QuantHealth, bringing its total funding to $30M. In 2025, the FDA announced plans to phase out animal testing requirements for monoclonal antibodies in favor of human-relevant methods, including AI-driven computational models—with the EMA moving in the same direction. Both industry and regulators, it seems, are taking this technology seriously.

👥 How it Works

A digital twin is a virtual replica of a physical object, continuously updated with real-world data so it mirrors the original’s behavior in real time. The concept, first applied by NASA in the 1960s, has since migrated from engineering into healthcare.

The applications are wide-ranging: optimizing industrial processes as Eli Lilly did to boost production of their GLP-1 drugs, predicting equipment failures, streamlining supply chains, and accelerating product development.

In a clinical trial patients are generally divided into two groups, also known as arms. The intervention arm receives the experimental treatment; the control arm receives a placebo, standard-of-care treatment or sham, serving as the baseline against which results are measured. Randomized controlled trials (RCTs) are the gold standard because randomization minimizes bias, but that randomization isn’t always flawless.

The traditional workaround of external controls drawn from historical trials, health records, or registries all carry their own limitations. For instance, data like those don’t include underrepresented groups or don’t account for placebo effect due to their observational nature.

Digital twins go a step further: using AI models augmented with historical data, they generate individualized predictions of how a patient might respond under different treatment scenarios. When used to simulate outcomes for patients who do not receive the experimental therapy, these models can produce a synthetic control arm.

These trial-level twins build on a foundation of patient-specific digital twin modeling (virtual replicas of individual physiology shaped by genomics, imaging, and clinical history) which we covered in our earlier overview of biological and patient-specific twins.

In clinical trials, an AI-powered digital twin typically operates in three steps:

• Build virtual patients — AI integrates biomarkers, imaging, genetics, and real-world evidence to generate synthetic profiles capturing the full variability of real populations.

• Run simulated trials — virtual cohorts replace placebo groups or test experimental therapies in silico, probing efficacy and safety without exposing patients to unnecessary risk.

• Optimize continuously — trial parameters like dosing and sample size are continuously refined in real time, anchored by validation against real-world data.

A less computationally demanding synthetic control arm approach uses AI-generated patient data based on registries, and real-world evidence but unlike DTs not modelling it on a particular individual. The appeal is sharpest in rare diseases, where finding enough eligible control patients is often impractical. The FDA, EMA, and NICE have all endorsed the approach, and it’s gaining traction: recent Phase II/III myeloma and lymphoma trials have leaned on external control data, and in at least one case (blinatumomab for acute lymphoblastic leukemia), a synthetic control arm helped support accelerated regulatory approval. AstraZeneca used over 300M synthetic patient records to advance its clinical trials, allegedly saving up to $100M per drug in development.

Synthetic control arms built from historical data have already supported label expansions and accelerated approvals with alectinib, blinatumomab, palbociclib among them. AI-generated individualized digital twins, however, have not yet served as primary evidence in a completed approval.

🦾 An Industry Arm

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Shortly prior to last Christmas the European Commission published a proposal of a European Biotech Act, a strategic initiative aimed at setting up a regulatory framework to strengthen the life sciences sector across the EU. The document has been mostly positively received by the sector leaders as a needed step towards fostering local biotech innovation. Brussels isn’t stopping there. Commission President Ursula von der Leyen pitched EU-Inc, a pan-European company structure meant to solve what many see as the EU’s core startup problem of navigating 27 different bureaucratic regimes. The proposal would let one register in 48 hours, fully online and in English.

The Case for Urgency

Why does it matter? Europe gave the world its first blockbuster pharmaceutical (Aspirin in 1899) and just 30 years ago produced half of all new treatments globally. Today, that share has fallen to just one in five. Even though the EU biotech industry has grown twice as fast as the overall union’s economy over the last decade, it struggles to convert the world’s top science into commercially viable products.

Europe holds a comparable share of the top 10% most-cited biomedical research to the US and China, yet lags significantly behind in venture investment — a gap caused by underdeveloped private equity markets and fragmented, complex regulatory frameworks. The disparity is also visible in listing trends, with 66 of the 67 EU companies that went public over the past six years choosing foreign stock exchanges.

To address this, the Biotech Act includes measures like:

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Transformer architectures trained on biological data are beginning to predict drug response, generate therapeutic hypotheses, and identify which patients are likely to benefit from which treatments (part of a broader push that includes early attempts at virtual cell models) tasks that previously required years of wet-lab iteration. Some of that work is still early, though a handful of results have made it far enough through validation to be worth paying attention to.

• Google Research, Google DeepMind, and Yale spent much of 2025 scaling C2S-Scale, a language model that reads single-cell RNA data as text; the 27-billion-parameter version, released in April, came in October with wet-lab validation of a model-generated hypothesis about making immune-”cold” tumors visible to T cells.

• A collaboration between Microsoft Research, Providence Health, and the University of Washington took a complementary approach: GigaTIME, published in Cell in December, routinely converts pathology slides into virtual immune-protein maps, surfacing over 1,200 significant associations across 14,256 patients.

• At Davos in January, Demis Hassabis now put Isomorphic Labs‘ first trials, primarily oncology candidates, at end of 2026; the company followed this month with IsoDDE, a general-purpose drug design engine that reportedly doubles AlphaFold 3’s accuracy, already deployed across its oncology programs.

Not all of it is language-model work.

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Just a day earlier, MIT Technology Review published its annual 10 Breakthrough Technologies list. This year, three of the highlighted technologies were in genomics: personalized gene editing, embryo scoring, and gene resurrection. From there, it seems like genomic applications are moving more into the mainstream technology discourse.

Another just-in data point from a few days ago is a report out of San Diego, where Element Biosciences says its newly announced VITARI benchtop sequencer can deliver a whole genome for $100, positioning it as a lower-cost alternative to Illumina’s high-throughput systems.

Looking at these and many of last year’s developments, genomics come into view as an integrated technology wave that extends from data generation to interpretation, intervention, and biological reconstruction.

With those early-2026 pings as a starting point, let’s do a selective pass through a few genomics patterns that seem to be carrying momentum into 2026.

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At first glance, these headlines span different companies and medical areas. But they share a common thread: each centers on advanced therapeutic modalities (ATMs)—a new generation of medicines that go beyond the limits of conventional drugs.

According to BCG, eight of the top ten best-selling biopharma products in 2025 are new-modality drugs, and the global pipeline value for these therapies has reached $197B. ATMs are becoming a more established part of the industry and are noticeably contributing to its growth.

Reject Tradition, Embrace Modernity

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In DealForma’s figures cited by CEO Chris Dokomajilar, deal flow between large-cap biopharma and Chinese biopharma accelerated in 2024-2025. In 2025, big pharma completed 18 in-licensing and asset purchase deals (just one in 2020) from Chinese companies with $50M+ upfronts, totaling $57.3B in deal value and $3.9B in upfront cash and equity. By 2026, China continues to emerge as a major source of globally licensable, clinical-stage biotech assets, backed by an increasingly complete innovation stack, even as new policy constraints complicate cross-border data flows and outsourcing.

In late January, speaking at the Asian Financial Forum in Hong Kong, executives from Merck and Amgen pointed to China as a likely early approval market for fully AI-designed drugs. Merck China president Marc Horn suggested that 2026 could mark the shift from AI-assisted discovery to compounds designed end-to-end by AI entering regulatory pipelines, citing China’s patient datasets, clinical execution, and the government’s recent “AI Plus” policy push. Amgen’s chief medical officer Paul Burton pointed to a similar timeline, seeing 2026 as a year when AI-driven and human genetics–led discovery could begin translating more directly into drug candidates.

For perspective, among recent big pharma deals involving Chinese companies, this year’s JPM week had AbbVie’s $5.6B partnership with RemeGen around a bispecific oncology asset. Looking back at just 2025, Pfizer licensed a bispecific from 3SBio with $1.25B upfront, AstraZeneca entered a multi-year $5.3B AI-enabled small-molecule discovery collaboration with CSPC Pharmaceuticals, and GSK’s x Jiangsu Hengrui agreements included $500M upfront and up to about $12B in potential milestones.

From Generics to Innovation

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Invasive & Minimally Invasive BCIs

Brain-computer interface (BCI) systems are being explored and used as a way to restore lost motor, speech, or sensory functions, particularly in patients with paralysis or neurodegenerative conditions. They work by placing electrodes on or in the brain to capture high-resolution neural activity, which is then translated into actions like moving a cursor, generating speech, or triggering stimulation.

Typically, BCIs include implanted pulse generators and wireless connections to external processors, which decode brain signals such as spikes or local field potentials from targeted brain areas, then use trained algorithms to translate those activity patterns into outputs such as cursor motion, text, or stimulation commands.

In 2025, several programs moved into multi-center or early pivotal territory:

• Neuralink extended its PRIME program into Great Britain with the GB-PRIME study at UCLH and Newcastle, evaluating the fully implantable N1 interface in patients with motor neuron disease and spinal cord injury, and reporting the first UK patient controlling a computer within hours after surgery. The same implant was used at home by ALS patient Brad Smith to control a motorized Insta360 webcam, demonstrating extended real-world use beyond cursor control.

• Paradromics received FDA IDE approval for its Connexus system to start the Connect-One early feasibility study, targeting speech restoration and computer control in people with severe paralysis via a high-bandwidth, fully implantable BCI. The Connect-One trial is designed around speech restoration as a primary endpoint rather than generic cursor control.

• Precision Neuroscience advanced its thin-film Layer 7 cortical interface. The 1,024-electrode subdural array, FDA-cleared as a temporary mapping device, was profiled in first human recipients as a minimally invasive, high-density platform that sits on the cortical surface rather than penetrating tissue.

• CorTec’s Brain Interchange BCI system reached first-in-human use in a stroke patient as a fully wireless, closed-loop implant capable of recording and stimulating cortex in real time, positioning it as a European competitor in implantable neuromodulatory BCIs.

• Synchron introduced an updated version of its endovascular Stentrode BCI that integrates Nvidia AI and the Apple Vision Pro headset to let people with severe paralysis control digital and physical environments using neural signals. Later, Synchron publicly demonstrated a person with ALS using its implanted Stentrode to control an iPad entirely by thought by converting neural motor-intent signals into native iPadOS inputs.

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There are other speakers highlighting the promises of AI for solving aging. Anthropic CEO Dario Amodei said at 2025 WEF that if AI dramatically accelerates biological research, doubling the human lifespan by around 2030 isn’t unrealistic because it could compress “100 years of progress” into 5–10 years. Such claims are controversial, but they reflect a real trend: AI is impacting both basic geroscience and emerging longevity medicine. Before delving deeper into the intersection of AI and longevity, let’s overview the history of this field before machines came.

Nothing Lasts Forever

Aging is the gradual, time-dependent decline in the physiological functions required for survival and reproduction. Unlike age-related diseases (such as cancer or heart disease), the defining features of aging are shared by all individuals within a species.

As an integral part of life, aging has caused a multitude of philosophical disputes throughout history, tracing back to 350 BCE when Aristotle first tried to explain senescence, viewing it as a ‘natural illness’. However, conventional aging research started much later, in the 20th century.

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Terray’s work in diffusion methods prompted a broader reflection on generative AI in biology. Today, most conversations and publications center on Transformer-based systems, especially large language models (LLMs) and other foundation models (FMs). LLMs make up a major subset of FMs, but whereas language models are trained primarily on textual data like natural language, code, or biological sequences, foundation models extend the paradigm to additional modalities, including images, audio, video, and even multimodal combinations.

Recent meta-reviews in biomedical NLP collectively catalog nearly 300 LLM instances across hundreds of studies. Foundation models are also proliferating, with over 200 tools developed since 2022 in drug discovery alone. In contrast, the literature on diffusion models for biological and chemical applications remains comparatively modest. So far, there have been only a handful of reviews capturing the diffusion generators. Yet despite lower popularity, diffusion architectures are carving out a meaningful and distinctive role in biotech research and industry.

Before diving deeper into their role in biomedicine, let’s briefly review how diffusion models work in general.

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This transformation is happening thanks t…

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EvolutionaryScale emerged in 2023 after Rives, along with Tom Sercu and Sal Candido, left Meta’s AI protein group (FAIR) during the company’s “year of efficiency” (there are, again, plans to cut 600 AI jobs after a $14.3 billion Scale AI investment and hiring spree this summer). Backed by the likes of Amazon and Nvidia, the team raised $142 million to develop large-scale generative models for protein design and became known for the ESM family of protein language models (PLMs) trained directly on amino-acid sequences.

Its flagships, ESM3 and ESM Cambrian, extended this work to fully generative modeling of protein structure and function. ESM3, trained on 2.7 billion proteins, has already been used to design molecules like the novel green fluorescent protein variant, esmGFP, said to represent roughly 500 million years of natural evolution.

CZI’s Biohub folds this hire into its broader “virtual biology” plan, setting out four scientific challenges: building an AI-based model of the cell, advancing imaging, instrumenting inflammation, and using AI to reprogram the immune system, with the Virtual Immune System as one of the flagship projects. The ES team is brought in “to help advance this initiative.” In the VIS roadmap, the molecular-interactions axis explicitly calls for protein language models that “can learn the universal grammar of immune recognition and enable the rational design of novel receptors.”

With that, let’s step back and look closer at what protein language models are, what kinds of applications companies are building them for, and where pharma is already involved.

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Last year, OpenAI partnered with Formation Bio and Sanofi as well as signed agreements with Moderna, Eli Lilly; followed by a Thermo Fisher Scientific deal in 2025. At the same time xAI advertises Grok for Government with support for science and healthcare purposes, while DeepSeek gains adoption across Chinese hospitals.

Today we’ll look at LLMs entering biomedical workflows, examine what these systems can do in lab- and clinic-adjacent tasks, and how hybrid designs aim to mitigate common failure modes.

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This May, researchers from the Centre for Genomic Regulation (CRG) reported the first use of generative AI to design DNA sequences that regulate gene expression within living mammalian cells. The team focused on synthetic enhancers—short DNA “switches” that control when and where genes are activated. In their proof-of-concept, they tasked the AI with generating enhancer sequences able to switch on a fluorescent reporter gene in specific mouse blood cells, while remaining silent elsewhere. The ~250-base-pair sequences were synthesized, delivered into cells by a virus (notably, they were integrated at random locations), and performed as intended in healthy mouse blood cells.

What CRG demonstrated fits into the typical synthetic biology workflow—where we define a functional specification, generate design candidates, synthesize them, and test them in cells. DNA elements are treated as modular parts composed into higher-order circuits and systems. Borrowing principles from classical engineering like standardization, abstraction, iteration, synthetic biology turns cellular programs into designable constructs.

These days, AI and machine learning are being applied all over the life sciences, and we’ve been tracking this closely across recent updates. Before diving into what’s happening in AI-SynBio today, let’s zoom out and see how the field emerged.

Historical Throughline

💠 1961–1999: The Origins.
Although the term “synthetic biology” was first introduced in 1912 by Stéphane Leduc in his work on the physico-chemical basis of life and spontaneous generation, the field’s origins are often traced to 1961, when François Jacob and Jacques Monod introduced the lac operon model in E. coli, showing that cells use regulatory circuits to respond to their environment. This framed biology as a system of logic and control. Early achievements included Meselson’s discovery of restriction enzymes in 1968, Boyer and Cohen’s recombinant DNA technology in 1973 — which paved the way for the first production of a synthetic protein, human insulin, in E. coli by Riggs and Itakura in 1978—and Kary Mullis’s invention of PCR in 1983. At the start of the decade, Barbara Hobom reintroduced the term ‘synthetic biology’ describing genetically modified bacteria with recombinant DNA. Advances in sequencing, genome mapping, and “omics” in the 1990s generated vast cellular catalogs; at the same time computational biology revealed networks of genes and proteins as structured, modular systems.

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Despite the extraordinary effort, the odds of success remain bleak. Only about 1 in 10 drug candidates entering clinical trials ultimately achieve regulatory approval, with failures most often linked to safety issues or insufficient efficacy. Even high-throughput screening (HTS), once celebrated as a breakthrough, delivers a discouraging hit rate of just 2.5%. Such low yields amplify delays, inflate costs, and exhaust resources.

Artificial intelligence (AI) and machine learning (ML) are emerging as powerful alternatives meant to accelerate discovery, improve prediction accuracy, and break the limitations of traditional methods. Importantly, the story of AI in drug discovery has evolved alongside advances in computer tech by building on decades of incremental progress:

• 1960s: The drug discovery field took its first computational step with the development of the QSAR (Quantitative Structure–Activity Relationship) method. The groundwork for QSAR was laid by Corwin Hansch and his colleagues in 1962 when they researched the correlation of molecular properties with biological activity.

• 1980s: The release of the CHARMM program in 1983 enabled general molecular simulation. In the meantime DOCK developed by Kuntz’s group in UCSF became the first molecular docking software.

• 1990s: Molecular modelling software platforms like Schrödinger made computer-aided drug design widely accessible, embedding computational tools into the pharmaceutical workflow. Additionally, open-source alternatives like GROMACS appeared (University of Groningen in 1991).

• 2010s: Deep learning catalyzed a wave of AI-first biotech startups like Recursion and Insilico Medicine. In June 2025 Insilico released the Phase IIa results for an AI-designed compound Rentosertib against idiopathic pulmonary fibrosis, which is believed to be a considerable milestone for a solely AI-inspired drug candidate up to date.

• 2020s: In CASP14, DeepMind’s AlphaFold achieved a median Global Distance Test (GDT - the main CASP metric for prediction precision evaluation) score of 92.4 —an unprecedented leap in accuracy. In 2024, the Nobel Prize in Chemistry recognized Demis Hassabis and John Jumper (for AlphaFold) and David Baker (for computational protein design).

Over a decade ago, strategists at Big Pharma noted the possibility of a broader AI’s potential for R&D transformation, considering progress in deep learning at the time, and started testing grounds for wider adoption. One of the earliest examples took place back in 2012, with Merck tapping into the online data science community Kaggle to crowdsource solutions for a core drug discovery challenge: predicting biological activity of molecules, both on-target and off-target. The 60-day competition awarded $40,000, with the top prize going to a team led by George Dahl (University of Toronto) for their use of neural networks and deep learning—a hint at how these methods would reshape drug R&D.

Today, we will follow stages of the drug discovery pipeline as outlined in the January 2025 Nature article by researchers from Wenzhou Medical University. We’ll look at each stage in detail and highlight how leading biopharma companies are leveraging AI across the entire drug development lifecycle.

The pipeline unfolds across six critical phases: target identification → discovery → preclinical/clinical → regulatory → post-market. The first three stages represent unique challenges and opportunities for innovation, and AI is increasingly shaping how the pharmaceutical industry approaches them.

Target Identification

Finding the right molecular target,usually a protein or nucleic acid, has always been a bottleneck in drug discovery. Classic approaches like pull-down assays or genome-wide screens work, but they’re slow and costly..

AI is taking over the target detection by uncovering hidden molecular patterns and disease links that traditional tools miss.

• NLP models like word2vec have been used to map gene functions in high-dimensional space, boosting sensitivity when data overlap is sparse.

• Graph deep learning takes this further by combining network structure with deep models to identify key targets and explain its reasoning, a good example of which is CGMega—a GNN-based tool for cancer gene module dissection.

• Platforms like Insilico’s PandaOmics show what’s possible: by linking omics data with biomedical literature, it flagged TRAF2- and NCK-interacting kinase as an anti-fibrotic target, leading to a new inhibitor (INS018_055).

In 2025, AstraZeneca accelerated its AI-driven oncology strategy with a clear focus on target identification and validation. In April, it launched a $200M partnership with Tempus AI and Pathos AI to build a multimodal foundation model to uncover novel targets and accelerate therapeutic development.

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In July, Deloitte released a report Trends Shaping Biopharma in 2025, highlighting two big changes.

• The first is that data and AI have become the new competitive advantage. With healthcare data growing rapidly and AI tools advancing, pharma companies can no longer just react to patient needs. They must predict them, provide proactive support, and design personalized treatments. Many firms are already bringing patient services in-house, and more than half of industry leaders admit their business models need to be updated.

• The second change is the rise of smarter supply chains and manufacturing. Geopolitical risks and supply chain disruptions are pushing companies to adapt, and technologies like digital supply networks, cloud platforms, digital twins, and advanced analytics are starting to reshape how drugs are produced—making the process faster, leaner, and more resilient.

  In this article: Alphabet/Google — Amazon — Apple — Microsoft — NVIDIA — Meta — Intel — IBM — Samsung — Oracle — Big Tech, Biopharma and Society

Tech companies, with their infrastructure, data power, and global reach are well positioned for the transformation biopharma needs. What draws them to healthcare is data. Biopharma holds one of the richest but least used data resources—genomic sequences, clinical trial results, real-world evidence, and patient feedback. Combined with AI and machine learning, this data could fuel breakthroughs in drug discovery, diagnosis, prognosis, and personalized medicine.

And tech giants started investing into biopharma a while ago. Amazon’s $3.9B acquisition of One Medical in 2023 signaled its healthcare ambitions. In July 2025, the White House’s “Make Health Tech Great Again” event highlighted the process: more than 60 healthcare and technology players — including Apple, Amazon, Anthropic, Google, and OpenAI — pledged to build a next-generation digital health ecosystem. Networks committed to CMS interoperability standards, health systems promised to ease patient data use, and EHR vendors vowed to streamline exchange.

All the signs indicate tech giants are here for a while, so let’s look at the key steps notable players have taken in biopharma this year.

Alphabet/Google

A recent InvestorsObserver analysis of Alphabet’s 13F equity positions suggests ~40% are in healthcare/biotech, which makes the company one of the most active investors in U.S. healthcare. The company strives for global AI leadership, considering healthcare and biotech as one of the major fields to fulfil this goal.

In March 2025 Google introduced TxGemma, a suite of “open” AI models aimed at advancing drug discovery. Scheduled for release later this month, the models are part of the company’s Health AI Developer Foundations program. TxGemma is designed to process both natural language and molecular structures—ranging from small molecules to proteins and other therapeutic entities. By enabling predictions about critical drug properties like safety and efficacy, Google expects the platform will accelerate the long, expensive, and high-risk process of drug development.

With this announcement, Google enters a crowded and increasingly competitive field of AI-powered drug discovery, which gathered 87% of all alliance investment in 2025. While the technology holds promise, with the 30% estimate for new drugs discovered with AI, results so far have been mixed. Several AI-designed drugs have failed in clinical trials, despite successes like Rentosertib - an IPF drug developed by Insilico Medicine which recently reached 2a phase trials.

Google’s most notable drug discovery player is Isomorphic Labs, the DeepMind spinout founded in 2021 and led by Demis Hassabis. The London-based company has raised this year $600M in its first external funding round, as it works to translate its AI drug-design platforms into clinical programs. President Colin Murdoch has said the company is “getting very close” to starting human trials, initially focusing on cancer therapeutics. Additionally, in April DeepMind and Isomorphic made AlphaFold3 (protein structure prediction tool) available for non-commercial use.

Note: In October 2024 the Nobel Prize in Chemistry went to David Baker for computational protein design, and jointly to Demis Hassabis and John Jumper for AlphaFold.

Drawing on AlphaFold’s capabilities, Isomorphic has already signed partnerships with Novartis and Eli Lilly, though it reported losses of £60M in 2023 due to heavy R&D spending and hiring.

Meanwhile, Alphabet’s other major healthcare initiative, Verily (previously Google Life Sciences), is navigating its own transformation. The life sciences arm plans to convert from an LLC to a C-corp as it prepares for a fresh funding round. CEO Stephen Gillett told employees the restructuring is intended to improve investor appeal, though no financing has been confirmed. At the same time, staff were informed that their equity had been revalued at roughly 80% below 2024 levels, reflecting what Gillett described as a gap between earlier valuations and current earnings. Nevertheless, the work is still ongoing and in early 2026 Verily plans to release Lightpath - an AI-backed chronic care solution aimed to help members with weight loss, e.g. providing support during and after GLP-1 use.

Amazon

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We might be entering a new era of space exploration as nations and private companies are racing to push the limits of what lies beyond Earth. China made the Tiangong space station fully operational in 2022; NASA has advanced with the Artemis program, launching its Space Launch System on an uncrewed mission as the first step toward a permanent lunar base and, eventually, crewed missions to Mars; SpaceX drew wide coverage in 2023 with the first orbital flight-test attempt of Starship—a reusable spacecraft built to carry heavy payloads into orbit and one day ferry settlers to Mars. India, too, is carving its place in human spaceflight: ISRO’s Gaganyaan mission is entering its final phase, now set for launch in 2027.

🌌 Microgravity

One of the unique features of the space environment is the microgravity condition. In 2020, the Center for the Advancement of Science in Space (CASIS, the nonprofit that manages the ISS National Lab) and the University of Pittsburgh’s McGowan Institute for Regenerative Medicine co-hosted the Biomanufacturing in Space Symposium. Held virtually, the event brought together leading experts in tissue engineering, regenerative medicine, and space-based research to explore how the ISS could be utilized to improve biomanufacturing. The event marked an initial move toward building a roadmap for the space-based biomanufacturing market.

Participants identified and prioritized three major areas of opportunity for R&D:

1. Disease modeling using microphysiological systems (tissue chips) and organoids

2. Stem cells and stem-cell-derived products

3. Biofabrication

What’s special about microgravity? One illustrative example comes from Merck’s research on Keytruda. By leveraging the International Space Station (ISS) for crystallization studies, Merck achieved remarkably uniform 39 μm particles, compared to the irregular 13-102 μm range typically produced on Earth. This improved consistency is beneficial for drug formulation due to improving manufacturing efficiency and delivery methods. Similarly, a promising therapy for Duchenne Muscular Dystrophy (DMD, a devastating muscle-wasting disease) was developed from a protein crystal studied aboard the ISS. TAS-205, an HPGDS inhibitor informed by ISS protein crystallography data, entered Phase 3 but was discontinued in July 2025 after missing co-primary endpoints.

Beyond protein crystallization, microgravity alters cell growth, differentiation, and tissue formation. In stem cells, microgravity reshapes the cytoskeleton, extracellular matrix, and gene expression; for example, human iPSC-derived cardiomyocytes in space showed altered calcium handling and 2,635 differentially expressed genes, while blood-derived stem cells lost stemness markers and differentiated earlier into bone. The promise of space-based stem cell research is underscored by a recent Mayo Clinic experiment, launched last month aboard the SpaceX Dragon to the ISS, which investigates how bone-forming stem cells interact with the signaling protein IL-6.

Cancer research shows that microgravity drives re-differentiation: lung cancer stem cells lost stemness and underwent apoptosis, while colorectal CSCs increased CD133/CD44 double-positive populations.

These conditions also promote scaffold-free 3D spheroids and organoids, made to more accurately model tumors and improve drug testing. A great example of organoid use in disease modeling is the NIH’s Tissue Chips in Space initiative, led by NCATS in partnership with NASA and the ISS National Lab in 2017. The program investigates how organs function under the unique conditions of microgravity. By 2021, kidney tissue chips (developed by Nortis; later acquired by Quris-AI) had already flown twice to the ISS, providing valuable insight into how kidneys respond to toxic and pharmacokinetic stress. These models allow researchers to observe drug effects that might remain hidden during conventional preclinical testing.

In regenerative medicine, microgravity enables engineering of bone, cartilage, vasculature, skin, liver, and heart tissues with enhanced differentiation compared to Earth. For instance, rabbit MSCs in microgravity bioreactors formed cartilage expressing collagen I/II and aggrecan, while vascular progenitors displayed improved angiogenic potential. Additionally, in September 2023, Redwire announced that it had successfully 3D bioprinted the first human knee meniscus in space using its upgraded BioFabrication Facility aboard the ISS. The tissue, cultured for 14 days in Redwire’s Advanced Space Experiment Processor, was returned to Earth on the SpaceX Crew-6 mission for analysis. Building on this success, since late August 2024 Redwire has been equipping its bioprinting efforts with advanced 3D bioprinters supplied by the Finnish company Brinter AM Technologies.

📖 History Brief

The Space Race ignited in the heat of the Cold War, sparked by the Soviet Union’s 1957 launch of Sputnik 1—the world’s first artificial satellite. In response, the United States established NASA the very next year, determined to match and surpass its rival’s extraterrestrial achievements. Just a few years later, human spaceflight became a reality: Yuri Gagarin’s historic orbital mission in 1961 and Alan Shepard’s pioneering Mercury flight set the stage for a new era of exploration. Alongside these milestones, biomedical research into life beyond Earth gained momentum. NASA’s early Mercury (1958–1963) and Gemini (1965–1966) programs pushed the limits of human endurance in space, proving tolerance to microgravity, validating spacewalks, and extending mission durations. These foundational steps culminated in humanity’s giant leap—the Apollo 11 lunar landing in 1969.

But the leap from short missions to long-duration spaceflight brought a new set of challenges. When the US launched Skylab, its first space station, in 1973, astronauts encountered profound physiological difficulties: prolonged existence in microgravity led to bone demineralization, muscle atrophy, and cardiovascular deconditioning—even with carefully designed exercise programs. These conditions created a unique opportunity to study aging, disease progression, and therapeutic interventions in fast-forward.

To neutralize these effects, researchers tested countermeasures such as lower body negative pressure devices, which provided useful data on how the cardiovascular system adapts in microgravity, even though concerns about long-term health risks remained. The Apollo–Soyuz mission in 1975 also marked an important shift: for the first time, American and Soviet crews worked together in space, exchanging medical monitoring practices and setting the stage for later international cooperation in protecting astronaut health.

The Space Shuttle Era (1981–2011) opened an entirely new chapter for biomedical research by transforming microgravity into a powerful experimental tool. Scientists could now probe musculoskeletal physiology, cardiovascular regulation, and immune function in ways impossible on Earth, all while supporting astronaut health during longer missions. Shuttle flights also deepened collaboration with Russia’s Mir space station, creating a bridge for joint biomedical studies. Cooperation reached new heights with the 1998 launch of the ISS and the arrival of its first permanent crew in 2000.

Since then, the ISS has become humanity’s primary laboratory for studying life sciences beyond Earth.

📚 Space Flavors of Biology

In January 2024 Aaron J. Berliner with the team from the Center for the Utilization of Biological Engineering in Space (CUBES) released a comprehensive overview “Domains of life sciences in spacefaring: what, where, and how to get involved” in npj Microgravity. There, authors define three major fields integrating space research and biology—Astrobiology (AB), Bioastronautics (BA), and Space Bioprocess Engineering (SBE).

➕ Astrobiology

Astrobiology investigates the origins, evolution, and distribution of life, searching for habitable environments beyond Earth and signs of biology on other worlds. Mars, with evidence of past liquid water, remains a prime candidate, while icy moons like Jupiter’s Europa and Saturn’s Enceladus, with subsurface oceans and geysers, seem as well quite promising. Curiosity and Cassini missions have uncovered organic molecules, suggesting that the building blocks of life may be widespread across the cosmos. Astrobiology also emphasizes planetary protection, preventing Earth microbes from contaminating alien environments and safeguarding Earth from potential extraterrestrial life.

Spaceflight studies aboard the ISS have shown that bacteria like Salmonella and Serratia marcescens can become more virulent in microgravity, highlighting both risks for astronaut health and the importance of countermeasures.

Apart from microbes, astrobiology considers the possibility of intelligent life and its societal impact. Ultimately, this subfield advances our knowledge of life on Earth while preparing us for safe exploration and potential settlement beyond our planet.

➕ Bioastronautics

Bioastronautics is the study of how spaceflight affects living systems, with a focus on human health and performance in extraterrestrial environments. It addresses the challenges of long-duration missions while developing technologies to safeguard crews. Prolonged exposure to microgravity and radiation can cause bone loss, cardiovascular strain, immune dysfunction, and vision problems like Spaceflight-Associated Neuro-Ocular Syndrome. Isolation and confinement add further risks, including stress, depression, and cognitive decline.

To mitigate these effects, bioastronautics develops countermeasures ranging from exercise and radiation shielding to advanced air and water recycling. Model organisms play a central role: NASA’s Rodent Research program and JAXA’s Mouse Habitat Unit provide vital data on mammalian health in space, and zebrafish, fruit flies, worms, and plants contribute insights into how gravity shapes biology and how agriculture could sustain future crews. As noted above with Salmonella and Serratia marcescens, spaceflight alters microbiomes and can boost virulence in pathogens.

Addressing these risks requires not only medical strategies but also innovations in spacecraft and habitat design. Incorporating microbial control measures (e.g. advanced air and water filtration systems) reduces the likelihood of harmful microorganisms spreading in closed environments. This area of research lies at the heart of international initiatives like the European Space Agency’s MELiSSA (Micro-Ecological Life Support System Alternative) and NASA’s CUBES (Center for the Utilization of Biological Engineering in Space).

➕ Space Bioprocess Engineering

The idea of biotechnology as essential for space was first highlighted in the 1992 National Academies report Putting Biotechnology to Work. Today, with deep-space missions on the horizon, Space Bioprocess Engineering (SBE) is emerging as a defined discipline. SBE integrates synthetic biology and bioprocess engineering to design, build, and manage biological systems that sustain astronauts when resupply from Earth is limited. Unlike bioastronautics, which studies the effects of spaceflight on life, SBE develops the technologies that make long-term living in space possible.

Core SBE goals include in situ resource utilization (ISRU), loop closure (LC) for recycling, in situ manufacturing (ISM), and food and pharmaceutical synthesis (FPS). Efforts range from ultra-efficient carbon and nitrogen capture to programmable biomanufacturing for foods, medicines, materials, and even self-healing structures. Central to this are resilient platform organisms—microbes and plants engineered to thrive in extreme environments. Some examples: Arthrospira platensis (cyanobacteria for nutrients and pharmaceuticals), Cupriavidus necator (bioplastics), Methanobacterium thermoautotrophicum, and higher plants like lettuce and potatoes.

Challenges remain in safety, containment, and reliability, and NASA’s Decadal Survey (2023–2032) calls for bold investment. Its proposed BLiSS campaign (Bioregenerative Life Support Systems) seeks to harness biology for food, air, water, and waste management—making sustainable offworld habitation possible.

Let’s look at some of the companies involved in integrating biotech with space travel and research.

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