Backed by returning and new regional and strategic investors, the round more than doubles the company’s prior raise and reflects growing conviction in both the technology and the progress behind it.

For nearly four decades, biomaterials have tried to win what surgeons and scientists call the “race for the surface,” the contest between a patient’s healing cells and bacteria to colonize a material first, by killing bacteria after they arrive.

Also Read: CurifyLabs Raises $14M Series A to Power the Future of Personalized Medicine

Auxilium takes a different approach. Its Aer platform is an engineered matrix-like product that mimics the body’s own extracellular matrix, built to win that race structurally by resisting bacterial attachment while welcoming regenerative cells, all without relying on antibiotics.

The new capital will advance the company’s lead product toward FDA clearance and into first-in-human use while deepening the team and the research behind it. Over the last year, Auxilium’s full-time team has doubled, attracting top researchers and scientists to relocate to Cleveland to join the mission.

“Last year, the question was whether the science was real. This year, it’s how fast we can get it to patients,” said Isaiah Kaiser, PhD, Founder and CEO of Auxilium Health. “We’re grateful to our investors and partners who believe in the science and the team behind what we’re building. Their confidence lets us move with the urgency this problem deserves.”

Source: PRNewswire

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Proceeds will be used to expand U.S. operations, strengthen supply chain infrastructure, enhance customer support, and accelerate product innovation.

CurifyLabs’ Compounding System Solution (CSS) combines proprietary software, GMP-manufactured excipient bases, and advanced 3D printing technology to automate the preparation of personalized medications. With advanced quality control processes built in, the CSS produces tailored doses and dosage forms with greater precision and speed than the manual processes typically used in pharmacies today.

Pharmacies in 21 U.S. states and across Europe use CurifyLabs technology to compound and dispense thousands of doses every day. As the company continues to scale, it will invest in its supply chain to ensure its growing network of pharmacy partners has what it needs, when it needs it.

Also Read: Inocras and AimedBio Partner for Cancer Drug Discovery

“Personalized medicine is one of the most important frontiers in healthcare enabling better patient outcomes and is experiencing solid growth. We are enabling more pharmacies to deliver personalized medicine in a safer and more efficient way,” said Morten E. Iversen, partner at Sandwater. “CurifyLabs has built something rare technology that combines clinical rigor with the speed and precision that busy pharmacy teams depend on. We are excited to support their rapid growth in the U.S. and beyond.”

“We have backed more than 136 life sciences companies over nearly three decades, and what sets the best founders apart is their ability to combine scientific depth with real-world execution,” said Daniel Karsberg, partner at HealthCap. “The CurifyLabs team has done exactly that.”

“CurifyLabs is the kind of company we want to support innovative, globally ambitious, and built on deep scientific expertise,” said Joni Karsikas, Investment Director at Tesi. “Their growth in the U.S. is further proof that Finnish health technology can compete and win on the world stage.”

“This investment reflects the conviction our partners have in what we’re building,” said Charlotta Topelius, CEO and Founder, CurifyLabs. “We have set a high bar for clinical rigor, product quality, and customer support, and this funding gives us the resources to raise that bar further.”

The funding will support ongoing development of the CurifyLabs platform, including its most recent product, the PharmaPrinter Aurum, which compounds up to nine times faster than manual processes. CurifyLabs’ technology is ISO 13485 certified and designed in compliance with FDA 503A/503B standards for non-sterile compounding.

Source: PRNewswire

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Also Read: SK bioscience Leverages AI to Optimize Global Vaccine R&D Decision-Making 

Highlighting the strategic importance of the acquisition, Fiona Marshall, President of Biomedical Research at Novartis, stated, “ADCs have become an important part of cancer treatment, but there remains a clear need for new payload mechanisms to overcome resistance and expand their impact for patients. Myricx Bio has developed a promising NMTi payload platform with a differentiated mechanism that could broaden the use of ADCs across multiple tumor settings. This proposed acquisition reflects our strategy to scale innovative platforms, as we have with radioligand therapies, to deliver more durable, transformative treatments for patients.” By targeting the NMT enzyme essential for cancer cell survival and growth, this novel mechanism hopes to overcome previous limitations and pioneer transformative cancer care.

Read More: Novartis agrees to acquire Myricx Bio, advancing next-generation antibody-drug conjugate innovation with a novel NMTi payload, expanding options for cancer patients

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Also Read: Boltz Partners with Takeda to Deploy Frontier Biomolecular AI Models

Initially developed and validated using rotavirus vaccine datasets from SK bioscience and PATH, the scalable platform aims to expand to other disease areas, thereby enhancing the research and development capabilities of vaccine developers in low- and middle-income countries (LMICs) while accelerating global public health equity. This initiative aligns with SK bioscience’s broader digital transformation strategy, which incorporates AI-driven experimental design systems and digital twin technologies to optimize manufacturing efficiency and shrink development timelines. Highlighting the initiative’s strategic vision, Jaeyong Ahn, CEO of SK bioscience stated, “This project represents a new approach to reducing uncertainty in vaccine development through AI and enabling more scientific and efficient decision-making. Through this consortium, we aim to drive innovation in vaccine R&D while contributing to improved vaccine access worldwide.”

Read More: SK bioscience Launches AI-Powered Initiative to Reduce Uncertainty in Vaccine-Development Decisions

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Strategic agreement brings Inocras’ cancer whole genome data and cancer intelligence platform into AimedBio’s ADC clinical trials, supported by a strategic equity investment

Inocras Inc., a U.S.-based precision medicine company, announced a strategic partnership with AimedBio, a South Korean biopharmaceutical company developing antibody-drug conjugates (ADCs) for cancer. As part of the agreement, AimedBio has made a strategic equity investment in Inocras, and the two companies have signed a joint research agreement to integrate Inocras’s whole genome sequencing (WGS) capabilities into AimedBio’s clinical programs.

Under the agreement, AimedBio will apply Inocras’ whole genome sequencing (WGS), cancer intelligence platform, and multi-omics analysis across its ADC clinical trials to support biomarker identification, patient selection, and novel cancer target discovery. By leveraging comprehensive cancer whole genome data, the collaboration aims to accelerate clinical development while uncovering new opportunities for precision drug discovery. The two companies also plan to explore joint commercial opportunities, connecting AimedBio’s precision drug screening business with Inocras’s diagnostic platform across global markets.

Also Read: Inocras and KAIST Launch DNAChunker for DNA Language Models 

“This partnership reflects what we’ve believed from the start, that cancer whole genome data has a role beyond diagnosing cancer. It can directly shape how new treatments are developed and identify which patients are most likely to benefit,” said Jehee Suh, CEO of Inocras. “Bringing our cancer intelligence platform into active clinical trials with AimedBio is an important step toward accelerating precision oncology and drug discovery, and we’re happy to welcome them as a strategic partner.”

Nam-Gu Her, CEO of AimedBio, said: “As ADC development grows more competitive, identifying which patients are most likely to respond to a given therapy becomes a decisive advantage. Inocras‘s genomic capabilities will support biomarker research, patient stratification, and the discovery of next-generation cancer targets, further strengthening AimedBio’s precision oncology platform.”

Source: Businesswire

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For companies that deal with Life Sciences, Biotech, and Pharmaceuticals, this is a landmark event, since it implies moving away from treating AI as a drafting tool and making it an actively involved agent in the laboratory process.

Inside Claude Science

Claude Science provides a way of combining the fragmented parts of a scientist’s everyday work environment into a single user interface. Instead of switching among scripting platforms, data science notebook platforms, and various research databases, scientists can now coordinate their workflow through natural language.

The system incorporates a central coordinating agent that uses more than 60 pre-built skills and database connectors for genomics, proteomics, structural biology, and cheminformatics. The platform connects directly to publicly available models and frameworks, including Evo 2 and Boltz-2 from NVIDIA’s BioNeMo framework and key open resources such as UniProt, PDB, and ChEMBL.

Crucially, the platform emphasizes reproducibility and accuracy. Claude Science introduces a “reviewer agent” that automatically validates mathematical steps and cross-references citations to eliminate hallucinations. Furthermore, every output is tied to a complete audit trail, preserving the exact code, container environment, and messaging history used to generate a specific result or 3D protein structure.

Also Read: Lenovo Launches Hybrid AI Advantage to Slash Enterprise Inference Costs 

Disrupting the Biotech and Life Sciences Industry

The introduction of Claude Science will profoundly affect the operational models of biotech and pharmaceutical firms. Historically, computational biology teams have spent excessive time and resources on “toolchain integration” manually cleaning data schemas, configuring endpoints, and shifting massive genomic or molecular datasets across various silos.

By wrapping these technical steps inside an agentic, natural-language interface, Anthropic lowers the technical barrier to entry. Lab scientists who are not deeply trained in machine learning operations (MLOps) can now execute massive computational jobs, like screening libraries of millions of chemical compounds or designing a genome-wide CRISPR knockout screen, directly within a chat thread.

Furthermore, because Claude Science runs natively on a lab’s local infrastructure, SSH networks, or elastic high-performance computing (HPC) nodes via integrations like Modal, it directly addresses the strict data compliance and security regulations governing private genomic data and proprietary intellectual property.

Overall Business Effects on Industry Players

For businesses operating in the life sciences space, the deployment of platforms like Claude Science yields several strategic and financial advantages:

1. Drastically Reduced R&D Timelines: The earliest stages of drug discovery target identification and lead optimization traditionally take years and cost millions of dollars. By scaling multi-step analyses over hundreds of parallel containers in minutes, businesses can compress these timelines from months to days. Accelerated discovery means faster pathways to clinical trials and an expanded intellectual property portfolio.
2. Mitigation of the “Reproducibility Crisis”: Scientific errors and irreproducible data cost the pharma industry billions annually in abandoned or failed clinical drug pipelines. The automated audit logs and code-tracing mechanics built into Claude Science assure business stakeholders and regulatory bodies that data is verifiable from its inception. This auditability aligns perfectly with stricter data standards being requested by top-tier academic journals and regulatory authorities.
3. Savings on Operational Costs and Optimizing Talent: Rather than hiring huge teams to handle only data engineering and workflow integration, companies can realign their talent towards more productive work such as customizing models and analyzing data.
4. Levelling the Playing Field for Agile Biotech: Cloud-native, flexible compute platforms allow smaller startup biotechs to rent top-tier computational power on demand. A lean startup can now use Claude Science to orchestrate an advanced enzyme engineering workflow that previously required the massive, capital-intensive compute centers of legacy pharmaceutical conglomerates.

Looking Ahead

Anthropic’s push into the laboratory environment complemented by its concurrent launch of internal pre-clinical drug programs for neglected diseases shows that AI providers are moving aggressively into vertical-specific applications.

For life sciences and biotech businesses, adopting these specialized AI workbenches is no longer an experimental luxury; it is becoming a core competitive requirement. Companies that rapidly integrate agentic workbenches into their R&D loops will likely lead the next generation of medical breakthroughs, leaving siloed, manual workflows behind.

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Model matches the accuracy of leading 1.2 billion-parameter DNA language models while using only 172 million parameters

Inocras, a bioinformatics-led company harnessing the power of whole genome data and proprietary analytics to deliver curated insights that advance precision health, announced that “DNAChunker: Learnable Tokenization for DNA Language Models,” a joint research paper with the Korea Advanced Institute of Science and Technology (KAIST), has been accepted for paper publication at the International Conference on Machine Learning (ICML) 2026.

The paper introduces DNAChunker, a learnable adaptive tokenization approach for DNA language models that dynamically segments genomic sequences into biologically meaningful, variable-length units. Unlike conventional DNA language models that process genomic sequences using fixed-size or externally defined segments, DNAChunker learns how to group genetic code based on biological context, enabling more accurate and efficient representation of complex genomic patterns.

DNAChunker achieves state-of-the-art performance while matching the accuracy of leading 1.2 billion-parameter DNA language models with only 172 million parameters, making it more than seven times smaller. By reducing model size while preserving performance, DNAChunker may help make advanced genomic AI models more practical for large-scale research, translational discovery and future clinical applications.

“DNA language models depend heavily on how genomic sequences are represented before they are interpreted by AI,” said Wonchul Lee, CIO at Inocras and co-lead of the paper. “By replacing rigid tokenization with a learnable approach, DNAChunker provides a more precise and efficient foundation for downstream genomic modeling.”

Also Read: Tempus Introduces ‘Preview’: Bridging the Critical Time Gap Between Diagnostic Order and Definitive Results

“Our ICML acceptance marks a major milestone for Inocras’ Cancer Foundation Model, developed in collaboration with KAIST and trained on thousands of whole genomes from diverse cancer types,” said Jehee Suh, CEO of Inocras. “DNAChunker provides the biologically informed genome representation layer underlying that broader vision, helping foundation models move beyond pattern recognition toward clinically meaningful cancer interpretation. Together with KAIST, we are advancing the core technologies needed to make whole-genome AI more accurate, efficient, and scalable.”

KAIST led foundational algorithm design, model implementation and validation, while Inocras contributed large-scale computational resources, key technical ideas and validation efforts to align the model with practical and clinical applications.

“DNAChunker shows that sequence representation is a central challenge in building effective DNA language models,” said Prof Sungsoo Ahn and Insu Han from KAIST and corresponding authors of the paper. “Our collaboration with Inocras helped connect advanced AI methodology with the scale and practical requirements of whole-genome analysis.”

Source: Businesswire

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Also Read: Catalent Launches Qai to Transform Manufacturing QA

In fact, by switching to AI-powered simulations in virtual mode instead of experiencing structural biology with the traditional, labor-intensive experimental methods, this collaboration structure shall Really reduce the preclinical period, allow more efficient validation of the targets and finally pave the way for the design of completely new, highly potent therapeutics. Thanks to a state-of-the-art secure data infrastructure, this implementation brings for Takeda’s use of leading-edge machine learning setups while always respecting the strictly confidential and intellectual rights of the target data and molecules. The cooperation achieving such a high degree of technological advancement and industrialization of ultra-modern AI models in the pharmaceutical deep tech research field is a big step forward and So going beyond merely isolated proofs-of-concept to a fully standardized and scalable engine for autonomy in molecular discovery.

Read More: Boltz Announces Collaboration with Takeda to Deploy Frontier Biomolecular AI Models Across Takeda’s Research Organization

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Also Read: SandboxAQ Integrates Quantitative AI Models With Claude to Accelerate Scientific Discovery 

“The launch of Qai represents an important milestone in how Catalent is applying advanced technologies, including AI, across our operations to improve consistency, accelerate insight and enable better decision-making. Innovative AI solutions like Qai strengthen the quality of our operations and better support the teams delivering critical therapies to patients around the world.” Emphasizing the industrial value of high-trust data governance, Todd Mersch, General Manager, U.S. Life Sciences and MedTech, Microsoft, added: “Qai reflects what we see as an important application of AI in life sciences: enhancing already robust data analytics and governance to deliver meaningful patient impact. By strengthening oversight and consistency across manufacturing processes, Qai supports Catalent’s commitment to delivering for customers and the patients they serve, helping to transform lives.” Ultimately, this roll-out marks a significant step in Catalent’s broader, ethically grounded digital transformation strategy, empowering global operations teams to minimize risk and advance a “Patient First” culture.

Read More: Catalent Launches Qai to Reimagine Quality Assurance for its Manufacturing Services

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By integrating these new gene expression tools, laboratory automation partnerships, and specialized analysis software, QIAGEN aims to accelerate the adoption of digital PCR across large-scale molecular biology research and regulatory compliance environments.

“Gene expression represents one of the largest application areas in molecular biology and a significant opportunity for digital PCR,” said Thierry Bernard, CEO of QIAGEN. “By expanding the QIAcuity ecosystem with new assays, enhanced multiplexing capabilities and workflow solutions, we are helping customers apply digital PCR to a broader range of research and biopharma applications.”

Also Read: Tempus Introduces ‘Preview’: Bridging the Critical Time Gap Between Diagnostic Order and Definitive Results

Expanding the QIAcuity Digital PCR Framework

The updated QIAcuity portfolio delivers key operational updates across four core segments of the biotechnology and life sciences market:

1. Advanced Gene Expression Solutions for dPCR

QIAGEN intends to expand its testing commercial lines later in 2026 by rolling out new QIAcuity Gene Expression Assays. The upcoming products are optimized to support precise expression profiling across human, mouse, and rat research models. Additionally, the developer plans to launch the QIAcuity OneStep High Multiplex Probe PCR Kit, a solution designed to maximize sample utility by enabling complex, high-order multiplex targets to run simultaneously inside a single nanoplate partition.

2. Enhanced Quality Control for Cell and Gene Therapy (CGT)

To support the stringent manufacturing regulations governing biopharmaceutical production, QIAGEN is building out its residual DNA testing suite. The expansion includes new quantification kits optimized for an array of producer cell systems. These tools enable biopharma operations to achieve reproducible, high-precision analytics during process development, batch release testing, and host-cell impurity monitoring.

3. Standardized Laboratory Automation Integration

Recognizing the throughput demands of industrial and clinical research laboratories, QIAGEN has partnered with Hamilton to automate the initial handling of QIAcuity nanoplate configurations. By transitioning fluid transfer, loading, and setup workflows to automated liquid handling systems, the collaboration significantly increases daily laboratory productivity, optimizes reagent usage, and minimizes the human errors inherent to manual pipetting.

4. Automated Analysis and Compliance Reporting via QIAcuity Software 3.5

Concurrently, the upcoming release of QIAcuity Software 3.5 introduces an updated data analysis layer. The software features pre-configured analysis templates and automated reporting frameworks built to standardize data interpretation across distributed laboratory setups. The platform upgrade streamlines verification speeds, making it easier for biopharma networks to preserve data integrity and maintain strict regulatory compliance across high-volume experimental runs.

Driving Data Efficiency via Nanoplate Partitioning

The QIAcuity platform distinguishes itself from legacy infrastructure by utilizing microfluidic nanoplates to disperse a biological sample into thousands of individual, isolated partitions. The system then executes and reads the target reactions simultaneously, allowing it to quantify even faint, low-abundance genetic signals with absolute accuracy. By combining sample partitioning, thermocycling, and optical imaging into a single unified instrument, the platform condenses traditional digital PCR workflows from six hours down to just two.

The comprehensive portfolio updates and software enhancements are scheduled to roll out across global biopharma and life sciences markets throughout the remainder of 2026. Laboratories can explore platform specifications, review automation integration protocols, and access relevant application notes via the official QIAGEN digital portal.

Source: BusinessWire

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