Pathos inks back-to-back cancer contracts with AstraZeneca, China's Alphamab. August 4, 2026 | Pathos AI could pay more than $2 billion to a Jiangsu Alphamab Biopharmaceuticals subsidiary for a late-stage bispecific antibody-drug conjugate for breast cancer, while the financial details of the AstraZeneca agreement were not disclosed. Pathos AI has linked up with AstraZeneca and Suzhou-based Jiangsu Alphamab Biopharmaceuticals in a pair of partnerships that will beef up the New York drugmaker's clinical cancer pipeline. With Jiangsu Alphamab, a subsidiary of Alphamab Oncology, Pathos is paying $125 million upfront and has earmarked up to $2.093 billion in milestones for rights to a bispecific antibody-drug conjugate in late-stage development for breast cancer, according to a Monday release. The Chinese company also stands to receive tiered royalties in the high-single-digit to low-double-digit percentage range, based on annual net sales - granted the alliance asset reaches the market. Financial details of the AstraZeneca agreement, on the other hand, weren't revealed but the deal centers around the pharma's preclinical PROTAC degrader that is also targeting breast cancer, per a separate announcement on Monday. Across both Monday partnerships, Pathos will leverage its proprietary AI platform Foundry, which according to the company's website is built on the "largest oncology foundation model in the industry." By using "thousands" of AI agents, Foundry is able to analyze complex clinical, biological and real-world data to optimize trial design and dosing, as well as match patients to the safest treatments that are most likely to elicit therapeutic benefit, Pathos said on Monday. Under the AstraZeneca deal, Pathos will use Foundry to advance AZD4241, which targets and degrades the estrogen receptor. The asset is being proposed for ER+/HER2- breast cancer. Pathos will take charge of AZD4241's early-stage development informed directly by insights from Foundry. It is unclear at what point of the drug development process AstraZeneca will take over. From Jiangsu Alphamab, Pathos has in-licensed JSKN016 - itself a molecule "identified and prioritized through Foundry," the AI specialist said on Monday. JSKN016 is currently in pivotal development for later-line triple-negative breast cancer in China, where Jiangsu Alphamab will retain full rights over the asset, as well as in Hong Kong, Macau and Taiwan. With the deal, Pathos will have full control over JSKN016 in all other territories and be responsible for the asset's development and commercialization globally - again, as informed by insights from Foundry. "Every major asset-level decision at Pathos flows through Foundry," the company said on Monday. Jiangsu Alphamab is also studying JSKN016 in various types of breast cancer, non-small cell lung cancer and other advanced solid tumors. With the two new agreements, Pathos now has four assets in the clinic, alongside the early-stage pocenbrodib for prostate cancer, breast cancer and multiple myeloma, as well as Phase 2 P-500 for solid tumors.

Pathos AI enters collaboration and co-exclusive licensing agreement to advance novel erα PROTAC for ER+ breast cancer into the clinic. * 4 hrs ago Pathos AI, a clinical-stage AI and technology company advancing its own pipeline of cancer therapies, today announced a collaboration and co-exclusive licensing agreement with AstraZeneca. Under this agreement, the companies will advance AZD4241, a preclinical investigational therapy for patients with estrogen receptor-positive, human epidermal growth factor receptor 2-negative (ER+/HER2-) breast cancer, with Pathos assuming responsibility for the early clinical development of the asset. AZD4241 is an investigational oral therapy known as a PROTAC (proteolysis-targeting chimera), specifically designed to degrade the estrogen receptor, targeting both normal and mutated forms of the receptor. Mutated forms of the estrogen receptor are present in up to 30-40% of patients whose disease progresses on standard hormone therapies. Powered By Keir Starmer arrives in Ankara for Nato summit Translating the full potential of this mechanism into patient benefit requires a fundamentally different approach. By harnessing large-scale multimodal patient data and a proprietary AI platform, Pathos redesigns drug development end-to-end. The company's capabilities span early asset identification, data driven portfolio optimization, and AI-native trial monitoring, with the specific early clinical execution for AZD4241 driven directly by its Foundry platform. Accelerated by AI: The Foundry Advantage In complex indications like ER+ breast cancer, identifying the exact patient subpopulation, optimal dose, and ideal treatment setting is a massive data challenge. Foundry was built to solve this problem. Foundry utilizes thousands of AI agents working in parallel, powered by the Pathos Oncology Foundation Model. These agents analyze complex biological, clinical, and real-world data to intelligently design clinical trials, optimize dosing, and match the right patients to the right therapies faster and more safely than traditional methods allow. "The bottleneck in oncology is not finding molecules. It is proving they work in the right patients. AZD4241 has a compelling mechanism. Foundry's job is to design the trial that proves it - matching this drug to the patients whose biology demands it. That is how we compress time. Patients are waiting." - Iker Huerga, CEO of Pathos AI A Growing Mission to Transform Oncology This collaboration and co-exclusive licence agreement to bring AZD4241 into the clinic expands Pathos' clinical pipeline to three major assets across four distinct tumor types. AZD4241, targeting ER+/HER2- breast cancer, joins pocenbrodib, currently in development for metastatic castration-resistant prostate cancer (mCRPC) and relapsed/refractory multiple myeloma (RRMM), and DO-2 for MET-altered non-small-cell lung cancer (NSCLC). Every program in the Pathos pipeline has been identified, evaluated, or accelerated by the Foundry platform, reinforcing the company's commitment to AI-native drug development. About Pathos AI Pathos AI is an AI and technology company advancing its own pipeline of cancer therapies. Its Foundry platform redesigns drug development from first principles: identifying undervalued clinical assets, making portfolio decisions with continuously updated probability-of-success calculations, and executing clinical trials with AI-native monitoring. Pathos's pipeline includes clinical-stage programs in mCRPC, multiple myeloma, and MET-altered NSCLC. Forward-Looking Statements This press release contains forward-looking statements. These statements involve risks and uncertainties that may cause actual results to differ materially. Pathos AI undertakes no obligation to update any forward-looking statement. Media gallery Sections.

GSK, Relation to build cellular datasets for AI drug targets. GSK agreed a research collaboration with Relation Therapeutics worth up to $110 million for large-scale human cell datasets and AI model training to identify drug targets. GSK has signed a research collaboration with Relation Therapeutics worth up to $110 million. Relation will generate large-scale datasets that measure how human cells respond to genetic changes and drug interventions, and will use those data to train AI models for drug-target discovery. Under the agreement, Relation will produce human cellular datasets and apply them to models within its MORGAN platform. The company's Lab-in-the-Loop approach links computational analysis with laboratory experiments. The planned work includes tissue profiling, single-cell and spatial transcriptomics, sequencing, functional assays and perturbation experiments that record how specific genetic changes alter cell states relevant to disease. The collaboration builds on earlier GSK-Relation projects focused on fibrotic diseases and osteoarthritis that created two functional disease datasets. Relation has also developed Osteomics, a proprietary functional single-cell bone atlas that combines patient-derived samples with single-cell and spatial omics, imaging, genomics, proteomics and clinical phenotype data. Hospitals and research partners in the UK and Australia are participating in that study. Public repositories provide large volumes of single-cell data, but differences in sampling, sequencing and processing pipelines can introduce technical noise and batch effects. Dataset overlap across resources can create risks of data leakage when training and test sets share similar cells. A recent study that trained 400 models on a corpus of 22.2 million cells and evaluated them across thousands of experiments reported that performance often plateaued after training on a fraction of the data and did not show the clear data-scaling behavior seen in some other AI fields. A separate assessment of two single-cell foundation models reported inconsistent performance gains over simpler approaches and highlighted ongoing challenges from batch effects and dataset composition. Pharmaceutical companies are seeking disease-specific, curated datasets through partnerships, licensing and joint development. Examples include a separate GSK agreement with Ochre Bio for human liver single-cell and perfused-organ data and a multi-party deal involving AstraZeneca, Pathos AI and Tempus to develop oncology models using de-identified clinical, genomic and imaging data. Limited access to suitable training data has been identified by industry participants as a constraint for AI applications in drug discovery. The GSK-Relation collaboration combines biological data generation with model development so the experimental datasets produced by Relation can be used directly to train and test AI models for target identification, prioritisation and validation. The companies plan to produce disease-focused single-cell datasets alongside machine-learning workflows to align laboratory measurements with computational predictions for drug-target discovery. Content on BlockPort is provided for informational purposes only and does not constitute financial guidance. Blockport Company strive to ensure the accuracy and relevance of the information Blockport Company share, but Blockport Company do not guarantee that all content is complete, error-free, or up to date. BlockPort disclaims any liability for losses, mistakes, or actions taken based on the material found on this site. Always conduct your own research before making financial decisions and consider consulting with a licensed advisor. For further details, please review its Terms of Use, Privacy Policy, and Disclaimer.