[{"data":1,"prerenderedAt":111},["ShallowReactive",2],{"uc-org-takeda":3},{"organization":4,"includeUnpublished":12,"evidence":13},{"slug":5,"name":6,"country":7,"region":8,"industry":9,"records":10,"useCases":10,"indexable":11},"takeda","Takeda","JP","asia-pacific","pharma-and-life-sciences",3,true,false,[14,44,68],{"title":15,"useCases":16,"organization":18,"vendors":19,"summary":23,"stage":24,"year":25,"channels":26,"languages":28,"metrics":30,"outcomeDisclosed":12,"sources":31,"verification":36,"grade":39,"id":40,"useCaseTitles":41},"Takeda: collaboration to run Iambic's AI platform on oncology and GI programs",[17],"ai-drug-discovery-platform",{"name":6,"anonymized":12,"country":7,"region":8,"industry":9},[20],{"name":21,"role":22},"Iambic","platform","Takeda and Iambic, a clinical stage AI drug discovery company, announced a collaboration spanning several years in February 2026 to use Iambic's AI models, including its NeuralPLexer model for predicting protein and ligand structures, and its automated wet lab capabilities on a set of Takeda's small molecule programs, initially in oncology and in gastrointestinal and inflammation. Takeda's chief scientific officer is quoted saying the platform offers the potential to lower the risk in candidate selection, improve probability of success, and more quickly advance select programs from early project start to an investigational new drug filing. Iambic is eligible for success based payments that could exceed 1.7 billion US dollars plus royalties; no program outcome is disclosed yet.","pilot",2026,[27],"internal-tools",[29],"en",[],[32],{"url":33,"title":34,"publisher":21,"date":35},"https://www.iambic.ai/post/iambic-announces-collaboration-with-takeda","Iambic Announces Collaboration with Takeda to Advance AI-Driven Design of Small Molecules","2026-02-09",{"level":37,"checkedAt":38},"source-verified","2026-09-29","C","takeda-iambic-ai-drug-discovery",[42],{"slug":17,"title":43},"AI native platform for drug target discovery and molecule design",{"title":45,"useCases":46,"organization":48,"vendors":49,"summary":52,"stage":24,"year":53,"channels":54,"languages":55,"metrics":56,"outcomeDisclosed":11,"sources":57,"verification":62,"grade":63,"id":64,"useCaseTitles":65},"Takeda: an internally built generative AI tool for regulatory submission analysis and tabulation",[47],"clinical-and-regulatory-document-drafting",{"name":6,"anonymized":12,"country":7,"region":8,"industry":9},[50],{"name":6,"role":51},"in-house","Takeda's medical writing organization, more than 100 full time writers and contractors across the United States, China and Japan, is building its own generative AI tool that automates the analysis and tabulation of clinical study data behind a regulatory submission, identifying which findings are significant and drafting clearer, more concise regulatory text. The tool was first tested on the efficacy analysis section of a clinical study report template, where Takeda reports one test summarized 70 pages of underlying data into two sentences with what it calls remarkable accuracy. Takeda says its writers will still be writing, editing, reviewing and verifying the data analyses.",2024,[27],[29],[],[58],{"url":59,"title":60,"publisher":6,"date":61},"https://www.takeda.com/our-impact/our-stories/aiming-to-enhance-regulatory-submissions-with-genai/","Aiming to Enhance Regulatory Submissions with GenAI","2024-12-12",{"level":37,"checkedAt":38},"B","takeda-genai-regulatory-submission-analysis",[66],{"slug":47,"title":67},"AI drafting of clinical study reports and regulatory documents",{"title":69,"useCases":70,"organization":72,"vendors":73,"summary":76,"stage":77,"year":78,"channels":79,"languages":80,"metrics":81,"outcomeDisclosed":11,"sources":101,"verification":105,"grade":39,"id":107,"useCaseTitles":108},"Takeda: AI expense audit across 63 countries",[71],"travel-and-expense-audit-agent",{"name":6,"anonymized":12,"country":7,"region":8,"industry":9},[74],{"name":75,"role":22},"AppZen","Takeda deployed AppZen's Expense Audit solution to review 100% of employee expense reports across its global operations, replacing a manual process that, at times, flagged as much as 70 to 100% of expenses for audit based on trigger rules yet still left the company vulnerable to duplicates and employee spend leakage. The AI models include translation capability to handle the language differences across Takeda's European and Asian operations, and auditors now focus their attention on high risk expenses.","scaled",2022,[],[],[82,90,96],{"kpi":83,"value":84,"unit":85,"qualifier":86,"claimant":87,"quote":88,"sourceUrl":89},"automation-rate",63,"percent","exact","vendor","Takeda achieved 63% auto-approval across 63 countries, processing 400K expense audits annually while saving 4,000 auditor hours quarterly with AppZen.","https://www.appzen.com/resources/case-studies/how-takeda-is-transforming-global-expense-auditing-with-ai",{"kpi":91,"value":92,"unit":93,"qualifier":94,"period":95,"claimant":87,"quote":88,"sourceUrl":89},"interactions-handled",400000,"count","approximately","per year",{"kpi":97,"value":98,"unit":99,"qualifier":94,"period":100,"claimant":87,"quote":88,"sourceUrl":89},"hours-saved",4000,"hours","per quarter",[102],{"url":89,"title":103,"publisher":75,"archivedUrl":104},"Takeda Saves 4,000 Auditor Hours Quarterly","https://web.archive.org/web/20220702200424/https://www.appzen.com/resources/case-studies/how-takeda-is-transforming-global-expense-auditing-with-ai?hsLang=en",{"level":37,"checkedAt":106},"2026-09-28","takeda-expense-audit-automation",[109],{"slug":71,"title":110},"AI agent for travel and expense report audit",1790783102023]