Clinical Trial Management

Astellas Pharma becomes latest pharma giant to join Evinova's AI platform, following Bristol Myers Squibb and parent AstraZeneca in backing cross-industry clinical trial collaboration >> 🔘 Three major pharma companies are now sharing operational clinical trial data with Evinova's AI platform, marking a rare moment of cross-industry collaboration in drug development 🔘 The platform uses multi-agent AI to tackle one of pharma's most persistent problems: fragmented systems and manual processes that drag out timelines and inflate costs. 🔘 It converts protocols into machine-readable formats and generates optimized study designs in minutes, benchmarked across cost, timelines, patient experience, and even carbon footprint, replacing weeks of manual work. 🔘 A single clinical trial requires over 200 interconnected document types. AI authoring agents now handle intelligent recommendations across regulatory, scientific, and operational inputs, cutting costly protocol amendments 🔘 Early results show 5 to 7 percent savings minimum per study, translating to hundreds of millions of dollars across a top-10 pharma portfolio 🔘 The architecture is modular and cloud-native, letting organizations plug in their own AI models with built-in privacy and regulatory compliance across global markets 💬 The broader signal here: clinical development is finally moving from a document-heavy, siloed process to an AI-first workflow, and the opt-in data sharing model could set a new industry standard for how sponsors learn from each other #digitalhealth #pharma #AI

We’ve optimized everything around the trial — except the documents that govern it. That blind spot is exactly where new AI-first players are gaining ground. In a world where trial documentation, regulatory compliance, and study-start-up workflows remain painfully manual, companies like Cori Clinical are rewriting the rules. They bring AI and automation directly into the heart of trial-planning and documentation — accelerating timelines while preserving compliance and control. ✅ AI-assisted drafting and co-authoring of protocols, investigator brochures, informed-consent forms, site-packs, SOPs — going from weeks to minutes. ✅ Regulatory-ready review & compliance checks: AI flags potential compliance or GxP issues, quality and patient-focus requirements — reducing risk and manual burden. ✅ Automated amendment & version control + audit-ready documentation — simplifying management across multiple trial sites and stakeholders. ✅ Workflow integration — works directly with tools like Microsoft Word and Veeva, preserving familiar workflows for teams while adding automation. For biotech, pharma and med-device sponsors — especially smaller or emerging companies — this kind of “clinical-grade AI workbench” can dramatically reduce time, cost, and administrative overhead. As the industry increasingly moves toward modular, tech-enabled services, the shift will reward teams that build flexible, automation-ready workflows capable of scaling across diverse trial portfolios. #ClinicalDevelopment #LifeSciencesAI #DigitalTrials #ClinicalOperations #AIDrivenInnovation #DrugDevelopment #PharmaTech #ClinicalResearch #R&DTransformation #HealthTech

A landmark report by two French government ministries calls for a national law requiring the results of *all* clinical trials to be rapidly made public on trial registries. According to the French Open Science Monitor, only 44% of French clinical trials completed during 2022 had made their results public one year later, including only 15% of academic trials. The World Health Organisation has demanded all clinical trial results to be made public on registries within one year of trial end. For drug trials, this is already a legal requirement in France and all other European Union countries. The report points out that "there is no ethical, scientific or public health rationale that justifies the current situation where nonpharmacological clinical trials are exempt from the [legal] obligation to post their results.” The new report sets out a long list of recommendations. Highlights: - Starting this year, via regulation, make it mandatory to post the results of all clinical trials onto clinical trial registries within one year. Then additionally adopt legislation. - Change the assessment criteria of sponsors and take into account results posting in funding indicators. - Send each sponsor a report that includes a list of their trials with unposted results in order to prompt them to identify and correct posting failures. - Send reminders to principal investigators who have not transmitted to the sponsor the information needed to post the results of completed trials, before the deadline (one year after the trial ends) is reached. - All funders should publish indicators on the posting and scientific publishing of results for the trials they fund. - Make payment of the final funding instalment conditional on posting results within twelve months after the trial ends. - Develop an open-source tool to generate a template of results to be posted directly onto clinical trial registries. (AMEN.) - Explicitly include trial publication in the scientific integrity policies of clinical trial sponsoring organisations. “The most important thing in our report is that we involve everyone” in fixing the problem, working group chair Professor Philippe Ravaud from Cochrane France said. The report is available online in both English and French language. It includes multiple annexes summarising relevant global norms and legal frameworks. More details, including an update on the UK national strategy and the status quo in the United States, here: https://lnkd.in/du6C5frr #clinicaltrials #essaiscliniques

AI could make clinical trials faster, cheaper, and more inclusive, but success depends on explainability, interoperability, and trust. 1️⃣ 80% of trials face recruitment delays, and 50% of datasets contain quality issues; AI aims to fix both. 2️⃣ Machine learning improves protocol design accuracy (80% vs. 65%) and accelerates site selection and feasibility assessments. 3️⃣ AI tools boost enrollment by up to 65% and cut screening time by 78%, though real-world deployment can be costly and complex. 4️⃣ NLP and digital systems help identify underrepresented groups, supporting more diverse and inclusive recruitment. 5️⃣ AI-driven digital biomarkers enable 90% sensitivity in real-time safety monitoring, improving adverse event detection. 6️⃣ Risk-based monitoring powered by AI detects data integrity issues within 48 hours, much faster than manual reviews. 7️⃣ Predictive models achieve 85-90% accuracy in forecasting outcomes and enable adaptive, personalized trial designs. 8️⃣ High-dimensional, noisy, and heterogeneous data challenge AI systems; success requires strong data harmonization and validation. 9️⃣ Regulatory gaps, stakeholder distrust, and lack of explainability remain major barriers to clinical adoption. 🔟 Real-world trials show AI's promise, but also its high cost, customization demands, and integration hurdles. ✍🏻 David Olawade (MPH, FRSPH, FHEA), Sandra Chinaza Fidelis (RN, BNSc, MSc, MPH), Sheila Marinze, Eghosasere Egbon, Ayodele Osunmakinde, Augustus Osborne. Artificial intelligence in clinical trials: A comprehensive review of opportunities, challenges, and future directions. International Journal of Medical Informatics. 2026. DOI: 10.1016/j.ijmedinf.2025.106141

AI in Clinical Data Management (CDM) AI is transforming Clinical Data Management by automating manual and error-prone tasks — from CRF design to data cleaning, query handling, and database lock. Here’s how AI fits across the CDM lifecycle: 1. Protocol → Study Build (Setup Phase) AI Use Cases: Protocol Parsing: Automatically extract study design elements (arms, visits, endpoints, assessments). Auto-generate eCRFs: Create CDASH-compliant CRFs and edit checks from protocols and standards. CDISC Mapping: Suggest CDASH/SDTM mappings for CRF fields. Data Dictionary Creation: Build metadata tables with variable names, labels, datatypes, and units. Approach/Tools: LLMs fine-tuned on CDISC/CDASH, NLP (spaCy, transformers) for structured extraction, integration with EDC APIs (Rave, Veeva, Oracle). 2. During Data Collection AI Use Cases: Smart Data Entry: Auto-fill/validate fields using context (e.g., logical consistency, range checks). Dynamic Edit Checks: Predict likely invalid data (e.g., male + pregnancy test). Duplicate/Outlier Detection: Flag duplicate IDs or implausible lab values in real time. Techniques: ML models for anomaly detection and predictive validation integrated into data pipelines (Python, R, SAS). 3. Data Cleaning & Query Management AI Use Cases: Automated Query Generation: Identify discrepancies and raise queries. Query Prioritization: Rank by data quality impact or timeline risk. Resolution Suggestions: NLP models draft likely responses. Outlier/Trend Analysis: Detect abnormal site or patient patterns. Tools: ML models trained on query logs; Power BI/Tableau dashboards highlighting predicted risks. 4. Data Review & Reconciliation AI Use Cases: Cross-System Reconciliation: Compare EDC, IVRS, and lab data. SDTM Validation: Ensure CDASH → SDTM mappings meet CDISC rules. Medical Coding: NLP suggests MedDRA/WHODrug terms. Signal Detection: Identify unusual subject or visit patterns. Techniques: Python/Pandas for rule-based checks; transformers (BERT, BioBERT) for coding normalization. 5. Database Lock & Post-Lock Activities AI Use Cases: Final QC Automation: Validate edit checks and metadata completeness. Timeline Prediction: Forecast lock delays using site/query metrics. Regulatory Submission: Auto-generate define.xml, reviewer guides, annotated CRFs. 6. Operational Metrics & Oversight AI Use Cases: Automated Dashboards: Track data entry lag, query closure, CRF completion. Risk-Based Monitoring: Predict high-risk sites/patients for focused review. Resource Forecasting: Estimate workload based on query trends. Tools: Power BI, Streamlit, Python ML models via Flask/FastAPI. 7. Emerging & Advanced Applications Generative AI Assistants: Natural language queries like “Show subjects missing visit dates last 7 days.” Automated SDTM/ADaM Drafting: LLMs create SAS-ready datasets and define.xml templates. AI Audit Companion: Summarize audit logs and deviations. Knowledge Bots: Train on SOPs, standards, and prior study documents.

The EMA put a sentence in writing last month that some sponsors will read twice. [UPDATE]                                                                                                           The date in this post is wrong. I attributed the EMA Notice to Sponsors on Validation and Qualification of Computerised Systems to 8 April 2026. The original Notice was published on 7 April 2020 and was superseded by the EMA Guideline on Computerised Systems and Electronic Data in Clinical Trials, effective 9 September 2023. The source I used carried the wrong date. I did not verify it. My mistake.    This is still critical to the use of software in clinical research. These principles do not need a fresh date to be operationally critical. Any sponsor or CRO running clinical trials on vendor-supplied systems is bound by them today under the 2023 EMA Guideline, ICH E6(R3), and Annex 11. Treat the date references in the body below as historic, not current. The substance is still the right starting point. Original post continues..... "A failure to document validation may result in regulatory authorities rejecting clinical trial data for MAAs." On 8 April 2026, the EMA's GCP Inspectors Working Group issued a Notice to Sponsors on the Validation and Qualification of Computerised Systems Used in Clinical Trials. The trigger was inspection findings, not a theoretical exercise. Inspectors had been seeing inadequate validation practices and insufficient documentation of qualification activities across both sponsor-owned and vendor-supplied systems. The notice clarifies the responsibilities and the consequences. The headline for sponsors is clarity on accountability. Qualification of a computerised system can be performed by the vendor, the sponsor, or the two jointly. Validation responsibility remains with the sponsor regardless of who performed the underlying activity. Documented evidence must exist throughout the system lifecycle, from design through to decommissioning, and the sponsor must have access to it whether their team or the vendor's team built the system. The vendor agreement is now load-bearing. The notice describes "clear and formal contractual agreements between sponsors and vendors" defining validation and qualification responsibilities as essential. A handshake or a service agreement that does not name validation explicitly will not survive inspection contact. If you operate trials on computerised systems supplied or maintained by a vendor, three things to check this week: 🔹 Whether your vendor agreements explicitly name who is responsible for validation and qualification activities, and where the documentation lives 🔹 Whether your audit trail design captures both initial entries and all subsequent changes, with named users and access controls 🔹 Whether your sponsor team can produce validation evidence on request, without needing to email the vendor and wait Worth reading in full: https://lnkd.in/eWS4vixz

From 28 April 2026, UK clinical research is entering a new era, and it is not just about new rules. It is about a shift in mindset. The UK is implementing updated regulations for Clinical Trials of Investigational Medicinal Products (CTIMP), alongside major changes to Good Clinical Practice (GCP) training. These updates align with the latest international standard, ICH E6(R3), developed by the International Council for Harmonisation to ensure clinical research is ethical, credible and globally comparable. The changes are designed to enhance patient safety, streamline trial approvals, and strengthen the UK’s position as a global leader in life sciences and innovation. At the heart of this change is a move away from “one size fits all” compliance. GCP training will no longer rely solely on generic e-learning. Instead, there is a strong emphasis on proportionality, role-specific competence, and consolidation sessions that focus on how trials are actually run. This reflects the principle of Quality by Design. Quality should be built into studies from the start, not checked for after things go wrong. Language is also changing, and this matters more than we may like to admit. The term “subject” is being replaced with “participant.” This is not semantics. Words shape culture. This change reinforces that research is done WITH people, not TO them. It reinforces that people are active partners in research and aligns the UK with a more respectful, participant-centred research culture. One of the most significant changes concerns who can serve as an investigator in a clinical trial of an investigational medicinal product. The updated framework recognises that high-quality research is delivered by multidisciplinary teams. Physicians are no longer assumed to be the only professionals capable of acting as investigators. Experienced nurses, pharmacists and other appropriately trained healthcare professionals can take on investigator roles, provided they have the right expertise and oversight. The role of Principal Investigator (PI) remains clear as the site's responsible leader. This change moves towards a more risk-based, proportionate, and flexible approach to trial management. These reforms aim to reduce unnecessary burdens without lowering standards, streamline UK research, and keep the focus on what truly matters: participant safety, meaningful outcomes, and data we can trust. The opportunity is huge, but only if implementation is thoughtful, accessible and honest. This is a moment to modernise not just our regulations, but our research culture. Are we ready? #ClinicalResearch #GoodClinicalPractice #CTIMP #ICH #QualityByDesign #ResearchLeadership #ResearchWorkforce #EquityInResearch #DrBookiesNuggets OncoDaily Health Research Authority Medicines and Healthcare products Regulatory Agency

Artificial intelligence is rapidly reshaping the clinical research landscape, and this new review, “Artificial intelligence in clinical trials: A comprehensive review of opportunities, challenges, and future directions”, published in the International Journal of Medical Informatics, provides one of the most detailed analyses to date. The paper outlines how AI can improve nearly every phase of the trial lifecycle, from protocol design and patient recruitment to data monitoring and predictive outcome modelling, showing tangible performance gains such as shorter timelines, lower costs, and improved data quality. Yet, what stands out most to me is not the promise of automation but the depth of the implementation challenges. Data interoperability gaps, regulatory uncertainty, and stakeholder trust remain critical bottlenecks. Without addressing these, efficiency gains risk being offset by ethical, technical, and governance concerns. The review’s emphasis on risk-stratified implementation, explainability, and bias mitigation provides a timely reminder that AI in clinical research is not merely a technical evolution but a systemic transformation, one that demands transparency, validation, and interdisciplinary collaboration. This study serves as a valuable reference point for researchers, developers, and regulators aiming to translate AI’s potential into real clinical impact while maintaining patient safety and scientific integrity. https://lnkd.in/gUfAZeBy #ClinicalResearch #ArtificialIntelligence #DigitalHealth #ClinicalTrials #AIinHealthcare #HealthInnovation #EthicsInAI #DataQuality #PatientCentricCare #TranslationalMedicine #HealthTechnology

I’m pleased to share a new publication on the “Current Opportunities for the Integration and Use of Artificial Intelligence and Machine Learning in Clinical Trials: Good Clinical Practice Perspectives.” This paper is the result of a cross-disciplinary working group of AI and clinical research experts convened by FDA’s Office of Scientific Investigations (OSI). The initiative reflects our attempt to assess the integration of AI/ML in clinical trials not just through the lens of technical performance but through Good Clinical Practice (GCP), inspectional oversight, and operational implementation at sites. While enthusiasm for AI continues to grow, its deployment in regulated clinical environments raises unique challenges related to data integrity, patient safety, and auditability. This paper offers a structured framework for addressing those concerns. Our key findings include: - AI/ML is already influencing trial design, monitoring, recruitment, and data capture; but formal governance and oversight remain inconsistent. - The current discourse often overlooks how AI affects real-world trial execution, particularly protocol adherence and inspection readiness. - The use of large language models (LLMs) in documentation and decision support is expanding rapidly, with limited guardrails. - Federated learning and privacy-preserving architectures offer promising alternatives to centralized data sharing. - Context-specific validation, not just general accuracy, is essential for safe, effective use in regulated settings. Based on these findings, we developed the following recommendations: - Align all AI/ML use in trials with GCP principles, ensuring traceability, transparency, and risk management. - Separate generative or adaptive systems from trial-critical decision pathways unless robust oversight is in place. - Establish clear SOPs, governance structures, and version control protocols for AI systems used by sponsors or sites. - Prioritize validation strategies tailored to the AI tool’s intended use, potential impact, and operational context. - Foster collaboration across stakeholders to build shared expectations for inspection readiness and responsible AI conduct. As AI becomes more deeply embedded in clinical research, structured, context-aware implementation will be critical. Our paper provides a foundation for moving forward responsibly as FDA continues to augment both its internal AI capabilities and its oversight mechanisms to advance national public health priorities. https://lnkd.in/dpbizggB