About ReBEC

The Brazilian Clinical Trials Registry (ReBEC) is an open-access virtual platform for registering experimental and non-experimental studies involving human beings conducted in Brazilian territory by Brazilian and foreign researchers. ReBEC is a joint project of the Ministry of Health (DECIT/MS), the Pan American Health Organization (PAHO), and the Oswaldo Cruz Foundation (FIOCRUZ). The Executive Committee of ReBEC is composed of the aforementioned institutions and the National Health Surveillance Agency (ANVISA). ReBEC is an important source of information for patients, healthcare professionals, researchers, companies, and research centers. Its support services feature generative artificial intelligence ReBEC@, the first artificial intelligence for clinical trial registration in the world. Learn more

Everything you need to know about ReBEC

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Instructions for the Registrant

We recommend reading the Registrant's Manual.

1 - Register or log in and fill out the forms normally in Portuguese and English.
For English, if you experience any difficulty, use Google Translate
(or another free translation tool).
2 - Attach the mandatory ethics committee documentation and submit the form.
3 - Save the support number in your WhatsApp contacts. Send a message with your name and username.
It is not necessary or recommended to send your password. 
4 - A reviewer will respond to you. Choose your preferred communication mode:
chat, audio recordings, or scheduled voice call.
5 - Your form will be reviewed in both languages. Follow further instructions to complete approval.

Clinical Research Volunteer

Would you like to become a volunteer in a clinical trial registered in ReBEC?

Access our FAQ to find out how.

10/27/2026  |  Anthropic

https://www.anthropic.com/research/early-progress-drug-development 

Anthropic’s life sciences team is working to accelerate curing disease. Over the past several months, our scientists have engaged in deep research partnerships with six organizations developing new medicines: BigHat Biosciences, Bristol Myers Squibb, Divergence Labs, Enveda, Generate Biomedicines, and The Column Group. We've also begun drug discovery efforts of our own. In doing so, we've learned a lot about how Claude can support drug development, and how we’ll need to improve Claude further to realize AI's potential in medicine. We plan to scale this work up substantially from here.

There are many opportunities to speed up drug development, even beyond the most commonly discussed applications of AI, like proposing initial designs for new molecules. Developing a drug involves many varied tasks: doing early research to identify a target for a drug and the right type of drug to engage it, designing a molecule with the right properties, optimizing it to be manufactured reliably at scale, drafting the regulatory filings needed to run clinical trials, designing and administering those trials, and much more.

Our strategy to realize AI’s medical benefits is based on having AI increasingly involved in all of these tasks. There is no single problem to solve; the bottlenecks are distributed all throughout the drug development process. The best way to identify and work through these bottlenecks is to get deeply involved in real drug programs, across many different disease areas and stages of development.

So, over the past several months, we’ve partnered with six organizations to do so. We’ve also started drug discovery efforts of our own, including programs for rare childhood forms of muscular dystrophy, where Claude…

10/27/2026  |  Rodrigo Dienstmann  |  ESMO

https://www.esmo.org/insights-by-esmo/all-news/bringing-ai-to-the-data-the-future-of-privacy-compliant-cancer-research 

The rapid development of AI requires the creation of new mechanisms that enable learning from data in situ, without moving it or accessing it directly to comply with EU’s GDPR

Research beyond the clinical trial setting depends on large, diverse, real-world data sets, but sharing that data across institutions remains difficult under General Data Protection Regulation (GDPR). Although EU law does not inherently act as a gatekeeper, it effectively serves this function. Even when patients have provided full consent to data sharing, the use of their data may still be restricted if the intended purpose differs from that for which consent was originally obtained. Navigating the beneficial use of artificial intelligence (AI) for real-world evidence generation while retaining GDPR compliance is a delicate balance.

Federated learning is the mechanism institutions are now adopting under the European Health Data Space (EHDS) regulation that officially entered into force in 2025 and aims to establish a common framework for the use and exchange of electronic health data across the EU. Rather than moving patient data to a central location, the algorithm travels to each institution’s data set and only the algorithm’s learning and not the underlying data is shared onward. This does, however, rely on hospitals transforming their data to a standard format using the Observational Medical Outcomes Partnership (OMOP) framework so that a common model can interpret data from every site in the same way. In practice, AI algorithms can be tested and validated at individual institutions using their local data, with…

10/27/2026  |  Janice Worley/Lori Eads  |  Applied Clinical Trials

https://www.appliedclinicaltrialsonline.com/view/closing-medical-writing-capacity-gap-why-training-beats-technology 

Medical writing capacity pressure is fundamentally a talent development problem, not a technology problem, since hybrid resourcing and AI cannot substitute for the experienced judgment needed to review AI output and mentor the next generation of writers.

When Applied Clinical Trials looked at resourcing and outsourcing trends in drug development last year, the pattern was clear: biopharma leaders are moving away from a single full-service CRO relationship and toward hybrid models that blend in-house expertise, trusted consultants, and targeted outsourcing. As leaders of functional service provider (FSP) medical writing teams, we've seen a similar shift in medical writing, though it largely reflects capacity demands rather than a change in resourcing strategy. More importantly, it has revealed a challenge that hybrid contracting alone cannot solve: a shrinking talent pool with increasingly uneven levels of experience.

Medical writing teams are expected to deliver a higher volume at greater speed while meeting growing demands for specialized expertise, driven in part by the explosion of activity in obesity and metabolic disease that is compressing timelines across the industry. Artificial intelligence (AI) is often touted as the solution. Although it has a role to play and can get sponsors part of the way there, it cannot do the one thing this momentrequires: develop the next generation of writers who know how to use it well. Based on our experience, we believe this challenge is fundamentally about talent development and training, not technology.

Why hybrid models are winning Read more... Read less... Open