Synterex Blog

The FDA’s recent Evaluation of Labeling Submissions with AI (ELSA) pilot program has generated both curiosity and skepticism in the regulatory community. Early responses from reviewers using ELSA have been mixed, with some finding it helpful for catching inconsistencies or typos, while others found it redundant or disconnected from their workflows. While the effort is laudable, the ELSA pilot highlights deeper limitations in how general-purpose AI tools perform in complex, re

Integrating technology-assisted content reuse into medical writing can reduce stress for writers by preventing unnecessary rework and maintaining consistent document quality and compliance. This ensures timely delivery of important new therapies to the patients who need them. 

The integration of artificial intelligence (AI) and machine learning (ML) in healthcare has transformed how medical technology is developed, evaluated, and deployed. This innovation calls for adherence to rigorous quality standards to ensure safety, efficacy, and compliance. While traditional Good Practice (GxP) guidelines—such as Good Manufacturing Practice (GMP), Good Clinical Practice (GCP), and Good Laboratory Practice.

As artificial intelligence (AI) continues to transform clinical trials, ensuring transparency, accountability, and compliance has become critical. Central to achieving these goals is the implementation of a robust audit trail system.

The U.S. Food and Drug Administration's (FDA) draft guidance, Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, offers a comprehensive framework for the integration of artificial intelligence (AI) in drug development. For regulatory professionals, understanding the various engagement options beyond traditional FDA meetings is crucial for effectively navigating AI applications in clinical trials.

The European Union’s Artificial Intelligence Act (EU AI Act) has become a significant milestone in the global regulation of artificial intelligence. As the world’s first comprehensive AI regulation, it introduces a risk-based framework designed to ensure the safe and ethical deployment of AI technologies. For industries relying heavily on innovation, like biopharma clinical trials, understanding this regulation is crucial.

In clinical trials and healthcare, data comprise the foundation of every decision, from designing study protocols to analyzing patient outcomes. As artificial intelligence (AI) increasingly shapes clinical operations, data governance has become a critical factor in ensuring data quality, compliance, and ethical AI deployment. Data governance involves the management of data availability, usability, integrity, and

In healthcare and biopharma, ensuring patient safety, product quality, and regulatory compliance has always been paramount. A Quality Management System (QMS) serves as the backbone for these priorities, providing a structured framework for risk management, process standardization, and continuous improvement.

Digital transformation in the biopharma industry isn't just about adopting the latest technologies—it's about fundamentally reimagining how clinical trials are designed, conducted, and analyzed. This shift involves integrating artificial intelligence (AI), cloud computing, and data analytics to streamline operations, accelerate drug development, and improve patient experiences.

In the evolving landscape of regulatory writing, it has become clear that creating clear and compliant documents is no longer enough. Building off the principles of lean authoring, regulatory writers must now consider holistically the experience of those who will review the documents.

Artificial Intelligence (AI) is revolutionizing the field of cancer immunotherapy by accelerating the discovery, development, and delivery of innovative treatments. Here is a breakdown of its potential role: Drug Discovery: AI algorithms can analyze vast amounts of genomic, proteomic, and clinical data to identify potential drug targets and predict the efficacy of immunotherapies.

The curb-cut effect refers to the broad and often unexpected benefits of designing for accessibility. Originally intended to assist people with physical disabilities, curb cuts have since proven beneficial for many others—parents with strollers, delivery workers with carts, and even travelers with rolling luggage.

In the rapidly evolving landscape of artificial intelligence, ensuring that generative AI systems adhere to robust standards is crucial. The National Institute of Standards and Technology (NIST), for example, has developed the Generative AI Intelligence Profile, a comprehensive framework designed to guide the development and deployment of generative AI technologies.

In the fast-evolving biopharmaceutical industry, the integration of artificial intelligence (AI) into clinical documentation processes presents an unprecedented opportunity to enhance efficiency and accuracy across the entire value chain. By adopting a technology-agnostic AI system, biopharma companies can significantly streamline operations, reduce costs, and accelerate drug development, ultimately delivering innovative therapies to patients more quickly.

As artificial intelligence (AI) permeates various aspects of life and business, the importance of explainable AI (XAI) grows, particularly in content creation tools—especially when those are being used in a regulated environment. Explainable AI enhances user trust by making the workings of AI systems transparent and comprehensible.

In recent years, lean authoring principles have emerged as a cornerstone for efficient and effective communication. These principles, which emphasize planning, avoiding redundancy, and using precise language, are especially relevant in technical and professional writing.

The World Health Organization (WHO) has long been a guiding force in global health issues, setting standards and offering recommendations to ensure the well-being and safety of populations worldwide.

Artificial intelligence (AI) systems are increasingly integrated into various sectors, including healthcare, finance, and customer service, making their reliability and accuracy critical. However, one lesser-known issue that can arise with AI systems is confabulation (aka hallucinations, fabrications).

In today’s rapid digital transformation of the clinical trials space, artificial intelligence (AI) is becoming an indispensable part of meeting business goals and improving patient care and treatment.

In the rapidly evolving landscape of artificial intelligence (AI), the 11 × 11 tipping point has emerged as a critical concept. This tipping point refers to the moment when AI technologies reach an optimal level of efficiency and effectiveness.