drug development

Finalized Guidance for Industry: Investigator Responsibilities for Safety Reporting

Finalized Guidance for Industry: Investigator Responsibilities for Safety Reporting

01/08/2026

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On December 15th, 2025, the US Food and Drug Administration (FDA) released the finalized guidance for industry “Investigator Responsibilities — Safety Reporting for Investigational Drugs and Devices”. Unlike most updates, this finalized guidance is replacing the FDA’s recommendations from two separate final guidance documents: “Safety Reporting Requirements for INDs and BA/BE Studies” from 2012 and “Adverse Event Reporting to IRBs—Improving Human Subject Protection” from 2009, both of which are now withdrawn.

Upcoming Public FDA Workshop on AI in Drug and Biological Product Development

Upcoming Public FDA Workshop on AI in Drug and Biological Product Development

07/23/2024

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The US Food and Drug Administration (FDA) in collaboration with the Clinical Trials Transformation Initiative (CTTI) announced an upcoming hybrid public workshop titled “Artificial Intelligence (AI) in Drug & Biological Product Development” on August 6th, 2024 at 10:00am-5:30pm EST. AI refers the area of computer science that creates intelligent machines intended to mirror human cognitive functions such as learning, recognizing patterns and relationships within datasets, adapting, and problem solving. AI is already being put to use in the field of clinical research.

ICH Q9(R1) Quality Risk Management Guidance Adopted

01/24/2023

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The ICH Q9(R1) international guideline on Quality Risk Management reached step 4 on January 18, 2023. Step 4 is when the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Regulatory Members of the ICH Assembly have adopted the guideline as a harmonized guideline.

How Can Artificial Intelligence Help Clinical Research?

2/05/2020

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The United States Government Accountability Office (GAO) released a report, “Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning in Drug Development.” It outlines six options for policymakers in response to challenges in the use of machine learning in drug development. But why would machine learning be useful in clinical research?