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Implementation of ICH M12 Guidance in 2024: What’s New for Drug Interactions?

The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) adopted a harmonized drug-drug interaction (DDI) guidance (ICH M12) in May 2024. The next step is for this guidance to be implemented by ICH members; the FDA and EMA have already implemented the guidance. Read our blog for key highlights of the 2024 ICH M12 Guidance.

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FMQs vs SMQs 

Sponsors often rely on Standardized MedDRA Queries (SMQs) to group adverse events for detection of safety signals across clinical trials. Additionally, the FDA has introduced their own version of adverse event groupings – FDA MedDRA Queries (FMQs). So, what’s the difference? 

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Statistical Challenges with Site Enrollment in Clinical Trials 

Did you know that insufficient enrollment is the leading cause for clinical trials being halted? Study sponsors rightly embrace those sites which are high performing as they give a study the best opportunity to meet its enrollment targets. However, is it possible for there to be overreliance on these high enrolling sites? Unfortunately, the answer is yes. 

patricia stephenson
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Overcoming statistical challenges in rare disease drug development

Regulatory agencies like the FDA require substantial evidence of the drug’s effectiveness for its intended use and sufficient information to conclude that the drug is safe.  However, flexibility is given in how the standard can be met given the challenges associated with the limited number of subjects available in rare disease.

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Where did the odds ratios go?

Reviewing recent FDA approvals, you may be struck by the total absence of odds ratios. Browsing the labels from the 2023 novel approvals, you can find proportions, differences in proportions, Chi-Squared analyses, CMH and variants, but logistic regression and odds ratios have practically disappeared from labeling. What gives?

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Study-Size Adjusted Percentages in Integrated Adverse Event Displays

To those of us who regularly create or review adverse event (AE) incidence tables for randomized controlled trials, it may come as a surprise that your typical AE incidence table can be misleading if data was combined from more than one trial. This is due to “Simpson’s paradox,” which, simply put, is the phenomenon that the mere grouping of data can introduce confounding or bias otherwise not present.