Author: Agneta Lehane

The tutorial titled “Full Random Effects Models (FREM): A Practical Usage Guide” delves into the innovative FREM methodology, a robust complement to traditional covariate modeling techniques. We believe this tutorial is a valuable resource for researchers and practitioners in the field of pharmacometrics, offering insights into the application of FREM to improve learnings from the analyses of clinical trial data, to better inform decision making in drug development and clinical practice.

We would like to highlight important work for the treatment of post-kala-azar dermal leishmaniasis. 

“The study provides the first accurate measurements of miltefosine penetration into the skin, demonstrating substantial exposure and prolonged retention of miltefosine within the skin. These findings support the use of miltefosine in cutaneous manifestations of leishmaniasis.”

In this article, Jakob Ribbing and co-authors under the ISoP Standards & Best Practice Committee, present perspectives on best practices for covariate analyses, aiming to guide pharmacometrics decision making.

With an abundance of sources to learn from, the need for a summary was identified. Through covering several aspects of covariate analysis that are common to (nearly) all approaches outlined, along with recommendations for what approaches and tools to use in different modeling applications, an overview with generic points of consideration is presented. This can support in the planning, execution, reporting and interpreting covariate analysis whether you operate in industry, academia or regulatory.

A publication about formulation and dose selection for further clinical development of a sustained release formulation in the treatment of cryptococcal meningoencephalitis. 

This new research suggests buprenorphine in CAM2038 formulation inhibits drug liking at low plasma concentrations, and suppresses the opioid cravings and withdrawal at even lower concentrations.

In this new publication, about the properties of full random effects models (FREM) and full fixed effects models (FFEM) abilities to handle large amounts of missing covariate data, the results suggest that FREM is an appropriate approach to covariate modeling for datasets with more than trivial degrees of missing covariate data, such as in global health studies or multi-study pharmacometric analyses in late phase drug development settings geared towards regulatory filing.