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#pharmacometrics — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #pharmacometrics, aggregated by home.social.

  1. The estimand framework, formalized in ICH E9(R1) regulatory guidance, provides a structured approach to define scientific objectives with precision. We apply the estimand framework to dose–exposure–response analyses. ... strategy to improve exposure–response analyses for dose selection, particularly when the relevant evidence includes data from multiple studies.

    #estimand #exposure-response #dose-response #causal #pharmacometrics #pmx

    doi.org/10.1002/psp4.70202

  2. The estimand framework, formalized in ICH E9(R1) regulatory guidance, provides a structured approach to define scientific objectives with precision. We apply the estimand framework to dose–exposure–response analyses. ... strategy to improve exposure–response analyses for dose selection, particularly when the relevant evidence includes data from multiple studies.

    -response -response

    doi.org/10.1002/psp4.70202

  3. The estimand framework, formalized in ICH E9(R1) regulatory guidance, provides a structured approach to define scientific objectives with precision. We apply the estimand framework to dose–exposure–response analyses. ... strategy to improve exposure–response analyses for dose selection, particularly when the relevant evidence includes data from multiple studies.

    #estimand #exposure-response #dose-response #causal #pharmacometrics #pmx

    doi.org/10.1002/psp4.70202

  4. The estimand framework, formalized in ICH E9(R1) regulatory guidance, provides a structured approach to define scientific objectives with precision. We apply the estimand framework to dose–exposure–response analyses. ... strategy to improve exposure–response analyses for dose selection, particularly when the relevant evidence includes data from multiple studies.

    #estimand #exposure-response #dose-response #causal #pharmacometrics #pmx

    doi.org/10.1002/psp4.70202

  5. The estimand framework, formalized in ICH E9(R1) regulatory guidance, provides a structured approach to define scientific objectives with precision. We apply the estimand framework to dose–exposure–response analyses. ... strategy to improve exposure–response analyses for dose selection, particularly when the relevant evidence includes data from multiple studies.

    #estimand #exposure-response #dose-response #causal #pharmacometrics #pmx

    doi.org/10.1002/psp4.70202

  6. Our colleagues at LAP&P are hosting a target-mediated drug disposition course using at in A Coruña, Spain!

    This is your chance to see nlmixr2 put through its paces in real-world by a world-class consulting team.

    lapp.nl/lapp-page-course/

  7. Hi everyone! This is the official home of on Mastodon. We are a constellation of packages aimed at supporting easy and robust nonlinear mixed-effects models in R. We are free and and will be forever.

    Stay tuned for announcements of blog postings, chat, trivia, and whatever you all want to talk about!

    nlmixr2.org

  8. I'm on fosstodon.org because I do a lot of development, most notably as a member of the development team. nlmixr2 is a set of packages - let's call it the - for R that provides an alternative for nonlinear mixed-effects () model development, which are the core of most workflows (amongst others).

  9. It's not just about the drugs themselves. and models are also an area in which continues to have an impact - maintains a list the ones they've developed internally, including examples for and , although there are many, many more.

    fda.gov/about-fda/center-drug-

  10. So can help us understand how drugs behave in different people. The model includes body weight - the bigger you are, the bigger your organs are (usually) and the more machinery you have for metabolizing substances like ethanol, so the slower you get drunk, and if you've eaten something, the alcohol will take longer to get into your system (although these are just two aspects of a very complex system).

  11. Wikipedia defines () as a field of study of the methodology and application of for disease and pharmacological measurement. It applies mathematical models of , , , and to describe and quantify interactions between (drugs) and patients (human and non-human), including both beneficial and adverse effects.

  12. time! I’m Justin Wilkins, a consultant living in Germany. I work for a small CRO called Occams and I post occasionally about , modeling and simulation, and the current wretched state of British politics. I’m a co-developer of the nonlinear mixed-effects model fitting package in R, as well as the package.