Pharmacometrics rolls up modeling and simulation for #Pharmacokinetics, #Pharmacodynamics, and #DiseaseProgression, with a focus on populations and variability. A major focus is to understand variability in drug response, which can be predictable (e.g. due to differences in body weight or kidney function) or unpredictable (differences between subjects seem random, but likely reflect a lack of knowledge or data).

https://en.wikipedia.org/wiki/Pharmacometrics

I promised you #Beer! It's actually a pretty good example. PK describes what happens to the alcohol (ethanol) you consume between the glass and the bathroom, and PD describes what it does while it's circulating in your blood (quite a few things, including making you tipsy). Ethanol is a pretty interesting case, because it's eye-wateringly complex. The #DrinkMe simulation on Nick Holford's website is a fun interactive example of how it fits together! #PMx

http://holford.fmhs.auckland.ac.nz/research/ethanol

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

https://www.fda.gov/about-fda/center-drug-evaluation-and-research-cder/division-pharmacometrics

Modeling tools in our area are largely closed-source and massively expensive, and are a gigantic entry barrier for new people, especially in low and middle-income countries (and borderline unaffordable even for CROs like mine). #nlmixr2 is intended to be a solution to this problem. I also maintain the #pmxTools package, which provides a handy set of general #PMx functions.

@nlmixr2

https://www.nlmixr2.org