The main backbone of the coffee simulator comes from a pharmacokinetic model detailed in this paper by KY Seng et al. https://pubmed.ncbi.nlm.nih.gov/19125908/

Interestingly, there are a lot of papers related to caffeine out there for various purposes, such as defense science and sports science! There are a ton of models too, on dosing caffeine in premature infants to treat apnea by simulating the respiratory system, which is particularly useful, as getting data in such young children is often difficult. In our case, we used a model built by the Defense Science Organization (DSO), Singapore to make our app.

DSO first had to give known doses of caffeine to their 59 healthy volunteers and collect serial blood samples from them over a period of 24 hours. The full details of their dosing regimens are listed in the paper. After, caffeine concentration from these blood samples are quantified on a LCMS machine. By plotting these data points over their collection time, we now have 59 individual PK profiles of caffeine from these volunteers.

The raw data is very informative, as from it, we can tell how fast caffeine is being absorbed and cleared. If we know things such as a therapeutic window, we can also tell how many people got the desired benefits using the caffeine doses reported in the study.

To take this information further to simulate new scenarios, we then need to build a PK model. In this case, we are using compartmental models (see picture) which describe the fate of a drug in the body by dividing the whole body into one or more compartments. While the compartments are not always linked to actual organs directly, we can use these models to mathematically describe what the body does to the drug in terms of absorption (KA), distribution  (V), and clearance (CL). We can then use these parameters to simulate new scenarios like what many of us have done with the coffee simulator.

Stay tuned for part 3 where I share how you can run simulations in R too!