Going beyond dose – why we need PK parameters

Good morning. In today’s weather report, we are expecting 1000L rain. – said no one ever.

If I were to read the weather report before going to work, I intuitively want to know 3 things: the location the rain will cover (location – does this affect my commute?), how heavy the rain will be (rate: how fast and how much rain is falling? will I get drenched?), and how long it will last (duration: should I wait for the rain to stop before I go out?).  From the weather app, I am told to expect scattered showers from 8am to 10am around where I live. I thus pack my foldable umbrella in my bag and head out.

Granted, to take a tablet, it is more convenient to simply know the amount, e.g. 1000mg. (no one consumes paracetamol at 100mg/min.) However, to optimize a dosing regimen, we also need to know similar things to the weather report such as

  • How much drug gets to the site of action? (location)
  • How fast is the drug absorbed and eliminated? (rate)
  • How long will the drug last in the body at a therapeutic concentration? (duration)

To answer these questions, we cannot simply rely on dose alone. We have to understand absorption (how fast and how much of the drug is absorbed), distribution (where does the drug go), metabolism and elimination (which together tell us how long the drug will last in the body). (Or ADME, for short) These processes work together to tell us how much drug is in the body and how long it will remain. This gives us a much better picture of how much drug to give.

Hope this short sharing allowed you to learn some PK on your commute!

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About janice goh

Dr. Janice Goh graduated from NUS Pharmacy and is a registered pharmacist with the Singapore Pharmacy Council. She recently completed her PhD in the lab of Professor Rada Savic at the University of California, San Francisco (UCSF) School of Pharmacy. She is currently a senior scientist at the Bioinformatics Institute, A*STAR. Her work focuses on using quantitative systems pharmacology using translational pharmacometrics tools by capitalising on preclinical data to predict clinical outcomes prior to actual trials.
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