Pharmacokinetics and Pharmacodynamics

There are two main branches of pharmacology: pharmacodynamics and pharmacokinetics.

The former relates to how a drug (or other substance) impacts the organism that ingests or absorbs it, while the latter focuses on how the organism affects the drug.

So figuring out how taking 162.5mg of aspirin will affect the countless variables that inform the functioning of an average adult body, versus that of an average child or an adult who is suffering from a specific autoimmune disease, is the wheelhouse of pharmacodynamic research.

Pharmakinetics, on the other hand, helps us understand how one’s body might tweak the potency of that aspirin based on how well or not-well (or said another way: how typically or atypically) one’s biological systems metabolize or alter that category of substance, once it’s ingested.

One’s body might also be more or less efficient in terms of distributing a given substance, which means (for instance) a drug that one takes orally and which is absorbed into one’s bloodstream—through which it eventually reaches a specific patch of infected skin on one’s arm—may reach that patch of skin earlier or later, with more or less potency, based on one’s level of cardiovascular health, or based on how effectively one’s liver and kidneys filter one’s blood for those sorts of substances.

These two concepts overlap a bit in terms of what they measure and the consequent knowledge they grant us, but understanding that there’s a push-pull dynamic at play, rather than a mono-directional influence of drug-on-body, makes more evident the incredible complexity of attempting to gauge how any foreign substance will interact with any organism’s (utterly complex) microbiome, which itself consists of a boggling array of interconnected relationships between organisms and systems made up of organisms.

This is part of why clinical trials are so fundamental to the drug-making process.

Even if we know, in a general sense, how most human bodies will respond to a given drug, some drugs have highly variant pharmacological impacts, while others are less-effective on average, but more predictable in how they (generally) interact with human bodies.

It’s possible to externally model some of these effects, and there are complex equations that help drug-makers figure out a range of possibilities in terms of desired and undesired impacts to biological systems, even before they start administering trial test-doses to closely watched subjects.

But because of how unique our bodies are, and how many variables influence the shape and substance and functionality of those bodies moment to moment, it’s an open question as to whether we’ll ever be able to accurately and broadly quantify the effects of drugs on even the majority of people at a useful resolution, which is part of why clinical trials remain a necessary component of the drug-making process, and why that process seems so utterly slow compared to the development and deployment of other new technologies.

This is interesting in part because it can help us understand why even well-tread pharmaceutical territory can be so rife with side-effects and unknowns, and because it can serve as a heuristic for understanding the mechanisms and frameworks that allow us to imperfectly engage with complex (to the point of being chaotic) systems in a manner that results in a remarkable number of positive outcomes, despite all the complexity and gray areas inherent in those systems.