by Dr. Jana Olson | 22 October 2019
This is the second article in a 2-part series. In case you missed it, here's more information on how to work together in the complex field of toxicology.
One of the main reasons to study toxicology is to understand the effects of a substance on living organisms and the environment. At Eurotox this year, many scientists presented their successes in not just studying toxicological effects, but also predicting them.
Many of the posters presented at Eurotox 2019 were also available in electronic format at stations around the venue, making them available even after the poster presentations were over. Check out the PMI presentations from Eurotox 2019.
Three categories of toxicology: in silico, in vitro, and in vivo
I learned a lot about the concept of read-across, which is that similar chemicals are likely to have similar effects. That means we can sometimes predict toxicological outcomes of target chemicals we know little about based on what we already know about similar ones. This makes read-across an important factor in making toxicology better able to predict the effects of chemicals. The more information researchers have about a chemical, the more likely they are to be able to predict a chemical’s effects.
Toxicology can be roughly broken down into three categories:
● in silico is literally "in silicon," meaning computer models.
● in vitro translates to “in glass” and refers to studies done on cell cultures of various kinds.
● in vivo studies are done “in a living organism,” such as a rodent, zebrafish, fruit fly, or others.
By combining the results of all these studies, researchers can better predict the effects of the tested substances in people. These three categories can be combined into many different kinds of studies to produce experimental data capable of measuring the biological impact of substances.
Toxicology studies generally fit into one or more categories: in silico, in vitro, and in vivo. You can learn more about how we apply these toxicology methods to study our smoke-free products.
Regulators in many industries and countries require in vivo studies as a safeguard for human life, especially in the pharmaceutical industry, as one example. So far, no computer model or cell culture can reach the level of complexity of a living organism. Even so, that doesn’t stop toxicologists from working hard to try to replace animal studies with other kinds of studies to achieve the same outcome. This point was also emphasized on day 3 of the conference as well, where researchers across both academia and multiple industries encouraged emphasis on the 3Rs of toxicology (replacement, reduction, and refinement of animal studies) and the need for sound, science-based decision making.
It makes perfect sense that the people closest to the animal studies – those who are required to perform them – would be looking for an effective replacement for those studies. In fact, I’ve recently seen a new publication by researchers at the FDA who are also working to further non-animal approaches to toxicology. It seems like just about everybody involved in toxicology studies is working on a way to eliminate animal studies. If anyone can figure out how to achieve the necessary results without having to involve animals in research, it’s these guys.
Combatting bias and logical fallacies in science
The third day of the conference was a short one, and it started off with the Bo Holmstedt Memorial Fund Lecture by Dr. Wout Slob, of the National institute of Public Health and Environment in the Netherlands. He pointedly introduced himself as not, in fact, a toxicologist, but rather a mathematician. And he critiqued the mistakes that a toxicologist – in fact, just about everyone – tends to make by using intuition, relying too much on quick-decision tactics, and failing to be critical of their own thought processes.
Dr. Wout Slob of the National Institute of Public Health and Environment, Netherlands, asked and answered the question "Why do toxicologists make so many of the same 'mistakes'?"
He presented three fallacies that even experienced scientists tend to fall prey to. The first is what he calls “What you see is all there is.” As he explained, just because you don’t see an effect doesn’t mean there isn’t one. There might be an effect, or there might not be. There might be an effect that is just extremely rare. The second fallacy is thinking in categories. People prefer to categorize into hot and cold, tall and short, effect or no effect, but the world is almost always more complicated than that. Finally, the third fallacy is assuming everything is linear. For example, many things in the real world grow according to a logarithmic scale: cells in a petri dish or even interest on investments for example. Expecting log-scale data to make sense on a linear-scale graph is a bad idea. Basically, we as scientists have to make sure we’re looking at our data clearly and without applying our own biases if we want to make sense of it all.
The presentations at Eurotox were much more technical than those presented at the earlier conference we covered, the Global Forum on Nicotine. Even so, it was inspiring to see what’s going on in the wider field of toxicology outside of tobacco research, and better understand what's on the horizon for toxicology.