Dr. Carolyn Baglole is an Associate Professor in the Department of Medicine at McGill University. Her laboratory focuses on how chronic lung diseases such as COPD are associated with environmental exposures, particularly cigarette smoke. We were able to sit down with her to learn more about her research and get her thoughts on preclinical pulmonary toxicology research!
A: I would say that pursuing respiratory toxicology as a research direction wasn’t necessarily on my radar as a career. I came into this field because of an opportunity that presented itself during my postdoc. I was finishing my Ph.D. at the University of Calgary and was initially going to go into industry. I had a job offer in Europe, but I felt that going right from my Ph.D. into an industry position could impact any potential future career choices that I might be able to make. So, I turned down the industry position and decided instead to do a postdoc.
My intention when looking for a postdoc position was to go into colon cancer research. My Ph.D. was in the field of gastrointestinal physiology and pharmacology, so I started searching for postdoc positions specifically in that field and just completely by chance came across a postdoc position at the University of Rochester in the Department of Environmental Medicine. There were several different postdoc positions available in that lab, and one of those positions was to study mechanisms of smoke-induced lung damage. I applied for that position even though it wasn’t what I intended to pursue- and I guess as you can say the rest is history…
In essence, my curiosity got the better of me and ultimately it ended up being a bit of a combination of the lab and department environment that I was going into, the project and the type of mentorship I felt they needed at the time. It all seemed like such a good fit even though I didn’t have any intention of going into that field. I would say that it’s not really unlike now that I have ventured into the cannabis research space. A lot of it is actually driven by sheer curiosity and intrigue, that’s really how my interest in cannabis research started. Once legalization was on the table for Canada, this made it ultimately easier to conduct the research. It became clear to me just how little we know about cannabis inhalation. The fact that there’s still so little known really makes this for me a particularly intriguing field to study.
A: During my postdoc, I started to learn more and more about smoke-induced diseases, particularly as it relates to the lung, and it became apparent that there is still such a great need for this research even though smoking rates are at an all-time low in North America, compared to their peak in the 1960s. This is of course not the case everywhere, and diseases caused by smoking are still a major health problem all over the world, Canada included. The burden of disease is still significant, and we clearly know that smoking causes disease, including chronic lung diseases such as COPD. We also know that not smoking largely prevents diseases, but we still don’t have much that can be done to reduce the disease in people who do smoke and we don’t know why only some smokers develop these diseases. Ultimately, I hope that my research can uncover new biological pathways that can be used as targets to help people that get chronic lung disease from these types of exposures. If you just think of it in terms of the current Covid-19 pandemic, there is still no clear consensus about whether or not smokers are more susceptible to the SARS-Cov-2 virus. This is certainly an area of considerable speculation and concern: right now, we are at the point of asking the questions without having a concrete answer.
When it comes to cannabis and the lungs, the landscape is even fuzzier: here we don’t know much, and therefore we cannot offer much information. It becomes more complicated with the introduction of new products that can be inhaled. I like to think of the landscape for cannabis research like looking out over the ocean: it is vast and sometimes turbulent, and the underlying currents really make it difficult to navigate. We have to get a handle on this kind of research to be able to make better informed scientific observations and conclusions.
A: We use preclinical models in our studies to try and more quickly gain insight into the inhalation toxicology of these products, whether it’s tobacco or cannabis, in a way that we cannot do at the population level. Preclinical models certainly have their limitations and are not a substitute for people, but we can use our models, for example, to investigate potential biological targets for smoking-related diseases or to investigate if there are alterations in lung structure and functions from cannabis products. This represents a starting point for us: we can use these inhalation models to investigate the impact of these products and susceptibility to emerging respiratory infections, the current COVID-19 pandemic being a perfect example. So, the real-world implications are that we can provide some information a bit more quickly on potential health implications that we may not immediately be able to investigate in humans.
A: It’s a challenge to decide upon which animal model would best fit the needs of what you’re trying to accomplish overall in the research. For example, we use the mouse model for several different reasons. In the literature, in addition to there being standardized protocols for these exposures, for some of the common mouse strains that we have access to, we already know what their overall susceptibility is to smoking-related lung diseases. For us, this represents a good starting point for our studies and this kind of fits into the second reason why we choose this model as our representative model: it’s because we can get genetically modified mice to study the biological pathways that we’re interested in. For example, if we already know that a certain mouse strain is moderately susceptible to smoke-induced lung damage, having a knockout for that protein in the same strain will allow us to determine if that protein confers protection or if it increases damage. It is a way to show causation in a biological pathway that we can then take to the next level and look at the validity of this pathway in biological samples from people for example with the disease. This helps facilitate the translation of our research results. We can take that knowledge that we have for tobacco smoke and apply it in much the same way to cannabis smoke. While knowing that there are fundamental differences between the cannabis and tobacco, we can take what we know, know how to do, and use these models to now investigate cannabis. There are some commonalities between tobacco smoke and cannabis smoke so we can use that knowledge as the starting point for our studies related to cannabis exposure.
A: With the inExpose we can really investigate the inhalation toxicology to both tobacco smoke and cannabis smoke, as well as a whole host of other types of inhalable products like E-cigarettes. It gives us good flexibility in terms of the exposure scenarios and the products that can be tested. There is a lot of variation in how people at an individual level use these products: this inter-individual variability applies to how people smoke tobacco and how they smoke cannabis. The way in which people use these different products is very different and variable. The inExpose allows us to tailor this information. For example, we can have defined protocols of exposure scenarios that mimic as best as possible how people use tobacco and how they use cannabis; the same goes with different E-cigarette products that are coming out. Overall, it really allows better reproducibility and translatability to how people are actually using these products.
A: The flexiVent has been a good confirmatory tool. For example, with our tobacco studies using the inExpose, we expose different inbred strains, who differ in their susceptibility to disease development, to tobacco smoke for a certain length of time. Then we look at overall lung damage as an indicator for an emphysema-like phenotype, which is a component of COPD. The nice part about having access to both the inExpose for exposures and then the flexiVent to test lung function is we can then show not only lung tissue destruction but that there are also alterations in lung function that mimics certain features of people with COPD. So again, we are combining the technologies so we can get a better handle on what’s actually happening in the lungs of our preclinical models in response to these inhaled products.
A: One of the limitations that SCIREQ has already addressed with the inExpose system was the initial inability to test different vape devices. The E-cigarette market is one that is changing really fast. Not being able to use JUUL devices, for example, which now commands upwards of 80% of the market, was a huge limitation. The fact that SCIREQ was able to hear and recognize that and then really quickly develop an extension that can adapt to JUUL and other types of e-cigarette devices was one of the reasons why we knew that this equipment would meet our needs in terms of research, both today and moving forward.
A: That’s a great question: tobacco is tobacco, and though there are slight variations between manufacturers, it’s really the same product. We cannot say the same when it comes to either E-cigarettes or cannabis. In particular, for cannabis, there are so many different varieties and so many different strains that vary in the composition of cannabinoids, which is the main secondary metabolite that folks are so interested in. If we want to compare, for example, cannabis smoke with a vaporized product, how do we standardize that? That is really the part where we’re at right now: to develop a standardized methodology like there is for tobacco and to know that when we use this product in the inExpose system, we are delivering physiologically relevant concentrations of cannabinoids. We can use that standardized methodology to then compare different products utilized in the same way. Having the ability now, with the inExpose, to standardize this methodology will be enormously helpful moving on with the research.
A: That’s another good question: I guess I only know what’s next when we actually get the data. In other words, we try as much as possible to let the data tell us what we’re going to do for our next experiments, papers, projects or grants. Sometimes, the “what’s next” is unintentional and completely unexpected. For example, even though I gained experience in pulmonary fibrosis research during my postdoc, I never really intended on pursuing that for my own independent research path- but we actually had a serendipitous finding from one experiment that led us to start pursuing the mechanistic basis for idiopathic pulmonary fibrosis (IPF); now we have a research program in my lab dedicated to studying fibrotic lung disease even though I really had no prior intention. So, with respect to the inhalation aspect, once the data starts to emerge, then we will know what that next step will be. It’s almost impossible to predict.
A: I would say what I like most is that I never know what we’re going to find. Every day is a new day, and you may think you know what you’re going to get in a given experiment or given project, but often times what you end up with is completely unexpected. Along those lines, if I were to give somebody advice it would be to keep an open mind. No matter what field you’re starting out in, this one included, keep an open mind and let the data tell the story as it is meant to be told.
I think sometimes when you’re starting out in a new field, one of the most disconcerting aspects is negative data and the assumption that something went wrong with the experiment or the experiment failed. It is easy to fall into the trap of repeating the experiment many times, which is obviously good but do not discard those first negative results as completely meaningless. As long as the experiment is properly designed, with the right positive and negative controls, those results are really part of the story and actually will provide some of the next steps and next sets of experiments you should conduct. So keep an open mind with all of the data that you’re generating, be diligent in the analysis but accept the results as they come. Be willing to embrace that most of the hypotheses that you come up with will be wrong. Eventually, you will see that being wrong will lead you to more interesting projects!