What Million Things are Circulating Inside You? with Jenna Hua

WEBVTT
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We are exposed to thousands of chemicals at one time, but we only have been measuring one at a time.
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All the study have been singly studying one chemical at a time.
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It's really problematic.
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Welcome to the Climate Biotech podcast, where we explore the most important problems at the intersection of climate and biology, and most importantly, how we can solve them.
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I'm Dan Goodwin, a technologist who spent years transitioning from software and neuroscience to a career in climate biotechnology.
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As your host, I will interview our sector's most creative voices, from scientists and entrepreneurs to policymakers and investors.
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We're thrilled to welcome Dr.
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Jenna Hua for a discussion about climate biotech.
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Jenna is one of my heroes.
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The great thing about running this podcast is I can put my bias on my sleeve.
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She's one of my heroes and a dear friend.
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For six years, she's been building a company called Million Marker, which is the category-defining company to understand how the toxins inside our body come in from the world we live in.
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By the end of this, I hope listeners will understand why the company is called Million Marker, why Jenna is amazing as a scientist, and why she's impressive as an entrepreneur.
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What we hope is this inspires people to get into the world of exposomics, to see the link between climate issues and environmental health issues through the fulcrum of human health.
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Jenna is a registered dietitian and environmental health scientist.
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She earned her Bachelor of Science in Nutrition, a Master of Public Health, and a PhD in environmental health sciences from UC Berkeley.
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After her doctorate, she pursued a postdoctoral research at Stanford University's School of Medicine.
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Then she created a company called Million Marker, which we'll get into soon and we can't wait to discuss it.
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But first, let's meet Jenna.
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So, Jenna, very excited to have you here.
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We've wanted to have this conversation for a long time.
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For people who haven't met you, who are you?
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Where did you grow up?
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Thank you for this really wonderful introduction.
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I'm thrilled to be here.
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Thrilled to have a friend like you, thrilled to be part of the climate biotech community, part of the homeworld community.
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So I'm Jenna.
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I'm Chinese American, grew up half, I would say half in China.
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It's in a town called Kuoming, Yunnan province, southwest of China, and half in the US.
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I came to the US when I was 15 and have lived here ever since.
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And then you went back later as a Fulbright fellow, right?
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Was it to this to where you grew up?
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Yes.
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So I went back to China to do my dissertation research.
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My research was focusing on childhood obesity and the food environment.
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At the time, food environment was rapidly changing in China.
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So lots of Western fast food coming in, people have reduced physical activity and more car driving, less biking.
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So I was very interested in how does food environment impact childhood obesity risk in China.
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And at the time, like actually China has the childhood obesity rate have reached almost the same as US.
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Now it's really similar.
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Over 30% of kids in China or in many parts of Asia actually have the same obesity rate as the US.
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Wow, that's incredible.
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All right, let's put a pin in that and come back.
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The big question that has that we always ask people is did you always know you'd be doing what you're doing now?
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Did you always know that you'd be the tip of the spear on changing how humans understand the way the toxins affect our health?
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No.
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But no, but it was actually a natural flow.
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I was studying, I was studying nutrition.
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I love food.
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And it was through that dissertation research in China studying the food environment made me realize actually it's more than just the food environment, it's more than where we shop, where we buy our food.
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It's also what's in our food, right?
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And then this comes in the exposure.
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So if you're exposed to a lot of heavy metals in food or pesticides or these plasticizers, you'll get into your body, it will ultimately change your physiology and change your disease outcome, and then actually change your obesity outcome or any other health conditions.
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So that's a natural way for me to think about okay, if it's besides the food environment, what else is changing people's health and impacting people's health?
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And then it comes down to exposure.
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And that's what's like opening up my eyes to okay, let's think about exposure.
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And adding on my personal experience with fertility struggles, it just made me like, okay, this is actually it's a low-hanging fruit, in my opinion, for people to understand their exposure and do something about it before getting to the bigger problems.
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Bigger problem saying that, okay, can we change the air we're breathing?
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It's a lot harder to change than something more immediate.
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Okay, your products use less plastics, stuff like that.
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I think there's gonna be a theme in this conversation.
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And as an old man now, I can't help but speaking cliches, and there's the cliche of the man searching for his keys underneath the lamppost.
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And that happens, I think, a lot in biotech, and especially where we're gonna be talking about today, because the things that biotech really likes to search for in our keys underneath the flat streetlight is we're looking for proteins solutions, we're looking for things that we can easily sequence with RNA, DNA, single cell, whatever.
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But the stuff that you're talking about is things that don't easily fall into that streetlight, right?
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So we can say how many calories in your food, and that's what we've done forever.
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But what I think is much less easy to visualize or imagine is the microplastics that come out from when you get takeout and it gets put in there, or I think that's and this falls into this world of metabolomics, I think, right?
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So I would love to just prompt you shake your tree of knowledge here, which is that why is metabolomics hard?
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Why are there not more streetlights?
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I think metabolomic itself, it feels like it's not hard.
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It's it's measurement of your metabolites, whatever comes out of your body.
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It seems to be pretty straightforward.
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I think what's hard is putting that searchlight is where how do you understand your input that drives that metabolomics output?
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And that input is literally everything.
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That's why it's hard.
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Okay, it's the air you're breathing, right?
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And then the air you're breathing, the chemical goes into your air, goes into your lung, and goes into your system, that ultimately comes out as one of the metabolites, right?
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It's the air, it's the food you're eating.
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And I'm eating all kinds of food, you know, it's packaged food is veggies, the fruit and carbs, and you can get down to macronutrients, micronutrients, and then you get to your the products you're using, right?
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And then it's not just that, it's also your stress levels.
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Who are you interacting with?
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And are they giving you stress?
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Your social life matters, right?
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And then your home environment, your office environment, your occupational exposure, then what job are you doing?
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If you're like working at a toll booth, constantly touching that thermal receipt all the time, that's gonna turn in also getting into your body and become a metabolite coming out of your metabolomics.
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So I think it's like understanding where the sources of your external output that driving the metabolomics inside, that's really challenging.
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And how do you even document that?
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Interesting.
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Yeah, I I can see why it can almost be disheartening where it's just it's so much in that it's soup, and then trying to pull a little signal from that.
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This I think comes to the name of million marker, right?
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And so why did you I think a lot of people here probably might not ever speak the word metabolomics, right?
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And like I think I just think of it as the small molecules that are non-templated.
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We don't know how to read out easily.
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But why, yeah, what did you call it million marker?
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It's a mouthful.
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At the time when I was thinking about a name, actually, my PhD advisor helped come up with that name because the eventual goal is for us to be able to discover over a million biomarkers one day to inform precision medicine and personal health.
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So, in order to do that, you need a large database and you'll be able to screen for all metabolomics and maybe proteomics too, single cell.
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You name it.
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It's all markers, it's all biomarkers.
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It could be biomarkers of exposure, it could be biomarkers of disease, you name it.
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Now, for bio, I think there's two kinds of things.
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So I order, let's say I order Thai food from my favorite local place.
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It comes in steaming hot in this plastic five.
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I eat that, I go in.
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Now, what we'd pull out from a metabolomic signature would be the raw plastics, probably, right?
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And then there might also be my body's reactions to the plastic.
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Do you have a preference, one or the other?
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Do you think there's more signal in one?
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I think you might see more in metabolomics.
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You might see more is your body's reaction to that plastic rather than the virgin plastic chemicals.
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There could be some in there is like unprocessed, but you will see majority is the metabolite that's what coming out of your body that's being already processed.
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Interesting.
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Okay, so actually, this is a good time to root people in really what million marker is, because I'm jumping right in, assuming everyone knows million marker, but I think it's worth maybe just starting briefly from the beginning, which is that you were a postdoc at Stanford, you could have done anything, and you chose to create a company.
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Can you just take from there and then getting to how, in my example of eating Thai food, I could now get a million marker kit pee in a cup and you could tell me how bad it was.
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But to go back to 2019, I'm so curious about postdoc Jenna and what took you to creating a company.
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I actually had the idea of creating million marker even right after my PhD program.
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I started experiencing fertility struggles at the time.
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So I have a super, super rare autoimmune condition called pregnancy-induced cushion syndrome that manifests as high cortisol level.
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Anything could go wrong, would go wrong in pregnancy, preclampsia, gestation diabetes, kidney failure, like everything.
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It's like really dangerous.
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But I was not diagnosed until my fourth late stage pregnancy loss.
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I was just told good luck every single time.
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Good luck.
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And then I would have loss at 22 weeks, something like that.
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It was just really hard, and then there was like no answer.
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So I'm seeing all the specialists and everything, and yes, only answer I got was they couldn't figure out what's going on.
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Good luck next time.
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It was like incredibly frustrating.
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And knowing what I already know was environmental exposures, I know they could impact fertility.
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So when I went to the doctor, I was like, could I get an environmental screen?
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And they said the only test available that was more than 10 years ago is heavy metal.
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Yes, sure, I got a heavy metal test and everything came back to be normal.
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But what I'm like, what about other things?
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What about phthalates?
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What about bispanols?
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What about these endocrine disrupting chemicals?
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They do impact fertility, yet there's no test.
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But I know they are available.
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They we literally have been running these tests through the national biomonitoring program for decades, like literally decades.
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But me as a scientist, I wasn't really paying attention on these chemicals until I got to my PhD, right?
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And then not until I experienced my fertility struggle, I started really paying attention to how these chemicals actually impact my health and my pregnancy and everything.
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And there's no tools out there.
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And then there's no, if you look into the literature, we only see majority of the time, see only correlational studies.
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So it's not a causal, it's only saying that okay, people with high exposure to this, you would have leads to worse outcomes.
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So how do you even study this?
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We are exposed to thousands of chemicals at one time, but we only have been measuring one at a time.
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All the studies have been singly studying one chemical at a time.
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It's really problematic.
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So it's twofold.
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A research problem that I ran into during my postdoc research.
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How do we understand these chemicals?
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How do we utilize exposome?
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How do we incorporate behavior change to make it actionable for people?
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At the same time, it was my own personal struggle.
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I just really want to get a sense of assurance.
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We only have a correlation of studies, but at least give me a sense of assurance, okay, this is not causing me having these issues, I can just move on.
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Like I isolated this, I don't have to think about it anymore.
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So it was a professional struggle of not having data, studying these things, and then not having the tools.
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And then the second was the personal, and I was like, okay, since nobody's reading my papers anyway, like I should just do something different.
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Wow.
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I one of my one of my sayings is that our sufferings become our superpowers to help others.
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And I didn't know if four four late stage losses is for that's a lot, and that helps me understand the incredible energy that you've brought in to this company because I think there's several times in this company's life cycle where a lesser entrepreneur would have let the company go.
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And you have fought so hard.
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So you have this personal experience, very motivating, incredible resilience, by the way.
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And I don't mean just to pat you on the back, it's just flooring to me.
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And then you create this company and then it gets picked up by Y Combinator.
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And so I'm gonna I'm gonna ask you this question in a really cheesy way, which is that okay, huge problem.
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Everyone agrees that diseases caused by pollution are a big thing.
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Everyone agrees that we need to detect these things better.
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Everyone wants something.
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So obviously, it's a totally crushing business from day one, right?
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Investors lined up around the block to fund you.
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Tell us how it went.
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Not really.
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So, yes, I got extremely lucky getting to Y Combinator.
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At the time I was still debating whether to stay in academia or do something different.
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Y Combinator is the driving force for me to completely leave academia because it gave me the first pot of money, the resource to even set it up to start exploring what this looked like.
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But obviously, I have no idea what I was doing as an entrepreneur.
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But I think there's actually a lot of now that I've gone through six years of this journey, I think there's actually quite a bit of overlap as academia.
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As a new professor, you set up your own lab, you manage your own people, it's running your own startup.
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The admin structure is a little bit different, but majority of the time it's actually quite similar.
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But obviously, as a business now, as an entrepreneur, you have to think about other things that you're not traditionally thinking about in academia.
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You think about technology development more, you think about communication more, you think about marketing anymore, you think about how to actually build a product that people actually want and that you can make money.
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And I think these things are in academia, people, I think people tend to think about, oh, if you're making money, then you're not doing the pure science.
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But I actually think the two can actually go in hand together because the problem we're facing is actually not having enough data, not having enough real world data, not a controlled study because this exposure, as we talk about, is everywhere, right?
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Not having real data to actually decipher the relationship between exposure and disease.
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So you actually need the real world data.
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So if you don't have a lot of people buying to this, you can there's no way to even create this real world data.
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So, like in order to even study exposure, study these exposures, you actually have to do something that like make something that people want, so then you can actually study this.
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So I'm glad I got to that point.
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But I think, yes, going into my combinator, I wasn't able to raise a lot of money.
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Like I was under the assumption, yes, it's something that people really wanted, and an investor would want to pour money on it.
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It was actually not the case.
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You what we discovered that people who really wanted it are what we call it the worried well.
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So people highly educated, with a lot of disposable income, able to make their changes.
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But if you think about this part of people, it's it's not a lot.
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And if you think about people who actually needed help to reduce exposure, those are not the typical worried well.
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People have lower socioeconomic status, say living next to freeway, don't have access to clean food and clean product, they probably need help the most, right?
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So that's actually like a huge market to address.
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But how we get there, that's a different story.
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And I love the point of you need to scale up into real-world data, but now the scientist has to learn how to make an app that people can use to order a product that is intuitive to not screw up, to then find you in the first place to send you a sample on the right timescale that you can process it and get meaningful data, right?
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I think there's you're and I'm saying this like it totally lovingly, like there's you're stepping over all these things which had to be done to be able to get out to the real world.
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And I think it's really worth celebrating this because the I think there's a lot of toxic nonsense in academia.
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One of them is as you said, like making money.
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The other thing is it has to be super controlled.
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And I think the easiest thing that I would dump on is some of the just the ridiculous social studies experiments that are done on grad students to undergrads who want 20 bucks, right?
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And they're saying like that's a representative of come on, dude.
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Like, right?
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So so I just want to really sing out this idea that yeah, to do population state scale metabolomic analysis, you need to go out to the public, but then to go to the public, it has to be done through a company.
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And to do it as a company, you need to learn packaging.
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And so, how was that?
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Uh when you left White Combinator, where were you in terms of that?
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Had you built the packaging?
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Were you out in people's hands?
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No.
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Uh literally join YC was the idea.
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Everything was from scratch.
00:17:14.720 --> 00:17:19.839
So the first step was is are people like how do we yes come up with the packaging?
00:17:19.920 --> 00:17:21.920
How do we even make people pee in a cup?
00:17:22.240 --> 00:17:27.519
First is actually for these analyses, we don't need that much urine, we only need 10 mils of urine.
00:17:27.759 --> 00:17:35.039
So the first version of the kit was like, I had it, I gave people a peacock and then actually gave people uh a tube.
00:17:35.200 --> 00:17:39.200
So I have them transfer the pea in a tube and then give me the tube.
00:17:39.440 --> 00:17:42.319
And then as a scientist, that's exactly what you do, right?
00:17:43.759 --> 00:17:45.119
Because that's why you need it.
00:17:45.279 --> 00:17:47.839
But like this is obviously horrible usability.
00:17:47.920 --> 00:17:52.079
Like, why would someone wants to transfer their pea into the tube and then give you the tube?
00:17:52.400 --> 00:17:54.160
Just pee in the 15 mil tube.
00:17:54.240 --> 00:17:55.279
Come on, how hard can it be?
00:17:55.519 --> 00:17:55.920
Exactly.
00:17:56.000 --> 00:17:57.839
So then just like a pea in a copy instead.
00:17:58.079 --> 00:18:02.960
So then we like had the kit iteration, and then you face the issue of like, how do you ship it?
00:18:03.279 --> 00:18:06.720
So if you can I use we talk about plastic, right?
00:18:06.799 --> 00:18:13.920
Can I have a peacup that's made of glass or stainless steel, so we can avoid plastic?
00:18:14.160 --> 00:18:17.920
That turned out to be also like a rabbit hole that we got into.
00:18:18.079 --> 00:18:19.039
Can we actually do that?
00:18:19.119 --> 00:18:24.720
The the short answer is no, because shipping, any a lot of stuff can happen in shipping.
00:18:24.960 --> 00:18:33.839
And even if you so if you want to use aluminum cup, your lid has to be lined with some kind of plasticizer, which will contaminate the sample.
00:18:34.079 --> 00:18:38.319
And if you use glass, it can easily broken during shipping, that's also problematic.
00:18:38.400 --> 00:18:44.240
So then we end up doing controls and then using better plastic to use as a peacup.
00:18:44.640 --> 00:18:47.119
Another thing is that we ran into is okay.
00:18:47.200 --> 00:18:54.559
If you want to be sustainable, if you use a glass, then you want to or metal, then you want to recycle that, then you have to auto-clave it.
00:18:54.720 --> 00:18:58.880
And also, what's the perception of people using a used peacup?
00:18:59.680 --> 00:19:06.640
So it was like like you think about as okay, if you go to a public toilet, that toilet is being used again and again, right?
00:19:06.720 --> 00:19:07.680
You don't really think about it.
00:19:07.759 --> 00:19:14.240
But then if you're like thinking about your using a used peacup or recycled a peacup, right?
00:19:14.559 --> 00:19:16.240
The average user sees this word.
00:19:16.400 --> 00:19:17.599
Don't worry, it's been autoclaved.
00:19:17.759 --> 00:19:20.400
And as they're holding this, what the hell is autoclaved?
00:19:20.640 --> 00:19:21.599
Exactly.
00:19:22.240 --> 00:19:27.039
So a lot of these things as a scientist, you don't like doing research, you don't really think about it.
00:19:27.200 --> 00:19:35.839
But now that you're actually building a product, that you have to think about it, and then you have to think about user journey, and then what does your box design look like?
00:19:36.000 --> 00:19:40.480
And also, where do we find this biologistic company can help me put together the kit?
00:19:40.559 --> 00:19:52.079
So when I first started, I was literally packing up the kit in my garage and then ship it to people, and or pick the first version was me picking up samples, people picking up people's pea samples all over the Bay Area.
00:19:52.559 --> 00:19:56.160
Then that's because the shipping, then we're able to ship the samples.
00:19:56.960 --> 00:19:58.000
Oh my gosh.
00:19:58.240 --> 00:20:07.680
Well, it really has to be appreciated that I think to make any progress on these diseases caused by pollution, you need to do things at the population scale, right?
00:20:07.839 --> 00:20:10.319
And to do that, you need to have all these things solved.
00:20:10.480 --> 00:20:15.279
But it's so interesting that it's one of those kind of uh big gaps where everyone will say this is true.
00:20:15.359 --> 00:20:22.240
So academia will say this is true, but then in the next breath or the department level, they'll say, Yeah, but we never want to have to handle that.
00:20:22.319 --> 00:20:23.599
We'll just right.
00:20:23.680 --> 00:20:28.079
And so exactly to have people to price that, I think, is really remarkable.
00:20:28.160 --> 00:20:33.279
And so to hop through, and then we can geek out on specifics because I think people would want to know what are you testing and why.
00:20:33.440 --> 00:20:37.119
How did it go on the what did you do to keep the company funded through that?
00:20:37.200 --> 00:20:42.640
So you go pitch, standard investors are saying, This doesn't look like software as a service, I don't know how to price this.
00:20:42.799 --> 00:20:45.119
And biotech people will say, What's your druggable target?
00:20:45.200 --> 00:20:47.440
And you say, I'm not a standard therapeutics company.
00:20:47.599 --> 00:20:51.119
So, what did you have to do for to keep the company going?
00:20:51.680 --> 00:20:56.720
I did try to pitch a lot, obviously, was able only getting a few angel checks.
00:20:56.799 --> 00:21:00.799
I think the biggest hurdle at the time was the awareness, it was still low.
00:21:01.039 --> 00:21:04.000
So a lot of people there you there's so much education to be done.
00:21:04.160 --> 00:21:08.240
And then there isn't, it's not like selling a supplement, there is a known business model.
00:21:08.319 --> 00:21:09.759
This is so unknown.
00:21:10.000 --> 00:21:13.680
So no one wants to fund this endeavor.
00:21:13.839 --> 00:21:19.039
And then we're also facing a chicken and egg problem at the time because the testing cost is high.
00:21:19.279 --> 00:21:23.920
Um, but in order to reduce the test cost, I need to build a lab.
00:21:24.000 --> 00:21:25.599
That's the only way to do it.
00:21:25.839 --> 00:21:29.119
But in order to fund the lab, you need to have the demand.
00:21:29.440 --> 00:21:31.759
So we don't have any of these.
00:21:31.920 --> 00:21:45.680
And even when you're like trying to go higher and we're like trying to get people to pay more, say, work with the doctor, doctors always ask, can you demonstrate by reducing exposures, I can change clinical outcome by what percent?
00:21:45.920 --> 00:21:47.440
We also don't have that data.
00:21:47.599 --> 00:21:48.880
So I was a little bit stuck.
00:21:49.039 --> 00:21:58.960
I raised a few Angel Tracks, family and friends, even raised a crowdfunding round from over 400 microinvestors, people giving us between$100 to$20,000.
00:21:59.519 --> 00:22:01.920
So that kept the company afloat for a while.
00:22:02.079 --> 00:22:05.680
Then I ended up applying for a lot of government grants.
00:22:05.839 --> 00:22:10.960
So through the Small Business Innovation Grants section, SBIR through NIH.
00:22:11.200 --> 00:22:21.759
So we have been funded ever since with a ton of SBIR money, not a ton, but enough to keep us going that we are able to take a step back.
00:22:21.839 --> 00:22:29.920
Let's demonstrate the science so then we can reach out to more people, reach out to doctors, and then being able to eventually sell to business.
00:22:30.640 --> 00:22:31.039
So good.
00:22:31.200 --> 00:22:35.279
If you go to millionmarker.com, it does not look like a bootstrap company, right?
00:22:35.359 --> 00:22:37.039
It really looks polished and beautiful.
00:22:37.119 --> 00:22:38.880
And so it just shows so much care.
00:22:38.960 --> 00:22:43.039
And if you go to millionmarker.com, you'll see that you now have two big studies going on, right?
00:22:43.119 --> 00:22:53.839
One with California and one with Nevada, which to me feels like a big kind of proof of concept that a startup can be doing kind of academic grade plus science.
00:22:53.920 --> 00:22:57.119
And so I think people will probably be pretty interested in what these two studies are right now.
00:22:57.440 --> 00:23:01.039
So the we're actually opened up enrollment nationwide.
00:23:01.200 --> 00:23:05.039
We started with Nevada because we have a partner, the Healthy Nevada Project.
00:23:05.119 --> 00:23:06.160
We started with them.
00:23:06.480 --> 00:23:16.079
The two studies, what we're trying to do is to actually give people the tool besides testing, but also education to help them reduce exposures.
00:23:16.240 --> 00:23:28.880
And what's more important in these studies, I think it'll be the first time that we're trying to demonstrate this causal relationship that by reducing exposures, we can actually change clinical outcome.
00:23:29.119 --> 00:23:30.960
Before it was all association.
00:23:31.119 --> 00:23:39.680
And we've been asked, like, we even get criticized in our NIH grant that okay, there's not enough causal information around these chemicals.
00:23:39.839 --> 00:23:44.319
But I'm like, well, you can't not really do a prospective study.
00:23:44.480 --> 00:23:50.960
You cannot recruit a group of pregnant women and give them an expose them to autonomous phalliase and observe what happens, right?
00:23:51.279 --> 00:23:51.759
Do that.
00:23:51.920 --> 00:23:59.119
So then the only way to do it is okay, if I'm able to reduce people's exposure, can I see any changes in outcome?
00:23:59.440 --> 00:24:00.799
That's what we're trying to do.
00:24:00.960 --> 00:24:02.079
So that's one study.
00:24:02.240 --> 00:24:04.480
The second study, we actually have multiple studies.
00:24:04.640 --> 00:24:14.160
Another study that we're really excited about is these hormone disrupting chemicals, besides besides the impact fertility, they actually are obesogens and diabesogens.
00:24:14.319 --> 00:24:17.440
That means they impact obesity and they impact diabetes.
00:24:17.680 --> 00:24:27.920
So another research that we're running, a study we're running is we're looking at can people going through reducing exposures to actually reduce weight at the same time.
00:24:28.480 --> 00:24:43.839
And then furthermore, as weight loss drug has becoming more and more popular, our hypothesis is that if you are actually on weight loss drug, reducing exposures can actually make the drug effect more, can make the drug more effective.
00:24:44.480 --> 00:24:45.839
Oh, that's fascinating.
00:24:46.079 --> 00:24:47.440
That's really interesting.
00:24:47.759 --> 00:24:57.279
And when you're analyzing these from because we're this is very interesting because you're analyzing people on the phenotypic level and you're analyzing them on the metabolomic level.
00:24:57.440 --> 00:25:04.960
And so I think the phenotyp, the phenotypic level is pretty intuitive, but the metabolic level, I think, is I think worth understanding for why it's so difficult, right?
00:25:05.039 --> 00:25:11.279
And so we talked about the p logistics, which is hilarious, and there's a lot of fulsome street fair jokes to make that we're not gonna make.
00:25:11.440 --> 00:25:14.799
But there is gonna be the question of well, what are you measuring in that?
00:25:14.880 --> 00:25:17.920
And so there's a million metabolites potentially that you could measure.
00:25:18.079 --> 00:25:26.319
There's gonna be a and so I think just during the brief overview of you could do untargeted mass spec, or you could do targeted mass spec, which have you chosen and why?
00:25:26.880 --> 00:25:27.839
Really good question.
00:25:28.079 --> 00:25:30.559
Right now, we're doing targeted metabolomics.
00:25:30.880 --> 00:25:39.920
Targeted right now, we're measuring 13 chemical metabolites that includes bisphenols, phthalates, some that phthalates, and parabens, and oxybenzone.
00:25:40.079 --> 00:25:47.599
These are all common hormone disrupting chemicals that are either plasticizers or they occur a lot in your personal care product.
00:25:47.839 --> 00:25:51.279
The good thing with these chemicals is that they're transient.
00:25:51.519 --> 00:25:55.839
Not saying they're good things, but they're at least they're addressable because they're transient.
00:25:56.000 --> 00:26:03.279
That means if you eliminate your exposure within 24 to 48 hours, you will see them being eliminated from your body.
00:26:03.440 --> 00:26:08.160
So your body actually has a natural detox system to get rid of them, which is very encouraging.
00:26:08.319 --> 00:26:16.160
And then that's the reason we pick these metabolites to measure and then pick these memicals to intervene first, because it's actionable.
00:26:16.480 --> 00:26:19.519
So you there are simple steps you could do.
00:26:19.759 --> 00:26:23.680
As simple as swapping out a product, you can already reduce your exposure.
00:26:23.920 --> 00:26:37.200
If you go, if you get your takeout on that Thai restaurant that we just talked about, if you bring your own container, a glass or stainless steel container, and have them putting your food in that container, you're already reducing a ton of exposures already.
00:26:37.359 --> 00:26:42.640
So things like that people can already immediately do, in my opinion, is okay.
00:26:42.799 --> 00:26:49.680
I think in order to build a product that people really want it, it has to be personal, it has to be actionable.
00:26:49.839 --> 00:26:54.400
And then measuring these metabolized and then using these strategies fits the bill, right?
00:26:54.480 --> 00:27:04.000
Like it's entry level to get people are interested in reducing their understanding their exposures and then reducing their exposures, the lowest of the lowest hanging fruit.
00:27:04.400 --> 00:27:08.640
Yeah, I was always curious about the 13 that you chose to because this is your custom panel, right?
00:27:08.720 --> 00:27:09.200
That you design.
00:27:09.440 --> 00:27:16.400
And I never thought about the temporal nature of intentionally choosing things that get out of your system relatively quick.
00:27:16.640 --> 00:27:18.640
So that way you're giving the actionable component.
00:27:18.799 --> 00:27:20.000
I think that's super clever.
00:27:20.160 --> 00:27:24.400
Are though and those are all the direct molecule themselves, right?
00:27:24.480 --> 00:27:30.240
You're not measuring the there's a cell-free DNA or like an antibody that increases in the blood.
00:27:30.319 --> 00:27:34.319
Like you're not measuring that, you're measuring the plastics themselves, right?
00:27:34.640 --> 00:27:37.119
Yes, the metabolite of the plastic themselves.
00:27:37.279 --> 00:27:42.559
They're not a parent compound, the metabolized on the plasticizers and these chemicals.
00:27:42.960 --> 00:27:43.680
Oh, interesting.
00:27:43.759 --> 00:27:46.160
So it's the liver enzymes have chopped them up a little bit.
00:27:46.480 --> 00:27:46.640
Exactly.
00:27:46.960 --> 00:27:47.920
There's a little bit left in the body.
00:27:48.160 --> 00:27:52.400
Is there any is it ever interesting to do a ratio between the two to see how fast they're being metabolized?
00:27:52.640 --> 00:27:54.079
Each person is a little bit different.
00:27:54.240 --> 00:27:56.799
In general, it's within 24 to 40 hours.
00:27:56.960 --> 00:28:01.200
Some of the metabolized, some of the chemicals actually have a half-life of six hours.
00:28:01.440 --> 00:28:12.000
But it's okay, if you really want to do a comprehensive measurement of your exposure on these chemicals, you have to collect 24-hour urine sample because that will give you the most comprehensive view.
00:28:12.079 --> 00:28:17.119
But it's impossible to have people collect 24-hour urine samples, pull them, and then send you a sample.
00:28:17.440 --> 00:28:18.559
Just not possible.
00:28:18.799 --> 00:28:23.359
And urine usually tracks about 90 to 95% of these chemicals.
00:28:23.519 --> 00:28:29.920
So the rest of them are deposited a bit in your blood, deposit a bit in your fat, in your gut, and also your sweat.
00:28:30.160 --> 00:28:34.480
Um, you can even measure some of these in your hair, but those are percentage and it's saliva.
00:28:34.559 --> 00:28:41.119
So but the percentages are so low, it's not a good, it's not like the main surrogate to look at your exposure.
00:28:41.200 --> 00:28:45.599
So urine is the ideal biomatrice to look at these exposures.
00:28:45.920 --> 00:28:50.720
Yeah, I think that's another one of those subtle decisions that you made that I think are easy to step over now.
00:28:50.880 --> 00:28:52.720
You could have done toenails, right?
00:28:52.799 --> 00:28:56.079
But you decided that you said urine is the best one to work with.
00:28:56.240 --> 00:28:59.920
Do you ever consider genomics for people for different people's clearance?
00:29:00.240 --> 00:29:01.839
Yes, that would be really interesting.
00:29:01.920 --> 00:29:07.599
And then that actually that's one of the reasons we are working with the Healthy Nevada project.
00:29:07.759 --> 00:29:15.680
This is a really we I think of this again, like if I stayed in academia, there's no way I would be working with Healthy Nevada project.
00:29:16.000 --> 00:29:22.319
As an entrepreneur, I was lucky enough to be able to get introduction to them and start working with them.
00:29:22.559 --> 00:29:25.599
I would not, I seriously would not get that opportunity to do that.
00:29:25.759 --> 00:29:27.519
That study is so impressive.
00:29:27.680 --> 00:29:41.119
They are a population cohort based in Nevada, and the goal for establishing the project is that to try and get everyone's exon genetic sequence so then they can use that data to inform a healthcare delivery to save costs.
00:29:41.279 --> 00:29:48.880
So if you screen someone for their breast cancer surgeries, if you found out that can really help inform the future spending and care of that patient.
00:29:49.039 --> 00:29:59.920
Because they already have this focus on genetic screening, the natural next step is to look at okay, are there gene environment interactions on certain diseases and then environmental.
00:30:00.319 --> 00:30:02.240
Is also part of their eventual goal.
00:30:02.400 --> 00:30:05.920
So it was a natural connection for us to work with them.
00:30:06.160 --> 00:30:22.480
And now that they have collected a ton of genetic data and then we're collecting both external and internal exposure information, eventually we'll be able to marry the genetic data with environmental data to tease up some of the effect that you were just talking about.
00:30:22.640 --> 00:30:28.799
I think it will be extremely helpful for people to understand how they're clearing these chemicals.
00:30:29.119 --> 00:30:31.920
Because right now we're measuring 13 metabolites, right?
00:30:32.079 --> 00:30:38.400
For example, phthalates we're measuring five of the metabolites, but your body could be like generating another like seven.
00:30:38.559 --> 00:30:44.559
So there are like known phthalates metabolites, there are like 13 or 14 of them, but we're only screening five.
00:30:44.799 --> 00:30:55.519
Five most common metabolites people would metabolize into, but you as a special individual, you could be metabolized into the other seven that we're not measuring.
00:30:55.759 --> 00:30:58.559
That's a limitation for any screening.
00:30:58.960 --> 00:31:10.559
But if we actually have the genetic component, we'll be able to see, hey, like among these people, these are what they're metabolizing, or you might have a genetic predisposition that you don't detox well.
00:31:10.799 --> 00:31:15.440
So then you don't get you don't actually get metabolized, nothing comes out.
00:31:15.680 --> 00:31:21.039
So if you knew that, then you better control your exposures because you don't metabolize well.
00:31:21.200 --> 00:31:23.680
So you don't want to expose yourself to more stuff.
00:31:24.000 --> 00:31:25.359
And there's the other, there's two things.
00:31:26.960 --> 00:31:31.759
The quick point is that the other thing is that sometimes the way your body breaks it down makes it more toxic.
00:31:32.000 --> 00:31:36.720
Stage of the liver is to make things more reactive so you can functionalize as something and then pee it out.
00:31:36.799 --> 00:31:39.839
But when it's more reactive, it can do more damage, right?
00:31:40.000 --> 00:31:40.400
Exactly.
00:31:40.559 --> 00:31:48.960
And I think there's the other subtle part, which is why I really admire, I think this is why I say like your category defining in the space, because it's one of those too big to tackle problems.
00:31:49.119 --> 00:31:56.799
Paul Reginado, co-founder of Homeworld, has this quote, which I say like almost every day, which he's like, when we work on clo- when we worked on the brain, we worked on things that are too small to see.
00:31:56.880 --> 00:31:59.839
And when we work on climate, we work on things that are too big to see.
00:32:00.079 --> 00:32:09.359
And I think this holds in the body too, because when you talk about tox, you're simultaneously, we're both talking about tiny molecules, right, that have been cleaved in the body floating around.
00:32:09.440 --> 00:32:18.640
But then at the same time, we're talking about the whole meter scale of the human body where it comes in through your mouth and then it goes through your gut and then it goes into your blood, then processed by the liver, right?
00:32:18.720 --> 00:32:20.000
And deposited into fat.
00:32:20.079 --> 00:32:26.960
And so it becomes this multi-organ, multi-molecule, things that are not easy to study, trying to be studied.
00:32:27.279 --> 00:32:29.599
And so I think it's I'm really curious.
00:32:29.839 --> 00:32:32.160
Kind of we want to go to some of the technical challenges.
00:32:32.240 --> 00:32:42.400
I'm very curious as to appreciate what the difficulty of the biology, kind of what are some of your wish lists for the technology solutions that we're gonna get in the years ahead.
00:32:42.799 --> 00:32:45.359
Uh, we touched a little bit on on targeted.
00:32:45.519 --> 00:32:47.519
I mentioned that we're doing targeted, right?
00:32:47.680 --> 00:33:02.559
I think I really hope on targeted to be developed more because the whole reason I got into the whole exposome and then doing the measurement is eventually that we're able to do this on targeted screen.
00:33:02.880 --> 00:33:08.960
Because you can just imagine the on-targeted, meaning that you're not fishing for anything, you're looking at everything.
00:33:09.200 --> 00:33:18.480
So if we can get on target to a certain sensitivity and accuracy level, then you literally see all your exposures.
00:33:18.720 --> 00:33:28.400
And this will enable so many things, not just biomarker discovery, but a ton of teasing out this like a multiple chemical effect, for example.
00:33:28.640 --> 00:33:31.200
So we're able to see all exposure at the same time.
00:33:31.359 --> 00:33:33.279
And then it's not just exposures, right?
00:33:33.359 --> 00:33:41.200
You could have even seen your nutrition because if you eat any macro neat micronutrients, uh ultimately it's all metabolites coming out.
00:33:41.279 --> 00:33:48.559
If you eat red meat, like I did I studied the nutrition, like a dietary assessment, that's like the holy grail of nutrition.
00:33:48.880 --> 00:33:53.200
I felt like on-targeted metabolite blowness can actually solve that problem.
00:33:53.440 --> 00:34:00.559
But if you ask people to do a dietary recall asking them what did you eat in the last 24 hours, sometimes you ask them what did you eat in the last week.
00:34:00.720 --> 00:34:01.759
Who remembers that?
00:34:02.000 --> 00:34:03.519
There's so much bias, right?
00:34:03.680 --> 00:34:04.400
Like going to it.
00:34:04.480 --> 00:34:09.119
And then some people underreporting because I don't want to be judged like what I'm eating.
00:34:09.280 --> 00:34:13.519
But if you can't just like uh screen people's peas, they don't have to tell you what they ate.
00:34:13.679 --> 00:34:16.079
Everything, the instrument, your pea doesn't lie.
00:34:16.159 --> 00:34:18.559
Like everything just comes out of it, right?
00:34:18.880 --> 00:34:21.039
So I see that as like eventual.
00:34:21.199 --> 00:34:32.800
I want to see that as eventual where we get to is using untargeted metabolomics to look at disease outcome to do true exposome screening.
00:34:33.440 --> 00:34:38.960
Now I think there's something we need to appreciate is what untargeted means in the context of mass spec, right?
00:34:39.119 --> 00:34:44.320
Because mass spec is in people are gonna get mad at me, but it's in some ways it's a primitive calculation, right?
00:34:44.400 --> 00:34:45.679
Where it's mass overcharge.
00:34:45.840 --> 00:34:51.360
And so with targeted, you're what you're looking for, and so you can pre-process for that.
00:34:51.840 --> 00:35:00.239
But then untargeted very barbarically, like from my understanding, which I have not done a mass experiment, so my opinion does not matter here, but you're putting everything in, right?
00:35:00.320 --> 00:35:06.719
And then you're just getting these this 1D spectra out or 2D, depending on if you do like GCMI MS.
00:35:07.039 --> 00:35:18.960
Anyway, you're getting this soup out, and so to me, it's then to be able to back out a spectra into suddenly here's a million possible things that are there, and we're gonna give you a count is pretty wild, right?
00:35:19.039 --> 00:35:23.440
And I guess the question I would have is that what you're doing gives you potential data advantage, right?
00:35:23.519 --> 00:35:34.480
If you could, in theory, if it takes 10 mils to do targeted, could I just pee 20 mils so you can do 10 mil untargeted, 10 mils targeted, and then one day use that as a data platform?
00:35:34.960 --> 00:35:35.920
We could do that.
00:35:36.079 --> 00:35:46.000
So one of the reasons so we're collecting sample is to be able to build a biobank that we eventually enable on-targeted experiment and discovery.
00:35:46.159 --> 00:35:57.519
And besides that, we talked about a little bit the metadata, the external stuff that comes in, which would be really important to later on to link to the signals for what's coming out of this on-targeted thing.
00:35:57.679 --> 00:36:12.960
Because eventually, okay, even if you identify the signal, what it is, you still, in order to make it to a product level for a human being to be able to use it, you have to be able to tell them what does this even mean to them, right?
00:36:13.119 --> 00:36:15.519
And then what are they doing about it?
00:36:15.840 --> 00:36:31.760
And so we're collecting that all the metadata that could be eventually feeds into the analysis, allow us to build a more mature and product to be able to actually inform what does this even mean to people and what can they do about it.
00:36:32.159 --> 00:36:36.480
So I totally get why you would do urine that is transient.
00:36:36.719 --> 00:36:43.440
I wonder just, and this is going in speculation, so we don't have to hover if it's not a good topic area, but I wonder about the opposite.
00:36:43.519 --> 00:36:45.519
What about the really long-standing things?
00:36:45.599 --> 00:36:52.559
And so I think about you know, when I've heard similar fertility stories, it often comes like, oh, this is where I grew up, right?
00:36:52.639 --> 00:36:58.320
And then this maybe in like prepubescent era, this stuff might have disrupted things, and decades later, this is what I got.
00:36:58.639 --> 00:37:04.719
So I wonder one, if there's like a metabolomic search to do for the kind of long tail or like permanent things.
00:37:04.880 --> 00:37:12.400
And then the it starts with a personal question, which is that uh have you ever done fertility screens for the people who grew up where you did in China?
00:37:12.719 --> 00:37:20.320
Okay, first uh fertility screen for people I grew up in China, no, but China's fertility rate has been dropping like crazy.
00:37:20.639 --> 00:37:23.599
And and also not just fertility rate.
00:37:23.760 --> 00:37:30.480
Yes, sure, you can argue that people aren't having less kids, but pollution is such a big issue.
00:37:30.719 --> 00:37:35.920
And one example, counter-argument that I once heard from Dr.
00:37:36.000 --> 00:37:44.320
Shauna Swan is that sure, you can blame, say, humans are getting more educated, they're having less kids, but animals didn't choose that.
00:37:44.480 --> 00:37:48.480
We're actually seeing fertility decline in a lot of animals as well.
00:37:48.960 --> 00:37:53.440
And what attributes to that, it's the pollution that attributes to that.
00:37:53.679 --> 00:37:54.800
So that's that question.
00:37:54.880 --> 00:37:58.639
And then the second question was the long-term exposures.
00:37:58.960 --> 00:38:01.920
So I think the long-term exposures are really important.
00:38:02.320 --> 00:38:03.119
Really important.
00:38:03.280 --> 00:38:08.000
The reason we haven't focused on that is you can hardly change that.
00:38:08.239 --> 00:38:15.440
So if you screen for you, yours and my blood today, we would still find DDT in our blood.
00:38:15.679 --> 00:38:17.920
DDT has been banned for decades.
00:38:18.559 --> 00:38:20.480
There's nothing you can do about it.
00:38:20.639 --> 00:38:26.400
Yes, you can make sure you have a healthy lifestyle, take your antioxidant, exercise, sleep well.
00:38:26.559 --> 00:38:30.239
That will help you decrease and eat a lot of fiber, getting rid of it.
00:38:30.400 --> 00:38:33.679
But you can only get rid of them to a certain degree.
00:38:33.920 --> 00:38:37.840
Once you expose to them, you just don't get rid of them for a long time.
00:38:38.079 --> 00:38:40.639
And it's depressing to know, right?
00:38:40.800 --> 00:38:43.440
But I think as a scientist, I definitely want to know.
00:38:43.519 --> 00:38:47.519
I have definitely tested my own myself for, say, for example, PIPAS.
00:38:47.679 --> 00:38:49.280
But there's not too much I can do.
00:38:49.360 --> 00:38:53.920
I can prevent my future exposure, but once I got exposed to this, there's not much I can do.
00:38:54.079 --> 00:38:55.280
The damage is already done.
00:38:55.440 --> 00:39:05.199
But I think what we could do is is yes, we have more people understanding these persistent exposures, then we really need to prevent our future generations from exposing to those things.
00:39:05.599 --> 00:39:15.440
Yeah, and there's the a lot of in this space is the phrase that my my professor, when I did my PhD, would say it's discoveries so dangerous that they were promptly ignored.
00:39:15.679 --> 00:39:19.920
And when you look at when you look at the studies using subsequent generations, right?
00:39:20.000 --> 00:39:23.519
So first line of mouse is given a bunch of PCBs, right?
00:39:23.840 --> 00:39:28.719
Second generation from that are fine, and then third generation's fat, right?
00:39:28.800 --> 00:39:39.599
You just see these studies all over, and you just look at like what model of biology fits that the immediate offspring is fine, and the next one is statistically way more likely to be disrupted.
00:39:39.760 --> 00:39:45.199
I think these studies I'm saying back your field to you, but I just when I read these, I'm like, I don't know what to do with that.
00:39:45.360 --> 00:39:47.760
I don't know how to imagine what the mechanism is.
00:39:47.920 --> 00:39:49.760
Is it a double strand break?
00:39:49.840 --> 00:39:51.039
Is it a weird binding?
00:39:51.199 --> 00:39:58.159
Is it a an adduct that somehow is passing through, or is it just that we're missing some additional dimension to biology?
00:39:58.480 --> 00:40:01.760
But I think these are such an important question to answer, right?
00:40:01.840 --> 00:40:10.480
And then that's why I also really appreciate homeworld also being as a think tank to bring people together from different fields to think about this problem.
00:40:10.719 --> 00:40:18.079
Because me being trained as an epidemiologist, like understanding this, I'm coming from like the macro aspect.
00:40:18.239 --> 00:40:20.800
Like I never dig into single cell sequencing, right?
00:40:20.880 --> 00:40:23.519
Like I never dig into so much of the mechanism stuff.
00:40:23.599 --> 00:40:32.559
But there's so much technology and the knowledge development in other fields that can be completely applied to pollution to this field.
00:40:32.800 --> 00:40:36.639
Like so many tools that we're literally not using in public health.
00:40:36.800 --> 00:40:46.800
It's like public health is like ancient, so ancient that we're still using surveys, paper, we're not really using technology, we're not embracing a ton of AI.
00:40:46.960 --> 00:41:04.079
There's and then the lab methods, one of the my wish lists, also besides the on-targeted metabolics, is that okay, can we even like add robotic arms, add automation to pollution, like to even the current targeted screening?
00:41:04.239 --> 00:41:11.920
Everything is so manual because there's no innovation in the field for a long time and there's no cross-breeding in the field.
00:41:12.239 --> 00:41:13.360
So I think it's really important.
00:41:13.599 --> 00:41:28.800
All the question you ask is so important, which I think if we can foster cross-field collaborations and then bring new ideas from other fields, from neuroscience, from other fields into pollution, into public health, it will really help.
00:41:29.119 --> 00:41:30.639
Yeah, I can't help but just say this.
00:41:30.719 --> 00:41:36.960
And this is me speaking my own personal opinion, but it's one of those things where you know the more the ethical quandaries pop up.
00:41:37.119 --> 00:41:38.639
I think I have an empathy.
00:41:38.719 --> 00:41:42.960
I started life and I still do talk a lot of trash and like critically.
00:41:44.320 --> 00:41:52.239
But then I'm beginning to also understand the epidemiologist side, where sometimes there's almost too much to know, and then you start getting yourself in a weird pickle, right?
00:41:52.320 --> 00:41:56.000
So a single person doing single cell sequencing, trying to understand the cause of their cancer.
00:41:56.079 --> 00:41:57.280
That's one thing, right?
00:41:57.519 --> 00:42:10.559
If you had population scale data of single cell sequencing, and you see some populations are doing better or worse from a you can quickly see where it's the same ideas that scared people about the Beijing Genomics Institute 15 years ago, right?
00:42:10.639 --> 00:42:13.840
But now it's like, oh, we're doing the exact same thing, but we're doing it for good.
00:42:14.480 --> 00:42:16.639
It's one of those very interesting topics.
00:42:16.880 --> 00:42:26.960
I think is one of the necessary ethical and moral reframings to say that, yeah, there's it's we do need to bring technology and there's a moral urgency for all these diseases.
00:42:27.119 --> 00:42:35.039
But to do that, we need to do things like talking about pediatric health that's being disrupted in a subclinical way, but is absolutely causing stuff, right?
00:42:35.280 --> 00:42:39.440
Or even infertility, I would say, falls under this subclinical thing.
00:42:39.519 --> 00:42:43.840
And for that, and many reasons, if this is a huge chunk of female health, it's totally overlooked.
00:42:44.000 --> 00:42:55.440
So, anyways, this is my I'll get off my soapbox, but this is why I think that what your work does is it uncovers a data element to what's so far been much more population-scale statistics.
00:42:56.000 --> 00:42:57.360
Yep, exactly.
00:42:57.519 --> 00:43:04.159
We actually want to personalize it, and I think it'll be awesome to have a single-cell sequencing database for pollution.
00:43:04.480 --> 00:43:07.920
If you collect the sample, that would be so amazing.
00:43:08.239 --> 00:43:09.920
You can do all sorts of things.
00:43:10.320 --> 00:43:19.519
Well, I'm curious to say what is the technology areas, and I we talked about on target, but what are the topics get you most excited, looking forward for the next six years of million marker?
00:43:20.079 --> 00:43:27.440
Doing yes, doing more on-target testing and not just on humans, but also on products, which then you can marry the two.
00:43:27.599 --> 00:43:30.079
Again, it's the building lesson meta database.
00:43:30.239 --> 00:43:38.239
And because another issue we have faced, yes, we give people actionable feedback to tell them swap out a product, eat better diet.
00:43:38.559 --> 00:43:42.719
But there's a whole other supply chain issue that we haven't really talked about.
00:43:43.119 --> 00:43:50.079
Your product label everything in your product is not many times as not as what you anticipate it to be, it's not as accurate.
00:43:50.320 --> 00:43:56.639
So even today, I use a good product supposed to be labeled phalli-free, PPA-free, paraben-free.
00:43:56.800 --> 00:43:58.800
Many times it's actually not.
00:43:59.440 --> 00:44:02.880
And there's a whole other issue there.
00:44:03.199 --> 00:44:12.719
So, in order to enable that, we need a better screening methods, we need people to work together, we need to work with businesses, we need to work with policymakers.
00:44:12.880 --> 00:44:14.800
There are so many components to that.
00:44:14.960 --> 00:44:21.440
So, I'm hoping we have enough technology to be able to screen for cost-effectively.
00:44:21.760 --> 00:44:26.320
Because once you put a product, you put human sample, everything into a machine, they don't really lie.
00:44:26.480 --> 00:44:27.280
People lie.
00:44:28.079 --> 00:44:28.400
Right.
00:44:28.559 --> 00:44:30.639
It's like hooray, you succeeded.
00:44:30.800 --> 00:44:43.280
Everyone's saying that their products are now phthalate-free and plastics-free, and then boo, that means the liars are also coming in to claim the things that they want to, they would want Million Marker to be able to say.
00:44:43.440 --> 00:44:47.519
So, are you actually doing product testing now?
00:44:48.400 --> 00:44:52.400
We are doing product testing through Million Marker Research Institute.
00:44:52.559 --> 00:45:01.119
It's our effort of screening more product, and then also exploring how do we push for this on-target technology.
00:45:01.280 --> 00:45:07.920
So the on-targeted stuff happening in a lab, but there's a whole data pipeline coming out of it.
00:45:08.000 --> 00:45:10.320
How do you make sense of the on-targeted data, right?
00:45:10.400 --> 00:45:15.199
And it's not just fund the chemist side that picking up the spectrus and figure out what it is.
00:45:15.360 --> 00:45:21.280
But then when you have all of these chemical well at the same time, what does that mean to people using this product?
00:45:21.599 --> 00:45:32.880
So, one idea, one effort that we have been putting, we have been working on is uh to okay, if we have the on-target screening data from a product, and what does that mean to people?
00:45:33.039 --> 00:45:34.880
What does that mean to a manufacturer?
00:45:34.960 --> 00:45:37.519
What does that mean to them manufacturing pipeline?
00:45:37.679 --> 00:45:38.960
What does that mean to their packaging?
00:45:39.119 --> 00:45:40.079
Can they change that?
00:45:40.159 --> 00:45:43.119
And what does that mean to a consumer who's using that product?
00:45:43.519 --> 00:45:43.840
Right.
00:45:44.000 --> 00:45:45.840
Yeah, here's your MS fingerprint.
00:45:45.920 --> 00:45:46.880
Do what we want.
00:45:47.119 --> 00:45:48.480
It isn't very useful, right?
00:45:48.639 --> 00:45:52.719
I think the fact that thellates, I still can't spell it, and I've been doing this for five years, right?
00:45:52.880 --> 00:46:00.079
The fact that people are even saying thellates, I think is a big credit to the fact that there's actionable stuff they can do thanks to Million Marker.
00:46:00.320 --> 00:46:02.239
Um, so I think there's been a big anyway.
00:46:02.320 --> 00:46:14.639
I would say just in the past few years since I really got into this space, there is a lot of change happening and definitely more increased demand combined with more people appreciating that we're just there's not a lot we can do yet, right?
00:46:14.719 --> 00:46:18.079
Which is increasing the demand to build those technologies.
00:46:18.639 --> 00:46:19.119
All right.
00:46:19.280 --> 00:46:20.800
There's a thousand ways to take this up.
00:46:20.880 --> 00:46:23.840
I wish we could spend more time on the research institute that you've set up.
00:46:24.000 --> 00:46:30.239
And so maybe at the end you can pull point people to wherever people can find you think have resources that people can find.
00:46:30.480 --> 00:46:32.239
Excuse me for stumbling over my words.
00:46:32.320 --> 00:46:34.719
This was a red eye I'm getting off of.
00:46:34.880 --> 00:46:38.400
But all right, so we always finished with four rapid fire questions at the end, Jenna.
00:46:38.480 --> 00:46:46.320
So the first question: what's a single book, paper, art piece, or idea that blew your mind and shaped your development as a scientist?
00:46:47.119 --> 00:46:52.559
The ExpoZome paper started by not Christopher Wire, that's actually uh later.
00:46:52.880 --> 00:46:54.960
I had learned about that paper actually later.
00:46:55.119 --> 00:46:57.840
It was actually from Steve Rappaport and Martin Smith.
00:46:57.920 --> 00:47:01.440
Those are two professors at UC Berkeley, and actually my PhD program.
00:47:01.760 --> 00:47:04.239
Steve was the person who told me about Exposome.
00:47:04.320 --> 00:47:12.400
And Jenna, did you know that the technology now is mature enough that we can screen for thousands of metabolized chemicals at the same time?
00:47:12.719 --> 00:47:25.039
That was the single piece that I felt blew my mind because I was thinking of exposure in an exposure journal, like an external sort of a way, doing a survey figure out exposures, but not actually measuring internally.
00:47:25.360 --> 00:47:28.960
And that sort of got me thinking about the idea of the marker.
00:47:29.760 --> 00:47:30.639
Amazing.
00:47:30.880 --> 00:47:34.000
What's the best advice line that a mentor gave you?
00:47:34.400 --> 00:47:35.599
I actually have two.
00:47:35.840 --> 00:47:38.480
One of my mentors, he told me two things.
00:47:38.639 --> 00:47:42.480
I have always told my mentees and then told everyone around me one.
00:47:42.719 --> 00:47:46.239
Number one is the rules are made to be broken.
00:47:46.719 --> 00:47:51.119
And the second one is uh never turn down a job you were not offered.
00:47:51.440 --> 00:47:53.039
So that means you need to apply, right?
00:47:53.119 --> 00:48:00.159
I think like I use that all the time that we we apply for a ton of grant grants to keep the company bootstrapped and keep it aflow.
00:48:00.400 --> 00:48:08.559
And I felt that sentence have always told me that you can't imagine you get you have all the resources in the world, but you have to make an effort to get there, right?
00:48:09.039 --> 00:48:15.679
That's yeah, and that's very easy to be underappreciated because of course Jenna has NIH grants and SBR grants, of course, right?
00:48:15.760 --> 00:48:19.360
And then no one knows all the other ones that you applied for didn't get.
00:48:19.679 --> 00:48:21.199
So, okay, third question.
00:48:21.360 --> 00:48:26.480
If you had a magic wand to get more attention or resources into one part of biology, what would it be?
00:48:26.719 --> 00:48:28.159
Exognomics.
00:48:28.559 --> 00:48:29.119
All right.
00:48:29.360 --> 00:48:30.559
Fourth question.
00:48:30.800 --> 00:48:31.440
I agree.
00:48:31.679 --> 00:48:33.840
Yes, I like strong, simple answers.
00:48:34.000 --> 00:48:34.800
Fourth question.
00:48:35.039 --> 00:48:40.400
What is one aspect of personal development you think biotechnologists need to spend more time on?
00:48:40.800 --> 00:48:42.639
Communication and marketing.
00:48:43.360 --> 00:48:53.920
As a scientist, I think we were not trained in communicating our research and research results in a meaningful, easily understandable way to the lay audience.
00:48:54.239 --> 00:48:57.119
That's so important for people to understand.
00:48:57.360 --> 00:49:04.880
We're talking about raising awareness for scientific discovery, we're talking about human health, but this communication is so important.
00:49:05.119 --> 00:49:06.639
And I think I was not trained.
00:49:06.719 --> 00:49:09.440
I have no understanding of how to even do marketing, right?
00:49:09.679 --> 00:49:20.079
Okay, I had no idea what SEO even means, but like all of these are so important for I feel like for scientists to be trained to understand how do they even communicate?
00:49:20.239 --> 00:49:22.719
Like, how do they utilize some marketing strategies?
00:49:22.880 --> 00:49:34.159
I used to think marketing is bullshit, but now I don't, I really appreciate like marketing strategies and then ways you could do to amplify your scientific discovery and results.
00:49:34.480 --> 00:49:38.000
It's a good point for me to point out that you've done a lot of Instagram live usage.
00:49:38.079 --> 00:49:40.719
So what if this is all new to you, you've really picked it up well.
00:49:40.800 --> 00:49:47.199
I the one thing I can't help but add to this is that I think in a lot of scientific training is to be able to communicate defensively, right?
00:49:47.280 --> 00:49:49.360
So, what are you gonna say that people couldn't shred?
00:49:49.679 --> 00:49:52.320
But a lot of entrepreneurship is offensively, right?
00:49:52.480 --> 00:49:56.400
You're trying to set out new territory, you need people to care before they can criticize.
00:49:56.559 --> 00:50:02.320
And I think that's to me, that's the big difference of communication as a scientist versus an entrepreneur.
00:50:02.960 --> 00:50:07.039
Yeah, I also think entrepreneur needs, especially working in a science, you need both.
00:50:07.280 --> 00:50:11.119
You need to be able to convince your fellow scientists, right?
00:50:11.280 --> 00:50:12.880
You still need that defensive thing.
00:50:13.039 --> 00:50:18.960
But then more importantly, you really we really need to train us on the front side offensive stuff.
00:50:19.280 --> 00:50:19.679
All right.
00:50:19.760 --> 00:50:23.360
Well, as we conclude, everyone who listened to this knows that I'm 100% biased.
00:50:23.440 --> 00:50:26.719
I think the work you do is amazing and I want to see more of it.
00:50:26.880 --> 00:50:28.960
I want to see more million marker success.
00:50:29.119 --> 00:50:34.000
And for everyone else who listened to this and wants to learn more, where would you point them to you and your work?
00:50:34.639 --> 00:50:38.079
Come to our website, www.millionmarker.com.
00:50:38.239 --> 00:50:41.440
We, as mentioned before, we also set up a nonprofit.
00:50:41.599 --> 00:50:46.000
We actually publish white paper on our product testing results.
00:50:46.159 --> 00:50:48.239
So I encourage everyone to check it out.
00:50:48.320 --> 00:50:55.199
They might be useful to you because we compare what's on the product label versus what's actually being screened.
00:50:55.360 --> 00:50:58.320
What are some tips you can pick out good products?
00:50:58.559 --> 00:51:01.679
We are also doing a project on dentures.
00:51:02.000 --> 00:51:06.559
So if you think about your night guards, invisal lines, what are actually in those things?
00:51:06.719 --> 00:51:10.480
And is there a brand that you can pick that's better than others?
00:51:10.639 --> 00:51:12.320
So we're doing those work too.
00:51:12.480 --> 00:51:18.719
So everyone can come to millenmarker.org to check out our work on the nonprofit side.
00:51:19.119 --> 00:51:19.760
That's so cool.
00:51:19.920 --> 00:51:23.119
Genoa, thank you so much for coming on the Climate Biotech podcast.
00:51:23.440 --> 00:51:25.119
Thank you so much for having me.
00:51:25.920 --> 00:51:29.760
Thank you so much for tuning into this episode of the Climate Biotech Podcast.
00:51:30.000 --> 00:51:38.400
We hope this has been educational, inspirational, and fun for you as you navigate your own journey and bring the best of biotech into planetary scale solutions.
00:51:38.559 --> 00:51:40.000
We'll be back with another one soon.
00:51:40.079 --> 00:51:45.039
And in the meantime, stay in touch with Homeworld Collective on LinkedIn, Twitter, or Blue Sky.
00:51:45.199 --> 00:51:46.800
Links are all in the show notes.
00:51:47.119 --> 00:51:52.800
Huge thanks to our producer, David Clark, and Operations Lead all in this time for making these episodes happen.
00:51:53.039 --> 00:51:54.480
Catch you on the next one.