October 21, 2025 • 46 mins
The data gap in women’s health has always been a major issue and challenge. Ridhi Tariyal, CEO and co-founder of NextGen Jane, is on a mission to de-risk the women’s health field and fill in the blanks with critical information and research. Host Swaril Mathur speaks with Tariyal on how NextGen Jane is collecting data through menstrual blood to make diagnoses and treatments easier. Tariyal also shares advice on the pros and cons of going the cash-pay consumer route and pitching the right business proposal to gain investors’ attention, as well as NextGen’s plans and focus points.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Swaril Mathur (00:03):
Welcome to MedTech Talk.
I'm your host, Swaril Mathur,and I'm thrilled to be joined
today by Ridhi Tariyal, CEO andco-founder of NextGen Jane.
Ridhi, thank you for beinghere.
Thanks so much for having me.
So I'm so excited for this conversation Ridhi
because you've been working onsomething really interesting in
a sector of healthcare that isso often overlooked and often
(00:25):
really challenging to dig into.
And before we get there, Ireally want to talk about how
you got to this place in yourcareer and what your most
formative experiences have been.
So can you tell us a little bitabout your professional journey
and how you landed in the worldof women's health?
Ridhi Tariyal (00:40):
Yes, absolutely.
I am an engineer by background,so went to Georgia Tech,
majored in industrialengineering, and actually did uh
banking and consulting at thestart of my career, if you can
believe it.
Um, did not like either, andreally wanted to dedicate my
time to something that I foundworthwhile.
And so joined Bristol MeyerSquibb for two years in their RD
(01:01):
group and thought, this is it.
This feels like the right home,but still too big, you know, um
massive infrastructure, lots ofbureaucracy, but doing the type
of work that I found exciting.
Um, and this was back in theday, um, I'm gonna time myself,
but somewhere around 2005, 2006.
And when I asked uh uh people,you know, what should I go into
(01:22):
if I want to do some, you know,genomics and precision medicine
and uh more startup type work.
And they were like, yeah, youthe startup's the right place to
go.
That's where a lot of the coolcutting-edge science is
happening.
Um, but you have to have anMBA, which, you know, I don't
think that would be the advicetoday.
I don't, I don't, you know, Iprobably wouldn't even take that
advice today, but I was like,okay, great, I'll go get my MBA.
(01:42):
Um, got my MBA at Harvard, andwhile I was there, I got a
secondary master's in biomedicalenterprise from MIT.
Um, really uh, you know, wantedto double down on um
healthcare, and MIT offer thisgreat program where you go
through med school classes, youround in the clinic, and the
thought is if you want to be amedical entrepreneur, um, sure,
go get your MBA, but you shouldalso know the jargon in medicine
(02:05):
and you should know theproblems on the front lines.
Um, and that will make you abetter life science
entrepreneur.
Um, loved it.
Uh, graduated uh from MIT, youknow, after this three-year dual
degree program, and took mythesis um and tried to turn it
into my first startup.
Um, this was uh around 2010.
So it was still, we were allstill recovering from the
(02:26):
housing crisis.
And um, you know, it was it wasme solo sort of pitching this
idea that was targeted towardsum an a consumer base in India,
genomics play consumer base inIndia, and uh couldn't quite get
anyone to bite.
So I ended up saying, okay, Iknow I want to do emerging
market genomics.
Um, and I got an opportunity toum do exactly that at the
(02:46):
Broad, um, which is a genomicsinstitute based in Cambridge,
Massachusetts.
I was uh leading the financeand operations for a large GWAS,
which is a genome-wideassociation study looking into
viral hemorrhagic fevers,specifically loss of fever in
West Africa.
And it requires you to be onthe ground for like four months
(03:07):
out of the year and you know,gave you access.
I was managing like a $10million budget.
And I was like, this is sort oflike a startup budget as well.
Um, I loved it, you know, itfelt like it was still a very
entrepreneurial environment.
Um, and uh did it for twoyears.
And after that, HBS had thisgreat opportunity.
It was called the BlavatnikFellowship, um, where they were
bringing back people who hadgraduated with their MBA within
(03:30):
the last five years and umgiving them what they called a
little bit of walking aroundmoney, you know, paying them
some sort of salary, uh, givingthem access to the entire IP
portfolio across the university.
So, you know, from the college,from the med school, um, and and
saying, try to spin somethingout.
You know, this is uh reallyhigh quality IP.
There's a lot of PI that spenta lot of time um in on this
(03:53):
basic research, but it's it'sthere, you know, all of this
research encounters the Valleyof Death, um, where it's not
quite a product yet.
So it's not ready for VC, andyet, you know, it's it's too far
along for basic research, andso it just dies.
Um, and I was looking at allthese cool things, you know,
very fascinating, um novel uhmaterial for our uh warfighters
(04:14):
in theater, um, you know, newinteresting machine vision
systems that were going totransform how we developed um
psychiatry psych psychiatrydrugs.
Swaril Mathur (04:23):
Wow.
Ridhi Tariyal (04:24):
Like, you know, was I I loved those projects.
I worked on them.
And at the same time, you know,I was in my early 30s and I I
was uh talking to my OBGYNsaying, well, um, how can I find
out the state of the state interms of my reproductive health?
And um, you know, I had just ascontext come from the broad
where genomics was justtransforming everything.
It cancer was a completelydifferent field, right?
(04:47):
Everything from how it'sclassified, diagnosed, how drugs
are developed for it, how it'streated, um, was all being
driven by molecular medicine.
And um, even uh microbiome wasnext, right?
There's so much researchhappening there.
And you go to your uh OBGYN,everything is analog, not
digital.
Um, you know, at Papsmere,they're looking for
(05:07):
morphological changes in thecell shape.
Um, you know, fibroids,adenomiosis, they're looking,
they're doing imaging to lookfor structural changes in the
uterus.
Um, endometriosis is probablythe worst, where they're still
doing surgery and going in withendoscopes, trying to identify
ectopic lesions.
Um, and none of it was reallydriven by molecular medicine.
And it was evident not only inthe tools that they had for
(05:28):
diagnostics, but in the uhoptions women had for drugs.
Swaril Mathur (05:32):
Yeah.
Ridhi Tariyal (05:32):
You know, very blunt instruments.
And so um I thought, oh,there's uh there's an
opportunity here.
Uh clearly this space needsmolecular uh medicine.
It needs that kind of uhunderpinning to understand why,
why we get these diseases, howto uh treat them in a more
targeted way.
Um, and that's really where Iwould say my career um, you
know, bent towards uh women'shealth in particular.
(05:53):
Wow.
Swaril Mathur (05:54):
Wow.
You know what something thatstrikes me about your background
and kind of the first chapteror maybe chapters of your career
is just how many differentthings you did.
And I'm curious, you know, Ithink it's always interesting
with careers when you talk aboutthen you can look back and
backwards justify why everytransition was logical.
But in a moment, it's notalways, it's not always so
(06:14):
seamless.
But I'm curious across allthese different things,
consulting and finance, youknow, rounding in hospitals,
being an engineer, doinggenomics in West Africa, were
there any like specific kind ofaha moments or any
transformative learnings thatyou find yourself now applying
in your in your current role?
Ridhi Tariyal (06:33):
That's a great question.
Um, one I would say that, youknow, I I learned very early on
that I don't do well inextremely constrained
environments or where, you know,environments where uh that are
so big that it takes um 15decision makers to to reach a
decision.
And so uh I would say that alot of my uh career decisions,
(06:54):
you know, as they evolved anddefinitely today, are oriented
towards how can you make how canyou be nimble, how can you be
um quick, how can you respond ina way that's that's um you know
thoughtful and yet reflectiveof how fast the world is moving.
And I would say that that, youknow, giving into that impulse
early on in my career andsaying, oh, this is too slow, I
(07:17):
don't want to move at this pace,um, has absolutely optimized me
for the pace at which scienceis moving now and the types of
decisions we have to make in inthis start of uh next change.
Swaril Mathur (07:29):
Yeah, yeah.
That's so interesting.
And and you know, like one ofthe things that comes to mind
for me is one of the reasonsthings sometimes move slowly or
decisions often take so manydifferent seats at the table is
because everybody's trying tomitigate risk and everyone's
trying to um, you know, uhmitigate their own personal
liability involved in making arisky decision.
(07:51):
And so uh I'm sure as we talkabout the NextGen Jane story,
we'll get we'll get into all thedifferent types of risk and
things that you as the as aco-founder have had to take on
and assess.
Um, but I'm curious, justbefore we dive in, if that's if
that's a consideration that, youknow, at all for you.
Ridhi Tariyal (08:07):
Um in terms of just to clarify the question and
how to think through risk inthis environment?
Swaril Mathur (08:12):
Yeah, and uh, you know, you described that that
you've intentionally pivotedyour career towards things that
can move faster.
A trade-off of moving faster isoften taking on more risk or
making riskier decisions withless information or less input.
Does does that actually doesthat assumption actually feel
true to you, first of all?
I'm just stating that as anassumption.
And then second, how do youhandle it?
Ridhi Tariyal (08:30):
Yeah, it's actually very liberating.
I think that part of the the umproblem that's inherent in
women's health is that there's adata gap.
So you're just functioning in alack of information, right?
Inherently the risk profile ishigher.
Um, because you're, you know,will menstrual blood be the
right aperture by which tounderstand uterine biology and
have insights into inflammatorydiseases that are female
(08:52):
dominant?
That's a question mark, right?
It's it's been a question markfor a long time because you're
not that not like you go in theliterature and there's 2,000
papers that you can reference.
There you go.
That's your that's yourbenchmark for where the risk
profile already is.
Nobody can actually mitigatethat risk.
If you think that it's aninteresting idea theoretically,
(09:12):
you're going to have to take onrisk to take a swing at it.
Swaril Mathur (09:15):
Yeah, yeah.
Well, I I mean, and it soundslike you're exactly the right
person to be doing that, givenhow energized you are about it.
So tell us so that we coulddive into it, tell us a little
bit about NextGen Jane.
Ridhi Tariyal (09:25):
Um so the the best way to describe it is that
is the core thesis, is thatuterine biology is a singular
aperture by which to understandinflammatory and immune-mediated
diseases that that areoverwhelmingly impact women.
Um, and examples are autoimmunediseases and endometriosis,
which you know is often thoughtof as a reproductive disease,
(09:47):
but is a chronic inflammatorycondition.
Um, and fundamentally, um, youknow, we believe that that part
of the reason that there are notgreat tests to help you find
out early on that you have thesediseases and not great medical
options to as to how to treatthese diseases is because there
is a gap in understanding themolecular underpinnings of these
(10:08):
diseases.
And so fundamentally, whatwe're trying to do is we we've
we've put a stake in the groundto say um the uterine, the
uterus is a singular organ.
And if you could understand themolecular infrastructure of
this dynamic biology, right?
An organ that that goes throughan entire developmental cycle
every 20 to 32 days, right?
Um, grows, sheds, you know,knows when to become placenta.
(10:31):
Um, if you could actuallyunderstand what's happening in
that uh uh dynamic catalog, thenyou have unique insight to be
able to help understand what isactually causing some of these
diseases and what are some noveluh insights that you can use to
actually think about new drugs?
Swaril Mathur (10:47):
Yeah, yeah.
And that thesis is socompelling.
I mean, that the the potentialof what the applications could
be, if all of that holds true,is really interesting.
But you you called out a momentago that it is a question mark.
So how did you how are youtackling that, right?
Why, why start a company?
Why is this a company, not aresearch project in a lab?
(11:08):
How are you de-risking this?
What's your thought processaround that?
Ridhi Tariyal (11:11):
Yeah, I think there's two great questions in
there.
One is how?
Um, and that's a reallyimportant question is how would
you even begin to answer it?
Um, and then there's asecondary sort of question is is
a startup the right context todo this?
Like why not do it, you know,just in academia, where that's
the traditional way life sciencehappens, right?
There's years and years ofbasic research that happened in
a university setting.
(11:32):
And then you license that IPout, and there you go, you're
off to the races buildingsomething um uh more
productized.
Um, you know, for us, um, maybeI'll answer the the second
question first, which is umthere when we started, which was
a long time ago, you know, Iwas in this fellowship around
2012, 2013, um, there stillwasn't that much of an appetite
(11:52):
to take a serious look at thissubstrate as an undervalue
overlooked uh medical specimen.
And um uh, you know, the thecontext is actually the
landscape's different now.
I think that every month almostI see a new call out for
menstruation uh in grantsthrough NIH, as well as um, you
know, funding opportunitiesthrough other nonprofit
(12:14):
organizations and foundations.
I hear all, you know, oftenhear of different universities
having labs that are arebeginning to evaluate this.
Um, and so maybe if I wasstarting this company in 2025, I
would have thought, oh yeah,this should just stay in a
university setting for a fewyears before we take it out.
That just was not the case in2012.
You know, we we hadconversations with academics
(12:36):
where they were very uh frankand said that that this is not
something that we are interestedin looking into, that labs are
are not going to be friendly toworking with this specimen type.
Um, and so at the time it feltlike there was no avenue through
the traditional routes, right?
Um, as well as um my co-founderand I were neither of us, we
you know we were both at thebroad and we had uh uh done
(12:59):
research in that setting, but weweren't PIs in our own right.
And so the thought of like,okay, we, you know, where how do
we how do we set up theapparatus for writing grants?
Um, you know, ironically, now,years later, we won millions of
dollars in grants through SBIRsand uh, you know, our are find
ourselves to be pretty adept atit and enjoy writing grants.
But at the time we thought,okay, so you know, writing
(13:20):
grants and going the traditionalroute uh through through how
this should should work andpublic through from public
funding is probably not for us.
Um and so that that just leftthe only option, right?
Which is we've got to getinvestors interested in this.
We've got to get someone whowho loves deep tech um to see
the value in this and and wantto be on the journey for it.
Swaril Mathur (13:37):
Yeah, yeah, absolutely.
And then going back to to kindof the first question, which was
the the why.
Ridhi Tariyal (13:43):
The how.
Swaril Mathur (13:44):
The how.
Ridhi Tariyal (13:46):
There's a why though.
No, that's that's the that'sthe next good question.
The why.
The how is, you know, so wesaid, all right, um, you're
gonna we're gonna collect thisthis specimen, we're gonna,
we're gonna have to do it acrossmany individuals, we're gonna
have to do longitudinalcollections, right?
Because we can't, we can'tbegin to understand such a
dynamic organ with a single timepoint analysis.
Um, and so when we took all ofthose things into consideration,
(14:08):
we landed on having an at-homeum specimen collection kit,
number one.
Um, number two, we wanted tomake sure that we were not um
asking uh individuals to do likethings like go get wet ice and
dry ice and make an entirescience experiment at home.
You know, in general, the moreum asks you layer on for a
participant, the more you drivedown activation energy.
(14:30):
Um and, you know, we were verykeen that we would only be able
to solve this conundrum of like,is there value here if we could
collect multiple samples acrossa single cycle, multiple cycles
across, you know, a year andmultiple years across a
lifetime.
Um so we spent a lot of timereally optimizing how do you do
all of the, if you can imagineeverything from um the UI of
(14:54):
like I have instructions for usethat the patients can use at
home, um, you know, the theintuitiveness of the design.
Um, there were things like thatwe had to engineer around, uh,
like um when women were droppingtheir tampon into the device in
the early days, they wereleaving the tampon string out
and it created a wicking path.
Um and so we designed amechanism by which as they
(15:19):
closed the device, the tamponlooped it around and and was
driven into the device.
There, and this is like asingle example.
There were so many exampleslike this where you are
discovering how the patient canis is actually using the device
and way where there are naturalplaces of misunderstanding.
Swaril Mathur (15:36):
Yeah.
Ridhi Tariyal (15:36):
And there are only two ways to tackle that
really is you can try tooptimize your instructions for
use for more and more clarity,but there's a plateau there.
Um, or you can design for themto say, I see that that mistake
you keep making.
I'm gonna actually design theproduct so you cannot make that
mistake.
Swaril Mathur (15:53):
Yeah, yeah, yeah.
Well, and and that right thereis just a great lesson in in
like product development and andtrue primary user research.
Um, not asking someone how theywould put the tampon in it, but
just seeing how they do it inreal world.
But just so take a step back.
This is like back to the thebroader how you decided, okay,
we have to start a likeventure-backed company to go and
(16:17):
do this basic science research.
And then everything you'retalking about right now is the
mechanics of how to get likestudy participants to give you
samples of menstrual blood sothat you can do the basic
science research to see if thethesis holds true, that genomic
data from menstrual blood can beused for diagnostic and
potentially therapeuticindications.
Ridhi Tariyal (16:38):
Correct.
Swaril Mathur (16:38):
Wow.
And what was the process liketo get investors?
What are the types of investorsyou were looking for?
What story were you tellingthem?
And what were they pressuretesting?
Because in in the same way thatyou just described, that, you
know, the the universityresearch environment maybe
wasn't optimized for this whenyou started it.
I am curious whether theventure environment was ready
(16:58):
for it.
Ridhi Tariyal (16:59):
Uh, more ready.
As evidenced by like how theyshould be played out.
Um, you know, I I don't know ifthis is would be a
controversial statement, but Iwould say that when you're
raising a seed round, people arewilling to take risks.
Um and and the further you go,like it's harder to raise the A
because you need to have hit acertain proof point.
It's harder to raise the Bbecause you need to have reached
(17:20):
an even higher proof point.
Um, but at the seed stage, youknow, having a uh really good
thesis of what you're doing andwhy you're doing it, um, and
having uh a really compellingsort of vision as to what kind
of value we could unlock isalmost sufficient to get
investors that are sort of havethe right risk profile
interested in taking that smallbet, right?
(17:41):
It's it's not gonna be bigmoney, like our seed round was
$2 million.
Um, but there were enough peoplearound the table that said,
okay, we get it, you know, um uhthis, this, there's not a lot
out here in terms of what'sgoing on with the specimen.
If you actually were able todiagnose some of these diseases,
it could really unlock value interms of both unmet clinical
need um as well as shorterdiagnostic odysseys, as well as,
(18:05):
you know, obviously potentiallymaking a lot of money because
there's such a demand to haveanswers.
And and women are extremelyelectrified uh individuals when
it comes to wanting moreinformation about their bodies.
So that's enough of a valueproposition that we're willing
to take that risk at the seedstage.
Um, and so that was, you know,our approach was making sure
that there was enough of areally compelling thesis as well
(18:27):
as a enough of a um the homerun if we hit the home run is
really big.
Swaril Mathur (18:31):
Yeah, yeah.
No, that resonates.
And uh, you know, it in theseed stage, it sort of sounds
like you're selling a story.
And if that story resonates,they're willing to take the risk
on the technology becausethat's by definition what what
the seed is.
Yeah, that's that's what it is.
Yeah.
Um, and you mentioned the valueproposition, and there's
there's multiple layers to that.
But just speaking to kind ofthe business and financial value
(18:54):
proposition, you know, havinghaving spent some time in kind
of the women's health arena ofmed tech, I've seen countless
examples of solutions that aredesigned to address a legitimate
unmet need, but are trying toslot into pockets of healthcare
where the dollars havehistorically been small, either
because, you know, the setreimbursement rate for a certain
(19:16):
category is really low, um, orbecause the procedure volumes
have historically been low.
And all of that makes it reallychallenging to try to pitch a
business case that's compellingeither to investors or to
eventual acquirers.
Um, and that's a reallimitation.
I mean, I've I've seenfirsthand from my time in in BD
at Axonics how how you know asmaller TAM makes it really
(19:39):
challenging to make a compellinginvestment or acquisition
thesis.
So how how has that come up foryou?
Um, how have you thought aboutthe the business you know
proposition?
Ridhi Tariyal (19:49):
Um it's it's been a moving target.
Uh and it sometimes in partdepends on where you are in like
the venture cycle of whatthey're interested in investing
in.
I would say when we startedout, um there was uh an even
more, and you know, it stillexists to this day.
I think everyone knows inVenture that um uh a pure
diagnostics play is just not ascompelling as a drug play at
(20:12):
some points, as a data play, asa platform play, you know, in
search or favorite um uhcategory.
And the the reasons are sort ofum understandable.
You know, you you mentionedreimbursement and TAMS being
small in women's health.
Um, reimbursement is just sortof hardened diagnostics across
the board, right?
I mean, if you get a diagnosticFTA approved, there's no
guaranteed you're gonna get itreimbursed.
(20:34):
Um and that is uh a lot of riskfor a VC to take on.
First, they're taking on thescientific risk of like, does it
will it work in the way thatyou assume that it'll that it
will work?
Then they're taking on theregulatory risk of like, will it
make through make it throughthe appropriate regulatory
bodies?
And then there's still consumerand payment risk.
Swaril Mathur (20:52):
Mm-hmm.
Ridhi Tariyal (20:52):
Yeah, that's like again, that's just inherent to
diagnostics, much less layeringon that if you were to get
reimbursement, by the way, ifit's a women's health product,
the reality of the data, datadata show that women's health
interventions are just uh morepoorly reimbursed.
And so it's not even going tobe as lucrative as something
that would be for um, you know,a non-women's health uh product.
(21:14):
Um, so there are a couple ofthings like that have helped
sort of thread the needle at theappropriate moment.
Um, you know, one has been it'swhy you saw early on so many
women's health companies havinga fertility plate, because there
are no known exceptions, right?
Like if you think about um verysuccessful businesses that sort
(21:35):
of are at the intersection ofhealthcare and and um a cash
pay, um infertility IVF is is abig one, right?
Um and it it I don't thinkincorrectly in some ways, of
course, family building involvesmultiple um parties, but like
in some ways you could think ofit as a woman's health business.
Um, and it does really well.
And so uh that's why there wereso many startups initially that
(21:57):
were, and you know, includingus, to say, all right, this is
this is a really viable marketentry point.
And it's something that thenumbers show that it's a vibrant
marketplace and andreimbursement so difficult that
if you need to prove out thatthere is a demand um for the
product that you're building umthat's gonna have to be
self-paid, that is the right,you know, uh proof point, that
(22:18):
is the right call point to go tobecause there's already a
willingness to pay.
There are other trends thathave changed over time.
So clearly, telehealth um andDTC everything, like there's a
lot of diagnostic companies,have also transformed how people
think about this becausethere's now this sort of
willingness to accept um thatconsumers for convenience and
(22:40):
maybe privacy would pay out ofpocket for things, that they
could get in a queue with aspecialist and wait for a
reimbursement.
And so there was a tensionthere for a while.
And again, especially when westarted out, um, there was this
sort of fundamental belief inamongst life science VCs that,
like, unless you're goingenterprise, meaning like, you
(23:00):
know, reimbursement through theclinic, it would be a tough slog
if you went the alternative.
If you were gonna go directconsumer, you know, sell this at
whatever price point that theconsumer was willing to pay, it
would never be as attractive asa reimbursed product being sold
through through the traditionalchannels.
And I actually think, I mean,some of that still exists, but I
actually think that over thelast decade, we've actually seen
a lot of transformation andproof points that maybe that's
(23:23):
not always the case, right?
People are buying birth controlpills online and people are
buying um hair loss uh pillsonline.
And there's just this really uhvibrant market showing that
people are willing to make a lotof these decisions, um, uh not
necessarily through thetraditional channels.
Swaril Mathur (23:40):
Yeah, yeah.
And those business models areinteresting and I think have
have probably contributed tosome of the growth that we've
we've seen and more med tech,more women's health med tech
innovations actually getting outinto the real world, um, which
is a positive.
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Swaril Mathur (24:11):
Uh a tension or a balance that I'm curious for
your thoughts on is, you know,there's a pro to getting things
into patients' hands morequickly by going the cash pay
consumer route.
Um, but there's also atrade-off of that's not going to
be accessible to everybody,right?
Inherently, there is a segmentof the population for whom that
will, no matter how motivatedthey are, that is just not an
(24:31):
option.
Um, how do you, how do you guysthink about that at Nextgen
Chain?
Ridhi Tariyal (24:36):
Yeah, it's very important for us that we are on
the path to reimbursement.
I think the the way we try toum reach balance in that
decision making is look, if wesaid we're gonna wait for
reimbursement, investors wouldnever be interested, right?
They would be like, that's whatis that?
After you get the product uhworking is that two-year
clinical utility study?
Like what you know, and whatare the economics that you're
(24:57):
convinced, convince convincingpayers of?
Like, and I've spoken to payersand uh, you know, they're
they're um often transparentwhere they say, listen, uh, you
know, I'm a commercial payer, Icover people for 12 to 24
months.
And uh my expectation is thatanything you offer actually has
to save me money across mycovered population within that
(25:18):
time frame.
And so anything like an easierway to diagnose a disease that
is likely to cause morehealthcare expenses in the year
of diagnosis, um, that's gonnabe a tough sum, right?
There, and they're sort ofhonest about that.
And I still think that there areclinical utility studies that
can show them that that there'sstill ways to save money, that
they're gonna take time, right?
They're gonna take time to showexactly um at a statistically
(25:41):
significant number and awell-powered analysis analysis
to say, look, over X number ofyears, X number of patients, we
were able to sell save you Xdollars.
You know, who funds thatclinical utility study?
Um, and so one of the best waysto do that is just to get the
product on market and start withcash pay as you do that utility
study and work with pairs toreally get them to buy in and
(26:04):
say that this is somethingthat's worth reimbursing.
Um and so that that's how we wetackle it.
We say what we're just startingat at cash pay, you know, to
make this attractive enough thatit continues to be a fundable
priority with the intent to uhreach a point where we can make
this, you know, reimbursed.
Yeah.
Swaril Mathur (26:20):
Yeah, that makes a lot of sense.
And I think, you know, it's ait's kind of the phase strategy
of you solve for the thing youcan solve for at the time, and
you can't, you can't ever reachthat giant population um that
requires reimbursement if you ifthe company doesn't exist.
Um the company has to do whatit has to do to exist, which
sounds so basic, but that'spretty important.
Ridhi Tariyal (26:40):
You know what?
One of the interestingphenomena, again, this is also
context specific, is umobviously there's there is
something happening inpharmaceutical, right?
There, there are there's now amovement to sell drugs, DTC.
Swaril Mathur (26:52):
Yeah.
Ridhi Tariyal (26:52):
Right.
We we we see the war happeningfor GLPs um in terms of who's
gonna get access through whatchannel.
I mean, I think my favoriteheadline recently was Costco's
gonna start selling it.
Oh my gosh.
I was like, game over, blackblack, game over.
Um, but the the interestingthing is gonna be that as and
more and more pharma companiesare signing on, right?
Not just for GLPs.
They're they are understandingthe importance of this channel.
(27:15):
Um, and so you know, if youhave a DTC uh uh, you know,
business arm um for your drugs,you will need a DTC diagnostic
arm for your business, right?
You can't prescribe a drugwithout having a diagnosis.
Um and I am sort of waitingwith bated breath.
I think it's just an excitingmovement for diagnostics to say,
okay, now pharma is going to becompelled, right?
(27:37):
To be like, we need a uh acompanion diagnostic in a
completely different way.
We need a companion diagnosticthat we can prescribe so that we
can unlock reimbursement forthe drug.
Swaril Mathur (27:47):
Yeah.
No, it's a it's a great pointthat if once you go consumer,
the whole, the whole carejourney actually kind of has to
be compatible with that.
Yeah.
Yeah.
Ridhi Tariyal (27:57):
It introduces a market dynamic that that is, I
think, in net favorable fordiagnostic companies.
Swaril Mathur (28:03):
Yeah, yeah.
And, you know, as a as aconsumer, there's something very
motivating about the idea thatyou could take control of all
these things and and and reallybe empowered to, you know, to
move the journey forward at yourown pace and not be hamstrung
by lack of availabilityavailability of doctor's
appointments or specialists orthings.
So um, I think there's a lot ofreally motivating aspects
(28:25):
there.
Um, I want to get to some ofkind of the key lessons that
you've learned in the Next GenJane journey.
We've alluded to some of them,of course, but before we go
there, can you just give us asense of like where have where
has the company come so far?
What since from founding tonow, you know, what have been
the major milestones, the majorkind of de-risking pieces?
And then what's what's next onthe horizon for you?
(28:45):
What are you focused on rightnow?
Ridhi Tariyal (28:47):
Yeah, I mean, I think there's been a lot of
major de-risking milestones.
I think the best way tounderstand it is this um stack
that I always describe, which isthe core asset of the company.
Um, you know, I think thatthere's been a lot of focus over
the last few years on, oh, youknow, a smart tampon was like
nice clickbaity title that theygave Next Gen Jane for a while,
or um, you know, um a way todiagnose, you know, hardcore
(29:11):
disease at as at home that oftenhad surgery as the only
alternative, which none of thesethings are wrong.
Um, but interestingly enough,the the sample collection system
and the fact that it enableslongitudinal narratives is is
the foundation of what we'vebuilt, but it's only the bottom
layer.
Um the layer you know, abovethat is great.
You've got the sample type, youknow, and it it you've got
you've done some pre-analyticalstandardization, you've got the
(29:33):
sample type in your lab.
Now what?
Um, and we we actually generatehigh-dimensional data from it.
It's not like we're doingtarget uh sequencing, we're not
looking at PCR, we're sayingnext generation sequencing.
We're doing full RNA seq.
We are um understanding 19,000gene profiles from the host, so
the actual woman, as well as thefull metatranscriptomics, which
is the activity level of thebacteria in that sample, um,
(29:57):
which just is such key insight.
Um And then we layer on DNAsequencing, right?
And we we're doing that verydeliberately because we know
we've taken it with that, likeone of the bets we're making is
that in this environment whereyou know AI is going to
transform everything, youactually you actually need
high-dimensional data and youneed matched RNA and DNA
(30:17):
samples.
And that is what makes yourdata extremely valuable.
And so you know we've donewe're deliberately going down
that path.
So great, you've got thishigh-dimensional data.
It's your ability to doanything with it is only as good
as your ability to understandwho gave it to you.
And so we try to do clinicalgrade annotations, which means
we both extract valuable labelsfrom EMR, so medical records
(30:38):
that we get, as well as we we itum have instituted this
proprietary next-genjane survey.
It's pretty intense.
It's over like a thousandquestions.
And over time we have foundthat, you know, the EMR is
spotty.
You never, you it's not likeyou have EMR from the last 20
years.
You've probably gone to 50different doctors.
Um anything that they're you'regetting from an quote-unquote
EMR perspective is sort of aspot chuck of like what your
(31:00):
latest doctor understands aboutyou.
Yep, that resonates.
We ask, we've asked patientsquestions like, oh, do you have
any autoimmune diseases?
And they will be like, Yes, Iwas diagnosed with lupus and so
and so.
Not in the EMR.
Nothing to anything recordsthat they've given us, right?
As you can imagine, reallyrelevant information to know
when you're thinking about likewhat data you're looking at.
We've asked women who are like,have you ever had any
(31:20):
miscarriages?
We'll have women be like, Yeah,I've had four miscarriages in a
row, not in the EMR.
Again, really germane touterine biology and and
everything that we're thinkingabout and looking at.
And so we have found that theannotations are extremely uh
improved by just engagingpatients themselves to say, we'd
love we'd love to know thingsabout your health.
Um, and then finally, the mostimportant sort of de-risking for
(31:44):
us came through when wedeveloped the what we call a
female-specific ontology, whichis, you know, especially so
female-specific labels for thedata that have everything to do
with femobiology.
The most evident one thateverybody knows now is sex as a
variable, right?
You should take sex as avariable into consideration.
It's a very important covariatein your analysis.
(32:05):
Um, and and great, awesome, youknow, most data sets that we
know track that.
What we found over the years isthings like cycle day of when
you did a sample collection,extremely important uh
covariant.
How heavy of a bleeder you are,extremely important covariate.
And so I would say really inthis journey, um, probably the
most important invention that wehave come up with are these the
(32:28):
female specific labels thatdrive our understanding of the
data.
Um, and my, you know, in termsof your like, what why was that
like a uh unlock in terms ofde-risk moment?
Um, we have this beautiful uhchart that we we show, which is
uh two charts, histograms, thatum plot out our what we call our
endometriosis score, which islike a compound um score based
(32:50):
on RNA-seq data.
And it, you know, it spits outa score that essentially is uh
our assessment of whether or notyou have endometriosis, right?
And we collect cases and wecollect controls.
And ideally, what you want tosee is that there's this nice
separation in those two curves,right?
And so we we plot this data andon the left hand side um you
see cases versus controls, andthe curves are completely
(33:10):
overlapping.
Swaril Mathur (33:12):
Oh my gosh.
Ridhi Tariyal (33:13):
Not ideal.
And on the right hand side,there's like a five-fold
separation.
Beautiful, exactly what youwould want to see.
Um, and they were put throughthe same algorithm, same data
points, like absolutely the samedata.
The only difference is on theright hand side, we actually
told the algorithm all of thefemale specific labels.
We told it what age she was inher cycle when we collected the
(33:34):
sample.
We told her what type ofbleeding phenotype she had, et
cetera.
On the left hand side, we justdidn't.
We said pretend this is a dataset exactly like everyone else
collects.
And you know, the curves werecompletely overlapped.
And so for us, that was it.
We we were like, this is thefuture of women's health.
Wow.
This is massive de-riskment,which is if you have these
(33:55):
labels in place, you areactually able to see signal from
noise.
And that that to me is is thereal value of NextGen Jane.
Forget about the diagnosticpiece of it, is this data
architecture and this stackthat, you know, if you have a
belief that uh, you know, themost valuable thing in this, in
this um changing, very quicklychanging landscape is
proprietary walled gardens ofdata that are well labeled and
(34:18):
well annotated and are specificto very um you know, clear
populations that you understand,that is what we are building.
Swaril Mathur (34:25):
Wow.
That is incredible.
I love, I love that example ofthe uh, you know, the
histograms.
And I think what what it bringsto light, you know, as a again,
as a consumer who hasn't spenttwo decades in genomics, right?
Who's who's trying tounderstand, you know, the impact
of all this is conditions likeendometrioses for so long, the
talk track around them has justbeen they're multifactorial and
(34:48):
it's so complicated, and youcan't predict when you're gonna
have a flare-up or what howsignificant the lesions are.
There's all these things thatthe answer has just been it's
too complicated.
Throw your hands up in the airand just uh like brute force
with the bluntest toolspossible.
We'll just rummage aroundinside your body and try to pick
out weird lesions that we find.
(35:09):
And that's not a commentary onlike the wonderfully skilled
physicians.
It's a commentary on the lackof the basic science to support
anything different up until now,right?
Um, but the nuance with whichyou and your team have thought
through what are all of thelayers on top of the data and
what are the ways they need tocome together to make this data
(35:30):
truly actionable and todifferentiate and tease out
these, you know, highlyintermingled, um, you know,
highly complex conditions isreally interesting.
This is really profound.
Ridhi Tariyal (35:42):
Thank you so much.
And I will just telescope outto make two other comments.
One is is that um imagine, soyou know, if if the way that you
diagnose the disease is throughsurgery, right?
Uh what is the clinicalendpoint you're chasing in uh
drug trials?
You know, it's not actuallydegree disease regression right
now.
The clinical endpoint forendometriosis trials is pain.
One of the most subjective uhendpoints you could imagine,
(36:06):
right?
And you know, people whodevelop drugs for endometriosis
know this, that this is aweakness in the entire way we
think about drug development forendometriosis is you need a
molecular phenotype to change tocomment on whether or not your
drugs are making an impact onthe progression of the disease.
If you do not have thatmolecular phenotype, what are
you assessing?
(36:26):
Just reduction in pain, whichagain could be placebo effect.
Like is this right?
Swaril Mathur (36:30):
Right, right.
Like the level of clinical riskin running that trial just
increases tenfold because youdon't have an objective,
reliable metric.
Ridhi Tariyal (36:38):
And and I'm just tying this to like this is why
this is so much beyond data,right?
Once you establish a molecularphenotype, great, sure, it can
be used to diagnose, but thinkabout how it could transform the
small molecule options thatwomen are going to have in the
future.
Yeah.
Right.
Now you're actually saying goup against disease progression.
Like that's what you should beevaluating as to whether or not
these drugs work.
And then if you telescope outfurther, it's, you know, why not
(37:01):
just endometriosis, it's notjust reproductive disorders,
it's immune-mediated disorders.
You know, it is all thesedifferent conditions that affect
women.
And, you know, uh, there isthere's this really interesting
um comment I always make when Ithink about like what why would
menstrual effluence be the rightum uh uh aperture for thinking
about all inflammatory andimmune mediated diseases?
(37:23):
The the biggest answer is thatmenstruation in itself is a
programmed inflammatory event,right?
Like women, female biologypresents model systems you can
you can use to study, you know,loosely good inflammation.
So like ovulation is an is aninflammatory event, but it's not
it's natural, right?
You're you're gonna getinflammation during ovulation,
(37:45):
implantation, inflammatoryevent, menstruation,
inflammatory event.
And menstruation is reallyunique in because it is, it
provides you the opportunity tounderstand the kinetics of
inflammation.
What happens duringmenstruation?
You have this huge inflammatorycascade that kicks off because
your body is saying, shed,right?
Kill these cells and shed theendometrial lining.
And then you have this repairprocess where you now have
(38:08):
growth of the new lining.
And so over the course of threeto five days, you have a model
system by which to observe thenatural cascade of inflammation
to repair, right?
And and we would offer up likeinflammation to rep, well,
inflammation is the consensuspathophysiology of so many
diseases.
If you could understand therelationship between
inflammation and repair, you nowhave insight into so many
(38:32):
diseases.
Absolutely.
Swaril Mathur (38:34):
And I just think, you know, as we as you zoom
back out that way, the thingthat comes to mind for me is
we've talked before about thefact that so many other
conditions that aren't women'shealth conditions, they are just
human conditions, have beenstudied primarily in the context
of men, right?
The research studies, clinicalstudies have historically been
mostly male because menstruationis a confounding factor.
(38:54):
And be because that's it, youknow, if you're if you're a
scientist trying to maximize thecleanliness of your data to get
a result, it's better tocontrol for that factor by not
introducing it.
And um, and it makes me thinkthat this could be a possibility
to kind of catch up on thatresearch and and use this as a
lens into those chronicconditions that have been
studied so broadly in otherpopulations.
But maybe there's a gap in theunderstanding for women.
(39:17):
And this could become a tool toto catch up on that and maybe
in a more accelerated waybecause of the ease of measuring
it with a natural biopsy, likea a natural, a natural source
for sample collection.
Ridhi Tariyal (39:30):
Yeah, totally.
I mean, even so, you know,again, I I tend to focus on like
the scientific aspects becausethey're just so super cool.
But even from a um complianceperspective, think about how
hard it is to get patients tocome back for blood draws.
That is incredibly expensive.
And it, you know, you lose alot of patients from that.
Imagine just mailing them asample collection kit and being
(39:50):
able to get them to regularlygive you uh, you know, molecular
updates in that way, right?
Just just clinical coordinationcould be improved by
integrating something like this.
Imagine how many um phase onesafety studies uh don't have
women.
Right.
I mean, just just and and it,you know, we think that like the
it's fine, but if you thinkabout like the sheer number of
(40:11):
drugs that have been pulled offthe market are mostly for for
side effects that they had onwomen, right?
It's it's just it's the the theutility of it is just, you
know, when you um when I haveput my big vision hat on, I'm
like the utility of it is justendless.
Swaril Mathur (40:25):
Yeah, yeah, absolutely.
You know, I think there's thisis an incredible opportunity.
Um, we've talked a lot aboutthe NextGen Jane story, and I I
kind of want to zoom back outnow as we've as we've explored
it and talk about your journeythrough this.
I mean, as you look back um onall of this, what have been some
of maybe the the morechallenging moments for you, the
moments that have really testedyou and pushed you?
(40:46):
What have you learned fromthat?
And and maybe to summarize itall, like how are you different
as a leader and as a person nowthan you were before you started
this journey?
Ridhi Tariyal (40:54):
Oh, those are tough questions.
Um, maybe I will answer theeasier one first uh about uh the
challenges and in the journey.
I mean, I think one bigchallenge is um, you know,
women's health tends to get dripcapital.
Um and this science could havemoved so much faster if there
were bigger checks cut for it,right?
But um I always say that, youknow, when you give uh certain
(41:16):
areas of science uh smallchecks, you are telling them to
make incremental progress.
And when you write big checks,you're telling them to shoot for
the stars.
Um, and so part of, you know, Iwill I will fully say part of
the tenure journey that we'vebeen on is really because
there's been no foundation,right?
So build we've been buildingthe scaffolding as we go, right?
Truly, truly being like, oh,this this is a really important
(41:38):
feature.
Now we have to go back andrecollect and to understand
exactly what the impact is.
I mean, even to figure out allof this, like uh the kinetics of
cycle day and how important itis, and some of these
covariates, you have to collectmultiple multiple, like day one,
day two, day three from like 50people across multiple cycles.
Like, you know, that's it'slike a huge undertaking to even
set the groundwork.
(41:58):
And so um, you know, some ofthat has been just this is just
gonna take time because it'snew, but absolutely some of it
has been so frustrating umbecause it's there's just not
enough capital that's gone intoit, right?
And you you can't, you know,again, once you get past the
series seeds date, it's harderand harder to unlock bigger and
bigger checks unless there isthis clear path to commercial
data, right?
(42:19):
And this is this tension of ifyou're doing something that is
more basic science oriented in ayou know capitalistic
environment, um, you are gonnaface that pressure, right?
To be like, when's this babycoming to market?
Right.
Love science, love, love theleaps and bounds, but like you
got to show me the path tomarket.
Um, and and that's beennecessary and frustrating.
(42:40):
And, you know, I've I've oftenwished to be like, uh, this
needs a $50 million truck to gofrom um zero to a hundred.
If you want the zero to ahundred and eighteen months,
then I need a bigger truck.
Swaril Mathur (42:51):
Yeah.
Ridhi Tariyal (42:51):
If if I can't get a bigger truck, then you're
just gonna expand the timelinefrom how we get to a hundred.
Swaril Mathur (42:56):
Yeah.
Yeah.
Yeah.
It's an interesting umrelationship between between
dollars in and timeline that Ihadn't really appreciated.
And and it's interestingactually to hear that in the
context of kind of the basicscience research, because those
of us who maybe haven't engagedas deeply in basic science
research might assume that itjust moves at the pace it moves.
Um, and basic science researchin the university setting, you
(43:18):
know, certainly isn't isn'talways that quick.
But I had never thought aboutit through the lens of there are
probably pieces of that thatare true.
And then there are probablypieces that if you throw some
accelerant on it, you could movemore quickly.
Ridhi Tariyal (43:31):
Let me tell you, I mean, like there are more
money at product development, itmoves more quickly.
If you throw more money atclinical trial enrollment, it
moves more quickly.
Yeah.
Swaril Mathur (43:38):
Yeah.
Yeah.
There you go.
Well, okay.
Um, and then what about kind ofyour your experience as a as a
leader?
I mean, there's just, I'm justimagining there's so much
uncertainty that you are facingevery day that you've been
leading through.
You are going after such animportant challenge, but that
also means on many days, Iimagine you're just doing things
that nobody has ever donebefore.
(43:59):
How, how has that, you know,transformed your leadership or
affected you?
Ridhi Tariyal (44:03):
Yeah.
I mean, and maybe two thingsthat stand out is curiosity,
persistence.
Um, I think curiosity becauselike again to your point, it's
everything's so new that you'vegot to be curious about it.
Um, and you know, you can youcan start to have conviction,
but it better be loosely held.
Cause the next day you willfind a data point where you'll
say, oh, oh my goodness.
Um and so, you know, you youcan't be uh again, when you're
(44:25):
when you're when you are umtraveling such novel terrain,
you've got to be open to that tosay, like, I am just gonna
learn.
I'm gonna ask a lot ofquestions.
I am, I'm gonna try to hold offon assumptions um and be ready
for wherever the data's gonnatake me.
Um and so I would say that isjust that's a skill set I have
had to hone to be okay withthat.
Um and I would say umpersistence because you know,
(44:48):
you do, you do want to quit,right?
You do there's there's manyopportunities where you're like,
um, you know, I've beenapproached by uh like scientists
that have really, again, reallycool tech, right?
That there's so much happeningin the world that I've that have
like actually asked me to say,are you done with NextGen Jane?
And like this is I I would lovefor you to come run this.
And um, you know, you're alwaystempted, like, because you're
(45:08):
like, oh, this this is the newcool shiny toy.
Yeah.
Um, but but knowing that youare working on something that
really is is super novel in it.
It's it may you may have beenworking on it for seven years,
but it is still super novel tothe world.
Um and saying just stay thecourse, stay the course.
There there are breakthroughshappening and there are bigger
(45:28):
breakthroughs coming.
And if you can be patient withit and persistent with it, that
that like that the you knowoutcome is gonna be way more
than you anticipated.
Um and that's that's it, thathas been a like a skills, skill
to hone in itself, to say, like,no, of course there's gonna be
new shiny toys.
You've just you you've got tofinish this.
Like there's somethinginteresting here.
(45:49):
And the reason, you know, be--forget about the money for a
second and the capital acquiredto do something like this.
Like the reason that these teamthings take a long time is
because they're hard.
Like, and if you don't have thepatience for that, then you're
never gonna get to that theEaster egg at the end of this.
Swaril Mathur (46:03):
Yeah, yeah, absolutely.
I love the way you articulatedthat.
And I think your passion forthis is so palpable in the way
that you talk about this entirejourney, this mission, and
what's at the end of the finishline.
And I am, I am just so excitedto watch it come to fruition and
to continue tracking thejourney.
Ridhi Tariyal (46:21):
Thank you so much.
Swaril Mathur (46:22):
Ridhi, this has been a really wonderful
conversation.
Thank you for sharing yourpersonal story and the story of
NextGen Jane.
You were tackling some enormousproblems that will have
transformative impact on a lotof people and excited to
continue to watch it come tolife.
Awesome.
I appreciate the opportunity tochat with you.
Thanks for being on.
Welcome to MedTech Talk.
I'm your host, Swaril Mathur,and I'm thrilled to be joined
today by Ridhi Tariyal, CEO andco-founder of NextGen Jane.
Ridhi, thank you for beinghere.
Thanks so much for having me.
So I'm so excited for this conversation Ridhi
because you've been working onsomething really interesting in
a sector of healthcare that isso often overlooked and often
(00:25):
really challenging to dig into.
And before we get there, Ireally want to talk about how
you got to this place in yourcareer and what your most
formative experiences have been.
So can you tell us a little bitabout your professional journey
and how you landed in the worldof women's health?
Ridhi Tariyal (00:40):
Yes, absolutely.
I am an engineer by background,so went to Georgia Tech,
majored in industrialengineering, and actually did uh
banking and consulting at thestart of my career, if you can
believe it.
Um, did not like either, andreally wanted to dedicate my
time to something that I foundworthwhile.
And so joined Bristol MeyerSquibb for two years in their RD
(01:01):
group and thought, this is it.
This feels like the right home,but still too big, you know, um
massive infrastructure, lots ofbureaucracy, but doing the type
of work that I found exciting.
Um, and this was back in theday, um, I'm gonna time myself,
but somewhere around 2005, 2006.
And when I asked uh uh people,you know, what should I go into
(01:22):
if I want to do some, you know,genomics and precision medicine
and uh more startup type work.
And they were like, yeah, youthe startup's the right place to
go.
That's where a lot of the coolcutting-edge science is
happening.
Um, but you have to have anMBA, which, you know, I don't
think that would be the advicetoday.
I don't, I don't, you know, Iprobably wouldn't even take that
advice today, but I was like,okay, great, I'll go get my MBA.
(01:42):
Um, got my MBA at Harvard, andwhile I was there, I got a
secondary master's in biomedicalenterprise from MIT.
Um, really uh, you know, wantedto double down on um
healthcare, and MIT offer thisgreat program where you go
through med school classes, youround in the clinic, and the
thought is if you want to be amedical entrepreneur, um, sure,
go get your MBA, but you shouldalso know the jargon in medicine
(02:05):
and you should know theproblems on the front lines.
Um, and that will make you abetter life science
entrepreneur.
Um, loved it.
Uh, graduated uh from MIT, youknow, after this three-year dual
degree program, and took mythesis um and tried to turn it
into my first startup.
Um, this was uh around 2010.
So it was still, we were allstill recovering from the
(02:26):
housing crisis.
And um, you know, it was it wasme solo sort of pitching this
idea that was targeted towardsum an a consumer base in India,
genomics play consumer base inIndia, and uh couldn't quite get
anyone to bite.
So I ended up saying, okay, Iknow I want to do emerging
market genomics.
Um, and I got an opportunity toum do exactly that at the
(02:46):
Broad, um, which is a genomicsinstitute based in Cambridge,
Massachusetts.
I was uh leading the financeand operations for a large GWAS,
which is a genome-wideassociation study looking into
viral hemorrhagic fevers,specifically loss of fever in
West Africa.
And it requires you to be onthe ground for like four months
(03:07):
out of the year and you know,gave you access.
I was managing like a $10million budget.
And I was like, this is sort oflike a startup budget as well.
Um, I loved it, you know, itfelt like it was still a very
entrepreneurial environment.
Um, and uh did it for twoyears.
And after that, HBS had thisgreat opportunity.
It was called the BlavatnikFellowship, um, where they were
bringing back people who hadgraduated with their MBA within
(03:30):
the last five years and umgiving them what they called a
little bit of walking aroundmoney, you know, paying them
some sort of salary, uh, givingthem access to the entire IP
portfolio across the university.
So, you know, from the college,from the med school, um, and and
saying, try to spin somethingout.
You know, this is uh reallyhigh quality IP.
There's a lot of PI that spenta lot of time um in on this
(03:53):
basic research, but it's it'sthere, you know, all of this
research encounters the Valleyof Death, um, where it's not
quite a product yet.
So it's not ready for VC, andyet, you know, it's it's too far
along for basic research, andso it just dies.
Um, and I was looking at allthese cool things, you know,
very fascinating, um novel uhmaterial for our uh warfighters
(04:14):
in theater, um, you know, newinteresting machine vision
systems that were going totransform how we developed um
psychiatry psych psychiatrydrugs.
Swaril Mathur (04:23):
Wow.
Ridhi Tariyal (04:24):
Like, you know, was I I loved those projects.
I worked on them.
And at the same time, you know,I was in my early 30s and I I
was uh talking to my OBGYNsaying, well, um, how can I find
out the state of the state interms of my reproductive health?
And um, you know, I had just ascontext come from the broad
where genomics was justtransforming everything.
It cancer was a completelydifferent field, right?
(04:47):
Everything from how it'sclassified, diagnosed, how drugs
are developed for it, how it'streated, um, was all being
driven by molecular medicine.
And um, even uh microbiome wasnext, right?
There's so much researchhappening there.
And you go to your uh OBGYN,everything is analog, not
digital.
Um, you know, at Papsmere,they're looking for
(05:07):
morphological changes in thecell shape.
Um, you know, fibroids,adenomiosis, they're looking,
they're doing imaging to lookfor structural changes in the
uterus.
Um, endometriosis is probablythe worst, where they're still
doing surgery and going in withendoscopes, trying to identify
ectopic lesions.
Um, and none of it was reallydriven by molecular medicine.
And it was evident not only inthe tools that they had for
(05:28):
diagnostics, but in the uhoptions women had for drugs.
Swaril Mathur (05:32):
Yeah.
Ridhi Tariyal (05:32):
You know, very blunt instruments.
And so um I thought, oh,there's uh there's an
opportunity here.
Uh clearly this space needsmolecular uh medicine.
It needs that kind of uhunderpinning to understand why,
why we get these diseases, howto uh treat them in a more
targeted way.
Um, and that's really where Iwould say my career um, you
know, bent towards uh women'shealth in particular.
(05:53):
Wow.
Swaril Mathur (05:54):
Wow.
You know what something thatstrikes me about your background
and kind of the first chapteror maybe chapters of your career
is just how many differentthings you did.
And I'm curious, you know, Ithink it's always interesting
with careers when you talk aboutthen you can look back and
backwards justify why everytransition was logical.
But in a moment, it's notalways, it's not always so
(06:14):
seamless.
But I'm curious across allthese different things,
consulting and finance, youknow, rounding in hospitals,
being an engineer, doinggenomics in West Africa, were
there any like specific kind ofaha moments or any
transformative learnings thatyou find yourself now applying
in your in your current role?
Ridhi Tariyal (06:33):
That's a great question.
Um, one I would say that, youknow, I I learned very early on
that I don't do well inextremely constrained
environments or where, you know,environments where uh that are
so big that it takes um 15decision makers to to reach a
decision.
And so uh I would say that alot of my uh career decisions,
(06:54):
you know, as they evolved anddefinitely today, are oriented
towards how can you make how canyou be nimble, how can you be
um quick, how can you respond ina way that's that's um you know
thoughtful and yet reflectiveof how fast the world is moving.
And I would say that that, youknow, giving into that impulse
early on in my career andsaying, oh, this is too slow, I
(07:17):
don't want to move at this pace,um, has absolutely optimized me
for the pace at which scienceis moving now and the types of
decisions we have to make in inthis start of uh next change.
Swaril Mathur (07:29):
Yeah, yeah.
That's so interesting.
And and you know, like one ofthe things that comes to mind
for me is one of the reasonsthings sometimes move slowly or
decisions often take so manydifferent seats at the table is
because everybody's trying tomitigate risk and everyone's
trying to um, you know, uhmitigate their own personal
liability involved in making arisky decision.
(07:51):
And so uh I'm sure as we talkabout the NextGen Jane story,
we'll get we'll get into all thedifferent types of risk and
things that you as the as aco-founder have had to take on
and assess.
Um, but I'm curious, justbefore we dive in, if that's if
that's a consideration that, youknow, at all for you.
Ridhi Tariyal (08:07):
Um in terms of just to clarify the question and
how to think through risk inthis environment?
Swaril Mathur (08:12):
Yeah, and uh, you know, you described that that
you've intentionally pivotedyour career towards things that
can move faster.
A trade-off of moving faster isoften taking on more risk or
making riskier decisions withless information or less input.
Does does that actually doesthat assumption actually feel
true to you, first of all?
I'm just stating that as anassumption.
And then second, how do youhandle it?
Ridhi Tariyal (08:30):
Yeah, it's actually very liberating.
I think that part of the the umproblem that's inherent in
women's health is that there's adata gap.
So you're just functioning in alack of information, right?
Inherently the risk profile ishigher.
Um, because you're, you know,will menstrual blood be the
right aperture by which tounderstand uterine biology and
have insights into inflammatorydiseases that are female
(08:52):
dominant?
That's a question mark, right?
It's it's been a question markfor a long time because you're
not that not like you go in theliterature and there's 2,000
papers that you can reference.
There you go.
That's your that's yourbenchmark for where the risk
profile already is.
Nobody can actually mitigatethat risk.
If you think that it's aninteresting idea theoretically,
(09:12):
you're going to have to take onrisk to take a swing at it.
Swaril Mathur (09:15):
Yeah, yeah.
Well, I I mean, and it soundslike you're exactly the right
person to be doing that, givenhow energized you are about it.
So tell us so that we coulddive into it, tell us a little
bit about NextGen Jane.
Ridhi Tariyal (09:25):
Um so the the best way to describe it is that
is the core thesis, is thatuterine biology is a singular
aperture by which to understandinflammatory and immune-mediated
diseases that that areoverwhelmingly impact women.
Um, and examples are autoimmunediseases and endometriosis,
which you know is often thoughtof as a reproductive disease,
(09:47):
but is a chronic inflammatorycondition.
Um, and fundamentally, um, youknow, we believe that that part
of the reason that there are notgreat tests to help you find
out early on that you have thesediseases and not great medical
options to as to how to treatthese diseases is because there
is a gap in understanding themolecular underpinnings of these
(10:08):
diseases.
And so fundamentally, whatwe're trying to do is we we've
we've put a stake in the groundto say um the uterine, the
uterus is a singular organ.
And if you could understand themolecular infrastructure of
this dynamic biology, right?
An organ that that goes throughan entire developmental cycle
every 20 to 32 days, right?
Um, grows, sheds, you know,knows when to become placenta.
(10:31):
Um, if you could actuallyunderstand what's happening in
that uh uh dynamic catalog, thenyou have unique insight to be
able to help understand what isactually causing some of these
diseases and what are some noveluh insights that you can use to
actually think about new drugs?
Swaril Mathur (10:47):
Yeah, yeah.
And that thesis is socompelling.
I mean, that the the potentialof what the applications could
be, if all of that holds true,is really interesting.
But you you called out a momentago that it is a question mark.
So how did you how are youtackling that, right?
Why, why start a company?
Why is this a company, not aresearch project in a lab?
(11:08):
How are you de-risking this?
What's your thought processaround that?
Ridhi Tariyal (11:11):
Yeah, I think there's two great questions in
there.
One is how?
Um, and that's a reallyimportant question is how would
you even begin to answer it?
Um, and then there's asecondary sort of question is is
a startup the right context todo this?
Like why not do it, you know,just in academia, where that's
the traditional way life sciencehappens, right?
There's years and years ofbasic research that happened in
a university setting.
(11:32):
And then you license that IPout, and there you go, you're
off to the races buildingsomething um uh more
productized.
Um, you know, for us, um, maybeI'll answer the the second
question first, which is umthere when we started, which was
a long time ago, you know, Iwas in this fellowship around
2012, 2013, um, there stillwasn't that much of an appetite
(11:52):
to take a serious look at thissubstrate as an undervalue
overlooked uh medical specimen.
And um uh, you know, the thecontext is actually the
landscape's different now.
I think that every month almostI see a new call out for
menstruation uh in grantsthrough NIH, as well as um, you
know, funding opportunitiesthrough other nonprofit
(12:14):
organizations and foundations.
I hear all, you know, oftenhear of different universities
having labs that are arebeginning to evaluate this.
Um, and so maybe if I wasstarting this company in 2025, I
would have thought, oh yeah,this should just stay in a
university setting for a fewyears before we take it out.
That just was not the case in2012.
You know, we we hadconversations with academics
(12:36):
where they were very uh frankand said that that this is not
something that we are interestedin looking into, that labs are
are not going to be friendly toworking with this specimen type.
Um, and so at the time it feltlike there was no avenue through
the traditional routes, right?
Um, as well as um my co-founderand I were neither of us, we
you know we were both at thebroad and we had uh uh done
(12:59):
research in that setting, but weweren't PIs in our own right.
And so the thought of like,okay, we, you know, where how do
we how do we set up theapparatus for writing grants?
Um, you know, ironically, now,years later, we won millions of
dollars in grants through SBIRsand uh, you know, our are find
ourselves to be pretty adept atit and enjoy writing grants.
But at the time we thought,okay, so you know, writing
(13:20):
grants and going the traditionalroute uh through through how
this should should work andpublic through from public
funding is probably not for us.
Um and so that that just leftthe only option, right?
Which is we've got to getinvestors interested in this.
We've got to get someone whowho loves deep tech um to see
the value in this and and wantto be on the journey for it.
Swaril Mathur (13:37):
Yeah, yeah, absolutely.
And then going back to to kindof the first question, which was
the the why.
Ridhi Tariyal (13:43):
The how.
Swaril Mathur (13:44):
The how.
Ridhi Tariyal (13:46):
There's a why though.
No, that's that's the that'sthe next good question.
The why.
The how is, you know, so wesaid, all right, um, you're
gonna we're gonna collect thisthis specimen, we're gonna,
we're gonna have to do it acrossmany individuals, we're gonna
have to do longitudinalcollections, right?
Because we can't, we can'tbegin to understand such a
dynamic organ with a single timepoint analysis.
Um, and so when we took all ofthose things into consideration,
(14:08):
we landed on having an at-homeum specimen collection kit,
number one.
Um, number two, we wanted tomake sure that we were not um
asking uh individuals to do likethings like go get wet ice and
dry ice and make an entirescience experiment at home.
You know, in general, the moreum asks you layer on for a
participant, the more you drivedown activation energy.
(14:30):
Um and, you know, we were verykeen that we would only be able
to solve this conundrum of like,is there value here if we could
collect multiple samples acrossa single cycle, multiple cycles
across, you know, a year andmultiple years across a
lifetime.
Um so we spent a lot of timereally optimizing how do you do
all of the, if you can imagineeverything from um the UI of
(14:54):
like I have instructions for usethat the patients can use at
home, um, you know, the theintuitiveness of the design.
Um, there were things like thatwe had to engineer around, uh,
like um when women were droppingtheir tampon into the device in
the early days, they wereleaving the tampon string out
and it created a wicking path.
Um and so we designed amechanism by which as they
(15:19):
closed the device, the tamponlooped it around and and was
driven into the device.
There, and this is like asingle example.
There were so many exampleslike this where you are
discovering how the patient canis is actually using the device
and way where there are naturalplaces of misunderstanding.
Swaril Mathur (15:36):
Yeah.
Ridhi Tariyal (15:36):
And there are only two ways to tackle that
really is you can try tooptimize your instructions for
use for more and more clarity,but there's a plateau there.
Um, or you can design for themto say, I see that that mistake
you keep making.
I'm gonna actually design theproduct so you cannot make that
mistake.
Swaril Mathur (15:53):
Yeah, yeah, yeah.
Well, and and that right thereis just a great lesson in in
like product development and andtrue primary user research.
Um, not asking someone how theywould put the tampon in it, but
just seeing how they do it inreal world.
But just so take a step back.
This is like back to the thebroader how you decided, okay,
we have to start a likeventure-backed company to go and
(16:17):
do this basic science research.
And then everything you'retalking about right now is the
mechanics of how to get likestudy participants to give you
samples of menstrual blood sothat you can do the basic
science research to see if thethesis holds true, that genomic
data from menstrual blood can beused for diagnostic and
potentially therapeuticindications.
Ridhi Tariyal (16:38):
Correct.
Swaril Mathur (16:38):
Wow.
And what was the process liketo get investors?
What are the types of investorsyou were looking for?
What story were you tellingthem?
And what were they pressuretesting?
Because in in the same way thatyou just described, that, you
know, the the universityresearch environment maybe
wasn't optimized for this whenyou started it.
I am curious whether theventure environment was ready
(16:58):
for it.
Ridhi Tariyal (16:59):
Uh, more ready.
As evidenced by like how theyshould be played out.
Um, you know, I I don't know ifthis is would be a
controversial statement, but Iwould say that when you're
raising a seed round, people arewilling to take risks.
Um and and the further you go,like it's harder to raise the A
because you need to have hit acertain proof point.
It's harder to raise the Bbecause you need to have reached
(17:20):
an even higher proof point.
Um, but at the seed stage, youknow, having a uh really good
thesis of what you're doing andwhy you're doing it, um, and
having uh a really compellingsort of vision as to what kind
of value we could unlock isalmost sufficient to get
investors that are sort of havethe right risk profile
interested in taking that smallbet, right?
(17:41):
It's it's not gonna be bigmoney, like our seed round was
$2 million.
Um, but there were enough peoplearound the table that said,
okay, we get it, you know, um uhthis, this, there's not a lot
out here in terms of what'sgoing on with the specimen.
If you actually were able todiagnose some of these diseases,
it could really unlock value interms of both unmet clinical
need um as well as shorterdiagnostic odysseys, as well as,
(18:05):
you know, obviously potentiallymaking a lot of money because
there's such a demand to haveanswers.
And and women are extremelyelectrified uh individuals when
it comes to wanting moreinformation about their bodies.
So that's enough of a valueproposition that we're willing
to take that risk at the seedstage.
Um, and so that was, you know,our approach was making sure
that there was enough of areally compelling thesis as well
(18:27):
as a enough of a um the homerun if we hit the home run is
really big.
Swaril Mathur (18:31):
Yeah, yeah.
No, that resonates.
And uh, you know, it in theseed stage, it sort of sounds
like you're selling a story.
And if that story resonates,they're willing to take the risk
on the technology becausethat's by definition what what
the seed is.
Yeah, that's that's what it is.
Yeah.
Um, and you mentioned the valueproposition, and there's
there's multiple layers to that.
But just speaking to kind ofthe business and financial value
(18:54):
proposition, you know, havinghaving spent some time in kind
of the women's health arena ofmed tech, I've seen countless
examples of solutions that aredesigned to address a legitimate
unmet need, but are trying toslot into pockets of healthcare
where the dollars havehistorically been small, either
because, you know, the setreimbursement rate for a certain
(19:16):
category is really low, um, orbecause the procedure volumes
have historically been low.
And all of that makes it reallychallenging to try to pitch a
business case that's compellingeither to investors or to
eventual acquirers.
Um, and that's a reallimitation.
I mean, I've I've seenfirsthand from my time in in BD
at Axonics how how you know asmaller TAM makes it really
(19:39):
challenging to make a compellinginvestment or acquisition
thesis.
So how how has that come up foryou?
Um, how have you thought aboutthe the business you know
proposition?
Ridhi Tariyal (19:49):
Um it's it's been a moving target.
Uh and it sometimes in partdepends on where you are in like
the venture cycle of whatthey're interested in investing
in.
I would say when we startedout, um there was uh an even
more, and you know, it stillexists to this day.
I think everyone knows inVenture that um uh a pure
diagnostics play is just not ascompelling as a drug play at
(20:12):
some points, as a data play, asa platform play, you know, in
search or favorite um uhcategory.
And the the reasons are sort ofum understandable.
You know, you you mentionedreimbursement and TAMS being
small in women's health.
Um, reimbursement is just sortof hardened diagnostics across
the board, right?
I mean, if you get a diagnosticFTA approved, there's no
guaranteed you're gonna get itreimbursed.
(20:34):
Um and that is uh a lot of riskfor a VC to take on.
First, they're taking on thescientific risk of like, does it
will it work in the way thatyou assume that it'll that it
will work?
Then they're taking on theregulatory risk of like, will it
make through make it throughthe appropriate regulatory
bodies?
And then there's still consumerand payment risk.
Swaril Mathur (20:52):
Mm-hmm.
Ridhi Tariyal (20:52):
Yeah, that's like again, that's just inherent to
diagnostics, much less layeringon that if you were to get
reimbursement, by the way, ifit's a women's health product,
the reality of the data, datadata show that women's health
interventions are just uh morepoorly reimbursed.
And so it's not even going tobe as lucrative as something
that would be for um, you know,a non-women's health uh product.
(21:14):
Um, so there are a couple ofthings like that have helped
sort of thread the needle at theappropriate moment.
Um, you know, one has been it'swhy you saw early on so many
women's health companies havinga fertility plate, because there
are no known exceptions, right?
Like if you think about um verysuccessful businesses that sort
(21:35):
of are at the intersection ofhealthcare and and um a cash
pay, um infertility IVF is is abig one, right?
Um and it it I don't thinkincorrectly in some ways, of
course, family building involvesmultiple um parties, but like
in some ways you could think ofit as a woman's health business.
Um, and it does really well.
And so uh that's why there wereso many startups initially that
(21:57):
were, and you know, includingus, to say, all right, this is
this is a really viable marketentry point.
And it's something that thenumbers show that it's a vibrant
marketplace and andreimbursement so difficult that
if you need to prove out thatthere is a demand um for the
product that you're building umthat's gonna have to be
self-paid, that is the right,you know, uh proof point, that
(22:18):
is the right call point to go tobecause there's already a
willingness to pay.
There are other trends thathave changed over time.
So clearly, telehealth um andDTC everything, like there's a
lot of diagnostic companies,have also transformed how people
think about this becausethere's now this sort of
willingness to accept um thatconsumers for convenience and
(22:40):
maybe privacy would pay out ofpocket for things, that they
could get in a queue with aspecialist and wait for a
reimbursement.
And so there was a tensionthere for a while.
And again, especially when westarted out, um, there was this
sort of fundamental belief inamongst life science VCs that,
like, unless you're goingenterprise, meaning like, you
(23:00):
know, reimbursement through theclinic, it would be a tough slog
if you went the alternative.
If you were gonna go directconsumer, you know, sell this at
whatever price point that theconsumer was willing to pay, it
would never be as attractive asa reimbursed product being sold
through through the traditionalchannels.
And I actually think, I mean,some of that still exists, but I
actually think that over thelast decade, we've actually seen
a lot of transformation andproof points that maybe that's
(23:23):
not always the case, right?
People are buying birth controlpills online and people are
buying um hair loss uh pillsonline.
And there's just this really uhvibrant market showing that
people are willing to make a lotof these decisions, um, uh not
necessarily through thetraditional channels.
Swaril Mathur (23:40):
Yeah, yeah.
And those business models areinteresting and I think have
have probably contributed tosome of the growth that we've
we've seen and more med tech,more women's health med tech
innovations actually getting outinto the real world, um, which
is a positive.
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Swaril Mathur (24:11):
Uh a tension or a balance that I'm curious for
your thoughts on is, you know,there's a pro to getting things
into patients' hands morequickly by going the cash pay
consumer route.
Um, but there's also atrade-off of that's not going to
be accessible to everybody,right?
Inherently, there is a segmentof the population for whom that
will, no matter how motivatedthey are, that is just not an
(24:31):
option.
Um, how do you, how do you guysthink about that at Nextgen
Chain?
Ridhi Tariyal (24:36):
Yeah, it's very important for us that we are on
the path to reimbursement.
I think the the way we try toum reach balance in that
decision making is look, if wesaid we're gonna wait for
reimbursement, investors wouldnever be interested, right?
They would be like, that's whatis that?
After you get the product uhworking is that two-year
clinical utility study?
Like what you know, and whatare the economics that you're
(24:57):
convinced, convince convincingpayers of?
Like, and I've spoken to payersand uh, you know, they're
they're um often transparentwhere they say, listen, uh, you
know, I'm a commercial payer, Icover people for 12 to 24
months.
And uh my expectation is thatanything you offer actually has
to save me money across mycovered population within that
(25:18):
time frame.
And so anything like an easierway to diagnose a disease that
is likely to cause morehealthcare expenses in the year
of diagnosis, um, that's gonnabe a tough sum, right?
There, and they're sort ofhonest about that.
And I still think that there areclinical utility studies that
can show them that that there'sstill ways to save money, that
they're gonna take time, right?
They're gonna take time to showexactly um at a statistically
(25:41):
significant number and awell-powered analysis analysis
to say, look, over X number ofyears, X number of patients, we
were able to sell save you Xdollars.
You know, who funds thatclinical utility study?
Um, and so one of the best waysto do that is just to get the
product on market and start withcash pay as you do that utility
study and work with pairs toreally get them to buy in and
(26:04):
say that this is somethingthat's worth reimbursing.
Um and so that that's how we wetackle it.
We say what we're just startingat at cash pay, you know, to
make this attractive enough thatit continues to be a fundable
priority with the intent to uhreach a point where we can make
this, you know, reimbursed.
Yeah.
Swaril Mathur (26:20):
Yeah, that makes a lot of sense.
And I think, you know, it's ait's kind of the phase strategy
of you solve for the thing youcan solve for at the time, and
you can't, you can't ever reachthat giant population um that
requires reimbursement if you ifthe company doesn't exist.
Um the company has to do whatit has to do to exist, which
sounds so basic, but that'spretty important.
Ridhi Tariyal (26:40):
You know what?
One of the interestingphenomena, again, this is also
context specific, is umobviously there's there is
something happening inpharmaceutical, right?
There, there are there's now amovement to sell drugs, DTC.
Swaril Mathur (26:52):
Yeah.
Ridhi Tariyal (26:52):
Right.
We we we see the war happeningfor GLPs um in terms of who's
gonna get access through whatchannel.
I mean, I think my favoriteheadline recently was Costco's
gonna start selling it.
Oh my gosh.
I was like, game over, blackblack, game over.
Um, but the the interestingthing is gonna be that as and
more and more pharma companiesare signing on, right?
Not just for GLPs.
They're they are understandingthe importance of this channel.
(27:15):
Um, and so you know, if youhave a DTC uh uh, you know,
business arm um for your drugs,you will need a DTC diagnostic
arm for your business, right?
You can't prescribe a drugwithout having a diagnosis.
Um and I am sort of waitingwith bated breath.
I think it's just an excitingmovement for diagnostics to say,
okay, now pharma is going to becompelled, right?
(27:37):
To be like, we need a uh acompanion diagnostic in a
completely different way.
We need a companion diagnosticthat we can prescribe so that we
can unlock reimbursement forthe drug.
Swaril Mathur (27:47):
Yeah.
No, it's a it's a great pointthat if once you go consumer,
the whole, the whole carejourney actually kind of has to
be compatible with that.
Yeah.
Yeah.
Ridhi Tariyal (27:57):
It introduces a market dynamic that that is, I
think, in net favorable fordiagnostic companies.
Swaril Mathur (28:03):
Yeah, yeah.
And, you know, as a as aconsumer, there's something very
motivating about the idea thatyou could take control of all
these things and and and reallybe empowered to, you know, to
move the journey forward at yourown pace and not be hamstrung
by lack of availabilityavailability of doctor's
appointments or specialists orthings.
So um, I think there's a lot ofreally motivating aspects
(28:25):
there.
Um, I want to get to some ofkind of the key lessons that
you've learned in the Next GenJane journey.
We've alluded to some of them,of course, but before we go
there, can you just give us asense of like where have where
has the company come so far?
What since from founding tonow, you know, what have been
the major milestones, the majorkind of de-risking pieces?
And then what's what's next onthe horizon for you?
(28:45):
What are you focused on rightnow?
Ridhi Tariyal (28:47):
Yeah, I mean, I think there's been a lot of
major de-risking milestones.
I think the best way tounderstand it is this um stack
that I always describe, which isthe core asset of the company.
Um, you know, I think thatthere's been a lot of focus over
the last few years on, oh, youknow, a smart tampon was like
nice clickbaity title that theygave Next Gen Jane for a while,
or um, you know, um a way todiagnose, you know, hardcore
(29:11):
disease at as at home that oftenhad surgery as the only
alternative, which none of thesethings are wrong.
Um, but interestingly enough,the the sample collection system
and the fact that it enableslongitudinal narratives is is
the foundation of what we'vebuilt, but it's only the bottom
layer.
Um the layer you know, abovethat is great.
You've got the sample type, youknow, and it it you've got
you've done some pre-analyticalstandardization, you've got the
(29:33):
sample type in your lab.
Now what?
Um, and we we actually generatehigh-dimensional data from it.
It's not like we're doingtarget uh sequencing, we're not
looking at PCR, we're sayingnext generation sequencing.
We're doing full RNA seq.
We are um understanding 19,000gene profiles from the host, so
the actual woman, as well as thefull metatranscriptomics, which
is the activity level of thebacteria in that sample, um,
(29:57):
which just is such key insight.
Um And then we layer on DNAsequencing, right?
And we we're doing that verydeliberately because we know
we've taken it with that, likeone of the bets we're making is
that in this environment whereyou know AI is going to
transform everything, youactually you actually need
high-dimensional data and youneed matched RNA and DNA
(30:17):
samples.
And that is what makes yourdata extremely valuable.
And so you know we've donewe're deliberately going down
that path.
So great, you've got thishigh-dimensional data.
It's your ability to doanything with it is only as good
as your ability to understandwho gave it to you.
And so we try to do clinicalgrade annotations, which means
we both extract valuable labelsfrom EMR, so medical records
(30:38):
that we get, as well as we we itum have instituted this
proprietary next-genjane survey.
It's pretty intense.
It's over like a thousandquestions.
And over time we have foundthat, you know, the EMR is
spotty.
You never, you it's not likeyou have EMR from the last 20
years.
You've probably gone to 50different doctors.
Um anything that they're you'regetting from an quote-unquote
EMR perspective is sort of aspot chuck of like what your
(31:00):
latest doctor understands aboutyou.
Yep, that resonates.
We ask, we've asked patientsquestions like, oh, do you have
any autoimmune diseases?
And they will be like, Yes, Iwas diagnosed with lupus and so
and so.
Not in the EMR.
Nothing to anything recordsthat they've given us, right?
As you can imagine, reallyrelevant information to know
when you're thinking about likewhat data you're looking at.
We've asked women who are like,have you ever had any
(31:20):
miscarriages?
We'll have women be like, Yeah,I've had four miscarriages in a
row, not in the EMR.
Again, really germane touterine biology and and
everything that we're thinkingabout and looking at.
And so we have found that theannotations are extremely uh
improved by just engagingpatients themselves to say, we'd
love we'd love to know thingsabout your health.
Um, and then finally, the mostimportant sort of de-risking for
(31:44):
us came through when wedeveloped the what we call a
female-specific ontology, whichis, you know, especially so
female-specific labels for thedata that have everything to do
with femobiology.
The most evident one thateverybody knows now is sex as a
variable, right?
You should take sex as avariable into consideration.
It's a very important covariatein your analysis.
(32:05):
Um, and and great, awesome, youknow, most data sets that we
know track that.
What we found over the years isthings like cycle day of when
you did a sample collection,extremely important uh
covariant.
How heavy of a bleeder you are,extremely important covariate.
And so I would say really inthis journey, um, probably the
most important invention that wehave come up with are these the
(32:28):
female specific labels thatdrive our understanding of the
data.
Um, and my, you know, in termsof your like, what why was that
like a uh unlock in terms ofde-risk moment?
Um, we have this beautiful uhchart that we we show, which is
uh two charts, histograms, thatum plot out our what we call our
endometriosis score, which islike a compound um score based
(32:50):
on RNA-seq data.
And it, you know, it spits outa score that essentially is uh
our assessment of whether or notyou have endometriosis, right?
And we collect cases and wecollect controls.
And ideally, what you want tosee is that there's this nice
separation in those two curves,right?
And so we we plot this data andon the left hand side um you
see cases versus controls, andthe curves are completely
(33:10):
overlapping.
Swaril Mathur (33:12):
Oh my gosh.
Ridhi Tariyal (33:13):
Not ideal.
And on the right hand side,there's like a five-fold
separation.
Beautiful, exactly what youwould want to see.
Um, and they were put throughthe same algorithm, same data
points, like absolutely the samedata.
The only difference is on theright hand side, we actually
told the algorithm all of thefemale specific labels.
We told it what age she was inher cycle when we collected the
(33:34):
sample.
We told her what type ofbleeding phenotype she had, et
cetera.
On the left hand side, we justdidn't.
We said pretend this is a dataset exactly like everyone else
collects.
And you know, the curves werecompletely overlapped.
And so for us, that was it.
We we were like, this is thefuture of women's health.
Wow.
This is massive de-riskment,which is if you have these
(33:55):
labels in place, you areactually able to see signal from
noise.
And that that to me is is thereal value of NextGen Jane.
Forget about the diagnosticpiece of it, is this data
architecture and this stackthat, you know, if you have a
belief that uh, you know, themost valuable thing in this, in
this um changing, very quicklychanging landscape is
proprietary walled gardens ofdata that are well labeled and
(34:18):
well annotated and are specificto very um you know, clear
populations that you understand,that is what we are building.
Swaril Mathur (34:25):
Wow.
That is incredible.
I love, I love that example ofthe uh, you know, the
histograms.
And I think what what it bringsto light, you know, as a again,
as a consumer who hasn't spenttwo decades in genomics, right?
Who's who's trying tounderstand, you know, the impact
of all this is conditions likeendometrioses for so long, the
talk track around them has justbeen they're multifactorial and
(34:48):
it's so complicated, and youcan't predict when you're gonna
have a flare-up or what howsignificant the lesions are.
There's all these things thatthe answer has just been it's
too complicated.
Throw your hands up in the airand just uh like brute force
with the bluntest toolspossible.
We'll just rummage aroundinside your body and try to pick
out weird lesions that we find.
(35:09):
And that's not a commentary onlike the wonderfully skilled
physicians.
It's a commentary on the lackof the basic science to support
anything different up until now,right?
Um, but the nuance with whichyou and your team have thought
through what are all of thelayers on top of the data and
what are the ways they need tocome together to make this data
(35:30):
truly actionable and todifferentiate and tease out
these, you know, highlyintermingled, um, you know,
highly complex conditions isreally interesting.
This is really profound.
Ridhi Tariyal (35:42):
Thank you so much.
And I will just telescope outto make two other comments.
One is is that um imagine, soyou know, if if the way that you
diagnose the disease is throughsurgery, right?
Uh what is the clinicalendpoint you're chasing in uh
drug trials?
You know, it's not actuallydegree disease regression right
now.
The clinical endpoint forendometriosis trials is pain.
One of the most subjective uhendpoints you could imagine,
(36:06):
right?
And you know, people whodevelop drugs for endometriosis
know this, that this is aweakness in the entire way we
think about drug development forendometriosis is you need a
molecular phenotype to change tocomment on whether or not your
drugs are making an impact onthe progression of the disease.
If you do not have thatmolecular phenotype, what are
you assessing?
(36:26):
Just reduction in pain, whichagain could be placebo effect.
Like is this right?
Swaril Mathur (36:30):
Right, right.
Like the level of clinical riskin running that trial just
increases tenfold because youdon't have an objective,
reliable metric.
Ridhi Tariyal (36:38):
And and I'm just tying this to like this is why
this is so much beyond data,right?
Once you establish a molecularphenotype, great, sure, it can
be used to diagnose, but thinkabout how it could transform the
small molecule options thatwomen are going to have in the
future.
Yeah.
Right.
Now you're actually saying goup against disease progression.
Like that's what you should beevaluating as to whether or not
these drugs work.
And then if you telescope outfurther, it's, you know, why not
(37:01):
just endometriosis, it's notjust reproductive disorders,
it's immune-mediated disorders.
You know, it is all thesedifferent conditions that affect
women.
And, you know, uh, there isthere's this really interesting
um comment I always make when Ithink about like what why would
menstrual effluence be the rightum uh uh aperture for thinking
about all inflammatory andimmune mediated diseases?
(37:23):
The the biggest answer is thatmenstruation in itself is a
programmed inflammatory event,right?
Like women, female biologypresents model systems you can
you can use to study, you know,loosely good inflammation.
So like ovulation is an is aninflammatory event, but it's not
it's natural, right?
You're you're gonna getinflammation during ovulation,
(37:45):
implantation, inflammatoryevent, menstruation,
inflammatory event.
And menstruation is reallyunique in because it is, it
provides you the opportunity tounderstand the kinetics of
inflammation.
What happens duringmenstruation?
You have this huge inflammatorycascade that kicks off because
your body is saying, shed,right?
Kill these cells and shed theendometrial lining.
And then you have this repairprocess where you now have
(38:08):
growth of the new lining.
And so over the course of threeto five days, you have a model
system by which to observe thenatural cascade of inflammation
to repair, right?
And and we would offer up likeinflammation to rep, well,
inflammation is the consensuspathophysiology of so many
diseases.
If you could understand therelationship between
inflammation and repair, you nowhave insight into so many
(38:32):
diseases.
Absolutely.
Swaril Mathur (38:34):
And I just think, you know, as we as you zoom
back out that way, the thingthat comes to mind for me is
we've talked before about thefact that so many other
conditions that aren't women'shealth conditions, they are just
human conditions, have beenstudied primarily in the context
of men, right?
The research studies, clinicalstudies have historically been
mostly male because menstruationis a confounding factor.
(38:54):
And be because that's it, youknow, if you're if you're a
scientist trying to maximize thecleanliness of your data to get
a result, it's better tocontrol for that factor by not
introducing it.
And um, and it makes me thinkthat this could be a possibility
to kind of catch up on thatresearch and and use this as a
lens into those chronicconditions that have been
studied so broadly in otherpopulations.
But maybe there's a gap in theunderstanding for women.
(39:17):
And this could become a tool toto catch up on that and maybe
in a more accelerated waybecause of the ease of measuring
it with a natural biopsy, likea a natural, a natural source
for sample collection.
Ridhi Tariyal (39:30):
Yeah, totally.
I mean, even so, you know,again, I I tend to focus on like
the scientific aspects becausethey're just so super cool.
But even from a um complianceperspective, think about how
hard it is to get patients tocome back for blood draws.
That is incredibly expensive.
And it, you know, you lose alot of patients from that.
Imagine just mailing them asample collection kit and being
(39:50):
able to get them to regularlygive you uh, you know, molecular
updates in that way, right?
Just just clinical coordinationcould be improved by
integrating something like this.
Imagine how many um phase onesafety studies uh don't have
women.
Right.
I mean, just just and and it,you know, we think that like the
it's fine, but if you thinkabout like the sheer number of
(40:11):
drugs that have been pulled offthe market are mostly for for
side effects that they had onwomen, right?
It's it's just it's the the theutility of it is just, you
know, when you um when I haveput my big vision hat on, I'm
like the utility of it is justendless.
Swaril Mathur (40:25):
Yeah, yeah, absolutely.
You know, I think there's thisis an incredible opportunity.
Um, we've talked a lot aboutthe NextGen Jane story, and I I
kind of want to zoom back outnow as we've as we've explored
it and talk about your journeythrough this.
I mean, as you look back um onall of this, what have been some
of maybe the the morechallenging moments for you, the
moments that have really testedyou and pushed you?
(40:46):
What have you learned fromthat?
And and maybe to summarize itall, like how are you different
as a leader and as a person nowthan you were before you started
this journey?
Ridhi Tariyal (40:54):
Oh, those are tough questions.
Um, maybe I will answer theeasier one first uh about uh the
challenges and in the journey.
I mean, I think one bigchallenge is um, you know,
women's health tends to get dripcapital.
Um and this science could havemoved so much faster if there
were bigger checks cut for it,right?
But um I always say that, youknow, when you give uh certain
(41:16):
areas of science uh smallchecks, you are telling them to
make incremental progress.
And when you write big checks,you're telling them to shoot for
the stars.
Um, and so part of, you know, Iwill I will fully say part of
the tenure journey that we'vebeen on is really because
there's been no foundation,right?
So build we've been buildingthe scaffolding as we go, right?
Truly, truly being like, oh,this this is a really important
(41:38):
feature.
Now we have to go back andrecollect and to understand
exactly what the impact is.
I mean, even to figure out allof this, like uh the kinetics of
cycle day and how important itis, and some of these
covariates, you have to collectmultiple multiple, like day one,
day two, day three from like 50people across multiple cycles.
Like, you know, that's it'slike a huge undertaking to even
set the groundwork.
(41:58):
And so um, you know, some ofthat has been just this is just
gonna take time because it'snew, but absolutely some of it
has been so frustrating umbecause it's there's just not
enough capital that's gone intoit, right?
And you you can't, you know,again, once you get past the
series seeds date, it's harderand harder to unlock bigger and
bigger checks unless there isthis clear path to commercial
data, right?
(42:19):
And this is this tension of ifyou're doing something that is
more basic science oriented in ayou know capitalistic
environment, um, you are gonnaface that pressure, right?
To be like, when's this babycoming to market?
Right.
Love science, love, love theleaps and bounds, but like you
got to show me the path tomarket.
Um, and and that's beennecessary and frustrating.
(42:40):
And, you know, I've I've oftenwished to be like, uh, this
needs a $50 million truck to gofrom um zero to a hundred.
If you want the zero to ahundred and eighteen months,
then I need a bigger truck.
Swaril Mathur (42:51):
Yeah.
Ridhi Tariyal (42:51):
If if I can't get a bigger truck, then you're
just gonna expand the timelinefrom how we get to a hundred.
Swaril Mathur (42:56):
Yeah.
Yeah.
Yeah.
It's an interesting umrelationship between between
dollars in and timeline that Ihadn't really appreciated.
And and it's interestingactually to hear that in the
context of kind of the basicscience research, because those
of us who maybe haven't engagedas deeply in basic science
research might assume that itjust moves at the pace it moves.
Um, and basic science researchin the university setting, you
(43:18):
know, certainly isn't isn'talways that quick.
But I had never thought aboutit through the lens of there are
probably pieces of that thatare true.
And then there are probablypieces that if you throw some
accelerant on it, you could movemore quickly.
Ridhi Tariyal (43:31):
Let me tell you, I mean, like there are more
money at product development, itmoves more quickly.
If you throw more money atclinical trial enrollment, it
moves more quickly.
Yeah.
Swaril Mathur (43:38):
Yeah.
Yeah.
There you go.
Well, okay.
Um, and then what about kind ofyour your experience as a as a
leader?
I mean, there's just, I'm justimagining there's so much
uncertainty that you are facingevery day that you've been
leading through.
You are going after such animportant challenge, but that
also means on many days, Iimagine you're just doing things
that nobody has ever donebefore.
(43:59):
How, how has that, you know,transformed your leadership or
affected you?
Ridhi Tariyal (44:03):
Yeah.
I mean, and maybe two thingsthat stand out is curiosity,
persistence.
Um, I think curiosity becauselike again to your point, it's
everything's so new that you'vegot to be curious about it.
Um, and you know, you can youcan start to have conviction,
but it better be loosely held.
Cause the next day you willfind a data point where you'll
say, oh, oh my goodness.
Um and so, you know, you youcan't be uh again, when you're
(44:25):
when you're when you are umtraveling such novel terrain,
you've got to be open to that tosay, like, I am just gonna
learn.
I'm gonna ask a lot ofquestions.
I am, I'm gonna try to hold offon assumptions um and be ready
for wherever the data's gonnatake me.
Um and so I would say that isjust that's a skill set I have
had to hone to be okay withthat.
Um and I would say umpersistence because you know,
(44:48):
you do, you do want to quit,right?
You do there's there's manyopportunities where you're like,
um, you know, I've beenapproached by uh like scientists
that have really, again, reallycool tech, right?
That there's so much happeningin the world that I've that have
like actually asked me to say,are you done with NextGen Jane?
And like this is I I would lovefor you to come run this.
And um, you know, you're alwaystempted, like, because you're
(45:08):
like, oh, this this is the newcool shiny toy.
Yeah.
Um, but but knowing that youare working on something that
really is is super novel in it.
It's it may you may have beenworking on it for seven years,
but it is still super novel tothe world.
Um and saying just stay thecourse, stay the course.
There there are breakthroughshappening and there are bigger
(45:28):
breakthroughs coming.
And if you can be patient withit and persistent with it, that
that like that the you knowoutcome is gonna be way more
than you anticipated.
Um and that's that's it, thathas been a like a skills, skill
to hone in itself, to say, like,no, of course there's gonna be
new shiny toys.
You've just you you've got tofinish this.
Like there's somethinginteresting here.
(45:49):
And the reason, you know, be--forget about the money for a
second and the capital acquiredto do something like this.
Like the reason that these teamthings take a long time is
because they're hard.
Like, and if you don't have thepatience for that, then you're
never gonna get to that theEaster egg at the end of this.
Swaril Mathur (46:03):
Yeah, yeah, absolutely.
I love the way you articulatedthat.
And I think your passion forthis is so palpable in the way
that you talk about this entirejourney, this mission, and
what's at the end of the finishline.
And I am, I am just so excitedto watch it come to fruition and
to continue tracking thejourney.
Ridhi Tariyal (46:21):
Thank you so much.
Swaril Mathur (46:22):
Ridhi, this has been a really wonderful
conversation.
Thank you for sharing yourpersonal story and the story of
NextGen Jane.
You were tackling some enormousproblems that will have
transformative impact on a lotof people and excited to
continue to watch it come tolife.
Awesome.
I appreciate the opportunity tochat with you.
Thanks for being on.
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