December 4, 2024 • 20 mins
Discover how their AI technology is revolutionizing drug discovery with Dr. Vimal Mehta, Co-Founder and CEO BioXcel and how this is leading to the approval of their first drug for agitation in bipolar I and II and schizophrenia, and paving the way for new treatments.
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Episode Transcript
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Dr. Moira Gunn (00:11):
We hear the term AI all the time, but what is it
actually doing? Today, we learnhow AI was used by one company
to discover a drug now approvedby the FDA. The company's AI has
already gone on to discoverother drugs as well. Doctor
Vimal Mehta is the CEO ofBioXcel Therapeutics. Well,
(00:36):
doctor Mehta, welcome to theprogram.
Dr. Vimel Mehta (00:38):
Thank you very much. I'm very happy to be here.
Dr. Moira Gunn (00:42):
Now this is a very interesting interview for
me because it's not just aboutthe drug candidates, the
treatments you're working on,but it's also about a a
different and advanced approachor a new approach to drug
discovery, how you come toidentify what would make a good
drug candidate. The scientist islooking to create this
(01:05):
intervention in the body, islooking to figuring out how to
deliver it. And, and in doingso, they they read everything
they can in the literature andalong the way they form
experiments and continue to workon what might be possible and,
you know, as they learn, theyevolve that. So when we look at
(01:25):
this approach, the firstlimiting factor is the
scientist's knowledge based onreading everything in the
literature. Why is this alimiting factor?
Dr. Vimel Mehta (01:38):
There are 50,000,000 publications and
there's a overload ofinformation. It's very difficult
for a scientist to digest allthe information. That's where we
applied artificial intelligenceand machine learning approaches
to make the informationdigestible for the scientist.
Because reading one publicationat a time will not get you,
(02:01):
where you need to be assimilateall the information. So machine
come in, they can read50,000,000 publication and they
never get tired.
And scientists will get tired.So, ultimately, machines can
really help us find insightwhich are not possible by just
reading one publication.
Dr. Moira Gunn (02:18):
Well, there's also an old adage in networking
that says, it's it's not justwho you know, it's who who you
know knows. And a scientistreading a publication takes that
information and just adds it onto himself, but there's a
there's a deeper aspect to whatartificial intelligence can
(02:42):
learn.
Dr. Vimel Mehta (02:43):
Artificial intelligence in a unbiased man,
manner creates a knowledge graphor a as you said, network of
information, which no scientistcan do. So it can bring all the
information that may exist inone part of the world or the
other part of the world andbring it together and create
(03:03):
some unique insights, which arenetwork maps. So you can look at
first degree connection, which ascientist can identify by
reading a book. But second, 3rddegree connections are very
difficult to assimilate becauseinformation is so complex. So
that's where the artificialintelligence platform, machine
learning technique helps us sortout and bring the efficiency
(03:26):
that we need to enhance our drugdiscovery and development
process.
Dr. Moira Gunn (03:31):
So it's not just who you know, it's who who you
know knows, and who they know,and it knows. And so it's a much
deeper understanding than justthe paper at hand or what
they're looking for at hand. So,that that kinda brings together.
It's like, oh, it it it enablesyou to know far more. Now you're
(03:51):
not just looking for theinformation.
You are also looking for thosesame capabilities among already
approved drugs. Why is that?
Dr. Vimel Mehta (04:03):
The reason we are trying to take advantage of
the existing knowledge becausethere is a 100 of years of
knowledge available about thedisease. There are so many
molecules that either make it tothe market and never their full
biology has been understood orit has been exploited or
leverage. And there aremolecules which fail after phase
(04:25):
2, and they get stuck in phase 2and phase 3. So we go with the
unbiased approach. We try to seethat what is the underlying
disease drivers.
Do we understand them? What arethe cause causal drivers for the
disease? If we understand, thenwe try to relate that to the
drugs and their underlyingbiology and the mechanism, what
(04:46):
exists, and then try tocorrelate and see, can we find a
novel way of treating that,patient population. And, that's
exactly the underlyingfoundation for our drug
candidates at BioXcelTherapeutics.
Dr. Moira Gunn (05:02):
Well, this was certainly the example of your
first drug, Igalmi, and let mespell it for pea for listeners.
It's I g a l m I, Igalmi, whichwas approved last spring and
it's a drug to treat agitationassociated with schizophrenia
and bipolar 1 and 2. This was adrug that had been approved
(05:26):
originally for a differentmedical condition. What was it
originally approved for?
Dr. Vimel Mehta (05:32):
It was originally approved as a
sedative and assertive given ina surgical unit.
Dr. Moira Gunn (05:38):
Now obviously, if you're trying to settle down
agitation in someone, you say,well, let's just pull the IV
out. That's not going to happen.You had to develop a new
delivery method. What did youdo?
Dr. Vimel Mehta (05:51):
So we converted this drug into a sublingual thin
film because you want some sortof a treatment that is patient
centric and friendly for thepatient. They don't feel
threatened. If you go at apatient with a needle, they can
feel threatened and they canhurt you also if you're trying
to go, to them with a needle. Sothat was fundamental reason that
(06:15):
we converted this drug into asublingual thin film. It's green
in color.
It's a minty in taste. You putit under the tongue. It has a
muco adhesive and it startsonset of action immediately.
That was the important factorfor the doctor that once you
give the drug, you have a rapidonset of action. Because you
want to calm the patient, assoon as possible.
(06:38):
We are doing microdosing,because that's what we realized.
That's what is needed to calmthe patient. So, film, we
believe was the ideal treatmentdelivery option for these
patient and we are seeing thatin the real market since we have
launched the Calmi in themarketplace.
Dr. Moira Gunn (06:57):
And if you took a pill, if you got the person to
take a pill, they're prettyagitated at this point. It would
take a long time to get intotheir system.
Dr. Vimel Mehta (07:06):
It will because it will go through the stomach
and, our drug that we are usingon Egalmi, it has some sort of a
metabolism. So it won't get tothe blood level to we need
within a short amount of time.So that's part of the reason we
did the sublingual delivery sothat drug can reach to the blood
levels and it can start treatingthe patient. There's another
(07:28):
problem to that. If you givebill, sometimes these patient
cheek it, they and then when thedoctor leave, they spit it out.
So with the film, with themucradysy, they can do that.
Dr. Moira Gunn (07:38):
A drug candidate that was original right from the
lab bench, never previouslyused, would take 12 to 15 years
to become approved. This wasalready approved. What territory
did you not have to revisitbecause it was already approved?
Dr. Vimel Mehta (07:59):
First is that we knew so much about the drug.
The fundamental properties ofthe drug because it has been in
humans for over 20 years in10,000,000 patient, there are
10,000 publications already. Sothat gives us a very good base.
The biggest advantage was thatwe had a very good understanding
of the safety for this drugbecause it has been used in the
(08:21):
human. So that's, that keycharacteristic that you look
into the drug, like, what is thepharmacokinetics, what is the
pharmacodynamics, what is thesafety, that's already
established.
We need you to figure out whatthose and what is the delivery
mechanism and what smart trialwe need to design to prove it.
(08:42):
And that's exactly what we did.That's part of the reason we
have were able to go from ourIND all the way to the approval
within a 3.5 year rather thantaking the, 12 to 15 year cycle.
And there has been no innovationin this area. So it's a highly
innovative drug from thoseperspectives.
Dr. Moira Gunn (09:01):
So for those of you listening who are outside
the drug development field, INDis investigational new drug.
That means you tell the FDA,this is what we wanna do. So
that's day 1 in a in a drug likethis. And from there too, it was
actually approved with justthree and a half years instead
of the 12 to 15. And you had alot more confidence because so
(09:25):
much more was done.
Dr. Vimel Mehta (09:27):
Certainly. It gives us a high level of
confidence, a deriskopportunity, for our
stakeholders because we have ahigh level of degree of
confidence that we can bring it,in the clinic and we can if
clinical data is good, we canbring it to the regulators to
get approved. So those were thefundamental reason for forming
(09:49):
BioXcel Therapeutics andinitiating this agitation
program, massive program wherethere are 140,000,000 episodes.
And it's a huge use societalproblem. So we are very pleased
with the outcome we had with theIgALMIA.
And as you and I speak today,it's already helping the
patient.
Dr. Moira Gunn (10:08):
You said a 140,000,000 episodes. Do you
mean a 140,000,000 episodes peryear?
Dr. Vimel Mehta (10:14):
That's right.
Dr. Moira Gunn (10:15):
And what was the what's the current treatment for
that before Igal may showed up?
Dr. Vimel Mehta (10:20):
So let me just divide that. So agitation
happens because ofschizophrenia, bipolar 1 and 2,
and agitation happens indementia patient or Alzheimer's
patient. So there are about40,000,000 episode that happened
for schizophrenia, bipolar 1 or2 patient, and there are about
100,000,000 episode thathappened for Alzheimer's and
(10:41):
dementia patient. So currently,there's no approved therapy for
Alzheimer's related agitation.They give them antipsychotic.
They give them Benzodiazepine.But what our treatment options
are more tranquilizing and theyare black box warning. So they
have not been, there is noapproved therapy as of today. In
bipolar, schizophrenia, arena,there are few approved
(11:04):
therapies, but they don't workthat well. And some of them are
I'm injections.
And they have, like, in a longeffect in patients sedating the
patient or tranquilizing thepatient. So what we have come
over is a very ideal option totreat a patient agitation and
involve the patient in the careand making a treatment choice.
(11:25):
So physician can tell them Ihave this I'm injection or I
have this innocuous film totreat your agitation. What do
you want to take? And some ofthese patients had this
experience that they come to theemergency room multiple times so
they know they did not like thedrugs or choices they were on
before because they weretranquilized for a long time.
(11:48):
So patient really know what theside effects were. So with a new
drug like a galmi, they have anew treatment option. And, same
for the health care providersand the physicians.
Dr. Moira Gunn (11:59):
Now I know you're also studying, bringing
eigalmi at home. Right now, youwould have to go you'd have to
be in a facility of some sort orgo to the ER to get this. How do
you study it at home?
Dr. Vimel Mehta (12:15):
We just initiated that program last
year, our pivotal phase 3program. We call it as a
serenity 3. And we it has twoparts to it. One part is
efficacy. We are testing theefficacy in a medical supervised
setting like we did before.
Like, where we got approval ofthe economy. Just we are using a
lower dose. So in our economy,we got 120 microgram and 180
(12:40):
microgram, 2 doses approvedacross the spectrum, mild,
moderate, and severe agitation.And now we are using half the
dose 60 and showing theefficacy. Once we demonstrate
efficacy under the samecondition as our previous trial,
Then second part of safety willbe done at home.
And it's no different than anyother drug that is being
(13:03):
developed. They test the safetyat home. So we will be using the
same where patient will bereporting how he feels. Does he
feel any safety or, issues orhis informant will be providing
that information. So it's nodifferent than the regular drug
development.
Dr. Moira Gunn (13:18):
And you're also studying Alzheimer's as well.
Right?
Dr. Vimel Mehta (13:22):
That's right. That's that's a very very large
opportunity and very high unmetmedical need. So our, like, you
know parents or grandparents,they end up in the assisted
living facilities or nursinghome not because of the
Alzheimer's or dementia. Theyend up there because agitation
(13:43):
cannot be controlled by theirfamily member. They really don't
know how to manage that.
Because whenever agitationhappen, they send them to the
emergency room. So it's a hugeunmet medical need. There's no
approved therapy, and we have abreakthrough therapy designation
from the FDA using our phase 2data. So we are in a good place
(14:04):
and now we are running pivotaltrials. We call them tranquility
23 to show that we can, helpthese patient and then if data
is positive, be able to getapproval from the FDA.
And there are about 100,000,000episodes related to just
Alzheimer's related agitation.
Dr. Moira Gunn (14:23):
Now since AI engines will search for whatever
you're asking for, I know you'realso working in immuno oncology.
Keytruda, of course, is wellknown as as leading immuno
oncology drug, and it works formany cancers, but it doesn't
work for all cancers. Here'swhere you're operating. Right?
Dr. Vimel Mehta (14:45):
That's right. So we are working on tumor that
are hard to treat. What I meanby that hard to treat tumors is
these tumors by definition arealso called as cold tumors where
immunotherapy doesn't work. Ifyou change the microenvironment
inside the tumor and make themhard, there is a high likelihood
that KEYTRUDA will providebetter benefit to the patient.
(15:08):
So that's exactly what we aredoing with our lead product that
was, again, identified using ourAI platforms.
We are combining with Keturah.We are presenting a data next
week at Escogu, full 28 patientdata to show how well, it can
help the patient. So that datawill be presented by our
(15:29):
principal investigator. Andcurrently, KEYTRUDA responses
are really, really low, like,under 5% in these rare forms of
the prostate cancer. So if youcan enhance the response rate
into the mid twenties, like, youknow, that's a big win, for,
these patients because, and alsoif you can stabilize the disease
(15:51):
and you can have a durableresponse.
Dr. Moira Gunn (15:55):
And is your drug one that was previously approved
as well?
Dr. Vimel Mehta (16:00):
This drug had a different story. So this drug
has gone to the phase threetrial in other tumor types, and
it has done multiple phase twotrials. It was a biotech
company. They spend a 100 of1,000,000 of dollar, but it was
stuck. Using AI, we identifiedthat this drug, which was tried
(16:21):
before the immunotherapyrevolution in early 2000 where
everything combination was achemotherapy.
Chemotherapy and the drug, thisdrug didn't work. With the new
information about theimmunotherapy and our AI
platform being able to identifythat, that it's a innate
immunity activator, and thiscould be a relevant candidate to
(16:44):
combine. So we have publishedthe mechanism. It's a completely
novel mechanism for this drug.We have established the safety
with Keytruda and now, efficacywith Keytruda.
So this will give us a good pathto double up how to bring this
drug to the marketplace.
Dr. Moira Gunn (17:03):
It occurs to me that you just don't walk up to
your AI engine and say, hey. Weneed another drug. You've gotta
have data and you've got to beable to ask the question, you
know, or the questions to get itdown to those things that are
usable, actions that are usable.Tell us about that.
Dr. Vimel Mehta (17:27):
You're absolutely right. There's no
magical solution. And in termsof the question, we decide that
we want to work in this, medicalcondition. Our experts, they
design question that can be aslong as 500 to 5000 word
queries, and we feed in themachine. Then machine reads
(17:48):
whatever information isavailable and create what we
call as a network map or aknowledge graph.
These are dynamic. It's not adatabase. These are dynamic
network maps or a knowledgegraph, which we can update as
new information will come. Andwe use that to see what are the
key insights we can drive usingthe machine learning. Like, how
(18:10):
can we figure out somethingthat's not easily accessible to
the human brain because there isso much information.
So it's a quite a process, andwe have built this over a now,
15 years. The parent company ofBioXcel Therapeutics, BioXcel
Corporation started as a bigdata company, developed the
(18:31):
algorithm, did this work for thepharmaceutical company for over,
like, in a 200 companies, andthen applied this to create
BioXcel Therapeutics pipeline,which are our current drugs. So
we are a very patient centricorganization. We like to make
societal impact, and that'sexactly what we like to do as a
(18:53):
team at BioXcel Therapeutics.
Dr. Moira Gunn (18:55):
Well, with this telling of your journey, your
motto for the company could beone thing leads to another, and
it certainly does. Doctor Mehta,thank you so much for joining
me. I hope you'll come back andsee us again.
Dr. Vimel Mehta (19:11):
Certainly. Thank you very much for hosting
us. I appreciate that.
Dr. Moira Gunn (19:15):
Doctor Vimal Mehta is the CEO of BioXcel
Therapeutics. More informationis available at bioexcel.com.
That's bio, the letter x, and celbioexcel.com.
We hear the term AI all the time, but what is it
actually doing? Today, we learnhow AI was used by one company
to discover a drug now approvedby the FDA. The company's AI has
already gone on to discoverother drugs as well. Doctor
Vimal Mehta is the CEO ofBioXcel Therapeutics. Well,
(00:36):
doctor Mehta, welcome to theprogram.
Dr. Vimel Mehta (00:38):
Thank you very much. I'm very happy to be here.
Dr. Moira Gunn (00:42):
Now this is a very interesting interview for
me because it's not just aboutthe drug candidates, the
treatments you're working on,but it's also about a a
different and advanced approachor a new approach to drug
discovery, how you come toidentify what would make a good
drug candidate. The scientist islooking to create this
(01:05):
intervention in the body, islooking to figuring out how to
deliver it. And, and in doingso, they they read everything
they can in the literature andalong the way they form
experiments and continue to workon what might be possible and,
you know, as they learn, theyevolve that. So when we look at
(01:25):
this approach, the firstlimiting factor is the
scientist's knowledge based onreading everything in the
literature. Why is this alimiting factor?
Dr. Vimel Mehta (01:38):
There are 50,000,000 publications and
there's a overload ofinformation. It's very difficult
for a scientist to digest allthe information. That's where we
applied artificial intelligenceand machine learning approaches
to make the informationdigestible for the scientist.
Because reading one publicationat a time will not get you,
(02:01):
where you need to be assimilateall the information. So machine
come in, they can read50,000,000 publication and they
never get tired.
And scientists will get tired.So, ultimately, machines can
really help us find insightwhich are not possible by just
reading one publication.
Dr. Moira Gunn (02:18):
Well, there's also an old adage in networking
that says, it's it's not justwho you know, it's who who you
know knows. And a scientistreading a publication takes that
information and just adds it onto himself, but there's a
there's a deeper aspect to whatartificial intelligence can
(02:42):
learn.
Dr. Vimel Mehta (02:43):
Artificial intelligence in a unbiased man,
manner creates a knowledge graphor a as you said, network of
information, which no scientistcan do. So it can bring all the
information that may exist inone part of the world or the
other part of the world andbring it together and create
(03:03):
some unique insights, which arenetwork maps. So you can look at
first degree connection, which ascientist can identify by
reading a book. But second, 3rddegree connections are very
difficult to assimilate becauseinformation is so complex. So
that's where the artificialintelligence platform, machine
learning technique helps us sortout and bring the efficiency
(03:26):
that we need to enhance our drugdiscovery and development
process.
Dr. Moira Gunn (03:31):
So it's not just who you know, it's who who you
know knows, and who they know,and it knows. And so it's a much
deeper understanding than justthe paper at hand or what
they're looking for at hand. So,that that kinda brings together.
It's like, oh, it it it enablesyou to know far more. Now you're
(03:51):
not just looking for theinformation.
You are also looking for thosesame capabilities among already
approved drugs. Why is that?
Dr. Vimel Mehta (04:03):
The reason we are trying to take advantage of
the existing knowledge becausethere is a 100 of years of
knowledge available about thedisease. There are so many
molecules that either make it tothe market and never their full
biology has been understood orit has been exploited or
leverage. And there aremolecules which fail after phase
(04:25):
2, and they get stuck in phase 2and phase 3. So we go with the
unbiased approach. We try to seethat what is the underlying
disease drivers.
Do we understand them? What arethe cause causal drivers for the
disease? If we understand, thenwe try to relate that to the
drugs and their underlyingbiology and the mechanism, what
(04:46):
exists, and then try tocorrelate and see, can we find a
novel way of treating that,patient population. And, that's
exactly the underlyingfoundation for our drug
candidates at BioXcelTherapeutics.
Dr. Moira Gunn (05:02):
Well, this was certainly the example of your
first drug, Igalmi, and let mespell it for pea for listeners.
It's I g a l m I, Igalmi, whichwas approved last spring and
it's a drug to treat agitationassociated with schizophrenia
and bipolar 1 and 2. This was adrug that had been approved
(05:26):
originally for a differentmedical condition. What was it
originally approved for?
Dr. Vimel Mehta (05:32):
It was originally approved as a
sedative and assertive given ina surgical unit.
Dr. Moira Gunn (05:38):
Now obviously, if you're trying to settle down
agitation in someone, you say,well, let's just pull the IV
out. That's not going to happen.You had to develop a new
delivery method. What did youdo?
Dr. Vimel Mehta (05:51):
So we converted this drug into a sublingual thin
film because you want some sortof a treatment that is patient
centric and friendly for thepatient. They don't feel
threatened. If you go at apatient with a needle, they can
feel threatened and they canhurt you also if you're trying
to go, to them with a needle. Sothat was fundamental reason that
(06:15):
we converted this drug into asublingual thin film. It's green
in color.
It's a minty in taste. You putit under the tongue. It has a
muco adhesive and it startsonset of action immediately.
That was the important factorfor the doctor that once you
give the drug, you have a rapidonset of action. Because you
want to calm the patient, assoon as possible.
(06:38):
We are doing microdosing,because that's what we realized.
That's what is needed to calmthe patient. So, film, we
believe was the ideal treatmentdelivery option for these
patient and we are seeing thatin the real market since we have
launched the Calmi in themarketplace.
Dr. Moira Gunn (06:57):
And if you took a pill, if you got the person to
take a pill, they're prettyagitated at this point. It would
take a long time to get intotheir system.
Dr. Vimel Mehta (07:06):
It will because it will go through the stomach
and, our drug that we are usingon Egalmi, it has some sort of a
metabolism. So it won't get tothe blood level to we need
within a short amount of time.So that's part of the reason we
did the sublingual delivery sothat drug can reach to the blood
levels and it can start treatingthe patient. There's another
(07:28):
problem to that. If you givebill, sometimes these patient
cheek it, they and then when thedoctor leave, they spit it out.
So with the film, with themucradysy, they can do that.
Dr. Moira Gunn (07:38):
A drug candidate that was original right from the
lab bench, never previouslyused, would take 12 to 15 years
to become approved. This wasalready approved. What territory
did you not have to revisitbecause it was already approved?
Dr. Vimel Mehta (07:59):
First is that we knew so much about the drug.
The fundamental properties ofthe drug because it has been in
humans for over 20 years in10,000,000 patient, there are
10,000 publications already. Sothat gives us a very good base.
The biggest advantage was thatwe had a very good understanding
of the safety for this drugbecause it has been used in the
(08:21):
human. So that's, that keycharacteristic that you look
into the drug, like, what is thepharmacokinetics, what is the
pharmacodynamics, what is thesafety, that's already
established.
We need you to figure out whatthose and what is the delivery
mechanism and what smart trialwe need to design to prove it.
(08:42):
And that's exactly what we did.That's part of the reason we
have were able to go from ourIND all the way to the approval
within a 3.5 year rather thantaking the, 12 to 15 year cycle.
And there has been no innovationin this area. So it's a highly
innovative drug from thoseperspectives.
Dr. Moira Gunn (09:01):
So for those of you listening who are outside
the drug development field, INDis investigational new drug.
That means you tell the FDA,this is what we wanna do. So
that's day 1 in a in a drug likethis. And from there too, it was
actually approved with justthree and a half years instead
of the 12 to 15. And you had alot more confidence because so
(09:25):
much more was done.
Dr. Vimel Mehta (09:27):
Certainly. It gives us a high level of
confidence, a deriskopportunity, for our
stakeholders because we have ahigh level of degree of
confidence that we can bring it,in the clinic and we can if
clinical data is good, we canbring it to the regulators to
get approved. So those were thefundamental reason for forming
(09:49):
BioXcel Therapeutics andinitiating this agitation
program, massive program wherethere are 140,000,000 episodes.
And it's a huge use societalproblem. So we are very pleased
with the outcome we had with theIgALMIA.
And as you and I speak today,it's already helping the
patient.
Dr. Moira Gunn (10:08):
You said a 140,000,000 episodes. Do you
mean a 140,000,000 episodes peryear?
Dr. Vimel Mehta (10:14):
That's right.
Dr. Moira Gunn (10:15):
And what was the what's the current treatment for
that before Igal may showed up?
Dr. Vimel Mehta (10:20):
So let me just divide that. So agitation
happens because ofschizophrenia, bipolar 1 and 2,
and agitation happens indementia patient or Alzheimer's
patient. So there are about40,000,000 episode that happened
for schizophrenia, bipolar 1 or2 patient, and there are about
100,000,000 episode thathappened for Alzheimer's and
(10:41):
dementia patient. So currently,there's no approved therapy for
Alzheimer's related agitation.They give them antipsychotic.
They give them Benzodiazepine.But what our treatment options
are more tranquilizing and theyare black box warning. So they
have not been, there is noapproved therapy as of today. In
bipolar, schizophrenia, arena,there are few approved
(11:04):
therapies, but they don't workthat well. And some of them are
I'm injections.
And they have, like, in a longeffect in patients sedating the
patient or tranquilizing thepatient. So what we have come
over is a very ideal option totreat a patient agitation and
involve the patient in the careand making a treatment choice.
(11:25):
So physician can tell them Ihave this I'm injection or I
have this innocuous film totreat your agitation. What do
you want to take? And some ofthese patients had this
experience that they come to theemergency room multiple times so
they know they did not like thedrugs or choices they were on
before because they weretranquilized for a long time.
(11:48):
So patient really know what theside effects were. So with a new
drug like a galmi, they have anew treatment option. And, same
for the health care providersand the physicians.
Dr. Moira Gunn (11:59):
Now I know you're also studying, bringing
eigalmi at home. Right now, youwould have to go you'd have to
be in a facility of some sort orgo to the ER to get this. How do
you study it at home?
Dr. Vimel Mehta (12:15):
We just initiated that program last
year, our pivotal phase 3program. We call it as a
serenity 3. And we it has twoparts to it. One part is
efficacy. We are testing theefficacy in a medical supervised
setting like we did before.
Like, where we got approval ofthe economy. Just we are using a
lower dose. So in our economy,we got 120 microgram and 180
(12:40):
microgram, 2 doses approvedacross the spectrum, mild,
moderate, and severe agitation.And now we are using half the
dose 60 and showing theefficacy. Once we demonstrate
efficacy under the samecondition as our previous trial,
Then second part of safety willbe done at home.
And it's no different than anyother drug that is being
(13:03):
developed. They test the safetyat home. So we will be using the
same where patient will bereporting how he feels. Does he
feel any safety or, issues orhis informant will be providing
that information. So it's nodifferent than the regular drug
development.
Dr. Moira Gunn (13:18):
And you're also studying Alzheimer's as well.
Right?
Dr. Vimel Mehta (13:22):
That's right. That's that's a very very large
opportunity and very high unmetmedical need. So our, like, you
know parents or grandparents,they end up in the assisted
living facilities or nursinghome not because of the
Alzheimer's or dementia. Theyend up there because agitation
(13:43):
cannot be controlled by theirfamily member. They really don't
know how to manage that.
Because whenever agitationhappen, they send them to the
emergency room. So it's a hugeunmet medical need. There's no
approved therapy, and we have abreakthrough therapy designation
from the FDA using our phase 2data. So we are in a good place
(14:04):
and now we are running pivotaltrials. We call them tranquility
23 to show that we can, helpthese patient and then if data
is positive, be able to getapproval from the FDA.
And there are about 100,000,000episodes related to just
Alzheimer's related agitation.
Dr. Moira Gunn (14:23):
Now since AI engines will search for whatever
you're asking for, I know you'realso working in immuno oncology.
Keytruda, of course, is wellknown as as leading immuno
oncology drug, and it works formany cancers, but it doesn't
work for all cancers. Here'swhere you're operating. Right?
Dr. Vimel Mehta (14:45):
That's right. So we are working on tumor that
are hard to treat. What I meanby that hard to treat tumors is
these tumors by definition arealso called as cold tumors where
immunotherapy doesn't work. Ifyou change the microenvironment
inside the tumor and make themhard, there is a high likelihood
that KEYTRUDA will providebetter benefit to the patient.
(15:08):
So that's exactly what we aredoing with our lead product that
was, again, identified using ourAI platforms.
We are combining with Keturah.We are presenting a data next
week at Escogu, full 28 patientdata to show how well, it can
help the patient. So that datawill be presented by our
(15:29):
principal investigator. Andcurrently, KEYTRUDA responses
are really, really low, like,under 5% in these rare forms of
the prostate cancer. So if youcan enhance the response rate
into the mid twenties, like, youknow, that's a big win, for,
these patients because, and alsoif you can stabilize the disease
(15:51):
and you can have a durableresponse.
Dr. Moira Gunn (15:55):
And is your drug one that was previously approved
as well?
Dr. Vimel Mehta (16:00):
This drug had a different story. So this drug
has gone to the phase threetrial in other tumor types, and
it has done multiple phase twotrials. It was a biotech
company. They spend a 100 of1,000,000 of dollar, but it was
stuck. Using AI, we identifiedthat this drug, which was tried
(16:21):
before the immunotherapyrevolution in early 2000 where
everything combination was achemotherapy.
Chemotherapy and the drug, thisdrug didn't work. With the new
information about theimmunotherapy and our AI
platform being able to identifythat, that it's a innate
immunity activator, and thiscould be a relevant candidate to
(16:44):
combine. So we have publishedthe mechanism. It's a completely
novel mechanism for this drug.We have established the safety
with Keytruda and now, efficacywith Keytruda.
So this will give us a good pathto double up how to bring this
drug to the marketplace.
Dr. Moira Gunn (17:03):
It occurs to me that you just don't walk up to
your AI engine and say, hey. Weneed another drug. You've gotta
have data and you've got to beable to ask the question, you
know, or the questions to get itdown to those things that are
usable, actions that are usable.Tell us about that.
Dr. Vimel Mehta (17:27):
You're absolutely right. There's no
magical solution. And in termsof the question, we decide that
we want to work in this, medicalcondition. Our experts, they
design question that can be aslong as 500 to 5000 word
queries, and we feed in themachine. Then machine reads
(17:48):
whatever information isavailable and create what we
call as a network map or aknowledge graph.
These are dynamic. It's not adatabase. These are dynamic
network maps or a knowledgegraph, which we can update as
new information will come. Andwe use that to see what are the
key insights we can drive usingthe machine learning. Like, how
(18:10):
can we figure out somethingthat's not easily accessible to
the human brain because there isso much information.
So it's a quite a process, andwe have built this over a now,
15 years. The parent company ofBioXcel Therapeutics, BioXcel
Corporation started as a bigdata company, developed the
(18:31):
algorithm, did this work for thepharmaceutical company for over,
like, in a 200 companies, andthen applied this to create
BioXcel Therapeutics pipeline,which are our current drugs. So
we are a very patient centricorganization. We like to make
societal impact, and that'sexactly what we like to do as a
(18:53):
team at BioXcel Therapeutics.
Dr. Moira Gunn (18:55):
Well, with this telling of your journey, your
motto for the company could beone thing leads to another, and
it certainly does. Doctor Mehta,thank you so much for joining
me. I hope you'll come back andsee us again.
Dr. Vimel Mehta (19:11):
Certainly. Thank you very much for hosting
us. I appreciate that.
Dr. Moira Gunn (19:15):
Doctor Vimal Mehta is the CEO of BioXcel
Therapeutics. More informationis available at bioexcel.com.
That's bio, the letter x, and celbioexcel.com.
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