Could tissue engineering mean personalized medicine? - Nina Tandon
I'd<00:00:16.420> like<00:00:16.660> to<00:00:16.779> show<00:00:16.900> you<00:00:16.930> a<00:00:17.050> video<00:00:17.290> of<00:00:17.410> some<00:00:17.800> of
I'd like to show you a video of some of
I'd like to show you a video of some of
the<00:00:17.950> models<00:00:18.250> I<00:00:18.400> work<00:00:18.580> with<00:00:18.960> they're<00:00:19.960> all<00:00:20.050> the
the models I work with they're all the
the models I work with they're all the
perfect<00:00:20.500> size<00:00:20.710> and<00:00:20.740> they<00:00:21.310> don't<00:00:21.610> have<00:00:21.759> an
perfect size and they don't have an
perfect size and they don't have an
ounce<00:00:21.910> of<00:00:22.119> fat<00:00:23.369> did<00:00:24.369> I<00:00:24.430> mention<00:00:24.580> they're
ounce of fat did I mention they're
ounce of fat did I mention they're
gorgeous<00:00:25.090> and<00:00:25.630> their<00:00:26.320> scientific<00:00:26.770> models<00:00:28.169> as
gorgeous and their scientific models as
gorgeous and their scientific models as
you<00:00:29.320> might<00:00:29.470> have<00:00:29.560> guessed<00:00:29.619> I'm<00:00:30.130> a<00:00:30.250> tissue
you might have guessed I'm a tissue
you might have guessed I'm a tissue
engineer<00:00:30.970> and<00:00:31.210> this<00:00:31.989> is<00:00:32.140> a<00:00:32.320> video<00:00:32.619> of<00:00:32.680> some<00:00:33.010> of
engineer and this is a video of some of
engineer and this is a video of some of
the<00:00:33.190> beating<00:00:33.520> heart<00:00:33.700> that<00:00:34.090> I've<00:00:34.420> engineered
the beating heart that I've engineered
the beating heart that I've engineered
in<00:00:34.989> the<00:00:35.020> lab<00:00:35.200> and<00:00:35.500> one<00:00:36.280> day<00:00:36.460> we<00:00:36.940> hope<00:00:36.969> that
in the lab and one day we hope that
in the lab and one day we hope that
these<00:00:37.329> tissues<00:00:37.809> can<00:00:38.079> serve<00:00:38.710> as<00:00:38.829> replacement
these tissues can serve as replacement
these tissues can serve as replacement
parts<00:00:39.579> for<00:00:39.640> the<00:00:39.790> human<00:00:40.030> body<00:00:40.210> but<00:00:41.140> what<00:00:41.290> I'm
parts for the human body but what I'm
parts for the human body but what I'm
going<00:00:41.770> to<00:00:41.829> tell<00:00:42.010> you<00:00:42.040> about<00:00:42.219> today<00:00:42.520> is<00:00:43.000> how
going to tell you about today is how
going to tell you about today is how
these<00:00:43.809> tissues<00:00:44.260> make<00:00:44.469> awesome<00:00:45.030> models<00:00:46.859> well
these tissues make awesome models well
these tissues make awesome models well
let's<00:00:48.190> think<00:00:48.370> about<00:00:48.429> the<00:00:48.609> drug<00:00:48.820> screening
let's think about the drug screening
let's think about the drug screening
process<00:00:49.510> for<00:00:49.690> a<00:00:49.719> moment<00:00:49.929> you<00:00:50.589> go<00:00:50.710> from<00:00:50.920> drug
process for a moment you go from drug
process for a moment you go from drug
formulation<00:00:51.760> lab<00:00:52.089> testing<00:00:52.570> animal<00:00:52.780> testing
formulation lab testing animal testing
formulation lab testing animal testing
and<00:00:53.559> then<00:00:53.649> clinical<00:00:53.980> trials<00:00:54.309> which<00:00:54.399> you<00:00:54.519> might
and then clinical trials which you might
and then clinical trials which you might
call<00:00:54.820> human<00:00:55.120> testing<00:00:55.570> before<00:00:56.140> the<00:00:56.409> drugs<00:00:56.530> get
call human testing before the drugs get
call human testing before the drugs get
to<00:00:56.920> mark<00:00:57.159> it<00:00:57.339> costs<00:00:58.239> a<00:00:58.359> lot<00:00:58.480> of<00:00:58.539> money<00:00:58.629> a<00:00:59.309> lot<00:01:00.309> of
to mark it costs a lot of money a lot of
to mark it costs a lot of money a lot of
time<00:01:00.699> and<00:01:01.079> sometimes<00:01:02.079> even<00:01:02.589> when<00:01:02.859> a<00:01:02.890> drug<00:01:03.100> hits
time and sometimes even when a drug hits
time and sometimes even when a drug hits
the<00:01:03.550> market<00:01:03.879> it<00:01:04.089> acts<00:01:04.449> in<00:01:04.629> an<00:01:04.720> unpredictable
the market it acts in an unpredictable
the market it acts in an unpredictable
way<00:01:05.260> and<00:01:05.890> actually<00:01:06.100> hurts<00:01:06.490> people<00:01:06.940> and<00:01:07.710> later
way and actually hurts people and later
way and actually hurts people and later
it<00:01:08.860> fails<00:01:09.130> the<00:01:10.120> worse<00:01:10.300> the<00:01:10.510> consequences<00:01:11.909> it
it fails the worse the consequences it
it fails the worse the consequences it
all<00:01:13.060> boils<00:01:13.210> down<00:01:13.450> to<00:01:13.720> two<00:01:13.840> issues<00:01:14.200> one<00:01:14.550> humans
all boils down to two issues one humans
all boils down to two issues one humans
are<00:01:15.640> not<00:01:15.730> wrapped<00:01:16.030> and<00:01:16.330> two<00:01:17.260> despite<00:01:17.890> our
are not wrapped and two despite our
are not wrapped and two despite our
incredible<00:01:18.670> similarities<00:01:19.330> to<00:01:19.480> one<00:01:19.600> another
incredible similarities to one another
incredible similarities to one another
actually<00:01:20.770> those<00:01:20.890> tiny<00:01:21.160> differences<00:01:21.700> between
actually those tiny differences between
actually those tiny differences between
you<00:01:22.180> and<00:01:22.300> I<00:01:22.390> have<00:01:22.960> huge<00:01:23.230> impacts<00:01:23.620> with<00:01:23.830> how<00:01:23.980> we
you and I have huge impacts with how we
you and I have huge impacts with how we
metabolize<00:01:24.580> drugs<00:01:24.820> and<00:01:25.420> how<00:01:25.510> those<00:01:25.630> drugs
metabolize drugs and how those drugs
metabolize drugs and how those drugs
affect<00:01:26.380> us<00:01:26.560> so<00:01:26.890> what<00:01:27.580> if<00:01:27.850> we<00:01:28.150> had<00:01:28.420> better
affect us so what if we had better
affect us so what if we had better
models<00:01:29.140> in<00:01:29.380> the<00:01:29.650> lab<00:01:29.830> that<00:01:30.160> could<00:01:30.310> not<00:01:30.460> only
models in the lab that could not only
models in the lab that could not only
mimic<00:01:31.840> us<00:01:32.050> better<00:01:32.500> than<00:01:32.590> rats<00:01:32.890> but<00:01:33.820> also
mimic us better than rats but also
mimic us better than rats but also
reflect<00:01:34.450> our<00:01:34.540> diversity<00:01:36.510> let's<00:01:37.510> see<00:01:37.660> how<00:01:37.810> we
reflect our diversity let's see how we
reflect our diversity let's see how we
can<00:01:38.110> do<00:01:38.200> it<00:01:38.290> with<00:01:38.410> tissue<00:01:38.590> engineering<00:01:40.530> one<00:01:41.530> of
can do it with tissue engineering one of
can do it with tissue engineering one of
the<00:01:41.710> key<00:01:41.830> technologies<00:01:42.430> that's<00:01:42.610> really
the key technologies that's really
the key technologies that's really
important<00:01:43.390> is<00:01:43.540> what's<00:01:44.290> called<00:01:44.560> induced
important is what's called induced
important is what's called induced
pluripotent<00:01:45.340> stem<00:01:45.940> cells<00:01:46.330> they<00:01:46.810> were
pluripotent stem cells they were
pluripotent stem cells they were
developed<00:01:47.260> in<00:01:47.320> Japan<00:01:47.650> pretty<00:01:47.950> recently<00:01:48.690> okay
developed in Japan pretty recently okay
developed in Japan pretty recently okay
induced<00:01:50.140> pluripotent<00:01:50.350> stem<00:01:51.040> cells<00:01:51.460> they're<00:01:52.060> a
induced pluripotent stem cells they're a
induced pluripotent stem cells they're a
lot<00:01:52.270> like<00:01:52.420> embryonic<00:01:52.720> stem<00:01:53.230> cells<00:01:53.500> except
lot like embryonic stem cells except
lot like embryonic stem cells except
without<00:01:54.310> the<00:01:54.460> controversy<00:01:55.210> we<00:01:56.020> induced<00:01:56.530> cells
without the controversy we induced cells
without the controversy we induced cells
okay<00:01:57.970> say<00:01:58.240> skin<00:01:58.480> cells<00:01:58.750> by<00:01:59.200> adding<00:01:59.470> a<00:01:59.530> few
okay say skin cells by adding a few
okay say skin cells by adding a few
genes<00:01:59.920> to<00:02:00.160> them<00:02:00.280> culturing<00:02:01.180> them<00:02:01.360> and<00:02:01.570> then
genes to them culturing them and then
genes to them culturing them and then
harvesting<00:02:02.260> them<00:02:02.520> so<00:02:03.520> there's<00:02:03.730> skin<00:02:04.000> cells
harvesting them so there's skin cells
harvesting them so there's skin cells
that<00:02:04.630> can<00:02:04.750> be<00:02:04.900> tricked<00:02:05.380> kind<00:02:05.980> of<00:02:06.010> like
that can be tricked kind of like
that can be tricked kind of like
cellular<00:02:06.610> amnesia<00:02:06.970> into<00:02:07.390> an<00:02:07.480> embryonic<00:02:07.780> state
cellular amnesia into an embryonic state
cellular amnesia into an embryonic state
so<00:02:08.950> without<00:02:09.099> the<00:02:09.369> controversy<00:02:09.910> that's<00:02:10.060> cool
so without the controversy that's cool
so without the controversy that's cool
thing<00:02:10.539> number<00:02:10.690> one<00:02:10.989> cool<00:02:11.620> thing<00:02:11.650> number<00:02:11.920> two
thing number one cool thing number two
thing number one cool thing number two
you<00:02:12.370> can<00:02:12.550> grow<00:02:12.700> any<00:02:12.910> type<00:02:13.180> of<00:02:13.209> tissue<00:02:13.930> out<00:02:14.110> of
you can grow any type of tissue out of
you can grow any type of tissue out of
them<00:02:14.500> brain<00:02:14.980> heart<00:02:15.250> liver<00:02:15.489> you<00:02:15.640> get<00:02:15.760> the
them brain heart liver you get the
them brain heart liver you get the
picture<00:02:16.349> but<00:02:17.349> out<00:02:17.470> of<00:02:17.620> yourself<00:02:18.060> so<00:02:19.060> we<00:02:19.180> can
picture but out of yourself so we can
picture but out of yourself so we can
make<00:02:19.540> a<00:02:19.749> model<00:02:20.439> of<00:02:20.489> your<00:02:21.489> heart<00:02:21.819> your<00:02:22.209> brain<00:02:22.239> on
make a model of your heart your brain on
make a model of your heart your brain on
a<00:02:22.900> chip
a chip
a chip
generating<00:02:25.960> tissues<00:02:26.380> of<00:02:26.590> predictable
generating tissues of predictable
generating tissues of predictable
density<00:02:27.610> and<00:02:27.700> behaviors<00:02:28.150> the<00:02:28.300> second<00:02:28.630> piece
density and behaviors the second piece
density and behaviors the second piece
and<00:02:29.230> will<00:02:29.890> be<00:02:30.010> really<00:02:30.280> key<00:02:30.520> towards<00:02:30.910> getting
and will be really key towards getting
and will be really key towards getting
these<00:02:31.240> models<00:02:31.690> to<00:02:31.810> be<00:02:31.900> adopted<00:02:32.350> for<00:02:32.380> drug
these models to be adopted for drug
these models to be adopted for drug
discovery<00:02:32.860> and<00:02:33.250> this<00:02:33.910> is<00:02:34.060> a<00:02:34.090> schematic<00:02:34.360> of<00:02:34.840> a
discovery and this is a schematic of a
discovery and this is a schematic of a
bioreactor<00:02:35.380> we're<00:02:35.800> developing<00:02:36.160> in<00:02:36.280> our<00:02:36.430> lab
bioreactor we're developing in our lab
bioreactor we're developing in our lab
to<00:02:37.180> help<00:02:37.240> engineer<00:02:37.780> tissues<00:02:38.170> in<00:02:38.380> a<00:02:38.440> more
to help engineer tissues in a more
to help engineer tissues in a more
modular<00:02:38.770> scalable<00:02:39.460> way<00:02:40.020> going<00:02:41.020> forward
modular scalable way going forward
modular scalable way going forward
imagine<00:02:41.920> a<00:02:41.980> massively<00:02:42.400> parallel<00:02:42.700> version<00:02:43.300> of
imagine a massively parallel version of
imagine a massively parallel version of
this<00:02:43.510> with<00:02:43.960> thousands<00:02:44.530> of<00:02:44.620> pieces<00:02:44.830> of<00:02:44.980> human
this with thousands of pieces of human
this with thousands of pieces of human
tissue<00:02:45.520> it<00:02:46.360> would<00:02:46.480> be<00:02:46.570> like<00:02:46.600> having<00:02:46.870> a
tissue it would be like having a
tissue it would be like having a
clinical<00:02:47.440> trial<00:02:47.710> on<00:02:47.890> a<00:02:47.920> chip<00:02:49.650> but<00:02:50.650> another
clinical trial on a chip but another
clinical trial on a chip but another
thing<00:02:51.190> about<00:02:51.310> these<00:02:51.580> induced<00:02:52.210> pluripotent
thing about these induced pluripotent
thing about these induced pluripotent
stem<00:02:53.050> cells<00:02:53.500> is<00:02:53.770> that<00:02:54.160> if<00:02:54.250> we<00:02:54.400> take<00:02:54.430> some<00:02:55.060> skin
stem cells is that if we take some skin
stem cells is that if we take some skin
cells<00:02:55.540> let's<00:02:55.780> say<00:02:55.960> from<00:02:56.590> people<00:02:56.830> with<00:02:56.920> a
cells let's say from people with a
cells let's say from people with a
genetic<00:02:57.400> disease<00:02:57.580> and<00:02:58.060> we<00:02:58.570> engineer<00:02:59.080> tissues
genetic disease and we engineer tissues
genetic disease and we engineer tissues
out<00:02:59.620> of<00:02:59.770> them<00:02:59.920> we<00:03:00.790> can<00:03:00.940> actually<00:03:01.090> use<00:03:01.300> tissue
out of them we can actually use tissue
out of them we can actually use tissue
engineering<00:03:02.080> techniques<00:03:02.560> to<00:03:03.040> generate
engineering techniques to generate
engineering techniques to generate
models<00:03:03.850> of<00:03:04.000> those<00:03:04.210> diseases<00:03:04.600> in<00:03:04.900> the<00:03:04.930> lab
models of those diseases in the lab
models of those diseases in the lab
here's<00:03:06.700> an<00:03:06.820> example<00:03:07.090> from<00:03:07.420> kevin<00:03:07.750> egan<00:03:07.900> slab
here's an example from kevin egan slab
here's an example from kevin egan slab
at<00:03:08.320> harvard<00:03:08.790> he<00:03:09.790> generated<00:03:10.330> neurons<00:03:11.200> from
at harvard he generated neurons from
at harvard he generated neurons from
these<00:03:12.430> induced<00:03:12.760> pluripotent<00:03:13.000> stem<00:03:13.660> cells
these induced pluripotent stem cells
these induced pluripotent stem cells
from<00:03:14.920> patients<00:03:14.980> who<00:03:15.430> have<00:03:15.550> Lou<00:03:15.670> Gehrig's
from patients who have Lou Gehrig's
from patients who have Lou Gehrig's
disease<00:03:16.260> and<00:03:17.260> he<00:03:17.440> differentiated<00:03:18.070> them<00:03:18.190> into
disease and he differentiated them into
disease and he differentiated them into
neurons<00:03:18.610> and<00:03:18.910> what's<00:03:19.090> amazing<00:03:19.360> is<00:03:19.750> that<00:03:19.930> these
neurons and what's amazing is that these
neurons and what's amazing is that these
neurons<00:03:20.410> also<00:03:20.740> show<00:03:20.920> symptoms<00:03:21.280> of<00:03:21.490> the
neurons also show symptoms of the
neurons also show symptoms of the
disease<00:03:22.050> so<00:03:23.050> with<00:03:23.170> disease<00:03:23.530> models<00:03:23.890> like
disease so with disease models like
disease so with disease models like
these<00:03:24.250> we<00:03:24.490> can<00:03:24.610> fight<00:03:24.850> back<00:03:25.000> faster<00:03:25.450> than<00:03:25.600> ever
these we can fight back faster than ever
these we can fight back faster than ever
before<00:03:26.170> and<00:03:26.290> understand<00:03:26.740> the<00:03:26.860> disease<00:03:27.160> better
before and understand the disease better
before and understand the disease better
than<00:03:27.640> ever<00:03:27.790> before<00:03:28.180> and<00:03:28.360> maybe<00:03:29.050> discover
than ever before and maybe discover
than ever before and maybe discover
drugs<00:03:29.830> even<00:03:29.950> faster<00:03:31.230> this<00:03:32.230> is<00:03:32.380> another
drugs even faster this is another
drugs even faster this is another
example<00:03:32.710> of<00:03:33.160> patient-specific<00:03:33.660> stem<00:03:34.660> cells
example of patient-specific stem cells
example of patient-specific stem cells
that<00:03:35.320> were<00:03:35.530> engineered<00:03:36.160> from<00:03:36.730> someone<00:03:37.120> with
that were engineered from someone with
that were engineered from someone with
retinitis<00:03:37.540> pigmentosa<00:03:37.920> this<00:03:38.920> is<00:03:39.070> a
retinitis pigmentosa this is a
retinitis pigmentosa this is a
degeneration<00:03:39.730> of<00:03:39.940> the<00:03:40.060> retina<00:03:40.420> it's<00:03:40.810> a
degeneration of the retina it's a
degeneration of the retina it's a
disease<00:03:41.110> that<00:03:41.290> runs<00:03:41.470> in<00:03:41.590> my<00:03:41.680> family<00:03:41.950> and<00:03:42.340> we
disease that runs in my family and we
disease that runs in my family and we
really<00:03:43.120> hope<00:03:43.300> that<00:03:43.570> sells<00:03:43.780> like<00:03:43.930> these<00:03:44.140> will
really hope that sells like these will
really hope that sells like these will
help<00:03:44.440> us<00:03:44.590> find<00:03:44.800> a<00:03:44.890> cure<00:03:45.150> so<00:03:46.150> some<00:03:46.420> people<00:03:46.750> think
help us find a cure so some people think
help us find a cure so some people think
that<00:03:47.140> these<00:03:47.260> models<00:03:47.650> sound<00:03:47.800> well<00:03:47.950> and<00:03:48.100> good
that these models sound well and good
that these models sound well and good
but<00:03:48.340> asked<00:03:49.090> will<00:03:49.240> are<00:03:49.660> these<00:03:49.810> really<00:03:50.020> as<00:03:50.260> good
but asked will are these really as good
but asked will are these really as good
as<00:03:50.710> the<00:03:50.830> rat<00:03:51.330> the<00:03:52.330> rat<00:03:52.360> is<00:03:52.750> an<00:03:52.959> entire<00:03:53.709> organism
as the rat the rat is an entire organism
as the rat the rat is an entire organism
after<00:03:54.459> all<00:03:54.610> with<00:03:54.790> interacting<00:03:55.330> networks<00:03:55.630> of
after all with interacting networks of
after all with interacting networks of
organs<00:03:56.110> a<00:03:56.410> drug<00:03:57.040> for<00:03:57.190> the<00:03:57.250> heart<00:03:57.520> can<00:03:58.090> get
organs a drug for the heart can get
organs a drug for the heart can get
metabolized<00:03:58.480> in<00:03:59.080> the<00:03:59.140> liver<00:03:59.440> and<00:04:00.060> some<00:04:01.060> of<00:04:01.150> the
metabolized in the liver and some of the
metabolized in the liver and some of the
byproducts<00:04:01.600> may<00:04:01.840> be<00:04:01.900> stored<00:04:02.200> in<00:04:02.350> the<00:04:02.440> fat
byproducts may be stored in the fat
byproducts may be stored in the fat
don't<00:04:03.760> you<00:04:03.850> miss<00:04:03.970> all<00:04:04.150> that<00:04:04.180> with<00:04:04.870> these
don't you miss all that with these
don't you miss all that with these
tissue<00:04:05.230> engineered<00:04:05.650> models<00:04:06.930> well<00:04:07.930> this<00:04:08.260> is
tissue engineered models well this is
tissue engineered models well this is
another<00:04:08.620> trend<00:04:08.950> in<00:04:09.040> the<00:04:09.130> field<00:04:09.430> by<00:04:10.360> combining
another trend in the field by combining
another trend in the field by combining
tissue<00:04:11.050> engineering<00:04:11.530> techniques<00:04:11.950> with<00:04:12.070> micro
tissue engineering techniques with micro
tissue engineering techniques with micro
fluidics<00:04:12.760> the<00:04:12.940> field<00:04:13.120> is<00:04:13.209> actually<00:04:13.480> evolving
fluidics the field is actually evolving
fluidics the field is actually evolving
towards<00:04:14.110> just<00:04:14.320> that<00:04:14.530> a<00:04:14.770> model<00:04:15.280> of<00:04:15.340> the<00:04:15.400> entire
towards just that a model of the entire
towards just that a model of the entire
ecosystem<00:04:16.570> of<00:04:16.660> the<00:04:16.810> body<00:04:17.020> complete<00:04:17.770> with
ecosystem of the body complete with
ecosystem of the body complete with
multiple<00:04:18.250> organ<00:04:18.549> systems<00:04:18.580> to<00:04:19.180> be<00:04:19.270> able<00:04:19.299> to
multiple organ systems to be able to
multiple organ systems to be able to
test<00:04:19.720> how<00:04:19.900> a<00:04:19.930> drug<00:04:20.200> you<00:04:20.380> might<00:04:20.530> take<00:04:20.709> for<00:04:20.859> your
test how a drug you might take for your
test how a drug you might take for your
blood<00:04:21.100> pressure<00:04:21.310> might<00:04:21.549> affect<00:04:21.790> your<00:04:21.850> liver
blood pressure might affect your liver
blood pressure might affect your liver
or<00:04:22.330> an<00:04:22.690> antidepressant<00:04:23.020> might<00:04:23.500> affect<00:04:23.770> your
or an antidepressant might affect your
or an antidepressant might affect your
heart<00:04:24.190> these<00:04:24.970> systems<00:04:25.360> are<00:04:25.510> really<00:04:25.840> hard<00:04:26.020> to
heart these systems are really hard to
heart these systems are really hard to
build<00:04:26.320> but<00:04:27.070> we're<00:04:27.190> just<00:04:27.220> starting<00:04:27.670> to<00:04:27.850> be<00:04:28.000> able
build but we're just starting to be able
build but we're just starting to be able
to<00:04:28.270> get<00:04:28.450> there<00:04:28.750> and<00:04:28.930> so<00:04:29.430> watch<00:04:30.430> out
to get there and so watch out
to get there and so watch out
but<00:04:32.540> that's<00:04:32.720> not<00:04:32.900> even<00:04:33.140> all<00:04:33.230> of<00:04:33.290> it<00:04:33.500> because
but that's not even all of it because
but that's not even all of it because
once<00:04:33.980> a<00:04:34.070> drug<00:04:34.280> is<00:04:34.400> approved<00:04:34.490> tissue
once a drug is approved tissue
once a drug is approved tissue
engineering<00:04:35.510> techniques<00:04:35.990> can<00:04:36.140> actually<00:04:36.500> help
engineering techniques can actually help
engineering techniques can actually help
us<00:04:36.710> develop<00:04:36.890> more<00:04:37.190> personalized<00:04:37.760> treatments
us develop more personalized treatments
us develop more personalized treatments
this<00:04:38.840> is<00:04:38.990> an<00:04:39.110> example<00:04:39.820> that<00:04:40.820> you<00:04:40.970> might<00:04:41.150> care
this is an example that you might care
this is an example that you might care
about<00:04:41.480> someday<00:04:42.140> and<00:04:42.380> I<00:04:42.590> hope<00:04:42.770> you<00:04:42.950> never<00:04:43.040> do
about someday and I hope you never do
about someday and I hope you never do
because<00:04:44.600> imagine<00:04:45.230> if<00:04:45.410> you<00:04:45.560> ever<00:04:45.680> get<00:04:45.860> that
because imagine if you ever get that
because imagine if you ever get that
call<00:04:46.310> that<00:04:47.270> gives<00:04:47.450> you<00:04:47.600> that<00:04:47.720> bad<00:04:48.080> news<00:04:48.110> that
call that gives you that bad news that
call that gives you that bad news that
you<00:04:48.740> might<00:04:48.920> have<00:04:49.070> cancer<00:04:49.600> wouldn't<00:04:50.600> you
you might have cancer wouldn't you
you might have cancer wouldn't you
rather<00:04:50.840> test<00:04:51.260> to<00:04:51.560> see<00:04:51.710> if<00:04:51.740> those<00:04:51.950> cancer<00:04:52.310> drugs
rather test to see if those cancer drugs
rather test to see if those cancer drugs
you're<00:04:52.760> going<00:04:52.910> to<00:04:52.970> take<00:04:53.180> are<00:04:53.390> going<00:04:53.510> to<00:04:53.570> work
you're going to take are going to work
you're going to take are going to work
on<00:04:53.870> your<00:04:53.990> cancer<00:04:54.850> this<00:04:55.850> is<00:04:56.000> an<00:04:56.090> example<00:04:56.390> from
on your cancer this is an example from
on your cancer this is an example from
Karen<00:04:56.930> Berg<00:04:57.140> slab<00:04:57.470> where<00:04:57.680> they're<00:04:57.830> using
Karen Berg slab where they're using
Karen Berg slab where they're using
inkjet<00:04:58.430> technologies<00:04:59.030> to<00:04:59.240> print<00:04:59.600> breast
inkjet technologies to print breast
inkjet technologies to print breast
cancer<00:05:00.290> cells<00:05:00.500> and<00:05:00.740> study<00:05:01.250> its<00:05:01.400> progressions
cancer cells and study its progressions
cancer cells and study its progressions
and<00:05:02.180> treatments<00:05:02.780> and<00:05:02.990> some<00:05:03.680> of<00:05:03.800> our
and treatments and some of our
and treatments and some of our
colleagues<00:05:04.100> at<00:05:04.310> Tufts<00:05:04.850> are<00:05:04.970> mixing<00:05:05.540> models
colleagues at Tufts are mixing models
colleagues at Tufts are mixing models
like<00:05:06.110> these<00:05:06.380> with<00:05:06.920> tissue<00:05:07.190> engineered<00:05:07.670> bone
like these with tissue engineered bone
like these with tissue engineered bone
to<00:05:08.150> see<00:05:08.300> House<00:05:08.450> cancer<00:05:08.840> might<00:05:08.990> spread<00:05:09.320> from
to see House cancer might spread from
to see House cancer might spread from
one<00:05:09.860> part<00:05:10.190> of<00:05:10.250> the<00:05:10.340> body<00:05:10.460> to<00:05:10.670> the<00:05:10.760> next<00:05:11.120> and<00:05:11.300> you
one part of the body to the next and you
one part of the body to the next and you
can<00:05:11.900> imagine<00:05:11.990> those<00:05:12.350> kinds<00:05:12.590> of<00:05:12.650> multi<00:05:12.950> tissue
can imagine those kinds of multi tissue
can imagine those kinds of multi tissue
chips<00:05:13.580> to<00:05:14.120> be<00:05:14.210> the<00:05:14.300> next<00:05:14.420> generation<00:05:14.990> of<00:05:15.050> these
chips to be the next generation of these
chips to be the next generation of these
kinds<00:05:15.470> of<00:05:15.530> studies<00:05:15.770> and<00:05:16.220> so<00:05:17.210> thinking<00:05:17.390> about
kinds of studies and so thinking about
kinds of studies and so thinking about
the<00:05:17.810> models<00:05:18.170> that<00:05:18.260> we've<00:05:18.380> just<00:05:18.410> discussed<00:05:18.950> you
the models that we've just discussed you
the models that we've just discussed you
can<00:05:19.400> see<00:05:19.580> going<00:05:19.790> forward<00:05:19.910> that<00:05:20.360> tissue
can see going forward that tissue
can see going forward that tissue
engineering<00:05:20.990> is<00:05:21.170> actually<00:05:21.440> poised<00:05:21.710> to<00:05:21.980> help
engineering is actually poised to help
engineering is actually poised to help
revolutionize<00:05:22.400> drug<00:05:23.060> screening<00:05:23.480> at<00:05:23.630> every
revolutionize drug screening at every
revolutionize drug screening at every
single<00:05:24.620> step<00:05:25.100> of<00:05:25.340> the<00:05:25.610> path<00:05:25.900> disease<00:05:26.900> models
single step of the path disease models
single step of the path disease models
making<00:05:27.620> for<00:05:27.740> better<00:05:27.830> drug<00:05:28.160> formulations
making for better drug formulations
making for better drug formulations
massively<00:05:29.630> parallel<00:05:29.960> human<00:05:30.560> tissue<00:05:30.770> models
massively parallel human tissue models
massively parallel human tissue models
helping<00:05:31.460> to<00:05:31.580> revolutionize<00:05:32.090> lab<00:05:32.330> testing
helping to revolutionize lab testing
helping to revolutionize lab testing
reduce<00:05:33.470> animal<00:05:33.860> testing<00:05:34.310> and<00:05:34.520> human<00:05:35.180> testing
reduce animal testing and human testing
reduce animal testing and human testing
and<00:05:35.630> clinical<00:05:35.930> trials<00:05:36.350> an<00:05:36.970> individualized
and clinical trials an individualized
and clinical trials an individualized
therapies<00:05:38.450> that<00:05:38.480> disrupts<00:05:39.080> what<00:05:39.200> we<00:05:39.290> even
therapies that disrupts what we even
therapies that disrupts what we even
consider<00:05:39.620> to<00:05:40.010> be<00:05:40.160> a<00:05:40.190> market<00:05:40.580> at<00:05:40.670> all
consider to be a market at all
consider to be a market at all
essentially<00:05:42.980> we're<00:05:43.160> dramatically<00:05:43.730> speeding
essentially we're dramatically speeding
essentially we're dramatically speeding
up<00:05:44.300> that<00:05:44.570> feedback<00:05:45.080> between<00:05:45.350> developing<00:05:45.890> a
up that feedback between developing a
up that feedback between developing a
molecule<00:05:46.310> and<00:05:46.670> learning<00:05:47.210> about<00:05:47.360> how<00:05:47.540> it<00:05:47.690> acts
molecule and learning about how it acts
molecule and learning about how it acts
in<00:05:48.050> the<00:05:48.110> human<00:05:48.410> body<00:05:48.730> our<00:05:49.730> process<00:05:50.090> for<00:05:50.270> doing
in the human body our process for doing
in the human body our process for doing
this<00:05:50.570> isn't<00:05:50.870> centrally<00:05:51.260> transforming
this isn't centrally transforming
this isn't centrally transforming
biotechnology<00:05:52.670> and<00:05:52.870> pharmacology<00:05:53.870> into<00:05:54.710> an
biotechnology and pharmacology into an
biotechnology and pharmacology into an
information<00:05:55.400> technology<00:05:56.140> helping<00:05:57.140> us
information technology helping us
information technology helping us
discover<00:05:57.680> and<00:05:57.890> evaluate<00:05:58.310> drugs<00:05:58.550> faster<00:05:59.090> more
discover and evaluate drugs faster more
discover and evaluate drugs faster more
cheaply<00:06:00.110> and<00:06:00.580> more<00:06:01.580> effectively<00:06:02.230> gives<00:06:03.230> new
cheaply and more effectively gives new
cheaply and more effectively gives new
meaning<00:06:03.440> to<00:06:03.800> models<00:06:04.550> against<00:06:04.850> animal<00:06:05.150> testing
meaning to models against animal testing
meaning to models against animal testing
doesn't<00:06:05.840> it<00:06:06.670> thank<00:06:07.670> you
you
Nina Tandon, medicine, tissue, tissue, engineer, stem, cells, TED, Fellow, TED-Ed, TEDEducation, Ed
