1. Development
of
a
Screening
Informa3cs
System
at
the
UNM
Center
for
Molecular
Discovery
Jeremy Yang, Oleg Ursu, Stephen Mathias, Cristian Bologa, Anna Waller, Annette Evangelisti, Gergely Záhoransky-Köhalmi, and Tudor Oprea
University of New Mexico, Albuquerque, New Mexico, USA
ACS National Meeting, San Diego, March 25-29, 2012
What
is
screening
informa3cs?
Major
challenges
Automa3ng
when
every
assay
is
special
•
Informa=cs
in
support
of
screening
for
biomolecular
discovery,
usually
•
New
methodology,
such
as
high-­‐content
and
mul=plex
bioassays
Flow
cytometry
generates
mul=ple
fluorescence
measurements
per
sample
pharma
discovery:
Acquisi=on,
processing
and
storage
of
bioassay
data
•
More
data,
internal
and
external
for
use
during
projects
and
for
retrospec=ve
analyses.
and
per
target,
where
mul=plex
=
mul=-­‐target.
Even
“singleplex”
assays
•
New
privacy
and
collabora=on
models
may
employ
mul=ple
posi=ve
and
nega=ve
control
targets.
Assays
can
differ
•
Searching
over
molecules,
assays,
ac=vi=es,
targets,
etc.
I/O
&
•
Advances
in
cheminforma=cs
and
bioinforma=cs
methodology
integra=on
in
conformance
with
contractual,
legal/regulatory,
business,
greatly
in
raw
data
outputs
and
analysis
protocols
to
calculate
a
“response”
•
Development
concurrent
with
ongoing
projects
and
deadlines
represen=ng
a
biological
outcome
(e.g.
binding
to
a
target).
In
some
cases,
and
scien=fic
requirements.
requiring
con=nually
opera=onal
system.
it
may
seem
more
appropriate
to
conceive
an
API
(programming
interface)
•
Applica=ons
and
interfaces
suited
to
trans-­‐disciplinary
audience
(biology,
chemistry,
pharmacology,
medicine,
etc.).
to
recode
each
assay
analysis
rather
than
an
informa=cs
system,
flexible
but
No
shrink-­‐wrapped
solu3ons
generally
constant,
and
in
fact,
our
solu=on
combines
elements
of
both.
Why
screening
informa3cs?
Due
to
the
complexity
of
modern
screening
informa=cs,
and
in
par=cular
our
novel,
highly
versa=le
mul=plex
flow-­‐cytometry
plasorm
MicroSoP
Excel,
not
going
away
soon
(patented,
and
commercialized
as
HyperCyt),
there
cannot
be
a
shrink-­‐ Excel
remains
an
important
tool
for
wrapped
solu=on
providing
all
needed
func=onality
for
all
possible
scien=fic
data
processing,
analysis
and
experiments.
visualiza=on,
at
UNMCMD
and
elsewhere.
But
it
has
fundamental
limita=ons
and
drawbacks,
esp.
data
One
of
the
primary
mo=va=ons
for
cheminforma=cs
has
been
drug
Solu3on:
hybrid,
agile
system
of
apps
&
APIs
and
code
access
and
version
control.
discovery
which
involves
bioassay
screening
and
increasingly,
high-­‐ Heterogeneous
so8ware
components
from
(1)
commercial
vendors,
throughput
screening
(HTS).
(2)
open
source
projects,
and
(3)
custom
code
developed
at
UNM.
E.g.
Bcl-­‐2
assay
analysis
worksheets,
UNMCMD,
2007
(PubChem
AID=1693).
Screening
Informa3cs
≠
Cheminforma3cs
!!
Cheminforma=cs
is
a
key
part
of
screening
informa=cs
but
biology
is
primary.
Plates,
wells,
samples,
and
measurements
are
physically
real
and
informa=cally
authorita=ve
while
structure
data
is
a
model
which
Custom
code:
Using
the
right
tools
for
the
tasks
may
be
incorrect
or
imprecise.
Chem-­‐
and
bio-­‐
contexts
must
be
Custom
so8ware
development
has
included:
Oracle
SQL
w/
AEI,
Excel
integrated
for
successful
system.
E.g.
EC50
=
1.7µM
is
about
a
sample,
macros,
Perl,
Java,
Python,
NCBI
EntrezU=ls
apps,
custom
PP
protocols,
a
well,
a
plate,
an
assay,
a
biological
system…
eventually
we
hope
about
Intro
to
Flow
Cytometry
at
UNMCMD
Prism
batch
code,
and
more.
Interfaces
include
command
line
apps,
web
a
lead
compound.
apps,
and
in-­‐house
APIs
for
rapid
development.
UNMCMD
specialized
for
flow
cytometry
AEVA
(Assay
Explora3on,
Viewing
&
Analysis)
web
app
Mul=plexed!
AEI
&
Pipeline
Pilot
&
customiza3on
A8er
licensing
the
Accelrys
Accord
Enterprise
Informa=cs
(AEI)
and
Pipeline
Pilot
(PP)
so8ware
in
2009,
efforts
began
to
configure
and
customize
AEI/PP.
Accelrys-­‐UNMCMD
consulta=on,
customiza=on
and
training,
revealed
(1)
what
components
could
be
used
with
minor
Accurate
data
acquisi3on
key
pre-­‐requisite
configura=on
efforts,
and
(2)
scope
of
required
custom
coding.
This
experience
was
essen=al
and
decisive
in
the
evolu=onary
design
Screening
informa=cs
depends
on
accurate
measurements
with
process.
addi=onal
informa=cs
challenges,
such
as
“binning”,
i.e.
correla=ng
fluorescence
data
to
wells
and
substances.
Conclusion
The
good
news
is
that
advances
in
so8ware
and
informa=cs
provide
choices
of
solu=ons
and
opportuni=es
to
effec=vely
manage
screening.
The
complexity
of
the
so8ware
landscape
is
truly
both
a
challenge
and
opportunity.
It
is
hoped
that
our
experiences
will
be
helpful
to
others
similarly
tasked
with
designing
and
implemen=ng
a
screening
informa=cs
system.
PP
protocol,
via
WebPort,
to
generate
PubChem
compliant
depositor
upload.
References:
1. Flow
Cytometry
Shi8ing
Gears,
Gene=c
Eng
&
Biotech
News,
Nov
15,
2011
(Vol.
31,
No.
20)
,
Hit
Defini3on:
various
assays,
various
methods
hJp://www.genengnews.com/gen-­‐ar=cles/flow-­‐cytometry-­‐shi8ing-­‐gears/3913.
2. Edwards
BS,
Young
SM,
Saunders
MJ,
Bologa
C,
Oprea
TI,
Ye
RD,
Prossnitz
ER,
Graves
SW,
Sklar
LA.
High-­‐
• Response:
>(ac=va=on)
or
<(inhibi=on)
cutoff
throughput
flow
cytometry
for
drug
discovery.
Expert
Opin.
Drug
Discov.
2,
685-­‐696,
2007.
3. Haynes
MK,
Strouse
JJ,
Waller
A,
Leitão
A,
Curpan
RF,
Bologa
C,
Oprea
TI,
Prossnitz
ER,
Edwards
BS,
Sklar
LA,
• SD:
>(ac=va=on)
or
<(inhibi=on)
cutoff
SDs
from
plate
mean.
Thompson
TA.
Detec=on
of
intracellular
granularity
induc=on
in
prostate
cancer
cell
lines
by
small
molecules
• Custom:
custom
func=on
specified
for
assays
with
"special
needs“.
using
the
HyperCyt®
high
throughput
flow
cytometry
system.
J.
Biomol.
Screening,
14,
596-­‐609,
2009
4. The
NIH's
Molecular
Libraries
Program
-­‐
What's
Next?
|
SLAS
Electronic
Laboratory
Neighborhood,
hJp://
c/o
Anna
Waller,
UNMCMD
HyperViewSession_20110603
Custom
may
include
counter-­‐targets,
mul=ple
+/-­‐
controls,
etc.,
etc.
www.eln.slas.org/story/1/52-­‐the-­‐nihs-­‐molecular-­‐libraries-­‐program-­‐whats-­‐next.
Powered by:
Mesa OpenEye OpenBabel SciTouch