Presentation given at AMIA 2010 TBI - Depression - predicting recovery course and treatment selection

1. Translational Medicine:!
Using Systems of Differential Equations to Identify Patterns
in Symptom Remission in Response to Treatment and the !
Underlying Dynamics of their Interactions!
Joanne
S.
Luciano,
Ph.D.
Predictive
Medicine,
Inc.
Belmont,
MA
2010
AMIA
Summit
on
Translational
Bioinformatics
Parc
55
Hotel
San
Francisco
San
Francisco,
California,
USA
March
11,
2010

2. Take Home Messages !!
!A neural network model is capable
of predicting and describing recovery
patterns in depression!
!Recovery patterns differ treatment!
• Cognitive Behavioural Therapy!
» is sequential!
• Desipramine!
» is simultaneous and delayed
!!
Predictive Medicine, Inc. © 2010! 2!

3. Overview!
• Why we did this work - to improve quality of life for millions
of people suffering from depression!
• How we did it - used differential equations (“neural
network”) to model and compare response to different
antidepressant treatments!
• What we found - different response patterns for the two
treatments - the order and timing of improvement of
symptoms were different!
• What we think it means - improvement in selection of
treatment thereby reducing unnecessary costs
and suffering. Potentially saving lives!
Predictive Medicine, Inc. © 2010! 3!

4. Overview!
• Why we did this work - to improve quality of
life for millions of people suffering from
depression!
• How we did it - used differential equations (“neural
network”) to model and compare response to
different antidepressant treatments!
• What we found - different response patterns for
the two treatments - the order and timing of
improvement of symptoms were different!
• What we think it means - improvement in selection
of treatment thereby reducing unnecessary costs
and suffering. Potentially saving lives!
Predictive Medicine, Inc. © 2010! 4!

5. Translational Medicine!
• Rapid transformation of laboratory findings into
clinically focused applications !
• ʻFrom bench to bedside and backʼ!
Predictive Medicine, Inc. © 2010! 5!

6. Depression is a BIG problem!
Characterized by persistent and pathological sadness,
dejection, and melancholy!
Prevalence (US)!
!6% year (18 million)!
!16% experience it in their lifetime!
Cost !
!44 Billion (1990)!
Impact!
!1% Improvement means (180, 000 people helped)!
!1% Improvement means (440 million in savings)!
Predictive Medicine, Inc. © 2010! 6!

7. The
Economic
Burden
of
Depression
Depression is the
highest of the health
care cost for business
http://www.preventingdepression.com/costs.htm
PredictiveHealthy Thinking Initiative!© 2010!
Source: The Medicine, Inc. 7!

8. Depression is a BIG Problem!
Predictive Medicine, Inc. © 2010!

9. Treatment Choice Vague!
Predictive Medicine, Inc. © 2010!

10. Overview!
• Why we did this work - to improve quality of life for millions
of people suffering from depression!
• How we did it - used differential equations
(“neural network”) to model and compare
response to different antidepressant
treatments!
• What we found - different response patterns for the two
treatments - the order and timing of improvement of
symptoms were different!
• What we think it means - improvement in selection of
treatment thereby reducing unnecessary costs and
suffering. Potentially saving lives!
Predictive Medicine, Inc. © 2010! 10!

11. Research Goals!
Illuminate recovery
course
Predictive Medicine, Inc. © 2010! 11!

12. Treatment Response Study!
Today’s
talk:
Response
to
treatment
Predictive Medicine, Inc. © 2010! 12!

13. Depression Background!
• Clinical Depression!
• Treatment!
• Symptom Measurement!
• No specific diagnosis!
• No specific treatment!
Predictive Medicine, Inc. © 2010! 13!

14. Clinical Data!
Symptoms!
! -HDRS (0-4 scale)!
!
Treatment!
-Desipramine (DMI)!
-Cognitive Behavioral Therapy (CBT)!
!
Outcome!
! - Responders!
Predictive Medicine, Inc. © 2010! 14!

15. Hamilton Psychiatric Scale for
Depression!
Predictive Medicine, Inc. © 2010! 15!

16. Modelling !
Recast
problem
into
mathematical
terms
!
Easier to understand!
Easier to manipulate!
Easier to analyze!
Predictive Medicine, Inc. © 2010! 16!

17. Predictive Medicine, Inc. © 2010! 17!

18. Understanding Recovery!
Predictive Medicine, Inc. © 2010! 18!

19. Depression Data!
• 7 Symptoms ! !!
!Physical:! !E Sleep ! !
! ! !M, L Sleep ! ! ! !
! ! !Energy ! ! ! ! !
!Performance: !Work & Interests ! ! ! !
!Psychological: !Mood ! ! ! ! !
! ! !Cognitions ! ! ! !
! ! !Anxiety ! ! !!
• 2 Treatments ! !Cognitive Behavioural Therapy (CBT)! !
! ! !Desipramine (DMI)!
!
• Clinical Data ! !Responders = improvement >= 50% ! !
! ! !N ! = 6 patient each study!
! !6 weeks ! = 252 data points each study!
!
Predictive Medicine, Inc. © 2010! 19!

20. Overview
Recovery Model and Parameters!
A
W E
C ES
M MS
Predictive Medicine, Inc. © 2010! 20!

21. Modeling Time to Response !
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22. Modeling Treatment Effects!
Predictive Medicine, Inc. © 2010! 22!

23. Recovery Model Equation!
= -
+
+
+
Predictive Medicine, Inc. © 2010! 23!

24. Training the model!
Predictive Medicine, Inc. © 2010! 24!

25. Recovery Pattern and Error
Example Patient (CBT)!
Predictive Medicine, Inc. © 2010! 25!

26. Recovery Pattern and Error
Patient Group (CBT)!
Predictive Medicine, Inc. © 2010! 26!

27. Overview!
• Why we did this work - to improve quality of life for millions
of people suffering from depression!
• How we did it - used differential equations (“neural network”)
to model and compare response to different antidepressant
treatments!
• What we found - different response patterns
for the two treatments - the order and timing
of improvement of symptoms were different!
• What we think it means - improvement in selection of
treatment - less trial and error !
Predictive Medicine, Inc. © 2010! 27!

28. Results
Optimized parameters specify model
Initial conditions predict pattern trajectory !
A
W E
C ES
M MLS
Predictive Medicine, Inc. © 2010! 28!

29. Latency!
Predictive Medicine, Inc. © 2010! 29!

30. Mean ½ Reduction Time!
CBT varies 3.7 wks
DMI varies 1.8 wks
Predictive Medicine, Inc. © 2010! 30!

31. Direct Effect of Treatment!
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32. Direct Treatment Intervention Effect!
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33. Treatment Effects and
Interactions!
DMI: > 2x interactions and loops DMI
CBT (delayed)
Sequential CONCURRENT
Predictive Medicine, Inc. © 2010! 33!

34. Order and Time of Symptoms
Improve is Different for CBT and DMI!
Predictive Medicine, Inc. © 2010!

35. Overview!
• Why we did this work - to improve quality of life for millions
of people suffering from depression!
• How we did it - used differential equations (“neural network”)
to model and compare response to different antidepressant
treatments!
• What we found - different response patterns for the two
treatments - the order and timing of improvement of
symptoms were different!
• What we think it means - improvement
in selection of treatment thereby
reducing unnecessary costs and
suffering. Potentially saving lives.!
Predictive Medicine, Inc. © 2010! 35!

36. Conclusions!
• An neural network model is capable of predicting
and describing recovery patterns in depression!
• We can do better than trial and error treatment
protocols!
• Recovery patterns differ by treatment!
• Cognitive Behavioural Therapy!
is sequential!
• Desipramine!
is concurrent (after delay)!
• Recovery patterns provide insights to patient
response that can inform treatment choices!
Predictive Medicine, Inc. © 2010! 36!

37. Limitations!
• Model:!
• Assumes symptoms interact!
• Assumes treatment acts directly!
• Permanent vs. transient!
• Causal vs. sequential!
• Statistical fluctuations not handled!
• Study:!
• CBT measurement intervals vary!
• Small sample size!
• Initial 6 weeks of CBT (entire=16)!
• Finer resolution of measurements (2-3/day)!
Predictive Medicine, Inc. © 2010! 37!

38. Thank
you!
Predictive Medicine, Inc. © 2010! 38!

39. Backup
Slides
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40. Recovery Model!
Predictive Medicine, Inc. © 2010!

41. Predictive Medicine, Inc. © 2010! 41!

42. Predictive Medicine, Inc. © 2010! 42!

43. Spanning
disciplines
Emerging
disciplines
Clinical Practice Research Life
Medicine Signs and Symptoms Findings
Anatomy Sciences
Diagnosis & Treatment
Neuroscience
Clinical
Research Molecular
Diabetes Huntington s Genomics
Biology
Disease
Depression Biochemistry
Translational
Influenza Medicine Genetics
Electronic Ontology
Medical
Records Bioinformatics
Computer
Mathematical
simulation
Models
Machine Learning
Semantic Web
Information Systems
Predictive Medicine, Inc. © 2010! 43!

44. World Congress
on Neural Patents
Networks, Offered at
July 11-15,
1993, Portland, Timeline
Ocean Tomo
Auction
Oregon Chicago, IL
US Patents Patents Sold
SIG No. to Advanced
Mental Function 6,063,028 Biological
and PhD
Dysfunction Thesis
Awarded
EMPWR Laboratories
Belgium
Sam Levin Proposal
BioPAX
Approved
1995 1997 2001 2006
1993 1994 1996 2000 2008 ?
Jackie Workshop
Poster
2009
Neural Linked Data
Samson, Presented W3C HCLS
Mc Lean Modeling of
Cognitive ISMB 1997 BioDASH
Hospital PSB 1998 US Patent No. EPOS
Depression and Brain 6,317,73
Research Disorders
Predictive Medicine, Inc. © 2010! Awarded 44!

45. Workshop 1995
Book 1996
Neural
Modeling
of
Depression
1996 Luciano, J., Cohen, M. Samson, J.
”Neural Network Modeling of Unipolar
Depression,” Neural Modeling of
Cognitive and Brain Disorders, World
Scientific Publishing Company, eds. J.
Reggia and E. Ruppin and R. Berndt.
Book cover; chapter pp 469-483.
Luciano Model highlighted on book cover
Predictive2008
27 October Medicine, Inc. © 2010! 45!

46. Establishing
Communities
of
Interest/
Practice
• BioPathways
Consortium
• BioPAX
• W3C
Semantic
Web
for
Health
Care
and
Life
Sciences
(HCLSIG)
Predictive2008
27 October Medicine, Inc. © 2010! 46!

47. BioPAX
-­‐
Enabling
Cellular
Network
Process
Modeling
Metabolic Molecular Signaling Gene
Pathways Interaction Pathways Regulatory
Networks Networks
Predictive2008
27 October Medicine, Inc. © 2010! 47!

48. EMPWR
Collaboration
with
Manchester,
UK
• Use
instanceStore
to
reason
over
BioPAX
formatted
(OWL)
pathway
data
• Goal:
discover
new
scientific
facts
• Method:
Utilize
power
of
reasoners
and
OWL
through
coupling
BioPAX
data
and
Manchester
Technology
• Results:
BioPAX
semantics
lacking
thus
had
to
educate
BioPAX
community
and
course-­‐correct
initiative
• Extending
BioPAX
to
enable
the
computational
exploration
Predictive2008
27 October Medicine, Inc. © 2010! 48!

49. Diabetes
Type
2
! 90-­‐95%
diagnosed
cases
of
diabetes
(adults)
! Usually
begins
as
insulin
resistance
! Associated
with
age,
obesity,
family
history,
history
of
gestatinal
diabestes,
impared
glucose
metabolish,
physical
inactivity,
race/
ethnicity
! Rare
in
children,
but
increasing
Predictive Medicine, Inc. © 2010! 49!

50. Understanding
the
role
of
risk
factors
in
insulin
resistance
Figure: Integration of genomic and proteomic/metabolomic data (text boxes shaded in gray) proposed for current project. We hypothesize that diabetes risk factors result in altered
gene and protein expression in skeletal muscle and adipose tissue (genomic data), leading to insulin resistance and inflammation. This, in turn, results in abnormal tissue function, as
indicted by accumulation of long-chain fatty acyl CoA and oxidative damage (proteomic and metabolomic data), further insulin resistance and beta-cell failure, and ultimately to type 2
Predictive Medicine, Inc. © 2010!
diabetes
50!

51. Enhance
pathway
capability
! Optimize
! Speed
! Accuracy
! Completeness
! Single
query
over
multiple
of
databases
! Validate,
test
and
evaluationr
! Incorporate
into
diabetes
research
workflow
Predictive Medicine, Inc. © 2010! 51!

52. Enhance
pathway
capability
Cell Designer model of adipose tissue cell. Add gene
expression, standard metadata terms (BioPAX, GenBank)!
Use with expression data constrained by proteomic data!
towards target ID, biomarker ID, patient population ID!52!
Predictive Medicine, Inc. © 2010!

53. 2008
Received
inquiry
and
put
up
for
auction
(Chicago)
2009
Sold
to
Advanced
Biomedical
Labs
(Belgium)
US Patent No. 6,063,028 May 2000 US Patent No. 6,317,731 Nov 2001
AUTOMATED TREATMENT METHOD FOR PREDICTING THE
SELECTION METHOD OUTCOME OF A TREATMENT
OCEAN TOMO LLC Live Auction, Chicago, USA Oct 30, 2008
Expected Value $800,000+
Predictive2008
27 October Medicine, Inc. © 2010! 53!

54. Take
Home
Message
• We
need
to
shorten
the
time
• tighten
the
loop
between
research
and
practice;
• 15
years
+
is
too
long,
way
too
long
Predictive Medicine, Inc. © 2010! 54!

55. Acknowledgements
• Sam
Levin
Eric Neumann! ME Patti!
• Dan
Levine
Chris Sander! Mark Musen!
Mike Cary! Zak Kohane!
• Dan
Bullock
Jeremy Zucker! Brian Athey!
• Ennio
Alan Ruttenberg! David States!
Mingola
Jonathan Rees! !
• Michiro
Robert Stevens! !
Negishi
Phil Lord! !
• Jacqueline
Alan Rector!
Sampson
Andy Brass!
• Larry
Hunter
Paul Fisher!
• Rick
Lathrop,
Carole Goble!
• Larrie
Hutton
George Church!
Matt Temple!
• Tim
Clark
Christopher Brewster!
55!
Predictive Medicine, Inc. © 2010!

56. Pre-­‐diabetes
! Increased
risk
of
developing
type
2
diabetes,
heart
disease,
and
stroke
! Blood
glucose
levels
higher
than
normal
(but
not
high
enough
to
be
characterized
as
diabetes)
! Impaired
fasting
glucose
(IFG),
impaired
glucose
tolerance
(IGT)
or
both.
! IFG
100
to
125
milligrams
per
deciliter
(mg/dL)
! IGT
140
to
199
mg/dL
! 19%
adults
(US,
2007)
! 7
%
IFG
adolescents
(US,
1999
to
2000)
Source: http://diabetes.niddk.nih.gov/DM/PUBS/statistics/!
Predictive Medicine, Inc. © 2010! 56!

57. Diabetes
57!
http://diabetes.niddk.nih.gov/DM/PUBS/statistics/!
Predictive Medicine, Inc. © 2010!

58. Data
! 130
nondiabetic
subjects
Subjects Recruited (May 2006)
! Characterized
(Mean + SD)
metabolically
Number 130
! Family
history
pos
and
Age 36 + 10 years
neg
BMI 27 + 5 kg/m2
Gender 53 M, 77 F
Family History DM 61 FH- neg, 69 FH+ pos
! FH-­‐
more
insulin
Fasting Glucose 93 + 17 mg/dl
sensitive
than
FH+
Fasting Insulin 9 + 8 µU/ml
SI 5.8 + 4.3
SG 0.0245 + 0.0211
! Broad
range
of
insulin
AIRg 469 + 404
sensitivity,
quartiles
of
SI
values
with
limits
of
2.6,
5.3,
and
8.4
Predictive Medicine, Inc. © 2010! 58!

59. Research
Aims
! Enhance
diabetes
research
with
pathway
capability
for
target
identification,
biomarker
identification,
patient
population
identification
! Enable
simulation
and
reasoning:
extend
and
integrate
computational
technologies:
web
services,
workflows,
metadata,
ontologies
BioPAX
pathway
representation
! Optimize
the
speed,
accuracy
and
completeness:
single
query
over
multiple
of
databases.
! Deploy
into
diabetes
research
workflow
Predictive Medicine, Inc. © 2010! 59!

60. Research
and
Practice
! Computational
modelers
construct
in
silico
representations
of
organic
phenomena
! Basic
researchers
construct
in
vitro
! Clinical
Researcher’s
conduct
in
vivo
studies
on
patient
populations
! Clinical
practioners
apply
the
results
of
clinical
research
Predictive Medicine, Inc. © 2010! 60!

61. Questions!
• Some people on antidepressants
commit suicide. Is it possible that the
antidepressant drug can cause this to
happen?!
!
• How can differential equations help us
to understand what is going on?!
Predictive Medicine, Inc. © 2010! 61!