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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
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Predictive Medicine, Inc. © 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
!!
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Predictive Medicine, Inc. © 2010! 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!
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Predictive Medicine, Inc. © 2010! 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!
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Predictive Medicine, Inc. © 2010! 5!
Translational Medicine!• Rapid transformation of laboratory findings into
clinically focused applications !• ʻFrom bench to bedside and backʼ!
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Predictive Medicine, Inc. © 2010! 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)!
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Predictive Medicine, Inc. © 2010! 7!
The Economic Burden of Depression
Source: The Healthy Thinking Initiative!
http://www.preventingdepression.com/costs.htm
Depression is the highest of the health care cost for business
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Predictive Medicine, Inc. © 2010!
Depression is a BIG Problem!
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Predictive Medicine, Inc. © 2010!
Treatment Choice Vague!
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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!
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Predictive Medicine, Inc. © 2010! 11!
Research Goals!
Illuminate recovery course
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Predictive Medicine, Inc. © 2010! 12!
Today’s talk: Response to treatment
Treatment Response Study!
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Predictive Medicine, Inc. © 2010! 13!
Depression Background!
• Clinical Depression!• Treatment!• Symptom Measurement!• No specific diagnosis!• No specific treatment!
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Predictive Medicine, Inc. © 2010! 14!
Clinical Data! Symptoms!
! -HDRS (0-4 scale)!!
Treatment!-Desipramine (DMI)!-Cognitive Behavioral Therapy (CBT)!
!
Outcome!! - Responders!
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Predictive Medicine, Inc. © 2010! 15!
Hamilton Psychiatric Scale for Depression!
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Predictive Medicine, Inc. © 2010! 16!
Modelling !
!
Easier to understand!Easier to manipulate!Easier to analyze!
Recast problem into mathematical terms
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Predictive Medicine, Inc. © 2010! 17!
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Predictive Medicine, Inc. © 2010! 18!
Understanding Recovery!
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Predictive Medicine, Inc. © 2010! 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!!
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Predictive Medicine, Inc. © 2010! 20!
Overview Recovery Model and Parameters!
M
E W
MS
ES
A
C
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Predictive Medicine, Inc. © 2010! 21!
Modeling Time to Response !
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Predictive Medicine, Inc. © 2010! 22!
Modeling Treatment Effects!
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Predictive Medicine, Inc. © 2010! 23!
Recovery Model Equation!
+
++
- ==
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Predictive Medicine, Inc. © 2010! 24!
Training the model!
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Predictive Medicine, Inc. © 2010! 25!
Recovery Pattern and ErrorExample Patient (CBT)!
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Predictive Medicine, Inc. © 2010! 26!
Recovery Pattern and ErrorPatient Group (CBT)!
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Predictive Medicine, Inc. © 2010! 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 !
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Predictive Medicine, Inc. © 2010! 28!
ResultsOptimized parameters specify model
Initial conditions predict pattern trajectory !
M C
W A
E ES
MLS
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Predictive Medicine, Inc. © 2010! 29!
Latency!
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Predictive Medicine, Inc. © 2010! 30!
Mean ½ Reduction Time!
CBT varies 3.7 wks
DMI varies 1.8 wks
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Predictive Medicine, Inc. © 2010! 31!
Direct Effect of Treatment!
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Predictive Medicine, Inc. © 2010! 32!
Direct Treatment Intervention Effect!
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Predictive Medicine, Inc. © 2010! 33!
Treatment Effects and Interactions!
CBT Sequential
DMI (delayed)
CONCURRENT
DMI: > 2x interactions and loops
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Predictive Medicine, Inc. © 2010!
Order and Time of Symptoms Improve is Different for CBT and DMI!
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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.!
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Predictive Medicine, Inc. © 2010! 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!
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Predictive Medicine, Inc. © 2010! 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)!
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Predictive Medicine, Inc. © 2010! 38!
Thank you!
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Predictive Medicine, Inc. © 2010! 39!
Backup Slides
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Predictive Medicine, Inc. © 2010!
Recovery Model!
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Predictive Medicine, Inc. © 2010! 41!
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Predictive Medicine, Inc. © 2010! 42!
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Predictive Medicine, Inc. © 2010! 43!
Spanning disciplines Emerging disciplines
Diagnosis & Treatment
Depression Translational
Medicine
Huntington’s Disease
Medicine Life Sciences
Information Systems
Neuroscience Molecular Biology
Clinical Practice Signs and Symptoms
Biochemistry
Mathematical Models
Computer simulation
Ontology Genetics
Genomics
Anatomy
Machine Learning Semantic Web
Influenza
Research Findings
Bioinformatics Electronic Medical Records
Clinical Research Diabetes
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Predictive Medicine, Inc. © 2010! 44!
Timeline
2009 1993
World Congress on Neural Networks,
July 11-15, 1993, Portland,
Oregon
SIG Mental Function
and Dysfunction Sam Levin
Jackie Samson, Mc Lean Hospital
Depression Research
1996
1995
2008 1994
Patents Sold to Advanced
Biological Laboratories
Belgium
Patents Offered at
Ocean Tomo Auction
Chicago, IL
US Patent No. 6,317,73 Awarded
US Patents No.
6,063,028 Awarded
2001
2000
PhD Thesis
Proposal Approved
Workshop Neural
Modeling of Cognitive and Brain Disorders
BioPAX
? Linked Data W3C HCLS BioDASH
EPOS
2006
EMPWR
Poster Presented
ISMB 1997 PSB 1998
1997
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Predictive Medicine, Inc. © 2010! 45! 27 October 2008
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
Workshop 1995 Book 1996
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Predictive Medicine, Inc. © 2010! 46! 27 October 2008
• BioPathways Consortium • BioPAX • W3C Semantic Web for Health Care and Life Sciences (HCLSIG)
Establishing Communities of Interest/Practice
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Predictive Medicine, Inc. © 2010! 47! 27 October 2008
BioPAX -‐ Enabling Cellular Network Process Modeling
Metabolic Pathways
Molecular Interaction Networks
Signaling Pathways
Gene Regulatory Networks
Glycolysis Protein-Protein Apoptosis TFs in E. coli
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Predictive Medicine, Inc. © 2010! 48! 27 October 2008
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
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Predictive Medicine, Inc. © 2010! 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
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Predictive Medicine, Inc. © 2010! 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 diabetes
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Predictive Medicine, Inc. © 2010! 51!
Enhance pathway capability ! Optimize
! Speed
! Accuracy
! Completeness
! Single query over multiple of databases
! Validate, test and evaluationr ! Incorporate into diabetes research
workflow
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Predictive Medicine, Inc. © 2010! 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!
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Predictive Medicine, Inc. © 2010! 53! 27 October 2008
2008 Received inquiry and put up for auction (Chicago) 2009 Sold to Advanced Biomedical Labs (Belgium)
US Patent No. 6,063,028 May 2000
AUTOMATED TREATMENT SELECTION METHOD
US Patent No. 6,317,731 Nov 2001
METHOD FOR PREDICTING THE OUTCOME OF A TREATMENT
OCEAN TOMO LLC Live Auction, Chicago, USA Oct 30, 2008
Expected Value $800,000+
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Predictive Medicine, Inc. © 2010! 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
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Predictive Medicine, Inc. © 2010! 55!
Acknowledgements • Sam Levin • Dan Levine • Dan Bullock • Ennio
Mingola • Michiro
Negishi • Jacqueline
Sampson • Larry Hunter • Rick Lathrop, • Larrie Hutton • Tim Clark
Eric Neumann!Chris Sander!Mike Cary!Jeremy Zucker!Alan Ruttenberg!Jonathan Rees!Robert Stevens!Phil Lord!Alan Rector!Andy Brass!Paul Fisher!Carole Goble!George Church!Matt Temple!Christopher Brewster!
ME Patti!Mark Musen!Zak Kohane!Brian Athey!David States!!!!
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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/!
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Diabetes
http://diabetes.niddk.nih.gov/DM/PUBS/statistics/!
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Data ! 130 nondiabetic subjects
! Characterized metabolically
! Family history pos and neg
! FH-‐ more insulin sensitive than FH+
! Broad range of insulin sensitivity, quartiles of SI values with limits of 2.6, 5.3, and 8.4
Subjects Recruited (May 2006)
(Mean + SD) Number 130 Age 36 + 10 years BMI 27 + 5 kg/m2 Gender 53 M, 77 F Family History DM 61 FH- neg, 69 FH+ pos Fasting Glucose 93 + 17 mg/dl Fasting Insulin 9 + 8 µU/ml SI 5.8 + 4.3 SG 0.0245 + 0.0211 AIRg 469 + 404
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Predictive Medicine, Inc. © 2010! 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
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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
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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?!