amia tbi 2010_pmi_luciano.ppt

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

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

!!


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!


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!




Translational Medicine!•  Rapid transformation of laboratory findings into

clinically focused applications !•  ʻFrom bench to bedside and backʼ!


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)!


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

Predictive Medicine, Inc. © 2010!

Depression is a BIG Problem!


Treatment Choice Vague!



of people suffering from depression!•  How we did it - used differential equations

(“neural network”) to model and compare response to different antidepressant treatments!




Research Goals!

Illuminate recovery course


Today’s talk: Response to treatment

Treatment Response Study!


Depression Background!

•  Clinical Depression!•  Treatment!•  Symptom Measurement!•  No specific diagnosis!•  No specific treatment!


Clinical Data! Symptoms!

! -HDRS (0-4 scale)!!

Treatment!-Desipramine (DMI)!-Cognitive Behavioral Therapy (CBT)!

!

Outcome!! - Responders!


Hamilton Psychiatric Scale for Depression!


Modelling !

!

Easier to understand!Easier to manipulate!Easier to analyze!

Recast problem into mathematical terms


Understanding Recovery!


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!!


Overview Recovery Model and Parameters!

M

E W

MS

ES

A

C


Modeling Time to Response !


Modeling Treatment Effects!


Recovery Model Equation!

+

++

- ==


Training the model!


Recovery Pattern and ErrorExample Patient (CBT)!


Recovery Pattern and ErrorPatient Group (CBT)!



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 think it means - improvement in selection of treatment - less trial and error !


ResultsOptimized parameters specify model

Initial conditions predict pattern trajectory !

M C

W A

E ES

MLS


Latency!


Mean ½ Reduction Time!

CBT varies 3.7 wks

DMI varies 1.8 wks


Direct Effect of Treatment!


Direct Treatment Intervention Effect!


Treatment Effects and Interactions!

CBT Sequential

DMI (delayed)

CONCURRENT

DMI: > 2x interactions and loops


Order and Time of Symptoms Improve is Different for CBT and DMI!



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 think it means - improvement in selection of treatment thereby reducing unnecessary costs and suffering. Potentially saving lives.!


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!


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)!


Thank you!


Backup Slides


Recovery Model!


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


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

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


•  BioPathways Consortium •  BioPAX •  W3C Semantic Web for Health Care and Life Sciences (HCLSIG)

Establishing Communities of Interest/Practice


BioPAX -‐ Enabling Cellular Network Process Modeling

Metabolic Pathways

Molecular Interaction Networks

Signaling Pathways

Gene Regulatory Networks

Glycolysis Protein-Protein Apoptosis TFs in E. coli


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


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


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


Enhance pathway capability !   Optimize

!   Speed

!   Accuracy

!   Completeness

!   Single query over multiple of databases

!   Validate, test and evaluationr !   Incorporate into diabetes research

workflow


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!


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+


Take Home Message

•  We need to shorten the time • tighten the loop between research and practice;

•  15 years + is too long, way too long


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!!!!


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/!


Diabetes

http://diabetes.niddk.nih.gov/DM/PUBS/statistics/!


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


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


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


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?!

amia tbi 2010_pmi_luciano.ppt

Health & Medicine

treatment predictive

translational medicine

depression data

treatment response study

clinical depression

aw ec esmmspredictive

different response patterns

selection of treatment