Translational Medicine:Using Systems of Differential Equations to Identify Patterns
in Symptom Remission in Response to Treatment
Joanne S. Luciano, Ph.D.Predictive Medicine, Inc.
Belmont, MA
2010 AMIA Summit on Translational BioinformaticsParc 55 Hotel San Francisco
San Francisco, California, USAMarch 11, 2010
Knowledge Recovery
© 2010 PREDICTIVE MEDICINE, INC.2
Overview• Why we did this work - to explore the utility of
computational methods in medicine• How we did it - used differential equations (“neural
networks”) to model recovery and compared recovery oftwo different antidepressant treatments
• What we found - recovery patterns for the two treatmentswere different - the order and timing of improvement ofsymptoms were different
• What we think it means -• Improved treatment selection• Reduced costs• Reduced suffering, possibly saving lives.
© 2010 PREDICTIVE MEDICINE, INC.3
Overview• Why we did this work - to explore the utility of
computational methods in medicine• How we did it - used differential equations (“neural
networks”) to model recovery and compared recovery oftwo different antidepressant treatments
• What we found - recovery patterns for the two treatmentswere different - the order and timing of improvement ofsymptoms were different
• What we think it means -• Improved treatment selection• Reduced costs• Reduced suffering, possibly saving lives
© 2010 PREDICTIVE MEDICINE, INC.4
Depression is a BIG problemCharacterized by persistent and pathological sadness,
dejection, and melancholyPrevalence (US)
6% year (18 million)16% experience it in their lifetime
Cost44 Billion (1990)
Potential Impact1% Improvement would mean 180, 000 people helped1% Improvement would mean 440 million in savings
© 2010 PREDICTIVE MEDICINE, INC.5
The Economic Burden of Depression
Source: The Healthy Thinking Initiative
http://www.preventingdepression.com/costs.htm
Depression is thehighest of the healthcare cost for business
© 2010 PREDICTIVE MEDICINE, INC.6
The Economic Burden of Depression
© 2010 PREDICTIVE MEDICINE, INC.7
Translational Medicine• Rapid transformation of laboratory findings into clinically focused
applications• ‘From bench (computer) to bedside (psychiatrists couch) and
back’
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Timeline(15+ yrs is too long!)
20091993
World Congress onNeural Networks,
July 11-15, 1993,Portland, Oregon
SIGMental Function
andDysfunctionSam Levin
JackieSamson,Mc LeanHospital
DepressionResearch
1996
1995
20081994
Patents Soldto Advanced
BiologicalLaboratories
Belgium
PatentsOffered at
Ocean TomoAuction
Chicago, IL
US Patent No.6,317,73Awarded
US PatentsNo. 6,063,028
Awarded
2001
2000
PhDThesis Proposal
Approved
WorkshopNeural
Modeling ofCognitive and
Brain Disorders
BioPAX
?Linked DataW3C HCLSBioDASH
EPOS
2006
EMPWR
PosterPresented
ISMB 1997PSB 1998
1997
Licensed toEvivar for HIVand Hepatitis B
© 2010 PREDICTIVE MEDICINE, INC.9
Overview• Why we did this work - to explore the utility of
computational methods in medicine• How we did it - used differential equations (“neural
networks”) to model recovery and compared recovery oftwo different antidepressant treatments
• What we found - recovery patterns for the two treatmentswere different - the order and timing of improvement ofsymptoms were different
• What we think it means -• Improved treatment selection• Reduced costs• Reduced suffering, possibly saving lives
© 2010 PREDICTIVE MEDICINE, INC.10
Research Goals
Illuminate recoverycourse
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Today’s talk:Response totreatment
Treatment Response Study
© 2010 PREDICTIVE MEDICINE, INC.12
Background
• Clinical Depression (Major DepressiveDisorder (MDD)
• Current Treatments
• Clinical Symptom (Hamilton Scale)
• MDD has no sub-diagnosis• MDD treatment guidelines vague• MDD treatment not specific to patients
Characteristics
Measurements
Problems with current status
© 2010 PREDICTIVE MEDICINE, INC.13
Clinical DataSymptom Intensity
-Hamilton Depression Rating Scale (0-4 scale)
Treatment-Desipramine (DMI)-Cognitive Behavioral Therapy (CBT)
Outcome - Recovery studied
- those patients who responded to treatment
© 2010 PREDICTIVE MEDICINE, INC.14
Hamilton Scale for DepressionExample Questions
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Modelling
•Easier to understand•Easier to manipulate•Easier to analyze
Recast problem into mathematical terms
© 2010 PREDICTIVE MEDICINE, INC.16
© 2010 PREDICTIVE MEDICINE, INC.17
Understanding Recovery
© 2010 PREDICTIVE MEDICINE, INC.18
Data• 7 Symptoms Physical: Early Sleep Disturbance
(Hamilton Mid, Late Sleep Disturbancequestionnaire Energyvalues Performance: Work & Interestsmeasure Psychological: Mood (sadness, hopelessness)severity) Cognitions (guilt, suicidal)
Anxiety
• 2 Treatments Cognitive Behavioural Therapy (CBT)Desipramine (DMI)
• Clinical Data Responders = improvement >= 50%N = 6 patient each study6 weeks = 252 data points each study
© 2010 PREDICTIVE MEDICINE, INC.19
Linking HypothesesSymptoms and Brain Region Activity
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OverviewRecovery Model and Parameters
M
EW
MS
ES
A
C
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Modelling Time to Response
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Modelling Treatment Effects
© 2010 PREDICTIVE MEDICINE, INC.23
Recovery Model Equation
+
++
-==
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Training the model
Pj
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Results of Training(treatment group)
CBT DMI
© 2010 PREDICTIVE MEDICINE, INC.26
Results of Training(Example CBT Patient)
(we’re not there yet)
© 2010 PREDICTIVE MEDICINE, INC.27
Overview• Why we did this work - to explore the utility of
computational methods in medicine• How we did it - used differential equations (“neural
networks”) to model recovery and compared recovery oftwo different antidepressant treatments
• What we found - recovery patterns for the two treatmentswere different - the order and timing of improvement ofsymptoms were different
• What we think it means -• Improved treatment selection• Reduced costs• Reduced suffering, possibly saving lives
© 2010 PREDICTIVE MEDICINE, INC.28
ResultsOptimized parameters specify model
Initial conditions predict pattern trajectory
MC
WA
EES
MLS
© 2010 PREDICTIVE MEDICINE, INC.29
Latency
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Mean ½ Reduction Time
CBT varies 3.7 wks
DMI varies 1.8 wks
© 2010 PREDICTIVE MEDICINE, INC.31
Direct Effect of Treatment
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Treatment Direct EffectsImmediate and Delayed
© 2010 PREDICTIVE MEDICINE, INC.33
Treatment Effects andInteraction Effects
CBTSequential
DMI(delayed)
CONCURRENT
© 2010 PREDICTIVE MEDICINE, INC.
Order and Time of SymptomsImprove is Different for CBT and DMI
© 2010 PREDICTIVE MEDICINE, INC.35
Overview• Why we did this work - to explore the utility of
computational methods in medicine• How we did it - used differential equations (“neural
networks”) to model recovery and compared recovery oftwo different antidepressant treatments
• What we found - recovery patterns for the two treatmentswere different - the order and timing of improvement ofsymptoms were different
• What we think it means -• Improved treatment selection• Reduced costs• Reduced suffering, possibly saving lives
© 2010 PREDICTIVE MEDICINE, INC.36
Conclusions• Recovery patterns differ by treatment
• Cognitive Behavioural Therapy– is sequential
• Desipramine– is concurrent (after delay)
• Suggests CBT better serves patients with strong cognition andmood symptoms DMI may better serve patients with all thesymptoms but are not suicidal
License available for US Patent No. 6,063,028 and 6,317,731 fromAdvanced Biomedical Labs, SA (www.ablsa.com) (Luxembourg)
Note: Fee free license for non-profit and academic institutions.
© 2010 PREDICTIVE MEDICINE, INC.
Acknowledgements
• Michael Cohen - Boston University• Jacqueline Samson - McLean Hospital,
Harvard Medical School• Michiro Negishi -Yale University• Dan Bullock - Boston University• Joseph Shildkraut - Harvard Medical School• Chalom Sayada - Advanced Biomedical Labs.
© 2010 PREDICTIVE MEDICINE, INC.
Thank you!
