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

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

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  • 1. Translational Medicine:!Using Systems of Differential Equations to Identify Patternsin 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.htmPredictiveHealthy 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 coursePredictive 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 MSPredictive Medicine, Inc. © 2010! 20!
  • 21. Modeling Time to Response !Predictive Medicine, Inc. © 2010! 21!
  • 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 MLSPredictive 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 wksPredictive Medicine, Inc. © 2010! 30!
  • 31. Direct Effect of Treatment!Predictive Medicine, Inc. © 2010! 31!
  • 32. Direct Treatment Intervention Effect!Predictive Medicine, Inc. © 2010! 32!
  • 33. Treatment Effects and Interactions! DMI: > 2x interactions and loops DMI CBT (delayed) Sequential CONCURRENTPredictive 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  Predictive Medicine, Inc. © 2010! 39!
  • 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 SystemsPredictive 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 AdvancedMental 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 coverPredictive2008 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 NetworksPredictive2008 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 alteredgene 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, asindicted 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 2Predictive 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!