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Big Data and Analytic Strategy for Clinical Research


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How the utilization of big data and analytic strategy simplifies clinical research, and thus, makes trials cost-effective

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Big Data and Analytic Strategy for Clinical Research

  1. 1. Big  data  and  analy>c  strategies:     Simplifying  Clinical  Research,   making  Trials  Cost-­‐Effec>ve   Candida  Fratazzi  MD   President   Boston  Biotech  Clinical  Research,  LLC   Simplifying  Clinical  Research  
  2. 2. Henry  Ford  devised  a  manufacturing  system  of  mass  produc>on,  using   specialized  machinery  and  standardized  products   ü  Ford  changed  the  way  we  made  cars  –  and  transformed  work  itself       Big  Data  not  only  refers  to  very  large  data  sets  and  the  tools  and  procedures   used  to  manipulate  and  analyze  them,  but  also  to  a  computa>onal  turn  in   thought  and  research  (Burkholder  1992)   ü  Big  Data  creates  a  radical  shiO  in  how  we  think  about  research    
  3. 3. Presenta>on’s  Map   Big Data challenge and opportunity Better disease treatments for a costefficient healthcare Algorithm development Personalized medicine Innovation in clinical research
  4. 4. Big  Data  Technology  Challenge   data size Volume data  source   speed  of  change   Variety   Velocity   Data complexity
  5. 5. Big  Data  Opportuni>es   •  Modern  medicine  collects  huge  amounts  of   informa>on  about  pa>ents  through  imaging   technology  (CAT  scans,  MRI),  gene>c  analysis   (DNA  microarrays),  and  other  forms  of   diagnos>c  equipment     •  Applying  data  mining  to  data  sets  for  large   numbers  of  pa>ents,  medical  researchers  are   gaining  fundamental  insights  into  the  gene>c   and  environmental  causes  of  diseases,  and   crea>ng  more  effec>ve  means  of  diagnosis  
  6. 6. Personalized  Medicine   “……..personalized  medicine  is  a  sort  of  shorthand  used  to  represent  the   logical  next  steps  in  progression  of  medical  science  toward  greater   mechanis>c  understanding  of  health,  disease,  and  treatment.”     Janet  Woodcock  
  7. 7. From  Blockbusters  to  Personalized  Medicine   •  The  biggest  challenges  for  the  biotechnology  and  pharmaceu>cal   companies  is  to  develop  and  deliver  drugs  that  fit  the  individual   pa>ent’s  biology  and  pathophysiology   •  Change  from  blockbuster  medicine  to  personalized  medicine  will   influence  the  way  that  drugs  are  developed,  and  prescribed  in  the   future     •  Personalized  medicine  is  a  stepwise  process  to  stra>fy  pa>ents  into   different  molecular/  biological  subgroups     •  Cancer  Medicine  is  expec>ng  to  deliver  in  10–15  years  many  more   drugs  using  CDx  
  8. 8. Right  drug,  Right  pa>ent,  Right  dose   Adapted  from  Vikas  Kumar,  The  role  of  pharmacogenomics  in  drug  development  
  9. 9. Lack  of  Efficacy  and…Side  Effects   ü   20-­‐75%  of  pa>ents  do  not  receive  effec>ve  treatment   ü   >100,000  deaths  per  year  from  adverse  drug  reac>ons  in  the  US  only    
  10. 10. Rheumatoid  Arthri>s  case  study   TNF-driven IL-6-driven Comorbidities T cell-driven Heterogeneity based on response To Enbrel B Cell-driven
  11. 11. Right  drug,  Right  pa>ent,  Right  dose   Personalized  Medicine  requires   Innova8on  in  Clinical  Research  to:   •  Reduce  clinical  development  and  CRO  cost   •  Improve  pa>ent  recruitment  and  accelerate  trial  execu>on   •  Reduce  clinical  failure  and  generate  reproducible  data   To  create  evidence-­‐based  data  driven  trials    
  12. 12.   S C I O     Strategic  Clinical     Innova>on  Organiza>on        a  new  class  of  service       B o s t o n   B i o t e c h   C l i n i c a l   R e s e a r c h  
  13. 13. Finding  the  Meaning  in  “  Meaningful  Use  ”   Trial  Protocol  Variables   1.  2.  3.  4.  5.  6.  7.  8.  9.  10.  Product  (Drug,  device,  or  diagnos>c)  MOA   Disease  pathology   Unmet  medical  needs   Disease  incidence  and  geography   Compe>>on  and  products  in  development   Select  subset  pa>ent  popula>on  likely  to  show  a  significant  improvement   considering  MOA   Iden>fy  clinically  meaningful  endpoint/s   Control  for  co-­‐morbidi>es   Consider  selec>ng  an  ac>ve  control  to  assist  in  pharmaco-­‐economic  support  for   reimbursement   Select  meaningful  outcomes  and  define  the  clinically  significant  minimal   difference  
  14. 14. Innova>on  in  Clinical  Research   Design Endpoints PK/PD Diagnostics Biomarkers Regulators SCIO   Patients’ stratification Safety Market analysis Disease staging CRO   Discovery  /  Pre-­‐Clinical   Drug-Target Study  protocol  
  15. 15. Cytokine  Signaling  Pathways  relevant  to  NFarly  RA   E B   SYK, (& BTK) K signalling cascade signalling cascade MAPK signalling cascade JAK signalling cascade Lipid   messengers   Syk   PI3K   PI3K   PI3K   PI3K   BTK   Kinases   e.g.  MKK3,   MKK6   JAK   Second   messengers   IKK   JAK   STAT   Kinases   STAT   JNK   ERK   NFκB   STAT   p38   Gene  transcrip>on   STAT   CYTOPLASM NUCLEUS Adapted from Mavers M, et al. Curr Rheum Rep 2009; 11: 378–385; and Rommel C, et al. Nat Rev Immunol 2007; 7: 191–201.
  16. 16. Therapeu>c  Indica>on  Selec>on  for  POC   Ankylosing   spondyli8s  (AS)   Serum  MMP-­‐3   IL-­‐6   VEGF   CRP   Ulcera8ve  coli8s  (UC)   An8-­‐S.  Cerevisiae  Ab   Perinuclear  an8neutrophil   cytoplasmic  Abs   Laminaribioside   Chitobioside   SLE   CD27  high  plasma  cells   Soluble  IL-­‐2  receptor  (CD25)   Soluble  thrombomodulin   Soluble  TNF  receptor   Soluble  VCAM-­‐1   Type  I  INF-­‐a,  IFB-­‐b   An8-­‐dsDNA   BLyS   CD19+  B  cells   CD40  ligand  =  lymphocytes   IL-­‐6,  10,  12,  p40,  12,  16,  18     Rheumatoid   arthri8s(RA)   An8nuclear  Ab   Rheumatoid  factor   CRP   Erythrocyte   sedimenta8on  rateHLA-­‐ B27           Crohn’s  disease  (CD)   An8-­‐S.  Cerevisiae  Ab   Perinuclear  an8neutrophil   cytoplasmic  Abs   Laminaribioside   Chitobioside   Juvenile  idiopathic   arthri8s  (JIA)   IgM  RF   IgA  RF   An8cyclic   citrullinated  pep8de   Abs    
  17. 17.   Dis>lling  Meaning  from  Big  Data   Metabolomics   ü  ü  ü  ü  Blood     Urine     Fluids     Tissue     Proteomics   Genomics   Epigene>cs   Microbiome   Imaging     A n   a l g o r i t h m   t o   d i s 8 l l   a   c o h e r e n t   p i c t u r e   o f   d i s e a s e     f r o m   a   w i d e   r a n g e   o f   d i s p a r a t e   d a t a     ü  ü  ü  ü    RNA     Protein   Metabolites   Images     A p p r o x i m a t e l y 4 0 0 0 N e w Te s t s / n e x t 1 0 y e a r s
  18. 18. There  is  No  App  for  Clinical  Research  INNOVATION   Tech  solu>ons  alone  are  not  enough  !!   •  Applying  new  technology  to  old  development  models  is  not   good  enough   •  Use  of  technology  alone  cannot    innovate  clinical  research     •  Technology  advancement  is  key  to  improve  Variety,  Volume   and  Velocity  management   •  Clinical  Research  INNOVATION  requires  algorithm/s  that   dis>lls  meanings  from  big  data    
  19. 19. Finding  the  meaning:  the  Algorithm  
  20. 20. The  Ul>mate  Goal     •  Simplifying  Clinical  Research  meets  the  requirements  of  investors,   partners  and  regulators   •  Strategizing  Clinical  Plan  accelerates  value  crea>on  from  phase  I   and  II  of  clinical  trials   •  Genera8ng  Evidence-­‐based  Medicine  reduces  clinical  risk  and   maximizing  the  chance  of  a  successful  outcome   •  Design  Focused  Trials  results  in  cost-­‐effec>ve  clinical  trials   •  Develop  Algorithm/s  to  integrate  Big  Data  for  decision  making    
  21. 21. Working  on  Algorithms  Development      
  22. 22. Algorithms  for  Clinical  Research  
  23. 23. Simplifying  Clinical  Research  affects  Healthcare   •  Personalized  medicine  is  poised  to  transform  healthcare     •  New  diagnos>c  and  prognos>c  tools  will  increase  our  ability  to   predict  drug  therapy  outcomes   •  Expanded  use  of  biomarkers  will  result  in  targeted  drug   development     Treatments  developed  for  Personalized  Medicine     improve  Healthcare  Quality  and  make  Healthcare   Cost-­‐effec>ve