Candida  Fratazzi  MD  President  

Boston  Biotech  Clinical  Research,  LLC  Simplifying  Clinical  Research  

www.bostonclinicalresearch.com  

Big  data  and  analy>c  strategies:    

Simplifying  Clinical  Research,  

making  Trials  Cost-­‐Effec>ve  

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    

Presenta>on’s  Map  

Big Data challenge and

opportunity

Personalized medicine

Innovation in clinical

research Algorithm

development

Better disease treatments for a cost-efficient healthcare

Data complexity

data  source  

Variety  

data size

Volume

speed  of  change  

Velocity  

Big  Data  Technology  Challenge  

•  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  

B ig  Data  Opportuni>es  

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  

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  

Right  drug,  Right  pa>ent,  Right  dose  

Adapted  from  Vikas  Kumar,  The  role  of  pharmacogenomics  in  drug  development  

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  

 

Rheumatoid  Arthri>s  case  study  

Heterogeneity based on response To Enbrel

B Cell-driven

TNF-driven IL-6-driven

T cell-driven

Comorbidities

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  

 

 

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  

Finding  the  Meaning   in  “  Meaningful  Use  ”  Trial  Protocol  Variables  

1.  Product  (Drug,  device,  or  diagnos>c)  MOA  2.  Disease  pathology  3.  Unmet  medical  needs  4.  Disease  incidence  and  geography  5.  Compe>>on  and  products  in  development  6.  Select  subset  pa>ent  popula>on  likely  to  show  a  significant  improvement  

considering  MOA  7.  Iden>fy  clinically  meaningful  endpoint/s  8.  Control  for  co-­‐morbidi>es  9.  Consider  selec>ng  an  ac>ve  control  to  assist  in  pharmaco-­‐economic  support  for  

reimbursement  10.  Select  meaningful  outcomes  and  define  the  clinically  significant  minimal  

difference  

Discovery  /  Pre-­‐Clinical  

CRO  

Innova>on  in  Cl inical  Research  

SCIO  PK/PD

Diagnostics Biomarkers

Drug-Target Design

Endpoints

Patients’ stratification

Safety

Regulators Disease staging

Study  protocol  

Market analysis

Cytokine  Signaling  Pathways  relevant  to  Early  RA  

CYTOPLASM NUCLEUS

Kinases  

Kinases  

p38  

JNK  ERK  

Syk  

IKK  

NFκB  

JAK  JAK  

STAT  

STAT  

STAT  STAT  

Gene  transcrip>on  

PI3K  

PI3K  PI3K  

Lipid  messengers  

Second  messengers  

MAPK signalling cascade

SYK, (& BTK) signalling cascade

NFKB  signalling cascade

JAK signalling cascade

PI3K  

BTK  

e.g.  MKK3,  MKK6  

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.

Therapeu>c   Indica>on  Selec>on   for  POC  

Ankylosing  spondyli8s  (AS)  Serum  MMP-­‐3  

IL-­‐6  VEGF  CRP  

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      

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  

         

Ulcera8ve  coli8s  (UC)  An8-­‐S.  Cerevisiae  Ab  

Perinuclear  an8neutrophil  cytoplasmic  Abs  Laminaribioside  Chitobioside  

 Dis>l l ing  Meaning  from  Big  Data  

Approximate ly 4000 New Tests / next 10 years

An  a l go r i thm   to  d i s8 l l   a   coherent  p i c tu re  o f  d i sease     f rom  a  w ide   range  o f  d i spa ra te  da ta  

Metabolomics  

Proteomics  Genomics  Epigene>cs  Microbiome  Imaging    

ü  Blood    ü  Urine    ü  Fluids    ü  Tissue    

 ü  RNA    ü  Protein  ü  Metabolites  ü  Images      

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    

Finding  the  meaning:  the  Algorithm  

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  

 

Working  on  Algorithms  Development      

Algorithms  for  Cl inical  Research  

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