Using	
  Computa.onal	
  Vaccinology	
  to	
  Design	
  
Genome-­‐Derived	
  Vaccines	
  for	
  Infec.ous	
  Diseases,	
  ...
Your	
  Speaker	
  –	
  Annie	
  De	
  Groot	
  MD	
  

2	
  
The	
  Company:	
  EpiVax	
  

hOp://bit.ly/EpiPubs	
  	
  

3	
  
EpiVax	
  Collaborates	
  with	
  the	
  	
  
Ins*tute	
  for	
  Immunology	
  and	
  Informa*cs	
  @	
  URI	
  

Collabor...
Addi.onal	
  Collaborators	
  

Bill	
  Mar<n	
  
Lenny	
  Moise	
  
Frances	
  Terry	
  
Leslie	
  Cousens	
  
Ryan	
  Ta...
Outline
•  Why Computational Immunology
•  Tools to Produce IDVs
–  Antigen selection
–  Vaccine design
–  New concepts

•...
Predic<ng	
  the	
  future	
  is	
  something	
  that	
  weather	
  experts	
  do	
  
with	
  the	
  assistance	
  of	
  i...
In	
  todays	
  talk,	
  I	
  will	
  discuss	
  the	
  use	
  of	
  immunoinforma<cs	
  tools	
  for	
  
vaccine	
  desig...
“Old	
  Style”	
  Vaccines	
  	
  

Grow	
  .	
  .	
  .	
  and	
  use	
  whole	
  pathogen
The	
  focus	
  of	
  our	
  work	
  
Can	
  we	
  make	
  vaccines	
  beJer/faster	
  	
  
BeOer	
  understanding	
  
	
 ...
iVAX	
  Vaccine	
  Design	
  Toolkit	
  
Why?	
  New	
  Vaccines	
  Needed	
  
•  For Example:
–  HIV
–  HCV
–  Malaria
–  Universal Influenza Vaccine
–  Vaccines ...
Why?	
  Unacceptable	
  Delays	
  
•  For Example: Pandemic influenza 2009
–  Traditional flu vaccine production methods
r...
Emergent	
  H7N9	
  disease	
  in	
  China	
  

hOp://bit.ly/EpiPubs	
  	
  

14	
  
Spread	
  to	
  Beijing	
  on	
  4/13/13	
  .	
  .	
  .	
  
Spread	
  to	
  Hong	
  Kong	
  on	
  12/6/	
  13	
  

15	
  
Markedly	
  Increased	
  ac.vity	
  in	
  
late	
  2013	
  and	
  early	
  2014!	
  

hOp://bit.ly/EpiPubs	
  	
  

16	
  
Con.nuing	
  Expansion	
  of	
  H7N9	
  
First	
  confirmed	
  cases	
  occurred	
  in	
  Shanghai	
  (3/30/13)	
  but	
  c...
Ci.es	
  that	
  are	
  one	
  stop	
  from	
  H7N9	
  

An	
  es<mated	
  70%	
  of	
  the	
  world	
  popula<on	
  resid...
H7N9	
  Morbidity	
  and	
  Mortality	
  
Quick	
  numbers...	
  
•  Total	
  confirmed	
  human	
  cases	
  of	
  
influenz...
Virus	
  Transmission	
  Mechanism	
  –	
  	
  
source	
  is	
  s.ll	
  at	
  large	
  
•  Human	
  to	
  human	
  
transm...
Distribu.on	
  of	
  Cases	
  

This	
  picture	
  
shows	
  the	
  
geographically	
  
wide	
  distribu<on	
  
of	
  flu	
...
Why	
  are	
  immunoinforma.cs	
  tools	
  
important	
  in	
  this	
  sedng?	
  
•  Immunoinforma<cs	
  predicted	
  low	...
(reminder)	
  Flu	
  Vaccine	
  –	
  HA	
  protein	
  

Ian	
  Mackey	
  hOp://www.uq.edu.au/vduVDUInfluenza_H7N9.htm	
  
h...
What	
  Can	
  We	
  Learn	
  About	
  H7N9?	
  	
  

HA	
  (hemagglu<nin)	
  is	
  the	
  ‘Cri<cal	
  An<gen’	
  
used	
 ...
H7N9	
  is	
  a	
  unique	
  virus	
  
•  Low	
  conserva<on	
  of	
  HA,	
  NA	
  surface	
  proteins	
  
is	
  not	
  su...
New	
  H7N9	
  Flu	
  is	
  Predicted	
  to	
  be	
  
80
POORLY	
  IMMUNOGENIC	
   Thrombopoietin
70
-

60

-

-

50

-

-...
Why	
  are	
  immunoinforma.cs	
  tools	
  
important	
  in	
  this	
  sedng?	
  
•  Immunoinforma<cs	
  predicted	
  low	...
Unadjuvanted Influenza
Vaccine Effectiveness
Why	
  are	
  immunoinforma.cs	
  tools	
  
important	
  in	
  this	
  sedng?	
  

.	
  .	
  .	
  Low	
  and	
  S predicte...
Why	
  are	
  immunoinforma.cs	
  tools	
  
important	
  in	
  this	
  sedng?	
  
•  Immunoinforma<cs	
  predicted	
  low	...
Outline
•  Why Computational Immunology
•  Tools to Produce IDVs
–  Antigen selection
–  Vaccine design
–  New concepts

•...
Computational Vaccinology:
Genomes-to-Vaccines	
  
Selection of vaccine antigens is key
•  Lots of Genomes now Published!
•  On line tools for Pathogen Gene finding
(GLIMMER...
Comparative Genomics Impacts
Vaccine Immunogen Selection	
  
Strain 1

dispensable	
  genes	
  

core	
  genome	
  

Strai...
Immunome-Derived Vaccines . . .	
  
Payload	
  

Adjuvant	
  

Delivery	
  
Vehicle	
  

.	
  .	
  .	
  Need	
  “informa*o...
Payload:	
  Predic.ng	
  Epitopes	
  that	
  Drive	
  
Immune	
  Response	
  is	
  our	
  Exper.se	
  
Protein

MHC II Poc...
How	
  do	
  we	
  measure	
  Immunogenicity?	
  	
  
Vaccine	
  an<gen	
  
epitope	
  

epitope	
  

epitope	
  

1	
  	
...
“Immunogenicity	
  Scale”	
  

Immunogenic	
  
proteins	
  

Non	
  	
  
Immunogenic	
  
proteins	
  

hOp://bit.ly/EpiPub...
Easy	
  easy	
  to	
  deliver	
  as	
  pep<des	
  

ClustiMer: Screen for Epitope Clusters

DRB1*0101
DRB1*0301
DRB1*0401
...
Conservatrix:
Overcome the Challenge of Variability

HIV

HCV

Influenza

43	
  
Conservatrix Finds Conserved 9-mers

CTRPNNTRK
CTRPNNTRK
CTRPNNTRK
CTRPNNTRK
CTRPNNTRK
CTRPNNTRK

CTRPNNTRK

Conserved
epi...
BlastiMer: Epitope Exclusion

Foreign	
  

Self	
  

In	
  all	
  of	
  our	
  vaccines	
  we	
  eliminate	
  cross-­‐reac...
Epitope	
  Cross-­‐Reac<vity	
  Impacts	
  
Vaccine	
  Immunogen	
  Selec<on	
  

Human
Poten.ally	
  
detrimental	
  cros...
JanusMatrix	
  
TCR

Each MHC ligand has two faces,
The MHC-binding face (aggretope),
and the TCR-interacting face (epitop...
HCV	
  T	
  Effector	
  Epitopes	
  
HCV_G1_NS2_732

HCV_G1_1941

HCV_G1_DEXDC_1246
HCV_G1_1605

HCV_G1_NS2_748

HCV_G1_NS4...
Treg-­‐like-­‐Epitope:	
  HCV	
  

HC

V_

G

1_

NS

2_

79

4
Outline
•  Why Computational Immunology
•  Tools to Produce IDVs
–  Antigen selection
–  Vaccine design

•  Case Studies

...
EpiAssembler Constructs
Immunogenic Consensus Sequences

CTRPNNTRK
CTRPNNTRK
CTRPNNTRK
CTRPNNTRK
CTRPNNTRK
CTRPNNTRK

Epi-...
EpiAssembler: Core Epitope
STRAIN 01
STRAIN 02
STRAIN 03
STRAIN 04
STRAIN 05
STRAIN 06
STRAIN 07
STRAIN 08
STRAIN 09
STRAI...
EpiAssembler: Flanking Epitopes
STRAIN 01
STRAIN 02
STRAIN 03
STRAIN 04
STRAIN 05
STRAIN 06
STRAIN 07
STRAIN 08
STRAIN 09
...
EpiAssembler:
Final Immunogenic Consensus Sequence
STRAIN 01
STRAIN 02
STRAIN 03
STRAIN 04
STRAIN 05
STRAIN 06
STRAIN 07
S...
VaxCAD Identifies and
Eliminates Junctional Epitopes
VaxCAD will identify junctional epitopes and rearrange chosen epitope...
-10

Epitope Cluster Score
Junctional Cluster Score

20

10

0

Peptides in Default order in construct HP_IIB
50

40

-10
...
Multi-Epitope Gene Design
Intended Protein Product: Many epitopes strung together in a “String-of-Beads”

DNA insert

DNA
...
Immunogenic Consensus Sequence
Formulations
DNA	
  –	
  chain	
  of	
  epitopes,	
  or	
  
pep<de	
  in	
  liposomes	
  

...
In Vivo Model for Validation:
HLA Transgenic Mice

	
  


	
  


	
  


	
  


	
  


	
  


HLA A2

HLA B7

HLA A2/...
Outline
•  Why Computational Immunology
•  Tools to Produce IDVs
•  Case Studies
–  Tularemia
–  Smallpox
–  H. pylori
–  ...
Current	
  Vaccine	
  Design	
  Pipeline	
  
Burk/Tuly/
MP

Epitope
Discovery

Epitope
Validation

Construct
Design

Immun...
GDV	
  Approach	
  Applied	
  to	
  F.	
  tularensis	
  
In 24 months:
•  Took one genome
•  Mapped class I + Class II
•  ...
High	
  Responder	
  Frequency	
  to	
  Class	
  II	
  
Epitopes	
  in	
  Pa.ents	
  with	
  Prior	
  Exposure	
  
22/25	
...
TulyVax:	
  6	
  epitope	
  in	
  	
  
LVS	
  Challenge	
  Strain	
  
IFN-g SFC/10^6 splenocytes
over background

TulyVax	
  Immunogenicity	
  in	
  HLA	
  Tg	
  	
  
Epitope-­‐specific	
  IFNγ...
TulyVax Efficacy
100%
TuliVax Immunized Mice
Placebo Recipient Mice

Percent Survival

80%
60%

57%

Rapidity:	
  from	
  ...
Immunome-Derived Smallpox Vaccine:
VennVax

vaccinia

	
  	
  	
  	
  	
  	
  

	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  

...
VennVax Class II Epitopes are
Antigenic in Dryvax Vaccinees

20	
  

88%	
  of	
  predicted	
  T	
  cell	
  epitopes	
  co...
VennVax Immunization
in HLA DR3 Transgenic Mice
Immunizations
Days 0, 14, 28, 42
1. epitope DNA vaccine prime (IM)
2. epit...
Survival	
  of	
  VennVax-­‐Vaccinated	
  
Mice	
  Aqer	
  Aerosol	
  Challenge	
  
100%	
  survival	
  of	
  Vaccinated	
...
Protection Without
Vaccine-Induced Antibodies
3
Pre-challenge Placebo
Pre-challenge Vaccine

2.5

Post-challenge Placebo
P...
Therapeutic H. pylori Vaccination
Week 0

Week 6

Week 12-19

H. pylori
SS1

H. pylori SS1 lysate IN

H. pylori
SS1

Week ...
HelicoVax: Broad Epitope Recognition

IFN-gamma Secretion in Response to Splenocyte Restimulation following immunization
A...
HelicoVax Eradicates H. pylori Infection
***	
  P<0.001	
  
**	
  P<0.01	
  
***	
  P<0.001	
  

800
600

H. pylori qPCR
(...
VEEV IDV Development:
Comparison with Whole Antigen Vaccine

Two Whole Gene Constructs
–  Ebola Zaire GP
–  VEEV 26S*
–  s...
IFNγ ELISpot responses to
VEEV peptide pools

VEEV E1

VEEV E2
VEEV IDV Elicits Antibody Response

USAMRIID DR3 Mouse Study
VEEV Challenge Group ELISA
Day 56 Serum Samples
5

Log10 Tite...
VEEV IDV Protects
Against Lethal Challenge

100
90
80
70
60
50
40
30
20
10
0

USAMRIID DR3 Mouse Study
VEEV Challenge Weig...
What Drives Protection?

T	
  helper	
  Epitopes	
  

B	
  cell	
  
epitopes	
  

Other?	
  	
  CTL?	
  	
  
Th2?	
  	
  
...
T	
  cells	
  =	
  Immune	
  System	
  Body	
  Armor
	
  
T	
  cell	
  response	
  cannot	
  prevent	
  Infec<on	
  but	
 ...
The "New" Flu 
(H1N1 2009 California)

hOp://bit.ly/EpiPubs	
  	
  

84	
  
2009	
  Worry:	
  CDC	
  –	
  	
  
No	
  Cross-­‐reac.ve	
  Ab	
  
• 
• 
• 

Preliminary	
  studies	
  of	
  individuals	
...
2009	
  H1N1	
  contains	
  conserved	
  epitope	
  
Sequences	
  –	
  Predicted	
  Cross	
  Protec.on	
  

Immunogenic
T ...
EpiVax	
  Predicted	
  Cross-­‐Protec.on	
  

hOp://www.ncbi.nlm.nih.gov/pubmed/19660593
	
  
hOp://bit.ly/EpiPubs	
  	
  ...
Immuniza.on	
  with	
  FluVax	
  cross-­‐conserved	
  	
  
T	
  cell	
  epitopes	
  decreases	
  lung	
  viral	
  load	
  ...
H1N1	
  Conclusions	
  
•  This work recapitulates other projects already completed:
Complete protection using ONLY T cell...
What about H7N9?

hOp://bit.ly/EpiPubs	
  	
  

92	
  
What	
  Can	
  We	
  Learn	
  About	
  H7N9?	
  	
  
Epitopes	
  Novel	
  or	
  Conserved?	
  

H7N9	
  

Circula<ng	
  Fl...
New	
  H7N9	
  Flu	
  is	
  Predicted	
  to	
  be	
  
80
POORLY	
  IMMUNOGENIC	
   Thrombopoietin
70
-

60

-

-

50

-

-...
This	
  is	
  a	
  unique	
  virus	
  
•  Low	
  conserva<on	
  of	
  HA,	
  NA	
  surface	
  proteins	
  
is	
  not	
  su...
Differen<al	
  Cross-­‐reac<vity	
  with	
  the	
  human	
  
genome-­‐	
  significance?	
  	
  
New	
  and	
  unpublished:	
...
This	
  is	
  a	
  unique	
  virus	
  
•  Unusually	
  low	
  immunogenicity	
  
•  Cross-­‐reac<vity	
  with	
  human	
  ...
hOp://bit.ly/EpiPubs	
  	
  

99	
  
Immunoinforma.cs	
  Toolkit	
  
•  EpiMatrix – maps T cell epitopes
•  ClustiMer - Promiscuous / Supertype Epitopes
Seamle...
FastVax: Vaccines on demand
•  High throughput computing
•  Immunoinformatics
•  Vaccine design algorithms

	
  
Rapid	
  ...
20	
  hours	
  -­‐	
  April	
  05	
  –	
  April	
  06	
  2013	
  
Extremely	
  Rapid	
  H7N9	
  Vaccine	
  Design	
  
Apri...
Gedng	
  FastVax	
  into	
  the	
  clinic:	
  4	
  Steps	
  
Emergency	
  use	
  
authoriza<on	
  

1.	
  In	
  silico	
  ...
H7N9	
  at	
  EpiVax	
  
•  String-­‐of-­‐epitopes	
  DNA	
  vaccine	
  (Doug	
  Lowrie)	
  
•  String-­‐of-­‐epitopes	
  ...
H7N9 Delivery vehicles
DNA	
  –	
  chain	
  of	
  epitopes,	
  or	
  
pep<de	
  in	
  liposomes	
  

ICS-­‐op<mized	
  who...
And	
  .	
  .	
  .	
  Cancer,	
  Allergy	
  and	
  Autoimmune	
  Disease?	
  
•	
  	
  	
  Payload+Adjuvant+	
  Delivery	
...
Outline
•  Why Computational Immunology
•  Tools to Produce IDVs
–  Antigen selection
–  Vaccine design
–  New concepts

•...
EpiVax:	
  Four	
  Core	
  Strengths	
  

Contact:	
  Anthony	
  Marcello,	
  BDA,	
  amarcello@epivax.com	
  	
  
Confiden...
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Introduction to Computational Vaccinology and iVAX by EpiVax

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This presentation was developed for Dr. Anna Durbin's vaccine class at Johns Hopkins. It was delivered simultaneously to my vaccine class at URI. Both classes had their first introductory lecture at the same time, so we joined them by webinar. The slides cover the EpiVax approach to computational vaccinology, which is relatively novel as compared to other groups working in the field. A number of case studies, including H7N9, are provided.

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Introduction to Computational Vaccinology and iVAX by EpiVax

  1. 1. Using  Computa.onal  Vaccinology  to  Design   Genome-­‐Derived  Vaccines  for  Infec.ous  Diseases,   Cancer,  Allergy  and  Autoimmune  Disease   22  January  2014   Anne  S.  De  Groot,  Lenny  Moise,  Leslie  Cousens,  Frances  Terry,   William  Mar<n   Ins<tute  for  Immunology  and  Informa<cs,  University  of  Rhode   Island  and  EpiVax,  Inc.     www.epvax.com  www.immunome.org       1  
  2. 2. Your  Speaker  –  Annie  De  Groot  MD   2  
  3. 3. The  Company:  EpiVax   hOp://bit.ly/EpiPubs     3  
  4. 4. EpiVax  Collaborates  with  the     Ins*tute  for  Immunology  and  Informa*cs  @  URI   Collabora<ve  Research  on  Immunome-­‐Derived  Accelerated  Vaccine  Design  and  Development   Funded  by  the  NIH  CCHI  U19,  COBRE,  and  P01  awards.  www.immunome.org   hOp://bit.ly/EpiPubs     4  
  5. 5. Addi.onal  Collaborators   Bill  Mar<n   Lenny  Moise   Frances  Terry   Leslie  Cousens   Ryan  Tassone   Howie  La<mer   Mindy  Cote   Lauren  Levitz   Chris<ne  Boyle   Mark  Poznansky   Tim  Brauns   Pierre  LeBlanc     Ted  Ross   Don  Drake,  Brian  Schanen   AI058326,  AI058376,     AI078800,  AI082642   Alan  Rothman   Carey  Medin   Andres  Gui<errez   Danielle  Aguirre   Joe  Desrosiers   Thomas  Mather   Wendy  Coy   Loren  Fast   Hardy  Kornfeld   Jinhee  Lee   Liisa  Selin   Sharon  Frey   Mark  Buller   hOp://bit.ly/EpiPubs     Jill  Schreiwer   Connie  Schmaljohn   Lesley  C.  Dupuy   5  
  6. 6. Outline •  Why Computational Immunology •  Tools to Produce IDVs –  Antigen selection –  Vaccine design –  New concepts •  Case Studies 6  
  7. 7. Predic<ng  the  future  is  something  that  weather  experts  do   with  the  assistance  of  informa<cs  models.       These  forecasts  enable  us  to  make  decisions  on  a  daily   basis,  and  they  are  accurate  enough  to  mobilize  millions  if   and  when  severe  storms  are  predicted.       Why  then,  are  we  so  slow  to  use  informa<cs  in  vaccine  and   protein  therapeu<cs  design?    
  8. 8. In  todays  talk,  I  will  discuss  the  use  of  immunoinforma<cs  tools  for   vaccine  design,  mechanism  of  ac<on  studies,  and  efficacy   evalua<ons.    I  believe  that  the  <me  is  ripe  for  vaccine  developers  to   ac<vely  apply,  evaluate  and  improve  vaccines  through  the  use  of   computa<onal  immunogenicity  predic<on  tools.  
  9. 9. “Old  Style”  Vaccines     Grow  .  .  .  and  use  whole  pathogen
  10. 10. The  focus  of  our  work   Can  we  make  vaccines  beJer/faster     BeOer  understanding    of  vaccine  MOA   Whole  (live/ killed)  vaccines   Subunit  vaccines   (Flu,  Hepa<<s  B,   HPV  vaccines,  for   example)   Genome-­‐ Derived,  Epitope   Driven  (GD-­‐ED)   Vaccines   Improve  vaccine   safety  and   efficacy   Accelerate   Vaccine  Design   hOp://bit.ly/EpiPubs     10  
  11. 11. iVAX  Vaccine  Design  Toolkit  
  12. 12. Why?  New  Vaccines  Needed   •  For Example: –  HIV –  HCV –  Malaria –  Universal Influenza Vaccine –  Vaccines against Cancer –  Vaccines for immunotherapy of AI –  Vaccines for diseases affecting food animals
  13. 13. Why?  Unacceptable  Delays   •  For Example: Pandemic influenza 2009 –  Traditional flu vaccine production methods require large lead time –  20 weeks to first vaccine dose –  “Pandemic” influenza had already peaked by the time the first shots were being delivered. –  Vaccine manufacturing failed the test. –  Is H7N9 the next pandemic? If so, we are worried. . .
  14. 14. Emergent  H7N9  disease  in  China   hOp://bit.ly/EpiPubs     14  
  15. 15. Spread  to  Beijing  on  4/13/13  .  .  .   Spread  to  Hong  Kong  on  12/6/  13   15  
  16. 16. Markedly  Increased  ac.vity  in   late  2013  and  early  2014!   hOp://bit.ly/EpiPubs     16  
  17. 17. Con.nuing  Expansion  of  H7N9   First  confirmed  cases  occurred  in  Shanghai  (3/30/13)  but  case  ac<vity   rapidly  increased  in  Zheijang  and  Jiangsu  provinces  shortly  aier.     Now,  we  have  a  problem!     hOp://bit.ly/EpiPubs     17   Image  credit  to  VDU  and  Dr.  Ian  M  Mackay  hOp://www.uq.edu.au/vdu/VDUInfluenza_H7N9.htm  
  18. 18. Ci.es  that  are  one  stop  from  H7N9   An  es<mated  70%  of  the  world  popula<on  resides  within  two  hours’  travel  <me  of  des<na<on   airports  (calculated  using  gridded  popula<on-­‐density  maps  and  a  data  set  of  global  travel   <mes,  map  supplied  by  A.  J.  Tatem,  Z.  Huang  and  S.  I.  Hay  (2013).    
  19. 19. H7N9  Morbidity  and  Mortality   Quick  numbers...   •  Total  confirmed  human  cases  of   influenza  A  virus  H7N9:  >  200   •  Total  deaths  aOributed  to  infec<on   with  influenza  A  virus  H7N9:  >  50   •  Case  Fatality  Rate  (CFR):  29%  (current)     •  Average  <me  from  illness  onset  to  first   confirma<on  of  H7N9  (days):  <10     •  Median  age  of  the  H7N9-­‐confirmed   cases  (including  deaths;  years):  63     •  Males:  71%  of  cases,  74%  of  deaths     •  Younger  pa<ents  are  recovering  .  .  .     hOp://pandemicinforma<onnews.blogspot.com   hOp://www.uq.edu.au/vdu/VDUInfluenza_H7N9.htm  19  
  20. 20. Virus  Transmission  Mechanism  –     source  is  s.ll  at  large   •  Human  to  human   transmission  has  not  been   proved  (or  disproved)  many   cases  show  uninfected  family   members     •  Poultry  iden<fied  as  poten<al   natural  host  and  H7N9   samples  were  found  in   poultry  market  environment   in  Shanghai.  However  not   many  poultry  vendors   infected  and  many  cases  have   no  indica<on  of  poultry   exposure   hOp://bit.ly/EpiPubs     Image  credit  to  VDU  and  Dr.  Ian  M  Mackay  hOp:// 20   www.uq.edu.au/vdu/VDUInfluenza_H7N9.htm  
  21. 21. Distribu.on  of  Cases   This  picture   shows  the   geographically   wide  distribu<on   of  flu  cases  -­‐   sugges<ng   widespread   distribu<on  of  the   virus  rather  than   a  point  outbreak.       hOp://bit.ly/EpiPubs     21  
  22. 22. Why  are  immunoinforma.cs  tools   important  in  this  sedng?   •  Immunoinforma<cs  predicted  low   immunogenicity  of  ‘cri<cal  an<gen’  H7  HA   •  hOp://bit.ly/H7N9_2013  
  23. 23. (reminder)  Flu  Vaccine  –  HA  protein   Ian  Mackey  hOp://www.uq.edu.au/vduVDUInfluenza_H7N9.htm   hOp://bit.ly/EpiPubs     23  
  24. 24. What  Can  We  Learn  About  H7N9?     HA  (hemagglu<nin)  is  the  ‘Cri<cal  An<gen’   used  for  Flu  vaccines,  especially   recombinant  vaccines    –     –  which  are  currently  in  produc*on.     hOp://bit.ly/EpiPubs     24  
  25. 25. H7N9  is  a  unique  virus   •  Low  conserva<on  of  HA,  NA  surface  proteins   is  not  surprising   •  Internal  proteins  are  more  conserved   hOp://bit.ly/EpiPubs     25  
  26. 26. New  H7N9  Flu  is  Predicted  to  be   80 POORLY  IMMUNOGENIC   Thrombopoietin 70 - 60 - - 50 - - 40 - HA  A/California/07/2009  (H1N1)   Tetanus Toxin - 30 - Influenza-HA HA  A/Victoria/361/2011  (H3N2)   - 20 - - 10 - - 00 - - -10 - - -20 - IgG FC Region - -30 - Fibrinogen-Alpha - -40 - - -50 - - -60 - - -70 - - -80 H7  HA   Immunogenic  Poten.al   - Human EPO EBV-BKRF3 HA  A/Texas/50/2012    (H3N2)   Albumin Follitropin-Beta Random  Expecta.on   HA  A/chicken/Italy/13474/1999  (H7N1)    .  .  .  .  .  .  .  .  .  -­‐6.23   HA  A/Shanghai/1/2013  (H7N9)  .  .  .  .  .  .  .    ..  .  .  .  .  .  .  .  -­‐8.11   HA  A/mallard/Netherlands/09/2005  (H7N7)  .  .  .  .  .  .  -­‐8.63   gB-2 (EPX Score: -24.56) HA  A/mallard/Netherlands/12/2000  (H7N3)  ..  .  .  .  .  .-­‐9.91   hOp://bit.ly/EpiPubs    
  27. 27. Why  are  immunoinforma.cs  tools   important  in  this  sedng?   •  Immunoinforma<cs  predicted  low   immunogenicity  of  ‘cri<cal  an<gen’  H7  HA   •  Vaccine  was  developed  but  is  low   immunogenicity  as  predicted.   hOp://bit.ly/H7N9_NovaVax  
  28. 28. Unadjuvanted Influenza Vaccine Effectiveness
  29. 29. Why  are  immunoinforma.cs  tools   important  in  this  sedng?   .  .  .  Low  and  S predicted   •  Immunoinforma<cs  low  .  .  .   low   immunogenicity  of  ‘cri<cal  an<gen’  H7  HA   •  Vaccine  was  developed  but  is  low   immunogenicity  as  predicted   •  Sero-­‐conversion  is  delayed,  diminished  in   pa<ents  infected  with  H7N9.   hOp://bit.ly/H7N9_Serology  
  30. 30. Why  are  immunoinforma.cs  tools   important  in  this  sedng?   •  Immunoinforma<cs  predicted  low   immunogenicity  of  ‘cri<cal  an<gen’  H7  HA   •  Vaccine  was  developed  but  is  low   immunogenicity  as  predicted   •  Sero-­‐conversion  is  delayed,  diminished  in   pa<ents  infected  with  H7N9.   •  New  vaccine  approaches  are  needed.   •  .  .  .  Now  that  you  are  convinced,  let’s  talk   about  computa<onal  vaccine  design  
  31. 31. Outline •  Why Computational Immunology •  Tools to Produce IDVs –  Antigen selection –  Vaccine design –  New concepts •  Case Studies 31  
  32. 32. Computational Vaccinology: Genomes-to-Vaccines  
  33. 33. Selection of vaccine antigens is key •  Lots of Genomes now Published! •  On line tools for Pathogen Gene finding (GLIMMER, ORPHEUS, GeneMark) •  Tools for selecting subsets of protein – such as subcellular localization of hypothetical proteins (PSORTb, CELLO, Proteome Analyst)
  34. 34. Comparative Genomics Impacts Vaccine Immunogen Selection   Strain 1 dispensable  genes   core  genome   Strain 2 pangenome   Strain 3     strain-­‐specific  genes  
  35. 35. Immunome-Derived Vaccines . . .   Payload   Adjuvant   Delivery   Vehicle   .  .  .  Need  “informa*on”     =  T  cell  and  B  cell  epitopes     .  .  .  And  the  correct  “milieu”     =  delivery  vehicle,  adjuvants/TLR  ligands     “Fine  tune”  the  immune  response?   Vaccine   . . And there is ample evidence that this approach to vaccine design produces protective immunity
  36. 36. Payload:  Predic.ng  Epitopes  that  Drive   Immune  Response  is  our  Exper.se   Protein MHC II Pocket Peptide Epitope HLA (Human MHC), are comprised of peptide specific pockets EpiMatrix predicts how well a peptide sequence will bind to a specific pocket. Binding is the prerequisite for immunogenicity 8 class II HLA supertypes which taken together incorporate 95% of human populations (and pockets) worldwide. Mature APC Each 9-mer/10-mer is analyzed for binding potential to each of those 8 allele matrices. The  EpiMatrix  Score  describes  the  binding  affinity   . of  the  pep<de  sequence  to  the  HLA  complex   Southwood et al. J. Immunology 1998 Sturniolo et al. Nature Biotechnology, 1999 hOp://bit.ly/EpiPubs     37  
  37. 37. How  do  we  measure  Immunogenicity?     Vaccine  an<gen   epitope   epitope   epitope   1    +    1    +    1        =    Response   Immune  response  to  a  vaccine  an<gen  can  be  predicted  by  measuring   the  number  of  T  cell  epitopes  contained  in  the  an<gen  with   immunoinforma<cs  tools.     hOp://bit.ly/EpiPubs    
  38. 38. “Immunogenicity  Scale”   Immunogenic   proteins   Non     Immunogenic   proteins   hOp://bit.ly/EpiPubs     41  
  39. 39. Easy  easy  to  deliver  as  pep<des   ClustiMer: Screen for Epitope Clusters DRB1*0101 DRB1*0301 DRB1*0401 DRB1*0701 DRB1*0801 DRB1*1101 DRB1*1301 DRB1*1501 42  
  40. 40. Conservatrix: Overcome the Challenge of Variability HIV HCV Influenza 43  
  41. 41. Conservatrix Finds Conserved 9-mers CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK Conserved epitope Identifying the most conserved 9-mers allows for protection against more strains with fewer epitopes 44  
  42. 42. BlastiMer: Epitope Exclusion Foreign   Self   In  all  of  our  vaccines  we  eliminate  cross-­‐reac<ve  epitopes   Confidential 45  
  43. 43. Epitope  Cross-­‐Reac<vity  Impacts   Vaccine  Immunogen  Selec<on   Human Poten.ally   detrimental  cross-­‐ reac.ve  epitopes   Human Microbiome Pathogen     Protec.ve  epitopes   Poten.ally   detrimental  cross-­‐ reac.ve  epitopes   hOp://bit.ly/EpiPubs     46  
  44. 44. JanusMatrix   TCR Each MHC ligand has two faces, The MHC-binding face (aggretope), and the TCR-interacting face (epitope) The JanusMatrix algorithm searches for putative MHC ligands which are identical at the contact residues but may vary at the MHC-binding residues. http://bit.ly/JanusMatrix MHC TCR Find predicted 9-mer ligands with: •  Identical T cell-facing residues •  Same HLA allele and minimally different MHC-facing residues 48 MHC/HLA
  45. 45. HCV  T  Effector  Epitopes   HCV_G1_NS2_732 HCV_G1_1941 HCV_G1_DEXDC_1246 HCV_G1_1605 HCV_G1_NS2_748 HCV_G1_NS4B_1769 HCV_G1_2941 HCV_G1_2440 HCV_G1_2898 HCV_G1_NS4B_1725 HCV_G1_ENV_359 HCV_G1_2485 HCV_G1_NS4B_1876 HCV_G1_NS4B_1910 HCV_G1_ENV_255 HCV_G1_2879 HCV_G1_NS4B_1790 HCV_G1_NS4b_1798 HCV_G1_2913 HCV_G1_NS5A_1988 HCV_G1_2840 HCV_G1_NS2_909
  46. 46. Treg-­‐like-­‐Epitope:  HCV   HC V_ G 1_ NS 2_ 79 4
  47. 47. Outline •  Why Computational Immunology •  Tools to Produce IDVs –  Antigen selection –  Vaccine design •  Case Studies 51  
  48. 48. EpiAssembler Constructs Immunogenic Consensus Sequences CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK CTRPNNTRK Epi-Assembler Immunogenic consensus
  49. 49. EpiAssembler: Core Epitope STRAIN 01 STRAIN 02 STRAIN 03 STRAIN 04 STRAIN 05 STRAIN 06 STRAIN 07 STRAIN 08 STRAIN 09 STRAIN 10 STRAIN 11 STRAIN 12 STRAIN 13 STRAIN 14 STRAIN 15 STRAIN 16 STRAIN 17 STRAIN 18 STRAIN 19 STRAIN 20 Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q X Q Q Q X A X A X A X A X A A A A A A X A X A A S S S S S S S S S S S S S S S S S S S S W W W W W W W W W W W W W W W W W W W W P P P X P P P X P P P X P X P P X P X P K K K K K K K K K R x K K K K K K K K K K V V X V V X V V X V V V V X V V V X V V E E E E E E E E E E E E E E E E E E E E Q X Q Q Q Q Q Q Q Q Q Q Q Q X Q Q Q Q Q F F F F F F F F F F F F F F F F F F F F W W W W W W W W W W W W W W W W W W W W A A A A A A A A A A A A A A X A A A A A K K K K K X K K K K K X K K K K K K K X H H H H H H H H H H H H H H H H H H H H X M M M M M M M M M M M M M M M M M M M W W W W W W X W W W W W W W W W W W W W N N N N N N N N N N N N N N N N N N N N X F F F F F F F F F F F F F F F F X F F F W A K H M W N F I I I X I I I I X I I I I I I I I I I I S S S S S S S S S X S S S S S X S S S S X G G X G G G G X G G G G X G G G G X G I I I I I I I I I I I I I I I I I I I I Q Q Q Q Q Q Q Q X Q Q Q Q Q Q Q Q Q Q Q Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y L L X L L X L L X L L X L L L L X L L L
  50. 50. EpiAssembler: Flanking Epitopes STRAIN 01 STRAIN 02 STRAIN 03 STRAIN 04 STRAIN 05 STRAIN 06 STRAIN 07 STRAIN 08 STRAIN 09 STRAIN 10 STRAIN 11 STRAIN 12 STRAIN 13 STRAIN 14 STRAIN 15 STRAIN 16 STRAIN 17 STRAIN 18 STRAIN 19 STRAIN 20 Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q X Q Q Q Q X A X A X A X A X A A A A A A X A X A A A S S S S S S S S S S S S S S S S S S S S S W W W W W W W W W W W W W W W W W W W W P P P X P P P X P P P X P X P P X P X P K K K K K K K K K R x K K K K K K K K K K V V X V V X V V X V V V V X V V V X V V E E E E E E E E E E E E E E E E E E E E Q X Q Q Q Q Q Q Q Q Q Q Q Q X Q Q Q Q Q F F F F F F F F F F F F F F F F F F F F W W W W W W W W W W W W W W W W W W W W A A A A A A A A A A A A A A X A A A A A K K K K K X K K K K K X K K K K K K K X H H H H H H H H H H H H H H H H H H H H X M M M M M M M M M M M M M M M M M M M W W W W W W X W W W W W W W W W W W W W N N N N N N N N N N N N N N N N N N N N X F F F F F F F F F F F F F F F F X F F I I I X I I I I X I I I I I I I I I I I S S S S S S S S S X S S S S S X S S S S X G G X G G G G X G G G G X G G G G X G I I I I I I I I I I I I I I I I I I I I Q Q Q Q Q Q Q Q X Q Q Q Q Q Q Q Q Q Q Q F W A K H M W N F M W N F I S G I Q W P K V E Q F W A W P K V E Q N F I S G I Q Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y L L X L L X L L X L L X L L L L X L L L Y L
  51. 51. EpiAssembler: Final Immunogenic Consensus Sequence STRAIN 01 STRAIN 02 STRAIN 03 STRAIN 04 STRAIN 05 STRAIN 06 STRAIN 07 STRAIN 08 STRAIN 09 STRAIN 10 STRAIN 11 STRAIN 12 STRAIN 13 STRAIN 14 STRAIN 15 STRAIN 16 STRAIN 17 STRAIN 18 STRAIN 19 STRAIN 20 Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q X Q Q Q Q X A X A X A X A X A A A A A A X A X A A A S S S S S S S S S S S S S S S S S S S S S W W W W W W W W W W W W W W W W W W W W P P P X P P P X P P P X P X P P X P X P K K K K K K K K K R x K K K K K K K K K K V V X V V X V V X V V V V X V V V X V V E E E E E E E E E E E E E E E E E E E E Q X Q Q Q Q Q Q Q Q Q Q Q Q X Q Q Q Q Q F F F F F F F F F F F F F F F F F F F F W W W W W W W W W W W W W W W W W W W W A A A A A A A A A A A A A A X A A A A A K K K K K X K K K K K X K K K K K K K X H H H H H H H H H H H H H H H H H H H H X M M M M M M M M M M M M M M M M M M M W W W W W W X W W W W W W W W W W W W W N N N N N N N N N N N N N N N N N N N N X F F F F F F F F F F F F F F F F X F F I I I X I I I I X I I I I I I I I I I I S S S S S S S S S X S S S S S X S S S S X G G X G G G G X G G G G X G G G G X G I I I I I I I I I I I I I I I I I I I I Q Q Q Q Q Q Q Q X Q Q Q Q Q Q Q Q Q Q Q F W A K H M W N F M W N F I S G I Q W P K V E Q F W A W P K V E Q N F I S G I Q Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y L L X L L X L L X L L X L L L L X L L L Y L Q A S W P K V E Q F W A K H M W N F I S G I Q Y L
  52. 52. VaxCAD Identifies and Eliminates Junctional Epitopes VaxCAD will identify junctional epitopes and rearrange chosen epitopes to reduce junctional epitope formation
  53. 53. -10 Epitope Cluster Score Junctional Cluster Score 20 10 0 Peptides in Default order in construct HP_IIB 50 40 -10 HP4117 HP4061 HP4181 HP4111 HP4018 HP4070 HP4060 HP4157 HP4065 HP4001 HP4193 HP4034 HP4068 HP4168 HP4160 HP4175 HP4127 HP4126 HP4007 HP4154 HP4164 HP4119 HP4100 HP4120 HP4179 30 EpiMatrix Cluster Score 50 HP4117 HP4179 HP4007 HP4111 HP4018 HP4070 HP4034 HP4193 HP4065 HP4181 HP4157 HP4060 HP4068 HP4164 HP4160 HP4175 HP4127 HP4120 HP4126 HP4154 HP4168 HP4119 HP4100 HP4001 HP4061 EpiMatrix Cluster Score VaxCAD Example Epitope Cluster Score Junctional Cluster Score 40 30 20 10 0 Peptides in Optimized order in construct HP_IIB 57  
  54. 54. Multi-Epitope Gene Design Intended Protein Product: Many epitopes strung together in a “String-of-Beads” DNA insert DNA Vector Protein product (folded) 58  
  55. 55. Immunogenic Consensus Sequence Formulations DNA  –  chain  of  epitopes,  or   pep<de  in  liposomes   ICS-­‐op<mized  whole  proteins   ICS-­‐op<mized  proteins  in  VLP  
  56. 56. In Vivo Model for Validation: HLA Transgenic Mice                   HLA A2 HLA B7 HLA A2/DR1 HLA DR2 HLA DR3 HLA DR4
  57. 57. Outline •  Why Computational Immunology •  Tools to Produce IDVs •  Case Studies –  Tularemia –  Smallpox –  H. pylori –  VEEV (multi-pathogen vaccine) –  Influenza 61  
  58. 58. Current  Vaccine  Design  Pipeline   Burk/Tuly/ MP Epitope Discovery Epitope Validation Construct Design Immunogenicity Animal Model Validation Epitope Discovery Epitope Validation Construct Design Immunogenicity Animal Model Validation Tularemia Epitope Discovery Epitope Validation Construct Design Immunogenicity Animal Model Validation Smallpox Epitope Discovery Epitope Validation Construct Design Immunogenicity Animal Model Validation H. pylori Epitope Discovery Epitope Validation Construct Design Immunogenicity Animal Model Validation VEEV Epitope Discovery Epitope Validation Construct Design Immunogenicity Animal Model Validation Influenza Epitope Discovery Epitope Validation Construct Design Immunogenicity Animal Model Validation HIV/TB 62
  59. 59. GDV  Approach  Applied  to  F.  tularensis   In 24 months: •  Took one genome •  Mapped class I + Class II •  Selected 165 epitopes •  Confirmed in human •  Cloned into vaccine •  Performed Challenge studies. . . McMurry  JA,  Gregory  SH,  Moise  L,  Rivera  DS,  Buus  S,  and  De  Groot  AS.  Diversity  of  Francisella  tularensis  Schu4  an<gens  recognized  by  T   lymphocytes  aier  natural  infec<ons  in  humans:  Iden<fica<on  of  candidate  epitopes  for  inclusion  in  a  ra<onally  designed  tularemia  vaccine.   Vaccine  2007  Apr  20;25(16):3179-­‐91.   63  
  60. 60. High  Responder  Frequency  to  Class  II   Epitopes  in  Pa.ents  with  Prior  Exposure   22/25  pep<des;   Average  response  to   the  pool  was  over   1,000  gamma   producing  cells  per   million  above   background.     Percent  of  subjects  responding  by  IFN  gamma  ELISpot   Significant  Spot  Forming  Cells  averaged  across  subjects   64  
  61. 61. TulyVax:  6  epitope  in     LVS  Challenge  Strain  
  62. 62. IFN-g SFC/10^6 splenocytes over background TulyVax  Immunogenicity  in  HLA  Tg     Epitope-­‐specific  IFNγ  Response   950 300 Placebo-immunized 250 900 - FT_II_v1-immunized 200 150 100 50 Schu4 peptides with perfect LVS match Schu4 peptides with partial LVS match 3025 3024 3023 3007 3019 3015 3003 3001 F176 F102 3018 3017 3005 3004 0 Schu4 peptides without LVS match Nearly identical immunogenicity profile observed in HLA DR3 mouse immunizations performed in collaboration with Dr. Terry Wu (UNM), illustrating broad reactivity of immunoinformatic predicted epitopes.
  63. 63. TulyVax Efficacy 100% TuliVax Immunized Mice Placebo Recipient Mice Percent Survival 80% 60% 57% Rapidity:  from  genome  to  candidate  vaccine  in  24  months  .  .  .     40% Efficacy:  14  epitope  vaccine  protects  against  live  challenge   20% 0% 0% 0 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Days after lethal bacterial challenge 14  epitopes:  T  cell-­‐epitope-­‐immunized  mice  were  protected  against  live   challenge  with  tularemia.  Placebo-­‐recipient  mice  died  within  10  days.   McMurry et al. Vaccine 2007;25:3179-91 and Gregory et al. Vaccine 2009 27:5299-306
  64. 64. Immunome-Derived Smallpox Vaccine: VennVax vaccinia                                 smallpox Immunogenic Epitopes Vaccine   Shared Immunogenic Epitopes
  65. 65. VennVax Class II Epitopes are Antigenic in Dryvax Vaccinees 20   88%  of  predicted  T  cell  epitopes  confirmed  in  vitro  using  hu  PBMC   Moise et al. Vaccine. 2009 27:6471-9
  66. 66. VennVax Immunization in HLA DR3 Transgenic Mice Immunizations Days 0, 14, 28, 42 1. epitope DNA vaccine prime (IM) 2. epitope peptide boost (IN) Moise L et al. Vaccine. 2011;29:501-11 Immunogenicity Day 56 Challenge Day 65
  67. 67. Survival  of  VennVax-­‐Vaccinated   Mice  Aqer  Aerosol  Challenge   100%  survival  of  Vaccinated  mice  vs.  17%  of  placebo     100 90 Percent Survival 80 Placebo 70 Vaccinated 60 50 40 30 20 10 0 DNA   00 100 boost   DNA   520 boost   Challenge   10 40 15 60 Day Post Immunization 17%     20 80 25 73   Moise et al. Vaccine. 2011; 29:501-11
  68. 68. Protection Without Vaccine-Induced Antibodies 3 Pre-challenge Placebo Pre-challenge Vaccine 2.5 Post-challenge Placebo Post-challenge Vaccine OD 490 2 1.5 Post-challenge 1 0.5 Pre-challenge 0 100 200 400 800 1600 1/Dilution Factor 3200 6400 12800
  69. 69. Therapeutic H. pylori Vaccination Week 0 Week 6 Week 12-19 H. pylori SS1 H. pylori SS1 lysate IN H. pylori SS1 Week 51 1. epitope DNA vaccine prime IN 2. epitope peptide boost IN IFN-gamma and IL-4 ELISpot H. pylori SS1 1. epitope DNA vaccine prime IM 2. epitope peptide boost IN Histology H. pylori SS1 1. control DNA prime IN 2. control peptide boost IN
  70. 70. HelicoVax: Broad Epitope Recognition IFN-gamma Secretion in Response to Splenocyte Restimulation following immunization Average Helico-Vax Average SS1 600 500 400 300 200 100 SS1 (whole lysate-immunized mice) recognized few epitopes (white bars); HelicoVax-immunized mice recognized 45 of 50 (dark bars). 45/50 were immunogenic. ConA HP POO L 6 HP POO L 5 HP POO L 4 HP 4179 HP 4175 HP 4164 HP 4160 HP 4157 HP 4154 HP 4127 HP 4120 HP 4119 HP 4117 HP 4111 HP 4070 HP 4068 HP 4060 HP 4018 HP POO L 3 HP POO L 2 HP POO L 1 HP 4199 HP 4197 HP 4189 HP 4174 HP 4165 HP 4156 HP 4153 HP 4152 HP 4077 HP 4071 HP 4067 HP 4055 HP 4054 HP 4040 HP 4032 HP 4029 0 HP 4009 SFC/10^6 over background 700
  71. 71. HelicoVax Eradicates H. pylori Infection ***  P<0.001   **  P<0.01   ***  P<0.001   800 600 H. pylori qPCR (SSA/GAPDH) 180 160 140 120 This result accomplished in just over 24 months . . . 100 80 60 40 20 0 Lysate pVAX DNA IM DNA IN Moss et al, Vaccine 2011;29:2085-91
  72. 72. VEEV IDV Development: Comparison with Whole Antigen Vaccine Two Whole Gene Constructs –  Ebola Zaire GP –  VEEV 26S* –  subcloned into pWRG-7077 VS. One Multi-Epitope Construct –  Ebola Zaire/Sudan GP epitopes –  VEEV 26S epitopes –  subcloned into pWRG-7077 *Dupuy LC, Richards MJ, Ellefsen B, Chau L, Luxembourg A, Hannaman D, Livingston BD, Schmaljohn CS. A DNA Vaccine for Venezuelan Equine Encephalitis Virus Delivered by Intramuscular Electro-poration Elicits High Levels of Neutralizing Antibodies in Multiple Animal Models and Provides Protective Immunity to Mice and Nonhuman Primates. Clin Vaccine Immunol. 2011 Mar 30.
  73. 73. IFNγ ELISpot responses to VEEV peptide pools VEEV E1 VEEV E2
  74. 74. VEEV IDV Elicits Antibody Response USAMRIID DR3 Mouse Study VEEV Challenge Group ELISA Day 56 Serum Samples 5 Log10 Titer 4 3 2 1 0 Neg Con Arm Pos Con Arm Vaccine Arm Whole Antigen Epitope-Driven Negative Control Vaccine Vaccine
  75. 75. VEEV IDV Protects Against Lethal Challenge 100 90 80 70 60 50 40 30 20 10 0 USAMRIID DR3 Mouse Study VEEV Challenge Weights % Mean Starting Weight Percent survival USAMRIID DR3 Mouse Study VEEV Challenge Survival 0 5 10 Days postchallenge Neg Con Arm 100 Pos Con Arm Vaccine Arm 90 Neg Con Control Negative Arm Pos Con Arm Whole Antigen Epitope-Driven Vaccine Arm 80 70 60 50 Vaccine 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Days Postchallenge
  76. 76. What Drives Protection? T  helper  Epitopes   B  cell   epitopes   Other?    CTL?     Th2?     Negative Control Whole Antigen Vaccine Subset of Th epitopes stimulate IFNγ secretion" " Combination of immunogenic Th epitopes that overlap B cell epitopes???" " Contribution from other Th epitopes (stimulate other cytokines) that overlap with Bcell epitopes" " " " Th epitopes that stimulate different subpopulations" " Epitope-Driven " Vaccine " What is clear: that whole Ag is not necessary for protection"
  77. 77. T  cells  =  Immune  System  Body  Armor   T  cell  response  cannot  prevent  Infec<on  but  .  .  .     T  cell  response  can  arm  against  Disease  
  78. 78. The "New" Flu (H1N1 2009 California) hOp://bit.ly/EpiPubs     84  
  79. 79. 2009  Worry:  CDC  –     No  Cross-­‐reac.ve  Ab   •  •  •  Preliminary  studies  of  individuals  showed  that   an<bodies  induced  by  seasonal  influenza   vaccina<on  were  not  cross-­‐reac<ve  with  novel   H1N1.   What  if  the  T  cell  epitopes  were  cross-­‐reac<ve?   Would  that  help?     (Note  that  the  situa<on  is  very  similar  for  H7N9   –  no  cross-­‐reac<ve  an<body).         Centers  for  Disease  Control  and  Preven<on.  Serum  an<body  response  to  a  novel  influenza   A  (H1N1)  virus  aier  vaccina<on  with  seasonal  influenza  vaccine.  MMWR  Morb  Mortal   Wkly  Rep  2009;58(19):521–4.     hOp://bit.ly/EpiPubs     85  
  80. 80. 2009  H1N1  contains  conserved  epitope   Sequences  –  Predicted  Cross  Protec.on   Immunogenic T cell epitopes Enough  Cross-­‐ protec<ve  Epitopes   that  Seasonal  Flu   vaccina<on  or   exposure  may  protect   Conserved T-Cell Epitopes hOp://bit.ly/EpiPubs     86   De Groot et al. Vaccine 2009;27:5740-7
  81. 81. EpiVax  Predicted  Cross-­‐Protec.on   hOp://www.ncbi.nlm.nih.gov/pubmed/19660593   hOp://bit.ly/EpiPubs     87  
  82. 82. Immuniza.on  with  FluVax  cross-­‐conserved     T  cell  epitopes  decreases  lung  viral  load   10 8   1.00E+08   P=  0.002   PFU/ml   * 10 1.00E+07   7   10   A  handful  of   conserved   epitopes   protected   against  disease   6   1.00E+06   Placebo   FluVax   2009   Placebo   2  Days   hOp://bit.ly/H1N1_DR3_2013   hOp://bit.ly/Moise_Universal_Flu   Post-­‐Infec.on   hOp://bit.ly/EpiPubs     FluVax   2009   4  Days   90  
  83. 83. H1N1  Conclusions   •  This work recapitulates other projects already completed: Complete protection using ONLY T cell epitopes (H. pylori, Tularemia, VennVax) •  Results of our published studies demonstrate that conserved T cell epitope sequences, important to viral fitness, also may be immunologically significant contributors to protection against newly emerging influenza strains. •  The conserved epitope approach promises to answer the need for prompt preparedness and delivery of a safe, efficacious vaccine without requiring a new vaccine for every emergent influenza strain. hOp://bit.ly/H1N1_DR3_2013   hOp://bit.ly/Moise_Universal_Flu   hOp://bit.ly/EpiPubs     91  
  84. 84. What about H7N9? hOp://bit.ly/EpiPubs     92  
  85. 85. What  Can  We  Learn  About  H7N9?     Epitopes  Novel  or  Conserved?   H7N9   Circula<ng  Flu   As  it  turns  out  -­‐  -­‐  -­‐   Very  Poor  Cross-­‐Conserva<on  –  Only  within  Internal  Proteins   hOp://bit.ly/EpiPubs     93  
  86. 86. New  H7N9  Flu  is  Predicted  to  be   80 POORLY  IMMUNOGENIC   Thrombopoietin 70 - 60 - - 50 - - 40 - HA  A/California/07/2009  (H1N1)   Tetanus Toxin - 30 - Influenza-HA HA  A/Victoria/361/2011  (H3N2)   - 20 - - 10 - - 00 - - -10 - - -20 - IgG FC Region - -30 - Fibrinogen-Alpha - -40 - - -50 - - -60 - - -70 - - -80 - H7  HA   Immunogenic  Poten.al   Human EPO EBV-BKRF3 HA  A/Texas/50/2012    (H3N2)   Albumin Follitropin-Beta hOp://bit.ly/H7N9_HVandI   Random  Expecta.on   HA  A/chicken/Italy/13474/1999  (H7N1)    .  .  .  .  .  .  .  .  .  -­‐6.23   HA  A/Shanghai/1/2013  (H7N9)  .  .  .  .  .  .  .    ..  .  .  .  .  .  .  .  -­‐8.11   HA  A/mallard/Netherlands/09/2005  (H7N7)  .  .  .  .  .  .  -­‐8.63   gB-2 (EPX Score: -24.56) HA  A/mallard/Netherlands/12/2000  (H7N3)  ..  .  .  .  .  .-­‐9.91  
  87. 87. This  is  a  unique  virus   •  Low  conserva<on  of  HA,  NA  surface  proteins   is  not  surprising   •  Internal  proteins  are  more  conserved   •  And  –  HA  is  has  unusually  low  immunogenicity   •  Could  that  explain  why  infec<on  is   widespread?   •  Difficult  to  make  an<bodies  to  the  HA   hOp://bit.ly/EpiPubs     96  
  88. 88. Differen<al  Cross-­‐reac<vity  with  the  human   genome-­‐  significance?     New  and  unpublished:  The  “Classic  Epitope”   Is  much  more  cross-­‐conserve  with  the  human  genome  in  the  case  of  H7N9.   H1N1   H7N9   hOp://bit.ly/EpiPubs     97  
  89. 89. This  is  a  unique  virus   •  Unusually  low  immunogenicity   •  Cross-­‐reac<vity  with  human  genome   •  How  do  we  overcome  this  problem?   hOp://bit.ly/EpiPubs     98  
  90. 90. hOp://bit.ly/EpiPubs     99  
  91. 91. Immunoinforma.cs  Toolkit   •  EpiMatrix – maps T cell epitopes •  ClustiMer - Promiscuous / Supertype Epitopes Seamless  Vaccine   •  BlastiMer - Avoiding “self” - autoimmunity Design   •  Conservatrix – Identifies Conserved Segments   Integrated  toolkit  is   •  EpiAssembler - Immunogenic Consensus Sequences unique  to  iVax   •  Aggregatrix – Optimizing the coverage of vaccines •  VaxCAD - Processing and Assembly hOp://bit.ly/EpiPubs     100  
  92. 92. FastVax: Vaccines on demand •  High throughput computing •  Immunoinformatics •  Vaccine design algorithms   Rapid  deployment   when  genome   sequence  is  in  hand     •  Vaccine Production •  Delivery device •  Animal safety/tox/immunogenicity/validation •  Deployment by established distribution systems Pilot  program     Funded  by  DARPA   Prebuilt   hOp://bit.ly/EpiPubs     101  
  93. 93. 20  hours  -­‐  April  05  –  April  06  2013   Extremely  Rapid  H7N9  Vaccine  Design   April  05,  2013:  Obtain  H7N9  Sequences  (4  human-­‐sourced;  GISAID)     Obtain  all  available     H7N9  sequences   EpiMatrix  Analysis:  Iden<fica<on  of  H7N9  Class  I  and  Class  II  Epitopes   Compare  with  previous  epitopes  (IEDB)   And  other  H7N9  strains;  create  final  list   20  hours  (Logged).   101  H7N9  ICS*  Class  II  Epitopes  +  586  Class  I  Epitopes       Eliminate  Epitopes     highly  conserved  with  Human   Design  vaccine:  12  hours  (Logged).   April  06,  2013:  H7N9  Vaccine:  Two  Constructs,  Class  I  and  Class  II   hOp://bit.ly/EpiPubs     102  
  94. 94. Gedng  FastVax  into  the  clinic:  4  Steps   Emergency  use   authoriza<on   1.  In  silico   Design   2.  Produc<on   and  Packaging   3.  Clinical   Trial   (correlates  of   immunity)   4.   Deployment   Regulatory   Agency  approval   As  Currently  Proposed  with  Genome-­‐derived  Epitope-­‐driven  Influenza  Vaccines  (R21  /  NIAID  /  NIH)   hOp://bit.ly/EpiPubs   104  
  95. 95. H7N9  at  EpiVax   •  String-­‐of-­‐epitopes  DNA  vaccine  (Doug  Lowrie)   •  String-­‐of-­‐epitopes  Phage  vaccine  (Ft.  Detrick)   •  Op<mized  HA  (fix  epitopes)  recombinant   (TBD?)   •  Op<mized  HA  +  epitope  string  VLP  (Ted  Ross)   •  Collabora<on  with  NIID/Japan  –  in  progress   EpiVax  Contacts:     Anthony  Marcello,  BDA,  amarcello@epivax.com     Anne  S.  De  Groot  CEO/CSO  annied@epivax.com   105  
  96. 96. H7N9 Delivery vehicles DNA  –  chain  of  epitopes,  or   pep<de  in  liposomes   ICS-­‐op<mized  whole  proteins   ICS-­‐op<mized  proteins  in  VLP  
  97. 97. And  .  .  .  Cancer,  Allergy  and  Autoimmune  Disease?   •      Payload+Adjuvant+  Delivery  vehicle  =  Vaccine   •  Cancer  Vax  =  Epitopes  +  Adjuvant  +  ?     •  Tregitope  =  Novel  “adjuvant”  that  induces  tolerance   •  Allergy  Vax  =  Epitopes  +Tregitope+Delivery  vehicle   •  Autoimmunity  Vax=  AutoAg+Tregitope+Del.  vehicle   107  
  98. 98. Outline •  Why Computational Immunology •  Tools to Produce IDVs –  Antigen selection –  Vaccine design –  New concepts •  Case Studies •  . . . Questions? 108  
  99. 99. EpiVax:  Four  Core  Strengths   Contact:  Anthony  Marcello,  BDA,  amarcello@epivax.com     Confiden<al  
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