Presentation by Piero Olliaro about "Anti-TB Drug R&D: Peculiarities, Pipeline and Initiatives." Piero Olliaro Bio: http://www.opensourcepharma.net/participants/piero-olliaro Conference Agenda (see Day 1, Session 1): http://www.opensourcepharma.net/agenda.html

1. Harrogate, 30Mar09
ANTI-TUBERCULOSIS DRUG R&D
PECULIARITIES, PIPELINE &
INITIATIVES
P. Olliaro
WHO/TDR, Geneva, CH
&
University of Oxford, UK
Bellagio, July 2014

2. Harrogate, 30Mar09
Generic
R&D ProcessTarget identification:
Discovery: Lead identification
(hit to lead)
Translation
(druggability, non-clinical
pharmacology) 
Development candidate, IND
Development (Clinical Ph 1-3
+ CMC + Non-clinical) 
Registration
Access, Post-marketing
studies/surveillance,
Intervention &
Implementation/Operational
research

3. Harrogate, 30Mar09
WHAT'S SPECIAL ABOUT TB DRUG R&D
Treating TB means dealing (simultaneously and
sequentially) with bacilli in different metabolic status and
in different environments
Which requires a combination of compounds with
different but integrated PK/PDs
Need new COMBINATIONS, not individual drugs

4. Harrogate, 30Mar09
Target identification:
Discovery: Lead identification
(hit to lead)
Translation
(druggability, non-clinical
pharmacology) 
Development candidate, IND
Development (Clinical Ph 1-3
+ CMC + Non-clinical) 
Registration
Access, Post-marketing
studies/surveillance,
Intervention &
Implementation/Operational
research

5. Harrogate, 30Mar09
WHO DOES IT – PHARMA-DRIVEN:
1. Novartis Institute for Tropical Diseases (NITD)
http://www.nibr.com/research/developing_world/NITD/
2. GSK Tres Cantos Open Lab Foundation
http://www.gsk.com/research/research-funding/tres-cantos-
open-lab-foundation.html
3. Lilly TB Discovery Initiative (TBDDI) (not-for-profit PDP)
https://openinnovation.lilly.com/dd/about-open-innovation/tb-
drug-discovery-initiative.html
4. TB Drug Accelerator (TBDA)
http://www.bioendeavor.net/BDDirectory_2658.asp?itemId=108
73
http://www.astrazeneca.com/Research/news/Article/25062012
--seven-pharmaceutical-companies-join-academic-research
http://drugdiscovery.pharmaceutical-business-
review.com/news/eisai-joins-tuberculosis-drug-accelerator-
partnership-to-discover-new-tuberculosis-treatments-251113

6. Harrogate, 30Mar09
WHO DOES IT – OTHERS:
1. Tuberculosis drug discovery TBD-UK http://www.tbd-
uk.org.uk/
2. Institute for TB Research (ITR) at UIC
http://www.tuberculosisdrugresearch.org/
3. Genome databases at the Broad Institute
http://www.broadinstitute.org/annotation/genome/mt
b_drug_resistance.1/DirectedSequencingHome.html

7. Harrogate, 30Mar09
ISSUES:
1. For overview see e.g. Koul et al, Nature 2011 -
http://www.nature.com/nature/journal/v469/n7331
/full/nature09657.html
2. General antibiotic resistance: a problem for some
classes of anti-tuberculosis drugs (aminoglycosides,
fluoroquinolones, etc.) in general use
3. Pharma overall disinvesting from infectious diseases
and antibiotics
4. Limited innovation (novel chemical classes); little
chance of ‘piggy-backing’ from anti-infective R&D
5. 'Pool' of compounds + sources (private, public)

8. Harrogate, 30Mar09
Target identification:
Discovery: Lead identification
(hit to lead)
Translation
(druggability, non-clinical
pharmacology) 
Development candidate, IND
Development (Clinical Ph 1-3
+ CMC + Non-clinical) 
Registration
Access, Post-marketing
studies/surveillance,
Intervention &
Implementation/Operational
research

9. Harrogate, 30Mar09
WHO DOES IT:
1. Individual companies/sponsors
2. PreDiCT-TB - public-private partnership
www.predict-tb.eu
Funded by the EU Innovative Medicines Initiative, [3
pharma (GSK, Sanofi, Janssen) + 2 biotech (ZF Screens,
Microsens Medtech) + 15 academic partners (headed
by the University of Liverpool)]
Multidisciplinary consortium "to create a new
integrated framework for TB drug development,
making optimal use of preclinical information to design
the most efficient clinical trials"

10. Harrogate, 30Mar09
ISSUES:
1. No mechanisms to move candidates to 'developers'
(apart from GATB?)
2. Drug action depends on metabolic status of MTB.
Need multiple models + way to integrate them for log-
phase, mid-phase & dormant MTB
3. How to test combinations as opposed to individual
compounds
4. Predictivity of animal models?
5. How to account for immune response?

11. Harrogate, 30Mar09
Target identification:
Discovery: Lead identification
(hit to lead)
Translation
(druggability, non-clinical
pharmacology) 
Development candidate, IND
Development (Clinical Ph 1-3
+ CMC + Non-clinical) 
Registration
Access, Post-marketing
studies/surveillance,
Intervention &
Implementation/Operational
research

12. Harrogate, 30Mar09
WHO DOES IT – THE PIPELINE:
1. http://www.newtbdrugs.org/pipeline.php (last
update 2013 = out-of-date)
2. Individual companies; GATB main actor; how many
companies still involved?
3. Limited spectrum of chemicals
4. No drug in Phase I
5. No headway in treatment shortening: gatifloxacin 4-
month regimen not non-inferior to 6-month standard
regimen (exc'pt non-cavitary disease ?)
6. Newly-diagnosed vs. multidrug resistant TB (a false
dichotomy?)
7. REGULATORY AGENCY role – requirements need to be
adapted to TB

13. Harrogate, 30Mar09
WHO DOES IT – "CLINICAL DATABASES":
1. Critical Path to TB Drug Regimens (CPTR) – supported
by BMGF http://cptrinitiative.org
2. Innovative medicine Initiative (IMI) – supported by EU
& Pharma
http://www.imi.europa.eu/content/predict-tb

14. Harrogate, 30Mar09
ISSUES – 1
LACK OF SURROGATE/BIO-MARKERS:
1. Inefficient system to select for candidate regimens for
Phase III
a. Phase II (IIa = EBA, extended EBA; IIb + SSCC, 8-
week Rx) measures only rate of decrease in colony
counts; cannot measure sterilizing activity, hence
not predictive of relapse rates in Phase III
b. Drug substitution (into standard treatment) as
opposed to new regimens (single developer vs. co-
development; regulatory issues)
2. Lack of biomarkers for Phase III studies  long
follow-up
1. 1 + 2 = costs, time, waste (= it takes a lot of time and
money to fail!)

15. Harrogate, 30Mar09
Limited tools to measure & predict
?

16. Harrogate, 30Mar09
ISSUES - 2:
1. Need pharmacologically-driven drug
design/development (PK/PDs) – which requires
understanding of MTB metabolism/dynamics + drug
PKs & interactions
2. Importance of standardizing study design, esp. core
outcome measures, duration of follow-up, non-
inferiority margin = 'community of practice'
3. Study design and outcomes for newly-diagnosed vs.
chronic multi-drug resistant TB (standard vs.
individual-tailored regimens; esp. testing of regimens
with newer drugs delamanid, bedaquiline)
4. Importance of PK/PD component in clinical trials
5. Clinical data-sharing critical for all the above

17. Harrogate, 30Mar09
ISSUES - 3:
1. Phase III clinical trial complexity:
a. For NDTB: compare to standard 6-month Rx (non-
inferiority trial design)
b. 1-2 year post-Rx follow-up
c. Numbers required ~800 pts/arm
d. Total trial duration 6+ years all going well
e. Cumbersome for staff
f. Expensive
2. Overall clinical trial capacities & capabilities (GCP,
GCLP compliant) to absorb Phase 2-3 trials in (highly)
endemic countries
a. Merits (and complexity) of sharing investments!
b. All public, no-profit, pharma, EDCTP?

18. Harrogate, 30Mar09

19. Harrogate, 30Mar09
SITUATION ANALYSIS
1. Where are the bottlenecks
2. Which ones could be addressed by which form
of open*
3. Tailored solutions
4. Who could have the solution; how many steps
away they are

20. Harrogate, 30Mar09
Bottlenecks
that could
be
addressed
through
open-source
approaches
Target identification:
Discovery: Lead identification
(hit to lead)
Translation
(druggability, non-clinical
pharmacology) 
Development candidate, IND
Development (Clinical Ph 1-3
+ CMC + Non-clinical) 
Registration
Access, Post-marketing
studies/surveillance,
Intervention &
Implementation/Operational
research

21. Harrogate, 30Mar09
Improving R&D
EfficiencyTarget identification:
Discovery: Lead identification
(hit to lead)
Translation
(druggability, non-clinical
pharmacology) 
Development candidate, IND
Development (Clinical Ph 1-3
+ CMC + Non-clinical) 
Registration
Access, Post-marketing
studies/surveillance,
Intervention &
Implementation/Operational
research
Models to reliably predict
effects in humans
Identify combinations
More efficient regimen selection in
PhII
Simplified Ph III trials
Develop combinations
Access
Optimised use in real-life
Data pooling from real-life studies
Shareassays,compounds,
data,knowledgeDatapooling,standardmethods
Target & compound diversity
Suitable screening approaches

22. Harrogate, 30Mar09
THE LANDSCAPE:
Incentives needed to further improve sharing of research data, 30th May
2014 Report commissioned for: Wellcome Trust, MRC, Cancer Research
UK and the Economic and Social Research Council
http://www.wellcome.ac.uk/News/Media-office/Press-
releases/2014/WTP056505.htm
Key findings
a. making data accessible to others can carry a significant cost to
researchers
b. funders encourage data access, but data management & sharing plans
they request of researchers are often not resourced adequately, and
delivery not monitored nor enforced;
c. very little, if any, formal recognition
d. data managers: vital role as members of research teams, but often low
status
e. the infrastructures needed to support researchers in data
management and sharing, and to ensure the long-term preservation
and curation of data, are often lacking

23. Harrogate, 30Mar09
TDR CLINICAL DATA SHARING :
1. Data sharing: facilitation of research through greater access to
data
2. Encouraged by numerous research funders and journal editors
3. Practical expression hampered by technical, cultural and ethical
issues
a. When, where and how to share data and who can access and
reuse it for secondary analysis?
b. How/Where to store and curate quality database; requires
resources and skilled personal
4. So far no single repository or best practice approach to sharing
clinical trial data
5. TDR is exploring this further using TB clinical trial data as
working example

24. Harrogate, 30Mar09
TB R&D OPEN* & DATA SHARING :
1. Priority:
a. simplifying, shortening, reducing costs of TB drug R&D;
b. develop drug combinations (multiple partners)
2. How:
a. Resolving methodological issues
b. Sharing information
c. (a) cannot happen without (b)
3. Arguments: improved efficiency
a. Patients will get more effective treatments sooner
b. Economic gain: savings by developers/funding
agencies
c. Economic cost of data-sharing
d. Balance: G >> C

25. Harrogate, 30Mar09
Reduce
Wastage, duplications
R&D risks, time, costs
Improve efficiencies
Increase innovation
(combine strengths, fill
weaknesses of
different stakeholders)
Create a more
conducive ecosystem
TB OPEN* R&D & DATA SHARING
Common understanding
of definitions: open-
source, open research,
data sharing (between
whom)
Where best applied to
R&D path: Pre-
competitive' space?
Clinical phases?
IP?
No one-fit-all solution

26. Harrogate, 30Mar09
HERE THERE
SHARE DATA
DEVELOP METHODS
SHARE METHODS
SHARE OUTCOMES