Someone who knows how to cope with innovation and no financial return on investment is Melissa Penny, professor at the Swiss TPH. She shared her insights on finding therapies for neglected diseases, which are often non-profitable. Her approach is to ensure evidence of a new therapeutic or cure for those diseases is “profitable” from a global health perspective. By utilising modelling and simulating as well as data analytics the effectiveness of an intervention throughout the whole value chain can be demonstrated to support payers to make the best decision possible.

1. Epidemiology and Public Health
Infectious Disease Modelling
The global health perspective
(innovation for neglected diseases)
Prof Melissa Penny, Swiss Tropical and Public Health Institute
melissa.penny@unibas.ch
7th May 2019

2. New therapeutics for global health
Bench to field
Discovery, Development, Implementation
Innovation value chain in global health

3. New therapeutics for global health
Preclinical Early clinical Clinical Implementation
Translation Translation Translation
Public health benefit
Save lives
Save costs

4. New therapeutics for global health
Modelling and simulation –
supporting product development and evaluation of novel therapeutics
across the innovation value chain
Preclinical Early clinical Clinical Implementation
Translation Translation Translation
Modelling and simulation throughout development to reach implementation
Modelling and simulation to inform innovation/development

5. New therapeutics for global health
Models
DATA
Policy & investment decisions
or evidence for payer perspective

6. Donor driven R&D to global policy and implementation
Multiple
actors for
innovation
Product development
partnerships$$$
Innovative vector control consortium
$$$
R&D funders
Payers: National
Malaria programs,
Global Fund, PMI,
GAVI, & &
Recommendations
1) WHO Pre-qualification
2) WHO Policy recommendations
3) National Health Ministry
Academia
Industry

7. Lessons learnt
Data!
Innovation and knowledge gaps
Equitable effectiveness
Actors
Incentives
Partnerships

8. Decision making in
healthModels
Iterative
Intervention Target product
profiles/use cases of new
malaria tools
Evidence generation for quantitative intervention Target Product Profiles
Identify innovation gaps for greater impact
Identify limitations
Iterative
DATA
Lessons learnt –
we need to identify innovation and knowledge gaps
Machine
LearningPartnerships
and decision
makers

9. Funding:
Thank you and acknowledgements
Melissa Penny
Guojing Yang
Monic Golumbeanu
Flavia Camponovo
Tom Smith
Members of Swiss TPH Infectious Disease modelling unit
Collaborators at Swiss TPH
Collaborators from MVI, IVCC, MMV, and Bill & Melinda Gates Foundation