Jack is the CEO of Etheros, a biotech start-up developing small molecule enzyme mimetics to treat neurodegeneration and inflammation. However, he currently best known for his work on R&D productivity. He coined the term “Eroom’s Law” to describe the contrast between the exponential decline in R&D output efficiency since 1950 in the face of huge gains in the brute force efficiency of the scientific activities on which drug discovery depends. His work has focused on the predictive validity of screening and disease models in drug discovery and preclinical development, where formal analysis shows that “quality beats quantity”. 

Jack Scannell has published extensively on the predictive validity of screening and disease models in drug discovery and preclinical development, where formal analysis shows that “quality beats quantity”. 

A lot can go wrong if you do not have the patient and other stakeholders in mind when designing your Target Product Profile (TPP). Regardless of whether you are looking to develop a drug, medical device or digital tool, adopting a patient-centric and market-centric approach is key to developing a highly differentiated product with advantaged market access and strong reimbursement potential.

This session is equally relevant for MedTech, Biotech and Digital Health.

Examples of things gone wrong:

• Research teams continue to develop medical devices and drugs for elderly/severely ill patients that are just too complex and not sufficiently convenient in use (e.g. mobile phone apps, thrice daily dosing, need for leukapheresis);

• No clear view on what a truly attractive first indication is and how additional indications or applications of your device could make investors/licensors dream about the broader opportunity across multiple patient types;

• Biotechs with no clear hypothesis of their product value and supporting evidence will lack the foundation for negotiation with investors/ licensors/acquirors;

• Lack of true differentiation vs standard of care – in most cases your product needs to deliver significantly better efficacy with better healthcare economics vs standard of care. For example, linagliptin did not show significant enough improvements (endpoints, delta to standard of care) to achieve acceptable reimbursement in a top-5 market;

• Choosing poorly predictive screening and disease model in drug discovery and preclinical development.