Pharmaceutical targeting has some of the lowest success rates in the industry. Very few pharmaceutical targets make it to the end of the approval process. Due to things like differences in animal models compared with humans, acceptable safety and toxicity rates in the target drugs themselves, or efficacy rates simply not panning out – successful clinical trials are elusive.
As a result, research and development is costly and patients usually end up footing the bill on the backend to justify discovery costs. Alternatively, life-saving medicines might simply not be produced because they may be deemed too expensive early on.
Analytics is changing all of that. With smarter drug targeting, pharmaceutical companies can justify up-front costs with a higher probability of success downstream.
Below is an example of what a predictive model could look like “in silico” to help scientists choose which targets to focus on. Below, “MedA” is the clear choice for concentrating resources.