The Grand Challenge

Inventing a new medicine is very difficult and at 95%, the rate of failure is staggeringly high. We shouldn’t be surprised at this. Human biology is very complex and still far from being understood, especially in disease, when processes fail. Even when scientists begin to unravel the causes of disease, finding a drug with just the right properties to repair the failure while not causing harm is a massive task. As a consequence, new medicines are expensive and slow to appear. The industry is very good at managing these risks and alert to new technologies and methods that can reduce the risks, but the attrition rate remains stubbornly high and the cost of inventing a new medicine and bringing it to patients continues to climb.

We founded Opportunity Pharma with a long term commitment to helping decision makers in life science investment firms, executives in Pharma and other organisations such as charities and public-private partnerships get better returns on their investment. Better investment returns will bring more funding for new medicine discovery and more new medicines for patients. We see the 95% failure is an opportunity for improvement, the fulcrum of better output, since a relatively minor improvement to 90% doubles the flow of new medicines. The positive impact from reducing attrition is far greater than could be achieved in any other way.

We focus our efforts on building software to enable objective and transparent investment decisions in a portfolio of new drug opportunities. We build foundations on curated and objective data and the physics of molecules and their interactions. We build on these solid foundations with well proven methods that learn and can provide new insights. Finally we build an interaction layer that maps onto the way executives make decisions within portfolios of opportunities, evaluated by scientific, technical, and commercial factors at stage-gates in the drug discovery and development process.

With software providing an objective, transparent and rigorous framework for portfolio decision-making, we can deploy “Virtual Experts” to prioritise across thousands of opportunities as well as offer support to the human experts when it comes to the crunch on whether to invest or not. The Virtual Experts can take advantage of the explosion of new technologies using Large Language Models, they can learn and improve, monitor and update.

We are building a radically better process for executive decision-making in drug discovery and development. These are early days, but we are in it for the long term.