A bright new opportunity for pharmaceutical companies waits, because the future of medicine is mathematics.
Last year scientists published a paper showing that they had developed an algorithm, which can calculate the risk of a woman contracting breast cancer over the next 10 and 20 year period. Their study included 122,000 women, and these scientists showed that they could accurately predict those who would develop cancer, particularly breast cancer.
In this case, the future opportunity for pharmaceutical companies is the feasibility of running prevention trials, when you can more accurately identify a high-risk population. This makes potential timelines for future trials feasible and any successful treatments cost effective, as the at-risk group is more clearly defined.
As long ago as 1960, the first mathematical model of how cardiac cells behave was developed, and more recently computer modelling of blood flow has changed our understanding of sickle-cell anaemia, as we learned that it produces four different types of red blood cells.
The pharmaceutical industry and researchers, already use computer models of an artificial heart to screen potential drug candidates. That model is made up of 30 different algorithms.
Insurance companies in the US, like WellPoint, are exploring the potential of using the IBM Watson supercomputer to aid decisions on what treatments are most likely to succeed.
Whether it is algorithms to identify at risk patients, super computers supporting physicians with making diagnoses, or computer models of an entire human being to predict how a particular patient will respond to a treatment approach, the implications for pharmaceutical companies will be significant.
On the one hand there is the opportunity to run trials more quickly and efficiently, speeding up development time. Even before the trial stage, there is the potential to increase the likelihood of a successful trial outcome for a molecule through modelling.
On the other hand, with insurers and prescribers using computer models to decide on treatment, companies will have to be much clearer on who the patient is and have the data to support that decision.
The question that really needs to be asked is how will healthcare providers and companies share data to their mutual benefit? One possible answer should be, by identifying unmet needs and aligning research towards those patients.
However, what these studies guarantee is that no matter what, mathematics and computers will be a key part of the future of healthcare.