The current trial process is time consuming and costly. It costs, on average, $2.6 billion to bring a new treatment to patients including each approved drug must cover its own costs as well as the costs of other treatments that failed the trial process.
The process challenges start with a clinical plan and development of a protocol. Sites are then asked how many patients they can recruit. Some recruit the number they promise. Some get more or less than the number forecasted. Many trials are cancelled for not recruiting enough patients. Approximately 50% under enroll patients. Approximately 10% will not enroll a single patient.
Machine learning can be used to determine if the number of patients you require for the study is attainable. Next it will tell you which cancer patients would be the best fit for your product, based on personal characteristics and characteristics of their tumor. You can also eliminate those patients who will likely not benefit from the treatment. Incidence of disease is not the same in all areas of the world, so you can determine which countries will most likely provide access to the patients you require. Once you identify the right countries in which to perform your trial, you can also identify which clinical sites in that country will be most successful at recruiting the patients you require. You could also determine which CRO would be the best fit for your study, based on global presence, expertise, and past performance. And best of all, that required information could be gleaned by using hard data, not by human guesswork.
The result would be more successful trials, they would take less time, and the number of failures would decrease. The cost of trials, and the corresponding cost of medicines, would also fall. That, in a nutshell, is the opportunity of AI and machine learning. $2.6 billion average cost to bring a new drug to market could potentially drop by as much as one-third.