A mobile drug trial that monitors side effects in real-time
Controlling Drug Monotony: With more complex drugs and much less time to develop and test them, pharma companies must constantly tweak their treatments to ensure they’re providing the best outcomes for the patient. A program that uses predictive models to predict the likely drug sequence of specific trial participants could provide pharmaceutical companies with real-time guidance to weed out bad patients and optimize trial outcomes.
Taking this next step, patient-driven models can also be used to prevent drug failures by highlighting harmful side-effects before they develop.
Drug Trials and Therapies
Manufacturers can use health data to predict what patient will become most susceptible to a treatment and allow them to consider possible combinations before they put a medication on the market.
This kind of predictive analysis also provides better patient outcomes and lessens drug toxicity.
Here’s an example using DHP, the Drug Effectiveness Network. It enables companies to explore not only the effectiveness of one drug but also how it interacts with others in combinations that maximizes treatment effectiveness:
These insights can have long-term consequences for clinicians who are going through trials. They can look back in time to design experiments and learn how to tweak medications to improve outcomes and improve patient welfare.
Our world is a never-ending cycle of new insights and challenges. Data science allows us to develop and assess new ideas and new products faster. However, the pace of change can take a toll on the value of data science for businesses. Companies must continue to innovate and adapt to change, and data science is just one example of these changes.
Appendix: A Working Paper That Presents Many of the Developments in this Section
From Dose to Effect in the Pharmaceutical Industry/article titleA Working Paper that presents many of the developments in this section
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