The pharmaceutical industry brings us several benefits, including new drugs, so we can lead healthier and better lives, but the industry is not without it’s a fair share of problems. Duplicating workflow, multiple data sources and the risk associated with new drug discovery are just some of the problems the industry has to overcome in the discovery of new drugs. Fortunately, there is a solution thanks to predictive analytics software. In this blog post, I am going to explain the role of predictive analytics in drug development.
The current challenges in developing new drugs
To appreciate what predictive analytics software brings to the table, we need to take a look at some of the challenges the industry faces. Developing new drugs is not an easy process. Medical researchers have to overcome a slew of problems to make a new drug. Some of these problems include the risk associated with drug discovery, communicating with other departments and replicated workflows.
Developing new drugs comes with several risks. Some of these risks include, but are not limited to, portfolio risks (does the medicine add value?), operational risks (data management) and resource risks. Pharmaceutical companies must manage all these risks while developing a new product. The challenge is further enhanced by the rise of personalised medicine model, which more pharmaceutical companies are adopting.
How does predictive analytics software address these problems?
Predicting drug behaviour
Anticipating how drug molecules will behave is one of the most challenging aspects of drug development. In the past, researchers would have to conduct a lot of in vivo testing to find the right drug molecule combination. However, with predictive analytics software, developers can now simulate drug molecule combination using mathematical simulations. Using this method, drug researchers can start with a wide number of compound combinations and narrow down the selection over time. The end result is an easier method of drug molecule combination, one that delivers more accurate results.
Predictive analytics software makes it easier for drug researchers to work with others. Drug development often happens in a silo but, with analytics, collaboration with external partners becomes much easier. For example, some companies collect and store information on millions of compounds for possible candidate molecules. Drug researchers can cross-reference this information with their own research to predict the behaviour of newly discovered compounds. Thus, researchers have an easier time finding new drugs formulas that will succeed on the market.
Drug development often occurs in isolation, however, predictive analytics software will encourage a silos breakdown, encouraging medical research departments to work with CROs, manufacturing and sales departments in drug development. The breakdown occurs because predictive technologies need to map out trends and make recommendations to better reflect the organisation’s interest.
For example, pharmaceutical organisations will not only anticipate possible compound combinations but also anticipate trends past the point of production, like how the final product will perform in the market. However, for this to work, medical data needs to be contextualised appropriately. The need to contextualise data accurately will see medical research departments work with CROs manufacturing and sales department.
Better selection of candidates for patient trials
Patient trials in drug development are often a bureaucratic process. However, with predictive analytics software, you can improve the process tremendously by finding the best batch of candidates based on other merits besides being first in line. Furthermore, you can seek out candidates who are often underrepresented in the sample, allowing clinical researchers to get unique results by studying the biological effects of medicine on different body types. As a result of this, the trial process would become more accurate and easier to accomplish.
Enable smarter decisions
If organisations are to manage RnD funds properly, they need to make smart decisions about allocating their resources. Unfortunately, many organisations lack the proper tools to support their choices in asset allocation and financial investment. Predictive analytics software can help decision-making by providing more in-depth information about potential scenarios. With data analytics, organisations will have accurate tools to help substantiate their decision making.
The role of predictive analytics software in drug development
Predictive analytics software improves the process of drug research and development. Research companies will have an easier time conducting research and working with different departments. Furthermore, many of the uncertainties associated with developing new drugs are removed thanks to predictive analytics, improving the success rate and making the research process much more efficient.