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Next-Gen Informatics Help Discover Better Drugs Faster
Article, Innovation, Pharma

Next-Gen Informatics Help Discover Better Drugs Faster

As the pharmaceutical industry adjusts to new challenges, information technology supporting the drug development process must advance and be redefined to improve research efficiency and success rates.

These challenges may be particularly burdensome for biotech and pharmaceutical researchers working on life-saving drugs and treatments.

Signals, a data management and analysis platform from PerkinElmer Informatics Inc., enable researchers all over the world to quickly share enormous amounts of structured data.

Why is Data Management Such a Difficult Task in Drug Development?

Although the biggest potential for machine learning is in especially data-rich areas of the drug development process, a key issue, according to the paper, is a “shortage of high-quality data, which is necessary for machine learning to be effective.

Every scientific subject has experts, and they all have their own areas of specialty and “worlds” in which they live.

However, when it comes to drug development, researchers won’t easily available and comprehensible data in order to make faster and more informed judgments before advancing medicines to clinical trials.

With patients’ health on the line, the previous methods of data silos and non-real-time data access no longer function. 

Furthermore, they argue that obtaining, curating, and keeping data is costly, and that ambiguity about privacy regulations impedes data sharing, which may also be hampered by a lack of economic incentives.

In this context, pharmaceutical firms are searching for methods to use the cloud to decrease data storage costs and facilitate cooperation.

Visibility into enormous amounts of data across various storage systems, transferring petabytes of data to the cloud and being able to quickly search and access data in the cloud are all new issues that pharmaceutical firms should be thinking about right now.

In Search of a New Definition of Discovery Informatics:

The benefits achieved by a discovery business from using discovery informatics software have usually been limited by the rate, frequency, and effectiveness of existing communication between computational scientists and laboratory scientists.

The considerable commercial cost of needing laboratory chemists to make judgments before software-generated decision-support information is available pales in contrast to the costs of requiring such chemists to make decisions before software-generated decision-support information is available.

This cost, in turn, is insignificant when compared to the costs of allowing computational chemists to spend their time.

As a result, today’s discovery informatics apps give more researchers, especially laboratory scientists, better decision-making tools and more autonomy.

This set of ‘front-line’ scientific applications, which are part of a category of software known as ‘laboratory informatics,’ is a crucial component in the redefining of discovery informatics and is being utilized by many laboratory scientists, including laboratory chemists and biologists.

Other laboratory informatics applications include ‘predictive chemistry’ software, such as virtual library design tools and structure-activity relationship data mining, which enable more fast and autonomous decision-making directly in the wet lab.

The Near Future

The transformation of discovery informatics from computational tools reserved for small groups of specialized scientists to laboratory decision support systems and knowledge bases available to all members of the discovery project team is already underway.

Many early adopters are already profiting from these new applications, while other pharmaceutical firms are already busily establishing cross-functional working groups and executive steering committees to examine how such applications may best be used to their particular discovery processes.

While advancing things in the fundamental algorithms employed by computational informatics are leading to improvements in scientific innovation and laboratory informatics is introducing greater autonomy and confident decision making in for the lab, hence improving the efficiency with which the industry operates its discovery laboratories.

Outsourcing and innovation

Before pursuing potential remedies, the industry must re-examine existing business structures and, as some have proposed, reconsider the role of Big Pharma in drug research.

Furthermore, an industry that was once highly respected and celebrated for its contributions to the world now faces ongoing societal backlash for high drug prices, despite rising R&D costs, and for perceived drug safety issues, despite the benefits that millions of people enjoy every day from pharmaceutical products.

The productivity, innovation, and efficiency gains required by the industry will not be achieved solely through new point discovery informatics applications working independently, but rather through software platforms that enable greater access to information, more rapid and autonomous decision-making, and collaboration among discovery teams. 

As the pharmaceutical business continues to evolve, discovery informatics is being reimagined to meet the emerging needs for more inventive science, greater laboratory efficiency, and, perhaps most significantly, research team cooperation.

How has this Platform Influenced Medication Development?

Improving collaborations and information sharing among scientists has always been at the heart of some of the most exciting pharmaceutical discoveries, as seen by the COVID-19 vaccines and treatments developed in less than two years.

One way that modern technology has assisted in the development of better drugs is by allowing pharmaceutical researchers to fail much faster than in the past. 

We are assisting scientists in more successfully analyzing greater and larger volumes of data.

For example, we have customers that require the daily processing of terabytes of microscopic images.

The more effectively and intuitively the material is arranged and presented, the simpler it is for them to progress.

Our Signals solutions are quite effective at aiding in the identification of those fascinating leads since all of the data is readily available in one spot.

The term “data-driven choices” is commonly used in the informatics industry.

Conclusion

Self-directed learning is especially important since institutions are limited in their ability to provide students with the skills they need to be prepared for the future role of AI in research.

Some of the most sweeping promises about AI’s potential to change drug development may turn out to be overstated. Critics point out that economic interests are at risk, and that no AI-developed medications are presently approved.

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