Big data is a game-changer in many industries. The healthcare sector can gain even more than most, considering how advances here can save lives, not just money. Medicine development, in particular, could take some massive steps forward thanks to big data.

What Does the Medicine Development Process Look Like?

Developing a new medicine is a long, expensive process. It takes an average of 12 years to take a drug from discovery to launch, and it costs millions of dollars. These extreme timelines and expenses mainly stem from two factors — there are many regulatory hurdles to clear and a lot of information to collect and process.

The typical development timeline falls into five overall phases. First, scientists must discover drug candidates, which are molecules that show potential to address a given condition. Next, they perform preclinical research to test and turn them into usable medicines.

Once pharmaceutical companies have a medicine, they must test it through a series of clinical trials. This is a four-stage process, and only 30% of drugs make it to the final stage. After completing these trials and refining the drug as necessary, businesses submit the results to the FDA for approval.

After a medicine gets FDA approval, pharma companies can release it to the public. However, they still need to monitor it. This last stage of development involves ongoing monitoring to watch for any issues that didn’t come up in clinical trials or the FDA review.

Big Data’s Role in Medicine Development

Big data significantly improves almost every stage of this process. Here’s a closer look at its growing role in medicine development.

1. Fueling AI Drug Discovery

The first and one of the most impactful applications of big data in drug development is in the discovery phase. Large data volumes lay the groundwork for machine learning models to simulate interactions between various molecules. These AI models can find promising medicine candidates in record time.

Some AI drug discovery tools have identified potential treatments in a matter of days when it would otherwise take months. From there, machine learning models can predict a medicine’s performance to streamline the preclinical research phase. This speed means life-saving medications can make it to market sooner, which wouldn’t be possible without big data.

2. Identifying Underserved Needs

Similarly, big data can make finding opportunities for new medicines easier. Creating an effective new treatment is largely a matter of finding an area where current options don’t meet everyone’s needs. Medical data from across demographics can reveal these gaps so pharma companies know what to look into.

This kind of predictive analytics is already common in healthcare. Some companies use big data to find poor patient outcomes that suggest a need for improvement. Others analyze it to predict disease outbreaks, kickstart the drug development process, and ensure faster treatment.

3. Streamlining Clinical Trials

Big data also has extensive applications for the lengthy clinical trial phase. First, it can help identify ideal testing areas. Finding a population with enough willing patients with needed conditions and sufficient diversity is challenging. Collecting and analyzing big data on an area’s demographics makes it much faster.

Pharma companies can also pull big data from these trials once they’re underway. Collecting as much real-time information as possible throughout this testing process gives researchers the evidence they need for future FDA review. Big data’s velocity also means they can spot and address potential safety issues sooner.

4. Monitoring for Potential Issues

Big data can also improve the post-market monitoring stage of medicine development. The FDA recalls more than 1,000 medications each year. Recognizing the need for these actions sooner would ensure fewer people experience issues.

Gleaning data across various sources and locations for warning signs of medicine-related issues helps regulators detect problems early. They can then modify the drug itself, its prescription recommendations, or anything else to protect people’s health.

Challenges With Big Data in Drug Development

As beneficial as these use cases are, big data faces some obstacles in healthcare. Chief among these is the issue of patient privacy. Regulations like HIPAA make it challenging to access some medical records, and big data applications must ensure privacy to avoid leaking sensitive health information.

Big data tools also often come with a learning curve. Many pharmaceutical companies cite a lack of relevant talent as a leading obstacle to using this technology. This talent gap makes it challenging to implement these tools and tailor them to the specific company effectively.

Costs are another problem. Drug development is already expensive, and the digital infrastructure and AI software needed to store and process big data are far from cheap. Consequently, smaller pharma businesses may struggle to use this technology to its full extent.

Potential Solutions

Thankfully, there are possible solutions to these issues. A promising 55.3% of healthcare organizations have increased their cybersecurity budgets in the last year. As AI and other data technologies become common, more HIPAA-compliant big data services will emerge, too. These trends will make investing in big data safer for pharma companies.

While it’s still challenging to attract tech talent, pharma businesses can tackle shortages by reskilling their existing workforce. Many big data and AI platforms are also becoming increasingly user-friendly as this market matures. Consequently, these talent gaps will become less of a concern over time.

Similarly, big data costs will fall as technology improves and the market grows. Pharma companies can also spread out these costs through gradual implementation. Applying this technology in a small use case before slowly expanding it to others will produce a better return on investment.

Big Data Is Changing the Pharmaceutical Industry

While challenges remain, big data is already making waves in the pharmaceutical industry. This technology has the power to change the way researchers develop new medicines.

These improvements could lead to cheaper, more accessible drugs coming out in much shorter time frames. In turn, health outcomes would improve for a greater variety of patients. It all starts with recognizing the potential of big data.