Data science is advancing into nearly every industry from pharma and banking to sales and logistics. And why not — when data science is properly integrated it results in a performance boost, faster implementation, or process automation (read: money, money, money).

The pharmaceutical and medical industries are no exception. Every top pharmaceutical company uses data science to optimize processes and improve results. We know this from experience – Appsilon helps several Fortune 100 pharma companies build enterprise Shiny applications to this end.

Why You Should Use R Shiny for Enterprise Application Development

Today you’ll read about the top 10 use cases for data science in pharma, including drug discovery, marketing and sales, and gene editing.

Table of contents:

1. Personalized Medication Plans
2. Marketing and Sales
3. Enhanced Drug Discovery and Development
4. Improved Drug Trials
5. Genomics
6. Genome Editing
7. Machine Learning
8. Patient Follow-ups
9. Safety and Risk Management
10. Operational Optimization

1. Personalized Medication Plans

Big data technologies can process and integrate endless amounts of data from multiple sources. That’s good news since it’s a key requirement for personalized medication plans. Companies can’t provide optimal individual-level plans before analyzing and mining data on a large scale, so big data technologies and machine learning are a requirement.

These technologies combined with genomic sequencing, patient’s medical sensor data, and medical records are a go-to way to provide personalized medication plans.

Do you want to stay on track with personalized medicine? Read the most recent news on ScienceDaily.

Further, personalized medication plans can be improved and extended by continuous analysis of the treatment progress. Pharmaceutical experts can see how the treatment is going and adjust the dosing accordingly.

2. Marketing and Sales

Niche markets are increasingly in demand, especially with improvements in personalized medication plans. Pharmaceutical companies can leverage data science to detect underserved markets and analyze them further. And hopefully coming up with a solution for those in need.

Data science in pharma can also be used to track sales efforts and provide feedback received during the sales process. There’s no shortage of ways to outsmart your competitors, and data science can make it a little bit easier.

At Appsilon, we’ve built an iPad app for the sales team of a leading US Pharma company — you can watch the presentation here.

3. Enhanced Drug Discovery and Development

Going from research to a ready-to-ship product is a long and tedious process in the pharmaceutical industry. Basically, it boils down to clinical trials, which oftentimes fail to meet their objectives, resulting in delays and an increase in cost.

But there’s a lot of work that has to be done before the first trial even begins. For example, pharmaceutical companies need to find a drug candidate, which is yet another time-consuming process.

Want to hear the good news? Data science can help automate that.

Improve efficiency and reduce errors with Appsilon’s open source data.validator package. A Crucial step in any data science project. 

Data science and automation can help pharmaceutical professionals screen millions of compounds to identify drug candidates for trials. It’s a simple process if you know what you’re doing. You need to go through a vast sea of information and filter out the results not matching your criteria.

It’s easy to automate the process, and when done correctly, it can radically increase the speed of drug discovery and the development cycle.

Experts agree that AI can accelerate drug discovery. Read more details on a report from Frost & Sullivan.

4. Improved Drug Trials

Pharmaceutical companies want to avoid spending time and money running suboptimal clinical trials. Big data ensures the right mix of patients is present for any given trial by precisely targeting specific user groups.

Big data technology allows pharmaceutical companies to analyze historical data on demographics, past behaviors and conditions, and previous clinical trials. It opens the possibility to predict potential side effects and prevent them on time. In addition, big data considers more factors than analysts ever reasonably could.

An optimized drug trial process significantly reduces the time needed to properly test the drug, hence decreasing the money spent in the process. It’s a two-birds-one-stone moment.

Quality data means big savings. Learn how Appsilon saved clients real money with data validation. 

But that’s not all because, after adequate testing, the drug goes through an approval process. And as it turns out, you can use machine learning in this step, too! Big data is changing the game for how fast, efficient, accurate, and competitive pharmaceutical companies want to be.

5. Genomics

Scientists can now sequence genome data in a few hours, thanks to The Human Genome Project. The project gave researchers access to billions of databases, containing information on genes, mutations, and so on.

As a result, the data provides valuable insights for the medical field by automatically annotating specific genes. This task is no easy feat and borderline impossible through manual work. That’s where data science chimes in, providing tools and frameworks to track, receive, store, analyze, and interpret gene data automatically.

Appsilon ML Engineers helped build a computer vision model to assist genetic research.

Data science in pharma is a promising career. There’s even an entire field of study combining genomics and data science — Genomics Data Science. It’s an interdisciplinary field that applies statistics and the tools of data science to analyze and interpret the data generated by modern genomics technologies.

6. Genome Editing

Genome editing is a method that lets scientists change the DNA of many organisms, including plants, bacteria, and animals. It leads to changes in physical traits, like eye color, and disease risk, according to the National Human Genome Research Institute.

It still has a long way to go before being implemented in therapies, but researchers can use machine learning and artificial intelligence to minimize potential damaging, off-target effects.

Combining gene editing, genomics, and big data could have a significant impact on the world. You can read this presentation about how combining these three will positively affect the future of Africa.

7. Machine Learning

Machine learning is such a broad field that grasping all of its capabilities will make your head spin. So let’s start with a generic example. Pharmaceutical companies spend most of their time and money on screening compounds that will be tested in preclinical trials. It turns out that machine learning can help quite a lot here.

Machine learning saves pharmaceutical companies both time and money by narrowing down the search area for researchers. They can spend more time examining promising drug candidates because machine learning pointed them in the right direction. Companies can also use machine learning to optimize clinical trials, improve sales, marketing, and so on — but more on that below.

Appsilon is an emerging player in the pharmaceutical industry. Read about our involvement in the R/Medicine Conference 2021.

8. Patient Follow-ups

A tremendous amount of work has gone into the development of biosensors, sophisticated at-home devices, smart pills, smart bottles, and smartphone apps. It’s safe to say tracking a patient’s health has never been easier than now.

Monitoring a patient’s health in real-time tells pharmaceutical companies how to improve their current offering, and also helps them to analyze the efficacy of a drug or treatment.

In addition, by collecting data from one specific patient, pharmaceutical companies can shorten the implementation time for future patients with similar characteristics — reducing both time and cost.

9. Safety and Risk Management

The internet contains seemingly endless amounts of information (or misinformation) on almost any topic. This is both a blessing and a curse for pharmaceutical companies. It’s often the case that pharmaceutical companies are spread globally, with large quantities of information on their products existing online, in the form of reviews, articles, and forum discussions.

Researching and going through all of this online data is impractical for manual labor. So companies typically write scrapers and save large volumes of unstructured data using big data technologies.

Automate text mining and provide drug safety with NLP.

However, the information is useless when scraped. There’s just too much to go through. With advancements in data science in pharma and machine learning — NLP in particular — analyzing sentiment and mentions is easier than ever. Monitoring communications on products can provide valuable insight. It’s important you don’t miss out on trending mentions of products. Who knows, perhaps there’s some side effect of a medication discussed on a forum in Poland — make sure to catch it before it’s too late.

10. Operational Optimization

Automation is a trending topic, especially with more companies looking to benefit from digital transformation. Most people want to avoid getting stuck in a repetitive loop every day, five days a week. The good news is they don’t have to, as machine learning and automation are here to help.

We’ve already discussed patient follow-ups and described how a lot of effort went into developing apps that remind patients to take their therapy on time. Just imagine how this would look like without an automated solution. Would there be a team of representatives calling patients every day?

Operational optimization isn’t specific to the pharmaceutical industry. Learn how to optimize your business. 

Conclusion: Implementing Data Science in Pharma

And there you have it, ten use cases of data science and artificial intelligence in the pharmaceutical industry. But there’s still one area we haven’t mentioned that has a tremendous impact at the organizational level — accurate, timely, visual data representation.

That’s where Appsilon can help. We’ve developed custom analytical dashboards for many Fortune 500 players in the pharmaceutical sector. Read what our customers are saying about Appsilon products on our Clutch profile. We are developing Shiny applications for automated report generation for big pharmaceutical companies like Merck. These reports include manufacturing quality checks that are required by the FDA. Automating these processes saves thousands of personnel hours and millions of dollars.

We have an experienced team of data scientists, computer scientists, frontend specialists, and infrastructure engineers with a specialization in enterprise Shiny dashboards. Our team of dedicated ML Engineers also builds custom AI and Computer Vision solutions. We’ve yet to find a pharmaceutical project that is too ambitious. Appsilon is an RStudio Full Service Certified Partner and can implement the full range of RStudio products. Browse our R Shiny Dashboards Demos and reach out if you need help building custom data science solutions.

Article Using Data Science in Pharma – Top 10 Real-World Examples comes from Appsilon | End­ to­ End Data Science Solutions.