Data Science in the Pharmaceutical Industry

The Promise of Big Data
For every 5,000 compounds starting in the laboratory, five are tested in humans and one makes it to market.

Moreover, it takes approximately 10 years and an average cost of $2-3 billion to develop each new drug. That adds up to a vast amount of molecular and clinical data stored in proprietary networks, just ripe for analytics.

There has been a push in recent years to increase collaboration – both internally and with the outside world. To gain a competitive edge, increase their expertise and enlarge their ever-growing databanks, pharmas are now working with:

The power to forecast the future has applications for drug discovery and avoiding negative outcomes.

In terms of drug discovery, Pharmas spend a vast amount of money screening compounds to test in preclinical trials. To speed up the process, drug companies are using predictive models to search gargantuan virtual databases of molecular and clinical data. Analysts zoom in on likely drug candidates with the help of criteria based on chemical structure, diseases/targets and other characteristics.

For example, Numerate, which works with companies like Boehringer Ingelheim and Merck, designs its predictive models with specific drug targets and treatment goals in mind.

In relation to avoiding negative outcomes, predictive modeling can also be used to short-circuit potential disasters such as deaths from risk factors.

Predictive analytics can also be used to optimize clinical trials through the selection of optimal patients through genetic clustering, and to improve marketing efforts.

In recent years, pharmaceutical companies and institutions have sponsored crowdsourced contests to predict patient and clinical outcomes, sales patterns, molecule activity, and anything else involving big data.

Examples of these include:

Data scientists can help to reduce the costs of clinical trials by enabling drug companies to implement:

Once upon a time, pharmaceutical companies would send their reps on lengthy doctors’ visits and invest in expensive, broad-scale product promotion.

In a March 2013 survey from Accenture, Life in the New Normal: The Customer Engagement Revolution, respondents noted that around 25% of their pharmaceutical marketing was delivered over a digital platform and that 87% intend to increase their use of analytics to target spending and drive improved ROI. Six years later, in 2019, many pharmaceutical companies are now planning to spend more than half of their budgets on digital marketing.

Some of that money is likely to go into monitoring doctors’ therapeutic tastes, geographic trends, peak prescription rates – anything that has a direct relevance to the sales cycle. This data then feeds into:

With the development of miniature biosensors, sophisticated at-home devices, smart pills and bottles, smartphones and health apps, monitoring a patient’s health has never been easier. Pharmaceutical companies are increasingly interested in how the real-time data from these tools can be used to support R&D, analyze efficacy and increase drug sales.

In addition to knowing how their drugs are being used, companies also typically want to hear how customers view their products. Opinions about new drugs are often generated through patient/physician and patient/patient experiences in a way that creates messy, unstructured data sets.

However, if properly organized and analyzed, this data can be a rich trove of information on:

By the time it reached the 21st century, the pharmaceutical industry had amassed quantities of structured and semi-structured data in separate, mutually inaccessible “silos”.

Unfortunately, it’s having trouble bringing those silos together. However analysts slice the numbers, the ultimate goal is to turn the data into information that can play a strategic business role. The prerequisite for that is making the data sources communicate with one another in a meaningful way.

What’s more, drug companies are coping with a flood of unstructured information – including social sentiment – that’s coming at them from outside sources. Integrating, manipulating, organizing, and interpreting this data to support some coherent course of action is causing more than a few headaches.

Data integration raises the issue of patient privacy. The more databases are shared among institutions, CROs, partners, software companies, etc., the more pharmas run the risk of exposing sensitive patient information to the eyes of those who shouldn’t be seeing it.

That means drug companies need to reduce their exposure to running afoul of federal laws and regulations. For example, the HIPAA and its younger brother the 2009 HITECH Act clearly state that covered entities must:

“Protect individuals’ health records and other identifiable health information by requiring appropriate safeguards to protect privacy, and setting limits and conditions on the uses and disclosures that may be made of such information without patient authorization.”

Data scientists should also be aware of the FDA’s Sentinel Initiative. This is a legally mandated electronic-surveillance system linking and analyzing health care data on millions of patients from multiple sources. Its purpose is to collect data on safety issues and enable regulators to take quick action.

In the late 19th century, Colonel Eli Lilly, founder of Eli Lilly and Company, had:

By the end of his life, he was a millionaire.

Lilly, Heinrich Emanuel Merck, Charles Pfizer, Friedrich Bayer, Edward Robinson Squibb – before they were brand names, they were pioneers of the pharmaceutical industry. They were also some of the first men to use data-driven methods to cut costs and improve products.

Fast-forward to the 20th century. Witness the rise of air travel, fortune cookies, Einstein’s Theory of Relativity, jazz – and the megalithic manufacturers we know today.

The shift from small independent companies to conglomerates got started in the 1940s. With so many lives on the line, World War II spurred intense collaboration between governments and pharmaceutical companies. In the 1940s, a mind-boggling collaborative effort between the government, Merck, Pfizer and Squibb (among others) resulted in the mass production of penicillin.

By the time the troops came back from the war, pharma was becoming big business. The second half of the 20th century saw the development of many pharmaceutical breakthroughs, including ibuprofen, the contraceptive pill, Valium, and the war on cancer, among others. Advances in genetics – including automated protein and DNA sequencing – and psychiatric treatments opened new markets.

This was also the age when data began to make its mark.

Take Electronic Data Capture (EDC). In the years leading up to the 1970s, pharmaceutical companies had been receiving clinical research data on paper forms. This often resulted in data entry errors and delays.

The 1970s also saw the introduction of Cambridge Structural Database (CSD) and the Protein Data Bank (PDB), as well as genetics-focused resources like the Staden Package for DNA Sequences.

With the arrival of Professor Norman Allinger’s the Journal of Computational Chemistry in 1980, the first decade of computer-assisted drug development had begun. As Sean Ekins discusses in his book, Computer Applications in Pharmaceutical Research and Development, scientists were now empowered to use computational chemistry programs on personal computers.

Software companies blossomed, anxious to provide drug companies with useful tools. Examples of their functions included:

Due to the massive influx of data, Lilly was the first pharmaceutical company to purchase a supercomputer (the Cray-2), and many of its competitors soon followed.

The volume of information only increased with the advent of the World Wide Web and the new millennium:

Effective data mining became critical to drug development.

Last updated: June 2020