Big Data in the Pharmaceutical Industry

Mankind is generating more data in two days than we did from the dawn of man until 2003 (Eric Schmidt, Google).

The use of Big Data has become a very important tool in helping businesses to outperform competition. In most industries, existing businesses and new entrants will use data-driven strategies to innovate, compete successfully and capture value.

Among the biggest challenges confronting the pharmaceutical industry is bringing a drug to market. This is among the riskiest and most expensive endeavours a pharmaceutical company can undertake. Here are some statistics –

  • Only 10 -12% drugs make it from discovery to commercial launch.
  • Cost per successful drug averages US $4 Billion
  • Average time from discovery to launch is 12 years

Against this backdrop, pharmaceutical business are constantly looking for ways and means of getting new drugs to market quickly, cost-effectively, at reduced risk and ensuring maximum patient benefit. Pharmaceutical businesses are constantly looking at ways to help take more intelligent decisions on which drugs to pursue.

To be able to take these decisions, pharmaceutical businesses needs to access / analyse huge volumes of clinical, market and legal data to help zero in which drug has the best chance of moving from the R & D labs to the consumer.

Areas where big data comes into play are:

Tracking data pertaining to previous clinical trials

There are a number of sources of information pertaining to historical clinical data that provide valuable insight into negative or other effects on trial patient populations.

Commercial viability of a product

A historical analysis of similar products factoring current and future regulatory environments, market conditions and market size helps assess profitability of a potential new drug.

Tracking regulations – globally and in regional markets

Pharmaceutical businesses are global. Any new drug that is launched is almost necessarily for the global market. In this context understanding regulatory frameworks in global markets and their impact on a new endeavour is critical and timely, successful global launch.

Monitoring current clinical trials

Tracking current clinical trials and any insights to be had from such trials helps re-adjust and re-align on-going trials factoring in any new insights that might be available.

Providing personalised treatment

Doctors always aim for treatment which will have the highest probability of success. In such cases, knowing the patient’s past history and marrying it with results of clinical trial results for a particular drug could result in the most optimal course of treatment.

Big Data offers the ability to analyse vast quantities of data which is central to making tailored treatment plans the norm. With the in-depth analyses that are made possible through Big Data analytics, pharma companies can match patients to a specific drug or course of treatment that is most likely to work for them. It also enables medical organisations to access information that will help to develop comparative effectiveness models for specific treatment scenarios which can, in turn, enable accelerated development of more cost-effective approaches to the delivery of a wide range of healthcare services.

Drug Safety

Predictable information on the side effects of a certain drug or drug combinations is the outcome of clinical trial reports. However field reports of a drug i.e. after the drug has been released and used by patient groups can be more easily accessible by accessing data from a range of sources- prescription data combined with online forums and patient records.

Applicability of drugs across other conditions

Innovation is not merely about discovering new drugs. It is also about being able to reuse active ingredients to treat other medical conditions. Accessing bio-chemical data on a drug along with patient outcomes and information on side-effects could help the healthcare industry take informed decisions on the wider applicability of a drug.

In each of the quoted instances above, there is a necessity to access tomes of data both internal and external. Internal in the form of clinical trial data, bio-chemical information on a product; external in the form of social media, drug safety data available in the public domain etc. There is also the challenge of sifting through and reacting in real-time to the large volume of chatter on the social web.

Given these humongous data volumes that need to be manipulated, processing times and response times could run into days – even weeks. Clichéd as it sounds, time is money. This is literally so in context of pharmaceutical businesses looking to get meaningful information to enable them take decisions. Decisions to discontinue an R & D initiative, to modify a product recipe, to modify a research initiative to incorporate new clinical findings etc…

SAP’s High Performance Analytic Appliance – SAP HANA – can potentially allow companies to process millions of interactions in real-time to deliver deep, actionable insights. HANA reduces processing times from months, weeks and days to days, hours and seconds. Some of SAP’s partners like Invenio have actually set up HANA labs where they are actually demonstrating the effectiveness of HANA in manipulating terabytes of data in real-time.

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