Personalized Medicine and Big Data

Modern drug ampoules

Modern drug ampoules (Photo credit: Wikipedia)

While many doomsayers describe the Pharmaceutical industry as one where the golden days are over and where more and more enterprises are bound to be falling off the “Patent Cliff” or get lost in space, many rather visionary companies have ignited their rocket boosters and are catapulting themselves onto firm ground again. This rocket booster consists of treatments for rare diseases and Personalized Medicine. The two chambered booster combines especially targeted diagnostics with targeted therapies and utilises cloud services alongside with Big Data analytics. This new method of transport brings with it new demands on research, clinical trials, production, logistics, information systems and the overall business model.

The pharmaceutical industry has already seen some rapid advancement during the last decade and a half. Who would have thought back in 2000 that in just 13 years researchers would have the capability to gain access to patient’s genome data and tailor treatments for specific genotypes and their specific diseases? Or that they could sift through large amounts of data to detect correlations in drug actions that could be beneficial in other disease contexts (drug re-purposing)?

However, with the advancements have also come challenges, with pharmaceutical companies facing unprecedented difficulties, including shrinking pipelines, early loss of patent protection due to long development and clinical trial times, increase in regulatory bylaws as well as novel demands on compliance and governance.

These challenges and more importantly how Pharma companies tackle them, are shaping the future of the industry, with some key trends already emerging:

  • To solve complex tasks, “coopetition” with other companies will become the norm
  • The journey towards Personalized Medicine
  • Race to Biologics
  • Pairing of drugs with their accompanying diagnostics
  • Stricter regulations on compliance
  • New demands on privacy and security
  • Big Data approaches yielding new insight into drug action correlations

These challenges put new demands on governance, processes, business models and the information systems, which will build the foundation for these new endeavours. New trends in technology adaptation will support and enable these objectives.

From Competition to “Coopetition” and Cooperation

Coopetition — Source:

Whereas in the past, companies kept all operations within their own perimeter; budget limitations, increasingly complex research and development tasks and a lack of available resources will lead to a trend in the future, where increasingly large areas of the drug discovery process will be outsourced to third parties. This may include outsourcing to

  • Small and flexible small research companies where risk taking is easier than in a large corporation;
  • Academic bodies where publicly financed research programs can focus on long-term search for new action mechanisms, drug classes, genotype/phenotype correlations and new substances isolated from natural sources like tropical forests or marine organisms – including the genomic sequencing of identified organisms.

But it may also mean that competitors team up to share the burden of research, something which is already being done in the Malaria research sector. With more companies working together the lines between competitor and partner will be blurred, but the need to maintain competitive advantage will be just as strong.

Simultaneously, an industry wide trend will kick in where rigid hierarchical organizations are giving yield to more loosely coupled competence centers, which will provide their services “in house” as well as on the market. We have seen such trends primarily in areas outside the companies’ core business. Many companies have out-placed their IT operations forming a more or less independent company serving also others in the market.

The challenges for the information systems that will have to support such business models can be answered with approaches including cloud computing, sophisticated identity and access management, and collaboration spaces where birds of a feather from different companies and organisations can securely collaborate in a protected environment. Social media tools (forums, blogs, chats, etc.) will increasingly be used to enable true collaboration in a secured environment in such settings.

Rise of personalized pharmaceuticals

Drugs will become less generic (and low-cost) and more tailored for specific demographics in the future (and more high value/high cost). There are huge benefits to this, including the fact that these drugs are harder to copy so they retain their value better for the producer and they are also more effective as they can meet more specialist needs. While the current trend is looking at groups of people, maybe based on age or ethnicity, this will evolve in the 21st Century into truly personalised drugs on an individual basis reflecting the patient’s genetic predisposition.

The author's depiction of Personalization in the pharmaceutical and health care indiustry. (c) H.Broda 2013

The author’s depiction of Personalization in the pharmaceutical and health care indiustry. (c) H.Broda 2013. Click to enlarge

We are currently observing a significant shift towards both biologics and diagnostic-guided treatment. This increasing personalisation of therapy will significantly benefit patients, by both raising efficacy and reducing side-effects. In order to maximise success in this new era, companies will have to revamp their processes across the entire value chain.

The race to Biologics

Biologics–symbolic picture. Source:

Many common classes of drugs have been shown to be effective in less than half of patients. A new generation of therapies is moving in. Many are biologics, created by biological processes rather than being chemically synthesized, making them much more difficult to copy. Biologics are also likely to dominate over the next years, since legislation allowing bio-similars (unbranded copies of biologics) has only recently been introduced, and bio-similars will have to be marketed as new drugs, unlike traditional generics.

Since biologics require cold-chain distribution, have relatively short shelf-lives, are expensive, and may require a molecular test that also requires cold chain, a highly nimble, secure and traceable supply chain is required. This becomes even more important as companies set their sights on emerging markets, where warm climates and large distances may present additional challenges. Cold-chain needs a multi-functional team including regulatory, quality and logistics, working together with external partners. For example, specialised logistics companies are now providing temperature sensors that detect and transmit data on temperature excursions.

‘Just-In-Time’ and potentially ‘Between-Centre’ product delivery can ensure optimal use of expensive inventory. Compliance with tracking and authentication regulations may be required (for example the E-Pedigree in California, and bar code Track and Trace in Turkey). Deployment of more advanced tracking and anti-counterfeiting technologies may also be considered for these high-value products. Bringing this all together into a robust distribution information system is essential. For emerging markets, comprehensive but simple distribution tracking systems, which provide secondary sales visibility, have successfully been deployed with Consumer Packaged Goods companies, and may be adaptable for pharmaceutical and diagnostic products (1).

Drugs and their diagnostics walk hand-in-hand

Walking hand in hand. Credits: Elora Daphne on Flickr

As therapies are becoming more and more targeted to smaller groups the categorization of patients into such groups is of crucial importance. This will lead to a significant increase in diagnostic capabilities and lead to a change in business models: Today most diagnostic companies are completely independent of the drug producing companies.

Tomorrow we will see drugs packaged with their prerequisite diagnostic sets and many drugs will require a diagnostic test confirming the classification of the patient into the predetermined recipient group for this drug. Pharma might benefit from shifting to a model where drugs and accompanying diagnostics will be co-produced, co-marketed, co-provisioned by the distributors, and sold together at pharmacies and health care providers.

The logistics and information systems supporting such business models will have to be adapted. Frameworks that will support such co-developments of drugs and their diagnostics will need to be based on new collaboration models and sharing of processes, procedures and information.

Regulations on compliance set to increase

Despite the fact that companies are already struggling with existing regulations, the increase in patient data and increased sensitivity of patient records mean that regulations are set to become stricter. The impact on the industry will e two-fold:

  • Requirements on document management, audit traceability, and policies will increase leading to a
  • Demand to hire more people to manage or adopt specialist systems to deal with the volume and complexity of regulation

The increase in sheer data volume in context with Big Data analysis and the handling of privacy sensitive patient data at massive scale will require new procedures, management models and tools that can reduce the load by automating the standard processes and providing relevant information at the right place at the right time.

Increasing demands on privacy and security

Privacy and Security. Source:

As more partnerships and cross-company collaborations form, the security requirements for the industry will be tightened while perimeter security, which we could count on in the past, all but vanishes. This will force companies to reconsider their security priorities. In the future, protecting assets cannot be based on protecting the entire data centre any longer. We will have to protect and control access rights to applications, data, and networks on the fields and islands of collaboration.

Patient data from masked electronic patient records will serve as one of the major sources for Big Data analytics. But trust of the public into such systems has to be earned and managed. While organizationally and technically such obfuscation of personal identifiable information (PII) can be achieved with relative ease due to available methods and solutions, winning public trust in such systems will require an open information policy and public discussions at every level in society. Pharma has to gain a lot from access to electronic patient data and will have to start to engage in building trusted reliable data stores for electronic patient records and become an active member in the public discourse on such endeavors.

To achieve this we will need to implement advanced identity and access management systems that will deal with contributors’ identities, roles in the system, authentication, authorization, and policies on governance while enabling audit tracks to adequately satisfy compliance regulations.

Future innovations will be based on Big Data

Big Data. Symbolic picture at

Though already a trend we can currently observe, the pharmaceutical sector will see rewards in decisions being made more accurately and more effectively thanks to improvements in scalable cloud based information processing, applications and data storage. This new compute model is providing novel analytical capabilities, which utilize and harvest information presented as structured as well as unstructured data. Industry refers to such previously generally unavailable data as “Dark Data.”

Such Dark Data may include sources from (a) research lab notebooks—especially from experiments that did not yield the expected or a (sometimes surprising) different result; from (b) sifting through literature sources detecting side effects in published literature using semi-automated text analysis methods; from (c) sources such as de-personalised (anonymized, pseudonymized or masked) globally accessible patient data depositories (electronic patient’s records).

Analysis with appropriate methods and tools will allow the pharmaceutical industry to unveil correlations between treatments and results with potentially huge sample sizes yielding significant correlations hitherto unattainable: Most “real life” data (on medical conditions, co-morbidities, treatment, response, side-effects) today reside in the healthcare system, in patient notes (paper and electronic). These data, combined with those from clinical trials, could generate valuable novel insights.

In a few years it will become a standard practice to have one’s genome sequenced as cost will come down to the equivalent of a dentist’s check-up visit. The genome sequence might then be stored in the individual’s electronic health record or put (privacy protected) onto a research accessible data base. With 30.000 genes per individual and millions of individuals’ genomes in data stores genomics researchers will have access to billions of genetic markers to better understand correlations between disease and classes and types of patients. Big Data is becoming huge.

To achieve such flexibility in acceptance of types of information for analysis information systems must enable access to such information for those empowered from anywhere and any future end user devices. Information has to be presented such that easy access while preserving the original semantics of the data can be achieved. The focus will lie on discovery, integration, analysis and exploitation of this vast information.

The success of Big Data technologies will depend on natural language processing capabilities, pattern recognition algorithms for image and video sources and on new statistical analysis methodologies, large storage capacities in the cloud and advanced search technologies surpassing the capabilities of HAL in “A Space Odyssey,” which will allow us to find the literal information needle in the haystack of data.

These are interesting times


The pharmaceutical industry will see significant changes in their business models where cooperation balances competition, where networking with others will become the standard model of operation and where the patient is not just consumer but partner.

The health care system will move from a repair shop business to a managed health system where diagnosis and prevention will become as important as therapy.

Specialization to small but high-value niches will be the path leading away from the “Patent Cliff” and co-marketing of drugs and diagnostics will become more and more standard. Big Data analytics will help to find correlations overlooked so far due to relatively small sample sizes and research data will be revisited yielding hitherto hidden drug action mechanisms.

Focusing on the core business and partnering with other companies and industry expert service providers will characterise the winners in this new century – those who did not get lost in space.


  1. Currie E, Broda H., (2013) The Key to Personalized Medicine Success. Pharmaceutical Executive Global Digest, April 2013
  2. This blog is based on an earlier version of this article:
    Broda, H. (2013) Big Pharma 2.0: What Does the Future Hold? European Pharmaceutical Contractor, June 2013
  3. Broda, H. (2013) Big Data Trends–A Basis for Personalized Medicine (Presentation on

About Hellmuth Broda

Independent Information and Communications Technology Strategist with an interest in the construction sites between business, society and technology.
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1 Response to Personalized Medicine and Big Data

  1. Pingback: Personalized Medicine and Big Data | Sykes' Blog

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