The knowledge and the tools that enable the administration of personalised medicine and clinical trials are coming to maturity at just the right time. Most accept that the current trial methodologies have become expensive and wasteful, and turn off potential recruits, with only a small minority of trials leading to any real advancements in understanding.

Failure starts at the very beginning with recruitment of the wrong drug targets, people whose genetics and disease characteristics mean that they are unlikely from the outset to benefit from the treatment they are trialling.

A more holistic approach – which makes use of advances in genomics, and takes into account the many environmental factors that can affect a patient’s susceptibility to medicine – is broadly acknowledged to be the future. The carefully tailored trials of drugs such as Herceptin – a breast cancer drug that targets the HER2 protein, which only some people possess – point the way to a brighter future driven by patients’ needs.

“The model of modest increases in benefit may have defined successful clinical trials of the 20th century, but it is no longer pertinent for the PPM-enabled approaches of the molecular era,” wrote Denis Horgan, executive director of the European Alliance for Personalised Medicine, in medical journal Public Health Genomics. “Clinical trials and clinical cancer research must increasingly be more cooperative (bridging the academia-industry intersect) and deliver benefit – our patients deserve and demand transformative rather than incremental change.”

A lot has to be done before this future becomes a pervasive reality. Personalisation is great for patients, but it presents challenges that, if not approached in the right way, could exacerbate existing problems with the clinical trial supply chain, and further impede the path of drugs to market. For the benefits of personalised medicine to be felt at the bedside, every link in a long, complicated supply chain will have to run efficiently.

The trouble of complexity

Clinical trials are already much bigger and more complicated than they were a decade ago, with large numbers of internal and external parties being involved. According to research by the Tufts Center of Drug Development, the average number of procedures performed in a trial increased by 48% between 2000 and 2010, and the number of criteria that potential participants needed to meet nearly doubled over the same period.

At the same time, drug companies have moved deeper into emerging markets, adding more far-flung links to the chain that bring with them their own logistic, regulatory and technological challenges. The Clinical Trials website (, which keeps track of clinical trials around the world, reported that during 2012, about 40% of clinical trials took place in Asia, Latin America and Africa.

New research partnerships must define how clinical data will be collected, curated and shared for research purposes, and also how this information can be shared with outside groups and fed into clinical practice.

Personalised medicine will further complicate the supply chain, leading to an even greater increase in the number of partnerships needed to bring a drug to the patient. In addition, many of these partners are bringing highly specialised knowledge from outside the biopharma realm, requiring a new knowledge base that drug developers will have to learn.

“Development of personal medicines is prompting changes in the way drug sponsors do R&D, leading to new partnerships and alliances,” the Tufts team writes in its ‘Outlook 2014’ report. “Co-development of drugs and diagnostics requires careful life cycle planning and management, as teaming with external partners raises questions regarding project stewardship and intellectual property rights. Factors developers need to manage and include assay and platform improvements, regional differences in technologies, testing requirements, barriers to diagnostic testing and how to demonstrate clinical utility.”

A large part of the management complexity involves incorporating and overseeing the new technology platforms. At the start of the chain are the bio-bank specimens and biomarkers, which have to be thoroughly catalogued. For big data analytics to work as well as possible, patient data needs to be extracted from multiple sources. These sources include national and regional databases (such as the NCBI), medical images, data from clinical research and increasingly digital devices that are used to monitor day-to-day patient lifestyle factors such as activity levels and sleep patterns.

Overseeing the collection and safe storage of this data is a massive job. The EU has helped drive collaboration in this area by funding organisations such as the Research Data Alliance and public-private partnerships such as the Innovative Medicines Initiative. Collaboration between the European Commission and the European Federation of Pharmaceutical Industries and Associations are also helping push things forward. However, there is still a long way to go.

“The ICT challenge is multiple,” Horgan writes in Public Health Genomics. “Aspects include how to store and provide access to huge amounts of human health-related sensitive data under a secure and common standard; how to optimise and support the computing capacity needed to perform the actual computation of huge datasets, taking into account the fact that storage may be either centralised or decentralised; how to interrogate such data; and how to link such data to experimental data. Furthermore, it needs to be determined who finances such activities and who will reap the benefits.”

One of the biggest obstacles to collating and classifying all this data is making research institutions and drug companies understand the value of sharing it, and creating a partnership framework in which they can have 100% faith. As well as dealing with partners in the pharmaceutical and biotech industries, personalised medicine will see big data specialists and software developers become key players in the pharmaceutical supply chain. This could become a bit of a culture clash and will require a lot of groundwork to ensure smooth integration.

“These new research partnerships must define how clinical data will be collected, curated and shared for research purposes, and also how this information can be shared with outside groups and fed into clinical practice,” writes Horgan. “An environment in which data is shared securely needs to be encouraged, standardised in terms of language, item collection and storage, and its value maximised through low-threshold access while ensuring appropriate levels of security, privacy and confidentiality.”

Particularly in the US, questions about intellectual property rights are another major stumbling block to pharma companies looking to embrace personalisation. The US Supreme Court made a ruling in June 2013 that bans the patenting of naturally occurring genes and processes, not just in humans but plants, animals and microbes too. That case concerned Utah-based Myriad Genetics, which had patents on tests used to detect mutations in the genes BRCA1 and BRCA2, common precursors to breast and ovarian cancer. Questions about whether drug companies can benefit economically from such products could prove a deterrent to investment and suggests that governments will have to carry a bulk of the risk, at least until more clarification has been provided on ownership.

Patent protection

“The absence of patent protection for these and related discoveries creates an economic vulnerability in the innovation supply chain, reflecting the absence of a period of market exclusivity for these discoveries characteristically granted through patent protection,” writes Scott A Waldman, chair of pharmacology and experimental therapeutics at Thomas Jefferson University Hospital in Philadelphia in a faculty paper entitled ‘Managing the innovation supply chain to maximize personalized medicine’. “This economic vulnerability threatens the flow of innovation by constraining investment in the development of intellectual property that cannot be patent protected. This absence of market exclusivity could strangle the supply of novel diagnostic products that are essential for downstream processes translating scientific innovation into the clinical tools supporting the evolution of personalised medicine.”

Optimising the supply chain for personalised medicine will require a change in the mindset of how drugs are developed. The partnership model has always been important in drug development, but the number of supply chain relationships that pharmaceutical companies will need to cultivate, and the nature of those relationships, will grow considerably. It also requires holistic thinking: the ability to consider all the actors in the supply chain, all the inputs and outputs, how they interact and how best to ensure efficiencies at each level.

The supply chain is the set of organisations and processes linked by one or more upstream and downstream flows of products or information from a source to an end user.

Waldman writes that the industry could benefit from looking at manufacturing sciences. At the moment, it considers the development of a drug to be a linear, left-to-right process that runs in “discreet sequential steps… from fundamental discovery of molecular principles and therapeutic targets to development involving clinical trials that prove efficacy and safety, through regulatory approval that certifies the utility of the management approach, to application in patients and populations”.

The problem with this is that it is simplistic and, Waldman writes “obscures the integral contributions by individual practitioners and domain-specific experts, and the impact of the external environment, which shape the process at every phase”. Without focusing on individual components – many of which sit outside the main supply chain – and their relationship with the whole, the precise idea of where vulnerabilities lie and improvements can be made is not grasped.

“Supply chain strategies require a total-systems view of the links in the chain that work together efficiently to create customer satisfaction at the endpoint of product delivery to the consumer,” Waldman writes. “Efficiencies must be at maximum at each of the component steps to produce added value across the entire continuum to optimise the benefit to the end user. This concept focuses on total-systems efficiency to produce optimum value by generating the best product while minimising waste and inefficiency.

“In this model, the supply chain is the set of organisations and processes linked by one or more upstream and downstream flows of products or information from a source to an end user.”

The development of personalised medicine will require a less linear R&D model, which medical practitioners, researchers, pharma companies, regulators and patients will have to adjust to. The financial and operational risk is great, but the rewards – better outcomes, lower development costs and improved profits – are too good to ignore.