Ian Peirson head of product planning at IDBS discusses how pharma companies can effectively outsource to gain a competitive advantage.
Outsourcing
The rise in outsourced research and development has brought a shift in what activities pharmaceutical companies are focusing on. Many pharmaceutical companies are now relying on multiple partners and collaborators to develop, test and evaluate new chemical and biological entities to meet the increased pace of competition.
In the outsourcing space, pharmaceutical companies have moved up the value chain, from secondary support functions such as IT and Human Resources, to core functions such as R&D and manufacturing.
Recent surveys suggest that more than 80% of pharmaceutical companies outsource significantly. Often, the benefit of outsourcing is the ability to access specialist business knowledge, technical expertise and bespoke intellectual property. Pharma companies also benefit from on-demand scalability for projects to accelerate new product development, yielding a faster time-to-market. Additionally, the improvements in efficiency can yield substantial cost savings and significant benefits.
Cost‐reduction strategies
Over the past two decades, the outsourcing approach has reshaped the industry as a whole. Historically, most IP and innovation was internally developed, with only tactical partnerships used for specific discovery and development processes. Projects were created to overcome a resourcing bottleneck, or to improve profitability by externalising to countries where labour was cheaper. The result was an implicit cost‐reduction strategy driven at a departmental or divisional level.
Over time, this evolved to external innovation, where intellectual property was generated externally and then acquired (i.e. the business model dependent on strategic partnerships and milestones). Many companies have successfully used outsourcing to lower costs, but, unless a company’s efforts are unusually good, true competitive advantage is fleeting when competitors begin outsourcing and achieving similar results.
Sustainable competitive advantage
To build sustainable competitive advantage, it is expected that the current transactional collaboration will extend even further to a fully distributed innovation model, where the IP and innovation will be orchestrated by sponsors in a dynamic fashion, across multiple external parties in a R&D network. With this approach of global collaboration, companies are expected to drive new revenue, quicken time‐to‐market, and increase innovation.
As more work is externalised, cultural working practises must be considered. Organisations need to trust that the partner will fulfil the request on time and to a sufficient standard of quality, while also understanding the status of a request across the network. Human resource aspects should also be considered, both from changes required to skill sets as well as employee concerns about the impact to their careers. Indeed, for a sponsor pharmaceutical company, the work of R&D may shift away from research towards design and project management.
In the new networked R&D model, the design and execution are less prescriptive, with partners and collaborators given a higher degree of autonomy to contribute new ideas. It is equally important for companies to be willing to learn from their global partners, to take on board the partner’s experience and recommendations for methodology improvements.
Partners should use reviews to discuss and address difficulties and mistakes in the global collaboration effort. Given that partners will themselves outsource to others, this requires an additional layer of joint and iterative communication with all partners to align the corporate strategy, objectives and timeframes, which need to be cascaded through the network. Formal end‐of‐project reviews are an important tool for resolving lessons learned and to disseminate information.
The complexity and volume of scientific data
From a research perspective, this parallel effort across partners increases the pressure to correctly capture, manage, interpret and share data from all sources. This, in turn, brings its own challenges, as the complexity and volume of scientific data associated with a study can be vast. Having multiple partners collaborating and sharing information across a network also implies decentralised security and IP protection, often of sensitive data. The introduction of cloud computing makes data sharing easier alongside a reduced IT overhead, however, these networks and systems need to be secure. ISO27001 compliance, in a quality management system and good IT practices are all necessary to provide assurance.
To best leverage the value from the data, it’s essential that a full record of the data is captured and shared with the sponsor, not just the conclusions of summary reports. Leveraging data from the partners helps to develop the sponsor’s corporate knowledge base. Remember, all data (including failed experiments) has value.
The full exchange of all research data is a key requisite, as this is a key component of the growing interest of ‘big data’ analysis within the pharmaceutical domain. The R&D IT environment is complex and may be dominated by several scientific applications with customised extensions and niche applications. At a high level, many of the scientific vendors provide strong scientific capabilities but do not have the requisite IT process capabilities that contribute to a full enterprise value for an organisation, with data often existing in silos.
Transitioning to an intelligent platform
The drive for change is forcing organisations to consider a digital strategy, that supports the transition to an intelligent platform where both data, and insights from data, can be easily leveraged. This requires organisations to reassess existing technologies to identify their maximum potential and understand how they can be augmented to fit into the new digital landscape.
The application of semantic enrichment and data analytic tools to process large amounts of scientific experimental and clinical data in the pharmaceutical arena is an extension of its application in insurance, financial, automotive & energy sectors. Leveraging dictionaries and ontologies helps to provide additional context for standardised searching, and automatically aggregating data provides a facile means for scientists and designers to better leverage historical data, to provide insights and new proposals for research areas.
In contrast to the legacy siloed data architecture and rigid processes, the anticipated digital future state of life science R&D is for a more flexible and open approach to data, leveraging the cloud environment – going beyond the traditional firewall to facilitate shared collaboration to drive innovation. All data that is acquired should be automatically indexed and enriched to add logic and context to support dynamic learning, with the intent to decrease R&D costs, improve safety/efficacy and accelerate the time to market. So data can both be the biggest asset of an R&D organisation, and also the biggest liability if not managed appropriately.
A robust digital strategy
Outsourcing is rightly seen as a powerful cost reduction mechanism – but confining global sourcing to this limited role leaves greater opportunities untapped. As the industry has matured, global services companies are now able to provide a much richer palette of offerings, providing value‐add far beyond cost reduction. It is expected that the pharmaceutical industry will come under increasing pressures in the future, as the rising cost of healthcare impacts will influence the need to have greater efficiencies.
Working with an externalised partner network is a viable solution to reduce internal overheads, whilst maintaining the necessary quality and regulatory compliance. Whilst the selection of a partner in the past was based on cost, location, capabilities and efficiency, in future it will be increasingly important to select a partner that employs a robust digital strategy to support data science.