Despite the hype generated by the Genomics revolution, hardly any outputs produced in ‘omics’ research projects actually lead to the development of commercial products. Notable examples of developments that have made it into medical practice are tests such as Oncotype Dx (a 21-gene assay that provides an individualized prediction of chemotherapy benefit) and MammaPrint (a microarray-based gene signature assay to assess risk for distant metastasis for breast cancer patients).
The reasons for the apparent lack of ‘Translation’ are manifold but attention has recently focused on issues pertaining the quality and validity of research outputs in the discovery phase. It appears that a critical assessment and re-design of current ‘omics’ research and development (R&D) practices is warranted, failing which the promises made by the field won’t come to fruition.
As an organisation that runs a large number of ‘omics’ projects every year and has an interest in stimulating bio-economic development in South Africa, generating outputs that can actually progress along the innovation chain and understanding the dynamic relationship of project-specific risk/return across the innovation chain, are essential core capabilities. In order to enhance the CPGR’s ability to facilitate ‘omics’ translation, we are combining available ‘omics’ translation guidelines and principles of portfolio, lean and quality management into a fit-for-purpose ‘omics’ project management process.
1. Lost in translation
Considerable sums of money are invested into biotech research every year. In Genomics alone, the annual spend on research by public funders is at least USD 3 bn worldwide (ref. 1). This is in stark contrast to the number of biomedical applications (e.g. diagnostic tests) that are generated from the corresponding research outputs. Current (conservative) estimates are that less than 1 % of genomic outputs move into further stages of the innovation chain (ref. 2), prompting questions regarding the quality of these outputs and the effectiveness of the innovation process as a whole. Poor performance of the research & development (R&D) process is a concern for industry as well as public funders. The current situation must be of particular concern for stakeholders in those National Systems of Innovation (NSI) that are facing systemic constraints in research funding, often coupled to an increasing pressure from funding agencies to demonstrate rapid returns on investments.
The pharmaceutical industry is often used as an indicator for the effectiveness of research spending and R&D productivity. It must be quite worrying then that the number of new drugs approved per billion USD spent on R&D has halved roughly every 9 years since 1950, falling around 80-fold in inflation adjusted terms. Not only has the productivity decreased; the costs of developing new therapies have gone up considerably. Current estimates put the development costs of one new FDA-approved molecular entity at USD 1.8 bn. This figure is based on risk-adjusted costs per development phase but excludes the costs of early R&D (e.g. the one done at Universities) (ref. 3).
The advent of new ‘omics’ technologies, stimulated by the success of the Human Genome project, has left biotech stakeholders excited all over the world. However, ‘industrialised’ efforts in tackling biological complexity, for example based on Next-Generation Sequencing (NGS), have not (yet) yielded the desired returns; what’s more, the vast array of possibilities offered by these new technologies has possibly distracted researchers, policy makers and funders from the real issues underlying the current biotech R&D productivity crisis.
‘Omics’ is generally viewed as a generator of a myriad of biomedical innovations, including biomarker assays for the optimisation of clinical trials, tests that guide the choice of treatment (Personalised Medicine) and assays for the early diagnosis of diseases (e.g. cancer). Unfortunately, for reasons laid out elsewhere (ref. 4 & 5), ‘omics’ has not lived up to the expectations. In particular, ‘Translation’ (i.e. the conversion of research outputs into practical applications of social or economic value) is just not happening, prompting questions regarding the efficiency of the ‘omics’ R&D process and the outputs generated in the wake of it.
A concern that we have at the CPGR is that in the absence of addressing the underlying problems, a great many projects employing ‘omics’ technologies (such as DNA microarrays, next-generation sequencing, or mass spectrometry) may fail to generate outputs that can be converted into innovative biomedical applications. Our concerns are shared by experts involved in ‘omics’ research, biomarker development and Translational medicine across the world (ref. 5).
The prospect of failing to realize the great potential in using ‘omics’ technologies to unravel biological complexity is of particular concern at times when funders are forced to cut research spending because of economic and fiscal constraints. We have therefore asked ourselves how ‘omics’ projects can be managed so that high-quality research outputs are achieved and, consequently, more effective progression of these outputs across the innovation chain is attained.
We came up with a set of interventions, considering the role the CPGR plays in advancing the development of the biotech sector in South Africa. Ultimately, our aim is to optimise the CPGR project management process so that it enhances the return-on-investment into ‘omics’ projects and to enable effective translation of research outputs into socio-economic value. The solutions presented below have been devised with a focus on ‘omics’ but the approach is generic and will have utility across the biotech spectrum.
2. A systems-based approach to ‘omics’ R&D management
At the CPGR, we are tackling ‘omics’ translation from 3 interrelated perspectives:
Perspective 1: Management of project specific risks. We are enhancing our internal project preparation process so that it addresses ‘omics’ specific risks. Generally speaking, risks can be put into 2 categories: specific and systemic. In ‘omics’ projects, specific risks can arise because of poor study design, poor quality of samples or sample management in general, lack of workflow validation, corruption of data and poor data management, and poor documentation of inputs, processes and outputs, to name just a few. Systemic risks are those that are common to all projects of a similar kind. For example, projects aimed at the discovery of biomarkers for the early diagnosis of cancer have certain risks in common (due to the complexity of cancer biology) while projects aimed at the prediction of relapse after treatment in TB (Tuberculosis) have other risk factors in common. In order to deal with specific risks, we propose the implementation of a rigorous and systematic assessment of each project using criteria related to those features that are critical for project success with relevance its position on the innovation chain. A focus on quality of outputs and outcomes is built into the assessment process. Our aim is a more effective prioritization of projects, e.g. by avoiding delays and by systematically eradicating typical confounders that can hamper project success.
Perspective 2: Management of systemic risks. We are developing a project portfolio management approach to deal with systemic risk. Using the process highlighted under (1) in conjunction with a thematic diversification strategy (e.g. cancer, TB, diabetes; drug discovery, biomarkers) , we will manage projects in portfolios. Whereas the approach highlighted in (1) is meant to optimise the stage-specific project success (e.g. discovery), the portfolio approach is geared towards enhancing the effective progression of projects along the innovation chain. Dedicated attention will be given to 3 key risks in biotech innovation: (i) technical (including science), (ii) financial, and (iii) execution.
Perspective 3: Trans-innovation chain stakeholder management. We will prepare projects so that they can be moved effectively across the innovation chain. This will require a long-term perspective on the project evolution and include an assessment of post-research project costs (e.g. for validation, clinical trials etc.). Eventually, every project will be subjected to an assessment of net present scientific, social and economic value. This will also require effective stakeholder cooperation across the innovation chain and an efficient monitoring system to determine performance of the system and impact on innovation/R&D productivity. A relationship with University-based TTOs (Technology Transfer Offices) will be critical in order to optimise this process with respect to the discovery / development transition. A relationship with industry, and entrepreneurs, will be important with respect to the development/commercialisation transition. Advocacy vis-a-vis funders and investors will be necessary in order to feed trans-innovation chain activities. Outcomes and performance will have to be assessed on a regular basis and made available for public scrutiny, in the interest of continuous improvement.
3. Seven key strategic interventions to enhance ‘omics’ innovation
A) Identify, secure and manage all the key resources and core capabilities required to run (large) omics projects. Strategically manage resources and capabilities in order to control risk and to enhance the quality of outputs. Where internal capabilities are not sufficient, collaboration with external partners will be sought (e.g. IP and technology transfer related matters).
B) Organise and prioritise resources and capabilities according to their relevance along the innovation chain (e.g. biomarker development process) and facilitate a value-pull development process. Consider all resources, capabilities, costs and risks early in the R&D planning process. Drive all projects with a ‘value-pull’ perspective, i.e. keeping end-user requirements in mind from the beginning.
For example, a research (discovery) project will be designed having in mind the inputs required in the validation stage. Similarly, a validation project will be designed from a development perspective.
For each project, define go/no-go criteria (pass criteria) for every development stage that are based on a sound risk/return assessment of each project.
Project management will be adaptive in order to absorb perturbations in the process.
C) Feed every project through a stratification process, using pre-defined criteria (disqualifiers, qualifiers, enhancers) as gate-keepers. This is to ensure that issues that could risk the stop or discontinuation of a project are identified as early as possible in the project management cycle. Prioritisation of projects will follow a defined, transparent process.
D) Govern projects on the basis of a well-controlled stage/gate process, ensuring quality of inputs, processes and outputs for each individual stage. This is to maximise the quality of outputs, and outcomes, in the interest of driving innovation. In each stage, critical issues (risks) will be mapped to allow better control of projects and to increase the overall success rate of each project.
E) Assess the value of each project in terms of its prospective scientific, social and commercial return as early as possible in the process. Identify possible exit scenarios (e.g. licensing of a patent or full-blown development into a commercial product) for each project on the basis of the risk assessment. Develop adequate value creation strategies on a project-by-project basis (e.g. partnering for licensing purposes). Identify and organise the key role players responsible for driving the corresponding processes.
F) Manage projects in portfolios that reflect risk/return ratios and alignment with specific (scientific) focus areas. Make sure that these focus areas are in line with the bio-economy strategy. Develop proper metrics to track failure/success and progression along the innovation chain effectively.
G) Engage in cross-innovation chain relationships in order to stimulate innovation. This includes interactions with TTOs, PIs (principal investigators), companies, and funders. Enable an open flow of information between ‘innovation spheres’ (clusters), to some extent substituting for a linear (one-dimensional) stage-gate process.
4. References
1. Pohlhaus JR & Cook-Deegan RM. (2008) Genomics research: world survey of public funding. BMC Genomics, 9: 472 – 490.
2. Ioannidis JP & Khoury MJ. (2011) Improving validation practices in “omics” research. Science, 334: 1230 – 1232.
3. Scannell JW, Blanckley A, Boldon H & Warrington B. (2012) Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov., 11: 191 – 200.
4. Fixes that fail in ‘omics’ research and translation.
5. National Research Council. Evolution of Translational Omics: Lessons Learned and the Path Forward. Washington, DC: The National Academies Press, 2012.
Recent Comments