We identify three dimensions with which to classify heuristically the routes to widespread adoption of cellular therapies. The first dimension is based on the relative involvement of clinicians and companies in a particular cellular therapy. The second dimension is based on cell type and consequent scale of manufacture. The third dimension classifies the therapeutic intervention as a procedure or product and has perhaps received less attention. We suggest that for those cellular therapies that require therapeutic procedures, close collaboration between companies and clinicians will reduce the time to widespread adoption. For selected cellular therapies we make predictions of the likely time to widespread adoption. STEM CELLS TRANSLATIONAL MEDICINE 2012;1:438 -447

The rapid progress in computer technology in recent years has enabled the development of increasingly complex simulators, which can handle large amounts of data. It is often assumed that this automatically leads to more accurate static and dynamic reservoir models. In reality, however, there is still much evidence that the predicted performance of a reservoir often differs vastly from the actual production behaviour. These deviations are an indication of the failure to understand the processes involved and to recognize the uncertainty inherent in the definition of important reservoir characteristics. In this paper, a classification scheme is proposed, in which uncertainty is expressed as fuzziness, incompleteness and randomness. Each of these elements is described in detail and illustrated within the context of reservoir appraisal, although the approach can be applied to the wider aspects of petroleum geoscience. It is believed that adopting this classification scheme will enable the geoscientist to build a more extensive picture of uncertainty in reservoir appraisal. It will also be invaluable as a tool with which to inform management of the existing uncertainty, using a consistent language, thus providing guidance in the decision-making process.

Regenerative medicine therapies are showing great clinical promise in providing cures to life-threatening diseases such as cancer and diabetes. However, little emphasis has been placed on the industrialisation of these therapies for commercial purposes. The inability to scale production up and out to meet pending demand is of increasing concern to both regulators and funding agencies. Using an open innovation theoretic lens, this paper explores the importance of involving commercial partners within the lengthy research and development phases. We adopt a case study method to show that laboratory processes that incorporate innovative manufacturing techniques produce significant reduction in cost of quality (errors) and improved scalability, while satisfying regulatory requirements. We demonstrate that this approach enables faster industrialisation, and improves the funding efficiency of clinical trial outcomes in terms of quality, cost and commercial success of therapies.

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