The WHO R&D Blueprint: 2018 review of emerging infectious diseases requiring urgent research and development efforts.

The Research and Development (R&D) Blueprint is a World Health Organization initiative to reduce the time between the declaration of a public health emergency and the availability of effective diagnostic tests, vaccines, and treatments that can save lives and avert a public health crisis. The scope of the Blueprint extends to severe emerging diseases for which there are insufficient or no presently existing medical countermeasures or pipelines to produce them. In February 2018, WHO held an informal expert consultation to review and update the list of priority diseases, employing a prioritization methodology which uses the Delphi technique, questionnaires, multi-criteria decision analysis, and expert review to identify relevant diseases. The committee determined that, given their potential to cause a public health emergency and the absence of efficacious drugs and/or vaccines, there is an urgent need for accelerated R&D for (in no order of priority) Crimean-Congo haemorrhagic fever, Ebola virus and Marburg virus disease, Lassa fever, Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS), Nipah and henipaviral diseases, Rift Valley fever and Zika virus disease. The experts also included "Disease X," representing the awareness that a previously unknown pathogen could cause a major public health emergency. This report describes the methods and results of the 2018 prioritization review.

World Health Organization Methodology to Prioritize Emerging Infectious Diseases in Need of Research and Development.

The World Health Organization R&D Blueprint aims to accelerate the availability of medical technologies during epidemics by focusing on a list of prioritized emerging diseases for which medical countermeasures are insufficient or nonexistent. The prioritization process has 3 components: a Delphi process to narrow down a list of potential priority diseases, a multicriteria decision analysis to rank the short list of diseases, and a final Delphi round to arrive at a final list of 10 diseases. A group of international experts applied this process in January 2017, resulting in a list of 10 priority diseases. The robustness of the list was tested by performing a sensitivity analysis. The new process corrected major shortcomings in the pre-R&D Blueprint approach to disease prioritization and increased confidence in the results.

Optimizing Multiple Analyte Injections in Surface Plasmon Resonance Biosensors with Analytes having Different Refractive Index Increments.

Surface plasmon resonance-based biosensors have been successfully applied to the study of the interactions between macromolecules and small molecular weight compounds. In an effort to increase the throughput of these SPR-based experiments, we have already proposed to inject multiple compounds simultaneously over the same surface. When specifically applied to small molecular weight compounds, such a strategy would however require prior knowledge of the refractive index increment of each compound in order to correctly interpret the recorded signal. An additional experiment is typically required to obtain this information. In this manuscript, we show that through the introduction of an additional global parameter corresponding to the ratio of the saturating signals associated with each molecule, the kinetic parameters could be identified with similar confidence intervals without any other experimentation.

Increasing throughput of surface plasmon resonance-based biosensors by multiple analyte injections.

Surface plasmon resonance-based biosensors are now acknowledged as robust and reliable instruments to determine the kinetic parameters related to the interactions between biomolecules. These kinetic parameters are used in screening campaigns: there is a considerable interest in reducing the experimental time, thus improving the throughput of the surface plasmon resonance assays. Kinetic parameters are typically obtained by analyzing data from several injections of a given analyte at different concentrations over a surface where its binding partner has been immobilized. It has been already proven that an iterative optimization approach aiming at determining optimal analyte injections to be performed online can significantly reduce the experimentation time devoted to kinetic parameter determination, without any detrimental effect on their standard errors. In this study, we explore the potential of this iterative optimization approach to further reduce experiment duration by combining it with the simultaneous injection of two analytes.

Estimation of analyte concentration by surface plasmon resonance-based biosensing using parameter identification techniques.

Surface plasmon resonance-based biosensors have been applied to the determination of macromolecule concentration. Up to now, the proposed experimental approaches have relied either on the generation of a calibration curve that exploits only a few data points from each sensorgram or on multiple injections of the unknown sample at various flow rates. In this article, we show that prior knowledge of the kinetic parameters related to the interaction of the species with a given partner could advantageously reduce the number of injections required by both aforementioned methods, thereby reducing experimental time while maintaining a good level of confidence on the determined concentrations.