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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., Highlights • Emerging infectious diseases pose an important threat for humans. • Many of these diseases do not have medical countermeasures. • WHO and partners are tackling this issue to advance product R&D for a set of emerging priority diseases. • A methodology has been developed and externally reviewed in 2016 to identify a priority list of diseases in need of R&D. • This methodology was applied in 2017 and 2018 to review the WHO R&D Blueprint priority list of diseases.

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.

Summary Background The spread of antibiotic-resistant bacteria poses a substantial threat to morbidity and mortality worldwide. Due to its large public health and societal implications, multidrug-resistant tuberculosis has been long regarded by WHO as a global priority for investment in new drugs. In 2016, WHO was requested by member states to create a priority list of other antibiotic-resistant bacteria to support research and development of effective drugs. Methods We used a multicriteria decision analysis method to prioritise antibiotic-resistant bacteria; this method involved the identification of relevant criteria to assess priority against which each antibiotic-resistant bacterium was rated. The final priority ranking of the antibiotic-resistant bacteria was established after a preference-based survey was used to obtain expert weighting of criteria. Findings We selected 20 bacterial species with 25 patterns of acquired resistance and ten criteria to assess priority: mortality, health-care burden, community burden, prevalence of resistance, 10-year trend of resistance, transmissibility, preventability in the community setting, preventability in the health-care setting, treatability, and pipeline. We stratified the priority list into three tiers (critical, high, and medium priority), using the 33rd percentile of the bacterium's total scores as the cutoff. Critical-priority bacteria included carbapenem-resistant Acinetobacter baumannii and Pseudomonas aeruginosa , and carbapenem-resistant and third-generation cephalosporin-resistant Enterobacteriaceae. The highest ranked Gram-positive bacteria (high priority) were vancomycin-resistant Enterococcus faecium and meticillin-resistant Staphylococcus aureus . Of the bacteria typically responsible for community-acquired infections, clarithromycin-resistant Helicobacter pylori , and fluoroquinolone-resistant Campylobacter spp, Neisseria gonorrhoeae , and Salmonella typhi were included in the high-priority tier. Interpretation Future development strategies should focus on antibiotics that are active against multidrug-resistant tuberculosis and Gram-negative bacteria. The global strategy should include antibiotic-resistant bacteria responsible for community-acquired infections such as Salmonella spp, Campylobacter spp, N gonorrhoeae , and H pylori . Funding World Health Organization.

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. Copyright © 2012 John Wiley & Sons, Ltd.

In order to improve the throughput of surface plasmon resonance-based biosensors, an on-line iterative optimization algorithm has been presented aiming at reducing experimental time and material consumption without any loss of confidence on kinetic parameters [De Crescenzo (2008) J. Mol Recognit., 21, 256-66.]. This algorithm was based on a simple Langmuirian model to compute the confidence and predict optimal injections. However, this kinetic model is not suitable for all interactions, as it does not include mass transfer limitation that may occur for fast interaction kinetics. If a simple model was to be used when this phenomenon influenced the interactions, kinetic parameters would be biased. On the other hand, we show in this paper that data analysis with a kinetic model including a mass transfer limitation step would lead to longer experiments and poorer confidence if the interactions were simple. So, in this manuscript, we present an on-line model discrimination and optimization approach to increase the throughput of surface plasmon resonance biosensors.

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.