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A systematic review and critical assessment of incentive strategies for discovery and development of novel antibiotics.

ABSTRACT: Despite the growing threat of antimicrobial resistance, pharmaceutical and biotechnology firms are reluctant to develop novel antibiotics because of a host of market failures. This problem is complicated by public health goals that demand antibiotic conservation and equitable patient access. Thus, an innovative incentive strategy is needed to encourage sustainable investment in antibiotics. This systematic review consolidates, classifies and critically assesses a total of 47 proposed incentives. Given the large number of possible strategies, a decision framework is presented to assist with the selection of incentives. This framework focuses on addressing market failures that result in limited investment, public health priorities regarding antibiotic stewardship and patient access, and implementation constraints and operational realities. The flexible nature of this framework allows policy makers to tailor an antibiotic incentive package that suits a country's health system structure and needs.

SUBMITTER: Renwick MJ

PROVIDER: S-EPMC4775540 | biostudies-other | 2016 Feb

REPOSITORIES: biostudies-other

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Project description:BackgroundThe effectiveness of breast cancer screening is still under debate. Our objective was to systematically review studies assessing personalized breast cancer screening strategies based on women's individual risk and to conduct a risk of bias assessment.MethodsWe followed the standard methods of The Cochrane Collaboration and PRISMA declaration and searched the MEDLINE, EMBASE and Clinical Trials databases for studies published in English. The quality of the studies was assessed using the ISPOR-AMCP-NPC Questionnaire and The Cochrane Risk of Bias Tool. Two independent reviewers screened full texts and evaluated the risk of bias.ResultsOut of the 1533 initially retrieved citations, we included 13 studies. Three studies were randomized controlled trials, while nine were mathematical modeling studies, and one was an observational pilot study. The trials are in the recruitment phase and have not yet reported their results. All three trials used breast density and age to define risk groups, and two of them included family history, previous biopsies, and genetic information. Among the mathematical modeling studies, the main risk factors used to define risk groups were breast density, age, family history, and previous biopsies. Six studies used genetic information to define risk groups. The most common outcome measures were the gain in quality-adjusted life years (QALY), absolute costs, and incremental cost-effectiveness ratio (ICER), while the main outcome in the observational study was the detection rate. In all models, personalized screening strategies were shown to be effective. The randomized trials were of good quality. The modeling studies showed moderate risk of bias but there was wide variability across studies. The observational study showed a low risk of bias but its utility was moderate due to its pilot design and its relatively small scale.ConclusionsThere is some evidence of the effectiveness of screening personalization in terms of QUALYs and ICER from the modeling studies and the observational study. However, evidence is lacking on feasibility and acceptance by the target population.Review registrationPROSPERO: CRD42018110483.

Project description:There is a great interest in reliable ways to obtain absolute protein abundances at a proteome-wide scale. To this end, label-free LC-MS/MS quantification methods have been proposed where all identified proteins are assigned an estimated abundance. Several variants of this quantification approach have been presented, based on either the number of spectral counts per protein or MS1 peak intensities. Equipped with several datasets representing real biological environments, containing a high number of accurately quantified reference proteins, we evaluate five popular low cost and easily implemented quantification methods (APEX, emPAI, iBAQ, Top3 and MeanInt). Our results demonstrate considerably improved abundance estimates upon implementing accurately quantified reference proteins; i.e. using spiked in SIS peptides or a standard protein mix, to generate a properly calibrated quantification model. We show that only the Top3 method is directly proportional to protein abundance over the full quantification range and is the preferred method in the absence of reference protein measurements. Additionally, we demonstrate that spectral count based quantification methods are associated with higher errors than MS1 peak intensity based methods. Furthermore, we investigate the impact of mis-cleaved, modified and shared peptides as well as protein size and the number of employed reference proteins on quantification accuracy. The .raw data submitted to PRIDE correspond to replicate DDA LC-MS/MS analysis of the UPS2 mix (Universal Proteomics Standard, UPS2, Sigma-Aldrich), as well as triplicate DDA LC-MS/MS analysis of the UPS2 mix spiked into samples from the following organisms M. pneumoniae, L. interrogans and D. melanogaster. The accompanying R-script called calculatePQIs.R was used to calculate the different Quantification Indices, provided that the peptide XICs or Spectral Counts have been loaded into a data.frame R object (which can be produced by SafeQuant).

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A systematic review and critical assessment of incentive strategies for discovery and development of novel antibiotics.

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