| 1. |
- Freire-Moran, Laura, et al.
(författare)
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Critical shortage of new antibiotics in development against multidrug-resistant bacteria-Time to react is now
- 2011
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Ingår i: Drug resistance updates. - : Elsevier BV. - 1368-7646 .- 1532-2084. ; 14:2, s. 118-124
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Forskningsöversikt (refereegranskat)abstract
- Two commercial databases (Pharmaprojects and Adis Insight R&D) were queried for antibacterial agents in clinical development. Particular attention was given to antibacterial agents for systemic administration. For each agent, reviewers were requested to indicate whether its spectrum of activity covered a set of selected multidrug-resistant bacteria, and whether it had a new mechanism of action or a new target. In addition, PubMed was searched for antibacterial agents in development that appeared in review articles. Out of 90 agents that were considered to fulfil the inclusion criteria for the analysis, 66 were new active substances. Fifteen of these could be systemically administered and were assessed as acting via a new or possibly new mechanism of action or on a new or possibly new target. Out of these, 12 agents were assessed as having documented in vitro activity against antibiotic-resistant Gram-positive bacteria and only four had documented in vitro activity against antibiotic-resistant Gram-negative bacteria. Of these four, two acted on new or possibly new targets and, crucially, none acted via new mechanisms of action. There is an urgent need to address the lack of effective treatments to meet the increasing public health burden caused by multidrug-resistant bacteria, in particular against Gram-negative bacteria.
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| 2. |
- So, Michelle, et al.
(författare)
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Advances in Type 1 Diabetes Prediction Using Islet Autoantibodies : Beyond a Simple Count
- 2021
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Ingår i: Endocrine Reviews. - : The Endocrine Society. - 0163-769X .- 1945-7189. ; 42:5, s. 584-604
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Forskningsöversikt (refereegranskat)abstract
- Islet autoantibodies are key markers for the diagnosis of type 1 diabetes. Since their discovery, they have also been recognized for their potential to identify at-risk individuals prior to symptoms. To date, risk prediction using autoantibodies has been based on autoantibody number; it has been robustly shown that nearly all multiple-autoantibody-positive individuals will progress to clinical disease. However, longitudinal studies have demonstrated that the rate of progression among multiple-autoantibody-positive individuals is highly heterogenous. Accurate prediction of the most rapidly progressing individuals is crucial for efficient and informative clinical trials and for identification of candidates most likely to benefit from disease modification. This is increasingly relevant with the recent success in delaying clinical disease in presymptomatic subjects using immunotherapy, and as the field moves toward population-based screening. There have been many studies investigating islet autoantibody characteristics for their predictive potential, beyond a simple categorical count. Predictive features that have emerged include molecular specifics, such as epitope targets and affinity; longitudinal patterns, such as changes in titer and autoantibody reversion; and sequence-dependent risk profiles specific to the autoantibody and the subject's age. These insights are the outworking of decades of prospective cohort studies and international assay standardization efforts and will contribute to the granularity needed for more sensitive and specific preclinical staging. The aim of this review is to identify the dynamic and nuanced manifestations of autoantibodies in type 1 diabetes, and to highlight how these autoantibody features have the potential to improve study design of trials aiming to predict and prevent disease.
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