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Objectives Supersaturating formulations hold great promise for delivery of poorly soluble active pharmaceutical ingredients (APIs). To profit from supersaturating formulations, precipitation is hindered with precipitation inhibitors (PIs), maintaining drug concentrations for as long as possible. This review provides a brief overview of supersaturation and precipitation, focusing on precipitation inhibition. Trial-and-error PI selection will be examined alongside established PI screening techniques. Primarily, however, this review will focus on recent advances that utilise advanced analytical techniques to increase mechanistic understanding of PI action and systematic PI selection.
Key Findings. Advances in mechanistic understanding have been made possible by the use of analytical tools such as spectroscopy, microscopy and mathematical and molecular modelling, which have been reviewed herein. Using these techniques, PI selection can instead be guided by molecular rationale. However, more work is required to see wide-spread application of such an approach for PI selection.
Conclusions PIs are becoming increasingly important in enabling formulations. Trial-and-error approaches have seen success thus far. However, it is essential to learn more about the mode of action of PIs if the most optimal formulations are to be realised. Robust analytical tools, and the knowledge of where and how they can be applied, will be essential in this endeavour.
Aims: Carotid intima media thickness (CIMT) predicts cardiovascular (CVD) events, but the predictive value of CIMT change is debated. We assessed the relation between CIMT change and events in individuals at high cardiovascular risk.
Methods and results: From 31 cohorts with two CIMT scans (total n = 89070) on average 3.6 years apart and clinical follow-up, subcohorts were drawn: (A) individuals with at least 3 cardiovascular risk factors without previous CVD events, (B) individuals with carotid plaques without previous CVD events, and (C) individuals with previous CVD events. Cox regression models were fit to estimate the hazard ratio (HR) of the combined endpoint (myocardial infarction, stroke or vascular death) per standard deviation (SD) of CIMT change, adjusted for CVD risk factors. These HRs were pooled across studies.
In groups A, B and C we observed 3483, 2845 and 1165 endpoint events, respectively. Average common CIMT was 0.79mm (SD 0.16mm), and annual common CIMT change was 0.01mm (SD 0.07mm), both in group A. The pooled HR per SD of annual common CIMT change (0.02 to 0.43mm) was 0.99 (95% confidence interval: 0.95–1.02) in group A, 0.98 (0.93–1.04) in group B, and 0.95 (0.89–1.04) in group C. The HR per SD of common CIMT (average of the first and the second CIMT scan, 0.09 to 0.75mm) was 1.15 (1.07–1.23) in group A, 1.13 (1.05–1.22) in group B, and 1.12 (1.05–1.20) in group C.
Conclusions: We confirm that common CIMT is associated with future CVD events in individuals at high risk. CIMT change does not relate to future event risk in high-risk individuals.