Raman spectroscopy as a PAT for pharmaceutical blending: Advantages and disadvantages.

Raman spectroscopy has been positively evaluated as a tool for the in-line and real-time monitoring of powder blending processes and it has been proved to be effective in the determination of the endpoint of the mixing, showing its potential role as process analytical technology (PAT). The aim of this study is to show advantages and disadvantages of Raman spectroscopy with respect to the most traditional HPLC analysis. The spectroscopic results, obtained directly on raw powders, sampled from a two-axis blender in real case conditions, were compared with the chromatographic data obtained on the same samples. The formulation blend used for the experiment consists of active pharmaceutical ingredient (API, concentrations 6.0% and 0.5%), lactose and magnesium stearate (as excipients). The first step of the monitoring process was selecting the appropriate wavenumber region where the Raman signal of API is maximal and interference from the spectral features of excipients is minimal. Blend profiles were created by plotting the area ratios of the Raman peak of API (AAPI) at 1598cm-1 and the Raman bands of excipients (AEXC), in the spectral range between 1560 and 1630cm-1, as a function of mixing time: the API content can be considered homogeneous when the time-dependent dispersion of the area ratio is minimized. In order to achieve a representative sampling with Raman spectroscopy, each sample was mapped in a motorized XY stage by a defocused laser beam of a micro-Raman apparatus. Good correlation between the two techniques has been found only for the composition at 6.0% (w/w). However, standard deviation analysis, applied to both HPLC and Raman data, showed that Raman results are more substantial than HPLC ones, since Raman spectroscopy enables generating data rich blend profiles. In addition, the relative standard deviation calculated from a single map (30 points) turned out to be representative of the degree of homogeneity for that blend time.

Predicting heart failure outcome from cardiac and comorbid conditions: the 3C-HF score.

BACKGROUND: Prognostic stratification in heart failure (HF) is crucial to guide clinical management and treatment decision-making. Currently available models to predict HF outcome have multiple limitations. We developed a simple risk stratification model, based on routinely available clinical information including comorbidities, the Cardiac and Comorbid Conditions HF (3C-HF) Score, to predict all-cause 1-year mortality in HF patients. METHODS: We recruited in a cohort study 6274 consecutive HF patients at 24 Cardiology and Internal Medicine Units in Europe. 2016 subjects formed the derivation cohort and 4258 the validation cohort. We entered information on cardiac and comorbid candidate prognostic predictors in a multivariable model to predict 1-year outcome. RESULTS: Median age was 69 years, 35.8% were female, 20.6% had a normal ejection fraction, and 65% had at least one comorbidity. During 5861 person-years follow-up, 12.1% of the patients met the study end-point of all-cause death (n=750) or urgent transplantation (n=9). The variables that contributed to outcome prediction, listed in decreasing discriminating ability, were: New York Heart Association class III-IV, left ventricular ejection fraction <20%, no beta-blocker, no renin-angiotensin system inhibitor, severe valve heart disease, atrial fibrillation, diabetes with micro or macroangiopathy, renal dysfunction, anemia, hypertension and older age. The C statistic for 1-year all-cause mortality was 0.87 for the derivation and 0.82 for the validation cohort. CONCLUSIONS: The 3C-HF score, based on easy-to-obtain cardiac and comorbid conditions and applicable to the 1-year time span, represents a simple and valuable tool to improve the prognostic stratification of HF patients in daily practice.