Application of a wavelength angle mapper for variable selection in iterative optimization technology predictions of drug content in pharmaceutical powder mixtures.

Process analytical technology (PAT) is an essential tool within pharmaceutical manufacturing to ensure consistent quality and maintain process control. Near-infrared (NIR) spectroscopy is one of the most popular PAT techniques, particularly for monitoring active pharmaceutical ingredient (API) concentrations. To interpret the spectral outputs of NIR spectroscopy, advanced multivariate models are required. Calibration-free models such as iterative optimization technology (IOT) algorithms are increasingly of interest, due primarily to their reduced material and time burdens. Variable/wavelength selection is a common method to improve prediction performance and robustness for IOT by focusing on spectral regions with the most relevant information. However, currently proposed wavelength selection approaches rely on training sets for optimization, therefore reducing or removing the advantages of IOT over empirical calibration-dependent models. In this work, a true calibration-free wavelength selection method is proposed based on measuring the difference between individual wavelengths of a mixture spectra and the net analyte signals via a wavelength angle mapper (WAM). An extension of the WAM utilizing a spectral window of wavelength instead of individual wavelengths, called SWAM, was also developed. However, the SWAM method does require a small training set to optimize wavelength selection parameters. The WAM and SWAM methods showed similar prediction performance for API in pharmaceutical powder blends when compared against other calibration-dependent models and the base IOT algorithm.

Comparison Between Pure Component Modeling Approaches for Monitoring Pharmaceutical Powder Blends with Near-Infrared Spectroscopy in Continuous Manufacturing Schemes.

Near-infrared (NIR) spectroscopy has become an important process analytical technology (PAT) for monitoring and implementing control in continuous manufacturing (CM) schemes. However, NIR requires complex multivariate models to properly extract the relevant information and the traditional model of choice, partial least squares, can be unfavorable on account of its high material and time investments for generating calibrations. To account for this, pure component-based approaches have been gaining attention due to their higher flexibility and ease of development. In the present study, the application of two pure component approaches, classical least squares (CLS) models and iterative optimization technology (IOT) algorithms, to pharmaceutical powder blends in a continuous feed frame was considered. The approaches were compared from both a model performance and practical implementation perspective. IOT were found to demonstrate superior performance in predicting drug content compared to CLS. The practical implementation of each modelling approach was also given consideration.

Development of calibration-free/minimal calibration wavelength selection for iterative optimization technology algorithms toward process analytical technology application.

As continuous manufacturing (CM) processes are developed, process analytical technology (PAT) via NIR spectroscopy has become an integral tool in process monitoring. NIR spectroscopy requires the deployment of complex multivariate models to extract the relevant information. The model of choice for the pharmaceutical industry is Partial Least Squares (PLS). However, the development of PLS can be burdensome due to the time and resource intensive requirements of calibration. To overcome this challenge, calibration-free/minimal calibration approaches have become of increasing interest. Iterative optimization technology (IOT) algorithms are a favorable calibration-free/minimal calibration approach with only the requirement of pure component spectra for successful active pharmaceutical ingredient (API) quantification. IOT algorithms were utilized to monitor potency trends (qualitative) and API content (quantitative) in a CM system and compared to a traditional PLS model. To overcome the reduced prediction performance of IOT during non-steady state conditions, a novel wavelength method based on variable importance in projection scores was employed. Overall, the success and value of IOT algorithms for application in CM settings was demonstrated.

Dose response of a novel exogenous ketone supplement on physiological, perceptual and performance parameters.

BACKGROUND: Interest into the health, disease, and performance impact of exogenous ketone bodies has rapidly expanded due to their multifaceted physiological and signaling properties but limiting our understanding is the isolated analyses of individual types and dose/dosing protocols. METHODS: Thirteen recreational male distance runners (24.8 ± 9.6 years, 72.5 ± 8.3 kg, VO2max 60.1 ± 5.4 ml/kg/min) participated in this randomized, double-blind, crossover design study. The first two sessions consisted of a 5-km running time trial familiarization and a VO2max test. During subsequent trials, subjects were randomly assigned to one (KS1: 22.1 g) or two (KS2: 44.2 g) doses of beta-hydroxybutyrate (ßHB) and medium chain triglycerides (MCTs) or flavor matched placebo (PLA). Blood R-ßHB, glucose, and lactate concentrations were measured at baseline (0-min), post-supplement (30 and 60 min), post-exercise (+ 0 min, + 15 min). Time, heart rate (HR), rating of perceived exertion (RPE), affect, respiratory exchange ratio, oxygen consumption (VO2), carbon dioxide production, and ventilation were measured during exercise. Cognitive performance was evaluated prior to and post-exercise. RESULTS: KS significantly increased R-ßHB, with more potent and prolonged elevations in KS2, illustrating an administrative and dosing effect. R-ßHB was significantly decreased in KS1 compared to KS2 illustrating a dosing and exercise interaction effect. Blood glucose elevated post-exercise but was unchanged across groups. Blood lactate significantly increased post-exercise but was augmented by KS administration. Gaseous exchange, respiration, HR, affect, RPE, and exercise performance was unaltered with KS administration. However, clear responders and none-responders were indicated. KS2 significantly augmented cognitive function in pre-exercise conditions, while exercise increased cognitive performance for KS1 and PLA to pre-exercise KS2 levels. CONCLUSION: Novel ßHB + MCT formulation had a dosing effect on R-ßHB and cognitive performance, an administrative response on blood lactate, while not influencing gaseous exchange, respiration, HR, affect, RPE, and exercise performance.