Volatile Organic Compound Sensing Array and Optoelectronic Filter System using Ion-Pairing Dyes with a Wide Visible Spectrum

An ideal dye-based sensing array has essential design requirements, including facile preparation methodology, tolerance to water vapor, a broad range of color-responsive changes, and a simple readout system. Here, a brief synthetic route is developed for ion-pairing dyes exhibiting unusual chromatic changes across the entire visible spectrum. It requires only mixing and precipitation under mild conditions. The dyes are applied to a sensing array containing 12 sensing elements with different initial states. Owing to the numerous color variations of the dyes, the color map generated by the array is highly simple yet sufficiently accurate to distinguish among the different functional groups (such as amines, aldehydes, and carboxylic acids) as well as carbon chain lengths. Principle component analysis (PCA) demonstrates that volatile organic compounds (VOCs) can be well classified according to the color changes of the sensing array. The ion-pairing dyes are embedded into 3D stacked nanofibers via electrospinning, and function as effective harmful-gas (e.g., formaldehyde) sensors with sub-ppm theoretical detection limits (0.15 ppm). Finally, the 3D stacked nanofibers can be employed in an optoelectronic filter system that automatically checks for formaldehyde in the surroundings and also confirms the effective removal of the detected formaldehyde by the gas filter cartridge.

Extraction of Reduced Infrared Biomarker Signatures for the Stratification of Patients Affected by Parkinson’s Disease: An Untargeted Metabolomic Approach

An untargeted Fourier transform infrared (FTIR) metabolomic approach was employed to study metabolic changes and disarrangements, recorded as infrared signatures, in Parkinson’s disease (PD). Herein, the principal aim was to propose an efficient sequential classification strategy based on SELECT-LDA, which enabled optimal stratification of three main categories: PD patients from subjects with Alzheimer’s disease (AD) and healthy controls (HC). Moreover, sub-categories, such as PD at the early stage (PDI) from PD in the advanced stage (PDD), and PDD vs. AD, were stratified. Every classification step with selected wavenumbers achieved 90.11% to 100% correct assignment rates in classification and internal validation. Therefore, selected metabolic signatures from new patients could be used as input features for screening and diagnostic purposes.

Probabilistic modeling of an injectable aqueous crystalline suspension using influence networks

Probabilistic modeling using influence networks is an efficient, intuitive, and easy to communicate strategy in the development of complex pharmaceutical products. This study was aimed to use a risk-based approach to explore the complex interactions between product and process design parameters affecting size and shape of the particles in injectable aqueous crystalline suspensions (ACS). Based on a risk assessment, a design of experiments (DOE) was applied to evaluate the most important parameters, i.e., four critical material attributes and two critical process parameters. A model hydrophobic drug (carbamazepine) was milled and homogenized in a multistep process (dispersion and milling steps). The final formulations were characterized with automated at-line image analysis of thousands of individual particles. The particle size and shape distributions were summarized with descriptive parameters, and the relationship of these parameters and the DOE was modeled using influence networks (INs). This approach was compared and contrasted with a classical modeling approach based on multivariate linear regression (MVLR). INs had a superior visual interpretation capability of the complex and multivariate ACS system making the risk-based decision making more accessible. The probability and causality were included in the IN, i.e., the relationships between size and shape. Moreover, IN allowed to incorporate prior knowledge in a systematic way by implementing a 'black and white list'. An IN based model was created with the following model performance: a mean absolute percentage error of 1.7% and 1.1% for the size and 6.2% and 5.0% for the shape, respectively for dispersed and milled ACS. Parameters with the highest and lowest probability to control the critical quality attributes of ACS could be identified. Consequently, the parameter settings giving the optimum particle size and shape could be predicted using a Monte Carlo simulation to calculate the probability of success including the uncertainty of the model. The cubic MVLR model for the size of milled ACS was comparable to the IN in terms of the mean absolute percentage error, i.e., 1.1%. However, IN was more efficient in visualizing the complex and multivariate data set, including all the critical quality attributes and formulation/process parameters of the ACS at the same time. Moreover, the prior knowledge used in probabilistic modeling of IN could be systematically documented.

Robust freeze-drying process re-design of a legacy product based on risk analysis and design of experiments

In this study, a QbD freeze-drying process re-design applied to a lyophilized injectable drug product is presented. The main objective was to assess the freeze-drying process robustness using risk analysis and a proper experimental design. First, the product's thermal fingerprint was characterized by thermal analysis and freeze-drying microscopy. Then, according to the output of the risk analysis, primary drying temperature and pressure were studied by a Doehlert DoE design with four responses; primary drying time, appearance, residual moisture content, and reconstitution time. Statistically significant MLR models were obtained for residual moisture content and primary drying time. In the latter, the temperature factor was the predominant factor to predict the duration of the primary drying stage. Two additional lab-scale batches were run to confirm the mathematical model predictions. Finally, optimal primary drying conditions (30 °C, 0.400 mbar) were selected to minimize the duration of the primary drying stage, while preserving the quality of the product. It was possible to set high temperature and pressure values because no collapse temperature was found during the thermal characterization of the product. Secondary drying temperature and time were defined based on the residual moisture content results. It was shown that secondary drying is robust between 30 °C and 50 °C and from 3 to 16 h. In conclusion, we were able to define a robust freeze-drying process which was further validated at an industrial scale with satisfactory results and approved by the health authorities in different countries around Europe.

Current progress in identifying endogenous biomarker candidates for drug transporter phenotyping and their potential application to drug development

Drug transporters play important roles in the elimination of various compounds from the blood. Genetic variation and drug-drug interactions underlie the pharmacokinetic differences for the substrates of drug transporters. Some endogenous substrates of drug transporters have emerged as biomarkers to assess differences in drug transporter activity-not only in animals, but also in humans. Metabolomic analysis is a promising approach for identifying such endogenous substrates through their metabolites. The appropriateness of metabolites is supported by studies in vitro and in vivo, both in animals and through pharmacogenomic or drug-drug interaction studies in humans. This review summarizes current progress in identifying such endogenous biomarkers and applying them to drug transporter phenotyping.

Non-invasive plant disease diagnostics enabled by smartphone-based fingerprinting of leaf volatiles

Plant pathogen detection conventionally relies on molecular technology that is complicated, time-consuming and constrained to centralized laboratories. We developed a cost-effective smartphone-based volatile organic compound (VOC) fingerprinting platform that allows non-invasive diagnosis of late blight caused by Phytophthora infestans by monitoring characteristic leaf volatile emissions in the field. This handheld device integrates a disposable colourimetric sensor array consisting of plasmonic nanocolorants and chemo-responsive organic dyes to detect key plant volatiles at the ppm level within 1 min of reaction. We demonstrate the multiplexed detection and classification of ten individual plant volatiles with this field-portable VOC-sensing platform, which allows for early detection of tomato late blight 2 d after inoculation, and differentiation from other pathogens of tomato that lead to similar symptoms on tomato foliage. Furthermore, we demonstrate a detection accuracy of ≥95% in diagnosis of P. infestans in both laboratory-inoculated and field-collected tomato leaves in blind pilot tests. Finally, the sensor platform has been beta-tested for detection of P. infestans in symptomless tomato plants in the greenhouse setting.