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In therapies involving therapeutic antibodies, therapeutic drug monitoring (TDM) has the potential to substantially increase clinical efficacy and to enable more economical drug use, because the clearance from the bloodstream after administration is patient dependent. Since TDM must be performed continuously and over extended time periods, it should ideally be achieved through an economical and easy to perform point-of-care (POC) assay. Here, we introduce a new analytical method for POC TDM of the clinically important therapeutic antibody cetuximab, using microfluidic thread-based analytical devices (µTADs) as a platform for a bioluminescence resonance energy-transfer (BRET) switching sensor protein for simple quantitative antibody detection. The µTAD is pre-treated with NaCl for blood plasma separation and a BRET switching sensor protein and its substrate for cetuximab detection, integrating all elements required for a reagent-free, single-step assay. A change in bioluminescence emission color depending on cetuximab concentrations occurs after whole blood application. The device design enables the quantification of cetuximab within the therapeutically relevant blood concentration range in 2.5 min using only 2 µL of whole blood. The results obtained are independent of the blood volume. This µTAD can be stored for about one month at − 20 °C, a temperature compatible with commonly available freezers. The application of mathematical morphology recognition (MMR)-integrated image processing techniques for the automatic selection of regions of interest (ROIs) yields results with minimal error. The simplicity of use, rapidity, and cost-effectiveness of this µTAD are expected to offer the possibility of achieving TDM of therapeutic antibodies at POC for individual patients.

Although fabrication of colorimetric paper-based analytical devices (PADs) has drawn increasing attention recently, the signal readout method is still a crucial challenge on the way to practical exploitations. We herein introduce an integration of digital image processing with a model PAD for easing and improving the signal readout procedure. The colorimetric detection mechanism of PAD relies on in-situ induced yellowish silver nanoparticles via the interaction of silver ions, poly(vinyl alcohol), and ammonia with isoniazid. The observed color value is related with the concentration of isoniazid. The proposed algorithm is based on mathematical morphology recognition, and minimizes the errors arising from manual area selection. Besides, it allows the recognition of both circle and square shapes in 96-well plate and A4 size array designs. Since this algorithm automatically provides the blank-corrected numerical matrixes and image profiles of red, green and blue channels, further investigations such as outlier classification, construction of regression/prediction models and calculation of detection limit can be easily performed. Comparison of signal readout results of the developed algorithm with ImageJ software demonstrates significant improvements in analysis speed, reproducibility, accuracy and color values. Therefore, application of the proposed algorithm is promising as a robust technique for practical applications.

Although microfluidic paper-based analytical devices (μPADs) get a lot of attention in the scientific literature, they rarely reach the level of commercialization. One possible reason for this is a lack of application of machine learning techniques supporting the design, optimization and fabrication of such devices. This work demonstrates the potential of two chemometric techniques including design of experiments (DoE) and digital image processing to support the production of μPADs. On the example of a simple colorimetric assay for isoniazid relying on the protonation equilibrium of methyl orange, the experimental conditions were optimized using a D-optimal design (DO) and the impact of multiple factors on the μPAD response was investigated. In addition, this work demonstrates the impact of automatic image processing on accelerating color value analysis and on minimizing errors caused by manual detection area selection. The employed algorithm is based on morphological recognition and allows the analysis of RGB (red, green, and blue) values in a repeatable way. In our belief, DoE and digital image processing methodologies are keys to overcome some of the remaining weaknesses in μPAD development to facilitate their future market entry.

In this work, an application of a design of experiments approach for the optimization of an isoniazid assay on a single-area inkjet-printed paper-based analytical device (PAD) is described. For this purpose, a central composite design was used for evaluation of the effect of device geometry and amount of assay reagents on the efficiency of the proposed device. The factors of interest were printed length, width, and sampling volume as factors related to device geometry, and amounts of the assay reagents polyvinyl alcohol (PVA), NH4OH, and AgNO3. Deposition of the assay reagents was performed by a thermal inkjet printer. The colorimetric assay mechanism of this device is based on the chemical interaction of isoniazid, ammonium hydroxide, and PVA with silver ions to induce the formation of yellow silver nanoparticles (AgNPs). The in situ-formed AgNPs can be easily detected by the naked eye or with a simple flat-bed scanner. Under optimal conditions, the calibration curve was linear in the isoniazid concentration range 0.03-10 mmol L-1 with a relative standard deviation of 3.4% (n = 5 for determination of 1.0 mmol L-1). Finally, the application of the proposed device for isoniazid determination in pharmaceutical preparations produced satisfactory results.

This manuscript reports on the application of chemometric methods for the development of an optimized microfluidic paper-based analytical device (μPAD). As an example, we applied chemometric methods for both device optimization and data processing of results of a colorimetric uric acid assay. Box-Behnken designs (BBD) were utilized for the optimization of the device geometry and the amount of thermal inkjet-deposited assay reagents, which affect the assay performance. Measurement outliers were detected in real time by partial least squares discriminant analysis (PLS-DA) of scanned images. The colorimetric assay mechanism is based on the on-device formation of silver nanoparticles (AgNPs) through the interaction of uric acid, ammonia, and poly(vinyl alcohol) with silver ions under mild basic conditions. The yellow color originating from visible light absorption by localized surface plasmon resonance of AgNPs can be detected by the naked eye or, more quantitatively, with a simple flat-bed scanner. Under optimized conditions, the linearity of the calibration curve ranges from 0.1-5 × 10 -3 mol L -1 of uric acid with a limit of detection of 33.9 × 10 -6 mol L -1 and a relative standard of deviation 4.5% (n = 3 for determination of 5.0 × 10 -3 mol L -1 uric acid). Graphical abstract A chemometrics-assisted microfluidic paper-based analytical device was developed as a low-cost and rapid platform for the determination of uric acid (UA). The detection method is based on the chemical interaction of UA, ammonia, and polyvinyl alcohol under mild basic condition with silver ions inducing formation of yellow silver nanoparticles (AgNPs).