Designing electrochemical microfluidic multiplexed biosensors for on-site applications.

Clinical assessment based on a single biomarker is in many circumstances not sufficient for adequate diagnosis of a disease or for monitoring its therapy. Multiplexing, the measurement of multiple analytes from one sample and/or of the same target from different samples simultaneously, could enhance the accuracy of the diagnosis of diseases and their therapy success. Thus, there is a great and urgent demand for multiplexed biosensors allowing a low-cost, easy-to-use, and rapid on-site testing. In this work, we present a simple, flexible, and highly scalable strategy for implementing microfluidic multiplexed electrochemical biosensors (BiosensorX). Our technology is able to detect 4, 6, or 8 (different) analytes or samples simultaneously using a sequential design concept: multiple immobilization areas, where the assay components are adsorbed, followed by their individual electrochemical cells, where the amperometric signal readout takes place, within a single microfluidic channel. Here, first we compare vertical and horizontal designs of BiosensorX chips using a model assay. Owing to its easier handling and superior fluidic behavior, the vertical format is chosen as the final multiplexed chip design. Consequently, the feasibility of the BiosensorX for multiplexed on-site testing is successfully demonstrated by measuring meropenem antibiotics via an antibody-free ß-lactam assay. The multiplexed biosensor platform introduced can be further extended for the simultaneous detection of other anti-infective agents and/or biomarkers (such as renal or inflammation biomarkers) as well as different (invasive and non-invasive) sample types, which would be a major step towards sepsis management and beyond.

Unraveling the impact of therapeutic drug monitoring via machine learning for patients with sepsis.

Clinical studies investigating the benefits of beta-lactam therapeutic drug monitoring (TDM) among critically ill patients are hindered by small patient groups, variability between studies, patient heterogeneity, and inadequate use of TDM. Accordingly, definitive conclusions regarding the efficacy of TDM remain elusive. To address these challenges, we propose an innovative approach that leverages data-driven methods to unveil the concealed connections between therapy effectiveness and patient data, collected through a randomized controlled trial (DRKS00011159; 10th October 2016). Our findings reveal that machine learning algorithms can successfully identify informative features that distinguish between healthy and sick states. These hold promise as potential markers for disease classification and severity stratification, as well as offering a continuous and data-driven "multidimensional" Sequential Organ Failure Assessment (SOFA) score. The positive impact of TDM on patient recovery rates is demonstrated by unraveling the intricate connections between therapy effectiveness and clinically relevant data via machine learning.

A three-level model for therapeutic drug monitoring of antimicrobials at the site of infection.

The silent pandemic of bacterial antimicrobial resistance is a leading cause of death worldwide, prolonging hospital stays and raising health-care costs. Poor incentives to develop novel pharmacological compounds and the misuse of antibiotics contribute to the bacterial antimicrobial resistance crisis. Therapeutic drug monitoring (TDM) based on blood analysis can help alleviate the emergence of bacterial antimicrobial resistance and effectively decreases the risk of toxic drug concentrations in patients' blood. Antibiotic tissue penetration can vary in patients who are critically or chronically ill and can potentially lead to treatment failure. Antibiotics such as ß-lactams and glycopeptides are detectable in non-invasively collectable biofluids, such as sweat and exhaled breath. The emergence of wearable sensors enables easy access to these non-invasive biofluids, and thus a laboratory-independent analysis of various disease-associated biomarkers and drugs. In this Personal View, we introduce a three-level model for TDM of antibiotics to describe concentrations at the site of infection (SOI) by use of wearable sensors. Our model links blood-based drug measurement with the analysis of drug concentrations in non-invasively collectable biofluids stemming from the SOI to characterise drug concentrations at the SOI. Finally, we outline the necessary clinical and technical steps for the development of wearable sensing platforms for SOI applications.

End-to-end design of wearable sensors.

Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease, through biophysical signals. Next-generation wearable sensors that enable the multimodal and/or multiplexed measurement of physical parameters and biochemical markers in real time and continuously could be a transformative technology for diagnostics, allowing for high-resolution and time-resolved historical recording of the health status of an individual. In this Review, we examine the building blocks of such wearable sensors, including the substrate materials, sensing mechanisms, power modules and decision-making units, by reflecting on the recent developments in the materials, engineering and data science of these components. Finally, we synthesize current trends in the field to provide predictions for the future trajectory of wearable sensors.

Biosensor-Enabled Multiplexed On-Site Therapeutic Drug Monitoring of Antibiotics.

Personalized antibiotherapy ensures that the antibiotic concentration remains in the optimal therapeutic window to maximize efficacy, minimize side effects, and avoid the emergence of drug resistance due to insufficient dosing. However, such individualized schemes need frequent sampling to tailor the blood antibiotic concentrations. To optimally integrate therapeutic drug monitoring (TDM) into the clinical workflow, antibiotic levels can either be measured in blood using point-of-care testing (POCT), or can rely on noninvasive sampling. Here, a versatile biosensor with an antibody-free assay for on-site TDM is presented. The platform is evaluated with an animal study, where antibiotic concentrations are quantified in different matrices including whole blood, plasma, urine, saliva, and exhaled breath condensate (EBC). The clearance and the temporal evaluation of antibiotic levels in EBC and plasma are demonstrated. Influence of matrix effects on measured drug concentrations is determined by comparing the plasma levels with those in noninvasive samples. The system's potential for blood-based POCT is further illustrated by tracking ß-lactam concentrations in untreated blood samples. Finally, multiplexing capabilities are explored successfully for multianalyte/sample analysis. By enabling a rapid, low-cost, sample-independent, and multiplexed on-site TDM, this system can shift the paradigm of "one-size-fits-all" strategy.

Low-Cost Optical Assays for Point-of-Care Diagnosis in Resource-Limited Settings.

Readily deployable, low-cost point-of-care medical devices such as lateral flow assays (LFAs), microfluidic paper-based analytical devices (µPADs), and microfluidic thread-based analytical devices (µTADs) are urgently needed in resource-poor settings. Governed by the ASSURED criteria (affordable, sensitive, specific, user-friendly, rapid and robust, equipment-free, and deliverability) set by the World Health Organization, these reliable platforms can screen a myriad of chemical and biological analytes including viruses, bacteria, proteins, electrolytes, and narcotics. The Ebola epidemic in 2014 and the ongoing pandemic of SARS-CoV-2 have exemplified the ever-increasing importance of timely diagnostics to limit the spread of diseases. This review provides a comprehensive survey of LFAs, µPADs, and µTADs that can be deployed in resource-limited settings. The subsequent commercialization of these technologies will benefit the public health, especially in areas where access to healthcare is limited.

On-Site Therapeutic Drug Monitoring.

Recent technological advances have stimulated efforts to bring personalized medicine into practice. Yet, traditional application fields like therapeutic drug monitoring (TDM) have remained rather under-appreciated. Owing to clear dose-response relationships, TDM could improve patient outcomes and reduce healthcare costs. While chromatography-based routine practices are restricted due to high costs and turnaround times, biosensors overcome these limitations by offering on-site analysis. Nevertheless, sensor-based approaches have yet to break through for clinical TDM applications, due to the gap between scientific and clinical communities. We provide a critical overview of current TDM practices, followed by a TDM guideline to establish a common ground across disciplines. Finally, we discuss how the translation of sensor systems for TDM can be facilitated, by highlighting the challenges and opportunities.