Application of programmable bio-nano-chip system for the quantitative detection of drugs of abuse in oral fluids

Salivary Biomarkers of Anti-Epileptic Drugs: A Narrative Review

Saliva is a biofluid that reflects general health and that can be collected in order to evaluate and determine various pathologies and treatments. Biomarker analysis through saliva sampling is an emerging method of accurately screening and diagnosing diseases. Anti-epileptic drugs (AEDs) are prescribed generally in seizure treatment. The dose-response relationship of AEDs is influenced by numerous factors and varies from patient to patient, hence the need for the careful supervision of drug intake. The therapeutic drug monitoring (TDM) of AEDs was traditionally performed through repeated blood withdrawals. Saliva sampling in order to determine and monitor AEDs is a novel, fast, low-cost and non-invasive approach. This narrative review focuses on the characteristics of various AEDs and the possibility of determining active plasma concentrations from saliva samples. Additionally, this study aims to highlight the significant correlations between AED blood, urine and oral fluid levels and the applicability of saliva TDM for AEDs. The study also focuses on emphasizing the applicability of saliva sampling for epileptic patients.

Microsystem Advances through Integration with Artificial Intelligence

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

Smart Diagnostics: Combining Artificial Intelligence and In Vitro Diagnostics

We are beginning a new era of Smart Diagnostics-integrated biosensors powered by recent innovations in embedded electronics, cloud computing, and artificial intelligence (AI). Universal and AI-based in vitro diagnostics (IVDs) have the potential to exponentially improve healthcare decision making in the coming years. This perspective covers current trends and challenges in translating Smart Diagnostics. We identify essential elements of Smart Diagnostics platforms through the lens of a clinically validated platform for digitizing biology and its ability to learn disease signatures. This platform for biochemical analyses uses a compact instrument to perform multiclass and multiplex measurements using fully integrated microfluidic cartridges compatible with the point of care. Image analysis digitizes biology by transforming fluorescence signals into inputs for learning disease/health signatures. The result is an intuitive Score reported to the patients and/or providers. This AI-linked universal diagnostic system has been validated through a series of large clinical studies and used to identify signatures for early disease detection and disease severity in several applications, including cardiovascular diseases, COVID-19, and oral cancer. The utility of this Smart Diagnostics platform may extend to multiple cell-based oncology tests via cross-reactive biomarkers spanning oral, colorectal, lung, bladder, esophageal, and cervical cancers, and is well-positioned to improve patient care, management, and outcomes through deployment of this resilient and scalable technology. Lastly, we provide a future perspective on the direction and trajectory of Smart Diagnostics and the transformative effects they will have on health care.

A Rapid and Sensitive Microfluidics-Based Tool for Seroprevalence Immunity Assessment of COVID-19 and Vaccination-Induced Humoral Antibody Response at the Point of Care

As of 8 August 2022, SARS-CoV-2, the causative agent of COVID-19, has infected over 585 million people and resulted in more than 6.42 million deaths worldwide. While approved SARS-CoV-2 spike (S) protein-based vaccines induce robust seroconversion in most individuals, dramatically reducing disease severity and the risk of hospitalization, poorer responses are observed in aged, immunocompromised individuals and patients with certain pre-existing health conditions. Further, it is difficult to predict the protection conferred through vaccination or previous infection against new viral variants of concern (VoC) as they emerge. In this context, a rapid quantitative point-of-care (POC) serological assay able to quantify circulating anti-SARS-CoV-2 antibodies would allow clinicians to make informed decisions on the timing of booster shots, permit researchers to measure the level of cross-reactive antibody against new VoC in a previously immunized and/or infected individual, and help assess appropriate convalescent plasma donors, among other applications. Utilizing a lab-on-a-chip ecosystem, we present proof of concept, optimization, and validation of a POC strategy to quantitate COVID-19 humoral protection. This platform covers the entire diagnostic timeline of the disease, seroconversion, and vaccination response spanning multiple doses of immunization in a single POC test. Our results demonstrate that this platform is rapid (~15 min) and quantitative for SARS-CoV-2-specific IgG detection.

Modified ELISA for Ultrasensitive Diagnosis

An enzyme-linked immunosorbent assay (ELISA) can be used for quantitative measurement of proteins, and improving the detection sensitivity to the ultrasensitive level would facilitate the diagnosis of various diseases. In the present review article, we first define the term 'ultrasensitive'. We follow this with a survey and discussion of the current literature regarding modified ELISA methods with ultrasensitive detection and their application for diagnosis. Finally, we introduce our own newly devised system for ultrasensitive ELISA combined with thionicotinamide adenine dinucleotide cycling and its application for the diagnosis of infectious diseases and lifestyle-related diseases. The aim of the present article is to expand the application of ultrasensitive ELISAs in the medical and biological fields.

Point-of-Care Drug of Abuse Testing in the Opioid Epidemic

CONTEXT.—

The United States is experiencing an opioid overdose epidemic. Point-of-care (POC) drug of abuse testing is a useful tool to combat the intensified opioid epidemic.

OBJECTIVES.—

To review commercially available POC drug of abuse testing involving opioids, to review opportunities and challenges for POC opioid testing and emerging testing methods in research literature, and finally to summarize unmet clinical needs and future development prospects.

DATA SOURCES.—

The Google search engine was used to access information for commercial opioid POC devices and the Google Scholar search engine was used to access research literature published from 2000 to 2019 for opioid POC tests.

CONCLUSIONS.—

The opioid epidemic provides unprecedented opportunities for POC drug testing, with significant clinical needs. Compared with gold standard tests, limitations for commercially available opioid POC testing include lower analytical sensitivity, lower specificity, and cross-reactivity. In response to unmet clinical needs, novel methods have emerged in research literature, such as microfluidics and miniature mass spectrometry. Future prospects include the development of quantitative POC devices and smarter and real-time drug testing.

Determination of morphine and its metabolites in the biological samples: an updated review

Morphine (MO) as an opioid analgesic is used for the treatment of moderate-to-severe pains, particularly cancer-related pains. Pharmacologic studies on MO are complicated due to drugs binding to the protein or metabolization to active metabolites, and even inter-individual variability. This necessitates the selection of a reliable analytical method for monitoring MO and the concentrations of its metabolites in the biological samples for the pharmacokinetic or pharmacodynamic investigations. Therefore, this study was conducted to review all the analytical research carried out on MO and its metabolites in the biological samples during 2007-2019 as an update to the study by Bosch et al. (2007).

Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Cyclic Olefin Copolymer Microfluidic Devices for Forensic Applications

Microfluidic devices offer important benefits for forensic applications, in particular for fast tests at a crime scene. A large portion of forensic applications require microfluidic chip material to show compatibility with biochemical reactions (such as amplification reactions), and to have high transparency in the visible region and high chemical resistance. Also, preferably, manufacturing should be simple. The characteristic properties of cyclic olefin copolymer (COC) fulfills these requirements and offers new opportunities for the development of new forensic tests. In this work, the versatility of COC as material for lab-on-a-chip (LOC) systems in forensic applications has been explored by realizing two proof-of-principle devices. Chemical resistance and optical transparency were investigated for the development of an on-chip presumptive color test to indicate the presence of an illicit substance through applying absorption spectroscopy. Furthermore, the compatibility of COC with a DNA amplification reaction was verified by performing an on-chip multiple displacement amplification (MDA) reaction.

Sensors that Learn: The Evolution from Taste Fingerprints to Patterns of Early Disease Detection

The McDevitt group has sustained efforts to develop a programmable sensing platform that offers advanced, multiplexed/multiclass chem-/bio-detection capabilities. This scalable chip-based platform has been optimized to service real-world biological specimens and validated for analytical performance. Fashioned as a sensor that learns, the platform can host new content for the application at hand. Identification of biomarker-based fingerprints from complex mixtures has a direct linkage to e-nose and e-tongue research. Recently, we have moved to the point of big data acquisition alongside the linkage to machine learning and artificial intelligence. Here, exciting opportunities are afforded by multiparameter sensing that mimics the sense of taste, overcoming the limitations of salty, sweet, sour, bitter, and glutamate sensing and moving into fingerprints of health and wellness. This article summarizes developments related to the electronic taste chip system evolving into a platform that digitizes biology and affords clinical decision support tools. A dynamic body of literature and key review articles that have contributed to the shaping of these activities are also highlighted. This fully integrated sensor promises more rapid transition of biomarker panels into wide-spread clinical practice yielding valuable new insights into health diagnostics, benefiting early disease detection.

Innovative Programmable Bio-Nano-Chip Digitizes Biology Using Sensors That Learn Bridging Biomarker Discovery and Clinical Implementation

The lack of standard tools and methodologies and the absence of a streamlined multimarker approval process have hindered the translation rate of new biomarkers into clinical practice for a variety of diseases afflicting humankind. Advanced novel technologies with superior analytical performance and reduced reagent costs, like the programmable bio-nano-chip system featured in this article, have potential to change the delivery of healthcare. This universal platform system has the capacity to digitize biology, resulting in a sensor modality with a capacity to learn. With well-planned device design, development, and distribution plans, there is an opportunity to translate benchtop discoveries in the genomics, proteomics, metabolomics, and glycomics fields by transforming the information content of key biomarkers into actionable signatures that can empower physicians and patients for a better management of healthcare. While the process is complicated and will take some time, showcased here are three application areas for this flexible platform that combines biomarker content with minimally invasive or non-invasive sampling, such as brush biopsy for oral cancer risk assessment; serum, plasma, and small volumes of blood for the assessment of cardiac risk and wellness; and oral fluid sampling for drugs of abuse testing at the point of need.

Programmable Bio-nanochip Platform: A Point-of-Care Biosensor System with the Capacity To Learn

The combination of point-of-care (POC) medical microdevices and machine learning has the potential transform the practice of medicine. In this area, scalable lab-on-a-chip (LOC) devices have many advantages over standard laboratory methods, including faster analysis, reduced cost, lower power consumption, and higher levels of integration and automation. Despite significant advances in LOC technologies over the years, several remaining obstacles are preventing clinical implementation and market penetration of these novel medical microdevices. Similarly, while machine learning has seen explosive growth in recent years and promises to shift the practice of medicine toward data-intensive and evidence-based decision making, its uptake has been hindered due to the lack of integration between clinical measurements and disease determinations. In this Account, we describe recent developments in the programmable bio-nanochip (p-BNC) system, a biosensor platform with the capacity for learning. The p-BNC is a "platform to digitize biology" in which small quantities of patient sample generate immunofluorescent signal on agarose bead sensors that is optically extracted and converted to antigen concentrations. The platform comprises disposable microfluidic cartridges, a portable analyzer, automated data analysis software, and intuitive mobile health interfaces. The single-use cartridges are fully integrated, self-contained microfluidic devices containing aqueous buffers conveniently embedded for POC use. A novel fluid delivery method was developed to provide accurate and repeatable flow rates via actuation of the cartridge's blister packs. A portable analyzer instrument was designed to integrate fluid delivery, optical detection, image analysis, and user interface, representing a universal system for acquiring, processing, and managing clinical data while overcoming many of the challenges facing the widespread clinical adoption of LOC technologies. We demonstrate the p-BNC's flexibility through the completion of multiplex assays within the single-use disposable cartridges for three clinical applications: prostate cancer, ovarian cancer, and acute myocardial infarction. Toward the goal of creating "sensors that learn", we have developed and describe here the Cardiac ScoreCard, a clinical decision support system for a spectrum of cardiovascular disease. The Cardiac ScoreCard approach comprises a comprehensive biomarker panel and risk factor information in a predictive model capable of assessing early risk and late-stage disease progression for heart attack and heart failure patients. These marker-driven tests have the potential to radically reduce costs, decrease wait times, and introduce new options for patients needing regular health monitoring. Further, these efforts demonstrate the clinical utility of fusing data from information-rich biomarkers and the Internet of Things (IoT) using predictive analytics to generate single-index assessments for wellness/illness status. By promoting disease prevention and personalized wellness management, tools of this nature have the potential to improve health care exponentially.

Cardiac ScoreCard: A Diagnostic Multivariate Index Assay System for Predicting a Spectrum of Cardiovascular Disease

Clinical decision support systems (CDSSs) have the potential to save lives and reduce unnecessary costs through early detection and frequent monitoring of both traditional risk factors and novel biomarkers for cardiovascular disease (CVD). However, the widespread adoption of CDSSs for the identification of heart diseases has been limited, likely due to the poor interpretability of clinically relevant results and the lack of seamless integration between measurements and disease predictions. In this paper we present the Cardiac ScoreCard-a multivariate index assay system with the potential to assist in the diagnosis and prognosis of a spectrum of CVD. The Cardiac ScoreCard system is based on lasso logistic regression techniques which utilize both patient demographics and novel biomarker data for the prediction of heart failure (HF) and cardiac wellness. Lasso logistic regression models were trained on a merged clinical dataset comprising 579 patients with 6 traditional risk factors and 14 biomarker measurements. The prediction performance of the Cardiac ScoreCard was assessed with 5-fold cross-validation and compared with reference methods. The experimental results reveal that the ScoreCard models improved performance in discriminating disease versus non-case (AUC = 0.8403 and 0.9412 for cardiac wellness and HF, respectively), and the models exhibit good calibration. Clinical insights to the prediction of HF and cardiac wellness are provided in the form of logistic regression coefficients which suggest that augmenting the traditional risk factors with a multimarker panel spanning a diverse cardiovascular pathophysiology provides improved performance over reference methods. Additionally, a framework is provided for seamless integration with biomarker measurements from point-of-care medical microdevices, and a lasso-based feature selection process is described for the down-selection of biomarkers in multimarker panels.

Quantitative determination of methamphetamine in oral fluid by liquid-liquid extraction and gas chromatography/mass spectrometry

Methamphetamine abuse is one of the most medical and social problems many countries face. In spite of the ban on the use of methamphetamine, it is widely available in Iran's drug black market. There are many analytical methods for the detection of methamphetamine in biological specimen. Oral fluid has become a popular specimen to test for the presence of methamphetamine. The purpose of the present study was to develop a method for the extraction and detection of methamphetamine in oral fluid samples using liquid-liquid extraction (LLE) and gas chromatography/mass spectrometry (GC/MS) methods. An analytical study was designed in that blank and 50 authentic oral fluid samples were collected to be first extracted by LLE and subsequently analysed by GC/MS. The method was fully validated and showed an excellent intra- and inter-assay precision (reflex sympathetic dystrophy ˂ 10%) for external quality control samples. Recovery with LLE methods was 96%. Limit of detection and limit of quantitation were 5 and 15 ng/mL, respectively. The method showed high selectivity, no additional peak due to interfering substances in samples was observed. The introduced method was sensitive, accurate and precise enough for the extraction of methamphetamine from oral fluid samples in forensic toxicology laboratories.

Challenges and opportunities for translating medical microdevices: insights from the programmable bio-nano-chip

This perspective highlights the major challenges for the bioanalytical community, in particular the area of lab-on-a-chip sensors, as they relate to point-of-care diagnostics. There is a strong need for general-purpose and universal biosensing platforms that can perform multiplexed and multiclass assays on real-world clinical samples. However, the adoption of novel lab-on-a-chip/microfluidic devices has been slow as several key challenges remain for the translation of these new devices to clinical practice. A pipeline of promising medical microdevice technologies will be made possible by addressing the challenges of integration, failure to compete with cost and performance of existing technologies, requisite for new content, and regulatory approval and clinical adoption.

Oral fluid drug analysis in the age of new psychoactive substances

Oral fluid has become an important matrix for drugs of abuse analysis. These days the applicability is challenged by the fact that an increasing number of new psychoactive drugs are coming on the market. Synthetic cannabinoids and synthetic cathinones have been the main drug classes, but the diversity is increasing and other drugs like piperazines, phenethylamines, tryptamines, designer opioids and designer benzodiazepines are becoming more prevalent. Many of the substances are very potent, and low doses ingested will lead to low concentrations in biological media, including oral fluid. This review will highlight the phenomenon of new psychoactive substances and review methods for oral fluid drug testing analysis using on-site tests, immunoassays and chromatographic methods.

Annual banned-substance review: analytical approaches in human sports drug testing

The aim of improving anti-doping efforts is predicated on several different pillars, including, amongst others, optimized analytical methods. These commonly result from exploiting most recent developments in analytical instrumentation as well as research data on elite athletes' physiology in general, and pharmacology, metabolism, elimination, and downstream effects of prohibited substances and methods of doping, in particular. The need for frequent and adequate adaptations of sports drug testing procedures has been incessant, largely due to the uninterrupted emergence of new chemical entities but also due to the apparent use of established or even obsolete drugs for reasons other than therapeutic means, such as assumed beneficial effects on endurance, strength, and regeneration capacities. Continuing the series of annual banned-substance reviews, literature concerning human sports drug testing published between October 2014 and September 2015 is summarized and reviewed in reference to the content of the 2015 Prohibited List as issued by the World Anti-Doping Agency (WADA), with particular emphasis on analytical approaches and their contribution to enhanced doping controls.

Next Generation Programmable Bio-Nano-Chip System for On-Site Detection in Oral Fluids

Current on-site drug of abuse detection methods involve invasive sampling of blood and urine specimens, or collection of oral fluid, followed by qualitative screening tests using immunochromatographic cartridges. Test confirmation and quantitative assessment of a presumptive positive are then provided by remote laboratories, an inefficient and costly process decoupled from the initial sampling. Recently, a new noninvasive oral fluid sampling approach that is integrated with the chip-based Programmable Bio-Nano-Chip (p-BNC) platform has been developed for the rapid (~ 10 minutes), sensitive detection (~ ng/ml) and quantitation of 12 drugs of abuse. Furthermore, the system can provide the time-course of select drug and metabolite profiles in oral fluids. For cocaine, we observed three slope components were correlated with cocaine-induced impairment using this chip-based p-BNC detection modality. Thus, this p-BNC has significant potential for roadside drug testing by law enforcement officers. Initial work reported on chip-based drug detection was completed using 'macro' or "chip in the lab" prototypes, that included metal encased "flow cells", external peristaltic pumps and a bench-top analyzer system instrumentation. We now describe the next generation miniaturized analyzer instrumentation along with customized disposables and sampling devices. These tools will offer real-time oral fluid drug monitoring capabilities, to be used for roadside drug testing as well as testing in clinical settings as a non-invasive, quantitative, accurate and sensitive tool to verify patient adherence to treatment.

Programmable bio-nano-chip system: a flexible point-of-care platform for bioscience and clinical measurements

The development of integrated instrumentation for universal bioassay systems serves as a key goal for the lab-on-a-chip community. The programmable bio-nano-chip (p-BNC) system is a versatile multiplexed and multiclass chemical- and bio-sensing system for bioscience and clinical measurements. The system is comprised of two main components, a disposable cartridge and a portable analyzer. The customizable single-use plastic cartridges, which now can be manufactured in high volumes using injection molding, are designed for analytical performance, ease of use, reproducibility, and low cost. These labcard devices implement high surface area nano-structured biomarker capture elements that enable high performance signaling and are index-matched to real-world biological specimens. This detection modality, along with the convenience of on-chip fluid storage in blisters and self-contained waste, represents a standard process to digitize biological signatures at the point-of-care. A companion portable analyzer prototype has been developed to integrate fluid motivation, optical detection, and automated data analysis, and it serves as the human interface for complete assay automation. In this report, we provide a systems-level perspective of the p-BNC universal biosensing platform with an emphasis on flow control, device integration, and automation. To demonstrate the flexibility of the p-BNC, we distinguish diseased and non-case patients across three significant disease applications: prostate cancer, ovarian cancer, and acute myocardial infarction. Progress towards developing a rapid 7 minute myoglobin assay is presented using the fully automated p-BNC system.