Additive Manufacturable Materials for Electrochemical Biosensor Electrodes

With the impending Industrial Revolution 4.0, the information produced by sensors will be central in many applications. This includes the healthcare sector, where affordable healthcare and precision medicine are highly sought after. Electrochemical sensors have the potential to produce affordable, high sensitivity and specificity, intuitive, and rapid point‐of‐care diagnostics. Underpinning these achievements is the choice of material and the fabrication thereof. In this review, the different types of materials used in electrochemical biosensors are reported, with a focus on synthetic conductive materials. The review demonstrates that there is an abundance of materials to select from, and compositing different types of materials further widens their applicability in biosensors. In addition, the fabrication of such materials using the state‐of‐the‐art of fabrication technology, additive manufacturing (AM), is also detailed. The need for compositing is evident in AM, as the feedstock for certain AM technologies is inherently nonconductive. Both material choice and fabrication technologies limitations are also discussed to highlight opportunities for growth. The review highlights how recent technological advancements have the potential to drive the healthcare industry toward achieving its primary goals

Optical biosensors - Illuminating the path to personalized drug dosing

Optical biosensors are low-cost, sensitive and portable devices that are poised to revolutionize the medical industry. Healthcare monitoring has already been transformed by such devices, with notable recent applications including heart rate monitoring in smartwatches and COVID-19 lateral flow diagnostic test kits. The commercial success and impact of existing optical sensors has galvanized research in expanding its application in numerous disciplines. Drug detection and monitoring seeks to benefit from the fast-approaching wave of optical biosensors, with diverse applications ranging from illicit drug testing, clinical trials, monitoring in advanced drug delivery systems and personalized drug dosing. The latter has the potential to significantly improve patients' lives by minimizing toxicity and maximizing efficacy. To achieve this, the patient's serum drug levels must be frequently measured. Yet, the current method of obtaining such information, namely therapeutic drug monitoring (TDM), is not routinely practiced as it is invasive, expensive, time-consuming and skilled labor-intensive. Certainly, optical sensors possess the capabilities to challenge this convention. This review explores the current state of optical biosensors in personalized dosing with special emphasis on TDM, and provides an appraisal on recent strategies. The strengths and challenges of optical biosensors are critically evaluated, before concluding with perspectives on the future direction of these sensors

Electrochemical biosensors: a nexus for precision medicine

Precision medicine is a field with huge potential for improving a patient's quality of life, wherein therapeutic drug monitoring (TDM) can provide actionable insights. More importantly, incorrect drug dose is a common contributor to medical errors. However, current TDM practice is time-consuming and expensive, and requires specialised technicians. One solution is to use electrochemical biosensors (ECBs), which are inexpensive, portable, and highly sensitive. In this review, we explore the potential for ECBs as a technology for on-demand drug monitoring, including microneedles, continuous monitoring, synthetic biorecognition elements, and multi-material electrodes. We also highlight emerging strategies to achieve continuous drug monitoring, and conclude by appraising recent developments and providing an outlook for the field

Disrupting 3D printing of medicines with machine learning.

3D printing (3DP) is a progressive technology capable of transforming pharmaceutical development. However, despite its promising advantages, its transition into clinical settings remains slow. To make the vital leap to mainstream clinical practice and improve patient care, 3DP must harness modern technologies. Machine learning (ML), an influential branch of artificial intelligence, may be a key partner for 3DP. Together, 3DP and ML can utilise intelligence based on human learning to accelerate drug product development, ensure stringent quality control (QC), and inspire innovative dosage-form design. With ML's capabilities, streamlined 3DP drug delivery could mark the next era of personalised medicine. This review details how ML can be applied to elevate the 3DP of pharmaceuticals and importantly, how it can expedite 3DP's integration into mainstream healthcare

Primary Care Workforce And Continuous Medical Education In China: Lessons To Learn From A Nationwide Cross-sectional Survey

published_or_final_versio

Inkjet drug printing onto contact lenses: Deposition optimisation and non-invasive dose verification

Inkjet printing has the potential to advance the treatment of eye diseases by printing drugs on demand onto contact lenses for localised delivery and personalised dosing, while near-infrared (NIR) spectroscopy can further be used as a quality control method for quantifying the drug but has yet to be demonstrated with contact lenses. In this study, a glaucoma therapy drug, timolol maleate, was successfully printed onto contact lenses using a modified commercial inkjet printer. The drug-loaded ink prepared for the printer was designed to match the properties of commercial ink, whilst having maximal drug loading and avoiding ocular inflammation. This setup demonstrated personalised drug dosing by printing multiple passes. Light transmittance was found to be unaffected by drug loading on the contact lens. A novel dissolution model was built, and in vitro dissolution studies showed drug release over at least 3 h, significantly longer than eye drops. NIR was used as an external validation method to accurately quantify the drug dose. Overall, the combination of inkjet printing and NIR represent a novel method for point-of-care personalisation and quantification of drug-loaded contact lenses

Integrating pressure sensor control into semi-solid extrusion 3D printing to optimize medicine manufacturing

Semi-solid extrusion (SSE) is a three-dimensional printing (3DP) process that involves the extrusion of a gel or paste-like material via a syringe-based printhead to create the desired object. In pharmaceuticals, SSE 3DP has already been used to manufacture formulations for human clinical studies. To further support its clinical adoption, the use of a pressure sensor may provide information on the printability of the feedstock material in situ and under the exact printing conditions for quality control purposes. This study aimed to integrate a pressure sensor in an SSE pharmaceutical 3D printer for both material characterization and as a process analytical technology (PAT) to monitor the printing process. In this study, three materials of different consistency were tested (soft vaseline, gel-like mass and paste-like mass) under 12 different conditions, by changing flow rate, temperature, or nozzle diameter. The use of a pressure sensor allowed, for the first time, the characterization of rheological properties of the inks, which exhibited temperature-dependent, plastic and viscoelastic behaviours. Controlling critical material attributes and 3D printing process parameters may allow a quality by design (QbD) approach to facilitate a high-fidelity 3D printing process critical for the future of personalized medicine

Machine learning predicts 3D printing performance of over 900 drug delivery systems

Three-dimensional printing (3DP) is a transformative technology that is advancing pharmaceutical research by producing personalized drug products. However, advances made via 3DP have been slow due to the lengthy trial-and-error approach in optimization. Artificial intelligence (AI) is a technology that could revolutionize pharmaceutical 3DP through analyzing large datasets. Herein, literature-mined data for developing AI machine learning (ML) models was used to predict key aspects of the 3DP formulation pipeline and in vitro dissolution properties. A total of 968 formulations were mined and assessed from 114 articles. The ML techniques explored were able to learn and provide accuracies as high as 93% for values in the filament hot melt extrusion process. In addition, ML algorithms were able to use data from the composition of the formulations with additional input features to predict the drug release of 3D printed medicines. The best prediction was obtained by an artificial neural network that was able to predict drug release times of a formulation with a mean error of ±24.29 min. In addition, the most important variables were revealed, which could be leveraged in formulation development. Thus, it was concluded that ML proved to be a suitable approach to modelling the 3D printing workflow

Associations of stunting in early childhood with cardiometabolic risk factors in adulthood

Abstract Early life stunting may have long-term effects on body composition, resulting in obesity-related comorbidities. We tested the hypothesis that individuals stunted in early childhood may be at higher cardiometabolic risk later in adulthood. 1753 men and 1781 women participating in the 1982 Pelotas (Brazil) birth cohort study had measurements of anthropometry, body composition, lipids, glucose, blood pressure, and other cardiometabolic traits at age 30 years. Early stunting was defined as height-for-age Z-score at age 2 years below -2 against the World Health Organization growth standards. Linear regression models were performed controlling for sex, maternal race/ethnicity, family income at birth, and birthweight. Analyses were stratified by sex when p-interaction<0.05. Stunted individuals were shorter (β=-0.71 s.d.; 95% CI: -0.78 to -0.64), had lower BMI (β=-0.14 s.d.; 95%CI: -0.25 to -0.03), fat mass (β=-0.28 s.d.; 95%CI: -0.38 to -0.17), SAFT (β=-0.16 s.d.; 95%CI: -0.26 to -0.06), systolic (β=-0.12 s.d.; 95%CI: -0.21 to -0.02) and diastolic blood pressure (β=-0.11 s.d.; 95%CI: -0.22 to -0.01), and higher VFT/SAFT ratio (β=0.15 s.d.; 95%CI: 0.06 to 0.24), in comparison with non-stunted individuals. In addition, early stunting was associated with lower fat free mass in both men (β=-0.39 s.d.; 95%CI: -0.47 to -0.31) and women (β=-0.37 s.d.; 95%CI: -0.46 to -0.29) after adjustment for potential confounders. Our results suggest that early stunting has implications on attained height, body composition and blood pressure. The apparent tendency of stunted individuals to accumulate less fat-free mass and subcutaneous fat might predispose them towards increased metabolic risks in later life.The last phase of the 1982 Pelotas (Brazil) birth cohort study was supported by the Wellcome Trust and the Fundação de Aparo à Pesquisa do Estado do Rio Grande do Sul; Brazil (Edital 04/2012 – PQG; Processo 12/2185-9). Earlier phases were funded by the International Development Research Centre (Canada), the WHO (Department of Child and Adolescent Health and Development and Human Reproduction Programme) to BLH, the Overseas Development Administration (currently the Department for International Development, United Kingdom), the European Union, the United Nations Development Fund for Women, the National Program for Centres of Excellence, the Pastorate of the Child (Brazil), the National Council for Scientific and Technological Development (CNPq; Brazil), and the Ministry of Health (Brazil). GVAF was supported by the Brazilian Coordination of Improvement of Higher Education Personnel (scholarship process BEX 5077/13-3). EDLR and KKO are supported by the Medical Research Council [Unit Programme number MC_UU_12015/2]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript