A Comprehensive Review on Potential Chemical and Herbal Permeation Enhancers Used in Transdermal Drug Delivery Systems

Using skin patches to deliver drugs is dependable and doesn't have the same issues as permeation enhancers, which help drugs get through the skin but struggle because of the skin's natural barrier. Strategies are required to increase topical bioavailability to enhance drug absorption. Natural compounds offer a promising solution by temporarily reducing skin barrier resistance and improving drug absorption. Natural substances allow a wider variety of medications to be distributed through the stratum corneum, offering a dependable approach to enhancing transdermal drug delivery. Natural substances have distinct advantages as permeability enhancers. They are pharmacologically effective and safe, inactive, non-allergenic, and non-irritating. These characteristics ensure their suitability for use without causing adverse effects. Natural compounds are readily available and well tolerated by the body. Studies investigating the structure-activity relationship of natural chemicals have demonstrated significant enhancer effects. By understanding the connection between chemical composition and enhancer activity, researchers can identify effective natural compounds for improving drug penetration. In conclusion, current research focuses on utilizing natural compounds as permeability enhancers in transdermal therapy systems. These substances offer safety, non-toxicity, pharmacological inactivity, and non-irritation. Through structure-activity relationship investigations, promising advancements have been made in enhancing drug delivery. Using natural compounds holds enormous potential for improving the penetration of trans-dermally delivered medications.

A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning

Robust, fault tolerant, and available systems are fundamental for the adoption of Internet of Things (IoT) in critical domains, such as finance, health, and safety. The IoT infrastructure is often used to collect a large amount of data to meet the business demands of Smart Cities, Industry 4.0, and Smart Home, but there is a opportunity to use these data to intrinsically monitor an IoT system in an autonomous way. A Test Driven Development (TDD) approach for automatic module assessment for ESP32 and ESP8266 IoT development devices based on unsupervised Machine Learning (ML) is proposed to monitor IoT device status. A framework consisting of business drivers, non-functional requirements, engineering view, dynamic system evaluation, and recommendations phases is proposed to be used with the TDD development tool. The proposal is evaluated in academic and smart home study cases with 25 devices, consisting of 15 different firmware versions collected in one week, with a total of over 550,000 IoT status readings. The K-Means algorithm was applied to free memory available, internal temperature, and Wi-Fi level metrics to automatically monitor the IoT devices under development to identify device constraints violation and provide insights for monitoring frequency configuration of different firmware versions. To the best of the authors' knowledge, it is the first TDD approach for IoT module automatic assessment which uses machine learning based on the real testbed data. The IoT status monitoring and the Python scripts for model training and inference with K-Means algorithm are available under a Creative Commons license.

Three-variate trajectories of metabolic control, body mass index, and insulin dose: Heterogeneous response to initiation of pump therapy in youth with type 1 diabetes

OBJECTIVE

Continuous subcutaneous insulin infusion (CSII) in youths with type 1 diabetes (T1D) is often associated with lower HbA1c, lower total daily insulin dose (TDD), and lower body mass index (BMI) compared with multiple daily injections (MDI). Individual responses to CSII are diverse. The aim was to identify unique three-variate patterns of HbA1c, BMI standard deviation score (SDS), and TDD after switching to CSII.

METHODS

Five thousand one hundred and thirty-three youths (≤20 years; 48% boys; median age at pump start 12.5 years) with T1D duration ≥3 years at CSII initiation were selected from the multicenter DPV registry. We applied group-based multitrajectory modeling to identify groups of individuals following similar trajectories. Measurements were aggregated quarterly during a 3-year follow-up period. Trajectory variables were changes of HbA1c, BMI-SDS, and TDD from baseline (delta = quarterly aggregated values at each time point [i] minus the respective baseline value).

RESULTS

Four groups of diverging Delta-HbA1c, Delta-BMI-SDS, and Delta-TDD patterns were identified. All showed improvements in HbA1c during the first 3 months. Group 1 (12%) was characterized by modest HbA1c increase thereafter, TDD reduction, and stable BMI-SDS. In Group 2 (39%), increasing HbA1c, decreasing BMI-SDS, and stable TDD were found. By contrast, sustainably improved HbA1c, increasing BMI-SDS, and stable TDD were observed in Group 3 (32%). Group 4 (17%) was characterized by increasing levels for HbA1c, BMI-SDS, and TDD. Between-group differences in baseline HbA1c, BMI-SDS, TDD as well as in sex ratio, age at diabetes onset and at pump start were observed.

CONCLUSIONS

Definite trajectories of glycemic control, BMI, and TDD over 3 years after CSII initiation were identified in youths with T1D allowing a more personalized treatment recommendation.

Recent advances in phenotypic drug discovery

There is a great need for innovative new medicines to treat unmet medical needs. The discovery and development of innovative new medicines is extremely difficult, costly, and inefficient. In the last decade, phenotypic drug discovery (PDD) was reintroduced as a strategy to provide first-in-class medicines. PDD uses empirical, target-agnostic lead generation to identify pharmacologically active molecules and novel therapeutics which work through unprecedented drug mechanisms. The economic and scientific value of PDD is exemplified through game-changing medicines for hepatitis C virus, spinal muscular atrophy, and cystic fibrosis. In this short review, recent advances are noted for the implementation and de-risking of PDD (for compound library selection, biomarker development, mechanism identification, and safety studies) and the potential for artificial intelligence. A significant barrier in the decision to implement PDD is balancing the potential impact of a novel mechanism of drug action with an under-defined scientific path forward, with the desire to provide infrastructure and metrics to optimize return on investment, which a known mechanism provides. A means to address this knowledge gap in the future is to empower precompetitive research utilizing the empirical concepts of PDD to identify new mechanisms and pharmacologically active compounds.

Determination of Effective Mode Selection for Ensuring Spectrum Efficiency with Massive MIMO in IoT Systems

Wireless Sensor Networks (WSNs) based on Internet of Things (IoT) applications are increasing day by day. These applications include healthcare, infrastructure monitoring, smart homes, wearable devices and smart cars. However, considering the fact that many different application areas will emerge in next generation wireless communication systems, efficient use of frequency spectrum is important. Because the whole frequency spectrum is now very crowded, it is important to ensure maximum spectrum efficiency for effective WSNs based on IoT. This study sought to determine which mode more effectively achieves spectrum efficiency for the performance of effective IoT systems under given conditions with respect to the length of the pilot sequence, Time Division Duplexing (TDD) or Frequency Division Duplexing (FDD). The results were obtained by Monte Carlo simulations. To the best of our knowledge, a study of effective mode selection analysis for spectrum efficiency in IoT based systems has not been available in the literature yet. The results of this study are useful for determining the appropriate design conditions for WSNs based on IoT.

Integrating EMDR into an evolutionary-based therapy for depression: a case study

We present an intervention in a case of major depression, where eye movement desensitization and reprocessing (EMDR) therapy was integrated into an evolutionary-based psychotherapy for depression. At the end of the treatment and at follow up assessment we observed a more accepting disposition and decreased depressive but not anxiety symptoms.

Neoclassic drug discovery: the case for lead generation using phenotypic and functional approaches

Innovation and new molecular entity production by the pharmaceutical industry has been below expectations. Surprisingly, more first-in-class small-molecule drugs approved by the U.S. Food and Drug Administration (FDA) between 1999 and 2008 were identified by functional phenotypic lead generation strategies reminiscent of pre-genomics pharmacology than contemporary molecular targeted strategies that encompass the vast majority of lead generation efforts. This observation, in conjunction with the difficulty in validating molecular targets for drug discovery, has diminished the impact of the "genomics revolution" and has led to a growing grassroots movement and now broader trend in pharma to reconsider the use of modern physiology-based or phenotypic drug discovery (PDD) strategies. This "From the Guest Editors" column provides an introduction and overview of the two-part special issues of Journal of Biomolecular Screening on PDD. Terminology and the business case for use of PDD are defined. Key issues such as assay performance, chemical optimization, target identification, and challenges to the organization and implementation of PDD are discussed. Possible solutions for these challenges and a new neoclassic vision for PDD that combines phenotypic and functional approaches with technology innovations resulting from the genomics-driven era of target-based drug discovery (TDD) are also described. Finally, an overview of the manuscripts in this special edition is provided.