Revolutionizing drug formulation development: The increasing impact of machine learning

Peptide-Hitchhiking for the Development of Nanosystems in Glioblastoma

Glioblastoma (GBM) remains the epitome of aggressiveness and lethality in the spectrum of brain tumors, primarily due to the blood-brain barrier (BBB) that hinders effective treatment delivery, tumor heterogeneity, and the presence of treatment-resistant stem cells that contribute to tumor recurrence. Nanoparticles (NPs) have been used to overcome these obstacles by attaching targeting ligands to enhance therapeutic efficacy. Among these ligands, peptides stand out due to their ease of synthesis and high selectivity. This article aims to review single and multiligand strategies critically. In addition, it highlights other strategies that integrate the effects of external stimuli, biomimetic approaches, and chemical approaches as nanocatalytic medicine, revealing their significant potential in treating GBM with peptide-functionalized NPs. Alternative routes of parenteral administration, specifically nose-to-brain delivery and local treatment within the resected tumor cavity, are also discussed. Finally, an overview of the significant obstacles and potential strategies to overcome them are discussed to provide a perspective on this promising field of GBM therapy.

Lipid nanoparticle (LNP) mediated mRNA delivery in cardiovascular diseases: Advances in genome editing and CAR T cell therapy

Cardiovascular diseases (CVDs) are the leading cause of global mortality among non-communicable diseases. Current cardiac regeneration treatments have limitations and may lead to adverse reactions. Hence, innovative technologies are needed to address these shortcomings. Messenger RNA (mRNA) emerges as a promising therapeutic agent due to its versatility in encoding therapeutic proteins and targeting "undruggable" conditions. It offers low toxicity, high transfection efficiency, and controlled protein production without genome insertion or mutagenesis risk. However, mRNA faces challenges such as immunogenicity, instability, and difficulty in cellular entry and endosomal escape, hindering its clinical application. To overcome these hurdles, lipid nanoparticles (LNPs), notably used in COVID-19 vaccines, have a great potential to deliver mRNA therapeutics for CVDs. This review highlights recent progress in mRNA-LNP therapies for CVDs, including Myocardial Infarction (MI), Heart Failure (HF), and hypercholesterolemia. In addition, LNP-mediated mRNA delivery for CAR T-cell therapy and CRISPR/Cas genome editing in CVDs and the related clinical trials are explored. To enhance the efficiency, safety, and clinical translation of mRNA-LNPs, advanced technologies like artificial intelligence (AGILE platform) in RNA structure design, and optimization of LNP formulation could be integrated. We conclude that the strategies to facilitate the extra-hepatic delivery and targeted organ tropism of mRNA-LNPs (SORT, ASSET, SMRT, and barcoded LNPs) hold great prospects to accelerate the development and translation of mRNA-LNPs in CVD treatment.

The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies

The advancement of artificial intelligence (AI), algorithm optimization and high-throughput experiments has enabled scientists to accelerate the discovery of new chemicals and materials with unprecedented efficiency, resilience and precision. Over the recent years, the so-called autonomous experimentation (AE) systems are featured as key AI innovation to enhance and accelerate research and development (R&D). Also known as self-driving laboratories or materials acceleration platforms, AE systems are digital platforms capable of running a large number of experiments autonomously. Those systems are rapidly impacting biomedical research and clinical innovation, in areas such as drug discovery, nanomedicine, precision oncology, and others. As it is expected that AE will impact healthcare innovation from local to global levels, its implications for science and technology in emerging economies should be examined. By examining the increasing relevance of AE in contemporary R&D activities, this article aims to explore the advancement of artificial intelligence in biomedical research and health innovation, highlighting its implications, challenges and opportunities in emerging economies. AE presents an opportunity for stakeholders from emerging economies to co-produce the global knowledge landscape of AI in health. However, asymmetries in R&D capabilities should be acknowledged since emerging economies suffers from inadequacies and discontinuities in resources and funding. The establishment of decentralized AE infrastructures could support stakeholders to overcome local restrictions and opens venues for more culturally diverse, equitable, and trustworthy development of AI in health-related R&D through meaningful partnerships and engagement. Collaborations with innovators from emerging economies could facilitate anticipation of fiscal pressures in science and technology policies, obsolescence of knowledge infrastructures, ethical and regulatory policy lag, and other issues present in the Global South. Also, improving cultural and geographical representativeness of AE contributes to foster the diffusion and acceptance of AI in health-related R&D worldwide. Institutional preparedness is critical and could enable stakeholders to navigate opportunities of AI in biomedical research and health innovation in the coming years.

Nucleic acids in modern molecular therapies: A realm of opportunities for strategic drug design

RNA vaccines have made evident to society what was already known by the scientific community: nucleic acids will be the "drugs of the future." By modifying the genome, interfering in transcription or translation, and by introducing new catalysts into the cell or by mimicking antibody effects, nucleic acids can generate therapeutic activities that are not accessible by any other therapeutic agents. There are, however, challenges that need to be solved in the next few years to make nucleic acids usable in a wide range of therapeutic scenarios. This review illustrates how simulation methods can help achieve this goal.

Pharmacokinetics and relative bioavailability study of two cefquinome sulfate intramammary infusions in cow milk

In this study, two intramammary infusions of cefquinome sulfate were investigated for pharmacokinetics and bioavailability. Twelve lactating cows for each group were administered an effective dose of 75 mg/gland for cefquinome, with milk samples collected at various time intervals. The concentrations of cefquinome in milk at different times were determined by the UPLC-MS/MS method. Analyses of noncompartmental pharmacokinetics were conducted on the concentration of cefquinome in milk. Mean pharmacokinetic parameters of group A and group B following intramammary administration were as follows: AUClast 300558.57 ± 25052.78 ng/mL and 266551.3 ± 50654.85 ng/mL, Cmax 51786.35 ± 11948.4 ng/mL and 59763.7 ± 8403.2 ng/mL, T1/2 5.69 ± 0.62 h and 5.25 ± 1.62 h, MRT 7.43 ± 0.79 h and 4.8 ± 0.78 h, respectively. Pharmacokinetic experiments showed that the relative bioavailability of group B was 88.69% that of group A. From our findings, group B (3 g: 75 mg) shows a quicker drug elimination process than group A (8 g: 75 mg), which suggests that the withdrawal period for the new formulation may be shorter.

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the "State-of-the-Art and Gaps Extracted from Results and Discussions" section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the "Literature Review Guided by Artificial Intelligence" section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field.

Integrated machine learning and deep learning for predicting diabetic nephropathy model construction, validation, and interpretability

OBJECTIVE

To construct a risk prediction model for assisted diagnosis of Diabetic Nephropathy (DN) using machine learning algorithms, and to validate it internally and externally.

METHODS

Firstly, the data was cleaned and enhanced, and was divided into training and test sets according to the 7:3 ratio. Then, the metrics related to DN were filtered by difference analysis, Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination (RFE), and Max-relevance and Min-redundancy (MRMR) algorithms. Ten machine learning models were constructed based on the key variables. The best model was filtered by Receiver Operating Characteristic (ROC), Precision-Recall (PR), Accuracy, Matthews Correlation Coefficient (MCC), and Kappa, and was internally and externally validated. Based on the best model, an online platform had been constructed.

RESULTS

15 key variables were selected, and among the 10 machine learning models, the Random Forest model achieved the best predictive performance. In the test set, the area under the ROC curve was 0.912, and in two external validation cohorts, the area under the ROC curve was 0.828 and 0.863, indicating excellent predictive and generalization abilities.

CONCLUSION

The model has a good predictive value and is expected to help in the early diagnosis and screening of clinical DN.

Rising role of 3D-printing in delivery of therapeutics for infectious disease

Modern drug delivery to tackle infectious disease has drawn close to personalizing medicine for specific patient populations. Challenges include antibiotic-resistant infections, healthcare associated infections, and customizing treatments for local patient populations. Recently, 3D-printing has become a facilitator for the development of personalized pharmaceutic drug delivery systems. With a variety of manufacturing techniques, 3D-printing offers advantages in drug delivery development for controlled, fine-tuned release and platforms for different routes of administration. This review summarizes 3D-printing techniques in pharmaceutics and drug delivery focusing on treating infectious diseases, and discusses the influence of 3D-printing design considerations on drug delivery platforms targeting these diseases. Additionally, applications of 3D-printing in infectious diseases are summarized, with the goal to provide insight into how future delivery innovations may benefit from 3D-printing to address the global challenges in infectious disease.

Accelerated chemical science with AI

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.