Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Optimizing asymmetric antibody purification: a semi-automated process and its digital integration

Over the past decades, immunization and display technologies have considerably increased the potential for generating new binders against cell surface targets. Concomitantly, the complexity of biologic therapeutic drugs has also increased, with new asymmetric formats such as bispecific antibodies or antibody fusion proteins making the supply of molecules for preclinical drug discovery more challenging. The purification of those molecules is crucial, and an efficient purification platform for drug discovery research units should have multiple aims. First, it needs to deliver the highest quality proteins for research activities at a fast pace in order to increase screening capacities. Second, it has to deliver protein with sufficient yield in order to cover the project requirements and minimize the repetition of production cycles. Through a case study for a bispecific antibody, we describe a semi-automated and digitalized purification platform aiming at accelerating and optimizing the supply of asymmetric antibodies for drug discovery. We show how the automation of repetitive tasks and the digitalization of the process can lead to increased throughput in the context of complex purifications, including a cation exchange chromatography separation step. Furthermore, we highlight how process digitalization leads to enhanced data capture and accessibility, facilitating decision-making along the purification process. With a maximal throughput of 36 asymmetric antibodies per week and data proving the consistency of the quality delivered, this platform represents a step forward in the supply of complex antibody formats for preclinical drug discovery.

Advancing Alzheimer's Therapy: Computational strategies and treatment innovations

Alzheimer's disease (AD) is a multifaceted neurodegenerative condition distinguished by the occurrence of memory impairment, cognitive deterioration, and neuronal impairment. Despite extensive research efforts, conventional treatment strategies primarily focus on symptom management, highlighting the need for innovative therapeutic approaches. This review explores the challenges of AD treatment and the integration of computational methodologies to advance therapeutic interventions. A comprehensive analysis of recent literature was conducted to elucidate the broad scope of Alzheimer's etiology and the limitations of conventional drug discovery approaches. Our findings underscore the critical role of computational models in elucidating disease mechanisms, identifying therapeutic targets, and expediting drug discovery. Through computational simulations, researchers can predict drug efficacy, optimize lead compounds, and facilitate personalized medicine approaches. Moreover, machine learning algorithms enhance early diagnosis and enable precision medicine strategies by analyzing multi-modal datasets. Case studies highlight the application of computational techniques in AD therapeutics, including the suppression of crucial proteins implicated in disease progression and the repurposing of existing drugs for AD management. Computational models elucidate the interplay between oxidative stress and neurodegeneration, offering insights into potential therapeutic interventions. Collaborative efforts between computational biologists, pharmacologists, and clinicians are essential to translate computational insights into clinically actionable interventions, ultimately improving patient outcomes and addressing the unmet medical needs of individuals affected by AD. Overall, integrating computational methodologies represents a promising paradigm shift in AD therapeutics, offering innovative solutions to overcome existing challenges and transform the landscape of AD treatment.

The current landscape of aromatase inhibitors for the treatment of estrogen receptor-positive breast carcinoma

Estrogen receptor-positive (ER+) breast carcinoma represents a significant portion of breast cancer cases and is characterized by the presence of estrogen receptors that promote tumor growth upon estrogen binding. ER + breast cancer progression involves hormonal influences, interactions within the tumor microenvironment, and genetic mutations that may lead to treatment resistance. Successful therapeutic options include hormonal therapies, particularly aromatase inhibitors (AIs), which aim to block the effects of estrogen or reduce its synthesis. With higher efficacy than tamoxifen, AIs such as anastrozole, letrozole, and exemestane have become widely employed in adjuvant and first-line treatments for advanced breast cancer. AIs function by inhibiting the enzyme aromatase, which converts androgens into estrogens in the peripheral tissues. Because too much estrogen might promote tumor growth, this decrease in estrogen levels is essential for treating ER+ malignancies. To provide a comprehensive overview of AIs in the treatment of ER+ breast cancer, this study examined the pharmacokinetics, clinical uses, mechanisms of action, and problems with treatment resistance. To maximize therapeutic approaches and enhance patient outcomes in the treatment of ER breast cancer, it is imperative to understand these characteristics.

Epigenetic targets and their inhibitors in the treatment of idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a deadly lung disease characterized by fibroblast proliferation, excessive extracellular matrix buildup, inflammation, and tissue damage, resulting in respiratory failure and death. Recent studies suggest that impaired interactions among epithelial, mesenchymal, immune, and endothelial cells play a key role in IPF development. Advances in bioinformatics have also linked epigenetics, which bridges gene expression and environmental factors, to IPF. Despite the incomplete understanding of the pathogenic mechanisms underlying IPF, recent preclinical studies have identified several novel epigenetic therapeutic targets, including DNMT, EZH2, G9a/GLP, PRMT1/7, KDM6B, HDAC, CBP/p300, BRD4, METTL3, FTO, and ALKBH5, along with potential small-molecule inhibitors relevant for its treatment. This review explores the pathogenesis of IPF, emphasizing epigenetic therapeutic targets and potential small molecule drugs. It also analyzes the structure-activity relationships of these epigenetic drugs and summarizes their biological activities. The objective is to advance the development of innovative epigenetic therapies for IPF.

Artificial intelligence in drug resistance management

This review highlights the application of artificial intelligence (AI), particularly deep learning and machine learning (ML), in managing antimicrobial resistance (AMR). Key findings demonstrate that AI models, such as Naïve Bayes, Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), have significantly advanced the prediction of drug resistance patterns and the identification of novel antibiotics. These algorithms have effectively optimized antibiotic use, predicted resistance phenotypes, and identified new drug candidates. AI has also facilitated the detection of AMR-associated mutations, offering new insights into the spread of resistance and potential interventions. Despite data privacy and algorithm transparency challenges, AI presents a promising tool in combating AMR, with implications for improving patient outcomes, enhancing disease management, and addressing global public health concerns. However, realizing its full potential requires overcoming issues related to data scarcity, ethical considerations, and fostering interdisciplinary collaboration.

Synthetic and Biogenic Materials for Oral Delivery of Biologics: From Bench to Bedside

The development of nucleic acid and protein drugs for oral delivery has lagged behind their production for conventional nonoral routes. Over the past decade, the evolution of DNA- and RNA-based technologies combined with the innovation of state-of-the-art delivery vehicles for nucleic acids has brought rapid advancements to the biopharmaceutical field. Nucleic acid therapies have the potential to achieve long-lasting effects, or even cures, by inhibiting or editing genes, which is not possible with conventional small-molecule drugs. However, challenges and limitations must be addressed before these therapies can provide cures for chronic conditions and rare diseases, rather than only offering temporary relief. Nucleic acids and proteins face premature degradation in the acidic, enzyme-rich stomach environment and are rapidly cleared by the liver. To overcome these challenges, various delivery vehicles have been developed to transport therapeutic compounds to the intestines, where the active compounds are released and gut microbiota and mucosal immune system also play an important role. This review provides a comprehensive overview of the promises and pitfalls associated with the oral route of administration of biologics, current delivery systems, applications of orally delivered therapeutics, and the challenges and considerations for translation of nucleic acid and protein therapeutics into clinical practice.

Mechanism Exploration of Dietary Supplement Astaxanthin on Improving Atherosclerosis through an Integrated Strategy Encompassing Artificial Intelligence Virtual Screening and Experimental Validation

Atherosclerosis (AS) is a major and common pathological basis of ischemic intestinal infarction, myocardial infarction, stroke, renal failure, and other highly lethal and disabling diseases. Current pharmacological interventions (e.g., statins) often cause adverse effects, limiting their long-term use. Natural compounds, with their multitarget efficacy and superior safety profiles, have emerged as promising alternatives for AS treatment. As a potent antioxidant carotenoid, astaxanthin exhibits unique therapeutic potential by simultaneously targeting inflammation, oxidative stress, and lipid metabolism, which are key drivers of AS pathogenesis. This study will systematically decipher astaxanthin's therapeutic mechanisms through an integrative strategy encompassing artificial intelligence virtual screening and experimental validation. Notably, five proteins, including CTSD, DPP4, FABP5, ITGAL, and MMP9, were identified as core targets for astaxanthin intervention in AS via network pharmacology and machine learning. Meanwhile, the results from molecular dynamic simulations confirmed that these core targets can stable binding with astaxanthin. Furthermore, in vitro experiments further validated astaxanthin can inhibit foam cell formation, restore redox balance, and suppress inflammation. Moreover, a close correlation has been found between them. These findings position astaxanthin as a multitarget natural agent to combat AS, addressing both efficacy advantage and safety concerns of current therapies.

Advances in artificial intelligence-envisioned technologies for protein and nucleic acid research

Artificial intelligence (AI) and machine learning (ML) have revolutionized pharmaceutical research, particularly in protein and nucleic acid studies. This review summarizes the current status of AI and ML applications in the pharmaceutical sector, focusing on innovative tools, web servers, and databases. This paper highlights how these technologies address key challenges in drug development including high costs, lengthy timelines, and the complexity of biological systems. Furthermore, the potential of AI in personalized medicine, cancer drug response prediction, and biomarker identification is discussed. The integration of AI and ML in pharmaceutical research promises to accelerate drug discovery, reduce development costs, and ultimately lead to more effective and personalized therapeutic strategies.

Perfluorooctane sulfonic acid: a possible risk factor of endothelial dysfunction based on in silico and in vitro studies

Perfluorooctane sulfonic acid (PFOS) is a fluorinated chemical utilized in a variety of industrial and household products. PFOS has been detected in human serum and is associated with multiple human adverse health effects. Epidemiological evidence has linked PFOS exposure to endothelial dysfunction, which is a key contributor to atherosclerosis. However, the underlying mechanisms of PFOS-induced endothelial dysfunction associated atherosclerosis has not been investigated. In the present study, human microvascular endothelial cells (HMEC-1) exposed to PFOS (15 μM) for 72 h, mimicking long-term exposure. We further employed integrated RNA-sequencing (RNA-seq) and transcriptomic analysis to identify differentially expressed genes (DEGs) for biological alterations: gene ontology (GO), pathway enrichment analysis (KEGG), protein-protein interaction network and modular clustering analysis. Furthermore, the Metascape database was used for disease association, tissue specificity, and transcription factor analysis. Hub genes were verified using atherosclerosis patient data sets from the GEO dataset. Alteration of hub genes in patients was then validated using immunoblotting and ELISA. Our results revealed that PFOS altered amino acid biosynthesis, lipid metabolism and induced the ER-stress response through the HRI/eIF2α/ATF4 pathway, leading to endothelial dysfunction. Interestingly, we found that PFOS induced inflammation by increasing COX-2, ICAM-1 and IL-6 expression through NF-κB and JAK2/STAT3 pathway in endothelial cells. Moreover, up-regulated C/EBPβ and ATF4 were observed in both patients and PFOS-exposed endothelium, which may use as an early biomarker and may have a potential role in PFOS-induced endothelial dysfunction. These findings provide novel insight into the underlying molecular mechanisms of PFOS-induced endothelial dysfunction associated with atherosclerosis.

Supervised machine learning for predicting drug release from acetalated dextran nanofibers

Electrospun drug-loaded polymeric nanofibers can improve the efficacy of therapeutics for a variety of implications. By design, these biomaterial platforms can enhance drug bioavailability and site-specific delivery while reducing off-target toxicities when compared to other conventional formulations. By incorporating biocompatible and biodegradable polymers with tunable degradation rates, such as acetalated dextran (Ace-DEX), drug-loaded nanofibers can enhance the safety and efficacy of treatment regimens while improving patient compliance through controlled release. Despite these benefits, clinical translation of electrospun formulations is challenged by labor-intensive in vitro studies for ensuring that release kinetics are accurately characterized and reproducible. In this study, we report a novel workflow for assessing in vitro drug release from Ace-DEX nanofibers using machine learning (ML) and develop a predictive model to streamline this rate-limiting step. The developed Gaussian process regression (GPR) model was trained, validated, and optimized using in vitro release profiles from thirty electrospun Ace-DEX scaffolds. The results of GPR model simulations reveal consistent performance across all Ace-DEX formulations considered in this study while also demonstrating a drug-agnostic approach to predict fractional drug release over time.

An update on knowledge graphs and their current and potential applications in drug discovery

INTRODUCTION

Knowledge graphs are becoming prominent tools in computational drug discovery. They effectively integrate heterogeneous biomedical data and generate new hypotheses and knowledge.

AREAS COVERED

This article is based on a literature review using Google Scholar and PubMed to retrieve articles on existing knowledge graphs relevant to the drug discovery field. The authors compare the types of entities, relationships, and data sources they encompass. Additionally, the authors provide examples of their use in the drug discovery field and discuss potential strategies for advancing this research area.

EXPERT OPINION

Knowledge graphs are crucial in drug discovery, but their construction leads to challenges in data integration and consistency. Future research should prioritize the standardization of data sources and data modeling. More efforts are needed for the integration in knowledge graphs of diverse data types, such as chemical structures and epigenetic data, to enhance their effectiveness. Additionally, advancements in large language models should be pursued to aid the development of knowledge graphs, provide intuitive querying capabilities for non-expert users, and explain knowledge graphs -derived predictions, thereby making these tools more accessible and their insights more interpretable for a wider audience.

Artificial intelligence in digital pathology - time for a reality check

The past decade has seen the introduction of artificial intelligence (AI)-based approaches aimed at optimizing several workflows across many medical specialties. In clinical oncology, the most promising applications include those involving image analysis, such as digital pathology. In this Perspective, we provide a comprehensive examination of the developments in AI in digital pathology between 2019 and 2024. We evaluate the current landscape from the lens of technological innovations, regulatory trends, deployment and implementation, reimbursement and commercial implications. We assess the technological advances that have driven improvements in AI, enabling more robust and scalable solutions for digital pathology. We also examine regulatory developments, in particular those affecting in-house devices and laboratory-developed tests, which are shaping the landscape of AI-based tools in digital pathology. Finally, we discuss the role of reimbursement frameworks and commercial investment in the clinical adoption of AI-based technologies. In this Perspective, we highlight both the progress and challenges in AI-driven digital pathology over the past 5 years, outlining the path forward for its adoption into routine practice in clinical oncology.

Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review

BACKGROUND

Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence of their real-world effectiveness remains unclear.

OBJECTIVE

To summarise the evidence on the effectiveness of NLP/ML in detecting ADEs from unstructured EHR data and ultimately improve pharmacovigilance in comparison to other data sources.

METHODS

A scoping review was conducted by searching six databases in July 2023. Studies leveraging NLP/ML to identify ADEs from EHR were included. Titles/abstracts were screened by two independent researchers as were full-text articles. Data extraction was conducted by one researcher and checked by another. A narrative synthesis summarises the research techniques, ADEs analysed, model performance and pharmacovigilance impacts.

RESULTS

Seven studies met the inclusion criteria covering a wide range of ADEs and medications. The utilisation of rule-based NLP, statistical models, and deep learning approaches was observed. Natural language processing/ML techniques with unstructured data improved the detection of under-reported adverse events and safety signals. However, substantial variability was noted in the techniques and evaluation methods employed across the different studies and limitations exist in integrating the findings into practice.

CONCLUSIONS

Natural language processing (NLP) and machine learning (ML) have promising possibilities in extracting valuable insights with regard to pharmacovigilance from unstructured EHR data. These approaches have demonstrated proficiency in identifying specific adverse events and uncovering previously unknown safety signals that would not have been apparent through structured data alone. Nevertheless, challenges such as the absence of standardised methodologies and validation criteria obstruct the widespread adoption of NLP/ML for pharmacovigilance leveraging of unstructured EHR data.

Strategies for the design of biomimetic cell-penetrating peptides using AI-driven in silico tools for drug delivery

Cell-penetrating peptides (CPP) have gained rapid attention over the last 25 years; this is attributed to their versatility, customisation, and 'Trojan horse' delivery that evades the immune system. However, the current CPP rational design process is limited, as it requires several rounds of peptide synthesis, prediction and wet-lab validation, which is expensive, time-consuming and requires extensive knowledge in peptide chemistry. Artificial intelligence (AI) has emerged as a promising alternative which can augment the design process, for example by determining physiochemical characteristics, secondary structure, solvent accessibility, disorder and flexibility, as well as predicting in vivo behaviour such as toxicity and peptidase degradation. Other more recent tools utilise supervised machine learning (ML) to predict the penetrative ability of an amino acid sequence. The use of AI in the CPP design process has the potential to reduce development costs and increase the chances of success with respect to delivery. This review provides a survey of in silico tools and AI platforms which can be utilised in the design process, and the key features that should be taken into consideration when designing next generation CPPs.

Revolutionizing prostate cancer therapy: Artificial intelligence - Based nanocarriers for precision diagnosis and treatment

Prostate cancer is one of the major health challenges in the world and needs novel therapeutic approaches to overcome the limitations of conventional treatment. This review delineates the transformative potential of artificial intelligence (AL) in enhancing nanocarrier-based drug delivery systems for prostate cancer therapy. With its ability to optimize nanocarrier design and predict drug delivery kinetics, AI has revolutionized personalized treatment planning in oncology. We discuss how AI can be integrated with nanotechnology to address challenges related to tumor heterogeneity, drug resistance, and systemic toxicity. Emphasis is placed on strong AI-driven advancements in the design of nanocarriers, structural optimization, targeting of ligands, and pharmacokinetics. We also give an overview of how AI can better predict toxicity, reduce costs, and enable personalized medicine. While challenges persist in the way of data accessibility, regulatory hurdles, and interactions with the immune system, future directions based on explainable AI (XAI) models, integration of multimodal data, and green nanocarrier designs promise to move the field forward. Convergence between AI and nanotechnology has been one key step toward safer, more effective, and patient-tailored cancer therapy.

Artificial intelligence in cardiovascular procedures: a bibliometric and visual analysis study

Background

The integration of artificial intelligence (AI) into cardiovascular procedures has significantly advanced diagnostic accuracy, outcome prediction, and robotic-assisted surgeries. However, a comprehensive bibliometric analysis of AI's impact in this field is lacking. This study examines research trends, key contributors, and emerging themes in AI-driven cardiovascular interventions.

Methods

We retrieved relevant publications from the Web of Science Core Collection and analyzed them using VOSviewer, CiteSpace, and Biblioshiny to map research trends and collaborations.

Results

AI-related cardiovascular research has grown substantially from 1993 to 2024, with a sharp increase from 2020 to 2023, peaking at 93 publications in 2023. The USA (127 papers), China (79), and England (31) were the top contributors, with Harvard University leading institutional output (17 papers). Frontiers in Cardiovascular Medicine was the most prolific journal. Core research themes included "machine learning," "mortality," and "cardiac surgery," with emerging trends in "association," "implantation," and "aortic stenosis," underscoring AI's expanding role in predictive modeling and surgical outcomes.

Conclusion

AI demonstrates transformative potential in cardiovascular procedures, particularly in diagnostic imaging, predictive modeling, and patient management. This bibliometric analysis highlights the growing interest in AI applications and provides a framework for integrating AI into clinical workflows to enhance diagnostic accuracy, treatment strategies, and patient outcomes.

Artificial intelligence, pharmaceutical development and dual-use research of concern: a call to action

Fervent attention was paid to what is coined dual-use research (DUR), or research that can both benefit and harm humanity, and dual-use research of concern (DURC), a particular subset of DUR that is reasonably anticipated to be a safety and security concern if misapplied. The aim of this paper is not to reiterate the challenges of DURC governance but to look at a new turn in DURC, namely the challenges posed by the use of artificial intelligence (AI) in pharmaceutical development. This is important, as AI is increasingly being used for pharmaceutical development in the industry. There is growing recognition that AI is DURC, and there is a dearth of industry and governmental guidance.

Predicting response to neoadjuvant chemotherapy in muscle-invasive bladder cancer via interpretable multimodal deep learning

Building accurate prediction models and identifying predictive biomarkers for treatment response in Muscle-Invasive Bladder Cancer (MIBC) are essential for improving patient survival but remain challenging due to tumor heterogeneity, despite numerous related studies. To address this unmet need, we developed an interpretable Graph-based Multimodal Late Fusion (GMLF) deep learning framework. Integrating histopathology and cell type data from standard H&E images with gene expression profiles derived from RNA sequencing from the SWOG S1314-COXEN clinical trial (ClinicalTrials.gov NCT02177695 2014-06-25), GMLF uncovered new histopathological, cellular, and molecular determinants of response to neoadjuvant chemotherapy. Specifically, we identified key gene signatures that drive the predictive power of our model, including alterations in TP63, CCL5, and DCN. Our discovery can optimize treatment strategies for patients with MIBC, e.g., improving clinical outcomes, avoiding unnecessary treatment, and ultimately, bladder preservation. Additionally, our approach could be used to uncover predictors for other cancers.

Unlocking the microbial treasure trove: advances in Streptomyces derived secondary metabolites in the battle against cancer

Streptomyces is widely recognized as the "biological factory" of specialized metabolites comprising a huge variety of bioactive molecules with diverse chemical properties. The potential of this Gram-positive soil bacteria to produce such diversified secondary metabolites with significant biological properties positions them as an ideal candidate for anticancer drug discovery. Some of the Streptomyces-derived secondary metabolites include siderophores (enterobactin, desferrioxamine), antibiotics (xiakemycin, dinactin) pigments (prodigiosin, melanin), and enzymes (L-methioninase, L-asperginase, cholesterol oxidase) which exhibit a pronounced anticancer effect on both in vitro and in vivo system. These secondary metabolites are endowed with antiproliferative, pro-apoptotic, antimetastatic, and antiangiogenic properties, presenting several promising characteristics that make them suitable candidates in the battle against this deadly disease. In this comprehensive review, we have dived deep and explored their history of discovery, their role as anticancer agents, underlying mechanisms, the approaches for the discovery of anticancer molecules from the secondary metabolites of Streptomyces (isolation of Streptomyces, characterization of bacterial strain, screening for anticancer activity and determination of in vitro and in vivo toxicity, structure-activity relationship studies, clinical translation, and drug development studies). The hurdles and challenges associated with this process and their future prospect were also illustrated. This review highlights the efficacy of Streptomyces as a "microbial treasure island" for novel anticancer agents, which warrants sustained research and exploration in this field to disclose more molecules from Streptomyces that are unidentified and to translate the clinical application of these secondary metabolites for cancer patients.

Recent advances in DNA nanotechnology for cancer detection and therapy: A review

Deoxyribonucleic acid (DNA) nanotechnology has rapidly emerged as a transformative field in biomedical research, offering innovative solutions for the detection and treatment of cancer. This review provides a comprehensive analysis of the role of DNA-based nanosystems in oncology, emphasizing their potential to address the limitations of conventional diagnostic and therapeutic approaches. Key advancements in DNA nanotechnology include the development of highly specific and sensitive nanostructures for early cancer detection, as well as precision-targeted delivery systems that enhance the efficacy of cancer therapies while minimizing side effects. The objectives of this review are threefold: first, to summarize the latest advancements in DNA nanotechnology, highlighting innovations in cancer biomarker detection and therapeutic applications; second, to explore the molecular mechanisms that enable these DNA-based nanosystems to interact with cancer cells with remarkable precision, including their design principles, self-assembly processes, and biological interactions; and third, to discuss the future implications of these technologies, considering the challenges, potential breakthroughs, and the steps needed to integrate DNA nanotechnology into clinical practice. By achieving these objectives, the review aims to offer insights into how DNA nanotechnology could revolutionize cancer care, providing new strategies for more personalized and effective treatments, and ultimately improving patient outcomes in the battle against cancer.

Revolutionizing Drug Delivery: The Impact of Advanced Materials Science and Technology on Precision Medicine

Recent progress in material science has led to the development of new drug delivery systems that go beyond the conventional approaches and offer greater accuracy and convenience in the application of therapeutic agents. This review discusses the evolutionary role of nanocarriers, hydrogels, and bioresponsive polymers that offer enhanced drug release, target accuracy, and bioavailability. Oncology, chronic disease management, and vaccine delivery are some of the applications explored in this paper to show how these materials improve the therapeutic results, counteract multidrug resistance, and allow for sustained and localized treatments. The review also discusses the translational barriers of bringing advanced materials into the clinical setting, which include issues of biocompatibility, scalability, and regulatory approval. Methods to overcome these challenges include surface modifications to reduce immunogenicity, scalable production methods such as microfluidics, and the harmonization of regulatory systems. In addition, the convergence of artificial intelligence (AI) and machine learning (ML) is opening new frontiers in material science and personalized medicine. These technologies allow for predictive modeling and real-time adjustments to optimize drug delivery to the needs of individual patients. The use of advanced materials can also be applied to rare and underserved diseases; thus, new strategies in gene therapy, orphan drugs development, and global vaccine distribution may offer new hopes for millions of patients.

Pharmacist-led surgical medicines prescription optimization and prediction service improves patient outcomes - a machine learning based study

Background

Optimizing prescription practices for surgical patients is crucial due to the complexity and sensitivity of their medication regimens. To enhance medication safety and improve patient outcomes by introducing a machine learning (ML)-based warning model integrated into a pharmacist-led Surgical Medicines Prescription Optimization and Prediction (SMPOP) service.

Method

A retrospective cohort design with a prospective implementation phase was used in a tertiary hospital. The study was divided into three phases: (1) Data analysis and ML model development (1 April 2019 to 31 March 2022), (2) Establishment of a pharmacist-led management model (1 April 2022 to 31 March 2023), and (3) Outcome evaluation (1 April 2023 to 31 March 2024). Key variables, including gender, age, number of comorbidities, type of surgery, surgery complexity, days from hospitalization to surgery, type of prescription, type of medication, route of administration, and prescriber's seniority were collected. The data set was divided into training set and test set in the form of 8:2. The effectiveness of the SMPOP service was evaluated based on prescription appropriateness, adverse drug reactions (ADRs), length of hospital stay, total hospitalization costs, and medication expenses.

Results

In Phase 1, 6,983 prescriptions were identified as potential prescription errors (PPEs) for ML model development, with 43.9% of them accepted by prescribers. The Random Forest (RF) model performed the best (AUC = 0.893) and retained high accuracy with 12 features (AUC = 0.886). External validation showed an AUC of 0.786. In Phase 2, SMPOP services were implemented, which effectively promoted effective communication between pharmacists and physicians and ensured the successful implementation of intervention measures. The SMPOP service was fully implemented. In Phase 3, the acceptance rate of pharmacist recommendations rose to 71.3%, while the length of stay, total hospitalization costs, and medication costs significantly decreased (p < 0.05), indicating overall improvement compared to Phase 1.

Conclusion

SMPOP service enhances prescription appropriateness, reduces ADRs, shortens stays, and lowers costs, underscoring the need for continuous innovation in healthcare.

AI-driven innovations in smart multifunctional nanocarriers for drug and gene delivery: A mini-review

The convergence of artificial intelligence (AI) and nanomedicine has revolutionized the design of smart multifunctional nanocarriers (SMNs) for drug and gene delivery, offering unprecedented precision, efficiency, and personalization in therapeutic applications. AI-driven approaches enhance the development of these nanocarriers by accelerating their design, optimizing drug loading and release kinetics, improving biocompatibility, and predicting interactions with biological barriers. This review explores the transformative role of AI in the fabrication and functionalization of SMNs, emphasizing its impact on overcoming challenges in targeted drug delivery, controlled release, and theranostics. We discuss the integration of AI with advanced nanomaterials-such as polymeric, lipidic, and inorganic nanoparticles-highlighting their potential in oncology and hematology. Furthermore, we examine recent clinical and preclinical case studies demonstrating AI-assisted nanocarrier development for personalized medicine. The synergy between AI and nanotechnology paves the way for next-generation precision therapeutics, addressing critical limitations in traditional drug delivery systems. However, data standardization, regulatory compliance, and translational scalability challenges remain. This review underscores the need for interdisciplinary collaboration to unlock AI's potential in nanomedicine fully, ultimately advancing the clinical application of SMNs for more effective and safer patient care.

Advances in gut microbiota-related treatment strategies for managing colorectal cancer in humans

Colorectal cancer (CRC) is a major contributor to global cancer-related mortality with increasing incidence rates in both developed and developing regions. Therefore, CRC presents a significant challenge to global health. The development of innovative tools for enhancing early CRC screening and diagnosis, along with novel treatments and therapies for improved management, remains an urgent necessity. CRC is intricately associated with the gut microbiota, which is integral to food digestion, nutrient generation, drug metabolism, metabolite production, immune enhancement, endocrine regulation, neurogenesis modulation, and the maintenance of physiologic and psychological equilibrium. Dysbiosis or imbalances in the gut microbiome have been implicated in various disorders, including CRC. Emerging evidence highlights the critical role of the gut microbiome in CRC pathogenesis and treatment, which presents potential opportunities for early detection and diagnosis. Despite substantial advances in understanding the relationship between the gut microbiota and CRC, significant challenges persist. Gaining a deeper and more detailed understanding of the interactions between the human microbiota and cancer is essential to fully realize the potential of the microbiota in cancer management. Unlike genetic factors, the gut microbiome is subject to modification, offering a promising avenue for the development of CRC treatments and drug discovery. This review provides an overview of the interactions between the human gut microbiome and CRC, while examining prospects for precision management of CRC.

Exosomes in Precision Oncology and Beyond: From Bench to Bedside in Diagnostics and Therapeutics

Exosomes have emerged as pivotal players in precision oncology, offering innovative solutions to longstanding challenges such as metastasis, therapeutic resistance, and immune evasion. These nanoscale extracellular vesicles facilitate intercellular communication by transferring bioactive molecules that mirror the biological state of their parent cells, positioning them as transformative tools for cancer diagnostics and therapeutics. Recent advancements in exosome engineering, artificial intelligence (AI)-driven analytics, and isolation technologies are breaking barriers in scalability, reproducibility, and clinical application. Bioengineered exosomes are being leveraged for CRISPR-Cas9 delivery, while AI models are enhancing biomarker discovery and liquid biopsy accuracy. Despite these advancements, key obstacles such as heterogeneity in exosome populations and the lack of standardized isolation protocols persist. This review synthesizes pioneering research on exosome biology, molecular engineering, and clinical translation, emphasizing their dual roles as both mediators of tumor progression and tools for intervention. It also explores emerging areas, including microbiome-exosome interactions and the integration of machine learning in exosome-based precision medicine. By bridging innovation with translational strategies, this work charts a forward-looking path for integrating exosomes into next-generation cancer care, setting it apart as a comprehensive guide to overcoming clinical and technological hurdles in this rapidly evolving field.

AI-Driven Drug Discovery for Rare Diseases

Rare diseases (RDs), affecting 300 million people globally, present a daunting public health challenge characterized by complexity, limited treatment options, and diagnostic hurdles. Despite legislative efforts, such as the 1983 US Orphan Drug Act, more than 90% of RDs lack effective therapies. Traditional drug discovery models, marked by lengthy development cycles and high failure rates, struggle to meet the unique demands of RDs, often yielding poor returns on investment. However, the advent of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), offers groundbreaking solutions. This review explores AI's potential to revolutionize drug discovery for RDs by overcoming these challenges. It discusses AI-driven advancements, such as drug repurposing, biomarker discovery, personalized medicine, genetics, clinical trial optimization, corporate innovations, and novel drug target identification. By synthesizing current knowledge and recent breakthroughs, this review provides crucial insights into how AI can accelerate therapeutic development for RDs, ultimately improving patient outcomes. This comprehensive analysis fills a critical gap in the literature, enhancing understanding of AI's pivotal role in transforming RD research and guiding future research and development efforts in this vital area of medicine.

Absorption of Toxicants from the Ocular Surface: Potential Applications in Toxicology

In relation to the eye, the body can absorb substances from the ocular surface fluid (OSF) in a few ways: directly through the conjunctival sac, through the nasal mucosa as the fluid drains into the nose, or through ingestion. Regardless of the absorption method, fluid from the conjunctival sac should be used as a toxicological matrix, even though only small quantities are needed. Contemporary analytical techniques make it a suitable matrix for toxicological research. Analyzing small quantities of the matrix and nano-quantities of the analyte requires high-cost, sophisticated tools, which is particularly relevant in the high-throughput environment of new drug or cosmetics testing. Environmental toxicology also presents a challenge, as many pollutants can enter the system using the same ocular surface route. A review of the existing literature was conducted to assess potential applications in clinical and forensic toxicology related to the absorption of toxicants from the ocular surface. The selection of the studies used in this review aimed to identify new, more efficient, and cost-effective analytical technology and diagnostic methods.

Engineering Useful Microbial Species for Pharmaceutical Applications

Microbial engineering has made a significant breakthrough in pharmaceutical biotechnology, greatly expanding the production of biologically active compounds, therapeutic proteins, and novel drug candidates. Recent advancements in genetic engineering, synthetic biology, and adaptive evolution have contributed to the optimization of microbial strains for pharmaceutical applications, playing a crucial role in enhancing their productivity and stability. The CRISPR-Cas system is widely utilized as a precise genome modification tool, enabling the enhancement of metabolite biosynthesis and the activation of synthetic biological pathways. Additionally, synthetic biology approaches allow for the targeted design of microorganisms with improved metabolic efficiency and therapeutic potential, thereby accelerating the development of new pharmaceutical products. The integration of artificial intelligence (AI) and machine learning (ML) plays a vital role in further advancing microbial engineering by predicting metabolic network interactions, optimizing bioprocesses, and accelerating the drug discovery process. However, challenges such as the efficient optimization of metabolic pathways, ensuring sustainable industrial-scale production, and meeting international regulatory requirements remain critical barriers in the field. Furthermore, to mitigate potential risks, it is essential to develop stringent biocontainment strategies and implement appropriate regulatory oversight. This review comprehensively examines recent innovations in microbial engineering, analyzing key technological advancements, regulatory challenges, and future development perspectives.

Oculomics: Current concepts and evidence

The eye provides novel insights into general health, as well as pathogenesis and development of systemic diseases. In the past decade, growing evidence has demonstrated that the eye's structure and function mirror multiple systemic health conditions, especially in cardiovascular diseases, neurodegenerative disorders, and kidney impairments. This has given rise to the field of oculomics-the application of ophthalmic biomarkers to understand mechanisms, detect and predict disease. The development of this field has been accelerated by three major advances: 1) the availability and widespread clinical adoption of high-resolution and non-invasive ophthalmic imaging ("hardware"); 2) the availability of large studies to interrogate associations ("big data"); 3) the development of novel analytical methods, including artificial intelligence (AI) ("software"). Oculomics offers an opportunity to enhance our understanding of the interplay between the eye and the body, while supporting development of innovative diagnostic, prognostic, and therapeutic tools. These advances have been further accelerated by developments in AI, coupled with large-scale linkage datasets linking ocular imaging data with systemic health data. Oculomics also enables the detection, screening, diagnosis, and monitoring of many systemic health conditions. Furthermore, oculomics with AI allows prediction of the risk of systemic diseases, enabling risk stratification, opening up new avenues for prevention or individualized risk prediction and prevention, facilitating personalized medicine. In this review, we summarise current concepts and evidence in the field of oculomics, highlighting the progress that has been made, remaining challenges, and the opportunities for future research.

Tailored therapies for triple-negative breast cancer: current landscape and future perceptions

Triple-negative breast cancer (TNBC) has become one of the most challenging cancers to date due to its great variability in biological features, high growth rate, and rare options for treatment. This review examines several innovative strategies for tailored treatment of TNBC, focusing mainly on the most recent developments and potential directions. The molecular landscape of TNBC is covered in the first section, which keeps the focus on transcriptome and genomic profiling while highlighting key molecular targets like mutations in the BRCA1/2, PIK3CA, androgen receptors (AR), epidermal growth factor receptors (EGFR), and immunological checkpoint molecules. This review also covers novel therapies that aim to block well-defined pathways, including immune checkpoint inhibitors (ICI), EGFR inhibitors, drugs that target AR, poly ADP ribose polymerase (PARP) inhibitors, and drugs that disrupt the PI3K/AKT/mTOR pathway. Additionally, it covers novel strategies focusing on combination therapy, targeting the DNA damage response pathway, and epigenetic modulators. Conclusively, it emphasizes perspectives and directions on topics such as personalized medicine, artificial intelligence (AI), predictive biomarkers, and treatment planning with the inclusion of machine learning (ML).

A meta-analysis of AI and machine learning in project management: Optimizing vaccine development for emerging viral threats in biotechnology

OBJECTIVES

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies across various industries, including healthcare, biotechnology, and vaccine development. These technologies offer immense potential to improve project management efficiency, decision-making, and resource utilization, especially in complex tasks such as vaccine development and healthcare innovations.

METHODS

A systematic meta-analysis was conducted by reviewing studies from databases like PubMed, IEEE Xplore, Scopus, Web of Science, EMBASE, and Google Scholar until September 2024. The analysis focused on the application of AI and ML in project management for vaccine development, biotechnology, and broader healthcare innovations using the PICO framework to guide study selection and inclusion. Statistical analyses were performed using Review Manager 5.4 and Comprehensive Meta-Analysis (CMA) software.

RESULTS

The meta-analysis reviewed 44 studies examining the integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, biotechnology, and vaccine development project management. Results demonstrated significant improvements in efficiency, resource allocation, decision-making, and risk management. AI/ML applications notably accelerated vaccine development, from candidate identification to clinical trial optimization, and improved predictive modeling for efficacy and safety. Subgroup analysis revealed variations in effectiveness across healthcare sectors, with the highest pooled effect sizes observed in infectious disease control (1.2; 95 % CI: 0.85-1.50) compared to medical imaging (0.85; 95 % CI: 0.75-0.95). Studies employing AI techniques demonstrated a pooled effect size of 0.83 (95 % CI: 0.78-1.08). Despite the observed high heterogeneity (I2 = 99.04 %) and moderate-to-high risks of bias, sensitivity analyses confirmed the robustness of the findings. Overall, AI/ML integration offers transformative potential to enhance project management and vaccine development, driving innovation and efficiency in these critical fields.

CONCLUSION

AI and ML technologies show significant potential to transform project management practices in healthcare, biotechnology, and vaccine development by enhancing efficiency, predictive analytics, and decision-making capabilities. Their integration paves the way for more innovative, data-driven solutions that can adapt to evolving challenges in these fields.

Embracing Generative Artificial Intelligence in Clinical Research and Beyond: Opportunities, Challenges, and Solutions

To explore threats and opportunities and to chart a path for safely navigating the rapid changes that generative artificial intelligence (AI) will bring to clinical research, the Duke Clinical Research Institute convened a multidisciplinary think tank in January 2024. Leading experts from academia, industry, nonprofits, and government agencies highlighted the potential opportunities of generative AI in automation of documentation, strengthening of participant and community engagement, and improvement of trial accuracy and efficiency. Challenges include technical hurdles, ethical dilemmas, and regulatory uncertainties. Success is expected to require establishing rigorous data management and security protocols, fostering integrity and trust among stakeholders, and sharing information about the safety and effectiveness of AI applications. Meeting insights point towards a future where, through collaboration and transparency, generative AI will help to shorten the translational pipeline and increase the inclusivity and equitability of clinical research.

Advances in Materials Science for Precision Melanoma Therapy: Nanotechnology-Enhanced Drug Delivery Systems

Melanoma, a highly aggressive form of skin cancer, poses a major therapeutic challenge due to its metastatic potential, resistance to conventional therapies, and the complexity of the tumor microenvironment (TME). Materials science and nanotechnology advances have led to using nanocarriers such as liposomes, dendrimers, polymeric nanoparticles, and metallic nanoparticles as transformative solutions for precision melanoma therapy. This review summarizes findings from Web of Science, PubMed, EMBASE, Scopus, and Google Scholar and highlights the role of nanotechnology in overcoming melanoma treatment barriers. Nanoparticles facilitate passive and active targeting through mechanisms such as the enhanced permeability and retention (EPR) effect and functionalization with tumor-specific ligands, thereby improving the accuracy of drug delivery and reducing systemic toxicity. Stimuli-responsive systems and multi-stage targeting further improve therapeutic precision and overcome challenges such as poor tumor penetration and drug resistance. Emerging therapeutic platforms combine diagnostic imaging with therapeutic delivery, paving the way for personalized medicine. However, there are still issues with scalability, biocompatibility, and regulatory compliance. This comprehensive review highlights the potential of integrating nanotechnology with advances in genetics and proteomics, scalable, and patient-specific therapies. These interdisciplinary innovations promise to redefine the treatment of melanoma and provide safer, more effective, and more accessible treatments. Continued research is essential to bridge the gap between evidence-based scientific advances and clinical applications.

Artificial Intelligence and Internet of Things Integration in Pharmaceutical Manufacturing: A Smart Synergy

Background: The integration of artificial intelligence (AI) with the internet of things (IoTs) represents a significant advancement in pharmaceutical manufacturing and effectively bridges the gap between digital and physical worlds. With AI algorithms integrated into IoTs sensors, there is an improvement in the production process and quality control for better overall efficiency. This integration facilitates enabling machine learning and deep learning for real-time analysis, predictive maintenance, and automation-continuously monitoring key manufacturing parameters. Objective: This paper reviews the current applications and potential impacts of integrating AI and the IoTs in concert with key enabling technologies like cloud computing and data analytics, within the pharmaceutical sector. Results: Applications discussed herein focus on industrial predictive analytics and quality, underpinned by case studies showing improvements in product quality and reductions in downtime. Yet, many challenges remain, including data integration and the ethical implications of AI-driven decisions, and most of all, regulatory compliance. This review also discusses recent trends, such as AI in drug discovery and blockchain for data traceability, with the intent to outline the future of autonomous pharmaceutical manufacturing. Conclusions: In the end, this review points to basic frameworks and applications that illustrate ways to overcome existing barriers to production with increased efficiency, personalization, and sustainability.

A review of chitosan-based multifunctional nanocomposites for drug/gene/protein delivery and gene therapy in cancer treatments: Promises, challenges and outlooks

This study provides a comprehensive examination of chitosan-based multifunctional nanocomposites and their extensive applications in drug/gene/protein delivery, tissue engineering and cancer therapy. As a natural polymer with eco-friendly characteristics and both antimicrobial and anti-cancer properties, chitosan has garnered attention in numerous medical and pharmaceutical domains. The research explores diverse chitosan nanocomposites, including those incorporating magnetic nanoparticles, carbon nanotubes, and clay- and alginate-based nanocomposites. Additionally, the study addresses the obstacles encountered in developing these materials and their potential for creating advanced drug delivery systems and targeted treatments. The study highlights the applications of these nanocomposites in bone, cartilage, and skin tissue regeneration, as well as their potential in neural tissue engineering. in conclusion, the research underscores the promising future of chitosan-based nanocomposites in revolutionizing drug delivery, tissue engineering, and cancer therapy. It emphasizes the need for further studies to fully harness the potential of these materials and translate laboratory findings into clinical applications, paving the way for more effective and personalized medical treatments. Our reason for writing this article appears to be a comprehensive exploration of the potential and challenges of chitosan-based multifunctional nanocomposites in medicine, particularly in drug/gene/protein delivery and cancer therapy. The aim is to provide a detailed analysis of the material's versatility, its integration with advanced nanotechnologies, and its applications in targeted treatments, and regenerative medicine. we seek to address existing challenges, such as safety, scalability, and regulatory compliance, while highlighting the promising future of these materials in personalized and efficient medical treatments.

Computational Design and Optimization of Multi-Compound Multivesicular Liposomes for Co-Delivery of Traditional Chinese Medicine Compounds

Study explored the synergistic anti-tumor effects of a combination of compounds from Traditional Chinese Medicine, including rosmarinic acid (RA), chlorogenic acid (CA), and scoparone (SCO), in the formulation of multivesicular liposomes (MVLs). Optimization of formulations and process parameters was essential to achieve effective liposomal encapsulation and optimal release profiles for these three compounds with diverse properties. Traditional trial-and-error approaches are inefficient for the optimization of complex multi-compound MVLs. We developed a new formulation optimization model, which could address this issue by predicting the optimal multi-compound MVLs formulation. Our machine learning model integrated support vector machine regression (SVR) algorithm and cuckoo search (CS) algorithm, resulting in three CS-SVR models to predict single-compound MVLs. The CS algorithm, with various weighting rules, was then applied to search the best formulation parameters across three CS-SVR models and to maximize the encapsulation efficiency for all three compounds. The multi-compound MLVs were subsequently prepared under the predicted conditions, achieving an optimized particle size of 15.12 µm, with encapsulation efficiencies of 82.93 ± 2.43% for CA, 82.22 ± 1.25% for RA, and 95.60 ± 0.18% for SCO. The predicted optimal multi-compound MVLs were further validated through in vitro characterization and in vivo anti-tumor experiments, showing a promising synergistic anti-tumor effect consistent with in vitro results. This model accurately predicted optimal encapsulation conditions, which were validated experimentally, demonstrating improved encapsulation efficiencies and reduced trial-and-error iterations. Collectively, our model provides a predictive pathway for multi-compound MVLs formulation, indicating the ability of this model to significantly reduce experimental burden and accelerate formulation development.

Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors

The global impact of SARS-CoV-2 highlights the need for treatments beyond vaccination, given the limited availability of effective medications. While Pfizer introduced Paxlovid, an FDA-approved antiviral targeting the SARS-CoV-2 main protease (Mpro), this study focuses on designing new antivirals against another protease, papain-like protease (PLpro), which is crucial for viral replication and immune suppression. NCATS/NIH performed a high-throughput screen of ∼15,000 molecules from an internal molecular library, identifying initial hits with a 0.5% success rate. To improve the hit rate and identify potent inhibitors, machine learning-based virtual screens were applied to ∼150,000 compounds, yielding 125 top predicted hits. Biochemical evaluation revealed 25 promising compounds, with a 20% hit-rate and IC50 values from 1.75 μM to <36 μM across 13 chemotypes. Further analog screening of those chemotypes, as part of the structure-activity relationships, led to 20 additional hits. Additionally, the hit-to-lead optimization of chemotype 7 produced 10 more analogs. These PLpro inhibitors provide promising templates for antiviral development against COVID-19.

Correlation of rivaroxaban solubility in mixed solvents for optimization of solubility using machine learning analysis and validation

In this study, the solubility of rivaroxaban, a poorly water-soluble drug, was investigated in mixed solvent systems to address challenges in pharmaceutical formulation and bioavailability enhancement. Solubility optimization is essential for the effective delivery and therapeutic performance of rivaroxaban, as its low aqueous solubility limits oral bioavailability and necessitates innovative approaches for drug formulation. The study explored the role of primary alcohols combined with dichloromethane in improving solubility, emphasizing their industrial relevance in crystallization, purification, and drug manufacturing processes. To complement experimental insights, machine learning models were employed to predict rivaroxaban solubility based on temperature, solvent type, and mass fraction of dichloromethane. Three models-AdaBoost Gaussian process regression (ADAGPR), AdaBoost multilayer perceptron (ADAMLP), and AdaBoost LASSO regression (ADALASSO)-were evaluated using [Formula: see text], RMSE, and MAPE metrics. Among these, ADAGPR demonstrated superior performance with an R² score of [Formula: see text], outperforming ADAMLP [Formula: see text] and [Formula: see text]. It also achieved the lowest total RMSE [Formula: see text] and MAPE [Formula: see text], confirming its predictive precision and reliability. Optimal solubility conditions were identified at [Formula: see text] with a mass fraction of 0.8190 in a dichloromethane-methanol mixture, yielding a predicted solubility of [Formula: see text]. These findings highlight the potential of combining chemical engineering principles with advanced predictive modeling to optimize solubility in complex solvent systems, offering significant value to pharmaceutical development and process optimization.

Flavonoids and Flavonoid-Based Nanopharmaceuticals as Promising Therapeutic Strategies for Colorectal Cancer-An Updated Literature Review

Colorectal cancer (CRC) represents one of the most serious health issues and the third most commonly diagnosed cancer worldwide. However, the treatment options for CRC are associated with adverse reactions, and in some cases, resistance can develop. Flavonoids have emerged as promising alternatives for CRC prevention and therapy due to their multitude of biological properties and ability to target distinct processes involved in CRC pathogenesis. Their innate disadvantageous properties (e.g., low solubility and stability, reduced bioavailability, and lack of tumor specificity) have delayed the potential inclusion of flavonoids in CRC treatment regimens but have hastened the design of nanopharmaceuticals comprising a flavonoid agent entrapped in a nanosized delivery platform that not only counteract these inconveniences but also provide an augmented therapeutic effect and an elevated safety profile by conferring a targeted action. Starting with a brief presentation of the pathological features of CRC and an overview of flavonoid classes, the present study comprehensively reviews the anti-CRC activity of different flavonoids from a mechanistic perspective while also portraying the latest discoveries made in the area of flavonoid-containing nanocarriers that have proved efficient in CRC management. This review concludes by showcasing future perspectives for the advancement of flavonoids and flavonoid-based nanopharmaceuticals in CRC research.

Deep learning: A game changer in drug design and development

The lengthy and costly drug discovery process is transformed by deep learning, a subfield of artificial intelligence. Deep learning technologies expedite the procedure, increasing treatment success rates and speeding life-saving procedures. Deep learning stands out in target identification and lead selection. Deep learning greatly accelerates initial stage by analyzing large datasets of biological data to identify possible therapeutic targets and rank targeted drug molecules with desired features. Predicting possible adverse effects is another significant challenge. Deep learning offers prompt and efficient assistance with toxicology prediction in a very short time, deep learning algorithms can forecast a new drug's possible harm. This enables to concentrate on safer alternatives and steer clear of late-stage failures brought on by unanticipated toxicity. Deep learning unlocks the possibility of drug repurposing; by examining currently available medications, it is possible to find whole new therapeutic uses. This method speeds up development of diseases that were previously incurable. De novo drug discovery is made possible by deep learning when combined with sophisticated computational modeling, it can create completely new medications from the ground. Deep learning can recommend and direct towards new drug candidates with high binding affinities and intended therapeutic effects by examining molecular structures of disease targets. This provides focused and personalized medication. Lastly, drug characteristics can be optimized with aid of deep learning. Researchers can create medications with higher bioavailability and fewer toxicity by forecasting drug pharmacokinetics. In conclusion, deep learning promises to accelerate drug development, reduce costs, and ultimately save lives.

Future prospective of AI in drug discovery

Drug discovery and development is very expensive and long with an inferior success rate. It is quite inefficient and costly due to huge R&D costs and lower productivity in pharmaceutical industries, to discover effective drugs and their development. AI can revolutionize the history of drug discovery and development because it will solve all these problems. AI can identify some promising drug candidates, reduce costs, and increase precision. AI algorithms analyze large datasets, predict molecular interactions, and help optimize the design of clinical trials, making the process of drug discovery and biomedical research much more efficient. By combining cutting-edge computation with more conventional pharmaceutical strategy, AI aids in expediting the process of therapeutics development. This chapter is an investigation of the core reasons behind lower approval rates of new drugs, the potential scope of AI to improve the drug discovery and development scenario, and the practical applications in the field. This article will further explore future opportunities, key methodologies, and challenges in the implementation of AI in pharmaceutical research.

New Perspectives of Hydrogels in Chronic Wound Management

Chronic wounds pose a substantial healthcare concern due to their prevalence and cost burden. This paper presents a detailed overview of chronic wounds and emphasizes the critical need for novel therapeutic solutions. The pathophysiology of wound healing is discussed, including the healing stages and the factors contributing to chronicity. The focus is on diverse types of chronic wounds, such as diabetic foot necrosis, pressure ulcers, and venous leg ulcers, highlighting their etiology, consequences, and the therapeutic issues they provide. Further, modern wound care solutions, particularly hydrogels, are highlighted for tackling the challenges of chronic wound management. Hydrogels are characterized as multipurpose materials that possess vital characteristics like the capacity to retain moisture, biocompatibility, and the incorporation of active drugs. Hydrogels' effectiveness in therapeutic applications is demonstrated by how they support healing, including preserving ideal moisture levels, promoting cellular migration, and possessing antibacterial properties. Thus, this paper presents hydrogel technology's latest developments, emphasizing drug-loaded and stimuli-responsive types and underscoring how these advanced formulations greatly improve therapy outcomes by enabling dynamic and focused reactions to the wound environment. Future directions for hydrogel research promote the development of customized hydrogel treatments and the incorporation of digital health tools to improve the treatment of chronic wounds.

AI-Driven Innovation in Skin Kinetics for Transdermal Drug Delivery: Overcoming Barriers and Enhancing Precision

Transdermal drug delivery systems (TDDS) offer an alternative to conventional oral and injectable drug administration by bypassing the gastrointestinal tract and liver metabolism, improving bioavailability, and minimizing systemic side effects. However, widespread adoption of TDDS is limited by challenges such as the skin's permeability barrier, particularly the stratum corneum, and the need for optimized formulations. Factors like skin type, hydration levels, and age further complicate the development of universally effective solutions. Advances in artificial intelligence (AI) address these challenges through predictive modeling and personalized medicine approaches. Machine learning models trained on extensive molecular datasets predict skin permeability and accelerate the selection of suitable drug candidates. AI-driven algorithms optimize formulations, including penetration enhancers and advanced delivery technologies like microneedles and liposomes, while ensuring safety and efficacy. Personalized TDDS design tailors drug delivery to individual patient profiles, enhancing therapeutic precision. Innovative systems, such as sensor-integrated patches, dynamically adjust drug release based on real-time feedback, ensuring optimal outcomes. AI also streamlines the pharmaceutical process, from disease diagnosis to the prediction of drug distribution in skin layers, enabling efficient formulation development. This review highlights AI's transformative role in TDDS, including applications of models such as Deep Neural Networks (DNN), Artificial Neural Networks (ANN), BioSIM, COMSOL, K-Nearest Neighbors (KNN), and Set Covering Machine (SVM). These technologies revolutionize TDDS for both skin and non-skin diseases, demonstrating AI's potential to overcome existing barriers and improve patient care through innovative drug delivery solutions.

Regulation of autophagy and cellular signaling through non-histone protein methylation

Autophagy is a highly conserved catabolic pathway that is precisely regulated and plays a significant role in maintaining cellular metabolic balance and intracellular homeostasis. Abnormal autophagy is directly linked to the development of various diseases, particularly immune disorders, neurodegenerative conditions, and tumors. The precise regulation of proteins is crucial for proper cellular function, and post-translational modifications (PTMs) are key epigenetic mechanisms in the regulation of numerous biological processes. Multiple proteins undergo PTMs that influence autophagy regulation. Methylation modifications on non-histone lysine and arginine residues have been identified as common PTMs critical to various life processes. This paper focused on the regulatory effects of non-histone methylation modifications on autophagy, summarizing related research on signaling pathways involved in autophagy-related non-histone methylation, and discussing current challenges and clinical significance. Our review concludes that non-histone methylation plays a pivotal role in the regulation of autophagy and its associated signaling pathways. Targeting non-histone methylation offers a promising strategy for therapeutic interventions in diseases related to autophagy dysfunction, such as cancer and neurodegenerative disorders. These findings provide a theoretical basis for the development of non-histone-methylation-targeted drugs for clinical use.

Precision medicine and nucleotide-based therapeutics to treat steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a complex multifactorial disease and becoming the leading cause of liver-related morbidity and mortality. MASLD spans from isolated steatosis to metabolic dysfunction-associated steatohepatitis (MASH), that may progress to cirrhosis and hepatocellular carcinoma (HCC). Genetic, metabolic, and environmental factors strongly contribute to the heterogeneity of MASLD. Lifestyle intervention and weight loss represent a viable treatment for MASLD. Moreover, Resmetirom, a thyroid hormone beta receptor agonist, has recently been approved for MASLD treatment. However, most individuals treated did not respond to this therapeutic, suggesting the need for a more tailored approach to treat MASLD. Oligonucleotide-based therapies, namely small-interfering RNA (siRNA) and antisense oligonucleotide (ASO), have been recently developed to tackle MASLD by reducing the expression of genes influencing MASH progression, such as PNPLA3 and HSD17B13. Here, we review the latest progress made in the synthesis and development of oligonucleotide-based agents targeting genetic determinants of MASH.

Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues

Non-communicable diseases (NCDs) such as cardiovascular diseases, chronic respiratory diseases, cancers, diabetes, and mental health disorders pose a significant global health challenge, accounting for the majority of fatalities and disability-adjusted life years worldwide. These diseases arise from the complex interactions between genetic, behavioral, and environmental factors, necessitating a thorough understanding of these dynamics to identify effective diagnostic strategies and interventions. Although recent advances in multi-omics technologies have greatly enhanced our ability to explore these interactions, several challenges remain. These challenges include the inherent complexity and heterogeneity of multi-omic datasets, limitations in analytical approaches, and severe underrepresentation of non-European genetic ancestries in most omics datasets, which restricts the generalizability of findings and exacerbates health disparities. This scoping review evaluates the global landscape of multi-omics data related to NCDs from 2000 to 2024, focusing on recent advancements in multi-omics data integration, translational applications, and equity considerations. We highlight the need for standardized protocols, harmonized data-sharing policies, and advanced approaches such as artificial intelligence/machine learning to integrate multi-omics data and study gene-environment interactions. We also explore challenges and opportunities in translating insights from gene-environment (GxE) research into precision medicine strategies. We underscore the potential of global multi-omics research in advancing our understanding of NCDs and enhancing patient outcomes across diverse and underserved populations, emphasizing the need for equity and fairness-centered research and strategic investments to build local capacities in underrepresented populations and regions.

Evaluation of Drug Permeation Enhancement by Using In Vitro and Ex Vivo Models

Drugs administered by means of extravascular routes of drug administration must be absorbed into the systemic circulation, which involves the movement of the drug molecules across biological barriers such as epithelial cells that cover mucosal surfaces or the stratum corneum that covers the skin. Some drugs exhibit poor permeation across biological membranes or may experience excessive degradation during first-pass metabolism, which tends to limit their bioavailability. Various strategies have been used to improve drug bioavailability. Absorption enhancement strategies include the co-administration of chemical permeation enhancers, enzymes, and/or efflux transporter inhibitors, chemical changes, and specialized dosage form designs. Models with physiological relevance are needed to evaluate the efficacy of drug absorption enhancement techniques. Various in vitro cell culture models and ex vivo tissue models have been explored to evaluate and quantify the effectiveness of drug permeation enhancement strategies. This review deliberates on the use of in vitro and ex vivo models for the evaluation of drug permeation enhancement strategies for selected extravascular drug administration routes including the nasal, oromucosal, pulmonary, oral, rectal, and transdermal routes of drug administration.

Nanotechnology and Artificial Intelligence in Dyslipidemia Management-Cardiovascular Disease: Advances, Challenges, and Future Perspectives

This narrative review explores emerging technologies in dyslipidemia management, focusing on nanotechnology and artificial intelligence (AI). It examines the current treatment recommendations and contrasts them with the future prospects enabled by these innovations. Nanotechnology shows significant potential in enhancing drug delivery systems, enabling more targeted and efficient lipid-lowering therapies. In parallel, AI offers advancements in diagnostics, cardiovascular risk prediction, and personalized treatment strategies. AI-based decision support systems and machine learning algorithms are particularly promising for analyzing large datasets and delivering evidence-based recommendations. Together, these technologies hold the potential to revolutionize dyslipidemia management, improving outcomes and optimizing patient care. In addition, this review covers key topics such as cardiovascular disease biomarkers and risk factors, providing insights into the current methods for assessing cardiovascular risk. It also discusses the current understanding of dyslipidemia, including pathophysiology and clinical management. Together, these insights and technologies hold the potential to revolutionize dyslipidemia management, improving outcomes and optimizing patient care.