Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

Epithelial-to-mesenchymal transition (EMT) and cancer metastasis: the status quo of methods and experimental models 2025

Epithelial-to-mesenchymal transition (EMT) is a crucial cellular process for embryogenesis, wound healing, and cancer progression. It involves a shift in cell interactions, leading to the detachment of epithelial cells and activation of gene programs promoting a mesenchymal state. EMT plays a significant role in cancer metastasis triggering tumor initiation and stemness, and activates metastatic cascades resulting in resistance to therapy. Moreover, reversal of EMT contributes to the formation of metastatic lesions. Metastasis still needs to be better understood functionally in its major but complex steps of migration, invasion, intravasation, dissemination, which contributes to the establishment of minimal residual disease (MRD), extravasation, and successful seeding and growth of metastatic lesions at microenvironmentally heterogeneous sites. Therefore, the current review article intends to present, and discuss comprehensively, the status quo of experimental models able to investigate EMT and metastasis in vitro and in vivo, for researchers planning to enter the field. We emphasize various methods to understand EMT function and the major steps of metastasis, including diverse migration, invasion and matrix degradation assays, microfluidics, 3D co-culture models, spheroids, organoids, or latest spatial and imaging methods to analyze complex compartments. In vivo models such as the chorionallantoic membrane (CAM) assay, cell line-derived and patient-derived xenografts, syngeneic, genetically modified, and humanized mice, are presented as a promising arsenal of tools to analyze intravasation, site specific metastasis, and treatment response. Furthermore, we give a brief overview on methods detecting dissemination and MRD in carcinomas, highlighting its significance in tracking the course of disease and response to treatment. Enhanced lineage tracking tools, dynamic in vivo imaging, and therapeutically useful in vivo models as powerful preclinical tools may still better reveal functional interdependencies between metastasis and EMT. Future directions are discussed in light of emerging views on the biology, diagnosis, and treatment of EMT and metastasis.

Beyond the Hype: The potential and challenges of semi-solid extrusion 3D Printing in pharmaceutical applications through the lens of Portuguese 3D experts

In recent years, the technological landscape has experienced a notable surge in the exploration of 3D Printing technologies (3DPT), particularly as an additive manufacturing method. Among the various 3DPT available, semi-solid extrusion 3DP (SSE-3DP) has emerged as a prominent solution for pharmaceutical applications, owing to its versatility and adaptability for customization. Nevertheless, achieving further technological advancement is hindered by a myriad of challenges related to materials, process, and quality control. Therefore, this research aimed to explore, uncover, and critically discuss both the potential and the challenges of SSE-3DP, by capturing the insights, vision, and perspectives of Portuguese 3DP experts, through a focus group analysis. The focus group discussions revealed an initial sense of positivity, fascination, and novelty regarding the technology, later evolving into mixed emotions of doubt and aspiration, effectively highlighting both the challenges and opportunities that lie ahead for the advancement of SSE-3DP. Moreover, the analysis yielded a total of 148 sub-codes, related to materials, process, printers' specifications, quality control testing, needs and lack of knowledge, solutions, business applications and personalization strategies. Notably, 3DP experts underscored the necessity for investment in multi-materials research, printer functionalities, process optimization through automation and artificial intelligence (AI) or advanced dynamic tools, and quality control using standard guidelines or machine learning models. Overall, this article provides a foundational framework for future research, addressing the gaps, needs, and solutions highlighted by 3DP experts.

Advancements and implications of artificial intelligence for early detection, diagnosis and tailored treatment of cancer

The complexity and heterogeneity of cancer makes early detection and effective treatment crucial to enhance patient survival and quality of life. The intrinsic creative ability of artificial intelligence (AI) offers improvements in patient screening, diagnosis, and individualized care. Advanced technologies, like computer vision, machine learning, deep learning, and natural language processing, can analyze large datasets and identify patterns that permit early cancer detection, diagnosis, management and incorporation of conclusive treatment plans, ensuring improved quality of life for patients by personalizing care and minimizing unnecessary interventions. Genomics, transcriptomics and proteomics data can be combined with AI algorithms to unveil an extensive overview of cancer biology, assisting in its detailed understanding and will help in identifying new drug targets and developing effective therapies. This can also help to identify personalized molecular signatures which can facilitate tailored interventions addressing the unique aspects of each patient. AI-driven transcriptomics, proteomics, and genomes represents a revolutionary strategy to improve patient outcome by offering precise diagnosis and tailored therapy. The inclusion of AI in oncology may boost efficiency, reduce errors, and save costs, but it cannot take the role of medical professionals. While clinicians and doctors have the final say in all matters, it might serve as their faithful assistant.

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry

The deployment of artificial intelligence (AI) is revolutionizing neuropharmacology and drug development, allowing the modulation of neurotransmitter systems at the personal level. This review focuses on the neuropharmacology and regulation of neurotransmitters using predictive modeling, closed-loop neuromodulation, and precision drug design. The fusion of AI with applications such as machine learning, deep-learning, and even computational modeling allows for the real-time tracking and enhancement of biological processes within the body. An exemplary application of AI is the use of DeepMind's AlphaFold to design new GABA reuptake inhibitors for epilepsy and anxiety. Likewise, Benevolent AI and IBM Watson have fast-tracked drug repositioning for neurodegenerative conditions. Furthermore, we identified new serotonin reuptake inhibitors for depression through AI screening. In addition, the application of Deep Brain Stimulation (DBS) settings using AI for patients with Parkinson's disease and for patients with major depressive disorder (MDD) using reinforcement learning-based transcranial magnetic stimulation (TMS) leads to better treatment. This review highlights other challenges including algorithm bias, ethical concerns, and limited clinical validation. Their proposal to incorporate AI with optogenetics, CRISPR, neuroprosthesis, and other advanced technologies fosters further exploration and refinement of precision neurotherapeutic approaches. By bridging computational neuroscience with clinical applications, AI has the potential to revolutionize neuropharmacology and improve patient-specific treatment strategies. We addressed critical challenges, such as algorithmic bias and ethical concerns, by proposing bias auditing, diverse datasets, explainable AI, and regulatory frameworks as practical solutions to ensure equitable and transparent AI applications in neurotransmitter modulation.

Neem gum and its derivatives as potential polymeric scaffold for diverse applications: a review

Naturally occurring polymers, particularly polysaccharides, are gaining significant attention for their eco-friendly, non-toxic nature and abundant availability. Neem Gum (NEG), a natural exudate from the neem tree (Azadirachta indica), is secreted as a defense mechanism to protect against microbial invasion and physical damage. Unlike common polysaccharides, NEG exhibits a distinct composition rich in bioactive constituents, including heteropolysaccharides and secondary metabolites, contributing to its diverse functional and therapeutic potential. These unique characteristics make NEG a promising biopolymer for applications in pharmaceuticals, food, cosmetics, and environmental industries, where it serves as a binding, emulsifying, gelling, and stabilizing agent. Recent advancements have focused on developing NEG composites and derivatives with enhanced properties and broader applications. Structural modifications like grafting and carboxymethylation have improved its utility in drug delivery, wound healing, and biodegradable materials. Modified NEG derivatives exhibit superior antimicrobial, anti-inflammatory, and antioxidant effects, expanding their biomedical potential in tissue engineering and controlled drug release. NEG-based hydrogels and films show promise in eco-friendly packaging and self-healing biomaterials. This review compiles NEG's diverse applications, highlighting its role in sustainable technologies and emerging fields like self-healing materials and smart polymers. It addresses challenges in scaling production, regulatory compliance, and technical constraints.

Machine learning: Python tools for studying biomolecules and drug design

The increasing adoption of computational methods and artificial intelligence in scientific research has led to a growing interest in versatile tools like Python. In the fields of medical chemistry, biochemistry, and bioinformatics, Python has emerged as a key language for tackling complex challenges. It is used to solve various tasks, such as drug discovery, high-throughput and virtual screening, protein and genome analysis, and predicting drug efficacy. This review presents a list of tools for these tasks, including scripts, libraries, and ready-made programs, and serves as a starting point for scientists wishing to apply automation or optimization to routine tasks in medical chemistry and bioinformatics.

Precision Targeting in Metastatic Prostate Cancer: Molecular Insights to Therapeutic Frontiers

Metastatic prostate cancer (mPCa) remains a significant cause of cancer-related mortality in men. Advances in molecular profiling have demonstrated that the androgen receptor (AR) axis, DNA damage repair pathways, and the PI3K/AKT/mTOR pathway are critical drivers of disease progression and therapeutic resistance. Despite the established benefits of hormone therapy, chemotherapy, and bone-targeting agents, mPCa commonly becomes treatment-resistant. Recent breakthroughs have highlighted the importance of identifying actionable genetic alterations, such as BRCA2 or ATM defects, that render tumors sensitive to poly-ADP ribose polymerase (PARP) inhibitors. Parallel efforts have refined imaging-particularly prostate-specific membrane antigen (PSMA) positron emission tomography-computed tomography-to detect and localize metastatic lesions with high sensitivity, thereby guiding patient selection for PSMA-targeted radioligand therapies. Multi-omics innovations, including liquid biopsy technologies, enable the real-time tracking of emergent AR splice variants or reversion mutations, supporting adaptive therapy paradigms. Nonetheless, the complexity of mPCa necessitates combination strategies, such as pairing AR inhibition with PI3K/AKT blockade or PARP inhibitors, to inhibit tumor plasticity. Immuno-oncological approaches remain challenging for unselected patients; however, subsets with mismatch repair deficiency or neuroendocrine phenotypes may benefit from immune checkpoint blockade or targeted epigenetic interventions. We present these pivotal advances, and discuss how biomarker-guided integrative treatments can improve mPCa management.

Transforming Pharmacogenomics and CRISPR Gene Editing with the Power of Artificial Intelligence for Precision Medicine

Background: Advancements in pharmacogenomics, artificial intelligence (AI), and CRISPR gene-editing technology are revolutionizing precision medicine by enabling highly individualized therapeutic strategies. Artificial intelligence-driven computational techniques improve biomarker discovery and drug optimization while pharmacogenomics helps to identify genetic polymorphisms affecting medicine metabolism, efficacy, and toxicity. Genetically editing based on CRISPR presents a precise method for changing gene expression and repairing damaging mutations. This review explores the convergence of these three fields to enhance improved precision medicine. Method: A methodical study of the current literature was performed on the effects of pharmacogenomics on drug response variability, artificial intelligence, and CRISPR in predictive modeling and gene-editing applications. Results: Driven by artificial intelligence, pharmacogenomics allows clinicians to classify patients and select the appropriate medications depending on their DNA profiles. This reduces the side effect risk and increases the therapeutic efficacy. Precision genetic modifications made feasible by CRISPR technology improve therapy outcomes in oncology, metabolic illnesses, neurological diseases, and other fields. The integration of artificial intelligence streamlines genome-editing applications, lowers off-target effects, and increases CRISPR specificity. Notwithstanding these advances, issues including computational biases, moral dilemmas, and legal constraints still arise. Conclusions: The synergy of artificial intelligence, pharmacogenomics, and CRISPR alters precision medicine by letting customized therapeutic interventions. Clinically translating, however, hinges on resolving data privacy concerns, assuring equitable access, and strengthening legal systems. Future research should focus on refining CRISPR gene-editing technologies, enhancing AI-driven pharmacogenomics, and developing moral guidelines for applying these tools in individualized medicine going forward.

Use of Computational Intelligence in Customizing Drug Release from 3D-Printed Products: A Comprehensive Review

Computational intelligence (CI) mimics human intelligence by expanding the capabilities of machines in data analysis, pattern recognition, and making informed decisions. CI has shown promising contributions to advancements in drug discovery, formulation, and manufacturing. Its ability to analyze vast amounts of patient data and optimize drug formulations by predicting pharmacokinetic and pharmacodynamic responses makes it a very useful platform for personalized medicine. The integration of CI with 3D printing further strengthens this potential, as 3D printing enables the fabrication of personalized medicines with precise doses, controlled-release profiles, and complex formulations. Furthermore, the automated and digital capabilities of 3D printing make it suitable for integration with CI. CI has proven useful in predicting material printability, optimizing drug release rates, designing complex structures, ensuring quality control, and improving manufacturing processes in 3D printing. In the context of customizing drug release from 3D-printed products, CI techniques have been applied to predict drug release from input variables and to design geometries that achieve the desired release profile. This review explores the role of CI in customizing drug release from 3D-printed formulations. It provides overview of limitations of 3D printing; how CI can overcome these challenges, and its potential in customizing drug release; a comparison of CI with other methods of optimization; and real-world examples of CI integration in 3D printing.

Recent advances in cutaneous drug delivery by iontophoresis

INTRODUCTION

Iontophoresis has been extensively studied for topical and transdermal drug delivery to stimulate the absorption of molecules that would hardly pass through the outermost layer of the skin passively. Recent research has focused on its combination with nanoparticle-based systems or microneedles to expand its therapeutic applications.

AREAS COVERED

This review explores the fundamental principles of iontophoresis, focusing on key factors influencing its drug transport mechanisms, and provides a discussion of the field's current state. A comprehensive analysis of articles published or available online in 2024 was conducted, categorizing studies by their application areas, drug delivery systems, iontophoretic conditions, and experimental limitations.

EXPERT OPINION

The findings reveal a recent focus on wound healing and skin repair, and advancements in treating inflammation, pain, and skin cancer. Market translation requires standardized experimental protocols, particularly for iontophoretic parameters and preclinical models, along with the development of cost-effective commercial devices. Additionally, while advancements in cutaneous drug delivery have increasingly benefited from machine learning approaches, their application to iontophoresis remains underexplored. With the growing interest in associating iontophoresis with the Internet of Things, such an integration, if combined with AI tools, could offer promising opportunities for personalized, real-time treatments in modern dermatology, and therapeutic systems.

The Role of Artificial Intelligence in Managing Bipolar Disorder: A New Frontier in Patient Care

Background/Objectives: Bipolar disorder (BD) is a complex and chronic mental health condition that poses significant challenges for both patients and healthcare providers. Traditional treatment methods, including medication and therapy, remain vital, but there is increasing interest in the application of artificial intelligence (AI) to enhance BD management. AI has the potential to improve mood episode prediction, personalize treatment plans, and provide real-time support, offering new opportunities for managing BD more effectively. Our primary objective was to explore the potential role of AI in transforming the management of BD, specifically in mood tracking, prediction, and personalized treatment regimens. Methods: To explore the potential role of AI in transforming BD management, we conducted a review of recent literature using key search terms. We included studies that discussed AI applications in mood tracking, prediction, and treatment personalization. The studies were selected based on their relevance to AI's role in BD management, with attention to the PICO criteria: Population-individuals diagnosed with BD; Intervention-AI tools for mood prediction, treatment personalization, and real-time support; Comparison-traditional treatment methods (when available); Outcome-measures of mood episode prediction, treatment effectiveness, and improvements in patient care. Results: The findings from recent research reveal promising developments in the use of AI for BD management. Studies suggest that AI-powered tools can enable more proactive and personalized care, improving treatment outcomes and reducing the burden on healthcare professionals. AI's ability to analyze data from wearable devices, smartphones, and even social media platforms provides valuable insights for early detection and more dynamic treatment adjustments. Conclusions: While AI's application in BD management is still in its early stages, it presents transformative potential for improving patient care. However, further research and development are crucial to fully realize AI's potential in supporting BD patients and optimizing treatment efficacy.

Advances in bioinformatic methods for the acceleration of the drug discovery from nature

BACKGROUND

Drug discovery from nature has a long, ethnopharmacologically-based background. Today, natural resources are undeniably vital reservoirs of active molecules or drug leads. Advances in (bio)informatics and computational biology emphasized the role of herbal medicines in the drug discovery pipeline.

PURPOSE

This review summarizes bioinformatic approaches applied in recent drug discovery from nature.

STUDY DESIGN

It examines advancements in molecular networking, pathway analysis, network pharmacology within a systems biology framework and AI for assessing the therapeutic potential of herbal preparations.

METHODS

A comprehensive literature search was conducted using Pubmed, SciFinder, and Google Database. Obtained data was analyzed and organized in subsections: AI, systems biology integrative approach, network pharmacology, pathway analysis, molecular networking, structure-based virtual screening.

RESULTS

Bioinformatic approaches is now essential for high-throughput data analysis in drug target identification, mechanism-based drug discovery, drug repurposing and side-effects prediction. Large datasets obtained from "omics" approaches require bioinformatic calculations to unveil interactions, and patterns in disease-relevant conditions. These tools enable databases annotations, pattern-matching, connections discovery, molecular relationship exploration, and data visualisation.

CONCLUSION

Despite the complexity of plant metabolites, bioinformatic approaches assist in characterization of herbal preparations and selection of bioactive molecule. It is perceived as powerful tool for uncovering multi-target effects and potential molecular mechanisms of compounds. By integrating multiple networks that connect gene-disease, drug-target and gene-drug-target, drug discovery from natural sources is experiencing a remarkable comeback.

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.

Emerging 3D printing technologies for solid oral dosage forms: Processes, materials and analytical tools for real-time assessment

Three-dimensional (3D) printing is an emerging technology with the potential to increase manufacturing flexibility and enable personalized drug delivery. 3D printing may form tablets using digitally controlled layer-by-layer material deposition, permitting the tailoring of solid oral dosage geometry and facile modifications of drug release profiles without requiring extensive alterations to the pharmaceutical formulation and process. The challenge to assure the quality of drugs still lies in monitoring and controlling critical steps in the 3D printing process. Optimizing an 3D printing process requires a comprehensive understanding of the critical process parameters, material attributes and their impact on the performance of 3D-printed tablets. This review focuses on recent advances in 3D printing technologies for solid oral dosage forms, emphasizing critical process parameters and material attributes that may be considered for optimizing printing processes and enhancing the quality of printed tablets. Additionally, this review explores real-time analytical tools and the crucial considerations for ensuring the performance of building materials, printing processes, and manufactured solid drug products. This review contributes to the ongoing discourse on harnessing the potential of 3D printing in the pharmaceutical field while emphasizing the imperative need for quality assurance throughout additive manufacturing processes.

Smart nanomedicines powered by artificial intelligence: a breakthrough in lung cancer diagnosis and treatment

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, primarily due to challenges in early detection, suboptimal therapeutic efficacy, and severe adverse effects associated with conventional treatments. The convergence of nanotechnology and artificial intelligence (AI) offers transformative potential in precision oncology, enabling innovative solutions for lung cancer diagnosis and therapy. Intelligent nanomedicines facilitate targeted drug delivery, enhanced imaging, and theranostic applications, while AI-driven models harness big biomedical data to optimize nanomedicine design, functionality, and clinical application. This review explores the synergistic integration of AI and nanotechnology in lung cancer care, highlighting recent advancements, key challenges, and future directions for clinical translation. Ethical considerations, including data standardization and privacy concerns, are also addressed, providing a comprehensive roadmap to overcome current barriers and advance the adoption of AI-driven intelligent nanomedicines in precision oncology. This synthesis underscores the critical role of emerging technologies in revolutionizing lung cancer management.

The Future of Medicine: How 3D Printing Is Transforming Pharmaceuticals

Three-dimensional printing technology is transforming pharmaceutical manufacturing by shifting from conventional mass production to additive manufacturing, with a strong emphasis on personalized medicine. The integration of bioinks and AI-driven optimization is further enhancing this innovation, enabling drug production with precise dosages, tailored drug-release profiles, and unique multi-drug combinations that respond to individual patient needs. This advancement is significantly impacting healthcare by accelerating drug development, encouraging innovative pharmaceutical designs, and enhancing treatment efficacy. Traditional pharmaceutical manufacturing follows a one-size-fits-all approach, which often fails to meet the specific requirements of patients with unique medical conditions. In contrast, 3D printing, coupled with bioink formulations, allows for on-demand drug production, reducing dependency on large-scale manufacturing and storage. AI-powered design and process optimization further refine dosage forms, printability, and drug release mechanisms, ensuring precision and efficiency in drug manufacturing. These advancements have the potential to lower overall healthcare costs while improving patient adherence to medication regimens. This review explores the potential, challenges, and environmental benefits of 3D pharmaceutical printing, positioning it as a key driver of next-generation personalized medicine.

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.

MrgX2-Targeting Ligand Screen for Antipseudoallergic Agents by Immobilized His-Tag-Fused Protein Technology

Mas-related G protein-coupled receptor X2 (MrgX2) plays a key role in pseudoallergy reactions; thus, it is of great significance to screen compounds with antipseudoallergy activity via MrgX2. Cell membrane chromatography (CMC) demonstrates great potential in drug screening, but it requires further optimization to improve its specificity and stability. In this study, a new CMC system incorporating His-tag-oriented immobilized proteins was constructed to screen MrgX2 antagonists. Single His-tag-fused MrgX2 was extracted intactly and covalently bond to divinyl sulfone-modified amino silica gel to obtain bioaffinity composites. The characterized composites were utilized to establish a MrgX2-His-tag@VS/CMC system to screen MrgX2 antagonists. Compound Z-3578 was screened from a G protein-coupled receptor compound library of 3010 compounds and revealed its efficient antipseudoallergy activity in vitro and in vivo via MrgX2. In conclusion, the new oriented-immobilized CMC system will provide an efficient analytical tool for screening active precursors.

Pharmaceutical 3D Printing Technology Integrating Nanomaterials and Nanodevices for Precision Neurological Therapies

Pharmaceutical 3D printing, combined with nanomaterials and nanodevices, presents a transformative approach to precision medicine for treating neurological diseases. This technology enables the creation of tailored dosage forms with controlled release profiles, enhancing drug delivery across the blood-brain barrier (BBB). The integration of nanoparticles, such as poly lactic-co-glycolic acid (PLGA), chitosan, and metallic nanomaterials, into 3D-printed scaffolds improves treatment efficacy by providing targeted and prolonged drug release. Recent advances have demonstrated the potential of these systems in treating conditions like Parkinson's disease, epilepsy, and brain tumors. Moreover, 3D printing allows for multi-drug combinations and personalized formulations that adapt to individual patient needs. Novel drug delivery approaches, including stimuli-responsive systems, on-demand dosing, and theragnostics, provide new possibilities for the real-time monitoring and treatment of neurological disorders. Despite these innovations, challenges remain in terms of scalability, regulatory approval, and long-term safety. The future perspectives of this technology suggest its potential to revolutionize neurological treatments by offering patient-specific therapies, improved drug penetration, and enhanced treatment outcomes. This review discusses the current state, applications, and transformative potential of 3D printing and nanotechnology in neurological treatment, highlighting the need for further research to overcome the existing challenges.

Artificial intelligence driven innovations in biochemistry: A review of emerging research frontiers

Artificial intelligence (AI) has become a powerful tool in biochemistry, greatly enhancing research capabilities by enabling the analysis of complex datasets, predicting molecular interactions, and accelerating drug discovery. As AI continues to evolve, its applications in biochemistry are poised to expand, revolutionizing both theoretical and applied research. This review explores current and potential AI applications in biochemistry, with a focus on data analysis, molecular modeling, enzyme engineering, and metabolic pathway studies. Key AI techniques-such as machine learning algorithms, natural language processing, and AI-based molecular modeling-are discussed. The review also highlights emerging research areas benefiting from AI, including personalized medicine and synthetic biology. The methodology involves an extensive analysis of existing literature, particularly peer-reviewed studies on AI applications in biochemistry. AI-driven tools like AlphaFold, which have significantly advanced protein structure prediction, are evaluated alongside AI's role in expediting drug discovery. The review also addresses challenges such as data quality, model interpretability, and ethical considerations. Results indicate that AI has expanded the scope of biochemical research by facilitating large-scale data analysis, enhancing molecular simulations, and opening new avenues of inquiry. However, challenges remain, particularly in data handling and ethical concerns. In conclusion, AI is transforming biochemistry by driving innovation and expanding research possibilities. Future advancements in AI algorithms, interdisciplinary collaboration, and integration with automated techniques will be crucial to fully unlocking AI's potential in advancing biochemical research.

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.

The molecular code of kidney cancer: A path of discovery for gene mutation and precision therapy

Renal cell carcinoma (RCC) is a malignant tumor with highly heterogeneous and complex molecular mechanisms. Through systematic analysis of TCGA, COSMIC and other databases, 24 mutated genes closely related to RCC were screened, including VHL, PBRM1, BAP1 and SETD2, which play key roles in signaling pathway transduction, chromatin remodeling and DNA repair. The PI3K/AKT/mTOR signaling pathway is particularly important in the pathogenesis of RCC. Mutations in genes such as PIK3CA, MTOR and PTEN are closely associated with metabolic abnormalities and tumor cell proliferation. Clinically, mTOR inhibitors and VEGF-targeted drugs have shown significant efficacy in personalized therapy. Abnormal regulation of metabolic reprogramming, especially glycolysis and glutamine metabolic pathways, provides tumor cells with continuous energy supply and survival advantages, and GLS1 inhibitors have shown promising results in preclinical studies. This paper also explores the potential of immune checkpoint inhibitors in combination with other targeted drugs, as well as the promising application of nanotechnology in drug delivery and targeted therapy. In addition, unique molecular mechanisms are revealed and individualized therapeutic strategies are explored for specific subtypes such as TFE3, TFEB rearrangement type and SDHB mutant type. The review summarizes the common gene mutations in RCC and their molecular mechanisms, emphasizes their important roles in tumor diagnosis, treatment and prognosis, and looks forward to the application prospects of multi-pathway targeted therapy, metabolic targeted therapy, immunotherapy and nanotechnology in RCC treatment, providing theoretical support and clinical guidance for individualized treatment and new drug development.

Nanostructures in Orthopedics: Advancing Diagnostics, Targeted Therapies, and Tissue Regeneration

Nanotechnology, delving into the realm of nanometric structures, stands as a transformative force in orthopedics, reshaping diagnostics, and numerous regenerative interventions. Commencing with diagnostics, this scientific discipline empowers accurate analyses of various diseases and implant stability, heralding an era of unparalleled precision. Acting as carriers for medications, nanomaterials introduce novel therapeutic possibilities, propelling the field towards more targeted and effective treatments. In arthroplasty, nanostructural modifications to implant surfaces not only enhance mechanical properties but also promote superior osteointegration and durability. Simultaneously, nanotechnology propels tissue regeneration, with nanostructured dressings emerging as pivotal elements in accelerating wound healing. As we navigate the frontiers of nanotechnology, ongoing research illuminates promising avenues for further advancements, assuring a future where orthopedic practices are not only personalized but also highly efficient, promising a captivating journey through groundbreaking innovations and tailored patient care.

Artificial Intelligence and Machine Learning Applications to Pharmacokinetic Modeling and Dose Prediction of Antibiotics: A Scoping Review

Background and Objectives: The use of artificial intelligence (AI) and, in particular, machine learning (ML) techniques is growing rapidly in the healthcare field. Their application in pharmacokinetics is of potential interest due to the need to relate enormous amounts of data and to the more efficient development of new predictive dose models. The development of pharmacokinetic models based on these techniques simplifies the process, reduces time, and allows more factors to be considered than with classical methods, and is therefore of special interest in the pharmacokinetic monitoring of antibiotics. This review aims to describe the studies that use AI, mainly oriented to ML techniques, for dose prediction and analyze their results in comparison with the results obtained by classical methods. Furthermore, in the review, the techniques employed and the metrics to evaluate the precision are described to improve the compression of the results. Methods: A systematic search was carried out in the EMBASE, OVID, and PubMed databases and the results obtained were analyzed in detail. Results: Of the 13 articles selected, 10 were published in the last three years. Vancomycin was monitored in seven and none of the studies were performed on new antibiotics. The most used techniques were XGBoost and neural networks. Comparisons were conducted in most cases against population pharmacokinetic models. Conclusions: AI techniques offer promising results. However, the diversity in terms of the statistical metrics used and the low power of some of the articles make the overall assessment difficult. For now, AI-based ML techniques should be used in addition to classical population pharmacokinetic models in clinical practice.