Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Alternative Cancer Therapeutics: Unpatentable Compounds and Their Potential in Oncology

Cancer remains a leading cause of death globally. Cancer patients often seek alternative therapies in addition to, or instead of, conventional treatments like chemotherapy, radiation, and surgery. The progress in medical advancements and early detection provides more treatment options; however, the development of cancer drugs requires a significant amount of time, demands substantial investments, and results in an overall low percent of regulatory approval. The complex relationship between patent protection and pharmaceutical innovation complicates cancer drug development and contributes to high mortality rates. Adjusting patent criteria for alternative cancer therapeutics could stimulate innovation, enhance treatment options, and ultimately improve outcomes for cancer patients. This article explores the potential of alternative cancer therapeutics, chemopreventive agents, natural products, off-patent drugs, generic unpatentable chemicals, and repurposed drugs in cancer treatment, emphasizing the mechanisms and therapeutic potential of these unconventional compounds as combinatorial cancer therapies. The biological pathways, therapeutic effects, and potential to enhance existing therapies are reviewed, demonstrating their cost-effective and accessible options as adjuvant cancer therapies.

Artificial intelligence as a tool in drug discovery and development

The rapidly advancing field of artificial intelligence (AI) has garnered substantial attention for its potential application in drug discovery and development. This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry. AI, encompassing machine learning algorithms, deep learning, and data analytics, offers unprecedented opportunities to streamline and enhance various stages of drug development. This opinion review delved into the current landscape of AI-driven approaches, discussing their utilization in target identification, lead optimization, and predictive modeling of pharmacokinetics and toxicity. We aimed to scrutinize the integration of large-scale omics data, electronic health records, and chemical informatics, highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies. Despite the considerable potential of AI, the review also addressed inherent challenges, including data privacy concerns, interpretability of AI models, and the need for robust validation in real-world clinical settings. Additionally, we explored ethical considerations surrounding AI-driven decision-making in drug development. This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends, presenting critical insights and addressing potential hurdles. In conclusion, this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.

Similarity-based metric analysis approach for predicting osteogenic differentiation correlation coefficients and discovering the novel osteogenic-related gene FOXA1 in BMSCs

Background

As a powerful tool, bioinformatics analysis is playing an increasingly important role in many fields. Osteogenic differentiation is a complex biological process involving the fine regulation of numerous genes and signaling pathways.

Method

Osteogenic differentiation-related genes are collected from the online databases. Then, we proposed two indexes Jaccard similarity and Sorensen-Dice similarity to measure the topological relevance of genes in the human PPI network. Furthermore, we selected three pathways involving osteoblast-related transcription factors, osteoblast differentiation, and RUNX2 regulation of osteoblast differentiation for investigation. Subsequently, we performed functional a enrichment analysis of these top-ranked genes to check whether these candidate genes identified by similarity-based metrics are enriched in some specific biological functions and states. we performed a permutation test to investigate the similarity score with four well-known osteogenic differentiation-related pathways including hedgehog signaling pathway, BMP signaling, ERK pathway, and Wnt signaling pathway to check whether these osteogenic differentiation-related pathways can be regulated by FOXA1. Lentiviral transfection was used to knockdown and overexpress gene FOXA1 in human bone mesenchymal stem cells (hBMSCs). Alkaline phosphatase (ALP) staining and Alizarin Red staining (ARS) were employed to investigate osteogenic differentiation of hBMSCs.

Result

After data collection, human PPI network involving 19,344 genes is included in our analysis. After simplifying, we used Jaccard and Sorensen-Dice similarity to identify osteogenic differentiation-related genes and integrated into a final similarity matrix. Furthermore, we calculated the sum of similarity scores with these osteogenic differentiation-related genes for each gene and found 337 osteogenic differentiation-related genes are involved in our analysis. We selected three pathways involving osteoblast-related transcription factors, osteoblast differentiation, and RUNX2 regulation of osteoblast differentiation for investigation and performed functional enrichment analysis of these top-ranked 50 genes. The results collectively demonstrate that these candidate genes can indeed capture osteogenic differentiation-related features of hBSMCs. According to the novel analyzing method, we found that these four pathways have significantly higher similarity with FOXA1 than random noise. Moreover, knockdown FOXA1 significantly increased the ALP activity and mineral deposits. Furthermore, overexpression of FOXA1 dramatically decreased the ALP activity and mineral deposits.

Conclusion

In summary, this study showed that FOXA1 is a novel significant osteogenic differentiation-related transcription factor. Moreover, our study has tightly integrated bioinformatics analysis with biological knowledge, and developed a novel method for analyzing the osteogenic differentiation regulatory network.

Synthetic Approaches, Properties, and Applications of Acylals in Preparative and Medicinal Chemistry

Diesters of geminal diols (R-CH(O-CO-R')2, RR'C(OCOR″)2, etc. with R = H, aryl or alkyl) are termed acylals according to IUPAC recommendations (Rule P-65.6.3.6 Acylals) if the acids involved are carboxylic acids. Similar condensation products can be obtained from various other acidic structures as well, but these related "non-classical acylals", as one might call them, differ in various aspects from classical acylals and will not be discussed in this article. Carboxylic acid diesters of geminal diols play a prominent role in organic chemistry, not only in their application as protective groups for aldehydes and ketones but also as precursors in the total synthesis of natural compounds and in a variety of organic reactions. What is more, acylals are useful as a key structural motif in clinically validated prodrug approaches. In this review, we summarise the syntheses and chemical properties of such classical acylals and show what potentially under-explored possibilities exist in the field of drug design, especially prodrugs, and classify this functional group in medicinal chemistry.

A comprehensive review on organ-on-chips as powerful preclinical models to study tissue barriers

In the preclinical stage of drug development, 2D and 3D cell cultures under static conditions followed by animal models are utilized. However, these models are insufficient to recapitulate the complexity of human physiology. With the developing organ-on-chip (OoC) technology in recent years, human physiology and pathophysiology can be modeled better than traditional models. In this review, the need for OoC platforms is discussed and evaluated from both biological and engineering perspectives. The cellular and extracellular matrix components are discussed from a biological perspective, whereas the technical aspects such as the intricate working principles of these systems, the pivotal role played by flow dynamics and sensor integration within OoCs are elucidated from an engineering perspective. Combining these two perspectives, bioengineering applications are critically discussed with a focus on tissue barriers such as blood-brain barrier, ocular barrier, nasal barrier, pulmonary barrier and gastrointestinal barrier, featuring recent examples from the literature. Furthermore, this review offers insights into the practical utility of OoC platforms for modeling tissue barriers, showcasing their potential and drawbacks while providing future projections for innovative technologies.

Exploring the genetic basis of childhood monogenic diabetes

Monogenic diabetes is caused by one or even more genetic variations, which may be uncommon yet have a significant influence and cause diabetes at an early age. Monogenic diabetes affects 1% to 5% of children, and early detection and genetically focused treatment of neonatal diabetes and maturity-onset diabetes of the young can significantly improve long-term health and well-being. The etiology of monogenic diabetes in childhood is primarily attributed to genetic variations affecting the regulatory genes responsible for beta-cell activity. In rare instances, mutations leading to severe insulin resistance can also result in the development of diabetes. Individuals diagnosed with specific types of monogenic diabetes, which are commonly found, can transition from insulin therapy to sulfonylureas, provided they maintain consistent regulation of their blood glucose levels. Scientists have successfully devised materials and methodologies to distinguish individuals with type 1 or 2 diabetes from those more prone to monogenic diabetes. Genetic screening with appropriate findings and interpretations is essential to establish a prognosis and to guide the choice of therapies and management of these interrelated ailments. This review aims to design a comprehensive literature summarizing genetic insights into monogenetic diabetes in children and adolescents as well as summarizing their diagnosis and management.

Oral food-derived whey protein isolate-Tremella fuciformis polysaccharides pickering emulsions with adhesive ability to delivery magnolol for targeted treatment of ulcerative colitis

Magnolol (Mag) is a promising natural compound with therapeutic potential for ulcerative colitis (UC). Here we designed and fabricated an oral food-grade whey protein isolate-Tremella fuciformis polysaccharides (WPI-TFPS) stabilized pickering emulsions to encapsulate Mag (Mag-WPI-TFPS) for targeted treatment of UC. With the assistance of the WPI-TFPS, pickering emulsions were well encapsulated and formed stable microparticles with a particle size of approximately 9.49 ± 0.047 μm, a 93.63 ± 0.21 % encapsulation efficiency and a loading efficiency of 21.53 ± 0.01 %. In vitro, the formulation exhibited sustained-release properties in simulated colon fluid with a cumulative release rate of 60.78 % at 48 h. In vivo, the Mag-WPI-TFPS specifically accumulated in the colon tissue for 24 h with stronger fluorescence intensity, which demonstrated that TFPS and WPI had a good adherence ability to inflamed mucosa by electrostatic attraction and ligand-receptor interactions. As expected, compared with Free-Mag, the oral administration of Mag-WPI-TFPS remarkably alleviated the symptoms of UC and protected the colon tissue in DSS-induced UC mice. More importantly, WPI-TFPS enhanced gut microbiota balance by increasing the diversity and relative abundances of Lactobacillaceae and Firmicutes. Overall, this study presents a convenient, eco-friendly, food-derived oral formulation with potential as a dietary supplement for targeted UC treatment.

Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and the need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. Addressing these challenges and fostering collaboration among AI experts, clinicians, and researchers is crucial for developing equitable and effective AI applications. The future of AI in HNC holds significant promise, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes.

Artificial Intelligence to Close the Gap between Pharmacokinetic/Pharmacodynamic Targets and Clinical Outcomes in Critically Ill Patients: A Narrative Review on Beta Lactams

Antimicrobial dosing can be a complex challenge. Although a solid rationale exists for a link between antibiotic exposure and outcome, conflicting data suggest a poor correlation between pharmacokinetic/pharmacodynamic targets and infection control. Different reasons may lead to this discrepancy: poor tissue penetration by β-lactams due to inflammation and inadequate tissue perfusion; different bacterial response to antibiotics and biofilms; heterogeneity of the host's immune response and drug metabolism; bacterial tolerance and acquisition of resistance during therapy. Consequently, either a fixed dose of antibiotics or a fixed target concentration may be doomed to fail. The role of biomarkers in understanding and monitoring host response to infection is also incompletely defined. Nowadays, with the ever-growing stream of data collected in hospitals, utilizing the most efficient analytical tools may lead to better personalization of therapy. The rise of artificial intelligence and machine learning has allowed large amounts of data to be rapidly accessed and analyzed. These unsupervised learning models can apprehend the data structure and identify homogeneous subgroups, facilitating the individualization of medical interventions. This review aims to discuss the challenges of β-lactam dosing, focusing on its pharmacodynamics and the new challenges and opportunities arising from integrating machine learning algorithms to personalize patient treatment.

Recent advancements toward the incremsent of drug solubility using environmentally-friendly supercritical CO2: a machine learning perspective

Inadequate bioavailability of therapeutic drugs, which is often the consequence of their unacceptable solubility and dissolution rates, is an indisputable operational challenge of pharmaceutical companies due to its detrimental effect on the therapeutic efficacy. Over the recent decades, application of supercritical fluids (SCFs) (mainly SCCO2) has attracted the attentions of many scientists as promising alternative of toxic and environmentally-hazardous organic solvents due to possessing positive advantages like low flammability, availability, high performance, eco-friendliness and safety/simplicity of operation. Nowadays, application of different machine learning (ML) as a versatile, robust and accurate approach for the prediction of different momentous parameters like solubility and bioavailability has been of great attentions due to the non-affordability and time-wasting nature of experimental investigations. The prominent goal of this article is to review the role of different ML-based tools for the prediction of solubility/bioavailability of drugs using SCCO2. Moreover, the importance of solubility factor in the pharmaceutical industry and different possible techniques for increasing the amount of this parameter in poorly-soluble drugs are comprehensively discussed. At the end, the efficiency of SCCO2 for improving the manufacturing process of drug nanocrystals is aimed to be discussed.

Lessons learnt from broad-spectrum coronavirus antiviral drug discovery

INTRODUCTION

Highly pathogenic coronaviruses (CoVs), such as severe acute respiratory syndrome CoV (SARS-CoV), Middle East respiratory syndrome CoV (MERS-CoV), and the most recent SARS-CoV-2 responsible for the COVID-19 pandemic, pose significant threats to human populations over the past two decades. These CoVs have caused a broad spectrum of clinical manifestations ranging from asymptomatic to severe distress syndromes (ARDS), resulting in high morbidity and mortality.

AREAS COVERED

The accelerated advancements in antiviral drug discovery, spurred by the COVID-19 pandemic, have shed new light on the imperative to develop treatments effective against a broad spectrum of CoVs. This perspective discusses strategies and lessons learnt in targeting viral non-structural proteins, structural proteins, drug repurposing, and combinational approaches for the development of antivirals against CoVs.

EXPERT OPINION

Drawing lessons from the pandemic, it becomes evident that the absence of efficient broad-spectrum antiviral drugs increases the vulnerability of public health systems to the potential onslaught by highly pathogenic CoVs. The rapid and sustained spread of novel CoVs can have devastating consequences without effective and specifically targeted treatments. Prioritizing the effective development of broad-spectrum antivirals is imperative for bolstering the resilience of public health systems and mitigating the potential impact of future highly pathogenic CoVs.

Advanced drug delivery technologies for postmenopausal effects

Postmenopause is the 12-month absence of menstrual periods, characterized by decreased estrogen and progesterone levels, leading to physical and psychological alterations such as hot flashes, mood swings, sleep disruptions, and skin changes. Present postmenopausal treatments include hormone replacement therapy, non-hormonal drugs, lifestyle modifications, vaginal estrogen therapy, bone health treatments, and alternative therapies. Advanced drug delivery systems (ADDSs) are essential in managing postmenopausal effects (PMEs), offering targeted and controlled delivery to alleviate symptoms and improve overall health. This review emphasizes such ADDSs for addressing PMEs. Emerging trends such as artificial ovaries are also reviewed. Additionally, the prospects of technologies such as additive manufacturing (3D and 4D printing) and artificial intelligence in further tailoring therapeutic strategies against PMEs are provided.

Advancing Pharmacy Practice: The Role of Intelligence-Driven Pharmacy Practice and the Emergence of Pharmacointelligence

The field of healthcare is experiencing a significant transformation driven by technological advancements, scientific breakthroughs, and a focus on personalized patient care. At the forefront of this evolution is artificial intelligence-driven pharmacy practice (IDPP), which integrates data science and technology to enhance pharmacists' capabilities. This prospective article introduces the concept of "pharmacointelligence", a paradigm shift that synergizes artificial intelligence (AI), data integration, clinical decision support systems (CDSS), and pharmacy informatics to optimize medication-related processes. Through a comprehensive literature review and analysis, this research highlights the potential of pharmacointelligence to revolutionize pharmacy practice by addressing the complexity of pharmaceutical data, changing healthcare demands, and technological advancements. This article identifies the critical need for integrating these technologies to enhance medication management, improve patient outcomes, and streamline pharmacy operations. It also underscores the importance of regulatory and ethical considerations in implementing pharmacointelligence, ensuring patient privacy, data security, and equitable healthcare delivery.

Artificial Intelligence, the Digital Surgeon: Unravelling Its Emerging Footprint in Healthcare - The Narrative Review

Background

Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions for diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into healthcare systems, it promises advancements across various domains. This review explores the diverse applications of AI in healthcare, along with the challenges and limitations that need to be addressed. The aim is to provide a comprehensive overview of AI's impact on healthcare and to identify areas for further development and focus.

Main Applications

The review discusses the broad range of AI applications in healthcare. In medical imaging and diagnostics, AI enhances the accuracy and efficiency of diagnostic processes, aiding in early disease detection. AI-powered clinical decision support systems assist healthcare professionals in patient management and decision-making. Predictive analytics using AI enables the prediction of patient outcomes and identification of potential health risks. AI-driven robotic systems have revolutionized surgical procedures, improving precision and outcomes. Virtual assistants and chatbots enhance patient interaction and support, providing timely information and assistance. In the pharmaceutical industry, AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy. Additionally, AI improves administrative efficiency and operational workflows in healthcare, streamlining processes and reducing costs. AI-powered remote monitoring and telehealth solutions expand access to healthcare, particularly in underserved areas.

Challenges and Limitations

Despite the significant promise of AI in healthcare, several challenges persist. Ensuring the reliability and consistency of AI-driven outcomes is crucial. Privacy and security concerns must be navigated carefully, particularly in handling sensitive patient data. Ethical considerations, including bias and fairness in AI algorithms, need to be addressed to prevent unintended consequences. Overcoming these challenges is critical for the ethical and successful integration of AI in healthcare.

Conclusion

The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency. However, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare. Future efforts should focus on enhancing the reliability, transparency, and ethical standards of AI technologies to ensure they contribute positively to global health outcomes.

The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review

Currently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. Numerous research studies and advancements have demonstrated how these computational technologies are used in various pharmaceutical research and development aspects, including drug discovery, personalized medicine, drug formulation, optimization, predictions, drug interactions, pharmacokinetics/ pharmacodynamics, quality control/quality assurance, and manufacturing processes. Using advanced modeling techniques, these computational technologies can enhance efficiency and accuracy, handle complex data, and facilitate novel discoveries within minutes. Furthermore, these technologies offer several advantages over conventional statistics. They allow for pattern recognition from complex datasets, and the models, typically developed from data-driven algorithms, can predict a given outcome (model output) from a set of features (model inputs). Additionally, this review discusses emerging trends and provides perspectives on the application of AI with quality by design (QbD) and the future role of AI in this field. Ethical and regulatory considerations associated with integrating AI into pharmaceutical technology were also examined. This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.

Artificial intelligence integration in the drug lifecycle and in regulatory science: policy implications, challenges and opportunities

Artificial intelligence tools promise transformative impacts in drug development. Regulatory agencies face challenges in integrating AI while ensuring reliability and safety in clinical trial approvals, drug marketing authorizations, and post-market surveillance. Incorporating these technologies into the existing regulatory framework and agency practices poses notable challenges, particularly in evaluating the data and models employed for these purposes. Rapid adaptation of regulations and internal processes is essential for agencies to keep pace with innovation, though achieving this requires collective stakeholder collaboration. This article thus delves into the need for adaptations of regulations throughout the drug development lifecycle, as well as the utilization of AI within internal processes of medicine agencies.

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

A Human Feedback Strategy for Photoresponsive Molecules in Drug Delivery: Utilizing GPT-2 and Time-Dependent Density Functional Theory Calculations

Photoresponsive drug delivery stands as a pivotal frontier in smart drug administration, leveraging the non-invasive, stable, and finely tunable nature of light-triggered methodologies. The generative pre-trained transformer (GPT) has been employed to generate molecular structures. In our study, we harnessed GPT-2 on the QM7b dataset to refine a UV-GPT model with adapters, enabling the generation of molecules responsive to UV light excitation. Utilizing the Coulomb matrix as a molecular descriptor, we predicted the excitation wavelengths of these molecules. Furthermore, we validated the excited state properties through quantum chemical simulations. Based on the results of these calculations, we summarized some tips for chemical structures and integrated them into the alignment of large-scale language models within the reinforcement learning from human feedback (RLHF) framework. The synergy of these findings underscores the successful application of GPT technology in this critical domain.

High-Throughput Molecular Modeling and Evaluation of the Anti-Inflammatory Potential of Açaí Constituents against NLRP3 Inflammasome

The search for bioactive compounds in natural products holds promise for discovering new pharmacologically active molecules. This study explores the anti-inflammatory potential of açaí (Euterpe oleracea Mart.) constituents against the NLRP3 inflammasome using high-throughput molecular modeling techniques. Utilizing methods such as molecular docking, molecular dynamics simulation, binding free energy calculations (MM/GBSA), and in silico toxicology, we compared açaí compounds with known NLRP3 inhibitors, MCC950 and NP3-146 (RM5). The docking studies revealed significant interactions between açaí constituents and the NLRP3 protein, while molecular dynamics simulations indicated structural stabilization. MM/GBSA calculations demonstrated favorable binding energies for catechin, apigenin, and epicatechin, although slightly lower than those of MCC950 and RM5. Importantly, in silico toxicology predicted lower toxicity for açaí compounds compared to synthetic inhibitors. These findings suggest that açaí-derived compounds are promising candidates for developing new anti-inflammatory therapies targeting the NLRP3 inflammasome, combining efficacy with a superior safety profile. Future research should include in vitro and in vivo validation to confirm the therapeutic potential and safety of these natural products. This study underscores the value of computational approaches in accelerating natural product-based drug discovery and highlights the pharmacological promise of Amazonian biodiversity.

Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.

The United States Food and Drug Administration's Platform Technology Designation to Expedite the Development of Drugs

Drug development costs can be significantly reduced if proven "platform" technologies are allowed to be used without having to validate their use. The most recent US Food and Drug Administration (FDA) guideline brings more clarity, as well as a greater focus on the most complex technologies that can now be used for faster drug development. The FDA has highlights the use of lipid nanoparticles (LNPs) to package and deliver mRNA vaccines, gene therapy, and short (2-20 length) synthetic nucleotides (siRNA). Additionally, monoclonal antibody cell development is targeted. The FDA provides a systematic process of requesting platform status to benefit from its advantages. It brings advanced science and rationality into regulatory steps for the FDA's approval of drugs and biologicals.

Biosimilars in the Era of Artificial Intelligence-International Regulations and the Use in Oncological Treatments

This research is based on three fundamental aspects of successful biosimilar development in the challenging biopharmaceutical market. First, biosimilar regulations in eight selected countries: Japan, South Korea, the United States, Canada, Brazil, Argentina, Australia, and South Africa, represent the four continents. The regulatory aspects of the countries studied are analyzed, highlighting the challenges facing biosimilars, including their complex approval processes and the need for standardized regulatory guidelines. There is an inconsistency depending on whether the biosimilar is used in a developed or developing country. In the countries observed, biosimilars are considered excellent alternatives to patent-protected biological products for the treatment of chronic diseases. In the second aspect addressed, various analytical AI modeling methods (such as machine learning tools, reinforcement learning, supervised, unsupervised, and deep learning tools) were analyzed to observe patterns that lead to the prevalence of biosimilars used in cancer to model the behaviors of the most prominent active compounds with spectroscopy. Finally, an analysis of the use of active compounds of biosimilars used in cancer and approved by the FDA and EMA was proposed.

Evaluating Molecular Complexity with Open-Source Machine Learning Approaches to Predict Process Mass Intensity

The application of green chemistry is critical for cultivating environmental responsibility and sustainable practices in pharmaceutical manufacturing. Process mass intensity (PMI) is a key metric that quantifies the resource efficiency of a manufacturing process, but determining what constitutes a successful PMI of a specific molecule is challenging. A recent approach correlated molecular features to a crowdsourced definition of molecular complexity to determine PMI targets. While recent machine learning tools show promise in predicting molecular complexity, a more extensive application could significantly optimize manufacturing processes. To this end, we refine and expand upon the SMART-PMI tool by Sheridan et al. to create an open-source model and application. Our solution emphasizes explainability and parsimony to facilitate a nuanced understanding of prediction and ensure informed decision-making. The resulting model uses four descriptors-the heteroatom count, stereocenter count, unique topological torsion, and connectivity index chi4n-to compute molecular complexity with a comparable 82.6% predictive accuracy and 0.349 RMSE. We develop a corresponding app that takes in structured data files (SDF) to rapidly quantify molecular complexity and provide a PMI target that can be used to drive process development activities. By integrating machine learning explainability and open-source accessibility, we provide flexible tools to advance the field of green chemistry and sustainable pharmaceutical manufacturing.

A Smart Strategy for Photoresponsive Molecules: Utilizing Generative Pre-trained Transformer and TDDFT Calculations in Drug Delivery

Photoresponsive drug delivery stands as a pivotal frontier in smart drug administration, leveraging the non-invasive, stable, and finely tunable nature of light-triggered methodologies. The Generative Pre-trained Transformer (GPT) has been employed for generating molecular structures. In our study, we harnessed GPT-2 on the QM7b dataset to refine a UV-GPT model with adapters, enabling the generation of molecules responsive to UV light excitation. Utilizing the Coulomb matrix as a molecular descriptor, we predicted the excitation wavelengths of these molecules. Furthermore, we validated the excited state properties through Quantum chemical simulations. The synergy of these findings underscores the successful application of GPT technology in this critical domain.

Artificial Intelligence-Powered Molecular Docking and Steered Molecular Dynamics for Accurate scFv Selection of Anti-CD30 Chimeric Antigen Receptors

Chimeric antigen receptor (CAR) T cells represent a revolutionary immunotherapy that allows specific tumor recognition by a unique single-chain fragment variable (scFv) derived from monoclonal antibodies (mAbs). scFv selection is consequently a fundamental step for CAR construction, to ensure accurate and effective CAR signaling toward tumor antigen binding. However, conventional in vitro and in vivo biological approaches to compare different scFv-derived CARs are expensive and labor-intensive. With the aim to predict the finest scFv binding before CAR-T cell engineering, we performed artificial intelligence (AI)-guided molecular docking and steered molecular dynamics analysis of different anti-CD30 mAb clones. Virtual computational scFv screening showed comparable results to surface plasmon resonance (SPR) and functional CAR-T cell in vitro and in vivo assays, respectively, in terms of binding capacity and anti-tumor efficacy. The proposed fast and low-cost in silico analysis has the potential to advance the development of novel CAR constructs, with a substantial impact on reducing time, costs, and the need for laboratory animal use.

Pharmacovigilance in Vaccines: Importance, Main Aspects, Perspectives, and Challenges-A Narrative Review

Pharmacovigilance plays a central role in safeguarding public health by continuously monitoring the safety of vaccines, being critical in a climate of vaccine hesitancy, where public trust is paramount. Pharmacovigilance strategies employed to gather information on adverse events following immunization (AEFIs) include pre-registration data, media reports, clinical trials, and societal reporting. Early detection of AEFIs during clinical trials is crucial for thorough safety analysis and preventing serious reactions once vaccines are deployed. This review highlights the importance of societal reporting, encompassing contributions from community members, healthcare workers, and pharmaceutical companies. Technological advancements such as quick response (QR) codes can facilitate prompt AEFI reporting. While vaccines are demonstrably safe, the possibility of adverse events necessitates continuous post-marketing surveillance. However, underreporting remains a challenge, underscoring the critical role of public engagement in pharmacovigilance. This narrative review comprehensively examines and synthesizes key aspects of virus vaccine pharmacovigilance, with special considerations for specific population groups. We explore applicable legislation, the spectrum of AEFIs associated with major vaccines, and the unique challenges and perspectives surrounding pharmacovigilance in this domain.

Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review

BACKGROUND

Drug safety relies on advanced methods for timely and accurate prediction of side effects. To tackle this requirement, this scoping review examines machine-learning approaches for predicting drug-related side effects with a particular focus on chemical, biological, and phenotypical features.

METHODS

This was a scoping review in which a comprehensive search was conducted in various databases from 1 January 2013 to 31 December 2023.

RESULTS

The results showed the widespread use of Random Forest, k-nearest neighbor, and support vector machine algorithms. Ensemble methods, particularly random forest, emphasized the significance of integrating chemical and biological features in predicting drug-related side effects.

CONCLUSIONS

This review article emphasized the significance of considering a variety of features, datasets, and machine learning algorithms for predicting drug-related side effects. Ensemble methods and Random Forest showed the best performance and combining chemical and biological features improved prediction. The results suggested that machine learning techniques have some potential to improve drug development and trials. Future work should focus on specific feature types, selection techniques, and graph-based methods for even better prediction.

Revolutionizing Neurology: The Role of Artificial Intelligence in Advancing Diagnosis and Treatment

Artificial intelligence (AI) has emerged as a powerful tool in the field of neurology, significantly impacting the diagnosis and treatment of neurological disorders. Recent technological breakthroughs have given us access to a plethora of information relevant to many aspects of neurology. Neuroscience and AI share a long history of collaboration. Along with great potential, we encounter obstacles relating to data quality, ethics, and inherent difficulty in applying data science in healthcare. Neurological disorders pose intricate challenges due to their complex manifestations and variability. Automating image interpretation tasks, AI algorithms accurately identify brain structures and detect abnormalities. This accelerates diagnosis and reduces the workload on medical professionals. Treatment optimization benefits from AI simulations that model different scenarios and predict outcomes. These AI systems can currently perform many of the sophisticated perceptual and cognitive capacities of biological systems, such as object identification and decision making. Furthermore, AI is rapidly being used as a tool in neuroscience research, altering our understanding of brain functioning. It has the ability to revolutionize healthcare as we know it into a system in which humans and robots collaborate to deliver better care for our patients. Image analysis activities such as recognizing particular brain regions, calculating changes in brain volume over time, and detecting abnormalities in brain scans can be automated by AI systems. This lessens the strain on radiologists and neurologists while improving diagnostic accuracy and efficiency. It is now obvious that cutting-edge artificial intelligence models combined with high-quality clinical data will lead to enhanced prognostic and diagnostic models in neurological illness, permitting expert-level clinical decision aids across healthcare settings. In conclusion, AI's integration into neurology has revolutionized diagnosis, treatment, and research. As AI technologies advance, they promise to unravel the complexities of neurological disorders further, leading to improved patient care and quality of life. The symbiosis of AI and neurology offers a glimpse into a future where innovation and compassion converge to reshape neurological healthcare. This abstract provides a concise overview of the role of AI in neurology and its transformative potential.

A novel cell-free therapy using exosomes in the inner ear regeneration

Cellular and molecular alterations associated with hearing loss are now better understood with advances in molecular biology. These changes indicate the participation of distinct damage and stress pathways that are unlikely to be fully addressed by conventional pharmaceutical treatment. Sensorineural hearing loss is a common and debilitating condition for which comprehensive pharmacologic intervention is not available. The complex and diverse molecular pathology that underlies hearing loss currently limits our ability to intervene with small molecules. The present review focuses on the potential for the use of extracellular vesicles in otology. It examines a variety of inner ear diseases and hearing loss that may be treatable using exosomes (EXOs). The role of EXOs as carriers for the treatment of diseases related to the inner ear as well as EXOs as biomarkers for the recognition of diseases related to the ear is discussed.

The recent advances in the approach of artificial intelligence (AI) towards drug discovery

Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium is showing the potential in unprecedented advancements in truth and efficiency. The intersection of AI has the potential to revolutionize drug discovery. However, AI also has limitations and experts should be aware of these data access and ethical issues. The use of AI techniques for drug discovery applications has increased considerably over the past few years, including combinatorial QSAR and QSPR, virtual screening, and denovo drug design. The purpose of this survey is to give a general overview of drug discovery based on artificial intelligence, and associated applications. We also highlighted the gaps present in the traditional method for drug designing. In addition, potential strategies and approaches to overcome current challenges are discussed to address the constraints of AI within this field. We hope that this survey plays a comprehensive role in understanding the potential of AI in drug discovery.

Bioprospecting of Aspergillus sp. as a promising repository for anti-cancer agents: a comprehensive bibliometric investigation

Cancer remains a significant global health challenge, claiming nearly 10 million lives in 2020 according to the World Health Organization. In the quest for novel treatments, fungi, especially Aspergillus species, have emerged as a valuable source of bioactive compounds with promising anticancer properties. This study conducts a comprehensive bibliometric analysis to map the research landscape of Aspergillus in oncology, examining publications from 1982 to the present. We observed a marked increase in research activity starting in 2000, with a notable peak from 2005 onwards. The analysis identifies key contributors, including Mohamed GG, who has authored 15 papers with 322 citations, and El-Sayed Asa, with 14 papers and 264 citations. Leading countries in this research field include India, Egypt, and China, with King Saud University and Cairo University as the leading institutions. Prominent research themes identified are "endophyte," "green synthesis," "antimicrobial," "anti-cancer," and "biological activities," indicating a shift towards environmentally sustainable drug development. Our findings highlight the considerable potential of Aspergillus for developing new anticancer therapies and underscore the necessity for further research to harness these natural compounds for clinical use.

Drug repurposing for cancer

In the dynamic landscape of cancer therapeutics, the innovative strategy of drug repurposing emerges as a transformative paradigm, heralding a new era in the fight against malignancies. This book chapter aims to embark on the comprehension of the strategic deployment of approved drugs for repurposing and the meticulous journey of drug repurposing from earlier times to the current era. Moreover, the chapter underscores the multifaceted and complex nature of cancer biology, and the evolving field of cancer drug therapeutics while emphasizing the mandate of drug repurposing to advance cancer therapeutics. Importantly, the narrative explores the latest tools, technologies, and cutting-edge methodologies including high-throughput screening, omics technologies, and artificial intelligence-driven approaches, for shaping and accelerating the pace of drug repurposing to uncover novel cancer therapeutic avenues. The chapter critically assesses the breakthroughs, expanding the repertoire of repurposing drug candidates in cancer, and their major categories. Another focal point of this book chapter is that it addresses the emergence of combination therapies involving repurposed drugs, reflecting a shift towards personalized and synergistic treatment approaches. The expert analysis delves into the intricacies of combinatorial regimens, elucidating their potential to target heterogeneous cancer populations and overcome resistance mechanisms, thereby enhancing treatment efficacy. Therefore, this chapter provides in-depth insights into the potential of repurposing towards bringing the much-needed big leap in the field of cancer therapeutics.

Development of Oral Tablets of Nebivolol with Improved Dissolution Properties, Based on Its Combinations with Cyclodextrins

New oral tablets of nebivolol have been developed aiming to improve, by cyclodextrin (CD) complexation, its low solubility/dissolution properties-the main reason behind its poor/variable oral bioavailability. Phase-solubility studies, performed using βCD and highly-soluble βCD-derivatives, indicated sulfobutylether-βCD (SBEβCD) as the best solubilizing/complexing agent. Solid drug-SBEβCD systems were prepared by different methods and characterized for solid-state and dissolution properties. The coevaporated product was chosen for tablet development since it provided the highest dissolution rate (100% increase in dissolved drug at 10 min) and almost complete drug amorphization/complexation. The developed tablets reached the goal, allowing us to achieve 100% dissolved drug at 60 min, compared to 66% and 64% obtained, respectively, with a reference tablet without CD and a commercial tablet. However, the percentage dissolved after 10 min from such tablets was only 10% higher than the reference. This was ascribed to the potential binding/compacting abilities of SBEβCD, reflected in the greater hardness and longer disintegration times of the new tablets than the reference (7.64 vs. 1.06 min). A capsule formulation with the same composition of nebivolol-SBEβCD tablets showed about a 90% increase in dissolved drug after 5 min compared to the reference tablet, and reached 100% dissolved drug after only 20 min.

Bee Venom-Loaded Niosomes as Innovative Platforms for Cancer Treatment: Development and Therapeutical Efficacy and Safety Evaluation

Despite past efforts towards therapeutical innovation, cancer remains a highly incident and lethal disease, with current treatments lacking efficiency and leading to severe side effects. Hence, it is imperative to develop new, more efficient, and safer therapies. Bee venom has proven to have multiple and synergistic bioactivities, including antitumor effects. Nevertheless, some toxic effects have been associated with its administration. To tackle these issues, in this work, bee venom-loaded niosomes were developed, for cancer treatment. The vesicles had a small (150 nm) and homogeneous (polydispersity index of 0.162) particle size, and revealed good therapeutic efficacy in in vitro gastric, colorectal, breast, lung, and cervical cancer models (inhibitory concentrations between 12.37 ng/mL and 14.72 ng/mL). Additionally, they also revealed substantial anti-inflammatory activity (inhibitory concentration of 28.98 ng/mL), effects complementary to direct antitumor activity. Niosome safety was also assessed, both in vitro (skin, liver, and kidney cells) and ex vivo (hen's egg chorioallantoic membrane), and results showed that compound encapsulation increased its safety. Hence, small, and homogeneous bee venom-loaded niosomes were successfully developed, with substantial anticancer and anti-inflammatory effects, making them potentially promising primary or adjuvant cancer therapies. Future research should focus on evaluating the potential of the developed platform in in vivo models.

Potential Applications of Artificial Intelligence (AI) in Managing Polypharmacy in Saudi Arabia: A Narrative Review

Prescribing medications is a fundamental practice in the management of illnesses that necessitates in-depth knowledge of clinical pharmacology. Polypharmacy, or the concurrent use of multiple medications by individuals with complex health conditions, poses significant challenges, including an increased risk of drug interactions and adverse reactions. The Saudi Vision 2030 prioritises enhancing healthcare quality and safety, including addressing polypharmacy. Artificial intelligence (AI) offers promising tools to optimise medication plans, predict adverse drug reactions and ensure drug safety. This review explores AI's potential to revolutionise polypharmacy management in Saudi Arabia, highlighting practical applications, challenges and the path forward for the integration of AI solutions into healthcare practices.

Predictive modeling of skin permeability for molecules: Investigating FDA-approved drug permeability with various AI algorithms

The transdermal route of drug administration has gained popularity for its convenience and bypassing the first-pass metabolism. Accurate skin permeability prediction is crucial for successful transdermal drug delivery (TDD). In this study, we address this critical need to enhance TDD. A dataset comprising 441 records for 140 molecules with diverse LogKp values was characterized. The descriptor calculation yielded 145 relevant descriptors. Machine learning models, including MLR, RF, XGBoost, CatBoost, LGBM, and ANN, were employed for regression analysis. Notably, LGBM, XGBoost, and gradient boosting models outperformed others, demonstrating superior predictive accuracy. Key descriptors influencing skin permeability, such as hydrophobicity, hydrogen bond donors, hydrogen bond acceptors, and topological polar surface area, were identified and visualized. Cluster analysis applied to the FDA-approved drug dataset (2326 compounds) revealed four distinct clusters with significant differences in molecular characteristics. Predicted LogKp values for these clusters offered insights into the permeability variations among FDA-approved drugs. Furthermore, an investigation into skin permeability patterns across 83 classes of FDA-approved drugs based on the ATC code showcased significant differences, providing valuable information for drug development strategies. The study underscores the importance of accurate skin permeability prediction for TDD, emphasizing the superior performance of nonlinear machine learning models. The identified key descriptors and clusters contribute to a nuanced understanding of permeability characteristics among FDA-approved drugs. These findings offer actionable insights for drug design, formulation, and prioritization of molecules with optimum properties, potentially reducing reliance on costly experimental testing. Future research directions include offering promising applications in pharmaceutical research and formulation within the burgeoning field of computer-aided drug design.

Nuclear Cardiology in the Era of Precision Medicine: Tailoring Treatment to the Individual Patient

Nuclear cardiology, employing advanced imaging technologies like positron emission tomography (PET) and single photon emission computed tomography (SPECT), is instrumental in diagnosing, risk stratifying, and managing heart diseases. Concurrently, precision medicine advocates for treatments tailored to each patient's genetic, environmental, and lifestyle specificities, promising a revolution in personalized cardiovascular care. This review explores the synergy between nuclear cardiology and precision medicine, highlighting advancements, potential enhancements in patient outcomes, and the challenges and opportunities of this integration. We examined the evolution of nuclear cardiology technologies, including PET and SPECT, and their role in cardiovascular diagnostics. We also delved into the principles of precision medicine, focusing on genetic and molecular profiling, data analytics, and individualized treatment strategies. The integration of these domains aims to optimize diagnostic accuracy, therapeutic interventions, and prognostic evaluations in cardiovascular care. Advancements in molecular imaging and the application of artificial intelligence in nuclear cardiology have significantly improved the precision of diagnostics and treatment plans. The adoption of precision medicine principles in nuclear cardiology enables the customization of patient care, leveraging genetic information and biomarkers for enhanced therapeutic outcomes. However, challenges such as data integration, accessibility, cost, and the need for specialized expertise persist. The confluence of nuclear cardiology and precision medicine offers a promising pathway toward revolutionizing cardiovascular healthcare, providing more accurate, effective, and personalized patient care. Addressing existing challenges and fostering interdisciplinary collaboration is crucial for realizing the full potential of this integration in improving patient outcomes.

Exploiting Nanotechnology for Drug Delivery: Advancing the Anti-Cancer Effects of Autophagy-Modulating Compounds in Traditional Chinese Medicine

Background

Cancer continues to be a prominent issue in the field of medicine, as demonstrated by recent studies emphasizing the significant role of autophagy in the development of cancer. Traditional Chinese Medicine (TCM) provides a variety of anti-tumor agents capable of regulating autophagy. However, the clinical application of autophagy-modulating compounds derived from TCM is impeded by their restricted water solubility and bioavailability. To overcome this challenge, the utilization of nanotechnology has been suggested as a potential solution. Nonetheless, the current body of literature on nanoparticles delivering TCM-derived autophagy-modulating anti-tumor compounds for cancer treatment is limited, lacking comprehensive summaries and detailed descriptions.

Methods

Up to November 2023, a comprehensive research study was conducted to gather relevant data using a variety of databases, including PubMed, ScienceDirect, Springer Link, Web of Science, and CNKI. The keywords utilized in this investigation included "autophagy", "nanoparticles", "traditional Chinese medicine" and "anticancer".

Results

This review provides a comprehensive analysis of the potential of nanotechnology in overcoming delivery challenges and enhancing the anti-cancer properties of autophagy-modulating compounds in TCM. The evaluation is based on a synthesis of different classes of autophagy-modulating compounds in TCM, their mechanisms of action in cancer treatment, and their potential benefits as reported in various scholarly sources. The findings indicate that nanotechnology shows potential in enhancing the availability of autophagy-modulating agents in TCM, thereby opening up a plethora of potential therapeutic avenues.

Conclusion

Nanotechnology has the potential to enhance the anti-tumor efficacy of autophagy-modulating compounds in traditional TCM, through regulation of autophagy.

Spectral insights: Navigating the frontiers of biomedical and microbiological exploration with Raman spectroscopy

Raman spectroscopy (RS), a powerful analytical technique, has gained increasing recognition and utility in the fields of biomedical and biological research. Raman spectroscopic analyses find extensive application in the field of medicine and are employed for intricate research endeavors and diagnostic purposes. Consequently, it enjoys broad utilization within the realm of biological research, facilitating the identification of cellular classifications, metabolite profiling within the cellular milieu, and the assessment of pigment constituents within microalgae. This article also explores the multifaceted role of RS in these domains, highlighting its distinct advantages, acknowledging its limitations, and proposing strategies for enhancement.

Special Issue "Computer-Aided Drug Discovery and Treatment"

This Special Issue aims to highlight some of the latest developments in drug discovery [...].

Nanomedicine facilitated cell signaling blockade: difficulties and strategies to overcome glioblastoma

Glioblastoma (GBM) is a highly aggressive and lethal type of brain tumor with complex and diverse molecular signaling pathways involved that are in its development and progression. Despite numerous attempts to develop effective treatments, the survival rate remains low. Therefore, understanding the molecular mechanisms of these pathways can aid in the development of targeted therapies for the treatment of glioblastoma. Nanomedicines have shown potential in targeting and blocking signaling pathways involved in glioblastoma. Nanomedicines can be engineered to specifically target tumor sites, bypass the blood-brain barrier (BBB), and release drugs over an extended period. However, current nanomedicine strategies also face limitations, including poor stability, toxicity, and low therapeutic efficacy. Therefore, novel and advanced nanomedicine-based strategies must be developed for enhanced drug delivery. In this review, we highlight risk factors and chemotherapeutics for the treatment of glioblastoma. Further, we discuss different nanoformulations fabricated using synthetic and natural materials for treatment and diagnosis to selectively target signaling pathways involved in GBM. Furthermore, we discuss current clinical strategies and the role of artificial intelligence in the field of nanomedicine for targeting GBM.

A Review of the Potential of Poly-(lactide-co-glycolide) Nanoparticles as a Delivery System for an Active Antimycobacterial Compound, 7-Methyljuglone

7-Methyljuglone (7-MJ) is a pure compound isolated from the roots of Euclea natalensis A. DC., a shrub indigenous to South Africa. It exhibits significant promise as a potential treatment for the highly communicable disease tuberculosis (TB), owing to its effective antimycobacterial activity against Mycobacterium tuberculosis. Despite its potential therapeutic benefits, 7-MJ has demonstrated in vitro cytotoxicity against various cancerous and non-cancerous cell lines, raising concerns about its safety for consumption by TB patients. Therefore, this review focuses on exploring the potential of poly-(lactide-co-glycolic) acid (PLGA) nanoparticles as a delivery system, which has been shown to decrease in vitro cytotoxicity, and 7-MJ as an effective antimycobacterial compound.

Digital Health in Diabetes and Cardiovascular Disease

BACKGROUND

Digital health technologies are rapidly evolving and transforming the care of diabetes and cardiovascular disease (CVD).

PURPOSE OF THE REVIEW

In this review, we discuss emerging approaches incorporating digital health technologies to improve patient outcomes through a more continuous, accessible, proactive, and patient-centered approach. We discuss various mechanisms of potential benefit ranging from early detection to enhanced physiologic monitoring over time to helping shape important management decisions and engaging patients in their care. Furthermore, we discuss the potential for better individualization of management, which is particularly important in diseases with heterogeneous and complex manifestations, such as diabetes and cardiovascular disease. This narrative review explores ways to leverage digital health technology to better extend the reach of clinicians beyond the physical hospital and clinic spaces to address disparities in the diagnosis, treatment, and prevention of diabetes and cardiovascular disease.

CONCLUSION

We are at the early stages of the shift to digital medicine, which holds substantial promise not only to improve patient outcomes but also to lower the costs of care. The review concludes by recognizing the challenges and limitations that need to be addressed for optimal implementation and impact. We present recommendations on how to navigate these challenges as well as goals and opportunities in utilizing digital health technology in the management of diabetes and prevention of adverse cardiovascular outcomes.

Cell-penetrating peptides for transmucosal delivery of proteins

Enabling non-invasive delivery of proteins across the mucosal barriers promises improved patient compliance and therapeutic efficacies. Cell-penetrating peptides (CPPs) are emerging as a promising and versatile tool to enhance protein and peptide permeation across various mucosal barriers. This review examines the structural and physicochemical attributes of the nasal, buccal, sublingual, and oral mucosa that hamper macromolecular delivery. Recent development of CPPs for overcoming those mucosal barriers for protein delivery is summarized and analyzed. Perspectives regarding current challenges and future research directions towards improving non-invasive transmucosal delivery of macromolecules for ultimate clinical translation are discussed.

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.

AI-driven covalent drug design strategies targeting main protease (mpro) against SARS-CoV-2: structural insights and molecular mechanisms

The emergence of new SARS-CoV-2 variants has raised concerns about the effectiveness of COVID-19 vaccines. To address this challenge, small-molecule antivirals have been proposed as a crucial therapeutic option. Among potential targets for anti-COVID-19 therapy, the main protease (Mpro) of SARS-CoV-2 is important due to its essential role in the virus's life cycle and high conservation. The substrate-binding region of the core proteases of various coronaviruses, including SARS-CoV-2, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV), could be used for the generation of new protease inhibitors. Various drug discovery methods have employed a diverse range of strategies, targeting both monomeric and dimeric forms, including drug repurposing, integrating virtual screening with high-throughput screening (HTS), and structure-based drug design, each demonstrating varying levels of efficiency. Covalent inhibitors, such as Nirmatrelvir and MG-101, showcase robust and high-affinity binding to Mpro, exhibiting stable interactions confirmed by molecular docking studies. Development of effective antiviral drugs is imperative to address potential pandemic situations. This review explores recent advances in the search for Mpro inhibitors and the application of artificial intelligence (AI) in drug design. AI leverages vast datasets and advanced algorithms to streamline the design and identification of promising Mpro inhibitors. AI-driven drug discovery methods, including molecular docking, predictive modeling, and structure-based drug repurposing, are at the forefront of identifying potential candidates for effective antiviral therapy. In a time when COVID-19 potentially threat global health, the quest for potent antiviral solutions targeting Mpro could be critical for inhibiting the virus.

Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin

The influence of artificial intelligence (AI) has drastically risen in recent years, especially in the field of medicine. Its influence has spread so greatly that it is determined to become a pillar in the future medical world. A comprehensive literature search related to AI in healthcare was performed in the PubMed database and retrieved the relevant information from suitable ones. AI excels in aspects such as rapid adaptation, high diagnostic accuracy, and data management that can help improve workforce productivity. With this potential in sight, the FDA has continuously approved more machine learning (ML) software to be used by medical workers and scientists. However, there are few controversies such as increased chances of data breaches, concern for clinical implementation, and potential healthcare dilemmas. In this article, the positive and negative aspects of AI implementation in healthcare are discussed, as well as recommended some potential solutions to the potential issues at hand.

Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review

Artificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Through machine learning (ML), deep learning, natural language processing (NLP), and robotic automation, AI has enhanced efficiency, accuracy, and innovation in the field. The purpose of this review is to shed light on the practical applications and potential of AI in various pharmaceutical fields. These fields include medicinal chemistry, pharmaceutics, pharmacology and toxicology, clinical pharmacy, pharmaceutical biotechnology, pharmaceutical nanotechnology, pharmacognosy, and pharmaceutical management and economics. By leveraging AI technologies such as ML, deep learning, NLP, and robotic automation, this review delves into the role of AI in enhancing drug discovery, development processes, and personalized patient care. It analyzes AI's impact in specific areas such as drug synthesis planning, formulation development, toxicology predictions, pharmacy automation, and market analysis. Artificial intelligence integration into pharmaceutical sciences has significantly improved medicinal chemistry, drug discovery, and synthesis planning. In pharmaceutics, AI has advanced personalized medicine and formulation development. In pharmacology and toxicology, AI offers predictive capabilities for drug mechanisms and toxic effects. In clinical pharmacy, AI has facilitated automation and enhanced patient care. Additionally, AI has contributed to protein engineering, gene therapy, nanocarrier design, discovery of natural product therapeutics, and pharmaceutical management and economics, including marketing research and clinical trials management. Artificial intelligence has transformed pharmaceuticals, improving efficiency, accuracy, and innovation. This review highlights AI's role in drug development and personalized care, serving as a reference for professionals. The future promises a revolutionized field with AI-driven methodologies.