The significance of artificial intelligence in drug delivery system design

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

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

The Prediction of the In Vitro Release Curves for PLGA-Based Drug Delivery Systems with Neural Networks

Background/Objectives: The accurate prediction of drug release profiles from Poly (lactic-co-glycolic acid) (PLGA)-based drug delivery systems is a critical challenge in pharmaceutical research. Traditional methods, such as the Korsmeyer-Peppas and Weibull models, have been widely used to describe in vitro drug release kinetics. However, these models are limited by their reliance on fixed mathematical forms, which may not capture the complex and nonlinear nature of drug release behavior in diverse PLGA-based systems. Method: In response to these limitations, we propose a novel approach-DrugNet, a data-driven model based on a multilayer perceptron (MLP) neural network, aiming to predict the drug release data at unknown time points by fitting release curves using the key physicochemical characteristics of PLGA carriers and drug molecules, as well as in vitro drug release data. We establish a dataset through a literature review, and the model is trained and validated to determine its effectiveness in predicting different drug release curves. Results: Compared to the traditional Korsmeyer-Peppas and Weibull semi-empirical models, the MSE of DrugNet decreases by 20.994 and 1.561, respectively, and (R2) increases by 0.036 and 0.005. Conclusions: These results demonstrate that DrugNet has a stronger ability to fit drug release curves and better capture nonlinear relationships in drug release data. It can deal with the nonlinear change of data better, has stronger adaptability and advantages than traditional models, and overcomes the limitations of the mathematical expressions in traditional models.

Implantable systems for neurological chronotherapy

Implantable systems for neurological chronotherapy are poised to revolutionize the treatment of central nervous system diseases and disorders. These devices enable precise, time-controlled drug delivery aligned with the body's circadian rhythms, optimizing therapeutic outcomes. By bypassing the blood-brain barrier, they achieve high local drug concentrations while minimizing systemic side effects, offering significant advantages for conditions where traditional therapies often fall short. Platforms like SynchroMed II and CraniUS showcase this innovation, providing programmable delivery for conditions such as epilepsy and glioblastoma, with customizable profiles ranging from continuous infusion to timed bolus administration. Preclinical and clinical studies underscore the efficacy of aligning drug delivery with circadian rhythms, enhancing outcomes in chrono-chemotherapy and anti-epileptic treatments. Despite their promise, challenges remain, including the invasiveness of implantation within the brain, device longevity, synchronization complexities, and cost(s). Accordingly, this review explores the current state of implantable neurological systems that may be leveraged for chronotherapy, their applications, limitations, and potential to transform neurological disease/disorder management.

Bioactive structures for inhibitors of Candida auris polymerase enzyme by artificial intelligence

AIMS

Present new bioactive compounds, created by De novo Drug Design and artificial intelligence (AI), as possible inhibitors of C. auris polymerase.

MATERIALS & METHODS

MolAICal's AI module was configured to identify FDA-approved molecular fragments with therapeutic effectiveness against C. auris polymerase, where the model with optimized synthetic accessibility and structural complexity was subjected to docking and molecular dynamics simulations and pharmacokinetic prediction.

RESULTS

Among 1,722 new forms, the Hit-960 compound stood out for its high bioaffinity and stability, with a binding energy of -9.12 kcal/mol and 75% synthetic accessibility.

CONCLUSIONS

Clinical studies are recommended to test its efficacy, contributing to the development of new treatments for C. auris infections.

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.

Utilizing machine learning for predicting drug release from polymeric drug delivery systems

Polymeric drug delivery systems (PDDS) play a crucial role in controlled drug release, providing improved therapeutic outcomes. However, formulating PDDS and predicting their release profiles remain challenging due to their complex structures and the numerous variables that influence their behavior. Traditional mathematical and empirical prediction methods are limited in capturing these complexities. Recent studies have unveiled the potential of Machine Learning (ML) in revolutionizing drug delivery, particularly in formulating complex PDDS. This article provides an overview of the significant and fundamental principles of various ML strategies in estimating PDDS drug release behavior. Our focus extends to the accomplishments and pivotal discoveries in current research, spanning seven distinct sustained-release drug delivery systems: matrix tablets, microspheres, implants, hydrogels, films, 3D-printed dosage forms, and other innovations. Furthermore, it addresses the challenges associated with ML-based drug release prediction and presents current solutions while delving into future perspectives. Our investigation underscores the significance of Artificial Neural Networks in ML-based PDDS release profile prediction, surpassing both traditional and alternative ML-based methods. These extensive datasets can be drawn from literature-based resources or enhanced through specific algorithms. Moreover, ensemble-based models have proven advantageous in scenarios involving intricate relationships, such as a high number of output parameters. ML-based drug release prediction notably exhibits substantial promise in 3D-printed dosage forms, presenting a frontier for personalized medicine and precise drug delivery.

Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research

One area of study within machine learning and artificial intelligence (AI) seeks to create computer programs with intelligence that can mimic human focal processes in order to produce results. This technique includes data collection, effective data usage system development, conclusion illustration, and arrangements. Analysis algorithms that are learning to mimic human cognitive activities are the most widespread application of AI. Artificial intelligence (AI) studies have proliferated, and the field is quickly beginning to understand its potential impact on medical services and investigation. This review delves deeper into the pros and cons of AI across the healthcare and pharmaceutical research industries. Research and review articles published throughout the last few years were selected from PubMed, Google Scholar, and Science Direct, using search terms like 'artificial intelligence', 'drug discovery', 'pharmacy research', 'clinical trial', etc. This article provides a comprehensive overview of how artificial intelligence (AI) is being used to diagnose diseases, treat patients digitally, find new drugs, and predict when outbreaks or pandemics may occur. In artificial intelligence, neural networks and deep learning are some of the most popular tools; in clinical research, Bayesian non-parametric approaches hold promise for better results, while smartphones and the processing of natural languages are employed in recognizing patients and trial monitoring. Seasonal flu, Ebola, Zika, COVID-19, tuberculosis, and outbreak predictions were made using deep computation and artificial intelligence. The academic world is hopeful that AI development will lead to more efficient and less expensive medical and pharmaceutical investigations and better public services.

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

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

SynBioNanoDesign: pioneering targeted drug delivery with engineered nanomaterials

Synthetic biology and nanotechnology fusion represent a transformative approach promoting fundamental and clinical biomedical science development. In SynBioNanoDesign, biological systems are reimagined as dynamic and programmable materials to yield engineered nanomaterials with emerging and specific functionalities. This review elucidates a comprehensive examination of synthetic biology's pivotal role in advancing engineered nanomaterials for targeted drug delivery systems. It begins with exploring the fundamental synergy between synthetic biology and nanotechnology, then highlights the current landscape of nanomaterials in targeted drug delivery applications. Subsequently, the review discusses the design of novel nanomaterials informed by biological principles, focusing on expounding the synthetic biology tools and the potential for developing advanced nanomaterials. Afterward, the research advances of innovative materials design through synthetic biology were systematically summarized, emphasizing the integration of genetic circuitry to program nanomaterial responses. Furthermore, the challenges, current weaknesses and opportunities, prospective directions, and ethical and societal implications of SynBioNanoDesign in drug delivery are elucidated. Finally, the review summarizes the transformative impact that synthetic biology may have on drug-delivery technologies in the future.

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

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

Integrative computational approaches in pharmaceuticals: Driving innovation in discovery and delivery

In recent years, the pharmaceutical industry has increasingly emphasized the role of lead compound identification in developing new therapeutic agents. Lead compounds show promising pharmacological activity against specific targets and are critical in drug development. Integrative computational approaches streamline this process by efficiently screening chemical libraries and designing potential drug candidates. This chapter highlights various computational techniques for lead compound discovery, including molecular modeling, cheminformatics, ligand- and structure-based drug design, molecular dynamics simulations, ADMET prediction, drug-target interaction analysis, and high-throughput screening. These methods improve drug discovery's efficiency, cost-effectiveness, and target-specific focus. Computational pharmaceutics has gained popularity due to the longer formulation development time which in turn increases the cost as well as decrease in the drug discovery production. Conventionally, formulation development relied on costly and unpredictable trial-and-error methods. However, analyzing the big data, artificial intelligence, and multi-scale modeling in computational pharmaceutics is transforming drug delivery. This chapter provides valuable insights throughout pre-formulation, formulation screening, in vivo predictions, and personalized medicine applications. Multiscale computational modeling is advancing drug delivery systems, enabling targeted treatments with multifunctional nanoparticles. Although in its early stages, this approach helps understand complex interactions between drugs, delivery systems, and patients.

Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine

Artificial Intelligence (AI) and Machine Learning (ML) are transforming drug discovery by overcoming traditional challenges like high costs, time-consuming, and frequent failures. AI-driven approaches streamline key phases, including target identification, lead optimization, de novo drug design, and drug repurposing. Frameworks such as deep neural networks (DNNs), convolutional neural networks (CNNs), and deep reinforcement learning (DRL) models have shown promise in identifying drug targets, optimizing delivery systems, and accelerating drug repurposing. Generative adversarial networks (GANs) and variational autoencoders (VAEs) aid de novo drug design by creating novel drug-like compounds with desired properties. Case studies, such as DDR1 kinase inhibitors designed using generative models and CDK20 inhibitors developed via structure-based methods, highlight AI's ability to produce highly specific therapeutics. Models like SNF-CVAE and DeepDR further advance drug repurposing by uncovering new therapeutic applications for existing drugs. Advanced ML algorithms enhance precision in predicting drug efficacy, toxicity, and ADME-Tox properties, reducing development costs and improving drug-target interactions. AI also supports polypharmacology by optimizing multi-target drug interactions and enhances combination therapy through predictions of drug synergies and antagonisms. In nanomedicine, AI models like CURATE.AI and the Hartung algorithm optimize personalized treatments by predicting toxicological risks and real-time dosing adjustments with high accuracy. Despite its potential, challenges like data quality, model interpretability, and ethical concerns must be addressed. High-quality datasets, transparent models, and unbiased algorithms are essential for reliable AI applications. As AI continues to evolve, it is poised to revolutionize drug discovery and personalized medicine, advancing therapeutic development and patient care.

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.

Targeting Drug Delivery System to Skeletal Muscles: A Comprehensive Review of Different Approaches

The skeletal muscle is one of the largest organs in the body and is responsible for the mechanical activity required for posture, movement and breathing. The effects of current pharmaceutical therapies for skeletal muscle diseases are far from satisfactory; approximately 24% of Duchenne muscular dystrophy (DMD) trials have been terminated because of unsatisfactory outcomes. The lack of a skeletal muscle-targeting strategy is a major reason for these unsuccessful trials, contributing to low efficiency and severe side effects. The development of targeting strategies for skeletal muscle-specific drug delivery has shown the potential for increasing drug concentrations in the skeletal muscle, minimising off-target effects, and thereby improving the therapeutic effects of drugs. Over the past few decades, novel methods for specifically delivering cargo to skeletal muscles have been developed. In this review, we categorise targeting methods into four types: peptides, antibodies, small molecules and aptamers. Most research has focused on peptide and antibody ligands, and there are several well-established drugs in this category; however, drawbacks such as protease degradation and immunogenicity limit their use. Aptamers and small molecules have low immunogenicity and are simple to chemically produce. However, small molecule ligands generally exhibit lower affinity because of their small size and high mobility. Aptamers are promising ligands for skeletal muscle-targeting delivery systems. Additionally, if the active site of the cargo is located inside the cell, an internalisation pathway becomes necessary. The order of internalisation ligands and targeting ligands in the complex is a crucial factor, because an inappropriate order could lead to much lower targeting and internalisation efficiencies. Moreover, ligand density also merits consideration, as increasing the density of the targeting ligands may result in steric hindrance, which could impact the accessibility of the receptor and cause enlargement of the targeted ligands. More efforts are required to optimise drug delivery systems that specifically recognise skeletal muscle, with the aim of enhancing quality of life and promoting patient well-being.

Advancing Cancer Drug Delivery with Nanoparticles: Challenges and Prospects in Mathematical Modeling for In Vivo and In Vitro Systems

Mathematical models are crucial for predicting the behavior of drug conjugate nanoparticles and optimizing drug delivery systems in cancer therapy. These models simulate interactions among nanoparticle properties, tumor characteristics, and physiological conditions, including drug resistance and targeting specificity. However, they often rely on assumptions that may not accurately reflect in vivo conditions. In vitro studies, while useful, may not fully capture the complexities of the in vivo environment, leading to an overestimation of nanoparticle-based therapy effectiveness. Advancements in mathematical modeling, supported by preclinical data and artificial intelligence, are vital for refining nanoparticle-based therapies and improving their translation into effective clinical treatments.

Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes

INTRODUCTION

Artificial intelligence (AI) is changing the field of nanomedicine by exploring novel nanomaterials for developing therapies of high efficacy. AI works on larger datasets, finding sought-after nano-properties for different therapeutic aims and eventually enhancing nanomaterials' safety and effectiveness. AI leverages patient clinical and genetic data to predict outcomes, guide treatments, and optimize drug dosages and forms, enhancing benefits while minimizing side effects. AI-supported nanomedicine faces challenges like data fusion, ethics, and regulation, requiring better tools and interdisciplinary collaboration. This review highlights the importance of AI regarding patient care and urges scientists, medical professionals, and regulators to adopt AI for better outcomes.

AREAS COVERED

Personalized Nanomedicine, Material Discovery, AI-Driven Therapeutics, Data Integration, Drug Delivery, Patient Centric Care.

EXPERT OPINION

Today, AI can improve personalized health wellness through the discovery of new types of drug nanocarriers, nanomedicine of specific properties to tackle targeted medical needs, and an increment in efficacy along with safety. Nevertheless, problems such as ethical issues, data security, or unbalanced data sets need to be addressed. Potential future developments involve using AI and quantum computing together and exploring telemedicine i.e. the Internet-of-Medical-Things (IoMT) approach can enhance the quality of patient care in a personalized manner by timely decision-making.

The impact of artificial intelligence on the knowledge, attitude, and practice of pharmacists across diverse settings: A cross-sectional study

The pharmacy practice landscape is undergoing a significant transformation with the increasing integration of artificial intelligence (AI). As essential members of the healthcare team, pharmacists' readiness and willingness to adopt AI technologies is critical. This cross-sectional study explores pharmacists' knowledge, attitudes, and practices (KAP) regarding AI in various practice settings. Utilizing a descriptive survey methodology, we collected data through a structured questionnaire targeting pharmacists across diverse working environments. Statistical analyses were conducted to calculate KAP scores. Results revealed that 44.8 % of participants possessed a moderate level of knowledge about AI, while 49.1 % expressed positive attitudes toward its potential applications in pharmacy. However, their current practices related to AI were rated as adequate (57.3 %). Notably, a significant association was found between knowledge, attitudes, and practices (p < 0.001). This study provides valuable insights into pharmacists' readiness to incorporate AI into their practice, emphasizing the need for targeted educational interventions to enhance knowledge and promote positive attitudes. Furthermore, efforts must be directed towards facilitating the integration of AI into pharmacy workflows to fully leverage this transformative technology and improve patient care outcomes.

Artificial intelligence in nanotechnology for treatment of diseases

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterising and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalise drug delivery based on individual patient factors, optimise formulation design and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective DDSs can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-DDSs, with a focus on their therapeutic applications. The use of AI in DDSs has the potential to revolutionise treatment optimisation and improve patient care.

Advances in artificial intelligence-based technologies for increasing the quality of medical products

Artificial intelligence (AI) is a technology that combines machine learning (ML) and deep learning. It has numerous usages in the domains of medicine and other sciences. Artificial intelligence can forecast the behavior of a drug's target protein and predict its desired physicochemical qualities. AI's potential to enhance healthcare services offerings formerly unheard-of opportunities for cost reserves, enhanced overall clinical and patient outcomes. The recent development of research in the biomedical field, encompassing fields such as genomics, computational medicine, AI, and algorithms for learning, has led to the demand for novel technology, a fresh workforce, and new standards of practice set the stage for the revolution in healthcare. By connecting these health statistics with cutting-edge AI technologies, precise insights into patient treatment can be obtained. Moreover, AI can aid in the search for new drugs by foretelling the target protein's two-dimensional structure. In the current review, an overview of the latest AI-based technologies and how they may be employed to reduce product development time to market and snowballing product quality, cost-effectiveness, as well as security throughout the manufacturing process is detailed.

AI for biofabrication

Biofabrication is an advanced technology that holds great promise for constructing highly biomimeticin vitrothree-dimensional human organs. Such technology would help address the issues of immune rejection and organ donor shortage in organ transplantation, aiding doctors in formulating personalized treatments for clinical patients and replacing animal experiments. Biofabrication typically involves the interdisciplinary application of biology, materials science, mechanical engineering, and medicine to generate large amounts of data and correlations that require processing and analysis. Artificial intelligence (AI), with its excellent capabilities in big data processing and analysis, can play a crucial role in handling and processing interdisciplinary data and relationships and in better integrating and applying them in biofabrication. In recent years, the development of the semiconductor and integrated circuit industries has propelled the rapid advancement of computer processing power. An AI program can learn and iterate multiple times within a short period, thereby gaining strong automation capabilities for a specific research content or issue. To date, numerous AI programs have been applied to various processes around biofabrication, such as extracting biological information, designing and optimizing structures, intelligent cell sorting, optimizing biomaterials and processes, real-time monitoring and evaluation of models, accelerating the transformation and development of these technologies, and even changing traditional research patterns. This article reviews and summarizes the significant changes and advancements brought about by AI in biofabrication, and discusses its future application value and direction.

Exploring the Artificial Intelligence and Its Impact in Pharmaceutical Sciences: Insights Toward the Horizons Where Technology Meets Tradition

The technological revolutions in computers and the advancement of high-throughput screening technologies have driven the application of artificial intelligence (AI) for faster discovery of drug molecules with more efficiency, and cost-friendly finding of hit or lead molecules. The ability of software and network frameworks to interpret molecular structures' representations and establish relationships/correlations has enabled various research teams to develop numerous AI platforms for identifying new lead molecules or discovering new targets for already established drug molecules. The prediction of biological activity, ADME properties, and toxicity parameters in early stages have reduced the chances of failure and associated costs in later clinical stages, which was observed at a high rate in the tedious, expensive, and laborious drug discovery process. This review focuses on the different AI and machine learning (ML) techniques with their applications mainly focused on the pharmaceutical industry. The applications of AI frameworks in the identification of molecular target, hit identification/hit-to-lead optimization, analyzing drug-receptor interactions, drug repurposing, polypharmacology, synthetic accessibility, clinical trial design, and pharmaceutical developments are discussed in detail. We have also compiled the details of various startups in AI in this field. This review will provide a comprehensive analysis and outline various state-of-the-art AI/ML techniques to the readers with their framework applications. This review also highlights the challenges in this field, which need to be addressed for further success in pharmaceutical applications.

Discovering Photoswitchable Molecules for Drug Delivery with Large Language Models and Chemist Instruction Training

Background: As large language models continue to expand in size and diversity, their substantial potential and the relevance of their applications are increasingly being acknowledged. The rapid advancement of these models also holds profound implications for the long-term design of stimulus-responsive materials used in drug delivery. Methods: The large model used Hugging Face's Transformers package with BigBird, Gemma, and GPT NeoX architectures. Pre-training used the PubChem dataset, and fine-tuning used QM7b. Chemist instruction training was based on Direct Preference Optimization. Drug Likeness, Synthetic Accessibility, and PageRank Scores were used to filter molecules. All computational chemistry simulations were performed using ORCA and Time-Dependent Density-Functional Theory. Results: To optimize large models for extensive dataset processing and comprehensive learning akin to a chemist's intuition, the integration of deeper chemical insights is imperative. Our study initially compared the performance of BigBird, Gemma, GPT NeoX, and others, specifically focusing on the design of photoresponsive drug delivery molecules. We gathered excitation energy data through computational chemistry tools and further investigated light-driven isomerization reactions as a critical mechanism in drug delivery. Additionally, we explored the effectiveness of incorporating human feedback into reinforcement learning to imbue large models with chemical intuition, enhancing their understanding of relationships involving -N=N- groups in the photoisomerization transitions of photoresponsive molecules. Conclusions: We implemented an efficient design process based on structural knowledge and data, driven by large language model technology, to obtain a candidate dataset of specific photoswitchable molecules. However, the lack of specialized domain datasets remains a challenge for maximizing model performance.

AI empowering traditional Chinese medicine?

For centuries, Traditional Chinese Medicine (TCM) has been a prominent treatment method in China, incorporating acupuncture, herbal remedies, massage, and dietary therapy to promote holistic health and healing. TCM has played a major role in drug discovery, with over 60% of small-molecule drugs approved by the FDA from 1981 to 2019 being derived from natural products. However, TCM modernization faces challenges such as data standardization and the complexity of TCM formulations. The establishment of comprehensive TCM databases has significantly improved the efficiency and accuracy of TCM research, enabling easier access to information on TCM ingredients and encouraging interdisciplinary collaborations. These databases have revolutionized TCM research, facilitating advancements in TCM modernization and patient care. In addition, advancements in AI algorithms and database data quality have accelerated progress in AI for TCM. The application of AI in TCM encompasses a wide range of areas, including herbal screening and new drug discovery, diagnostic and treatment principles, pharmacological mechanisms, network pharmacology, and the incorporation of innovative AI technologies. AI also has the potential to enable personalized medicine by identifying patterns and correlations in patient data, leading to more accurate diagnoses and tailored treatments. The potential benefits of AI for TCM are vast and diverse, promising continued progress and innovation in the field.

Nucleic acid-responsive smart systems for controlled cargo delivery

Stimulus-responsive delivery systems allow controlled, highly regulated, and efficient delivery of various cargos while minimizing side effects. Owing to the unique properties of nucleic acids, including the ability to adopt complex structures by base pairing, their easy synthesis, high specificity, shape memory, and configurability, they have been employed in autonomous molecular motors, logic circuits, reconfigurable nanoplatforms, and catalytic amplifiers. Moreover, the development of nucleic acid (NA)-responsive intelligent delivery vehicles is a rapidly growing field. These vehicles have attracted much attention in recent years due to their programmable, controllable, and reversible properties. In this work, we review several types of NA-responsive controlled delivery vehicles based on locks and keys, including DNA/RNA-responsive, aptamer-responsive, and CRISPR-responsive, and summarize their advantages and limitations.

Application of artificial intelligence in drug design: A review

Artificial intelligence (AI) is a field of computer science that involves acquiring information, developing rule bases, and mimicking human behaviour. The fundamental concept behind AI is to create intelligent computer systems that can operate with minimal human intervention or without any intervention at all. These rule-based systems are developed using various machine learning and deep learning models, enabling them to solve complex problems. AI is integrated with these models to learn, understand, and analyse provided data. The rapid advancement of Artificial Intelligence (AI) is reshaping numerous industries, with the pharmaceutical sector experiencing a notable transformation. AI is increasingly being employed to automate, optimize, and personalize various facets of the pharmaceutical industry, particularly in pharmacological research. Traditional drug development methods areknown for being time-consuming, expensive, and less efficient, often taking around a decade and costing billions of dollars. The integration of artificial intelligence (AI) techniques addresses these challenges by enabling the examination of compounds with desired properties from a vast pool of input drugs. Furthermore, it plays a crucial role in drug screening by predicting toxicity, bioactivity, ADME properties (absorption, distribution, metabolism, and excretion), physicochemical properties, and more. AI enhances the drug design process by improving the efficiency and accuracy of predicting drug behaviour, interactions, and properties. These approaches further significantly improve the precision of drug discovery processes and decrease clinical trial costs leading to the development of more effective drugs.

Synergizing blockchain and artificial intelligence to enhance healthcare

This perspective paper explores the synergistic potential of blockchain and artificial intelligence (AI) in transforming healthcare. It begins with an overview of blockchain's role in healthcare data management, security, the pharmaceutical supply chain, clinical trials, and health insurance. The discussion then shifts to the impact of AI on healthcare, followed by an examination of integrated AI-blockchain platforms and their benefits. Technical challenges, limitations, and solutions related to these technologies are scrutinized. The paper addresses regulatory compliance and ethical considerations, and proposes future directions for their implementation. It concludes with research and implementation guidelines, offering a roadmap for harnessing blockchain and AI to enhance healthcare 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.

Design strategies, advances and future perspectives of colon-targeted delivery systems for the treatment of inflammatory bowel disease

Inflammatory bowel diseases (IBD) significantly contribute to high mortality globally and negatively affect patients' qualifications of life. The gastrointestinal tract has unique anatomical characteristics and physiological environment limitations. Moreover, certain natural or synthetic anti-inflammatory drugs are associated with poor targeting, low drug accumulation at the lesion site, and other side effects, hindering them from exerting their therapeutic effects. Colon-targeted drug delivery systems represent attractive alternatives as novel carriers for IBD treatment. This review mainly discusses the treatment status of IBD, obstacles to drug delivery, design strategies of colon-targeted delivery systems, and perspectives on the existing complementary therapies. Moreover, based on recent reports, we summarized the therapeutic mechanism of colon-targeted drug delivery. Finally, we addressed the challenges and future directions to facilitate the exploitation of advanced nanomedicine for IBD therapy.

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.

Preparation and anti-tumor activity of paclitaxel silk protein nanoparticles encapsulated by biofilm

In order to overcome the poor bioavailability of paclitaxel (PTX), in this study, self-assembled paclitaxel silk fibronectin nanoparticles (PTX-SF-NPs) were encapsulated with outer membrane vesicles of Escherichia coli (E. coil), and biofilm-encapsulated paclitaxel silk fibronectin nanoparticles (OMV-PTX-SF-NPs) were prepared by high-pressure co-extrusion, the size and zeta potential of the OMV-PTX-SF-NPs were measured. The antitumor effects of OMV-PTX-SF-NPs were evaluated by cellular and pharmacodynamic assays, and pharmacokinetic experiments were performed. The results showed that hydrophobic forces and hydrogen bonding played a major role in the interaction between paclitaxel and filipin proteins, and the size of OMV-PTX-SF-NPs was 199.8 ± 2.8 nm, zeta potential was -17.8 ± 1.3 mv. The cellular and in vivo pharmacokinetic assays demonstrated that the OMV-PTX-SF-NPs possessed a promising antitumor effect. Pharmacokinetic experiments showed that the AUC0-∞ of OMV-PTX-SF-NPs was 5.314 ± 0.77, which was much larger than that of free PTX, which was 0.744 ± 0.14. Overall, we have successfully constructed a stable oral formulation of paclitaxel with a sustained-release effect, which is able to effectively increase the bioavailability of paclitaxel, improve the antitumor activity, and reduce the adverse effects.

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.

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.

TumFlow: An AI Model for Predicting New Anticancer Molecules

Melanoma is the fifth most common cancer in the United States. Conventional drug discovery methods are inherently time-consuming and costly, which imposes significant limitations. However, the advent of Artificial Intelligence (AI) has opened up new possibilities for simulating and evaluating numerous drug candidates, thereby mitigating the requisite time and resources. In this context, normalizing flow models by employing machine learning techniques to create new molecular structures holds promise for accelerating the discovery of effective anticancer therapies. This manuscript introduces TumFlow, a novel AI model designed to generate new molecular entities with potential therapeutic value in cancer treatment. It has been trained on the NCI-60 dataset, encompassing thousands of molecules tested across 60 tumour cell lines, with an emphasis on the melanoma SK-MEL-28 cell line. The model successfully generated new molecules with predicted improved efficacy in inhibiting tumour growth while being synthetically feasible. This represents a significant advancement over conventional generative models, which often produce molecules that are challenging or impossible to synthesize. Furthermore, TumFlow has also been utilized to optimize molecules known for their efficacy in clinical melanoma treatments. This led to the creation of novel molecules with a predicted enhanced likelihood of effectiveness against melanoma, currently undocumented on PubChem.

Artificial Intelligence-Assisted Multiparameter Size Discrimination of Silver Nanoparticles through Electrochemical Collision

Single particle collision is an important tool for size analysis at the individual particle level; however, due to complex dynamic behaviors of nanoparticles on the surface of an electrode, the accuracy of size discrimination is limited. A silver (Ag) nanoparticle (NP) was chosen as the research target, and the dynamic behavior of Ag NPs was simplified by enhancing adsorption between Ag NP and Au ultramicroelectrode (UME) in alkaline media. Immediately after, accurate dynamic and thermodynamic information on single Ag NP was accurately extracted from collision events, including current intensity, transferred charge, and duration time. On the basis that there were differences between parameters of different-sized Ag NPs, multiparameter size discrimination was proposed, which improved the accuracy compared to single-parameter discrimination. More intriguingly, multiparameter analysis was combined with artificial intelligence, a tool adept at processing multidimensional data, for the first time. Finally, artificial intelligence-assisted multiparameter size discrimination was successfully used to intelligently distinguish mixed Ag NPs, with an optimal accuracy of more than 95%. To sum up, the artificial intelligence-assisted multiparameter method showed an excellent ability to quickly achieve the most accurate size discrimination of nanoparticles at the level of individual particle and provide an effective guidance for the application of nanoparticles.

Risks and benefits associated with the primary functions of artificial intelligence powered autoinjectors

Objectives

This research aims to present and assess the Primary Functions of autoinjectors introduced in ISO 11608-1:2022. Investigate the risks in current autoinjector technology, identify and assess risks and benefits associated with Artificial Intelligence (AI) powered autoinjectors, and propose a framework for mitigating these risks. ISO 11608-1:2022 is a standard that specifies requirements and test methods for needle-based injection systems intended to deliver drugs, focusing on design and function to ensure patient safety and product effectiveness. 'KZH' is an FDA product code used to classify autoinjectors, for regulatory purposes, ensuring they meet defined safety and efficacy standards before being marketed.

Method

A comprehensive analysis of autoinjectors problems is conducted using data from the United States Food and Drug Administration (FDA) database. This database records medical device reporting events, including those related to autoinjectors, reported by various sources. The analysis focuses on events associated with the product code KZH, covering data from January 1, 2008, to September 30, 2023. This research employs statistical frequency analysis and incorporates pertinent the FDA, United Kingdom, European Commission regulations, and ISO standards.

Results

500 medical device reporting events are assessed for autoinjectors under the KZH code. Ultimately, 188 of these events are confirmed to be associated with autoinjectors, all 500 medical devices were seen to lack AI capabilities. An analysis of these events for traditional mechanical autoinjectors revealed a predominant occurrence of malfunctions (72%) and injuries (26%) among event types. Device problems, such as breakage, defects, jams, and others, accounted for 45% of incidents, while 10% are attributed to patient problems, particularly missed and underdoses.

Conclusion

Traditional autoinjectors are designed to assist patients in medication administration, underscoring the need for quality control, reliability, and design enhancements. AI autoinjectors, sharing this goal, bring additional cybersecurity and software risks, requiring a comprehensive risk management framework that includes standards, tools, training, and ongoing monitoring. The integration of AI promises to improve functionality, enable real-time monitoring, and facilitate remote clinical trials, timely interventions, and tailored medical treatments.

Unleashing the Potential of Oral Deliverable Nanomedicine in the Treatment of Inflammatory Bowel Disease

Inflammatory bowel disease (IBD), marked by chronic gastrointestinal tract inflammation, poses a significant global medical challenge. Current treatments for IBD, including corticosteroids, immunomodulators, and biologics, often require frequent systemic administration through parenteral delivery, leading to nonspecific drug distribution, suboptimal therapeutic outcomes, and adverse effects. There is a pressing need for a targeted drug delivery system to enhance drug efficacy and minimize its systemic impact. Nanotechnology emerges as a transformative solution, enabling precise oral drug delivery to inflamed intestinal tissues, reducing off-target effects, and enhancing therapeutic efficiency. The advantages include heightened bioavailability, sustained drug release, and improved cellular uptake. Additionally, the nano-based approach allows for the integration of theranostic elements, enabling simultaneous diagnosis and treatment. Recent preclinical advances in oral IBD treatments, particularly with nanoformulations such as functionalized polymeric and lipid nanoparticles, demonstrate remarkable cell-targeting ability and biosafety, promising to overcome the limitations of conventional therapies. These developments signify a paradigm shift toward personalized and effective oral IBD management. This review explores the potential of oral nanomedicine to enhance IBD treatment significantly, focusing specifically on cell-targeting oral drug delivery system for potential use in IBD management. We also examine emerging technologies such as theranostic nanoparticles and artificial intelligence, identifying avenues for the practical translation of nanomedicines into clinical applications.

Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects

The impact of the coronavirus disease 2019 (COVID-19) pandemic on the everyday livelihood of people has been monumental and unparalleled. Although the pandemic has vastly affected the global healthcare system, it has also been a platform to promote and develop pioneering applications based on autonomic artificial intelligence (AI) technology with therapeutic significance in combating the pandemic. Artificial intelligence has successfully demonstrated that it can reduce the probability of human-to-human infectivity of the virus through evaluation, analysis, and triangulation of existing data on the infectivity and spread of the virus. This review talks about the applications and significance of modern robotic and automated systems that may assist in spreading a pandemic. In addition, this study discusses intelligent wearable devices and how they could be helpful throughout the COVID-19 pandemic.

Exploring the current and prospective role of artificial intelligence in disease diagnosis

Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems, providing assistance in a variety of patient care and health systems. The aim of this review is to contribute valuable insights to the ongoing discourse on the transformative potential of AI in healthcare, providing a nuanced understanding of its current applications, future possibilities, and associated challenges. The authors conducted a literature search on the current role of AI in disease diagnosis and its possible future applications using PubMed, Google Scholar, and ResearchGate within 10 years. Our investigation revealed that AI, encompassing machine-learning and deep-learning techniques, has become integral to healthcare, facilitating immediate access to evidence-based guidelines, the latest medical literature, and tools for generating differential diagnoses. However, our research also acknowledges the limitations of current AI methodologies in disease diagnosis and explores uncertainties and obstacles associated with the complete integration of AI into clinical practice. This review has highlighted the critical significance of integrating AI into the medical healthcare framework and meticulously examined the evolutionary trajectory of healthcare-oriented AI from its inception, delving into the current state of development and projecting the extent of reliance on AI in the future. The authors have found that central to this study is the exploration of how the strategic integration of AI can accelerate the diagnostic process, heighten diagnostic accuracy, and enhance overall operational efficiency, concurrently relieving the burdens faced by healthcare practitioners.

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.

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.

Current Insights into Targeting Strategies for the Effective Therapy of Diseases of the Posterior Eye Segment

The eye is one a unique sophisticated human sense organ with a complex anatomical structure. It is encased by variety of protective barriers as responsible for vision. There has been a paradigm shift in the prevalence of several major vision threatening ocular conditions with enhanced reliance on computer-based technologies in our workaday life and work-from-home modalities although aging, pollution, injury, harmful chemicals, lifestyle changes will always remain the root cause. Treating posterior eye diseases is a challenge faced by clinicians worldwide. The clinical use of conventional drug delivery systems for posterior eye targeting is restricted by the ocular barriers. Indeed, for overcoming various ocular barriers for efficient delivery of the therapeutic moiety and prolonged therapeutic effect requires prudent and target-specific approaches. Therefore, for efficient drug delivery to the posterior ocular segment, advancements in the development of sustained release and nanotechnology-based ocular drug delivery systems have gained immense importance. Therapeutic efficacy and patient compliance are of paramount importance in clinical translation of these investigative drug delivery systems. This review provides an insight into the various strategies employed for improving the treatment efficacies of the posterior eye diseases. Various drug delivery systems such as systemic and intraocular injections, implants have demonstrated promising outcomes, along with that they have also exhibited side-effects, limitations and strategies employed to overcome them are discussed in this review. The application of artificial intelligence-based technologies along with an appreciation of disease, delivery systems, and patient-specific outcomes will likely enable more effective therapy for targeting the posterior eye segment.

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics

Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neural networks (NNs) approaches for novel algorithm and hypothesis development by training the machines in multiple ways. AI-based drug development from molecule identification to clinical approval tremendously reduces the cost of development and the time over conventional methods. The COVID-19 vaccine development and approval by regulatory agencies within 1-2 years is the finest example of drug development. Hence, AI is fast becoming a boon for scientific researchers to streamline their advanced discoveries. AI-based FDA-approved nanomedicines perform well as target selective, synergistic therapies, recolonize the theragnostic pharmaceutical stream, and significantly improve drug research outcomes. This comprehensive review delves into the fundamental aspects of AI along with its applications in the realm of pharmaceutical life sciences. It explores AI's role in crucial areas such as drug designing, drug discovery and development, traditional Chinese medicine, integration of multi-omics data, as well as investigations into drug repurposing and polypharmacology studies.

Application and development of hydrogel biomaterials for the treatment of intervertebral disc degeneration: a literature review

Low back pain caused by disc herniation and spinal stenosis imposes an enormous medical burden on society due to its high prevalence and refractory nature. This is mainly due to the long-term inflammation and degradation of the extracellular matrix in the process of intervertebral disc degeneration (IVDD), which manifests as loss of water in the nucleus pulposus (NP) and the formation of fibrous disc fissures. Biomaterial repair strategies involving hydrogels play an important role in the treatment of intervertebral disc degeneration. Excellent biocompatibility, tunable mechanical properties, easy modification, injectability, and the ability to encapsulate drugs, cells, genes, etc. make hydrogels good candidates as scaffolds and cell/drug carriers for treating NP degeneration and other aspects of IVDD. This review first briefly describes the anatomy, pathology, and current treatments of IVDD, and then introduces different types of hydrogels and addresses "smart hydrogels". Finally, we discuss the feasibility and prospects of using hydrogels to treat IVDD.

Use of tree-based machine learning methods to screen affinitive peptides based on docking data

Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees (CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model (C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4 %). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.

Advances in Designing Essential Oil Nanoformulations: An Integrative Approach to Mathematical Modeling with Potential Application in Food Preservation

Preservation of foods, along with health and safety issues, is a growing concern in the current generation. Essential oils have emerged as a natural means for the long-term protection of foods along with the maintenance of their qualities. Direct applications of essential oils have posed various constraints to the food system and also have limitations in application; hence, encapsulation of essential oils into biopolymers has been recognized as a cutting-edge technology to overcome these challenges. This article presents and evaluates the strategies for the development of encapsulated essential oils on the basis of fascination with the modeling and shuffling of various biopolymers, surfactants, and co-surfactants, along with the utilization of different fabrication processes. Artificial intelligence and machine learning have enabled the preparation of different nanoemulsion formulations, synthesis strategies, stability, and release kinetics of essential oils or their bioactive components from nanoemulsions with improved efficacy in food systems. Different mathematical models for the stability and delivery kinetics of essential oils in food systems have also been discussed. The article also explains the advanced application of modeling-based encapsulation strategies on the preservation of a variety of food commodities with their intended implication in food and agricultural industries.

DeepTPpred: A Deep Learning Approach With Matrix Factorization for Predicting Therapeutic Peptides by Integrating Length Information

The abuse of traditional antibiotics has led to increased resistance of bacteria and viruses. Efficient therapeutic peptide prediction is critical for peptide drug discovery. However, most of the existing methods only make effective predictions for one class of therapeutic peptides. It is worth noting that currently no predictive method considers sequence length information as a distinct feature of therapeutic peptides. In this article, a novel deep learning approach with matrix factorization for predicting therapeutic peptides (DeepTPpred) by integrating length information are proposed. The matrix factorization layer can learn the potential features of the encoded sequence through the mechanism of first compression and then restoration. And the length features of the sequence of therapeutic peptides are embedded with encoded amino acid sequences. To automatically learn therapeutic peptide predictions, these latent features are input into the neural networks with self-attention mechanism. On eight therapeutic peptide datasets, DeepTPpred achieved excellent prediction results. Based on these datasets, we first integrated eight datasets to obtain a full therapeutic peptide integration dataset. Then, we obtained two functional integration datasets based on the functional similarity of the peptides. Finally, we also conduct experiments on the latest versions of the ACP and CPP datasets. Overall, the experimental results show that our work is effective for the identification of therapeutic peptides.

Oral delivery of RNAi for cancer therapy

Cancer is a major health concern worldwide and is still in a continuous surge of seeking for effective treatments. Since the discovery of RNAi and their mechanism of action, it has shown promises in targeted therapy for various diseases including cancer. The ability of RNAi to selectively silence the carcinogenic gene makes them ideal as cancer therapeutics. Oral delivery is the ideal route of administration of drug administration because of its patients' compliance and convenience. However, orally administered RNAi, for instance, siRNA, must cross various extracellular and intracellular biological barriers before it reaches the site of action. It is very challenging and important to keep the siRNA stable until they reach to the targeted site. Harsh pH, thick mucus layer, and nuclease enzyme prevent siRNA to diffuse through the intestinal wall and thereby induce a therapeutic effect. After entering the cell, siRNA is subjected to lysosomal degradation. Over the years, various approaches have been taken into consideration to overcome these challenges for oral RNAi delivery. Therefore, understanding the challenges and recent development is crucial to offer a novel and advanced approach for oral RNAi delivery. Herein, we have summarized the delivery strategies for oral delivery RNAi and recent advancement towards the preclinical stages.

Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence

Cell-based drug delivery systems are new strategies in targeted delivery in which cells or cell-membrane-derived systems are used as carriers and release their cargo in a controlled manner. Recently, great attention has been directed to cells as carrier systems for treating several diseases. There are various challenges in the development of cell-based drug delivery systems. The prediction of the properties of these platforms is a prerequisite step in their development to reduce undesirable effects. Integrating nanotechnology and artificial intelligence leads to more innovative technologies. Artificial intelligence quickly mines data and makes decisions more quickly and accurately. Machine learning as a subset of the broader artificial intelligence has been used in nanomedicine to design safer nanomaterials. Here, how challenges of developing cell-based drug delivery systems can be solved with potential predictive models of artificial intelligence and machine learning is portrayed. The most famous cell-based drug delivery systems and their challenges are described. Last but not least, artificial intelligence and most of its types used in nanomedicine are highlighted. The present Review has shown the challenges of developing cells or their derivatives as carriers and how they can be used with potential predictive models of artificial intelligence and machine learning.