Artificial intelligence to bring nanomedicine to life

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.

Designing nanotheranostics with machine learning

The inherent limits of traditional diagnoses and therapies have driven the development and application of emerging nanotechnologies for more effective and safer management of diseases, herein referred to as 'nanotheranostics'. Although many important technological successes have been achieved in this field, widespread adoption of nanotheranostics as a new paradigm is hindered by specific obstacles, including time-consuming synthesis of nanoparticles, incomplete understanding of nano-bio interactions, and challenges regarding chemistry, manufacturing and the controls required for clinical translation and commercialization. As a key branch of artificial intelligence, machine learning (ML) provides a set of tools capable of performing time-consuming and result-perception tasks, thus offering unique opportunities for nanotheranostics. This Review summarizes the progress and challenges in this emerging field of ML-aided nanotheranostics, and discusses the opportunities in developing next-generation nanotheranostics with reliable datasets and advanced ML models to offer better clinical benefits to patients.

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.

Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance

The advent of artificial intelligence (AI) has catalyzed a profound transformation in the pharmaceutical industry, ushering in a paradigm shift across various domains, including drug discovery, formulation development, manufacturing, quality control, and post-market surveillance. This comprehensive review examines the multifaceted impact of AI-driven technologies on all stages of the pharmaceutical life cycle. It discusses the application of machine learning algorithms, data analytics, and predictive modeling to accelerate drug discovery processes, optimize formulation development, enhance manufacturing efficiency, ensure stringent quality control measures, and revolutionize post-market surveillance methodologies. By describing the advancements, challenges, and future prospects of harnessing AI in the pharmaceutical landscape, this review offers valuable insights into the evolving dynamics of drug development and regulatory practices in the era of AI-driven innovation.

Develop Targeted Protein Drug Carriers through a High-Throughput Screening Platform and Rational Design

Protein-based drugs offer advantages, such as high specificity, low toxicity, and minimal side effects compared to small molecule drugs. However, delivery of proteins to target tissues or cells remains challenging due to the instability, diverse structures, charges, and molecular weights of proteins. Polymers have emerged as a leading choice for designing effective protein delivery systems, but identifying a suitable polymer for a given protein is complicated by the complexity of both proteins and polymers. To address this challenge, a fluorescence-based high-throughput screening platform called ProMatch to efficiently collect data on protein-polymer interactions, followed by in vivo and in vitro experiments with rational design is developed. Using this approach to streamline polymer selection for targeted protein delivery, candidate polymers from commercially available options are identified and a polyhexamethylene biguanide (PHMB)-based system for delivering proteins to white adipose tissue as a treatment for obesity is developed. A branched polyethylenimine (bPEI)-based system for neuron-specific protein delivery to stimulate optic nerve regeneration is also developed. The high-throughput screening methodology expedites identification of promising polymer candidates for tissue-specific protein delivery systems, thereby providing a platform to develop innovative protein-based therapeutics.

Mapping the evolution and research landscape of ferroptosis-targeted nanomedicine: insights from a scientometric analysis

Objective

Notable progress has been made in "ferroptosis-based nano drug delivery systems (NDDSs)" over the past 11 years. Despite the ongoing absence of a comprehensive scientometric overview and up-to-date scientific mapping research, especially regarding the evolution, critical research pathways, current research landscape, central investigative themes, and future directions.

Methods

Data ranging from 1 January 2012, to 30 November 2023, were obtained from the Web of Science database. A variety of advanced analytical tools were employed for detailed scientometric and visual analyses.

Results

The results show that China significantly led the field, contributing 82.09% of the total publications, thereby largely shaping the research domain. Chen Yu emerged as the most productive author in this field. Notably, the journal ACS Nano had the greatest number of relevant publications. The study identified liver neoplasms, pancreatic neoplasms, gliomas, neoplasm metastases, and melanomas as the top five crucial disorders in this research area.

Conclusion

This research provides a comprehensive scientometric assessment, enhancing our understanding of NDDSs focused on ferroptosis. Consequently, it enables rapid access to essential information and facilitates the extraction of novel ideas in the field of ferroptotic nanomedicine for both experienced and emerging researchers.

Application of artificial intelligence in cancer diagnosis and tumor nanomedicine

Cancer is a major health concern due to its high incidence and mortality rates. Advances in cancer research, particularly in artificial intelligence (AI) and deep learning, have shown significant progress. The swift evolution of AI in healthcare, especially in tools like computer-aided diagnosis, has the potential to revolutionize early cancer detection. This technology offers improved speed, accuracy, and sensitivity, bringing a transformative impact on cancer diagnosis, treatment, and management. This paper provides a concise overview of the application of artificial intelligence in the realms of medicine and nanomedicine, with a specific emphasis on the significance and challenges associated with cancer diagnosis. It explores the pivotal role of AI in cancer diagnosis, leveraging structured, unstructured, and multimodal fusion data. Additionally, the article delves into the applications of AI in nanomedicine sensors and nano-oncology drugs. The fundamentals of deep learning and convolutional neural networks are clarified, underscoring their relevance to AI-driven cancer diagnosis. A comparative analysis is presented, highlighting the accuracy and efficiency of traditional methods juxtaposed with AI-based approaches. The discussion not only assesses the current state of AI in cancer diagnosis but also delves into the challenges faced by AI in this context. Furthermore, the article envisions the future development direction and potential application of artificial intelligence in cancer diagnosis, offering a hopeful prospect for enhanced cancer detection and improved patient prognosis.

Nanotechnology-empowered combination therapy for rheumatoid arthritis: principles, strategies, and challenges

Rheumatoid arthritis (RA) is an autoimmune disease with multifactorial etiology and intricate pathogenesis. In RA, repeated monotherapy is frequently associated with inadequate efficacy, drug resistance, and severe side effects. Therefore, a shift has occurred in clinical practice toward combination therapy. However, conventional combination therapy encounters several hindrances, including low selectivity to arthritic joints, short half-lives, and varying pharmacokinetics among coupled drugs. Emerging nanotechnology offers an incomparable opportunity for developing advanced combination therapy against RA. First, it allows for co-delivering multiple drugs with augmented physicochemical properties, targeted delivery capabilities, and controlled release profiles. Second, it enables therapeutic nanomaterials development, thereby expanding combination regimens to include multifunctional nanomedicines. Lastly, it facilitates the construction of all-in-one nanoplatforms assembled with multiple modalities, such as phototherapy, sonodynamic therapy, and imaging. Thus, nanotechnology offers a promising solution to the current bottleneck in both RA treatment and diagnosis. This review summarizes the rationale, advantages, and recent advances in nano-empowered combination therapy for RA. It also discusses safety considerations, drug-drug interactions, and the potential for clinical translation. Additionally, it provides design tips and an outlook on future developments in nano-empowered combination therapy. The objective of this review is to achieve a comprehensive understanding of the mechanisms underlying combination therapy for RA and unlock the maximum potential of nanotechnology, thereby facilitating the smooth transition of research findings from the laboratory to clinical practice.

Development of Polymethine Dyes for NIR-II Fluorescence Imaging and Therapy

Fluorescence imaging in the second near-infrared window (NIR-II) is burgeoning because of its higher imaging fidelity in monitoring physiological and pathological processes than clinical visible/the second near-infrared window fluorescence imaging. Notably, the imaging fidelity is heavily dependent on fluorescence agents. So far, indocyanine green, one of the polymethine dyes, with good biocompatibility and renal clearance is the only dye approved by the Food and Drug Administration, but it shows relatively low NIR-II brightness. Importantly, tremendous efforts are devoted to synthesizing polymethine dyes for imaging preclinically and clinically. They have shown feasibility in the customization of structure and properties to fulfill various needs in imaging and therapy. Herein, a timely update on NIR-II polymethine dyes, with a special focus on molecular design strategies for fluorescent, photoacoustic, and multimodal imaging, is offered. Furthermore, the progress of polymethine dyes in sensing pathological biomarkers and even reporting drug release is illustrated. Moreover, the NIR-II fluorescence imaging-guided therapies with polymethine dyes are summarized regarding chemo-, photothermal, photodynamic, and multimodal approaches. In addition, artificial intelligence is pointed out for its potential to expedite dye development. This comprehensive review will inspire interest among a wide audience and offer a handbook for people with an interest in NIR-II polymethine dyes.

Advances in the treatment of atherosclerosis with ligand-modified nanocarriers

Atherosclerosis, a chronic disease associated with metabolism, poses a significant risk to human well-being. Currently, existing treatments for atherosclerosis lack sufficient efficiency, while the utilization of surface-modified nanoparticles holds the potential to deliver highly effective therapeutic outcomes. These nanoparticles can target and bind to specific receptors that are abnormally over-expressed in atherosclerotic conditions. This paper reviews recent research (2018-present) advances in various ligand-modified nanoparticle systems targeting atherosclerosis by specifically targeting signature molecules in the hope of precise treatment at the molecular level and concludes with a discussion of the challenges and prospects in this field. The intention of this review is to inspire novel concepts for the design and advancement of targeted nanomedicines tailored specifically for the treatment of atherosclerosis.

Nanomedicine Targeting Myeloid-Derived Suppressor Cells Enhances Anti-Tumor Immunity

Cancer immunotherapy, a field within immunology that aims to enhance the host's anti-cancer immune response, frequently encounters challenges associated with suboptimal response rates. The presence of myeloid-derived suppressor cells (MDSCs), crucial constituents of the tumor microenvironment (TME), exacerbates this issue by fostering immunosuppression and impeding T cell differentiation and maturation. Consequently, targeting MDSCs has emerged as crucial for immunotherapy aimed at enhancing anti-tumor responses. The development of nanomedicines specifically designed to target MDSCs aims to improve the effectiveness of immunotherapy by transforming immunosuppressive tumors into ones more responsive to immune intervention. This review provides a detailed overview of MDSCs in the TME and current strategies targeting these cells. Also the benefits of nanoparticle-assisted drug delivery systems, including design flexibility, efficient drug loading, and protection against enzymatic degradation, are highlighted. It summarizes advances in nanomedicine targeting MDSCs, covering enhanced treatment efficacy, safety, and modulation of the TME, laying the groundwork for more potent cancer immunotherapy.

A Journey of Challenges and Victories: A Bibliometric Worldview of Nanomedicine since the 21st Century

Nanotechnology profoundly affects the advancement of medicine. Limitations in diagnosing and treating cancer and chronic diseases promote the growth of nanomedicine. However, there are very few analytical and descriptive studies regarding the trajectory of nanomedicine, key research powers, present research landscape, focal investigative points, and future outlooks. Herein, articles and reviews published in the Science Citation Index Expanded of Web of Science Core Collection from first January 2000 to 18th July 2023 are analyzed. Herein, a bibliometric visualization of publication trends, countries/regions, institutions, journals, research categories, themes, references, and keywords is produced and elaborated. Nanomedicine-related academic output is increasing since the COVID-19 pandemic, solidifying the uneven global distribution of research performance. While China leads in terms of publication quantity and has numerous highly productive institutions, the USA has advantages in academic impact, commercialization, and industrial value. Nanomedicine integrates with other disciplines, establishing interdisciplinary platforms, in which drug delivery and nanoparticles remain focal points. Current research focuses on integrating nanomedicine and cell ferroptosis induction in cancer immunotherapy. The keyword "burst testing" identifies promising research directions, including immunogenic cell death, chemodynamic therapy, tumor microenvironment, immunotherapy, and extracellular vesicles. The prospects, major challenges, and barriers to addressing these directions are discussed.

Exploring and Analyzing the Systemic Delivery Barriers for Nanoparticles

Most nanomedicines require efficient in vivo delivery to elicit diagnostic and therapeutic effects. However, en route to their intended tissues, systemically administered nanoparticles often encounter delivery barriers. To describe these barriers, we propose the term "nanoparticle blood removal pathways" (NBRP), which summarizes the interactions between nanoparticles and the body's various cell-dependent and cell-independent blood clearance mechanisms. We reviewed nanoparticle design and biological modulation strategies to mitigate nanoparticle-NBRP interactions. As these interactions affect nanoparticle delivery, we studied the preclinical literature from 2011-2021 and analyzed nanoparticle blood circulation and organ biodistribution data. Our findings revealed that nanoparticle surface chemistry affected the in vivo behavior more than other nanoparticle design parameters. Combinatory biological-PEG surface modification improved the blood area under the curve by ~418%, with a decrease in liver accumulation of up to 47%. A greater understanding of nanoparticle-NBRP interactions and associated delivery trends will provide new nanoparticle design and biological modulation strategies for safer, more effective, and more efficient nanomedicines.

Tolerogenic Nano-/Microparticle Vaccines for Immunotherapy

Autoimmune diseases, allergies, transplant rejections, generation of antidrug antibodies, and chronic inflammatory diseases have impacted a large group of people across the globe. Conventional treatments and therapies often use systemic or broad immunosuppression with serious efficacy and safety issues. Tolerogenic vaccines represent a concept that has been extended from their traditional immune-modulating function to induction of antigen-specific tolerance through the generation of regulatory T cells. Without impairing immune homeostasis, tolerogenic vaccines dampen inflammation and induce tolerogenic regulation. However, achieving the desired potency of tolerogenic vaccines as preventive and therapeutic modalities calls for precise manipulation of the immune microenvironment and control over the tolerogenic responses against the autoantigens, allergens, and/or alloantigens. Engineered nano-/microparticles possess desirable design features that can bolster targeted immune regulation and enhance the induction of antigen-specific tolerance. Thus, particle-based tolerogenic vaccines hold great promise in clinical translation for future treatment of aforementioned immune disorders. In this review, we highlight the main strategies to employ particles as exciting tolerogenic vaccines, with a focus on the particles' role in facilitating the induction of antigen-specific tolerance. We describe the particle design features that facilitate their usage and discuss the challenges and opportunities for designing next-generation particle-based tolerogenic vaccines with robust efficacy to promote antigen-specific tolerance for immunotherapy.

Emergence and impact of theranostic-nanoformulation of triple therapeutics for combination cancer therapy

Cancer remains a major global health threat necessitating the multipronged approaches for its prevention and management. Traditional approaches in the form of chemotherapy, surgery, and radiotherapy are often encountered with poor patient outcomes evidenced by high mortality and morbidity, compelling the need for precision medicine for cancer patients to enable personalized and targeted cancer treatment. There has been an emergence of smart multimodal theranostic nanoformulation for triple combination cancer therapy in the last few years, which dramatically enhances the overall safety of the nanoformulation for in vivo and potential clinical applications with minimal toxicity. However, it is imperative to gain insight into the limitations of this system in terms of clinical translation, cost-effectiveness, accessibility, and multidisciplinary collaboration. This review paper aims to highlight and compare the impact of the recent theranostic nanoformulations of triple therapeutics in a single nanocarrier for effective management of cancer and provide a new dimension for diagnostic and treatment simultaneously.

Advanced optical imaging for the rational design of nanomedicines

Despite the enormous potential of nanomedicines to shape the future of medicine, their clinical translation remains suboptimal. Translational challenges are present in every step of the development pipeline, from a lack of understanding of patient heterogeneity to insufficient insights on nanoparticle properties and their impact on material-cell interactions. Here, we discuss how the adoption of advanced optical microscopy techniques, such as super-resolution optical microscopies, correlative techniques, and high-content modalities, could aid the rational design of nanocarriers, by characterizing the cell, the nanomaterial, and their interaction with unprecedented spatial and/or temporal detail. In this nanomedicine arena, we will discuss how the implementation of these techniques, with their versatility and specificity, can yield high volumes of multi-parametric data; and how machine learning can aid the rapid advances in microscopy: from image acquisition to data interpretation.

Nanomedicine Combats Drug Resistance in Lung Cancer

Lung cancer is the second most prevalent cancer and the leading cause of cancer-related death worldwide. Surgery, chemotherapy, molecular targeted therapy, immunotherapy, and radiotherapy are currently available as treatment methods. However, drug resistance is a significant factor in the failure of lung cancer treatments. Novel therapeutics have been exploited to address complicated resistance mechanisms of lung cancer and the advancement of nanomedicine is extremely promising in terms of overcoming drug resistance. Nanomedicine equipped with multifunctional and tunable physiochemical properties in alignment with tumor genetic profiles can achieve precise, safe, and effective treatment while minimizing or eradicating drug resistance in cancer. Here, this work reviews the discovered resistance mechanisms for lung cancer chemotherapy, molecular targeted therapy, immunotherapy, and radiotherapy, and outlines novel strategies for the development of nanomedicine against drug resistance. This work focuses on engineering design, customized delivery, current challenges, and clinical translation of nanomedicine in the application of resistant lung cancer.

Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics

Materials informatics (MI) has immense potential to accelerate the pace of innovation and new product development in biotechnology. Close collaborations between skilled physical and life scientists with data scientists are being established in pursuit of leveraging MI tools in automation and artificial intelligence (AI) to predict material properties in vitro and in vivo. However, the scarcity of large, standardized, and labeled materials data for connecting structure-function relationships represents one of the largest hurdles to overcome. In this Highlight, focus is brought to emerging developments in polymer-based therapeutic delivery platforms, where teams generate large experimental datasets around specific therapeutics and successfully establish a design-to-deployment cycle of specialized nanocarriers. Three select collaborations demonstrate how custom-built polymers protect and deliver small molecules, nucleic acids, and proteins, representing ideal use-cases for machine learning to understand how molecular-level interactions impact drug stabilization and release. We conclude with our perspectives on how MI innovations in automation efficiencies and digitalization of data-coupled with fundamental insight and creativity from the polymer science community-can accelerate translation of more gene therapies into lifesaving medicines.

Revolutionizing drug formulation development: The increasing impact of machine learning

Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic demonstrated the impact of formulation science. Yet, the design of advanced pharmaceutical formulations is non-trivial and primarily relies on costly and time-consuming wet-lab experimentation. In 2021, our group published a review article focused on the use of ML as a means to accelerate drug formulation development. Since then, the field has witnessed significant growth and progress, reflected by an increasing number of studies published in this area. This updated review summarizes the current state of ML directed drug formulation development, introduces advanced ML techniques that have been implemented in formulation design and shares the progress on making self-driving laboratories a reality. Furthermore, this review highlights several future applications of ML yet to be fully exploited to advance drug formulation research and development.

How can Electrospinning Further Service Well for Pharmaceutical Researches?

The past two decades have witnessed the enormous success and progress of electrospinning, as well as its broad and useful applications in pharmaceutics as a laboratory pharmaceutical nanotechnology. Everything in the past is a preface, in which the large screen opens for electrospinning and electrospun nanofibers (particularly those multiple-fluid electrospinning processes and the related multiple-chamber nanostructures) to stride into a new stage and the real commercial applications. In this commentary, four hot regions are identified for the further progress of the applications of electrospinning in pharmaceutics, in which electrospinning and its products can provide more and better services to the development of pharmaceutics.

Interpretable XGBoost-SHAP Model Predicts Nanoparticles Delivery Efficiency Based on Tumor Genomic Mutations and Nanoparticle Properties

Understanding the complex interaction between nanoparticles (NPs) and tumors in vivo and how it dominates the delivery efficiency of NPs is critical for the translation of nanomedicine. Herein, we proposed an interpretable XGBoost-SHAP model by integrating the information on NPs physicochemical properties and tumor genomic profile to predict the delivery efficiency. The correlation coefficients were 0.66, 0.75, and 0.54 for the prediction of maximum delivery efficiency, delivery efficiency at 24 and 168 h postinjection for test sets. The analysis of the feature importance revealed that the tumor genomic mutations and their interaction with NPs properties played important roles in the delivery of NPs. The biological pathways of the NP-delivery-related genes were further explored through gene ontology enrichment analysis. Our work provides a pipeline to predict and explain the delivery efficiency of NPs to heterogeneous tumors and highlights the power of simultaneously using omics data and interpretable machine learning algorithms for discovering interactions between NPs and individual tumors, which is important for the development of personalized precision nanomedicine.

Research in the Field of Drug Design and Development

The processes used by academic and industrial scientists to discover new drugs have recently experienced a true renaissance, with many new and exciting techniques being developed over the past 5-10 years alone. Drug design and discovery, and the search for new safe and well-tolerated compounds, as well as the ineffectiveness of existing therapies, and society's insufficient knowledge concerning the prophylactics and pharmacotherapy of the most common diseases today, comprise a serious challenge. This can influence not only the quality of human life, but also the health of whole societies, which became evident during the COVID-19 pandemic. In general, the process of drug development consists of three main stages: drug discovery, preclinical development using cell-based and animal models/tests, clinical trials on humans and, finally, forward moving toward the step of obtaining regulatory approval, in order to market the potential drug. In this review, we will attempt to outline the first three most important consecutive phases in drug design and development, based on the experience of three cooperating and complementary academic centers of the Visegrád group; i.e., Medical University of Lublin, Poland, Masaryk University of Brno, Czech Republic, and Comenius University Bratislava, Slovak Republic.

Functional Materials for Subcellular Targeting Strategies in Cancer Therapy: Progress and Prospects

Neoadjuvant and adjuvant therapies have made significant progress in cancer treatment. However, tumor adjuvant therapy still faces challenges due to the intrinsic heterogeneity of cancer, genomic instability, and the formation of an immunosuppressive tumor microenvironment. Functional materials possess unique biological properties such as long circulation times, tumor-specific targeting, and immunomodulation. The combination of functional materials with natural substances and nanotechnology has led to the development of smart biomaterials with multiple functions, high biocompatibilities, and negligible immunogenicities, which can be used for precise cancer treatment. Recently, subcellular structure-targeting functional materials have received particular attention in various biomedical applications including the diagnosis, sensing, and imaging of tumors and drug delivery. Subcellular organelle-targeting materials can precisely accumulate therapeutic agents in organelles, considerably reduce the threshold dosages of therapeutic agents, and minimize drug-related side effects. This review provides a systematic and comprehensive overview of the research progress in subcellular organelle-targeted cancer therapy based on functional nanomaterials. Moreover, it explains the challenges and prospects of subcellular organelle-targeting functional materials in precision oncology. The review will serve as an excellent cutting-edge guide for researchers in the field of subcellular organelle-targeted cancer therapy.

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.

Nanomedicine in cancer therapy

Cancer remains a highly lethal disease in the world. Currently, either conventional cancer therapies or modern immunotherapies are non-tumor-targeted therapeutic approaches that cannot accurately distinguish malignant cells from healthy ones, giving rise to multiple undesired side effects. Recent advances in nanotechnology, accompanied by our growing understanding of cancer biology and nano-bio interactions, have led to the development of a series of nanocarriers, which aim to improve the therapeutic efficacy while reducing off-target toxicity of the encapsulated anticancer agents through tumor tissue-, cell-, or organelle-specific targeting. However, the vast majority of nanocarriers do not possess hierarchical targeting capability, and their therapeutic indices are often compromised by either poor tumor accumulation, inefficient cellular internalization, or inaccurate subcellular localization. This Review outlines current and prospective strategies in the design of tumor tissue-, cell-, and organelle-targeted cancer nanomedicines, and highlights the latest progress in hierarchical targeting technologies that can dynamically integrate these three different stages of static tumor targeting to maximize therapeutic outcomes. Finally, we briefly discuss the current challenges and future opportunities for the clinical translation of cancer nanomedicines.

Nanoparticles-induced potential toxicity on human health: Applications, toxicity mechanisms, and evaluation models

Nanoparticles (NPs) have become one of the most popular objects of scientific study during the past decades. However, despite wealth of study reports, still there is a gap, particularly in health toxicology studies, underlying mechanisms, and related evaluation models to deeply understanding the NPs risk effects. In this review, we first present a comprehensive landscape of the applications of NPs on health, especially addressing the role of NPs in medical diagnosis, therapy. Then, the toxicity of NPs on health systems is introduced. We describe in detail the effects of NPs on various systems, including respiratory, nervous, endocrine, immune, and reproductive systems, and the carcinogenicity of NPs. Furthermore, we unravels the underlying mechanisms of NPs including ROS accumulation, mitochondrial damage, inflammatory reaction, apoptosis, DNA damage, cell cycle, and epigenetic regulation. In addition, the classical study models such as cell lines and mice and the emerging models such as 3D organoids used for evaluating the toxicity or scientific study are both introduced. Overall, this review presents a critical summary and evaluation of the state of understanding of NPs, giving readers more better understanding of the NPs toxicology to remedy key gaps in knowledge and techniques.

Towards artificial intelligence-enabled extracellular vesicle precision drug delivery

Extracellular Vesicles (EVs), particularly exosomes, recently exploded into nanomedicine as an emerging drug delivery approach due to their superior biocompatibility, circulating stability, and bioavailability in vivo. However, EV heterogeneity makes molecular targeting precision a critical challenge. Deciphering key molecular drivers for controlling EV tissue targeting specificity is in great need. Artificial intelligence (AI) brings powerful prediction ability for guiding the rational design of engineered EVs in precision control for drug delivery. This review focuses on cutting-edge nano-delivery via integrating large-scale EV data with AI to develop AI-directed EV therapies and illuminate the clinical translation potential. We briefly review the current status of EVs in drug delivery, including the current frontier, limitations, and considerations to advance the field. Subsequently, we detail the future of AI in drug delivery and its impact on precision EV delivery. Our review discusses the current universal challenge of standardization and critical considerations when using AI combined with EVs for precision drug delivery. Finally, we will conclude this review with a perspective on future clinical translation led by a combined effort of AI and EV research.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning

Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity is a critical parameter that describes their interaction with the living organisms screened in every bio-related research. To prevent excessive experiments, such properties have to be pre-evaluated. Several existing ML models partially fulfill the gap by predicting whether a nanomaterial is toxic or not. Yet, this binary categorization neglects the concentration dependencies crucial for experimental scientists. Here, an ML-based approach is proposed to the quantitative prediction of inorganic nanomaterial cytotoxicity achieving the precision expressed by 10-fold cross-validation (CV) Q²  = 0.86 with the root mean squared error (RMSE) of 12.2% obtained by the correlation-based feature selection and grid search-based model hyperparameters optimization. To provide further model flexibility, quantitative atom property-based nanomaterial descriptors are introduced allowing the model to extrapolate on unseen samples. Feature importance is calculated to find an interpretable model with optimal decision-making. These findings allow experimental scientists to perform primary in silico candidate screening and minimize the number of excessive, labor-intensive experiments enabling the rapid development of nanomaterials for medicinal purposes.

Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.

Artificial intelligence aids in development of nanomedicines for cancer management

Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.

Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence

INTRODUCTION

Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines.

AREAS COVERED

the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods.

EXPERT OPINION

The development of integrated workflows based on automated experimentation and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.

The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research

The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocarriers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated.

The role of imaging in targeted delivery of nanomedicine for cancer therapy

Nanomedicines overcome the pharmacokinetic limitations of traditional drug formulations and have promising prospect in cancer treatment. However, nanomedicine delivery in vivo is still facing challenges from the complex physiological environment. For the purpose of effective tumor therapy, they should be designed to guarantee the five features principle, including long blood circulation, efficient tumor accumulation, deep matrix penetration, enhanced cell internalization and accurate drug release. To ensure the excellent performance of the designed nanomedicine, it would be better to monitor the drug delivery process as well as the therapeutic effects by real-time imaging. In this review, we summarize strategies in developing nanomedicines for efficiently meeting the five features of drug delivery, and the role of several imaging modalities (fluorescent imaging (FL), magnetic resonance imaging (MRI), computed tomography (CT), photoacoustic imaging (PAI), positron emission tomography (PET), and electron microscopy) in tracing drug delivery and therapeutic effect in vivo based on five features principle.

Hotspot Mining in the Field of Library and Information Science under the Environment of Big Data

Currently, with the implementation of big data strategies in countries all over the world, big data has achieved vigorous development in various fields. Big data research and application practices have also rapidly attracted the attention of the library and information field. Objective. The study explored the current state of research and research hotspots of big data in the library and information field and further discussed the future research trends. Methods. In the CNKI database, 16 CSSCI source journals in the discipline of library information and digital library were selected as data sources, and the relevant literature was retrieved with the theme of “big data.” The collected literature was excluded and expanded according to the citation relationship. Then, with the help of Bicomb and SPSS, co-word analysis and cluster analysis would be carried out on these literature results. Results. According to the findings of the data analysis, the research hotspots on the topic mainly focus on five major research themes, namely, big data and smart library, big data and intelligence research, data mining and cloud computing, big data and information analysis, and library innovation and services. Limitations. At present, the research scope and coverage on this topic are wide, which leads to the research still staying at the macro level. Conclusions. Big data research will remain one of the hotspots in the future. However, the most study is still limited to the perspective of library and information and has not yet analyzed the research status, research hotspots, and development trends in this field from the perspective of big data knowledge structure. Moreover, machine learning, artificial intelligence, knowledge services, AR, and VR may be new directions for future attention and development.

Involvement of Resveratrol against Brain Cancer: A Combination Strategy with a Pharmaceutical Approach

A brain tumor (BT) is a condition in which there is growth or uncontrolled development of the brain cells, which usually goes unrecognized or is diagnosed at the later stages. Since the mechanism behind BT is not clear, and the various physiological conditions are difficult to diagnose, the success rate of BT is not very high. This is the central issue faced during drug development and clinical trials with almost all types of neurodegenerative disorders. In the first part of this review, we focus on the concept of brain tumors, their barriers, and the types of delivery possible to target the brain cells. Although various treatment methods are available, they all have side effects or toxic effects. Hence, in the second part, a correlation was made between the use of resveratrol, a potent antioxidant, and its advantages for brain diseases. The relationship between brain disease and the blood-brain barrier, multi-drug resistance, and the use of nanomedicine for treating brain disorders is also mentioned. In short, a hypothetical concept is given with a background investigation into the use of combination therapy with resveratrol as an active ingredient, the possible drug delivery, and its formulation-based approach.