Cancer Modeling-on-a-Chip with Future Artificial Intelligence Integration

Investigation of tumour environments through advancements in microtechnology and nanotechnology

Cancer has a significant negative social and economic impact on both developed and developing countries. As a result, understanding the onset and progression of cancer is critical for developing therapies that can improve the well-being and health of individuals with cancer. With time, study has revealed, the tumor microenvironment has great influence on this process. Micro and nanoscale engineering techniques can be used to study the tumor microenvironment. Nanoscale and Microscale engineering use Novel technologies and designs with small dimensions to recreate the TME. Knowing how cancer cells interact with one another can help researchers develop therapeutic approaches that anticipate and counteract cancer cells' techniques for evading detection and fighting anti-cancer treatments, such as microfabrication techniques, microfluidic devices, nanosensors, and nanodevices used to study or recreate the tumor microenvironment. Nevertheless, a complicated action just like the growth and in cancer advancement, and their intensive association along the environment around it that has to be studied in more detail.

Targeted Cancer Therapy-on-A-Chip

Targeted cancer therapy (TCT) is gaining increased interest because it reduces the risks of adverse side effects by specifically treating tumor cells. TCT testing has traditionally been performed using two-dimensional (2D) cell culture and animal studies. Organ-on-a-chip (OoC) platforms have been developed to recapitulate cancer in vitro, as cancer-on-a-chip (CoC), and used for chemotherapeutics development and testing. This review explores the use of CoCs to both develop and test TCTs, with a focus on three main aspects, the use of CoCs to identify target biomarkers for TCT development, the use of CoCs to test free, un-encapsulated TCTs, and the use of CoCs to test encapsulated TCTs. Despite current challenges such as system scaling, and testing externally triggered TCTs, TCToC shows a promising future to serve as a supportive, pre-clinical platform to expedite TCT development and bench-to-bedside translation.

Skin-on-a-chip technologies towards clinical translation and commercialization

Skin is the largest organ of the human body which plays a critical role in thermoregulation, metabolism (e.g. synthesis of vitamin D), and protection of other organs from environmental threats, such as infections, microorganisms, ultraviolet radiation, and physical damage. Even though skin diseases are considered to be less fatal, the ubiquity of skin diseases and irritation caused by them highlights the importance of skin studies. Furthermore, skin is a promising means for transdermal drug delivery, which requires a thorough understanding of human skin structure. Current animal andin vitrotwo/three-dimensional skin models provide a platform for disease studies and drug testing, whereas they face challenges in the complete recapitulation of the dynamic and complex structure of actual skin tissue. One of the most effective methods for testing pharmaceuticals and modeling skin diseases are skin-on-a-chip (SoC) platforms. SoC technologies provide a non-invasive approach for examining 3D skin layers and artificially creating disease models in order to develop diagnostic or therapeutic methods. In addition, SoC models enable dynamic perfusion of culture medium with nutrients and facilitate the continuous removal of cellular waste to further mimic thein vivocondition. Here, the article reviews the most recent advances in the design and applications of SoC platforms for disease modeling as well as the analysis of drugs and cosmetics. By examining the contributions of different patents to the physiological relevance of skin models, the review underscores the significant shift towards more ethical and efficient alternatives to animal testing. Furthermore, it explores the market dynamics ofin vitroskin models and organ-on-a-chip platforms, discussing the impact of legislative changes and market demand on the development and adoption of these advanced research tools. This article also identifies the existing obstacles that hinder the advancement of SoC platforms, proposing directions for future improvements, particularly focusing on the journey towards clinical adoption.

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.

From animal testing to in vitro systems: advancing standardization in microphysiological systems

Limitations with cell cultures and experimental animal-based studies have had the scientific and industrial communities searching for new approaches that can provide reliable human models for applications such as drug development, toxicological assessment, and in vitro pre-clinical evaluation. This has resulted in the development of microfluidic-based cultures that may better represent organs and organ systems in vivo than conventional monolayer cell cultures. Although there is considerable interest from industry and regulatory bodies in this technology, several challenges need to be addressed for it to reach its full potential. Among those is a lack of guidelines and standards. Therefore, a multidisciplinary team of stakeholders was formed, with members from the US Food and Drug Administration (FDA), the National Institute of Standards and Technology (NIST), European Union, academia, and industry, to provide a framework for future development of guidelines/standards governing engineering concepts of organ-on-a-chip models. The result of this work is presented here for interested parties, stakeholders, and other standards development organizations (SDOs) to foster further discussion and enhance the impact and benefits of these efforts.

The progressive trend of modeling and drug screening systems of breast cancer bone metastasis

Bone metastasis is considered as a considerable challenge for breast cancer patients. Various in vitro and in vivo models have been developed to examine this occurrence. In vitro models are employed to simulate the intricate tumor microenvironment, investigate the interplay between cells and their adjacent microenvironment, and evaluate the effectiveness of therapeutic interventions for tumors. The endeavor to replicate the latency period of bone metastasis in animal models has presented a challenge, primarily due to the necessity of primary tumor removal and the presence of multiple potential metastatic sites.The utilization of novel bone metastasis models, including three-dimensional (3D) models, has been proposed as a promising approach to overcome the constraints associated with conventional 2D and animal models. However, existing 3D models are limited by various factors, such as irregular cellular proliferation, autofluorescence, and changes in genetic and epigenetic expression. The imperative for the advancement of future applications of 3D models lies in their standardization and automation. The utilization of artificial intelligence exhibits the capability to predict cellular behavior through the examination of substrate materials' chemical composition, geometry, and mechanical performance. The implementation of these algorithms possesses the capability to predict the progression and proliferation of cancer. This paper reviewed the mechanisms of bone metastasis following primary breast cancer. Current models of breast cancer bone metastasis, along with their challenges, as well as the future perspectives of using these models for translational drug development, were discussed.

Artificial intelligence in cancer diagnosis: Opportunities and challenges

Since cancer is one of the world's top causes of death, early diagnosis is critical to improving patient outcomes. Artificial intelligence (AI) has become a viable technique for cancer diagnosis by using machine learning algorithms to examine large volumes of data for accurate and efficient diagnosis. AI has the potential to alter the way cancer is detected fundamentally. Still, it has several disadvantages, such as requiring a large amount of data, technological limitations, and ethical concerns. This overview looks at the possibilities and restrictions of AI in cancer detection, as well as current applications and possible future developments. We can better understand how to use AI to improve patient outcomes and reduce cancer mortality rates by looking at its potential for cancer detection.

Translational Research Techniques for the Facial Plastic Surgeon: An Overview

The field of facial plastic and reconstructive surgery (FPRS) is an incredibly diverse, multispecialty field that seeks innovative and novel solutions for the management of physical defects on the head and neck. To aid in the advancement of medical and surgical treatments for these defects, there has been a recent emphasis on the importance of translational research. With recent technological advancements, there are now a myriad of research techniques that are widely accessible for physician and scientist use in translational research. Such techniques include integrated multiomics, advanced cell culture and microfluidic tissue models, established animal models, and emerging computer models generated using bioinformatics. This study discusses these various research techniques and how they have and can be used for research in the context of various important diseases within the field of FPRS.

Micro-engineering and nano-engineering approaches to investigate tumour ecosystems

The interactions among tumour cells, the tumour microenvironment (TME) and non-tumour tissues are of interest to many cancer researchers. Micro-engineering approaches and nanotechnologies are under extensive exploration for modelling these interactions and measuring them in situ and in vivo to investigate therapeutic vulnerabilities in cancer and extend a systemic view of tumour ecosystems. Here we highlight the greatest opportunities for improving the understanding of tumour ecosystems using microfluidic devices, bioprinting or organ-on-a-chip approaches. We also discuss the potential of nanosensors that can transmit information from within the TME or elsewhere in the body to address scientific and clinical questions about changes in chemical gradients, enzymatic activities, metabolic and immune profiles of the TME and circulating analytes. This Review aims to connect the cancer biology and engineering communities, presenting biomedical technologies that may expand the methodologies of the former, while inspiring the latter to develop approaches for interrogating cancer ecosystems.

Cancer Models on Chip: Paving the Way to Large-Scale Trial Applications

Cancer kills millions of individuals every year all over the world (Global Cancer Observatory). The physiological and biomechanical processes underlying the tumor are still poorly understood, hindering researchers from creating new, effective therapies. Inconsistent results of preclinical research, in vivo testing, and clinical trials decrease drug approval rates. 3D tumor-on-a-chip (ToC) models integrate biomaterials, tissue engineering, fabrication of microarchitectures, and sensory and actuation systems in a single device, enabling reliable studies in fundamental oncology and pharmacology. This review includes a critical discussion about their ability to reproduce the tumor microenvironment (TME), the advantages and drawbacks of existing tumor models and architectures, major components and fabrication techniques. The focus is on current materials and micro/nanofabrication techniques used to manufacture reliable and reproducible microfluidic ToC models for large-scale trial applications.

Organ-on-a-chip meets artificial intelligence in drug evaluation

Drug evaluation has always been an important area of research in the pharmaceutical industry. However, animal welfare protection and other shortcomings of traditional drug development models pose obstacles and challenges to drug evaluation. Organ-on-a-chip (OoC) technology, which simulates human organs on a chip of the physiological environment and functionality, and with high fidelity reproduction organ-level of physiology or pathophysiology, exhibits great promise for innovating the drug development pipeline. Meanwhile, the advancement in artificial intelligence (AI) provides more improvements for the design and data processing of OoCs. Here, we review the current progress that has been made to generate OoC platforms, and how human single and multi-OoCs have been used in applications, including drug testing, disease modeling, and personalized medicine. Moreover, we discuss issues facing the field, such as large data processing and reproducibility, and point to the integration of OoCs and AI in data analysis and automation, which is of great benefit in future drug evaluation. Finally, we look forward to the opportunities and challenges faced by the coupling of OoCs and AI. In summary, advancements in OoCs development, and future combinations with AI, will eventually break the current state of drug evaluation.

On-chip modeling of tumor evolution: Advances, challenges and opportunities

Tumor evolution is the accumulation of various tumor cell behaviors from tumorigenesis to tumor metastasis and is regulated by the tumor microenvironment (TME). However, the mechanism of solid tumor progression has not been completely elucidated, and thus, the development of tumor therapy is still limited. Recently, Tumor chips constructed by culturing tumor cells and stromal cells on microfluidic chips have demonstrated great potential in modeling solid tumors and visualizing tumor cell behaviors to exploit tumor progression. Herein, we review the methods of developing engineered solid tumors on microfluidic chips in terms of tumor types, cell resources and patterns, the extracellular matrix and the components of the TME, and summarize the recent advances of microfluidic chips in demonstrating tumor cell behaviors, including proliferation, epithelial-to-mesenchymal transition, migration, intravasation, extravasation and immune escape of tumor cells. We also outline the combination of tumor organoids and microfluidic chips to elaborate tumor organoid-on-a-chip platforms, as well as the practical limitations that must be overcome.

Transformative Materials to Create 3D Functional Human Tissue Models In Vitro in a Reproducible Manner

Recreating human tissues and organs in the petri dish to establish models as tools in biomedical sciences has gained momentum. These models can provide insight into mechanisms of human physiology, disease onset, and progression, and improve drug target validation, as well as the development of new medical therapeutics. Transformative materials play an important role in this evolution, as they can be programmed to direct cell behavior and fate by controlling the activity of bioactive molecules and material properties. Using nature as an inspiration, scientists are creating materials that incorporate specific biological processes observed during human organogenesis and tissue regeneration. This article presents the reader with state-of-the-art developments in the field of in vitro tissue engineering and the challenges related to the design, production, and translation of these transformative materials. Advances regarding (stem) cell sources, expansion, and differentiation, and how novel responsive materials, automated and large-scale fabrication processes, culture conditions, in situ monitoring systems, and computer simulations are required to create functional human tissue models that are relevant and efficient for drug discovery, are described. This paper illustrates how these different technologies need to converge to generate in vitro life-like human tissue models that provide a platform to answer health-based scientific questions.

Multifunctional nanostructures: Intelligent design to overcome biological barriers

Over the past three decades, nanoscience has offered a unique solution for reducing the systemic toxicity of chemotherapy drugs and for increasing drug therapeutic efficiency. However, the poor accumulation and pharmacokinetics of nanoparticles are some of the key reasons for their slow translation into the clinic. The is intimately linked to the non-biological nature of nanoparticles and the aberrant features of solid cancer, which together significantly compromise nanoparticle delivery. New findings on the unique properties of tumors and their interactions with nanoparticles and the human body suggest that, contrary to what was long-believed, tumor features may be more mirage than miracle, as the enhanced permeability and retention based efficacy is estimated to be as low as 1%. In this review, we highlight the current barriers and available solutions to pave the way for approved nanoformulations. Furthermore, we aim to discuss the main solutions to solve inefficient drug delivery with the use of nanobioengineering of nanocarriers and the tumor environment. Finally, we will discuss the suggested strategies to overcome two or more biological barriers with one nanocarrier. The variety of design formats, applications and implications of each of these methods will also be evaluated.

Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy

The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.

Opportunities and challenges to engineer 3D models of tumor-adaptive immune interactions

Augmenting adaptive immunity is a critical goal for developing next-generation cancer therapies. T and B cells infiltrating the tumor dramatically influence cancer progression through complex interactions with the local microenvironment. Cancer cells evade and limit these immune responses by hijacking normal immunologic pathways. Current experimental models using conventional primary cells, cell lines, or animals have limitations for studying cancer-immune interactions directly relevant to human biology and clinical translation. Therefore, engineering methods to emulate such interplay at local and systemic levels are crucial to expedite the development of better therapies and diagnostic tools. In this review, we discuss the challenges, recent advances, and future directions toward engineering the tumor-immune microenvironment (TME), including key elements of adaptive immunity. We first offer an overview of the recent research that has advanced our understanding of the role of the adaptive immune system in the tumor microenvironment. Next, we discuss recent developments in 3D in-vitro models and engineering approaches that have been used to study the interaction of cancer and stromal cells with B and T lymphocytes. We summarize recent advancement in 3D bioengineering and discuss the need for 3D tumor models that better incorporate elements of the complex interplay of adaptive immunity and the tumor microenvironment. Finally, we provide a perspective on current challenges and future directions for modeling cancer-immune interactions aimed at identifying new biological targets for diagnostics and therapeutics.

Towards a New 3Rs Era in the construction of 3D cell culture models simulating tumor microenvironment

Three-dimensional cell culture technology (3DCC) sits between two-dimensional cell culture (2DCC) and animal models and is widely used in oncology research. Compared to 2DCC, 3DCC allows cells to grow in a three-dimensional space, better simulating the in vivo growth environment of tumors, including hypoxia, nutrient concentration gradients, micro angiogenesis mimicism, and the interaction between tumor cells and the tumor microenvironment matrix. 3DCC has unparalleled advantages when compared to animal models, being more controllable, operable, and convenient. This review summarizes the comparison between 2DCC and 3DCC, as well as recent advances in different methods to obtain 3D models and their respective advantages and disadvantages.

Spatiotemporal dissection of tumor microenvironment via in situ sensing and monitoring in tumor-on-a-chip

Real-time monitoring in the tumor microenvironment provides critical insights of cancer progression and mechanistic understanding of responses to cancer treatments. However, clinical challenges and significant questions remain regarding assessment of limited clinical tissue samples, establishment of validated, controllable pre-clinical cancer models, monitoring of static versus dynamic markers, and the translation of insights gained from in vitro tumor microenvironments to systematic investigation and understanding in clinical practice. State-of-art tumor-on-a-chip strategies will be reviewed herein, and emerging real-time sensing and monitoring platforms for on-chip analysis of tumor microenvironment will also be examined. The integration of the sensors with tumor-on-a-chip platforms to provide spatiotemporal information of the tumor microenvironment and the associated challenges will be further evaluated. Though optimal integrated systems for in situ monitoring are still in evolution, great promises lie ahead that will open new paradigm for rapid, comprehensive analysis of cancer development and assist clinicians with powerful tools to guide the diagnosis, prognosis and treatment course in cancer.

The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review

Organs-on-chips (OoCs) are miniature microfluidic systems that have arguably become a class of advanced in vitro models. Deep learning, as an emerging topic in machine learning, has the ability to extract a hidden statistical relationship from the input data. Recently, these two areas have become integrated to achieve synergy for accelerating drug screening. This review provides a brief description of the basic concepts of deep learning used in OoCs and exemplifies the successful use cases for different types of OoCs. These microfluidic chips are of potential to be assembled as highly potent human-on-chips with complex physiological or pathological functions. Finally, we discuss the future supply with perspectives and potential challenges in terms of combining OoCs and deep learning for image processing and automation designs.

Engineering organ-on-a-chip systems to model viral infections

Infectious diseases remain a public healthcare concern worldwide. Amidst the pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, increasing resources have been diverted to investigate therapeutics targeting the COVID-19 spike glycoprotein and to develop various classes of vaccines. Most of the current investigations employ two-dimensional (2D) cell culture and animal models. However, 2D culture negates the multicellular interactions and three-dimensional (3D) microenvironment, and animal models cannot mimic human physiology because of interspecies differences. On the other hand, organ-on-a-chip (OoC) devices introduce a game-changer to model viral infections in human tissues, facilitating high-throughput screening of antiviral therapeutics. In this context, this review provides an overview of thein vitroOoC-based modeling of viral infection, highlighting the strengths and challenges for the future.

Spheroid Engineering in Microfluidic Devices

Two-dimensional (2D) cell culture techniques are commonly employed to investigate biophysical and biochemical cellular responses. However, these culture methods, having monolayer cells, lack cell-cell and cell-extracellular matrix interactions, mimicking the cell microenvironment and multicellular organization. Three-dimensional (3D) cell culture methods enable equal transportation of nutrients, gas, and growth factors among cells and their microenvironment. Therefore, 3D cultures show similar cell proliferation, apoptosis, and differentiation properties to in vivo. A spheroid is defined as self-assembled 3D cell aggregates, and it closely mimics a cell microenvironment in vitro thanks to cell-cell/matrix interactions, which enables its use in several important applications in medical and clinical research. To fabricate a spheroid, conventional methods such as liquid overlay, hanging drop, and so forth are available. However, these labor-intensive methods result in low-throughput fabrication and uncontrollable spheroid sizes. On the other hand, microfluidic methods enable inexpensive and rapid fabrication of spheroids with high precision. Furthermore, fabricated spheroids can also be cultured in microfluidic devices for controllable cell perfusion, simulation of fluid shear effects, and mimicking of the microenvironment-like in vivo conditions. This review focuses on recent microfluidic spheroid fabrication techniques and also organ-on-a-chip applications of spheroids, which are used in different disease modeling and drug development studies.

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

Microfluidic Protocols for the Assessment of Anticancer Therapies in 3D Tumor-Stromal Cocultures

Microfluidic technologies allow the generation of large datasets using smaller quantities of cells and reagents than with traditional well plate assays. Such miniaturized methods can also facilitate the generation of complex 3D preclinical models of solid tumors with controlled size and cell composition. This is particularly useful in the context of recreating the tumor microenvironment for preclinical screening of immunotherapies and combination therapies at a scale, to reduce the experimental costs during therapy development while using physiologically relevant 3D tumor models, and to assess the therapy's efficacy. Here, we describe the fabrication of microfluidic devices and the associated protocols to culture tumor-stromal spheroids for assessing the efficacy of anticancer immunotherapies as monotherapies and as part of combination therapy regimes.

Towards establishing human body-on-a-chip systems

Body-on-a-chip (BoC) platforms are established from multiple organs-on-chips (OoCs) to recapitulate the interactions between different tissues. Recently, Vunjak-Novakovic and colleagues reported the creation of a BoC system comprising four fluidically linked OoCs. Herein, the major innovations in their BoC system are discussed, followed by our future perspectives on enhancing the physiological relevance and scalability of BoCs for applications in studying disease mechanisms, testing potential therapeutics, and developing personalized medicine.

Breakthroughs and Applications of Organ-on-a-Chip Technology

Organ-on-a-chip (OOAC) is an emerging technology based on microfluid platforms and in vitro cell culture that has a promising future in the healthcare industry. The numerous advantages of OOAC over conventional systems make it highly popular. The chip is an innovative combination of novel technologies, including lab-on-a-chip, microfluidics, biomaterials, and tissue engineering. This paper begins by analyzing the need for the development of OOAC followed by a brief introduction to the technology. Later sections discuss and review the various types of OOACs and the fabrication materials used. The implementation of artificial intelligence in the system makes it more advanced, thereby helping to provide a more accurate diagnosis as well as convenient data management. We introduce selected OOAC projects, including applications to organ/disease modelling, pharmacology, personalized medicine, and dentistry. Finally, we point out certain challenges that need to be surmounted in order to further develop and upgrade the current systems.

In Vitro Cancer Models: A Closer Look at Limitations on Translation

In vitro cancer models are envisioned as high-throughput screening platforms for potential new therapeutic discovery and/or validation. They also serve as tools to achieve personalized treatment strategies or real-time monitoring of disease propagation, providing effective treatments to patients. To battle the fatality of metastatic cancers, the development and commercialization of predictive and robust preclinical in vitro cancer models are of urgent need. In the past decades, the translation of cancer research from 2D to 3D platforms and the development of diverse in vitro cancer models have been well elaborated in an enormous number of reviews. However, the meagre clinical success rate of cancer therapeutics urges the critical introspection of currently available preclinical platforms, including patents, to hasten the development of precision medicine and commercialization of in vitro cancer models. Hence, the present article critically reflects the difficulty of translating cancer therapeutics from discovery to adoption and commercialization in the light of in vitro cancer models as predictive tools. The state of the art of in vitro cancer models is discussed first, followed by identifying the limitations of bench-to-bedside transition. This review tries to establish compatibility between the current findings and obstacles and indicates future directions to accelerate the market penetration, considering the niche market.

Biomimetic hydrogel supports initiation and growth of patient-derived breast tumor organoids

Patient-derived tumor organoids (PDOs) are a highly promising preclinical model that recapitulates the histology, gene expression, and drug response of the donor patient tumor. Currently, PDO culture relies on basement-membrane extract (BME), which suffers from batch-to-batch variability, the presence of xenogeneic compounds and residual growth factors, and poor control of mechanical properties. Additionally, for the development of new organoid lines from patient-derived xenografts, contamination of murine host cells poses a problem. We propose a nanofibrillar hydrogel (EKGel) for the initiation and growth of breast cancer PDOs. PDOs grown in EKGel have histopathologic features, gene expression, and drug response that are similar to those of their parental tumors and PDOs in BME. In addition, EKGel offers reduced batch-to-batch variability, a range of mechanical properties, and suppressed contamination from murine cells. These results show that EKGel is an improved alternative to BME matrices for the initiation, growth, and maintenance of breast cancer PDOs.

Patient-derived tumour organoids are important preclinical models but suffer from variability from the use of basement-membrane extract and cell contamination. Here, the authors report on the development of mimetic nanofibrilar hydrogel which supports tumour organoid growth with reduced batch variability and cell contamination.

Micro and nanofluidics for high throughput drug screening

In this book chapter, we elaborate on the state-of-the-art technology developments in high throughput screening, microfluidics and nanofluidics. This book chapter further elaborated on the application of microfluidics and nanofluidics for high throughput drug screening with respect to communicable diseases and non-communicable diseases such as cancer. As a future perspective, there is tremendous potential for microfluidics and nanofluidics to be applied in high throughput drug screening which could be applied for various biotechnology applications such as in cancer precision medicine, point-of-care diagnostics and imaging. With the integration of Fourth industrial revolution (4IR) technologies with micro and nanofluidics technologies, it envisioned that such integration along with digital health would enable next generation technology development in medical field.

Cancer-on-a-Chip: Models for Studying Metastasis

The microfluidic-based cancer-on-a-chip models work as a powerful tool to study the tumor microenvironment and its role in metastasis. The models recapitulate and systematically simplify the in vitro tumor microenvironment. This enables the study of a metastatic process in unprecedented detail. This review examines the development of cancer-on-a-chip microfluidic platforms at the invasion/intravasation, extravasation, and angiogenesis steps over the last three years. The on-chip modeling of mechanical cues involved in the metastasis cascade are also discussed. Finally, the popular design of microfluidic chip models for each step are discussed along with the challenges and perspectives of cancer-on-a-chip models.

Organ on Chip Technology to Model Cancer Growth and Metastasis

Organ on chip (OOC) has emerged as a major technological breakthrough and distinct model system revolutionizing biomedical research and drug discovery by recapitulating the crucial structural and functional complexity of human organs in vitro. OOC are rapidly emerging as powerful tools for oncology research. Indeed, Cancer on chip (COC) can ideally reproduce certain key aspects of the tumor microenvironment (TME), such as biochemical gradients and niche factors, dynamic cell-cell and cell-matrix interactions, and complex tissue structures composed of tumor and stromal cells. Here, we review the state of the art in COC models with a focus on the microphysiological systems that host multicellular 3D tissue engineering models and can help elucidate the complex biology of TME and cancer growth and progression. Finally, some examples of microengineered tumor models integrated with multi-organ microdevices to study disease progression in different tissues will be presented.

Microfluidic-Based Oxygen (O2) Sensors for On-Chip Monitoring of Cell, Tissue and Organ Metabolism

Oxygen (O₂) quantification is essential for assessing cell metabolism, and its consumption in cell culture is an important indicator of cell viability. Recent advances in microfluidics have made O₂ sensing a crucial feature for organ-on-chip (OOC) devices for various biomedical applications. OOC O₂ sensors can be categorized, based on their transducer type, into two main groups, optical and electrochemical. In this review, we provide an overview of on-chip O₂ sensors integrated with the OOC devices and evaluate their advantages and disadvantages. Recent innovations in optical O₂ sensors integrated with OOCs are discussed in four main categories: (i) basic luminescence-based sensors; (ii) microparticle-based sensors; (iii) nano-enabled sensors; and (iv) commercial probes and portable devices. Furthermore, we discuss recent advancements in electrochemical sensors in five main categories: (i) novel configurations in Clark-type sensors; (ii) novel materials (e.g., polymers, O₂ scavenging and passivation materials); (iii) nano-enabled electrochemical sensors; (iv) novel designs and fabrication techniques; and (v) commercial and portable electrochemical readouts. Together, this review provides a comprehensive overview of the current advances in the design, fabrication and application of optical and electrochemical O₂ sensors.

Roles of Microvesicles in Tumor Progression and Clinical Applications

Microvesicles are extracellular vesicles with diameter ranging from 100 to 1000 nm that are secreted by tumor cells or other cells in the tumor microenvironment. A growing number of studies demonstrate that tumor-derived microvesicles are involved in tumor initiation and progression, as well as drug resistance. In addition, tumor-derived microvesicles carry a variety of immunogenic molecules and inhibit tumor response to immunotherapy; therefore, they can be exploited for use in tumor vaccines. Moreover, because of their high stability, tumor-derived microvesicles extracted from body fluids can be used as biomarkers for cancer diagnosis or assessment of prognosis. Tumor-derived microvesicles can also be deployed to reverse drug resistance of tumor regenerative cells, or to deliver chemotherapeutic drugs and oncolytic adenovirus for the treatment of cancer patients. This review summarizes the general characteristics of tumor-derived microvesicles, focusing on their biological characteristics, their involvement in tumor progression, and their clinical applications.

Leveraging advances in immunopathology and artificial intelligence to analyze in vitro tumor models in composition and space

Cancer is the leading cause of death worldwide. Unfortunately, efforts to understand this disease are confounded by the complex, heterogenous tumor microenvironment (TME). Better understanding of the TME could lead to novel diagnostic, prognostic, and therapeutic discoveries. One way to achieve this involves in vitro tumor models that recapitulate the in vivo TME composition and spatial arrangement. Here, we review the potential of harnessing in vitro tumor models and artificial intelligence to delineate the TME. This includes (i) identification of novel features, (ii) investigation of higher-order relationships, and (iii) analysis and interpretation of multiomics data in a (iv) holistic, objective, reproducible, and efficient manner, which surpasses previous methods of TME analysis. We also discuss limitations of this approach, namely inadequate datasets, indeterminate biological correlations, ethical concerns, and logistical constraints; finally, we speculate on future avenues of research that could overcome these limitations, ultimately translating to improved clinical outcomes.

Technological Advances in Tumor-On-Chip Technology: From Bench to Bedside

Simple Summary

Various 3D in vitro tumor models are rapidly advancing cancer research. Unlike animal models, they can be produced quickly and are amenable to high-throughput studies. Growing tumor spheroids in microfluidic tumor-on-chip platforms has particularly elevated the capabilities of such models. Tumor-on-chip devices can mimic multiple aspects of the dynamic in vivo tumor microenvironment in a precisely controlled manner. Moreover, new technologies for the on- and off-chip analysis of these tumor mimics are continuously emerging. There is thus an urgent need to review the latest developments in this rapidly progressing field. Here, we present an overview of the technological advances in tumor-on-chip technology by reviewing state-of-the-art tools for on-chip analysis. In particular, we evaluate the potential for tumor-on-chip technology to guide personalized cancer therapies. We strive to appeal to cancer researchers and biomedical engineers alike, informing on current progress, while provoking thought on the outstanding developments needed to achieve clinical-stage research.

Abstract

Tumor-on-chip technology has cemented its importance as an in vitro tumor model for cancer research. Its ability to recapitulate different elements of the in vivo tumor microenvironment makes it promising for translational medicine, with potential application in enabling personalized anti-cancer therapies. Here, we provide an overview of the current technological advances for tumor-on-chip generation. To further elevate the functionalities of the technology, these approaches need to be coupled with effective analysis tools. This aspect of tumor-on-chip technology is often neglected in the current literature. We address this shortcoming by reviewing state-of-the-art on-chip analysis tools for microfluidic tumor models. Lastly, we focus on the current progress in tumor-on-chip devices using patient-derived samples and evaluate their potential for clinical research and personalized medicine applications.

Transcending toward Advanced 3D-Cell Culture Modalities: A Review about an Emerging Paradigm in Translational Oncology

Cancer is a disorder characterized by an uncontrollable overgrowth and a fast-moving spread of cells from a localized tissue to multiple organs of the body, reaching a metastatic state. Throughout years, complexity of cancer progression and invasion, high prevalence and incidence, as well as the high rise in treatment failure cases leading to a poor patient prognosis accounted for continuous experimental investigations on animals and cellular models, mainly with 2D- and 3D-cell culture. Nowadays, these research models are considered a main asset to reflect the physiological events in many cancer types in terms of cellular characteristics and features, replication and metastatic mechanisms, metabolic pathways, biomarkers expression, and chemotherapeutic agent resistance. In practice, based on research perspective and hypothesis, scientists aim to choose the best model to approach their understanding and to prove their hypothesis. Recently, 3D-cell models are seen to be highly incorporated as a crucial tool for reflecting the true cancer cell microenvironment in pharmacokinetic and pharmacodynamics studies, in addition to the intensity of anticancer drug response in pharmacogenomics trials. Hence, in this review, we shed light on the unique characteristics of 3D cells favoring its promising usage through a comparative approach with other research models, specifically 2D-cell culture. Plus, we will discuss the importance of 3D models as a direct reflector of the intrinsic cancer cell environment with the newest multiple methods and types available for 3D-cells implementation.

Translating complexity and heterogeneity of pancreatic tumor: 3D in vitro to in vivo models

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive type of cancer with an overall survival rate of less than 7-8%, emphasizing the need for novel effective therapeutics against PDAC. However only a fraction of therapeutics which seemed promising in the laboratory environment will eventually reach the clinic. One of the main reasons behind this low success rate is the complex tumor microenvironment (TME) of PDAC, a highly fibrotic and dense stroma surrounding tumor cells, which supports tumor progression as well as increases the resistance against the treatment. In particular, the growing understanding of the PDAC TME points out a different challenge in the development of efficient therapeutics - a lack of biologically relevant in vitro and in vivo models that resemble the complexity and heterogeneity of PDAC observed in patients. The purpose and scope of this review is to provide an overview of the recent developments in different in vitro and in vivo models, which aim to recapitulate the complexity of PDAC in a laboratory environment, as well to describe how 3D in vitro models can be integrated into drug development pipelines that are already including sophisticated in vivo models. Hereby a special focus will be given on the complexity of in vivo models and the challenges in vitro models face to reach the same levels of complexity in a controllable manner. First, a brief introduction of novel developments in two dimensional (2D) models and ex vivo models is provided. Next, recent developments in three dimensional (3D) in vitro models are described ranging from spheroids, organoids, scaffold models, bioprinted models to organ-on-chip models including a discussion on advantages and limitations for each model. Furthermore, we will provide a detailed overview on the current PDAC in vivo models including chemically-induced models, syngeneic and xenogeneic models, highlighting hetero- and orthotopic, patient-derived tissues (PDX) models, and genetically engineered mouse models. Finally, we will provide a discussion on overall limitations of both, in vitro and in vivo models, and discuss necessary steps to overcome these limitations to reach an efficient drug development pipeline, as well as discuss possibilities to include novel in silico models in the process.

Strategies for inclusion of growth factors into 3D printed bone grafts

There remains a critical need to develop new technologies and materials that can meet the demands of treating large bone defects. The advancement of 3-dimensional (3D) printing technologies has allowed the creation of personalized and customized bone grafts, with specific control in both macro- and micro-architecture, and desired mechanical properties. Nevertheless, the biomaterials used for the production of these bone grafts often possess poor biological properties. The incorporation of growth factors (GFs), which are the natural orchestrators of the physiological healing process, into 3D printed bone grafts, represents a promising strategy to achieve the bioactivity required to enhance bone regeneration. In this review, the possible strategies used to incorporate GFs to 3D printed constructs are presented with a specific focus on bone regeneration. In particular, the strengths and limitations of different methods, such as physical and chemical cross-linking, which are currently used to incorporate GFs to the engineered constructs are critically reviewed. Different strategies used to present one or more GFs to achieve simultaneous angiogenesis and vasculogenesis for enhanced bone regeneration are also covered in this review. In addition, the possibility of combining several manufacturing approaches to fabricate hybrid constructs, which better mimic the complexity of biological niches, is presented. Finally, the clinical relevance of these approaches and the future steps that should be taken are discussed.

Low power consumption photoelectric coupling perovskite memristor with adjustable threshold voltage

Organic-inorganic halide perovskites (OHPs) have been proven to possess unique optical and electrical properties, and achieved more extensive application as excellent materials for memristors in recent years. Based on the traditional OHP-based memristors, the intermediate layer of the memristor was prepared using yttrium oxide (Y₂O₃)/OHP stacking structure in this manuscript. The potential barrier between Y₂O₃and perovskite is relatively high (ΔE_C = 2.13 eV) which leads to comparatively low current of the memristor, thus the power consumption can be reduced. Besides, by changing the external light conditions, one can realize sharp or slow switch between high resistance state (HRS) and low resistance state (LRS), so as to meet the requirement of multilevel data storage, which indicates its promising application prospect in information storage and biological simulation. In addition, based on characteristics of photoelectric coupling, the Y₂O₃/OHP memristor can also achieve the advantage of adjustable threshold voltage. The transition of HRS and LRS can be realized by changing the illumination condition at any voltage, which means the set and reset voltage are not fixed, so that the memristor with adjustable threshold voltage can adapt to various working conditions.

Tumor-on-a-chip: from bioinspired design to biomedical application

Cancer is one of the leading causes of human death, despite enormous efforts to explore cancer biology and develop anticancer therapies. The main challenges in cancer research are establishing an efficient tumor microenvironment in vitro and exploring efficient means for screening anticancer drugs to reveal the nature of cancer and develop treatments. The tumor microenvironment possesses human-specific biophysical and biochemical factors that are difficult to recapitulate in conventional in vitro planar cell models and in vivo animal models. Therefore, model limitations have hindered the translation of basic research findings to clinical applications. In this review, we introduce the recent progress in tumor-on-a-chip devices for cancer biology research, medicine assessment, and biomedical applications in detail. The emerging tumor-on-a-chip platforms integrating 3D cell culture, microfluidic technology, and tissue engineering have successfully mimicked the pivotal structural and functional characteristics of the in vivo tumor microenvironment. The recent advances in tumor-on-a-chip platforms for cancer biology studies and biomedical applications are detailed and analyzed in this review. This review should be valuable for further understanding the mechanisms of the tumor evolution process, screening anticancer drugs, and developing cancer therapies, and it addresses the challenges and potential opportunities in predicting drug screening and cancer treatment.

Visualizing Extracellular Vesicles and Their Function in 3D Tumor Microenvironment Models

Extracellular vesicles (EVs) are cell-derived nanostructures that mediate intercellular communication by delivering complex signals in normal tissues and cancer. The cellular coordination required for tumor development and maintenance is mediated, in part, through EV transport of molecular cargo to resident and distant cells. Most studies on EV-mediated signaling have been performed in two-dimensional (2D) monolayer cell cultures, largely because of their simplicity and high-throughput screening capacity. Three-dimensional (3D) cell cultures can be used to study cell-to-cell and cell-to-matrix interactions, enabling the study of EV-mediated cellular communication. 3D cultures may best model the role of EVs in formation of the tumor microenvironment (TME) and cancer cell-stromal interactions that sustain tumor growth. In this review, we discuss EV biology in 3D culture correlates of the TME. This includes EV communication between cell types of the TME, differences in EV biogenesis and signaling associated with differing scaffold choices and in scaffold-free 3D cultures and cultivation of the premetastatic niche. An understanding of EV biogenesis and signaling within a 3D TME will improve culture correlates of oncogenesis, enable molecular control of the TME and aid development of drug delivery tools based on EV-mediated signaling.

NIR-Controlled Treatment of Multidrug-Resistant Tumor Cells by Mesoporous Silica Capsules Containing Gold Nanorods and Doxorubicin

Multidrug resistance (MDR) is identified as a major impediment to the efficient chemotherapy of cancer, and considerable endeavors have been devoted to reverse MDR containing structuring varieties of multifunctional nanocarriers. Here, a specially light-activated hollow mesoporous silica nanocontainer with an in situ-synthesized Au nanorod (AuNR) core and a surface-modified hairpin structure DNA gatekeeper is reported for treating MDR tumor cells. In this system, the AuNR only fills part of the space in hollow mesoporous silica due to its controllable size, and the remaining space is used to load enough DOX. By controlling the near-infrared (NIR) laser intensity and exposure duration, the configuration of hairpin-structured DNA (T_m = 51.4 °C) can change reversibly and then trigger the controllable intracellular release of DOX, leading to a significantly enhanced chemotherapeutic efficacy and adjustable photothermal treatment for multidrug-resistant cancer cells. The in vitro experiments showed that this system could effectively overcome the MDR of HepG2-adm cells (a MDR cell line of human hepatocarcinoma cells) by the increased concentration of DOX intracellularly and the photothermal conversion of AuNRs, even at a low concentration (e.g., 30 μg mL^-1). Therefore, this NIR-triggered chemo-photothermal synergistic treatment system can be used as a promising efficient strategy in reversing the multidrug resistance for cancer therapy.

Construction of cancer-on-a-chip for drug screening

Cancer-on-a-chip has effectively contributed to the development of drug screening, holding great promise for more convenient and reliable drug development as well as personalized drug administration.

Breast tumor-on-chip models: From disease modeling to personalized drug screening

Breast cancer is one of the leading causes of mortality worldwide being the most common cancer among women. Despite the significant progress obtained during the past years in the understanding of breast cancer pathophysiology, women continue to die from it. Novel tools and technologies are needed to develop better diagnostic and therapeutic approaches, and to better understand the molecular and cellular players involved in the progression of this disease. Typical methods employed by the pharmaceutical industry and laboratories to investigate breast cancer etiology and evaluate the efficiency of new therapeutic compounds are still based on traditional tissue culture flasks and animal models, which have certain limitations. Recently, tumor-on-chip technology emerged as a new generation of in vitro disease model to investigate the physiopathology of tumors and predict the efficiency of drugs in a native-like microenvironment. These microfluidic systems reproduce the functional units and composition of human organs and tissues, and importantly, the rheological properties of the native scenario, enabling precise control over fluid flow or local gradients. Herein, we review the most recent works related to breast tumor-on-chip for disease modeling and drug screening applications. Finally, we critically discuss the future applications of this emerging technology in breast cancer therapeutics and drug development.

Tumor Microenvironment and the Role of Artificial Intelligence in Breast Cancer Detection and Prognosis

A critical knowledge gap has been noted in breast cancer detection, prognosis, and evaluation between tumor microenvironment and associated neoplasm. Artificial intelligence (AI) has multiple subsets or methods for data extraction and evaluation, including artificial neural networking, which allows computational foundations, similar to neurons, to make connections and new neural pathways during data set training. Deep machine learning and AI hold great potential to accurately assess tumor microenvironment models employing vast data management techniques. Despite the significant potential AI holds, there is still much debate surrounding the appropriate and ethical curation of medical data from picture archiving and communication systems. AI output's clinical significance depends on its human predecessor's data training sets. Integration between biomarkers, risk factors, and imaging data will allow the best predictor models for patient-based outcomes.

Environmental toxicology wars: Organ-on-a-chip for assessing the toxicity of environmental pollutants

Environmental pollution is a widespread problem, which has seriously threatened human health and led to an increase of human diseases. Therefore, it is critical to evaluate environmental pollutants quickly and efficiently. Because of obvious inter-species differences between animals and humans, and lack of physiologically-relevant microenvironment, animal models and in vitro two-dimensional (2D) models can not accurately describe toxicological effects and predicting actual in vivo responses. To make up the limitations of conventional environmental toxicology screening, organ-on-a-chip (OOC) systems are increasingly developing. OOC systems can provide a well-organized architecture with comparable to the complex microenvironment in vivo and generate realistic responses to environmental pollutants. The feasibility, adjustability and reliability of OCC systems make it possible to offer new opportunities for environmental pollutants screening, which can study their metabolism, collective response, and fate in vivo. Further progress can address the challenges to make OCC systems better investigate and evaluate environmental pollutants with high predictive power.

Advanced 3D Cell Culture Techniques in Micro-Bioreactors, Part II: Systems and Applications

In this second part of our systematic review on the research area of 3D cell culture in micro-bioreactors we give a detailed description of the published work with regard to the existing micro-bioreactor types and their applications, and highlight important results gathered with the respective systems. As an interesting detail, we found that micro-bioreactors have already been used in SARS-CoV research prior to the SARS-CoV2 pandemic. As our literature research revealed a variety of 3D cell culture configurations in the examined bioreactor systems, we defined in review part one “complexity levels” by means of the corresponding 3D cell culture techniques applied in the systems. The definition of the complexity is thereby based on the knowledge that the spatial distribution of cell-extracellular matrix interactions and the spatial distribution of homologous and heterologous cell–cell contacts play an important role in modulating cell functions. Because at least one of these parameters can be assigned to the 3D cell culture techniques discussed in the present review, we structured the studies according to the complexity levels applied in the MBR systems.