Recommended Guidelines for Developing, Qualifying, and Implementing Complex In Vitro Models (CIVMs) for Drug Discovery

Impact of mechanical cues on key cell functions and cell-nanoparticle interactions

In recent years, it has been recognized that mechanical forces play an important regulative role in living organisms and possess a direct impact on crucial cell functions, ranging from cell growth to maintenance of tissue homeostasis. Advancements in mechanobiology have revealed the profound impact of mechanical signals on diverse cellular responses that are cell type specific. Notably, numerous studies have elucidated the pivotal role of different mechanical cues as regulatory factors influencing various cellular processes, including cell spreading, locomotion, differentiation, and proliferation. Given these insights, it is unsurprising that the responses of cells regulated by physical forces are intricately linked to the modulation of nanoparticle uptake kinetics and processing. This complex interplay underscores the significance of understanding the mechanical microenvironment in shaping cellular behaviors and, consequently, influencing how cells interact with and process nanoparticles. Nevertheless, our knowledge on how localized physical forces affect the internalization and processing of nanoparticles by cells remains rather limited. A significant gap exists in the literature concerning a systematic analysis of how mechanical cues might bias the interactions between nanoparticles and cells. Hence, our aim in this review is to provide a comprehensive and critical analysis of the existing knowledge regarding the influence of mechanical cues on the complicated dynamics of cell-nanoparticle interactions. By addressing this gap, we would like to contribute to a detailed understanding of the role that mechanical forces play in shaping the complex interplay between cells and nanoparticles.

Complex In Vitro Model Characterization for Context of Use in Toxicologic Pathology: Use Cases by Collaborative Teams of Biologists, Bioengineers, and Pathologists

Complex in vitro models (CIVMs) offer the potential to increase the clinical relevance of preclinical efficacy and toxicity assessments and reduce the reliance on animals in drug development. The European Society of Toxicologic Pathology (ESTP) and Society for Toxicologic Pathology (STP) are collaborating to highlight the role of pathologists in the development and use of CIVM. Pathologists are trained in comparative animal medicine which enhances their understanding of mechanisms of human and animal diseases, thus allowing them to bridge between animal models and humans. This skill set is important for CIVM development, validation, and data interpretation. Ideally, diverse teams of scientists, including engineers, biologists, pathologists, and others, should collaboratively develop and characterize novel CIVM, and collectively assess their precise use cases (context of use). Implementing a morphological CIVM evaluation should be essential in this process. This requires robust histological technique workflows, image analysis techniques, and needs correlation with translational biomarkers. In this review, we demonstrate how such tissue technologies and analytics support the development and use of CIVM for drug efficacy and safety evaluations. We encourage the scientific community to explore similar options for their projects and to engage with health authorities on the use of CIVM in benefit-risk assessment.

Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease

Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.

Recommendations on fit-for-purpose criteria to establish quality management for microphysiological systems and for monitoring their reproducibility

Cell culture technology has evolved, moving from single-cell and monolayer methods to 3D models like reaggregates, spheroids, and organoids, improved with bioengineering like microfabrication and bioprinting. These advancements, termed microphysiological systems (MPSs), closely replicate tissue environments and human physiology, enhancing research and biomedical uses. However, MPS complexity introduces standardization challenges, impacting reproducibility and trust. We offer guidelines for quality management and control criteria specific to MPSs, facilitating reliable outcomes without stifling innovation. Our fit-for-purpose recommendations provide actionable advice for achieving consistent MPS performance.

Beyond the hype and toward application: liver complex in vitro models in preclinical drug safety

INTRODUCTION

Drug induced Liver-Injury (DILI) is a leading cause of drug attrition and complex in vitro models (CIVMs), including three dimensional (3D) spheroids, 3D bio printed tissues and flow-based systems, could improve preclinical prediction. Although CIVMs have demonstrated good sensitivity and specificity in DILI detection their adoption remains limited.

AREAS COVERED

This article describes DILI, the challenges with its prediction and the current strategies and models that are being used. It reviews data from industry-FDA collaborations and strategic partnerships and finishes with an outlook of CIVMs in preclinical toxicity testing. Literature searches were performed using PubMed and Google Scholar while product information was collected from manufacturer websites.

EXPERT OPINION

Liver CIVMs are promising models for predicting DILI although, a decade after their introduction, routine use by the pharmaceutical industry is limited. To accelerate their adoption, several industry-regulator-developer partnerships or consortia have been established to guide the development and qualification. Beyond this, liver CIVMs should continue evolving to capture greater immunological mimicry while partnering with computational approaches to deliver systems that change the paradigm of predicting DILI.

Progress in developing microphysiological systems for biological product assessment

Microphysiological systems (MPS), also known as miniaturized physiological environments, have been engineered to create and study functional tissue units capable of replicating organ-level responses in specific contexts. The MPS has the potential to provide insights about the safety, characterization, and effectiveness of medical products that are different and complementary to insights gained from traditional testing systems, which can help facilitate the transition of potential medical products from preclinical phases to clinical trials, and eventually to market. While many MPS are versatile and can be used in various applications, most of the current applications have primarily focused on drug discovery and testing. Yet, there is a limited amount of research available that demonstrates the use of MPS in assessing biological products such as cellular and gene therapies. This review paper aims to address this gap by discussing recent technical advancements in MPS and their potential for assessing biological products. We further discuss the challenges and considerations involved in successful translation of MPS into mainstream product testing.

Current advances in the use of bioluminescence assays for drug discovery: an update of the last ten years

INTRODUCTION

Bioluminescence is a well-established optical detection technique widely used in several bioanalytical applications, including high-throughput and high-content screenings. Thanks to advances in synthetic biology techniques and deep learning, a wide portfolio of luciferases is now available with tuned emission wavelengths, kinetics, and high stability. These luciferases can be implemented in the drug discovery and development pipeline, allowing high sensitivity and multiplexing capability.

AREAS COVERED

This review summarizes the latest advancements of bioluminescent systems as toolsets in drug discovery programs for in vitro applications. Particular attention is paid to the most advanced bioluminescence-based technologies for drug screening over the past 10 years (from 2013 to 2023) such as cell-free assays, cell-based assays based on genetically modified cells, bioluminescence resonance energy transfer, and protein complementation assays in 2D and 3D cell models.

EXPERT OPINION

The availability of tuned bioluminescent proteins with improved emission and stability properties is vital for the development of bioluminescence assays for drug discovery, spanning from reporter gene technology to protein-protein techniques. Further studies, combining machine learning with synthetic biology, will be necessary to obtain new tools for sustainable and highly predictive bioluminescent drug discovery platforms.

Functional brain region-specific neural spheroids for modeling neurological diseases and therapeutics screening

3D spheroids have emerged as powerful drug discovery tools given their high-throughput screening (HTS) compatibility. Here, we describe a method for generating functional neural spheroids by cell-aggregation of differentiated human induced pluripotent stem cell (hiPSC)-derived neurons and astrocytes at cell type compositions mimicking specific regions of the human brain. Recordings of intracellular calcium oscillations were used as functional assays, and the utility of this spheroids system was shown through disease modeling, drug testing, and formation of assembloids to model neurocircuitry. As a proof of concept, we generated spheroids incorporating neurons with Alzheimer's disease-associated alleles, as well as opioid use disorder modeling spheroids induced by chronic treatment of a mu-opioid receptor agonist. We reversed baseline functional deficits in each pilot disease model with clinically approved treatments and showed that assembloid activity can be chemogenetically manipulated. Here, we lay the groundwork for brain region-specific neural spheroids as a robust functional assay platform for HTS studies.

Report of the Assay Guidance Workshop on 3-Dimensional Tissue Models for Antiviral Drug Development

The National Center for Advancing Translational Sciences (NCATS) Assay Guidance Manual (AGM) Workshop on 3D Tissue Models for Antiviral Drug Development, held virtually on 7-8 June 2022, provided comprehensive coverage of critical concepts intended to help scientists establish robust, reproducible, and scalable 3D tissue models to study viruses with pandemic potential. This workshop was organized by NCATS, the National Institute of Allergy and Infectious Diseases, and the Bill and Melinda Gates Foundation. During the workshop, scientific experts from academia, industry, and government provided an overview of 3D tissue models' utility and limitations, use of existing 3D tissue models for antiviral drug development, practical advice, best practices, and case studies about the application of available 3D tissue models to infectious disease modeling. This report includes a summary of each workshop session as well as a discussion of perspectives and challenges related to the use of 3D tissues in antiviral drug discovery.

Replacement, Reduction, and Refinement of Animal Experiments in Anticancer Drug Development: The Contribution of 3D In Vitro Cancer Models in the Drug Efficacy Assessment

In the last decades three-dimensional (3D) in vitro cancer models have been proposed as a bridge between bidimensional (2D) cell cultures and in vivo animal models, the gold standards in the preclinical assessment of anticancer drug efficacy. 3D in vitro cancer models can be generated through a multitude of techniques, from both immortalized cancer cell lines and primary patient-derived tumor tissue. Among them, spheroids and organoids represent the most versatile and promising models, as they faithfully recapitulate the complexity and heterogeneity of human cancers. Although their recent applications include drug screening programs and personalized medicine, 3D in vitro cancer models have not yet been established as preclinical tools for studying anticancer drug efficacy and supporting preclinical-to-clinical translation, which remains mainly based on animal experimentation. In this review, we describe the state-of-the-art of 3D in vitro cancer models for the efficacy evaluation of anticancer agents, focusing on their potential contribution to replace, reduce and refine animal experimentations, highlighting their strength and weakness, and discussing possible perspectives to overcome current challenges.

Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond

The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.

Three-Dimensional Spheroids for Cancer Research

In vitro cell culture is one of the most widely used tools used today for increasing our understanding of various things such as protein production, mechanisms of drug action, tissue engineering, and overall cellular biology. For the past decades, however, cancer researchers have relied heavily on conventional two-dimensional (2D) monolayer culture techniques to test a variety of aspects of cancer research ranging from the cytotoxic effects of antitumor drugs to the toxicity of diagnostic dyes and contact tracers. However, many promising cancer therapies have either weak or no efficacy in real-life conditions, therefore delaying or stopping altogether their translating to the clinic. This is, in part, due to the reductionist 2D cultures used to test these materials, which lack appropriate cell-cell contacts, have altered signaling, do not represent the natural tumor microenvironment, and have different drug responses, due to their reduced malignant phenotype when compared to real in vivo tumors. With the most recent advances, cancer research has moved into 3D biological investigation. Three-dimensional (3D) cultures of cancer cells not only recapitulate the in vivo environment better than their 2D counterparts, but they have, in recent years, emerged as a relatively low-cost and scientifically accurate methodology for studying cancer. In this chapter, we highlight the importance of 3D culture, specifically 3D spheroid culture, reviewing some key methodologies for forming 3D spheroids, discussing the experimental tools that can be used in conjunction with 3D spheroids and finally their applications in cancer research.

Critical design parameters to develop biomimetic organ-on-a-chip models for the evaluation of the safety and efficacy of nanoparticles

INTRODUCTION

Organ-on-a-chip (OOC) models are based on microfluidics and can recapitulate the healthy and diseased microstructure of organs¹ and tissues and the dynamic microenvironment inside the human body. However, the use of OOC models to evaluate the safety and efficacy of nanoparticles (NPs) is still in the early stages.

AREAS COVERED

The different design parameters of the microfluidic chip and the mechanical forces generated by fluid flow play a pivotal role in simulating the human environment. This review discusses the role of different key parameters on the performance of OOC models. These include the flow pattern, flow rate, shear stress (magnitude, rate, and distribution), viscosity of the media, and the microchannel dimensions and shape. We also discuss how the shear stress and other mechanical forces affect the transport of NPs across biological barriers, cell uptake, and their biocompatibility.

EXPERT OPINION

We describe several good practices and design parameters to consider for future OOC research. We submit that following these recommendations will help realize the full potential of the OOC models in the preclinical evaluation of novel therapies, including NPs.

Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology

BACKGROUND

Conventional preclinical models often miss drug toxicities, meaning the harm these drugs pose to humans is only realized in clinical trials or when they make it to market. This has caused the pharmaceutical industry to waste considerable time and resources developing drugs destined to fail. Organ-on-a-Chip technology has the potential improve success in drug development pipelines, as it can recapitulate organ-level pathophysiology and clinical responses; however, systematic and quantitative evaluations of Organ-Chips' predictive value have not yet been reported.

METHODS

870 Liver-Chips were analyzed to determine their ability to predict drug-induced liver injury caused by small molecules identified as benchmarks by the Innovation and Quality consortium, who has published guidelines defining criteria for qualifying preclinical models. An economic analysis was also performed to measure the value Liver-Chips could offer if they were broadly adopted in supporting toxicity-related decisions as part of preclinical development workflows.

RESULTS

Here, we show that the Liver-Chip met the qualification guidelines across a blinded set of 27 known hepatotoxic and non-toxic drugs with a sensitivity of 87% and a specificity of 100%. We also show that this level of performance could generate over $3 billion annually for the pharmaceutical industry through increased small-molecule R&D productivity.

CONCLUSIONS

The results of this study show how incorporating predictive Organ-Chips into drug development workflows could substantially improve drug discovery and development, allowing manufacturers to bring safer, more effective medicines to market in less time and at lower costs.

Predictive validity in drug discovery: what it is, why it matters and how to improve it

Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which 'predictive validity' is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.

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.

Using artificial intelligence technology to fight COVID-19: a review

In late December 2019, a new type of coronavirus was discovered, which was later named severe acute respiratory syndrome coronavirus 2(SARS-CoV-2). Since its discovery, the virus has spread globally, with 2,975,875 deaths as of 15 April 2021, and has had a huge impact on our health systems and economy. How to suppress the continued spread of new coronary pneumonia is the main task of many scientists and researchers. The introduction of artificial intelligence technology has provided a huge contribution to the suppression of the new coronavirus. This article discusses the main application of artificial intelligence technology in the suppression of coronavirus from three major aspects of identification, prediction, and development through a large amount of literature research, and puts forward the current main challenges and possible development directions. The results show that it is an effective measure to combine artificial intelligence technology with a variety of new technologies to predict and identify COVID-19 patients.

Microphysiological Systems: Stakeholder Challenges to Adoption in Drug Development

Microphysiological systems (MPS) or tissue chips/organs-on-chips are novel in vitro models that emulate human physiology at the most basic functional level. In this review, we discuss various hurdles to widespread adoption of MPS technology focusing on issues from multiple stakeholder sectors, e.g., academic MPS developers, commercial suppliers of platforms, the pharmaceutical and biotechnology industries, and regulatory organizations. Broad adoption of MPS technology has thus far been limited by a gap in translation between platform developers, end-users, regulatory agencies, and the pharmaceutical industry. In this brief review, we offer a perspective on the existing barriers and how end-users may help surmount these obstacles to achieve broader adoption of MPS technology.

Pre-Clinical In Vitro Models Used in Cancer Research: Results of a Worldwide Survey

Simple Summary

Cancer cell lines, grown on plastic dishes i.e., two-dimensional (2D), are routinely used in cancer research, e.g., when evaluating the effectiveness of potential anti-cancer drugs before proceeding to studies in animal models and then human clinical trials. Stop/go decisions are generally made from these initial studies. As only ~10% of potential anti-cancer drugs succeed during clinical development, this suggests that these models are inadequate. Cells grown as three-dimensional (3D) models, akin to a tumor mass and with other cells that would naturally occur in its environment, should be more clinically relevant. We performed a worldwide survey, open to cancer researchers at all stages and in all settings, to find out what models they use; for what purposes, and why they chose those models. The majority reported using 2D models only, mainly due to lack of experience and costs but expressed interest in 3D cultures. Guidelines on how to develop such models cost-effectively are needed.

Abstract

To develop and subsequently get cancer researchers to use organotypic three-dimensional (3D) models that can recapitulate the complexity of human in vivo tumors in an in vitro setting, it is important to establish what in vitro model(s) researchers are currently using and the reasons why. Thus, we developed a survey on this topic, obtained ethics approval, and circulated it throughout the world. The survey was completed by 101 researchers, across all career stages, in academia, clinical or industry settings. It included 40 questions, many with multiple options. Respondents reported on their field of cancer research; type of cancers studied; use of two-dimensional (2D)/monolayer, 2.5D and/or 3D cultures; if using co-cultures, the cell types(s) they co-culture; if using 3D cultures, whether these involve culturing the cells in a particular way to generate spheroids, or if they use additional supports/scaffolds; techniques used to analyze the 2D/2.5D/3D; and their downstream applications. Most researchers (>66%) only use 2D cultures, mainly due to lack of experience and costs. Despite most cancer researchers currently not using the 3D format, >80% recognize their importance and would like to progress to using 3D models. This suggests an urgent need to standardize reliable, robust, reproducible methods for establishing cost-effective 3D cell culture models and their subsequent characterization.

Evaluation of a Three-Dimensional Primary Human Hepatocyte Spheroid Model: Adoption and Industrialization for the Enhanced Detection of Drug-Induced Liver Injury

Drug-induced liver injury is a leading cause of compound attrition during both preclinical and clinical drug development, and early strategies are in place to tackle this recurring problem. Human-relevant in vitro models that are more predictive of hepatotoxicity hazard identification, and that could be employed earlier in the drug discovery process, would improve the quality of drug candidate selection and help reduce attrition. We present an evaluation of four human hepatocyte in vitro models of increasing culture complexity (i.e., two-dimensional (2D) HepG2 monolayers, hepatocyte sandwich cultures, three-dimensional (3D) hepatocyte spheroids, and precision-cut liver slices), using the same tool compounds, viability end points, and culture time points. Having established the improved prediction potential of the 3D hepatocyte spheroid model, we describe implementing this model into an industrial screening setting, where the challenge was matching the complexity of the culture system with the scale and throughput required. Following further qualification and miniaturization into a 384-well, high-throughput screening format, data was generated on 199 compounds. This clearly demonstrated the ability to capture a greater number of severe hepatotoxins versus the current routine 2D HepG2 monolayer assay while continuing to flag no false-positive compounds. The industrialization and miniaturization of the 3D hepatocyte spheroid complex in vitro model demonstrates a significant step toward reducing drug attrition and improving the quality and safety of drugs, while retaining the flexibility for future improvements, and has replaced the routine use of the 2D HepG2 monolayer assay at GlaxoSmithKline.

Organ-on-chip applications in drug discovery: an end user perspective

Organ-on-chip (OoC) systems are in vitro microfluidic models that mimic the microstructures, functions and physiochemical environments of whole living organs more accurately than two-dimensional models. While still in their infancy, OoCs are expected to bring ground-breaking benefits to a myriad of applications, enabling more human-relevant candidate drug efficacy and toxicity studies, and providing greater insights into mechanisms of human disease. Here, we explore a selection of applications of OoC systems. The future directions and scope of implementing OoCs across the drug discovery process are also discussed.

The emergence of imaging mass spectrometry in drug discovery and development: Making a difference by driving decision making

The pharmaceutical industry is a dynamic, science-driven business constantly under pressure to innovate and morph into a higher performing organization. Innovations can include the implementation of new technologies, adopting new scientific methods, changing the decision-making process, compressing timelines, or making changes to the organizational structure. The drivers for the constant focus on performance improvement are the high cost of R&D as well as the lengthy timelines required to deliver new medicines for unmet needs. Successful innovations are measured against both the quality and quantity of potential new medicines in the pipeline and the delivery to patients. In this special feature article, we share our collective experience implementing matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) technology as an innovative approach to better understand the tissue biodistribution of drugs in the early phases of drug discovery to establish pharmacokinetic-pharmacodynamic (PK-PD) relationships, as well as in the development phase to understand pharmacology, toxicology, and disease pathogenesis. In our experience, successful implementation of MALDI IMS in support of therapeutic programs can be measured by the impact IMS studies have on driving decision making in pipeline progression. This provides a direct quantifiable measurement of the return to the organization for the investment in IMS. We have included discussion not only on the technical merits of IMS study conduct but also the key elements of setting study objectives, building collaborations, data integration into the medicine progression milestones, and potential pitfalls when trying to establish IMS in the pharmaceutical arena. We categorized IMS study types into five groups that parallel pipeline progression from the earliest phases of discovery to late stages of preclinical development. We conclude the article with some perspectives on how we see MALDI IMS maintaining relevance and becoming further embedded as an essential tool in the constantly changing environment of the pharmaceutical industry.

Operationalizing the Use of Biofabricated Tissue Models as Preclinical Screening Platforms for Drug Discovery and Development

A wide range of complex in vitro models (CIVMs) are being developed for scientific research and preclinical drug efficacy and safety testing. The hope is that these CIVMs will mimic human physiology and pathology and predict clinical responses more accurately than the current cellular models. The integration of these CIVMs into the drug discovery and development pipeline requires rigorous scientific validation, including cellular, morphological, and functional characterization; benchmarking of clinical biomarkers; and operationalization as robust and reproducible screening platforms. It will be critical to establish the degree of physiological complexity that is needed in each CIVM to accurately reproduce native-like homeostasis and disease phenotypes, as well as clinical pharmacological responses. Choosing which CIVM to use at each stage of the drug discovery and development pipeline will be driven by a fit-for-purpose approach, based on the specific disease pathomechanism to model and screening throughput needed. Among the different CIVMs, biofabricated tissue equivalents are emerging as robust and versatile cellular assay platforms. Biofabrication technologies, including bioprinting approaches with hydrogels and biomaterials, have enabled the production of tissues with a range of physiological complexity and controlled spatial arrangements in multiwell plate platforms, which make them amenable for medium-throughput screening. However, operationalization of such 3D biofabricated models using existing automation screening platforms comes with a unique set of challenges. These challenges will be discussed in this perspective, including examples and thoughts coming from a laboratory dedicated to designing and developing assays for automated screening.

Microphysiological systems: What it takes for community adoption

Microphysiological systems (MPS) are promising in vitro tools which could substantially improve the drug development process, particularly for underserved patient populations such as those with rare diseases, neural disorders, and diseases impacting pediatric populations. Currently, one of the major goals of the National Institutes of Health MPS program, led by the National Center for Advancing Translational Sciences (NCATS), is to demonstrate the utility of this emerging technology and help support the path to community adoption. However, community adoption of MPS technology has been hindered by a variety of factors including biological and technological challenges in device creation, issues with validation and standardization of MPS technology, and potential complications related to commercialization. In this brief Minireview, we offer an NCATS perspective on what current barriers exist to MPS adoption and provide an outlook on the future path to adoption of these in vitro tools.

Generation and Culture of Lingual Organoids Derived from Adult Mouse Taste Stem Cells

The sense of taste is mediated by taste buds on the tongue, which are composed of rapidly renewing taste receptor cells (TRCs). This continual turnover is powered by local progenitor cells and renders taste function prone to disruption by a multitude of medical treatments, which in turn severely impacts the quality of life. Thus, studying this process in the context of drug treatment is vital to understanding if and how taste progenitor function and TRC production are affected. Given the ethical concerns and limited availability of human taste tissue, mouse models, which have a taste system similar to humans, are commonly used. Compared to in vivo methods, which are time-consuming, expensive, and not amenable to high throughput studies, murine lingual organoids can enable experiments to be run rapidly with many replicates and fewer mice. Here, previously published protocols have been adapted and a standardized method for generating taste organoids from taste progenitor cells isolated from the circumvallate papilla (CVP) of adult mice is presented. Taste progenitor cells in the CVP express LGR5 and can be isolated via EGFP fluorescence-activated cell sorting (FACS) from mice carrying an Lgr5^{EGFP-IRES-CreERT2} allele. Sorted cells are plated onto a matrix gel-based 3D culture system and cultured for 12 days. Organoids expand for the first 6 days of the culture period via proliferation and then enter a differentiation phase, during which they generate all three taste cell types along with non-taste epithelial cells. Organoids can be harvested upon maturation at day 12 or at any time during the growth process for RNA expression and immunohistochemical analysis. Standardizing culture methods for production of lingual organoids from adult stem cells will improve reproducibility and advance lingual organoids as a powerful drug screening tool in the fight to help patients experiencing taste dysfunction.

Academic collaborative models fostering the translation of physiological in vitro systems from basic research into drug discovery

The success of preclinical drug discovery strongly relies on the ability of experimental models to resemble human pathophysiology. The number of compounds receiving approval for clinical use is limited, and this has led to the development of more physiologically relevant cellular models aimed at making preclinical results more prone to be successfully translated into clinical use. In this review, we summarize the technologies available in the field of high-throughput screening (HTS) using complex cellular models, and describe collaborative initiatives, such as EU-OPENSCREEN, which can efficiently support researchers to easily access state-of-the-art chemical biology platforms for improving the drug discovery process.

Advanced Multi-Dimensional Cellular Models as Emerging Reality to Reproduce In Vitro the Human Body Complexity

A hot topic in biomedical science is the implementation of more predictive in vitro models of human tissues to significantly improve the knowledge of physiological or pathological process, drugs discovery and screening. Bidimensional (2D) culture systems still represent good high-throughput options for basic research. Unfortunately, these systems are not able to recapitulate the in vivo three-dimensional (3D) environment of native tissues, resulting in a poor in vitro–in vivo translation. In addition, intra-species differences limited the use of animal data for predicting human responses, increasing in vivo preclinical failures and ethical concerns. Dealing with these challenges, in vitro 3D technological approaches were recently bioengineered as promising platforms able to closely capture the complexity of in vivo normal/pathological tissues. Potentially, such systems could resemble tissue-specific extracellular matrix (ECM), cell–cell and cell–ECM interactions and specific cell biological responses to mechanical and physical/chemical properties of the matrix. In this context, this review presents the state of the art of the most advanced progresses of the last years. A special attention to the emerging technologies for the development of human 3D disease-relevant and physiological models, varying from cell self-assembly (i.e., multicellular spheroids and organoids) to the use of biomaterials and microfluidic devices has been given.