Drug Research And Development Research Articles

IMMU-66. PHYSIOLOGICALLY RELEVANT MURINE GLIOMA MODELS THAT REPRESENT GBM HETEROGENEITY AND ENABLE THE NEXT GENERATION OF DISCOVERY AND THERAPY EFFICACY STUDIES

Abstract To transform cancer drug development pipelines and gain a deeper mechanistic understanding of cancer: immune interactions, we desperately need better experimental models that faithfully recapitulate the cellular heterogeneity, complex and dynamic cell:cell interactions, and therapy responses in humans. To address this need, we developed and characterized a cohort of five new mouse glioma models in the C57BL6/j background. These transplantable tumorsphere models were derived from spontaneous gliomas that formed in genetically engineered mouse models with different oncogenic drivers, including EGFRviii, PDGFA, PTEN, p53, and NF1 mutations. They show a range of responses to standard of care (TMZ+IR) similar to GBM patients and all are resistant, to immune checkpoint inhibitors, anti-PD1+CTLA4, treatment, unlike the GL261 model and similar to GBM patients. We deeply characterized each model, including immune phenotyping and single-cell RNA-sequencing analyses. We also performed a cross-species comparison of human and mouse GBM and stromal cell phenotypes at the single-cell level. Our results show that new mouse models recapitulate the heterogeneity of glioma cell states observed in individual patients and, importantly, also the complex and immune suppressive TME. We demonstrate at the single-cell level that glioma-associated T and myeloid cell types are molecularly similar and conserved across species. Leveraging these data sets and experimental models, we are testing now novel immunotherapy targets for GBM.

A microvascularized in vitro liver model for disease modeling and drug discovery

Drug discovery for complex liver diseases faces alarming attrition rates. The lack of non-clinical models that recapitulate key aspects of liver (patho)-physiology is likely contributing to the inefficiency of developing effective treatments. Of particular notice is the common omission of an organized microvascular component despite its importance in maintaining liver function and its involvement in the development of several pathologies. Increasing the complexity ofin vitromodels is usually associated with a lack of scalability and robustness which hinders their implementation in drug development pipelines. Here, we describe a comprehensive liver microphysiological system comprising stellates, liver-derived endothelial cells and hepatocytes conceived within a scalable and automated platform. We show that endothelial cells self-organize in a microvascular network when co-cultured with stellates in a hydrogel. In a tri-culture, hepatocytes polarize accordingly, with a basolateral side facing blood vessels and an apical side facing bile-canaliculi-like structures. Stellates interact and surround the hollow microvessels. Steatosis was induced by exogenous administration of fatty acids which could be prevented by co-administration of firsocostat. Administration of TGF-βresulted in an activated stellate cells phenotype which could be prevented by the co-administration of SB-431542. The model was implemented on a microtiter plate format comprising 64 chips which enabled the development of a fully automated, multiplexed fibrosis assay with a robust Z' factor suitable for high-throughput applications.

First-in-Class Small Molecule Degrader of Pregnane X Receptor Enhances Chemotherapy Efficacy.

Pregnane X receptor (PXR) is a ligand-activated transcription factor that binds diverse compounds and upregulates drug metabolism machinery in response. PXR activation is detrimental to drug efficacy and safety because it reduces active drug concentrations and increases reactive metabolites, leading to toxicity and/or drug-drug interactions. Thus, effort must be expended in drug development pipelines to assess PXR activation by lead candidates and chemically modify agonists to reduce PXR liabilities while maintaining on-target potencies. Coadministration of drugs with PXR antagonists could prevent PXR-mediated metabolism events, but such compounds are rare and may themselves be converted to agonists by metabolic enzymes or PXR mutations. Here, we report the design, synthesis, optimization, and biological validation of proteolysis targeting chimeras that induce PXR degradation through E3 ubiquitin ligase recruitment. PXR degradation blocks agonist-induced gene expression and enhances anticancer effects of the chemotherapy paclitaxel, a known PXR agonist and substrate of downstream metabolic enzymes.

Towards next-generation treatment options to combat Plasmodium falciparum malaria.

Malaria, which is caused by infection of red blood cells with Plasmodium parasites, can be fatal in non-immune individuals if left untreated. The recent approval of the pre-erythrocytic vaccines RTS, S/AS01 and R21/Matrix-M has ushered in hope of substantial reductions in mortality rates, especially when combined with other existing interventions. However, the efficacy of these vaccines is partial, and chemotherapy remains central to malaria treatment and control. For many antimalarial drugs, clinical efficacy has been compromised by the emergence of drug-resistant Plasmodium falciparum strains. Therefore, there is an urgent need for new antimalarial medicines to complement the existing first-line artemisinin-based combination therapies. In this Review, we discuss various opportunities to expand the present malaria treatment space, appraise the current antimalarial drug development pipeline and highlight examples of promising targets. We also discuss other approaches to circumvent antimalarial resistance and how potency against drug-resistant parasites could be retained.

Informatics and Artificial Intelligence-Guided Assessment of the Regulatory and Translational Research Landscape of First-in-Class Oncology Drugs in the United States, 2018-2022.

Cancer drug development remains a critical but challenging process that affects millions of patients and their families. Using biomedical informatics and artificial intelligence (AI) approaches, we assessed the regulatory and translational research landscape defining successful first-in-class drugs for patients with cancer. This is a retrospective observational study of all novel first-in-class drugs approved by the US Food and Drug Administration (FDA) from 2018 to 2022, stratified by cancer versus noncancer drugs. A biomedical informatics pipeline leveraging interoperability standards and ChatGPT performed integration and analysis of public databases provided by the FDA, National Institutes of Health, and WHO. Between 2018 and 2022, the FDA approved a total of 247 novel drugs, of which 107 (43.3%) were first-in-class drugs involving a new biologic target. Of these first-in-class drugs, 30 (28%) treatments were indicated for patients with cancer, including 19 (63.3%) for solid tumors and the remaining 11 (36.7%) for hematologic cancers. A median of 68 publications of basic, clinical, and other relevant translational science preceded successful FDA approval of first-in-class cancer drugs, with oncology-related treatments involving fewer median years of target-based research than therapies not related to cancer (33 v 43 years; P < .05). Overall, 94.4% of first-in-class drugs had at least 25 years of target-related research papers, while 85.5% of first-in-class drugs had at least 10 years of translational research publications. Novel first-in-class cancer treatments are defined by diverse clinical indications, personalized molecular targets, dependence on expedited regulatory pathways, and translational research metrics reflecting this complex landscape. Biomedical informatics and AI provide scalable, data-driven ways to assess and even address important challenges in the drug development pipeline.

Therapeutic targeting of senescent cells in the CNS.

Senescent cells accumulate throughout the body with advanced age, diseases and chronic conditions. They negatively impact health and function of multiple systems, including the central nervous system (CNS). Therapies that target senescent cells, broadly referred to as senotherapeutics, recently emerged as potentially important treatment strategies for the CNS. Promising therapeutic approaches involve clearing senescent cells by disarming their pro-survival pathways with 'senolytics'; or dampening their toxic senescence-associated secretory phenotype (SASP) using 'senomorphics'. Following the pioneering discovery of first-generation senolytics dasatinib and quercetin, dozens of additional therapies have been identified, and several promising targets are under investigation. Although potentially transformative, senotherapies are still in early stages and require thorough testing to ensure reliable target engagement, specificity, safety and efficacy. The limited brain penetrance and potential toxic side effects of CNS-acting senotherapeutics pose challenges for drug development and translation to the clinic. This Review assesses the potential impact of senotherapeutics for neurological conditions by summarizing preclinical evidence, innovative methods for target and biomarker identification, academic and industry drug development pipelines and progress in clinical trials.

Evaluating deep neural networks in optimizing drug discovery and precision medicine: A review

Introduction/Background: Deep neural networks have shown great promise in advancing drug discovery and precision medicine. By leveraging large amounts of complex biomedical and chemical data, deep learning approaches can identify novel targets, predict drug-target and drug-drug interactions, generate new molecular structures, and assist in personalized treatment selection and development. However, fully utilizing deep learning techniques for optimization across the drug development pipeline remains an ongoing challenge. Materials and Methods: A comprehensive literature review was conducted using major bibliographic databases including PubMed, Web of Science, and Scopus. Search terms included combinations of "deep learning", "drug discovery", "precision medicine", "biomedical data", and "neural networks". Over 200 papers published between 2010-2023 related to deep learning applications in pharmacology and genomics were identified and reviewed. Results: Deep learning has been widely applied at various stages of the drug discovery process including target identification/prioritization, lead generation/optimization, and prediction of molecular properties. Convolutional neural networks are commonly used for the representation and classification of biological sequence and image data for tasks such as gene expression analysis and pathogen detection from microscopy images. Graph neural networks effectively model compound structures and interactome networks to predict molecular bindings and disease associations. Multi-modal neural networks integrate diverse data types for personalized treatment response prediction and biomarker discovery. Challenges remain around data and model interpretation, generalization to new targets/diseases, and integration across domains. Discussion: While deep learning has shown promise, rigorous benchmarking and validation on real-world clinical endpoints are still needed to establish usefulness in decision-making. Data and model transparency must be improved to enable scientific insights. Privacy and security risks accompanying "real world" biomedical big data will require ethical practices. Standardization and sharing of resources/protocols could accelerate progress by enabling comparison of techniques. Combining deep learning with other AI paradigms like causal inference may further improve utility in drug discovery and precision healthcare. Conclusion: Deep neural networks demonstrate potential for optimizing drug development and precision medicine applications. Continued advancement relies on addressing challenges around data, models, validation, and ethics. Multi-disciplinary collaborations integrating machine learning, molecular biology, medicine, and other domains are needed to fully realize benefits to patients.

Beyond Antibiotics: What the Future Holds.

The prevalence of multidrug resistance (MDR) and stagnant drug-development pipelines have led to the rapid rise of hard-to-treat antibiotic-resistant bacterial infections. These infectious diseases are no longer just nosocomial but are also becoming community-acquired. The spread of MDR has reached a crisis level that needs immediate attention. The landmark O'Neill report projects that by 2050, mortality rates associated with MDR bacterial infections will surpass mortality rates associated with individuals afflicted with cancer. Since conventional antimicrobials are no longer very reliable, it is of great importance to investigate different strategies to combat these life-threatening infectious diseases. Here, we provide an overview of recent advances in viable alternative treatment strategies mainly targeting a pathogen's virulence capability rather than viability. Topics include small molecule and immune inhibition of virulence factors, quorum sensing (QS) quenching, inhibition of biofilm development, bacteriophage-mediated therapy, and manipulation of an individual's macroflora to combat MDR bacterial infections.

Mycobacterium abscessus strain variability in preclinical drug development: does it really matter?

Abstract Background New treatment options for Mycobacterium abscessus infections are urgently needed. Since a correlation between MICs and clinical outcomes is not clearly established, potency of novel drugs needs to be evaluated using additional in vitro drug activity assays. Preclinical drug activity assays generally use the M. abscessus type strain ATCC 19977. However, M. abscessus complex entails a genetically and morphologically diverse group, and it is questionable whether drug activity observed against ATCC 19977 is representative of drug activity against clinical M. abscessus isolates. Objectives To assess whether the relationship between MIC and the quantitative antimycobacterial activity of amikacin, imipenem and clofazimine differs between the ATCC 19977 strain and clinical M. abscessus isolates. Methods Experiments were performed with M. abscessus ATCC 19977 and a subset of six clinical isolates covering the three M. abscessus subspecies and the smooth and rough morphotypes. Cultures were exposed to the drugs at 4-fold increasing, MIC-standardized concentrations, and the mycobacterial load was assessed over time. Results Concentration- and time-dependent activity of amikacin, imipenem and clofazimine against the six clinical isolates was similar. Only slight variations in drug activity were observed between ATCC 19977 and clinical isolates. Conclusions Time- and concentration-dependent drug activity against the ATCC 19977 strain seems indicative for in vitro drug behaviour against M. abscessus complex clinical isolates. Including one clinical smooth morphotype isolate alongside ATCC 19977 seems appropriate for reliable interpretation of this particular in vitro drug activity assay as part of the M. abscessus preclinical drug development pipeline.

Crossing the Chasm: How to Approach Translational Pharmacokinetic-Pharmacodynamic Modeling of Phage Dosing.

Effectively treating multidrug-resistant bacterial infections remains challenging due to the limited drug development pipeline and a scarcity of novel agents effective against these highly resistant pathogens. Bacteriophages (phages) are a potential addition to the antimicrobial treatment arsenal. Though, phages are currently being tested in clinical trials for antibiotic-resistant infections, phages lack a fundamental understanding of optimal dosing in humans. Rationally designed preclinical studies using in vitro and in vivo infection models, allow us to assess clinically relevant phage +/- antibiotic exposure (pharmacokinetics), the resulting treatment impact on the infecting pathogen (pharmacodynamics) and host immune response (immunodynamics). A mechanistic modeling framework allows us to integrate this knowledge gained from preclinical studies to develop predictive models. We reviewed recently published mathematical models based on in vitro and/or in vivo data that evaluate the effects of varying bacterial or phage densities, phage characteristics (burst size, adsorption rate), phage pharmacokinetics, phage-antibiotic combinations and host immune responses. In our review, we analyzed study designs and the data used to inform the development of these mechanistic models. Insights gained from model-based simulations were reviewed as they help identify crucial phage parameters for determining effective phage dosing. These efforts contribute to bridging the gap between phage therapy research and its clinical translation.

Computer-aided pattern scoring (C@PS): a novel cheminformatic workflow to predict ligands with rare modes-of-action

The identification, establishment, and exploration of potential pharmacological drug targets are major steps of the drug development pipeline. Target validation requires diverse chemical tools that come with a spectrum of functionality, e.g., inhibitors, activators, and other modulators. Particularly tools with rare modes-of-action allow for a proper kinetic and functional characterization of the targets-of-interest (e.g., channels, enzymes, receptors, or transporters). Despite, functional innovation is a prime criterion for patentability and commercial exploitation, which may lead to therapeutic benefit. Unfortunately, data on new, and thus, undruggable or barely druggable targets are scarce and mostly available for mainstream modes-of-action only (e.g., inhibition). Here we present a novel cheminformatic workflow—computer-aided pattern scoring (C@PS)—which was specifically designed to project its prediction capabilities into an uncharted domain of applicability.

Multi-output prediction of dose–response curves enables drug repositioning and biomarker discovery

Drug response prediction is hampered by uncertainty in the measures of response and selection of doses. In this study, we propose a probabilistic multi-output model to simultaneously predict all dose–responses and uncover their biomarkers. By describing the relationship between genomic features and chemical properties to every response at every dose, our multi-output Gaussian Process (MOGP) models enable assessment of drug efficacy using any dose–response metric. This approach was tested across two drug screening studies and ten cancer types. Kullback-leibler divergence measured the importance of each feature and identified EZH2 gene as a novel biomarker of BRAF inhibitor response. We demonstrate the effectiveness of our MOGP models in accurately predicting dose–responses in different cancer types and when there is a limited number of drug screening experiments for training. Our findings highlight the potential of MOGP models in enhancing drug development pipelines by reducing data requirements and improving precision in dose–response predictions.

Shared etiology of Mendelian and complex disease supports drug discovery

BackgroundDrugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease.MethodsIn previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations.ResultsOur analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated.ConclusionsOur findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.

Characterization of novel double-reporter strains of Mycobacterium abscessus for drug discovery: a study in mScarlet.

Mycobacterium abscessus (Mab) is currently considered an "incurable nightmare." Its intrinsic resistance, high toxicity, long duration, and low cure rates of available therapies often lead to the clinical decision not to treat. Moreover, one of the significant drawbacks of anti-Mab drug development is the lack of correlation between in vitro susceptibility and clinical efficacy. Most drug screening assays are performed on Mab growing in liquid cultures. But being an intracellular pathogen, inducing granulomas and biofilm formation, the broth culture is far from ideal as in vitro drug-testing setup. This study presents new double-reporter Mab strains that allow direct real-time bacterial detection and quantification in a non-invasive way. These strains can be applied to an extensive range of experimental settings, far surpassing the utility of single-reporter bacteria. They can be used in all steps of the pre-clinical anti-Mab drug development pipeline, constituting a highly valuable tool to increase its success.

Enhancing the Intrinsic Antiplasmodial Activity and Improving the Stability and Selectivity of a Tunable Peptide Scaffold Derived from Human Platelet Factor 4.

The control of malaria, a disease caused by Plasmodium parasites that kills over half a million people every year, is threatened by the continual emergence and spread of drug resistance. Therefore, new molecules with different mechanisms of action are needed in the antimalarial drug development pipeline. Peptides developed from host defense molecules are gaining traction as anti-infectives due to theood of inducing drug resistance. Human platelet factor 4 (PF4) has intrinsic activity against P. falciparum, and a macrocyclic helix-loop-helix peptide derived from its active domain recapitulates this activity. In this study, we used a stepwise approach to optimize first-generation PF4-derived internalization peptides (PDIPs) by producing analogues with substitutions to charged and hydrophobic amino acid residues or with modifications to terminal residues including backbone cyclization. We evaluated the in vitro activity of PDIP analogues against P. falciparum compared to their overall helical structure, resistance to breakdown by serum proteases, selective binding to negatively charged membranes, and hemolytic activity. Next, we combined antiplasmodial potency-enhancing substitutions that retained favorable membrane and cell-selective properties onto the most stable scaffold to produce a backbone cyclic PDIP analogue with four-fold improved activity against P. falciparum compared to first-generation peptides. These studies demonstrate the ability to modify PDIP to select for and combine desirable properties and further validate the suitability of this unique peptide scaffold for developing a new molecule class that is distinct from existing antimalarial drugs.

Prediction of Drug-Induced Liver Injury: From Molecular Physicochemical Properties and Scaffold Architectures to Machine Learning Approaches.

The process of developing new drugs is widely acknowledged as being time-intensive and requiring substantial financial investment. Despite ongoing efforts to reduce time and expenses in drug development, ensuring medication safety remains an urgent problem. One of the major problems involved in drug development is hepatotoxicity, specifically known as drug-induced liver injury (DILI). The popularity of new drugs often poses a significant barrier during development and frequently leads to their recall after launch. In silico methods have many advantages compared with traditional invivo and invitro assays. To establish a more precise and reliable prediction model, it is necessary to utilize an extensive and high-quality database consisting of information on drug molecule properties and structural patterns. In addition, we should also carefully select appropriate molecular descriptors that can be used to accurately depict compound characteristics. The aim of this study was to conduct a comprehensive investigation into the prediction of DILI. First, we conducted a comparative analysis of the physicochemical properties of extensively well-prepared DILI-positive and DILI-negative compounds. Then, we used classic substructure dissection methods to identify structural pattern differences between these two different types of chemical molecules. These findings indicate that it is not feasible to establish property or substructure-based rules for distinguishing between DILI-positive and DILI-negative compounds. Finally, we developed quantitative classification models for predicting DILI using the naïve Bayes classifier (NBC) and recursive partitioning (RP) machine learning techniques. The optimal DILI prediction model was obtained using NBC, which combines 21 physicochemical properties, the VolSurf descriptors and the LCFP_10 fingerprint set. This model achieved a global accuracy (GA) of 0.855 and an area under the curve (AUC) of 0.704 for the training set, while the corresponding values were 0.619 and 0.674 for the test set, respectively. Moreover, indicative substructural fragments favorable or unfavorable for DILI were identified from the best naïve Bayesian classification model. These findings may help prioritize lead compounds in the early stage of drug development pipelines.

Uncovering the Elusive Structures and Mechanisms of Prevalent Antidepressants

AbstractMost treatments to alleviate major depression work by either inhibiting human monoamine transporters, vital for the reuptake of monoamine neurotransmitters, or by inhibiting monoamine oxidases, which are vital for their degradation. The analysis of the experimental 3D structures of those antidepressants in their drug formulation state is key to precision drug design and development. In this study, microcrystal electron diffraction (MicroED) is applied to reveal the atomic 3D structures for the first time of five of the most prevalent antidepressants (reboxetine, pipofezine, ansofaxine, phenelzine, and bifemelane) directly from the commercially available powder of the active ingredients. Their modes of binding are investigated by molecular docking, revealing the essential contacts and conformational changes into the biologically active state. This study underscores the combined use of MicroED and molecular docking to uncover elusive drug structures and mechanisms to aid in further drug development pipelines.

Artificial intelligence for ophthalmic drug discovery and development: Capabilities, applications, and challenges

The integration of artificial intelligence (AI) into ophthalmic drug discovery and development presents transformative opportunities to address the inherent complexities and challenges of creating targeted therapies for eye diseases. The ability of AI to process vast datasets can facilitate the discovery of novel drug candidates, improve predictions of drug efficacy and safety, and streamline the drug development pipeline. Applications can range from enhancing target identification and compound screening to refining predictive toxicology. However, challenges such as data limitations, computational demands, model interpretability, and ethical considerations remain. Despite these hurdles, the integration of AI with emerging technologies and its potential to optimize clinical trials signifies a new era of innovation in ophthalmology, emphasizing its critical role in addressing current challenges and advancing therapeutic development. In this paper, we explore the role of AI in ophthalmic drug discovery, highlighting its potential to address critical challenges in the field and delineating its impact across various stages of drug development.

Assessment and process optimization of high throughput biofabrication of immunocompetent breast cancer model for drug screening applications

Recent advancements in 3D cancer modeling have significantly enhanced our ability to delve into the intricacies of carcinogenesis. Despite the pharmaceutical industry’s substantial investment of both capital and time in the drug screening and development pipeline, a concerning trend persists: drug candidates screened on conventional cancer models exhibit a dismal success rate in clinical trials. One pivotal factor contributing to this discrepancy is the absence of drug testing on pathophysiologically biomimetic 3D cancer models during pre-clinical stages. Unfortunately, current manual methods of 3D cancer modeling, such as spheroids and organoids, suffer from limitations in reproducibility and scalability. In our study, we have meticulously developed 3D bioprinted breast cancer model utilizing decellularized adipose tissue-based hydrogel obtained via a detergent-free decellularization method. Our innovative printing techniques allows for rapid, high-throughput fabrication of 3D cancer models in a 96-well plate format, demonstrating unmatched scalability and reproducibility. Moreover, we have conducted extensive validation, showcasing the efficacy of our platform through drug screening assays involving two potent anti-cancer drugs, 5-Fluorouracil and PRIMA-1Met. Notably, our platform facilitates effortless imaging and gene expression analysis, streamlining the evaluation process. In a bid to enhance the relevance of our cancer model, we have introduced a heterogeneous cell population into the DAT-based bioink. Through meticulous optimization and characterization, we have successfully developed a biomimetic immunocompetent breast cancer model, complete with microenvironmental cues and diverse cell populations. This breakthrough paves the way for rapid multiplex drug screening and the development of personalized cancer models, marking a paradigm shift in cancer research and pharmaceutical development.

Real-time label-free three-dimensional invasion assay for anti-metastatic drug screening using impedance sensing.

Tumor metastasis presents a formidable challenge in cancer treatment, necessitating effective tools for anti-cancer drug development. Conventional 2D cell culture methods, while considered the "gold standard" for invasive studies, exhibit limitations in representing cancer hallmarks and phenotypes. This study proposes an innovative approach that combines the advantages of 3D tumor spheroid culture with impedance-based biosensing technologies to establish a high-throughput 3D cell invasion assay for anti-metastasis drug screening through multicellular tumor spheroids. In addition, the xCELLigence device is employed to monitor the time-dependent kinetics of cell behavior, including attachment and invasion out of the 3D matrix. Moreover, an iron chelator (deferoxamine) is employed to monitor the inhibition of epithelial-mesenchymal transition in 3D spheroids across different tumor cell types. The above results indicate that our integrated 3D cell invasion assay with impedance-based sensing could be a promising tool for enhancing the quality of the drug development pipeline by providing a robust platform for predicting the efficacy and safety of anti-metastatic drugs before advancing into preclinical or clinical trials.