Drug Screening Research Articles

Prenatal substance use and mental health comorbidities predict continued use.

Prenatal substance use is common and can affect maternal and infant health. In addition, prenatal substance use is associated with mental health comorbidities (depression, anxiety, and posttraumatic stress disorder). Unremitting prenatal substance use disorders and mental health comorbidities are associated with poor health outcomes for mothers and exposed infants. The purpose of this study was to examine how any substance use, type of substance use (polysubstance use vs. single substance use), and combinations of mental health comorbidities predict continued use during pregnancy (i.e., use in the 30 days prior to delivery). Health records of patients enrolled in a comprehensive prenatal program for women with substance use disorders were retrospectively analyzed (N = 281). Urine drug screen records were used to determine substance use, and diagnostic codes were used to identify mental health comorbidities. Thirty-seven percent of the sample (n = 105/281) tested positive for substances at admission and 42% (n = 119/281) demonstrated continued use. 30% of the sample (n = 85/281) had depression, anxiety, and posttraumatic stress disorder, 27% (n = 76/281) had two of the three mental health comorbidities, 26% (n = 73/281) had one of the three comorbidities, and 17% (n = 47/281) did not have a mental health comorbidity. Any substance use at admission or having all three mental health conditions were associated with continued use. Substance use at admission and number of mental health conditions were independent predictors of continued use, although substance use was the stronger predictor. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Tailoring esophageal tumor spheroids on a chip with inverse opal scaffolds for drug screening

Esophageal cancer (EC) is characterized by high morbidity and mortality, and chemotherapy has become an indispensable means for comprehensive treatment. However, due to the limitation of the effective in vitro disease model, the development of chemotherapeutic agents still faces great challenges. In this paper, we present a novel tumor spheroid on a chip platform based on inverse opal hydrogel scaffolds to screen chemotherapeutic agents for EC treatment. With the microfluidic emulsion approach, the inverse opal hydrogel scaffolds were generated with tunable and organized pores, which could provide spatial confinement for cell growth. Thus, the suspended KYSE-70 cells could successfully form uniform cell spheroids on the inverse opal hydrogel scaffolds. It was demonstrated that the tumor cell spheroids could recapitulate 3D growth patterns in vivo and exhibited higher sensitivity to the chemotherapy agents compared with monolayer cells. Besides, by employing the scaffolds into a microfluidics to construct esophageal tumor on a chip, the device could realize high-throughput tumor cell spheroids generation and drug screening, indicating its promising role in chemotherapy drug development.

AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery.

Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.

Scaling-law mechanical marker for liver fibrosis diagnosis and drug screening through machine learning

Studies of cell and tissue mechanics have shown that significant changes in cell and tissue mechanics during lesions and cancers are observed, which provides new mechanical markers for disease diagnosis based on machine learning. However, due to the lack of effective mechanic markers, only elastic modulus and iconographic features are currently used as markers, which greatly limits the application of cell and tissue mechanics in disease diagnosis. Here, we develop a liver pathological state classifier through a support vector machine method, based on high dimensional viscoelastic mechanical data. Accurate diagnosis and grading of hepatic fibrosis facilitates early detection and treatment and may provide an assessment tool for drug development. To this end, we used the viscoelastic parameters obtained from the analysis of creep responses of liver tissues by a self-similar hierarchical model and built a liver state classifier based on machine learning. Using this classifier, we implemented a fast classification of healthy, diseased, and mesenchymal stem cells (MSCs)-treated fibrotic live tissues, and our results showed that the classification accuracy of healthy and diseased livers can reach 0.99, and the classification accuracy of the three liver tissues mixed also reached 0.82. Finally, we provide screening methods for markers in the context of massive data as well as high-dimensional viscoelastic variables based on feature ablation for drug development and accurate grading of liver fibrosis. We propose a novel classifier that uses the dynamical mechanical variables as input markers, which can identify healthy, diseased, and post-treatment liver tissues.

Pattern Recognition of Alkaloids by Inhibiting the Catalytic Activity of Dopzymes for Dopamine.

Exploiting the specific recognition probe for all of the biomolecules is difficult in "lock-and-key" biosensors. The cross-reaction or the semispecific probe in pattern recognition mode is an alternative strategy through extracting a multidimensional signal array from recognition elements. Here, we design a pattern recognition sensor array based on the alkaloid-inhibited catalytic activity of dopzymes for the discrimination and determination of six alkaloids. In this sensor array, three different G-rich sequences, i.e., G-triplex (G3), G-quadruplex (GQ1), and the G-quadruplex dimer (2GQ1) possessing various peroxidase activities, conjugated with a dopamine aptamer and the dopzymes (G3-d-apt, GQ1-d-apt, and 2GQ1-d-apt) were obtained with an enhanced catalytic performance for the substrate. Through the interactions between six target alkaloids and G3, GQ1, and 2GQ1 regions, the pattern signal (6 alkaloids × 3 dopzymes × 5 replicates) was obtained from the diverse inhibited effect for the dopzyme activity. In virtue of the statistical method principal component analysis (PCA), the data array was projected into a new dimensional space to acquire the three-dimensional (3D) canonical scores and grouped into their respective clusters. The sensor array exhibited an outstanding discrimination and classification capability for six alkaloids with different concentrations with 100% accuracy. In addition, the nonspecific recognition elements of the sensor array showed high selectivity even though other alkaloids with similar structures to targets existed in the samples. Importantly, the levels of the six targets can be analyzed by the most influential discrimination factor, which represented the vector with the highest variance, evidencing that the sensor array has potential in drug screening and clinical treatment.

State of the Art Modelling of the Breast Cancer Metastatic Microenvironment: Where Are We?

Metastatic spread of tumour cells to tissues and organs around the body is the most frequent cause of death from breast cancer. This has been modelled mainly using mouse models such as syngeneic mammary cancer or human in mouse xenograft models. These have limitations for modelling human disease progression and cannot easily be used for investigation of drug resistance and novel therapy screening. To complement these approaches, advances are being made in ex vivo and 3D in vitro models, which are becoming progressively better at reliably replicating the tumour microenvironment and will in the future facilitate drug development and screening. These approaches include microfluidics, organ-on-a-chip and use of advanced biomaterials. The relevant tissues to be modelled include those that are frequent and clinically important sites of metastasis such as bone, lung, brain, liver for invasive ductal carcinomas and a distinct set of common metastatic sites for lobular breast cancer. These sites all have challenges to model due to their unique cellular compositions, structure and complexity. The models, particularly in vivo, provide key information on the intricate interactions between cancer cells and the native tissue, and will guide us in producing specific therapies that are helpful in different context of metastasis.

Research of Pulmonary Fibrosis Lesions Based on FLIM and SHG Imaging Microscopy.

Two-photon fluorescence lifetime microscopy (TP-FLIM) is a powerful quantitative imaging technique that characterizes and analyzes the structure and function of biological samples through a combination of intensity and lifetime imaging. Because TP-FLIM is independent of the fluorescence signal intensity and the fluorophore concentration, it is widely used in high-throughput, high-content drug screening and clinical diagnostics. Second harmonic generation (SHG) imaging technology has the advantages of high spatial resolution and imaging depth inherent to nonlinear optical imaging. Second harmonics often appear in noncentrosymmetric structures. Collagen tissue in biological organisms is a good example of these structures, showing strong harmonic effects. Therefore, SHG has been widely used for imaging of specific tissue structure imaging. TP-FLIM technology is highly sensitive for quantitatively detecting changes in microenvironments. The objective of this study is to examine pathological pulmonary fibrosis slices using a combined approach of TP-FLIM and SHG technology. The fluorescence lifetime data of pulmonary collagen fibers are analyzed by using phasor plot analysis methods, and normal collagen fibers and fibrotic collagen fibers are distinguished by calculating the aspect ratio from the SHG images formed by the collagen fibers. Our study provides a new method for a deeper understanding of the pathological mechanisms and clinical diagnosis of pulmonary fibrosis and other collagen fiber-related disorders.

One-Step Synthesis and Oriented Immobilization of Strep-Tag II Fused PDGFRβ for Screening Intracellular Domain-Targeted Ligands.

Accurate orientations and stable conformations of membrane receptor immobilization are particularly imperative for accurate drug screening and ligand-protein affinity analysis. However, there remain challenges associated with (1) traditional recombination, purification, and immobilization of membrane receptors, which are time-consuming and labor-intensive; (2) the orientations on the stationary phase are not easily controlled. Herein, a novel one-step synthesis and oriented-immobilization membrane-receptor affinity chromatography (oSOMAC) method was developed to realize high-throughput and accurate drug screening targeting specific domains of membrane receptors. We employed Strep-tag II as a noncovalent immobilization tag fused into platelet-derived growth factor receptor β (PDGFRβ) through CFPS, and meanwhile, the Strep-Tactin-modified monolithic columns are prepared in batches. The advantages of oSOMAC are as follows: (1) targeted membrane receptors can be expressed independent of living cell within 1-2 h; (2) orientation of membrane receptors can be flexibly controlled and active sites can expose accurately; and (3) targeted membrane receptors can be synthesized, purified, and orientation-immobilized on monolithic columns in one step. Accordingly, three potential PDGFRβ intracellular domain targeted ligands: tanshinone IIA (Tan IIA), hydroxytanshinone IIA, and dehydrotanshinone IIA were successfully screened out from Salvia miltiorrhiza extract through oSOMAC. Pharmacological experiments and molecular docking further demonstrated that Tan IIA could attenuate hepatic stellate cells activation by targeting the protein kinase domain of PDGFRβ with a KD value of 9.7 μM. Ultimately, the novel oSOMAC method provides an original insight for accurate drug screening and interaction analysis which can be applied in other membrane receptors.

Ocular Surface Fluid: More Than a Matrix

Although the eye can be subjected to therapeutic manipulation, some of its structures are highly inaccessible. Thus, conventional therapeutic administration pathways, such as topical or systemic routes, usually show significant limitations in the form of low ocular penetration or the appearance of side effects linked to physiology, among others. The critical feature of many xenobiotics is the drug gradient from the concentrated tear reservoir to the relatively barren corneal and conjunctival epithelia, which forces a passive route of absorption. The same is true in the opposite direction, towards the ocular surface (OS). With the premise that tears can be regarded as equivalent to or a substitute for plasma, researchers may determine drug concentrations in the OS fluid. Within this framework, a survey of scholarly sources on the topic was conducted. It provided an overview of current knowledge, allowing the identification of relevant theories, methods, and gaps in the existing research that can be employed in subsequent research. OS fluid (tears particularly) has enormous potential as a source of biological material for external drug screening and as a biomarker of various systemic diseases. Given the numerous alternate matrices, knowledge of their properties is very important in selecting the most appropriate specimens in toxicological analyses.

Bioengineered Silk Protein-Based 3D In Vitro Models for Tissue Engineering and Drug Development: From Silk Matrix Properties to Biomedical Applications.

3D in vitro model has emerged as a valuable tool for studying tissue development, drug screening, and disease modeling. 3D systems can accurately replicate tissue microstructures and physiological features, mirroring the in vivo microenvironment departing from conventional 2D cell cultures. Various 3D in vitro models utilizing biomacromolecules like collagen and synthetic polymers have been developed to meet diverse research needs andaddress the complex challenges of contemporary research. Silk proteins, bearing structural and functional similarities to collagen, have been increasingly employed to construct advanced 3D in vitro systems, surpassing the limitations of 2D cultures. This review examines silk proteins' composition, structure, properties, and functions, elucidating their role in 3D in vitro models. Furthermore, recent advances in biomedical applications involving silk-based organoid models are discussed. In particular, the unique physiological attributes of silk matrix constituents in in vitro tissue constructs are highlighted, providing a meticulous evaluation of their importance. Additionally, it outlines the current research hurdles and complexities while contemplating future avenues, thereby paving the way for developing complex and biomimetic silk protein-based microtissues.

Avenues and Future Prospects of 3D Bioprinting

3D bioprinting has opened numerous avenues in the medical field, particularly advancing biomedical techniques for administration of drugs, fabrication of prostheses, surgical operations and restoration of damaged body parts. In this review we discuss the principles of 3D bioprinting, its applications, advantages over traditional methods, recent advancements and the future prospects of automation within the field.. The review also delves into the creation of more physiologically relevant tissue models for drug screening and toxicity testing. Finally, we also explore more recent advancements showcasing the impact of printing in space, integration of artificial intelligence (AI), and development of 4D bioprinting. Expressing current limitations and challenges, the review underscores the potential of 3D bioprinting in personalized medicine.

Supercooled preservation of cultured primary rat hepatocyte monolayers

Supercooled preservation (SCP) is a technology that involves cooling a substance below its freezing point without initiating ice crystal formation. It is a promising alternative to prolong the preservation time of cells, tissues, engineered tissue products, and organs compared to the current practices of hypothermic storage. Two-dimensional (2D) engineered tissues are extensively used in in vitro research for drug screening and development and investigation of disease progression. Despite their widespread application, there is a lack of research on the SCP of 2D-engineered tissues. In this study, we presented the effects of SCP at −2 and −6°C on primary rat hepatocyte (PRH) monolayers for the first time and compared cell viability and functionality with cold storage (CS, + 4°C). We preserved PRH monolayers in two different commercially available solutions: Hypothermosol-FRS (HTS-FRS) and the University of Wisconsin (UW) with and without supplements (i.e., polyethylene glycol (PEG) and 3-O-Methyl-Α-D-Glucopyranose (3-OMG)). Our findings revealed that UW with and without supplements were inadequate for the short-term preservation of PRH monolayers for both SCP and CS with high viability, functionality, and monolayer integrity. The combination of supplements (PEG and 3-OMG) in the HTS-FRS solution outperformed the other groups and yielded the highest viability and functional capacity. Notably, PRH monolayers exhibited superior viability and functionality when stored at −2°C through SCP for up to 3 days compared to CS. Overall, our results demonstrated that SCP is a feasible approach to improving the short-term preservation of PRH monolayers and enables readily available 2D-engineered tissues to advance in vitro research. Furthermore, our findings provide insights into preservation outcomes across various biological levels, from cells to tissues and organs, contributing to the advancement of bioengineering and biotechnology.

NF2 regulates IP3R-mediated Ca2+ signal and apoptosis in meningiomas.

Meningiomas are the most common primary intracranial tumors and account for nearly 30% of all nervous system tumors. Approximately half of meningioma patients exhibit neurofibromin 2 (NF2) gene inactivation. Here, NF2 was shown to interact with the endoplasmic reticulum (ER) calcium (Ca2+) channel inositol 1,4,5-trisphosphate receptor 1 (IP3R1) in IOMM-Lee, a high-grade malignant meningioma cell line, and the F1 subdomain of NF2 plays a critical role in this interaction. Functional assays indicated that NF2 promotes the phosphorylation of IP3R (Ser 1756) and IP3R-mediated endoplasmic reticulum (ER) Ca2+ release by binding to IP3R1, which results in Ca2+-dependent apoptosis. Knockout of NF2 decreased Ca2+ release and promoted resistance to apoptosis, which was rescued by wild-type NF2 overexpression but not by F1 subdomain deletion truncation overexpression. The effects of NF2 defects on the development of tumors were further studied in mouse models. The decreased expression level of NF2 caused by NF2 gene knockout or mutation affects the activity of the IP3R channel, which reduces Ca2+-dependent apoptosis, thereby promoting the development of tumors. We elucidated the interaction patterns of NF2 and IP3R1, revealed the molecular mechanism through which NF2 regulates IP3R1-mediated Ca2+ release, and elucidated the new pathogenic mechanism of meningioma-related NF2 variants. Our study broadens the current understanding of the biological function of NF2 and provides ideas for drug screening of NF2-associated meningioma.

Survey of Potential Drug Interactions, Use of Non-Medical Health Products, and Immunization Status among Patients Receiving Targeted Therapies

In recent years, several changes have occurred in the management of chronic immunological conditions with the emerging use of targeted therapies. This two-phase cross-sectional study was conducted through structured in-person interviews in 2018–2019 and 2022. Additional data sources included ambulatory medical records and the itemized reimbursement reporting interface of the National Health Insurance Fund. Drug interactions were analyzed using the UpToDate Lexicomp, Medscape drug interaction checker, and Drugs.com databases. The chi-square test was used, and odds ratios (ORs) were calculated. In total, 185 patients participated. In 53% of patients (n = 53), a serious drug–drug interaction (DDI) was identified (mean number: 1.07 ± 1.43, 0–7), whereas this value was 38% (n = 38) for potential drug–supplement interactions (mean number: 0.58 ± 0.85, 0–3) and 47% (n = 47) for potential targeted drug interactions (0.72 ± 0.97, 0–5) in 2018. In 2022, 78% of patients (n = 66) were identified as having a serious DDI (mean number: 2.27 ± 2.69, 0–19), 66% (n = 56) had a potential drug–supplement interaction (mean number: 2.33 ± 2.69, 0–13), and 79% (n = 67) had a potential targeted drug interactions (1.35 ± 1.04, 0–5). Older age (>60 years; OR: 2.062), female sex (OR: 3.387), and polypharmacy (OR: 5.276) were identified as the main risk factors. Screening methods and drug interaction databases do not keep pace with the emergence of new therapeutics.

Enantioselective Surface-Enhanced Raman Scattering by Chiral Au Nanocrystals with Finely Modulated Chiral Fields and Internal Standards.

The chiral discrimination of enantiomers is crucial for drug screening and agricultural production. Surface-enhanced Raman scattering (SERS) is proposed for discriminating enantiomers benefiting from chiral plasmonic materials. However, the mechanism of enantioselective SERS is unclear, and fluctuating SERS intensities may result in errors. Herein, this work demonstrates a reliable SERS substrate using chiral Au nanocrystals with finely modulated chiral fields and internal standards. Chiral electromagnetic fields are enhanced after modulation, which is conducive to increasing the difference in the enantiomeric SERS intensity, as evidenced by the experimental and simulation results. Furthermore, the SERS stability is improved by the corrective effect of the internal standards, and the relative standard deviation is significantly reduced. Using finely modulated chiral fields and internal standards, L- and D-phenylalanine exhibit a stable six times difference in SERS ratio. Theoretical simulations reveal that linearly polarized light can also excite the chiral fields of chiral Au nanocrystals, indicating non-chiral far-field light is converted into chiral near-field sources by chiral Au nanocrystals. Thus, the mechanism of enantioselective SERS can be elucidated by the scattering difference of chiral molecules in chiral near fields. This study will pave the way for the development of enantioselective SERS and related chiroptical technologies.

Enhanced hepatotoxicity assessment through encapsulated HepG2 spheroids in gelatin hydrogel matrices: Bridging the gap from 2D to 3D culture

Conventional 2D drug screening often fails to accurately predict clinical outcomes. We present an innovative approach to improve hepatotoxicity assessment by encapsulating HepG2 spheroids in gelatin hydrogel matrices with different mechanical properties. Encapsulated spheroids exhibit sustained liver-specific functionality, enhanced expression of drug-metabolizing enzymes, and increased drug sensitivity compared to 2D cultures. The platform detects critical variations in drug response, with significant differences in IC50 values between 2D and spheroid cultures ranging from 1.3-fold to > 13-fold, particularly for acetaminophen. Furthermore, drug-metabolizing enzyme expression varies across hydrogel concentrations, suggesting a role for matrix mechanical properties in modulating hepatocyte function. This novel spheroid-hydrogel platform offers a transformative approach to hepatotoxicity assessment, providing increased sensitivity, improved prediction, and a more physiologically relevant environment. The use of such advanced in vitro models can accelerate drug development, reduce animal testing, and contribute to improved patient safety and clinical outcomes.

Prediction method of pharmacokinetic parameters of small molecule drugs based on GCN network model.

Accurately predicting plasma protein binding rate (PPBR) and oral bioavailability (OBA) helps to better reveal the absorption and distribution of drugs in the human body and subsequent drug design. Although machine learning models have achieved good results in prediction accuracy, they often suffer from insufficient accuracy when dealing with data with irregular topological structures. In view of this, this study proposes a pharmacokinetic parameter prediction framework based on graph convolutional networks (GCN), which predicts the PPBR and OBA of small molecule drugs. In the framework, GCN is first used to extract spatial feature information on the topological structure of drug molecules, in order to better learn node features and association information between nodes. Then, based on the principle of drug similarity, this study calculates the similarity between small molecule drugs, selects different thresholds to construct datasets, and establishes a prediction model centered on the GCN algorithm. The experimental results show that compared with traditional machine learning prediction models, the prediction model constructed based on the GCN method performs best on PPBR and OBA datasets with an inter-molecular similarity threshold of 0.25, with MAE of 0.155 and 0.167, respectively. In addition, in order to further improve the accuracy of the prediction model, GCN is combined with other algorithms. Compared to using a single GCN method, the distribution of the predicted values obtained by the combined model is highly consistent with the true values. In summary, this work provides a new method for improving the rate of early drug screening in the future.

Platinum nanowires/MXene nanosheets/porous carbon ternary nanocomposites for in situ monitoring of dopamine released from neuronal cells

Dopamine is an important neurotransmitter in the body and closely related to many neurodegenerative diseases. Therefore, the detection of dopamine is of great significance for the diagnosis and treatment of diseases, screening of drugs and unraveling of relevant pathogenic mechanisms. However, the low concentration of dopamine in the body and the complexity of the matrix make the accurate detection of dopamine challenging. Herein, an electrochemical sensor is constructed based on ternary nanocomposites consisting of one-dimensional Pt nanowires, two-dimensional MXene nanosheets, and three-dimensional porous carbon. The Pt nanowires exhibit excellent catalytic activity due to the abundant grain boundaries and highly undercoordinated atoms; MXene nanosheets not only facilitate the growth of Pt nanowires, but also enhance the electrical conductivity and hydrophilicity; and the porous carbon helps induce significant adsorption of dopamine on the electrode surface. In electrochemical tests, the ternary nanocomposite-based sensor achieves an ultra-sensitive detection of dopamine (S/N = 3) with a low limit of detection (LOD) of 28 nM, satisfactory selectivity and excellent stability. Furthermore, the sensor can be used for the detection of dopamine in serum and in situ monitoring of dopamine release from PC12 cells. Such a highly sensitive nanocomposite sensor can be exploited for in situ monitoring of important neurotransmitters at the cellular level, which is of great significance for related drug screening and mechanistic studies.

Investigating FSGS-like Injury in Zebrafish Larvae by Nifurpirinol: Efficacy and Molecular Insight.

Identifying effective drugs for focal segmental glomerulosclerosis (FSGS) treatment holds significant importance. Our high-content drug screening on zebrafish larvae relies on nitroreductase/metronidazole (NTR/MTZ)-induced podocyte ablation to generate FSGS-like injury. A crucial factor for successful drug screenings is minimizing variability in injury induction. For this, we introduce Nifurpirinol (NFP) as a more reliable prodrug for targeted podocyte depletion. NFP showed a 2.3-fold increase in efficiency at concentrations 1600-fold lower compared to MTZ-mediated injury induction. Integration into the screening workflow validated its suitability for the high-content drug screening. The presence of crucial FSGS hallmarks such as podocyte foot process effacement, proteinuria, and activation of parietal epithelial cells, were found. After the isolation of the glomeruli from the larvae, we identified essential pathways by proteomic analysis. This study shows that NFP serves as a highly effective prodrug to induce the FSGS-like disease in zebrafish larvae and is well-suited for a high-content drug screening to identify new candidates for the treatment of FSGS.

Biofabrication and Monitoring of a 3D Printed Skin Model for Melanoma.

There is an unmet need for in vitro cancer models that emulate the complexity of human tissues. 3D-printed solid tumor micromodels based on decellularized extracellular matrices (dECMs) recreate the biomolecule-rich matrix of native tissue. Herein a 3D in vitro metastatic melanoma model that is amenable for drug screening purposes and recapitulates features of both the tumor and the skin microenvironment is described. Epidermal, basement membrane, and dermal biocompatible inks are prepared by means of combined chemical, mechanical, and enzymatic processes. Bioink printability is confirmed by rheological assessment and bioprinting, and bioinks are subsequently combined with melanoma cells and dermal fibroblasts to build complex 3D melanoma models. Cells are tracked by confocal microscopy and surface-enhanced Raman spectroscopy (SERS) mapping. Printed dECMs and cell tracking allow modeling of the initial steps of metastatic disease, and may be used to better understand melanoma cell behavior and response to drugs.