Advances on Cancer-on-Chip Models

Dear Colleagues,

We are pleased to announce a Special Issue focusing on cancer-on-chip models and their recent advancements in fabrication, mathematical modeling, integration, potential applications, and their transformative impact on cancer research and drug development.

This Special Issue invites contributions that address cutting-edge approaches in cancer research using advanced in vitro platforms, with an emphasis on innovation, personalization, and technological integration. We welcome a broad range of submissions that explore the following topics:

1. Replicating Tumor Microenvironments

3D bioprinting, 3D mimicking, and efforts to mimic the complexity of tumor microenvironments are encouraged, including innovations in 3D cell cultures, tissue engineering, and the development of more sophisticated in vivo-like cancer models. Submissions that address critical biological processes such as tumor–stromal interactions, immune responses, and drug resistance within these platforms are highly welcomed.

2. Patient-Specific and Personalized Cancer Models

This category focuses on creating patient-derived cancer-on-chip models to predict individual responses to therapies. We encourage studies that use these platforms to test new treatment strategies, identify personalized combination therapies, and explore critical factors such as sex-based or genetic variability in cancer progression and treatment outcomes.

3. High-Throughput Drug Screening and Therapeutic Development

We seek contributions that leverage cancer-on-chip technologies for the efficient, high-throughput screening of drug candidates or combination therapies. Emphasis will be given to studies that demonstrate the clinical translatability of their findings and propose innovative tools to optimize drug development pipelines.

4. Integrating Multi-Omics Technologies

We invite studies that combine genomics, transcriptomics, proteomics, and metabolomics with cancer-on-chip platforms to uncover the molecular mechanisms of cancer progression, resistance, and therapy response. Submissions showcasing multi-omics-based approaches to identify novel biomarkers or therapeutic targets are of particular interest.

5. Machine Learning (ML) and Artificial Intelligence (AI) in Cancer-on-Chip Models

This category explores the transformative role of AI and ML in cancer-on-chip research. Submissions should focus on how these tools can enhance data analysis, predictive modeling, and decision-making, ultimately driving advances in personalized therapy, treatment response prediction, and cancer progression insights.

We look forward to receiving your valuable contributions to this Special Issue.

Dr. Mohammad Kohandel
Dr. Nafiseh Moghimi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.