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Applied Sciences
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Anticancer Drugs: New Developments and Discoveries
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Anticancer Drugs: New Developments and Discoveries

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 12507

Special Issue Editors

Dr. Ana C. Henriques

E-Mail Website
Guest Editor
Católica Biomedical Research Centre, Faculty of Medicine, Catholic University of Portugal, Oeiras, Portugal
Interests: cancer; targeted therapies; microbiology; mitosis; aging
Dr. Patrícia M. A. Silva

E-Mail Website
Guest Editor
1. UNIPRO—Oral Pathology and Rehabilitation Research Unit, University Institute of Health Sciences (IUCS-CESPU), 4585-116 Gandra, Portugal
2. Associate Laboratory i4HB—Institute for Health and Bioeconomy, University Institute of Health Sciences—CESPU, 4585-116 Gandra, Portugal
3. UCIBIO—Applied Molecular Biosciences Unit, Translational Toxicology Research Laboratory, University Institute of Health Sciences (1H-TOXRUN, IUCS-CESPU), 4585-116 Gandra, Portugal
Interests: anticancer strategies; targeted therapy; cancer biomarkers; mitosis; apoptosis; drug discovery; bioactive compounds
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Anticancer drugs remain a fundamental pillar of cancer treatment, supporting an increasingly diverse range of therapeutic strategies. This Special Issue will focus on recent breakthroughs in anticancer drug research and development, and we welcome original research articles and reviews that explore innovative approaches to overcoming the current challenges and limitations associated with these. Relevant topics may include, but are not limited to, the following: the development of novel agents with reduced systemic toxicity; improved drug delivery systems (e.g., nanotechnology-based approaches); targeted therapies (through pathway- or molecule-specific mechanisms, with or without nanotechnology); immunotherapies (including cancer vaccines, adoptive cell therapies, etc.); and computational drug repositioning.

Dr. Ana C. Henriques
Dr. Patrícia M. A. Silva
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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.

Keywords

  • anticancer drugs
  • mechanisms of drug resistance
  • targeted therapy
  • drug delivery systems
  • nanotechnology in cancer treatment
  • cancer immunotherapy
  • drug toxicity and safety
  • computational drug repositioning
  • bioinformatics in oncology

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Published Papers (4 papers)

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Research

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13 pages, 2516 KB  
Article
Clinical Evaluation of Commercial Deep Learning and Model-Based Segmentation Algorithms for Male Pelvic Structures in Prostate Cancer Computed Tomography Scans
by Nicola Maffei, Marco Saguatti, Ercole Mazzeo, Marco Vernaleone, Giulia Miranda, Maria Victoria Gutierrez, Domenico Finocchiaro, Giulia Stocchi, Dario Corbelli, Maria Pia Morigi, Bruno Meduri, Alessio Bruni and Gabriele Guidi
Appl. Sci. 2026, 16(3), 1399; https://doi.org/10.3390/app16031399 - 29 Jan 2026
Viewed by 606
Abstract
The performances of two autosegmentation algorithms were evaluated on 28 anonymized pelvic CT scans as a pilot study for the clinical implementation of a semi-automatic workflow. Four organs at risk (OARs), namely the rectum, bladder, and femoral heads, were contoured manually by an [...] Read more.
The performances of two autosegmentation algorithms were evaluated on 28 anonymized pelvic CT scans as a pilot study for the clinical implementation of a semi-automatic workflow. Four organs at risk (OARs), namely the rectum, bladder, and femoral heads, were contoured manually by an expert radiation oncologist (RO)—considered as the ground truth (GT)—and by model-based segmentation (MBS) and deep learning (DL) algorithms. Autocontouring performances were evaluated using a qualitative scoring system, contouring time analysis, and five geometrical indices: the 95th percentile Hausdorff Distance (95HD), Dice Similarity Coefficient (DSC), Surface Dice Similarity Coefficient (SDSC), Added Path Length (APL), and Relative Added Path Length (RAPL). Considering total median value for the four OARs, both MBS and DL showed clinically acceptable results with differences between the two algorithms being not statistically significant for almost all indices. The DL autocontouring algorithm achieved high geometric accuracy, high scores from the ROs, and consistent performances with all validation indices for every OAR. The MBS algorithm achieved high geometric accuracy for the femoral heads and bladder. The DL algorithm required 30 s to contour all the OARs, and the MBS algorithm required 90 s, showing a time gain compared with the manual contours, which took 20 min for each case. The DL autocontouring algorithm obtained promising but preliminary results with every evaluation metric and for every analyzed OAR. The application of the MBS algorithm as the only contouring tool still presents challenges. Full article
(This article belongs to the Special Issue Anticancer Drugs: New Developments and Discoveries)
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22 pages, 13716 KB  
Article
In Silico Identification of Six Mushroom-Derived Sterol and Triterpenoid Compounds as Potential P-Glycoprotein Modulators in Multidrug Resistance
by Jéssica Fonseca, Carlos S. H. Shiraishi, Rui M. V. Abreu, Sara Ricardo and Josiana A. Vaz
Appl. Sci. 2025, 15(16), 8772; https://doi.org/10.3390/app15168772 - 8 Aug 2025
Viewed by 1140
Abstract
The overexpression of P-glycoprotein (P-gp) is often directly related to multidrug resistance (MDR), one of the greatest challenges in cancer treatment. This transmembrane efflux pump decreases the intracellular concentrations of chemotherapy drugs, reducing their effectiveness and resulting in treatment failure. This work used [...] Read more.
The overexpression of P-glycoprotein (P-gp) is often directly related to multidrug resistance (MDR), one of the greatest challenges in cancer treatment. This transmembrane efflux pump decreases the intracellular concentrations of chemotherapy drugs, reducing their effectiveness and resulting in treatment failure. This work used in silico methods to assess the potential of bioactive chemicals produced from mushrooms as P-gp modulators. A database comprising 211 bioactive compounds from mushrooms was investigated using molecular docking and virtual screening techniques against the P-gp structure. The compounds ergosta-4,6,8(14),22-tetraen-3-one, lucidumol A, (22E,24S)-ergosta-4,22-dien-3-one, antcin K, 3,11-dioxolanosta-8,24(Z)-diene-26-oic acid, and (22E)-19-norergosta-5,7,9,22-tetraen-3β-ol were identified as the six best candidates from our database of mushroom compounds based on their binding affinities, toxicity predictions, and pharmacological properties assessed through ADME analyses (absorption, distributions, metabolism, and excretion). These six compounds exhibited strong binding affinities, with binding energies ranging from −12.31 kcal/mol to −10.93 kcal/mol, all showing higher affinities than the control, tariquidar, which had a binding energy of −10.78 kcal/mol. Toxicity predictions indicated favorable safety profiles for all six, while ADME analyses found that all six compounds had high oral bioavailability and a low probability of acting as P-gp substrates. These results position bioactive mushroom compounds, particularly these six, as promising P-gp modulators, suggesting positive outcomes in cancer treatment. Full article
(This article belongs to the Special Issue Anticancer Drugs: New Developments and Discoveries)
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Review

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45 pages, 2725 KB  
Review
Injectable Hydrogel Systems for Targeted Drug Delivery: From Site-Specific Application to Design Strategy
by Yeji Lee, Minji Kim, Nurihan Kim, Seonyeong Byun, Soonmin Seo and Jung Y. Han
Appl. Sci. 2025, 15(21), 11599; https://doi.org/10.3390/app152111599 - 30 Oct 2025
Cited by 12 | Viewed by 5017
Abstract
Injectable hydrogels are adaptable drug delivery systems capable of forming localized depots that align with the anatomical and physiological constraints of administration sites. Their performance depends on both the injection environment and the properties of the therapeutic cargo. Applications span ocular, intra-articular, subcutaneous, [...] Read more.
Injectable hydrogels are adaptable drug delivery systems capable of forming localized depots that align with the anatomical and physiological constraints of administration sites. Their performance depends on both the injection environment and the properties of the therapeutic cargo. Applications span ocular, intra-articular, subcutaneous, intramuscular, tumoral, central nervous system, and mucosal delivery, where hydrogels address challenges of clearance, retention, and compatibility. Beyond bulk depots, particulate hydrogel formats such as microgels and nanogels improve syringeability, modularity, and integration with nanoparticle carriers. Functional versatility arises from stimuli responsiveness, including pH, enzymatic, thermal, redox, and light triggers, and from hybrid designs that integrate multiple cues for precision control. Loading strategies range from passive encapsulation to affinity binding and covalent conjugation, with release governed by diffusion, degradation, and stimuli-modulated kinetics. Translational progress depends on reproducible fabrication, scalable manufacturing, and device integration, while site-dependent constraints and regulatory hurdles remain significant challenges. Full article
(This article belongs to the Special Issue Anticancer Drugs: New Developments and Discoveries)
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37 pages, 2129 KB  
Review
The Dual Role of Metformin: Repurposing an Antidiabetic Drug for Cancer Therapy
by Flávia Barbosa, Andrea Cunha, Joana Barbosa, Juliana Faria and Odília Queirós
Appl. Sci. 2025, 15(21), 11576; https://doi.org/10.3390/app152111576 - 29 Oct 2025
Cited by 3 | Viewed by 5320
Abstract
Maintaining glucose homeostasis is vital for normal physiological function, and any disturbance in this balance is associated with the development of degenerative and chronic diseases, like Type 2 Diabetes (T2D) and certain types of cancer, where altered glucose metabolism plays a central role. [...] Read more.
Maintaining glucose homeostasis is vital for normal physiological function, and any disturbance in this balance is associated with the development of degenerative and chronic diseases, like Type 2 Diabetes (T2D) and certain types of cancer, where altered glucose metabolism plays a central role. Epidemiological evidence indicates a positive association between diabetes and an increased risk of developing certain types of cancer. Such a correlation may be driven by shared risk factors, namely obesity, inflammation, and insulin resistance. The observed association between diabetes and an increased risk of certain cancers, along with the rising incidence of both diseases, has in recent years raised interest in treatments that may target both conditions. Among them, the biguanide metformin, the first-line drug prescribed for T2D, has attracted significant attention as a repurposed drug due to its potential role in cancer treatment. Metformin is a glucose-lowering drug that reduces hepatic glucose production and improves insulin sensitivity, promoting glucose uptake by the skeletal muscle, contributing to better glycemic control in individuals with T2D and prediabetic syndromes. However, beyond its metabolic effects, metformin also influences key signaling pathways involved in cell growth and survival, such as the AMP-activated protein kinase (AMPK)/mTOR axis, raising interest in its potential application as an anticancer agent. Furthermore, metformin inhibits mitochondrial complex I, disrupting cellular energy production, which is essential for cancer proliferation. This review aims to explore and clarify the multifunctional role of metformin in both T2D and cancer, focusing on the metabolic alterations observed in these diseases. It highlights how glucose metabolism dysregulation contributes to disease progression in both contexts and explores the molecular targets of metformin in each condition and its potential for dual therapeutic benefit. Finally, selected clinical trials concerning metformin use in cancer therapy, alone or in combination, will be presented, highlighting its potential to enhance treatment response, reduce resistance, and improve overall patient outcomes. Full article
(This article belongs to the Special Issue Anticancer Drugs: New Developments and Discoveries)
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