Pre-clinical Studies of Personalized Medicine for Cancer Research
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Biomarkers".
Deadline for manuscript submissions: 30 September 2024 | Viewed by 12303
Special Issue Editor
Interests: in silico pharmacology; PBPK; drug metabolism; drug combination; drug delivery; peptides; ADME; DMPK; pharmacokinetic
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Pre-clinical studies of personalized medicine for cancer involve the exploration and development of targeted therapies and treatment strategies that are tailored to the unique genetic and molecular characteristics of an individual's cancer. These studies play a crucial role in advancing the field of oncology by providing valuable insights into the efficacy, safety, and potential side effects of personalized treatments before they are tested in human clinical trials.
This Special Issue is dedicated to key aspects of pre-clinical studies for personalized medicine in cancer research, for patent-derived models (which can include cell lines, xenografts (tumors grown in mice using patient-derived cells), or organoids (3D cultures of cancer cells that mimic the original tumor)), genomic profiling (specific mutations, alterations, and biomarkers that may be targeted by personalized therapies), drug screening (targeted therapies, immunotherapies, and conventional chemotherapeutics, or drug repurposing), biomarker validation (identified through genomic profiling), combination therapies (synergies between different drugs or treatment modalities), toxicity and safety assessment (understanding the therapeutic window and ensuring that the treatment is safe for patients), mechanistic studies (how specific drugs interact with molecular targets and pathways within cancer cells), tumor heterogeneity (pre-clinical studies taking into account the heterogeneity of tumors, recognizing that different regions of a tumor may have distinct genetic profiles), animal studies (understanding how treatments perform in a whole organism and providing insights into potential systemic effects), and data analysis interpretation (statistical methods and bioinformatics play a crucial role in the effectiveness and safety of personalized treatments).
In this Special Issue, original articles and reviews would be highly appreciated.
I look forward to receiving your contributions.
Prof. Dr. Nuno Vale
Guest Editor
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 communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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. Cancers 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 2900 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
- patient-derived models
- genomic profiling
- drug screening
- biomarker validation
- combination therapies
- mechanistic studies
- tumor heterogeneity
- data analysis and interpretation
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