Special Issue "Pharmacogenomics: Challenges and Future"
Deadline for manuscript submissions: 1 December 2021.
Interests: pharmacogenetics; biomarkers; cancer; medicine for drug addiction; alcoholism
Interests: cancer; integrative omics analysis; mirna therapeutics; pharmacogenomics
Special Issues and Collections in MDPI journals
Over the past years, the growth of pharmacogenomics (PGx) within the medical community has contributed to improving the practice of precision medicine. The genetic make-up influences interindividual variability in drugs response in terms of dosing, drug efﬁcacy/toxicity, hypersensitivity reactions, drug resistance, and clinical outcome. The discovery of PGx biomarkers may lead to tailored prescription, with a major impact on healthcare costs. Until now, FDA recommendations are provided for over 200 drugs in several therapeutic areas, especially for cancer. However, PGx implementation is still limited, and several barriers need to be overcome. For this Special Issue, contributions from both experts and beginners in the PGx field are invited. We welcome reviews and original research articles covering many aspects of PGx, from the discovery of new PGx biomarkers to methodological strategies to increase PGx knowledge and implementation, including validation of PGx biomarkers for clinical translation. Moreover, studies on ethical, legal, and economic aspects and on the role of epigenetic and non-genetic factors are also welcome.Dr. Mariamena Arbitrio
Dr. Maria Teresa Di Martino
Dr. Francesca Scionti
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 papers will be 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 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. Genes 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 2000 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.
- ADME genes
- Regulation in interindividual variability
- Polymorphic variant
- Single-Nucleotide polymorphism (SNP)
- Rare variants and copy number variation (CNV)
- Pharmacokinetics (PK)/Pharmacodynamics (PD)
- PGx genotyping strategies
- PGx implementation
- PGx discovery tools
- PGx validation tools
- Preemptive dose modulation
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Machine Learning in pharmacogenetics: review of the current literature.
Abstract: The current review provides the state of art of current applications of machine learning (ML) procedures in pharmacogenomics. ML deals with the study, the design and the development of algorithms that give computers capability to learn without being explicitly programmed. ML algorithms belong to the field of artificial intelligence, and, to date, have demonstrated innovative performance improvements on a wide range of tasks in biomedicine. According to the final goal, ML can be defined as Unsupervised (UML) of as Supervised (SML). UML techniques are used when the outcome is not known and the goal of the research is revealing the underlying structure of the data. On the other hand, SML techniques are applied when prediction is the focus of the research. The increasing use of sophisticated ML algorithms will likely be instrumental in improving knowledge in pharmacogenetics.
Pharmacogenomics: a step forward precision medicine in childhood asthm.
Abstract: Personalized medicine, an approach to care in which an individual’s information is used to target interventions to maximize health outcomes, is rapidly becoming a reality for many diseases. Childhood asthma is a heterogeneous disease and many children have uncontrolled symptoms. Therefore, an individualized approach is needed to improve asthma outcomes in children. The rapidly involving field of genomics and pharmacogenomics may provide a way to achieve asthma control and reduce future risks in children with asthma. In particular, pharmacogenomics can provide tools to identify novel molecular mechanisms and biomarkers to guide treatment. Advanced high-throughput technologies together with the patient’s pheno-endotypization will expand our knowledge of underlying molecular mechanisms involved in asthma pathophysiology and will contribute to select and stratify appropriate therapeutic strategies for each patient.
A standardized machine learning approach for discovering pharmacogenomic interactions based on Random Forests.
Abstract: The identification of somatic mutations and copy number alterations in tumor tissues produces huge amounts of data, which can be correlated with the diversity of therapeutic responses. The analysis of such big data requires powerful and computationally efficient algorithms able to disentangle the contribution of different mutations/alterations in predicting drug sensitivity of cell lines. At the same time, there is the need to produce standardized performance indicators to allow comparability across different therapeutic options. In this study, we matched two databases from the Cancer Cell Line Encyclopedia (CCLE) project, and the Genomics of Drug Sensitivity in Cancer (GDSC) project. For the 648 shared cell lines, we considered 48270 gene mutation/alteration profiles gathered from CCLE as the input features, and the area under the dose-response curve (AUC) for 265 drugs gathered from GDSC as the outcome. We therefore develop a standardized machine learning approach involving a three-step data reduction (focusing on the selection of driver, informative, and non-redundant predictors), and a combination of random forest and the concordance correlation coefficient (CCC) for the assessment of the model predictive power.
Title: Human Cytochrome P450 Genes and their role in Drug Metabolism