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Background: This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes., Methods: Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. ML model efficiency was calculated by internal and external cross-validation during recursive feature elimination. Protein modelling was also performed for predicted highly damaging variants with lower frequencies. Genotype-phenotype correlations were implemented for top selected variants in terms of highest possibility of being damaging., Results: Five deleterious pharmacovariants in the RYR1, POLG, ANXA11, CCNH, and CDH23 genes identified in step (a) and subsequent analysis displayed high impact on drug-related phenotypes. Also, the utilization of recursive feature elimination achieved a subset of 175 malfunction pharmacovariants in 135 drug-related genes that were used by the RF model with fivefold internal cross-validation, resulting in an area under the curve of 0.9736842 with an average accuracy of 0.9818 (95% CI: 0.89, 0.99) on predicting whether a carrying individuals will develop adverse drug reactions or not. However, the external cross-validation of the same model indicated a possible false positive result when dealing with a low number of observations, as only 60 important variants in 49 genes were displayed, giving an AUC of 0.5384848 with an average accuracy of 0.9512 (95% CI: 0.83, 0.99)., Conclusion: While there are some technologies for functionally assess not-interpreted pharmacovariants, there is still an essential need for the development of tools, methods, and algorithms which are able to provide a functional prediction for every single pharmacovariant in both large-scale datasets and small cohorts. Our approaches may bring new insights for choosing the right computational assessment algorithms out of high throughput DNA sequencing data from small cohorts to be used for personalized drug therapy implementation., (© 2023. The Author(s).)

Myelodysplastic syndromes (MDS) consist of a group of hematological malignancies characterized by ineffective hematopoiesis, cytogenetic abnormalities, and often a high risk of transformation to acute myeloid leukemia (AML). So far, there have been only a very limited number of studies assessing the epigenetics component contributing to the pathophysiology of these disorders, but not a single study assessing this at a genome-wide level. Here, we implemented a generic high throughput epigenomics approach, using methylated DNA sequencing (MeD-seq) of LpnPI digested fragments to identify potential epigenomic targets associated with MDS subtypes. Our results highlighted that PCDHG and ZNF gene families harbor potential epigenomic targets, which have been shown to be differentially methylated in a variety of comparisons between different MDS subtypes. Specifically, CpG islands, transcription start sites and post-transcriptional start sites within ZNF124, ZNF497 and PCDHG family are differentially methylated with fold change above 3,5. Overall, these findings highlight important aspects of the epigenomic component of MDS syndromes pathogenesis and the pharmacoepigenomic basis to the hypomethylating agents drug treatment response, while this generic high throughput whole epigenome sequencing approach could be readily implemented to other genetic diseases with a strong epigenetic component., (© 2023. The Author(s).)

Background: There is an increasing interest worldwide in investigating healthcare stakeholders' perceptions and intentions to adopt pharmacogenomics (PGx) into clinical practice. However, the existing inquiries based on well-established theories and models that interpret their intentions to implement PGx are scarce. This study is the first that examines the impact of selected factors on health science students' intention to adopt genetic testing applications using the technology acceptance model while it compares two different cultural groups: Greeks (Europe; Christian) and Malays (Asia; Muslim)., Results: Malay students were more persuaded about benefits of genomics for drug management compared to their Greek counterparts. However, participants from both countries appear to be particularly convinced about the benefits of genomics on disease management. Moreover, students from both countries considered the potential misuse of genetic information by corporate or government bodies as their most important concern; Greek students appeared to be considerably less worried than Malay about other probable hazards such as the deficient protection of privacy and confidentiality, which could be attributed to their religious background. Participants from both samples expressed very positive attitudes towards genetic research and testing and their favourable intentions to adopt genetic testing for personal use. Exploratory factors analysis and path analysis yielded quite similar results for both samples. Path analysis revealed that the factors of attitudes, concerns, drug management benefits and disease management benefits significantly influenced students' intentions to adopt genetic testing for personal use, with attitudes being the most inspirational factor with rather high impact, while training did not seem to affect participant's intentions. The squared multiple correlation of both models was quite satisfactory reaching to 0.55 for the Malaysian sample., Conclusion: Similarities in the results of the two groups along with the relevant validity and reliability tests indicate that the proposed model is a good fit for future studies to interpret stakeholders' intentions to adopt genetic testing. Therefore, it can provide a promising and reliable basis for future model development to explain the relationships between intentions to adopt genetic testing and its predictors., (© 2022. The Author(s).)