Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics.

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.

Toward Integration of Pharmacogenomic Tests into Daily Clinical Practice: A General Survey for Polish Healthcare Workers.

BACKGROUND Pharmacogenomics (PGx) has a direct influence on personalized drug therapy for various types of disorders and has been proven to have an important role in the future of medicine. The present study evaluated the awareness of PGx testing of clinicians and healthcare workers in the Republic of Poland. To the best of our knowledge, this is the first direct assessment of Polish healthcare professionals' attitudes toward introducing PGx tests into daily clinical practice. MATERIAL AND METHODS We used a comprehensive anonymous questionnaire with queries on level of education, background knowledge of PGx tests, advantages and barriers for implementation of such tests, and clinicians' desire to order the test that was distributed online to doctors, healthcare workers, related students/Ph.D. students, and administrative staff managing healthcare units. RESULTS We gathered 315 responses. According to the answers, two-thirds of participants had heard about PGx before (64.4%). An overwhelming majority of respondents appreciated the benefits of PGx (93.3%). Indeed, prior knowledge and level of education showed significant associations with positive attitudes toward PGx clinical testing (P≤0.05). However, all participants agreed there are major challenges for including such tests as part of routine clinical practice. CONCLUSIONS While the awareness and interest in PGx clinical testing in Polish healthcare providers are rising, some main barriers for implementation of these tests still need to be addressed in Poland.

Identification with all humanity-A test of the factorial structure and measurement invariance of the scale in five countries.

Identification with all humanity measured as an individual characteristic is an important factor related to social and international relations, such as concern for global issues and human rights, prosocial attitudes, intergroup forgiveness, attitudes toward immigrants, solving global problems, reactions to hate crimes and dehumanisation. We examine the factorial structure, psychometric properties and measurement invariance of the Identification with All Humanity (IWAH) scale in student samples from five countries (the United States, Poland, France, Mexico and Chile; N = 1930). Separate confirmatory factor analyses (CFAs) for each country showed a second-order model of one superordinate factor with two subfactors. The cross-country validation of the scale, based on multigroup CFA, confirmed configural and metric invariance between countries for raw scores, and full metric invariance for "pure" scores. This study showed that the IWAH scale can be successfully used for cross-country research and the results from different countries can be compared and integrated.

Integration of human cell lines gene expression and chemical properties of drugs for Drug Induced Liver Injury prediction.

MOTIVATION: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI can bring a significant reduction in the cost of clinical trials. In this work we examined whether occurrence of DILI can be predicted using gene expression profile in cancer cell lines and chemical properties of drugs. METHODS: We used gene expression profiles from 13 human cell lines, as well as molecular properties of drugs to build Machine Learning models of DILI. To this end, we have used a robust cross-validated protocol based on feature selection and Random Forest algorithm. In this protocol we first identify the most informative variables and then use them to build predictive models. The models are first built using data from single cell lines, and chemical properties. Then they are integrated using Super Learner method with several underlying methods for integration. The entire modelling process is performed using nested cross-validation. RESULTS: We have obtained weakly predictive ML models when using either molecular descriptors, or some individual cell lines (AUC ∈(0.55-0.61)). Models obtained with the Super Learner approach have a significantly improved accuracy (AUC=0.73), which allows to divide substances in two categories: low-risk and high-risk.

Sensitivity analysis based on the random forest machine learning algorithm identifies candidate genes for regulation of innate and adaptive immune response of chicken.

Two categories of immune responses-innate and adaptive immunity-have both polygenic backgrounds and a significant environmental component. The goal of the reported study was to define candidate genes and mutations for the immune traits of interest in chickens using machine learning-based sensitivity analysis for single-nucleotide polymorphisms (SNPs) located in candidate genes defined in quantitative trait loci regions. Here the adaptive immunity is represented by the specific antibody response toward keyhole limpet hemocyanin (KLH), whereas the innate immunity was represented by natural antibodies toward lipopolysaccharide (LPS) and lipoteichoic acid (LTA). The analysis consisted of 3 basic steps: an identification of candidate SNPs via feature selection, an optimisation of the feature set using recursive feature elimination, and finally a gene-level sensitivity analysis for final selection of models. The predictive model based on 5 genes (MAPK8IP3 CRLF3, UNC13D, ILR9, and PRCKB) explains 14.9% of variance for KLH adaptive response. The models obtained for LTA and LPS use more genes and have lower predictive power, explaining respectively 7.8 and 4.5% of total variance. In comparison, the linear models built on genes identified by a standard statistical analysis explain 1.5, 0.5, and 0.3% of variance for KLH, LTA, and LPS response, respectively. The present study shows that machine learning methods applied to systems with a complex interaction network can discover phenotype-genotype associations with much higher sensitivity than traditional statistical models. It adds contribution to evidence suggesting a role of MAPK8IP3 in the adaptive immune response. It also indicates that CRLF3 is involved in this process as well. Both findings need additional verification.