In silico methods for the prediction of drug-induced cardiotoxicity

Unexpected adverse reactions, especially unsafe cardiac effects, are a major concern of pharmaceutical companies that can prompt them to both discontinue drugs currently in development and withdraw drugs already on the market. Therefore, the safety assessment is a key stage of both the drug development process and the current regulatory framework of clinical trials. Given the importance of unforeseen acute electrophysiological effects in precipitating potentially lethal arrhythmias, the current preclinical testing stages of drug development are largely focused on their detection. However, a substantial number of drugs also affect cardiac function on many other levels, including contractility, mitochondria function and cell signalling. A number of in vitro, in vivo and in silico approaches capable of detecting different types of possible cardiovascular side effects have been proposed recently. Among those, human-based computational methods hold a great potential to increase the productivity of drug discovery pipelines, drive a more rational drug design and replace costly animal experiments that have limited translational ability for humans. Therefore, the goal of this thesis is to propose a computational approach to predict drug-induced cardiotoxicity. A multi-label machine learning classification approach is used to simultaneously predict multiple forms of clinical cardiac side effects and take into account relationships between those forms of toxicity. In the last part of this thesis, the effects of trafficking impairment, as one of the cardiotoxicity mechanisms, are then investigated using simulations of action potential models

Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification

For the past several decades, research in understanding the molecular basis of human muscle aging has progressed significantly. However, the development of accessible tissue-specific biomarkers of human muscle aging that may be measured to evaluate the effectiveness of therapeutic interventions is still a major challenge. Here we present a method for tracking age-related changes of human skeletal muscle. We analyzed publicly available gene expression profiles of young and old tissue from healthy donors. Differential gene expression and pathway analysis were performed to compare signatures of young and old muscle tissue and to preprocess the resulting data for a set of machine learning algorithms. Our study confirms the established mechanisms of human skeletal muscle aging, including dysregulation of cytosolic Ca2+ homeostasis, PPAR signaling and neurotransmitter recycling along with IGFR and PI3K-Akt-mTOR signaling. Applying several supervised machine learning techniques, including neural networks, we built a panel of tissue-specific biomarkers of aging. Our predictive model achieved 0.91 Pearson correlation with respect to the actual age values of the muscle tissue samples, and a mean absolute error of 6.19 years on the test set. The performance of models was also evaluated on gene expression samples of the skeletal muscles from the Gene expression Genotype-Tissue Expression (GTEx) project. The best model achieved the accuracy of 0.80 with respect to the actual age bin prediction on the external validation set. Furthermore, we demonstrated that aging biomarkers can be used to identify new molecular targets for tissue-specific anti-aging therapies

Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers

Abstract There is an association between smoking and cancer, cardiovascular disease and all-cause mortality. However, currently, there are no affordable and informative tests for assessing the effects of smoking on the rate of biological aging. In this study we demonstrate for the first time that smoking status can be predicted using blood biochemistry and cell count results andthe recent advances in artificial intelligence (AI). By employing age-prediction models developed using supervised deep learning techniques, we found that smokers exhibited higher aging rates than nonsmokers, regardless of their cholesterol ratios and fasting glucose levels. We further used those models to quantify the acceleration of biological aging due to tobacco use. Female smokers were predicted to be twice as old as their chronological age compared to nonsmokers, whereas male smokers were predicted to be one and a half times as old as their chronological age compared to nonsmokers. Our findings suggest that deep learning analysis of routine blood tests could complement or even replace the current error-prone method of self-reporting of smoking status and could be expanded to assess the effect of other lifestyle and environmental factors on aging

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output the AAE uses a vector of binary fingerprints and concentration of the molecule. In the latent layer we also introduced a neuron responsible for growth inhibition percentage, which when negative indicates the reduction in the number of tumor cells after the treatment. To train the AAE we used the NCI-60 cell line assay data for 6252 compounds profiled on MCF-7 cell line. The output of the AAE was used to screen 72 million compounds in PubChem and select candidate molecules with potential anticancer properties. This approach is a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties

Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity in order to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic and (iv) machine learning methods applied to dissect, quantify and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology towards coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.Comment: 27 pages, 2 figure

Fulminant invasive group A streptococcal infection in children

Group A streptococcal infections dominate among invasive streptococcal infections, with the major causative agent, Streptococcus pyogenes, being quite stable in the environment and bearing a large number of chromosome encoded pathogenicity factors or transmitted by horizontal transfer through bacteriophages. Different genetic variants of S. pyogenes can have a different set of pathogenicity factors able to change during pathogen evolution and determine virulence level for specific isolate. With a short incubation period, the disease can proceed with developing invasive infection and toxic shock syndrome with unfavorable outcome within 7 days from disease onset. The purpose of this article is to increase the doctors’ alertness to early recognition and diagnosis, which directly affects adequate treatment in a timely manner and disease outcome. The data on streptococcal morbidity in Russia and worldwide, review of laboratory diagnostic methods and pathogen genetic typing are presented. The maximum number of cases of streptococcal septicemia in Russia was registered in 2022, which accounted for 69% of all cases during the 2014–2022 observation period. The article also describes two clinical cases of fulminant invasive group A streptococcal infection in children with symptoms of acute respiratory viral infections at the onset of the disease. The results of various laboratory diagnostics methods verifying the diagnosis are presented. The genetic characterization of microbial isolates was performed by deep DNA sequencing. In the biological material from patients (including autopsy in one case), S. pyogenes sequence type ST-28, serotypes emm-1.25 and emm-1.0 were identified. The increasing importance of invasive streptococcal infection for health care in Russia and other countries may be associated with a possible change in dominating S. pyogenes genetic variants. In this regard, the study on circulating S. pyogenes genotypes on an ongoing basis as part of surveillance of streptococcal infection and development of vaccine for specific prevention are required