Molecular determinants of drug-specific sensitivity for epidermal growth factor receptor (EGFR) exon 19 and 20 mutants in non-small cell lung cancer.

We hypothesized that aberrations activating epidermal growth factor receptor (EGFR) via dimerization would be more sensitive to anti-dimerization agents (e.g., cetuximab). EGFR exon 19 abnormalities (L747_A750del; deletes amino acids LREA) respond to reversible EGFR kinase inhibitors (TKIs). Exon 20 in-frame insertions and/or duplications (codons 767 to 774) and T790M mutations are clinically resistant to reversible/some irreversible TKIs. Their impact on protein function/therapeutic actionability are not fully elucidated.In our study, the index patient with non-small cell lung cancer (NSCLC) harbored EGFR D770_P772del_insKG (exon 20). A twenty patient trial (NSCLC cohort) (cetuximab-based regimen) included two participants with EGFR TKI-resistant mutations ((i) exon 20 D770>GY; and (ii) exon 19 LREA plus exon 20 T790M mutations). Structural modeling predicted that EGFR exon 20 anomalies (D770_P772del_insKG and D770>GY), but not T790M mutations, stabilize the active dimer configuration by increasing the interaction between the kinase domains, hence sensitizing to an agent preventing dimerization. Consistent with predictions, the two patients harboring D770_P772del_insKG and D770>GY, respectively, responded to an EGFR antibody (cetuximab)-based regimen; the T790M-bearing patient showed no response to cetuximab combined with erlotinib. In silico modeling merits investigation of its ability to optimize therapeutic selection based on structural/functional implications of different aberrations within the same gene

Laryngeal Cancer Diagnosis via miRNA-based Decision Tree Model

Purpose Laryngeal cancer (LC) is the most common head and neck cancer, which often goes undiagnosed due to the expensiveness and inaccessible nature of current diagnosis methods. Many recent studies have shown that microRNAs (miRNAs) are crucial biomarkers for a variety of cancers. Methods In this study, we create a decision tree model for the diagnosis of laryngeal cancer using a calculated miRNAs’ attributes, such as sequence-based characteristics, predicted miRNA target genes, and gene pathways. This series of attributes is extracted from both differentially expressed blood-based miRNAs in laryngeal cancer and random, non-associated with cancer miRNAs. Results Several machine-learning (ML) algorithms were tested in the ML model, and the Hoeffding Tree (HT) classifier yields the highest accuracy (86.8%) in miRNAs-based recognition of laryngeal cancer. Furthermore, HT-based model is validated with the independent laryngeal cancer datasets and can accurately diagnose laryngeal cancer with 86% accuracy. We also explored the biological relationships of the attributes used in HT-based model to understand their relationship with cancer proliferation or suppression pathways. Conclusion Our study demonstrates that the proposed model and an inexpensive miRNA testing strategy have the potential to serve as a cost-effective and accessible method for diagnosing laryngeal cancer

New inhibitors of p38 mitogen-activated protein kinase: Repurposing of existing drugs with deep learning

P38-alpha (MAPK14) is a protein kinase that is implicated in the pathological mechanisms of BAG3 P209L myofibrillar myopathy, cancers, Alzheimer’s disease and other diseases like rheumatoid arthritis. Inhibition of p38 has shown promise as treatment for these diseases. Traditional drug discovery methods were unable to create both effective and safe small molecule inhibitors, so we used machine learning to elucidate potential p38 blockers from existing FDA-approved drugs. Using available bioactivity data, we determined the best existing p38 inhibitors and applied fingerprint clustering to isolate the compounds with similar structures. Descriptors were calculated for these clustered compounds and the most important of these descriptors were determined through a machine-learning based feature selection algorithm. This data served as the training set for a deep neural network that was fine-tuned to a 92% validation accuracy. The neural network model was applied to a database of FDA-approved drugs, revealing 149 potential p38 inhibitors, whose efficacy were confirmed by docking simulations to be statistically significantly higher than random FDA drugs and slightly higher than known inhibitors. Our study not only reveals potential treatments for p38-mediated diseases but also demonstrates the capability of integrating various machine-learning techniques and computational algorithms to predict novel functions of existing pharmaceuticals

Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics

The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning (ML), the model was developed. We identified seven metabolic pathways related to TC: Pyrimidine metabolism, Tyrosine metabolism, Glycine, serine, and threonine metabolism, Pantothenate and CoA biosynthesis, Arginine biosynthesis, Phenylalanine Metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis. The ML classifications’ accuracies were confirmed through 10-fold cross validation, and the most accurate classification being 87.30%. The metabolic pathways identified in relation to TC and the changes within such pathways can contribute to more pattern recognition for diagnostics of TC patients and assistance to TC screening. The results also point to a possibility for the development of using ML methods for TC diagnostics and further applications of ML in general cancer-related metabolite analysis

Implementation of Machine Learning-Based System for Early Diagnosis of Feline Mammary Carcinomas through Blood Metabolite Profiling

Feline mammary carcinoma (FMC) is a prevalent and fatal carcinoma that predominantly affects unsprayed female cats. FMC is the third most common carcinoma in cats but is still underrepresented in research. Current diagnosis methods include physical examinations, imaging tests, and fine-needle aspiration. The diagnosis through these methods is sometimes delayed and unreliable, leading to increased chances of mortality. The objective of this study was to identify the biomarkers, including blood metabolites and genes, related to feline mammary carcinoma, study their relationships, and develop a machine-learning (ML) model for the early diagnosis of the disease. We analyzed blood metabolites of felines with mammary carcinoma using the pathway analysis feature in the MetaboAnalyst software. The metabolic pathways that were elucidated to be associated with this disease include Alanine, aspartate and glutamate metabolism, Glutamine and glutamate metabolism, Arginine biosynthesis, and Glycerophospholipid metabolism. Furthermore, we also elucidated several genes that play a significant role in the development of FMC, such as ERBB2, PDGFA, EGFR, FLT4, ERBB3, FIGF, PDGFC, PDGFB through STRINGdb, a database of known and predicted protein–protein interactions, and MetaboAnalyst 5.0. We utilized ML methods to recognize FMC using blood metabolites of sick patients. The best-performing model was able to predict metabolite class with an accuracy of 85%. In conclusion, our findings demonstrate that the identification of the biomarkers associated with FMC and the affected metabolic pathways can aid in the early diagnosis of feline mammary carcinoma

Repurposing of drugs for combined treatment of COVID 19 cytokine storm using machine learning

Context: SARS CoV 2 induced cytokine storm is the major cause of COVID 19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines production. This approach provided very limited success, since there are multiple proteins involved in the complex cell signaling disease mechanisms. Objective: To elucidate using machine learning (ML) the set of drugs targeting a group of proteins involved in the mechanism of cytokine storm. Methods: We selected for targeting five proteins: Angiotensin II receptor type 1 (AT1R), A disintegrin and metalloprotease 17 (ADAM17), Nuclear Factor Kappa B (NF B), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3) that are involved in the SARS CoV 2 induced cytokine storm pathway. We developed ML models for these five proteins, using known active inhibitors. After developing the model for each of these proteins, FDA-approved drugs were screened to find novel therapeutics for COVID 19. Results: We identified twenty drugs that are active for four proteins and eight drugs active for five proteins. Mitomycin C is the most active drug across all five proteins with an average prediction score of 0.886. For further validation of these results, we used the PyRx software to conduct protein–ligand docking experiments and calculated the binding affinity. The docking results support findings by the ML model. Conclusions: It is possible to elucidate the drugs, targeting simultaneously several proteins related to cytokine production to treat the cytokine storm in COVID 19 patients

Potential COVID-19 Protease Inhibitors: Repurposing FDAapproved Drugs

Using as a template the crystal structure of COVID-19 protease, we developed a pharmacophore of functional centers of the protease inhibitor-binding pocket. Then we conducted data mining of the conformational database of FDA-approved drugs. This search brought 64 compounds that can be potential inhibitors of COVID-19 protease. The conformations of these compounds undergone 3D fingerprint similarity clusterization. Then we conducted docking of possible conformers of these drugs to the binding pocket of protease. We also conducted the same docking of random compounds. Free energies of the docking interaction for the selected compounds were clearly lower than random compounds. Three of the selected compounds were carfilzomib, cyclosporine A, and azithromycin—the drugs that already are tested for COVID-19 treatment. Among the selected compounds are two HIV protease inhibitors and two hepatitis C protease inhibitors. We recommend testing of the selected compounds for treatment of COVID-19.<br /

Bovine leukemia virus relation to human breast cancer: Meta-analysis

Bovine leukemia virus (BLV) is a virus that infects cattle around the world and is very similar to the human T-cell leukemia virus (HTLV), which causes adult T-cell leukemia/lymphoma (ATL). Recently, presence of BLV DNA and protein was demonstrated in commercial bovine products and in humans. BLV DNA is generally found at higher rates in humans who have or will develop breast cancer, according to research done with subjects from several countries. These findings have led to a hypothesis that BLV transmission plays a role in breast cancer oncogenesis in humans. Here we summarize the current knowledge in the field

Repurposing of drugs for combined treatment of COVID-19 cytokine storm using machine learning

Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) induced cytokine storm is the major cause of COVID-19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines production. This approach provided very limited success, since there are multiple proteins involved in the complex cell signaling disease mechanisms. We targeted five proteins: Angiotensin II receptor type 1 (AT1R), A disintegrin and metalloprotease 17 (ADAM17), Nuclear Factor‑Kappa B (NF‑κB), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3), which are involved in the SARS‑CoV‑2 induced cytokine storm pathway. We developed machine-learning (ML) models for these five proteins, using known active inhibitors. After developing the model for each of these proteins, FDA-approved drugs were screened to find novel therapeutics for COVID‑19. We identified twenty drugs that are active for four proteins with predicted scores greater than 0.8 and eight drugs active for all five proteins with predicted scores over 0.85. Mitomycin C is the most active drug across all five proteins with an average prediction score of 0.886. For further validation of these results, we used the PyRx software to conduct protein–ligand docking experiments and calculated the binding affinity. The docking results support findings by the ML model. This research study predicted that several drugs can target multiple proteins simultaneously in cytokine storm-related pathway. These may be useful drugs to treat patients because these therapies can fight cytokine storm caused by the virus at multiple points of inhibition, leading to synergistically effective treatments