Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis.

BACKGROUND AND OBJECTIVES: The prediction of multiple drug efficacies using machine learning prediction techniques based on clinical and molecular attributes of tumors is a new approach in the field of precision medicine of oncology. The selection of suitable and effective therapeutic drugs among the potential drugs is performed computationally considering the tumor features. In this study, we developed and validated machine learning models to predict the efficacy of five anti-cancer drugs according to the clinical and molecular attributes of 30 oral squamous cell carcinoma (OSCC) cohorts. This sounds a bit odd - consider: Ranking of the drugs was achieved using their apoptotic priming. METHODS: We developed multiple drug efficacy prediction models based on three types of tumor characteristics by applying machine learning methods, including multi-target regression (MTR) and support vector regression (SVR). The prediction accuracy of existing machine learning methods was enhanced by introducing novel pre-processing techniques to develop Enhanced MTR (E_MTR), Enhanced Log-based MTR (EL_MTR), Enhanced Multi-target SVR (EM_SVR), and Enhanced Log-based Multi-target SVR (ELM_SVR). As a unique capability, ELM_SVR and EL_MTR rank the drugs based on their predicted efficacy. All the drug efficacy prediction models were built using OSCC real samples and theoretical samples. The best model was selected was based on dataset size and evaluation metrics, such as error terms, residuals and parameter tuning, and cross-validated (CV) using 30 real samples and 340 theoretical samples. RESULTS: When 30 real tumor samples were used for the train-test and CV methods, MTR models predicted the efficacy with less error than SVR models. Comparatively, using 340 theoretical samples for the train-test and CV methods, though MTR improved the performance, SVR predicted the efficacy with zero error. We found that, for small samples, the proposed MTR provided a 0.01 difference between actual apoptotic priming and predicted priming of five drugs. For large samples, the predicted values by the proposed SVR had a difference of 0.00001. The error terms (Actual vs. Predicted) also reveal that the enhanced log model is suitable when MTR is applied. Meanwhile, the enhanced model is suitable for SVR learning for multiple drug efficacy prediction. It was found that the predicted ranks of the drugs based on the multi-targeted efficacy prediction exactly match the actual rankings. CONCLUSION: We developed efficient statistical and machine learning models using MTR and SVR analysis for anticancer drug efficacy, which will be useful in the field of precision medicine to choose the most suitable drugs in personalized manner. The performance results of the proposed enhanced ranking techniques are described as follows: i) EL_MTR is the best to predict multiple anticancer drug efficacies and improve the accuracy of ranking drugs, irrespective of sample size; and ii) ELM_SVR performs better than other MTR models with a large sample size and precise ranking process.

Redox status and metabolomic profiling of thioredoxin reductase inhibitors and 4 kGy ionizing radiation-exposed Deinococcus radiodurans.

The gram-positive bacterium Deinococcus radiodurans can survive under extreme ionizing radiation environment. This study aims to rationalize the role of redox balance, antioxidant status, and metabolite content on the radiation survival of D. radiodurans. We found that the TrxR inhibitors, i.e., ebselen, auranofin, and epigallocatechin gallate (EGCG) (10 µM) treatment affects the radiation survival of D. radiodurans. The TrxR inhibitors treatment affects the redox status, activities of antioxidant enzymes, increases the intracellular ROS levels and protein carbonylation upon 4 kGy ionizing radiation treatments. Moreover, the alteration in cellular redox status affects the metabolites content of the organism. In addition, we noticed differential metabolomic profiles in sham control, radiation control (4 kGy), and TrxR inhibitors plus radiation-treated D. radiodurans. The TrxR inhibitors plus radiation treated groups exhibit more variation compare to sham control and 4 kGy radiation-exposed D. radiodurans. Further, some novel metabolites can possess the high antioxidant property and involved in vital cellular metabolism were found in sham control and radiation treated cells of D. radiodurans. Thus, the results illustrate the role of intracellular redox status in the survival and metabolomic profile of D. radiodurans.

Phytochemicals reverse P-glycoprotein mediated multidrug resistance via signal transduction pathways.

P-glycoprotein, encoded by ATP-binding cassette transporters B1 gene (ABCB1), renders multidrug resistance (MDR) during cancer chemotherapy. Several synthetic small molecule inhibitors affect P-glycoprotein (P-gp) transport function in MDR tumor cells. However, inhibition of P-gp transport function adversely accumulates chemotherapeutic drugs in non-target normal tissues. Moreover, most small-molecule P-gp inhibitors failed in the clinical trials due to the low therapeutic window at the maximum tolerated dose. Therefore, downregulation of ABCB1-gene expression (P-gp) in tumor tissues seems to be a novel approach rather than inhibiting its transport function for the reversal of multidrug resistance (MDR). Several plant-derived phytochemicals modulate various signal transduction pathways and inhibit translocation of transcription factors, thereby reverses P-gp mediated MDR in tumor cells. Therefore, phytochemicals may be considered an alternative to synthetic small molecule P-gp inhibitors for the reversal of MDR in cancer cells. This review discussed the role of natural phytochemicals that modulate ABCB1 expression through various signal transduction pathways in MDR cancer cells. Therefore, modulating the cell signaling pathways by phytochemicals might play crucial roles in modulating ABCB1 gene expression and the reversal of MDR.

Ginsenoside Rg1 Induces Apoptotic Cell Death in Triple-Negative Breast Cancer Cell Lines and Prevents Carcinogen-Induced Breast Tumorigenesis in Sprague Dawley Rats.

The objective of this study is to investigate the anticancer potential of ginsenoside Rg1 using in vitro and in vivo experimental models. In this study, we found that ginsenoside Rg1 induces cytotoxicity and apoptotic cell death through reactive oxygen species (ROS) generation and alterations in mitochondrial membrane potential (MMP) in the triple-negative breast cancer cells (MDA-MB-MD-231 cell lines). We found that ginsenoside Rg1 induces the formation of gamma H2AX foci, an indication of DNA damage, and subsequent TUNEL positive apoptotic nuclei in the MDA-MB-MD-231 cell lines. Further, we found that ginsenoside Rg1 prevents 7,12-dimethylbenz (a) anthracene (DMBA; 20 mg/rat) induced mammary gland carcinogenesis in experimental rats. We observed oral administration of ginsenoside Rg1 inhibited the DMBA-mediated tumor incidence, prevented the elevation of oxidative damage markers, and restored antioxidant enzymes near to normal. Furthermore, qRT-PCR gene expression studies revealed that ginsenoside Rg1 prevents the expression of markers associated with cell proliferation and survival, modulates apoptosis markers, downregulates invasion and angiogenesis markers, and regulates the EMT markers. Therefore, the present results suggest that ginsenoside Rg1 shows significant anticancer properties against breast cancer in experimental models.

An efficient wavelet-based optimization algorithm for the solutions of reaction-diffusion equations in biomedicine.

In this paper, a mathematical model of nonlinear reaction-diffusion equation with Michaelis-Menten kinetics in a solid of planar and spherical shape is discussed. The proposed model is based on non-stationary diffusion equation containing a non-linear term related to Michaelis-Menten kinetics of the enzymatic reaction. An efficient wavelet-based spectral method has been developed for the analytical expressions pertaining to substrate concentration for all parameter values. The efficiency of the proposed wavelet method is confirmed by mean of the computational CPU time. The proposed wavelet-based results are compared with Adomian Decomposition Method (ADM). Satisfactory agreement with ADM results is observed. Moreover, the use of the wavelet method is found to be simple, efficient, flexible, and straight forward. Also, it requires less computation costs.

Computational models for predicting anticancer drug efficacy: A multi linear regression analysis based on molecular, cellular and clinical data of oral squamous cell carcinoma cohort.

BACKGROUND AND OBJECTIVES: The computational prediction of drug responses based on the analysis of multiple clinical features of the tumor will be a novel strategy for accomplishing the long-term goal of precision medicine in oncology. The cancer patients will be benefitted if we computationally account all the tumor characteristics (data) for the selection of most effective and precise therapeutic drug. In this study, we developed and validated few computational models to predict anticancer drug efficacy based on molecular, cellular and clinical features of 31 oral squamous cell carcinoma (OSCC) cohort using computational methods. METHODS: We developed drug efficacy prediction models using multiple tumor features by employing the statistical methods like multi linear regression (MLR), modified MLR-weighted least square (MLR-WLS) and enhanced MLR-WLS. All the three developed drug efficacy prediction models were then validated using the data of actual OSCC samples (train-test ratio 31: 31) and actual Vs hypothetical samples (train-test ratio 31: 30). The selected best statistical model i.e. enhanced MLR-WLS has then been cross-validated (CV) using 341 theoretical tumor data. Finally, the performances of the models were assessed by the level of learning confidence, significance, accuracy and error terms. RESULTS: The train-test process for the real tumor samples of MLR-WLS method revealed the drug efficacy prediction enhancement and we observed that there was very less priming difference between actual and predicted. Furthermore, we found there was a less difference between actual apoptotic priming and predicted apoptotic priming for the tumors 6, 8, 21 and 30 whereas, for the remaining tumors there were no differences between predicted and actual priming data. The error terms (Actual Vs Predicted) also revealed the reliability of enhanced MLR-WLS model for drug efficacy prediction. CONCLUSION: We developed effective computational prediction models using MLR analysis for anticancer drug efficacy which will be useful in the field of precision medicine to choose the choice of drug in a personalized manner. We observed that the enhanced MLR-WLS model was the best fit to predict anticancer drug efficacy which may have translational applications.