Due to the advent of recent technologies, new candidate genes are explored and can be used as precise biomarkers for screening and developing drug targets.

BackgroundPoor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties.ResultsThe models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/−bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction.ConclusionThe logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability.

Hepatocellular carcinoma is the fifth most common malignant tumor in the world, both in terms of incidence and mortality in Asian and Western countries. There are currently limited therapeutic regimens available for effective treatment of this cancer. Carvacrol is a predominant monoterpenoic phenol believed to impede cancer promotion and progression. The present study was conducted to decipher the role of carvacrol during diethylnitrosamine (DEN)-induced hepatocarcinogenesis in male wistar albino rats. Carvacrol (15 mg/kg body weight) suppressed the elevation of serum tumor marker enzymes, carcinoembryonic antigen, and α-feto protein induced by DEN. The activities of phase I enzymes increased markedly during DEN induction, but was found to be significantly lowered upon carvacrol treatment. On the contrary, the phase II enzymes decreased in DEN-administered animals, which was improved normalcy upon carvacrol-treated animals. DEN-administered animals showed increased mast cell counts, argyrophilic nucleolar organizing regions, proliferating cell nuclear antigen, and matrix metalloproteinases (MMPs-2/9), whereas carvacrol supplementation considerably suppressed all the above abnormalities. The results suggest that the carvacrol exhibited the potential anticancer activity by inhibiting cell proliferation and preventing metastasis in DEN-induced hepatocellular carcinogenesis.

Heart failure (HF) is a clinical condition distinguished by structural and functional defects in the myocardium, which genetic and environmental factors can induce. HF is caused by various genetic factors that are both heterogeneous and complex. The incidence of HF varies depending on the definition and area, but it is calculated to be between 1 and 2% in developed countries. There are several factors associated with the progression of HF, ranging from coronary artery disease to hypertension, of which observed the most common genetic cause to be cardiomyopathy. The main objective of this study is to investigate heart failure and its association with cardiomyopathy with their genetic variants. The selected novel genes that have been linked to human inherited cardiomyopathy play a critical role in the pathogenesis and progression of HF. Research sources collected from the human gene mutation and several databases revealed that numerous genes are linked to cardiomyopathy and thus explained the hereditary influence of such a condition. Our findings support the understanding of the genetics aspect of HF and will provide more accurate evidence of the role of changing disease accuracy. Furthermore, a better knowledge of the molecular pathophysiology of genetically caused HF could contribute to the emergence of personalized therapeutics in future.

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