Abstract: Accumulating studies have shown that microRNAs (miRNAs) could be used as targets of small-molecule (SM) drugs to treat diseases. In recent years, researchers have proposed many computational models to reveal miRNA-SM associations due to the huge cost of experimental methods. Considering the shortcomings of the previous models, such as the prediction accuracy of some models is low or some cannot be applied for new SMs (miRNAs), we developed a novel model named Symmetric Nonnegative Matrix Factorization for Small Molecule-MiRNA Association prediction (SNMFSMMA). Different from some models directly applying the integrated similarities, SNMFSMMA first performed matrix decomposition on the integrated similarity matrixes, and calculated the Kronecker product of the new integrated similarity matrixes to obtain the SM-miRNA pair similarity. Further, we applied regularized least square to obtain the mapping function of the SM-miRNA pairs to the associated probabilities by minimizing the objective function. On the basis of Dataset 1 and 2 extracted from SM2miR v1.0 database, we implemented global leave-one-out cross validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold cross-validation to evaluate the prediction performance. Finally, the AUC values obtained by SNMFSMMA in these validation reached 0.9711 (0.8895), 0.9698 (0.8884), 0.8329 (0.7651) and 0.9644 +/- 0.0035 (0.8814 +/- 0.0033) based on Dataset 1 (Dataset 2), respectively. In the first case study, 5 of the top 10 associations predicted were confirmed. In the second, 7 and 8 of the top 10 predicted miRNAs related with 5-FU and 5-Aza-2'-deoxycytidine were confirmed. These results demonstrated the reliable predictive power of SNMFSMMA.

Abstract: Diabetes mellitus (DM) leads to microvascular, macrovascular, and neurological complications. Less is understood about the mechanisms of this disease that give rise to weak bones. The many molecular mechanisms proposed to explain the damage caused by chronic hyperglycemia are organ and tissue dependent. Since all the different treatments for DM involve therapeutic activity combined with side effects and each patient represents a unique condition, there is no generalized therapy. The alterations stemming from hyperglycemia affect metabolism, osmotic pressure, oxidative stress, and inflammation. In part, hemodynamic modifications are linked to the osmotic potential of the excess of carbohydrates implicated in the disease. The change in osmotic balance increases as the disease progresses because hyperglycemia becomes chronic. The aim of the current contribution is to provide an updated overview of the molecular mechanisms that participate in the development and treatment of diabetes.

Abstract: ATP-binding cassette (ABC) transporters are evolutionarily conserved membrane proteins that pump a variety of endogenous substrates across cell membranes. Certain subfamilies are known to interact with pharmaceutical compounds, potentially influencing drug delivery and treatment efficacy. However, the role of drug resistance-associated ABC transporters has not been examined in idiopathic pulmonary fibrosis (IPF) or its animal model: the bleomycin (BLM)-induced murine model. Here, we investigate the expression of two ABC transporters, P-gp (permeability glycoprotein) and BCRP (breast cancer resistance protein), in human IPF lung tissue and two different BLM-induced mouse models of pulmonary fibrosis. We obtained human IPF specimens from patients during lung transplantation and administered BLM to male C57BL/6J mice either by oropharyngeal aspiration (1 U/kg) or subcutaneous osmotic infusion (100 U/kg over 7 d). We report that P-gp and BCRP expression in lungs of patients with IPF was comparable to controls. However, murine lungs expressed increased levels of P-gp and BCRP after oropharyngeal and subcutaneous BLM administration. We localized this upregulation to multiple pulmonary cell types, including alveolar fibroblasts, endothelial cells, and type 2 epithelial cells. Functionally, this effect reduced murine lung exposure to nintedanib, a U.S. Food and Drug Administration-approved IPF therapy known to be a P-gp substrate. The study reveals a discrepancy between IPF pathophysiology and the common animal model of lung fibrosis. BLM-induced drug efflux in the murine lungs may present an uncontrolled confounding variable in the preclinical study of IPF drug candidates, and these findings will facilitate disease model validation and enhance new drug discoveries that will ultimately improve patient outcomes.

Abstract: Clear cell renal cell carcinoma (ccRCC) accounts for 70-80% of kidney cancer diagnoses and displays high molecular and histologic heterogeneity. Hence, it is necessary to reveal the underlying molecular mechanisms involved in progression of ccRCC to better stratify the patients and design effective treatment strategies. Here, we analyzed the survival outcome of ccRCC patients as a consequence of the differential expression of four transcript isoforms of the pyruvate kinase muscle type (PKM). We first extracted a classification biomarker consisting of eight gene pairs whose within-sample relative expression orderings (REOs) could be used to robustly classify the patients into two groups with distinct molecular characteristics and survival outcomes. Next, we validated our findings in a validation cohort and an independent Japanese ccRCC cohort. We finally performed drug repositioning analysis based on transcriptomic expression profiles of drug-perturbed cancer cell lines and proposed that paracetamol, nizatidine, dimethadione and conessine can be repurposed to treat the patients in one of the subtype of ccRCC whereas chenodeoxycholic acid, fenoterol and hexylcaine can be repurposed to treat the patients in the other subtype.

Abstract: Methadone continues to be an important medication for the treatment of paediatric and adult cancer-related pain. Appropriate patient selection to ensure safe and effective treatment by a team of clinicians who appreciate and are familiar with methadone and its unique pharmacology is crucial. Unlike morphine and other more common opioids, methadone is purported to have involvement with delta-opioid receptor and higher affinity as an N-methyl-D-aspartate-receptor antagonist. Clinically this gives it the advantage of being effective for both nociceptive and neuropathic pain, but also may be useful in the setting of tolerance to other opioids. Methadone also comes in multiple available formulations that can be administrated through a variety of routes beyond the oral route. Challenges with methadone in treating cancer-related pain include drug interactions specifically as it relates to new targeted cancer therapies. Recent guidelines recommend electrocardiogram monitoring with methadone and there is potential for additive cardiac toxicity in the oncology setting. Appropriate dosing of methadone for pain management given age, organ dysfunction, and patients who are on methadone maintenance therapy are also key factors. This article aims to provide clinicians with evidence and clinical practice guidelines for safe and appropriate use of methadone including indication, initiation, and monitoring given its complexity for management of pain in the dynamic oncology setting.

Abstract: Kaempferol is a ubiquitous flavonoid, found in various plants having a wide range of known pharmacological activities, including antioxidant, antiinflammatory, anticancer, antiallergic, antidiabetic, neuroprotective, cardioprotective and antimicrobial activities. Nonetheless various evidence suggest that kaempferol is also able to interact with many unknown therapeutic targets modulating signalling pathways, thus providing an opportunity to explore the potential target space of kaempferol. In this study, we have employed various ligand-based approaches to identify the potential targets of kaempferol, followed by validations using modelling and docking studies. Molecular dynamics, free energy calculations, volume and residue contact map analyses were made to delineate the cause of drug-resistance among mutants. We have discovered dihydropteroate synthase (DHPS) as a novel potential therapeutic target for kaempferol. Further studies employing molecular dynamics simulations and binding free energies indicate that kaempferol has potential to inhibit even the sulfone-resistant DHPS mutants, which makes it a very attractive antibiotic agent. The identification of natural-product based kaempferol opens up the door for the design of antibiotics in a quick and high throughput fashion for identifying antibiotic leads.

Abstract: Variations in disease onset and/or severity have often been observed in siblings with cystic fibrosis (CF), despite the same CFTR genotype and environment. We postulated that genomic variation (modifier and/or pharmacogenomic variants) might explain these clinical discordances. From a cohort of patients included in the Wisconsin randomized clinical trial (RCT) of newborn screening (NBS) for CF, we identified two brothers who showed discordant lung disease courses as children, with one milder and the other more severe than average, and a third, eldest brother, who also has severe lung disease. Leukocytes were harvested as the source of DNA, and whole-genome sequencing (WGS) was performed. Variants were identified and analyzed using in-house-developed informatics tools. Lung disease onset and severity were quantitatively different between brothers during childhood. The youngest, less severely affected brother is homozygous for HFE p.H63D. He also has a very rare PLG p.D238N variant that may influence host-pathogen interaction during chronic lung infection. Other variants of interest were found differentially between the siblings. Pharmacogenomics findings were consistent with the middle, most severely affected brother having poor outcomes to common CF treatments. We conclude that genomic variation between siblings with CF is expected. Variable lung disease severity may be associated with differences acting as genetic modifiers and/or pharmacogenomic factors, but large cohort studies are needed to assess this hypothesis.

Abstract: Most of the targeted discoveries in tuberculosis research have covered previously explored chemical structures but neglected physiochemical properties. Until now, no efficient prediction tools have been developed to discriminate the novelty of screened compounds at early stages. To overcome this deficit, a drastic novel approach must include physicochemical properties filters provided by Chemical Global Positioning System-Natural Product analysis (ChemGPS-NP). Three different screening schemes GSK, GVKBio, and NIAID provided 776, 2880, and 3779 compounds respectively and were evaluated based on their physicochemical properties and thereby proposed as deduction examples. Charting the physiochemical property spaces of these sets identified the merits and demerits of each screening scheme by simply observing the distribution over the chemical property space. We found that GSK screening set was confined to a certain space, losing potentially active compounds when compared with an in-house constructed 459 highly active compounds (active set), while the GVKBio and NIAID screening schemes were evenly distributed through space. The latter two sets had the advantage, as they have covered a larger space and presented compounds with additional variety of properties and activities. The in-house active set was cross-validated with MycPermCheck and SmartsFilter to be able to identify priority compounds. The model demonstrated undiscovered spaces when matched with Maybridge drug-like space, providing further potential targets. These undiscovered spaces should be considered in any future investigations. We have included the most active compounds along with permeability and toxicity filters as supplemented material.

Abstract: BACKGROUND: Many previous clinical studies have found that accumulated sequential mutations are statistically related to tumorigenesis. However, they are limited in fully elucidating the significance of the ordered-mutation because they did not focus on the network dynamics. Therefore, there is a pressing need to investigate the dynamics characteristics induced by ordered-mutations. METHODS: To quantify the ordered-mutation-inducing dynamics, we defined the mutation-sensitivity and the order-specificity that represent if the network is sensitive against a double knockout mutation and if mutation-sensitivity is specific to the mutation order, respectively, using a Boolean network model. RESULTS: Through intensive investigations, we found that a signaling network is more sensitive when a double-mutation occurs in the direction order inducing a longer path and a smaller number of paths than in the reverse order. In addition, feedback loops involving a gene pair decreased both the mutation-sensitivity and the order-specificity. Next, we investigated relationships of functionally important genes with ordered-mutation-inducing dynamics. The network is more sensitive to mutations subject to drug-targets, whereas it is less specific to the mutation order. Both the sensitivity and specificity are increased when different-drug-targeted genes are mutated. Further, we found that tumor suppressors can efficiently suppress the amplification of oncogenes when the former are mutated earlier than the latter. CONCLUSION: Taken together, our results help to understand the importance of the order of mutations with respect to the dynamical effects in complex biological systems.

Abstract: Inputs to molecular pathways that are the backbone of cellular activity drive the cell to certain outcomes and phenotypes. Here, we investigated proteins that topologically controlled different human pathways represented as independent molecular interaction networks, suggesting that a minority of proteins control a high number of pathways and vice versa. Transcending different topological levels, proteins that controlled a large number of pathways also controlled a network of interactions when all pathways were combined. Furthermore, control proteins that were robust when interactions were rewired or inverted also increasingly controlled an increasing number of pathways. As for functional characteristics, such control proteins were enriched with regulatory and signaling genes, disease genes and drug targets. Focusing on evolutionary characteristics, proteins that controlled different pathways had a penchant to be evolutionarily conserved as equal counterparts in other organisms, indicating the fundamental role that control analysis of pathways plays for our understanding of regulation, disease and evolution.