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This experimental study was aimed at assessing the total polyphenol and flavonoid content of Tragopogon pratensis (folium) vegetal extract and evaluating the histopathological status of mice with liver problems and diabetes during therapy with 20% hydroalcoholic extract of T. pratensis (folium). The diabetes in mice was induced by single intraperitoneal (i.p.) dose of 180 mg/kg b.w. streptozotocin. The experiment involved two congtrol groups: the first (I) group consisted of mice with normal pancreatic function; the second (II) group consisted of mice with experimentally induced diabetes. The third (III) group of mice with experimentally induced diabetes was treated with T. pratensis extract at a dose of 150 mg/kg b.w.. The first and second groups were not treated, while the third group received daily the established medication once a day, in the morning at the same time (9 a.m), for five weeks. The obtained results showed that the administration of T. pratensis tincture triggers the hepatic regenerative processes, restores functional activity of the liver, and positively influences functionality of the pancreas. The molecular docking analysis supports results of the experimental study.

Betulonic acid (BA) is a pentacyclic lupane-type triterpenoid possessing valuable pharmacological activities, exhibiting very low water solubility. Inclusion complexes between the substance and cyclodextrins (CDs) were obtained in order to improve its solubility and consequently its bioavailability. The aim of this study was to investigate the guest–host interaction of BA with γ-cyclodextrin (γ-CD) and its derivative, 2-hydroxypropyl-γ-cyclodextrin (HP-γ-CD), in solution and in solid state in order to prove the formation of inclusion complexes between the components. The kneading method was used for the inclusion complexes preparation, and different analytical techniques such as thermal analysis, powder X-ray diffractometry, universal attenuated total reflectance Fourier transform IR spectroscopy (UATR-FTIR) and UV spectroscopy were employed to investigate the interaction between substances. The stoichiometry of the inclusion complex BA/HP-γ-CD was found to be 1:1 by employing continuous variation method, and the apparent stability constant was calculated as 1855.55 M−1 using Benesi–Hildebrand equation. Structural studies of the inclusion complexes were carried out using molecular modeling techniques in order to explain the complexation mechanism. The results of this study confirm the formation of inclusion complexes between BA and cyclodextrins both in solution and in solid state.

We explored the relationship between obstructive sleep apnea (OSA) patients&rsquo, anthropometric measures and the CPAP treatment response. To that end, we processed three non-overlapping cohorts (D1, D2, D3) with 1046 patients from four sleep laboratories in Western Romania, including 145 subjects (D1) with one-night CPAP therapy. Using D1 data, we created a CPAP-response network of patients, and found neck circumference (NC) as the most significant qualitative indicator for apnea&ndash, hypopnea index (AHI) improvement. We also investigated a quantitative NC cutoff value for OSA screening on cohorts D2 (OSA-diagnosed) and D3 (control), using the area under the curve. As such, we confirmed the correlation between NC and AHI (&rho, =0.35, p&lt, 0.001) and showed that 71% of diagnosed male subjects had bigger NC values than subjects with no OSA (area under the curve is 0.71, with 95% CI 0.63&ndash, 0.79, p&lt, 0.001), the optimal NC cutoff is 41 cm, with a sensitivity of 0.8099, a specificity of 0.5185, positive predicted value (PPV) = 0.9588, negative predicted value (NPV) = 0.1647, and positive likelihood ratio (LR+) = 1.68. Our NC =41 cm threshold classified the D1 patients&rsquo, CPAP responses&mdash, measured as the difference in AHI prior to and after the one-night use of CPAP&mdash, with a sensitivity of 0.913 and a specificity of 0.859.

Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach&mdash, based on knowledge about the chemical structures&mdash, can not fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug&ndash, target interactions to infer drug properties. To this end, we define drug similarity based on drug&ndash, target interactions and build a weighted Drug&ndash, Drug Similarity Network according to the drug&ndash, drug similarity relationships. Using an energy-model network layout, we generate drug communities associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property, thus, we consider them potential drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. Using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure based on molecular docking to analyze Azelaic acid and Meprobamate&rsquo, s repurposing.

Complex network representations of reported drug-drug interactions foster computational strategies that can infer pharmacological functions which, in turn, create incentives for drug repositioning. Here, we use Gephi (a platform for complex network visualization and analysis) to represent a drug-drug interaction network with drug interaction information from DrugBank 4.1. Both modularity class- and force-directed layout ForceAtlas2 are employed to generate drug clusters which correspond to nine specific drug properties. Most drugs comply with their cluster's dominant property; however, some of them seem not to be in a proper position (i.e., in accordance with their already known functions). Such cases, along with cases of drugs that are topologically placed in the overlapping or bordering zones between clusters, may indicate previously unaccounted pharmacologic functions, thus leading to potential repositionings. Out of the 1141 drugs with relevant information on their interactions in DrugBank 4.1, we confirm the predicted properties for 85% of the drugs. The high prediction rate of our methodology suggests that, at least for some of the 15% drugs that seem to be inconsistent with the predicted property, we can get very good repositioning hints. As such, we present illustrative examples of recovered well-known repositionings, as well as recently confirmed pharmacological properties.

Proposal This paper investigates a novel screening tool for Obstructive Sleep Apnea Syndrome (OSAS), which aims at efficient population-wide monitoring. To this end, we introduce SASscore which provides better OSAS prediction specificity while maintaining a high sensitivity. Methods We process a cohort of 2595 patients from 4 sleep laboratories in Western Romania, by recording over 100 sleep, breathing, and anthropometric measurements per patient; using this data, we compare our SASscore with state of the art scores STOP-Bang and NoSAS through area under curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). We also evaluate the performance of SASscore by considering different Apnea–Hypopnea Index (AHI) diagnosis cut-off points and show that custom refinements are possible by changing the score’s threshold. Results SASscore takes decimal values within the interval (2, 7) and varies linearly with AHI; it is based on standardized measures for BMI, neck circumference, systolic blood pressure and Epworth score. By applying the STOP-Bang and NoSAS questionnaires, as well as the SASscore on the patient cohort, we respectively obtain the AUC values of 0.69 (95% CI 0.66-0.73, p < 0.001), 0.66 (95% CI 0.63-0.68, p < 0.001), and 0.73 (95% CI 0.71-0.75, p < 0.001), with sensitivities values of 0.968, 0.901, 0.829, and specificity values of 0.149, 0.294, 0.359, respectively. Additionally, we cross-validate our score with a second independent cohort of 231 patients confirming the high specificity and good sensitivity of our score. When raising SASscore’s diagnosis cut-off point from 3 to 3.7, both sensitivity and specificity become roughly 0.6. Conclusions In comparison with the existing scores, SASscore is a more appropriate screening tool for monitoring large populations, due to its improved specificity. Our score can be tailored to increase either sensitivity or specificity, while balancing the AUC value.

Essential hypertension is a chronic pathology that causes long-term complications due to late diagnosis of patients, the inability to control the disease through medication, or due to the complexity of associated risk factors. Our study sets out to identify specific patterns of response to arterial hypertension treatment, by taking into consideration the multiple connections between risk factors in a relevant population of hypertensive patients. Network science is an emerging paradigm, branching over multiple aspects of physical, biological and social phenomena. One such branch, which has brought significant contributions to medical science, is the field of network medicine. To apply this methodology, we create a complex network of hypertensive patients based on their common medical conditions. Consequently, we obtain a community-based representation which pinpoints specific—and previously uncharted—patterns of hypertension development. This approach creates incentives for evaluating patient’s treatment efficacy, by considering its network topological position. Distinct clusters of patients with common properties have emerged for each study group (group A—treated with nebivolol, group B—treated with perindopril and group C—treated with candesartan cilexetil). Therefore, our network-based clustering allows for a better treatment assessment.

Fosinopril, the unique phosphinic acid derivative among the angiotensin-converting enzyme inhibitors, is a poorly water-soluble and highly lipophilic substance, exhibiting poor bioavailability following oral administration. The inclusion complex between the drug substance and hydroxypropyl-β-cyclodextrin (HPBCD) was obtained in order to improve its solubility and, consequently, its bioavailability. The aim of this study was to investigate the thermoanalytical behavior of fosinopril sodium (FOS), of the inclusion complex between FOS and HPBCD (FOS/HPBCD) and its physical mixture with some pharmaceutical excipients commonly used in solid dosage forms. The analytical techniques used for this purpose have been thermogravimetry/derivative thermogravimetry (TG/DTG), differential thermal analysis (DTA) and Fourier transform infrared spectroscopy (FT-IR). Non-isothermal methods have been employed to determine the kinetic parameters using DTG data. The DTA curves of inclusion complex FOS/HPBCD and its 1:1 (m/m) physical mixtures with excipients (magnesium stearate, talc and starch) indicated a possible interaction of the inclusion complex (IC) with magnesium stearate and talc. FT-IR data and the values of the activation energy (E a) for physical mixture did not evidence any incompatibility between the IC and excipients. Concerning FOS, the thermoanalytical results and the value of E a for the binary mixture of drug with magnesium stearate confirmed the incompatibility between the two substances reported in the literature. Therefore, as a result of the encapsulation of FOS in the HPBCD cavity, the stabilization of drug substance in the presence of magnesium stearate has been realized.

Zofenopril calcium (ZOF)is one of the newest angiotensin converting enzyme (ACE) inhibitor, is one of the highly lipophilic newest angiotensin and with low water solubility. This research investigates the interaction between ZOF and a β-cyclodextrin chemically modified derivative, 2-hydroxypropyl-β-cyclodextrin (HPBCD), in order to prove the formation of an inclusion complex with an enhanced water solubility profile of ZOF. In this research is reported for the first time the physicochemical characterization and the solubility profile of an inclusion complex between ZOF and HPBCD. Different spectroscopic techniques (UV absorption spectrometry, powder X-ray diffraction, attenuated total reflectance Fourier transform IR spectroscopy) were applied in order to prove the formation of the ZOF/HPBCD inclusion complex, both in water and in solid state, backed by thermal analysis (TGA/DTG/HF). The obtained results confirm that the physicochemical properties of the ZOF/HPBCD binary system, prepared using the kneading method, are different in comparison both with the parent substances and the corresponding physical mixture, thus suggesting that an inclusion complex was formed. After the formation of the inclusion complex with HPBCD, the solubility test indicated that the water solubility of ZOF was increased by 5 times.

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications.