Novel method for the prediction of drug-drug Interaction based on gene expression profiles

Prediction of Adverse Effects of Drug-Drug Interactions on Cardiovascular System Based on the Analysis of Structure-Activity Relationships

Co-administration of drugs often leads to drug-drug interactions, which could be accompanied by various adverse drug reactions that pose a threat to life and health of the patient. The effect caused by adverse drug reactions on cardiovascular system is one of the most significant manifestations of drug-drug interaction. Clinical assessment of adverse drug reactions resulting from drug-drug interaction between all drug pairs used in therapeutic practice is not possible. The purpose of this work was to build models using structure-activity analysis to predict adverse effects of drugs on cardiovascular system, mediated by pairwise interactions between the drug pairs when they are taken together. Data on the adverse effects resulting from drug-drug interaction were obtained from the DrugBank database. The data on drug pairs that do not cause such effects, which are necessary for building accurate structure-activity models, were obtained from the TwoSides database, which contains the results of analysis of the spontaneous reports. Two types of descriptors were used to describe a pair of drug structures: PoSMNA descriptors and probabilistic estimates of the prediction of biological activities obtained using the PASS program. Structure-activity relationships were established using the Random Forest method. Prediction accuracy was calculated by means of five-fold cross-validation. The highest accuracy values were obtained using PASS probabilistic estimates as descriptors. The area under the ROC curve was 0.94 for bradycardia, 0.96 for tachycardia, 0.90 for arrhythmia, 0.90 for ECG QT prolongation, 0.91 for hypertension, 0.89 for hypotension.

Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome

High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually complementary; however, a direct comparison of these two analyses for omics data is poorly examined.We applied tensor decomposition (TD) to a dataset representing changes in the concentrations of 562 blood molecules at 14 time points in 20 healthy human subjects after ingestion of 75 g oral glucose. We characterized each molecule by individual dependence (constant or variable) and time dependence (later peak or early peak). Three of the four features extracted by TD were characterized by our previous hypothesis-driven study, indicating that TD can extract some of the same features obtained by hypothesis-driven analysis in a non-biased manner. In contrast to the years taken for our previous hypothesis-driven analysis, the data-driven analysis in this study took days, indicating that TD can extract biological features in a non-biased manner without the time-consuming process of hypothesis generation.

Emerging innate biological properties of nano-drug delivery systems: A focus on PAMAM dendrimers and their clinical potential

Drug delivery systems or vectors are usually needed to improve the bioavailability and effectiveness of a drug through improving its pharmacokinetics/pharmacodynamics at an organ, tissue or cellular level. However, emerging technologies with sensitive readouts as well as a greater understanding of physiological/biological systems have revealed that polymeric drug delivery systems are not biologically inert but can have innate or intrinsic biological actions. In this article, we review the emerging multiple innate biological/toxicological properties of naked polyamidoamine (PAMAM) dendrimer delivery systems in the absence of any drug cargo and discuss their correlation with the defined physicochemical properties of PAMAMs in terms of molecular size (generation), architecture, surface charge and chemistry. Further, we assess whether any of the reported intrinsic biological actions of PAMAMs such as their antimicrobial activity or their ability to sequester glucose and modulate key protein interactions or cell signaling pathways, can be exploited clinically such as in the treatment of diabetes and its complications.