Current Concepts on Drug Abuse and Dependence

Drug addiction is a complex disease characterized by compulsive and uncontrollable desire to seek and consume the drug. In time, drug-related terminology has undergone many changes, arising from the deepening of the mechanisms of action, but also about the need for a greater precision in the definition. Drug dependence can be assigned not only to pharmacological effects of the drugs of abuse, but also to their interaction with each particular neurological and psychological constitution. The research on the neurobiological mechanisms of addiction processes allows both a better understanding of current pharmacotherapy and the development of new treatment strategies in drug abuse and dependence. In this review we intend to present the current concepts related to drug abuse and dependence

Interaction of an Antituberculosis Drug with a Nanoscopic Macromolecular Assembly: Temperature-Dependent Förster Resonance Energy Transfer Studies on Rifampicin in an Anionic Sodium Dodecyl Sulfate Micelle

In this contribution, we report studies on the nature of binding of a potent antituberculosis drug, Rifampicin (RF) with a model drug delivery system, sodium dodecyl sulfate (SDS) micelle. Temperature dependent dynamic light scattering (DLS), conductometry, and circular dichroism (CD) spectroscopy have been employed to study the binding interaction of the drug with the micelle. The absorption spectrum of the drug RF in the visible region has been employed to study Förster resonance energy transfer (FRET) from another fluorescent drug Hoechst 33258 (H33258), bound to the micelle. Picosecond-resolved FRET studies at room temperature confirm the simultaneous binding of the two drugs to the micelle and the distance between the donor−acceptor pair is found to be 34 Å. The temperature dependent FRET study also confirms that the location and efficiency of drug binding to the micelle changes significantly at the elevated temperature. The energy transfer efficiency of the donor H33258, as measured from time-resolved studies, decreases significantly from 76% at 20 °C to 60% at 55 °C. This reveals detachment of some amount of the drug molecules from the micelles and increased donor−acceptor distance at elevated temperatures. The estimated donor−acceptor distance increases from a value of 33 Å at 20 °C to 37 Å at 55 °C. The picosecond resolved FRET studies on a synthesized DNA bound H33258 in RF solution have been performed to explore the interaction between the two. Our studies are expected to find relevance in the exploration of a potential vehicle for the vital drug rifampicin

Identification of Potential Drug Interaction with Complementary and Alternative Medicines Among Chronic Disease Outpatients

Chronic diseases such as congestive heart failure (CHF) and chronic kidney disease (CKD) are related with multiple drug prescription which can lead to drug interaction. The high usage of complementary and alternative medicines (CAM) in Indonesians can also increase the risk for drug interaction. The objective of this study is to describe CAMs use and identify potential drug interaction with CAMs in CHF and CKD outpatients. The study is a cross sectional study. Data of prescribed drugs and CAMs consumed by the patients was collected by using medication reconciliation process. Data of routine CAMs and prescribed medicines were compared to identify potential drug interactions which were then classified based on their mechanism and significance. The result showed that 6,90 % of CHF patients and 25 % of CKD patients consumed CAMs. Potential drug interaction between the CAMs and the prescribed drugs was identified in 2.74% of patients consuming CAMs. Based on the mechanism, interaction was dominated by pharmacodynamics interaction (69.2%) while interaction significancy was various. It can be concluded that CAMs usage was more familiar in CKD patients compared to CHF patients. Potential drug interaction with CAMs was able to be identified through medication reconciliation process and should be taken into awareness by the healthcare team

A network inference method for large-scale unsupervised identification of novel drug-drug interactions

Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others. Here we present a network inference algorithm to predict uncharacterized drug-drug interactions. Our algorithm takes, as its only input, sets of previously reported interactions, and does not require any pharmacological or biochemical information about the drugs, their targets or their mechanisms of action. Because the models we use are abstract, our approach can deal with adverse interactions, synergistic/antagonistic/suppressing interactions, or any other type of drug interaction. We show that our method is able to accurately predict interactions, both in exhaustive pairwise interaction data between small sets of drugs, and in large-scale databases. We also demonstrate that our algorithm can be used efficiently to discover interactions of new drugs as part of the drug discovery process

Design of hybrid gels based on gellan-cholesterol derivative and P90G liposomes for drug depot applications

Gels are extensively studied in the drug delivery field because of their potential benefits in therapeutics. Depot gel systems fall in this area, and the interest in their development has been focused on long-lasting, biocompatible, and resorbable delivery devices. The present work describes a new class of hybrid gels that stem from the interaction between liposomes based on P90G phospholipid and the cholesterol derivative of the polysaccharide gellan. The mechanical properties of these gels and the delivery profiles of the anti-inflammatory model drug diclofenac embedded in such systems confirmed the suitability of these hybrid gels as a good candidate for drug depot applications

Synergistic drug combinations from electronic health records and gene expression.

ObjectiveUsing electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding.MethodWe applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis.ResultsFrom EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence.ConclusionsThis is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing

Elucidating Signal Transduction Modulatory Drug Target Network of Colon Cancer: A Network Biology Approach

Latest evaluation and validation of cancer drugs and their targets has demonstrated the lack and inadequate development of new and better drugs, based on available protocols. Even though the specificity of drug targets is a great challenge in the pharmaco-proteomics field of cancer biology, for eradicating such hurdles and paving the way for the drugs of future, a novel step has been envisaged here to study the relation between drug target network and the corresponding drug network using the advanced concepts of proteomics and network biology. The literature mining was done for the collection of receptors and the ligands. About 1000 natural compounds were collected and out of those 300 molecules showed anti-cancer activity against colon cancer. Ligand Vs multiple receptor docking was done using the software Quantum 3.3.0; the results were further used for the designing of a well connected Protein Ligand Interaction (PLI) network of colon cancer. The obtained network is then extrapolated to sort out the receptors expressed in the specific cancer type. The network is then statistically analyzed and represented by the graphical interpretation, in order to ascertain the hub nodes and their locally parsed neighbours. Based on the best docking scores, the graphs obtained from the docking analysis are statistically validated with the help of VisANT. In the network three hub nodes Neutrophil cytosol factor 2, UV excision repair protein RAD23 homolog A, & Receptor-type tyrosine-protein phosphatase eta were identified, which showed the highest interaction with the ligands. Butyrate and Farnesol showed highest interaction as ligands. Multiple Sequence Alignment was done of the binding site sequence of the drug targets to find out the evolutionary closeness of the binding sites. The phylogenetic tree was also constructed to further validate the observation. Further in-vitro and in-vivo studies needs to be done to analyse the receptor specificity and anti tumor activity of these compounds in Colon cancer

The relationship of drug reimbursement with the price and the quality of pharmaceutical innovations

This paper studies the strategic interaction between pharmaceutical firms' pricing decisions and government agencies' reimbursement decisions which discriminate between patients by giving reimbursement rights to patients for whom the drug is most effective. We show that if the reimbursement decision preceeds the pricing decision, the agency only reimburses some patients if the private and public health benefits from the new drug diverge. That is, when (i) there are large externalities of consuming the drug and (ii) the difference in costs between the new drug and the alternative treatment is large. Alternatively, if the firm can commit to a price in advance of the reimbursement decision, we identify a strategic effect which implies that by committing to a high price ex ante, the firm can force a listing outcome and make the agency more willing to reimburse than in the absence of commitment