Diverse Set Of Drugs Research Articles

Complex drug use patterns and associated HIV transmission risk behaviors in an Internet sample of U.S. men who have sex with men.

Although the relationship between drug use and HIV risk among men who have sex with men (MSM) is well described, relatively few studies have employed empirical methods to assess underlying classes of drug use that may better predict the risk of HIV or sexually transmitted infections (STIs) among MSM. The aim of this study was to determine whether latent class analysis (LCA) would identify underlying drug classes reported prior to sex, as well as predict unprotected anal intercourse (UAI) in the last sexual encounter among MSM. From 2004 to 2005, an anonymous online survey was conducted among 8,717 sexually active MSM recruited from gay-affiliated U.S. websites. LCA clustered participants into six distinct drug use classes based on the specific types and number of drugs used: (1) low/no drug use, (2) recreational drug use, (3) poppers with prescription erectile dysfunction (ED) drug use, (4) poppers with both prescription and non-prescription ED drug use, (5) recreational, club, and ED drug use, and (6) high polydrug use. Compared with men in Class 1, men in the highest drug use class were 4.84 times more likely to report UAI in their last sexual encounter and 3.78 times more likely to report an STI in the past year (both ps<.001). Younger MSM aged 18-29 were significantly more likely to report an STI than men aged 50 and above (p<.001). There is a need to better understand the complex relationship between a diverse set of drugs used among MSM and how polydrug use impacts sexual negotiation over time.

Insights into the permeability of drugs and drug-likemolecules from MI-QSAR and HQSAR studies

Membrane-interaction QSAR (MI-QSAR) and Holographic QSAR (HQSAR) analyses have been performed on a diverse set of drugs and drug-like molecules. MI-QSAR combines a set of membrane-solute interaction properties calculated during molecular dynamics simulation with the set of classical solute descriptors to predict the biological behavior of drugs and drug-like molecules. HQSAR is a technique which employs fragment fingerprints or molecular holograms as predictive variables of biological activity. A data set of 60 structurally diverse molecules with permeability coefficients were used to construct significant MI-QSAR and HQSAR models of Caco-2 cell permeation. A statistically meaningful MI-QSAR model was obtained with r (2) = 0.805 and q (2) = 0.696. Subsequently, HQSAR models were developed on the same data set. The best HQSAR model (r (2) = 0.915, q (2) = 0.539) was obtained with fragment distinctions atom, bond, donor and acceptor with atom count 4 to 7. The predictions for training and test set molecules are in good agreement with experimental results and show the potential of models for untested compounds. This displays the importance of MI-QSAR and HQSAR analysis in estimating ADME properties characterized by the transport of solutes through biological membranes.

Psychedelics and the Human Receptorome

We currently understand the mental effects of psychedelics to be caused by agonism or partial agonism of 5-HT2A (and possibly 5-HT2C) receptors, and we understand that psychedelic drugs, especially phenylalkylamines, are fairly selective for these two receptors. This manuscript is a reference work on the receptor affinity pharmacology of psychedelic drugs. New data is presented on the affinity of twenty-five psychedelic drugs at fifty-one receptors, transporters, and ion channels, assayed by the National Institute of Mental Health – Psychoactive Drug Screening Program (NIMH-PDSP). In addition, comparable data gathered from the literature on ten additional drugs is also presented (mostly assayed by the NIMH-PDSP). A new method is introduced for normalizing affinity (Ki) data that factors out potency so that the multi-receptor affinity profiles of different drugs can be directly compared and contrasted. The method is then used to compare the thirty-five drugs in graphical and tabular form. It is shown that psychedelic drugs, especially phenylalkylamines, are not as selective as generally believed, interacting with forty-two of forty-nine broadly assayed sites. The thirty-five drugs of the study have very diverse patterns of interaction with different classes of receptors, emphasizing eighteen different receptors. This diversity of receptor interaction may underlie the qualitative diversity of these drugs. It should be possible to use this diverse set of drugs as probes into the roles played by the various receptor systems in the human mind.

Quantitative Structure-Retention-Pharmacokinetic Relationship Studies

Since the majority of lead compounds identified for drug clinical trials fail to reach the market due to poor efficacy in humans or poor pharmacokinetics (PKs), the prediction of PK properties in humans plays an important role in selection of potential drug candidates. The aim of the present study was to develop novel models for the prediction of separate PK parameters for a diverse set of drugs. Prediction would be based on the retention of each drug using micellar liquid chromatography (MLC) and selected theoretically-derived descriptors. Retention time, half life (t((1/2))), and volume of distribution (Vd) for each of the 26 training drugs were extracted from literature while molecular descriptors were generated using Molecular Modeling Pro. A total of 35 molecular descriptors describing molecular size, shape and solubility were calculated from the 3D molecular structure of each compound. Artificial neural network (ANN) modeling was used to correlate the calculated descriptors and retention time with half life and volume of distribution. A sensitivity analysis procedure was used to refine the models. The final predictive models showed significant correlations with literature values of t((1/2)) and Vd: 0.854 and 0.855 respectively for the internal testing data and 0.720 and 0.827 respectively for the external validation set of compounds. Absolute predicted values were in good agreement with literature values. Analysis of descriptors in the optimum models revealed a large degree of overlap. Solubility characteristics, hydrogen bonding, and molecular size and shape were shown to play important roles in determining drug t((1/2)) and Vd. The reciprocal of retention time was also included in both optimum models attesting to the significance of this particular physicochemical parameter and the complexity of the models developed. This novel combination of theoretical and experimental data for pharmacokinetic modeling may lead to further progress in drug development.