Medicinal Chemistry - Volume 4, Issue 1, 2008

The novel 4-phenoxy-1,2,3,4-tetrahydroisoquinolines 6a-c and their rigid congeners 4,5,6,6a- ydrochromeno[ 2,3,4-de]isoquinolines 7a,b were synthesized in order to obtain dopamine D2-like receptor ligands. The new compounds were evaluated for their in vitro binding affinities, in vivo behavioral activities on rats, and for their effects on rat brain neurochemistry. Compounds 6b (toward both D2 and D3 dopamine receptors) and 7a,b (toward D3 only dopamine receptors) showed the most significant affinities. However none of the new compounds was able to stimulate behavioral activity in non pre-treated rats, nor to influence brain neurochemistry.

In order to get insight into the trypanocidal mechanism of action of a series of 5-nitrofuryl containing hiosemicarbazones some studies related to their bioreduction were performed. Electron spin resonance spectra of radicals generated in T. cruzi by compounds' bioreduction were analyzed. Three different patterns of ESR signals were observed for the different assayed compounds. These results were in agreement with the changes in the T. cruzi-oxygen uptake promoted by these compounds. On the other hand, free-radical scavenger properties, measured as oxygen radical absorbance capacity (ORAC), did not seem to correlate with the trypanocidal activity.

Protein tyrosine phosphatase 1B (PTP1B) is an enzyme that plays a critical role in down-regulating insulin signaling through dephosphorylation of the insulin receptor. Inhibitors of PTP1B showed increased insulin sensitivity and normalize plasma glucose level and thus are useful therapeutic agents for the treatment of diabetes. A series of functionalized 4,5-dihydronaphthofurans and dibenzofurans were synthesized, studied through molecular docking and evaluated for their PTP1B inhibitory activity.

5-Aminosalicylic acid is one of the drugs most commonly used for inflammatory bowel disease treatment, although its use is limited due to side effects. The aim of this work was to synthesize four 5-ASA derivatives (1-4) and analyze their pharmacological effects. The compound structures were elucidated by spectral (IR and 1H and 13C-NMR) analysis, and their analgesic effects and lethal doses 50 (LD50) were evaluated in the mouse model. In addition, their Log Ps and affinities for both cyclooxygenase enzymes (COX I and COX II) were evaluated through theoretical calculations. All compounds showed analgesic activities from 0.1 mg/Kg to 16 mg/Kg in the mouse model. The imides showed more affinity by COX enzymes and their Log Ps were the highest. The docking calculations showed that all compounds have good affinities for COX I and COX II (≅ 1 μM), making π π, van der Waals interactions and hydrogen bonds. The toxicities of all compounds were low, judging by the LD50. Finally, the docking analysis show that the compounds act on COX enzymes and their analgesic effects could be mediated in part by the inhibition of these enzymes.

Mathematical models were developed for the estimation of human carbonic anhydrase (CA) II inhibition. A large set of 95 CA inhibitors incorporating diverse aromatic rings were used for this purpose. The numerical descriptors used were distance- and connectivity- based indices, quantum -theoretical descriptors and Balaban and Balaban type descriptors of molecular structure. After descriptor generation, multiple linear regression analysis was performed to find superior models for estimation. The obtained results indicate that: (i) models based on topological indices are superior to those based on quantum -theoretical descriptors; (ii) combinations of topological and quantum-theoretical descriptors improves the quality of regression; (iii) in both cases involvement of Balaban and Balaban type indices is beneficial. The results are described critically based on variety of statistical parameters.

A series of piperazinephenethylamines were synthesized to study the contribution of a basic amine to binding affinity at the melanocortin-4 receptor. Several potent compounds from this series possessed subnanomolar Ki values in a competition binding assay.

HIV protease inhibitors are the backbone of HIV therapy. In addition to blocking intracellular HIV protease and dramatically decreasing viral burden, the protease inhibitors also regulate apoptosis. A growing body of data has confirmed the immunomodulatory effects of HIV protease inhibitors which block CD4+ and CD8+ T cell death in models of HIV infection. The mechanism of this apoptosis inhibition is still under active investigation and supported by several proposed hypothesis for how they alter the fate of the cell. More recently, the anti-apoptotic effects of the HIV protease inhibitors has been extended to the non-HIV, non-immune cell, whereby protease inhibitors prevent apoptosis, and disease, in animal models of sepsis, hepatitis and stroke. Interestingly, when HIV protease inhibitors are used at supra-therapeutic concentrations, they exert pro-apoptotic effects. This has been demonstrated in a number of tumor models. Although it is unclear how HIV protease inhibitors can induce apoptosis at increased concentrations, future research will define the targets of the immunomodulation and reveal the full clinical potential of this intriguing class of drugs.

A method called “Emerging Chemical Patterns” (ECP) has recently been introduced as a novel approach to binary molecular classification (for example, “active” versus “inactive”). The underlying pattern recognition algorithm was first introduced in computer science and then adopted for applications in medicinal chemistry and compound screening. A special feature is its ability to accurately classify molecules on the basis of very small training sets containing only a few compounds. This feature is highly relevant for virtual compound screening when only very few experimental hits are available as templates. Here we adopt ECP calculations to simulate sequential screening using an experimental highthroughput screening (HTS) data set containing inhibitors of dihydrofolate reductase. In doing so, we focus on minimizing the number of database compounds that need to be evaluated in order to identify a substantial fraction of available hits. We demonstrate that iterative ECP calculations recover on average between ∼19% and ∼39% of available hits in the data set while dramatically reducing the number of compounds that need to be tested to between ∼0.002% and ∼9% of the screening database.