Liposomal formulations in the pharmacological treatment of leishmaniasis: a review.

Conventional chemotherapy for leishmaniasis includes considerably toxic drugs and reports of drug-resistance are not uncommon. Liposomal encapsulated drugs appear as an option for the treatment of leishmaniasis, providing greater efficacy for the active and reducing its side effects by promoting superior tissue absorption, favouring drug penetration into the macrophages, and retarding its clearance from the site of action. In this paper, a review on the advances achieved with liposome-based anti-leishmaniasis drug delivery systems is presented. Formulations prepared with either conventional or modified (sugar-coated, cationic, niosomes, peptides- and antibodies-bounded) liposomes for the delivery of pentavalent antimonials, amphotericin B, pentamidine, paromomycyn, and miltefosine were covered. This literature review depicts a scenario of no effective therapeutic agents for the treatment of this neglected disease, where liposomal formulations appear to improve the effectiveness of the available antileishmania agents.

Preparation, characterization, and topical delivery of paromomycin ion pairing.

Topical chemotherapy with paromomycin (PA) has been used as an alternative for the treatment of cutaneous leishmaniasis; however, poor skin penetration of this drug limits the efficacy of formulations. The objective of this work was to study the ability of the PA free base to form ion pairing with organic acids, as well as evaluate the effect of these compounds on the topical delivery of PA. PA permeation across intact skin was low, while drug penetration into skin from PA ion pairing was the higher than that observed for the PA base. Data obtained on the stripped skin, a damaged skin model, clearly showed that the ion pairing presented a potential to improve PA skin permeation.

Linear indices of the 'macromolecular graph's nucleotides adjacency matrix' as a promising approach for bioinformatics studies. Part 1: prediction of paromomycin's affinity constant with HIV-1 psi-RNA packaging region.

The design of novel anti-HIV compounds has now become a crucial area for scientists around the world. In this paper a new set of macromolecular descriptors (that are calculated from the macromolecular graph's nucleotide adjacency matrix) of relevance to nucleic acid QSAR/QSPR studies, nucleic acids' linear indices. A study of the interaction of the antibiotic Paromomycin with the packaging region of the HIV-1 psi-RNA has been performed as example of this approach. A multiple linear regression model predicted the local binding affinity constants [Log K (10(-4) M(-1))] between a specific nucleotide and the aforementioned antibiotic. The linear model explains more than 87% of the variance of the experimental Log K (R = 0.93 and s = 0.102 x 10(-4) M(-1)) and leave-one-out press statistics evidenced its predictive ability (q2 = 0.82 and s(cv) = 0.108 x 10(-4) M(-1)). The comparison with other approaches (macromolecular quadratic indices, Markovian Negentropies and 'stochastic' spectral moments) reveals a good behavior of our method.

Markovian negentropies in bioinformatics. 1. A picture of footprints after the interaction of the HIV-1 Psi-RNA packaging region with drugs.

MOTIVATION: Many experts worldwide have highlighted the potential of RNA molecules as drug targets for the chemotherapeutic treatment of a range of diseases. In particular, the molecular pockets of RNA in the HIV-1 packaging region have been postulated as promising sites for antiviral action. The discovery of simpler methods to accurately represent drug-RNA interactions could therefore become an interesting and rapid way to generate models that are complementary to docking-based systems. RESULTS: The entropies of a vibrational Markov chain have been introduced here as physically meaningful descriptors for the local drug-nucleic acid complexes. A study of the interaction of the antibiotic Paromomycin with the packaging region of the RNA present in type-1 HIV has been carried out as an illustrative example of this approach. A linear discriminant function gave rise to excellent discrimination among 80.13% of interacting/non-interacting sites. More specifically, the model classified 36/45 nucleotides (80.0%) that interacted with paromomycin and, in addition, 85/106 (80.2%) footprinted (non-interacting) sites from the RNA viral sequence were recognized. The model showed a high Matthews' regression coefficient (C = 0.64). The Jackknife method was also used to assess the stability and predictability of the model by leaving out adenines, C, G, or U. Matthews' coefficients and overall accuracies for these approaches were between 0.55 and 0.68 and 75.8 and 82.7, respectively. On the other hand, a linear regression model predicted the local binding affinity constants between a specific nucleotide and the aforementioned antibiotic (R2 = 0.83,Q2 = 0.825). These kinds of models may play an important role either in the discovery of new anti-HIV compounds or in the elucidation of their mode of action. AVAILABILITY: On request from the corresponding author (humbertogd@cbq.uclv.edu.cu or humbertogd@navegalia.com).