Current Drug Discovery Technologies - Volume 2, Issue 4, 2005

With the emergence and re-emergence of infectious diseases and development of multi-drug resistance, there is a dire need to find newer cures and to produce more drugs and vaccines in the pipeline. To meet these increasing demands biomedical researchers and pharmaceutical companies are combining advanced methods of drug discovery, such as combinatorial chemistry, high-throughput screening and genomics, with conventional approaches using natural products and traditional knowledge. However, such approaches require much international cooperation and understanding of international laws and conventions as well as local customs and traditions. This article reviews the forty years of cumulative experience at the National Institutes of Health (initiated by the National Cancer Institute) in natural products drug discovery. It presents (1) three major cooperative programs (2) the legal mechanisms for cooperation and (3) illustrative case studies from these programs. We hope that these discussions and our lessons learned would be helpful to others seeking to develop their own models of cooperation for the benefit of global health.

The acid dissociation constant (pKa) is the key parameter to define the extent of ionization of a drug molecule and is used for ADME properties evaluation via the pH-dependent distribution coefficient, logD. We present a method for pKa prediction using a predefined reference database and structural fingerprints based on a multilevel neighborhoods description of the ionizable atom(s). This database mining approach is suitable for screening large compound collections for HTS compound prioritization and external compound acquisition. In addition to pKa prediction it provides medicinal chemists rapid access to already available pKa measurements and hints for manipulating the chemical structure to increase or decrease pKa.

The purpose of this study was to design a chitosan based drug delivery system containing a new enzyme, cardosin A, which could hydrolyse interstitial collagens. Cardosin A is extracted from the pistils of the plant Cynara cardunculus L. and chitosan is a polysaccharide derived from chitin with valuable properties as a biomaterial. In this work we report our experiments on the synthesis of chitosan sponges and immobilisation of cardosin A, by entrapment. We observed that 10-15% of the incorporated cardosin A were released over 6 days of incubation. In addition, we could also note that this immobilisation procedure did not induce any specificity alterations on cardosin A. The specificity study of the enzyme, using β-chain of oxidised insulin, showed that the immobilised and released enzymes have the same hydrolysis pattern as the free enzyme. The ability of this enzyme to hydrolyse type I collagen was maintained, after the immobilisation procedure. The biocompatibility in vivo of these sponges was evaluated by histological staining after implantation in rats submitted to abdominal surgery. Results of this study demonstrated that these chitosan sponges are very promising vehicles for the application of cardosin A, in abdominal cavity for prevention and reduction of the adhesions formation.

Using short peptide fragments of proteins to elicit antibodies able to recognize the protein from which the peptide sequence was derived, is one of the main goals in immunotherapy today. Indeed, peptide-immunotherapy appears as an obliged way to obtain antibodies of predetermined specificity and exempt from the complications associated with whole cells/entire protein vaccines. However, effective peptide-immunotherapy remains an exciting theoretical speculation largely unrealized to date. The major obstacle in designing effective peptide vaccines is our incapacity to scientifically define peptide immunogenicity. This mini-review schematically describes: 1) the available methods to identify epitopic peptides; 2) the sequence redundancy concept as a possible basis for peptide immunogenicity.

Computational approaches are developed to design or rationally select, from structural databases, new lead trichomonacidal compounds. First, a data set of 111 compounds was split (design) into training and predicting series using hierarchical and partitional cluster analyses. Later, two discriminant functions were derived with the use of non-stochastic and stochastic atom-type linear indices. The obtained LDA (linear discrimination analysis)-based QSAR (quantitative structure-activity relationship) models, using non-stochastic and stochastic descriptors were able to classify correctly 95.56% (90.48%) and 91.11% (85.71%) of the compounds in training (test) sets, respectively. The result of predictions on the 10% full-out cross-validation test also evidenced the quality (robustness, stability and predictive power) of the obtained models. These models were orthogonalized using the Randic´ orthogonalization procedure. Afterwards, a simulation experiment of virtual screening was conducted to test the possibilities of the classification models developed here in detecting antitrichomonal chemicals of diverse chemical structures. In this sense, the 100.00% and 77.77% of the screened compounds were detected by the LDA-based QSAR models (Eq. 13 and Eq. 14, correspondingly) as trichomonacidal. Finally, new lead trichomonacidals were discovered by prediction of their antirichomonal activity with obtained models. The most of tested chemicals exhibit the predicted antitrichomonal effect in the performed ligand-based virtual screening, yielding an accuracy of the 90.48% (19/21). These results support a role for TOMOCOMD-CARDD descriptors in the biosilico discovery of new compounds.