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Current Drug Discovery Technologies
ISSN: 1570-1638
Current Drug Discovery Technologies
Volume 2, Number 4, December 2005
Contents
Nature’s Medicines: Traditional Knowledge
and Intellectual Property Management. Case Studies from
National Institutes of Health (NIH), USA
Pp. 203-219
Ranjan Gupta, Bjarne Gabrielsen and Steven M. Ferguson
[Abstract]
Database Mining for pKa Prediction Pp. 221-229
Thierry Kogej and Sorel Muresan
[Abstract]
Immobilisation of Cardosin A in Chitosan Sponges as
a Novel Implant for Drug Delivery Pp. 231-238
Anabela O. Pereira, Daniel J. Cartucho, Ana S. Duarte,
Maria H. Gil,António M. S. Cabrita, João A.
Patrício and Marlene M. T. Barros
[Abstract]
Peptimmunology: Immunogenic Peptides and Sequence
Redundancy Pp. 239-244
Darja Kanduc
[Abstract]
A Computer-Based Approach to the Rational Discovery
of New Trichomonacidal Drugs by Atom-Type Linear Indices Pp.
245-265
Yovani Marrero-Ponce, Yanetsy Machado-Tugores,
David Montero Pereira,José Antonio Escario, Alicia
Gómez Barrio, Juan José Nogal-Ruiz, Carmen Ochoa,Vicente
J. Arán, Antonio R. Martínez-Fernández,
Rory N. García Sánchez,Alina Montero-Torres,
Francisco Torrens and Alfredo Meneses-Marcel
[Abstract]
Abstracts
[Back to top]
Nature’s Medicines: Traditional Knowledge
and Intellectual Property Management. Case Studies from
National Institutes of Health (NIH), USA
Ranjan Gupta, Bjarne Gabrielsen and Steven M. Ferguson
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.
[Back to top]
Database
Mining for pKa Prediction
Thierry Kogej and Sorel Muresan
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.
[Back to top]
Immobilisation
of Cardosin A in Chitosan Sponges as a Novel Implant for Drug
Delivery
Anabela O. Pereira, Daniel J. Cartucho, Ana S. Duarte,
Maria H. Gil,António M. S. Cabrita, João A.
Patrício and Marlene M. T. Barros
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.
[Back to top]
Peptimmunology: Immunogenic Peptides
and Sequence Redundancy
Darja Kanduc
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.
[Back to top]
A Computer-Based Approach to the Rational Discovery of New
Trichomonacidal Drugs by Atom-Type Linear Indices
Yovani Marrero-Ponce, Yanetsy Machado-Tugores,
David Montero Pereira,José Antonio Escario, Alicia
Gómez Barrio, Juan José Nogal-Ruiz, Carmen Ochoa,Vicente
J. Arán, Antonio R. Martínez-Fernández,
Rory N. García Sánchez,Alina Montero-Torres,
Francisco Torrens and Alfredo Meneses-Marcel
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.
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