06-02-2013, 04:06 PM
Evolutionary combinatorial chemistry, a novel tool for SAR
studies on peptide transport across the blood–brain barrier.
[attachment=49884]
Abstract:
The use of high-throughput methods in drug discovery allows the generation and testing of a large number of
compounds, but at the price of providing redundant information. Evolutionary combinatorial chemistry combines the selection
and synthesis of biologically active compounds with artificial intelligence optimization methods, such as genetic algorithms (GA).
Drug candidates for the treatment of central nervous system (CNS) disorders must overcome the blood–brain barrier (BBB). This
paper reports a new genetic algorithm that searches for the optimal physicochemical properties for peptide transport across the
blood–brain barrier. A first generation of peptides has been generated and synthesized. Due to the high content of N-methyl
amino acids present in most of these peptides, their syntheses were especially challenging due to over-incorporations, deletions
and DKP formations. Distinct fragmentation patterns during peptide cleavage have been identified. The first generation of peptides
has been studied by evaluation techniques such as immobilized artificial membrane chromatography (IAMC), a cell-based assay,
log Poctanol/water calculations, etc. Finally, a second generation has been proposed. Copyright 2005 European Peptide Society
and John Wiley & Sons, Ltd.
INTRODUCTION
Abbreviations: Abbreviations used for amino acids and the designation
of peptides follow the rules of the IUPAC-IUB Commission of
Biochemical Nomenclature listed in J. Peptide Sci. 2003; 9: 1–8. The
following additional abbreviations are used: AcOH, acetic acid; Ac2O,
acetic anhydride; AM, p-(R,S)-α-{1-[(9-fluorenyl)methoxyformamido]-
2,4-dimethoxybenzyl}phenoxyacetic acid; BBB, blood–brain barrier;
BBEC, bovine brain endothelial cells; Barlos resin, 2-
chlorotrityl chloride resin; DMEM, Dulbecco’s modified Eagle
medium; EDT, 1,2-ethanedithiol; GA, genetic algorithm; HBSS,
Hank’s balanced salt solution; IAMC, immobilized artificial membrane
chromatography; log Poctanol/water, octanol–water partition
coefficient; p-MBHA, p-methylbenzhydrylamine; MeCN, acetonitrile;
MeOH, methanol; PC, phosphatidylcholine; PDA, photo diode array;
PyAOP, (7-azabenzotriazol-1-yloxy)-tris(pyrrolidino)phosphonium hexafluorophosphate;
TBME, tert-butylmethyl ether; TEER, transendothelial
electrical resistance; TIS, triisopropylsilane. Amino acid symbols
denote the L configuration unless otherwise stated. All reported solvent
ratios are expressed as volume/volume unless otherwise stated.
RESULTS AND DISCUSSION
Design and Use of a Genetic Algorithm
In terms of peptide design it was decided to explore
the use of evolutionary combinatorial chemistry. In
evolutionary combinatorial chemistry the selection and
synthesis of biologically active compounds is combined
with artificial intelligence optimization methods, such
as genetic algorithms. Genetic algorithms (GA) were first
proposed by Holland in 1975 [16] and they are based
on Darwinian theories of natural evolution, whereby
individuals with the highest degree of adaptation to
their environment have the greatest chance of survival.
By eliminating redundant information, genetic algorithms
are able to reduce the number of candidate
solutions needed for problem solving, hence accelerating
processes such as drug development. They are
particularly well suited for cases where little is known
about the search space.
Synthesis of the First Generation
The evolution starts with the creation and synthesis
of the first generation. The first generation of chromosomes
was created randomly and a peptide was selected
and synthesized for each chromosome. Table 1A shows
the 24 chromosomes corresponding to the first generation
and Table 1B illustrates the peptide sequences
corresponding to the first generation. The peptides are
grouped according to their C-terminal groups (CONH2,
CONHCH3, COOH).
studies on peptide transport across the blood–brain barrier.
[attachment=49884]
Abstract:
The use of high-throughput methods in drug discovery allows the generation and testing of a large number of
compounds, but at the price of providing redundant information. Evolutionary combinatorial chemistry combines the selection
and synthesis of biologically active compounds with artificial intelligence optimization methods, such as genetic algorithms (GA).
Drug candidates for the treatment of central nervous system (CNS) disorders must overcome the blood–brain barrier (BBB). This
paper reports a new genetic algorithm that searches for the optimal physicochemical properties for peptide transport across the
blood–brain barrier. A first generation of peptides has been generated and synthesized. Due to the high content of N-methyl
amino acids present in most of these peptides, their syntheses were especially challenging due to over-incorporations, deletions
and DKP formations. Distinct fragmentation patterns during peptide cleavage have been identified. The first generation of peptides
has been studied by evaluation techniques such as immobilized artificial membrane chromatography (IAMC), a cell-based assay,
log Poctanol/water calculations, etc. Finally, a second generation has been proposed. Copyright 2005 European Peptide Society
and John Wiley & Sons, Ltd.
INTRODUCTION
Abbreviations: Abbreviations used for amino acids and the designation
of peptides follow the rules of the IUPAC-IUB Commission of
Biochemical Nomenclature listed in J. Peptide Sci. 2003; 9: 1–8. The
following additional abbreviations are used: AcOH, acetic acid; Ac2O,
acetic anhydride; AM, p-(R,S)-α-{1-[(9-fluorenyl)methoxyformamido]-
2,4-dimethoxybenzyl}phenoxyacetic acid; BBB, blood–brain barrier;
BBEC, bovine brain endothelial cells; Barlos resin, 2-
chlorotrityl chloride resin; DMEM, Dulbecco’s modified Eagle
medium; EDT, 1,2-ethanedithiol; GA, genetic algorithm; HBSS,
Hank’s balanced salt solution; IAMC, immobilized artificial membrane
chromatography; log Poctanol/water, octanol–water partition
coefficient; p-MBHA, p-methylbenzhydrylamine; MeCN, acetonitrile;
MeOH, methanol; PC, phosphatidylcholine; PDA, photo diode array;
PyAOP, (7-azabenzotriazol-1-yloxy)-tris(pyrrolidino)phosphonium hexafluorophosphate;
TBME, tert-butylmethyl ether; TEER, transendothelial
electrical resistance; TIS, triisopropylsilane. Amino acid symbols
denote the L configuration unless otherwise stated. All reported solvent
ratios are expressed as volume/volume unless otherwise stated.
RESULTS AND DISCUSSION
Design and Use of a Genetic Algorithm
In terms of peptide design it was decided to explore
the use of evolutionary combinatorial chemistry. In
evolutionary combinatorial chemistry the selection and
synthesis of biologically active compounds is combined
with artificial intelligence optimization methods, such
as genetic algorithms. Genetic algorithms (GA) were first
proposed by Holland in 1975 [16] and they are based
on Darwinian theories of natural evolution, whereby
individuals with the highest degree of adaptation to
their environment have the greatest chance of survival.
By eliminating redundant information, genetic algorithms
are able to reduce the number of candidate
solutions needed for problem solving, hence accelerating
processes such as drug development. They are
particularly well suited for cases where little is known
about the search space.
Synthesis of the First Generation
The evolution starts with the creation and synthesis
of the first generation. The first generation of chromosomes
was created randomly and a peptide was selected
and synthesized for each chromosome. Table 1A shows
the 24 chromosomes corresponding to the first generation
and Table 1B illustrates the peptide sequences
corresponding to the first generation. The peptides are
grouped according to their C-terminal groups (CONH2,
CONHCH3, COOH).