Biomedical subjects

Mary L Bandu

Publications and source records attributed to Mary L Bandu.

4 recordsLinked to original sources

PubMed2005

Identifying lactone hydrolysis in pharmaceuticals. A tool for metabolite structural characterization.

Methods to characterize metabolic transformations in a rapid and reliable fashion are required for facilitating the development of all new pharmaceuticals. One metabolic transformation, which is the focus of this study, is lactone hydrolysis. For pharmaceuticals containing lactones, hydrolysis occurs readily due to both enzymatic and nonenzymatic processes. Hydrolysis affects both the bioavailability and the efficacy of lactone-containing drugs and pro-drugs. To facilitate the characterization of lactones and their corresponding hydrolysis products, we have developed a mass spectrometric method that can readily discriminate between a lactone and its corresponding carboxylic acid, even when these changes are accompanied by other modifications that occur during metabolism. This method uses characteristic product ions in MS/MS experiments, and the trends described herein can be applied broadly to several types of lactones. To demonstrate the efficacy of this approach, two different lactones that had undergone multiple modifications were characterized, and in both cases, lactone hydrolysis was readily discernible, based on the MS/MS data.

Carboxylic Acids↗

PubMed2005

The STEP method (statistical test of equivalent pathways): application to pharmaceuticals.

The STEP method (Statistical Test of Equivalent Pathways), recently developed to determine primary and secondary fragmentation in the MS/MS of peptides and carbohydrates, is applied in the current study to common pharmaceutical antibiotics. The classification of product ions as primary or secondary is then utilized to construct genealogy diagrams that aid in the structural characterization of the product ions. Four compounds were subjected to the MS/MS conditions used for the STEP method, and the method was used to correctly identify primary and secondary ions in three of the four pharmaceuticals. Calculated STEP values for erythromycin did not match previously characterized fragmentation assignments. This provided an opportunity to explore potential limitations of STEP analysis. It was determined that inaccurate STEP assignments could result, if the starting compound is classified as "fragile", because fragile ions, such as erythromycin can produce abnormally low STEP ratios. While this finding represents a limitation of using the STEP method to determine whether product ions are due to primary or secondary fragmentation for fragile ions, it suggests the possibility of identifying the presence of "fragile ions" by STEP analysis.

Anti-Bacterial Agents↗

PubMed2005

STEP (Statistical Test of Equivalent Pathways) analysis: a mass spectrometric method for carbohydrates and peptides.

We have recently developed a new mass spectrometry method, the STEP (statistical test of equivalent pathways) analysis that uses ion abundances in two tandem mass spectrometry experiments to obtain genealogy information about product ions present in mass spectra. The method requires minimal sample, and it can be performed using a conventional quadrupole ion trap mass spectrometer. To obtain genealogy information, STEP ratios are calculated by comparing the relative abundances of product ions in two MS/MS experiments. These ratios are directly related to the origin of the product ions. Product ions that result directly from the precursor ion always have STEP ratios that are </=1. Ions that result from secondary fragmentation pathways have STEP ratios that are significantly larger than the primary ions, based on a Q test of statistical significance. Consequently, the type (primary or secondary) of all the product ions in an MS/MS experiment can easily be identified in this analysis. The STEP method is applied herein to peptides and carbohydrates, and the STEP results are consistent with validation data for 95% of the ions in this study. This new method has many applications in carbohydrate and peptide analysis. It can be used to support mechanistic studies of peptide fragmentation, and it is useful for discriminating among various isomeric carbohydrates, without the need for reference standards. Several examples are presented to demonstrate the reliability of this method, and an example showing how the method benefits carbohydrate sequencing is also provided.

Amino Acid Sequence↗

PubMed2004

Prediction of MS/MS data. 1. A focus on pharmaceuticals containing carboxylic acids.

Metabolite identification is a necessary step in developing safe and effective drugs. Metabolite analysis typically involves rapid identification of the chemical composition of the metabolite by automated HPLC-MS methods, followed by the laborious process of identifying the structure of the metabolite. Since MS is typically utilized to identify the metabolite, it is logical to utilize MS/MS to structurally characterize the sample. However, interpretation of MS/MS data may not provide sufficient information, as fragmentation pathways are not well understood or predictable. Therefore, other more time-consuming methods of analysis are often undertaken. If the dissociation rules for low-energy MS/MS experiments were clearly defined for all classes of compounds, more information would be obtained from MS/MS data, and metabolite identification would proceed more rapidly. We are currently developing methods to define these fragmentation rules. By screening approximately 100 carboxylic acids at a time and applying knowledge of physical-organic chemistry, predictive rules are under development that describe how compounds dissociate under low-energy collision-induced dissociation conditions. Studies of carboxylic acid dissociation demonstrate that this approach is practical and reliable. Dissociation rules were predicted with a 90% success rate, when tested on acid-containing pharmaceuticals. This predictive power cannot be matched by any commercially available software. This study, and others like it, will be used to develop algorithms that more rapidly identify drug metabolites and degradation products, based on MS/MS data. Such algorithms will benefit drug development for all types of pharmaceuticals.

Algorithms↗