Bio-analytical method based on MALDI-MS analysis for the quantification of CIGB-300 anti-tumor peptide in human plasma.

A fully validated bio-analytical method based on Matrix-Assisted-Laser-Desorption/Ionization-Time of Flight Mass Spectrometry was developed for quantitation in human plasma of the anti-tumor peptide CIGB-300. An analog of this peptide acetylated at the N-terminal, was used as internal standard for absolute quantitation. Acid treatment allowed efficient precipitation of plasma proteins as well as high recovery (approximately 80%) of the intact peptide. No other chromatographic step was required for sample processing before MALDI-MS analysis. Spectra were acquired in linear positive ion mode to ensure maximum sensitivity. The lower limit of quantitation was established at 0.5 µg/mL, which is equivalent to 160 fmol peptide. The calibration curve was linear from 0.5 to 7.5 µg/mL, with R(2)>0.98, and permitted quantitation of highly concentrated samples evaluated by dilution integrity testing. All parameters assessed for five validation batches met the FDA guidelines for industry. The method was successfully applied to analysis of clinical samples obtained in a phase I clinical trial following intravenous administration of CIGB-300 at a dose of 1.6 mg/kg body weight. With the exception of Cmax and AUC, pharmacokinetic parameters were similar for ELISA and MALDI-MS methods.

Isoelectric point optimization using peptide descriptors and support vector machines.

IPG (Immobilized pH Gradient) based separations are frequently used as the first step in shotgun proteomics methods; it yields an increase in both the dynamic range and resolution of peptide separation prior to the LC-MS analysis. Experimental isoelectric point (pI) values can improve peptide identifications in conjunction with MS/MS information. Thus, accurate estimation of the pI value based on the amino acid sequence becomes critical to perform these kinds of experiments. Nowadays, pI is commonly predicted using the charge-state model [1], and/or the cofactor algorithm [2]. However, none of these methods is capable of calculating the pI value for basic peptides accurately. In this manuscript, we present an new approach that can significant improve the pI estimation, by using Support Vector Machines (SVM) [3], an experimental amino acid descriptor taken from the AAIndex database [4] and the isoelectric point predicted by the charge-state model. Our results have shown a strong correlation (R(2)=0.98) between the predicted and observed values, with a standard deviation of 0.32 pH units across the complete pH range.