Simultaneous characterization of insulin HMWP and protamine sulphate in complex formulations through SEC-coupled mass spectrometry.

High molecular weight protein aggregates present in a recombinant human insulin and analogs are conventionally quantified by SEC-HPLC and identified by SEC-MALS as oligomeric population which lacks precise identification of species. The limitation of these two techniques is vanquished though simultaneous separation and identification by SEC coupled with MS. The identification was established with organic solvent based isocratic elution followed by MS for parallel separation and identification of HMWP species. The developed SEC-MS method is validated to establish the method capability and variability. Further investigations under stress conditions of Insulin analogues revealed the capability of the method to separate higher order oligomeric (Trimeric, and Tetrameric) species alongside covalent dimeric species. Additionally, the method holds good in separating and sequencing protamine peptides used in suspension (Neutral Protamine Hagedorn) and biphasic/mixed (70/30) formulations of Human insulin using ETD-MSMS. The data presented here shows insight towards utilization of state-of-the-art SEC-MS technique in the biopharmaceutical field as a tool to establish the structural comparability of higher order species in biosimilars and to investigate the lot to lot batch variability for protamine sulphate in-terms of sequence confirmation.

MAWA∗-a memory-bounded anytime heuristic-search algorithm.

This paper presents a heuristic-search algorithm called Memory-bounded Anytime Window A∗ (MAWA∗), which is complete, anytime, and memory bounded. MAWA∗ uses the window-bounded anytime-search methodology of AWA∗ as the basic framework and combines it with the memory-bounded A∗ -like approach to handle restricted memory situations. Simple and efficient versions of MAWA∗ targeted for tree search have also been presented. Experimental results of the sliding-tile puzzle problem and the traveling-salesman problem show the significant advantages of the proposed algorithm over existing methods.