With ever increasing needs of regulatory requirements and quality consciousness at pharma industry; it is imperative for all pharma companies to deal with the impurities in detail including the minutest details. The cost of either having an impurity or a wrongly identified impurity can be seriously high and can even shake the stock price of the company. Therefore one of the key aspect of this is complete structure characterization of these impurities as early as possible and as conclusively as possible. Considering that impurities can be structurally related/similar to the pure compound the challenge is even more time consuming to work with them. This article shows  how  CASE (Computer Assisted Structure Elucidation) can help researchers in reducing the time consumed and increase dependability of the characterization results.

Computer-Assisted Structure Elucidation (CASE) is an area of scientific investigation on the frontier between organic chemistry, molecular spectroscopy, and computer science. The general wisdom for structure elucidation is that any proposed structure should always be considered a hypothesis and while these hypotheses can be proven to be incorrect, it can be very difficult to confirm a structure’s identity with 100% confidence. One of the legacies of the late Dr. David John Faulkner, a highly respected professor at the Scripps Institution of Oceanography in the area of natural products chemistry, was advice on how to avoid elucidation mistakes. One of Faulkner’s biggest philosophical contributions to the field of structure elucidation was the proposition of the following structure elucidation rules:

• Never propose a structure before you have accumulated ALL possible spectral data
• If the structure is incompatible with any measurement, however minor, then the structure is wrong
• Always find alternative structures, and evaluate ALL alternative structures in a systematic manner

Mikhail Elyashberg says in Journal of  Cheminformatics“The molecule under analysis acts like a specific coding machine (cipher machine) which codes structural information into each kind of spectrum using it’s own code. The goal of a researcher is to crack the codes [of spectral information] and extract the maximum structural information possible. Obviously, the human expert is frequently unable to derive and check all possible structures. Therefore, it is not surprising that sometimes incorrect structures are elucidated by chemists. The advantages of the systematic approach are obvious: all plausible structures are exhaustively enumerated, ranked, and automatically displayed.”  

The new ACD/Structure Elucidator is a complete software package that helps in the elucidation of unknown structures. It extracts information from various analytical techniques (NMR, MS, and IR) in all instrument formats to generate potential structures that are consistent with the data provided.

How the technology works
The software works with researcher at each step assisting rather than substituting. It uses MF and 1D and 2D NMR data to generate a Molecular Connectivity Diagram (MCD). The information in this diagram is used to generate potential structures.

The process goes like this

• From MS data it takes the mass and then generates the MF.
• With 1D NMR data it can count the number of Carbons and attached protons and hence restricts the MF to very few
• With 2D NMR data it Establishes connectivities and connectivity distances between the atoms.
• Now it checks to resolve ambiguities in different datasets and connectivities to create right correlations.    
• Now with this information it create ‘Molecular Connectivity Diagram’
• Based on this MCD it generates all possible structure (generally in tens of thousands) so that it leaves no stone unturned.
• Then starts the process of narrowing down on these structures and arrive at the most probable structure(s).
• The structures get filtered out based on matching spectral data, instability of the chemical structure, functional group confirmation based on IR, knowledge of starting material, pure compound etc.
• This leaves with less than 5% of the molecules.
• Now the theoretical spectrum is calculated for the structures and compared directly with the experimental spectra to calculate the deviation with it. Following this the molecules are ranked based on their match.

Based on this approach the scientists are able to complete their work with speed, accuracy, efficiency and in a much systematic way. Along with this there are tools inbuilt to perform de-replication using spectral data to save time of characterisation.