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5 things you need to STOP doing in your lab | Gain time and be awesome at your job

January 30, 2020
by Sanji Bhal, Director, Marketing & Communications, ACD/Labs

There just doesn’t seem to be enough hours in the day to get everything done. There are ways to gain time and be able to do more but they can be difficult to identify because until we’re exposed to them we carry on as we always have. If you are still doing any of the things listed below, then there are ways you can gain hours in your day and even do your job better than you are right now!


1.     Stop Running Trial and Error Experiments

If you remember your high-school days when learning acid-base chemistry, one of the experiments you were likely asked to do was to calculate the concentration of an unknown base by titrating it with an acid of known volume and concentration. Typically (if your teacher valued his/her time), you were instructed to do a quick and dirty pass to roughly identify how much acid was necessary to cause the indicator to change colour. Once you had an idea where the “bulls eye” was, subsequent experiments could then be done more efficiently with higher precision.

In a ‘grown-up’ science culture we call that first experiment trial-and-error. It was used to quickly scope out the limits of experiment so more refined work could follow. For those that were sneaky enough to wait until other’s were already running their trial and error experiments, they simply asked their lab mates for the volume and avoided the trial-and-error run. While in academia the journey to the answer is nearly as important as the answer itself, in commercial R&D the journey has limited value and we just really want to get to the destination as efficiently as possible.

Chromatographic separations are an example of experiments that still often begin with trial and error runs. If delving right into software that can help the entire process of method development and optimization (such as ACD/Method Selection Suite or ACD/AutoChrom) is too much of a commitment at the outset then try using pKa and logP/logD calculators to help you get a better starting point. There are also free tools for column selection and translation of HPLC methods to those under high pressure (UHPLC). Once you have success with these tools and see how they help you in your work, perhaps you will be more open to using more sophisticated tools that will help you reach robust methods, following QbD principles. Rather than relying only on your experience and ‘gut feeling’ of the best place to start, you can investigate the full experimental design space with a minimal number of initial separations; perhaps eventually, if you begin to create a database of separations those can serve as a starting point. These tools not only serve to eliminate trial and error but also help broaden your skillset beyond the columns and methods you are familiar with to take you from being good at your job to being awesome!

If you’re interested in investigating chromatography software further, check out the Smarter Method Development webinar series.

2.     Stop laboriously searching for property data in the literature

If trial and error experiments are a time suck, then searching the literature for property data is almost infinitely worse. If you are doing this, I hope you are at least using something like ChemSketch that will help you do structure, similar structure, or sub-structure searches of digital papers on your computer. Ultimately with the number of reliable property calculators available, this is a practice you need to relegate to your past today.

Whether you’re looking for physicochemical property information to guide chromatographic separations, to better understand the behavior of compounds, or to help guide lead structure optimization in discovery projects, property calculators (such as for logP, pKa, aqueous solubility) can provide accurate values directly from chemical structure. If you are working in novel chemical space, make sure you invest in tools that have the capability to be trained with your own data so you can expand the chemical space coverage to your own novel chemical space.

Predicted values can help scientists make confident decisions about molecules worth investing further effort in, and importantly those that should be sidelined at an early stage. You may also be interested to learn that tools are also available for metabolite prediction so you can see if early stage discovery compounds have a toxic metabolite to further guide decision-making. In development, when physical measurements become important, physicochemical/ADME and toxicity calculators can aid in narrowing experimental design space. Also, it is not to be forgotten that there are situations when predicted property data can be included in regulatory submissions as outlined for the EU’s REACH legislation around registration of chemicals.

3.     Stop turning to publicly available data for chemical nomenclature

We all learn how to name chemical structures in high school. The challenge with this task is the countless number of detailed rules and keeping up with the changing rules. While IUPAC only publishes major changes every few decades (the most recent update to the Blue book was made in 2013), new rules are continually added to keep up with the evolving R&D landscape.

Accurate chemical nomenclature is important because it helps to proliferate our research (when included in peer-reviewed publications), helps colleagues find the correct sample/data, and ensures intellectual property rights are protected. It’s far more productive to rely on software to give you the answer than doing literature searches (either in paper publication or online databases which can both include inaccuracies) for similar compounds to serve as a foundation, or trying to name from scratch.

A 2006 publication by G. Eller et. al claims that of 300 systematic names they investigated approximately 25% were named in such a way that it was impossible to generate the described chemical structure from the name alone. If names are supposed to be a unique identifier that helps communicate your science, we should be doing better. Hopefully the situation has improved over the last 14 years but it’s far better to ensure it than to cross your fingers. Software such as ACD/Name uses algorithmically programmed IUPAC rules to generate accurate chemical names. Our head nomenclature developer, Andrey Erin (who has been involved with IUPAC for many decades) keeps himself up-to-date about the changing landscape.

4.     Stop analyzing NMR, MS and IR data manually

I know that you are smart people and if I were to put a spectrum in front of you, I’m confident that you’d do a pretty good job of assigning it, and for the most part it would be correct. Now my question to you is, how long does that take? If you run a few different analyses to help identify a material, e.g., LC/MS, 1H and 13C NMR, and IR, are you equally comfortable with them all? What about when the structures differ significantly and/or you don’t have a proposed structure to work from?

In our experience, while less experienced scientists really appreciate assisted spectral analysis and interpretation, even the most experienced analytical chemist is grateful for assistance in spectral/chromatographic analysis. While it adds speed and confidence to the final decision for less experienced scientists, for experienced analysts it simply means they can get through more samples in less time and that’s what this blog post is all about, being able to do more in the time you have.

ACD/Labs offers software on the Spectrus Platform to meet the needs of every kind of user. From ACD/Spectrus Processor—the ideal entry-level application that supports all analytical techniques providing assisted spectral analysis, interpretation, and structure-spectrum verification; to technique-specific workbooks that offer more advanced tools for experienced users. To list just some of the useful functionality available for spectral/chromatographic interpretation and analysis:

  • NMR—attach a proposed structure to a spectrum then simply hover over the peaks in the spectrum for a suggestion of the atom assignment that fits best
  • MS—attach chemical structures to chromatographic peaks and the software automatically evaluates consistency providing a visual color-coded result (red=poor, green=good)
  • Optical—use the knowledgebase included with the software to aid functional group assignments

Once you are comfortable with the accuracy of the information provided, perhaps your organization will look to automate analytical data processing and analysis so your experts can review by exception and really ramp up their ability to not only analyze more samples but also find time to work on the really difficult problems that are deserving of their experience and attention.

5.     Stop creating NMR multiplet reports manually

When you or your colleagues are writing a patent or publication do you spend time painstakingly typing, formatting, and editing to prepare publication-ready multiplet reports like those below?

1H NMR (400 MHz, DMSO-d6) d ppm 2.34 (dd, J=15.97, 8.06 Hz, 1 H) 2.64 (dd, J=16.05, 5.35 Hz, 1 H) 3.81 (tt, J=7.71, 5.33 Hz, 1 H) 4.47 (d, J=7.47 Hz, 1 H) 4.87 (d, J=5.13 Hz, 1 H) 5.68 (d, J=2.20 Hz, 1 H) 5.88 (d, J=2.34 Hz, 1 H) 6.58 (dd, J=8.13, 1.98 Hz, 1 H) 6.68 (d, J=8.06 Hz, 1 H) 6.71 (d, J=1.91 Hz, 1 H) 8.82 (s, 1 H) 8.87 (s, 1 H) 8.94 (s, 1 H) 9.18 (s, 1 H)

I am delighted to tell you that you need never do any of those things again! Being one of our first products, ACD/Labs sells a fair amount of NMR data processing and analysis software to chemists. Over the years we have introduced lots of helpful (and cool) functionality to help our customers process and analyze their data more efficiently and confidently. It seems, however, that with all the useful “bells and whistles” we’ve added, the little piece of functionality that scientists get really excited about when they first see the software (or discover the functionality for the first time) is “J-Coupler”. J Coupler, which has been in the software for almost 20 years, allows you to automatically determine multiplet patterns, and coupling constants from NMR spectra.

While users tell us that the time savings of automatically measuring coupling constants is “useful” (you can definitely put away any rulers now), this isn’t the “killer” application. Surprisingly it’s the simple capability to create a multiplet report in journal format with a single mouse click.

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