Another pair of eyes: improved ability to measure consistency of ophthalmic drug dosages

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  • Published: Aug 17, 2016
  • Author: Ryan De Vooght-Johnson
  • Channels: Laboratory Informatics / Chemometrics & Informatics
thumbnail image: Another pair of eyes: improved ability to measure consistency of ophthalmic drug dosages

Pill conformity

Chinese physicians of bygone generations prescribed natural remedies for a multitude of ailments and maladies. Today, practitioners of Traditional Chinese Medicine are retracing their steps with modern analytical techniques in an attempt to elucidate the active components behind their therapeutic activity.

Have you ever taken one of your pills—say a standard, lightly dusted paracetamol tablet—and pondered on its likeness to the other allegedly identical packaged tablets? I have. And upon consultation of the nearest packet, it reads as clear as day, ‘Each tablet contains Paracetamol 500 mg.’ But how do the manufacturers maintain this dosage?

‘To ensure the therapeutic utility of a finished dosage unit,’ Professor Oliva and colleagues clarify in the Journal of Separation Science, ‘the drug content of each unit should be distributed in a narrow range around the label claim.’ But what can contribute to the slight variations in the content? Perusal of the Guidance for Industry: Powder Blends, and Finished Dosage Units regulatory guidelines drafted by the American Food and Drug Administration (FDA) reveals that this ‘Content Uniformity’, as chemists call it, is a combination of many factors.

Merely standardising the pill weight is not enough. For example, if the blend of active ingredient (paracetamol in this case) is not equally mixed, then some pills will be stronger than others. What’s more, the error introduced by your analytical method of choice may also contribute to non-conformity. A metric of accuracy and precision combined, the error of an analytical test should ideally be no greater than 2.25%, experts recommend. Of course, the lower this error the better.

DoE/MLR

The pharmaceutical industry has devised and deployed numerous experimental designs and data crunching methods over the years to test the limits of analytical tests. In times gone by, this analytical error was tested one-variable-at-a-time (OVAT), which generated huge volumes of data and did not take into account the potential interactions between variables.

In response to these shortcomings, the US FDA and the European Medicines Agency (EMA) joined forces in early 2013, subsequently developing the design of experiments (DoE) and multiple linear regressions (MLR) strategy. This strategy, write the authors, involves simultaneously toying with ‘all important variables…through a set of planned experiments followed by statistical analysis of the results.’ With the predictive power of these regression models, researchers are able to generate reels of information from minimal data sets.

Out of the need to analyse their ophthalmic hydrogel, the Tenerife-based chemists set out to develop an accessible strategy for measuring the uniformity of the active triamcinolone acetonide (TCA), which has been proven effective for treating many eye diseases. For this, they settled on HPLC-UV technology, which they reasoned would be cheap, rapid, and yield high-quality data, with subsequent number crunching on the free, open-source R software.

Role model

Having toiled away at the numerous permutations of column temperature, flow rate and MeOH mobile phase component one-variable-at-a-time, the configuration of 30°C, 0.4 mL/min and 53% was found to be the most accurate. All well, but the authors conceded that this simplistic approach overlooks any synergistic and agonistic influences, and therefore pressed on with their DoE/MLR strategy.

An advantage to this modelling is the volume of data that is churned out, including how much influence each variable has. The MeOH component (34% of variation) was most influential on the retention of TCA, the authors calculated, whilst fine-tuning the flow rate (11%) would translate to the greatest improvement in analytical accuracy. Next, their method of determining TCA uniformity was validated.

Their fast and accessible UHPLC-UV test maintained linearity over 2–10 μg TCA per 1 mL of hydrogel, and could quantitate down to 2.38 μg/mL. Furthermore, the test made nine determinations of 2, 6 and 10 μg/mL matrix-matched standards with an average accuracy of 98.3%, whilst multiple measurements taken over the course of the same and three different days were within 2.0% RSD.

‘The estimated analytic error from pre-study validation’, the authors conclude, ‘was lower than 2.5%, which can be considered appropriate to detect any change in drug content.’ Curiosity satisfied? Mine has been.

Related Links

J. Sep. Sci. 2016, Early View article,. Oliva et al. Development of an ultra high performance liquid chromatography method for determining triamcinolone acetonide in hydrogels using the design of experiments/design space strategy in combination with process capability index.

Article by Ryan De Vooght-Johnson

The views represented in this article are solely those of the author and do not necessarily represent those of John Wiley and Sons, Ltd.

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