Tall Poppy Syndrome: A hidden Social Phenomenon that Defies Conventional Wisdom

You cannot strengthen one by weakening another; and you can’t increase the height of a dwarf by cutting off giant’s legs.” Benjamin franklin

In January 1993, an unlabeled vial arrived at the white house. It was President Bill Clinton’s allergy medicine. The White House physician at the time Dr. Burton Lee a practitioner for 37 years, prominent scientist refused to administer the president the medicine until its content is being confirmed by the trusted authorities. On the following day Dr. Lee received an unexpected news to pack his belongings and leave the office. Despite his well-established reputation in the medical field and adherence to ethical practices, Dr. Lee found himself unfairly robbed from his position. Many were left to wonder if he had become a victim of the Tall Poppy Syndrome.

In this blog, we will discuss this psychological societal tendency. 

What is Tall Poppy Syndrome?

Have you been tall popped before?! Congratulations!! Unlike, the average Joe’s, the tall poppy syndrome is a cultural phenomenon that affects only individuals who stand out from the pack due to their competencies, and core qualities. The origin of the word poppy dates back to the 17th-century. Frequently Australians were using it to describe the conspicuous individuals with an implication to cutting them down. 

The concept has been used in different cultures with similar expressions for instance in Egypt is “plucking the feather”. In Japan, is “the nail that sticks up gets hammered down”. In the Netherlands, is “don’t put your head above ground level”

Why tall poppy syndrome prevails significantly in social or semi-social countries?

In a socially structured communities, the pursuit of maintaining an average status serves as an ideal method for tyrants to indoctrinate the masses effectively. These societies place a strong emphasis on conformity and modesty, discouraging individuals from standing out. Thus, the achievement of an individual is often viewed as a threat to the group, as it can create feelings of jealousy, insecurity, or even guilt among others who have not used their potential. 

Why do successful people bother the majority?

Successful people’s accomplishments act as a spotlight shining down on the majority’s shortcomings. They might feel that the hardworking is a threat to their ego. This can cause a good portion of the common many to say things like “why can’t you be like us” or make fun of the standout. This is because they feel jealous of the successful ones and they might not have the same values as the standout. 

What are the consequences of exposure to tall poppy syndrome?

Tall poppy syndrome is not a diagnosable condition. It is rather a social phenomenon that percolates through society. Repetitive exposure can lead to:

  • Personal insecurity, as they can’t comfortably express themselves.
  • Low self-esteem to share their achievements.

How to countereffect the tall poppy syndrome?

When noticing that you are being tall popped, first thing you might experience as a rookie is a feeling of anger, and angry mind is a narrow mind. Therefore, it is imperative to acknowledge that a challenge is coming your way to strengthen you not to humiliate you. It is not necessarily targeting you as a human being, but it is towards the success you have achieved that irritates the subject. However, it is your responsibility to be very perspicuous about such behavior. So, it important to train yourself to act calmly as possible. One of the great psychological techniques to help neutralize this behavior is a technique called Verbal Aikido.

Aikido is a Japanese martial art created during the 1920s by Morihei Ueshiba. It is translated as the path of unifying energy. 

As the old saying goes, “it’s better to be the warrior in the garden than the gardener in the war.” The primary goal in exercising aikido is to adopt the warrior mentality through overcoming oneself instead of responding to cutting down through violence or coping through caving in. 

The warrior has confidence that can control himself and can also redirect the opponent’s attack if the situation dictates that and that in itself gives power to the practitioner in knowing that he’s in control. 

Verbal aikido is a powerful psychological technique that emphasizes the importance of self-control. It teaches individuals to become capable and dangerous while also being able to manage their emotions and responses. To key tenant of this technique is relying on controlling one’s own energy and allowing the opponent to use their negative energy against them. Like water, the individual must adapt to the situation and redirect the attacker’s strength to harmlessly neutralize the threat. By mastering this principle, one can become more confident, self-assured, and effective in handling confrontational situations.

A very typical situation of tall poppy syndrome that takes place in every high school around the world can be exemplified in the following scenario:

We have Jim who started working out and was the youngest amongst his peers to win his first collegiate show. And we have the rest of the classmates, who never took that route. Here’s a snippet of a conversation between Jim and his classmates:

Classmate: I heard there were only few competitors on your show, looks like you did not have a tough show?

Jim: I had very respectable competitors, but my goal was to beat my old me.

Classmate: you probably did not have to suffer a lot because of your genetics?

Jim: I did not pay much attention to whether I am suffering or not. I was only focusing on what needs to be done. 

Classmate: you are so philosophical, life is too short to diet to spend it in the gym or not pigging out, why you are doing all of that work?

Rings a bell already?!

Moral of the story is, life will throw curve balls at you; this time is from your classmates, who were telling you, your achievement is nothing but luck.

This is a typical dialogue between the victim of the tall poppy and the perpetrator. As you can see in the above example, the classmates didn’t want to recognize the achievement of Jim. And kept on throwing shots at Jim. That could have an impact on Jim’s esteem, isn’t it?! The higher your perspicacity about the attack, the better you become at disarming the attack.

Some people are like those classmates, your success reminds them of their shortcomings. When we operate in a reactive mode, we become manipulated, depressed, and lose our inner peace. 

To avoid being in a reactive state when we are faced with a cut down situation we need to train and develop our emotional resiliency to register there is an attack, and we need to stand guard at our minds so that nothing can affect our well-being. Verbal aikido promotes the idea of calm mind is a resilient mind. A narrow mind is easily manipulated and used. Verbal aikido teaches us to remain collected during a cut down. To do this we must apply the three step process:

Figure 1: Dichotomy between reactive versus proactive during a cut down attack

The words of Rene Descartes are very helpful to remember during any form of social attack like a cut down situation, “Whenever anyone has offended me, I try to raise my soul so high that the offense can’t reach it.” In order to be able to raise your soul above attacks using verbal aikido as a mental tool to help in maneuvering properly during such attacks can provide immeasurable benefits. As it toughens your mind by calming it. It uses three core principles to do so where they have to intertwin together in coasting you through the cut down attack.

  1. Kamae: avoid crumbliness by preserving balance: In aikido, the body’s center of gravity is the anchor to execute a technique and face the opponent. In doing so, you’ve to position your body in a position called Kamae, which facilitates movement without affecting the body stability. By extension, this happens in verbal aikido you have to maintain your mental balance. Essentially, you want to connect with your inner feelings through your thoughts. One of the quickest things you can do is to be perspicuous of how individuals operate. When people try to tall poppy you it is because they are fearful of your shine. It is a form of recognition of being a competent individual. Cognitive empathetic thinking or as it is commonly known as empathetic accuracy it is implicitly a powerful tool to position yourself in the captain’s seat of your emotions and feelings. It allows you not to expend expansive amount of energy on the malicious acts but rather allows you to understand the attacker’s mind and it can show you that the attacker not always a bad person but someone who needs help. Adopting empathetic thinking in approaching such situations can help in sustaining the inner energy by rendering confidence and peace.
  2. Zanshin: neutralize the attack: The second step in antagonizing the tall poppy situation after maintaining our inner balance, is to neutralize the attack. For instance, if we want to bring a wall down the most effective way is to be on the side of the demolition. Same applied during a cut down situation instead of responding neck to neck, it is better to neutralize the action. Verbal aikido teaches you to use the attackers force to your advantage. When the attack starts you can immediately respond by a smile and expression of gratitude towards their concern. Caveat, it must be stemmed from sincerity That creates an imbalance that will help neutralize tall poppy, criticism. 
  3. Musubi: create healthy balance: The last step to disarm the effect of tall poppy attack is to create a new balance. This step overlaps with the first step of maintaining balance as both require empathetic accuracy. In verbal aikido the main objective is to maintain the calmness and focus on a solution. In order to achieve calmness, you should not trust your gut completely during heightened moment of attack. Instead, engaging your cerebral thinking has far superiority in establishing healthier environment as it allows you to step in the attacker’s shoes, and quickly analyze and evaluate the true motive behind the tall poppy this helps to calm your mind.

To put an end to the tall poppy syndrome we must take control inwardly and outwardly. Inwardly by rewiring our mind to take control of our minds. And outwardly/empathetically by genuinely understand that comparisons are unnecessary. We must put in the effort in training our minds to look empathetically at the preparators as people worth helping while standing our grounds not being a push over, in other words, be dangerous and learn how to control it – there is staggering feeling in knowing that you’re are capable of inflicting pain but you choose to be kind. Think of yourself as a panther, how does a panther move during an attack. Calm and steady allows you to be in charge and makes you confident enough to recognize that the attacks are always coming from below not from above.

What does LoD and LoQ tells us?

Why google is the best search engine out there? In my opinion, it is due to its capability of capturing the least amount of words provided by the user and comes up with the closest answer to your search. The same goes with analytical methods, one of the critical performance characteristics to identify the capability of the method is how much the method can detect and quantify reliably i.e. (with high repeatability and accuracy).

Limit of Blank (LOB), Limit of Detection (LOD), and Limit of Quantitation (LOQ) they’re like three legs of the stool if you short-change any one of them the whole stool is going to fall down. They are generally used to describe the smallest concentration of an analyte that can be detected by the analytical method. An analogy of google search engine helps to distinguish between these terms. LOB is analogous to no typed words in the search bar, just the google search web page; LOD is like typing few words with no key identifiers not enough to get the accurate search results. With the LOQ, the key identifying words are enough to get the correct search results.

Some definitions:

Limit of Detection LoD (Minimum detection limit, Minimum detectable value, EU directive CCa): is defined as the lowest conc. of analyte that can be detected in the test sample with a repeatability and precision. 

Limit of Quantitation LoQ (Minimum reporting limit/application limit): is the lowest conc. of analyte that can be quantified in the test sample with accepted accuracy.

There’re several approaches for determining LOD, and LOQ are possible according to the ICH Q2:

  • Based on visual evaluation
  • Based on the standard deviation of the response and the Slope
  • Based on the signal-to-noise ratio
  • Based on standard deviation of the blank (not required by ICH)

In this blog we are going to discuss the application of the last 3.

Determination based on standard deviation of the response of the calibration curve and the slope:

The standard deviation of the response curve is a measure of the variability in the instrument response at a given concentration of analyte. It is calculated by analyzing a series of replicate measurements of the instrument response at a fixed concentration of the analyte. The standard deviation of the response is typically used to estimate the noise level of the analytical method and is an important parameter for calculating the limit of detection and quantitation.

On the other hand, the slope of the response curve is a measure of the change in the instrument response with respect to changes in the concentration of the analyte. It is typically determined by constructing a calibration curve using a series of standard solutions of the analyte and measuring the corresponding instrument response. The slope is an important for determining the sensitivity of the analytical method, and is used to calculate the limit of detection and quantitation as well as to quantify the amount of analyte in unknown samples.

In the following example, I will demonstrate how to generate the slope and standard deviation data using a excel spreadsheet. By understanding the underlying concepts, you can effectively apply these methods to your own data sets. Moreover, proper interpretation of the results could lead to valuable insights and pave the way for more advanced statistical analysis.

Determination based on standard deviation of the blank:

Determining LoD and LoQ using the standard deviation of the blank is one of the common approach that are widely recognized. The blank is a measurement of the instrument response in the absence of any analyte, and it provides an estimate of the background noise level of the analytical method.

Figure 1: Regression Analysis Results

The regression function provides three outputs: regression statistics, ANOVA, and coefficients. Regression statistics: provides info on how well the regression equation fits the data. ANOVA: studies the level of variability within the regression model.Coefficient’s table: provides the slope of the curve and the standard deviation of the Y-intercept.

Determination based on the signal to noise ratio: ICH Q2 (R1) doc (1)

In metrology, signal in form of numbers is the language of the instrument where it communicates with us data to extract information. For instance, in the field of nutraceuticals, the main focus is not always the detection and quantification of main compounds, rather residuals, contaminants, heavy metal products in trace levels.

Why SNR is important data parameter?

Contextually, noise is a random small signal that provides information on the heath of the detector, pumping system, and contaminated reagents. It is a key component in determining limit of detection. Conceptually, If the signal of the analyte is smaller than the baseline noise of the analytical method, the analyte is not recognized. However, trying to determine SNR visually could be less accurate as it involves analyst bias. Examine the figure below.

Figure 2: Zoomed in SNR

How to determine LOD / LOQ using the SNR? 

Print a copy of the chromatogram and perform a vertical measurement of the baseline noise (N = h), next, measure the signal (S = H) from the middle of the baseline noise vertically to the top of the peak of interest. So, measurements are h= 50 and H = 300.According to the USP formula: S/N = 2H/h, therefore, S/N = [(2*300
) / 50] = 12.

Figure 3: SNR – Blank Vs. Sample

Takeaways:

  • LOD and LOQ determination based on the statistical performance of the calibration curve is instrumental from a scientific standpoint. 
  • Visual evaluation to determine LOD and LOQ is not as accurate as it involves analyst bias.
  • SNR technique is better to be used to confirm that the regression technique gives reasonable values. 
  • ICH requires analyzing minimum of 6 determinations at the LOD and LOQ.
  • Acceptable precision between samples of +/- 15%.

How to perform linearity study?

It is the ability of the method to obtain test results proportional to the concentration of analyte.

What are the components of linearity?

Figure 1

How to evaluate linearity?

Figure 2

As per ICH guidelines Q2B, R1, the linearity should be evaluated initially by visual inspection of the response versus concentration plot. Practically, upon the establishment of a linear relationship a statistical calculation can then be implemented to generate linearity data from the regression line analysis by the least square method.

As Socrates used to say, “The beginning of wisdom starts by the definition of terms”, let’s define the output of the regression analysis by reciting the following example:After running a six-point calibration curve for sodium (Na) on ICP-OES, we received the following responses as shown in the table below.

Figure 3

Figure 4

The quality of linearity data can be primarily judged by examining the correlation coefficient and y-intercept of the linear regression line for the response versus concentration plot. Correlation coefficients of > 0.990 for drug product, or > 0.998 for drug substance are well regarded for the fit of the data to the regression line. However, evaluating linearity data solely on those parameters can’t reflect a true measure of linearity as different datasets can provide identical regression statistics (Check source). Therefore, visual evaluation remains prominent while estimating linearity along with examining the residuals from the linear regression. 

The residuals are the difference between the experimental signal and the calculated signal. It provides information on how the line fits through the data points.

Figure 5

Why do you go into the trouble of creating the residual plot? 

It provides information on how the line fits through the data points. And whether the line is good in explaining the relationship between the concentration and response. Generally, the closer the points to the horizontal line the lesser the error in the predictive value. If you see the points randomly scattered above and below the horizontal line and you don’t discern any trend, then the line is probably a good model for the data. If the residual has an upward trend or if they were curving up and then curving down, or they had a down-ward trend then this line is not a good fit.

Figure 6

Figure 7

Figure 8

Practically, software like JMP can perform all the statistical data. But if you’re limited, data treatment can be done using the excel spreadsheet, as shown below:

The output from regression analysis is generated using the LINEST function in excel, as shown in the table below:

Figure 9

DataDescriptionValue calculated by LINEST
*Equation of the line, y = mx + cThe relationship between the independent variable (x), and the dependent variable (y), is expressed by the equation of the line.Y = 9312.1634x – 759.6204
*Intercept (c) The value of y when x equals zero.759.6204
*SE_InterceptThe standard error of the intercept113.7429
CI at 95% of intercept (-1075.42) – (-443.82)
*Slope (m)Gradient of the response curve. It indicates information about instrument response in respect to concentration9312.1634
SE_SlopeThe standard error of the slope36.8192
CI at 95% of Slope (9209.94) – (9414.39)
*Coefficient of determination, R2The square of the correlation coefficient, represents the variance in the outcome that the model is capable of predicting.0.9999374
*Correlation coefficient, R (Multiple R)The correlation between the predicted and observed value. The closer the value to 1, the better the correlation. Can be calculated using the data analysis under the data tab in excel then the regression function.0.9999687
*SS_RegressionThe regression sum of squares is the amount of variability in the response that is accounted for by the regression line.2292203183
*SS_Residual / Error sum of squaresThe residual sum of squares is the variability about the regression line. 143337.769
*SS_TotalThe total sum of squares is the total amount of variability in the response.2292346521

*You can calculate the above regression statistics data using excel spreadsheet:

How to carry out linearity?  

Beginning with the end in mind, let’s work our way backward to examine what parameters we need to fulfill in the linearity study. Typically, during the method development phase we’ve gained an idea on the working range after the LOQ has been established. So, during the validation there’re some guidelines regarding the ranges that should be considered as per ICH Q2B:

Figure 10

Figure 11

Let’s dive into the following example for better understanding of the process, our team received a newly developed method of Sodium (Na) to be validated. Based on the preliminary runs the consensus was to use matrix free standard to determine the content of sodium in the sample. After establishing the LOQ using (10 * SS / Slope), a six-point calibration curve is prepared from 80 – 120% of the test concentration. 

Choosing the linearity range is vital as it ensures we’re not overlooking any change in the dependent variables. In this example, estimating the linearity and determining the linear dynamic working range has to meet certain criteria that we will be discussing in this example.

The first step starts by preparing the calibration solution levels. While, the range’s minimum requirements for the assay is 80 – 120%, there’s no cookie cutter approach. Practically, you start with an educated guess on the proper range and eventually the range is evaluated by investigating the validation characteristics of linearity, accuracy, and precision. In this example wider range from 50 – 130% of the target sample concentration was chosen. The way you prepare the standards is first you need to know the target concentration of the compound under study (Analyte A), which is 1.0 mg/mL.

As indicated in the table below, the target concentration is 1.0 mg/mL. The 50% of the 1.0 mg/mL is equal to 0.5 mg/mL, how? 

Multiply, 1.0 mg/mL * (50/100) = 0.5 mg/mL. 

Now we know that the working standards concentration is from (0.5, 0.7, 0.85, 1, 1.15, 1.3) mg/mL. What we need to do know is to figure out the stock standard concentration in order to prepare the working standards by the serial dilutions. In this example, the stock solution has a concentration of 2.67 mg/mL.

Figure 12

Figure 13

After injecting the working standards through the instrument. The calibration curve was constructed for the visual examination to see how the data lie along the least squares line.

Figure 14

numbers are arbitrary in vacuum, the instrument gives out responses in form of numbers. It’s our responsibility to know how they’re generated and how they can benefit us. By the same line of thought, we want to examine the quality of the linear response over the specified concentration range. 

How to examine the quality of the linear response?

Examining the quality of the linear response is partly determined by inspecting the slope, ideally, the closer to zero the higher the quality of the linear response. In order to do that the response factor (RF) is first determined. 

Response factor is the calculated by dividing the area of response by concentration of analyte.

RF = Area / Conc.Generally, response factor is determined at each measured concentration and plotting this response factor against analyte concentration, where then the slope can be determined using the Linest function in excel. As you can in the example mentioned the response is independent of the concentration that alludes to a true linearity over the range of concentrations. Additionally, the slope in the example is inclined towards zero which improves the confidence in the quality of the linear response.

Figure 15

Having fulfilled the visual aspect of linearity, along with response factor. There still another layer of conformity that need to be performed to show a deeper understanding of the behavior of the model that is relying more on the statistical calculations to show if there are any deviations from the assumed linearity – the residual analysis. Essentially, residuals measure the difference between the measured value and the calculated value using the slope and the intercept determined by a fit of all data to predict the calculated value. Typically, residuals should be randomly distributed around the true mean of zero.

Figure 16

Figure 17

So now we’ve fulfilled the first parameter of the linearity study which is the correlation coefficient. Then the remaining are the y-intercept, residual standard deviation, and range. The validated working range is determined by the investigation of the accuracy and precision parameters of the method. 

The y-intercept and residual standard deviation calculation:The calculation is based on the regression line equation, Y = mx + b at 100% level. Where, Intercept = b, X variable 1 / Slope = m, x = 1068 ug/mL. The following table shows the calculation for the y-intercept and residual standard deviation.

Figure 18

Figure 19

Note, Correlation coefficient is not a measure of linearity but rather a measure of how well the data fits the model. It only reflects how much of the change in response is due to the change in concentration.

A simple introduction to method validation

Figure 1

Did you ever think of what the possible reasons for variation in the pharmaceutical drug product development could be? In other words, why does a process lose stability when a change is encountered?

So, what can you do about this? How do you reduce uncertainty without encumbering the production process?

That’s what we’re going to dive into. If you want the punchline, method validation. I’ll discuss the possible reasons of variation in the phase III QC lab. Additionally, through this article I’ll explain the practical steps on performing method validation and the calculations involved in each step. 

Managing of the process?

Peter Drucker talks about managing things. His mantra is what can’t get measured can’t get managed. 

Therefore, the fundamental task for validation is to reduce the error or better yet to estimate the error and account for it by conducting a series of controlled experiments to make information known and therefore, predictable. 

Validation of the method?

Method validation is a statistical validation component used to test the truth of something in any process. It uses the statistical tools and principles to collect data on the performance of the process that can be then provide information on the quality of the process and can predict errors. It serves as an assurance that the method is correctly fit for the intended use.

There is always a requirement to validate the stability of your process. The guidelines are referred to by ICH ‘Q2(R1): validation of analytical procedures: Text and Methodology.’ The guidelines include a harmonized set of terms and definitions together with basic requirements for validation. 

Analytical method validation is one type of validation that is required during drug development and manufacturing. 

Standard steps used in Analytical Method Validation:

It divides data capturing into five parts – Instrument, Sample preparation, Analyst, Method (quantitation), Environment. 

It evaluates data from different days, analyst.

It is crucial to have a solid validation protocol, because this is your insurance policy when uncertainty is encountered and a key to confirm safety and efficacy of a drug substance or drug product.

Areas of validation in the pharmaceutical industry:

Production processes 

Cleaning procedures

Analytical methods

In-process control test procedures

Computerized systems

The purpose of validation is to depict that processes involved in the development and manufacture of drugs, can be performed in effective and reproducible manner that propels to production operations efficiency.

To ensure that quality is built in at every step, and not just tested for at the end, cGMP requires validation of analytical method. Generally, we start by writing the validation protocol for the method. It details the design of the validation study. It provides information on which characteristics will be tested during the study, how the experiments will be performed, periodic revalidation, change control, stability studies, and what results will be calculated.

In our case, validation of the method consists of an evaluation stage to see if the method is fit for purpose as used in the laboratory along with any performance parameters that may be evaluated under method development.

When performing parameter implementation, there’s four types of outcomes that could occur while running a method.

  • Positive result from the sample containing the analyte (True positive).
  • Negative result from the sample, which doesn’t contain the analyte (True negative).
  • Positive result from the sample, which are structurally similar or closely related to the analyte (False positive).
  • Negative result from the sample containing other interfering compounds that hinder the analyte signal (False positive).

Once you understand these four outcomes then we can decide what parameters need to be evaluated and how to calculate them.Firstly, you can think of the freshly developed method as “hypothesis” we don’t know if the preliminary quantitative assay results are reproducible or not, and how robust the method is to any changes.  In our case, we have a draft method developed for our X product to separate and quantify (Compound X). Our method development team told us the method is yielding good results. This piqued couple questions: (1) how we ensure that the method is yielding for its intended use and reproducing accurate data every single time. (2) how quickly can we identify the root cause of any upcoming raised issues and work on remediation to generate data for product release. (3)how do we know when sample is failing, is it due to the method, the analyst, or the product. How to control the process and better yet predict the outcome which in turns translates to less lead times and high ROI. As Peter Drucker said what can’t get measured, can’t get managed. Now you guessed it right method validation!

Figure 2

In the next installement we will talk more about the the practical approach of each parameter.

How to perform the accuracy study in method validation?

Accuracy is a critical parameter in method validation as it confirms the suitability of the method and ensures accurate quantification of the analytes in the sample.

True positive and true negative is the observation that is correctly predicted and therefore shown in green. We want to minimize false positive and false negative, so they are shown in red. Confused already! Let’s deconstruct each term.

Figure 1

For any fellow chemist the Christmas happens when green dominates the red. Partly, this means less investigations are required. Although, these trues are good, however, they can be misleading when evaluated in isolation. So, let’s heed on the words of Socrates and start by understanding what each term means, how they’re generated.

Figure 2

True positive – these are correctly estimated positive values which means that the theoretical and the found concentration are within the acceptance criteria of less than or equal to 2.0%. 

Example: if the theoretical spike input is 649.2 mcg/mL and the found concentration came out to be 649.7 mcg/mL 

% Recovery = (649.7/649.2) * 100% = 100.07%

True Negative – these are correctly predicted negative values. The sample contains in correct amount of the analyte.

False Positives – due interference that enhances the signal or lack of selectivity.

False Negatives – due to interference that hinders the signal.

When we think of a one performance metric for evaluating the capability of the method, often we optimize for the true positive as a measuring while we’re assaying a compound.

Accuracy as per ICH, is the ratio of the found value to the theoretical value. But how close the measured value to the true value be in reality?

As per ICH guidelines on the validation of analytical procedures accuracy and trueness can be used synonymously. In fact, trueness is the closeness of the agreement between the average value obtained from a large cluster of test results and the accepted value. 

Accuracy = Reference value – Experimental value

In contrast, accuracy is the least amount of error rendered in an individual result. Therefore, accuracy is the summation of the following equation:

Accuracy = Trueness + Precision + Linearity + Selectivity

Consequently, when performing method validation, the accuracy is determined for each individual test result. However, the overall reporting as a mean, this value reflects the trueness of the method.In the confrontation between accuracy and precision the table below demonstrates the differences.

Figure 3

In the first row, the pattern of numbers is disproportionally spaced out and away from the mean. The precision is low which is reflected in a high %CV as numbers exhibits are variation and the trueness is low because the numbers are not close to the target mean. 

In the second row, the pattern is relatively in proximity but away from the target mean. The precision is high and reflected with low %CV. 

In the third row, the pattern of numbers is distant from each other but revolving around the target mean. The trueness is improved as the numbers are clustered around the target mean.

In the fourth row, the pattern of numbers is proportionally close to each other and clustered around the target mean. The precision is high as the numbers are clustered proximally and represented by a low %CV and the trueness is high as the numbers are around the target mean.

As we start performing accuracy study for a specific product the estimated result may be within the range but then it may fluctuate as the chosen concentration of the sample changes. The impact of concentration of the sample on the closeness of the results to the true value is due to bias. 

Trueness of the method is quantitatively expressed as bias, in which bias is defined as the estimate of the systematic error. The bias function:

B (X,Y,Z) = (aka bias is a function of X,Y, and Z)

X = bias due to matrix effect (i.e. ionization suppression / enhancement)

Y = bias due to purity of the standard, calibration of the volumetric glassware

Z = bias due to analyte loss during sample preparation, or stability of the analyte in the sample solution

Figure 4

As evident from the above picture the maximum possible accuracy can be achieved by minimizing the bias.

Figure 5

A) Comparison to standard

The most straightforward practice when assessing accuracy of the method is to use a certified reference material (CRM) or (SRM) that is as close as
possible to the matrix of interest if available and prepare the certified material in triplicates at the vicinity of low, middle, and high concentrations
of the linearity.

Figure 6

The following example shows you how to use standard reference material to determine accuracy.

Figure 7

This approach is commonly preferred for pure drug substance (DS), where the analyte is largely assayed a certified standard such as NIST or CRM can be sourced. However, this approach is not necessarily instrumental with complex sample matrices such as botanicals, multivitamin products, new drug candidates, biological fluids, etc. 

Conversely, if the standard is not widely available a special lot of the material can be used as a reference standard. But how can one qualify the lot?

It is paramount to ensure a highly purified and characterized material to assure authenticity as a standard.

Figure 8

B) Analyte Recovery

Another way of assessing the method’s accuracy of is to check the capability of the method to separate and quantify the analyte agnostically i.e., without any impact from the sample matrix as that could lead to false positive or false negative results. This can be done through measuring the analyte recovery.

This approach is highly instrumental when the subjected material is in complex from such as multivitamin finished product that contain many other ingredients.

To study the effect of the sample matrix on the compound of interest you spike a placebo/blank matrix with a pure standard of known concentration at the beginning of the sample preparation at 50 – 150% of the level expected for the analyte in triplicates. The determination of the concentration of the spiked amount has an acceptance recovery between 98.0 – 102.0%.

To better understand the & recovery lets use the following example of assaying of sodium (Na) by ICP-OES.

Figure 8

After running the test solutions at each level in triplicate. We can then use the following formula to calculate the accuracy as the % recovery.

C) Standard Addition Method

The third way of determining the accuracy of the method is by comparing the results to the standard addition method.

What is the standard addition method?

Every study sample is divided into aliquots of equal volumes, and the aliquots are spiked with known and varying amounts of the analyte to build the calibration curve.

How the sample calculation is calculated?

It is the negative x-intercept of the calibration line.

What are the advantages of method of standard addition?

This method is very accurate because it allows direct quantitation of endogenous analytes without manual subtraction of background peak areas.

What are the disadvantages?

It requires a large amount of sample and it could be time consuming and labor intensive.

The following example shows you how to use standard addition to determine accuracy.

Figure 9

Accuracy is a critical parameter in method validation as it confirms the suitability of the method and ensures accurate quantification of the analytes in the sample.

What does precision tells us about the method?

Have you ever wondered how the same exact cup of Starbucks coffee you’ve in D.C. tastes the same as the one in Cairo? Conversely, considering everything is the same in terms of supply chain and manpower, yet the competitors failed to offer consistent taste experience. Thus, how do you thrive in such an environment. There has to be a process that ensures consistency in every cup of coffee being served – you guessed it right, precision. Bar none, when it comes to the analytical methods like Starbucks, the goal is to ensure consistency with less variability to achieve safety and efficacy in every step, with less rework and leave no room for – random errors.

That brings us to another layer to evaluate the efficiency of a method, we must examine the precision. Precision is one of the main parameters that needs to be determined during method validation the ICH Q2 (R1) defines precision as the closeness of agreement between the series of measurements obtained by the replicate measurements on the same homogenous sample under the prescribed conditions. That’s improving the precision typically reduces the degree of variation of results, i.e. improves the consistency of the results. 

Precision should not be confused with accuracy. A method can be precise but not accurate and vice versa. Let’s explore this notion by looking at the following figure that illustrates the difference between precision and accuracy using the basketballer practicing 9 shots.

Figure 1: depicts the difference between accuracy and precision

1: Ball scored in the center net 9 times. This represents the optimal scenario, since all shots/results are inside the net around the mean.

2: Ball hit the outer left side of the ring 9 times. This shows a very precise method as all the shots/results are in proximity to each other. However, the method is inaccurate as the shots are outside the target.

3: Ball hit the outer side of the ring from all directions. The method requires optimization.

4: Ball landed inside the net but in different area. This represents an accurate method as all shots are within the net. However, individual shots show a high degree of variation.

Precision studies the degree of random error that could be encountered during measurements through assessing the spread of results. 

It consists of four components: system precision, repeatability, intermediate precision, and reproducibility each determines a different kind of variation. Not all four components need to be determined, the extent of the procedure depends on the intended use. 

We will see in the following examples when to use what. 

Effect of varying conditions:

System Precision: is the first level of precision which investigates the variability of the measurement, it is referred to as the instrument precision. 

Repeatability (Sr): expresses the precision of the method under the same operating conditions over a short interval of time. 

Intermediate precision (SRW): expresses within laboratories variations; different days, different analysts, different equipment.Reproducibility: is the precision between laboratories.

Figure 2: Depicts the varying levels of precision

Precision have different levels of variability. Generally, the more conditions you change within the method the larger the precision value will become. 

Repeatability

Repeatability provides understanding on the degree of variation between analytical results within the same homogenous sample in a short time frame. It’s occasionally referred to as intra-assay precision (Sr). The experiment is performed using the same analyst, same instrument, same set of reagents, and same day at different times. As per ICH Q2 (R1), repeatability should be assessed using:

  1. A minimum of 9 determinations covering the specified range for the procedure (3 concentrations/3 replicates each).
  2. A minimum of 6 determinations at 100% of the test concentration.

Figure 3: illustrates the repeatability concept. Same basketballer shots at different times on the same day, same ball, same court.

Intermediate PrecisionIntermediate precision studies the variation within the laboratory over a longer period of time by examining the scatter of analytical results that were obtained when a method is applied on different days, different analysts using different instruments (note, same model and same manufacturer).

Figure 4: illustrates the intermediate precision. Different basketballers shots at different times on different days, different balls, same court.

Reproducibility

Reproducibility illustrates the differences in a method by measuring the variation of analytical results obtained from different laboratories. Contrarily, to intermediate precision, reproducibility does not only involve different analysts, but also different ambient conditions, different manufacturer of instruments. The reproducibility study shows that the performance of the analytical method is location independent. Therefore, it is not a required parameter during method validation within one laboratory. However, it should be considered in case of standardization of an analytical procedure to be used in more than one laboratory.

Figure 5: illustrates the reproducibility. Different basketballers shots at different times in two different courts, and different balls.

The following example illustrates how to calculate precision within a laboratory. The experiment was carried out using SRM of NIST 3280 for determining the precision of Sodium.

Figure 6: NIST 3280 certificate of analysis

Figure 7: precision results.

Precision is a vital aspect of scientific analysis, and to ensure accuracy, results must be scrutinized through statistical analysis. The ICH Q2(R1) guidelines recommend presenting precision test results in standard deviation, relative standard deviation, and confidence intervals for each precision type. Among these, confidence intervals hold great significance in assessing precision, allowing us to gauge an instrument’s ability to deliver consistent data within a specific range of values. Essentially, it enables us to pinpoint the precise range in which an instrument can produce reliable and consistent results. Although the use of precision testing may not always be practical for repeatability, an overall confidence interval that includes both intermediate precision and repeatability results could be worth calculating.