The HPLC symposium series is one of the premier conferences for liquid phase separations and related techniques. The conference has grown to become one of the largest meetings in the world dedicated to liquid phase separation science. This international meeting is multi-disciplinary and brings together many of the world's leading authorities to address the practical and economic aspects of separation science, as well as the challenges that lie ahead.

The 37th International Symposium on High Performance Liquid Phase Separations and Related Techniques (HPLC 2011 Dalian)

 in Dalian, China. The conference will take place at the Dalian World Expo Centre, Dalian, China, an exhibition hall which integrates services for exhibition, conference and performance. 

will be comprised of plenary and keynote lectures, oral lectures, prominently featured poster presentations, and a comprehensive display of commercial products for separation scientists. The symposium will cover advances in all areas of liquid phase separations.  

Currently more than 40 specially invited speakers from abroad have been confirmed, as well as more than 35 from mainland China and 5 from Taiwan. A list of prominent invited speakers can be found on the conference website. 

, featuring theoretical models and insights and data analysis and chemometrics; 

Advances in Liquid Phase Separation Technology

, featuring column technology and stationary phases, monoliths and small particle technology, ultra-high pressure and high temperature chromatography, sample preparation methods and hyphenated separations such as LC–MS, CE–MS, LC–NMR and other hyphenated techniques; 

Applications in Life Sciences and Drug Discovery

, featuring metabolomics, proteomics, glycomics, biomarker discovery and validation and pharmaceutical separations and analysis; and 

, featuring food safety, environmental and agricultural analysis and polymer analysis. 

Young scientists (under the age of 35) are encouraged to submit abstract for inclusion in the oral programme, and those that do can be considered for the PSC (Permanent Scientific Committee of HPLC Symposium) Young Scientist Award. 

PSC Young Scientist Award aims to encourage the participation of young scientists in the HPLC symposium series and to recognise their contribution to the meeting. 

In order to help the presence of young scientists to attend the 

 symposium, travel awards sponsored by HPLC will be presented to students who will present either an oral or poster contribution at the symposium			

For more information visit the website at 

 or contact Profesor Dr Guowang Xu, Co-Chairman of HPLC 2011 Dalian, National Chromatographic R. & A. Center, Dalian Institute of Chemical Physics, The Chinese Academy of Sciences, Dalian 116023, China. 

The 17th International Symposium on Separation Sciences (17-ISSS)

Hal of Grand Hotel Napoca, ClujNapoca, Romania

Advanced Analytical Methodologies in Drug Discovery (SSPA 2011)

International Symposium of AOAC Europe Section: Quality Control of Botanicals, TCM, Herbal Food Supplements and Herbal Medicinal Products

 AOAC Europe Section and the German Chemical Society 

Articles

Departments

Event News

For method development or routine analysis, the minimum gradient time may be a limit of throughput.

When you are developing a new liquid chromatography (LC) method, speed is often the primary interest — both speed in development and speed in terms of run time for the final method. With the expanding interest in Quality by Design (QbD) (1), we would like to know how different variables affect a separation, — both to ensure that a method is robust and to provide information for method adjustment when necessary. Gradient elution is often a first choice for method development experiments because the run times are defined and gradient data can be converted to isocratic conditions with the help of software programs such as DryLab (Molnar Institute, Berlin, Germany).  

Because speed is of interest, it is good to know just how fast we can make gradient runs and still have "good" chromatography. After all, we don't want to just blast the sample through the column with no chance of separation. Fortunately, there is a well-developed theoretical basis for gradient elution (2) that allows us to make predictions of conditions that will satisfy our goals. 

Before we look at gradients, let's consider isocratic separations, in which the organic solvent (%B) in a reversed-phase (RP) mobile phase is at constant concentration. For isocratic conditions, an ideal separation will have retention factors, 

 < 10, but it is sometimes hard to fit all peaks within this range, so we settle for 1 < 

 < 20 for acceptable chromatographic behavior. For 

 < 1, there is a risk of interferences from unretained material from the sample matrix and retention is very sensitive to small changes in %B. For 

 > 20, peaks become broad, and thus harder to detect, and the run time gets inconveniently long.  

-value changes as the sample travels through the column. At the beginning of the separation, the %B is low and 

 is large; as the peak moves along the column, the %B rises and 

in the isocratic context is a moving target during a gradient run. For this reason, we use 

* to represent a single number in gradients; this can be thought of as an average 

* is approximately the same for all peaks in the chromatogram and can be estimated as: 

 is the flow-rate (mL/min), Δ%B is the gradient range (5–95% = 0.9), 

 is the slope of a plot of isocratic log 

 where MW is the molecular weight. A value of 

 = 5 will be used in the present discussion, representing a typical value for compounds of MW < 1000 Da. Because 

-values are approximately (but not exactly) the same for a sample mixture, 

*-values (Equation 1) will also be approximately the same for all peaks in a run under the same gradient conditions. Like isocratic runs, it is desirable to have 2 < 

 < 10 for gradients. If a pair of gradient runs is desired for use with predictive calculations, such as with DryLab, 

*-values differing by 2–3-fold are recommended (for example, runs with 

* = 3 and 6). For fast runs with "good" chromatography, the topic of this discussion, we'll use 

We can rearrange Equation 1 to estimate the gradient time required for various chromatographic conditions: 

Let's look at an example using Equation 3 for a separation using a 150 × 4.6 mm, 5-µm particle diameter, 

= 2) and a gradient range of 5–100%B (Δ%B = 0.95). An estimate of the column volume is: 

 is the column diameter, 150 mm and 4.6 mm here. (This assumes a total column porosity of ≈65%.) So

 ≈ (2 × 5 × 1.6 × 0.95)/2 ≈ 7.5 min 

You can find this summarized in the first line of Table 1. (Note that in this calculation and all those in Table 1, I've rounded the numbers, so if you repeat these calculations, your results may vary slightly.) 

It might also be interesting to estimate the pressure, 

, under these conditions. We can do this (in psi) with 

 [Equation 2.13a in (3)] where η is the mobile phase viscosity in cP. For gradients, we need to use the highest viscosity during the run. For acetonitrile as the B-solvent and 30 °C, η = 0.9 [Table 1.5 of (3)]. This gives a maximum pressure of ≈ 1275 psi; in my experience, this calculation is a bit low for real columns, so I've multiplied values from Equation 5 by 1.5 for Table 1 to give a better feel of the pressure that is likely to be observed. 

I've made similar calculations as described above and summarized these in Table 1 for several other popular column configurations. The columns all have approximately the same number of theoretical plates, 

 ≈ 10 000 (±10%) under realistic conditions, so they should give the same separations in all cases; the exception is the 50 × 2.1 mm, 3-µm column with 

≈ 5000. The flowrates were chosen to give pressures of ≈ 2000–3000 psi (135–205 bar) for conventional LC equipment and 10 000–15 000 psi (680–1020 bar) for ultrahigh-pressure high-performance LC (UHPLC), which represent "typical" method conditions. 

You can see in Table 1 that for conventional methods using 100–150 mm columns with 3- or 5-µm particles, gradient times of 7–8 min are used for 

* = 2. For a second calibration run for retention modeling software, use a run three-fold longer (20–25 min). For the 50 × 2.1 mm, 3 µm column popular for LC–MS, a gradient of ≈2 min is required. 

Under UHPLC conditions with a 2-µm column (last two rows in Table 1), run times of just under 2 min are required. For comparison, I've included a 2.5-µm shell-type column (third line from the bottom of Table 1); these columns have pressures reflected by the particle size, but performance more like a 2-µm particle. In the configuration chosen, gradient times are about the same as UHPLC columns, but with a somewhat lower pressure. 

So far so good. It looks like we can get "good" chromatography with runs of 7–8 min on conventional LC equipment and in 2 min for UHPLC conditions. These are fast enough to give us the ability to screen many possible variables in a reasonable amount of time. However, there are a couple additional factors that we'll consider next. 

One factor to take into account with fast gradients is the gradient delay. This is determined by the dwell volume, 

, of the system, which comprises the mixer, connecting tubing, sample loop and, for lowpressure mixing systems, the pump head volume. The dwell volume should be measured for each LC instrument; directions for this were given in a previous 

 instalment (4). Typical dwell volumes for conventional highpressure mixing LC systems are in the 1.5–3.0 mL range; their low-pressure mixing counterparts generally are slightly larger, with 2.5–4.0 mL dwell volumes. Highpressure mixing systems that have been tuned up for LC–MS work and UHPLC systems typically have dwell volumes of 0.3–0.5 mL, whereas low-pressure mixing UHPLCs have 1.0–1.5 mL. 

The practical impact of the dwell volume on the run time is that it extends the run because the 

 — the time it takes for the gradient to reach the column after it is started — is added to the gradient time. This is shown in the third column from the right of Table 1. I've assumed a 2.0-mL dwell volume for conventional LC systems (first three rows) and 0.4 mL for the LC–MS and UHPLC configurations. Note that for the 4.6-mm columns and 1.5–2 mL/min flow-rates (first two lines of Table 1), the added time is 15–20%, but if a 100 × 2.1 mm column is used, the total run time is nearly doubled by the gradient delay. With the 2.1-mm i.d. columns used for LC–MS or UHPLC, the delay can be substantial, 25–40% additional time for the run. For some instruments, it may be possible to delay the injection until the gradient reaches the column, but the dwell time must be paid for sometime — in such cases, it is moved from within the run to between consecutive runs. Additionally, the dwell volume must be thoroughly flushed out when changing the mobile phase from one condition to another during method development. 

It is clear that it is desirable to minimize the dwell volume to maximize throughput with gradients. In some cases it may be possible to reduce the dwell volume by changing system plumbing, but there is a minimum dwell volume that is characteristic of each LC system. 

Another potential problem related to the dwell volume is gradient distortion. This is illustrated in Figure 1. In each case, the gradient programme is shown with a dashed line and the solid line shows the actual gradient generated by the LC system. In the top case, the gradient is ramped at 3%/min and in the lower run, 14%/min. You can see that in one case, the actual gradient closely follows the intended one, whereas in the steep gradient there is considerable distortion in the actual gradient. Clearly the lower case is undesirable. Such gradients are very instrument-dependent and will be hard to transfer. Based on these data (5), it was concluded that as long as the gradient volume is at least twice as large as the dwell volume, gradient distortion should be minimal. The upper plot has a gradient volume twice the dwell volume, whereas the ratio for the lower one is only 0.3. 

Figure 1: Gradient distortion comparing 3%/min gradient (top) and 14%/min (bottom). Dashed lines show gradient programme; solid lines show actual gradient. Data from (5). 

The next-to-last column in Table 1 shows the gradient volume, 

), for each gradient condition. The maximum recommended dwell volume (half the gradient volume) is shown in the far right column of Table 1. We can conclude that gradient distortion is not likely to be a big problem with conventional LC equipment used with 4.6-mm i.d. columns or with LC–MS or UHPLC equipment used with 2.1-mm i.d. columns. In either case, however, when smaller diameter columns are used, gradient distortion is a factor that should not be ignored. In any case, it is wise to check to be sure that your LC system will generate a distortion-free gradient under the fastest desired gradient conditions. This can be done in the same manner as the dwell volume measurement discussed above (4), but using the desired gradient conditions. 

Table 1: Characteristics of various gradient configurations. 

Fast gradients can be generated reliably with conventional, LC–MS and UHPLC-type LC equipment if certain precautions are taken. Fast gradients allow for rapid screening and optimization of several variables during method development and the creation of short run times for routine analysis. It is important to know the dwell volume of each gradient system so that gradient delay can be taken into account. A check should also be made that gradient distortion is not observed, or the transfer of such gradients may be problematic. It should be obvious that longer, larger-volume gradients will be less subject to distortion, so only the shortest gradients anticipated need to be checked for potential distortion problems. 

, is vice president of LC Resources, Walnut Creek, California, USA. he is also a member of 

 editorial advisory board. Direct correspondence about this column should go to "LC Troubleshooting", 

, 4A Bridgegate Pavillion, Chester Business Park, Wrexham Road, Chester CH4 9QH, UK, or e-mail the editor, Alasdair Matheson, at 

1. International Committee on Harmonization, ICH Q8 Pharmaceutical Development (May 2006).  

(Wiley Interscience, New York, NY, 2007). 

3. L.R. Snyder, J.J. Kirkland and J.W. Dolan, 

Introduction to Modern Liquid Chromatography 

(Wiley, New York, NY, 3rd Edition, 2010). 

5. G. Hendricks, J.P. Franke and D.R.A. Uges, 

LC Troubleshooting

For method development or routine analysis the minimum gradient time may be a limit of throughput.

How Fast Can A Gradient be Run?

With the recent emphasis on high-throughput high performance liquid chromatography (HPLC) and ultrahigh-pressure liquid chromatography (UHPLC), chromatographers are looking for ways to improve productivity. This instalment discusses the use of method translation calculators to make conversions from conventional HPLC to high-throughput HPLC and UHPLC more convenient to perform without the need for manual calculations. Both isocratic and gradient method translations are discussed and applications are demonstrated. 

Nowadays everybody is interested in increasing productivity, and for good reason. Many analytical laboratories are experiencing increases in the number of samples with fewer personnel and the same number of instruments. Conventional gas chromatography (GC) and liquid chromatography (LC) methods were able to keep up with the sample load when only a few samples or tens of samples required analysis per day. Often, samples could be loaded into autosampler carousels and run overnight so that the next morning results could be provided to the requestor. However, many times those requesting the analytical data want (and perhaps need) the information more quickly than overnight. 

Techniques for increasing the speed in chromatography laboratories have been known for years — shorter columns, faster flow-rates, smaller internal diameter columns and so on. The recent trends in high performance liquid chromatography (HPLC) column technology with shorter columns packed with smaller particles (1) or superficially porous particles (2) used at a higher flow-rate are a direct result of the need for increasing sample throughput. In GC, shorter, smaller internal diameter columns perhaps with lower viscosity carrier gases, such as hydrogen, have resulted in faster separations (3). When higher throughput is required, analysts don't want to have to spend time redoing or revalidating the method but want to get the same elution order and resolution achieved with the original column but faster. To maintain the integrity of the separations, the experimental conditions must be adjusted so that everything looks the same, except that the analysis time is shorter.  

The process of converting a method is termed 

). There are standard equations available to set up the new conditions and sometimes it is easy to calculate these parameters. For isocratic (HPLC) and isothermal (GC) work, the calculations are relatively straightforward. However, when one wants to translate gradient (HPLC) or temperature programming (GC), the calculations become a bit more challenging, especially if column internal diameters and lengths are also changed simultaneously. Fortunately, there are software packages available, mostly for free, to assist one in making a method change. Most of these free software packages have the name "Translator" or "Calculator" associated with their identification. The purpose of this instalment is to demonstrate the use of method translation for some relatively simple HPLC isocratic and gradient methods and to provide sources where these calculators can be found. Because the equations forming the basis of these calculators can be found elsewhere, they won't be repeated here. I will provide some practical examples that can show one how to make a method conversion without having to perform manual calculations. 

The calculations used in HPLC are simpler than in GC, mainly because the liquid mobile phases are relatively noncompressible compared to GC carrier gases. GC method translation was covered in last month's column. 

Here, I will consider isocratic method translation and then gradient method translation. Parameters that are used in isocratic HPLC method development using a single column type (for example, monomeric C18) and mobile phase combination that are considered in method translation include particle size, column length, column internal diameter, flow-rate, retention factor, injection volume, sample concentration and column back pressure. At this point in time, we will not include column temperature as a variable since variation in retention factor 

 with temperature can be compound-dependent. Because viscosity is influenced by column temperature, temperature is used in the method translation to calculate the mobile phase viscosity for water–acetonitrile and water–methanol mixtures. The pertinent equations used in HPLC method translation are well known and will not be covered here.  

To determine if an isocratic method is ready for method translation, one must set performance goals for the new method. The availability of the new column geometry to achieve the necessary efficiency must be established. The instrument needs versus the method performance goals must be assessed. One must determine if there are any instrumental restrictions, such as extracolumn effects, available data rate and system pressure limitations. The flow-rate availability from the solvent delivery system must be established as per the method performance goal for analysis time. If a smaller column volume is the outcome, the injection volume must be adjusted to compensate for it. If the injection volume is too small, one must assess injection repeatability and sample solvent composition robustness.  

Table 1: Isocratic parameters for initial tocopherol method by HPLC.  

For our first example, let's consider a simple isocratic separation of vitamin E α-, β-, and γ-tocopherols on a conventional 400-bar LC system using UV detection. The initial conditions and chromatograms of the reversed-phase separation are shown in Table 1 and Figure 1 (right-hand side), respectively. A 5-µm microparticulate column with the dimensions 150 mm × 4.6 mm was used with a 1-mL/min flow-rate. Resolution of the three components was more than adequate for good quantification. The separation time was just under 14 min, which in today's world is considered to be somewhat slow for such a simple analysis. A shorter column packed with sub-2-µm reversed-phase packing of the same type at a higher flow-rate would provide a faster separation with equivalent efficiency. A goal of a factor of 5× in speed was set but using the existing equipment. In addition, saving solvent was a secondary goal for method translation. The question now arises about how much the method can be modified and which modifications are the most important. 

Figure 1: Comparison of conventional isocratic method vs. translated isocratic method at 3 mL/min.  

Method Translator is free online software that allows users to first enter the parameters for an existing method and then specify parameters for a new method that provides an improvement (4). Through an automated system of "checks and balances" to ensure that one's system is capable of making a successful conversion, users then enter a new set of parameters. Figure 2 is a screen capture of the software tool modified for isocratic method translation. The left side of the screen provides the original LC method with some added parameter values. For example, the extracolumn dispersion of the standard LC system had a value of 35 µL derived from the volume of connecting tubing (21.6 µL because the tubing internal diameter was 0.17 mm) plus the volume of the UV flow cell (13 µL). The mobile phase was 95% acetonitrile–5% water, which had a viscosity of 0.43 cP. In this simple case, we wanted to adopt one of the newer, short sub-2-µm columns that should give similar performance to the original column but provide a faster separation. 

Figure 2: Screen capture: simple method conversion for isocratic separation of tocopherols.  

The new method on the right side of Figure 2 shows that by use of a column one-third of the length, the time savings factor was 3.0 (constant flow of 1 mL/min) and the solvent savings was 67%; a substantial reduction in purchase and disposal costs. Because of the smaller column volume, a reduction in the injection volume to 0.33 µL was recommended. On the downside, because the original system was used without any further modification, the large system dispersion value (35 µL) gave rise to a loss of effective plates (down to 9849 plates from an expected 12077). On such a short column, some adjustment of the extracolumn volume would have to be made to get a better representation of the potential efficiency gains. To achieve a reduction in extracolumn volume, the original connecting tubing was replaced with smaller 0.12-mm i.d. tubing and the 13-µL flow cell was replaced with a 5-µL semimicro flow cell, cutting down the extracolumn volume to 11 µL. By reducing the extracolumn volume by roughly two-thirds, the effective plates now numbered 11300 and the resolution was unaffected. Still, our goal of a factor of five in speed was not achieved. A simple click on the "Speed Optimized" button on the Method Translator page showed a recommended increase in the flowrate to 3 mL/min as shown in Figure 3 (the right-hand side of the screen capture only). By making these method modifications recommended by the Method Translator program, we have decreased the separation time to 1.7 min (a factor of 8.9 over the original method) and have ended up saving nearly 10 mL of total solvent per run. Table 2 summarizes the new set of conditions. The chromatogram on the left hand side of Figure 1 shows that the peaks were still well resolved with good peak shape. The pressure did increase to 224 bar but this was within the pressure capability of our system so it presented no problem. 

Figure 3: New speed-optimized translated isocratic method for tocopherols.  

This example shows how using a method translation package, one can optimize an isocratic method without having to perform innumerable calculations. Many of the performance parameters are automatically adjusted as one enters the new column and instrument configuration. The calculations also showed the importance of minimizing the extracolumn effects on efficiency when using sub-2-µm ultrahigh-pressure liquid chromatography (UHPLC) columns. 

Table 2: Isocratic parameters for translated tocopherol method by HPLC.  

The use of gradient elution is the most popular approach to analysing complex samples, reducing analysis time, increasing peak capacity and increasing sensitivity. One can also use method translation to convert gradient methods to improve overall performance. When one considers converting a gradient method, Equation 1 is the relationship that makes it easier to understand the tradeoffs in modifying the chromatographic conditions:  

* = apparent retention factor under gradient conditions; note that the actual retention factor changes constantly during a gradient; 

 Δ%B = difference between the initial and final %B values; multistep gradients and those with gradient holds in the middle of the gradient profile are more difficult to translate; 

 = constant (~4–5 for molecules for small molecules (100–500 Da) and 10–1000 for large biomolecules such as peptides and proteins); 

In gradient method translation, one desires to keep the relative peak positions in the chromatogram unchanged and as long as Equation 1 is satisfied, a method should be translated easily. Any change in one parameter on the right-hand side of the equation should be offset by a proportional adjustment of another parameter. Table 3 outlines some simple changes that have to be made in order to balance Equation 1. 

Table 3: Method translation parameters affected by Equation 1.  

As with isocratic method translation, in gradient method translation performance goals must be established by first examining the current method. In addition to the performance parameters discussed in isocratic method translation, parameters such as column volume (

, affected by column length and internal diameter), the gradient programme (hopefully linear), dwell volume (also called 

 — the point of solvent mixing in the flow system to the head of the column), resolution of critical pairs and column back pressure (mobile phase viscosity can change dramatically during gradient elution) become more important and should be considered in gradient elution. Think of any tradeoffs that you may have to make in the new method: pressure or flow limitations of your LC system, columns on hand (different phases, dimensions, hardware configurations), extracolumn effects (some of which may be negated by the focusing of analytes at the head of the column), instrument gradient delay volumes (that may change with pressure due to presence of pulse dampers), post-gradient equilibration (regeneration) time, time constant of detector or data rate of your data system. 

Let's now consider the method translation of a gradient example. Using the same Method Translator online program as for the isocratic approach, we desired to replace an older method for the analysis of impurities of an active pharmaceutical ingredient (API) done by conventional HPLC. Figure 4(a) shows the conventional separation of the API and its impurities on a 250 mm × 4.6 mm column packed with 5-µm StableBond C18 reversed-phase particles (Agilent Technologies, Santa Calra, California, USA). The analysis was done under typical conditions often used in pharmaceutical QA/QC laboratories. At a temperature of 40 °C, the entire separation required about 20 min using a 5–90% gradient over 20 min with a 3-min isocratic hold and a 7-min regeneration step. It was desired to convert the method to a sub-2-µm column (100 mm × 4.6 mm) to maintain the resolution of the impurities present. Using the "Simple Conversion" mode (screen capture not shown here), the overall separation time was decreased to a little more than 7 min (a time savings factor of 2.5) with a 60% savings in solvent (42 mL in original method to 16.8 mL in translated method).  

Figure 4: (a) Original method using conventional HPLC. (b) Gradient method translation using simple conversion mode. (c) Gradient method translation using speed-optimized mode.  

The new chromatogram is depicted in Figure 4(b). Method Translator calculated all the setup parameters including the new gradient times and injection volume. The resolution was maintained but the pressure at the maximum solvent viscosity of the water–acetonitrile mixture (0.75 cP at 40 °C obtained from a viscosity table on another tab on the same Method Translator website) increased from 95 to 292 bar as a result of the smaller particles packed in the column. Finally, clicking on the "Speed Optimized" button on the Method Translator program, the flow rate was increased from 1.4 mL/min to 2.58 mL/min, taking into account the maximum pressure rating of the instrument. Figure 4(c) provides the highest speed separation with the separation time just under 4 min, a time savings factor of 4.6 compared with the original conventional LC method. As expected, the operating pressure increased to 540 bar, which is still below the 600-bar capability of the HPLC system that was used for this assay. A screen capture of the speed-optimized gradient translated method showing both initial and final conditions is depicted in Figure 5. A further calculation using the "Resolution Optimized" mode could have been performed but the overall analysis time would have been longer and more solvent would have been used. 

Figure 5: Speed-optimized gradient method translation for API and its impurities.  

If the method required the use of mass spectrometry (MS) detection where such high flow-rates are not MS friendly, then a 100 mm × 2.1 mm column could have been entered into the translated method, resulting in the use of only 3.5 mL of solvent per run (not shown here), because of the lower flow-rates used to maintain the proper linear velocity with this smaller internal diameter column. 

Although the number of displayed parameters in the Advanced Method Translation tool may look formidable, many of them are based on calculated values. Method Translator starts with a preset of values for every input field. There is also a "Basic Mode" of operation where certain values required for calculations (for example, flow resistance and porosity) that are not commonly known are preset and hidden from view to simplify the interface. All calculated values are updated immediately after any input value is changed. All inputs are checked for consistency: For example, is the flow-rate adequate for the column being used as well as the solvent and temperature? Are the gradient entries logical?  

The Method Translator software tool can be used for any commercial liquid chromatograph where some of the input parameter values must be entered manually. A convenient "Cost Savings Calculator" is available through another tab on the Method Translator website to help to determine total savings for adopting shorter, faster analytical columns. 

Where Can You Get Method Translation Programs?

There are a number of method translation programs or free services for HPLC that are available from both commercial and academic sources. As mentioned earlier, they are almost all "freeware" and can be used online or downloaded onto your PC. Table 4 lists the locations for some of these programs. 

Table 4: Method translation programs and services for HPLC.  

In this instalment, I have demonstrated the use of method translation software to make conversions from HPLC to high-throughput HPLC and UHPLC more convenient to perform without the need for labourious manual calculations. Both isocratic and gradient method translations were discussed and applications demonstrated. Most of the software packages are free and can be used online or downloaded. Sources for some of these programs have been provided. 

, is a senior scientist at the Columns and Supplies Division. Agilent Technologies, Wilmington, Delaware, USA and is a member of 

 editorial advisory board. Direct correspondence about this column should be addressed to "Column Watch", 

, 4A Bridgegate Pavilion, Chester Business Park, Wrexham Road, Chester CH4 9QH, UK or e-mail the editor, Alasdair Matheson, at 

Column Watch

An overview of the method translation calculators to make conversions from conventional HPLC to high-throughput HPLC and UHPLC.

Method Translation in Liquid Chromatography

In two recent Questions of Quality columns (1,2) the topic of why system suitability tests (SSTs) were not a substitute for analytical instrument qualification (AIQ) was discussed. In these two columns, the focus of the discussion was on the initial qualification of the chromatograph and that SSTs could not be a substitute for AIQ because the former is method specific and the latter is instrument specific. The columns looked at the first three phases of the 4Qs model outlined in USP <1058> (3): design qualification (DQ), installation qualification (IQ) and operational qualification (OQ).  

However, these two columns did not consider system suitability tests run during routine analysis because the focus was solely on the initial qualification of a chromatograph. The last stage of the 4Qs model — performance qualification (PQ) that is performed after the operational release of the instrument — was not discussed.  

After publication of the second article, I was contacted by Hermann Wätzig who mentioned that the articles had not considered how SST results could be used effectively in PQ. He, along with his colleagues, suggested that a third article focusing on the role of SSTs during PQ should be written. 

First we will consider what is meant by PQ using the definitions and explanations contained in USP <1058> (3) and then discuss how SSTs can be used to support the PQ phase of AIQ effectively. In this discussion we will use SSTs that are run during an HPLC analysis as an example, but the principles contained here can be modified and applied to GC. 

PQ is an important part of analytical instrument qualification (AIQ) and according to USP <1058> (3) is defined as 

the documented collection of activities necessary to demonstrate that an instrument consistently performs according to the specifications defined by the user, and is appropriate for the intended use.

 The aim of PQ is to demonstrate that a chromatograph remains qualified and is performing as specified by the users in the chromatography laboratory.

 Table 1: Performance qualification (PQ) parameters derived directly from existing SST execution.			

Table 1 contained in the USP <1058> (3) defines PQ in more detail: 							

Performed periodically at specified intervals for each instrument

: The period can either be at specified time intervals (the traditional PQ approach) or continuously when an SST is run before and during an analysis. However, the frequency and the acceptance criteria will need to be defined by each laboratory based on their business and regulatory needs. 

: A regular service programme for the chromatographic instrument with associated records needs to be established. If an instrument fails to meet PQ test specifications, then it requires maintenance or repair. After this has occurred, then, as a minimum, the relevant PQ tests should be repeated to demonstrate that instrument performance is within specification. If a repair is expensive then a complete requalification of a module or the whole instrument may be required. However, we will not discuss this area any further in this column. 

Establish practices to address operation, calibration, maintenance and change control

: This would involve instrument log books with regular peer review, how to requalify the instrument after minor or major repairs and a simple change control process. Again, this topic will not be discussed further here.  

: We will focus on this area of PQ in the rest of the article and look at ways to generate this information from activities that are already undertaken during an analysis, as well as further information that could be obtained with modification of current analytical practices.  

According to USP <1058> (3) the laboratory should establish a test or series of tests to verify the acceptable performance of the instrument for its intended use. Quoting from the general chapter: 

PQ tests are usually based on the instrument's typical on-site applications and may consist of analysing known components or standards. The tests should be based on good science and reflect the general intended use of the instrument. Some system suitability tests or quality control checks that are performed concurrently with the test samples can be used to demonstrate that the instrument is performing suitably

Some of the key points about performance checks are:					

The need to set specifications to compare the instrument performance against to determine if the instrument performance is acceptable or not.  

PQ tests may be modular (focussing on a specific module of the chromatograph. for example, the pump or detector) or holistic (looking at the whole system performance).  

It may be useful to repeat the same PQ tests each time the instrument is used so that a history of the instrument's performance can be compiled. This would allow trending of data and assessing if the instrument performance is maintained or is changing and would be a proactive approach to identifying potential problems before they adversely affected an analysis. 

Some PQ checks can be performed concurrently with the test samples to demonstrate that the chromatograph is working within a laboratory's defined limits for a specific analytical method. The development of this point is the focus of this column.

Although this column is discussing system suitability tests for chromatographs, other point-of-use checks are used to determine if an analytical instrument is functioning correctly before analysing samples. This is a common procedure in the majority of analytical laboratories because it is good analytical science. Consider the following examples: 					

: Before using a pH meter an analytical scientist would check that the electrode and the whole system were functioning correctly by checking with two or more calibrated solutions of known pH value that bracket the range of measurement. These solutions can be prepared in the laboratory or purchased with a calibration certificate from a supplier. 

: Again, the balance will be checked before use with either masses that have been calibrated to traceable national or international standards or laboratory calibrated masses that are traceable to externally calibrated masses. The masses used would also bracket the range of measurement on the balance. This would be in addition to the balance's internal calibration mechanisms that can also be used to ensure adequate performance. 

In both of these cases, the point-of-use check determines if the instrument is within acceptable limits from a holistic perspective. In exactly the same vein, a system suitability test confirms that a chromatograph is functioning and is fit to analyse samples for a specific analytical procedure.  

System Suitability Tests as Performance Checks

All of the SST parameters are defined in the appropriate chapters of the Pharmacopoeias, for example the United States Pharmacopoeia (USP) <621> (4) or European Pharmacopoeia (EP) 2.2.46 (5). During the development and validation of an analytical procedure, the SST parameters most applicable to demonstrating that the chromatographic system works will be selected by the method developer and then validated as part of the overall procedure. As an SST is run before every chromatographic analysis in a regulated environment, the following question arises: How can the information contained in the SST injection samples be analysed to produce meaningful performance data of a chromatograph?  

Traditional versus Continuous Performance Checks

Traditionally, a system check would be performed every three to six months depending how often the chromatograph was used. However if a problem was identified as a result of the performance check, up to the last six months of chromatographic results could be considered suspect. This is not an appealing situation for the manager of the analytical laboratory to be in. Therefore, an alternative approach should be considered to provide an effective and efficient means of assuring the performance of a chromatograph. 

An alternative is continuous PQ, which can be regarded as a major improvement because this modern approach allows a laboratory to reduce time and effort in gathering performance statistics without losing data quality. Even better, it allows for the continuous monitoring of the instrument performance (6,7). This approach uses a holistic look at the most relevant performance parameters of a liquid chromatograph such as:					

Precision and linearity of the injection volume

Thermostatting precision of the column oven

There are five parameters listed in Table 1, which can be determined from data obtained after running an existing SST (6,7). By using this approach there is the benefit of saving extra effort as these parameters can be checked on a daily basis compared with the traditional PQ approach where system performance is just checked every 3–6 months. Furthermore, it avoids raising the question that the results generated since the last traditional PQ may be questioned because issues with a chromatograph can be identified and resolved much sooner that using the traditional PQ. The tolerance values in the right hand columns of both Tables 1 and 2 were chosen according to reference 8 and this table also complies with the EDQM specifications (9) for HPLC performance.

Table 2: Additional performance qualification (PQ) parameters that can be derived by modifying an SST sequence. 			

Many of the parameters in Table 1, for example injection volume precision, mobile phase proportioning (if used), flow-rate precision and precision of column oven temperature, can be derived from the consistency of the retention time of a single peak over the time frame of an analysis.  

Table 2 shows the additional seven parameters that can be monitored holistically with little additional effort by adapting and extending the SST design. The thermostatting precision and accuracy of the autosampler tray, flow-rate accuracy, mobile phase proportioning, wavelength accuracy and absolute values for noise and drift have to be measured separately as outlined in Table 2 (6,7). 

The total list of PQ parameters shown in Tables 1 and 2 consists of up to 12 items that are necessary to monitor the PQ of an HPLC instrument thoroughly. This list combines both PQ parameters and some of the modular parameters of an operational qualification (OQ). Most of these parameters can be derived holistically by applying either normal (Table 1) or modified (Table 2) SSTs. Additionally, these parameters, which require modular testing, can be investigated without changing parts such as flow cells, columns or the mobile phase reservoir. Thus extra time for the sample and mobile phase preparation and long equilibration procedures is no longer necessary.  

Note, however, that not all instrument PQ parameters can be evaluated by this proposed approach. For example, detector lamp energy cannot be measured by the continuous PQ but this parameter would be a less frequent performance check in a PQ and is easily accomplished manually.  

If the principle of continuous PQ is of interest to you, how should it be accomplished? We suggest the following approach which is outlined in Figure 1 and in the text below (7).

Figure 1: A suggested approach for continuous performance qualification (PQ). 			

The basic continuous PQ test can derive five parameters from the system suitability test injections and the enhanced PQ can determine up to 12 parameters in a single run. 

1. Examine the SST of your method. If there are multiple methods running alternately on the system, take the simplest one(s). However, you must make sure that the respective standard substances are well defined. 

2. Look for a well-separated peak. You should find at least one as this is typically claimed for an SST (6,7). 

3. Run the injection sequence consisting of the following samples, which for the basic performance determination should be structured as follows: 

Questions of Quality

A look at the role of system suitability tests (SSTs) during performance qualification (PQ).

Why System Suitability Tests Are Not a Substitute for Analytical Instrument Qualification (Part 3): Performance Qualification (PQ)

Experimental design or sequential approaches can be used to optimize separations in pharmaceutical analysis However, the choices made by the analysts setting up the experiments are not always the best and may create problems. In this article the different steps of an experimental design-based approach are considered and the problems that arise from the wrong choices are discussed.

In previous articles we have discussed the experimental designs that can be applied in method development and optimization as well as the analysis of this data (1–4). In recent articles we described a method development approach for drug impurity profiles (5,6). This approach is a variant on the common experimental design one. In an experimental design approach one first determines the most important factors from screening design results. The responses of the method are then modelled as a function of these latter factors and the optimal factor combination determined from the results of response surface designs. For drug impurity profiling we could skip the screening step since we usually know in reversed-phase chromatography which factors have the largest influence on the selectivity. This allows the use of a sequential approach in method optimization, where some steps included small designs (5,6). 

However, in some manuscripts originating from both the pharmaceutical industry and the academic world, some steps from the experimental design [or design of experiments (DoE)] approach are not properly applied, leading to problems: either with the applied designs, in the practical execution of the design or in the data handling steps. We will limit ourselves in the rest of this article to the application response surface designs. The different steps in a DoE approach are the selection of the factors and their levels, the selection of the appropriate design to examine the factors, the selection of the response(s) to consider, the execution of the design experiments and the data handling. 

The number of factors to optimize should preferably be low (i.e., two or three). These most important factors are, for example, selected based on the results of a screening design performed earlier. Regularly, optimization designs are executed for four to six factors. Such designs require a considerable number of experiments, but determining the best conditions from it is far from obvious and we will discuss this later. Moreover, it is rare that such a high number of factors would significantly affect the method. Therefore it is better to make an 

 selection of the important factors in a preceding screening step. The factors are best defined in such a way that they have a physico-chemical meaning that is easy to understand by the users. For example, pH can be defined as a factor. Everybody knows its meaning and its influence could be derived and interpreted from the design results. Even though pH could also be split in the concentrations of its acidic and basic compounds that might be included as two factors in a design, the physicochemical interpretation of their effects is far less evident. 

Factor levels during optimization are selected in an interval that is as broad as possible and that are, for example, limited by the product to be optimized or the limits of the applied technique. For instance, if DoE is applied to optimize a formulation, such as a tablet, then the most extreme levels (as well as their combination) should be tested preliminarily to verify whether these extremes still result in the desired product (i.e., the tablet). In chromatography, the pH, for example, might be limited depending on the type of columns used. 

Based on the number of factors to be examined, an experimental design is selected. We discussed the most frequently applied response surface designs in reference 3. It concerns designs with at least three levels for each factor. When examining four or more factors the required number of experiments is high. For example, when applying a central composite design studying 4, 5 or 6 factors requires at least 25, 43 and 77 experiments, respectively. This is another reason to keep the number of factors examined limited. Often a number of replicated experiments, usually of the centre point, are added to the experimental set-up. They are used to estimate the experimental error and are later applied in the data interpretation to indicate non-significant terms in the models built. This will be discussed later. 

The responses measured should allow the prediction of the best conditions, such as those where the worst separated peak pair is best separated. At first sight a response indicating the degree or the quality of separation, such as the selectivity factor, α, or the resolution, Rs, seems appropriate for this purpose. To predict the best conditions the response is modelled as a function of the factors examined, usually applying a quadratic model. For instance, for considering two factors: 

 the model coefficients. For more than two factors similar models can be built containing more terms (4). 

However, modelling resolution or the selectivity factor from the measured design values is usually not recommended, unless no changes in elution sequence occur among all the experiments, which in method optimization is very improbable. When changes in elution sequence occur, there are then two objections against the modelling of the above responses. 

First, a situation where two peaks A and B are equally separated but eluting in a different sequence, that is once as AB and once as BA, both are indicated by the same number to represent a different situation. In the factor's domain between both conditions the separation will go through a situation of co-elution or Rs = 0. When modelling the above, the different situations AB and BA are not distinguished and no model will pass through zero in the intermediate domain. Thus to distinguish between both situations one should give one of the two resolutions a negative sign [e.g., Rs(AB) = 1.5 and Rs(BA) = –1.5]. In a study where resolution was modelled, it was reported that "almost all models for Rs had serious lack of fit problems". The reason for these problems can be easily found in the above. The optimum was then selected as the best result from the design experiments. There is nothing wrong with that approach, but then one does not use all the available information. 

Second, only the resolution between consecutive peaks is of interest to find the best conditions. However, the problem is that because elution sequence changes occur between design experiments, one does not know which peak pairs are relevant at a given set of conditions. Consequently, one should model the resolution between all possible peak pairs. This means 15 models for a mixture with six compounds and 78 models for a mixture of 13 compounds. Such modelling becomes tedious, while a majority of models will in fact be redundant. For the two reasons above one does better not to model resolution (or selectivity factor) during separation optimization. 

Alternatively, the retention times (and occasionally the peak widths) will be modelled. This approach requires one (occasionally two) models for each compound. Peak widths are also modelled for isocratic separations, while for gradient elution one observes from the measured peak widths whether they can be considered constant or not and thus don't need to be modelled. Then the relevant resolutions are calculated from the modelled responses for given experimental conditions, because;  

 are the retention times and baseline peak widths of two adjacent peaks. 

In a DoE approach a quadratic model (Equation 1) is usually built. In a number of publications the results of replicated experiments (e.g., at centre point level) are used to estimate experimental error and to determine which coefficients of the model are non-significant (i.e., equal to zero). Very often nothing is done with the observed results. Normally a new model without the non-significant terms should then be build. However, when one uses either the first model or the second to select the optimal conditions, it will not make a large difference. The models determine regions with proper response characteristics. When considering the region determined by the full quadratic model, one does not expect the selection to be largely changed from a model modelling the same data but where the nonsignificant contributions to the model are eliminated. 

Often in method optimization the appropriate conditions are visually derived from the response surfaces or the equivalent contour plots (Figure 1). These are visualizations of the quadratic model that has been built. Now when examining three factors, one has to fix one factor at a given value to be able to visualize a part of the entire response surface. This is similar to selecting a chromatogram from an LC–DAD measurement.

 Figure 1: Response surface and its corresponding contour plot, representing the behavior of a response as a function of two factors. 

The chromatogram selected at low wavelength does not necessarily resemble that at an intermediate or at a high wavelength. Each of them only shows a tiny part of the entire data set. The response surfaces observed at low, intermediate and high fixed value of the third factor also do not necessarily resemble each other and they also each show a fraction of the entire response surface. When evaluating four, five or six factors in an optimization step, one has to fix two, three or four factors to be able to draw a response surface for the two remaining. The more factors that need to be fixed the smaller the fraction of the entire response surface one is visualizing and observing. In fact one does not have an idea whether the observed best conditions represent a local optimum, which can be far away from the global one, or not. 

Now when considering our specific problem of separating a mixture of compounds, we will not determine the optimum from the response surface visualization because only models for retention time (and occasionally for peak width) are available. To find the conditions where the worst separated peak pair is separated best, a grid over the entire domain is considered. This approach is explained in detail in references 5 and 6. For each factor in its examined interval a feasible step size is defined. For example, for percentage of modifier

the step size might be 1%, 2% or 5%; for the pH it could be 0.1 or 0.2 pH units. These steps determine a number of feasible levels within the factor range, for example 20 for %modifier and 30 for the pH. The above grid is composed of all combinations of the different factor levels. For example, for pH and modifier in the above example, the grid contains 600 points (i.e., 20 × 30). When four or more factors are considered, easily ten- or hundred thousands of grid points are obtained, which makes the calculation work tedious. Occasionally then broader steps can be considered resulting in fewer grid points. 

At each grid point one predicts the retention time of a given compound from the model built and considers its corresponding width. The retention times (with their widths) are then sorted from high to low (representing consecutive peaks) and the resolutions between consecutive peaks are calculated. By this approach only the relevant resolutions are considered. At each point the smallest of these resolutions is selected because it represents the worst separated peak pair at that set of conditions. From all minimal resolutions one can derive the different conditions where the worst separated pair is best separated or where it is baseline separated, Rs > 1.5. This approach leads to the indication of the conditions were best separations are predicted. It does not require any visualization but indicates the proper optimum, even for higher numbers of examined factors, though there the calculation load is high. The predicted separation at the selected set of conditions is then verified experimentally. 

The following recommendation can be given from a practical point of view to separate mixtures with a considerable number of compounds (e.g., in drug impurity profiles), and regards the gradient or solvent strength conditions: When measuring experimental design results to apply an approach as described above, take care to choose the initial gradient or solvent strength conditions such that the elution time differences between the first and last eluting peaks are large enough. If not, one risks performing lots of calculations and ending up with best conditions where not all peaks are baseline separated. Speeding up the analysis once baseline separation is obtained belongs to the fine tuning of the method and can be done rather quickly in different ways (e.g., gradient adaption, flow-rate changes, column dimension changes), while a repetition of the experimental design conditions at an adapted gradient is much more tedious. 

The various steps of an experimental design-based approach are considered and the problems that arise from wrong choices are described.

LCGC Europe-08-01-2011

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