Joanne Ratcliff, PhD, is communications project manager, Laboratory Weighing, Mettler Toledo GmbH, Joanne.Ratcliff@mt.com.

Joanne Ratcliff, PhD

Capsule filling is common in preclinical and clinical studies. Companies are increasingly developing cytotoxic and potent compounds; containment of these substances is, therefore, a primary safety concern for laboratory managers. As the API dosed into capsules are typically not fully characterized at this stage, they must be treated as high-potency to protect the researchers handling them, because any spillage or exposure presents a potential safety risk.

	Often, only a limited quantity of the drug substance is available. When working with a new chemical entity, where the synthesis program may run concurrently with the early dosing studies, early batches may be of the order of several grams, with perhaps a gram or less available for development work. At this phase, the drug is expensive and precious, so it is important to avoid wasting any.

	Depending on the nature of the drug and test subject, new pharmaceuticals are often administered in gelatine capsules. The fill weight of each capsule must be accurately calculated and prepared according to the test subject’s weight. This preparation involves manually weighing highly potent, hazardous substances precisely with a spatula. Manual capsule filling, however, is tricky and challenging, especially with small capsules.

	Manual filling is usually performed manually when the sample series are too small for filling machines or different amounts are required per capsule, which is not easy to automate. Manual filling can be slow and tedious, especially if tens, hundreds, or even thousands of capsules have to be prepared each day.

	There are various solutions to overcome the manual filling challenges:

		Low-cost capsule boards are an extremely fast method of filling capsules, but have some drawbacks. Capsules cannot be filled with pure API because it would deliver too much API per capsule; hence, an excipient must be used. As segregation of powders cannot be controlled, homogeneous-proof samples are required and different formulations per capsule are not possible. Filling is achieved by volume, which is not highly reproducible, and capsule weights cannot be checked afterwards.

		Automated filling machines are a higher-throughput approach, but typically come at a high investment. Usually requiring at least a kilogram scale of material to run a batch, they are not flexible enough to fill different amounts per capsule. Additionally, these systems require time-consuming commitments of maintenance and cleaning between batches to prevent cross-contamination and ensure safety.

		Semi-automated capsule filling solutions bridge the gap between the manual and high-throughput machines. These flexible systems are usually compact and affordable, ideally suited for small batch capsule filling and accurately dosing different target amounts to each capsule gravimetrically (e.g., Quantos, Mettler Toledo GmbH).

	Reduce time and enhance safety using automation

 In a GMP lab, a validated filling system provides reduced labor time compared to manual filling. A technician may be capable of manually preparing 85-125 capsules per day at 3 mg target weights (technician dependent), whereas a semi-automated system produces 60 capsules per hour. Although the manual estimate depends on the standard operating procedure (SOP), a manual fill in a GMP lab would also require the eyes of two technicians: one to tare the balance, fill the capsule, and record the final fill weight; and the other present to verify each of these steps. Whereas, using a validated filling system requires only one operator to perform the task. Additionally, the tare and fill weights can be captured electronically and stored and/or printed automatically, which reduces the labor time associated with data recording/transcription.

 By using neat API in capsule, formulation development time can be reduced by simply evaluating the compatibility between the capsule shell and the API, instead of investigating excipient compatibility and fully formulating a dosage form. Analytical method development time can also be reduced because no specificity needs to be qualified, as no interfering excipients are present. Thus, the analytical method for the drug substance can suffice for the drug product.

	Typically, release testing is performed on all incoming materials and final dosage forms before release to the clinical side. Using neat API in capsule, the initial release determines the purity and potency of the API, which minimizes the release testing and allows substances to get to the clinic faster.

Minimize cleaning and cross contamination

. In setting up a typical fully automated encapsulation machine, there are a number of product contact parts that have to be cleaned and verified by swabbing after dosing to determine residual API left on the instrument. However, semi-automated filling systems are much simpler to clean, because no substance comes into contact with any other part of the system other than the dosing head and the sample changer, which is easily detached for cleaning. The rest of the system is considered non-product contact and thus needs no additional cleaning verification. Cross-contamination risk is eliminated, as separate dosing heads are used for each substance.

. With high-potency substances, semi-automated capsule filling offers significant safety benefits. Potential exposure is reduced using a self-contained dosing unit. The drug substance is stored in a standard jar or vial with a dispensing head screwed on top, protecting the analyst from the drug, and the substance from the environment. At this phase, when working with new chemical entities that can cost thousands of dollars per gram, it is important to avoid any substance wastage. Weighing small quantities such as 1-5 mg, manually with a spatula, can result in significant loss or spillage. Having a system that accurately dispenses exactly the required amount, without wasting anything is, therefore, beneficial.

	When referring to this article, please cite it as M. Greene and J. Ratcliff, “Overcoming the Challenges of Capsule Filling in Preclinical and Clinical Studies" sidebar to “Key Considerations in Capsule Filling," 

Articles

The authors discuss the challenges of capsule filling in preclinical and clinical studies.

Overcoming the Challenges of Capsule Filling in Preclinical and Clinical Studies

	A measurement of any kind is affected by the errors and uncertainties that exist in that measurement process. During calibration, the performance of an instrument is assessed and its limitations originating from errors and uncertainties are made evident; this is the essential foundation for achieving accurate measurement results.


	Calibration is more than just comparing a “standard” number with a displayed value on the balance. There is a calibration process that relates to the combination of uncertainties achieved when a balance weigh cell is deviated from its rest position. Calibration establishes the relationship between the displayed value and a standard or accepted true value. The values must fall within an assigned measurement uncertainty range (1, 2).

	A calibration is meaningless without the measurement uncertainty for that specific instrument. Only once the instrument uncertainty has been established, and providing that the number is within the required process tolerance, can it be classed as an accurate calibration.
	Measurement uncertainty is derived from known scientific methodology. It is not something that can be decided by a company or committee. There is an internationally correct and recognized way of calculating measurement uncertainty, described in the Guide to the Expression of Uncertainty of Measurement (GUM) (3).

	It is crucial that the weights used in routine testing are certified as traceable and that the certification is current. If the weight is out of certification, it is no longer guaranteed to give the maximum permissible error (MPE) that is expected.

Why is measurement uncertainty important?


	A modern electronic weigh cell works differently than a traditional mechanical balance. In an electronic weigh cell, the relationship between what is displayed on the screen and the errors involved is not linear. The current approach to metrology, however, is often based on history and familiarity, rather than a scientific study of the process. Traditional methods of balance testing need to be updated and replaced with scientifically meaningful tests, which include an estimation of measurement uncertainty. A knowledge of measurement uncertainty can be used to develop a streamlined, robust, and meaningful testing regime that meets scientific requirements but doesn’t take up several hours every week.

What contributes to total measurement uncertainty?


	The total measurement uncertainty is shown in Figure 1by the black line (U_tot). The significant factors that contribute to measurement uncertainty across the weighing range (4) are described as follows.

. Repeatability, also known as precision, is the largest single contributor to the total measurement uncertainty. At the lower end of the weighing range, the total measurement uncertainty detected is almost entirely due to the repeatability, shown by the yellow line (U_RP) in 

. The magnitude of the repeatability uncertainty completely obscures the chance to measure any other components in this range.

. The eccentricity or cornerload error is the error associated with not placing the weight in the center of the weigh pan. The eccentricity is only significant, and therefore detectable, at the higher end of the weighing range, as shown by the green line (U_EC) in Figure 1.

. Nonlinearity is the error due to the nonlinear behavior of the balance upon increasing the load on the weighing pan. It is not a dominant influence at any point on the weighing range. At the low end of the weighing range, the nonlinearity is superimposed by repeatability uncertainty. At the high end, it is dominated by sensitivity. In fact, at no point on the weighing range does it account for more than 0.1% of the total measurement uncertainty that can be detected. Nonlinearity is significant for the manufacturer because it provides information about the mechanical operation of the balance. For people who have worked for a long time in the pharmaceutical industry, where it may be normal to carry out linearity checks daily, it may come as a surprise to learn that these tests are not as significant as previously thought.

. Sensitivity (i.e., systematic deviation) is the key parameter (besides repeatability) that should be assessed periodically. At the higher end of the weighing range, the sensitivity and eccentricity become the dominant contributors to the overall measurement uncertainty. The high end of the weighing range is thus the ideal place to carry out these routine tests. 

 clearly shows that the sensitivity component of uncertainty (pink line, U_SE) is a completely horizontal line, which means it can be safely assumed that if the sensitivity measurement is performed at the high end of the weighing range, the result represents the whole of the nominal capacity of the balance, providing that the weigh cell is working correctly. Incidentally, if the weigh cell is not working correctly, a test carried out at the high end of the weighing range (as near to 100% of the nominal capacity of the balance) on a daily or regular basis will usually indicate any problems with the weigh cell immediately.


	A manufacturer or service provider will test the four significant components of measurement uncertainty when performing a calibration. These measurements are reflected on a calibration certificate. It is also expected that the balance user or internal metrology department will measure some of these contributors during routine balance testing operations.

	Good Weighing Practice (GWP), Mettler Toledo’s science-based standard for lifecycle management of weighing instruments, provides a strategy for reducing measurement errors and ensuring accurate weighing results. Based primarily on the user’s weighing requirements and prevailing weighing risks, GWP guides users on how to optimize routine testing procedures and how to avoid unnecessary or erroneous testing (4). GWP specifies which of the following tests should be carried out during routine testing.

. Perform (typically) 10 replicate weighings under the same conditions, using the same methodology, to give a statistically significant weighing sequence, according to the GUM (3). Calculate the standard deviation (s), which is the repeatability of the instrument.

Eccentricity test (i.e., cornerload error)

. Place a weight in the center of the weigh pan and zero the balance. Move the weight out to the corners of the weigh pan and take the readings. With a round weighing pan, the protocol is no different. The aim is to compare the difference between the extremities of the weigh pan to the center. Depending on the weighing process, this test can be omitted by the user.

Nonlinearity (i.e., error of indication) test

. Nonlinearity is not very significant at any point in the weighing range, so it is perfectly valid to omit this from the user routine testing regime. This test can be performed by the external service technician during the annual calibration or preventive maintenance visit. It is acceptable to perform a nonlinearity test only one or two times per year, even for a heavily used microbalance or analytical balance. Although it doesn’t need to be measured daily or frequently by the user, it does need to be included in the measurement uncertainty calculation, as per the GUM (3).

. This test is sometimes referred to as the highest “error of indication” test point. The aim is to measure the error of indication by placing a single weight on the balance and comparing it with a standard value. For accuracy, this test must be done at or close to the top of the nominal working range of the balance. Below that, increasingly lower uncertainty due to sensitivity can actually be detected. At the very low end, with very small weights, there is almost no sensitivity uncertainty detectable at all. At this point, the repeatability (i.e., precision of the balance) becomes the completely dominant factor.

	Although there are other contributors to uncertainty, such as external influences, the advantage of modern electronic weigh cell technology is that most of those uncertainties are included in the repeatability of the balance.


	Once the four uncertainty components have been determined during the full calibration by the manufacturer or service provider, the correct way to calculate the total measurement uncertainty (i.e., “given uncertainty budget”) for any balance or scale, according to metrology guidelines, is a two-step calculation. First combine the individual components to calculate the so-called standard uncertainty (u) for the instrument by using a root-sum-square method, as shown in the simplified model in 

 are the uncertainties due, respectively, to repeatability, eccentricity, nonlinearity, and sensitivity.

	Next, expand the uncertainty using standard scientific practices. For an expansion factor of k=2, which can be quite frequently applied, multiply the equation by 2 to give a 95.5% confidence level in the result (3).


	According to GWP, only two weights are required for a user to perform regular routine testing of their balance (4). The larger weight at 100% or close to the nominal capacity of the balance is used to perform the sensitivity test. The smaller weight (5% of the nominal weighing capacity) is used to perform the repeatability test.

	Linearity testing does require more than two weights. If a service provider performs the linearity testing as part of the annual calibration or preventative maintenance visit, however, then there is no scientific requirement for the user to check linearity more frequently.


	According to the laws of metrology, there is no traceable calibration without a statement of measurement uncertainty. Traditional balance testing should be replaced with scientifically meaningful test points. Testing at multiple points on the weighing range every day can take a lot of time, effort, and resources. In addition, the test points may not even be relevant if the metrology behind the testing is not considered. An understanding of the basic principles of balance and scale properties, such as measurement uncertainty, enables the user to achieve a qualified weighing process.

”, white paper no. 30260955 (May 2015),  accessed Feb. 29, 2016.
	2. K. Fritsch and I. Ciesniewski, NCSLI Measure: 

. 10 (3) 56-65 (September 2015).
	3. BIPM, Evaluation of measurement data-Guide to the expression of uncertainty in measurement, JCGM 100:2008, GUM 1995 with minor correction, first ed. (Sèvres, France, September 2008).
	4. K. Fritsch, NCSLI Measure: 


	Ian Ciesniewski, PhD, is technical director, and Joanne Ratcliff, PhD, is communications project manager, both at laboratory Weighing, Mettler Toledo.

:
	When referring to this article, please cite it as I. Ciesniewski and J. Ratcliff, "Understanding Measurement Uncertainty in Weighing," 

Understanding of the basic principles of balance and scale enables a user to achieve a qualified weighing process.

Scientific Approach Ensures Accurate Weighing Results

A program for calibration and routine testing of weighing instruments ensures accurate results.

Understanding Measurement Uncertainty in Weighing

Data integrity is a major regulatory topic in GMP-regulated laboratories. The problem is widespread, as cases of non-compliance have also been observed in laboratories regulated by good laboratory practice (GLP) and good clinical practice (GCP), not just GMP. Non-compliances from the European Medicines Agency (EMA) or warning letters from FDA show examples from companies in the United States, Canada, the United Kingdom, and Italy, as well as China and India. So, it is not just a problem for Asia-it is a global issue.

	The non-compliances are not confined to falsification and fraud. In fact, in the majority of labs, the main data integrity issues concern poor data management, which account for 95% of non-compliance cases; only 5% are a result of falsification or fraud. Problems arise from basic errors such as relying on paper as raw data and failing to protect electronic records, rather than deliberate manipulation of data. It is the latter, however, that gets the major headlines. In July 2014, FDA issued a stern warning that data integrity was a key focus of its enforcement efforts (1).


	In January 2015, the UK Medicines and Healthcare products Regulatory Agency (MHRA) went further and issued 

 (revised in March 2015) (2). This guidance consists of descriptions, definitions, and expectations based around those definitions, which include raw data, original records, file structures, and audit trail. The regulatory expectations presented in this guidance are useful. However, the definitions are simply presented as a shopping list; a diagram explaining and linking key definitions would be more beneficial.

	The guidance presents data integrity as the extent to which all data are “complete, consistent, and accurate” throughout the data lifecycle, which covers the period from data acquisition through to interpretation, reporting, and archiving and then destruction after the record retention period. There are similarities in the US regulations; a 20-year-old FDA definition of data integrity describes the degree to which a collection of data is complete, consistent, and accurate (3).

	In European GMP regulations, documentation constitutes a key part of quality assurance and, therefore, compliance with GMP (4). An organization must have good documentation, follow standard operating procedures (SOPs), and demonstrate compliance with the applicable regulations. Several requirements focus on data integrity for computerized systems (5). If critical data are entered into a data system, a second check is required, which can either be a second person or can be automated using the computer system itself.

	In the US, the regulations for laboratory records focus on the concept of complete data (6).

	A review of data integrity warning letters reveals several citations for failure to have complete data including a failure to have a complete procedure (7), a failure to fully document the work that is carried out (8), or being selective in reporting data (e.g., using test samples to “check” if an instrument is working correctly) (9). Complete data includes the actual observation, which can be visual or by computerized system. Contextual metadata, which puts the data or result in context, is also required. Examples of metadata include operator ID, units of measurement, sample information, identity, batch number, instrument suitability, and readiness. Audit trail events are also part of the metadata. If a hybrid system or a fully electronic system is used, FDA and European regulatory agencies require companies to review and evaluate audit trail events to see if data have been manipulated without authorization.

	FDA guidance comprises a list of commonly asked questions and answers that are crucial for maintaining data integrity (10). Detailed explanations are given for:

		Why paper records from a hybrid system should not be defined as raw data-instead it should be the underlying electronic records; Although the reasoning focuses on chromatography, it is applicable to any computerized system.

		Why sharing of user identities in a computerized system is not allowed-as it makes it impossible to attribute the work carried out to a single individual.

		Why actual samples should not be used as system suitability tests to see whether a batch passes or not. FDA warning letters reveal many citations for this transgression.


	The criteria for data integrity are defined by the acronyms ALCOA and ALCOA+. ALCOA, which stands for attributable, legible, contemporaneous, original, and accurate, was developed by an FDA GMP inspector in the late 1980s. ALCOA has been used in many areas that are regulated both by FDA and other regulatory agencies worldwide. In 2010, EMA published a paper on electronic source data for clinical records, in which another four requirements relating to electronic data were added: complete, consistent, enduring, and available (11). The Good Automated Manufacturing Practice (GAMP) Data Integrity Special Interest Group (SIG) now refers to ALCOA+, which includes the nine attributes or criteria for data integrity (see 


	In the past, an inspector would review piles of paper; to view a computer system, he would be shown print-outs of the screen. Now, the focus is on the computerized systems and the electronic records within; the paper output is secondary. Inspectors will focus on the electronic records, looking at how they were generated and manipulated within the application. In the light of these changes, consideration needs to be given to the person who will manage the system during an inspection. How is the system configured to protect the electronic records? Are electronic signatures being used in the application, ensuring that the configuration of the application is documented and reflects the settings within the software?

	Annex 11 now requires audit trail entries to be reviewed, and FDA considers these entries as part of the complete data. Many findings of non-compliance during inspection have been discovered by looking at audit trail entries, therefore, an approach to reviewing audit trail events is needed. Citations have noted when audit trails were been turned off, the audit trail had not been reviewed, or user identities were shared, which is not allowed under the regulations. Additional citations can be found in the September 2014 issue of 


	The 10 compliance commandments for computerized laboratory systems described in 

Where should the electronic data transfer begin?


	Let’s consider preparation of standards by way of example. In an analytical lab, standards are usually prepared using an analytical balance. The actual weight is typically printed on a strip printer and pasted into a lab journal. Afterwards, the standards are widely used for analytical methods, such as high-performance liquid chromatography (HPLC), gas chromatography (GC), titration, etc. Data management and documentation for analytical instruments are usually managed by dedicated software or a laboratory information management system (LIMS). This procedure is currently allowed by regulatory bodies, which state that print-outs representing original data for simple devices (such as balances) are acceptable (2) but is not the case for more complex devices. Nevertheless, it is important that reference standards are accurate and traceable because they represent the starting point of many analyses. Warning letters have cited “no details available on the preparation of standards or solutions, especially of analytical reference standards” (13, 14). Independent of process, it is important to ensure that all the data are available.

	This then begs the question: Where should the electronic transfer begin? In the process described above, there is a gap in the data transfer between the “simple instrument” (the balance) and the “complex instrument” (e.g., the HPLC). Clearly, this is not recommended because it introduces an additional level of risk; it is obvious why no gaps should occur.

	Capturing the data at the point of origin and transferring the data electronically throughout the whole workflow is a much lower risk approach and reduces the risk of errors during the early stages of a process, giving additional confidence in the compliance of a workflow or laboratory.


	The next question then becomes:  What data need to be transferred? It is essential to associate results with metadata to build context around the values collected. Although integrating even a simple instrument can be tricky, the advantages of an integrated solution are obvious. When electronic data transfer starts from the beginning of the process, each piece of information needs to be input only once for it to be available throughout the whole system. This allows seamless movement of data and other information from the start of a process to the end, without the need for manual effort, such as manual transcription, creating an efficient work environment.

	This transfer of data is achievable using a laboratory execution system (LES), such as LabX, which enables a variety of instruments to be directly connected (balances, titrators, density meters, refractometers, thermal analysis instruments, and pH meters). Useful features such as SOP-user guidance on the instrument terminal, automatic results capture in a database, and real-time data access support traceability of data and compliance with the GAMP data integrity criteria in ALCOA+.

	As regulators continue to tighten their inspection approaches, it is crucial for managers and scientists in regulated GXP laboratories to understand these criteria for data integrity and to assess and improve laboratory data management processes to ensure compliance with current regulations. Only after all these points have been addressed can data integrity truly be achieved.

Current Regulatory/Inspection Issues Related to Supply Chain

,” Food and Drug Law Institute (FDLI), Conference Understanding cGMPS-What Attorneys Need to Know, Washington DC, July 15, 2014.
	2. MHRA, 

Glossary of Computer System Software Development Terminology

EudraLex, The Rules Governing Medicinal Products in the European Union

 Volume 4, Good Manufacturing Practice, Chapter 4 Documentation (June 2011).
	5. European Commission,

, Volume 4, Good Manufacturing Practice, Annex 11 Computerised Systems (January 2011).
	6. US Electronic 

FDA Warning Letter to Ipca Laboratories Ltd

FDA Warning Letter to Micro Laboratories Ltd.

Questions and Answers on Current Good Manufacturing Practices, Good Guidance Practices, Level 2 Guidance-Records and Reports

GCP Inspectors Working Group publication, Reflection paper on expectations for electronic source data and data transcribed to electronic data collection tools in clinical trials

 (London, June 2010).
	12. R.D. McDowall, 

FDA Warning Letter to RPG Life Sciences Ltd.


	When referring to this article, please cite as B. McDowall and J. Ratcliff, "How Important is Data Integrity to Regulatory Bodies?" 

Data integrity is a widespread, global problem that must be addressed.