Process Analytical Technology Concepts and Principles
Process Analytical Technology Concepts and Principles
Process Analytical Technology Concepts and Principles
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<strong>Process</strong> <strong>Analytical</strong><br />
<strong>Technology</strong><br />
<strong>Concepts</strong> <strong>and</strong> <strong>Principles</strong><br />
Mark L. Balboni<br />
<strong>Process</strong> analytical technologies are a<br />
multifaceted group of concepts that<br />
comprise one of many initiatives that form<br />
FDA’s “Pharmaceutical GMPs for the 21st<br />
Century.”Yet what constitutes a process<br />
analytical technology or whether it is in fact<br />
a new technology remains unclear. This<br />
article outlines the key concepts that define<br />
process analytical technology <strong>and</strong><br />
emphasizes the relevant theory <strong>and</strong><br />
applications of chemometrics.<br />
PHOTODISC, INC.<br />
Mark L. Balboni is a senior compliance<br />
consultant at KMI, a division of PAREXEL<br />
International, LLC, 28241 Crown Valley<br />
Parkway, F601, Laguna Niguel, CA 92677,<br />
tel/fax 949.830.2355, mbalboni@belmont.<br />
kminc.com.<br />
The current process analytical technology (PAT) initiative<br />
underway within FDA exemplifies the latest consortium<br />
between FDA <strong>and</strong> the industry that aims to encourage<br />
the concepts of quality by design, use of computerized<br />
data gathering <strong>and</strong> evaluation techniques, <strong>and</strong> process- <strong>and</strong><br />
product-monitoring methods through advanced instrumentation<br />
<strong>and</strong> data evaluation. Although this partnership between<br />
FDA <strong>and</strong> the industry is relatively new (2001), methods related<br />
to PAT such as chemometrics have been studied <strong>and</strong> have been<br />
in use for quite some time. Yet, the PAT initiative has raised several<br />
questions: What does PAT really encompass? Is it a new<br />
technology or is it a series of proven scientific principles? How<br />
can PAT be used in a pharmaceutical operation to gain better<br />
process underst<strong>and</strong>ing <strong>and</strong> possibly reduce cycle times <strong>and</strong> associated<br />
costs?<br />
This article discusses the concepts that embody PAT. Emphasis<br />
is placed on chemometrics, which is the use of mathematical<br />
<strong>and</strong> statistical models to extract <strong>and</strong> interpret chemical<br />
data.<br />
What is PAT?<br />
FDA defines PAT as<br />
● a system for the analysis <strong>and</strong> control of manufacturing<br />
processes based on timely measurements of critical quality<br />
parameters <strong>and</strong> performance attributes of raw materials <strong>and</strong><br />
in-process materials<br />
● a process to ensure acceptable end-product quality at the completion<br />
of the processing (1).<br />
FDA also states that PAT involves<br />
● the optimal application of process analytical chemistry (PAC)<br />
tools<br />
● feedback process-control strategies<br />
● information management tools <strong>and</strong>/or product–process optimization<br />
strategies for the manufacture of pharmaceuticals<br />
(1).<br />
In summary, PAT can be defined as the optimal application of<br />
PAC tools, feedback process-control strategies, information<br />
management tools, <strong>and</strong>/or product–process optimization strategies<br />
to the manufacture of pharmaceuticals.<br />
PAT focuses on the principles of building quality into the<br />
product <strong>and</strong> process as well as continuous process improvement.<br />
A few examples of PAT tools <strong>and</strong> strategies are as follows:<br />
54 Pharmaceutical <strong>Technology</strong> OCTOBER 2003 www.pharmtech.com
● at-line, in-line, or on-line measurement of process quality<br />
<strong>and</strong> performance attributes using a variety of instrumentation<br />
<strong>and</strong> measurement strategies such as near-infrared (NIR),<br />
vibrational, acoustical, <strong>and</strong> X-ray spectroscopy<br />
● chemometric approaches such as multivariate statistical <strong>and</strong><br />
pattern recognition methods.<br />
● real-time data <strong>and</strong> information management systems for<br />
process control (2).<br />
FDA’s PAT initiative is supported by many large pharmaceutical<br />
corporations <strong>and</strong> distinguished members of academia.<br />
Another potential advantage of PAT is the opportunity to<br />
place more reliance on in-process testing as the basis for final<br />
product release. This type of product-release methodology requires<br />
both a significant amount of data to be compiled <strong>and</strong><br />
heavy correlations to be determined by analysis <strong>and</strong> evaluation<br />
(3). The theory is that product release could be based on relationships<br />
(i.e., correlations between observed in-process test results<br />
<strong>and</strong> predictive qualitative results of the final product) established<br />
during product–process development <strong>and</strong> confirmed<br />
by both validation <strong>and</strong> routine review of product–process data<br />
for commercial lots. These relationships coupled with confirmation<br />
testing of the finished product would serve as the basis<br />
for release, or the product could possibly be released on the<br />
basis of the observed in-process results <strong>and</strong> how a currently<br />
produced lot favorably compares with other previously released<br />
product lots.<br />
FDA’s PAT initiative is being spearheaded by the Center for<br />
Drug Evaluation <strong>and</strong> Research (CDER), Office of Pharmaceutical<br />
Science. Since 2001, FDA has held <strong>and</strong> participated in a series<br />
of PAT meetings as part of the <strong>Process</strong> <strong>Analytical</strong> Technologies<br />
Subcommittee for the Office of Pharmaceutical Sciences<br />
<strong>and</strong> with the FDA Science Board. These meetings have included<br />
not only FDA personnel but also pharmaceutical industry representatives,<br />
members of academia, <strong>and</strong> engineering <strong>and</strong> consulting<br />
professionals.<br />
The PAT initiative is part of a larger FDA initiative called<br />
“Pharmaceutical CGMPs for the 21st Century: A Risk-Based<br />
Approach”(4). The agency seeks to improve the regulation of<br />
pharmaceutical manufacturing using a science- <strong>and</strong> risk-based<br />
approach to product-quality regulation while incorporating an<br />
integrated quality-systems approach.<br />
Complete descriptions of FDA’s PAT initiative <strong>and</strong> the “Pharmaceutical<br />
CGMPs for the 21st Century” approach, including<br />
electronic links to associated documents <strong>and</strong> reference materials,<br />
are provided in References 1 <strong>and</strong> 4.<br />
PAC <strong>and</strong> in/at/on-line monitoring<br />
According to Hailey et al., PAC is the technique of “gathering<br />
analytical information in real time at the point of manufacture”<br />
(5). As they noted, PAC “places an emphasis on the process<br />
rather than the final product,” including “an underst<strong>and</strong>ing of<br />
the relationship between final product specification [sic] <strong>and</strong><br />
the critical variables during the manufacturing process.”<br />
Although the approaches <strong>and</strong> instrumentation currently<br />
being discussed are in some cases categorized as being novel or<br />
new, real-time measurement (PAC) has existed for some time<br />
(e.g., real-time temperature monitoring of reaction vessels during<br />
the synthesis of active pharmaceutical ingredients). One<br />
particular industry–university partnership, The Center for<br />
<strong>Process</strong> <strong>Analytical</strong> Chemistry (CPAC) at the University of Washington,<br />
has been in existence since at least 1986 (6).<br />
Although PAC is not a new approach, many of the related<br />
techniques have been tested <strong>and</strong> used only on a limited basis<br />
by a very small percentage of the pharmaceutical industry. The<br />
following is a partial list of the various sensors <strong>and</strong> instrumentation<br />
recently discussed at the 2003 Arden House Conference<br />
either in-use or currently being evaluated for feasible use for<br />
production monitoring:<br />
● NIR spectroscopy for moisture determination<br />
● X-ray spectroscopy<br />
● radio frequency for moisture determination<br />
● microwaves for moisture determination<br />
● RAMAN spectroscopy, with vibrational spectroscopy being<br />
the most common. RAMAN complements IR spectroscopy<br />
<strong>and</strong> is used for raw-material identification, polymorph differentiation,<br />
<strong>and</strong> reaction monitoring.<br />
● fluorescence for water quality<br />
● on-line measurement of color<br />
● X-ray fluorescence for the detection of inorganic materials<br />
● photoacoustic spectroscopy.<br />
Because most of these technologies are extremely sophisticated,<br />
one must realize that the key emphasis of PAT is not so<br />
much how to collect the data or what kind of instrumentation<br />
should be used, but rather what data should be collected, what<br />
is done with these data, <strong>and</strong> what associated conclusions are<br />
reached. Therefore, a complete <strong>and</strong> thorough underst<strong>and</strong>ing of<br />
the manufacturing process is paramount.<br />
Chemometrics<br />
To fully underst<strong>and</strong> PAT, one must first underst<strong>and</strong> the science<br />
behind manufacturing processes, including how these processes<br />
operate, their limitations, <strong>and</strong> their expected outcomes.<br />
In 1974 Sante Wold, from the Institute of Chemistry at Umea<br />
University (Umea, Sweden) described “the art of extracting<br />
chemically relevant information from data provided in chemical<br />
experiments” as chemometrics (7). He stated that this art is<br />
“heavily dependent on the use of different kinds of mathematical<br />
models” <strong>and</strong> that it was important to have knowledge in<br />
statistics, numerical analysis, <strong>and</strong> applied mathematics, including<br />
the challenge to “structure the chemical problem to a form that<br />
can be expressed in a mathematical relation.”<br />
Twenty years later, during a 1994 presentation, Professor Wold<br />
said his definition of chemometrics had not changed much. He<br />
said chemometrics requires us to ask ourselves “How do we get<br />
chemically relevant information out of measured chemical data;<br />
how do we represent <strong>and</strong> display this information; <strong>and</strong>, how do<br />
we get such information into data?”<br />
In 1996, Wise <strong>and</strong> Gallagher stated chemometrics “is the science<br />
of relating measurements made on a chemical system to<br />
the state of the system via application of mathematical or statistical<br />
methods” (8). Similarly, Hardy noted that data are “raw<br />
information, both qualitative <strong>and</strong> quantitative” (9). He observed<br />
that in <strong>and</strong> of themselves, “raw data are meaningless” <strong>and</strong> that<br />
a method of “analysis <strong>and</strong> a model” are needed to “gain knowl-<br />
56 Pharmaceutical <strong>Technology</strong> OCTOBER 2003 www.pharmtech.com
edge” from the data. He also identified the clear need for a<br />
process to “convert data to knowledge.”<br />
Dosage forms recently have been described as “complex<br />
multifactorial physiochemical systems” (10), sometimes referred<br />
to as multivariate. Multivariate analysis “consists of<br />
methods of statistical analysis of multivariate data, characterized<br />
as consisting of several observations on each set of objects<br />
or mathematically represented by a collection of points<br />
in a finite-dimensional Euclidean space R P ” (11). So, multivariate<br />
analysis is an important statistical method widely used<br />
by chemometricians.<br />
Described below are a few of the tools commonly used by<br />
chemometricians, all of which are helpful for evaluating processing<br />
data generated during pharmaceutical unit operations<br />
or when synthesizing active pharmaceutical ingredients.<br />
Principle component analysis (PCA). PCA is a technique used to<br />
express variations of many variables by a small number of indicies<br />
(11). Wise <strong>and</strong> Gallagher describe PCA as a favorite tool<br />
of chemometricians for data compression <strong>and</strong> information extraction<br />
<strong>and</strong> note that “PCA finds combinations of variables or<br />
factors that describe major trends in a data set” (8). Sans et al.<br />
(12) observed that PCA can be used to determine the number<br />
of components that influence the data of the process as well as<br />
to identify the chemical meaning of the components. They proposed<br />
that PCA enables one “to approach multivariate chemical<br />
problems in order to obtain underlying information about<br />
the correlation of the raw data <strong>and</strong> the real meaning interpretation.”<br />
Finally, Wise <strong>and</strong> Gallagher (8) acknowledged that “generally<br />
there is a great deal of correlated or redundant information<br />
in process measurements,” <strong>and</strong> “often essential information<br />
lies not in any individual process variable but how the variables<br />
change with respect to one another” or how they co-vary. They<br />
also noted that “some sort of signal averaging” would be useful<br />
in cases in which a large amount of noise is created from the<br />
volume of data <strong>and</strong> a lack of clear underst<strong>and</strong>ing of the data<br />
exists.<br />
However, Wu et al. point out two limitations of PCA:<br />
● When an object is added or removed as displayed in a plane,<br />
principal components (PCs) must be recalculated all over<br />
again by following the process of selection <strong>and</strong> interpretation<br />
of the PCs.<br />
● No more than two PCs at a time can be viewed (inspected)<br />
in a plane, <strong>and</strong> this prevents one from using the information<br />
contained in other PCs (13).<br />
The authors also note that their “star-plot” method could be<br />
used as an alternative way to display <strong>and</strong> analyze multivariate<br />
data.<br />
Application of PCA to chemical processes. Wise <strong>and</strong> Gallagher (8)<br />
studied data obtained from a slurry-fed ceramic reactor using<br />
thermocouples placed at 20 locations (8). They found a great<br />
deal of correlation as the data generated followed a sawtooth<br />
pattern. In addition, the study revealed the following:<br />
● PCA performed on this data found three factors that captured<br />
approximately 97% of the variance in the data set.<br />
● This previously noted finding allowed 16 variables to be replaced<br />
with three new ones, which were linear combinations<br />
of the original variables.<br />
● The sawtooth pattern was attributed to changes in the level<br />
of molten glass, which was a controlled variable.<br />
● The three factors (PCs) were identified as the level of molten<br />
glass in the reactor, the variation between two groups of measured<br />
locations, <strong>and</strong> the variation of overall process temperature<br />
(also a controlled variable).<br />
Sans et al. also used PCA to determine stoichiometric models<br />
from on-line spectroscopy for selecting the number of reactions<br />
<strong>and</strong> the number of chemical absorbing species to better<br />
describe a chemical reaction (12). They chose to use<br />
spectroscopy methods because they can reveal information<br />
about the dynamic evolution of the reaction mass during a<br />
chemical reaction. Although the outcome of this study is not<br />
discussed in this article, Sans et al. did note that “semibatch<br />
processes are examples of complex reaction networks that generally<br />
are difficult to interpret because of the large number of<br />
reactions occurring simultaneously <strong>and</strong> because of the effects<br />
related to the addition of materials that may cause complex volume<br />
changes during the processing time.”<br />
Multiway principle component analysis (MPCA). An analog of PCA<br />
is what is known as multiway PCA, which is “equivalent to performing<br />
PCA on a very large two-dimensional matrix formed by<br />
unfolding the three-way array X into one of six possible ways,<br />
only three of which are mathematically unique” (8). General PCA<br />
methods do not take into account the ordered nature of the data<br />
sets, meaning that the data were not collected in a sequential<br />
manner. Multiway methods take into account the ordered (sequential)<br />
nature regarding when data were generated “because<br />
the data usually are organized into time-ordered blocks that are<br />
each representative of a single sample or process run” (8).<br />
Nomikos <strong>and</strong> MacGregor (14) describe another aspect of<br />
MPCA; namely, the use of on-line measurements to identify<br />
“significant deviations from the normal operating behavior by<br />
using SPC charts.” An empirical model is based on data generated<br />
when the process is operating within a state of control.<br />
They noted, “future unusual events are detected by referencing<br />
the measured process behavior against the ‘in-control’ model<br />
<strong>and</strong> its statistical properties.”<br />
Partial least squares (PLS). Wise <strong>and</strong> Gallagher described PLS<br />
as a regression “related to both principle component regression<br />
(regression of properties on the principle component scores of<br />
the measured variable) <strong>and</strong> multiple linear regression (also<br />
known as inverse least squares)” (8). They observed that this<br />
analysis can be used to predict “properties of a system based on<br />
variables that are only indirectly related to the property.” Accordingly,<br />
by using PLS, one attempts to “find factors” that both<br />
“capture variation <strong>and</strong> achieve correlation.”<br />
Multiway partial least squares (MPLS). Nomikos <strong>and</strong> MacGregor<br />
observed that MPCA using statistical process-control charts<br />
“only makes use of the process variable trajectory measurements<br />
(X) taken throughout the duration of the batch” (14). In contrast,“multiway<br />
partial least squares (MPLS) can be performed<br />
using both the process data (X) <strong>and</strong> the product quality data<br />
(Y),” <strong>and</strong> “focuses more on the variance of X that is more predictive<br />
for the product quality Y” (14).<br />
Other statistical tools. Two other statistical tools that may be<br />
useful in PAT efforts are<br />
58 Pharmaceutical <strong>Technology</strong> OCTOBER 2003 www.pharmtech.com
● capability studies, which measure the ability of the process to<br />
consistently meet specifications by evaluating select process<br />
outputs <strong>and</strong> calculating the average <strong>and</strong> ranges over a specified<br />
time on control charts. From these studies, capability indicies<br />
Cp (used to evaluate the variation) <strong>and</strong> Cpk (used to<br />
evaluate the centering of the process) are calculated.<br />
● design of experiments, which are experiments that involve<br />
changing one or more of the process inputs <strong>and</strong> measuring<br />
the results to one or more of the process outputs (15).<br />
Rapid microbiology test methods<br />
Another part of FDA’s PAT initiative involves discussion<br />
about the feasibility of developing so-called rapid microbiology<br />
methods (emphasis added, as most microbiological testing<br />
can take anywhere from several days to a couple of weeks<br />
to complete). This topic is of heightened interest within the<br />
industry as manufacturers seek additional ways to reduce cycle<br />
times.<br />
Rapid microbiology testing was discussed during the FDA<br />
Advisory Committee for Pharmaceutical Science meeting on<br />
23–24 October 2002 (16). During that meeting, the committee<br />
identified at least two problems or risks associated with the development<br />
of rapid microbiology methods; namely,<br />
● validation of test methods may not yield results equal to those<br />
for traditional test methods<br />
● acceptance by regulators (e.g., FDA).<br />
The group also categorized microbial determinations as follows:<br />
● qualitative methods (presence or absence of microbes; e.g.,<br />
sterility testing)<br />
● quantitative methods (enumeration of microorganisms present;<br />
e.g., microbial limits tests)<br />
● microbial identification.<br />
FDA did concede that rapid microbiology test methods are<br />
an important part of the PAT initiative but that at this time it<br />
has not yet been discussed extensively (16). FDA also commented<br />
that the general guidance document on PAT would not<br />
specify details about rapid microbiology methods but would<br />
rather cover them in a general sense to encourage their use. Finally,<br />
the group felt that microbial identification probably has<br />
the most rapid method systems presently available, <strong>and</strong> quantitative<br />
testing probably has the least detection systems.<br />
Real-time data information <strong>and</strong> management systems<br />
Another PAT tool being discussed is the use of real-time data<br />
information <strong>and</strong> management systems. Although a detailed<br />
overview of this topic is not provided in this article, some of<br />
the possible concerns include possible 21 CFR Part 11 implications,<br />
adding complexity to already complex computer system<br />
architecture, <strong>and</strong> the amount of data retention necessary, given<br />
that continuous monitoring will generate very large volumes<br />
of data.<br />
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With FDA’s recent withdrawal of Part 11 guidance documents,<br />
it would be advisable to first review the anticipated PAT guidance<br />
document for information concerning data information<br />
<strong>and</strong> management issues <strong>and</strong> secondly review the 21 CFR Part<br />
11 implementation portion of the “Pharmaceutical CGMPs for<br />
the 21st Century Initiative,” which includes both a Notice of<br />
Availability <strong>and</strong> a draft guidance on Part 11. The draft guidance<br />
on Part 11 attempts to clarify the scope <strong>and</strong> applicability of the<br />
regulation, which ultimately may undergo revision to clarify<br />
the scope <strong>and</strong> requirements.<br />
Benefits <strong>and</strong> challenges: an industry perspective<br />
Most industry representatives that either were involved in discussions<br />
regarding the feasibility of PAT or who have conducted<br />
successful PAT efforts thus far have had many more positive<br />
than negative comments regarding the advantages of adopting<br />
PAT principles. Positive perceived benefits of PAT include<br />
● decrease in cycle times<br />
● lower costs<br />
● increased efficiency <strong>and</strong> batch-to-batch consistency<br />
● process fingerprinting (signature) that would be useful for validation,<br />
scale-up, <strong>and</strong> confirming acceptable h<strong>and</strong>ling of changes<br />
● increased process underst<strong>and</strong>ing <strong>and</strong> a decrease in variability,<br />
rejects, <strong>and</strong> lot failures<br />
● possible continuous processing <strong>and</strong> the ability to adjust process<br />
on the basis of real-time monitoring data.<br />
Conversely, the most-common perceived or actual challenges<br />
include<br />
● product-approval delays by inclusion of PAT methodologies<br />
into relatively traditional drug development <strong>and</strong> validation<br />
activities<br />
● lack of a written PAT guidance document from FDA<br />
● an increase in the amount of data being generated, including<br />
not only what one should do with the extra data but also possible<br />
Part 11 implications<br />
● increased pressures to meet aggressive filing timelines, added<br />
costs to make changes, lack of senior management support,<br />
<strong>and</strong> resource constraints (Source: Reference 1 <strong>and</strong> 2003 AAPS<br />
Arden House Conference.<br />
Where we go from here<br />
Although no one can clearly predict how widely accepted PAT<br />
will become, it is clear that FDA does support innovation. The<br />
agency is appealing to the industry to take a more active role in<br />
underst<strong>and</strong>ing its manufacturing processes <strong>and</strong> is seeking quick<br />
<strong>and</strong> effective resolution of problems associated with good manufacturing<br />
practices that result in rejected batches, stability failures,<br />
field alerts, <strong>and</strong> product recalls.<br />
Currently there are many actions a company can take to<br />
prepare for what FDA is calling the “GMPs for the 21st Century.”<br />
By reading the contents provided on FDA’s Web site regarding<br />
PAT (http://www.fda.gov/cder/OPS/PAT.htm), which<br />
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includes committee meeting minutes <strong>and</strong> dozens of presentations,<br />
one will see that many large pharmaceutical firms already<br />
have made significant accomplishments through the<br />
successful implementation of PAT principles. In spite of the<br />
industry’s dwindling profit margins, fewer new products in<br />
the pipeline, <strong>and</strong> a more competitive marketplace, these companies<br />
already have recognized the positive aspects of implementing<br />
PAT. The following are some active steps that a company<br />
can take right now.<br />
Develop a plan for the future.<br />
● Wait for FDA’s much anticipated guidance document about<br />
PAT before making significant decisions regarding how your<br />
company will h<strong>and</strong>le certain PAT principles (e.g., validation<br />
of new analytical methods for a unique in-process test).<br />
● Analyze existing product lines <strong>and</strong> determine which may benefit<br />
most from PAT. For example, the compounding <strong>and</strong> filling<br />
of a well-established product that is a true solution composed<br />
of 99.5% water is probably not the best c<strong>and</strong>idate for<br />
PAT. The manufacture of complex dosage forms such as<br />
tableted products, which also includes in-process monitoring<br />
using process control charts, would probably be a better<br />
choice. Products with recurring quality problems are also<br />
good c<strong>and</strong>idates because process deviations or exceptions are<br />
sometimes results of a less-than-complete underst<strong>and</strong>ing of<br />
the process rather than more-obvious causative factors.<br />
● Obtain/retain employees with education, training, <strong>and</strong> experience<br />
in disciplines necessary to be successful in PAT efforts.<br />
For example, FDA is seeking to hire persons experienced in<br />
in-process/chemical engineering, PAC, chemometrics, <strong>and</strong><br />
industrial pharmacy. For your company’s efforts to be successful,<br />
similar expertise will be needed.<br />
● Gain executive management’s support for PAT. Although it<br />
may be easier to make the scientific case, management must<br />
underst<strong>and</strong> how PAT could reduce cycle times <strong>and</strong> costs <strong>and</strong><br />
add value. Make a business case for adopting PAT.<br />
Benchmark with industry <strong>and</strong> academic partners. Speak with<br />
company representatives from firms that already are using PAT<br />
principles. Many major universities also are heavily involved in<br />
the PAT initiative <strong>and</strong> could provide guidance, including, but<br />
not limited to, the following schools that have had representatives<br />
at several FDA committee meetings about PAT:<br />
● MIT Pharmaceutical Manufacturing Initiative<br />
● Purdue University, School of Pharmacy<br />
● Center for <strong>Process</strong> <strong>Analytical</strong> Chemistry at the University of<br />
Washington.<br />
● Texas A&M Department of Chemistry <strong>and</strong> Chemical Engineering.<br />
Gather information about the topic. A significant amount of information<br />
concerning PAT can be found on FDA’s Web site (1).<br />
Additional new information continues to be added, <strong>and</strong> plenty<br />
of information already is available on many university <strong>and</strong> industry<br />
association Web sites regarding topics such as PAT, PAC,<br />
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PCA, <strong>and</strong> chemometrics. Internet keyword searches can yield<br />
significant additional information.<br />
Attend future PAT workshops, seminars, or symposia. Find out<br />
what FDA <strong>and</strong> others are saying first-h<strong>and</strong>. If the workshops<br />
are sponsored by an industry association, you may need to attend<br />
in person to get the h<strong>and</strong>outs <strong>and</strong> presentations because<br />
all of that information may not be published on FDA’s or the<br />
association’s Web site.<br />
Provide comments directly to FDA using the Internet. If you have<br />
substantial comments regarding FDA’s PAT initiative <strong>and</strong> you<br />
feel it is information that others could benefit from, consider<br />
providing electronic comments to FDA. The agency already has<br />
established a Web-based feedback tool for this purpose (www.<br />
fda.gov/ohrms/dockets/). You will need to search for docket<br />
number 03N-0059. Alternatively, you could send questions or<br />
comments regarding the PAT initiative by email to<br />
pat@cder.fda.gov.<br />
Ensure quality is designed into your products <strong>and</strong> processes.<br />
● Confirm compliance with good manufacturing practices. Remember,<br />
CGMP is the minimum st<strong>and</strong>ard according to 21<br />
CFR 211.1(a). Most of the successful companies strive for <strong>and</strong><br />
achieve substantial compliance with these regulations as well<br />
as carry out “best practices.” Poorly developed manufacturing<br />
processes, untrained employees, or equipment that has<br />
not been properly qualified will hinder any PAT efforts.<br />
● Complete thorough product <strong>and</strong> process development work<br />
to ensure processes are adequately defined. Include robust<br />
specifications <strong>and</strong> critical process-control points.<br />
● Confirm that active drug substances are well characterized.<br />
● Use validation efforts to demonstrate consistency <strong>and</strong> reproducibility,<br />
not as a means to conduct range finding, process<br />
adjustments, or enhancements. These are development tasks,<br />
not validation.<br />
● Optimize processes <strong>and</strong> improve yields after successful validation,<br />
not during validation.<br />
● Consider process or equipment changes carefully, especially<br />
postapproval, because this usually will result in much unanticipated<br />
work <strong>and</strong> undoubtedly more development activities<br />
<strong>and</strong> data. Complete risk or impact assessments on the<br />
basis of data <strong>and</strong> not on opinions or theories.<br />
Many years ago, Alex<strong>and</strong>er Hamilton said,<br />
Experience teaches that men are often so much governed<br />
by what they are accustomed to see <strong>and</strong> practice, that the<br />
simplest <strong>and</strong> most obvious improvements, in the most<br />
ordinary occupations, are adopted with hesitation, reluctance,<br />
<strong>and</strong> by slow gradations. Men would resist<br />
changes, so long as even bare support could be ensured<br />
by an adherence to ancient courses, <strong>and</strong> perhaps even<br />
longer.<br />
Sometimes changes do take time, so we should look to the<br />
future potential of PAT to enhance our pharmaceutical processes<br />
Continued on page 254<br />
Circle/eINFO 51<br />
66 Pharmaceutical <strong>Technology</strong> OCTOBER 2003 www.pharmtech.com
Continued from page 66<br />
<strong>and</strong> applaud FDA for encouraging the application of new technologies<br />
<strong>and</strong> for their willingness to work with industry on this<br />
important initiative.<br />
Acknowledgment<br />
The author would like to acknowledge the following individuals<br />
for their assistance in the writing of this article: KMI senior<br />
compliance consultant Eric S. Weilage <strong>and</strong> PAREXEL senior<br />
biostatistician Chunzhang Wu, PhD.<br />
References<br />
1. CDER, Office of Pharmaceutical Sciences, “<strong>Process</strong> <strong>and</strong> <strong>Analytical</strong><br />
Technologies Initiative,” http://www.fda.gov/cder/OPS/ PAT.htm.<br />
2. A. Hussain <strong>and</strong> J. Famulare, “FDA’s PAT Initiative <strong>and</strong> its Role in the<br />
Proposed Drug Quality System for the 21st Century,” presented at the<br />
AAPS Arden House Conference, 27 January 2003.<br />
3. T. Layloff, “<strong>Process</strong> <strong>Analytical</strong> <strong>Technology</strong> (PAT) Subcommittee Report,”<br />
presented at the ACPS meeting 21 October 2002.<br />
4. FDA,“Pharmaceutical CGMPs for the 21st Century: A Risk-Based Approach,”<br />
http://www.fda.gov/cder/gmp/index.htm.<br />
5. P. Hailey et al.,“Automated System for the On-line Monitoring of Powder<br />
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551–559 (1996).<br />
6. D. Illman,“CPAC: An Industry–University Cooperative Research Center<br />
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5 (7), 164–172 (1986).<br />
7. S. Wold,“Chemometrics: What Do We Mean with It <strong>and</strong> What Do We<br />
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8. B. Wise <strong>and</strong> N. Gallager, “The <strong>Process</strong> Chemometrics Approach to<br />
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(1996).<br />
9. J. Hardy, “Special Topics: Chemometrics,” lecture presentation associated<br />
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10. FDA,“Emerging Issues in Pharmaceutical Manufacturing: <strong>Process</strong> <strong>Analytical</strong><br />
<strong>Technology</strong>,” science board meeting presentation (16 November<br />
2001).<br />
11. N. Sugakkai,“Iwanami Sugaku Ziten,” original publication by Iwanami<br />
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13. W. Wu et al.,“The Star-Plot: an Alternative Display Method for Multivariate<br />
Data in the Analysis of Food <strong>and</strong> Drugs,” J. Pharma. Biomed.<br />
Analysis 17 (6-7), 1001–1013 (September 1998).<br />
14. P. Nomikos <strong>and</strong> J. MacGregor,“Multiway Partial Least Squares in Monitoring<br />
Batch <strong>Process</strong>es,” Chemometrics <strong>and</strong> Intelligent Laboratory Systems<br />
30, 97–108 (1995).<br />
15. FDA Global Harmonization Task Force Study Group #3, draft <strong>Process</strong><br />
Validation Guidance (1 June 1998).<br />
16. FDA Advisory Committee for Pharmaceutical Science transcripts, 23<br />
October 2002. PT<br />
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