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Identifying and setting
specifications to establish the quality dimensions of a drug substance
represents an important aspect of project management. With significant
cost and time-to-market pressures, a well thought out strategy regarding
a product's specifications can have huge implications on ease of
manufacturing and, ultimately, on the product's financial return. The
cost of measuring numerous specifications on a commercial product is
significant. An evolving specification "laundry list" generated during
the development process may become "engraved in stone," incurring
unnecessary costs year after year during the product's life cycle.
Surely, this is an undesired situation in today's cost-conscious
marketplace. Therefore, an important project management goal is to
establish material acceptance specifications that clearly assess the
API's quality as it is released from a validated manufacturing process.
From exploratory medicinal chemistry to clinical trial material and
ultimately on to commercial production, the specifications that define
the expected quality of the target molecule should be an evolutionary
process. For instance, the specifications for a promising lead candidate
must ensure proper molecular characterization and identification of the
target. Typically, structure elucidation techniques of proton and carbon
NMR, crystallography, mass spectroscopy and elemental analysis may be
used. Further, the impurity profile throughout the entire development
process should receive close attention from the project's stakeholders.
The goal of this exercise is to insure the conclusions drawn from costly
toxicological, safety and clinical studies are attributable to the
target drug's characteristics and not to impurities. However, as the new
drug candidate progresses toward commercialization, many of these
specifications can be exchanged for those indicating manufacturing
capability. Of course, the evolving specifications must adequately
insure that the chemical intent and integrity of the API is not
compromised.
For pharmaceutical companies wishing to outsource API production of
clinical trial material or to initiate validation batches for commercial
manufacturing, a specification and accompanying test method review is
warranted. This review may have a number of implications. For a mature
and formulated drug product, a Pre-Approval Supplement or a Change Being
Effected in 30 days (CBE-30) filing must be submitted 1 to the FDA, but
the upside potential from cost reductions in API manufacturing warrants
the activity.
Conversely, for a clinical trial material, a specification assessment
before scale-up can have a substantial net present value when the
accrual of analytical costs over years of production are included in the
manufacturing total costs. The analytical demands on the contract
manufacturer can be contained and the client receive long-term cost
benefit if the specifications representing molecular characterization
are separated and removed from those suitable as manufacturing
specifications. Ideally, manufacturing specifications should be based on
batch-to-batch variability using well documented standards for the API
and impurities. Obviously, using documented standards and a validated
analytical method allows avoidance of costly analytical techniques
during manufacture release testing merely to reassert structure
confirmation—an activity defined and well covered during the development
cycle 2,3 . The benefits accrued from separating structure elucidation
specifications from manufacturing specs allows the contract
manufacturer's plant management to focus on process robustness and
predictability 4,5 (basic essentials of process validation) and to
maintain these features throughout the API's lifecycle. Clearly,
manufacturing API specifications directly related to consistent, high
quality API delivery is everyone's goal. To that end, let's examine some
observations and aspects of specification development, its impact on
process validation and how these issues relate to the contract
manufacturer.
Specifications and Natural Variation
It is very important to recognize the impact of natural (random)
variation on a long list of specifications 6. Simply stated, nature
tends to work against you as the cumulative variation from testing each
specification is combined (a stacked tolerance). Unfortunately, many can
testify to this observation by their participation in the well-intended,
but often tedious Out-of-Specification (OOS) investigation process. The OOS investigation and follow-up can be cumbersome and costly. Clearly,
OOS situations contribute to variable cost variances and have a negative
impact on the financial health of the manufacturing process (as
discussed in the cited article). Often, an OOS situation represents
natural variation that could have been avoided by the thoughtful and
responsible selection of specifications, and the conclusions drawn from
the subsequent process validation study. An understanding of a process's
natural variation as measured against the product specifications should
be one of the desired outcomes from the process validation activity.
This understanding can ultimately have a respectable financial pay7.
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Setting
Specifications
It is rumored that setting product specifications is the leading cause
of project managers seeking professional psychiatric treatment. Few
other topics will bring the armchair philosophers to their feet in
tirades of dogma as they wave a map to the moral high ground. However,
adopting a rational approach to setting manufacturing specifications can
provide more predictable results and provide a data driven basis to
process control.
A number of scenarios can occur when specifications are not developed as
an integral whole or in a self-consistent fashion. For instance, a
combination of analytical techniques used to measure a given parameter
should be shown to yield comparable results with an expected variation.
Perhaps an obvious conclusion would be to eliminate one of the methods,
but often specifications added to maintain current GMP compliance makes
it difficult to remove the original spec and its accompanying test
methodology. To illustrate this, a drug substance specification list
containing both an (original) titrimetric assay and an (updated) HPLC
assay with analyte specificity is probably a non-value added redundancy.
Similarly, a TLC impurities test remaining on a drug substance
specification sheet after a validated HPLC impurity analysis has been
implemented should be avoided if possible. It's well known (okay, so I'm
exaggerating here) that performing the TLC test with each batch is
significantly cheaper than filing a Supplement!
Another example is the situation where related specifications are not
centered to one another. If two (or more) related specs are not centered
(or aligned), the "approval" range or overlap artificially reduces the
specification range of the individual specs. An example of this is the
amount of allowable enantiomer in a HPLC chirality assay and the
relationship to an optical rotation specification. In this case it may
be possible for the process to deliver material that meets one
specification but not the other(s). This situation necessitates that the
process include a search and sort mechanism that "inspects quality into
the product." Clearly, such an approach to meeting specifications is
unacceptable and would not represent a validated process 8.
Conversely, well-characterized standards and their validated analytical
methods are intended to, and easily obviate, the need for lengthy
specification sheets. Establishing the identity, assay, impurities and
confirming that the process itself did not contribute to undesired
materials should be the goal of consistent manufacture of the drug
substance. For manufacturing purposes, a logical set of specifications
that represent the API and are based on validated methods and standards
should be sought. Again, the steps of molecular characterization
activities should be separated from manufacturing specifications.
One rule of thumb for setting specifications is to set the spec range at
± four sigma for any given, independent specification. The steps to this
are to: 1) determine the natural variation of the process (including
measurement systems) to ± three sigma, 2) question and challenge whether
the product would meet other acceptance criteria if material was
produced in the ± three-to-four sigma range, and 3) if so, establish an
accepted specification range at four sigma. One criteria to this
approach is that the measurement systems must be "in control and
capable," as determined using a traceable standard. Secondly, it must be
remembered that specifications do not drive performance
requirements—just the opposite is true. However, setting specs that
reflect an honest assessment of a process's natural variation, while
simultaneously supporting the uncompromising delivery of a drug
substance's quality aspects, has long-term financial pay to all parties.
Specification Goals and Variation
Simply stated, API specifications provide a quantitative means to
evaluate product quality. Operationally, the manufacturing management
desires a process sufficiently robust that they can produce material
predictably and consistently within specification. Hence, the attention
drawn to manufacturing six-sigma programs is justified for an effective
means to demonstrate processes are in control. If process development
activities addressed control factors sufficiently to move process
averages (by design) and to understand the factors contributing to
random variation, then the process and the specifications can be
"centered." Perhaps the comments vide supra about establishing
specification ranges is more apparent. A ± four sigma specification
range based on ± three sigma process control limits provides a
manufacturing system statistically capable of producing the drug
substance correctly—every batch. This scenario represents a plant
manager's utopia, but unfortunately, life is usually not this simple.
Often, product specifications and manufacturing control limits are not
centered—at least not for every specification. At times, the statistical
process control limits are equal to, or greater than the specifications.
Instead of utopia, this keeps plant managers awake at night or on the
phone arranging "technical coverage" to see the batch through. If a
company culture rewards "fire-fighting" or the appearance of heroic
efforts, it will also inadvertently support the "arsonist." Ultimately,
higher productivity and good employee morale are achievable with
manufacturing processes that are in control and capable. Amazingly, it
all starts with the product specifications!
For the purists who are crying foul over this manufacturing-driven
approach to product quality, it should be firmly stated that product
performance in the application should drive product specifications; the
reciprocal is not necessarily true. Phrased another way, there is no
relationship between product specifications and process control limits.
However, the financial return from the described approach is significant
when off-quality costs are considered—OOS investigations, lost plant
time or opportunity costs, lost raw materials, stock-out situations,
etc.
Financial Leverage
From a project management perspective, it is important to understand
when and how to set manufacturing specifications versus how to handle
information obtained for molecular characterization purposes. Molecular
characterization is a scientific exercise to confirm a material's
structure. There is essentially no end to the tests available to
"explore" the molecule's structure. In nearly all cases, molecular
characterization is an R&D function. In contrast, manufacturing
specifications indicate the acceptable tolerances of a material for its
intended use when produced by a defined manufacturing process. Lastly,
specifications do not represent a scientific investigation process but
are used to confirm what is already known and expected from routine
manufacture of the substance.
In R&D activities, routine characterization of complex molecules would
probably include high field NMR techniques and the like—used to
characterize reference markers and analytical standards. Using the
analytical standards, validated analytical testing procedures can be
established 9 to confirm identity, assay and purity of the API produced
in manufacturing. Further, the validated analytical methods are an
integral part to establishing process validation and are often employed
during the cleaning validation activities.
Obviously, the importance of analytical standards to manufacturing
operations is significant. In fact, two financial benefits arise from
well documented standards employed in manufacturing QC labs:
1) standards obviate the need for capital intensive, high maintenance,
high budget,
sophisticated analytical laboratories attached to manufacturing
facilities, and
2) the variable cost contribution per API batch from expensive molecular
characterization instrumentation is eliminated.
If these reasons still seem insufficient to the cautious project
manager, consider the financial burden of retaining specifications
emanating from the discovery laboratory (as opposed to manufacturing
process development).
As a product matures and larger quantities are produced, actual
specifications per batch must be tested on a statistical sampling of the
batch. Often, this sampling is the square root of the number of
containers required to package the batch, plus one. The analytical cost
for release testing then becomes the summation of the number of tests
times their associated costs, times the statistical sampling. Sure,
there are ways to reduce this cost: One method is to validate testing on
a composite sample of the statistical sampling; another way is to
truncate the specification list to eliminate structure confirmation
tests. A careful selection of specifications for API release testing
essentially generates a financial annuity for the life of the product.
A few recommendations for establishing API specifications have been
offered that can substantially reduce the manufacturing cost of a drug
substance. Without question, choose specifications that represent the
API's quality when produced by a validated manufacturing process using
documented analytical standards. These specifications should, if
possible, be divorced from molecular confirmation techniques since
structure determination was accomplished long before the product reached
manufacturing. Secondly, use a statistical basis to establishing the
specification range based on the process and measurement capabilities of
the production facility. This approach is dependent upon the caveat that
performance requirements drive specifications, not what the plant can,
or wants to, do.
Lastly, an emphasis on analytical reference markers and standards is
well warranted. For instance, in the case of contract manufacturing, the
sponsor anticipates a cost benefit to the outsourcing activity. For a
new drug substance, standards may not have been prepared; for an older
drug, updating documentation to cGMP guidelines may be needed. In either
case, careful attention to preparing, characterizing and documenting
analytical standards is an investment paying a dividend on every API
batch produced. The project manager who effectively addresses these
issues without compromising API quality contributes significantly to the
financial well-being of their organization 10.
References
1. Guidance for Industry; Changes to an Approved NDA or ANDA; Food and
Drug
Administration, November 1999.
2. Chemical Engineering; June 6, 1977; pp. 168 – 182: "Design and
Analysis of
Industrial Experiments"; Thomas D. Murphy, Jr.
3. Chemical Engineering; November 1995; pp. 142 - 147: "Design of
Experiments";
Kymberly K. Hockman and David Berengut.
4. Quality, June 1996, pp. 28 – 30: "Put ‘Process' in SPC"; Edmund
S. Fine.
5. Chemical Processing's 1997 Project Engineering Annual, pp. 56 – 59:
"Basic
Elements of Chemical SPC"; Jodi Kay.
6. Contract Pharma, October 2000, pp. 54 – 64: "Improve Financial
Performance from
Statistical Process Control: Six Sigma Programs Are a Valuable Addition
to a
Manager's Toolbox"; Cliff R. King
7. Chemical Market Reporter; July 16, 2001; pp. fr10 – 12; "Six Sigma
and the
Bottom Line"; Pamela Sauer.
8. BioPharm, August 2001, p. 18: "Documentation: It's Not Just Paper
Work"; Dian
Feuerhelm and Gregory S. Blank.
9. Pharmaceutical Technology, October 1999, pp. 166 – 129: "A
Statistically
Integrated Approach to Analytical Method Validation"; Donald H. Weed,
Jr.
10. Chemical Market Reporter; September 3, 2001, pp. 27 – 30: "EVA
(Economic
Value Added) and the Chemical Industry"; John Ballow, Henri Perrson and
Red
Knechtel. |
India