STATISTICS IN PHARMACEUTICAL
DEVELOPMENT AND
MANUFACTURING
Statistics in Pharmaceutical Development and
Manufacturing
Abstract
The pharmaceutical industry is undergoing rapid change and facing numerous
challenges, including the demands of global competition, the need to speed up the
drug development process, and the Food and Drug Administration's (FDA’s)
expectations for the incorporation of the principles of quality by design (QbD) and
process analytical technology (PAT) in process and analytical development.
Statistical thinking and methods play a significant role in addressing these issues.
This article provides an overview of the use of statistical thinking and methods in
the R&D and manufacturing functions of the pharmaceutical industry. The exposition
includes the history of pharmaceutical quality and regulation, phases of
pharmaceutical development and manufacturing and the basic quality and statistical
tools employed in each, emerging statistical methods, the impact of statistical
software and information technology, and the role of statisticians in pharmaceutical
development and manufacturing. Four case studies are included to illustrate how
these issues play out in actuality. A summary provides a succinct synopsis
of those issues and concludes that the complex, technical nature of pharmaceutical
,development and manufacturing offers many opportunities for the effective use of
statistical thinking and methods and that those who use these methods can become
catalysts for both process development understanding and product quality
improvement.
KEY WORDS: Design Space; Drug Manufacturing; FDA; ICH; Opportunity; PAT;
Pharmaceutical Industry; Quality by Design; R&D; Statistical Methods; Statistical
Thinking; Technical Change
, Introduction
Statistical concepts and tools have been successfully applied for decades in such
sectors as chemicals, automobile manufacturing, and computer chip manufacturing, but
their use in the far more regulated pharmaceutical industry presents some unique
challenges. For example, many pharmaceutical companies hesitate to invest heavily in
large-scale manufacturing process quality before a drug is approved for marketing,
because failure in the clinic means product failure. Because time from inception to
clinical approval may span 12-15 years, 60-75% of product patent life may have expired
by the time the Phase III (confirmatory) trials have been completed. Even after a
successful set of Phase IIA (dose determination) and Phase IIB (proof of concept) clinical
trials are completed, 40-50% of product patent life may have expired. In addition, one in
three drugs is expected to fail in Phase I (first time in man) trials for assessing safety,
tolerability, and drug blood levels. Pharmaceutical companies are therefore under
considerable economic pressure to file for approval of the new drug application (NDA)
with regulatory authorities as soon as possible after completing successful Phase III
clinical trials, when a considerable amount of money must be invested in product launch.
As a result, quality and process understanding initiatives must compete for time and
money with potential losses from considerable 'at-risk' development activities.
1
, Nevertheless, a number of factors are converging to increase the need for
sophisticated, statistics-driven approaches to quality and process understanding in the
pharmaceutical industry, including:
Regulatory trends. Recent regulatory guidelines from the Food and Drug
Administration (FDA), the European Medicines Agency (EMEA), and the
International Conference on Harmonisation (ICH) encourage scientifically-based
approaches to quality and compliance. Implementing the concepts embodied in
those guidelines – Process Analytical Technology (PAT), Quality by Design
(QbD), and Design Space – will require new, more statistically rigorous and risk-
based ways of doing things.
The inherent characteristics of pharmaceutical manufacturing. Many of
these inherent characteristics – and the challenges they present – clearly call for
the increased use of sophisticated statistics-driven approaches. For example, in
most other manufacturing industries, product specifications are clearly tied to
product performance. In pharmaceuticals, however, it is difficult to tightly
connect, say, 'tablet dissolution' rates to drug efficacy and safety over a vast array
of potential product users, each with different body size, age, lifestyle, genetics,
and drug-metabolism chemistries. Furthermore, pharmaceutical companies must
maintain quality in a many-step production process that creates a complex
molecule that must have the proper molecular structure and be free of serious
chemical impurities or biological contaminants. In addition, up to now, there has
2
DEVELOPMENT AND
MANUFACTURING
Statistics in Pharmaceutical Development and
Manufacturing
Abstract
The pharmaceutical industry is undergoing rapid change and facing numerous
challenges, including the demands of global competition, the need to speed up the
drug development process, and the Food and Drug Administration's (FDA’s)
expectations for the incorporation of the principles of quality by design (QbD) and
process analytical technology (PAT) in process and analytical development.
Statistical thinking and methods play a significant role in addressing these issues.
This article provides an overview of the use of statistical thinking and methods in
the R&D and manufacturing functions of the pharmaceutical industry. The exposition
includes the history of pharmaceutical quality and regulation, phases of
pharmaceutical development and manufacturing and the basic quality and statistical
tools employed in each, emerging statistical methods, the impact of statistical
software and information technology, and the role of statisticians in pharmaceutical
development and manufacturing. Four case studies are included to illustrate how
these issues play out in actuality. A summary provides a succinct synopsis
of those issues and concludes that the complex, technical nature of pharmaceutical
,development and manufacturing offers many opportunities for the effective use of
statistical thinking and methods and that those who use these methods can become
catalysts for both process development understanding and product quality
improvement.
KEY WORDS: Design Space; Drug Manufacturing; FDA; ICH; Opportunity; PAT;
Pharmaceutical Industry; Quality by Design; R&D; Statistical Methods; Statistical
Thinking; Technical Change
, Introduction
Statistical concepts and tools have been successfully applied for decades in such
sectors as chemicals, automobile manufacturing, and computer chip manufacturing, but
their use in the far more regulated pharmaceutical industry presents some unique
challenges. For example, many pharmaceutical companies hesitate to invest heavily in
large-scale manufacturing process quality before a drug is approved for marketing,
because failure in the clinic means product failure. Because time from inception to
clinical approval may span 12-15 years, 60-75% of product patent life may have expired
by the time the Phase III (confirmatory) trials have been completed. Even after a
successful set of Phase IIA (dose determination) and Phase IIB (proof of concept) clinical
trials are completed, 40-50% of product patent life may have expired. In addition, one in
three drugs is expected to fail in Phase I (first time in man) trials for assessing safety,
tolerability, and drug blood levels. Pharmaceutical companies are therefore under
considerable economic pressure to file for approval of the new drug application (NDA)
with regulatory authorities as soon as possible after completing successful Phase III
clinical trials, when a considerable amount of money must be invested in product launch.
As a result, quality and process understanding initiatives must compete for time and
money with potential losses from considerable 'at-risk' development activities.
1
, Nevertheless, a number of factors are converging to increase the need for
sophisticated, statistics-driven approaches to quality and process understanding in the
pharmaceutical industry, including:
Regulatory trends. Recent regulatory guidelines from the Food and Drug
Administration (FDA), the European Medicines Agency (EMEA), and the
International Conference on Harmonisation (ICH) encourage scientifically-based
approaches to quality and compliance. Implementing the concepts embodied in
those guidelines – Process Analytical Technology (PAT), Quality by Design
(QbD), and Design Space – will require new, more statistically rigorous and risk-
based ways of doing things.
The inherent characteristics of pharmaceutical manufacturing. Many of
these inherent characteristics – and the challenges they present – clearly call for
the increased use of sophisticated statistics-driven approaches. For example, in
most other manufacturing industries, product specifications are clearly tied to
product performance. In pharmaceuticals, however, it is difficult to tightly
connect, say, 'tablet dissolution' rates to drug efficacy and safety over a vast array
of potential product users, each with different body size, age, lifestyle, genetics,
and drug-metabolism chemistries. Furthermore, pharmaceutical companies must
maintain quality in a many-step production process that creates a complex
molecule that must have the proper molecular structure and be free of serious
chemical impurities or biological contaminants. In addition, up to now, there has
2
