1、Designation: E2475 10 (Reapproved 2016)Standard Guide forProcess Understanding Related to PharmaceuticalManufacture and Control1This standard is issued under the fixed designation E2475; the number immediately following the designation indicates the year oforiginal adoption or, in the case of revisi
2、on, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.1. Scope1.1 The purpose of this guide is to establish a frameworkand context for process understanding for pharmac
3、euticalmanufacturing using quality by design (QbD) (Juran, 1992;2FDA/ICH Q8). The framework is applicable to both activepharmaceutical ingredient (API) and to drug product (DP)manufacturing. High (detailed) level process understandingcan be used to facilitate production of product which consis-tentl
4、y meets required specifications. It can also play a key rolein continuous process improvement efforts.1.2 Process Analytical Technology (PAT) is one elementthat can be used for achieving control over those inputsdetermined to be critical to a process. It is important for thereader to recognize that
5、PAT is defined as:“a system for designing, analyzing, and controlling manufacturing throughtimely measurements (i.e., during processing) of critical quality and performanceattributes of raw and in process materials and processes, with the goal ofensuring final product quality. It is important to not
6、e that the term analytical inPAT is viewed broadly to include chemical, physical, microbiological,mathematical, and risk analysis conducted in an integrated manner. The goal ofPAT is to enhance understanding and control the manufacturing process”(U.S. FDA PAT)1.3 This standard does not purport to ad
7、dress all of thesafety concerns, if any, associated with its use. It is theresponsibility of the user of this standard to establish appro-priate safety and health practices and determine the applica-bility of regulatory limitations prior to use.2. Referenced Documents2.1 ASTM Standards:3E456 Termino
8、logy Relating to Quality and StatisticsE2281 Practice for Process Capability and PerformanceMeasurementE2474 Practice for Pharmaceutical Process Design UtilizingProcess Analytical TechnologyE2617 Practice for Validation of Empirically Derived Mul-tivariate Calibrations2.2 U.S. Government Publication
9、s:4FDA/ICH Q8 Pharmaceutical DevelopmentFDA/ICH Q10 Pharmaceutical Quality SystemsU.S. FDA PAT Guidance Document, Guidance for IndustryPATA Framework for Innovative PharmaceuticalManufacturing and Quality Assurance3. Terminology3.1 Definitions of Terms Specific to This Standard:3.1.1 critical inputs
10、, ncritical process parameters andcritical raw material attributes for a given process.American Society for Quality53.1.2 empirical, adjany conclusion based on experimentaldata and past experience, rather than on theory.3.1.3 expert system, nan expert system is a computerprogram that simulates the j
11、udgment and behavior of a humanor an organization that has expert knowledge and experience ina particular field.3.1.3.1 DiscussionTypically, such a system contains aknowledge base containing accumulated experience and a setof rules for applying the knowledge base to each particularsituation that is
12、described to the program. Sophisticated expertsystems can be enhanced with additions to the knowledge baseor to the set of rules.3.1.4 first principles, na calculation is said to be from firstprinciples, or ab initio, if it starts directly at the level ofestablished laws of physics and does not make
13、 assumptionssuch as model and fitting parameters.3.1.5 mechanistic, adj(1) of, or relating to, theories thatexplain phenomena in purely physical or deterministic terms: amechanistic interpretation of nature.1This guide is under the jurisdiction of ASTM Committee E55 on Manufactureof Pharmaceutical a
14、nd Biopharmaceutical Products and is the direct responsibility ofSubcommittee E55.01 on Process Understanding and PAT System Management,Implementation and Practice.Current edition approved Sept. 1, 2016. Published September 2016. Originallyapproved in 2010. Last previous edition approved in 2010 as
15、E2475 10.DOI:10.1520/E2475-10R16.2Juran, J., Juran on Quality by Design: The New Steps for Planning Quality IntoGoods and Services, Free Press, New York, N.Y., 1992.3For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org. For Annual Bo
16、ok of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.4Available from U.S. Government Printing Office Superintendent of Documents,732 N. Capitol St., NW, Mail Stop: SDE, Washington, DC 20401, http:/www.access.gpo.gov.5Available from American Society
17、for Quality (ASQ), 600 N. Plankinton Ave.,Milwaukee, WI 53203, http:/www.asq.org.Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States13.1.6 process capability, nstatistical estimate of the out-come of a characteristic from a process that h
18、as been demon-strated to be in a state of statistical control. E22813.1.7 process inputs, nthe combination of all processparameters and raw material attributes for a given process.3.1.8 process understanding, vto recall and comprehendprocess knowledge such that product quality can be explainedlogica
19、lly or scientifically, or both, as a function of processinputs and respond accordingly.3.1.9 residual error, nthe difference between the observedresult and the predicted value (estimated treatment response);Observed Result minus Predicted Value. E4563.1.10 uncertainty, nan indication of the variabil
20、ity asso-ciated with a measured value that takes into account two majorcomponents of error: (1) bias, and (2) the random errorattributed to the imprecision of the measurement process. E4564. Process Understanding4.1 From physical, chemical, biological, and microbiologi-cal perspectives, a process is
21、 considered to be well understoodwhen:(1) All significant sources of variability in process inputsare identified and explained,(2) The effect of these sources of variability on productquality attributes can be accurately and reliably estimatedbased on the inputs to the process, and(3) Significant pr
22、ocess parameters are continuously man-aged and controlled to ensure that the process must produceproduct which is continuously within required specifications tothe user specified required degree or confidence.4.2 A well-controlled process is a process where the risk ofproducing product not meeting r
23、equired specifications is belowthe maximum acceptable level of risk as predetermined by theuser. Accordingly, process understanding requires the compre-hension and recall of process knowledge sufficient for thelogical, statistical, or scientific understanding, or combinationthereof, of how significa
24、nt process parameters and raw materialattributes relate to, or impact the quality attributes of, theproduct being produced. Sufficient process understandingshould be achieved to reduce risk to an acceptable level for thepatient, manufacturer, or any other stakeholder.4.3 A Lifecycle Commitment (Deve
25、lopment and CommercialManufacture):4.3.1 Process understanding is fundamental to QbD. It isimportant to realize that due to commercial realities (forexample, finite resources, time, and money), a process willtypically be commissioned as soon as the degree of processunderstanding is sufficient to per
26、mit operation of the processwith an acceptably low, user specified, level of risk ofproducing out of specification product. While it may beappropriate to commission a process once this minimumdegree of process understanding is achieved, the risk that theprocess may transition out of control steadily
27、 increases overtime (for example, process drift), and could exceed themaximum acceptable risk without warning, unless an ongoingprogram to enhance process understanding is in place.4.3.2 Accordingly, the development of process understand-ing should be treated as an ongoing process. Learning shouldco
28、ntinue throughout the product and process life cycle toimprove the level of process understanding to include processparameters and other factors (for example, environmental,changes of scale, changes in raw materials, changes in person-nel) which may have changed or which may have newlyemerged since
29、the time the process was first commissioned.Work to enhance process understanding continuously through-out the life cycle of the product and process can provideassurance that the process will continue to have an acceptablylow risk of producing out of specification results.4.3.3 Manufacturers are enc
30、ouraged to continuously moni-tor and improve upon their operations to enhance productquality.4.4 Process Understanding for the Whole Process:4.4.1 For each product, process understanding covers theprocess from the initial design of the chemical or biologicaldrug substance through manufacturing of th
31、e unit dose ordevice to final packaging. In addition, the critical qualityattributes of the raw materials will in turn become inputs to thedrug product manufacturing process, as will process param-eters.4.4.2 Fig. 1 schematically illustrates that the performance ofany process output (Y) is a functio
32、n of the inputs (X), which canbe classified into one of six categories (that is, operator,equipment, measurements, methods, materials, and environ-mental conditions).4.4.3 Comprehensive understanding of the relationships ofthe process inputs and operating parameters to quality attri-butes of the res
33、ulting product is fundamental to developing asuccessful risk mitigation or control strategy, or both. Identi-fication of critical process parameters (CPPs) and critical rawmaterial attributes should be carried out using suitable experi-mental and investigative techniques.An understanding of thesecri
34、tical inputs (CPPs and critical raw material attributes), andtheir monitoring and control, is essential when designing aprocess that is able to consistently and reliably deliver productof the desired quality.4.4.4 One method for achieving the desired state is throughmultivariate analysis and control
35、. The acceptable operatingenvelope of the critical inputs defines the relationship betweenthe design space, control strategy and operating range(s).4.4.5 Note that for raw materials, an additional source ofvariability derives from the potential for adulteration. Thisrequires that manufacturers under
36、stand their incoming supplychain and suppliers quality systems, and include methods todetect adulteration of materials in addition to confirmingidentity as necessary, bearing in mind that adulteration may bedifficult to detect by standard methods. It also requires thatmanufacturers use suppliers tha
37、t are aware of these concernsand are prepared to implement their own precautionarymeasures, and to permit transparency into their respectivesupply sources.4.5 Tools of Process Understanding:4.5.1 Process understanding begins with process design(Practice E2474) and usually a structured, small scale d
38、evel-opment program which focuses on efficiently delivering aE2475 10 (2016)2product meeting the required specifications. Tools that may beapplied during development and after commercialization in-clude:(1) Scientific theory,(2) Prior knowledge,(3) Design of experiments,(4) Simulation of unit operat
39、ions,(5) Selection of a suitable technology platform,(6) Mathematical models,(7) Validated empirical/statistical models,(8) Appropriate instrumentation, and(9) Appropriate analytical methods.4.5.2 The measurement technologies include but are notlimited to spectroscopic, acoustic, or other rapid sens
40、or tech-nologies. The development of these and other advanced tech-niques will continue to enable or enhance predictive control forcommercial pharmaceutical processes.4.5.3 The ability to measure process parameters and qualityattributes inline, online, or atline in real time can contribute toprocess
41、 understanding and the ability to control the process.These technologies offer the development scientist, commer-cial production engineer and manufacturing personnel theopportunity for additional insight. This is achieved through theincreased measurement frequency and availability of morecomprehensi
42、ve data.5. Process Knowledge5.1 Process knowledge is the cornerstone of process under-standing. There are various levels of process knowledge, andthese are listed from lowest to highest state of understanding:(1) Descriptive knowledge (what is occurring?),(2) Correlative knowledge (what correlations
43、 are empiri-cally observed?),(3) Causal knowledge (empirical, what causes what?),(4) Mechanistic knowledge (explanations for observedcausality), and(5) First principles knowledge (underlying physical,chemical, and biological phenomena of the mechanistic expla-nations).5.2 Process knowledge is the ac
44、cumulated facts about theprocess. This accumulated knowledge is generally embodied ina model of the process. Accordingly, process model is oftenused synonymously with process knowledge.5.3 Process understanding is demonstrated by the extent towhich process knowledge can be used to predict and contro
45、lthe process outcomes; a well understood process will combineknowledge from various sources to ensure a well controlledprocess and consistent product quality.5.4 At any point in time for any manufacturing process, thelevel of understanding will likely be a combination of variousFIG. 1 Input, Process
46、, and Output DiagramE2475 10 (2016)3levels of understanding. As more knowledge is obtainedthroughout the lifecycle of a product, the relative contributionto understanding of the various levels is likely to change.5.5 Prior knowledge is any knowledge that may be availablethrough previous experience.
47、Prior knowledge may come froma number of sources including scientific literature, companyexperience from research and development, and existing com-mercial products as a result of lab and manufacturing investi-gations. All knowledge that is available should be consideredand placed in context in orde
48、r to optimize the overall level ofunderstanding.5.6 Within most organizations in the early stages of QbDimplementation, process understanding tends to be basedmainly on descriptive and correlative and scientific knowledge.The framework outlined in the FDAs “Pharmaceutical cGMPSfor the 21st Century A
49、 Risk-Based Approach”6shouldencourage the pharmaceutical industry to enhance understand-ing by adding process knowledge at the causal, mechanistic,and first principles levels.5.7 Mechanistic and first principles process models canoffer advantages over process models which are a combinationof only descriptive, correlative, and causative process knowl-edge. Proper evaluation of risk may be more challenging in theabsence of mechanistic or first principles process knowledge.The user is responsible for determining the level of processknowledge which is appropriate fo
