Strategic Approaches to Process Optimization and Scale-up - Almac

Strategic Approaches to Process Optimization and Scale-upThe authors present three approaches that a contract development and manufacturing organization can consider whendesigning development and process-optimization studies that will provide useful data for scaling up a project.Sep 2, 2010By: Conor P. Long, John McQuaidPHARMACEUTICAL TECHNOLOGY - Volume 34, Issue 9As the development of a new drug product progresses, the batch sizes manufactured generally increase. The early FirstTime in Man (FTIM) clinical trials commonly involve dosing to a small number of subjects, with the majority of themanufactured product often being required for stability studies and analytical testing. As one proceeds from formulationdesign to Phase I clinical trials and subsequently through to Phase II & III, batch sizes generally increase. Following asuccessful clinical trial outcome, resulting in the granting of a marketing authorization, the batch sizes manufactured maybe further increased to cover the market demands.Current International Conference on Harmonization (ICH) guidelines state that data from stability studies should beprovided on at least three primary batches of the drug product (1). The primary batches should be of the sameformulation and packaged in the same container-closure system as proposed for the marketed formulation. Themanufacturing process used for these primary batches should simulate that to be applied to production batches andshould provide product of the same quality and meeting the same specification as that intended for marketing. Two of thethree batches should be at least pilot-scale batches and the third one can be smaller, if justified. Where possible, batchesof the drug product should be manufactured by using different batches of the drug substance. Typical batch sizes usedthroughout development as defined in the European Medicines Agency's (EMA) guidance for Process Validation arelaboratory-scale, pilot, and production-scale batches (2).Laboratory-scale batches. These are produced at the research and early development laboratory stage. They may beof very small size (e.g., 100–1000 times less than production scale). Laboratory-scale batches may be used to supportformulation and packaging development, early clinical and/or preclinical stages. Laboratory-scale batches can also beanalyzed to assist in the evaluation and definition of critical quality attributes (CQAs). A CQA is a physical, chemical,biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution toensure the desired product quality. CQAs are generally associated with the drug substance, excipients, intermediates,and drug product.Pilot-scale batches. These may be used in the process-development or optimization stage. They may be used tosupport preclinical and mid- to later-stage clinical evaluation and also to support formal stability studies. If supportingformal registration, a pilot-batch size should correspond to at least 10% of the production-scale batch. For oral soliddosageforms, this size should generally be 10% of production scale or 100,000 units, whichever is greater. The choice ofpilot scale is often difficult for the project team as members must balance parameters such as anticipated productvolumes, anticipated site of production, equipment constraints at that site, and regulatory expectations. With theincreasing trend toward developing orphan drugs, the authors believe that the regulatory expectation of pilot-scalebatches of 100,000 units is not always valid and should be discussed with the relevant regulatory authority.Production-scale batches. These batches are of the size that will be produced during the routine manufacturing andmarketing of the product. Data on production-scale batches may not always be available prior to granting marketingauthorization.Approaches to process optimization and scale-upPharmaceutical development activities around the scaling-up and optimizing for pilot- or commercial-scale manufactureshould include, at a minimum, the following elements (3):Pharmaceutical Tech ‐ Sep 10 ‐ Process Opt & Scale‐Up

• Defining the target product profile as it relates to quality, safety and efficacy, considering, for example, the routeof administration, dosage form, bioavailability, dosage, and stability• Identifying CQAs of the drug product, so that those product characteristics having an impact on product qualitycan be studied and controlled• Determining the quality attributes of the drug substance and excipients, and selecting the type and amount ofexcipients to deliver drug product of the desired quality and efficacy• Selecting an appropriate manufacturing process• Where possible, identifying a control strategy.The basis of quality-by-design (QbD) follows that well-designed experiments, carefully planned and thought out from theoutset of formulation development, can produce quality data from an early stage, which can minimize lengthy and costlyrepetition of manufacturing and subsequent studies and analysis. Nevertheless, before manufacturing registrationbatches, it is usual to invest additional time and effort in challenging, and, where necessary, optimizing the existingmanufacturing process. This is achieved by making additional development batches at laboratory-scale, pilot scale, andpossibly even at production scale. The output from the above development efforts provides the basis for justification thatscale up can be achieved without a consequent loss in quality and/or efficacy and provides the link between formulationand process development, pilot-scale manufacture, and eventual commercial production.Analysis of the data assists in determining the parameters that have a known effect upon a CQA of the drug product.These parameters are referred to as being critical process parameters (CPPs). Furthermore, data obtained creates apicture of the product and process, how its design has evolved, what is critical and must be controlled, and provenacceptable ranges (PARs) around specific process parameters.This article presents three approaches that a CDMO can consider in designing development and process optimizationstudies that will provide useful data on scaling up a product.Approach 1: Quality ensured by end-product testingThis method of manufacturing is a minimal approach to the mechanistic understanding of a process. The development ismainly empirical with research often conducted one factor at a time (OFAT). Following manufacture, there would be littleor no 'design space' defined, resulting in a strict manufacturing process that should be adhered to in order to minimizeany variability in product characteristics and thus the CQA profile. Validation will primarily be based on initial full-scalebatches. CPPs are determined using only prior knowledge. Proven acceptable ranges for CPPs are not fully determined.A minimal knowledge of PARs is determined. The following is an example of how this approach is used in developing afilm-coated tablet.Example 1: Blend, direct compression, and coating scale-up operation. A blending operation at a specified scale isperformed using a standard tumble blender. The drive unit is set to a fixed speed. The excipients are added to theblending shell and tumbling is initiated. The blend is mixed for a set period of time. Following this mixing time, the blend issampled to determine blend uniformity and bulk assay values. If the blend is determined to be uniform, the time andspeed used is recorded and would be used for future batches. If the blend is found to be nonuniform at this stage, anadditional mixing period is used, followed by further sampling for uniformity and bulk assay. This procedure is repeateduntil uniformity is achieved with the mixing time and speed recorded for future batches.The uniform blend is compressed using a suitable tablet press for the batch size in question. The compressionparameters are adjusted in order to obtain the desired product characteristics such as tablet weight, hardness, thickness,disintegration time, and dissolution time. Compression force and compression speed would be adjusted during set-upsand continually assessed with appropriate adjustments made. Intensive in process checks continuously monitor productquality characteristics in order to maintain an acceptable product.Pharmaceutical Tech ‐ Sep 10 ‐ Process Opt & Scale‐Up

The tablet cores are progressed for coating using a batch coating process. The coating parameters are initially set usingprior knowledge of the equipment being used and coating at a particular batch size. Intensive in-process checks, inaddition to close visual observations by trained operators, allow adjustments to be made throughout the coating process.The final parameters would be recorded and used for future coating operations at this scale.With the advent of the guideline ICH Q8 Pharmaceutical Development, this historical approach is falling out of favor, andwould likely invite greater scrutiny from regulatory authorities following submission (3).Approach 2: Full design of experimentsAn enhanced QbD approach to product development would additionally include a systematic evaluation, understanding,and refining of the formulation and manufacturing process, including (3):• Identifying–using prior knowledge, experimentation, and risk assessment–the material attributes and processparameters that can have an effect on product CQAs• Determining the functional relationships that link material attributes and process parameters to product CQAs• Using the enhanced process understanding in combination with quality risk management to establish anappropriate control strategy that can, for example, include a proposal for design space(s) and/or real-timerelease.Figure 1: An Ishikawa (fishbone) diagramthat identifies all potential parameters thatcan have an impact on the desired qualityattribute. (ALL FIGURES COURTESY OFTHE AUTHORS)higher level of process understanding.Risk-assessment tools can be used to identify and rank potential parameters deemedto have an impact on product quality based on prior knowledge and initial experimentaldata. The initial list of all possible parameters can be quite extensive, but is likely to benarrowed, as process understanding is increased, to a smaller list of potentialparameters. Narrowing the list has the advantage of reducing the number ofexperiments necessary in the modeling of a design space. The list can be refinedfurther through screening experimentation to determine the significance of individualparameters and potential interactions. Once the significant parameters are identified,they can be further studied (e.g., through a combination of design of experiments,mathematical models, or studies that lead to mechanistic understanding) to achieve aExample 2: Full design of experiments. Using prior scientific knowledge, the projectteam experts will use the target product profile to establish CQAs, and, in turn, CPPs.An approach could follow the five steps listed below.Step 1: Cause and effect analysis (Ishikawadiagram). The expert team compiles a cause andeffect (C&E) diagram, also known as an Ishikawadiagram, that maps out all potential parameters in aTable I: The potential parameters in anexample analysis.Figure 2: Example C&E diagram detailingmanufacturing process. The number of parameters can be very extensive. An exampledry-mixing and granulation parameters. analysis performed for a recent project at Almac resulted in 79 parameters, possiblyinfluencing the final tablet quality (see Figure 1). The parameters determined areshown in Table I. Figure 2 shows the potential CPPs making up the dry granulation step of the manufacturing process.Pharmaceutical Tech ‐ Sep 10 ‐ Process Opt & Scale‐Up

Step 2: Ranking of process parameters in order of importance. The list wasanalyzed using a risk-assessment approach to prioritize the parameters. Parametersgraded as high risk were progressed for investigation. An example parameter ratingform is presented in Figure 3. The example shows a parameter rating form focusing onthe nine parameters from the dry-granulation stage that may have an effect on theCQAs. A separate rating form would be completed for each stage of the manufacturingprocess. A scoring system, ranging from 1 being minimal effect, 3 being moderate,and 9 having a major effect on product attributes, was used. This scoring approachapplies a much greater weighting to parameters deemed to impact a product'sattributes significantly, thus increasing the necessity to further study the effect of theparticular variable.Figure 4: Example results parameterrating form.Figure 4 shows an example of results following the compilation of the scoring process.The example illustrated focuses on a single product attribute (tablet hardness) and asingle processing step (dry granulation). The full exercise produces individual tablescompiling project team views regarding each processing parameter in relation to eachCQA.Figure 5 shows how decisions can be made onwhich parameters may be investigated at the nextstage. The total score is the sum of the scoresobtained for process parameters across all theCQAs. This will provide a rank order of parametersthat may have an effect on CQAs. Using prior experience coupled with knowledge ofthe intended process, the number of parameters can be minimized. The number ofparameters investigated using experimental design will significantly increase thenumber of batches required.Figure 6: Detail of parameter riskassessment.Step 3: Assessing the impact of parameters on the product. The parameters werefurther screened by considering the higher ranked parameters in each processing stepas having a high influence on each CQA. Also, every parameter having a score of 40or more was classified as having a high influence on CQAs. An example matrixshowing the impact assessment for the drygranulation step is presented in Figure 6.Step 4: Determine the parameters to beinvestigated experimentally. Following a full analysis using prior knowledge andexperience of the formulation and the manufacturing techniques, conclusions will bedrawn from the risk-based classification process to list the parameters to beinvestigated using a first-screening experimental design (see Table II).The parameters were investigated using a 2-level screening design of experiments. Inthis example, 10 parameters were chosen to be screened.Figure 3: Example parameter rating form.Figure 5: Parameter evalution todetermine which parameters should beinvestigated.Table II: Parameters to be investigated inexample analysis.Figure 7: The relationship between thenumber of parameters and resolution ofdesign to the number of experimentsrequired.The number of parameters, in addition to the desired resolution of the design, impactson the number of experiments required in determining the critical parameters. Figure 7shows the relationship between number of parameters and resolution with number ofexperiments required. Additional considerations such as time and activepharmaceutical ingredient constraints may also influence the number of experimentsthat are possible at this stage.Pharmaceutical Tech ‐ Sep 10 ‐ Process Opt & Scale‐Up

The standard experimental design notation is presented in Figure 8, where X is thenumber of levels being investigated. A level is a particular parameter value. In thecase of compression speed, the two levels could be the maximum and minimum turretspeed that would potentially be used in an operation. K is the number of parametersbeing investigated. P is the degree of fractioning that is being performed in order toreduce the number of experiments. A full factorial design would not have a value for pdisplayed.For example, 25 would be a two-level design covering 5 parameters. This wouldrequire 32 experiments to investigate the parameter effects. 25-1 is a fractional designof this example where the number of experiments required is halved to 16. As the degree of fractioning increases thequality of information obtained from the design decreases. A typical approach would be to use a high-quality (highresolution) design for optimization and in-depth study of a process and use a low-resolution design for initial screening inorder to determine critical parameters. The design would then generate a number of experiments that detail the exactparameters to set for a manufacturing operation. Each experiment would be completed and the subsequent productattributes would be determined. Statistical software is used to analyze the effect of the process parameters on the productattributes and determine how critical each parameter is.Figure 9: The influence of granulator finalscreen size and roller pressure on tablethardness.screen size decreases, the tablet hardness increases.In the described example, the results of the experimental design allowed the numberof potential critical parameters for the initial screening to be reduced to eight. Theseeight parameters were then investigated using a more comprehensive 3-level design.This type of design, also referred to as a response surface method (RSM), will requiremore experiments. The previous 2-level screening design is used to minimize thenumber of parameters investigated so as to minimize the number of experiments atthe 3-level design stage. In this example, the results of the 3-level design enabledstatistically significant models to be calculated showing the effects of the criticalparameters on a number of product attributes. A graphical example of a modeldisplaying the influence of granulator final screen size and roller pressure on tablethardness is shown in Figure 9. As can be seen, as the roller pressure and granulatingStep 5: Design space. The linkage between the process inputs (input parameters and process parameters) and theCQAs can be described in the design space. The risk-assessment and process-development experiments described cannot only lead to an understanding of the linkage and effect of process inputs on product CQAs, but also help identify theparameters and their ranges within which consistent quality product can be achieved.Approach 3: Hybrid method combining elements of approaches 1 and 2.Figure 8: Standard experimental designnotation.The use of a full design of experiments approach can result in a significant amount of batches being manufactured tocover the design space. As the number of batches required to describe the design space increases, the developmenttime and cost increases. A third method combining elements from approaches 1 and 2 optimizes the balance betweendevelopment time and cost and maintains a good understanding of the manufacturing process. This hybrid approach canbe achieved by breaking the manufacturing process into defined sections. The initial blending step can be performedusing Approach 1. The blend could be manufactured to specification, and end-product testing will show the quality (e.g.blend uniformity/assay). Parameters such as blending time and blending speed can be determined by intensive blenduniformity and bulk-assay analysis allowing blending windows to be established. A second confirmatory batch could bemanufactured using the parameters obtained from the initial batch that could provide additional confidence in themanufacturing process.The blend will have been shown to meet specification and can then be progressed to the next processing steps. Anexperimental design approach can then be performed. For example, the next steps could involve dry granulation followedby compression into tablets. As described in approach 2, following completion of a full risk assessment and subsequentcause and effect diagram detailing the potential process parameters, a structured design of experiments plan can bePharmaceutical Tech ‐ Sep 10 ‐ Process Opt & Scale‐Up

devised. Rather than completing each experiment at the full scale, the experiment can be performed using a suitablysized aliquot of the blend manufactured already. As both dry granulation and compression are a continuous throughputprocess, the parameters investigated are mainly independent of batch size used. Using this approach, a 60-kg blendcould be divided into 3-kg sublots, allowing up to 20 experiments to be run.Following completion of the experiments and analysis of the data, a confirmatory batch could be manufactured using theoptimized parameters determined from statistical analysis and preparation of a design space. The resulting batch or tabletcores could be progressed to coating if required. Again, the coating process can be investigated using method 1 or 2.ConclusionStrategies for product development can vary from company to company and from product to product, and as such, acontract development and manufacturing organization (CDMO) must be able to provide a service that can suit therequirements of any customer. For a variety of reasons, a company might choose either an empirical approach or a moresystematic approach to product development. The approaches described can aid in the provision of a high-qualitydevelopment offering from the outset while striving to control timelines and costs, which are commonly seen as opposingforces in the pharmaceutical industry.Conor P. Long* is a senior formulation development scientist and John McQuaid is a technical development manager,both at Almac Pharma Services, 22 Seagoe Industrial Estate, Craigavon, BT63 5QD, UK, tel: +44 (0) 2838 363363, fax+44 (0) 2838 363300, conor.long@almacgroup.com.*To whom all correspondence should be addressed.References1. ICH, Q1A (R2) Stability Testing of New Drug Substances and Products, (ICH, Geneva, Feb. 2003).2. EMA, Note for guidance, Process Validation, CPMP/QWP/848/96, (EMA, London, Sept. 2001).3. ICH, Q8 (R2) Pharmaceutical Development, (ICH, Geneva, Aug, 2009).Pharmaceutical Tech ‐ Sep 10 ‐ Process Opt & Scale‐Up