Ajay Pazhayattil*, M.Pharm, DBA, Ajay.Pazhayattil@capcium.com, is vice-president, Scientific and Regulatory Affairs at Capcium.

Ajay Pazhayattil

Soft capsule formulation technology presents an advantage to certain drug substances by overcoming its solubility and physical constraints that may inhibit use of alternate dosage forms. The technology can provide a product differentiation advantage. The relatively established quality-by-design (QbD)-based formulation and process development approaches for soft capsules can result in a lower development cost, potentially reducing time-to-market. The soft capsule formulation technology is an ideal candidate for 505(b)(2) submission pathway, and at the same time best for emerging small-molecule pipelines. The availability of dedicated soft-capsule development and commercialization sites catering to a wide range of batch sizes fitting a spectrum of fill formulation types makes the technology highly accessible. The dosage form can be used to improve bioavailability of Class II and IV drug substances and improve product stability. Emergence of vegetarian capsule shell formulations annuls the dietary concerns stemming from use of animal-based gels. New developments in soft capsule technology allows for considering the dosage delivery system as one of the preferred solid oral formulations that can achieve a high rate of patient compliance.

Submitted: Aug. 3, 2022
Accepted: Sept. 2, 2022

Sam Turner is director of Technology and Development; Naresh Talwar, PhD, is director of Product Development; and Ajay Pazhayattil

, M.Pharm, DBA, Ajay.Pazhayattil@capcium.com, is vice-president, Scientific and Regulatory Affairs; all at Capcium.

To whom all correspondence should be addressed.


Volume 46, Number 11
November 2022
Pages: 32–35

When referring to this article, please cite it as S. Turner, N. Talwar, and A. Pazhayattil, “Novel Formulations and Line Extensions with Soft Capsule Technology," 

Articles

Advancements in soft capsule technology can enable the development of soft capsule formulations, which are becoming the preferred method of oral administration.

Novel Formulations and Line Extensions with Soft Capsule Technology

The pharmaceutical regulatory landscape and pharmaceutical development have been reshaped by International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Harmonised Guidelines. ICH guidelines Q8–Q12 (1–5), and those in development such as ICH Q14 (6), have applied science, risk management, and quality systems to enhance process and product quality. In 2011, FDA’s revised process validation guidance (7) extended ICH’s concepts to pharmaceutical product lifecycle, an approach that is now being applied to cleaning validation and other areas of manufacturing, as well as to clinical trials (8).

Legacy drug products developed before any these guidelines were established, however, have not undergone the same rigor during development as newer products. As a result, companies must categorize legacy products as being in Stage 1, 2, or 3 of the lifecycle and make a data-driven assessment to determine whether remediation or one of the following plans must be developed:

Stage 3b monitoring (for formulations deemed to be robust)

Stage 3a monitoring that calls for additional data to be gathered (in order to determine robustness)

This article proposes an independent structured review process for legacy products—those pharmaceuticals that predate FDA’s updated process validation and quality by design [QbD] guidance. This approach uses data to develop insights into product sustainability and risks. It focuses on the evaluation of intra- and inter-batch variability for uniformity and other critical quality attributes (9–10) and can be applied to small-molecule manufacturing processes.

This review process involves comprehensive process assessment (i.e., historical data review and the gathering of end-to-end manufacturing process data); extensive process monitoring, and characterization exercises that enable lifecycle quality gaps to be addressed.

By allowing sound pharmaceutical manufacturing science principles to be applied, as well as data analysis, this assessment permits legacy product quality to be on par with that of any products developed by applying QbD approaches. Going through this assessment process results in a heightened level of product and process knowledge, while substantially reducing the risk of potential failure.

Based on the 2011 FDA process validation guidance and relevant ICH guidelines, this assessment method requires the use of analytical and interpretive methods based on standard statistical and data visualization tools. The approach allows results to be verified at any time by both product owner and regulator, utilizing standard statistical tools. All data sets prepared, transposed, and analyzed should be subject to data verification procedures to assure data accuracy and integrity.

Documents required for the assessment include the following:

executed batch records for process and packaging

current specifications for in-process, finished product, and stability

analytical test methods and test method validation

analytical results (in-process,
finished product, microbiological, stability)

process performance qualification (PPQ) protocols and reports

The assessment uses current regulatory standards for validation (e.g., from FDA, the European Medicines Agency, Health Canada, and the World Health Organization). It also uses tools and methods such as cause-and-effect Ishikawa diagram, scientific rationale checklists, decision trees, as well as supporting analytical and statistical tools such as Minitab or JMP software.

The assessment focuses on analyzing the information and knowledge gained from the three stages of the process validation lifecycle: process design, process performance qualification, and continued process verification during commercial manufacturing. It is performed in the following two parts:

Part A involves a holistic regulatory compliance review solely focusing on the compliance with the lifecycle-associated regulatory guidance—such as ICH Q8, Q9, and Q10 requirements—and future industry trends—such as ICH Q12, Q13 (11), and Q14—and how they work in tandem

Part B is a diagnostic study of the current process control (a seven-step product assessment process) that results in determining the adequacy of the current control strategy.

Part A. Compliance review: Adherence to FDA process validation and ICH guidelines

A comprehensive compliance review is designed to summarize and confirm compliance with the FDA process validation guidance and ICH and other emerging guidance, based on observation and the available data. The recent ICH Q12 document allows use of lifecycle guidelines for seamless post-approval change management. Adherence to lifecycle guidance, therefore, becomes more important for ongoing operational flexibility and compliance.

Stage I, process design assessment, involves the following steps:

determining the quality target product profile and justification of critical quality attributes and critical process parameters (as per ICH Q8)

applying quality risk management tools (as per ICH Q9)

documenting a critical manufacturing equipment suitability assessment

determining degree, and understanding variability

determining the availability of scale-up trials to determine effects of scale.

Stage 2, process performance qualification review, covers aspects such as:

design considerations of a facility and qualification of utilities and equipment

validation of analytical methods per analytical QbD (ICH Q 14)

utilization of cumulative data from all relevant studies to establish the PPQ manufacturing conditions

number of batches and samples to provide sufficient statistical confidence

application of statistical process performance indicators

clear, documented justification for manufacturing process to be in a state of control.

Stage 3, continued process verification of the product, includes the following:

Continued monitoring and sampling of critical process parameters and quality attributes is at the level established during the process qualification stage until sufficient data is available.

An ongoing program is in place to collect and analyze product and process data, detecting unplanned departures from the process.

The data collection plan and statistical methods to determine process stability and capability is adequate in identifying variability in the process to signal potential process improvements.

Review of failures, non-conformances, and trends during commercial manufacturing are conducted.

Part B. Control strategy review: The seven-step legacy product assessment 

The seven steps involved in Part B are as follows:

Identify sources of variability (Step 1).


Variability is caused by contributing factors associated with materials, methods, machine, measurement, manpower, and mother-nature (12). In pharmaceutical manufacturing, this relates to attributes of material, formulation and associated processing parameters including scale-up factors, processing equipment, test method, personnel, and facility/utilities (

Figure 1. Sources of variability. CQA is critical quality attribute. All figures courtesy of the authors.

The overall critical quality attribute (CQA) variability is a function of individual component variability.

A cause-and-effect technique is utilized to identify underlying factors that could have an impact on CQA that would affect patients (i.e., label claim CQAs). The assessment includes factors and attributes of materials, formulation, processing parameters and scale-up, processing equipment, method and analytical, personnel, and facility/utilities as shown in 

Figure 2. Ishikawa example (material attributes, formulation, process parameter/scale, equipment, method/analytical, personnel, and facility/utilities/environmental). CMA is critical material attribute. CQA is critical quality attribute.


The rationale for the criticality assignment is discussed qualitatively (

). Upon identifying the risk factors through cause-and-effect techniques, a risk-rating decision tree (

) is used to analyze the legacy process upon risk assessment. The criticality assignment will be commensurate with the product/formulation characteristics. In addition to any product-specific CQAs that would affect the patient, the review should, at a minimum, assess the impact of variability factors on CQAs (i.e., assay, uniformity, weight variation, dissolution, and related substances). Process variability may affect the assay of the drug product, thus the impact of sources of variability on assay should be evaluated. Variability in uniformity and weight variation can affect safety and efficacy.

Figure 3. Example section of factors affecting uniformity variability. CQA is critical quality attributes. CPP is critical process parameters. PSD is particle size distribution. Ref. Std. is reference standards.

Formulation, process, and other variables may impact content uniformity and weight variation. Thus, the impact of sources of variability on content uniformity should also be evaluated. Related substances can impact safety and must be controlled based on FDA, compendia, or ICH requirements. Formulation, process, and other variables can impact related substances. Risk assessment must cover all the formulation and process attributes as shown in the below example for uniformity.

The decision tree with a data-driven semi-quantitatively risk-rating system (

) is used to prioritize the unexamined influencing factors labeled in yellow (

) identified for each element. Note that the example provided in Figure 3 pertains to factors affecting one quality attribute only (uniformity). Additional review is warranted when the rating value is higher.


The decision tree-based assessment determines whether the established current control strategy for each attribute is supported by the cumulative documented data from relevant Stage 1 studies (e.g., design of experiments; ranging studies, laboratory or pilot batches). The control strategies established to control and mitigate the identified risk factors are then reviewed to determine the missing parts.

This gap assessment also evaluates whether the established current control strategies are derived based on objective information/rationale and data from original and subsequent process design studies. The need for establishing additional controls, including manufacturing process controls, is determined at the end of the gap assessment.

The gap assessment also determines whether the established current control strategy is supported by the cumulative data from all relevant studies (e.g., designed experiments; laboratory, pilot, and PPQ batches). The control strategies established to control and mitigate the identified risk factors are reviewed to arrive at the conclusions. The residual risks and the need for establishing additional controls, including manufacturing process controls, are then identified to initiate a heightened sampling and testing Stage 3a plan for the product.

Lifecycle review and Stage 3a monitoring batch execution (Step 4).


The lifecycle review should determine, based on available lifecycle data, whether the current process is operating consistently within the originally established control strategy. The control strategy gap assessment step also may identify the need for additional controls or identify gaps in supporting data. The lifecycle review will determine if any commercial product data supports the identified gaps. The data from all three stages of the process validation lifecycle (e.g., design of experiments, manufacturing trials, statistical analysis, annual product review, and commercial batch data) may enable the development of a risk-based control strategy that would suitably minimize sources of variability. Data from previous credible experience with sufficiently similar products and processes may also be utilized to support the control strategy.

Data gaps for legacy products at Stage 1 QbD, Stage 2 equipment/facility/utility qualification and PPQ, and Stage 3 continued process verification require that a sampling and testing plan be developed under a Stage 3a protocol, to further enable prediction with adequate statistically significant data sets.

The type of Stage 3a sampling/testing plan developed is based on the product/process gaps that have been identified. The Stage 3a study focuses on processes that have not been characterized, or for which variability has not been determined. Extensive data must be collected and tested, and a monitoring protocol executed by the technical operations team responsible for Stage 1 lifecycle of the product. The Stage 3a lifecycle monitoring data that are collected supports the identified gaps or enables closure of existing Stage 1 data gaps for the product.

Data-driven, scientifically sound Stage 3a monitoring enables the following:

continual enhancement of the product control strategy

collection and evaluation of information and data, such that sources of variability can be detected, as recommended by FDA process validation guidance

closure of the characterization gaps that were not addressed during legacy product development and to meet QbD standards, per ICH Q8 (R2)

determination of intra and inter-batch uniformity as recommended by FDA process validation guidance

development of a baseline for Stage 3b monitoring as recommended by FDA’s process validation guidance.

ICH Q10 recommends management of product and process knowledge from development through commercialization and until discontinuation. The monitoring batches add to the body of knowledge on the product. FDA’s process validation guidelines allow for use of credible experience with sufficiently similar products, hence a bracketing approach for monitoring may be applied and conclusions can be used for control strategy enhancement of similar products.

The Stage 3a data collection protocol (13) developed after gap assessment enables in-depth statistical assessment of the legacy product. The Stage 3a assessment will also provide a baseline for continual Stage 3b program for the product. The approach supports the organization’s ability to make science- and risk-based continual process improvement decisions for the product, as well as to identify areas where QbD development should focus for similar manufacturing processes or products. The Stage 3a monitoring measures are developed in line with upcoming revisions to ICH Q2/Q14 and the proposed I General Chapter <1220> framework for analytical QbD, which requires identification of adverse trends, allowing proactive measures and facilitation of continued improvements and change control through continued monitoring.


The extensive data collection step includes batch data from development, process performance qualification, and commercial manufacturing. The new database will include potential variables that can impact the CQAs. This will include, but will not be limited to, API and excipient material attributes, process parameters, equipment parameters, hold times, operational variables, operation/analytical personnel, instrumentation, documented weights/time, facility/utility parameters, and incidents. This step will involve extensive data set collection, data set review, and data preparation for the subsequent statistical analysis.

As a result, the database must be developed so that it maximizes the utilization of existing product/process data for multivariate statistical analysis such as correlations. The database is created for variables that can impact CQAs. The database is utilized as a starting point for Stage 3b trending and ongoing signal detection of the legacy formulations that are determined to be robust post assessment.


The data collected from Stage 3a heightened sampling and the historical batches are statistically analyzed to identify patterns and insights into the impact of potential sources of variability on the CQAs. Statistical software tools such as Minitab and JMP may be utilized. The analysis determines the adequacy and suitability of the collected data sets to support the current control strategy. The analysis first attempts to determine whether the original control strategy developed for the legacy product adequately meets the current standards.

Enhancement requirements are then determined, based on the correlations and trends observed from batch data across commercial manufacturing. This step includes an assessment of within-batch and between-batch variability, and the associated controls available to minimize the variability.

The statistical assessment can identify patterns and insights showing the impact of potential sources of variability on the CQAs. The correlation between the uniformity results, such as blend uniformity (BU) or content uniformity (CU), can suggest whether adequate mixing is happening and therefore justify continuing stratified CU sampling in place of error-prone and disruptive BU sampling. A further statistical criterion can also be developed using the assessment: removing the CU sampling once adequate uniformity data have been generated. To augment the historical data, for example, the variability of uniformity is determined with Stage 3a monitoring batch data generated (14). Correlation of process parameters are conducted to determine the degree of their influence on the CQAs at manufacturing stages.

The statistical assessment tries to address each of the attribute gaps identified though Stage 3a heightened monitoring and analysis. The rating table is then updated based on the newly identified and generated data, as shown in 


Interpretation generates actionable recommendations based on the statistical analysis. The data-driven estimations allow for in-depth product/process understanding, proving product robustness and are in line with regulators’ expectations that science and data be used for product and process decision making. The determination is based on data signals identified through assessment of the historical data and the generated heightened results from Stage 3a monitoring batches.

When adequate data are available, the signals seen (i.e., CQAs, key performance parameters, and CPPs), can be categorized based on risk. All signals are not created equal, and different action plans are required for the yellow flags. The assessment suggests actions based on practical relevance and the statistical strength of the signal (15). As a result, this review process enables organizations to make data-driven justification on the product sustainability while implementing continuous improvement remediation for legacy drug products.

The protocol-driven legacy product assessment closely dissects the manufacturing process to determine the current state of control, to comprehend and address Stage 3a gaps for completeness of validation status. The exercise also determines the process robustness, allowing for business decision making. The review provides clear data-driven input for one of three actions:

developing a Stage 3b monitoring program, in cases where product has been determined to be robust,

developing a Stage 1 continuous improvement plan, or

A data-driven strategy can assess the quality of legacy drugs developed before 2011 process-validation requirements were established.

Assessing Legacy Drug Quality 

	The continuous manufacturing  concept originated from pig iron production using a blast furnace, where the process operates for multiple years without shutdown. The approach is followed in production processes for oil refining, chemicals, synthetic fibers, fertilizers, power generation, natural gas, and waste-water treatment. The application of continuous manufacturing in pharmaceutical and biopharmaceutical manufacturing has progressed in the past decade with adoption of the same approach as leading industries. So far, some pharmaceutical products are manufactured and marketed using this concept, notably by Janssen and Vertex (1).  

	The options of an in-line process analytical testing approach for process controls and automation in processing is providing some level of semi-continuous manufacturing, resulting in reduced waste and downtime with increased gross margin and profitability. The conversion of batch processes to continuous manufacturing is the future of the pharmaceutical industry, employing the continuous flow, end-to-end integration of manufacturing sub-processes with a significant level of control strategies. The benefits of continuous manufacturing over traditional batch processes are evident, but the implementation is still at snail speed (2). The pharmaceutical industry is acknowledging continuous manufacturing as an option to improve and sustain manufacturing operations. The pharmaceutical industry is making efforts by internally assessing, developing, and implementing semi-continuous manufacturing processes to improve manufacturing efficiencies.

	For a successful integration of continuous manufacturing, an orchestrated coordination amongst several industry partners is essential. The three major components for successful implementation are equipment and process analytical tool manufacturers, API and excipient manufacturers with a focus on process controls in providing consistent supply, and the finished product manufacturing facility in developing strategies for integration of techniques for implementation. The integration of a segmented batch processing and testing to a single flow is a major task in achieving continuous operations. As such, processes have inbuilt significant lag-time between operations from start to end and delivering to patients, which can range from three to six months. The reconfiguration of equipment to control the quality of the API and excipient can improve the effective asset utilization (EAU) by having a dedicated production line for large-volume product manufacturing. To achieve such an objective, the organizational mindset, regulation, and technology balance is a challenge in the pharmaceutical industry. The success lies in achieving the concept of raw material input into the system at the beginning of the process and discharging all at once as finished product sometime afterwards. The key to this process is that the finished product has uniform characteristics throughout the processing and meets set quality standards (3).

	Regulatory challenges of continuous manufacturing

	The level of regulatory submission requirements and inconsistent requirements around the world makes it difficult for the pharmaceutical industry to make any significant changes to an approved process, as any delay results in loss of market share in a highly competitive environment. There are minimal regulations or industry guidance documents on continuous manufacturing, and nothing is prohibiting implementation (4). Regulatory agencies do recognize that continuous manufacturing is a modern manufacturing approach with the potential to improve the assurance of quality and consistency of drugs, enabling quality to be directly built into process following quality-by-design (QbD) efforts. Because a true continuous manufacturing process is not feasible, producing a finished product batch in a defined production time period based on variations due to different lots of API or excipient as input or equipment cycling capability utilizing a chain of unit operations in continuous mode is an acceptable approach. It will require a significant reconsideration of the control strategy and well-defined methods employed to assure uniform characteristics and quality within specified limits for finished products. These goals can be achieved by well-defined characterization of input materials, selection of appropriate in-process sampling frequency of testing, setting of appropriate acceptance criteria, and statistical process controls with a feedback mechanism.

	Factors in processing, such as equipment, cycling capability, and interaction of unit operations, need to be controlled. FDA supports the implementation of continuous manufacturing using a science- and risk-based approach and recommends early and frequent discussion with the agency before implementation of manufacturing technology. FDA has developed a program to help industry through its new guidance on advancement of emerging technology applications to modernize pharmaceutical manufacturing base (5). The regulatory requirement for the quality of the product remains the same as in batch processing, but for continuous manufacturing, the sampling plan needs to be redefined, deviations need to be handled differently, variability should be controlled, manufacturing changes need proper management, and the rationale for testing of a continuous batch must be defined in comparison to the traditional model (6).

	Barriers to implement continuous manufacturing

	The current manufacturing process followed by most of the pharmaceutical industry has many unit operations performed in segregated locations to form a batch and has limitations in flexibility, as the process controls are not dynamic. Performing continuous manufacturing based on QbD principles with reorganized manufacturing equipment and testing instruments and improved technical skills of the professionals who run these processes is a challenging task. The process of switching from a traditional manufacturing process stream to continuous manufacturing requires a commitment for capital investment to upgrade manufacturing equipment and install appropriate process analytical tools to control the process and develop control strategies to ensure equivalency of the two processes (6). A decision to switch to continuous manufacturing is still a big question for users as risk of cross contamination, cleaning frequency, and equipment breakdown need to be addressed to a satisfactory level. The users are concerned that not all equipment can perform continuously. For instance, tablet compression tooling is constructed with stainless-steel of different grades that are not meant for continuous use because the force applied in the process generates stress on the stainless-steel, which results in wear and tear that can affect the product physical quality attributes. The product quality expectation remains the same for continuous manufacturing as for traditional batch manufacturing. The continuous manufacturing risk assessment requires a good understanding of the process dynamics, process conditions, and material attributes to manage the risk. The continuous manufacturing process requires use of model-based control, multivariate monitoring, and large database management for analysis in real time to support real-time release testing (RTRT). The communication of residual level of risk by acquiring effective control strategy to the risk assessment for product and process development, as well as product lifecycle management, requires linking of the system for effective feedback.

	Continuous manufacturing requires an understanding of the process and dynamics impacted by interaction with process parameters and material attributes. The process dynamics require a level of controls including raw material controls, process monitoring, and detecting and handling deviation in real time to support a real-time release testing (7). The batch definition and control strategy verification should be defined up front. Process understanding of the continuous manufacturing should be established based on design space and design of experiment (DoE) at development stage. A predictive model should be built with simulation capability to define action limits and an approach to manage process deviation with adjustment of process parameters. The process dynamics controls characterized by residence time distribution help identify the failure mode or deviation and changes in set-point impacting the start-up and shut-down on material quality. For an effective control strategy to keep continuous manufacturing in a state of control, an appropriate approach for individual process and product as assessed by risk analysis is required. The control strategy should provide consistent assurance of process performance and quality and should be designed to mitigate product quality risk in response to deviation over the time of continuous manufacturing.

	Any given process uses multiple raw material lots in a batch, which require a process to establish traceability in a finished product. For a smooth, continuous manufacturing process, particle size distribution and density of raw material should be controlled such that they do not affect the flow behavior, mixing of ingredients, and dosage uniformity. The monitoring process will require the appropriate use of process analytical tools and modeling capability. The decision to select a tool should consider interference generated in testing due to flow of powder, time of data acquisition with respect to flow, and locations and number of installed probes or sensors at key sampling points. There should be a backup plan for each sensor or probe in case of failure during the process, which can affect the process monitoring and data acquisition. The acquired data are critical as they support the real-time monitoring and multivariate analysis to keep the process in a controlled state.

	The cost of replacing traditional batch manufacturing with continuous manufacturing could be a significant investment; however, it offers cost savings subsequently by reducing waste, loss of batches due to failure, cost of testing, and inventory overhead cost. It is a smart move if the pharmaceutical industry starts with a hybrid continuous manufacturing approach to keep the initial investment cost lower and start generating gains. These gains can offset the cost of equipment and instrumentation faster and provide more confidence in the newer technology. The manufacturing cost of products varies with the type of equipment used.Integrating the existing equipment on site with upgrades to achieve hybrid continuous manufacturing can be a cost-effective approach, rather than building a complete integrated system to replace existing systems. It is more appropriate to have a complete continuous manufacturing system to start in R&D environment, which gives more opportunity to master the continuous manufacturing technology to implement subsequently in a commercial environment with more success. Future manufacturing processes involving continuous manufacturing with increased processing speed and controlled and monitored quality parameters, which lowers  the cost of manufacturing, gives a distinct advantage to generic-drug manufacturers.

Advancement of Emerging Technology Applications to Modernize the Pharmaceutical Manufacturing Base Guidance for Industry

International Symposium on Continuous Manufacturing of Pharmaceuticals: Implementation, Technology & Regulatory

The pharmaceutical industry is making efforts by internally assessing, developing, and implementing semi-continuous manufacturing processes to improve manufacturing efficiencies. 

Continuous Manufacturing: A Generic Industry Perspective

FDA’s focus on the quality culture and its request for quality metrics may ensure a successful company-CMO relationship. FDA’s draft guidance 

, Rev 1 (1) is now accessible after extensive deliberation and comments from the industry. 

 that measurements to evaluate quality of facilities and processes should aid the agency’s site inspection efforts (2). The guidance is expected to ensure that drug products are continually manufactured under high quality standards. In addition, it encourages manufacturers to self-monitor using robust quality measurements. 

	FDA recognizes the trends in current pharmaceutical business models with multiple contract manufacturing organizations (CMOs) involved in commercial manufacturing and supply-chain operations. FDA 

 (3) justify the need for clearly defining cGMP-related roles between product owners and contract facilities. The final FDA industry guidance on Contract Manufacturing Agreements for Drugs: Quality Agreements was issued in November 2016 (4).

 at a healthy rate of 7.5% annually; many of them converting from CMOs to contract development and manufacturing organizations (CDMOs) (5). FDA recognizes that CDMOs can enhance speed, efficiency, technical expertise, scale-up knowledge, and expertise (4). The guidance will ultimately benefit the patients who are prescribed drug products manufactured at the CMO sites under the agreement. Illustrative cases discussed in the guidance are typical with CMO projects. The agency considers the product owner ultimately responsible for ensuring their product, a human, veterinary, biological, biotechnology drug product, a drug substance, an in-process material or a combination product, is made in compliance to cGMPs even though a quality agreement may assign specific manufacturing activity to the CMOs. The European Union guidance also entails that outsourcing activities should be defined and a contract written to avoid misinterpretations that may result in poor quality (6). 

	International Council for Harmonization (ICH) Q10 and Pharmaceutical Inspection Co-operation Scheme (PIC/S) 

Good Manufacturing Practice for Medicinal Products

 require written agreement between contract giver and acceptor for outsourced pharmaceutical activities (7, 8). A written quality agreement is, therefore, a collective requirement in the industry, developed in consultation with a cross-functional team, ensuring partners operate with the same reference guidance (9). The collaborative development of the agreement provides a better sense of the CMO quality systems and quality focus, which will potentially pave the way to improved business relationships. Lack of clarity on cGMP responsibilities can put both product owners and contract facilities at risk. The resulting scenarios such as non-compliance, broken contracts, drug product shortage, and loss of market have a direct business impact for both parties. 

 guidance was issued in July 2015. This guidance document explained the intent of FDA’s Center for Drug Evaluation and Research (CDER) and Center for Biologics Evaluation and Research (CBER) to collect data and use 

 to ensure continuous improvement and innovation in the industry (10). FDA’s directors acknowledged that the guidance is an important first step on a shared industry/regulator effort toward further improving drug quality (2). Webinars and public consultations were held to communicate the key points about the guidance’s intent, namely encouraging the industry in implementing good quality management systems, improving regulators’ risk-based inspection management, and to better forecast potential for shortages in drug supply chain. FDA supplemented the draft with a technical conformance guide, providing the industry with specific technical details on the quality metric data submission requirements (11). The agency has considered 

 (12) on the first draft. The revised guidance post consultation now allows firms to report quality metrics by product or by site. FDA has also revised the requirements for data reporting and has added some examples. The initial intent is to have the reporting voluntary. One notable exclusion in the current revision is that of the Optional Metrics Related to Quality Culture and Process Capability/Performance. FDA has also incorporated a provision for firms to submit a 300-word comment to provide further explanation on the submitted data, and to elaborate on further plans for improvement. 

	The quality metrics guidance is a result of a carefully considered long-term quality improvement strategy from FDA. This guidance, as discussed in the background section, continues the efforts initiated by FDA since development of the final report on Pharmaceutical cGMPs for the 21st Century-A Risk Based Approach (13) from September 2004. The report identifies that product quality and productivity improvement share a common element (i.e., reduction of variability by virtue of process understanding though the lifecycle). It reinforces that reducing variability provides positive outcomes for both patients and the industry. 

	One reportable metric is the lot acceptance rate (LAR). This metric is an effective tool for estimating the cost of quality for manufacturing sites. It would reflect the real effectiveness of unit operations or continuous manufacturing lines, where current measurement norms focus primarily on productivity numbers. The LAR metric allows for senior management decision making and continuous improvement opportunities for products.  

	The second reportable metric is product quality complaint rate (PQCR), ensuring patient or customer focus. Regulator expectation is to ensure timely and effective corrective actions and preventive actions (CAPA) are taken when customer complaints arise. With an improved PQCR, the industry will achieve considerable patient confidence and professional goodwill, a desired outcome for any pharmaceutical organization. 

	The guidance indicates that invalidated out-of-specification rate (IOOSR) is an indicator of the laboratory operations effectiveness. This includes IOOSR from both routine release and stability testing. Analytical variability is one of the major sources of variability for test results (14, 15). Routine calculation and reporting of IOOSR will provide a threshold for further investigation into the root cause of such unintended analytical variability (e.g., analyst, instrument, test method, reagents, standards). The metric can be incorporated as part of the analytical quality by design (AQbD) continued monitoring stage (16). Commercial laboratory operations are equally critical as manufacturing operations. The index provides senior management clear insights into the true laboratory operations efficiency; highlighting the level of analyst expertise, need for instrumentation upgrades, performance of external laboratories (if any), and capturing analytical method robustness. 

	FDA intends to publish a list of establishments that voluntarily report quality metrics. By doing so, the agency would be effectively propelling industry participation in regularly reporting metrics by publicizing the efforts put in by those organizations to key stakeholders. The agency engagements during the voluntary reporting phase is an opportunity for organizations to demonstrate effectiveness of their quality management systems, product/process control strategies, and cGMP compliance. The guidance itself identifies that the list may be useful for identifying contract manufacturers and for healthcare purchasing/sourcing groups. The reporting organizations will be categorized and identified into top and mid tiers such that the stakeholders can recognize the level of data being submitted. 

	With the recent focus in quality culture and pharmaceutical quality systems by regulators, the industry is expected to ensure better quality and predictability, driving businesses to succeed. The 

Submission of Quality Metrics and Contract Manufacturing Agreements for Drugs: Quality Agreements

 guidance documents are rightly aligned with this goal. FDA may request information such as product recall and customer complains as per Section 704 of the Food, Drug, and Cosmetic (FD&C) Act (17). Also, 21 

) Part 211 (a) requires the company’s quality unit to be responsible for products manufactured by a CMO (18). The business benefits of effectively implementing the guidance requirement include prevention of manufacturing issues, prediction of supply disruptions, and development of a sustainable CMO relationship in addition to the non-tangible element of stakeholder confidence and goodwill. The new quality standard guidance’s developed by FDA will improve regulator-industry transparency. It will reduce process variability and encourage use of the best technology and innovation in the pharmaceutical industry. Adoption of guidance requirements will result in well-established quality systems through the manufacturing sites; potentially reducing the regulators resources required for monitoring (e.g., reduced site audit cycle). Both the patient and pharmaceutical companies are positioned to significantly benefit from the current regulatory strategies. 

Guidance for Industry, Submission of Quality Metrics Data, Rev 1, Draft Guidance

 (CDER, Silver Spring, MD, November 2016).
	2. A. Boam and M. Malarkey, “‘Quality Metrics’: FDA’s plan for a key set of measurements to help ensure manufactures are producing quality medications,” 

, July 2015.
	3. FDA Enforcement Actions, 

Guidance for Industry, Contract Manufacturing Agreements for Drugs: Quality Agreements

 (CDER, Silver Spring, MD, November 2016).
	5. N. Walker, 

(January 2016).
	6. European Commission, 

Good Manufacturing Practice for Medicinal Products for Human and Veterinary Use

, Volume 4, Chapter 7 (European Commission, January 2013).
	7. ICH, Q10 

Pharmaceutical Quality System, Quality Guidelines

, Part 1, Chapter 7 (PIC/S, January 2017).
	9. A. Pazhayattil, 

 (May 2008).
	10. FDA, Quality Metrics for Drug Manufacturing, cGMP’s for Drugs: Reports, Guidances and Additional Information, 

Technical Specification Document, Quality Metrics Technical Conformance Guide

Technical Specification Document, Comments-Docket ID: FDA-2012-D-1595

, FDA.
	13. FDA, Final Report, Pharmaceutical cGMPs for the 21st Century-A Risk-Based Approach (FDA, September 2004).
	14. B. Nunnally, 

 78-88 (Summer 2009).
	15. N. Sayeed-Desta, et al., “Assessment Methodology for Process Validation Lifecycle Stage 3A,” 

: 1-6 (2016).
	16. G. Martin, et al., “Lifecycle Management of Analytical Procedures: Method Development, Procedure Performance Qualification, and Procedure Performance Verification,” USP Stimuli Article. 
	17. FDA, 

Guidance for Industry, FDA Records Access Authority Under Sections 414 and 704 of the Federal Food, Drug, & Cosmetic Act

 Chapter 1, Subchapter C, Part 211, Subpart B, Section 211.22 (a), April 2016. 


	Vol. 41, No. 2
	Supplement
	February 2017
	Pages: s40-s42

	When referring to this article, please cite it as N. Sayeed-Desta, A. Pazhayattil, and J. Spes, "Quality Metrics and CMO Agreement," Supplement to 

FDA’s focus on the quality culture and its request for quality metrics may ensure a successful company-CMO relationship.

Quality Metrics and CMO Agreement

Establishing a commercial batch size is a crucial decision in pharmaceutical operations. It is influenced by the type of manufacturing technology being used, regulatory filing commitments, supply chain demand, and operational planning factors. To understand batch size, the differences between “batch,” “continuous,” “semi-batch,” and “semi-continuous” manufacturing must first be defined (1). In batch manufacturing, all materials are charged before the start of processing and discharged at the end of processing. Examples include bin blending and lyophilization. Continuous manufacturing involves materials simultaneously charged and discharged from the process; examples are found in petroleum refining, food processing, and, more recently, in pharmaceutical manufacturing. Other manufacturing variations include semi-batch (i.e., fed-batch) manufacturing, as found in wet granulation, tablet coating, and fermentation, in which materials are added during processing and discharged at the end of processing. In semi-continuous manufacturing, materials are simultaneously charged and discharged, but for a discrete time period. Examples include roller compaction, tablet compression, and encapsulation. For semi-continuous manufacturing processes, the process output is independent of batch size as long as the material input is set up to produce consistent output as per the controlled process. Therefore, a fixed batch size is not required for semi-continuous manufacturing processes.

	Regulatory guidances provide clarity on batch requirements. FDA’s 

Guidance for Industry: Immediate Release Solid Dosage Forms Scale Up and Post Approval Changes 

(2) outlines the  maximum allowable batch size as 10 times the size of the pilot/bio batch. FDA’s cGMP guidance 

 (3) provides clarity on batch and lot definitions. A European Medicines Agency (EMA) draft guideline on manufacture of finished dosage forms (4) notes that the batch size for a product to be marketed should normally be compatible with qualified equipment. It should be sufficient enough to allow process capability to be established. For example, a commercial batch size for solid oral dosage forms should be at least 100,000 units unless justification is provided. The equipment capacity and maximum quantity allowed determines the maximum batch size.

	Operational efficiency increases with use of smaller batch sizes for such semi-continuous operations as compaction, tablet compression, and encapsulation. Taking the product out faster achieves higher asset and line capacity utilization. Having a minimum possible batch size can also increase supply chain velocity. Examples of unit operations that are not affected by having non-standard batch size include: particle size reduction, fluid-bed drying, roller compaction, extrusion, tablet compression, and encapsulation (5).

	FDA’s 2015 guidance on emerging technology (6) promotes adoption of continuous manufacturing technology, and the recent approval of tablet production on a Janssen continuous manufacturing line highlights regulators’ acceptance of continuous manufacturing, as long as product quality is maintained. Traditional tablet compression and encapsulation operations are semi-continuous manufacturing operations, which implies that the same duration for the tablet compression or encapsulation run should yield similar quality product. The following sections discuss approaches for determining and provide justification for an appropriate minimum batch size for semi-continuous dosing unit operations, such as tableting and encapsulation.

	Control strategies for tablet compression and encapsulation manufacturing include in-process quality control specifications for such quality attributes as tablet weight, hardness, thickness, friability, and disintegration time. These attributes should be measured frequently (i.e., at intervals of 15 or 30 min.) throughout the run. The quality attributes are guaranteed through the compression run by force control established during product set-up. Any tablet compressed beyond the upper and lower limits of the set main compression forces will be rejected. For both tablets and capsules, 100% or frequent weight sorting/check are also used to assure product quality over the entire run.

	Powders, however, exhibit complex behavior during processing. The internal dynamics of powder systems under dosing are specific to a product. The start and end fraction samples are considered “worst case” and are indicative of segregation issues that may occur during a routine manufacturing process. Solid-dose tablet compression/encapsulation processes do not typically include purging as a control strategy to reduce segregation risk. Therefore, along with the stratified process validation (PV) Stage 1 content uniformity data, the content uniformity results from PV Stage 2 batches provide a clear indication of homogeneity. Typically, stratified samples are collected from across the dosing run from 20 locations to verify consistency and reproducibility throughout the batch, and replicate samples are tested for uniformity per location. The unit dose is also tested to confirm product-specific physical and chemical quality attributes (see 

). In addition to content uniformity, active ingredient homogeneity is evaluated in powder blends (i.e., blend uniformity) in all products at each batch size and bin size at Stage 2. The drug-content uniformity is determined by blend uniformity, stratified dosage uniformity, and assay. In-vitro performance of the dosage form is assured by dissolution testing. Based on the assurance of multi-component uniformity and in-vitro performance, it can be concluded that a semi-continuous tablet compression/encapsulation process is capable of producing consistent unit doses with any dosing batch size.  

			Table I. Testing conducted during Stage 2 Process Performance Qualification Studies.			

					Appearance, individual tablet weight, weight of 10 tablets, hardness, thickness, friability, disintegration 

					Stratified dosage uniformity (minimum of 20 samples), assay, dissolution profile, product-specific certificate of analysis and submission requirements. 

					Appearance, individual capsule weight, weight of 10 capsules 

	In a systematic approach to determine the minimum tablet compression batch size, the following factors can be considered: tablet weight, API percentage, manufacturing process, and set-up waste from similar products and/or processes. 

 The total run time for the dosing batch is based on the target tablet/capsule weight. For any assigned minimum batch size to be compressed, the products with minimum tablet weight will take more time to compress than products with maximum tablet weight. As per the Stage 2 stratified sampling, a total of 20 locations are sampled during the dosing run. Therefore, use of a minimum batch size can be evaluated with products of low and high unit weights. 

Robustness of unit dose weight uniformity.

 Tablet weight is typically monitored using force control mechanism throughout the tablet compression process, where rejection limits (S+ and S- rejection forces and M+ and M- adjustment forces) are defined. Any tablet outside the acceptable limits is rejected. Forces are specific to a tablet press model, although the concept is applied for all. Tablets or filled capsules can be subjected to weight check as well. Normally, higher tolerance limits for weight checking are maintained, which are tighter than the values in the master document.

 Low API % w/w in the formulation is considered a high risk factor due to potential segregation after final blend and a high risk for content uniformity. Therefore, a product with low unit weight and low API content can be selected to further evaluate the minimum tableting batch size. This factor can also be used to determine variability within a batch for a particular product. Data of comparable campaigns can provide a reasonable indication of the anticipated batch-to-batch variability. The magnitude of the batch-to-batch variability can be dependent on several different factors; one factor in particular is the API content or product label claim. To gain an understanding of batch-to-batch variability, historical dosage uniformity and dissolution data from a large number of campaigns (e.g., more than 200 Stage 2 campaigns encompassing over 700 individual batches and approximately 100 distinct molecules) can be compiled. The batch-to-batch variability can be extracted from each campaign by separating the intra-batch variability from the overall total campaign variability. The data can be then segregated and analyzed to assess the relative influence of several factors (e.g., manufacturing process, strength, batch size). Product active content or strength has a high probability to be a factor that correlates to batch-to-batch variability. Typically, batch-to-batch variability for both content uniformity (CU) and dissolution significantly increases for low-strength products (<1 mg). Thus, one relevant factor for batch-to-batch variability is active content or strength (7).

Another factor that affects the minimum required dosing batch size is the manufacturing process. For example, the potential for segregation is minimal with hot-melt extrusion processes, in which the API is formed into agglomerates by melting of liquid binders. A direct mix manufacturing process has a higher potential for API segregation than a compacted process wherein the API is locked into granules. Therefore, a product with low API content and low unit weight from a direct-mix manufacturing process and high API content and high unit weight from a granulation manufacturing process may be considered as extreme cases for the assessment. 

Start-up waste from similar product/process.

 Start-up waste is generated due to initial adjustments to achieve the in-process quality attributes (i.e., tablet weight, hardness, thickness, friability, and disintegration) prior to starting the compression process. Process performance qualification samples are collected after start up, where all the in-process specifications are achieved at an established consistent tablet-press speed. In-process quality attributes are maintained by the force-control mechanism throughout the compression process. Because the tablets manufactured as start-up waste are disposed of, the start-up waste generated should be considered for the minimum batch quantity requirement.

	Based on these four factors, the product portfolio can be assessed and summarized as shown in 

			Table II. Summary of product information. CU is content uniformity, CpK is process capability, and Pa is acceptance probability*.			

	The assessment is based on an underlying assumption that the equipment qualification and calibration tolerances of key parameters were considered, and that a compliant quality management system is in place to detect any adverse trends or deviations. Additional factors can be added to the assessment. A minimum dosing batch size can be assigned based on the factors (tablet weight, API percentage, manufacturing process, start-up waste) as well as equipment capacity/capability (e.g., hopper feeder, powder bowl capacities) and regulatory market requirements, with the underlying assumption that the product has an established control strategy. Application of a minimum batch size based on the described strategy is of low risk for the product with a defined manufacturing process from Stage 1: process design, confirmed at Stage 2: process performance qualification, continually monitored as part of Stage 3: continued process verification, and with a control strategy established, including in-process controls. Jana Spes, vice-president of Technical Operations, Apotex, summarizes that variable batch sizes for semi-continuous processes such as tablet compression and encapsulation, without compromising product quality, allows for operational flexibility and efficiency. With regulatory acceptance of continuous manufacturing technologies and the associated minimal impact of batch sizes, it is time to reconsider the approach to batch-size determination for traditional semi-continuous manufacturing processes as well.

		S. Chatterjee, “FDA Perspective on Continuous Manufacturing,” IFPAC Annual Meeting (Baltimore, MD, January 2012).	

Guidance for Industry: Immediate Release Solid Dosage Forms Scale Up and Post Approval Changes

Draft guideline on manufacture of the finished dosage form

Draft Guidance for Industry: Advancement of Emerging Technology Applications to Modernize the Pharmaceutical Manufacturing Base


	Naheed Sayeed-Desta is manager, Process Validation, 

; Ajay Pazhayattil is associate director, Process Validation; and Srihari Chowdari is manager, Technical Operations, all at Apotex.

 Supplement
	September 2016
	Pages: s16–s19

:
	When referring to this article, please cite it as N. Sayeed-Desta, A. Pazhayattil, and S. Chowdari, "Determining Minimum Batch Size," 

Flexible batch sizes for semi-continuous unit operations, such as tableting and encapsulation, can improve efficiency while maintaining quality.