batch size Archives - European Industrial Pharmacists Group (EIPG)

Real-world evidence for regulatory decision-making

by Giuliana Miglierini Digitalisation is rapidly advancing also in the regulatory field, as a tool to improve the efficiency and accuracy of processes used for the generation and use of data to inform the regulatory decision-making. To this instance, real-world Read more

Webinar: Implementation of Contamination Control Strategy Using the ECA template

The next EIPG webinar will be held in conjunction with PIER and University College Cork on Friday 21st of October 2022 (16.00 CEST), on the implementation of Contamination Control Strategy (CCS) using the ECA* template. This is the second Read more

ACT EU’s Workplan 2022-2026

by Giuliana Miglierini The implementation phase of the Accelerating Clinical Trials in the EU (ACT EU) initiative, launched in January 2022 by the European Commission, started with the publication of the2022-2026 Workplan jointly drafted by the Commission, the European Medicines Read more

Greatest common divisor for product traceability and batch definition in continuous biomanufacturing

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by Giuliana Miglierini

According to the draft ICH Q13 guideline on continuous manufacturing (CM), the definition of batch established by the ICH Q7 is applicable to all modes of CM, and it may refer to the quantity of output or input material, or to the run time at a defined mass flow rate. Other approaches to batch size definition are also possible and have to be justified taking into consideration their scientific rationale and the characteristics of the specific CM process.

The choice of a range for the batch size has to be justified in the regulatory dossier, including the approach used to define it. To this instance, changes in batch size that fall into the defined range can be managed through the Pharmaceutical Quality System, while variations have to be submitted (based on the availability of supporting data) to manage post-approval changes falling outside the approved range. ICH Q13 also asks manufacturers to define a suitable quantitative metric in order to establish batch-to-batch consistency and system robustness.

A possible approach to answer the complex challenges of batch definition in continuous integrated biomanufacturing has been proposed by an article published in the Journal of Chemical Technology and Biotechnology and signed by researchers of the University of Natural Resources and Life Sciences, Vienna, Austria, and the Austrian Centre of Industrial Biotechnology (ACIB). According to the authors, another important issue to be faced in CM is the ability to trace the raw materials through the entire process.

The usefulness of the greatest common divisor (GCD)

The deep understanding of a continuous manufacturing process is fundamental to support its regulatory acceptability; many are the different parameters to be considered to this instance, both regarding the attributes of input materials (e.g., potency, material flow properties) and process conditions (e.g., mass flow rates), in order to achieve the desired comprehension of the process dynamics.

The definition of the residence time distribution (RTD) for each individual unit operation, as well as for the integrated system, can be used to define the time a certain mass or fluid element remains in the continuous process. Challenges in the use of the RTD for batch definition in CM include the possibility to combine different production runs and the possible occurrence of process failures, which may cause great economic losses in case of batches of large dimensions.

The article by Lali et al. describes the use of the greatest common divisor (GCD) as a new parameter that may prove useful to lower “the spread of the RTD through continuous downstream process chains without the need for a redesign of individual unit operations for narrower RTD”.

Semi-continuous purification as the model example

The process used to model the new approach refers to the conventional semi-continuous purification of monoclonal antibodies using staphylococcal Protein A affinity chromatography, a process that may include runs performed on different columns.

The overall modelled process described in the article consists of six different steps, each characterized by a different RTD, starting from the alternating tangential flow filtration of the output material obtained from the upstream steps. A three-column periodic countercurrent chromatography (PCC) was used for protein capture, giving rise to a discrete output flow. This was collected in a surge tank or a continuous stirring tank reactor, from which a continuous outlet flow feeds the next unit operation, consisting of a fully continuous virus inactivation column. The last step of the process included polishing by flow-through chromatography and final concentration and buffer exchange obtained by ultrafiltration and diafiltration. The simulation first focused on each single step, to then consider the RTD of the integrated process.

The criticality assessed by the authors refers to the time-dependency of the RTD for the semicontinuous steps of the modelled process (whereas continuous steps are time-independent).

This is further complicated by the fact “each semicontinuous unit operation adds a periodic behavior to the product concentration profile, which leads to complex periodic behavior in the outlet of the process”.

The great common denominator is the parameter proposed in order to take into due account the time period of the semi-continuous steps, namely the time difference between elution peaks. A GCD of 2.29 hours was identified for the switching of the inlet flow to the next chromatographic column; this value was used to define batch size in comparison to a fixed arbitrary time (2 h). The same approach was also used to define outlet sections of the process and the resulting batches (also by pooling different outlet sections together to form a larger batch).

Based on different sectioning in the inlet, when we track the product profile after each unit operation, we see a chaotic pattern when using an arbitrary time of 2 h. However, when the inlets are sectioned based on the GCD of the period for semi-continuous unit operations, we see a predictable, constant periodic behavior in the outlets”, writes the authors.

According to Lali et al., the synchronisation of the semi-continuous unit operations to achieve the largest possible GCD or the smallest possible lower common multiple is the only requirement for this method to define the batch size; every multiple of the GCD can also be used. Authors provide some examples which may typically occur during the management of a CM process and suggest a possible procedure for the implementation of batch definition based on GCD.

Automation of aseptic manufacturing

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by Giliana Miglierini

The pharmaceutical industry is often the last industrial sector to implement many new manufacturing and methodological procedures. One typical example is Lean production, those concepts were developed in the automotive industry well before their adoption in the pharmaceutical field. The same may also apply to automation: it appears time is now mature to see an increasing role of automated operations in the critical field of aseptic manufacturing, suggests an article by Jennifer Markarian on

The main added value of automation is represented by the possibility to greatly reduce the risk of contamination associated to the presence of human operators in cleanrooms. A goal of high significance for the production of biotech, advanced therapies, which are typically parenterally administered. Automation is already taking place in many downstream processes, for example for fill/finish operations, packaging or warehouse management.

The advantages of the automation of aseptic processes

The biggest challenges engineers face when designing isolated fill lines are fitting the design into a small, enclosed space; achieving good operator ergonomics; and ensuring all systems and penetrations are leak-tight and properly designed for cleanability and [hydrogen peroxide] sterilization,” said Joe Hoff, CEO of robotics manufacturer AST, interviewed by Jennifer Markarian.

The great attention to the development of the Contamination Control Strategy (CCS) – which represents the core of sterile manufacturing, as indicated by the new Annex 1 to GMPs – may benefit from the insertion of robots and other automation technologies within gloveless isolators and other types of closed systems. This passage aims to completely exclude the human presence from the cleanroom and is key to achieve a completely segregated manufacturing environment, thus maximising the reduction of potential risks of contamination.

The new approach supports the pharmaceutical industry also in overcoming the often observed reluctance to innovate manufacturing processes: automation is now widely and positively perceived by regulators, thus contributing to lowering the regulatory risks linked to the submission of variations to the CMC part of the authorisation dossiers. High costs for the transitions to automated manufacturing – that might include the re-design of the facilities and the need to revalidate the processes – still represent significant barriers to the diffusion of these innovative methodologies for pharmaceutical production.

The elimination of human intervention in aseptic process was also a requirement of FDA’s 2004 Guideline on Sterile Drug Products Produced by Aseptic Processing and of the related report on Pharmaceutical CGMPs for the 21st Century: A Risk-Based Approach. According to Morningstar, for example, the FDA has recently granted approval for ADMA Biologics’ in-house aseptic fill-finish machine, an investment aimed to improve gross margins, consistency of supply, cycle times from inventory to production, and control of batch release.

Another advantage recalled by the PharmTech’s article is the availability of highly standardized robotics systems, thus enabling a great reduction of the time needed for setting up the new processes. The qualification of gloves’ use and cleaning procedures, for example, is no longer needed, impacting on another often highly critical step of manufacturing.

Easier training and higher reproducibility of operative tasks are other advantages offered by robots: machines do not need repeated training and testing for verification of the adherence to procedures, for example, thus greatly simplifying the qualification and validation steps required by GMPs. Nevertheless, training of human operators remains critical with respect to the availability of adequate knowledge to operate and control the automated systems, both from the mechanical and electronic point of view.

Possible examples of automation in sterile manufacturing

Robots are today able to perform a great number of complex, repetitive procedures with great precision, for example in the handling of different formats of vials and syringes. Automatic weighing stations are usually present within the isolator, so to weight empty and full vials in order to automatically adjust the filling process.

This may turn useful, for example, with respect to the production of small batches of advanced therapy medicinal products to be used in the field of precision medicine. Robots can also be automatically cleaned and decontaminated along with other contents of the isolator, simplifying the procedures that have to be run between different batches of production and according to the “Cleaning In Place” (CIP) and “Sterilisation In Place” (SIP) methodologies.

The design and mechanical characteristics of the robots (e.g. the use of brushless servomotors) make the process more smooth and reproducible, as mechanical movements are giving rise to a reduced number of particles.

Examples of gloveless fully sealed isolators inclusive of a robotic, GMP compliant arm were already presented in 2015 for the modular small-scale manufacturing of personalised, cytotoxic materials used for clinical trials.

Maintenance of the closed system may be also, at least partly, automated, for example by mean of haptic devices operated by remote to run the procedure the robotic arm needs to perform. Implementation of PAT tools and artificial intelligence algorithms offers opportunities for the continuous monitoring of the machinery, thus preventing malfunctioning and potential failures. The so gathered data may also prove very useful to run simulations of the process and optimization of the operative parameters. Artificial intelligence may be in place to run the automated monitoring and to detect defective finished products.

Automated filling machines allow for a high flexibility of batch’s size, from few hundreds of vials per hour up to some thousands. The transfer of containers along the different stations of the process is also automated. The implementation of this type of processes is usually associated with the use of pre-sterilised, single-use materials automatically inserted within the isolator (e.g. primary containers and closures, beta bags and disposal waste bags).

Automation may also refer to microbial monitoring and particle sampling operations to be run into cleanrooms, in line with the final goal to eliminate the need of human intervention.

Comparison of risks vs manual processes

A comparison of risks relative to various types of aseptic preparation processes typically run within a hospital pharmacy and performed, respectively, using a robot plus peristaltic pump or a manual process was published in 2019 in Pharm. Technol. in Hospital Pharmacy.

Production “on demand” of tailor-made preparations has been identified by authors as the more critical process, for which no significant difference in productivity is present between the manual and automated process. The robotic process proved to be superior for standardised preparations either from ready to use solutions or mixed cycles. A risk analysis run using the Failure Modes Effects and Criticality Analysis (FMECA) showed a lower level of associated risk.