The US Food and Drug Administration has made some excellent efforts in recent years to modernize its manufacturing regulation and guidance. However, its recent decision to move forward with its direct final rule exempting most Phase 1 investigational drugs from complying with the regulatory cGMPs is not one of them. The rule casts too wide a net, capturing both sensible changes and highly risky ones. 

Many aspects of this rule (published in the 

 on July 15) and the accompanying guidance (

) simply codify what has already been agency practice for years. FDA leaders, speaking at conferences, have often said that GMP requirements should be ramped up as a product progresses through clinical development, and agency enforcement has reflected that thinking.   

Indeed, many industry experts agree that some cGMP rules in 21 

 210 and 211 may not be necessary or appropriate in early development, such as the requirement for separate packaging and production areas, as the rule mentions. It also makes sense to acknowledge, for example, that a manufacturing process will not be fully validated at Phase 1.  

The problem, however, is that in seeking to make some appropriate GMP adaptations for early development, the agency has gone too far by relaxing quality control rules that are critical for ensuring drug safety. 

The most troublesome change is the way the guidance waters down the importance of the quality control function. The document goes so far as to say that in "very limited circumstances . . . all QC functions may be performed by the same individual(s) performing manufacturing." This is alarming. Even in publishing, where the consequences of an error are much lower, we don't proofread our own work. 

Also missing are other important provisions from part 211, such as the requirements to validate autoclave sterilization (a critical step to prevent patient exposure to nonsterile products) or to conduct identity checks on raw materials. Have we already forgotten the heparin crisis of earlier this year, in which at least 149 people died?  

In making important decisions, the FDA normally evaluates the balance of risks and benefits. So who will benefit from the modified rules?  

Large organizations, who already have robust quality systems in place, will most likely continue to follow full GMPs in Phase 1. So will many smaller companies, even if only for efficiency: Many firms manufacture a single lot for Phases 1 and 2, and because Phase 2 drugs still must be manufactured according to full GMPs, those combined lots also must follow the strict rules. So not much will change for these companies, either. 

Thus, the primary beneficiaries of the change will be start-ups, spun out of university research environments, that are bringing their first drugs to the clinic. Some of these companies will even be working in experimental areas like gene therapy. Those are precisely the firms who are least likely to know what they are doing in terms of GMPs and quality control.  

Annex 13 of the European GMP guidelines also acknowledges that good manufacturing practices should be "appropriate to the stage of development of the product." Yet in contrast to the new FDA documents, the European guidance emphasizes that because less is known about drugs in early development, quality systems are even more important at this stage. The FDA rules should do the same. 

Articles

Columns and Departments

GMPs in Phase 1 is more important now than ever before.

GMPs in Phase 1: Take 2

This paper discusses an approach for the establishment and lifecycle management of biological and biotechnology-derived product specifications. The views presented are consistent with the concept of Quality by Design (QbD), in which critical quality attributes (CQAs) are distinguished from parameters used to monitor process consistency. Specifications and the corresponding limits as applied to CQAs serve to ensure that the product is fit for use, whereas control limits are a manufacturer’s tool to monitor shifts and trends in the manufacturing process. In the current paradigm, inappropriate use of specifications creates a disincentive for continuous process understanding; more suitable approaches to analyzing development and manufacturing data are discussed. Statistical methods are presented for deriving and interpreting data against specifications that better manage the risk to the customer of receiving product with diminished safety or efficacy, as well as the risk to the manufacturer of earmarking a satisfactory lot as unacceptable. The recommendations are presented as a rational approach to setting and maintaining specifications, while recognizing that their applicability may not be suitable in all cases, given the heterogeneity of types of regulated biological and biotechnology-derived products and their unique challenges.

The purpose of this paper, which has been developed by the Working Group on Specifications and Formulations of the Pharmaceutical Research and Manufacturers of America (PhRMA) Biologics and Biotechnology Leadership Committee, is to provide guidance on a lifecycle approach to setting global specifications for biological and biotechnology-derived products. In the pharmaceutical industry, specifications are legally binding criteria that a product must meet in order to be marketed. They ensure the consistency and quality of the product and help ensure that it is safe and efficacious over the shelf life of the product. Specifications evolve during product development and ideally should embrace future process capability. This is true for biological and biotechnology-derived products for which there may be limited experience at the time of regulatory filings (including the marketing application), and for which early commercial production often is necessary to gain a better understanding of product quality attributes, methods, and limits.  

Parts 1 and 2 of this article, published in the June and July issues of 

 included four sections: Terminology; Stages of the Lifecycle of a Product; Components of a Biological and Biotechnology Product Specification; and Current Issues Related to the Development of Specifications. This final section describes a suggested approach for developing and maintaining a total quality system.  

A SUGGESTED APPROACH FOR DEVELOPING AND MAINTAINING A TOTAL QUALITY SYSTEM

A rational approach to setting and maintaining specifications should incorporate the following fundamental principles:  

A total quality system should seek to minimize risks to the customer and manufacturer alike. When in conflict, however, practice should err on the side of minimizing customer risk.

If possible, specifications should be established from sound scientific data and reflect fitness for use.

Specifications should be established to guarantee that the product is fit for use throughout the shelf life of the product.

Control limits are a manufacturer's tool to monitor shifts and trends in the manufacturing process, and to study the effect of process improvements.

Specifications should be set to control the average lot attribute rather than individual dosage units or measurements. This approach reflects current industry practice of mapping average quality attributes to population clinical response during product development.

A lifecycle approach should acknowledge that material and substantial information is necessary to establish specifications, and thus restrictive limits should not be set during development. In some cases, it may not be possible to set limits until post-licensure, after early product manufacture and test method experience have captured information adequate to set meaningful limits.

Product limits provide a basis for assessing product quality attributes at significant points in time, such as at release and at the end of shelf life. Limits should not be overused in studies that aim to explore process or product stability. 

On the basis of these principles, the following guidance is recommended for setting limits for biological and biotechnology-derived products. 

1. Capture scientific data during development to set limits that reflect fitness for use throughout the shelf life of the product.

With the goal of setting specifications that reflect fitness for use, development studies should be performed to identify quality attributes that may be linked to clinical outcomes, and to set release limits that guarantee the quality of the product throughout its shelf life. Setting specifications in this way requires a coordinated effort among the preclinical, clinical, and nonclinical development teams. The goal of identifying quality attributes that may be linked to fitness for use may be accomplished by using sound scientific judgment and by evaluating the historical performance of products with similar quality attributes.  

Preclinical studies in animals or cell culture can help assess how changes in quality attributes may affect clinical outcome. These studies form the beginning of our understanding about which quality attributes may be clinically meaningful, and also can provide a basis for setting preliminary or final limits on those attributes. Clinical studies in target populations may be designed to explore or support ranges for meaningful quality attributes, which together with preclinical experiments form the basis of specifications. Nonclinical studies on final product should be designed to explore and define the properties of the product and analytical methods under conditions that are likely to occur during manufacture, testing, handling, and product shelf life.  

The loss rates determined in stability studies can be used in conjunction with preclinical or clinically derived specifications, as well as release assay variability, to calculate release limits that provide assurance that product released to market is safe and effective and will remain so throughout its shelf life. Figure 4 shows an example of how to set release specifications for an attribute that shows loss over time. The lower specification limit corresponds to the mean of a lot that showed satisfactory performance in clinical trials. (Note that this represents the reportable value from release testing of the clinical lot, together with either additional information about the lot or a compendial or consensus limit for the quality attribute.) The product is earmarked for a 24-month shelf life to allow adequate time for product distribution and consumption. The total loss from release through expiry (solid line) is combined with uncertainties in the release assay and loss estimates to derive the lower release limit (LRL) for the lot. The total loss value may be determined from the sum of individual losses under various storage and handling conditions (e.g., room temperature handling during packaging, labeling, and inspection of a refrigerated product, long-term storage at the labeled temperature, loss after reconstitution and storage in a physician's office). 

Figure 4. An example of how to set final product release specifications for an attribute that shows loss over time, taking into account all stability losses during handling and storage (solid line)and release assay variability    

The fundamental concept behind the strategy of release limits is represented by the equations that follow: 

1.64 = statistical factor associated with 95% confidence 

 = release assay variability expressed    in the form of SD 

 = variability of loss rate associated with i

i indexes multiple storage and handling conditions. 

Finally, development data can be used to forecast process capability for manufactured product. Quality by design (QbD) tools can be used to mimic the ranges of normal operating conditions for the product, leading to a preliminary estimate of process capability. Those forecasts should be verified through early manufacturing experience, and updated as necessary to formulate control or process capability limits (LCL, UCL) for the process. Standards of practice for establishing control limits vary throughout the industry. The application of standard tools for quality assessment can help mitigate risk and facilitate the implementation of a strategic quality system.

The ideal quality system for final product is illustrated in Figure 5. The specification limits (LSL, USL) reflect restrictions within which product is fit for use, and must conform throughout shelf life. The release limits (LRL, URL) ensure these limits are met at release and throughout product shelf life, whereas the control or process capability limits (LCL, UCL) describe process and assay variability. Under this paradigm, final product would be out of specifications (OOS) at release if it falls outside the release limits. On the other hand, a lot would be out-of-trend (OOT) if it falls outside the control limits. An OOT lot signals a possible problem with the process, which is subject to investigation. An OOT lot would nevertheless be fit for use, and thereby could be safely released to the market. 

Figure 5. The ideal quality system for final product, showing the relationship among specification, release and control limits. The specification limits (LSL,USL) reflect restrictions within which product is fit for use,and must conform though tout shelf life. The release limits (LRL,URL)ensure these limits are met at release and throughout product shelf life,whereas the control or process capability limits (LCL,UCL) describe process and assay variability. Under this paradigm, final product would be out of specifications (OOS)at release if it falls outside the release limits. 

Process capability is reflected in the difference between control and release limits. Satisfactory process capability will allow the process to vary over long-term manufacturing experience while remaining within specifications. Unsatisfactory process capability, which will lead to frequent OOS results, will give the manufacturer an incentive to improve process consistency, and thus process capability. In keeping with the regulatory relief expected from the implementation of Quality by Design (QbD), the manufacturer is at liberty to update control limits without strict regulatory oversight, as long as these remain within the release limits, and to improve process capability without regulatory consequence in the form of more restrictive release limits.  

2. When limits are to be supported by process consistency rather than fitness for use, set limits that appropriately reflect the long-term process distribution, and provide incentives for continuous process monitoring and improvement.

Limits on attributes that are not linked to clinical safety or efficacy may be set based on process consistency and should appropriately acknowledge the long-term process distribution. 

Control limits that are based on process consistency are an integral part of a total quality system in which a robust process is developed and then maintained. In such a system, control limits are used to monitor the process. QbD principles should be acknowledged during development to ensure that manufacturing control limits are appropriate. Process understanding can help ensure that limits accurately reflect product consistency. Under this paradigm, control limits should be set with adequate full-scale manufacturing experience, where possible, changing the factors that impact consistency and define the design space for the process. 

Release limits that are set at the boundaries of process capability create the misconception that product that is atypical or "statistically aberrant" is unfit for use. Lots  manufactured using an adequately controlled process will appear unacceptable as more information is gathered and the process matures. Process improvements will become difficult to implement, because these are likely to result in a shift in the "quality" profile for the product. To avoid these problems, release limits should be set beyond the limits of process capability to provide opportunity to monitor for shifts and trends and to implement process improvements without affecting supply to the market. 

3. Take action that is appropriate to the type of attribute and the purpose of different types of limits.

The action that should be taken when a quality attribute exceeds its specifications is not the same as the action that should be taken when an attribute exceeds its control limits. Exceeding a specification signals that the product may not be fit for use, while exceeding a control limit signals that the process may not be in adequate control and should be monitored for shifts and trends. 

Figure 6 compares the actions that might be taken when a quality attribute exceeds a specification or control limit, depending on whether the attribute is linked to fitness for use or is a consistency attribute used to monitor the product or the process. Following down the left side of the flow chart,

a quality attribute that is linked to fitness for use may have specifications that were defined during development as well as control limits that have been updated throughout the lifecycle of the product. Failure to meet specifications results in a formal OOS investigation, which may result in withholding the lot or withdrawing it from the market. A lot that meets specifications but fails to meet its control limits will result in an OOT investigation. This may result in corrective action when an assignable cause has been linked to the OOT event.  

Figure 6. Flow chart that compares the actions that might be taken with a quality attribute that is linked to fitness for use with those that might be taken with a consistency attribute that is used to monitor the product or the process. 

On the other hand (following down the right side of the flow chart), a quality attribute that is not linked to fitness for use but is used to monitor product or process consistency may have control limits that are updated throughout the lifecycle of the product. A failure to meet control limits will result in an OOT investigation. That investigation may result in corrective action when an assignable cause has been linked to the OOT event. 

4. Treat post-marketing stability studies as a control strategy for the process, rather than an assessment of the lot's stability.

Features

Set limits to provide incentives for process improvements.

A Rational Approach for Setting and Maintaining Specifications for Biological and Biotechnology–Derived Products—Part 3

Line workers in an aseptic formulation, fill, and finish operation may seem pretty far removed from the clinic, but in reality, contract manufacturing organizations (CMOs) have a tremendous impact on patient wellness. The more CMOs understand this influence, the better able they will be to ensure the needs of their ultimate customers—patients—are fully met. 

Over the past year, Baxter BioPharma Solutions has put in place a two-fold campaign to reinforce a patient-centric focus, first, among its own employees and, second, throughout its partnerships with pharmaceutical customers. Our first goal is to help our manufacturing employees understand the higher purpose behind what they do each day, regardless of whether they are moving raw materials from the cooler to the production suites; working in the compounding, mixing, or filling suites; or labeling and inspecting vials before distribution. Using banners, videos, and educational programs, we encourage team members to appreciate how what they do each day may help save a child's life or make someone's golden years brighter.  

Our pharmaceutical partners have assisted with the effort, coming to our facility to educate our employees about the disease their products treat, the agents' mechanisms of action, and the symptoms patients may experience. This knowledge helps keep the person waiting for treatment on top of the mind and it fosters a sense of pride and motivation to work harder and better. Thus, a patient-centric mindset can influence operational performance, batch-release times, exceptions for batches, and the overall performance of the facility. 

Moreover, a patient focus influences the counsel we provide to our pharmaceutical partners on reformulations and enhanced packaging that ensure patient safety and help clinicians comply with regulatory requirements concerning admixture and administration of parenteral products. Packaging considerations also take into account patients who may self-administer medication for a particular disease or condition. Consider, for example, an individual with motor impairment. For that person, medication in a prefilled syringe or autoinjector may help them manage treatment on their own and comply with a chronic-treatment regimen.  

Of course, considering patients and clinicians is good business. When pharmaceutical companies make it easier for clinicians to comply with regulations, it improves market share. When CMOs help reformulate or repackage a molecule to improve ease of administration, those efforts may extend the commercial lifecycle of the products. 

But commerce cannot and should not be the ultimate objective. Producing high-quality therapeutics in a reliable fashion, and ensuring they meet the needs of patients, increases the likelihood that patients will get better, live longer, or manage a chronic disease more easily. If someone's life is better because of a therapeutic, then a day's work in the clinic, at a pharmaceutical company, and in a CMO was filled with purpose.  

And, ultimately, that is what makes an organization successful.  

is a product manager, both at Baxter BioPharma Solutions, Round Lake, IL, 

Manufacturers of biopharmaceuticals can improve productivity by taking patient wellness into account.

Connecting the Manufacturing Process to Patient Wellness

Process modeling can be a useful tool to aid in process development, process optimization, and process scale-up. When modeling a chromatography process, one must first select the appropriate models that describe the mass transfer and adsorption that occurs within the porous adsorbent. The theoretical understanding of chromatographic behavior can augment available experimental data and aid in the design of specific experiments to develop a more complete understanding of the behavior of a unit operation. 

Biotech unit operations, including process chromatography, are complex because of the large number of factors that affect process performance. A typical process chromatography step has 50–100 operating parameters that can impact its performance. In addition, a similar number of raw materials are used during the process step. It is not feasible to evaluate the effect of all of these variables on step performance. The current practice is to use risk analysis to identify process parameters that need to be examined during process characterization with the extent of process modeling limited to use of linear statistical models.

An example of such statistical modeling has been published before in this series.

 The case study involved the design of a process analytical technology-based control scheme for a cation-exchange chromatography step. A design of experiments (DoE) study consisting of 17 experiments was conducted with purity of the load material, start point of pooling (start collect), and end point of pooling (stop collect) as variables. Pool purity and step yield were monitored for each experiment. The results were analyzed using statistical software and are presented in Figure 1. It is seen that load purity and stop collect have a significant impact, where as start collect does not have a significant impact on pool purity. This model was then used to calculate the pool purity for all the experiments that were performed. Figure 2 illustrates a comparison of the pool purities calculated using the statistical model and the actual pool purity as measured by reversed-phase high-performance liquid chromatography (RP HPLC). It is seen that the two numbers correlate very well and support feasibility of this scheme. While this example highlights the usefulness of an empirical model, there are significant limitations of this approach. First, because the model is not based on fundamental principles that govern the unit operation, its predictions are limited to the ranges of data for process parameters that the model is based on, e.g., load impurity of 1.8–5.8% for the case study illustrated in Figure 1. Second, just like any other modeling approach, the underlying assumptions have to hold true for the model to be accurate.   

If possible, it is always preferable to create a process model based on fundamental principles. Such a process model can be used as a tool for optimization, scale-up, and manufacturing support. This article is the thirteenth in the 

 series and focuses on process modeling tools that can be useful in capturing the process understanding. We review some of the significant and recent contributions in the area of process modeling of process chromatography along with examples from existing biotech processes.

Figure 1.Statistical analysis of data from chromatography experiments 

CONSIDERATIONS FOR MODELING PROCESS CHROMATOGRAPHY

When modeling chromatographic separation processes, the model used to describe mass transport within the column must first be selected. Models used to describe the separation process are well established in the literature and are based on fundamental thermodynamic and mass-transfer relationships.

 A schematic of the separation process that occurs within the chromatography column during protein adsorption is shown in Figure 3. During the adsorption process, protein is first transported from the bulk-liquid phase to the surface of the porous adsorbent, at which point it diffuses into the porous structure and is adsorbed to the surface by electrostatic or hydrophobic interactions. The mode of adsorption is dependent on the type of chromatography ligand chosen for the adsorption process, such as affinity, ion exchange, or hydrophobic interaction. Model input parameters include the process operating conditions (feed concentration and operating velocity), properties of the packed bed (bed porosity, bed height, and HETP) and the protein-adsorbent equilibrium isotherm relationship. A material balance is used to relate the protein present in the liquid phase of the column to that adsorbed inside the porous adsorbent and predict the protein concentration in the column outlet.    

Figure 2. Comparison of pool purity measured by reversed-phase high performance liquid chromatography with that predicted by the statistical model 

 The adsorption and separation processes in a column are complex, and vary with both column position and time. Protein mass transport in the column is governed by two resistances: transport of protein from the bulk fluid to the surface of the adsorbent (called film mass transfer) and transport of protein within the porous adsorbent (called intraparticle mass transfer). Intraparticle mass transfer is composed of: protein diffusion into the adsorbent and protein binding to the adsorbent surface (surface reaction). Several assumptions are routinely made to simplify the models used to describe mass transport in the column. First, columns are assumed to be radially homogenous, and  therefore, properties vary only with the column bed height but not with the column radius. Second, for porous adsorbents, intraparticle mass transfer is governed by protein diffusion, as protein adsorption to the stationary phase (surface reaction) occurs much more rapidly and does not contribute significantly to mass transport. The adsorption process is assumed to occur instantaneously, with equilibrium between the protein adsorbed to the stationary phase and that present in the liquid solution.   

Figure 3. Schematic showing the mass transfer and adsorption processes within a chromatography column, and the equations used to model the process 

The adsorption equilibrium data, also known as the adsorption isotherm, are used to relate the protein concentration in the liquid phase inside the column to that adsorbed to the surface of the adsorbent over a range of concentrations.  Data for the adsorption isotherm are usually generated in separate experiments under equilibrium conditions. A variety of models can be used to describe the equilibrium adsorption isotherm, with the two most common being the Langmuir and Steric-Mass Action (SMA) models.

 Correlations for several of the other column model parameters, including the film mass-transfer coefficient and axial dispersion, are used to estimate values under the process operating conditions evaluated in the separation.   

Table 1.  Models used to describe intraparticle mass transfer (governed by diffusion) and adsorption equilibrium used to model chromatography separations  

Models are also required to describe intraparticle protein diffusion and are shown in Table 1. Two of the most commonly used models include the homogenous (surface) diffusion and the pore diffusion model.  These two models assume different physical mechanisms for the diffusion of proteins into the porous stationary adsorbents. The homogeneous diffusion model assumes that the protein adsorbed to the stationary phase is free to migrate, or diffuse, along the solid surface. The driving force for the homogeneous diffusion model is the protein concentration gradient in the adsorbed phase. On the other hand in the pore diffusion model, it is assumed that intraparticle mass transfer occurs by diffusion in liquid-filled pores with a driving force expressed in terms of the radial pore fluid concentration gradient. The adsorbed protein is assumed to be in equilibrium with that in the pore fluid at each radial position in the particle.  

Table 2.  Experimental and model predictions for the step yield and aggregate levels using a Phenyl Sepharose Fast Flow column. The feed used in the studies consisted of 87% monomer and 13% aggregate.1 

MODELING TO ESTIMATE DYNAMIC CAPACITY IN PROCESS CHROMATOGRAPHY

Models describing protein adsorption within a chromatography column can be used to predict the performance of the chromatography step, including the dynamic binding capacity, effects of column scale-up, and separation of complex multicomponent protein mixtures.

 Once the model parameters are known, dynamic binding capacity can be predicted over a range of operating conditions, such as different column bed heights and operating velocities. Because experimental measurement of the dynamic binding in high capacity resins can involve significant amounts of feed material (because protein breakthrough must be achieved), the prediction of the dynamic binding capacity can result in significant savings in development time and protein-feed requirements. Modeling predictions can also be used to determine which adsorbents will have the highest capacities over a range of operating conditions.   

Figure 4. Schematic of the competitive, binary adsorption process for a monomer and aggregate mixture. The aggregate species binds irreversibly in the case shown 

McCue, et al., have used modeling to predict the separation of highly complex, multicomponent protein mixtures after adsorption onto the column.

 As a result of recent advancements in computational power, this type of approach can be used to rapidly predict the separation of two or more components possessing similar adsorptive properties, such as the separation of protein and monomer species using hydrophobic interaction chromatography (HIC).

 Table 2 shows an example of model predictions and experimental results for the separation of monomer and aggregate species using Phenyl Sepharose Fast Flow (GE Healthcare) to evaluate product yield and aggregate removal over a range of operating conditions. As with all model formulations, the governing adsorption isotherm model must first be chosen. In the case of the described monomer or aggregate separation, a competitive Langmuir binary adsorption isotherm was selected, in which the aggregate species bound irreversibly to the HIC adsorbent, as shown schematically in Figure 4. When formulating model predictions, a sensitivity analysis should be performed to determine which input parameters have the greatest impact on the model predictions. Once this is known, highly sensitive parameters should be measured as accurately as possible to minimize the uncertainty in the predictions. A model parameter sensitivity analysis can also add further insight into the fundamental mechanisms that govern the particular adsorption or separation. For example, a sensitivity analysis may show the dynamic binding capacity is highly sensitive to moderate changes in the operating velocity. This information could be highly useful for resin selection, as it may be desirable to select a resin in which the performance is insensitive to flow rate, so that the process throughput could be increased without a loss in dynamic binding capacity. A sensitivity analysis could also help in troubleshooting investigations or manufacturing deviations involving chromatography steps. For example, if during the course of an investigation an input parameter was found to be above or below an expected range, the model could be used to predict the effect on column performance, as well as what corrective actions should be taken. Model predictions could be especially useful for troubleshooting if experimental data are not available, as could be the case when unexpected deviations occur in a manufacturing environment.          

Figure 5. Comparison of a simulated and an experimental purification of the four closely related components. the ordinate axis on the right hand side of the figure shows the concentration scale of the gradient 

Mollerup, et al., used a thermodynamic model to simulate elution of several closely related compounds in process chromatography characterized by four key components.

 They assumed that all activity coefficients except the activity coefficients of the solute proteins are unity or constant. In the case of ion-exchange, the data presented demonstrate that the activity coefficients in this case are of minor importance. As seen in Figure 5, the agreement between the adsorption models and the experimental adsorption data is good with respect to the actual chromatogram, impurity profile, yield, and collected fraction volume, thus indicating that the chosen model describes the actual process quite well. Though the conclusions made on the basis of simulation need to be confirmed experimentally, the authors state that proper use of simulations can reduce the number of experiments substantially.   

Figure 6. Comparison of results from traditional screening approach and modeling approach. Bars indicate individual contributions of variance. 

MODELING TO IDENTIFY OPTIMAL CONDITIONS FOR PROCESS CHROMATOGRAPHY

Kaltenbrunner, et al., recently compared the use of a chromatographic model to rank operational parameters to the traditional statistical experimental design approach.

 They performed both the typical screening of parameters in a fractional factorial experimental design and, independently, developed a theoretical model as described by Yamamoto, et al., for a particular ion-exchange chromatography operation.

 While the original model is based primarily on the response of protein elution to ionic strength, flow rate, and gradient slope; extensions for resin ligand density and pH were included.

 With a series of small-scale linear gradient experiments, a model for the ion-exchange operation based on established response relations was constructed. As interactions and curvature are inherent to the model assumptions, once the right model was chosen, fewer experiments were necessary to define system behavior than with a traditional statistical factorial approach, where no prior information about parameter relations was available. The model was then used to predict a matrix of potential experimental conditions similar to statistical experimental design. In this case though, there were no material restrictions and cost considerations and a full factorial matrix with several factor levels could be modeled easily and rapidly.   

Figure 7. Exemplary representation of a matrix of a 729 data point virtual three-level full factorial and response surface model. Profiles of normalized single factor response for all other factors held constant. 

Parameter screening by chromatographic modeling and by fractional factorial experimental design both rank three parameters—the ionic strength at the beginning of the elution gradient, pH, and resin ligand density—as the parameters that have the highest effect on separation behavior (Figure 6). Similarly, both methods identify gradient slope and flow rate—within the range considered in this study—as parameters that have a lesser effect on separation behavior. This comparison indicates that the modeling approach outlined by Yamamoto, et al., can be applied for the initial screening of operational parameters during process characterization. In this case of six model input factors, to obtain a traditional multiple regression model that could describe all possible two-factor interactions and simple quadratic curvature, fewer than 50 factor combinations have to be predicted by the model. To predict all factor combinations for 3 levels, 3 x 3 x 3 x 3 x 3 x 3 = 729 combinations must be calculated. This can easily be done when using computational analysis. As a result, normally neglected higher order interaction could be included in the analysis. Figure 7 represents a virtual full factorial experiment with all combinations of factor inputs at three levels. The panels show changes in predicted recovery and resolution to single factor changes within normalized input ranges from –1 to +1. All other factors were held at center point condition, and therefore, factor interactions were not detectable in this representation. As seen in Figure 8, interactions with pH are demonstrated with respect to two model outputs. The red areas represent factor combinations where the expected product purity is undesirable, and blue areas represent factor combinations where the expected product recovery is undesirable. The model can help inscribe the process design space in which the process performance is acceptable. In practice, the selection of a design space is much more complex than implied in these plots. In these plots, all other parameters were held constant at their target. Although two- or three-dimensional visualization is difficult because of  the multidimensional nature of the model, chosen parameter ranges can be tested in all their combinations by stochastic modeling.   

Figure 8. Exemplary representation of a matrix of a 729 data point virtual three-level full factorial and response surface model. Surface plots of all two-factor interactions and pH with respect to purity and recovery.(A)Flow Rate; (B)Initial ionic strength; (c)Bed hight; (D)Gradient Slope; (E) Ligand density 

Mollerup, et al., also demonstrated the usefulness of such simulations for process optimization and scale-up.

 As seen in Figure 5, the process model was shown to be in good agreement with the experimental data. This model was then used as a simulation tool to optimize the process. The column size and the properties of the loading solution were fixed and the independent variables that were examined included load volume, flow rate, and gradients. The concentration in the collected pool volume was stipulated to be within specified limits. Figure 9 compares the experimental and the simulated separation. The agreement between the experimental and the simulated chromatogram is satisfactory and sufficient to optimize the current separation. The experimental chromatogram is broader than the simulated one, which indicates that the collected fraction containing the product must be increased compared to that used in the simulation. The results in Table 3 further demonstrate the use of the simulation in optimization and increasing the overall productivity.   

Figure 9. A comparison of the simulation shown in figure 4 and the corresponding experimental chromatogram. Details shown in Table 3  

Simulations can be used to identify critical process parameters and support process design and development by making a true optimization with regard to pressure drop, flow rate, gradients, salt, pH, temperature, throughput, yield, purity, and robustness. Applying these modeling tools in the development of chromatographic separations can support the design of effective and efficient manufacturing processes that will consistently ensure a predefined quality at the end of the manufacturing process, consistent with the basic tenet of Quality by Design.   

Table 3.  Comparison of simulated and experimental results from an optimization.4 

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 12. Lewus, RK, Carta G, Binary protein adsorption on gel-composite ion-exchange media. AIChE Journal. 1999;45:512. 

A review of some recent contributions in process chromatography.

Modeling of Biopharmaceutical Processes. Part 2: Process Chromatography Unit Operation

In most countries, commercial biopharma depends to some extent on taxpayer-sponsored infrastructure and incentives in the form of state-supported universities and research facilities that supply researchers and are centers of innovation. The same is certainly true for Indian biopharma, although, as noted in the recent report, 

Advances in Biopharmaceutical Technology in India

, the Indian government's helping hand goes further than most.  

Almost all biopharmaceutical companies in India have had some form of government assistance in recent years such as public loan assistance enjoyed by many small biotech companies and Biocon's government-sanctioned Special Economic Zone (SEZ) in Bangalore, which will confer huge tax breaks on the company through 2015.

 approach to its economy. This attitude is a legacy of the anti-colonial movement and the polarizing influence of the Cold War, in which socialism was seen as the most viable option to exploitative Western capitalism. The Indian National Congress, the party that dominated Indian politics for decades after independence, was essentially a socialist party. Even now, two of the other five national parties describe themselves as communist, and Indian economic policies continue to be organized according to old-fashioned, Russian-style five-year plans. 

After the mid-1980s' decline of the Soviet empire, the West enjoyed an economic boom, and India's export markets expanded rapidly. The country began to outgrow its anti-Western sentiments, focusing instead on becoming a respected and powerful participant in the global economy. 

At the time, the successes of American companies like Genentech and the advent of technologies like the polymerase chain reaction (PCR) were fostering the biotechnology revolution.  India, therefore, decided to make biotechnology one of its priorities for industrial development, and in 1986, the Department of Biotechnology (DBT) was formed under the Ministry for Science and Technology. 

Because product patents were not legal in India from 1972 until 2005, India's pharmaceutical industry based itself on reverse-engineering Western drugs and patenting new manufacturing processes. Although the pharmaceutical industry developed, biotechnology was still considered new and difficult, and Indian drug makers focused on synthetic chemistry rather than biological processes. By 2005, when product patents became legal again under the Patents (Amendment) Act,  India was producing far more qualified graduates in chemistry than in biology.

  Moreover, most Indian biology graduates were doing their postdoctoral work in the West and ultimately emigrating there.

The relative scarcity of qualified Indian graduates in biotech-related disciplines is now seen as a major limiting factor in the development of India's biotech industry. The DBT is trying to remedy the situation with initiatives that include:

 The DBT supports the improvement of undergraduate programs in the life sciences and biotech-related disciplines by increasing the quality of teaching, promoting creative thinking among students, and improving infrastructure and the supplies of laboratory consumables. The Star College designation is being offered to academic institutions as an incentive to raise standards.

. At the postgraduate level, PhD programs are being expanded. Regional training centers are being set up to improve the quality of biotech-related teaching and industry-oriented courses are being introduced, such as degrees in regulatory affairs and biomedical enterprise. New research fellowships and overseas-training schemes are being offered for postdoctoral students, collaborative research programs with Western labora-tories are being set up, and re-entry packages are also being used to entice Indian postdoctorates who currently work abroad to return to India. 

 The DBT, which already funds a number of autonomous biotech centers, says it plans to assist in setting up several dozen more university-based, interdisciplinary research facilities by 2011. These are meant to specialize in areas from basic biology to biomedicine.

The Health Science Biotechnology Cluster.

 This DBT-funded project in the northern state of Haryana will include a Translational Health Science Technology Institute, a Vaccine Technology Center, a Diagnostics Development Center, a Biotech Platform Technology unit, an animal facility for biopharmaceuticals testing, and a Center for Biotechnology Education and Research, sponsored also by the UNESCO.

The DBT is also funding several programs to help push innovations out of academic laboratories and into production. These include:

The Biotechnology Industry Partnership Program. 

This is a  technology-transfer scheme, in which the DBT will connect Indian biotech companies with suitable  research laboratories, and will help to manage patenting and other IP-related issues as research findings are translated into marketable products.

The Biotechnology Industry Research Assistance Council. 

 This council will help small and medium biotech firms to grow and compete globally. Provisions include lending assistance and support for international patenting costs.

Expansion of the Small Business Innovation Research Industry Scheme. 

An existing scheme by which the DBT provides financial and other assistance to fledgling biotech firms.

A National Biotechnology Regulatory Authority.

 A national authority to be in place by 2009 and to act as a single-window regulator for the biotech industry.

The Indian tax system is complex and evolving, but currently the local biopharmaceutical industry enjoys a host of actual and potential tax breaks. Among these are:

Exemption from customs and excise taxes for all drugs and materials imported or produced domestically for clinical trials, and for certain biotech-related research and development (R&D) equipment.

A low rate of customs duty (5%) for most R&D equipment.

10-year deduction of all profits and gains for companies conducting R&D approved by the Indian Scientific and Industrial Research Organization (SIRO) before March 31, 2005.

Continuing tax exemptions for venture-capital funds that invest in biotech companies.

In 2006, India's legislature passed the SEZ Act to promote both local and foreign investment, boost the influx of know-how from the West, and foster India's export sector. In principle, any company or group of companies may set up a SEZ on its own. The DBT is helping to finance the establishment of several biotechnology parks with SEZ status before 2010. 

100% foreign-equity investment permitted for manufacturing all drugs except recombinant-DNA products and cell-targeted therapies

Customs and excise duty exemption to companies recognized by  SIRO

3-year excise-duty waiver on patented products.

Public funding for biopharma-related research in India comes from a variety of sources. The DBT is one of the largest contributors, and others include:

The Department of Scientific and Industrial Research

The Council of Scientific and Industrial Research

Biopharmaceutical industry suppliers also recognize that government support provides a level of stability, which leads to increased local investments. "The Indian government has encouraged an open-door policy with industry leaders," said Vinay Joban, general manager at Pall Life Sciences, Pall India. "The government has created excise-free zones to encourage pharmaceutical and biotechnology growth, and Pall and others are establishing new facilities and expanding existing facilities to meet international regulatory cGMP guidelines." 

In the recent years, India's state governments, much like those in the US, have been competing for biopharmaceutical business by providing incentives to would-be investors. These incentives include tax breaks, seed money, concessional pricing for land, subsidies for utilities and patent costs, and the promise of a single-window clearance on regulatory matters involving the state.

India's plans for promoting its biopharmaceutical and other biotech sectors are fairly ambitious and may be more difficult to realize as swiftly as the country's planners would like. As with many government initiatives around the globe, the DBT's budget (approaching $200 million in 2008) would seem to be spread thinly if it is to accommodate the long list of priority projects the organization intends to support.

Still, India clearly is intent on improving itself in the biotech sector, and the desire to make Indian biotech as good as Indian IT seems to be widespread. It is, therefore, likely that in another decade, India will at least have one of Asia's best biotech capabilities.  

 1. Biocon looks to Rs 650-cr SEZ for tax relief. The Hindu Business Line. 2005 Oct 20. Available from: 

www.thehindubusiness-line.com/2005/10/20/stories/2005102001630200.htm

 2. The Patents Amendment Act, 2005. Available from:  

www.patentoffice.nic.in/ipr/patent/patent_2005.pdf

What the Indian government is doing to make its biotech sector as strong as its IT sector.

The Visible Hand: Government Supports the Biopharmaceutical Industry

 With this month's column, we are introducing a new regular section dealing with the latest developments in disposable technology and, in particular, feedback from users about these developments. In putting this together, I will be helped by my colleague Miriam Monge, a well-known figure in the disposables business; she is also European chair of ISPE's Community of Practice for Disposable technologies.  

CHANGING MABS MARKET MAKES DISPOSABLES USE MORE FEASIBLE

Much of the current clinical pipeline is geared toward monoclonal antibodies (MAbs) and the current trend is for smaller market-volume requirements for MAbs. When linked with increasing product titers, this trend puts many new manufacturing facilities well within the reach of disposable technologies. Johannes Roebers made this point in a recent talk, noting that annual product requirements of about 500 kg/year will become the norm

 As disposables become more and more integrated in our manufacturing facilities, it is interesting to assess the implications for both design and project management.    

Implications for Facility Design and Project Management

If we consider a typical bulk MAb facility, designed to produce 500 kg/year with a titer of around 3 g/L and product recovery to bulk of about 60%, around 8,000 L of bioreactor capacity is needed.

  If processed as one batch, about 63,000 L of solutions are required for processing. At this scale, this facility is well within the comfort zone for using disposable technologies. Today, we would consider disposables for various unit operations in a plant, such as: production bioreactors (4 by 2,000 L or 8 by 1,000 L); depth filters for harvest; membrane chromatography for flow-through columns; process mixing; and product, media, and buffer hold and preparation.  

So, what are the implications for facility design? One clue is given by the amount of water used for cleaning a stainless-steel facility, in which much of the complexity of facility design is driven by pipework for steam-in-place (SIP) and clean-in-place (CIP). For example, if our model 500 kg bulk MAb plant were a stainless-steel facility, 155,000 L of solutions would be required annually for cleaning. In a disposable facility, on the other hand, we can:  

remove most CIP systems, restricting their use to packaged equipment

remove process pipework between the unit operations by replacing it with physical movement and disposable tubing.

Figure 1 shows a development of these design concepts, published in 2004.

  The result of this level of integration is a building containing cleanrooms but little process infrastructure. The process is configured by setting up process operations in designated serviced work stations minimally equipped with power, data links, and gases. In this way, the operational space becomes flexible and easily reconfigurable. This kind of facility design has more in common with traditional manufacturing operations, focusing on optimizing material and people flows and giving serious consideration to lean approaches (e.g., reducing changeover times, inventories, and waste). In the building project, there is less emphasis on the process that will take place in it, because we have now effectively separated the process from the building. Against this backdrop, below are the key considerations when undertaking this type of a design project.  

Figure 1. A disposable concept facility  

1. Speed in one area affects other parts of the project.

 The simplicity of the building, its layout, and lack of process infrastructure allow a project that is largely based on disposables to be built more quickly than a stainless-steel facility. This means that other aspects of the project, such as process and validation activities, vendor audits, and extractables and leachables studies, must be started earlier.  

2. Separating process design from building design.

 Process and technology are still important but they can be quickly separated from the building project. Here the emphasis is to define the mass balance and assess the available technologies for the particular applications. It is important that all effort be made to eliminate or minimize all clean utilities, CIP, and process pipework. Use of modeling tools helps in the cost–benefit assessment and the analysis of process, material, and people requirements. Where possible, develop vendor-independent disposables design specifications.  

 This type of facility relies more heavily on manual handling, rather than pipework, to move product and materials around the facility. Therefore, it is important that early prototypes be developed of the disposable systems that will be used and that the operations be laid out at scale to allow for ergonomic and functional evaluations to be completed before commission-ing and start up. 

 When a lot of disposable technologies will be used in a new facility, it is important to involve the procurement organization from the outset of the project design. The procurement team will need to carry out the vendor assessments in terms of pricing, risk, and security of supply, and ensure their requirements are effectively communicated to the engineering project team.  

As the industry demand for disposables increases, the suppliers are striving to add new technologies and innovations to their disposable technology offerings. Examples of most recently announced new developments include: 

New offerings in large-volume bags for holding fluids:

Pall Corporation has launched a new large-volume bag range called "Allegro" with capacities of 100, 200, and 500 L.  

The Millipore Mobius line now includes includes bags from 200 to 2,000 L. The Mobius stainless-steel totes have a patented registration plate that makes it possible to adapt the ports at the bottom of the container to work with bags from multiple suppliers. Millipore's large-volume bags (1,000 and 2,000 L), branded "EZ fold," also feature walk-away filling in which the bags unfold as they are filled.

Sartorius Stedim Biotech has recently added a 50-L volume Palletank to its Flexel 3D range. 

New measurement and control systems greatly expand the use of disposable bags. Of particular interest is Sartorius Stedim Biotech's newest single-use pH probes, which can measure pH levels from 0 to 11 with a +/–0.1 precision. 

When configuring fluid handling systems, aseptic connectors are an enabling technology that has greatly extended the use of disposable systems. This was spearheaded by the Pall KleenPak connector,but recently many other vendors have developed their own connectors:   

Saint Gobain has recently launched its new sterile connector, the Pure Fit SC. 

GE Healthcare's Bioquate is a genderless connector that is popular with end-users. 

Millipore offers a wide range of connectors in both dry-to-dry and dry-to-wet formats, with internal diameters (IDs) up to 1 inch. The Lynx ST, for connecting to stainless steel, was recently launched in 1-inch ID format. This is a membrane-free system that facilitates integrity testing and is simple to manipulate. 

In addition to making technical developments, vendors are also looking at ways to improve supply-chain security; a common approach is to achieve this through product standardization. By offering standard, off-the-shelf products, vendors are able to shorten lead times and improve the security of supply. This standardization should benefit the end-users as the industry develops generic design standards, thus making it easier for end-users to work with multiple suppliers.  

In addition to making technical developments, vendors are also looking at ways to improve supply-chain security. By offering standard, off-the-shelf products, vendors are able to shorten lead times and improve the security of supply.

BioPharm International-08-01-2008

GMPs in Phase 1: Take 2

A Rational Approach for Setting and Maintaining Specifications for Biological and Biotechnology–Derived Products—Part 3

Connecting the Manufacturing Process to Patient Wellness

Modeling of Biopharmaceutical Processes. Part 2: Process Chromatography Unit Operation

The Visible Hand: Government Supports the Biopharmaceutical Industry

Disposables Open Up Possibilities in Facility Design

FDA Seeks Active System for Monitoring Adverse Events