Abstract
Monoclonality of mammalian cell lines used for production of biologics is a regulatory expectation and one of the attributes assessed as part of a larger process to ensure consistent quality of the biologic. Historically, monoclonality has been demonstrated through statistics generated from limiting dilution cloning or through verified flow cytometry methods. A variety of new technologies are now on the market with the potential to offer more efficient and robust approaches to generating and documenting a clonal cell line.
Here we present an industry perspective on approaches for the application of imaging and integration of that information into a regulatory submission to support a monoclonality claim. These approaches represent the views of a consortium of companies within the BioPhorum Development Group and include case studies utilising imaging technology that apply scientifically sound approaches and efforts in demonstrating monoclonality. By highlighting both the utility of these alternative approaches and the advantages they bring over the traditional methods, as well as their adoption by industry leaders, we hope to encourage acceptance of their use within the biologics cell line development space and provide guidance for regulatory submission using these alternative approaches.
LAY ABSTRACT: In the manufacture of biologics produced in mammalian cells, one recommendation by regulatory agencies to help ensure product consistency, safety, and efficacy is to produce the material from a monoclonal cell line derived from a single, progenitor cell. The process by which monoclonality is assured can be supplemented with single-well plate images of the progenitor cell. Here we highlight the utility of that imaging technology, describe approaches to verify the validity of those images, and discuss how to analyze that information to support a biologic filing application. This approach serves as an industry perspective to increased regulatory interest within the scope of monoclonality for mammalian cell culture–derived biologics.
- Monoclonality
- Cell cloning
- Clonally derived
- Limited/limiting dilution
- Plate-based imaging
- Single-cell imaging
- CHO
Introduction
In mammalian cell production of biologics, the derivation of a cell line from a single progenitor cell, or monoclonality, is a regulatory expectation as part of a robust control strategy meant to reduce the risk of product heterogeneity and improve process robustness to ensure patient safety and efficacy. To clarify these expectations the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) has issued guidelines known as the ICH Q5D (1), which specifies that “for recombinant products, the cell substrate is the transfected cell containing the desired sequences, which has been cloned from a single cell progenitor”. Additional guidance from the World Health Organization (WHO) Expert Committee on Biological Standardization (WHO TRS 978) (2) also states that “the cloning procedure should be fully documented, with details of imaging techniques and/or appropriate statistics” and that “for proteins derived from transfection with recombinant plasmid DNA technology, a single fully documented round of cloning is sufficient, provided that product homogeneity and consistent characteristics are demonstrated throughout the production process and within a defined cell age beyond the production process”. In recent years the assurance of monoclonality for cell lines producing biopharmaceuticals has become an area of increasing focus of regulatory agencies (3⇓–5).
The biopharmaceutical industry strives to ensure that biologics are manufactured in such a way to deliver therapeutics with product quality characteristics that are consistent and meet pre-determined product specifications. A consistent product profile is important not only to maximize the efficacy of the recombinant therapeutic but also minimize any undesirable attributes that could pose a safety risk to patients. Monoclonality is one means by which product heterogeneity can be minimized, as it ensures that at the point of cloning the cell substrate is homogeneous. Ensuring monoclonality is one component of a robust process control strategy including assessment of cell line stability, process characterization, and rigorous testing of the final drug product.
Regulatory authorities, including the U.S. Food and Drug Administration (FDA), maintain that a well-documented cloning step is desired for ensuring product consistency. Thus, the industry has placed significant effort into developing systems that ensure monoclonality in a rigorous and expedient manner. Developing image-based techniques providing photographic documentation that a cell line is derived from a single progenitor is an area of intense focus. Here we describe methods by which advanced imaging technologies are used to provide assurance that a cell line is clonally derived.
Considerations for Implementation of Plate Imaging
Methods for Isolating Clonal Cell Lines
Traditional methods of cloning mammalian cells have included limiting dilution cloning (LDC) or flow cytometry–based single cell sorting (6⇓⇓–9). LDC is performed by plating an appropriately low cell density target per well in a multi-well plate. Based on Poisson distribution statistics, the number of wells expected to contain a target number of cells can be determined and this distribution can be used to calculate a probability of monoclonality given a plated cell concentration (10,11). The U.S. FDA has previously stated that two rounds of LDC at a sufficient dilution is adequate assurance of monoclonality (3). However, given the nature of the statistical calculation, this evidence of monoclonality only applies in the situation where the cell population exists entirely as singlets and the cells do not have a propensity to aggregate or clump. Similarly, flow cytometry–based cloning leverages characterization of system performance to assess a statistical confidence of monoclonality. This verification process then provides a numerical confidence that a given clone is from a single progenitor cell (9).
Recent guidance from the U.S. FDA has indicated that survival statistics should not be used to calculate statistical probability of monoclonality; rather, a prospective probability should be used (4). Cloning by limiting dilution requires significant time and increased resource commitment leading to lengthy cell line development timelines, a major limitation to this technique.
To address those limitations, methods of isolating monoclonal cell lines have been developed aimed at reducing cell line development timelines while ensuring adequate probability and assurance of monoclonality. The U.S FDA has also stated that “advances in technology (e.g., imaging) might allow for one round of cloning performed at an appropriate dilution when combined with this technology” (3). This suggestion aligns with the efforts from the members of this consortium towards development of image-based techniques to provide additional evidence in support of clonal origin. Application of plate-based, single-well imaging can further assure the confidence of monoclonality using traditional cloning methodologies.
Considerations for Implementation of Plate-Based Imaging in a Cloning Workflow
The physical documentation of single cells via plate-based, single-well imaging provides significant benefits in cell line development and can be used to increase the overall assurance of monoclonality. Developing, optimizing, and characterizing a plate-based imaging platform requires a significant investment, but it can increase confidence in the monoclonality of a cell line.
Many vendors develop and market automated, high-throughput instruments that can accurately and precisely image an entire SLAS (Society for Laboratory Automation and Screening)-format plate in a short time and are amenable to single cell cloning. However, careful consideration must be placed in introducing any protocol into a cell line development workflow, and cell culture plate imaging is no different.
Image quality is one of the key considerations for choosing a plate imager. High-resolution image acquisition that is clear and sensitive is available with most high-throughput imaging instruments on the market. Image resolution of 1 micron/pixel is routinely available; however, this resolution may not be required for the purpose of identifying Chinese hamster ovary (CHO) cells. In order to maximize image quality, careful consideration is placed on choosing the cell culture plate. This plate should be compatible with the media and cells used for the cloning process and should also minimize the number of artefacts visible during the imaging process. The selection of the imager and the plate define the framework of the image quality that will be used to identify the presence or absence of cells and how many cells are visible in each well.
Single-channel, bright-field imaging reduces time and resources required to identify cell lines compared to traditional manual identification. However, it can be challenging to identify a single progenitor cell at the time of cloning due to the limitation of image quality, the presence of cell shaped artefacts, and the impact of well edge effects. In addition, cloning media components, such as serum and peptone, can change the cell shape significantly and can introduce additional artefacts that limit the ability to distinguish single cells. Together with other factors, such as culture debris and imperfection of the culture plates, plate imaging can introduce additional complexity in determining monoclonality even given an image at the time of cloning. To mitigate this risk, imaging may be performed on multiple days, including pre-deposition imaging, to track cell doubling of the progenitor cell and to provide additional assurance that no other population of cells exist after the cloning. More imaging, while beneficial for demonstration of monoclonality, can be resource-intensive and can potentially be detrimental to cloning outgrowth and thus must also be considered carefully. Additionally, use of fluorescent dyes has enabled combined bright-field and fluorescent imaging techniques. Dual bright-field and fluorescent imaging protocols require staining cells prior to plating into multi-well plates and may improve a plate-based imaging protocol by providing a secondary method for detecting cells, further enhancing single-cell detection and statistical analyses. In addition, fluorescence imaging may reduce some of risks of bright-field imaging described above (Figure 1). The use of fluorescent dyes should be evaluated to ensure no negative impact on the resulting production cell line prior to implementation.
Sample image data from commercially available high-throughput imagers. Brightfield image of single-cell cloning experiment from CellMetric imager (A). Overlay brightfield and fluorescence image of single-cell cloning experiment from Cellavista (B) and Celigo imagers (C).
The speed of image acquisition may be a consideration if higher throughput is required. Typically, single-channel acquisition times range from 1 to 5 min per plate. Two- and three-channel image acquisition would double or triple the image acquisition time, respectively.
Consistent with the concept of maintaining high-throughput for cell line development, laboratory automation has been employed to streamline cell line development workflows. An automated single-cell cloning workflow may consist of cell culture plates being handled by a robotic arm that moves the plates between an automated liquid handler, plate centrifuge, high-throughput plate imager, and incubator. This automated approach has the added benefit of consistent and accurate plate handling in order to reduce disturbance to the cell culture.
In support of the high-throughput applications many plate imagers also provide automatic image processing software that is capable of identifying single cells without manual intervention. If properly refined, this software can dramatically reduce the need for operator intervention during the cloning workflow. However, manual verification of single-well images may still be desirable to provide a higher degree of assurance in the monoclonality of a selected cell line.
In order to generate cell lines that satisfy productivity and product quality requirements, many clones are evaluated and the automated workflows as described above can satisfy the throughput needs. This methodology will therefore also require the acquisition of tens of thousands to hundreds of thousands of high-resolution images in the generation of a single, commercial-ready cell line. Therefore, the storage, curation, and access to these images must also be considered such that these critical data are securely stored to ensure that there is no loss in data.
Case Studies in Plate-Based Imager Verification
Even with a complete well image immediately after the cloning step and a fully optimized imaging protocol, there is still the possibility of errors that could result in a non-clonal line that appeared clonal via imaging. When imaging single cells there are two types of errors that may occur: false positives and false negatives.
False positives can occur when a non-viable cell or cell-like debris is identified as a viable cell. In the case of a well with no other viable cells, this cell would be identified as clonal but would not produce a viable colony, as no cells are present. For a well containing one or more cells and non-viable cell-like objects, the well would grow but would not be identified as clonal and would therefore be rejected. Neither of these errors would result in a non-clonal cell line progressing into cell line selection and can therefore be disregarded for the evaluation of imager technologies.
False negatives occur when a cell that is not visible by imaging is actually present in the well. Similar to false positives there are two potential outcomes of a false negative. The first is a cell is present that is not counted via imaging and no other cells are present in the well. This could result in growth of a colony where no cells were visible during the imaging of the cloning step. The phenomenon of growth in wells with no apparent cell origin has been termed ghost wells (Figure 2). These cell lines will be rejected during monoclonality evaluation, as no progenitor cell was identified during the cloning process, and therefore present minimal risk to an imaging workflow. The second false-negative condition occurs when one or more cells is imaged but an additional cell or cells is present in the well that failed to be imaged. The case in which only a single cell is imaged but multiple cells are present is the primary risk of failure for an imaged cloning process. Under this condition, a cell line identified as monoclonal via imaging would be derived from more than one progenitor cell.
Outgrowth of a ghost well with no visible cell on day 0, immediately following cloning, and no cells visible on days 1, 2, and 3 after cloning.
A variety of techniques have been developed to reduce the rate of missed cells during imaging. Several of these methods have been implemented in routine cloning practices as part of cell line development activities. A description of these is provided in Box 1.
Techniques for Improving Imaging of Single Cells during Cloning
Due to the possibility of false negatives being identified via the imaging process, it can be necessary to characterize the robustness of a single-cell image. This characterization can provide statistical confidence that an image of a single cell corresponds to a well containing only one cell. That confidence can then be provided as additional assurance of monoclonality for a cell line.
However, there are some limitations with the information that can be extracted from a single image. Notably, there is no method to characterize how many cells are actually present in a well immediately after cloning, and in fact the best estimate of this value is the image under evaluation. As such, some other measures must be identified that can infer how many cells were present. To do this, three different verification methods were identified allowing characterization of the accuracy of an imaging system.
Limiting Dilution Goodness of Fit
The industry has confidence that the cells deposited during a LDC step will fit to a Poisson distribution (10). One straightforward assessment of an imager's confidence is to perform a goodness-of-fit assessment of a LDC to a Poisson distribution. This fit would allow an assessment of how well the imager reports the predicted distribution from this well-established cloning approach.
Data from five companies examining LDC is presented in Table I compared to a predicted Poisson distribution. Based on a visual evaluation, each of these samples clearly demonstrates a good fit to a Poisson distribution and based on this a qualitative degree of confidence can be assessed for an imager and an imaging method (Figure 3).
Goodness-of-Fit Analysis and Empirical Average Plating Density for Limiting Dilution Cloning Experiments across Five Companies. Cases where the empirical plating density was significantly different from the theoretical are highlighted.
Distribution of limiting dilution cloning (LDC) experiments across five companies (grey) compared to expected Poisson distribution (red). The target seeding density and the empirically calculated plating density for each experiment are noted beneath each distribution.
One potential numerical evaluation of this goodness of fit is a chi-squared (χ2) analysis, however this approach has several limitations which prevent an accurate evaluation of fit except under very limited conditions. For the presented data sets, only one passed a χ2 goodness of fit test with a p-value above 0.05 (Table 1), failing to confirm the alternative hypothesis that the data does not fit a Poisson distribution. The origin of this effect is from two sources, variation in the very rare events (e.g. greater than 3 cells/well observed) and overpowering of the studies. Based on a Poisson distribution with mean (λ) of 0.2 to 0.8, the prevalence of having four cells in a single well is between 0.005% and 0.769% of wells. Even for relatively large samples sizes (>1000 wells) this means a very small number of wells with growth are anticipated. Additionally, due to the calculation method of the χ2 statistic this expected low value can have a significant influence on the acceptance of the fit of the data. The difference for these very rare events may be affected by clumping of cells, resulting in an increase in the number of wells with more than one cell present. Secondly, the χ2 statistic is additive thus an overpowered study can result in rejection of the null hypothesis due to relatively low percentage variation in the observed and expected well counts. As a result, very high sample size can also result in the rejection of the null hypothesis observed for most of the presented data.
Although there are some weaknesses in a goodness-of-fit assessment, the fit of a Poisson distribution also allows for evaluation of the empirically observed plating density and can be compared directly to the theoretical target plating density for the experiment. Interestingly, for the data collected from two of the companies (Table I; Company D & E), the empirical plating density was significantly lower than the theoretical, and for one company the empirically calculated plating density was higher than the theoretical (Table I; Company B). This difference in theoretical and observed plating density could be attributed to error during the dilution of cells for the cloning process. Relying on the ability of the imager to accurately identify cells could be applied to assign a more accurate probability of monoclonality using Poisson statistics, and in some cases a higher confidence could be assessed using the theoretical plating target.
One additional deficiency of this verification approach is that it is only valid for a plating method with a known distribution. As such, it is not applicable for evaluation of a flow cytometry–based or single-cell plating device and instead could only be used to evaluate the imaging technology alone. However, this imager verification could potentially be applied to justify a lower dilution during a cloning process or a lower degree of verification for an alternative plating system. Additionally, it provides a more accurate measure of the actual number of cells plated than the theoretical count based on a bulk cell count and dilution factor.
In general, the success of a quantitative goodness-of-fit test in this evaluation is potentially affected by both a too low and too high sample size and will typically not serve as the best predictor of fit of LDC to the expected Poisson distribution. However, the qualitative result does provide a degree of assurance in the performance of LDC and can be easily assessed as part of a verification process or during routine cloning. Additionally, empirical cloning density can be determined to provide a more accurate assessment of the density of cells at cloning.
Ghost Well Error Rate
Perhaps the most obvious statistic to infer the number of cells that were actually present during the cloning stage is the outgrowth of colonies from the individual cloned cells. This value combined with imager data then offers an additional measure of the ability of the imager to detect the presence of cells.
Using well outgrowth data from ghost wells, wells identified by the imager to contain no cells but then produced viable cell growth, one estimation of the error rate of an imaging platform can be assessed. However, determination of an error rate for an imager using this ghost well count can be performed in a variety of different ways presenting different information in characterization of an imager and cloning process. Here we present two such calculations: a cloning ghost well rate that examines the rate of ghost wells relative to the total number of plated wells and an imager ghost well rate that examines the number of ghost wells compared to the total number of plated cells.
The cloning ghost well rate is defined as the number of ghost wells divided by the total number of wells plated in a cloning experiment.
Here outgrowth is defined as the presence of a colony at the end of a single-cell cloning study regardless of imager data. This measurement provides an estimation of the total error of the cloning and imaging process including the cloning approach, dilution of cells, and quality of images taken of those wells at the time of cloning. This error rate gives a measure of how often a cell or cells is completely missed by the imaging platform across an entire cloning experiment.
The imager ghost well rate adjusts the cloning rate by only accounting for wells that contain cells in them by examining the number of ghost wells observed divided by the total number of wells that contained cells.
The imager ghost well rate assesses the frequency at which cells present in a well were not successfully imaged. This provides a numerical measurement of how often an imaging platform fails to successfully identify cells present in a well normalized by the number of wells with cells plated in that experiment.
Although these equations allow for a determination of the number of wells with cells that are missed by the system, there is an additional variable that affects the error rate. Outgrowth of a colony from a single cell does not occur for all cells, and for cloning studies from BioPhorum member companies outgrowth of a colony from a single cell can vary from 10% to 98% depending on cloning media, cloning process, culture conditions, etc. As such an additional correction must be included to account for wells that contained cells that were not imaged and failed to outgrow. To make this correction one additional assumption was made: The outgrowth rate of ghost wells is equivalent to the average outgrowth percentage of all wells detected to contain cells via imaging.
Applying that assumption to each ghost well rate defined two corrected equations:
This simplifies to:
Using these equations one can then provide a measure of some aspects of the error in a cloning workflow. Unlike the goodness-of-fit assessment described above, it can also be applied for any plating method and can be used to calculate an error using any cloning approach. Data from seven separate companies is presented in Table II, including calculated outgrowth rates and corrected cloning and imager ghost well percentages. The corrected cloning ghost well rates observed ranged from 0.5% to 3.5% across the presented companies' data. As noted above, these values represent how often a ghost well was observed within a given number of wells plated, and this number is directly affected by the cloning method and cloning density. The corrected imager ghost well rate ranged from 1.2% to 8.1%. This value was consistently higher than the corrected cloning ghost well rate, as it is measured only from the number of wells containing growth. The imager ghost well rate accounts for only wells that had a deposited cell that grew into a colony, and so it is normalized by the cloning method and plating density.
Corrected Ghost Well Calculations for Seven Companies Examining Cloning Using Limiting Dilution or Flow Cytometry–Based Cloning
The corrected cloning ghost well rate and the corrected imager ghost well rate provide some information on the performance of a cloning workflow or an imager performance, respectively. However, these measurements have some limitations due to the nature of the error detected. Although ghost wells represent a cell that was not observed by the imaging platform, the cause of this failure can be due to systematic errors in the imaging process such as failure to focus in the correct plane of the well due to well artefacts. However, whole-well imaging errors would not affect the confidence of monoclonality because rejection of those images would also result in rejection of the associated cell lines. As a result the corrected cloning or imager ghost well rate is inherently inflated from the actual error rate of a clonal imaging process, and a higher ghost well rate may not be indicative of a less robust cloning process.
Although the corrected cloning and imager ghost well percentages have some limitations in terms of their direct correlation to confidence of monoclonality from a well image, they can provide a numerical estimation for the robustness of a cloning step. As noted above this estimation is inflated by mechanical failures of the imaging process, and as such these numbers correspond to a worst-case scenario with regard to numerical confidence. Given those limitations, this degree of error combined with a high probability of monoclonality could be applied to reduce the number of cloning steps required or could be applied as part of a verification package supporting the assurance around a flow cytometry–based or alternative single-cell plating system.
Distinguishable Cell Studies
The corrected ghost well rates described above provide an estimation of the error rate of a single cell image, but the value they assess is the probability that the imager identified zero cells when cells were actually present in the well. For a single cell image we are actually concerned with the probability that the imager indicates one cell is present and there actually are two cells. Due to the limitations in the ability to detect just one cell and that outgrowth can potentially occur from more than one cell, we require an additional variable to explore that probability. As such introduction of multiple, distinguishable cells into a cloning experiment provides a means to estimate the error rate of our imaging system when we detect a single cell.
To that end, cells expressing a reporter protein (e.g., fluorescent proteins) can be used as part of a verification procedure for both an imaging system and a cloning process. One such experiment examined a mixed population of green and red fluorescent protein-expression cells (12) (Table III). A pooled culture of these cells were co-cultured and then cloned via LDC and imaged on day 0 immediately after cloning. The wells were then assessed for fluorescence on day 20 of culture after cloning. From the day 0 cloning, 4403 wells were identified as monoclonal via imaging using automated image recognition software. These wells were then assessed for the presence of green and red cells after outgrowth. Of the 4403 wells identified as monoclonal by the automated imaging system, only 33 wells contained both red and green cells on day 20 of culture. Manual examination of the day 0 images revealed that 32 of these 33 would have been rejected as having multiple cells. Due to the low error rate by the image algorithm in identifying one cell at the time of plating and the ability to easily differentiate more than one cell in the image data by manual inspection, this indicates that plate imaging provides valuable data to ensure that our cell lines are clonally derived.
Results from a Distinguishable Cell Study Examining the Error Rate at Detecting 1 Cell/Well via Imaging for a Limiting Dilution Cloning Experiment
To assess this imaging error percentage, some similar manipulations to the corrected ghost well calculation must be made. These include a correction for outgrowth and for the potential occurrence of two same-color cells being plated together. For the outgrowth percentage the assumption is made that the outgrowth of each cell into a colony is equivalent to the average outgrowth rate of our 1 cell/well–containing wells. The two populations were plated at an approximately equivalent density and had similar cloning efficiencies; therefore we would assume that the probability of having a mixed population (green and red) is equivalent to the probability of a same color (green and green or red and red). As such, the corrected monoclonal imaging error rate is:
This simplifies to:
Applying that statistic to the example described above produces an error rate of 2.7% using the automated image analysis and 0.09% with the addition of manual image verification. This corresponds to a 97.3% or 99.9% confidence in an image of a single cell in a well corresponding to a clonal cell line. This high degree of confidence is greater than two rounds of cloning at 0.06 cells/well plating density, which is far below the plating density of common industry standards. As such, with this cloning verification, a single round of LDC or an alternative single-cell plating system could be applied and produce a higher degree of probability and assurance than well established and accepted cloning methods such as traditional, two-round LDC.
Approaches to Addressing Regulatory Requirements for Clonal Cell Lines
The verification methods described above provide a few techniques for translating results from new technologies into actionable data in support of monoclonality for mammalian cell lines. Using such data or applying similar approaches affords a variety of methods for the demonstration of monoclonality that meet the expectations from regulatory agencies. Monoclonality is often described in two categories: probability and assurance (3⇓–5). Probability refers to a numerical confidence of monoclonality as a result of cloning methods and verification data (e.g., Poisson distribution for LDC). Assurance contains the body of data supporting that numerical claim and any qualitative support for monoclonality such as images, technical set points for cloning systems, or retrospective monoclonality evaluations. Based on these categories one can imagine several approaches to meeting regulatory expectations with regard to demonstration of monoclonality of a mammalian cell line.
Two Rounds of Limiting Dilution Cloning (LDC)
LDC is a well-documented, widely used, straightforward, and cost-effective method to select a single progenitor cell from a polyclonal population of recombinant cells to produce therapeutic proteins. LDC assumes a Poisson distribution of the population of plated cells using the average plating density as the mean of the distribution. Therefore, if using an input seeding density of 0.5 cell/well, one round of LDC would provide a 77% assurance that the cell line was clonally derived. If a cell line that has undergone LDC at 0.5 cell/well is then used in another round of LDC at 0.5 cell/well, the confidence increases to 95%. Using the same analysis for a 0.4 cell/well seeding density, one round of LDC would provide an 81% assurance that the cell line was clonally derived. If this cell population was subjected to another round of LDC at 0.4 cell/well, the probability that the cell line was clonally derived would increase to 97% (10). The use of the input number of cells to determine the probability of a cell line being clonally derived is the most conservative assumption but still does not take into account cell-to-cell interactions and that not all cells grow after LDC. Additionally, using two rounds of LDC is time-consuming and may slow the cell line development process.
Single-Round Cloning Coupled with Verified Imaging
New technologies can facilitate an improved approach to demonstration of monoclonality. Notably, the implementation of high-throughput single-cell imaging allows for the direct assurance of whether a cell line is clonal. As such, combining imaging in support of a well characterized cloning approach such as LDC or a qualified flow cytometry–based system for single-cell plating can demonstrate monoclonality with more reliability than the current standard of two rounds of LDC. In this approach a more limited probability via the single round of LDC could be supplemented with an increased probability using an imager verification study performed on equipment and protocols as has been described here. Additional assurance could also be provided in the image verification approach and through the physical images collected as part of the cloning process.
As an intermediate approach, in-process verification such as Poisson goodness of fit or leveraging ghost well calculations could also serve as a supplemental assurance to a confidence generated from an LDC study.
Similarly, if a qualified flow cytometry–based system, or equivalent, is employed for single-cell plating and the verification of the workflow demonstrates a high degree of confidence, then additional assurance may be unnecessary.
In general, a single-cell imaging platform provides a framework by which to enhance the probability and assurance of monoclonality to meet the expectations of a clonal cell line. The application of imaging technology and the need for characterization of that technology is wholly dependent upon the degree of probability and assurance that exists in the cloning techniques applied independent of the imaging.
Process Consistency and Genomic Plasticity
While cloning of the cell line that will eventually generate the master and working cell banks used to produce clinical and commercial protein therapeutics reduces heterogeneity of the cell line, it is only one of many steps in product development to produce a robust manufacturing process to ensure safe and efficacious protein therapeutics and consistent product supply. Innate plasticity of the CHO genome is well documented and leads to heterogeneity of the cloned cell line and resulting cell bank during generation accrual (13,14). Given the potential heterogeneity of the master cell bank, demonstration of monoclonality post-cloning is not possible with absolute certainty. Moreover, proteins are highly complex molecules generated in living cells and, as such, are inherently heterogeneous. A clonal cell line does not guarantee process and product consistency; similarly, a heterogeneous cell line may not always result in significantly heterogeneous product characteristics. Therefore, development and demonstration of a robust manufacturing process that delivers consistent product quality attributes is most important. A primary concern regarding non-clonal cell lines is process improvements made post-approval that could result in selection of a sub-population of cells that generates product with significant changes in quality attributes that will not pass release tests and negatively affect product supply. If upstream or downstream process changes result in significant product attribute differences, then additional clinical trials may be required to ensure that these differences have no impact to product efficacy and safety. As such, monoclonality is a small piece of an overall control strategy including demonstration of cell line phenotypic and genotypic consistency, comparability of product quality from Phase I through licensure and post-licensure, supporting pre-clinical and clinical trials, process validation, and a well-defined control strategy required to demonstrate that the protein product is safe, efficacious, and available to patients.
Conclusions
Plate-based, single-cell imaging is one of many technologies affecting the methods applied for generation of clonal mammalian cell lines. The performance verification methods described here present an approach to qualitatively and quantitatively assess the performance of an imaging platform. These evaluations provide a straightforward means to ensure the robustness of a cloning process and to meet regulatory expectations for derivation of a cell line from single cell.
Beyond the scope of data here, application of verified imaging technology may afford the ability to confirm performance of other next-generation technologies for single-cell cloning. Such technologies include single cell printers that can employ in-line, active imaging as part of single-cell plating. Through the application of similar characterization and verification of these technologies, comparable or better confidence and assurance of monoclonality can be maintained while continuing to enhance throughput and timelines for cell line development activities.
Overall, the goal of single-cell cloning remains the production of a robust cell line that, when combined with a well-designed control strategy, can ensure consistent manufacturing performance resulting in safety and efficacy for the therapies serving each of our patients. As more technologies and approaches are developed to support these activities, we must ensure that we employ robust techniques to understand the benefits and the risks in their implementation.
Conflict of Interest Declaration
The authors declare that they have no competing interests.
Acknowledgements
Masahiro Kokubo, Keina Yamaguchi: Kyowa Hakko Kirin Co., Ltd., Gunma, Japan; Bernie Sweeney: UCB Pharma, Slough, UK.
This article describes a consensus view from the BPDG Monoclonality team. The authors sincerely thank the members of the team for their contribution at monthly BPDG discussions and in the preparation of this manuscript.
Since its inception in 2004, the BioPhorum has become a trusted environment where senior leaders of the biopharma industry come together to openly share and discuss the emerging trends and challenges facing their industry. BioPhorum currently comprises more than 2000 active participants in 6 Phorums; Drug Substance, The Development Group, Fill Finish, The Technology Roadmap, BioPhorum IT Group, and BioPhorum Supply Partners. The Monoclonality Collaboration is part of the Biophorum Development Group. The article is a composite view of opinions shared by the whole of the BPDG-Monoclonality and should not be attributed to the individual positions of the participating companies.
- © PDA, Inc. 2018
