	Secrecy and black-box operations are out; public disclosure is the mantra for regulators and manufacturers alike, as FDA explores more options for communicating agency policies, approval decisions, and concerns about product quality and safety. The drive for greater transparency in agency decision-making fits a range of health policy goals. More information on drug pricing, discounts, and rebates is considered key to limiting payer and patient outlays for prescription drugs. The Federal Open Payments, or “Sunshine”, policy tracks industry payments, gifts, and transactions with healthcare professionals to uncover any industry influence on prescribers. And more timely and complete information on product safety and recalls aims to prevent patient harm, as do efforts to make drug labeling more informative.

	A main transparency issue involves public access to clinical study data and results. While industry sponsors of clinical trials are meeting requirements for listing new studies on the ClinicalTrials.gov website, the record is weaker for timely posting of research results for newly approved medical products. The data transparency movement also has prompted biopharma companies to provide scientists and researchers with access to confidential clinical research data and to limit publications to open access journals. Meanwhile, academic researchers have lagged in disclosing clinical research activities, prompting the National Institutes of Health (NIH) to threaten to cancel grants to organizations that fail to meet requirements. 

	Transparency also is considered important for enhancing drug quality to reduce product recalls and shortages, according to a white paper from the 

 (OPQ) in the Center for Drug Evaluation and Research (CDER) (1). As part of efforts to encourage manufacturers to adopt continuous manufacturing and systems to better ensure quality, an OPQ pilot study is exploring strategies with potential to enhance the transparency and consistency of assessments and facilitate team-based review and communication. 

	Other nations are joining the transparency movement. The China Food and Drug Administration (CFDA) issued draft guidance on policies for disclosing information regarding the acceptance, review, and approval of drug applications. While aiming to keep trade or technical data confidential, CFDA will make public information on active ingredients, dosing, license holder, and patents 60 days following an approval. 

	Despite expanded information disclosure related to biomedical research, drug marketing, and product safety, stakeholders want to know more about the status of drug applications and FDA’s decision-making process in both rejecting and approving submissions. A main demand-from both consumer activists and free market deregulators-is for access to complete response letters (CRLs) sent to manufacturers that essentially delay or reject an application and outline what additional clinical or manufacturing information is needed to achieve approval. FDA currently posts summaries and reports on newly approved drugs and biologics and has indicated interest in also posting CRLs. But manufacturers strongly oppose such a move as CRLs usually contain trade secrets or confidential information. Current law requires FDA to redact such manufacturing information from public disclosures, and any move to change that policy may require legislative action. 

	These issues are not new and reflect decades of reviewing and updating FDA policies on public access to information on agency operations and regulated products. A Transparency Initiative launched in June 2009 under former FDA Commissioner Margaret Hamburg and former Principal Deputy Commissioner Joshua Sharfstein has established FDA online “dashboards” that track agency actions and programs, including inspections, recalls, imports, and compliance actions. A “Drug Trials Snapshots” initiative posts data from clinical trials on products approved since January 2015. FDA has expanded access to agency enforcement reports and adverse event data and has made its guidance development process more visible and efficient.

	Last year, Sharfstein, now affiliated with the Johns Hopkins Bloomberg School of Public Health, organized a group of experts to produce a Blueprint for Transparency at FDA, which was published March 2017 in a special issue of the 

(2). Among its 18 recommendations for making FDA decisions less opaque, the Blueprint calls for FDA to provide more public information on its evaluation process for new drugs, generics, and biosimilars, including what products are in the review queue and why certain applications are not approved. The rationale is that discussion of unsuccessful R&D would help researchers avoid studies unlikely to succeed, reducing costs and avoiding patient exposure to potential harm. The analysts also seek authority for FDA to correct misleading information issued by manufacturers, such as incomplete factors underlying a CRL, and for the agency to disclose data from clinical trials for approved products when sponsors fail to do so. 

	Such proposals reflect greatly expanded public access to information on drug testing and production through the Internet, social media, and smart phones-not all of it accurate or unbiased. Consumers and industry competitors can obtain reports of adverse events and enforcement actions, raising questions about the value and impact of limits on what FDA can or cannot disclose about a product or manufacturer. 

	FDA Commissioner Scott Gottlieb addressed these issues at a January 2018 forum on the Hopkins transparency report. He 

 a new pilot to test the impact of FDA posting more data from clinical study reports (CSRs) of approved drugs, asking that sponsors of nine new products voluntarily provide CSR data, protocols, and statistical analysis plans for pivotal studies (3). In addition, FDA aims to better track drug studies from initial Internet posting through FDA approval by adding the ClinicalTrails.gov identifier (NCT) number to all clinical data submitted to the agency. 

	However, Gottlieb hedged about publishing CRLs, proposing to further explore FDA’s authority to release these documents. While he acknowledged that some information in CRLs might enhance the safe use of already approved products, he noted that redacting proprietary information from these letters is burdensome and questioned the value of disclosing letters that cite manufacturing shortcomings.

	Gottlieb also mentioned FDA efforts to inform the public more quickly about adverse events and product recalls, which totaled more than 9000 in 2017, including 1200 involving drugs.  Up until now, FDA has delayed recall announcements until it determined whether a safety issue raises serious health consequences, as with a defective anesthesia product, or represents low risk. Now FDA’s Office of Regulatory Affairs (ORA) will include in weekly Enforcement Reports “not-yet-classified” recalls while the months-long classification process continues. FDA issued

 in January 2018 outlining when a manufacturer should issue a public warning about a recall, including timelines and important data to include and what information FDA will post in its reports (4). 

	Although FDA has authority to require recalls, most drug manufacturers initiate such actions voluntarily, aware that FDA can issue press releases and alerts if companies are slow to act. Recall announcements from companies seem to appear almost weekly, many involving particulates or contamination of sterile injectables. Unfortunately, the failures that lead to drug recalls often create shortages in needed medicines. FDA and manufacturers will be looking to see if the earlier recall listing process generates alarms that turn out to be unnecessary, or if the new policy gets high-risk products off the market more quickly.  

Office of Pharmaceutical Quality, FDA Pharmaceutical Quality Oversight, One Quality Voice

, Special Supplement to Vol. 45 (4) 7-23  (Winter 2017).

Fostering Transparency to Improve Public Health

Guidance for Industry: Product Recalls, Including Removals and Corrections 

(Office of Regulatory Affairs, Nov. 3, 2003).


	Vol. 31, No. 3
	March 2018
	Pages: 8–10

	When referring to this article, please cite it as J. Wechsler, "FDA Heightens Drive for Transparency," 

Articles

Regulatory Beat

Manufacturers face demands for timely information on clinical studies, product recalls, and approvals.

FDA Heightens Drive for Transparency

	Modern bioprocessing and antibody manufacturing are mature fields; however, challenges remain, and the advances themselves often bring new challenges. This is the nature of progress. The current challenges are exemplified by the ongoing advances and industry adoption of technologies such as continuous processing, the importance of modeling bioprocesses, increasingly seamless downscaling of processes, and other factors that are expected-and even required-by regulatory agencies. 

	Over the past 15 years, the industry has evolved into a mature, productivity-oriented segment. Of the dozens of trends evaluated in the annual BioPlan Associates survey (1), productivity continues to show the highest focus as an industry objective (See 

). This suggests that other operational aspects must address industry challenges around improving productivity and efficiency. 

Figure 1: Single most important biomanufacturing trend or operational area, 2014-2017. Source: Summary Data, 

 Annual Report and Survey of Biopharmaceutical Manufacturing

, BioPlan Associates, Inc., April 2017. (Figure courtesy of author)

	But to achieve successes, operational challenges must be worked out. As an example, continuous bioprocessing is one of, if not the most, challenging area for process development. Continuous processing-including both upstream perfusion and downstream continuous purification processing--remains rare and elusive despite the fact these technologies have been established for some time. These challenges particularly stand out when continuous and conventional batch processing options are compared. 

 shows survey results when bioprocessing professionals were asked to indicate issues with adoption of upstream perfusion that are much or somewhat bigger.

Figure 2: Selected concerns, perfusion vs. batch-fed processes. Red is much bigger concern with perfusion. Blue is somewhat more concern. White is at least somewhat of a concern, 2017 Source: Summary Data, 

, BioPlan Associates, Inc.,  April 2017. (Figure courtesy of author)

	Bioprocessing professionals clearly see a number operational problems remaining with upstream perfusion versus conventional batch processing. In 2017, a number of concerns were cited by more than 50% of respondents to BioPlan’s survey: 

		Upstream [process] development and characterization time 

		Process development control challenges.

	This daunting collection of concerns about perfusion persists despite it being in use and marketed for multiple decades-including for commercial biopharmaceutical manufacture-notably for Factor VIII. In many respects, perfusion may be viewed as a technology adoption model in bioprocessing that demonstrates the slow nature of adoption of rational, effective technologies. BioPlan market research studies (1) have shown that approximately 5% of bioreactors use perfusion, with many of these being feeder and not production bioreactors. Further, while perfusion is seen by users as highly productive, often better and cheaper versus batch processing, most using it in early manufacturing continue to avoid its use in commercial manufacturing, viewing the challenges as high and the risks significant, in terms of regulatory approvals. One challenge for perfusion process adoption is that single-use perfusion units only recently became available. This lack of familiarity could be a factor in the reporting of high levels of concerns with--and even fears of--perfusion versus batch bioreactors likely among those not using the technology. Many challenges remain before the industry gains experience and confidence in this option for continuous upstream processing.

	Downstream continuous operations, particularly chromatography, has more challenges; suitable hardware with built-in data systems are just starting to become available from suppliers. Downstream continuous bioprocessing was cited as the second single-most important bioprocessing trend or operational area on which the industry must focus its efforts. In BioPlan’s study, perceived need for improvements carries over to facility budgets with interviewees reporting an average increase of 8.8% in their 2017 downstream technology budgets vs. 7.7% for upstream; 33.8% cited continuous chromatography as a “top area where suppliers should place their efforts on.” 

	Adoption of continuous chromatography, generally involving use of multiple interfaced columns with integrated controls, is growing more rapidly than perfusion adoption; however, adoption has only recently started from a near zero baseline and is low. Combined with other downstream advances (e.g., increasing use of membranes in place of resin-filled columns, automated in-line buffer dilution and column packing, better resins, and more single-use and recyclable columns), downstream processing is in many respects changing and evolving more rapidly than upstream, but the adoption and integration into actual bioprocessing remain slow. When asked to cite the top downstream areas where suppliers should focus their development efforts, disposable purification systems were cited by 35.9% of respondents, followed closely by continuous purification cited by 33.9%. Geographically, European respondents are much more interested in continuous purification compared with those in the United States (44.4% vs. 25.3%).

	But basic problems remain with broader adoption of both upstream and downstream continuous bioprocessing, including the inability to integrate related manufacturing systems. Currently, implementation of end-to-end continuous bioprocessing, or even upstream or downstream segments, remains rare or nonexistent, with at best one or a few unit processes implemented as continuous. 

	Bioprocessing modeling and understanding of implications of changes in scale remains another challenging area. The industry has naturally concentrated on scale-up, as drug product volumetric requirements increase as products advance in development. More is known about scaling up versus scaling down. But the importance of scaling down is rapidly increasing, including the ability to model and predict what happens in research and development by using smaller scales, rather than having to run experimental studies at various scales, including full scale. Having scale-down capabilities and understanding is increasingly expected, if not required, in FDA and other major market regulatory filings, particularly for product approvals. Being able to scale down and have related models and knowledge are now key parts of process analytical technology, quality by design, and other regulatory/quality initiatives. In addition, validated process down-scaling methods often are required for cost-effective design and refinement of bioprocessing. This is exemplified in the use of down-scaled bioprocesses to validate process biosafety, including reduction in virus titers from virus filtration and chromatography steps. The costs of validating virus removal and inactivation would be too expensive and time-consuming if run for full-scale processes. 

	Bioprocess system designers also want the ability to conduct computer-based modeling to scale up and scale down a process using bioprocessing equipment from the same product line. 

	This particularly applies to purchases of bioreactors, mixers, chromatography, and other major bioprocessing systems, but also extends to filters and other equipment. Scalability, particularly including scale-down ability, is a top equipment/technology selection factor cited in bioprocessing supplies-related market research studies. Many facilities will not purchase equipment unless reasonable scalability is known and documented among products throughout a product line, particularly for bioreactors and chromatography and filtration systems. Bioprocessing professionals regularly cite an expectation that bioprocessing systems will seamlessly scale up and down. In addition, the users want to model or predict the operation of the systems at scales higher than anticipated use to provide better assurance of actual scalability throughout the range of scales they actually will use, such as from mini/desktop to 2000-L or larger production bioreactors. 

	To move forward, industry and its investors need to recognize this segment has historically been one of the slowest, but also the most persistent, in terms of adopting the advances that the industry requires. To achieve success, operational challenges must be worked out, but in this regulated, high-stakes environment, the timelines for development and adoption of new technologies create challenges that, sometimes, require years to resolve. 

Report and Survey of Biopharmaceutical Manufacturing Capacity and Production

, 13th Annual (BioPlan Associates, 2016).


	Vol. 31, No. 3
	March 2018
	Pages: 12–13

	When referring to this article, please cite it as R.A. Rader, "Current Challenges in Bioprocesses Development," 

Perspectives on Outsourcing

Development and adoption of new technologies create challenges that may take years to resolve. 

Current Challenges in Bioprocesses Development

	Advanced data analytics tools are used by industry to find the golden nuggets in historical data, to aid in process development, to fine-tune production, and to achieve long-term improvements in product quality and throughput. During recent years, four key stages of data analytics have been defined (1), which can be seen as being part of a data analytical continuum (see 

). Handling those key stages diligently by using advanced data analytics tools is expected to give manufacturers a competitive edge. 

	Along the data analytics continuum (described in detail in 

), the most advanced challenge is being able to predict what will happen in the future and, in the event of an undesirable outcome, prescribe certain activities or interventions to prevent it from happening. Although looking ahead into the future is of greatest commercial interest, value is created at every stage of data analytics; it all depends on the specific need and the tools and approach to the analytics process.

Figure 1. Illustration of the data analytics continuum. At the base level of descriptive analytics, data aggregation and data mining provide insight into the past and answer: "What has happened?". Diagnostic analytics examines data or content to answer the question: "Why did it happen?", characterized by drill-down, data discovery, mining, and correlations. Predictive analytics uses statistical models and forecast techniques to understand the future and answer: "What could happen?". At the pinnacle of data analytics, prescriptive analytics uses optimization and simulation algorithms to advise on possible outcomes and answer: "What should we do?" or "How can we avoid this happening?". All figures are courtesy of the authors.

	In the bioprocessing industry, parts or all stages of the data analytics continuum are applicable. In early-stage development, advanced data analytics is used to compare batches, for example, to figure out how to modify cell expansion steps so that they lead to higher cell densities and product titers. In late-stage development, advanced data analytics is used when scaling-up manufacturing processes to verify comparable performance at different scales. And in full production, advanced data analytics is used for real-time bioprocess monitoring and early fault detection of batches deviating from good, normal operating conditions.

	A primary objective in bioprocess development and scale-up is to accomplish a consistent, uniform, and predictable environment across scales. The following case study describes the essentials of a bioprocess characterization and scale comparison project at various stages of the data analytics continuum.

	In total, a development project for a fed-batch cell-culture biological process encompassed 75 batches. Metabolite measurements were taken once per day. The metabolite data included cell performance metrics, selected process conditions, and nutrient concentrations. In addition to the batch evolution trajectory measurements, initial and final batch conditions were registered. Some calculated parameters were added to the trajectory data to enhance information content and assist in interpretation. These data come from three operating scales (micro, micro+, and pilot) including both stable operating conditions and some runs planned using design of experiments (DOE) (2).

	There are multiple uses of these types of data. In the descriptive analytics stage, data analytics tools such as partial least squares (PLS) or orthogonal partial least squares (OPLS) can be used for batch evolution modeling to understand what happened within and between batches during the batch lifetime (3). Popular graphic representations involve control charts displaying time evolution trajectories of important process variables or model statistics to diagnose which batches conform to good normal developmental trajectories and which batches do not (see 

Figure 2. Control chart from a data analytics model capturing batch evolution. The horizontal axis shows batch lifetime in days. The vertical axis is the charted statistic (t), which is a parameter of the data analytics model. The red dashed lines represent upper and lower control limits. Each batch is represented by a single line. As long as a batch line stays within the control limits, normal operating conditions are inferred.

	Another perspective often used in descriptive analytics is to compare batch-to-batch variations by representing the complete batch trajectory in a single summary. In this comparison, an often-used data analytics tool is principal component analysis (PCA) (3). A typical output from a PCA is shown in 

, in which color coding is used to represent batch scale and marker sizing is used to represent peak viable cell density (VCD). The pilot scale has the most consistent operation with high peak VCD, as shown in 

, where the green markers are all of similar size and operating conditions. The micro scale is most similar to the pilot scale, as indicated by the blue markers in 

 The micro scale has consistent operation, but there is considerable variation in peak VCD, with some very low VCD runs shown by the smaller markers in 

) demonstrates extensive variation in its process performance with large variation in peak VCD.

Figure 3. Scatter graph arising from a batch-scale comparison model. Each plotted point represents one completed batch. Points that are close to each other correspond to batches of similar process operating conditions. Points that are far away from each other correspond to batches of very different process operating conditions. Symbol color indicates batch scale: blue is micro, red is micro+, and green is pilot. Symbol size indicates final peak viable cell density (VCD). In particular, there is a conspicuous deviation on the micro+ scale for some batches marked by the frame at the top of the graph.

	Subsequent to descriptive analytics, diagnostic analytics can be performed. In this case study, the batch-scale comparison model is interrogated to uncover why any differences detected (shown in 

) did occur. A data scientist could here be interested in comparing batches between scales. Given the deviating cluster on the micro+ scale, an equally relevant scenario would be to first try to interpret the split of the batches on this scale. Regardless of interpretation objective, similar diagnostics pathways are used.

 shows a plot often used in diagnostic analytics denoted “contribution plot”. This plot was created to find sources of variation contributing to the deviating cluster of micro+ batches framed in the top of 

 shows that this cluster of batches started with a higher than average sodium trajectory (brown line) and a lower than average osmolality profile (purple line), for example; other interesting features are also revealed. Conceptually, similar plots can be readily created to interpret scale differences.

Figure 4. Contribution plot visualizing process parameter contribution to the discovered deviation for the framed batches shown in Figure 3. Parameter lines far away from zero (both in the positive and negative numerical direction) point to variables that contribute to the deviation. These variables should be considered when taking action to remove batch and or scale differences.

	The third stage of the data analytics continuum corresponds to predictive analytics. The term predictive analytics may carry different connotations for different people. In one aspect, predictive analytics may refer to the act of, given a fresh set of batch process measurements, “classifying” (i.e., predicting) the currently evolving batch into a process monitoring model to uncover whether this batch conforms to, or deviates from, normal operating conditions. In another aspect, predictive analytics may mean an attempt to explain (i.e., predict) final batch critical quality attributes (CQAs), such as peak VCD or titer, in terms of their correlations to batch critical process parameters (CPPs). Irrespective of the connotation of the term, predictive analytics to find out what will happen is conveniently carried out by using tools like DOE, PCA, PLS, and OPLS (2, 3).

	Prescriptive analytics is the last stage of the data analytics continuum, and, at this stage, focus lies on forecasting the future, either to drive the manufacturing toward an optimal situation or to avoid certain undesirable process hiccups from happening. In general, in real-time applications, the stages of predictive and prescriptive analytics are often intimately linked (4). According to reference 4, “one approach that has become common in biologics manufacturing is the use of multivariate modeling and calibrate models to determine how a process should run. We turn such models into control charts and track a process as it is running.” Beyond this real-time monitoring step, model predictive control tools are described, which are available in terms of a real-time bioprocess forecasting, control, and optimization toolbox (4). With this toolbox, “smart changes” (i.e., “advised future”) to setpoints of bioprocess CPPs resulting in optimized production is given.

	The data analytics continuum is a way to concatenate the four major steps of data analytics into an organized and chronological work order. Alongside this continuum, the dimensions of “business value” and “complexity” are often appended to further enrich the understanding of the concept. The continuum is general in the sense that its stages apply to any type of data (e.g., production, finance, medical), and the different stages may be addressed using different types of data analytical tools. For example, as shown in this article, the various stages of bioprocess characterization and monitoring are stratifiable according to the steps of the continuum.

	This article illustrates how evaluation of batch type data, including initial and final conditions and batch evolution trajectories, can be accomplished using projection methods such as PCA, PLS, and OPLS. Such methods, preferably prudently combined with DOE, enable powerful scale-up modeling possibilities of bioreactors to support process development and improvement in a predictable, timely, and cost-effective manner. The main conclusion of the use case cited was that micro- and pilot-scale batches performed similarly, albeit with some variability in peak VCD, and that the batches of the micro+ scale deviated substantially and with a strong clustering among themselves. By using Â­contribution-based diagnostics, reasons for scale differences and clustering were visualized, interpreted, and mitigated. 

	1. Gartner, “2017 Planning Guide for Data and Analytics,” www.gartner.com/binaries/content/assets/events/keywords/catalyst/catus8/2017_planning_guide_for_data_analytics.pdf  (accessed Jan. 31, 2018).

Design of Experiment: Principles and Applications

, (MKS Umetrics AB, Sweden, 3rd Ed., January 2008).

 Multi- and Megavariate Data Analysis Basic Principles and Applications

, (MKS Umetrics AB, Sweden, 3rd Ed., May 2013).

 (November 2017) www.bioprocessintl.com/manufacturing/process-monitoring-and-controls/model-predictive-control-for-bioprocess-forecasting-and-optimization/ (accessed Jan. 31, 2018). 

	When referring to this article, please cite it as L. Eriksson and C. McCready, "Characterizing a Bioprocess with Advanced Data Analytics," 

	Lennart Eriksson, PhD, is senior lecturer and data scientist, lennart.eriksson@sartorius-stedim.com, and Chris McCready is lead data scientist, both at Sartorius Stedim Data Analytics.

Features

Modeling at various stages of the data analytics continuum aids scale comparison of a bioreactor.

Characterizing a Bioprocess with Advanced Data Analytics

Q. We are planning to upgrade several of our automated systems in production and in the laboratories. These upgrades are necessary so that we can implement functionality like audit trails, which are now required to achieve data integrity compliance. We contacted suitable vendors, and some have now offered to sell to us fully 21 

) Part 11 and data integrity-compliant software packages. Such a package seems like a very good deal, but it is not offered by the majority of suppliers. Can you give some insight on how other companies address this situation?

	A. You are in a very typical situation where not all your automated systems are of a technical standard that make compliance with the applicable regulations possible, unless you upgrade or replace certain systems. It is an unfortunate fact that there are still vendors out there who make misleading claims, either out of ignorance, or worse, knowingly. The only party that can legally commission and operate a computerized system in a fully compliant manner is the system owner (i.e., someone within your organization). Only you know how you are going to use the specific system and for what purpose. No vendor can do this for you. Therefore, automated system suppliers can merely offer to sell you systems that are designed and built in a manner that allows you, the customer, to operate them in a compliant manner (e.g., complying with 21 

Part 11 or other regulatory requirements).

	Let me give you an example to clarify this: You may purchase a system upgrade that provides audit trail functionality. Although the vendor gives you an audit trail as you requested, you may choose not to activate it (perhaps because it slows the system down too much). Now you may be in a non-compliant situation. Or, you decide to activate the audit trail, but upon review you find that it is not in human readable form, or that it only captures a fraction of the transaction, or that the amount of data in the audit trail is so overwhelming that it becomes unmanageable.

	Savvy companies have in-house experts with a sound understanding of the regulations covering automated systems, how to perform computerized systems validation, and how to optimally harness the vendors’ expertise. These experts will put together the user requirement specifications (URS) for the various systems. The URS is the document that will steer how the system (or system upgrade) will help you to operate in compliance with the regulations (i.e., what it takes to make sure that data are trustworthy and your system is fully validated). In the URS, companies will specify what they expect from the audit trail (e.g., it must be human readable, sortable, exportable, searchable, etc.). 

	The URS also forms the basis for the testing requirements, namely the testing by the users. Users may be quality unit personnel who need to verify that on an analytical instrument the series of injections for an analysis tally with the method, or that there were no rogue injections. Only these people will know what they are looking for and how they want to perform their review. Your system vendors are now tasked with providing you with a system that meets your needs, and not just a ‘one-size-fits-all’ solution.

	Don’t be lulled into a false sense of security by sales promises; instead, make sure you have experts at hand who can help you select the systems and suppliers who best meet your needs. Once you do this, you will find that validation and compliance even with the most demanding regulations become not only possible, but exciting. 

	When referring to this article, please cite it as S. Schmitt, "Computerized Systems Validation," 

Ask the Expert

In-house experts can help select the right systems and suppliers, making validation and compliance easy, says Siegfried Schmitt, principal consultant at PAREXEL.

Computerized Systems Validation

	A recent report from PwC’s Health Research Institute (1) summarizes long-term trends and changes in FDA activities that occurred in 2017, and the implications for the bio/pharma industry, and offers a performance evaluation for the agency and industry. According to the report, FDA had a good year in 2017; the agency approved 45 new molecular entities, the second highest total in history, and the most generic drugs ever (763 full approvals and 174 tentative approvals). These approval successes came as the number of new regulations declined but enforcement efforts increased.

	In addition, a record number of drugs (476) received orphan drug designation in 2017, an indication of the ongoing trend of companies developing specialty and oncology drugs for smaller patient populations. Under the 2017 tax legislation, the tax credit for research and development costs for orphan drugs was cut in half, to 25%, lowering incentive for companies to pursue such research.

	The report notes only six economically significant regulations-all unrelated to pharmaceuticals or biotechnology-were released by FDA in 2017, compared with 30 or more in the previous four years. This slowdown was the sole significant “sudden change” observed by the authors. Rulemaking, however, increased in the second years of the Bush and Obama administrations; the report notes that 18 pharmaceutical regulations are under development and could be published in 2018.

	While the number of rules decreased in 2017, FDA continued to make policy by issuing guidance documents with user fees, generic drugs, and procedural topics receiving the most attention. The 174 draft and final guidance documents exceeded the average of 152 per year issued under the Obama Administration. Nearly 100 guidance documents are on the Center for Drug Evaluation and Research’s (CDER) agenda for 2018, dealing with procedural changes; quality; chemistry, manufacturing, and controls; and generic drugs.

	Overall, FDA issued fewer warning letters in 2017 (510) compared with previous years. However, the 85 letters issued by CDER in 2017 were nearly double the amount issued in 2014. This jump in the number of warning letters was triggered by increased enforcement efforts authorized by the Drug Quality and Security Act of 2013 and many targeted offshore API and drug product manufacturing facilities.

	The number of deficiencies found during inspections held steady; failure to have or follow written procedures, failure to scientifically validate testing procedures, and failure to fully investigate discrepancies in manufacturing or testing processes were the most frequent offenses.

	Scott Gottlieb, who took over as FDA commissioner in May 2017, has demonstrated a strategy to “provide regulatory relief where appropriate, introduce competitive policies and eliminate restrictive ones, and promote innovation and the exchange of information” (1).

	That approach should appeal to pharmaceutical executives surveyed by PwC; 36% of the respondents would like to see standards for approval, including minimized regulations for lower-risk products, streamlined. Only 6% would like to see streamlined manufacturing regulations, including manufacturing quality and inspections.

	1. PWC Health Research Institute Insight, “Continue to Take as Prescribed,” February 2018.

	When referring to this article, please cite it as R. Peters, " Regulatory Role in Bio/Pharma Progress" 

From the Editor

FDA enforcement efforts and drug approvals trend upward. 

Regulatory Role in Bio/Pharma Progress

Transporting sensitive pharmaceuticals is a risky and complex business. “The lower deck conditions within a Boeing plane or a typical intermodal freight container are a long way from the GMP-validated cleanrooms and controlled laminar-flow environments of the pharma production environment,” says Alan Kennedy, founder and executive director of TEAM UP, an organization that focuses on pharma supply chain issues. 

	According to the International Air Transport Association (IATA), the global pharmaceuticals logistics market is valued at $64 billion and is one of the most regulated, expensive, and fragile cargo markets in the world today (1), says Kennedy.

	Pharmaceutical transport typically combines different modes, explains Kennedy. Air transport, prized for its speed and flexibility, is used mainly for long distance, intercontinental distribution of the most valuable therapeutics, he says, while road is the most widespread method and is also used to connect with both air and sea freight.

	Rail remains an insignificant factor, but this may change, Kennedy says, as new “silk route” connections between Asia and Europe are established (2).

	Ocean transport may conjure 1940’s images of the days when most people still crossed the ocean by ship. It has historically been the Cinderella of pharma distribution, says Kennedy, but, IATA notes that 3.5 million metric tons of pharmaceuticals are still shipped by ocean each year, compared with 0.5 million metric tons by air (3).

	Over the past five years, ocean transport has taken on much greater visibility in the pharma industry. Cost competitiveness is one factor driving acceptance, he says, since it is up to 80% less expensive than air transport.  In addition, Kennedy notes, although it takes much longer than air travel it is often more reliable, because there are far fewer product handoffs. According to some studies, air freight accounts for 80% of all reported temperature excursions, compared with 1% for sea journeys (4,5).

	With ocean freight, customs clearances and other time-consuming paperwork procedures can often be arranged during transit. “The fact that ocean freight entails a greater stock tie-up can also be mitigated through use of ‘floating warehouse’ principles whereby goods in transit are recognized as inventory,” he says.

	In addition, ocean transport has a carbon footprint that is 1/25th the size of air travel’s (i.e., 1000 grams per m.t./km of CO

 are released during air transport, compared with less than 40 grams by ocean transport (6), says Kennedy.

	A 2013 report by analysts from The Seabury Group (7) found significant quality-control problems with air freight.  As a result, Kennedy says, air cargo carriers began to set higher standards, establishing the IATA Center of Excellence for Independent Validators (CEIV) Pharma program to interpret general European Union and other good distribution practices (GDPs)  for air cargo (8). 

	That same year, the EU extended GDP regulations to cover controlled room temperature (CRT) products, which must nominally be kept from 15-25 °C, which only accelerated a switch from air to ocean transport, Kennedy says. 

	So far, most of the growth in pharma ocean transport has been around relatively low value, high-volume products such as solid-dose tablets, generic pharmaceuticals, APIs, and excipients. However, advocates see that it also has potential to handle more sensitive large-molecule biologics as well as personalized medicine therapies.

	Among ocean transport’s strongest pharma advocates is AstraZeneca, which has increased the percentage of products it ships by sea from 5% in 2012 to nearly 70% in 2017 (9). Eli Lilly, which has been conducting studies of ocean transport of biologicals, has found that transporting products by sea saves 80% in costs, reduces carbon footprint, reduces staffing requirement, packing, and storage needs, and also reduces vibration and shock to materials during transport (10).  

	Lilly has partnered with Modality Solutions for protocols and reports, with Q Products for thermal covers, and with Sensitech, using TempTales, monitors that allow for simultaneous tracking of both temperature and location.

	One test involved simulating worst-case scenarios for a 40-foot reefer transporting 18 pallets of placebo, evaluating temperatures ranging from 0 -115 °F.  The studies used Locus Traxx GPS and Lansmont’s Saver to monitor product shock and vibration.  Other tests compared results of trucking product to Long Beach, CA, a two-to-three-day trip, with shipping product to Australia, a 45-day trip one way.

	A Bayer study (11), meanwhile, showed ocean transport plus trucking to be an efficient way to transport pharmaceuticals inland in Brazil. Logistics efforts in that country can be challenging, because they involve driving long distances overland and, in some areas, facing potential security issues. 

	In 2016, at the European Temperature Control Logistics Conference, a number of stakeholders in pharma cold-chain shipping joined forces to form TEAM UP. Their aim was to explore ways to improve the shipping of pharmaceuticals. In January 2018, TEAM UP established Poseidon, a network involving the different types of companies involved in the ocean transport of pharmaceuticals (see 

	Led by pharmaceutical manufacturers, but involving shippers, logistics companies, and suppliers as full partners, the group is focusing on applying best collaborative and integration practices. Its goal is to leverage the latest technology to address any performance gaps that might still separate ocean from other transport modes. Technology vendor members currently include Maersk, H. Essers, Marsh, DowDuPont, Pelican Biothermal, Klinge Corp., Logtag Recorders, and Controlant. Members are collaborating on new projects, including a major validation study that will be completed in June 2018, as well as new insurance and container options for sea freight.

Pharmaceutical Transportation: How to Increase Air Cargo’s Share

,” aircargopedia.com, 2017.
	2. J. Webb, “

The New Silk Road: China Launches Beijing-London Freight Train Route

,” forbes.com,  January 3, 2017.
	3. IATA, “

Astra-Zeneca Eyes Major Shift from Air Freight to Ocean: A More-Controlled Environment for Drugs

,” theloadstar.co.uk, December 15, 2015.
	5. 

, fluglaerm.de, 2015.
	6. Opinion Editorial, “

Unreliable Air Cargo Industry Loses Pharma Traffic to Sea While IATA Sleeps

, coolchain.org, May 14, 2013.
	8. IATA, 

Centers of Excellence for Independent Validators

, iata.org.
	9. A. Guisbert-Wiliams, “Eli Lilly and Company NALO Logistics Paradigm Shift Air to Ocean (A2O),” 15th Cold Chain GDP and Temperature Management Logistics Summit, Canada, 2017.
	10. J. Wann, “Air, Ocean, Road Strategy at AstraZeneca,” Logipharma, April 2017.  
	11. R. Velazquez Trevino, “Modal Shifts: Where Are We Going?”  TCL London 2017, January 31, 2016.


	Vol. 31, No. 3
	March 2018
	Pages: 44â47

	When referring to this article, please cite it as A. Shanley, "Poseidon Takes on the Pharma Supply Chain," 

Lower costs, fewer opportunities for temperature excursions, and a smaller carbon footprint are making ocean transport more attractive for pharmaceuticals. Poseidon, a new collaborative pharma initiative, seeks to leverage benefits.

Poseidon Takes on the Pharma Supply Chain

	Alan Kennedy, executive director of TEAM UP, shared perspectives on Poseidon and ocean transport.

: Why was Poseidon established and what do you hope to achieve with the program?

: Like-minded individuals at the 2017 European Temperature Controlled Logistics Conference agreed that, in order for any degree of wholesale, across-the-board improvement in the pharma-cold chain, there would need to [be] more alignment between supply-chain stakeholders. Poseidon’s model is built around some of the principles of supply-chain collaboration and integration that are being successfully applied in other industries. Instead of a supply chain, Poseidon is a supply network that involves all of the stakeholders that are responsible for transporting pharmaceutical products. Currently, 19 companies are involved in the Poseidon project, including eight pharmaceutical manufacturers.

	Driven by pharmaceutical companies, the program is designed so that each pharma firm and its shippers, logistics companies, and suppliers sit around the same table as equal partners. A network partner agreement governs the relationships between all these parties, and they work together as a single team with common goals, rules, and performance incentives.

	Poseidon is putting together an end-to-end integrated network for pharma logistics. We hope that the model will serve as a template for pharmaceutical companies in their quest for more efficient, more competitive, and more concerted supply chains that are fit for purpose in today’s rapidly changing environment.

: How are you connecting the many stakeholders who would be involved in ocean transport? 

: A cloud-based Poseidon Collaboration Hub enables members to communicate in real time or whenever needed. Stakeholders around the world facilitate network communications, exchange ideas, disseminate news, share information, access up-to-date documentation, and manage projects. The Hub is also used for non-transactional network communications, for meetings, and to store documentation.

: What types of technology (e.g., temperature and condition monitoring) are being refined for ocean transport, and how would they differ from solutions that were developed for air transport?

: The challenges of sea freight broadly mirror those of air, although some of the protection and monitoring equipment had to be adapted to the different time parameters and operating conditions. For example, one of our participants, DuPont, is currently exploring new cargo-cover materials for ocean freight use.

	One of the advantages of sea freight is that it offers better shipment visibility so that shippers can monitor the geographical position and physical condition of freight in real-time. At this point, Poseidon is only using Maersk ‘smart’ reefers and US-built Klinge ‘redundancy’ reefers.

	For air freight, the US Federal Communications Commission requires transmitting devices such as data-loggers to be auto-switched off during flight for safety reasons. Although the loggers continue to capture data during the flight, this information can only be retrieved later. As a result, there is reduced opportunity to receive alerts or to intervene quickly, to fix a problem that has been reported while it is happening, such as an unexpected temperature excursion that might compromise product quality.

: Are insurance costs more of an issue for sea than air freight?

: Ocean transport insurance can sometimes be perceived as a black hole by shippers, who are often more comfortable with the comprehensible and comprehensive insurance provisions associated with air freight.  Shipping lines, for instance, tend to apply inconsistent liability limits, which, in any case, are invariably much lower than the typical value of a full container consignment of pharma products.  It’s a problem that currently prevents many otherwise suitable products from being shipped by water.

	Poseidon is actively addressing this issue and has brought the insurance industry into negotiations from the project’s start. As a result, Poseidon can now offer a wide-ranging insurance framework specially devised to provide satisfactory cover for all pharma shipment risks and values. Poseidon insurance will provide significantly improved coverage while demystifying the marine protection process.

: Have there been any regulatory changes regarding ocean shipment or position pronouncements by the World Health Organization (WHO) or other global authorities on ocean transport of pharmaceuticals?

: GDP legislation is indiscriminate, non-prescriptive and applies universally to all transport modes. So, the same standards must be met by all (The IATA CEIV program is a translation of these regulations to make them more air-freight-friendly).

	Although a high proportion of vaccines are currently transported by sea, the use of ocean freight has not yet been officially endorsed by WHO due to concerns surrounding the perceived implications of a ‘total’ loss situation (i.e., where a huge volume of urgent vaccines or drugs might be lost in a single event). WHO is expected to review this position, however.

: Have there been any detailed risk benefit analyses of ocean transport performed recently?

: Different pharmaceutical companies have launched proprietary pilot studies to ascertain savings and quantify results. 

	However, Poseidon is working with several pharma manufacturers to design and implement the largest ocean freight transport validation exercise to date. This study will involve multi-container intercontinental shipments of different pharmaceutical dosage forms (oral solid dosage forms, vial parenterals, and topical creams), which will be rigorously monitored and documented. This exercise will be completed by June 2018.

: Are there types of pharma companies that tend to rely more on ocean transportation?  What interest are you seeing in the industry?

: For the first time, the 2017 IQPC Temperature Controlled Logistics conference in London included a Sea Freight Focus Day. This event was sold out, and 70% of those attending worked at pharmaceutical manufacturers, almost all seeking to enter sea freight or to upgrade their sea freight usage.Most of Big Pharma is already shipping some of its products by sea. However, these companies typically deal in huge volumes that lend themselves well to full container load (FCL) shipments. 

	Small- and mid-sized pharma companies, which contribute half the industry’s output, do not currently have access to a GDP-compliant less than container load (LCL) service, also known as ‘groupage’ or shared container service. Therefore, they are forced to send materials by air. We expect this restriction to be lifted once Poseidon introduces its GDP-compliant LCL service later this year.

	When referring to this article, please cite it as A. Shanley, "From Supply Chains to Supply Networks: Poseidon Aims to Redefine Pharma Cold-Chain Logistics," 

Alan Kennedy, executive director of TEAM UP, shared perspectives on Poseidon and ocean transport.

From Supply Chains to Supply Networks: Poseidon Aims to Redefine Pharma Cold-Chain Logistics

	A key element of drug development and product/process characterization is the requirement to identify and control critical process parameters (CPPs) and critical material attributes (CMAs) that influence critical quality attributes (CQAs). CQAs have clinical relevance as they are defined to specifically measure drug attributes associated with safety and efficacy of the drug. In a previous article (1), a discussion of the selection of CPPs was presented. This article presents statistical methods to identify CPPs and CMAs and approaches on how to control them so they do not adversely affect the drug product or drug substance.

	International Council for Harmonization (ICH) Q8 

 (2) states in Part II, 2.5, “… These controls should be based on product, formulation and process understanding and should include, at a minimum, control of the critical process parameters and material attributes. A comprehensive pharmaceutical development approach will generate process and product understanding and identify sources of variability. Sources of variability that can impact product quality should be identified, appropriately understood, and subsequently controlled. Understanding sources of variability and their impact on downstream processes or processing, in-process materials, and drug product quality can provide an opportunity to shift controls upstream and minimize the need for end product testing. Product and process understanding, in combination with quality risk management (see ICH Q9), will support the control of the process such that the variability (e.g., of raw materials) can be compensated for in an adaptable manner to deliver consistent product quality.”

	Line-of-sight for all CQAs and CPPs/CMAs

	When working on drug development, there is a need to maintain line-of-sight (

) between CQAs and elements of drug development that may influence them. Elements of drug development that may impact CQAs are as follows:

		Formulation process for drug substance or drug product

		Processing method or sequence of operation

		Selection of normal operating ranges (NORs)

		Selection of proven acceptable ranges (PARs)

		Critical parameters in analytical methods. 

	By maintaining line-of-sight, the relationship of drug development parameters and CQAs is maintained and evaluated in a systematic manner, making knowledge gaps and oversights less likely. Quality risk management is a technique that evaluates and -manages development risks associated with CQAs.  ICH Q9 on quality risk management (3) states, “Two primary principles of quality risk management are: the evaluation of the risk to quality should be based on scientific knowledge and ultimately link to the protection of the patient; and the level of effort, formality, and documentation of the quality risk management process should be commensurate with the level of risk.”

Figure 1: Line of sight and critical process parameters and key responses. CQA is critical quality attributes. CPP is critical process parameters. CMA is critical material attributes. (Figure courtesy of the author)

	Quality risk management is normally used to identify development areas with risk to CQAs; however, it is not sufficient to identify the CPPs and CMAs. High-level risk assessments will indicate unit operations with risk, and low-level risk assessment will identify potential factors that may impact CQAs. Risk assessment alone is not sufficient to detect and control CPPs. It can only be based on measurement of how factors directly influence the response.

	CPPs may be found in formulation, media, upstream and downstream unit operations, and drug-product processing. Due to the large number of unit operations and media complexity, it is easy to overlook processing parameters and materials that may influence drug-substance and drug-product variation and CQAs. Failure to identify critical parameters will result in unexplainable variation during batch processing and lot acceptance.

	The key steps to CPP/CMA selection and their application to process control is as follows: 

		Identify CQAs for drug product and substance. Include responses for productivity (%viability or titer).

		Select API, excipients, processes, materials, and container closure.

		Define all unit operations and process sequence of operation.

		Define all product and process specification limits and acceptance criteria (include in-process controls and release testing).

		Qualify/validate all analytical methods.

		Complete high-level quality risk management and identify unit operations with risk to CQAs.

		For unit operations with risk, complete a low-level factor/response selection for all factors, interactions, quadratics, and materials.

		Generate design of experiments (DoEs) for unit operations or materials with risk.

		Explore the design space all key factors identified during the risk assessment using DoE or other multivariate methods.

		Determine the factor effect size and scaled estimates. 

Statistical methods to identify critical process parameters and critical material attributes-and approaches to control them-are needed to protect drug product and drug substances.

BioPharm International-03-01-2018

FDA Heightens Drive for Transparency

Current Challenges in Bioprocesses Development

Characterizing a Bioprocess with Advanced Data Analytics

Computerized Systems Validation

Regulatory Role in Bio/Pharma Progress

Poseidon Takes on the Pharma Supply Chain

From Supply Chains to Supply Networks: Poseidon Aims to Redefine Pharma Cold-Chain Logistics

Identifying and Controlling CPPs and CMAs

Employing Spectroscopic Tools in Downstream Process Control

Biologic NMEs Maintain Strong Presence in 2017 Drug Approvals

BioPharm International, March 2018 Issue (PDF)