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A Better Way to Study Leachables from Container Closures

BioPharm International, April 2010 Issue (PDF)

For anyone who likes to ponder the business of biopharmaceuticals, there has been plenty of news to chew on recently, as companies make moves that hint at important shifts in strategy. 

AstraZeneca (AZ), GlaxoSmithKline (GSK), and Pfizer all have announced significant cuts in drug discovery investment. Are they inching closer to becoming virtual companies? I exaggerate a bit, but many big firms have already embraced contract manufacturing, and one could argue that they have been outsourcing significant amounts of R&D for years, by buying individual drugs from start-ups and whole pipelines through acquisitions of large and medium-sized competitors. Will some of them outsource R&D completely, and focus on what Steven Burrill considers their core competencies: post-discovery drug development, and sales and marketing? 

Meanwhile, many are buying or partnering with generics companies, particularly firms selling in emerging countries. Recent deals involve AZ in India, GSK in India and South Africa, Sanofi-Aventis in Brazil, Mexico, and the Czech Republic, and Pfizer's failed bid for Germany's Ratiopharm. Big Pharma ownership of generics units is not new, but these recent deals show a strong interest in diversification among product types and geography. Pharma traditionally has made its biggest profits in the US because of the lack of price controls here, but they may wish to reduce their reliance on North America and take advantage of new opportunities in emerging markets.  

Given ongoing industry challenges and the evolving nature of the pharma business, traditional strategies were bound to change. But of course, there is one more big threat—and perhaps opportunity—on the horizon: US healthcare reform. Now that reform once again seems imminent, there are many questions to consider, such as: 					

How strongly will biologics manufacturers feel the effects of biosimilars? At what discount will biosimilars sell compared to the price of their reference products?

How will reform affect the way companies negotiate drug reimbursement and formulary coverage with government and private payors? 

As US government spending on healthcare increases, will the need to reduce costs lead to even greater use of small-molecule generics? 

Will we start to see more comparative effectiveness studies?

How will healthcare reform affect innovation, and the US role in the industry?

Will cost pressures lead to more outsourcing and offshoring of manufacturing?

Regular readers of this column know this topic fascinates me, but it's a complex issue and difficult to tackle. So when I began preparing the keynote event for the 2010 Interphex conference, I decided to bring in a panel of experts to share their views on what healthcare reform means for the industry: Matthew Hudes, US managing principal of Deloitte Consulting Life Sciences & Health Care Practice; John Engel, founding partner of Engel & Novitt, LLP; and Michael Mawby, VP of government affairs of Novo Nordisk. Chris Matthews of MSNBC will moderate the panel, which will ensure a lively discussion! For more information, please see 

www.biopharminternational.com/Interphex2010

I invite you to join us in New York on April 20 for this keynote panel. As it turns out, the topic couldn't be more timely. 

From the Editor

Given today's challenges, traditional strategies were bound to change. Now, there is one more big threat (or opportunity?) on the horizon.

Preparing For an Uncertain Future

In late February of this year, the US Food and Drug Administration released its 

Draft Guidance for Industry on Adaptive Design Clinical Trials for Drugs and Biologics

. Unlike traditional clinical studies, which require that the elements of the protocol are immutable throughout the study, adaptive trials can include elements that can be changed mid-trial, so long as the conditions under which changes are made and the nature of those changes are established in the study design. 

Adaptive trials promise to lower trial costs and speed up the development process by reducing the number of trials that must be conducted and increasing the probability that a trial will reach the desired endpoint. They are indicative of the growing complexity of clinical research as bio/pharmaceutical companies respond to the growing concern over the cost and safety of new drugs. Companies must confront an expanding range of challenges when developing and testing new drug candidates. Some of those challenges include:  

demonstrating superiority over existing therapies: this is becoming a touchstone for regulators concerned about the quality of new drugs coming to market and third-party payers unwilling to add new drugs to formularies unless they provide a tangible benefit

reducing trial costs: decreasing the costs of drug development is imperative as bio/pharmaceutical companies endure declining revenues in the phase of patent expirations and fewer approvals

expanding subject populations: the need to find sufficient numbers of trial subjects, combined with the desire to expand into new markets, has pushed bio/pharmaceutical companies to recruit more and more patients in emerging markets, including Latin America, Eastern Europe, India, and China

bringing more biologics into the clinic: as the clinical performance of traditional small-molecule compounds wanes, bio/pharmaceutical companies have increased their focus on biologics as the source of new drug approvals.

These challenges have increased the complexity of clinical trials. Examples of this increased complexity include the expanding number of clinical trials in which new drug candidates are tested against active controls (approved, marketed drugs); clinical trial designs incorporating more arms (patient sub-groups); adaptive trials; and the growing number of sites per study. 

The increased complexity of clinical trials is posing new challenges across the supply chain.  

 large multi-arm studies greatly increase the number of packaging configurations needed to conduct a trial. A study with at least one active control, one placebo, and a development candidate can easily have six or more packaging configurations, depending on the protocol. If the study is being conducted in multiple countries, labeling the packages to incorporate multiple languages is an additional challenge. 

 large multi-country studies make clinical supply forecasting very difficult. For example, estimating how many kits will be required in each country means being able to forecast enrollment rates with some accuracy, which is extremely difficult. Adaptive trials further complicate the problem.  

 shipping clinical supplies to multiple countries raises problems such as transportation and storage under good manufacturing practice (GMP) conditions. Because of the growing number of biologics, maintenance of temperature conditions across the supply chain, i.e., cold chain management, has become a major concern. 

The growing complexity of clinical trials and the clinical supply chain has opened up an "arms race" in the clinical supply chain industry because participants are competing to offer the most sophisticated capabilities. For instance, clinical packagers are investing millions in information systems that can track individual packages from the warehouse shelf through every step in the supply chain, all the way to the clinic or physician's office where the patient receives his drug supply.  

In addition, supply chain complexity has boosted the importance of a range of specialty providers and services across the supply chain. Some examples are below.  

that specialize in procuring drugs used as active controls in clinical trials. This is an intricate undertaking because bio/pharmaceutical companies often require large quantities of a drug from a single manufacturing lot when conducting active control trials.

 that specialize in shipping materials to clinical sites and depots around the world. This is a booming business, with high double-digit growth rates. 

that provide GMP-compliant storage for packaged clinical supplies. Depots enable bio/pharmaceutical companies to ship multiple kits to a country at one time, reducing shipping and customs-clearing costs, and ensuring timely delivery to patients. Once an ancillary service offered by a few contract research organizations (CROs), clinical supply depots now are a critical link in the supply chain.

The ability to deal with the complexity of the clinical supply process has shifted the balance of power in the clinical supply chain to CROs, especially the major clinical packagers and logistics services providers. Being able to handle clinical supply chain complexity requires major investments in information technology and specialized packaging and labeling equipment. It also rewards companies with the know-how to devise sophisticated solutions for complex supply chain problems and the experience to accurately and safely implement those sophisticated solutions.  

Only companies that handle hundreds of clinical supply projects annually can use the experience to devise and implement those sophisticated solutions and afford to make the necessary investments. There is no way that bio/pharmaceutical companies can hope to match those capabilities in their in-house clinical supply operations.  

is president of PharmSource Information Services, Inc. , Springfield, VA, 703.383.4903,

Outsourcing Insights

The ability to deal with the complexity of the clinical supply process has shifted the balance of power to clinical supply chain specialists.

Complex Clinical Trials are Posing New Challenges Across the Clinical Supply Chain

Despite a few minor bumps in the road, the biotech industry has maintained a positive climb since the beginning of the year, in lockstep with the performance of the general markets.  

The Burrill Biotech Select Index has posted a 4% jump in value year-to-date (YTD, Table 1), buoyed by companies such as Affymetrix, whose shares have surged and are up 25% and Amylin Pharmaceuticals (up 33 %), mainly because they reported strong quarterly financial results. In addition, most of the companies in the group have posted modest single or low double-digit percentage gains in their share values.  

Table 1. The Burrill Biotech Select Index posted a 4% jump in value year-to-date 

Although biotech's uptick so far is encouraging, investors still need to be cautious in their optimism. The market no longer embraces risk and the investors are not ready to put their faith in venture-backed biotechnology companies in their desire to go public. 

It was hoped that Ironwood Pharmaceuticals' initial public offering (IPO) debut in February would pave the way for several other aspirants to follow. However, the Ironwood IPO was tough to get out. The amount raised was below the prior private round pricing, and the investment bankers had to buy 50% of the offering to get it done. Companies on the IPO runway are now having second thoughts about their IPOs because of the uncertainty of reaching their intended share offering prices. California biotech Anthera Pharmaceuticals, for example, which planned to go public late February, delayed its offering until the beginning of March. It finally sold 6 million shares at $7 each, after cutting the size of its deal by 39%. Anthera had originally planned to sell 4.61 million shares at $13–15 each to finance the development of three drugs currently in clinical trials. 

I think the message from Wall Street is that biotech IPO deals will still be tough to do. The equity markets in general are in tough shape right now. If you are on the "buyside," it makes sense to be working with companies already public. This avoids the immediate risk of a downside event if you participate in an IPO offering. 

In Burril & Company's shortly to be released 24th annual report on the industry, 

Biotech 2010-Life Sciences: Adapting for Success

, we predict that at least 15 biotech IPOs will be completed in 2010, but supply will overwhelm demand, and the markets will be very selective—a situation we currently are witnessing now. 

We also predict that the worldwide financing environment in 2010 will be more robust than 2009, but choppy and selective at times. This environment favors risk-mitigated companies rather than earlier-stage development companies. Capital markets in the US and globally will continue to strengthen, building on a return of investor confidence. Much of the global economic recovery so far has been driven by stimulus funding, and as a result, real economic growth will remain uncertain. The large universe of small public companies and private companies looking for venture capital will still face challenges as they try to find ways to extend their runway and stretch out their remaining funds. We can expect to see further consolidation in 2010, although at a slower pace than we saw in 2009.  

Ironwood Pharmaceuticals priced its IPO at $11.25 per share at the beginning of February, less than the $14–16 the company had hoped for. The company raised $203 million by completing its IPO and exercising an over-allotment option, and will use the cash to develop and commercialize linaclotide being evaluated in a Phase 3 program.  

Anthera, the second biotech IPO of the year, has an anti-inflammatory drug known as A-002 in the last-stage of clinical testing. 

Late February, two biotech companies—AVEO Pharmaceuticals and Trius Therapeutics—set the terms of their offerings. AVEO, which is developing novel cancer therapeutics, announced plans to raise $98 million by offering 7 million shares in the price range of $13–15. Trius said it plans to sell 6 million shares to investors at a price range of $12–14 to raise $84 million. 

Also on the biotech IPO runway are companies including Alimera Sciences, Aldagen, AutoGenomics, Prometheus Laboratories, Rules Based Medicine, and Tengion. 

 is chief executive officer at Burrill & Company,  San Francisco, CA, 415.591.5400,  

Burrill on Biotech

As the capital markets in the US and globally continue to strengthen, the biotech industry can expect to see a rise in initial public offerings this year.

Biotech on a Roll to Start 2010

This article describes a systematic approach to evaluating the leachables profile from various biopharmaceutical formulations stored in rubber-stoppered glass vials. We evaluate leachables from rubber stoppers in 11 different formulations containing typical biopharmaceutical excipients to assess how differences in various formulation excipients affect leachables. The information obtained from this type of study is critical to support the Quality by Design paradigm of incorporating product quality into the product design process and using risk-based approaches to managing quality.

Biopharmaceutical packaging materials (container/closure systems) must be evaluated for compatibility with the drug formulation in the early stages of development to ensure the product is safe for use throughout its shelf life. This requirement also applies to the development of biopharmaceutical manufacturing processes including single-use components used in manufacturing equipment and in-process production conditions.

  Although compendial tests provide some preliminary extractables information about the container/closure systems, they do not identify or quantitate individual extractables. Therefore, they cannot correlate extractables from the container/closure systems to the leachables in the drug products. This article describes a systematic approach to determining extractables and leachables based on the concepts proposed by the Product Quality Research Institute's Leachables and Extractables Working Group for the development of orally inhaled and nasal drug products in concert with relevant regulatory guidelines.

 The study design focuses on identifying and elucidating the correlation between leachables caused by biopharmaceutical formulations stored in rubber-stoppered glass vials and its potential application in supporting Quality by Design (QbD) in the biopharmaceutical development process. 

Glass vials and rubber stoppers are widely used as container/closure systems for biopharmaceutical products and other drug formulations. Organic compounds in the stoppers, such as oligomers, antioxidants, and curing agents, can leach out into drug formulations and affect drug safety and efficacy. In addition, typical biopharmaceutical drug product formulation ingredients, such as co-solvents, surfactants, chelating agents, bulking agents, and pH modifiers, can alter the physicochemical properties of the drug formulation itself and also may have an impact on the leaching of organic compounds from rubber stoppers. This paper evaluates leachables from rubber stoppers in 11 different formulations containing typical biopharmaceutical excipients, and addresses how differences in various formulation excipients affect leachables. 

In this study, typical biopharmaceutical formulation components were chosen to create a variety of test formulations. The extractables profiles of chlorobutyl rubber stoppers were determined. The major extractables were then chosen as targets for a leachable assessment in which the test formulations were stored at 40 °C and 75% relative humidity (RH) for one month. The following formulation variations were evaluated: pH (5.0, 6.8, 8.2), chelating agent (0.1 to 0.5 mM EDTA), surfactant concentration (0.1–0.5% Tween 80), bulking agent (sucrose, mannitol, or trehalose), and the presence of a co-solvent (2% glycerol). 

Table 1. Aqueous formulations used for leachables evaluations 

Eleven aqueous placebo formulations were evaluated in this study (Table 1). The various test formulations included a simple phosphate buffer at pH 6.8, a buffer with a glycerol co-solvent, three formulations in which the pH was varied from slightly acidic to slightly alkaline, two formulations with different amounts of ethylenediaminetetraacetic acid (EDTA) included as a chelating agent, two formulations with different amounts of Tween 80 included as a surfactant, and two formulations with either mannitol or trehalose added as bulking agents. Commercially available glass vials and rubber stoppers were used in the study. 

Table 2. Headspace GCâMS chromatographic conditions 

Separate glass vials were filled with one of the 11 formulations listed in Table 1 and crimp-sealed with rubber stoppers. The stoppered vials containing various formulations were inverted and then stored at 40 °C and 75% RH. After one month, the formulations were tested for volatile, semi-volatile, and nonvolatile leachables using headspace gas chromatography–mass spectrometry (GC–MS), direct injection GC–MS, and gas chromatography–ultra violet mass spectrometry LC–UV–MS, respectively. The portions of each sample to be analyzed were transferred to headspace GC vials and high performance liquid chromatography (HPLC) vials without further treatment for headspace GC–MS and LC–UV–MS analysis, respectively. For direct injection GC–MS analysis, the samples were back extracted into methylene chloride, which is a more suitable solvent for this analysis. Control samples (portions of each formulation that were stored in glass volumetric flasks at 5 °C for the same time duration as the samples) also were analyzed. 

Table 3. GCâMS chromatographic conditions 

To correlate the leachables with the extractables from the stoppers, the stoppers were extracted with water and isopropanol and analyzed with the same headspace GC–MS, direct injection GC–MS, and LC–UV–MS conditions. For headspace GC–MS analysis, the stoppers were extracted in headspace GC vials at 90 °C for 24 h, and the vials were directly used for volatile extractables analysis, without opening the vial cap. All volatile extractables, including water insoluble extractables, were detectable using this approach. For direct injection GC–MS and LC–MS analyses, the stoppers were extracted by refluxing with water and isopropanol for 16 h. Tables 2–4 show the chromatographic conditions used for each analysis. 

Table 4. LCâMS chromatographic conditions 

Headspace GC–MS Analysis Extractables Evaluation

The water extracts of stoppers yielded seven extractables peaks (Figure 1). Six of the seven peaks were identified as 2-methylpentane (4.24 min), 3-methylpentane (4.62 min), hexane (5.14 min), methylcyclopentane (6.24 min), cyclohexane (7.93 min), and butylated hydroxytoluene (BHT, 33.26 min). The peak at 28.60 min was not identified; the GC–MS library search and manual spectral interpretation did not produce a good match or a tentative identification. The amounts for each peak are summarized in Table 5. The peak at 28.60 min was quantitated using cyclohexane as the surrogate standard. Because the relative response factor of the unknown peak against cyclohexane is not determined, the amount reported for this peak is only considered a semi-quantitative estimate. 

Table 5. Quantitation of extractables/leachables with headspace GCâMS 

Six leachables peaks were observed in the headspace GC–MS analysis from the formulations: 2-methylpentane, 3-methylpentane, hexane, methylcyclopentane, cyclohexane, and BHT. These leachables peaks correlate to stopper extractables. The unknown extractables at 28.60 min were not observed as leachables. This may be because of insolubility of the compound in the aqueous media. The amount of leachables in different formulations is summarized in Table 5. The effect of various formulation ingredients on the leachables profile is discussed in detail below. 

Phosphate Buffer with or without Glycerol Co-Solvent

Five leachables peaks were observed in the pH 6.8 phosphate buffer: 3 methylpentane, hexane, methylcyclopentane, cyclohexane, and BHT. All of the peaks were very small. Methylcyclopentane was the largest leachables peak, probably because it is more soluble in aqueous media than the other compounds. The addition of 2% glycerol in the phosphate buffer as a co-solvent did not significantly affect the amount of leachables observed. 

Formulation pH had an impact on leachables. The neutral pH (pH 6.8) provided slightly lower amounts of leachables compared to the slightly acidic (pH 5.0) and the slightly alkaline (pH 8.2) formulations (Figure 2). The acidic and alkaline formulations had similar leachable profiles. 

Based on the leachables profiles of formulations containing 0, 0.1, and 0.5 mM EDTA, EDTA did not significantly affect the amount of organic leachables. 

The excipient Tween 80 had the most significant effect on leachables compared to other formulation ingredients. The addition of Tween 80 significantly increased the leached amount of 2-methylpentane, 3-methylpentane, hexane, methylcyclopentane, and cyclohexane. The leached amounts of these compounds in the formulation with 0.5% Tween 80 is five to 10 times higher than in formulations without Tween 80. This is because the surfactant increases the solubility of these compounds in the aqueous formulation (Figure 3). However, the addition of Tween 80 did not affect the amount of leached BHT because no BHT was detected in the formulations with 0.5% or 0.1% Tween 80. 

Bulking Agents: Sucrose, Mannitol, and Trehalose

The use of bulking agents affected the amount of leachables observed. Higher amounts of leachables were observed in the formulation with 7% mannitol compared to the formulation with 7% trehalose, in which few leachables were observed (Figure 4). The formulation with 7% sucrose had leachables levels between those seen in formulations with mannitol and trehalose. 

Direct Injection GC–MS Analysis Extractables

There was only one semivolatile extractable peak observed from the water extracts. The peak was identified as BHT and is very small because of its limited solubility in water. 

There were many extractables peaks observed in the isopropanol extracts (Figure 5). The two dominant peaks are BHT (12.18 min) and an unknown hydrocarbon at 13.66 min. Other compounds observed were palmitic acid (15.35 min) and stearic acid (16.62 min). All other peaks appear to be various hydrocarbons. 

Leachables were determined by comparing the stressed formulations to the formulation blanks and the stopper extractables profile. There is no appreciable interference from the formulation blanks for the main extractables peaks. The only leachables observed was BHT, which was reported in formulation 3 (pH 5.0), formulation 6 (0.1 mM EDTA), and formulation 7 (0.5 mM EDTA). This result was consistent with headspace GC–MS data. The BHT peak is very small. No other peaks observed in the isopropanol extract were observed in any of the formulations. This can be explained by the insolubility of these compounds in the aqueous formulations. 

There were two additional peaks observed in formulation 1 (pH 6.8 phosphate buffer) and formulation 2 (phosphate buffer with 2% glycerol). The first elutes at 11.27 min and the second elutes at 12.11 min. The peak at 12.11 min is tentatively identified as 2,4-t-butylphenol, while the peak at 11.27 min was not identified. For all other formulations, the peak at 11.27 min was not observed. For the peak at 12.11 min, there is a co-eluting interference peak from all the formulation blanks except formulations 1 and 2, so it is not conclusive if the peak observed in formulations 1 and 2 was also present in those formulations. The source of the two peaks requires further evaluation. 

No extractables were observed with LC–MS from the water extracts of rubber stoppers. The isopropanol extract of the stoppers contained BHT (5.02 min) and three fatty acids (myristic acid, 5.21 min; palmitic acid, 5.80 min; and stearic acid, 6.30 min). 

The leachables were evaluated by comparing the stability samples to the formulation blanks and the stopper extractables profile. There is no appreciable interference from the formulation blanks for the main extractables peaks. There are no leachables observed in the LC–MS analysis of the 11 formulations. The observation of BHT in the GC–MS analysis but not in the LC–MS analysis is likely because of the increased sensitivity of GC–MS over LC–MS. 

Eleven different typical formulations of  biopharmaceutical products were evaluated to determine their effect on the leachables profile of a common rubber stopper after storage at 40 °C, 75% RH for one month. The key findings are as follows:  

Different formulation excipients have a significant impact on leachables. Especially of note is Tween 80, which significantly increased the amount of leachables as compared to other formulations. 

The impact of different formulation excipients does not always affect all leachable components to the same extent. For example, Tween 80 significantly increased the leachables 2-methylpentane, 3-methylpentane, hexane, methylcyclopentane, and cyclohexane. However, it does not appear to have an impact on BHT. The bulking agents used, such as sucrose, mannitol, and trehalose, lead to different leachables levels, with trehalose being a superior bulking agent from the standpoint of minimizing leachables.

Features

A systematic approach facilitates formulation component selection.

A Study of Leachables for Biopharmaceutical Formulations Stored in Rubber-Stoppered Glass Vials

An uncertain policy environment, a challenging financial situation for many companies, and ambiguity within the overall economy present us with an important opportunity to re-focus and re-energize our industry's mission to heal, fuel, and feed the world. Biotechnology's mission has never been more critical. The challenges confronting our world are enormous, but the place to look for change is among the great minds of scientists and inside research laboratories.  

Our companies continue to innovate at a breathtaking pace. Today, there are more than 200 biotechnology products and vaccines approved by the FDA, extending lives and providing new hope for patients living with debilitating diseases like cancer or HIV and AIDS. More than 600 new biotechnology medicines currently are either in human clinical trials or under review by the FDA for diseases including cancer, Parkinson's disease, diabetes, multiple sclerosis, and more than 100 other conditions. Many other potential treatments, vaccines, and diagnostics are in earlier phases of development. Cutting edge research in areas such as regenerative medicine, gene therapy, and synthetic biology may lead to even more effective ways to not only treat, but predict, preempt, and prevent disease.  

Researching and developing new therapies is a long, expensive, and risky process. On average, developing a new drug costs $1.2 billion over the course of 10 to 15 years from the time research begins until it receives FDA approval to go on the market. This includes the cost of dead ends and failed drug candidates along the away. Only five out of every 5,000 potential medicines tested ever reaches the stage of clinical trials. 

We must ensure that companies have the necessary support and funding for research and development to produce promising innovations that will lead to life-saving treatments. We must have a strong system in place that protects patents and encourages future innovation. We must prioritize new policies and regulations that will create an environment where innovation can flourish. 

Policies that encourage innovation will help provide the certainty biotech researchers and their investors need to continue to develop and improve breakthrough biologic medicines and treatments and, some day, cures.  

Millions of people are waiting. Some are waiting for a new treatment. Some are waiting for a cure. Biotech provides the best chance we have to end the waiting and provide new hope. 

Despite these challenges, biotech companies are dedicated to finding the next biologically based treatment or cure. They are willing to devote enormous energy, creativity, and resources to this endeavor, even though they know success is difficult to achieve. Now is the time to bring together the entire biotech community to work together to find solutions for the challenges we face.  

I invite you to join me in Chicago at the 2010 BIO International Convention as we tackle some of the many challenges currently facing our industry. The global event for biotechnology will bring together more than 15,000 industry leaders along with 1,700 exhibitors from around the world from May 3 to 6 in Chicago. Together, we can deliver on the promise and potential of biotech to help heal, fuel, and feed the world. 

is president and CEO of the Biotechnology Industry Organization (BIO), Washington, DC, 202.962.9200, 

Final Word

This year's BIO International Convention will be occurring against a backdrop of profound change in our industry.

Better Times Ahead for Biotech

The capabilities of computational fluid dynamics (CFD) as a tool for trouble-shooting and Quality by Design have been demonstrated through case studies in bioprocesses from upstream, to downstream, to fill–finish. The applications involve, for example, multiple physics, fluid dynamics, transport phenomenon, heat transfer, phase transition, and rheology. Flow types include laminar flow in emulsification, turbulent flow in bioreactor mixing, and porous media flow in chromatographic columns, filtration, and centrifuge machine. Part 1 of this article series is an overview of the technology, followed by a discussion of the model setup. Part 2 of this series will discuss how CFD is used to perform flow characterization, mixing, resuspension, fermentation, and cell culture.

Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical methods and algorithms to solve and analyze problems that involve fluid flows and associated phenomena. CFD is a highly sophisticated integration of applied computer science, physics, chemistry, and engineering science. Existing commercial CFD codes are capable of simulating a very wide variety of physical processes besides fluid flow. To date, there are more than a dozen commercially available codes (e.g., Ansys, Fluent, CFX, Star-CD, Flow3D, and Phoenics) and open-source codes (e.g., Open-Foam) for CFD modeling and simulations. Significant improvements are continually being made in both CFD software and the hardware used to run it. The software is becoming more user friendly and the hardware is providing more computing power for less money. 

Besides being a powerful instrument to analyze "what if" scenarios to improve the efficiency of existing operating systems, CFD also can be used as a Quality by Design (QbD) tool for the design of new systems. It can help shorten product and process development cycles; optimize processes to improve robustness, efficiency, and productivity; and solve problems as they arise in unit operations. CFD technology is well established and has been used successfully in the aerospace and automotive industries in optimizing control strategies and equipment, and shortening the time from design to production. It entered the chemical industries over the past two decades and has been applied to various chemical processes.

  Recently, CFD has seen increased interest in the pharmaceutical arena, where insights into fluid flow and related phenomena can help mitigate risks associated with scaling-up process equipment and aid in troubleshooting.

  Furthermore, it has demonstrated its potential as a QbD tool.  

Pharmaceutical and biopharmaceutical processes that can benefit from CFD analysis include turbulent flow in bioreactors; multiphase flow, heat, and mass transfer; chemical reactions; phase transitions; porous media flow; and granular particle material dynamics. These processes cover upstream, downstream, and fill–finish, and involve unit operations such as fermentation and cell culture, mixing, chromatography, filtration, centrifuge separation, product freezing and thawing, lyophilization, emulsification, spray drying, product packing, and transportation. With growing capabilities and flexibility, CFD has been the most widely applicable tool available to simulate the physical processes involved in process development, manufacturing, and drug delivery.  

There have many reviews of the application of CFD to various industrial processes, only a few of which have covered the role of CFD in the pharmaceutical industry.

  The biotechnology industry has its own unique processes, in addition to shared common unit operation processes with the pharmaceutical industry. This review will focus on applying CFD to bioreactor (fementer) operations.  

The fundamental basis of any CFD problem is the Navier-Stokes equations that describe the motion of fluids. Additional physiochemical processes are coupled with fluid flow, such as multiphase (gas–liquid, or solid–liquid, or solid–liquid–gas) interaction, species transport, heat transfer, mass diffusion, and chemical reactions. Numerical algorithms are used to solve conservation equations for mass, momentum, and energy, and provide solutions on flow and physical variables such as velocity, pressure, temperature, density, concentration, and volume or mass fraction, from which other derived variables such as shear stress, pH, height equivalent to a theoretical plate (HETP), etc., are obtained.  

CFD involves two procedures of discretization—domain discretization and equation discretization—followed by the application of a suitable algorithm to solve the resulting ordinary differential equations for unsteady problems and an algebraic equation for steady problems, subject to appropriate boundary conditions and initial conditions, to get solutions on the grid.

  Either an iterative solver or a direct solver can be used, depending on the size of the problem, available computer memory, and CPU speed. In the step of equation discretization, partial differential equations are discretized into a finite difference equation, whereas in domain discretization, the continuous spatial domain is discretized into small control volumes to form a volume mesh or grid. A postprocessor is used for the analysis and visualization of the resulting solution.  

Mesh-based methods include the finite difference method (FDM), the finite volume method (FVM), the finite element method (FEM), the boundary element method (BEM), and some high-resolution schemes that treat shocks and discontinuities. Non-mesh–based methods include smoothed particle hydrodynamics (SPH)—a Lagrangian method of solving fluid problems, Spectral methods—a technique where the equations are projected onto basis functions like the spherical harmonics and Chebyshev polynomials, and Lattice Boltzmann methods (LBM), which simulate an equivalent mesoscopic on a Cartesian grid instead of solving the macroscopic system (or the real microscopic physics).  

The key to a successful CFD analysis of a bioprocess is to choose correct models based on underlying physics and biochemistry. These models also require the right boundary conditions and initial conditions.  

Currently, there are two broadly categorized computational approaches for modeling the interaction between phases in multiphase flows: the Euler-Lagrange approach

  In the Euler-Lagrange approach, the fluid phase is treated as a continuum by solving the time-averaged Navier-Stokes equations, whereas the dispersed phase is solved by tracking a large number of particles through the calculated flow field. The Euler-Lagrange approach typically deals with the dispersed second phase by occupying a low volume fraction, even though high mass loading is acceptable. The particle trajectories are computed individually at specified intervals during the fluid phase calculation. In the Euler-Euler approach, the different phases are treated mathematically as interpenetrating continua. Because the volume of a phase cannot be occupied by the other phases, the concept of phasic volume fraction is introduced. These volume fractions are assumed to be continuous functions of space and time and their sum is equal to one. Conservation equations for each phase are derived to obtain a set of equations that have similar structures for all phases. In both approaches, the continuum phase and discrete phase momentum equations are coupled through the drag source and sink terms, and through the volume fraction of the dispersed phase.  

The flow in an agitated bioreactor typically is in a fully turbulent regime, although some emulsification processes may operate in a laminar or transitional regime because of the high viscosity of the mixture. Two methods often used to model the turbulent flow are the Reynolds-averaged Navier-Stokes (RANS) models and the unsteady large eddy simulation (LES) models. The direct numerical simulation (DNS) method is too computationally demanding for real applications. 

RANS-based models have different variations, such as the standard k-epsilon, the Chen-Kim, the renormalized group (RNG), the realizable k-epsilon, and the k-omega, which all assume isotropic turbulence and do not show superiority of one over another,

  and the Reynolds stress transport models (RSTM), which incorporate the anisotropy characteristics by solving transport equations for Reynolds stress terms. However, the improvement on the accuracy of RSTM predictions over the k–e models is not conclusive.

  Based on the Kolmogorov theory, the LES models solve only for the large eddies explicitly, while the small-scale effects, below the filter size with corresponding wave number lying in the inertial convective subrange of the energy spectrum, are modeled using a sub-grid scale model. Good agreement between experimental data and model predictions on mean velocities and turbulence quantities in agitated tanks have been reported.

  LES requires less computational effort than direct numerical simulation (DNS), which is the most exact approach to model turbulence and can resolve the smallest eddies, but uses significantly more effort than methods that solve RANS.

When modeling a rotating system such as in an agitated tank, the multiple reference frame (MRF) and the sliding mesh (SM) model often are used. The MRF model performs a steady-state calculation with a rotating reference frame in the impeller region and a stationary reference frame in the outer region. In this way, the effects of the impeller rotation are accounted for by the frame of reference, allowing for explicit modeling of the impeller geometry. The SM model allows the impeller region to slide relative to the outer region in discrete time-steps and performs time-dependent calculations using implicit or explicit interpolation of data at successive time-steps. The SM model is a more accurate representation of the actual phenomenon of the impeller rotation, but unfortunately a computationally demanding one. 

The predictive capabilities of all available CFD models when applied to agitated bioreactors equipped with various impellers in up- or down-pumping mode are discussed in the reviews.

  In summary, all turbulent models can predict the mean flow-field and power number very well,

  and also can capture most of the key features of near-impeller flows with sufficient accuracy, but provide various degrees of agreement with experimental data on turbulent characteristics. The standard k-epsilon turbulence model combined with the MRF model, as commonly used in engineering CFD simulations of stirred tanks and often faulted for its assumption of isotropic turbulence, can model the turbulent flow with adequate accuracy if fine enough grids coupled with higher-order discretization schemes are used.

  The LES for modeling flow in stirred tanks has the advantage of capturing the instantaneous velocity field and vortex structures.

Cells growing in bioreactors take up nutrients from the culture medium and release products, byproducts, and waste metabolites. Mixing and sparging greatly influence the mass transfer phenomena required for an adequate supply of nutrients and removal of waste metabolites.

  Agitation is used to maintain cells in suspension, provide a homogeneous mix of nutrients, and prevent the accumulation of toxic gases. Multiple tasks and numerous choices of impellers make process scale-up extremely challenging. Figure 1 illustrates how CFD provides assistance in understanding the extracellular environment, optimizing the operation conditions, designing hardware configuration, and implementing scale-up and scale-down strategies. 

Even though CFD has advanced remarkably, many challenging cases require CFD experts. Applying tools blindly without understanding the capabilities and limitations of the methods involved could lead to erroneous results. Besides good knowledge of numerical computation technologies (i.e., ensuring mesh quality and selecting time-steps for unsteady simulations), understanding the underlying physics, chemistry, and biology, and choosing the most appropriate physiochemical models are vital to successful CFD simulations. For example, a model taking interparticle and intraparticle mass transport and adsorption for chromatographic separations into account can be a powerful predictive tool. However, industrial process streams contain many proteins and an array of other materials such as lipids, nucleic acids, and cell debris, all of which may interact with the column matrix. Determining their competitive isotherms individually is not realistic. Simplified but reasonably accurate isotherm models based on theoretical study or through experimental data determination should be set up for any CFD modeling effort.  

Making prediction with high confidence would require including not only fluid dynamic modeling but also modeling of other physical quantities. Areas that relate to bioprocesses that still need extensive research include interphase drag laws, bubble breakup, and coalescence mechanisms; constitutive models for the deformation of soft porous media; the dependence of cell damage on energy dissipation rate; cohesion; and multiple-particle interactions for spherical and nonspherical particles, just to name a few. With regard to the computation technologies, a substantial reduction in computational time for 3D unsteady flow simulations can further promote the application of CFD in the biopharmaceutical industry. CFD simulations produce huge amounts of raw data for unsteady flow problems, therefore, data storage and management will become a critical issue. 

CFD model validation is necessary in any CFD modeling effort. Designing experimental verification requires the same level of understanding of the physiochemical mechanisms as for CFD model setup. Choosing parameters for comparison is a process of scientific reasoning, as well as artistic intuition. 

When designing fermentation or cell culture bioreactors, one needs to address various issues. Among them are the simultaneous dispersion of gas, the homogenization (mixing) of the nutrients or base, the suspension of living cells by the impellers, and mass transfer between cells and media. Media and bubbles are of vital importance to the performance of a bioreactor. In the case of airlift bioreactors, air flowing upward in a column-shaped bioreactor vessel generates sufficient mixing of gases and cells simultaneously, thereby replacing the need for the conventional impellers of a stirred tank bioreactor. Aeration coupled with agitation in conventional bioreactors generates complex flow dynamics in the tank. Furthermore, foaming resulting from the high volume of airflow will adversely affect process performance. Bubble busting at the air–medium interface has been reported to cause cell damage. As indicated in Figure 2, considerations of simultaneously suspending cells (solids), dispersing gas completely, achieving a sufficient ratio of surface area to gas volume for mass transfer, and minimizing detrimental hydrodynamic forces leave a very small design and performance space. Finding the overlapped optimal space for each bioreactor and using it as the scale-up and scale-down and design criterion is a challenging but rewarding task.  

The use of CFD has gone through many great developments, in terms of the computational technologies for robust, accurate, and efficient numerical analysis tools, as well as higher levels of sophistication of physical modeling in the areas such as turbulence and multiphase flow.  

Although the semi-empirical correlations or the lack of sound physics principles in CFD models limit its predictive capabilities, undoubtedly, with current computing power progressing unrelentingly, multiscale modeling and simulations from the particle level to the continuum level will become more and more realistic and uncover more fundamental physics. It is conceivable that CFD will continue to provide explanations for more and more flow-related phenomena. Fueled by science-centered regulatory initiatives and cost and quality concerns, the use of CFD modeling technologies will provide significant opportunities for optimization and quality enhancement in the future.  

The author would like to thank his former colleagues in process development, manufacturing and research at Amgen for explaining their needs to him and enriching his understanding of bioprocesses through many years of collaboration.  

 is the founder and principal consultant of Systems Quality-by-Design, Inc., Newbury Park, CA, 626.716.2016, 

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http://www.chemicalvision2020.org/cfd.html

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Computational fluid dynamics is a powerful tool to optimize processes.

Applying Computational Fluid Dynamics Technology in Bioprocesses-Part 1

This article presents a case study illustrating the benefits of using a high throughput process development (HTPD) approach for choice of media and optimization of the polishing step of a monoclonal antibody in a two-step purification process. The most commonly used format for HTPD is the 96-well plate to facilitate rapid screening of chromatographic parameters. The use of the 96-well plate format enabled the efficient screening of three different media and optimization of chromatographic parameters to maximize yield at the desired purity level.

Biopharmaceutical manufacturers are under increasing pressure to develop and produce biopharmaceuticals cost-effectively and within tight timelines. The US Food and Drug Administration is also nudging the industry to implement its Quality by Design (QbD) initiative in the manufacturing processes. QbD stipulates a better understanding of the influence of raw materials and intermediates, and control of process parameters. This initiative also is applicable to the development of downstream processes in the manufacture of monoclonal antibodies (MAbs). In this area, a new approach of high throughput process development (HTPD) is emerging as a useful tool for compliance with QbD. HTPD uses a screening format to facilitate the identification of optimal experimental conditions by directed, rapid screening of experimental parameters. A large experimental space can be investigated and characterized in a very short timeframe, enabling a better understanding of the effect of process conditions. 

Traditionally, MAbs are purified in a three-step process. First, the target is captured by chromatography with Protein A media, and two subsequent polishing steps are then performed according to a variety of protocols, often involving a combination of ion-exchange chromatography and hydrophobic interaction chromatography. For particular antibodies, it is possible to apply a more efficient two-step process that involves one capture step and a single polishing step. In the polishing step of a two-step process, a multimodal chromatography medium capable of several interactions (e.g., hydrophobic, ion-exchange interactions, hydrogen bonding) can be used, which allows the selective removal of impurities like antibody aggregates, host cell proteins (HCPs), and leaked ligands. The complexity of these media requires a more thorough process optimization study. The most commonly used format for HTPD is the 96-well plate to facilitate rapid screening of chromatographic parameters. 

This case study illustrates the benefits of using the HTPD approach for choice of media and optimization of the polishing step in a two-step MAb purification process. The use of a 96-well plate format enabled efficient screening of three different media and optimization of chromatographic parameters to maximize yield at the desired purity level. The initial capture step was performed using a Protein A medium (MabSelect SuRe, GE Healthcare), which was then used diluted or undiluted for the specific experiments.

  Three chromatography media have been investigated in this study (Table 1). 

Table 1. Chromatography media tested in this study 

INITIAL SCREENING OF CHROMATOGRAPHIC CONDITIONS

Initial experiments focused on finding the ideal incubation times and MAb concentrations for the remaining experiments (Table 2). These experiments were performed using 96-well plates (PreDictor, GE Healthcare) filled with the three chromatography media being studied, which eliminated the need for the more time-consuming column chromatography. 

Table 2. Initial screening conditions for the three chromatography media tested 

The chromatography media in the 96-well plate were first equilibrated at specific pH and salt concentrations, and the sample was then added at four different incubation times. The flow-through fraction was collected in a collection plate using centrifugation at 300

 and was analyzed using size-exclusion chromatography (SEC) to determine monomer and aggregate contents. 

Initial screening on the two anion-exchange media showed that antibody binding only occurred at pH 8.5 and 0 mM NaCl. The binding kinetics for all three media were fast for the monomer. After 10 min, the binding was complete, while the binding of the aggregates was approximately three-times slower—at least 30 min was required to achieve complete binding. The monomer content increased slightly at longer incubation times, possibly because of the displacement of monomer by aggregates. It was concluded that an incubation time of 1 h and a protein concentration of 5.3 g/L were the most appropriate conditions. 

OPTIMIZATION OF FLOW-THROUGH PURIFICATION

A further screening in 96-well plate format was performed for all these media to identify the chromatographic conditions to reduce the aggregate levels to <1%. The starting material had very low levels of HCPs, so these were not considered in this particular study. 

The MAb was loaded on the chromatography media in a range of pH values and NaCl concentrations (Table 3). After 60 min of incubation with the sample (5.3 g/L), the flow-through fractions were collected and the concentration of monomer and aggregates in the fractions was determined by SEC. 

Table 3. Screening conditions for flow-through purification 

Using the monomer and aggregate concentration in the starting material (

) and the concentration in the flow-through fraction (

) can be calculated according to the following equation: 

For the strong anion exchanger (Capto Q), the highest capacity for both monomer and aggregates was observed at high pH values and the lowest NaCl concentration (data not shown). For all conditions, the monomer capacity was higher than the aggregate capacity. The capacity for the weak anion exchanger (Capto DEAE) leveled off at higher pH levels (8.8–9.2, data not shown). This is thought to be because of the nature of the DEAE ligand, which loses its charge around this pH, while the quaternary ammonium ions remain charged at these conditions.  

The calculations with the multimodal medium (Capto adhere) revealed the highest capacity for both monomer and aggregates at high pH values and low NaCl concentrations, as shown in Figure 1. For phosphate, the capacity decreased with increasing NaCl concentration, and the opposite trend was seen for citrate. Given that this was a flow-through step, the objective was to identify conditions when the aggregate capacity was much higher than the monomer capacity. For all conditions, the monomer capacity was higher than the aggregate capacity, which would mean a low monomer yield in a flow-through step. 

The binding capacity data from the 96-well plate experiments were then used to predict yield and purity of the monomer in the flow-through fraction. This is possible using the assumption that the monomer plate binding capacity equals the dynamic binding capacity in a column, which is a good approximation for longer residence times. Purity and yield can then be calculated using the following equations: 

 is volume of sample loaded onto a column, 

 is the binding capacity found in the plates.  

A column prediction for the strong anion exchanger at a simulated load of 150 g/L is shown in Figure 2, which also shows the strong anion exchanger's predictions. The yield increased with decreased pH while the purity increased with increased pH. In this case, the purity was considered the most important factor for the flow-through step. The highest purity was identified by following the "ridge" from pH 8.0 without NaCl to pH 9.2 with approximately 50 mM NaCl. 

Predictions for yield and purity at different sample loads also were performed for the multimodal medium in the 96-well plate format and the prediction at the most favorable conditions was compared with the column data. Based on the previous data, a column prediction of purity and yield at various sample loads could be made to find the best conditions for the flow-through step. Figure 3 shows the data for a load of 122 g/L. The yield increased with decreased pH while the purity increased with increased pH. In this case, the purity was considered to be the most important factor for the flow-through step, with the highest purity prediction highlighted with the red box. 

These results were verified using a 1-mL prepacked column (Figure 4, HiTrap, GE Healthcare). The column was equilibrated with 25 mM sodium phosphate at pH 7.5. The sample (desalted MabSelect Sure eluate at approximately 7 mg/mL) was loaded at 10 min residence time. Experiments were performed with two sample loads, 130 and 260 g/L, and the corresponding prediction calculations were made for these two loads. 

The predicted yield and purity at both sample loads (130 and 260 g/L) were compared to the experimental values obtained using both 96-well plate and 1-mL HiTrap columns. The results correlated well between columns and plates (Figure 4), demonstrating that the experimental conditions in 96-well plates can be successfully scaled up to the column format. For both formats the yield increased with increased load, but the purity showed the opposite trend. With purity being the most important factor, the lower sample load was of greater interest because it provided 98% (96-well) and 97% (column) purity. However, in both cases the yields were significantly below the target 85%.  

The results from the screening experiments were used to compare the performance of the strong anion exchanger (Capto Q) with the weak anion exchanger (Capto DEAE) and the multimodal medium (Capto adhere). The predicted performance was almost identical at similar loads for all media: at the best purity (100% monomer content), the yield was at most 60%.  

Other factors must be considered to choose between the different media. One such factor could be the pH of the process step. At pH values >8, deamidation is a major problem, and the optimum conditions for Capto Q and Capto DEAE are at pH values of ≥8. Also, viral reduction is very effective with Capto adhere in a wider range of conductivities than for the other anion exchangers. Therefore, it is a better choice to use Capto adhere as the polishing step in the MAb purification process.  

SELECTIVE ELUTION OF THE MONOMERIC ANTIBODY

To increase the yield of the monomer, a selective elution study in the 96-well plate format was performed using the multimodal medium. The objective was to selectively elute the bound monomer after the flow-through step while retaining the aggregates on the column.   

The following parameters were studied after loading the plate at the optimal conditions for purity (Figure 3, red box):  

pH 6.0 to 7.0 with 50 mM sodium phosphate 

NaCl concentration from 0 to 550 mM NaCl. 

Thorough analysis of the resulting monomer and aggregate content (determined using SEC) for the different elution conditions led to the conclusions that the monomer content in the elution pool was higher when using phosphate buffers than when using citrate buffers and that the differences were because of buffer type and not pH value. Therefore, the remaining results are focused on elution conditions for the data for the phosphate buffer.  

To better visualize the relation between monomer and aggregates in the elution pool, the raw data were presented as an objective function (purity*yield) for all elution conditions (Figure 5). The highest values for the objective function were seen around pH 6 and NaCl concentrations around 250 mM.  

To verify the results from the plate experiment, a single-column experiment was performed. The MAb was bound using the same conditions as in the selective elution experiment. The elution condition was selected from within the optimal area highlighted in Figure 5. When all three fractions (flow-through, wash, and elution) were pooled, both the yield and the purity reached the preset criteria (Figure 6). The aggregate level was reduced to 0.5% with a yield of 87%. 

HTPD allows rapid screening of chromatographic parameters.

BioPharm International-04-01-2010