Cynthia A. Challener is a contributing editor to Pharmaceutical Technology.

Cynthia A. Challener

Before syringes are used on patients, they must go through rigorous quality control testing. Empty syringes must be tested at the site where filling will be completed as part of an incoming quality control protocol. Filled syringes are subjected to release testing.

	Prefilled syringes can help reduce medication errors; however, the devices pose challenges in manufacturing and require extensive testing.

	Knowledge and understanding of the various tests involved is essential for ensuring patient safety. A holistic approach to syringe manufacturing that includes formulation, process, packaging, device integration, quality control, and an understanding of the user needs is essential.

Biologics and Sterile Drug Manufacturing 2019

	When referring to this article, please cite it as C. Challener, "Test Methods and Quality Control for Prefilled Syringes," 

Pharmaceutical Technology Biologics and Sterile Drug Manufacturing 

Articles

Empty and prefilled syringes must pass a range of quality control tests.

Test Methods and Quality Control for Prefilled Syringes

Excipients are essential components in drug products. Although sometimes defined as “functional” or “inert,” each has a specific role within the formulation that is necessary for the overall function of the medicine. Variability in the properties and performance of excipients may, depending on the nature of the variability and the role and quantity of the excipient, have a significant impact on the safety and/or efficacy of the final drug product. Managing-minimizing-excipient variability is, therefore, an essential part of drug product development and manufacture.

	Excipient variability depends on many factors. One important determiner relates to whether the excipients are prepared from natural materials or via chemical synthesis, according to Liam Cullen, an engineering specialist with Servier. For example, he points to cellulose-based materials, which are derived from wood pulp that is then chemically treated. “Natural materials, particularly those that are grown, can be subject to environmental conditions such as soil type, weather, and the use of fertilizers. Secondary processing steps can also provide further material variability,” he explains.

	Materials that are manufactured by chemical synthesis alone tend to have less variability, but changes in the manufacturing environment, including the raw materials used, processes, and manufacturing locations can occur. For instance, Cullen notes that suppliers may elect to move the manufacturing of an excipient from one plant to another for cost reasons, and then replace some or all of the raw materials with locally sourced alternatives. “While the overall material specification ranges do not change, the once-typical values may be impacted,” he says.

	Variability is most commonly encountered at ACD/Labs in the impurity profiles of excipients, according to Andrew Anderson, the company’s vice-president of innovation and informatics strategy. Generally, excipient attributes such as rheology, granulometry, viscosity, and impurity profiles have been better indicators of change compared to more specific material characteristics such as purity, appearance, or loss on drying, according to Cullen.

	Because both changes in the raw materials and processes used to manufacture excipients can impact final drug product performance, Mike Tobyn, research fellow in the materials science and engineering group at Bristol-Myers Squibb (BMS) stresses that it is important to understand the sources of variability for any excipients used in drug formulations.

	In addition, he observes that the analytical methods used to generate results for certificates of analyses (CoAs) and by pharmaceutical companies upon receipt of excipients are often an unappreciated source of variability. “When debating whether a material is ‘different’ based on these results, it is necessary to consider whether the variability is in fact due to the different capabilities of the methods being used. In some cases, a change in the analytical results may be due to analytical ‘noise’ and not because the attributes of the material have changed,” states Tobyn.

	Impacts from development to commercial production

	Understanding the impact of variability in excipient properties or specifications on the critical quality attributes (CQAs) and performance of the final drug product is key to developing a robust strategy for selecting the right formulation and process that will result in a robust drug product, according to Anil Kane, executive director and global head of technical and scientific affairs at Thermo Fisher Scientific. “Challenges (and failures) start in development. Hence it is key to evaluate variability in the excipients, the API, and the process parameters during the development and pilot stages before pivotal clinical trials,” he states.

	Excipient variability can impact product consistency, stability, and dissolution profiles, among other properties. Most often these impacts occur with excipients that have functional roles, such as in drug release. Variability in inert excipients (diluents, fillers, etc.) can have an impact on product performance, but the impacts may be more difficult to detect at the development stage, according to Cullen.

	At both the development and commercial stage, excipient variability can result in a batch failing to meet specifications, which equates to a loss of time and money, according to Tobyn. “Failure could be obvious, such as sticking or tablet breakage for oral solid dosage products. Or it could be more subtle, such as interference with an established analytical method. In the latter case, the process understanding and control provided by the method may no longer be in place, which slow down release or threaten the batch,” Tobyn says.

	While all levels of development and commercial production can be affected by changes in the performance of excipients, some stages may not display any negative effects. “It is important to take a holistic approach that considers all of the information gathered from various sources and includes comparison with historical data as well as computer modeling of the data gathered,” Cullen asserts.

	He notes as an example issues that can develop with particle segregation that typically aren’t detected at development scale but become noticeable in the plant. In development, the distance from the feed intermediate bulk container to the tablet press feeder is approximately one meter; in commercial production, the feed IBC can be on the floor above the tablet press, and the acceleration involved when the material is released from the IBC to the press can result in a de-mixing phenomenon called segregation that affects smaller particle sizes in the feed mixture. “This issue is not immediately obvious in production but is normally identified during validation or standard product testing,” Cullen adds. As a result, he suggests that design-of-experiment (DoE) and quality-by-design (QbD) models currently used in smaller-scale development should be expanded to commercial-scale activities.

	Lack of variability at the development stage can also be a problem when processes are scaled up, according to Tobyn. “It is important during development to establish potential excipient variability issues that might occur and how they might affect product quality and performance. Tales abound of products developed using unusual or atypical batches throughout the development stage with no issues observed that fail in the plant due to an unexpected change. The formulator must work to avoid this situation,” he comments.

	Special considerations for continuous processes

	In continuous manufacturing, material characterization of each and every excipient is extremely crucial because continuous processes rely completely on uniform continuous blending and feeding to tableting or encapsulation machines, according to Kane. Physicochemical properties such as particle size distribution, densities, flow properties, surface morphology, and other characteristics play a critical role in developing an efficient continuous process.

	Unlike for conventional processes where the first step is usually blending, Tobyn adds that in a continuous process there is a period where the excipient is not blended but is transported via a feeder to the blender. “The flow of this powder is a consideration that does not exist for batch processes. Although loss-in-weight feeders can cope with changes in flow and feed rates, it is still beneficial to keep these changes to a minimum,” he notes.

	Here again, employing QbD for continuous manufacturing is an effective approach to risk mitigation, according to Anderson. He also notes that the use of process analytical technology is imperative for QbD implementation.

	Drug makers face many challenges when approaching the management of excipient variability. One basic issue is the locations of excipient production, according to Cullen. Excipients are often not manufactured nearby or even in the same country or on the same continent, particularly for specialized materials. “Issues of distance, language, and time zones make it challenging to maintain concurrent or even regular contact,” he notes. Online help desks often provide limited support due to insufficient staffing or unwillingness of suppliers to share process or material sensitive information, even where dedicated secure client accounts have been created, adds Cullen.

	There are also different types of suppliers, according to Cullen. Some are dedicated to the pharmaceutical industry and understand the extensive requirements of the industry. Others serve multiple sectors with the pharmaceutical industry comprising a significant part of their businesses. These suppliers generally recognize the special requirements of their pharma customers, but may need some additional support. There are also suppliers for whom the pharmaceutical industry is a small percentage of their business. These suppliers may not fully understand the specific needs of pharma companies and communication may be difficult.

	As one specific example, Cullen notes that some suppliers will indicate on CoAs that a specific attribute “complies” with the specification rather than providing an actual value, which makes it difficult to identify variations and to use data for trending analyses to determine patterns of variability that can assist in understanding the subtle changes in the CQAs of an excipient.

	A deeper understanding of how variability in excipients can affect drug product performance and proposed control strategies is essential for improving drug product development, according to Kane. Gaining that understanding can be difficult, however, because pharma companies don’t have the internal capability to manipulate excipients and their manufacturing processes. “Variability in excipients is a function of the control strategy used by the suppliers of these materials, and it can be challenging for drug makers to obtain ideal sets of samples for adequate investigations,” he observes.

	Furthermore, the number of excipient material properties combined with the number of excipients in a drug product formulation present cost and logistics challenges for executing manageable experimental designs. “Risk-based approaches may be helpful for identifying the excipient material properties (typically functional) that have the greatest impact on product performance,” says Kane.

	In addition to finding sufficient time to run a systematic DoE to evaluate the influence of excipients, Thermo Fisher is often also challenged by the need to work with minimal quantities of API at the clinical and pilot scale, according to Kane. For ACD/Labs’ customers, data analysis and reviews are a challenge, and the company is working with a variety of customers to address this issue, according to Anderson.

	Tobyn agrees that there are business and quality barriers to completing the processes necessary to ensure that excipient variability is minimized from the start. “The technical processes to establish which materials have low variability exist. We currently need the business processes to exploit that,” he adds.

	One of the most common tactics for pharma companies is to, where possible, choose excipients that have worked well in the past and to purchase them from reliable suppliers with a track record of providing excipients with minimal variability.

	For instance, BMS uses platform formulations that allow the company to work closely with and gain a greater understanding of a limited number of materials. “Because the platform is known, and how those materials interact has been demonstrated, the only material ‘variable’ is the API,” Tobyn remarks. He adds that the platform utilizes high-volume, widely available excipients that are expected to demonstrate lower variability.

	BMS has also used a range of techniques to identify vendors with proven track records in producing low-variability materials. “Use of these vendors, along with ongoing monitoring of the variability of their products, provides a stable base for the platform,” Tobyn explains. Other vendors can be introduced, along with more variability, at key stages if the business requires.

	Beyond the use of well-known excipients, the most basic step at Servier is to monitor and analyze the data from both CoAs and on-site testing results, according to Cullen. Functional excipients are monitored more closely than less functional materials, but all can have an impact. The data are trended along with the performance data of the finished product, and over time it is possible to identify which materials have the greatest effect on finished product. Servier also conducts regular supplier audits.

	Once the most impactful excipient material properties are identified, this understanding can be combined with knowledge of the API properties and the process parameters used to manufacture the drug product to develop an appropriate control strategy that ensures consistent supply of safe and efficacious drug product, according to Kane. He also stresses that the experience of formulation scientists is essential for developing robust optimized formulations.

	In addition, employing a QbD approach, and specifically accounting for each material’s CQAs, can help with developing appropriate procedures and mitigating the risk of commercial production failures when any excipient has attribute variability, according to Anderson.

	Of course, suppliers play a crucial role in reducing excipient variability. In general, BMS has found that the more experience a vendor has with a material the less variable it is likely to be, according to Tobyn. “A major excipient that has been produced for 50 years on a tonne-per-day basis and is in thousands of products is less likely (although not totally free from risk) to have variability that will influence final drug products,” he says. On the other hand, suppliers of niche, specialist, novel materials may need some time to develop process and product consistency.

	Cullen would like excipient suppliers to openly discuss planned and unplanned process changes with their suppliers, provide specific values of CoAs, not just “Complies”, and prepare annual summaries of CQAs for their processes and finished materials that also include timelines for any changes. He would also like to see more direct communication and opportunities for site visits to suppliers, such as workshops and plant tours, so pharma companies can gain a greater understanding of excipient manufacturing processes.

	Excipient suppliers should, in fact, be open to sharing their data, with appropriate controls, on the variability of their products, according to Tobyn. He notes that some vendors, mainly the ones who are aware that their products are low variability, do so, but others are reluctant. “Data sharing would allow customers to make informed decisions about which materials are low and high variability and potentially provide pressure for vendors with high variability to improve that variability,” he observes.

	BMS would also like to monitor excipient properties in real time with vendors in order to be able to select batches during the purchasing process, either in a regular process or opportunistically when suitable batches became available. “This type of purchasing, however, will require new processes for our company and the vendors,” Tobyn notes. In addition, he would like to see more focus on analytical methods and reduction of analytical “noise” so that “real” variability can be readily probed, identified, and minimized.

	Both Kane and Anderson would like to see more excipient suppliers adopt and apply QbD principles in the manufacture of their excipients.

	Essential to minimization of excipient variability is the building of relationships with suppliers, according to Cullen. “Waiting to commence a dialogue with suppliers until an issue arises or until it is time for an audit does not provide good results. It is important to establish and maintain contacts within supplier organizations so that issues can be discussed freely and directly as they occur or ideally in advance of their occurrence where issues during the manufacture of an excipient have arisen. It is an area for constant development. Drug manufacturers must work closely with their excipient suppliers at all times,” he asserts.


	Vol. 43, No. 5
	May 2019
	Pages: 24–26

	When referring to this article, please cite it as C. Challener, “Avoiding Excipient Variability,”

Knowing the source and understanding the impact on CQAs is crucial to optimum drug formulation and processing.

Avoiding Excipient Variability

The presence of the probable carcinogens N-nitrosodimethylamine (NDMA) and N-nitrosodiethylamine (NDEA) found in APIs used for the production of a number of generic versions of angiotensin II receptor blocker (ARB) medicines has raised concerns about how manufacturers and regulatory bodies address the potential for production of undesirable impurities (1). The products in question complied with regulations yet contained genotoxic compounds. FDA has taken several steps to address this particular issue (2,3). Drug makers also clearly need to consider what approaches they should be taking to ensure their API suppliers are supplying high-quality material that is safe for use in final drug formulations.

	“Despite the fact that manufacturers have the process knowledge and experience that regulatory bodies do not have, we are still dealing with massive recalls of very important medications, the scope of which just keeps increasing,” observes Macniell Esua, chief compliance officer at CordenPharma International.

	In this particular case, Esua points out that it appears that a trace impurity in a solvent reacted with a process reagent used, resulting in a genotoxic impurity. “Surprising levels of the impurity are being found in the drug that exceed the levels one might have predicted and that are allowed. One guess is that recycled solvents may be a contributing factor,” he notes. Esua adds that it is critical that the full investigation findings be shared with the industry in an open fashion, so everyone can learn from this potentially tragic situation.

	Looking specifically at the issues concerning valsartan, it is also important to note that after the initial recall of drug product containing an API manufactured by Zhejiang Huahai China, additional API manufacturers in China and India had their approvals recalled for the same reason. “The presence of nitrosamines is not only a ‘variation’ problem but has also been shown to be a GMP cross-contamination problem at some of the manufacturing sites involved,” says Rolf Arndt, senior quality assurance and regulatory affairs specialist with Cambrex.

	He adds that the fact that different nitrosamines were identified for both APIs and drug products makes the problem more challenging, because the required maximum specification limits of nitrosamines are substantially lower than for normal impurities.

	“The issue is not a simple question of ‘approval systems,’ but more a combination of better evaluation of the risks of formation of nitrosamines as well as better analytical techniques; but most importantly, that API manufacturers have sufficient controls within their GMP procedures to avoid cross contamination,” Rolf asserts. “Furthermore,” he states, “Because this field is so fast moving, it is not enough to merely comply with the regulations; it is important to be ahead of them.”

	During a process validation exercise related to an investigational new drug, a full fate and purge study should normally be conducted, according to Esua. In the current situation with valsartan and related products, Esua suggests a fate and purge study would have correctly identified NDMA as a potential impurity in the process. “As a result, work would have been conducted to identify the potential risk of introduction of NDMA in the final product and to develop mitigation strategies. Through the change control process, full consideration should have been given to the change in solvent, the potential impurities that the change was introducing, and the downstream implications,” he says. He also points out that this issue is again related to the scientific competence aspect-and whether the study was conducted with sufficient rigor or paid “mere lip-service.”

	Any process change must involve a risk assessment that includes a judgement of whether the proposed changes have the potential to result in the production of new or additional impurities, according to Arndt. The assessment should also identify whether there is a risk of formation of impurities that may fall under International Council for Harmonisation  M7 guidelines.

	“If there is a risk of formation of so-called ‘M7 impurities,’ further investigation must be performed, and the basis to exclude any process change must rest fully on supportive analytical data,” Arndt states. “These types of impurities cannot normally be detected by simple chromatographic methods that are used for ‘related substances’-substances similar to the API, so more advanced techniques must be used, for example, [tandem mass spectrometry] MS/MS,” he adds.

	When considering drug process changes, the burden resides rightfully with the manufacturer, according to Esua. It is also the manufacturers’ responsibility to complete a comprehensive scientific review of the proposed changes, with thorough consideration of all possible implications, and a detailed impact assessment as part of the change control process.

	“In the case of the NDMA contamination of valsartan, is it known that dimethylamine could be an impurity in the solvent [drug master file] DMF? Yes. Is it known that dimethylamine would react under the reaction conditions to give NDMA? Yes. Where, then, did the manufacturer go wrong? Were dimethylamine levels higher than anticipated in the DMF (since it’s not a typical impurity that would be screened during a DMF solvent release), or was the combination of these two factors (the presence of dimethyl amine and reactivity in the reaction) simply not considered?” Esua asks.

	What is important to remember, according to Esua, is that although a regulatory inspection is a tool to measure compliance against current manufacturing regulations, it does not, nor can it, dictate a certain level of scientific competence on the part of the manufacturers, or unfortunately, the regulatory agencies themselves.

	So how can drug makers be sure they are selecting API suppliers with the appropriate level of scientific competency? “It is imperative that pharma/biotech companies understand the partners they choose to do business with,” asserts Stephen Houldsworth, director of global small molecules and antibiotics platforms at CordenPharma International. “They must have confidence in the company’s quality systems and its manufacturing team, as well as the team’s scientific competence and ability to make the correct decisions,” he explains.

	Houldsworth points out that there are suppliers in the marketplace today that perhaps make decisions based on incorrect assumptions or poor science, the consequences of which are often not discovered until much later, as in the case of valsartan and similar drugs. “Unfortunately, price drives a lot of purchasing decisions-especially in the generic API business-but this cannot be the only priority. Considerations such as quality controls, process development, project management, and scientific expertise must also be factored into these decisions,” he stresses.

	The first and most important step is to evaluate and audit the API manufacturer to ensure that it has a sufficient standard of GMP protocols and procedures in place to minimize the risk for cross contamination, according to Jonathan Knight, director of marketing intelligence for Cambrex. The second step is to ensure further evaluation where necessary, such as for potential toxic elemental impurities originating from the environment, the raw materials used for manufacturing, and/or corrosion of manufacturing equipment. “Confirmation is needed that these types of impurities are not present in the API and thus do not impact the product quality,” Knight observes.

	In addition, Cambrex suggests reviewing the company’s regulatory history and its corrective actions around observations and GMP compliance and training. Additionally, Knight notes, it is important to assess the company’s experience in developing new chemical entities, and the proficiency and experience of its chemical-development, analytical-development, and quality-assurance departments. That includes checking the investment history of a manufacturer, especially with respect to the acquisition of the most up-to-date analytical equipment.

	“Having a good understanding of what the potential risks are can be determined by the development of good design of experiments. In addition, ensuring openness between parties and clear documentation can avoid problems,” Knight adds.

	The success of a company’s outsourcing efforts is driven by the quality of their vendor qualification and management systems, according to Houldsworth. “They need to be robust, structured, and detailed to provide a high level of confidence that the API suppliers in question are reliable and competent,” he concludes.

	Esua cautions that regulatory agencies should not respond to the valsartan contamination issue with a simple knee-jerk reaction and demand that all process changes must be approved by regulatory agencies before implementation. “Apart from crippling both the manufacturers (via delays in change approvals) and the regulatory agencies (by drastically increased burden of work), this approach would lead to rising costs in an industry that is already under intense pressure from a costing point of view, without any guarantee that problems will be avoided in the future,” he states.

After Valsartan Recalls, Regulators Grapple with Nitrosamine Contamination in APIs

Statement from FDA Commissioner Scott Gottlieb, M.D., and Janet Woodcock, M.D., Director of the Center for Drug Evaluation and Research on the FDA’s Ongoing Investigation into Valsartan and ARB Class Impurities and the Agency’s Steps to Address the Root Causes of the Safety Issues,

Vol. 43, No. 4
	April 2019
	Pages: 22–24

	When referring to this article, please cite it as C. Challener, “Select Suppliers with Demonstrated Expertise to Avoid Sourcing ‘Bad’ APIs," 

Confidence in the quality systems and scientific competence of the API manufacturing team is essential.

Select Suppliers with Demonstrated Expertise to Avoid Sourcing “Bad” APIs

Dissolution in the intestinal tract is an essential first step in the delivery of most oral solid-dosage drugs. A high percentage of drug candidates today are, however, considered to be poorly soluble according to the Biopharmaceutical Classification System (BCS). Formulators are challenged to develop delivery mechanisms to overcome these solubility issues. Many excipients can enhance solubility as well. The challenge is to select the right ones for the specific API and dosage form.

	A diverse array of excipients is used for solubility enhancement, including cellulosics, vinylpyrrolidones, acrylates, modified or copolymer variants of these chemistries, and various lipids such as triglycerides. Poorly soluble APIs can also be formulated with carriers such as cyclodextrins to form complexes that can enhance solubility and bioavailability.

	Each of these excipient classes are capable of drug/polymer interactions that stabilize the compound in the desired state (e.g., amorphous state) and deliver it as intended, according to Kevin O’Donnell, R&D manager in pharma excipients at DowDuPont Specialty Products (DuPont) Division. “With the exception of controlled-release systems, these polymers will also be hydrophilic, possibly with pH-dependent dissolution, to improve wettability of the final formulation of the often highly hydrophobic APIs,” he explains.

	Recent excipient introductions into the pharmaceutical market have been focused on overcoming processing limitations, because the formulation of poorly soluble compounds typically requires an enabling manufacturing technology, according to Paula Garcia-Todd, global strategic marketing manager for drug delivery technologies at DuPont Nutrition & Health.

	The rationale for selecting the appropriate excipients for solubility enhancement should be primarily driven by the chemical structure and physico-chemical properties of the API and the target drug load, which together lead to the desired final dosage form, according to Sanjay Konagurthu, senior director of science and innovation in the pharma services group, part of Thermo Fisher Scientific.

	Typical API properties used for selecting the appropriate excipients include aqueous solubility, LogP, LogD, pKa, melting point, glass-transition temperature, organic solubility, thermal stability, precipitation kinetics, chemical stability, and others.

	For example, O’Donnell notes that if the API is not soluble in organic solvents, it is likely that a fusion technology such as hot-melt extrusion (HME) will be used. In that case, excipients amenable to extrusion will be the first to be evaluated. Conversely, if the API is thermally unstable, excipients that are soluble in the same solvents as the API will be evaluated for spray drying.

	“A couple of the most fundamental considerations for developing robust formulations are ensuring that the API has sufficient solubility and stability in the solubility-enhancing excipients. These are the critical properties to overcome a solubility hurdle of the API,” adds Ronak Savla, scientific affairs manager, Catalent.

	Ultimately, formulation choices for excipients are primarily driven by the type of enabling technology used for solubility enhancement, according to Konagurthu. “We need to ensure we choose the right excipients for maintaining the physical and chemical stability of the intermediate(s) as well as the final dosage form,” he says.

	Lipid-based drug delivery systems (LBDDS) are one of the most widely used technologies for solubility enhancement, according to Savla. LBDDS can contain different combinations of oils (triglycerides or mixed glycerides), water-soluble surfactants (e.g., different forms of castor oil and polysorbates), water insoluble surfactants (e.g., oleic acid), and co-solvents (e.g., ethanol, PEG400, and propylene glycol).

	“These excipients solubilize drugs or form emulsions. Therefore, in addition to purity and safety of the excipients, the most important attributes to consider during LBDDS development are the solubility and stability of the drug in the formulation and the minimization of drug precipitation upon contact with gastrointestinal fluid,” explains Savla.

	Amorphous solid dispersions (ASDs) are increasingly used for solubility enhancement. In HME and spray-drying, the API is distributed throughout a polymer network, maintaining the API in a higher-energy amorphous state with greater solubility than its preferred lower-energy crystalline state. “To ensure stability of ASDs, the polymer should not only be compatible with the drug but also be miscible and enhance the supersaturation of the API in aqueous media,” Savla observes.

	A wide range of polymers are used in ASDs, including cellulosics, polyvinyl derivatives, and polymethacrylates. Hygroscopic or acidic polymers that might lead to hydrolysis or acidic degradation are an exception. Various additives such as surfactants, plasticizers, and permeation enhancers are also often employed by formulators.

	The dosage form itself will impact the choice of excipients as well. For oral solid-dosage forms, Garcia-Todd notes that the permitted daily dose of an excipient and the physical properties of the excipient (e.g., many lipids may not be suitable for tablet formulation) must be considered. For non-oral routes of administration, such as implantable formulations, she adds that regulatory or toxicology limitations may exist for an excipient that limits or prevents its use.

	Formulation requires time and resources to create and test different formulations in the laboratory. Modeling and simulation tools hold the promise to improve speed and reduce costs by helping formulators simulate API-polymer interactions and focus their experiments on the polymers that have the most promise, according to Savla. He does note, however, that these modeling and simulation technologies are still in their infancy and there needs to be a continued effort to build the dataset for these models and validate them against in-vitro experiments.

	For Garcia-Todd, modeling and simulation are already useful to some extent. “While modeling and simulation can be useful for eliminating certain excipient classes during excipient selection, they are not yet to the point of identifying a single best option. Often, these systems will identify a number of excipients that are most likely to be successful but it is then up to the formulator to test the set and identify which is indeed most capable of improving drug solubility for a given API,” she says.

	Konagurthu, on the other hand, notes that the speed and performance of computational modeling methods using quantum mechanics (QM) and molecular dynamics (MD) has significantly improved, allowing for rapid in-silico screening of drugs in more complex environments. “To further improve success rates and reduce development time and cost, early consideration of the drug delivery method and formulation approaches should begin in parallel with drug discovery and development programs,” he asserts.

	Thermo Fisher is actively leveraging modeling and simulation tools for excipient selection. The company has developed a novel in-silico approach/platform for identification of the appropriate solubilization technology and excipient selection for solubility enhancement, according to Konagurthu. The platform is designated as Quadrant 2 and is an agnostic approach toward formulation selection of excipients based on computer algorithms that use a combination of calculated descriptors and available experimental physico-chemical properties.

	“Excipient selection and drug loading is accomplished by calculating descriptors, energies of interactions from QM and MD simulations combined with machine learning. These provide the input to an algorithm that provides a rank ordering of recommended excipients and drug loading options,” Konagurthu explains.

	Thermo Fisher uses these simulations in the early phases of the development process to rapidly identify candidate excipients for drugs, predict key experimental properties of the formulations, and determine drug loading and formulation stability. Notably, the company has validated its models with experimental data for more than 175 compounds to a predictive accuracy of greater than 80%, according to Konagurthu.

	Many companies have their own unique protocols for excipient screening/selection for solubility enhancement formulations. “While methods will vary, the common goals will include (for ASDs), identifying the excipients most capable of holding the drug in the amorphous state (e.g., film casting and looking for residual crystallinity); identifying which of those are capable of generating strong supersaturation and maintenance thereof; identifying the potential drug load in the strongest candidates; and analyzing for potential incompatibilities. Additional steps may include evaluation of excipient properties of each candidate excipient in the intended manufacturing method,” O’Donnell explains.

	At Catalent, the first step is screening API solubility, stability, and potential solid-state changes in a panel of common excipients used in FDA-approved products. Based on these results, formulation prototype development with other ingredients (surfactants, co-solvents, etc.) is undertaken. “It is important to keep in mind that the amount of excipient in the formulation is under the maximum allowable potency per dose,” Salva notes.

	Thermo Fisher first uses its Quadrant 2 platform to select an available list of solubility enhancement technologies suitable for the API. The algorithm then identifies candidate excipients for each technology type and predicts factors such as maximum drug loading, stability, and dissolution performance for numerous different excipients. “Based on these results, a significantly reduced number of candidate excipients is generated. The top five to six are selected for experimental feasibility screening using small amounts of API (<1g),” Konagurthu says.

	“Every API is different, and unfortunately there is no silver bullet that is effective for all drugs,” Garcia-Todd asserts. “With each compound, the formulator must identify the best enabling technology and the best excipient concurrently, and often these do not match. Finding the proper balance of performance and manufacturability is crucial early in development to ensure a drug product is effective and does not fail because it cannot be made,” she says. Adds Konagurthu: “The most challenging aspect is to reduce the number of excipients to a manageable level and have a high probability of improving the solubility and bioavailability.”

	What will help ensure success? “First, be material agnostic,” says O’Donnell. Just because an excipient worked in the past does not mean it will be the best choice in all cases. “Second,” he continues, “understand the limitations of your excipients in manufacturing technologies: do not try to force fit an excipient into a technology. Third, solubility enhancement does not guarantee bioavailability enhancement; advance more than one formulation into 

	For Savla, because every drug is unique, it is important to comprehensively characterize the API to understand the challenges (e.g., poor solubility in acidic conditions) and develop potential formulation. It is also important to recognize that excipients work through different mechanisms in different enabling technologies. He also notes that some amount of experimentation is required.

	The first step to take, according to Konagurthu, is identifying the appropriate enabling technology for solubility enhancement based on the API’s physico-chemical properties. Next, it is important to avoid excessive time-consuming empiricism by having a rational selection and screening strategy for excipient selection without relying on “trial-and-error” approaches. Ultimately, he says the goal is to make sound decisions based on balancing the aspects of bioavailability, manufacturability, and stability to develop a robust, commercializable drug product.

Vol. 43, No. 3
	March 2019
	Pages: 22–24

	When referring to this article, please cite it as C. Challener, “Strategic Screening for Solubility Solutions," 

Understanding the API, delivery mechanism, and excipient functionality is essential to solving drug solubility challenges.


Strategic Screening for Solubility Solutions

Analytical instruments are increasing in sensitivity and accuracy. Pharmaceutical methods applied today generate increasing quantities of data. These data are stored and manipulated using a variety of software platforms from different suppliers. Pharmaceutical scientists must be able to integrate and interpret these data in an efficient manner that ensures reliability, security, and regulatory compliance.

	Today’s modern laboratory is highly dependent on a range of instrumental technologies, all reliant on data output that has to be interpreted, managed, and maintained in a retrievable, secure, and non-corruptible manner, according to Mark Rogers, global technical director at SGS Life Sciences. The volume and complexity of these data make the move to electronic capture inescapable, he adds.

	For many of these techniques, however, the inevitable evolution from paper-based data collection procedures to electronic systems has not been without difficulties. “It may be argued, for example, that the relatively simple process of recovering and reviewing chart recordings from a well-maintained paper archive has advantages over the recovery of data from older software/operating platforms that may have become obsolete, making data difficult to retrieve,” Rogers explains.

	Regulatory considerations have also now become intrinsically linked to the traceability, security, and non-corruptibility of scientific data, again resulting in difficulties with older equipment, he notes. “The electronic modernization of older-but well-established and effective-equipment may be one of the most significant and ongoing challenges,” asserts Rogers.

	In addition, despite advances in the digitalization of analytical data, significant human intervention and manipulation is still required during any decision-making process based on data acquisition. For lot release, once a specification (identify, purity, physical form, etc.) has been established and test methods validated for a drug substance, there are many human intervention steps involved to get from a request for release to fulfilling that request, explains Andrew Anderson, vice-president of innovation and informatics strategy at ACD/Labs. Spectra must be collected and separation analyses performed. The data obtained must be generated as a table, chart, or other picture that describes the results, which must then be compared to reference standards to determine whether they conform or not.

	“Many of these steps cannot be digitized or even easily automated. Bringing data back into the decision-making mechanism is very difficult without specialized informatic tools that handle all different types of data. This challenge is the greatest latent pain for development organizations,” Anderson asserts.

	There will also always be challenges with getting the right data, according to Anderson. “There are trade-offs between project timelines and the data that can possibly be generated and ultimately handled by existing infrastructure,” he explains.

	One example relates to the move to a quality-by-design approach to process development. With this approach comes a geometric expansion in the quantity of data being acquired to characterize a process. Each possible parameter variant requires a data point for the modeling system used to determine the optimum operating conditions.

	In addition, the data itself is becoming more complex. A nuclear magnetic resonance (NMR) spectrum provides a significant amount of detailed information about a given molecule and is therefore attractive for small-molecule API proof-of-structure analysis. Interpretation of this type of spectrum, such as comparison with a reference standard to confirm identity, requires an expert, however. “A method for plant operators to use for determining that a lot spectrum conforms to a standard should be rugged and allow for simple acquisition and validation. Ultraviolet (UV) analysis is one example. Such a method leaves some doubt however; NMR provides more fidelity, so there is a tradeoff between absolute confirmation and practicality,” Anderson observes.

	The complexity of analytical experiments will, in fact, always outpace the ability to implement them in quality processes, he adds. “There are often challenges to implementing more complex analytical techniques in a quality paradigm. All of the aspects of quality assurance are increasing in complexity: software systems and how they handle data, quality practices, standard operating procedures, and test methods. Each of these factors must be considered to ensure that drug products introduced to the market are of the highest quality,” notes Anderson.

	It is not only the volume and complexity of raw data being generated that is the key issue for Marco Galesio, R&D team leader in the analytical chemistry development group at Hovione, but the lack of specific and user-friendly software to treat and interpret these data. “Most of the time there is a lot of relevant data being generated, but due to lack of suitable software and sufficient time, it is not possible to take the most out of it,” he says.

	Biologic drugs pose some additional challenges beyond those associated with chemical APIs. Small molecules have one structure and composition. Because biologic drug substances are produced in cells or microorganisms, their structures and compositions can vary slightly from batch to batch, leading to inherent variability, according to Tiffani A. Manolis, senior director of Agilent Technologies’ global pharma strategic program.

	Added to this variability is the complexity of large-molecule structures, which necessitates the use of a greater number of more complex analytical methodologies, according to Íñigo Rodríguez-Mendieta, technical client manager biopharmaceuticals, SGS Life Sciences. In most cases, specific expertise is required to interpret large-molecule data, notes Constança Cacela, associate director of R&D in the analytical chemistry development group at Hovione.

	The necessity to fully define the relatively complex and diverse structural features of large molecule entities has led to expansion in the variability and sophistication of the analytical tools in the biologics field, according to Rodríguez-Mendieta. “Many of these techniques have traditionally been employed in the academic sector where data interpretation relies heavily on subjective manual review, and this is often difficult to translate to machine-based understanding,” he says. Approaches to digitalization have included automated reference to library data as in the case of circular dichroism (CD) and/or the application of complex algorithms as in the case of mass spectrometry (MS)-based sequencing. “None of these approaches has yet been perfected, however, particularly for large-molecule applications where structural diversity can prove a significant impediment to accurate interpretation,” adds Rodríguez-Mendieta.

	The key challenge lies in the fact that with biologic molecules, identity has various meanings related to structure (primary, secondary, etc.), post-translational modifications (PTMs), etc. “Orthogonal analyses are required to ensure full characterization. Chromatography may be used to determine purity, while other methods must be used to determine the identity (sequence, location of PTMs, etc.). Structural assays are often augmented with behavior immunoassays. All of these data must be ‘assembled’ together to create the complete picture. That can be challenging to do digitally, because orthogonal data are often generated in a variety of vendor formats,” Anderson explains.

	The same issues exist for biosimilar comparability studies, Rodríguez-Mendieta notes. “Results have to be considered holistically, which remains a considerable challenge, as the data can be somewhat contradictory and often produced from a wide range of techniques with instrumentation from multiple manufacturers with different data platforms,” he says.

	Next-generation drug substances can complicate the situation even further. Anderson points to antibody-drug conjugates (ADCs) as an example. “ADCs comprise an antibody, a small-molecule cytotoxic payload, and a linker of some kind, which can be an oligomer or other small to medium-sized component. The dynamic range of MS, NMR, or other instruments must therefore be sufficiently wide to allow analysis of small and large molecules, and of course also validatable,” he explains.

	Separation techniques such as gas chromatography (GC), high-performance liquid chromatography (HPLC), capillary electrophoresis (CE), and gel electrophoresis when coupled to UV, Raman, infrared, and fluorescence do not provide sufficient data per se, and particularly for large molecules, according to Cacela. “Data generated by these techniques must often be validated by more comprehensive techniques such as mass spectrometry,” she notes.

	In general, however, LC–MS methods, especially high-resolution MS (quadrupole time-of-flight [Q-TOF], triple quadrupole, etc.), generate overwhelmingly large data files, according to Manolis. “Data sets are becoming very large and pose challenges to any IT [information technology] infrastructure as they are transmitted across networks for storing and analysis,” she observes.

	An additional challenge is related to data interpretation. The acquisition of MS data occurs in much less time than it takes to interpret it, according to Anderson. “With TOF instruments it is possible to gather detailed spectra in just three minutes, so the technique can be useful for screening experiments or monitoring the progress of a synthetic reaction or cell culture. The data files that result can be a gigabyte in size, which is challenging not only from a hard disc perspective, but for interpretation, which can take much longer,” he comments. Furthermore, deep expertise is required to accurately evaluate and filter the meaningful data, Cacela adds.

	Similarly, the volume of data generated in NMR and surface plasmon resonance analyses can be challenging to manage.

	Another challenge given the volume and complexity of data generated today is the need for some sort of data abstraction in order to be able to efficiently leverage analytical data in the decision-making process. “Very large data sets need to be transformed in some way to make them manageable and interpretable in a practical and timely manner. The difficulty, however, is the potential for loss of information,” asserts Anderson.

	Reduction of data creates risk from a total analysis point of view, he adds. Centroided MS data vs. full-profile MS data is one example. The former involves a mathematical transformation of the data that provides simplification and easier interpretation, but the risk of missing important details must be weighed against that simplification.

	There is a big push, according to Anderson, to find ways to use data in a non-reduced way for more practical interpretations.  One example involves modifying the way signals are acquired. In NMR, for instance, Anderson points to nonuniform sampling, which involves pulsing the sample at nonuniform frequencies and allows the reconstruction of spectra close to what would be obtained with full-frequency pulsing but in a smaller data file.

	There is also a focus on the application of machine learning and artificial intelligence in this area, letting machines identify trends and make observations that humans could probably find but don’t have time to do, according to Anderson.

	A theme underlying all of the above areas of concern is the need for software platforms that enable viewing of data generated by different instruments from different suppliers. “The systems we have today generate huge amounts of data. But these same systems are not necessarily capable of integrating these large data sets and providing researchers with actionable information in a multi-modal view,” Manolis states. While data generation and usability of technologies are improving with better user interfaces and easier-to-use systems and applications, the integration of data sets and connectivity between data sets from different technologies and vendors remains a challenge.

	“Software analysis tools, rather than only gather data, should also be able to analyze and interpret that data and provide information as the output,” asserts Lucia Sousa, associate analytical chemist for R&D in the analytical chemistry development group at Hovione. “Vendors are creating software that interprets data, but even these advanced systems still require great effort from the user. Scientists in the laboratory should have knowledge in their fields of expertise and not need to be experts in software applications as well,” she adds. 

	Throughout pharma discovery, development, and commercial manufacturing, data are acquired and stored on separate and disparate systems that contain both structured and unstructured information, according to Manolis. The challenge is further complicated by collaboration with outsourcing partners, suppliers, distributors, and government agencies that have their own disparate systems. Overcoming these challenges will dramatically improve researchers’ abilities to make faster decisions and will ultimately accelerate drug development and approval, she adds. 

	Analytical manufacturers are working with pharma companies and others to build software solutions that ease these processes. The Agilent data system OpenLab CDS ChemStation Edition, according to Manolis, is the first to support the new Allotrope Data Format (ADF) (allotrope.org), an emerging standard developed by a consortium of pharmaceutical companies.

	“ADF standardizes the collection, exchange, and storage of analytical data captured in laboratory workflows and enables labs to transfer and share that data across platforms,” Manolis explains. “There needs to be continued focus on evolving customer requirements concerning analysis and integration of data sets, compliant solutions for analysis and storage, and the IT infrastructure challenges around large data sets, specifically those generated from high-resolution MS analyses,” she continues. 

	It is an exciting time with respect to advances in data analysis and interpretation, according to Anderson. “Instrument manufacturers and software vendors like ACD/Labs are facing a new paradigm. No longer are we dealing with the integration of monolithic systems through document exchange. Copying and pasting spectra into a word document isn’t good enough any longer. Integration of not only data, but applications is needed to provide a seamless user experience. Decision-making interfaces of the future will be interoperable and able to leverage a variety of heterogeneous and orthogonal data,” he explains.

	 The horizon for software solutions to address pharma customer needs is positive and a fast-moving and competitive space, agrees Manolis. “Broadly speaking, there are many organizations evaluating and using tools that were not designed specifically for pharma but perhaps for business or consumers, and these organizations have been adapting them to their needs. Moving forward, we see these boundaries continuing to blur as solutions are delivered from a variety of software vendors.”

	Pharmaceutical scientists will need to adapt as well. An understanding of data science is becoming essential. “This experience is key for analytical scientists to understand the data and challenge the data with the right questions and subsequent analyses,” Manolis says. “To avoid data silos and facilitate the sharing of data, the expertise gained by the data owner will be invaluable when developing ways to use existing information or to integrate additional data,” she adds.

	Furthermore, implementing end-to- end data integration requires capabilities that include trusted sources of data and documents, robust quality, and maintenance of data integrity. “The objective is to tackle the most important data first to achieve a rapid return on investment. Identifying the right infrastructure will therefore be of paramount importance,” states Manolis.

	Hovione also believes that data science will allow data-driven predictions from big data through modeling and statistical inference. “Data science will allow the extraction of meaningful information for decision making and consequently allow increased efficiency in pharma laboratories,” says Galesio. He does note, however, that many pharma researchers still lack expertise in this field. “Going forward it will be especially relevant to combine both data science and analytical expertise. We believe many companies are investing more and more in this area,” Galesio observes.

	Many pharma organizations are building structures or have built structures in the past 5–10 years specifically supporting data science and analysis, according to Manolis. “We would assume this investment will continue as modalities and data sets continue to increase in complexity,” she concludes.

With higher volumes of analytical date being generated in pharmaceutical processing, digitization and platform integration are becoming increasingly important to implement.

Digitization of Data Analysis and Interpretation

Engineering of protein therapeutics has advanced significantly since the first biologic drugs were introduced. Today, monoclonal antibodies (mAbs) have become one of the predominant forms of protein-based biopharmaceuticals, with humanization and fully human monoclonals contributing to their increasing success. Antibody-drug conjugates are a second-generation version leveraging the attributes of both small and large molecules. Bi/multispecific and fusion proteins are emerging as promising third-generation protein drug substances. Peptides and peptidomimetic therapeutics offer the advantages of mAbs but in smaller molecules that may be formulated for oral delivery. Advanced modeling tools are enabling the rapid engineering of more complex proteins with desirable properties. Companies are also paying close attention to the developability and manufacturability of proteins and considering patient needs at the earliest engineering stages, leading to safer and more effective biologic drugs.

	Fully human monoclonals and mAb humanization

	Fully human mAb therapeutics with fully human sequences have reduced immunogenicity potential and improved safety and efficacy, enabling chronic treatment without liabilities, according to Jennitte Stevens, director of process development at Amgen. Generation of fully human antibodies is achieved in transgenic animals. For instance, mouse cells that produce mAbs have been modified by knocking in human genes and knocking out mouse genes. Amgen’s XenoMouse technology has been used to produce the marketed mAb therapeutics denosumab, erenumab, and evolocumab.

	Recently, more fully human platform technologies have been developed, such as OmniAb (Ligand), Trianni Mouse (Trianni), and huTARG (Innovative Targeting Solutions). “The latter is a fully mammalian technology that generates in-vitro antibody diversity through ex-vivo V(D)J recombination in cultured cells. These technologies enhance both the speed and success of developing antibody therapeutics,” Stevens says.

	Humanization of mAbs is another well-established method to generate therapeutic antibodies when transgenic technology is not available or feasible, according to Stevens. “Humanization techniques using complementary determining regions grafting or phage display have enabled the advancement of numerous antibody therapeutics (such as trastuzumab, pembrolizumab, and romosozumab) to the clinic and ultimately to patients. Humanized antibodies may also have improved safety and immunogenicity profiles, which is a desirable outcome for patients,” she observes.

	Bispecific antibodies are an important emerging area in advancing protein engineering for pharmaceutical products, and several bispecifics based on immunoglobulin scaffolds are currently in clinical development, according to Mark Smythe, founder and currently vice-president of technology for Protagonist Therapeutics.

	“Protein therapeutics traditionally were designed as single receptor/protein antagonists or agonists. But to effectively combat disease, many future drugs will require multiple proteins being agonized or antagonized to achieve efficacy, namely in the immune-oncology and inflammation space,” Stevens explains.

	One example is bispecific T-cell engagers (BiTEs). Blincyto, a BiTE targeting CD3 and CD19, became the first bispecific biologic approved in the US market and demonstrates the impact bispecific molecules can have on treatment of disease, according to Stevens.

	“Ongoing IgG-based modular domain engineering and platform development in multispecifics present great potential to elicit synergistic activities, enhanced therapeutic index (efficacy, selectivity, novel signaling) or safety, effector cell retargeting, half-life extension and/or as a trojan horse as well as improved convenience, all of which can result in significantly improved outcomes for patients,” Stevens asserts.

	One example comes from Alligator Bioscience, a Swedish clinical-stage biotechnology company developing tumor-directed immuno-oncology antibody drugs. In January 2019, the company launched its IgG-like RUBY bispecific antibody format that allows plug-and-play generation of bispecific compounds from any two antibodies. The bispecific antibodies consist of immunoglobulins fused via the C-terminal heavy chains to Fab domains, which are attached via their light chains, allowing for unrestrained dual binding to two targets with a bivalent interaction in a manner similar to the natural antibody-antigen interaction. The result is bispecifics with excellent stability and manufacturability properties, superior binding, and dramatically shortened development timelines, according to the company.

	Bispecifics based on non-immunoglobulin scaffolds that overcome some of the limitations of immunoglobulin scaffolds are also being pursued by multiple developers, including Molecular Partners, Ablynx, and Covagen. More than 100 formats of bispecific/multispecific candidates are being engineered across the industry, many in the oncology space, according to Stevens.

	Peptides and peptidomimetic therapeutics

	Although mAbs have led to the development of many meaningful biotherapeutics and provide the clearest examples of successful protein engineering technology, Smythe notes that they have significant practical limitations in therapeutic use, such as the inability to deliver biologics or peptides by oral administration. “One type of recent advance in peptide engineering has been the design of protein-based therapeutics that can bind the same targets or block the same pathways as clinically validated antibodies, while allowing oral administration. This advance can be seen in drug candidates based on constrained peptides, such as those from Protagonist Therapeutics, and in the formulation design of an oral GLP-1 agonist from Novo Nordisk,” he says.

	For therapeutic applications that do not require effector function, engineering of an IgG scaffold lacking effector function can streamline development and commercialization of such a therapeutic, according Stevens. “Various strategies have been developed recently to eliminate Fc-associated effector binding and functions such as antibody-dependent cell-mediated cytotoxicity and complement dependent cytotoxicity activity,” she observes. For example, Amgen has developed a stable effector functionless IgG platform to eliminate off-target effects while maintaining the stability and half-life expected of a traditional antibody. 

	Advances have not been limited to novel protein formats. New strategies are also being applied to the development of engineered proteins that integrate developability prediction, manufacturability assessment, and quality-by-design early in the process. “This approach is critical to achieving right-first-time outcomes during the development and commercialization cycle of a biologic,” asserts Stevens.

	Amgen has developed a next-generation antibody engineering strategy that enables reliable and efficient identification of pre-candidate leads from early screening lead molecules. “We translate patient needs into protein design requirements during development and engineer in attributes to meet biological performance and molecule stability, which provide improved delivery for desired patient outcomes and improved processing during manufacturing. Incorporating patient needs into molecule designs starts with a translation of the target product profile into a quality target product profile followed by application of these targets during selection and engineering of molecules. This process enables faster and more efficient advancement of novel and effective therapies to patients while improving the overall patient experience,” Stevens explains.

	A combination of structure-based drug design with library approaches has become a powerful tool in protein engineering, according to Stevens. “Merging these approaches enables a focused library design that can yield desired protein characteristics rapidly by eliminating the repeated rounds of screening required in a random library approach. At the same time, it enables exploration of a much wider design space than is possible using pure rational design approaches,” she notes.

	More advances can be expected, too. Continued improvements in 

 approaches (modeling, software, and especially machine learning and artificial intelligence) for the prediction of critical molecule attributes such as aggregation, viscosity, and immunogenicity from primary sequences will significantly increase the success rate, accelerate molecule advancement, and reduce the overall resources required to advance a molecule from the discovery to the clinic, according to Stevens.

	Similarly, miniaturization and high-throughput versions of protein assays will reduce analysis times and enable exploration of wider design spaces. Stevens points to nanofluidic characterization assays for antibody identification and developability assessments as one example.

	Cell-free protein synthesis is also promising, but at this point is limited by the current capabilities in gene synthesis, which serves as a bottleneck for throughput. “Low cost, rapid, and precise gene synthesis will be hugely enabling in this area,” says Stevens.

Vol. 43, No. 2
	February 2019
	Pages: 20–21

	When referring to this article, please cite it as C. Challener, “Advances in Engineering of Protein-Based APIs," 

New platform technologies, advanced modeling tools, and addressing patient needs are important developments.