Susan Haigney

Drug compounding gives doctors and patients an alternative option for treatment. But these products come with some risk because of the nature of how they are manufactured.

FDA states, “Compounding is generally a practice in which a licensed pharmacist, a licensed physician, or, in the case of an outsourcing facility, a person under the supervision of a licensed pharmacist, combines, mixes, or alters ingredients of a drug to create a medication tailored to the needs of an individual patient. Although compounded drugs can serve an important medical need for certain patients, they also present a risk to patients” (1).

Quality Considerations for the Manufacture of Compounded Drugs

Quality and Regulatory Sourcebook March 2023

® spoke with Susan J. Schniepp, distinguished fellow, and Steven Lynn, executive principal consultant, Pharma and Biologics, at Regulatory Compliance Associates (RCA), and Purushottam Singnurkar PhD, VP and Head, Formulation Development, Syngene International Limited about quality measures that should be considered in the manufacture of compounded drugs.

Pharmaceutical Technology’s Quality and Regulatory Sourcebook 

When referring to this article, please cite it as Haigney, S. Quality Considerations for the Manufacture of Compounded Drugs. 

Pharmaceutical Technology Quality and Regulatory Sourcebook

Articles

Manual aseptic operations make quality practices necessary in the manufacturing of compounded drugs.

The World Health Organization defines pharmacovigilance as “the science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other medicine/vaccine related problem” (1). Drug safety includes quality control during the development and manufacture of a drug and monitoring the safety of the drug post-approval when it is in the market and being used by patients.

The European Medicines Agency (EMA) states that European Law requires a pharmacovigilance system be operated by EMA, the national competent authority, and the market authorization holder to monitor medicines throughout their use for adverse side effects that may occur (2). In the United States, FDA’s Adverse Event Reporting System database support’s the agency’s post-marketing safety surveillance program and contains adverse event reports, medication error reports, and quality complaints. Reports sent to FDA by drug manufacturers and healthcare professionals are used by the Center of Drug Evaluation and Research and the Center for Biologics Evaluation and Research to track the safety of a drug (3).

“Pharmacovigilance activities start with clinical trials to provide information on the safety and therapeutic benefits of a drug. The aim of [pharmacovigilance] monitoring during the clinical trials is to demonstrate that the therapeutic benefits outweigh the risk (severe side effects), and if proven, the respective data is submitted to regulatory agency (i.e., FDA) to gain approval to commercialize the drug,” says Dr. Foram Vaishnav, head of the North American pharmacovigilance and risk management team at Dr. Reddy’s Laboratories.

Gathering and reporting safety data to regulators is part of the responsibility of drug sponsors to ensure the safety of the products they manufacture and distribute. “Each sponsor is legally required to manage adverse events based on specific guidelines by territory. A sponsor with authorization to run clinical trials for a product will need to collect and report all adverse events per study protocol, with a subset requiring expedited reporting,” says Sharmila Sabaratnam, senior director, Vault Safety strategy at Veeva. “The most considerable volume of adverse event reports occurs post-market and can present challenges if sponsors don’t have the right systems, processes, and talent in place. In pharmacovigilance, you need this foundation to ensure patient safety, scientific rigor, and data integrity.”

Use of artificial intelligence (AI) in the monitoring and reporting of drug safety data is a tool companies can utilize to assist them in this process. “AI assists companies by automating information capture, yielding quick output, limiting errors, and helps to understand and classify clinical documentation,” says Aloka Srinivasan, PhD, principal and managing partner at RAAHA LLC.

“With the evolution of safety technology, AI is more commonly leveraged in case processing for intake, validation, and coding to assist case processors or automatically process cases,” says Sabaratnam. “AI can help extract and aggregate large data sets, applying natural language processing (NLP) to automate case intake. Accelerating the capture of cases drives earlier analysis so companies can report issues and make preventative changes. These AI technologies also significantly reduce data entry associated with case intake, lowering overhead costs.”

“Given the short time frame in which adverse events must be collected, evaluated, and reported to regulatory agencies, pharmaceutical safety teams are forced to deal with increasingly large volumes of data (due to an increase in safety data sources, including social media, online forums, and physical documents) that must be processed quickly,” says Updesh Dosanjh, practice leader, Pharmacovigilance Technology Solutions, IQVIA. “Not only does AI-driven automation expedite the process by supporting or replacing manual activities; it often does a more thorough job than manual reviewers facing deadline pressure in far shorter timescales, minutes vs. hours … AI tools can analyze both structured and unstructured data in real-time. [Pharmacovigilance] query tools automate the reporting and management of case documentation, while NLP tools analyze complex narratives, including patient charts, social media posts, articles, and other unstructured data.”

The importance of AI in drug development is seen in the way it can run multiple analytical methods in real-time and evaluate data from different angles, according to Dosanjh, giving an organization a “richer picture of the data.”

AI serves a variety of purposes in safety evaluations during drug development, confirms Dr. Vaishnav, including clinical, administrative, and research areas. However, Dr. Vaishnav warns, use of AI comes with challenges. “AI-enabled products, for example, have sometimes resulted in inaccurate, even potentially harmful, recommendations for treatment. These errors can be caused by unanticipated sources of bias in the information used to build or train the AI, inappropriate weight given to certain data points analyzed by the tool.” Vaishnav continues that while AI cannot replace clinical trials, “the machine learning software can run faster and more accurately to analyze the data generated from these clinical trials, producing results that, again, are checked by experts to see if the software is properly evaluating the data to generate insights that help drug manufacturers make better-informed decisions.”

Mark Moorman, vice president of Artificial Intelligence, TCG Digital and Analytics Business Development, LabVantage Solutions, states that a lack of data is a concern in drug development. “A better AI/predictive model for drug safety and vigilance would be a quantitative model which uses expert guidance,” he says. “With qualitative models, you can guide the learning with an expert’s opinion on what could or should happen. This can provide more insight when real data is limited, and it would be a good way of carrying out pharmacovigilance in the early stages.”

AI can also help sponsors with drug development by using data to choose which products are worth developing further, according to Dr. Vaishnav. “From [a] pharmacovigilance perspective, if the drug does not meet efficacy criteria, or demonstrates unusual and unexpected side effects during drug development, using AI supported data analysis, drug manufacturers can focus on redirecting resources towards developing and delivering medications that could have a positive impact on patient life,” says Dr. Vaishnav.

Srinivasan agrees that AI can be used to improve decision making. “One can use AI for natural language understanding and image recognition to improve the quality of data received from drug studies,” she says. “This, combined with the current developments in big data analytics and cloud-based pharmacovigilance platforms will enable more sophisticated analysis of large datasets from real-world experiments.” AI can also reduce human error, speed up risk assessments, and identify patterns and trends in data, adds Srinivasan.

AI can help pharmaceutical companies study, learn, and predict needed changes for products already on the market based on safety information attained post approval to protect patients. “The findings can provide signals around the effects of long-term use of medicines that are unknown and help drive changes in either dosage or patient instruction,” says Sabaratnam.

When it comes to post-marketing safety data, Vaishnav states that AI and machine learning assist drug sponsors to collate data and develop solutions to practical adverse events. “Computer-assisted coding (CAC) of International Council for Harmonisation Medical Dictionary for Regulatory Activities (MedDRA) code adverse events mentioned in product labels [can be used]; however, they must be collected and stored in a way that streamline processes and introduce efficiencies into manual review processes and enable the tools to convert them to real-world data (RWD). This analysis of RWD produces real-world evidence (RWE), which is clinical evidence that can provide information about the usage and potential benefits or risks of a drug product,” says Vaishnav.

AI and computational linguistics methodologies are also used in the reporting of adverse events to regulators by using NLP techniques, according to Vaishnav. But there are limitations to the use of AI, Vaishnav warns, such as “difficulties with language learning, arising from the need to understand context and interpret ambiguities, particularly affecting translation, and inadequacies of databases. These limitations require careful curation of adverse event data for accurate submissions.”

“Qualitative models use expert opinions (often reflected in rules or significant factors) to aggregate events as meaningful or not. For post-approval changes, causal models may be a better fit because they help identify the root cause of events and whether it leads to meaningful events, such as side effects. A causal analysis run by a machine/AI can look at all the events that happened post-approval and potentially identify problems that need to be reported to the FDA,” says Moorman.

AI provides higher quality data for submission to regulatory agencies, according to Dosanjh, by asking smarter questions. “This allows cleaner data to be easily sent in an ingestible format to the internal teams allowing them to focus on analysis rather than data collection and extraction. AI can detect potential signals earlier in the reporting process, again giving analysis teams more time to make the right determination,” says Dosanjh. “The biggest risk organizations run today is the very real reality that a human processer has missed an event and the risk profile of my product is incomplete or consistent. AI offers a consistent analysis of data that significantly reduces these risks.”

AI applications used in pharmacovigilance include optical character recognition (OCR), which converts handwritten and typed text into machine-readable text; RPA; autonomous software; desktop automation; NLP; speech to text conversion; and Natural Language Understanding (NLU). According to Srinivasan, these are used to collect data on adverse drug reactions (ADRs) and improve accuracy, speed, and scalability, as well as reduce costs. Some of the neural networks and deep learning models used to create real-world data from ADRs, according to Srinivasan, include FastText, long short-term memory recurrent neural network (LSTM), and convolutional neural network (CNN). “By using different combinations and integrations ofthese available technologies, there is apotential to simplify and standardize the intake of ICSR [Individual Case Safety Report] data into [a pharmacovigilance] system,” says Srinivasan.

Sabaratnam points to the automation of pharmacovigilance tasks such as rule-based robotic process automation (RPA) or lookups, cognitive machine learning and chatbots, and orchestration of workflows or blockchain. “Both regulators and life sciences are on a learning curve to determine appropriate use cases, GxP validation, and quality assurance in a highly regulated environment,” Sabaratnam says. “Most of the AI technologies available now aren’t providing scientific judgement. They allow pharmacovigilance teams to filter information to identify trends and deliver signals. For example, AI is helping identify the right patient populations for treatments that perform within that demographic. In areas like oncology, this is a critical factor.”

While the application of AI in drug safety operations is evolving, according to Vaishnav, using AI for the analysis of big data can assist sponsors to make drug-event associations and help anticipate benefits or adverse reactions when performing risk-benefit assessments. “These signals provide valuable real-world intelligence which cannot be determined by mining data from controlled clinical trials. Similarly, [NLG] technologies can be applied to generate aggregate reports, or its basic framework, which can help with human experts freed up to provide further analysis and finalization.”

NLP applications are used in pharmacovigilance to understand and classify post-marketing adverse events information from a variety of sources such as patients, healthcare providers, and clinical trials, explains Vaishnav. “NLP systems can analyze unstructured clinical notes on patients, giving incredible insight into understanding quality, improving methods, and better results for patients,” says Vaishnav.

Moorman stresses the benefits of NLP because of the textual or unstructured data involved in drug safety. “This area of AI continues to develop, but it is showing real-world usage and is ready to be applied to medical documents around adverse events. Nearest Neighbor models could also be used to categorize adverse effects and associate with either treatment, demographics, disease anomalies, personal information, etc. Also, the causal and qualitative models bring a lot of value—they are getting better and better every day.”

There are new modeling techniques for visual/image classification being developed as well, Moorman points out. “So, all in all, there’s been some incredible work on purifying natural language and image classification modeling that could prove very useful for drug development and safety.”

Post-marketing surveillance (PMS) or signal detection is used in the ongoing monitoring of adverse events. “PMS depends on analysis of data from sources such as medical assessment of spontaneous AEs, medical literature, regulatory agency databases, and clinical trial data, along with ‘real-world evidence’ such as electronic health records, medical device data, and data from social media and other online sources,” says Vaishnav.

The future of AI in pharmacovigilance may be found in new AI tools such as data hubs and medical evaluation support, confirms Dosanjh. Data hubs allow for the collation and sharing of information in real time across an organization, allowing for the combination of data across teams and a full view of the data. AI-driven medical evaluation support can also provide a more holistic view of the data and connect similar and relevant cases based on structured and unstructured data, according to Dosanjh. “An evaluator can compare their decision against an AI tool that has scanned the entire history of the product or similar events reported in all similar classes of products. This will bring a new dimension to evaluation and should support doctors in making better informed decisions,” says Dosanjh.

“As AI tools are used and have more data for the entire life cycle of an adverse event, we can extend data analytics capabilities and allow AI tools to not just identify potential issues, but also the mitigations that proved most successful, then suggesting those with smaller datasets in a far quicker manner than organizations can today,” says Dosanjh. “This will increase mitigations’ effectiveness and help organizations enact them quicker, reducing patients’ potential exposure to future [adverse events] or even preventing them.”

Pharmacovigilance: Overview. EMA.europa.eu.

Questions and Answers on FDA’s Adverse Event Reporting System (FAERS)

. FDA.gov. June 4, 2018 (accessed Feb. 7, 2023).

When referring to this article, please cite it as Haigney, S. Harnessing Technology to Ensure Drug Safety. 

The ability of artificial intelligence to process large amounts of diverse data cleanly creates more accurate reports for improved drug safety. 

Harnessing Technology to Ensure Drug Safety

The bio/pharma outsourcing industry continues to be integral in the development and manufacture of medicines. Contract research organizations, contract development and manufacturing organizations (CDMOs), and contract manufacturing organizations assist drug sponsor companies in bringing their products to market. These contractors offer the experience and resources some drug companies may lack to develop and manufacture drug products.

Outsourcing processes appears to be on the rise. When it comes to biopharmaceuticals specifically, BioPlan Associates states that more than half of biomanufacturers (65%) outsourced bioprocessing tasks in 2022, which is up 23% from 2006. The research company also predicts this trend will continue, citing that 79% of respondents to a 2022 survey have indicated they will be outsourcing some of their operations by 2027 (1). Analytical testing and toxicity testing were the most typical outsourced activities in 2022, according to BioPlan (2). “As the industry has become more complex, with increasingly advanced technologies, such as cell and gene therapies, in some cases, the need for the skills available from specialized CDMOs is indispensable,” said Shriya Bhatkhande, research associate with BioPlan Associates (2).

According to Ashley Wentworth, senior director, Vault Quality at Veeva Systems, contract partnerships are also entering the quality control space, with some companies collaborating with contractors in quality management. “Others, too concerned about security and data access, shied away from this approach. This, however, will change,” Wentworth said (3). In addition, contractors bringing suppliers into quality management systems will make acceptance, sign off, and approval of products easier, according to Wentworth. “Over time, companies will develop distinct information-sharing strategies, training programs, and new processes tailored to each partner” (3).

These and other outsourcing trends, such as fill/finish for messenger RNA therapies and technology transfer, are discussed in 

Pharmaceutical Technology’s® Bio/Pharma Outsourcing Innovation eBook, published in February 2023. The eBook can be found on PharmTech.com.

BioPlan Associates, Inc. 19th Annual Report and Survey of Biopharmaceutical Manufacturing Capacity and Production (19th annual edition, April 2022).

Bhatkhande, S. Biopharmaceutical Manufacturing Growth. Pharmaceutical Technology’s Bio/Pharma Outsourcing Innovation eBook, February 2023.

Wentworth, A. Forecasting Trends in Pharma Innovation. Pharmaceutical Technology Bio/Pharma Outsourcing Innovation eBook. February 2023.

When referring to this article, please cite it as Haigney, S. Outsourcing Still Vital. 

The bio/pharma outsourcing industry continues to be integral in the development and manufacture of medicines. 

Outsourcing Still Vital

The relationship between pharmaceutical companies and the contract organizations that perform development and manufacturing services comes with the necessary process of transferring intellectual information, processes, and product from one organization to another. Complex products such as biologics and messenger RNA (mRNA) vaccines come with additional challenges during the tech transfer process.

Communication is Key in Complex Tech Transfer

To gain more insight on the complexities of transfer of pharmaceutical technology between contractors and sponsors, 

® spoke with Petra Dieterich, scientific leader, and Jeffrey Mocny, scientific leader-Early Development, both with Abzena; Peter Ercoli, chief operating officer, Biologics, at BIOVECTRA; Deepak Thassu, PhD, MBA, senior vice president, R&D and Regulatory Submission, at LGM Pharma; Emma Rhodes, head of New Product Introduction at Sterling Pharma Solutions; Sara Schemel, senior process engineer, Dona York, principal process engineer, and Gauri Patki, senior process engineer, all at Catalent Biologics; Uwe Hanenberg, PhD, head of Product Development at Recipharm; and Rebecca Powell, associate director, Tech Transfer at Arranta Bio, a Recipharm company.

When referring to this article, please cite it as S. Haigney. Communication is Key in Complex Tech Transfer. 

Pharmaceutical Technology, Bio/Pharma Outsourcing Innovation 

The complexity of the pharmaceutical product brings challenges to the transfer of information and technology between sponsors and contractors.

Of the variety of FDA warning letters sent to bio/pharmaceutical manufacturing and distribution companies at the end of 2022, a good amount seemed to be targeted at those companies trying to market unadulterated COVID-19 treatments. But the agency did have some feedback for bio/pharma manufacturers producing legitimate medicines.

As expected, quality assurance and control were the top issues FDA pointed out to companies. Investigating deviations through corrective actions and preventive actions (CAPA), establishing robust quality control units (QU), and having written procedures in place were mentioned in a variety of warning letters sent to drug manufacturers in 2022.

The following are some notable comments from FDA.

In a warning letter (1) regarding an inspection done in March and April of 2022 at a Phoenix, Ariz. facility, the agency reproached the company for not thoroughly investigating batch discrepancies, failures, and out-of-specification results.

“You failed to effectively implement corrective actions and preventive actions (CAPA) and return your aseptic processing line and manufacturing operations to a state of control. Multiple media fill failures occurred between April and October 2021, when simulating aseptic processing operations on the filling line used for your drug product Abraxane (a sterile (b)(4) drug),” the letter stated.

According to FDA, the company did not adequately perform an evaluation and remediation of the loss of control of aseptic operations and released batches that may have been impacted by the batch failures. “Your investigations into the following media fill failures were inadequate in that they lacked sufficient rigor in determining root causes and scope of impact after the media fills revealed serious non-sterility risks in your aseptic process operations,” the letter stated.

After the agency’s inspection and findings, the company took steps that included a Field Alert Report, batch rejections, and suspension of operations. However, FDA stated these actions were not sufficient. “However, you continued to observe contaminated units in subsequent media fills. The recurring incidents of contaminated units in media fills are an indicator of an adverse trend in your aseptic filling line. You did not provide a comprehensive assessment of contamination risk factors in your aseptic processing operations that support robust and holistic remediations to your aseptic processing operation design. Systemic remediations are essential to address a pattern of recurring contamination events and ensure sustainable control of your aseptic processing operations,” the letter stated.

In response, the company was required to provide FDA with a comprehensive risk assessment of contamination hazards in its aseptic facility and processes. The company must also provide a remediation plan and a CAPA plan to implement routines, operations, and management. An evaluation of the company’s aseptic processing operations was also required by the agency.

Following good manufacturing practices (GMPs) helps produce a quality product that is effective and safe. Having written standard operating procedures (SOPs) is a staple of GMPs. These written procedures not only ensure that employees know the required steps to perform manufacturing operations, they also assure FDA that a company’s processes and methods are sound and efficient.

In a warning letter dated Sept. 27, 2022 (2), FDA inspectors found a company in India had not established adequate written procedures for equipment cleaning and for monitoring control of processing steps that may cause variability in quality characteristics. This lack of written procedures resulted in the agency asking the company to provide an independent, retrospective assessment of effectiveness of their cleaning processes to evaluate the possibility of cross-contamination. A CAPA plan and improvements in the company’s cleaning validation program were also required. The agency also asked for a procedure for a risked-based review of all of the company’s products.

A China facility also received notification from FDA that they lacked written procedures for equipment cleaning (3). “You manufacture drug products using the same equipment that you use to manufacture non-drug industrial products such as (b)(4) and (b)(4). Inadequate removal of residues from manufacturing equipment during cleaning can lead to contamination of drug products subsequently manufactured on the non-dedicated equipment … In your response, you provided a document outlining your procedures for cleaning and disinfecting manufacturing equipment. It is unacceptable as a matter of CGMP to continue manufacturing drugs using the same equipment that you use to manufacture industrial products due to the risk of cross-contamination,” FDA stated in the letter.

In addition to a risk assessment of the products manufactured on the shared equipment, the agency requested a summary of updated SOPs that showed an appropriate validation and verification program for cleaning procedures.

The creation of an adequate quality control unit has been something FDA has continually mentioned in warning letters to drug manufacturing companies. In October 2022, a Mexico facility (4) was given an FDA warning letter that pointed out the company’s quality control unit did not ensure the products manufactured met CGMP compliance or established specifications.

The letter stated the quality unit did not provide adequate oversight and did not have adequate procedures for handling vendor qualification, batch release, reserve samples, investigations, complaints, and training. The QU also did not properly test incoming components or adhere to a stability program.

In another FDA warning letter (5), a China facility failed to fully investigate API impurities in associated batches. “Though you identified at least four lots that failed your (b)(4) impurity specification, you did not extend your investigation to all lots manufactured prior to August 2018 after you identified (b)(4) had a higher risk of forming,” FDA stated in the letter. The firm also did not establish an impurity profile for identified and unidentified potential impurities. “Your firm failed to perform a systematic evaluation of the manufacturing process of (b)(4) API and have robust procedures to identify the potential formation of (b)(4) impurity,” the agency stated.

Computer and systems controls were mentioned in an FDA warning letter to a German facility (6), where the company did not have appropriate controls ensuring changes to master production and control records were limited to authorized personnel. “You failed to have adequate controls in place for your Fourier Transform Infrared Spectrometer (FTIR) system. For example, you did not establish unique usernames and passwords for each analyst. In addition, the audit trail function was disabled even though this Fourier Transform Infrared Spectrometer is routinely used for raw material and finished product testing … Your quality system does not adequately ensure the accuracy and integrity of data to support the safety, effectiveness, and quality of the drugs you manufacture,” the agency stated.

The issues included herein have been seen in a variety of FDA warning letters this year and in the past. It is apparent that the agency is still focused on ensuring that manufacturers are following GMPs and taking product quality seriously.

Warning Letter to Beijing Xinggu Lvsan Technology Co., Ltd. Formerly known as Beijing Lvsan Technology Co.,Ltd

Warning Letter to Eksa Mills S.A. de C.V.

Warning Letter to Zhejiang Tianyu Pharmaceutical Co.. Ltd

Warning Letter to System Kosmetik Produktionsgesellschaft fur kosmetische GmbH


Vol. 47, No. 1
January 2023
Page: 38–39

When referring to this article, please cite it as S. Haigney. Write it Down,
Investigate, and Control. 

FDA sent a variety of warning letters to drug manufacturers at the end of 2022 highlighting myriad CGMP and quality control violations.

Write it Down, Investigate, and Control

Certificates of analysis (CoAs) are used by materials suppliers to identify the product they are providing to a pharmaceutical manufacturer. They include information such as product name, material grade, batch number, expiration dates, testing dates, information on analytical testing performed on the material, as well as additional product information. According to Quincy Mehta, senior manager, Consumables Product Management at SCIEX, “CoAs are vital in ensuring both the outcome and quality of a product. CoAs contain key information relaying to a customer that each lot or unit of a product is manufactured, tested, and qualified for their application of use. SCIEX assigns test parameters and information in CoAs based on the needs and target applications of our customers. This provides a key level of support and confidence that SCIEX products are ready for use in our customer’s own quality controlled environments.”

spoke with industry experts about the importance of CoAs and best practices for writing and working with these important documents.

Experts interviewed include: Agata Ochocinska, head of Quality (Europe) at Actylis; Peter Phillips, VP of Quality Operations at BIOVECTRA; Sarah Breen, senior manager, Quality, at C

 PHARMA; Subbareddy Inta, senior director, Quality and Compliance, Swati Tipnis, lead, Analytical Quality and Compliance, and Mayank Nagar, vice president and head, Tech. Services & Product Launch Management, all at Dr. Reddy’s Laboratories; IPEC Americas; Shailesh Vengurlekar, senior VP, Quality & Regulatory Affairs, at LGM Pharma; Siegfried Schmitt, vice president, Technical, at Parexel; and Susan J.Schniepp, distinguished fellow at Regulatory Compliance Associates.

 How does one assure regulators that CoAs are accurate and reliable?

 Each organization must establish strict procedures governing the development of CoAs, including QC [quality control] methods, documentation practices, analytical methods and QA [quality assurance] processes in line with regulatory requirements.

These procedures should be outlined in SOPs [standard operating procedures] to guide the employees who sample, test and issue these documents. The system must be regularly verified, and its compliance confirmed (ex. by audit) providing proof of compliance and thus assuring regulators of the accuracy of generated documents, CoAs.

 Regulators from FDA and Health Canada carry out regular inspections to audit the manufacturing process and product release to ensure it includes the generation of an acceptable CoA. They review the raw data that is included and the methods used to transcribe data onto the CoA.

 Easing regulators’ concerns regarding CoA accuracy is best done through a comprehensive risk assessment and supplier qualification. If due diligence is performed, which should include an on-site audit, if possible, in which testing records and data traceability to the CoA are reviewed and verified, the concerns regarding CoA accuracy should be mitigated.

 By performing on-site audits, regulators are able to review the data in person and ensure it is accurate. Once this has been done, with subsequent CoAs, the regulators can request the data electronically. Having a quality assurance representative also sign the CoA verifies that the information has been reviewed and verified by a second party.

A standard operating procedure must be in place to document the creation, review, and approval of certificates of analysis. The [SOP] must detail the level of review that is performed on the CoA. Adequate control must be in place to ensure that the possibility of an incorrect CoA being released are kept to a minimum. For example including several level of review both within and between departments for CoA creation, review, and approval. Employers should champion quality culture making employees take quality focused actions.

The best practice to be able to assure regulators you have control over your supplier is to establish a robust quality agreement, periodically audit the supplier, and periodically verify the analytical results listed on the CoAs.

Nagar, Inta, and Tipnis (Dr. Reddy’s Laboratories):

 The tests mentioned on the CoA should be verified if they are compendial and validated. If they are in house tests, ensure they are accurate to be used for testing the product. Also, having one person to prepare the CoA and another review and approve it ensures reliability of the CoA. If the CoA is approved by QA who is independent of the manufacturing or QC unit, it adds an additional level of confidence and prevents bias.

Who is ultimately responsible for incorrect information on a CoA?

 An excipient CoA is a legal document that certifies the quality of the excipient and demonstrates that the batch conforms to the defined specifications, has been manufactured under appropriate GMP [good manufacturing practice], and is suitable for use in pharmaceuticals. The excipient manufacturer or supplier who has generated the CoA is responsible for its accuracy. The 

IPEC-PQG Good Manufacturing Practices Guide for Pharmaceutical Excipients

 provides best practices for ensuring the quality of excipients and integrity of CoA data.

This responsibility solely lies on the supplier, and more specifically, it falls upon the person who is generating and entering the information in the CoA. Most of the time, this responsibility will fall upon quality control as it is their role to ensure the information is accurate and complete.

In the rare event a mistake is made, it is whoever generated the CoA who is responsible. We have audit trails, all electronically recorded, to identify when in the process a mistake occurred.

To avoid incorrect information, an analyst tests the product, generates the result, and records it onto the CoA. Then, our quality assurance team reviews all the raw data and ensures accurate transcription onto the CoA. These processes help avoid mistakes.

On a manufacturing site some of the following people have the potential to impact the final CoA. The research chemists that create the synthetic route for the product must ensure that any impurities that are not purged are included in the final specification. The process team involved in the validation must ensure that the final output material is a homogeneous mixture as only meaningful results come from a homogeneous material. The quality control analyst must ensure that methods are validated correctly and are capable of detecting the required impurities. They are also responsible for ensuring the methods are executed appropriately and the data is transcribed correctly into the CoA. [QA] does a review of the batch record and the [QC] data in order to ensure compliance before creating the final CoA. [QA] are the final approval on the CoA, but rely on the competence of those in the previous steps.

Ultimately everyone on a site is responsible if incorrect information appears on a CoA as quality is everyone’s responsibility.

 What are some best practices for writing CoAs?

 They should be auto-generated by suitably configured documentation management systems. The data required to populate the CoA should not be entered manually, but should be electronically transferred from the instrument to the CoA-creating system, where this is possible. Typically, CoAs are created in Portable Document Format (PDF). To assure compliance with archiving requirements, PDF/A should be used. In fact, unless mandated by law, CoAs should only be created, signed, and issued electronically.

 One of the best practices for writing CoAs is to avoid listing ambiguous test results (i.e., pass/fail, < X, > X, ). When information on the CoAs isn’t specific, it is much harder for companies to perform deviation, investigation, or CAPA [corrective and preventive action] analysis into product failures.

 There are very specific guidelines already put into place for drafting a CoA by governing regulatory bodies. Each regulatory body is clear in their expectations for what information must be included, including the expected document format as well as the specific required information. It is best practice to stick to those expectations, so all information is accurately captured.

 In the year 2000, IPEC-Americas published the first version of the 

Certificate of Analysis Guide for Bulk Pharmaceutical Excipients

 to assist excipient manufacturers in preparing certificates of analysis. The most recent version of this guide [was published] in
October 2022. The guide provides a framework for [CoA] content and best practices. According to the guide, ‘A CoA for excipients should be prepared and issued by the company responsible for the material. It is expected that a complete and accurate CoA is provided to the user (medicinal product manufacturer) for each batch and/or delivery of excipient.’

 Certificates of analysis should be issued for each batch of intermediate or API or FP [finished product] manufactured/produced. The CoA for any product should be complete, legible, and comprehensive with all the information regarding the product. It should have at the bare minimum the name/logo of the manufacturer, name of the product, batch number, date of manufacture and expiry or retest, name of the tests performed with acceptance criteria and results. Finally, the CoA should be signed by the personnel preparing it and countersigned a personnel reviewing and approving it. The CoA should also be written and approved as close as possible to the date of testing.

 It’s essential to establish which of a product’s critical quality attributes (CQAs) need to be tested. Gathering data about CQAs defines the expected specifications, which are tied into the process and rely on the capability of the test methods. Standard tests measure appearance, identity, purity, and potency. Tests with greater specificity are needed for the measurement of things like process impurities. ICH [International Council of Harmonisation], which BIOVECTRA uses to govern its quality system, provides guidance on defining specifications.

A good CoA program needs electronic record-keeping to simplify version control, history, and change control. It’s best practice to have a document management system (DMS).

 Do sponsor companies or contract development and manufacturing organizations (CDMOs) have input on what is included in a supplier’s CoA?

Yes, CDMOs should have input for two reasons. If a CDMO is purchasing material for use in multiple customers’ products, they should be the primary negotiator as to what information is needed on the CoA so the material is accepted by all the affected customers. If the client is buying the material for use only in their products, the CDMOs should still have input on the contents of the CoA because the material will be used at their site and they need to know if it will impact any other products or if there is cross contamination considerations to be addressed.

Supplier CoAs can be adapted to specific requirements; however, minimum regulatory requirements must be adhered to in terms of the general content as pharmacopeial monograph (if applicable). Additional testing may be included or more rigorous testing standards than the compendial grade may be applied if necessary.

Yes, they absolutely can. If a supplier is not following the guidelines as required, then the sponsor company can request more information and ensure all guideline materials are provided. Occasionally, clarification on a certain test will also be required. I’ve seen CDMOs ask for additional details from tests that have been performed and are lacking all information. The main details of the CoA will not be altered, but usually it is in the case of clarification or meeting the guidelines.

 Most of the information in a certificate is similar from product to product. This is true for our core line of off-the-shelf chemical reagents, like dithiothreitol (DTT), for which we’ve established the specifications and provide those to our clients. Having said that, our clients will inform us of a particular CQA they need us to assess, which allows us to develop and apply the appropriate test methods and specs.

For our suppliers, an uncommon case would be if we identified an impurity in the supply of a critical raw material. In that case, we would work with our supplier to add this to their certificate.

Yes, CDMOs must have an input on what is included in a supplier CoA. A product can be released considering the pharmacopeia, for example Ph.Eur./USP/ChP.; however, the pharmacopeia does not fully cover impurities that may form in the production process as a result of the synthetic pathway. It is for this reason that CDMO and
supplier must work closely together for the analysis methods and specification of each product.

 Yes. Both product owners or sponsor companies and contract manufacturing organizations collaborate in defining the specification and designing the certificate of analysis. Input is valued from both to ensure the compliance to guidelines, systems, and submission requirements.

For standard (“catalog”) excipient products, CoA customization should not be expected by the customer. In some cases, information can be added to a supplier CoA, when agreed upon between customer and supplier. Typically, when information is added beyond the standard CoA, it is done as an addendum rather than modifying the supplier’s standard template. Custom CoAs, even as addendums, can be difficult to manage using standard CoA software, and thus are generally discouraged by suppliers.

What are some best practices for verifying the information in a CoA received from a supplier?

 Typically, there is a SOP in place to establish a procedure for verifying the information. From this SOP, the quality assurance person will be able to take the steps to ensure the information is verifiable. On the receiving end, the sponsor company or CDMO can also have their own procedures set. These procedures can include asking the supplier to provide copies of the lab notebooks which will ultimately verify that the information is correct.

: It is important not to rely on a supplier’s CoA, especially for critical raw materials. It is best practice to qualify suppliers, verifying their testing protocols and ensuring they have a quality system in place to provide accurate results. Our quality team qualifies suppliers by retesting the material to the supplier’s standard and validating that we get the same results. Ultimately, this reduces the need for us to test everything they supply, relying more on their data. We also conduct an annual review of our suppliers, including to find if there are any emerging FDA or Health Canada trends against a supplier.

Our clients usually audit us on an annual or biannual basis, as well as review all our raw data on a batch-by-batch basis. Some will send our product out for further testing.

] 211 the sponsor may accept a supplier’s CoA provided that they establish the reliability of the supplier’s analyses. The sponsor should determine how best to verify this, which may include an on-site audit of the supplier.

Nagar, Inta, and Tipnis (Dr. Reddy’s Laboratories): 

Pharmaceutical companies usually do analyze the API or excipient using the compendial method or the method shared by the API/excipient manufacturer and prepare their own CoA and compare the results to the drug substance/excipient manufacturer’s results in the CoA provided. Another practice is to perform vendor qualification and analyze three batches from the supplier and qualify the vendor based on the comparison of the results to the CoA from the supplier. After successfulvendor qualification, one batch could be tested on annual basis with full testing of all the tests on the specification and partial testing of the subsequent batches with only the most critical tests, and adapting the results from the supplier CoA for the non-critical tests based on the vendor qualification and verification of the results for certain no of lots. This practice should be periodically evaluated for potential risk, and should be part of organization’s quality management system

 A [CoA] for commercial release must always be created based on the submitted and approved registered detail. The S41 [the section of the filing that refers to the specifications that are registered for that product] is checked in order to compare the test methods/specification registered versus the test methods/specifications on the CoA. The stability data is checked to ensure that the correct re-test expiry date is present on the CoA. Results generated and reported on the CoA should be observed and if any untoward trends are noted, these must be investigated. The information and transcriptions must be thoroughly checked to ensure compliance. Signatures and dates applied to the CoA are checked to confirm the sequence of event. For example the date of manufacture cannot be after the date of release.

 Each CoA must be approved and dated to be valid. While verifying the information on a CoA, one should always refer to the product specification (compendial or agreed), its grade and type (intermediate, API, excipient, cell culture grade, etc.). The type of product will dictate the type of testing mandatory for the specific material allowing for document verification and acceptance.

 As a minimum, it is necessary to verify whether the correct form and format was used as agreed in the quality/technical agreement. Then, it must be verified that all required test results are included.

A CoA should be digitally signed, otherwise it is necessary to verify that the hand signature isn’t a facsimile. If only a (scanned) copy is provided,
the sponsor must verify during audits that the original and the supplied copy are identical.

The most conservative approach would be to verify all the information regarding the analytical results by performing the tests. This approach isn’t practical or cost effective. Currently, the best practice for verifying the information on the CoA is to establish a robust quality agreement with the supplier and the establish a program to periodically verify the test results. I would advise the program be tailored to the needs of the material being evaluated. For example, if you only receive a material two times a year, it isn’t realistic to verify the analytical results of that material every tenth lot. The analytical verification time frame for any material should be based on risk analysis.

Industry experts discuss best practices for certificates of analysis.