Facing new global challenges, pharmaceutical company leaders are looking to prioritize operational strategies to remain competitive in this rapidly evolving business environment. Recent events that range from the COVID-19 pandemic to new regulatory guidelines to economic inflation have required pharma manufacturers to find new ways to streamline workflows and minimize risks. Disruptions caused by the pandemic and related restrictions continue to impact supply chains and logistics worldwide. Additionally, many companies have shifted toward remote and hybrid working, and have increased their use of digital technologies to maintain business continuity and protect the health of employees.

These changes are accelerating the need for new innovations. One solution is the use of intelligent informatics in the pharmaceutical quality control (QC) laboratory to improve the quality and safety of drugs and reduce the risk of costly errors and recalls. However, while technology can simplify many workflows, it also comes with a learning curve and new burdens may be placed on its users. Pharma companies must determine the effectiveness of technological advancements and the potential challenges they may introduce in an environment like pharmaceutical quality assurance/quality control (QA/QC).

Pharmaceutical Technology's March 2023 Quality and Regulatory Sourcebook eBook

Mike Wilson, QA/QC Strategy, Waters Corporation

When referring to this article, please cite it as Wilson, M. Mitigating Human Error and Supporting Compliance with Smart Technology. 

Pharmaceutical Technology's Quality and Regulatory Sourcebook eBook 

Articles

Technological advances hold promise for mitigating human error in the pharmaceutical industry, especially in quality assurance/quality control, by reducing the incidence of, and risk associated with, mistakes.

Mitigating Human Error and Supporting Compliance with Smart Technology

Progressing a drug candidate from research to commercialization requires huge sums of money (approximately US$1.5 billion–$4.5 billion) (1) and is inherently high risk due to the high attrition rate. Only approximately
10% of total drug attrition is because of poor strategic planning or a lack of commercial needs (2). Highlighting solid strategy and funding alone is not enough to take a drug to market. Approximately 70–80% of clinical failures can be attributed to a lack of clinical efficacy or toxicity (3),
and the majority of drug failures occurring in pre-clinical and Phase I are due to pharmacokinetics or toxicity issues (4).

Paul Van Tilborg is director of analytical sciences at Ardena.

When referring to this article, please cite it as Van Tilborg, P. Phase-appropriate Analytical Methodology. 

Analytics

A phase-appropriate analytical development strategy is crucial, especially for complex (formulated) drug substances.

Phase-appropriate Analytical Methodology

Titanium dioxide (also known as E171 or TiO

) has a long history of safe use across many global industries with several applications in paints, cosmetics, medicines, and food. TiO

 has recently been banned for use in foods and dietary supplements in Europe, and there is a potential that TiO

 could also be banned in pharmaceuticals in the European Union (EU) as early as 2025. The EU is currently evaluating an extension of the E171 ban to pharmaceuticals, and the impact of the potential ban would have serious consequences for the availability of many drugs for European patients.

The European Food Safety Authority (EFSA) re-evaluated the safety of TiO

 (E171), and although there were no studies showing direct interaction of E171 with DNA, EFSA concluded in May 2021 that a genotoxicity concern could not be ruled out and that E171 can no longer be considered safe in food (1). A ban on its use in foods was then quickly proposed by the European Commission (EC).

Read this article in the Quality and Regulatory Sourcebook 2023

David R. Schoneker is the president/owner/consultant at Black Diamond Regulatory Consulting, LLC.

When referring to this article, please cite it as D. Schoneker, "How Banning TiO

Pharmaceutical Technology's Quality and Regulatory

The impact of the potential ban would have serious consequences for the availability of many drugs for European patients.

How Banning Titanium Dioxide Might Impact Pharma

A safety surveillance strategy is a requirement for pharmaceutical companies to monitor product safety in the post-marketing environment, as well as to keep physicians informed on product risks and how to prescribe appropriately. This is usually seen as a labor-intensive cost center that often overburdens teams with work, making it difficult to retain talent. As the industry becomes more cost-conscious and is handling more real-world data sources than ever, companies are seeking ways to improve the efficiency and effectiveness of their pharmacovigilance (PV) activities. Automation offers the solution for organizations prepared to embrace it and to do the hard work required to support digital transformation.

Why automation is vital in pharmacovigilance

Many reasons exist to support the adoption of automation in drug safety surveillance. Using artificial intelligence (AI) in safety surveillance can automate many aspects of the process, including non-electronic data collection, processing of inbound data, and triaging data according to its source. With these applications, companies can increase productivity, lower their technology costs, and reduce their reliance on manual labor.

Updesh Dosanjh is a practice leader for technology solutions at IQVIA.

When referring to this article, please cite it as U. Dosanjh, "Pharmacovigilance Automation," 

Organizations can better understand the maturity of AI-driven automation technology across their organization’s IT landscape through effective pharmacovigilance.

Pharmacovigilance Automation

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).

® 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

Manufacturing

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

Quality Considerations for the Manufacture of Compounded Drugs

 the use of machine learning (ML) in pharmacovigilance grows, so does the need for large and comprehensive data sets to ensure reliable and accurate models. However, obtaining and maintaining such data can be a challenge, as data gaps can arise for a variety of reasons. These gaps can have significant implications for the accuracy and effectiveness of ML models, and it is therefore important to explore new strategies for closing these gaps.

Data gaps can pose challenges to the development of reliable ML models in pharmacovigilance. There are various strategies that organizations can use to overcome these challenges, including active surveillance, using diverse and representative data sources, employing data augmentation techniques, and implementing data governance and management practices. By leveraging these strategies, organizations can effectively close data gaps and achieve more accurate and effective ML models in pharmacovigilance. Choosing the right technology partner is important when implementing data-driven technologies in pharmacovigilance and on the need for continuous monitoring, evaluation, and improvement of the models. Overcoming data gaps and achieving reliable ML models in pharmacovigilance can be done through a combination of data management, governance practices, and effective technology partnerships.

Pharmaceutical Technology’s Quality and Regulatory Sourcebook

Pankaj Bhardwaj serves as a senior product manager, and Ryanka Chauhan serves as a product manager; both at Datafoundry for DF mSignal AI.

When referring to this article, please cite it as Bhardwaj, P. and Chauhan, R. Overcoming Data Gaps in Pharmacovigilance. 

By leveraging certain strategies, organizations can effectively close data gaps and achieve more accurate and effective machine learning models in pharmacovigilance.


Overcoming Data Gaps in Pharmacovigilance

Biopharma systems and processes are outlined in the introductory section of this monograph. A new knowledge management (KM) paradigm is then described, beginning with an analysis of United States and European Union regulatory requirements, and of prevailing commissioning and qualification (C&Q) and quality risk management (QRM) guidance from the International Society for Pharmaceutical Engineering (ISPE) and International Council for Harmonisation (ICH).

The KM paradigm is a response to the expectation that C&Q and QRM be integrated and accelerated, on urgent fast-track projects (the new normal) in particular. It is based on pattern recognition, and on the insight that process-related critical quality attributes (CQAs) and critical process parameters (CPPs), and plant-related critical aspects (CAs, or functions) and critical design elements (CDEs, or components), are best managed via a previously unrecognized fundamental object called quality quartet, comprising CQA:CPP:CA:CDE parts. Several well-established and universally accepted benchmarks are listed in support of the paradigm.

Cliff Campbell is senior consultant at KPC.

When referring to this article, please cite it as Campbell, C. Points to Consider for Knowledge Management Acceleration. 

Knowledge Management

Quality quartets support an integrated and stable combination of
commissioning and qualification and quality risk management processes.