Wednesday, May 9, 2018

Growing Adoption of Open Innovation Models in Pharmaceutical and Biotechnology Companies

Dublin, May 09, 2018 (GLOBE NEWSWIRE) — The “High Throughput Screening Market by Technology, Application, Product, End User – Global Forecast to 2023” report has been added to ResearchAndMarkets.com’s offering.

The global high-throughput screening (HTS) market is projected to reach USD 21.69 Billion by 2023 from USD 14.87 Billion in 2018, at a CAGR of 7.8%.

The major factors driving the growth of the HTS market include initiatives undertaken by pharmaceutical and biotechnology companies, increasing R&D spending, technological advancements in HTS, and the availability of government funding and venture capital investments.

The report analyzes the global HTS market by product & service, technology, application, end user, and region. On the basis of product & service, the reagents & assay kits segment accounted for the largest share of the global HTS market in 2017. Factors such as the large numbers of reagents and assay kits used in HTS techniques, rising prevalence of a number of diseases, increasing pharmaceutical R&D, and increased government funding for life science research are driving the growth of this segments.

Based on technology, the label-free technology segment is expected to grow at the highest CAGR during the forecast period. The major advantage offered by label-free technology is that it can help in the study of varied cell types and targets. Label-free assays also provide simple methods for studying complex biological pathways. The label-free technology is set to reduce drug failure caused by toxicity. These factors are likely to boost the growth of this technology market.

On the basis of application, the target identification & validation segment accounted for the largest share of the global HTS market in 2017. The large share of this segment is attributed to the growing number of potential drug targets for screening.

Based on end user, the pharmaceutical and biotechnology companies accounted for the largest share of the global HTS market in 2017. Factors contributing to its largest share include the increasing use of HTS techniques by pharmaceutical and biotechnology companies for drug discovery applications along with the increasing pharmaceutical R&D expenditure.

Geographically, the global HTS market is segmented into North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa. In 2017, North America accounted for the largest share of the HTS market, followed by Europe and Asia Pacific. Factors such as the large spending on pharmaceutical R&D, growing adoption of HTS, availability of government funding, and the presence of major key players in the region are responsible for the large share of the North American HTS market.

The prominent players in the global HTS market are Agilent (US), Danaher (US), Thermo Fisher Scientific (US), PerkinElmer (US), Tecan (Switzerland), Axxam (Italy), Merck Group (Germany), Bio-Rad (US), Hamilton (US), Corning (US), BioTek (US), and Aurora Biomed (Canada).

Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary

4 Premium Insights
4.1 High-Throughput Screening: Market Overview
4.2 HTS Market: Developed vs Developing Countries (2018 vs 2023)
4.3 Geographic Snapshot: HTS Market (2017)
4.4 Geographic Mix: HTS Market
4.5 HTS Market, By Product & Services (2018 vs 2023)

5 Market Overview
5.1 Introduction
5.2 Market Dynamics
5.2.1 Market Drivers
5.2.1.1 Growing Adoption of Open Innovation Models in Pharmaceutical and Biotechnology Companies
5.2.1.2 Government Funding and Venture Capital Investments
5.2.1.3 Increasing R&D Spending
5.2.1.4 Technological Advancements
5.2.2 Market Restraints
5.2.2.1 Capital-Intensive Nature of HTS
5.2.2.2 Complexities in the Field of Assay Development
5.2.3 Market Opportunities
5.2.3.1 Emerging Markets
5.2.3.2 Growing Research Activities in Toxicology and Stem Cells
5.2.4 Market Challenges
5.2.4.1 Dearth of Skilled Operators

6 Industry Insights
6.1 Introduction
6.2 Technological Trends
6.2.1 Label-Free Technology
6.2.2 Automation & Miniaturization
6.2.3 Microfluidics
6.3 HTS in Drug Discovery Process

7 High-Throughput Screening Market, By Product & Service
7.1 Introduction
7.2 Reagents & Assay Kits
7.3 Instruments
7.4 Consumables & Accessories
7.5 Software
7.6 Services

8 High-Throughput Screening Market, By Technology
8.1 Introduction
8.2 Cell-Based Assays
8.2.1 2D Cell Culture
8.2.2 3D Cell Culture
8.2.2.1 Scaffold-Based Technology
8.2.2.1.1 Hydrogels
8.2.2.1.1.1 Animal-Derived Hydrogels
8.2.2.1.1.1.1 Matrigel
8.2.2.1.1.1.2 Collagen
8.2.2.1.1.2 Synthetic Hydrogels
8.2.2.1.1.3 Alginate/Agarose
8.2.2.1.2 Inert Matrix/Solid Scaffolds
8.2.2.1.3 Micropatterned Surfaces
8.2.2.2 Scaffold-Free Technology
8.2.2.2.1 Microplates
8.2.2.2.2 Hanging-Drop Plates
8.2.2.2.3 Ultra-Low Binding Plates
8.2.2.2.4 Other Scaffold-Free Technologies
8.2.3 Perfusion Cell Culture
8.3 Lab-On-A-Chip
8.4 Ultra-High-Throughput Screening
8.5 Bioinformatics
8.6 Label-Free Technology

9 High-Throughput Screening Market, By Application
9.1 Introduction
9.2 Target Identification and Validation
9.3 Primary and Secondary Screening
9.4 Toxicology Assessment
9.5 Other Applications

10 High-Throughput Screening Market, By End User
10.1 Introduction
10.2 Pharmaceutical and Biotechnology Companies
10.3 Academic and Government Institutes
10.4 Contract Research Organizations (CRO)
10.5 Other End Users

11 High-Throughput Screening Market, By Region

12 Competitive Landscape
12.1 Introduction
12.2 Market Leadership Analysis
12.3 Competitive Scenario
12.3.1 Product Launches and Upgrades
12.3.2 Partnerships, Collaborations, and Agreements
12.3.3 Expansions
12.3.4 Acquisitions
12.3.5 Other Developments

13 Company Profiles

  • Agilent Technologies, Inc.
  • Aurora Biomed
  • Axxam S.P.A.
  • Bio-Rad Laboratories
  • Biotek Instruments
  • Corning Incorporated
  • Danaher Corporation
  • Hamilton Company
  • Merck Group
  • Perkinelmer, Inc.
  • Tecan Group
  • Thermo Fisher Scientific Inc.

For more information about this report visit https://ift.tt/2K52CIw

CONTACT: ResearchAndMarkets.com
Laura Wood, Senior Manager
press@researchandmarkets.com
For E.S.T Office Hours Call 1-917-300-0470
For U.S./CAN Toll Free Call 1-800-526-8630
For GMT Office Hours Call +353-1-416-8900
Related Topics: Drug Discovery

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Deploying the Cloud in GxP Environments

Meeting the stringent cloud compliance and regulatory requirements in pharma

 

The traditional IT infrastructure for most life sciences organizations was not designed to meet the business challenges that companies are faced with today. It can take significant, sustained, and hugely disruptive investment in new technologies and infrastructure to bring internal systems to the required security, performance, and compliance level. At the same time, a life sciences company must do much more than maintain “business as usual.” It must reduce costs and increase productivity and innovation against a backdrop of continually changing market pressures and regulatory requirements. This is the reason that we’re seeing greater cloud adoption in other parts of the life sciences business. However, good practice quality guidelines (GxP) environments have their own unique requirements. There are very strict guidelines around application and system usage in key business functions, such as research and development, clinical trials, quality, and manufacturing, set by the FDA and other global regulators. This article looks at the cloud deployment models available for GxP environments and how to select the right one for a pharmaceutical company’s cost constraints and regulatory profile.

Three types of cloud service

The strengths and weaknesses of internal IT deployments are similar across industries. They are, however, exacerbated in the regulatory environment. A large life sciences company can have thousands of different IT architecture combinations and a large proportion of its overall IT budget is taken up with simply operating, maintaining, and supporting these existing systems. More importantly, the result can often be a lack of agility, if it takes IT too long to respond to changing business requirements. With the additional compliance constraints, it can take many months to deploy a new module or just add extra computing or storage capacity. In addition, users are often faced with slow and inefficient legacy systems and, worse, much of their data remains under-utilized, due to its storage in inaccessible silos throughout the organization.

Cloud services can help overcome many of the drawbacks of existing internal systems. There are infinite combinations of cloud deployments, however; generally, the following delivery types can enable a company to decide which elements of its IT infrastructure to continue to operate internally and which to have executed by a cloud service provider.

  • Infrastructure as a service (IaaS). IaaS provides a service to establish and run virtualized computer resources over the internet. Virtualization is the creation of virtual—rather than actual—versions of IT infrastructure, such as operating systems, servers, or storage devices. The services provider is responsible for managing and delivering hardware, storage, servers, and data center space that form the foundation of a cloud environment. 
  • Platform as a service (PaaS). PaaS is a cloud computing service that provides all the platform—hardware, middleware, and operating system—components needed for a company to develop, run, and manage applications. The cloud technology provider takes care of all the infrastructure while the pharma company manages its own application portfolio.
  • Application as a service (AaaS). Also known as software as a service, AaaS provides a completely hosted—and managed if required—IT package. The provider makes applications available to the company over the internet via a thin client PC.

 

Four types of cloud deployment 

Before looking at the four cloud deployment models, it’s worth considering the characteristics that all cloud services have in common. Using the internet allows many companies to connect securely to the same service, enabling collaboration and information sharing. Companies using the cloud service have access to shared resources that are continually improving so that they should always have access to the latest and best performing systems. With some cloud service providers, the service is delivered on-demand. Life sciences companies access the service as required and usage can be metered or architected in such a way that they only pay for what they use.

A major benefit of the cloud is its virtually limitless scalability and geographic agnosticism—that can be applied extremely quickly to meet demand. One life sciences company found that it would require 250 internal servers to meet peak processing times during certain phases of global clinical trials. This meant waiting for internal resource to be freed up, and as the project was estimated to cost $150 per second, that was a very costly delay.1 Switching to a cloud service meant that the company not only could meet its computing requirements quickly, but it could scale up for peak processing and scale down afterwards—only paying for the resource they used.

Further qualifying the virtualization tools themselves can greatly reduce qualification time, especially in the scenario where the underlying specifications of the servers are identical, allowing the rapid deployment of pre-qualified server packages.

The cloud deployment models available allow a company to access the benefits of cloud computing while ensuring that its working within the performance, security, and risk levels of the organization’s requirements.

Hosted public internet

A public cloud is a publicly accessible cloud environment owned by a third-party cloud service provider (CSP). Services are provisioned in a multi-tenant environment where many customers are using the same service. The infrastructure may be hosted on the premises of the service provider, a third-party data center, or, possibly, multiple third-party facilities and, further, may reside on equipment owned or leased by the CSP. It is vital before engaging with such a provider that a pharma company fully understands its provider’s architecture, the layers of service-level agreements (SLAs), and the relationships between all of the delivery partners. Ultimately, though, the environment will be operated by whoever is making use of it, be it life sciences companies, government organizations, or academic institutions. 

The service is delivered across the public internet and accessed via thin clients at the customer site. The main features of hosted public cloud include:

  • Fast and easy deployment of standardized solutions.
  • Easy to connect and collaborate with external customers, partners, and suppliers.
  • Complete management and support of IT infrastructure.
  • System performance and continuity guaranteed under SLA.
  • Reasonable levels of security.
  • Lack of auditability—while most public cloud providers will offer standard third-party audited accreditations, such as ISO27001 or SOC 2, they will not generally permit traditional GxP audits.

While companies have access to the latest web security standards, the hosted public cloud will not deliver the highest levels of security possible and is likely not to be up to the companies’ requirements if this is a foremost concern. 

In addition, the cloud provider is responsible for the creation and ongoing maintenance of the public cloud and its IT resources. It is more difficult to control patching and upgrade frequency and it is likely that the user will have little-to-no transparency over what happens below the operating system. 

Where application and infrastructure qualification and validation assurance is essential, a pharma company will need to find ways of working with the cloud provider to gain all the information it needs to meet the organization’s compliance requirements. Appendix 11 of the ISPE GAMP Good Practice Guide for IT Infrastructure Control and Compliance2 provides strategies for qualifying the suppliers for each of the different engagement types.

 

Hosted private network

A private cloud, as the name suggests, is solely owned by the cloud service provider. Deployed internally or externally, a hosted private network offers high levels of security using the provider’s private cloud and delivers data management and business continuity services. It is the ideal choice for organizations that need to manage their host applications and other applications used by their customers. The main features of a hosted private network are:

  • Ability to retain existing IT system customizations.
  • Flexibility to modify systems as required.
  • Flexibility on the control of upgrade and patch frequency.
  • Maximum levels of reliability and scalability.
  • Maximum levels of security.
  • Greater control over cloud infrastructure.
  • Typically running on dedicated hardware (though private clouds can be virtualized).

There isn’t a great deal of difference in the design structure between hosted public cloud and hosted private network. The biggest difference for the latter is that the provider is, effectively, delivering a single tenant service over a multi-tenant architecture. It is essential that the provider can prove complete customer and data isolation—that a company’s applications and data are completely isolated from that of any other customer using the provider’s services. As such, the security, performance, and compliance benefits of the private model will come at an increased cost.

Hybrid cloud

A hybrid cloud contains the best parts of the hosted public cloud and hosted private network models. In a hybrid cloud deployment, the cloud environment is comprised of two or more different cloud deployment models. For example, one may choose to deploy cloud services processing sensitive data to a private cloud and other, less-sensitive cloud services to a public cloud. A hybrid cloud delivers superior data management, security, scalability, and performance, but adds complexity in terms of management and reliability due to the diverse configurations that this model can create. The hybrid model potentially provides the best opportunity of balance for a GxP-regulated entity; higher-risk GxP applications and services can reside in a qualified cloistered environment, while non-GxP applications can exist outside of the more constrictive GxP control set. The main features of hybrid cloud are:

  • Ability to deploy primary solution on premise.
  • Ability to retain existing IT system customizations.
  • Flexibility to modify systems as required.
  • Flexibility on the control of upgrade and patch frequency.
  • Flexibility to deploy business continuity and disaster recovery capabilities externally.
  • High levels of reliability and scalability.
  • High levels of security.
  • Greater control over cloud infrastructure.

Hybrid cloud deployments can be complex and challenging to create and maintain due to the potential disparity in cloud environments. Life sciences companies need to work closely with the cloud service provider to know exactly who is responsible for managing every element of the IT infrastructure. Where qualification and validation is important, the cloud service provider must be able to demonstrate and record that all its activities meet a company’s GxP compliance requirements. 

On-premise cloud

Where security and control are paramount concerns, on-premise cloud deployments are preferred. In this model, all IT infrastructure remains within the organization. With on-premise cloud, a company uses cloud computing technology as a means of centralizing access to IT resources by different parts, locations, or departments of the organization. 

Even though the cloud infrastructure physically resides on the company’s premises, the IT resources it hosts are still considered “cloud-based,” as they are made remotely accessible via the cloud to both internal and external users. The service provider delivers the level of management and maintenance skills the pharma customer requires to operate the system. The main features of on-premise cloud are:

  • Ability to qualify the data center infrastructure, cloud stack, and virtualized architectures.
  • Ability to remain using existing hardware.
  • Ability to maintain system on-premise.
  • Ability to retain existing IT system customizations.
  • Flexibility to modify systems as required.
  • Flexibility on the control of upgrade and patch frequency.
  • Ability to use provider to flexibly resource IT infrastructure.
  • Maximum levels of security.
  • Maximum control over cloud infrastructure.

From the standpoints of data integrity, security, and software validation, on-premise cloud represents an attractive option. However, it does have drawbacks. Unsurprisingly, this cloud type suffers from some of the key weaknesses of internal IT systems. Key among these is the potential lack of scalability. A company is still bounded by the capabilities of its existing servers and can’t take advantage of the unlimited potential to quickly and securely scale computing capacity as business requires.

Further, with a hardware refresh rate of three to five years, and the internal costs of managing the solution and any associated regulated expectations, this deployment type can soon exceed the perceived value of an on-premise architecture.

 

The regulatory paradox

To meet the criteria for computing in a GxP environment, software applications have to be carefully validated and other IT infrastructure components—data center facilities, network components, and infrastructure software and tools—needed to be properly qualified. The life sciences industry had become very comfortable with using the GAMP 5 for the validation of applications. Until recently, similar guidance for cloud deployments was in short supply, but the International Society for Pharmaceutical Engineering (IPSE), the creator of GAMP 5, has addressed this with the publication of the GAMP Good Practice Guide: IT Infrastructure Control and Compliance rev 2.2 The guide directly addresses the vastly increased risk profile for cloud computing and provides a roadmap for transitioning from an internal self-managed relationship to a model for working with a qualified supplier, such as a CSP. 

The IPSE guidance for achieving compliance now places new emphasis on:

  • Supplier assessment and management.
  • Installation and operational qualification of infrastructure components (including facilities).
  • Configuration management and change control of infrastructure components and settings in a highly dynamic environment.
  • Management of risks to IT Infrastructure.
  • Involvement of service providers in critical IT Infrastructure processes.
  • SLAs with XaaS (i.e., IaaS, PaaS, SaaS) providers and third-party data center providers.
  • Security management in relation to access controls, availability of services, and data integrity.
  • Data storage, and in relation to this, security, confidentiality, and privacy.
  • Backup, restore, and disaster recovery.
  • Archiving.

This new guidance comes at a critical time, as regulatory pressure elsewhere in the business are likely to encourage life sciences companies to investigate cloud services. A slew of recent and forthcoming regulations across the European Union (EU) place an emphasis on information sharing and improved data management. The EU General Data Protection Regulation (GDPR), which deals with the management of personal information; the ISO Identification of Medicinal Products (IDMP), which involves improving information sharing and reporting of medicinal products; and the EU Clinical Trials Regulation (CTR) will affect every company that sells, markets, or works in Europe.

In all three cases, the regulations require enterprise-level of control and visibility of data within an organization—and, in some cases, its suppliers, partners, and customers. It involves bringing together different data in different formats from different parts of the business. In many cases, existing legacy systems will labor to meet performance, security, and transparency requirements to comply with these regulations. The scalability, reliability, and proven security capabilities of the cloud make it an increasingly attractive option. 

What to expect from a cloud provider

Delivering cloud services into a regulated environment places extra responsibility on service providers. Often, as the GAMP Cloud Special Interest Group has pointed out, this will involve them being willing to adapt their business model, as “it involves even greater movement of control toward the supplier, but still leaves the responsibility for the data and process within the regulated company. …The compliance concerns are just as valid, on infrastructure, platform, and application level, with little or nothing that we as life sciences companies can influence with regard to the provider’s management processes.”3

While true, many service providers have made significant efforts to tailor their service to meet GxP requirements. In addition to meeting all the latest cloud standards, such as SSAE and ISO 27001, some deliver against qualification standards and include validation packages that let a company take a risk-based approach to application development, delivery, and amendment. They will all provide the most stringent security, access, and change controls to meet the needs of regulated environments.

Where some providers differ is in their willingness or ability to deliver the level of audit rights and documented processes that life sciences companies require to meet their GxP compliance responsibilities. It is essential that companies are sure that the change control and documentation processes of the provider meet their requirements, especially within their qualification documentation practices.

Ready to go

The cloud is not an immature technology. Properly architected, built, and managed, it is a highly resilient, scalable, and secure platform that has been proven to successfully host mission-critical applications. More and more industries—even the US government—are quickly moving to adopt a “cloud-first” strategy. The GxP environment, like other regulated environments, has very stringent requirements and that has certainly slowed adoption. 

The lack of clear implementation guidance has been an issue. However, with the new IPSE guidance and a risk-based approach to cloud deployment backed by a cloud service provider whose services are designed for regulated environments, companies can now begin to benefit more fully from the cloud. Today, the cloud is better suited to deliver GxP-compliant services that will help life sciences organizations meet their key business challenges. As the GAMP Special Interest Group says: “We all know it’s the way to go.”3

 

Jaleel Shujath is Director, Life Sciences Strategy, at OpenText. Stephen Ferrell is a Partner at Promedim Ltd.

 

References

1. https://ift.tt/2jKZIgA

2. ISPE, GAMP Good Practice Guide: IT Infrastructure Control and Compliance (Second Edition), 2017

3. https://ift.tt/2I8nmyr

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Validation by Numbers | Pharmaceutical Technology


Validation by Design, Lynn Torbeck, PDA Books, Bethesda, MD, 2010, 200 pp., ISBN: 193372238X

How many samples should I take? Is the assay method validated? Is this result really out of specification? Should I adjust the tablet weight? Is this process under control? Should I reject the batch? The pharmaceutical industry has always faced questions such as these, and they may be answered best from a statistical perspective.

Unfortunately, many quality-assurance workers and production managers charged with answering these types of questions do not have an adequate working knowledge of statistics. They find it difficult to answer these questions and to understand answers that statisticians provide.

The book Validation By Design: The Statistical Handbook for Pharmaceutical Process Validation, by Lynn Torbeck, a member of Pharmaceutical Technology‘s Editorial Advisory Board, contains information useful to people who are new to statistics and to employees responsible for implementing statistical techniques that monitor and control pharmaceutical production processes and quality-assurance activities. The book was written specifically to address the statistical issues contained in the US Food and Drug Administration’s November 2008 draft guidance for industry titled Process Validation: General Principles and Practices, which is reproduced in its entirety in an appendix. Torbeck’s book can form the basis for interdepartmental discussions and for an understanding of the statistical techniques that are consistent with the intent of the guidance.

An important feature of the book is its interpretation of the guidance’s statistical implications. The author rewrote the guidance’s statistical content as a series of self-audit questions that cite specific lines in the guidance document. For example, lines 27–29 of the guidance state, “The lifecycle concept links the product and process development, qualification of the commercial manufacturing process, and the maintenance of the process in a state of control during routine commercial production.” The associated self-audit question asks, “Is the process in a state of control during routine commercial production?” The question is followed

by a brief explanation of the term “state of control” and a reference to the chapter of the book that describes the concept. This self-audit format leads the reader through the various statistical techniques that enable compliance with various sections of the guidance.

A second important feature of the book is the chapters that elucidate statistical methods and concepts. Each of these chapters is written in a standard format that contains subtopics such as “Other Names,” “Acronyms,” “Definition,” “Related Topics,” “Calculation,” “Illustration,” “Cautions,” “Advice,” and “References.” This consistent categorization enables the reader to understand the statistical concepts and decide whether their use is appropriate in a particular situation. The chapters explain simple statistical concepts such as average and relative standard deviation, as well as complicated ones such as control charts, root-cause analysis, process mapping, interquartile range, and Plackett–Burman designs.

The book should prove useful to employees charged with developing a self-audit program to measure the company’s level of compliance with the process-validation guidance. It also would be a solid basis for writing validation protocols.

The book cannot be considered an introductory text about statistics because the topics are not presented in depth. Still, its references provide necessary information for readers who wish to delve further into any of the subjects. According to the author’s preface, the book is for “those engaged in meeting the requirements of the FDA process-validation guidance.” The questions that interpret the guidance, the author’s explanations, and the chapters about statistical methods and techniques should do much to help personnel meet those requirements.


Russell Madsen is president of The Williamsburg Group, 18907 Lindenhouse Rd., Gaithersburg, MD 20879, tel. 301.938.4266, fax 301.869.5016, [email protected]
. He also is a member of Pharmaceutical Technology‘s Editorial Advisory board.

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Do Visible Residue Limits Make the 10-ppm Carryover Limit Obsolete?

Cleaning validation is documented evidence that provides a high degree of assurance that a cleaning procedure consistently removes residues to predetermined acceptable levels. These acceptable residue limits (ARL) for drug products are often based on health and adulteration criteria (1–3). The limit used to determine the appropriate cleaning level is the lower of the two criteria. A health-based limit is generated from toxicity data, which can be expressed as acceptable daily intake (ADI) (4, 5). The health limit is calculated using the ADI, or an alternative toxicity factor, and the parameters of the equipment used to manufacture the formulation (2, 6). For example, a health-based limit can be calculated as follows:

(ADI ÷ MDD) × (DUB ÷ SSA) = (μg/cm2)

where the ADI units are μg/day of the residue in question, which would have no pharmacological effect; MDD is the maximum daily dose (units per day) of the product manufactured in the equipment; DUB is the dose units per batch (units) of the subsequent product; and SSA is the shared surface area (cm2) for the product contact surface area of the manufacturing equipment.

For the adulteration-based limit, a carryover limit of 10 ppm or a baseline limit of 100 μg/swab is often used in industry. The following equation provides an example:

10 μg/g × (MBS ÷ SSA) = ARL (μg/cm2)

where 10 μg/g (10 ppm) is the adulteration-based limit; MBS is the minimum batch size (g) of the product; and SSA is the shared surface area (cm2) for the product contact surface area of the manufacturing equipment. Alternatively, an adulteration limit of 4 μg/cm2 or 100 μg/swab can be used as an adulteration-based cleaning limit (7).

A third level of acceptance criteria of any cleaning evaluation is that all equipment surfaces must be visibly clean. The visual cleanliness of the equipment must be established before any swabbing can take place to confirm compliance with a health-based or adulteration-based cleaning limit.


The 10-ppm limit

The concept of the 10-ppm carryover limit has been used since the beginning of cleaning validation. The idea of a carryover adulteration limit goes back much further. The latter is used in conjunction with a health-based residue limit; together, the two limits provide a well-defined residue limit to prevent carryover that causes adulteration in pharmaceutical formulation manufacturing. A defined limit of carryover adulteration makes sense for compounds, which have a relatively high health-based residue limit. Not only does this limit prevent unacceptable carryover, but it also ensures a pharmacologically safe formulation.

Fourman and Mullin popularized the 10-ppm criterion for cleaning validation (6). Their program was the first to couple the health-based and carryover criteria in a logical, straightforward manner. They took the 10-ppm limit, calculated it with the equipment-product contact-surface area and the subsequent batch size, and arrived at a swab limit for carryover. It has since been demonstrated that a flat, unadjusted 10-ppm or 100 μg/swab limit can also be appropriate for some applications without affecting compliance, which accompanies a constantly changing cleaning limit in a clinical-manufacturing facility (7). The US Food and Drug Administration cited the 10-ppm limit in its guide to Inspection of Validation of Cleaning Processes as one of several appropriate options for cleaning limits (8). Health Canada and the Pharmaceutical Inspection Cooperation Scheme also use the 10 ppm limit in their cleaning validation guidelines (9, 10). The European Medicines Agency uses similar wording regarding cleaning-validation limits, although the agency does not specifically use the 10 ppm limit (11). Several literature references (12–14) refer to the use of the 10-ppm limit and numerous commercial and research facilities (2, 15–17) have adopted the 10-ppm carryover limit as part of their internal cleaning validation programs.

The 10-ppm limit is used when it is lower than the corresponding health-based limit for the residue of interest. Although the 10-ppm limit has some historic precedent, there is a lack of scientific justification and validation as a limit for cleaning validation. However, the idea of a carryover limit was logical to industry and the 10-ppm limit not only filled this need but also made sense.

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The Importance of Equivalence in the Execution and Maintenance of Validation Activities

It is common in the global healthcare industry to have multiple pieces of identical equipment available for the purposes of added capacity and redundancy. These circumstances provide opportunities to streamline qualification and validation activities. When several pieces of equipment are identical in all respects, the qualification effort should seek to demonstrate their basic interchangeability for all uses to reduce the needless repetition of activities. The qualification protocols should identify essential performance criteria for the equipment that each unit must meet to demonstrate its equivalence. The criteria used for this evaluation should be formal, quantitative, reasonably tight, and realistic (the performance of a single piece of equipment will vary over time).* Once equivalence has been demonstrated during the qualification effort, subsequent performance-qualification efforts should be divided between the pieces of equipment to reduce the number of studies that would otherwise be required.

The principles of equivalence can be adapted to several other instances in processing and analysis (e.g., containers, materials, formulations, analytical instruments, and personnel) where basic similarities in performance can be exploited to simplify the overall effort. The principle of equivalence is relevant to even singular instances. The fundamental precept of the US Food and Drug Administration’s draft process-validation guidance requires that firms demonstrate the consistency of the process at various scales from development, through scale-up, and continuing into commercial manufacturing (1). At the core of that guidance is an expectation for process equivalence during the course of the initial effort and recommendations for controls that will ensure consistent production over the product’s life cycle. The guidance implies the expectation for equivalent performance over scale and time; however it is unfortunately not explicit.

Other citations in the regulatory arena refer to equivalence in the execution of validation. These citations also are somewhat more implicit. In a 1994 Warning Letter, FDA indicated the basic requirements for equivalence:


It is FDA’s position, however, that while it is possible to rely on validation data from one chamber to represent that of another, it is only possible to do so for chambers at the same location which are identical in all respects. That means the chambers are of identical construction and installation (i.e., identical plumbing, characteristics, steam supplies, operating environments, etc.) and the product(s) to be sterilized are equivalent in all respects (2).

This letter serves as perhaps the clearest statement with respect to equivalence ever made by FDA. As the letter applies to a sterilization process, one of the more crucial processes requiring validation, industry has every reason to believe that the agency would take a similar view with respect to less crucial process equipment.


FDA’s guidance document on revisions to new drug applications (NDAs) and abbreviated new drug applications (ANDAs) come close to touching on the subject of equivalence as it relates to equipment, but focus on the performance of the drug (3). This document and the individual scale-up and postapproval changes guidance documents clearly imply that when individual machines are identical, the implications for process equivalence are clearer and more certain (4).

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ANI Pharmaceuticals’ (ANIP) CEO Arthur Przybyl on Q1 2018 Results – Earnings Call Transcript

Statistically Justifiable Visible Residue Limits


The standard of visual cleanliness is
commonly applied to the evaluation of
surface contamination. Numerous published
studies have examined the visually
clean standard as a means of verifying
cleaning effectiveness in pharmaceutical
manufacturing, and methods for the
quantitation of visible residue limits (VRLs)
have been provided. Current methods
for establishing VRLs are not statistically
justifiable, however. The author proposes a
method for estimating VRLs based on logistic
regression.

Visually clean (VC), a term that refers to inspection with the naked eye, is a common cleanliness standard employed for evaluating surface contamination and cleaning in high-technology manufacturing, including that of pharmaceuticals, where surface cleaning is of utmost importance. The importance of the VC standard for pharmaceutical manufacturing is evident in the following facts:

  • It is one of the acceptance criteria for establishing the limits for cleaning-validation (CV) studies (1)
  • Visual examination of equipment surfaces for cleanliness immediately before use is required by good manufacturing practice (GMP) regulations (2)
  • Even before the issuance of the GMP regulations, most companies used to a VC standard (3)
  • A VC approach to controlling cross-contamination in processing and manufacturing operations provides a practical and effective method of risk management (4, 5)
  • It is one of the means of evaluating cleaned surfaces during the development, optimization, and validation of cleaning processes
  • It is the only tool available to operators for examining equipment surfaces to verify that they have been cleaned effectively
  • Manufacturers employ it for routine monitoring of the cleaning process.

Table I: Advantages and disadvantages of the visually clean standard.

Many manufacturers believe that compliance with a requirement that the surface be visually clean ensures only the absence of gross amounts of contamination and may be regarded as the lowest cleanliness standard because of its subjectivity and variability. Many studies of VC as one of several criteria for evaluating surface contamination have been published. In light of its advantages and disadvantages, which are listed in Table I, visual inspection of surfaces, combined with a few simple tools, is still regarded as an effective and inexpensive primary way for evaluating surface cleanliness.


Visually clean criterion for CV studies

In the pharmaceutical industry, cleaning is defined as limiting contamination to a level below practical, achievable, justifiable, and verifiable limits. CV is the documented evidence of cleanliness.

Common bases for establishing CV acceptance limits, as described in literature and in regulatory guidance documents, include the following (6, 7):

  • Therapeutic daily dose
  • Toxicological data
  • The 10-ppm criterion
  • The VC criterion.

Table II: Definitions of visually clean and visible residue limit from the literature.

The method that yields the lowest acceptance limit is selected, and the value is considered the maximum allowable carryover (MACO) limit for CV studies. The VC criterion still holds, however, and is independent from the established MACO limit. Regardless of whether the established visible residue limit (VRL) is lower or higher than the MACO values, noncompliance with the VC requirement indicates the failure of CV. The Pharmaceutical Inspection Convention and Pharmaceutical Inspection Cooperation Scheme (PIC/S) requires the VC criterion to be verified through well-documented spiking studies before it can be used for CV studies (1).

Because the VC standard is relevant to many technological areas, tremendous efforts have been devoted to defining and devising novel and efficient ways to develop justifiable and quantifiable VRLs for monitoring and validating cleaning procedures. Table II lists some definitions of the VC standard from the literature. The VC standard and VRLs are based on the following common principles:

  • Particles deposited on the surface tend to reduce the reflection of light.
  • The unaided human eye (with or without corrected vision) can detect particles as small as 40–50 μm under ideal conditions (8).
  • The viewer’s state of mind could affect his or her ability to detect the residue visually (e.g., the residue might be visible but unseen because the observer is inattentive).
  • The brighter a residue in comparison with its background, the higher the probability of its detection through visual inspection.

Although the VC standard may be highly subjective, personnel have successfully quantified VRLs by establishing well-controlled experiments and programs. For industries other than pharmaceuticals, variables and parameters associated with the VC standard (e.g., viewing distance and light intensity) have been quantified and well documented (8).

The most popular method, henceforth referred to as the current method, for determining VRLs in the pharmaceutical industry involves spiking the selected material surface with known amounts of residue at concentrations of about 0–10 μg/cm2. Trained inspectors then examine the surfaces under controlled viewing conditions (e.g., light, viewing angle, and viewing distance) for the presence of residue (9–11). The lowest level of residue that is detected is then considered the VRL for that particular residue. The only drawback with the method is that it is not statistically justifiable and, hence, not scientifically definable. The primary objective of this article is to establish a method for setting scientifically and statistically justifiable VRLs and to provide a meaningful definition of the VC standard.

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