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Quality Risk-Management Principles and PQRI Case Studies

A PQRI expert working group provides case study examples of risk-management applications.

The harmonized Q9 Quality Risk Management guideline from the International Conference on Harmonization (ICH) provides an excellent high-level framework for the use of risk management in pharmaceutical product development and manufacturing quality decision-making applications (1–2). It is a landmark document in acknowledging risk management as a standard and acceptable quality system practice to facilitate good decision-making with regard to risk identification, resource prioritization, and risk mitigation/elimination, as appropriate.

Recognizing the need to propagate and expedite holistic adoption of quality risk management across the pharmaceutical industry, the Product Quality Research Institute Manufacturing Technical Committee (PQRI–MTC) commissioned a small working group of industry and FDA representatives to seek out good case studies of actual risk-management practices used by large bio/pharmaceutical firms to share with the industry at large.

The working group spent approximately one year soliciting risk-management case studies from industry peers and contacts, and ultimately reviewed more than 20 of them. Each study was graded against six multiple criteria to assess applicability, usefulness, and alignment with ICH Q9. The highest graded case studies were measured against two additional criteria to ensure a balanced mix of examples for this report. Due to the size of a well-developed risk assessment, especially when applied to a complex problem or operating area, the presented case studies in most instances represent redacted versions of the actual assessments. Nonetheless, the provided summaries are effective in demonstrating the general thought process, risk application, and use of chosen risk methods.

As a byproduct of the working group's collaboration on risk-management practices, several common principles that reflect current industry and regulatory thinking emerged. These principles are aligned with, and in some instances expand beyond, those defined by ICH Q9 and are included in this report. In addition, several risk-management reference tools used by participating firms have been included as examples.

Risk-management principles, case studies, and supporting tools used by large bio/pharmaceutical manufacturers for effective quality oversight of product development and manufacturing operations are included in this report. Each case study notes the applicable corresponding quality system (i.e., Quality, Facilities & Engineering, Material, Production, Packaging & Labeling, or Laboratory Control) that is consistent with FDA's quality systems guidance document (3). In addition, the case studies identify the risk methodology that was used for ease of categorization, understanding, and potential application by the reader. Medical-device examples fall beyond the scope of this article, although the case studies and tools presented have relevance to device manufacturing. See the sidebar, "PQRI case studies," for details on the topics covered.

PQRI case studies

Principles and common practices

Core principles of quality risk management according to the ICH Q9 guideline include the following:

1. Compliance with applicable laws: Risk assessment should be used to assess how to ensure compliance and to determine the resulting prioritization for action—not for a decision regarding the need to fulfill applicable regulations or legal requirements.

2. Risk can only be effectively managed when it is identified, assessed, considered for further mitigation, and communicated. This principle embodies the four stages of an effective quality risk-management process as defined by ICH Q9: risk assessment (i.e., risk identification, analysis, and evaluation); risk control (i.e., risk reduction and acceptance); risk communication; and risk review.

3. All quality risk evaluations must be based on scientific and process-specific knowledge and ultimately linked primarily to the protection of the patient. Risk assessment is based on the strong understanding of the underlying science, applicable regulations, and related processes involved with the risk under analysis. Collectively, these components should be assessed first and foremost with regard to the potential impact to the patient (see Figure 1).

Figure 1: Quality risk-evaluation pyramid.

4. Effective risk management requires a sufficient understanding of the business, the potential impact of the risk, and ownership of the results of any risk-management assessment.

5. Risk assessment must take into account the probability of a negative event in combination with the severity of that event. This principle also serves as a useful working definition for risk (i.e., risk represents the combination of the probability and severity of any given event).

6. It is not necessary or appropriate to always use a formal risk-management process (e.g., standardized tools). Rather, the use of an informal risk-management process (e.g., empirical assessment) is acceptable for areas that are less complex and that have lower potential risk. Risk decisions are made by industry every day. The complexity of the events surrounding each decision and the potential risk involved are important inputs in determining the appropriate risk-assessment methodology and corresponding level of analysis required. For less complex, less risky decisions, a qualitative analysis (e.g., decision tree) of the options may be all that is required. In general, as the complexity and/or risk increases, so should the sophistication of the risk-assessment tool used. In the same regard, the level of documentation of the risk-management process to render an appropriate risk assessment should be commensurate with the level of risk (2). See Figure 2 for details.

Figure 2: Documentation level.

Risk-assessment supporting tools

A key early step in the execution of a risk analysis is to determine the appropriate risk-assessment tool, or methodology. There is no single best choice for any given assessment process, and the selection of the appropriate risk methodology should be based on the depth of analysis required, complexity of the subject risk of concern, and the familiarity with the assessment tool. Based on the industry examples reviewed by the PQRI–MTC working group, risk ranking and filtering (sometimes referred to as risk matrix) and flowcharting were the most popular tools used for basic risk-assessment activities. Correspondingly, failure-mode effect analysis (FMEA) appeared to be the most frequently used methodology for more advanced risk analysis. Some examples demonstrated the power of combining tools to help with more complex analysis. For example, fault-tree analysis (FTA) or a fishbone diagram can be used to initially scope and evaluate the fault modes of a particular problem and be used to feed a hazards analysis and critical control point (HACCP), or a similar tool to evaluate overall system control and effectiveness can be used. Table I provides a list of generally well-recognized risk-management tools.

Table I: Common risk-management tools.

Each risk subject and assessment warrants consideration of the applicable descriptors of potential risk and related consequences. Ideally, firms should establish a guidance document ahead of any risk analysis, such as the one provided in Table II, to help guide the risk-assessment process and provide for consistency in decision-making company-wide.

Table II: Severity categorization.

Risk trainers. In assembling this collection of case studies, the authors recognized the benefit of providing industry with additional background on core risk methodologies. Training tools for the application of risk ranking and filtering, FMEA, FTA, and HAZOP are available online with the web version of this article at PharmTech.com/PQRIstudies . These tools are meant to facilitate greater familiarity with the risk methodology used in each corresponding case study.

The PQRI–MTC Risk Management Working Group solicited and formatted a series of best-practice case studies aligned with ICH Q9 principles. The collected case studies demonstrate that there is a wide range of applications for the use of structured risk-management analysis to facilitate effective quality-decision activities. The studies demonstrate the baseline needed to choose the appropriate risk methodology for the targeted need, taking into account the degree of complexity and risk involved for the specific subject of concern. It is equally important to predefine the potential resulting risk categorizations so as to not be influenced by the assessment results in defining appropriate response actions. Finally, once risks have been appropriately assessed and prioritized, clear risk-mitigating actions must be defined, communicated, implemented and monitored for effectiveness.

Ted Frank is with Merck & Co; Stephen Brooks, Kristin Murray* and Steve Reich are with Pfizer; Ed Sanchez is with Johnson & Johnson; Brian Hasselbalch is with the FDA Center for Drug Evaluation and Research; Kwame Obeng is with Bristol Myers Squibb; and Richard Creekmore is with AstraZeneca.

*To whom all correspondence should be addressed, [email protected] .

1. FDA Global Harmonization Task Force, "Implementation of Risk Management Principles andActivities within a Quality Management System" (Rockville, MD, 2000).

2. ICH, Q9 Quality Risk Management, 2005.

3. FDA, Guidance for Industry: Quality Systems Approach to Pharmaceutical CGMP Regulations (Rockville, MD, 2006).

4. FDA, "Risk-Based Method for Prioritizing CGMP Inspections of Pharmaceutical Manufacturing Sites–A Pilot Risk Ranking Model," (Rockville, MD, 2004).

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An industrial case study: QbD to accelerate time-to-market of a drug product

  • Madalena Testas 1 ,
  • Tiago da Cunha Sais 2 ,
  • Leonardo Piccoli Medinilha 2 ,
  • Katia Nami Ito Niwa 2 ,
  • Lucas Sponton de Carvalho 2 ,
  • Silvia Duarte Maia 2 ,
  • Anderson Flores 3 ,
  • Lígia Pedroso Braz 1 ,
  • José Cardoso Menezes 1 &
  • Cássio Yooiti Yamakawa 2 , 4  

AAPS Open volume  7 , Article number:  12 ( 2021 ) Cite this article

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The use of a Quality by Design (QbD) approach in the development of pharmaceutical products is known to bring many advantages to the table, such as increased product and process knowledge, robust manufacturing processes, and regulatory flexibility regarding changes during the commercial phase. However, many companies still adhere to a more traditional pharmaceutical process development—in some cases due to the difficulty of going from a theoretical view of QbD to its actual application. This article presents a real-world case study for the development of an industrial pharmaceutical drug product (oral solid dosage form) using the QbD methodology, demonstrating the activities involved and the gains in obtaining systematic process and product knowledge.

Introduction

In 1992, Dr. Joseph M. Juran introduced the concept of quality being designed into a product and that most quality issues were related to the way in which the product was designed in the first place (Yu et al., 2014 ). Over time, this Quality by Design (QbD) approach was translated into the pharmaceutical industry, reaching its most important evolution steps with the publication of three guidelines by the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), namely, ICH Q8(R2), Q9, and Q10 (ICH, 2009 ; ICH, 2005 ; ICH, 2008 ). These guidelines describe the elements of QbD: pharmaceutical development, quality risk management, and pharmaceutical quality system.

ICH Q8(R2) defines QbD as “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management.” This is a clear and easy to understand description, at least in theory (ICH, 2009 ). In its form, QbD can be explained as an orderly, well-planned procedure to assemble and deliver quality. For that, it is required an extensive comprehension of how the product and process factors impact quality (Malik et al., 2019 ).

But how to go from definitions and guidelines to an actual process and product development in a real-world situation? The uncertainty in the answer drives many companies away from QbD and to adhere to a more traditional approach to pharmaceutical development. Figure 1 represents a workflow with all the important elements that must be present in a QbD development of a pharmaceutical product.

figure 1

Quality by Design methodology applied for a pharmaceutical product development

Just like the ICH Q8(R2) guideline indicates, one of the first elements to be defined is the Quality Target Product Profile (QTPP)—a summary of the desirable quality characteristics a product should have to ensure the desired quality, taking into account safety and efficacy of the drug product to the patient (Yu et al., 2014 ; ISPE, 2011 ). The end goal of process development is the definition of a control strategy that ensures that the process consistently delivers a product with the quality for which it was designed. The multidimensional combination and interaction of process inputs that have demonstrated to maintain the Critical Quality Attributes (CQA, i.e., the product quality characteristics that are critical for ensuring the safety and efficacy from a patient’s perspective) within their specification (and thus, assure quality) is called the Design Space (DS) (Yu et al., 2014 ; ICH, 2009 ; ISPE, 2011 ). This concept brings certain regulatory flexibility to the table, since alterations made within the DS are not considered changes (ICH, 2009 ). The elements represented in Fig. 1 are obtained using risk management and knowledge management methodologies. The combination of risk assessment (RA) and data analysis is one of the stone pillars for QbD and the opportunities for acquiring and managing knowledge based on this arrangement are central for a successful QbD pharmaceutical development and lifecycle management.

A drug product development case study

Herein, we describe how the QbD approach and its concepts, summarized in Fig. 1 , were applied to a real-case development of a generic pharmaceutical drug product (DP), i.e., of a drug intended to be submitted to the regulatory agencies as an alternative to a brand-name drug (patent-protected). The project’s goal was to develop a generic two-API (active pharmaceutical ingredient) solid dosage oral form using the QbD approach outlined in Fig. 1 , in order to obtain a deeper product and process understanding to expedite time to market, assure process assertiveness and reduce risk of defects after product launch. Limitations of this work are the ones typical of the development of a generic DP, where the physicochemical characteristics of the reference listed drug (patent-protected brand-name drug) must be considered. The proposed generic DP must be comparable to the innovator DP in dosage form, strength, route of administration, quality, performance characteristics, and intended use. So, the generic manufacturer must scientifically demonstrate that his product performs in the same way as the innovator drug with respect to pharmacokinetic and pharmacodynamic properties (e.g., by performing bioequivalence studies) for it to be approved for sale after the patent protections expire. This work is the result of a collaborative project between 4Tune Engineering and Libbs Farmacêutica.

Materials and methods

The pharmaceutical product considered in this case study consists of a generic two-API oral solid dosage form—coated tablets. Tablets are amongst the most common oral solid dosage forms and consist of a compressed powder formulation comprised of API(s) (or drug substance(s)) and inactive ingredients or excipients (e.g., fillers, binders, lubricants, disintegrants, coatings). A generic drug contains the same API as the original (patent-protected) innovator drug, but may vary in certain characteristics, such as the manufacturing process, formulation, and packaging. In this case study, the tablets have distinct dosages of the two active ingredients: one API is at very low amounts (2.5 mg), whereas the other is at a very high dose (up to 200 to 400 times higher). The reference DP is already available in the market to patients, with no reported risks related to drug-drug compatibility or with safety concerns to the patients. The unit operations involved are those typical of the manufacturing process of a coated tablet form, such as materials dispensing, fluid bed granulation and drying, blending, compression, and tablet coating. For confidentiality reasons, the names of raw materials, intermediates and DP, manufacturing operations, parameter ranges, and other manufacturing details are not disclosed throughout this article. The results presented serve only the purpose of exemplifying the methodology used.

The authors’ goal with this manuscript is to provide, in the form of a case study, a brief outline of the steps involved in the application of the QbD methodology in the development of a pharmaceutical product. It is out of this paper’s scope to give a technical review or discussion of the methodologies and techniques comprised in the QbD toolkit, such as design of experiments and modelling approaches, and quality risk management tools. The interested reader should consult specialized literature for further methods’ details. Methodology aspects related with design of experiments, multivariate analysis, modelling, and quality risk management are given, as required for the purpose of this work, along the “ 5 ” section, while going through the case study.

The designed experiments and analyses described herein were performed in software JMP® version 13 (SAS Institute Inc., Cary, NC, USA, 1989-2019). Principal Component Analysis (PCA) modelling and computer simulations were performed in MATLAB® version 2018a (The Math Works, Inc., Natick, MA, USA) and using PLS_Toolbox version 8.7 (Eigenvector Research, Inc., Manson, WA, USA).

The QbD methodology followed along this project for knowledge and risk management was supported by the use of the iRISK TM platform (version 2.8) (iRISK, 2021 ) by the interacting multidisciplinary technical team. Several iRISK TM tools were employed, such as Process Mapping, Critical Quality Attributes assessment tool, Cause-Effect matrix for risk assessment and criticality analysis, and Failure Mode and Effect Analysis (FMEA) for process risk assessment.

Results and discussion

How to combine risk and knowledge in pharmaceutical development.

Following the QbD methodology (Fig. 1 ), one of the first activities conducted in this work was a criticality assessment (CA) for the identification of CQAs. For this, the project team gathered as much product-related information as possible from literature, specific data of the reference product, and the QTPP. Having a list with the product quality attributes and their respective target values/ranges is standard: fulfilling these targets is mandatory for batch release. However, assessing these characteristics from a risk-to-patient perspective might be more complex. From a list of about 20 potential Critical Quality Attributes (pCQAs) collected by the team, a ranking system for pCQAs’ CA was applied based on a criticality score that considered the risk for the patient of each quality attribute. Specifically, the criticality score is a quantitative measure given by the product between uncertainty and impact . The uncertainty measures the relevance of the available information (e.g, literature, prior knowledge, in vitro, clinical data), i.e., if there is variation in a quality attribute, are the consequences for the patient well-known? The impact measures how severe will the change of a given quality attribute be in terms of efficacy, safety, and pharmacokinetics and pharmacodynamics. By setting up a criticality threshold and a numeric ranking, it is possible to have the quantification of risk and a more exact approach for defining the criticality. For the CA, the team employed a scoring scale with 5 levels (Impact score: 2 (none), 4 (low), 12 (moderate), 16 (high), and 20 (very high); Uncertainty score: 1 (very low), 2 (low), 3 (moderate), 5 (high), and 7 (very high)). During this exercise, the attributes with low uncertainty and low impact were not considered critical and, therefore, were classified as non-CQAs (Fig. 1 ); Quality attributes with low severity but high uncertainty were considered critical - unless more information had become available to lower their uncertainty. The use of a systematic quality risk management platform for this exercise, specifically iRISK TM CQA Assessment tool (iRISK, 2021 ), ensured standardization of the definition of critical quality by allowing an alignment of methodologies, concepts and evaluation criteria by the involved technical teams. At the end of this step, the project team identified about fifteen CQAs, such as assay, content uniformity and dissolution of each API, water content, and impurities.

With a clear definition of the critical quality elements and respective targets, the manufacturing process can now be designed to meet those requirements (Fig. 1 ). Five different manufacturing processes were then considered and evaluated by the technical team based on process knowledge and experience, and given the product’s specificities (namely the technical challenges related with the manufacture of a DP having two APIs at extremely different concentrations). Figure 2 shows the process flowchart for the chosen process comprising 10-unit operations, including materials dispensing, powdered material seiving, solution/suspension preparation steps, fluid bed granulation and drying, blending, compression, and tablet coating.

figure 2

Manufacturing process workflow of the pharmaceutical drug product (DP). The green boxes represent raw materials (RM)—both active pharmaceutical ingredients and excipients; the yellow boxes represent unit operations (UO ), and the blue box represents the final DP

In the next step of the QbD methodology (Fig. 1 ), the critical aspects of the product formulation and manufacturing process were assessed by following a combination of risk-based and data-driven approaches. A preliminary CA based on the reference product and/or similar products information (literature and prior knowledge) helped to identify which excipient and/or combination of excipients might present the highest risk of affecting the final product’s quality. This CA was performed using the risk tool Cause-Effect Matrix (CEM) (iRISK, 2021 ). In general terms, a CEM involves rating process inputs to process outputs based on their interaction impact, and then ranking process inputs based on the order of importance of the process output to the customer (ISPE, 2017 ; ISPE/PDA, 2019 ). For confidentiality reasons, the CEM generated at this stage of the project is not shown. It is similar to the CEM given in Fig. 4 , but has the formulation excipients in rows. The risk of a formulation component affecting a given final product’s CQA (entry of the CEM) was classified as low (score of 1), medium (score of 3), and high (score of 9) based on literature and prior knowledge, as stated above. This preliminary risk rank filtering of formulation components identified the two APIs and five excipients as having the highest impact on the product’s quality. Then, a design of experiments (DoE) approach (Montgomery, 2020 ) was followed to characterize these formulation components’ impact on the product’s CQAs, and their respective interactions, and therefore define the optimal quantities of each excipient in the drug formulation. A designed experiment consists of a set of trials, in which multiple input factors (independent variables) are manipulated to determine their effect on one or more response variables (dependent variables); these trails are run at different factor values (known as levels). DoE provides an efficient framework to do experimentation and thus increase process and product understanding and optimize processes. In fact, DoE can be applied for different investigation objectives, such as (1) screening studies (where the goal is to discover which are the most important factors that affect the process under study, given a large set of potential factors), (2) optimization studies (involve determining optimal factor settings to achieve a desired process objective), (3) regression modelling (where is goal is to produce a detailed mathematical model quantifying the dependence of response variables on process inputs, instead of just examining how factors contribute to a response), and (4) robustness studies (involve determining operational settings that are least affected by noise factors or uncontrolled factors variations (e.g., environmental variation, manufacturing variation) that might be expected during the process to ensure that the process is robust to them).

In this case, the formulation DoE was created using as factors the ratio between APIs (API-1/API-2, where API-1 is the low dosage API and API-2 is the high dosage one) and the percentage of five excipients (selected in the previous CA, as abovementioned). Based on the outcomes of the CA of the formulation components, the considered responses were the decrease in assay of each API, the amount of total impurities, and the amount of individual impurities of the final product. The type of screening design applied was a Definitive Screening Design (DSD) (SAS Institute, 2019 ). DSDs consist of an innovative and efficient class of screening designs, offering several advantages over standard screening designs (such as fractional factorial design). DSDs avoid confounding of effects (i.e., main effects are not confounded with each other or with two-way interactions) and can identify factors causing a nonlinear effect on the response (by employing three levels for each continuous factor—low, middle, and high—these designs allow estimation of quadratic model terms for continuous factors). Besides, DSDs require a small number of trials (e.g., with six or more factors, the minimum number of required runs is usually only a few more than twofold the number of factors). DSDs are appropriate for early-stage experimentation work, usually with four or more factors, and allow to perform screening, optimization, and robustness studies. These advantages of DSDs justified the selection of this type of screening against standard screening designs, such as fractional factorial designs, to perform the formulation screening and optimization studies, as a trade-off between budgetary constraints (time and resources) and knowledge expected to extract from the experiments. By applying a DSD, the formulation DoE therefore consisted of 13 trials, and each factor assumed three levels (low, middle, high).

Based on the DoE outcomes, multivariate linear regression models were built describing the relationship between the formulation components and the responses evaluated. These models were then used for formulation optimization (SAS Institute, 2019 ), i.e., to estimate the amount of each formulation component required to minimize the impurity profile of the drug product and minimize the decrease in assay. The formulation optimization was performed on the reduced models, i.e., models constructed after removing non-significant terms from the initial full DoE models (terms with a p -value above 0.05). The following three factors remained in the optimized multivariate linear models: API-1/API-2 ratio, amount of stabilizer, and amount of Excipient A. The optimal settings for the formulation components are represented in red in Fig. 3 (red dotted lines and red values). Each plot shows the effect of a given factor ( x -axis) on each of the responses ( y -axis). For example, the profiles indicate that: a) the % of Stabilizer in the formulation affects all the five responses (assay and impurity levels) and a lower content of Stabilizer has a detrimental effect on the DP assay; b) the amount of Excipient A in the formulation has no impact on Unknown Impurity B (flat line) but affects the other impurities; c) while the ratio of APIs has no effect on the DP assay (horizontal line), lower values of API-1/API-2 contribute to higher impurity levels of Unknown Impurity A and B.

figure 3

Optimization of the product formulation using DoE studies. Each plot shows the predicted effect of a given factor in the x -axis (formulation component) on each of the responses ( y -axis). The red dotted lines indicate the optimized solutions for the responses of interest (lowest impurity levels and lowest decrease in assay in the final drug product)

Next, a similar approach based on a CA exercise using the CEM risk tool (iRISK, 2021 ) was applied for defining the critical aspects of the manufacturing process, specifically to determine the Critical Process Parameters (CPPs)—Fig. 1 . As per ICH Q8(R2), a CPP is “a process parameter whose variability has an impact on a CQA and therefore should be monitored or controlled to ensure the process produces the desired quality” (ICH, 2009 ).

The first step involved applying the CEM tool to rate the unit operations in terms of their impact on the product’s CQAs (scoring scale: low = 1; medium = 3; high = 9). These results supported a prioritization, in which the unit operations having the potential strongest impact (highest overall score) on the product’s CQAs were assessed first. As shown in Fig. 4 , unit operations UO2 and UO6 were the top-ranking ones.

figure 4

Criticality assessment of the unit operations (UOs) using iRISK TM Cause-Effect Matrix. The risk of a given UO (in rows) affecting a given product’s CQA (in columns) was classified as low (score of 1), medium (score of 3), and high (score of 9). Due to confidentiality reasons, not all of the CQAs are shown. Given their higher overall score, operations UO2 and UO6 (highlighted) were identified as the top 2 UOs potentially affecting the product’s Critical Quality Attributes (CQAs)

These two process steps (UO2 and UO6) were then investigated by running DoEs with the goal of understanding which process parameters (PPs) were influencing the CQAs and in which extent. First, a prioritization step using the CEM tool (iRISK, 2021 ) was done in order to select from the original 20 PPs of UO2 and UO6, those to be considered for the DoEs. This PPs ranking was based on their level of impact in the final product’s quality (scoring scale: low = 1; medium = 3; high = 9), leading to the selection of 8 potential CPPs (pCPPs) for UO2, and 6 pCPPs for UO6 (not disclosed herein, due to confidentiality reasons). Then, in both cases, the DoE followed a DSD, given the advantages provided by this type of experimental design and the scope of the experimental work (process screening and optimization). Three levels were therefore considered for each factor in both DoEs (low, middle, and high); the DoEs considered 17 runs for UO2 (with 8 PPs used as factors and 7 CQAs as responses) and 13 runs for UO6 (with 6 PPs considered factors and 7 CQAs considered responses). The responses were the same for both DoEs and included relevant quality attributes of the final DP, such as assay, content uniformity, and dissolution. Figure 5 exemplifies how the knowledge obtained from the UO2 DoE analysis can support the identification of CPPs. The right-hand plot shows the true versus predicted values of Assay of API-1 obtained after fitting a multiple linear regression model to the UO2 DoE data. The model constructed to predict Assay of API-1 considers a quadratic term (UO2_PP4*UO2_PP4), three main effects (UO2_PP4, UO2_PP3, and UO2_PP6) and a two-way interaction (UO2_PP3*UO2_PP6). Criticality of UO2_PP3 and UO2_PP4 was thus set to critical (CPPs) in the criticality assessment table of iRISK TM (left-hand panel in Fig. 5 ) since the variability of these PPs is directly impacting at least one of the CQAs (assay of API-1 in this case) in a significant way (as given by the calculated p -values of the multiple linear models’ outcomes; model terms with a p -value below 0.05 are considered significant). Parameters UO2_PP1 and UO2_PP2 were also found to be critical, presenting a significant relationship with other CQAs at a 0.05 level (data not shown).

figure 5

Update of the criticality assessment for the process parameters of unit operation UO2 in the iRISK TM platform (left-hand panel) based on the DoE outcomes (right-hand panel; the plot shows the actual versus predicted values by the model fitted for one of the responses evaluated in the DoE (assay of API-1)). Criticality of parameters UO2_PP4 and UO2_PP3 was set as critical (CPP = critical process parameter) since their variation affects the assay of API-1 (at the 0.05 significance level)

Regarding UO6, parameters UO6_PP1 and UO6_PP3 were found to be critical (data not shown). Besides confirming the criticality of potential CPPs of UO2 and UO6, the DoE results allowed defining a preliminary operating range for their PPs to be tested on the scale-up stage. For unit operations without a DoE analysis, results from additional experimental work were used to justify the criticality of their respective PPs. In the absence of evidence to classify a given PP as critical or non-critical, the PP was considered a pCPP. Overall, more than 10 PPs were identified as CPPs in the entire manufacturing process. Since most of the information was obtained at a small scale, the scaling up was a step of utmost importance. A small-scale DS was initially defined considering the knowledge obtained from the CA and the DoE results.

Process knowledge, scale-up studies, and control strategy definition

In the early stages of pharmaceutical process development, investigations are performed at a small scale. Transformations of the small-scale observations into commercial-scale development (Fig. 1 ) require different design strategies and different equipment which may cause differences in product quality (Raval et al., 2018 ). To cope with these potential differences in quality due to the presence of scale-up effects when transferring from small scale to commercial scale, the DS in commercial scale must be adapted accordingly.

For the presented case study process, an assessment of the unit operations indicated that both UO2 and UO6 were scale dependent. Ideally, a DoE should be performed at a commercial scale, using the knowledge collected at a small scale as the foundation for selecting PPs to be tested and their respective ranges. This scale-up DoE would allow to (a) confirm the criticality of the PPs, (b) define the optimal ranges for commercial-scale manufacturing, and (c) develop statistical models linking the CPPs with the CQAs. As for this project, it was not feasible to perform a full DoE at a commercial scale for UO2 and UO6. Instead, the process operational ranges for UO2 and UO6 were defined based on a small set of commercial scale batches manufactured at specific conditions, supported by knowledge acquired during the small-scale activities, as described next.

The methodology involved the use of Principal Component Analysis (PCA) and the available production batches (observations): 9 compliant batches (i.e., batches conforming to the acceptance criteria for all CQAs) and one non-compliant batch (i.e., a batch that failed to meet the acceptance criteria for at least one CQA). PCA is a multivariate projection method of data reduction or data compression. It transforms a large set of variables into a smaller dimensional set of new variables designated as principal components (PC), each of which is a linear combination of the original ones. In PCA, the new variables are uncorrelated; the first PC to be extracted (PC1) captures the highest amount of variability in the data set and each successive component accounts for as much of the remaining variability as possible (Jackson, 1991 ; Esbensen and Geladi, 2009 ; Næs et al., 2017 ). The dimensionality reduction provided by PCA allows a simplified representation of the data set, which facilitates exploring and interpreting its correlation structure. This feature of PCA was thus applied at this stage of the project to estimate the process operational ranges for UO2 and UO6. First, a PCA model was built using the values of the selected CPPs for UO2 and UO6 (total of 6 CPPs: 4 for UO2 and 2 for UO6) for the 9 compliant batches. This model allowed to obtain a simplified bidimensional representation of the two major sources of variability of the CPPs for UO2 and UO6, as denoted by the so-called score plot for the first two principal components of the model (PC1 and PC2). The score plot is a scatter plot of the scores of each sample (i.e., the projection of the sample/observation in the PC) on the two components and allows to examine the relationship between samples (Jackson, 1991 ; Esbensen and Geladi, 2009 ; Næs et al., 2017 ). The score plot for PC2 versus PC1 is shown in Fig. 6 A, where each green dot corresponds to a compliant batch (total of 9 batches, as mentioned above); these two components capture about 75.4% of the total variability present in the data. The score plot (Fig. 6 A) also shows the predicted scores of the non-compliant production batch (red dot) whose CPPs values were not used to build the original PCA model. The score plot was then used to obtain an initial estimate of the DS for UO2 and UO6: this corresponds to the rectangle area delimited by PC1 and PC2 scores of the compliant batches (green dots), which is outlined by the blue rectangle in Fig. 6 A. This region intentionally excludes the predicted non-compliant batch (red dot), since the goal is to define the process operating ranges for UO2 and UO6 expected to result in compliance batches. Note that these two components (PC1 and PC2) can be described as a linear combination of the CPPs (not given here), so the selected score plot region can be converted in ranges for each of the 6 considered CPPs of UO2 and UO6.

figure 6

Definition of the operational ranges for UO2 and UO6 using PCA modelling and computer simulations. Top left panel ( A ): PCA score plot of the analysis of 9 compliant manufactured batches ( green dots ). The data set consists of the CPPs values measured for UO2 and UO6; the score plot represents the first two principal components (PC1 and PC2), which describe 53.7% and 21.7% of the variance in the data, respectively. The dashed line corresponds to the 95% confidence ellipse for the model scores. The red dot shows the model predicted scores for a non-compliant production batch. The blue outlined rectangle that covers only compliant batches defines a first estimate of UO2 and UO6 design space (DS). Top right panel ( B ): PCA model projections for two different sets of batch simulation runs (100,000 samples per run) made to refine the acceptable ranges for UO2 and UO6 CPPs. Each small point corresponds to the predicted model scores for a simulated batch (sample) that has been generated by considering random combinations of the CPPs values within a predefined admissible range (see text for further details). Predicted scores were projected onto the original score plot shown in panel A . The blue outlined rectangle represents the first DS estimate, as defined in panel A , and was used as an acceptance criterion for model predicted scores together with other two diagnostic statistics (Hotelling’s T 2 and sum of squared residuals). The small red and green dots correspond to a set of simulations made after a first refinement of the admissible values for CPPs, where simulated samples satisfying all the acceptance criteria are shown in green. The small blue dots represent the model outcomes for a final set of simulations made after a second (and final) refinement of the CPPs ranges, whereby all simulated batches were found to comply with the predefined acceptance criteria. Bottom panel ( C ): The table summarizes the initial experimental ranges of the manufactured compliant batches ( orange ) and the final restricted ranges ( blue ) obtained for each CPP of UO2 and UO6 based on the described methodology

The next stage of the procedure involved several batch simulation runs, whereby different combinations of the CPPs values within a specified range were randomly chosen to create new hypothetical batches (100,000 simulated batches). The first round of 100,000 simulated batches considered a broader range of possible values for UO2 and UO6 CPPs (namely, within 0.75 times below and 1.5 times above the lower and upper limits, respectively, reported by the 10 manufactured batches). The previously derived PCA model was then applied to these simulated batches, and only those batches satisfying the following three criteria were considered “acceptable” batches: (i) predicted score values for PC1 and PC2 within the defined DS estimate (blue rectangle in Fig. 6 A); (ii) Hotelling’s T 2 statistics below 80% of the maximum value obtained by the model, and (iii) a sum of squared residuals below 80% of the 95% confidence limit of the model residuals. Hotelling’s T 2 and squared residuals are two useful diagnostic statistics that allow assessing whether a sample has an unusual variance inside the model (sample with large Hotelling’s T 2 ) and/or outside the model (sample with large residuals). Hotelling’s T 2 (or sum of normalized squared scores) measures the distance from a sample to the centre of the model; The sum of squared residuals of a sample provides a measure of the distance between the sample and its projection on the model (i.e., lack of fit of the model to each sample) (Jackson, 1991 ; Esbensen and Geladi, 2009 ; Næs et al., 2017 ). These “acceptable” simulated batches were then employed to perform a first refinement of the CPPs ranges to use for UO2 and UO6, by assuming the 95% confidence interval for each CPP in the “acceptable” simulated batches. These new ranges of admissible values for CPPs were considered to generate a second set of random batches (100,000 batches). The resulting PCA model predictions are projected on the score plot in Fig. 6 B (small red and green dots) and were assessed based on the same acceptance criteria (i)–(iii) outlined above for prediction scores and the two diagnostic statistics. The simulated samples satisfying all the acceptance criteria correspond to the small green points shown in Fig.  6 B.

Finally, a second refinement of the allowable ranges for CPPs was made by running consecutive sets of batch simulations (100,000 batches per run) within decreasing ranges of CPPs values and assessing their PCA model predictions based on the previously defined acceptance criteria. The widest restricted ranges of CPPs values leading to a 100% “acceptance” rate of simulated batches were chosen as the final restricted ranges. The model projections for 100,000 simulations computed within these new restricted CPPs ranges are shown by the blue dots in Fig. 6 B. The restricted CPPs ranges are disclosed in Fig.  6 C (in blue; for comparison, the full ranges for the nine CQA-compliant production batches are included in orange). These CPPs ranges were employed to define the Normal Operating Range (NOR) for UO2 and UO6 and were applied at production to manufacture three validation batches, which fulfilled all quality requirements.

In parallel with the scale-up activities and following the QbD workflow (Fig. 1 ), a process RA was performed using the Failure Mode and Effect Analysis (FMEA) methodology. With a wide application in manufacturing industries, FMEA is a risk management tool used by many pharmaceutical companies for risk ranking; FMEA provides a systematic method of identifying and preventing system, product, and process problems before they occur (ICH, 2005 ; ISPE, 2017 ; ISPE/PDA, 2019 ; ASQ, 2020 ; Stamatis, 2003 ; Stamatis, 2019 ). Along the FMEA exercise (iRISK, 2021 ), a multidisciplinary technical team identified, analysed, and prioritized the risks, creating a list of all the failure modes that may occur during commercial manufacturing and the potential effects related to each failure. Additionally, the FMEA allowed the quantification of risks and prioritization for their mitigation and/or elimination by classifying the risk according to the severity of the effect, and the occurrence and detectability probabilities for the failure mode (Fig. 7 ). The risk priority number (RPN) allows the quantification of risk by multiplying severity, occurrence, and detectability values. Thus, FMEA represents a systematic methodology to rate the risks relative to each other. For that, a rating scale for severity, occurrence, and detectability was agreed between the technical team and applied along the FMEA activity. The scale considered a 5-level rank of even values ranging from 2 to 10. Additionally, it was defined beforehand a threshold value for RPN (in this case RPN of 288) above which mitigation actions should be defined to reduce the risk.

figure 7

Decomposition of risk in severity , occurrence and detectability , and identification of the source of knowledge used for their assessment (CQAs, critical quality attributes)

Severity was attributed according to the impact of the identified risk on the product’s quality and compliance, by extension, based on the impact for the patient. For example, if a risk describes an increase outside the operating range for a certain PP and that increase causes the CQA to go out of specification, adversely affecting the patient’s health, then the risk severity was classified as very high (rank of 10). If a given failure does not affect the product’s quality and the patient’s health and safety, its severity is ranked as very low (rank of 2). Likelihood of occurrence was quantified in terms of how often that event might occur during routine batch manufacturing (a rank of 2 if the failure is unlikely; a rank of 4 if the failure has a probability of occurrence below 1%; a rank of 6 if there are 5 occurrences in 100 events; a rank of 8 if the failure is frequent but with a probability below 10%; and a rank of 10 if the failure is very frequent with a probability of having more than 3 occurrences in 10 events).

During scale-up and additional batches manufacture, certain failure modes with a low occurrence frequency during development were often observed (i.e., had high probability of occurrence); this reveals the importance of revising the RA throughout the product’s lifecycle. The same principle applies to the likelihood of detection, which quantifies how easy and quick the detection of a certain failure mode is. The detectability scale ranged from 2 (if the failure mode was easily and always detected) to a rank of 10 (when the failure mode was hard to detect and only detected in less than 67% of the cases).

Along the FMEA, about 100 failure modes were identified, with the majority (90%) being classified as easily and always detected (detectability rank of 2; and with a RPN not greater than 72) since there were reliable detection controls in place and the process automatically prevented further processing. Moreover, only 10% of the identified failure modes had a RPN equal to or greater than 128, but none exceeded the predefined threshold (RPN of 288).

More than 40% of the failure modes identified in the RA were related to UO2 operation, followed by UO7 operation with about 25% of the failure modes.

A robust control strategy, with a strong monitoring plan, can help reduce the occurrence and/or improve the detectability of specific failure modes, thus mitigating risk.

After finalizing the RA, despite none of the classified failure modes surpassing the RPN threshold, the technical team decided to address some of the ones with high-ranking RPN values. For example, a mitigation action was defined for a failure mode with a RPN value of 256. The action was a verification step for a certain equipment to check for its integrity status. By implementing this mitigation action, the RPN dropped to an acceptable value (RPN = 128) due to an improvement in the failure mode’s detectability (detection rank dropped from 8 to a value of 4). This verification step was added to the control strategy as a preventive control.

By the end of process development (Fig. 1 ), the process control strategy was defined based on the RA exercise and the characteristics of the NOR/DS. The control strategy was formalized in several facilitation sessions with a multidisciplinary team and the support of iRISK TM risk management platform (iRISK, 2021 ). The control strategy was composed by preventive controls (e.g., equipment calibration), detective controls (e.g., alarms), and in-process controls, amongst others.

  • Product lifecycle management

By adopting QbD during pharmaceutical development, deep process and product understanding were obtained, allowing the creation of a knowledge base for the product. With a higher understanding of the relationship between the process and the product, it is possible to know what impact a certain change in process will have, supporting the decision-making flow (ISPE, 2011 ). Besides, it is important to update the knowledge base whenever a critical change (e.g., change in supplier, change of equipment) or deviation (e.g., equipment out of calibration, error in following a given operating instruction) occurs. The change control system handles changes done in the context of continuous improvement or by necessity (e.g., change of a raw material supplier). The change must be evaluated with a knowledge and risk-based approach, hence why it is important to keep the risk and knowledge base updated. Depending on the type of change, its implementation might require prior approval from the regulatory authorities (ICH, 2019 ). There is an interactive flow of information between the risk management and data/knowledge management systems, as represented in Fig. 8 . The use of monitoring systems and the establishment of a Continued Process Verification (CPV) plan, as well as the application of data analysis strategies, allow the continuous flow of knowledge regarding the state of the process. This information can be used to update the RA, supporting the identification of new risks that might be detected and revision of existing ones. Depending on their criticality, risks might have to be addressed and the control strategy may need to be improved by implementing risk mitigation actions. An improved control strategy should be able to keep the process in control; this can be monitored in the CPV programme. This flow of information should be managed during the entire product’s lifecycle for the resulting knowledge base to be representative of the current situation regarding the product’s quality and the process’s performance.

figure 8

Flow of data, knowledge, and risk: these are continuously being updated and iterating with each other throughout the product’s lifecycle

Product lifecycle management activities include all that was done through development until the product is no longer commercialized. It is important to look at lifecycle management at a commercial stage through a continuous improvement lens since it is about maximizing the value of the product to the patient (Tiene, 2017 ). This can include changes in formulation, process unit operations, packaging, delivery systems, or even the inclusion of Information Technology or automation solutions for improving and automatizing the collection and assessment of data and risks. The use of an up-to-date knowledge base regarding the product and process greatly supports the selection of improvement actions since their impact will be better understood.

This article describes a successful application of Quality by Design to the development of a pharmaceutical generic drug product (coated tablet form). By following a QbD approach, a significant reduction of 30% in the overall development and validation time was achieved when compared to a traditional approach. The collection of knowledge in a systematic manner allowed the definition of a robust process that will consistently achieve the desired product quality. Future decision-making and continuous improvement activities will likewise be supported by the gained product and process understanding. One may expect that its lifecycle management to be much less unpredictable given the much higher level of process and product knowledge established. Additionally, this methodology can be easily transferred to the development of other products, bringing in further acceleration to the standard pharmaceutical development process. Overall, a more efficient and with enhanced quality critical path was followed and shown feasible. This translates into higher quality, safety, and efficacy of medicines for patients.

Availability of data and materials

The datasets generated during and/or analysed during the current study are not publicly available due to confidentiality reasons.

Abbreviations

Active pharmaceutical ingredient

Criticality assessment

Cause-effect matrix

Critical material attribute

Critical process parameter

Continued process verification

Critical quality attribute

  • Design of experiments

Definitive screening design

Drug product

Design space

Failure mode and effect analysis

International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use

Normal operating range

Principal component analysis

Principal component

Potential critical material attribute

Potential critical process parameter

Potential critical quality attribute

Process parameter

  • Quality by design

Quality target product profile

Risk assessment

Raw material

Risk priority number

Unit operation

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Acknowledgements

This work was carried out as a collaborative project between 4Tune Engineering and Libbs Farmacêutica. 4Tune Engineering is a consulting company with over 18 years of experience in the pharmaceutical area. Founded in 1958, Libbs Farmacêutica is a pharmaceutical company in the forefront of key innovation projects within Brazil’s industry: it is a pioneer in the launch of biosimilars and monoclonal antibodies in Brazil and currently produces ninety different products. We would like to thank all the departments involved in the project at Libbs Farmacêutica for their support, commitment, and dedication; we would also like to thank João Almeida Lopes for his technical contribution.

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All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by MT, TCS, and LPM. The first draft of the manuscript was written by MT and critically reviewed, commented, and edited by LPB. All authors commented on previous versions of the manuscript. The final manuscript was prepared by MT and LPB. All authors read and approved the final manuscript.

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Testas, M., da Cunha Sais, T., Medinilha, L.P. et al. An industrial case study: QbD to accelerate time-to-market of a drug product. AAPS Open 7 , 12 (2021). https://doi.org/10.1186/s41120-021-00047-w

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Quality Risk Management in Pharmaceutical Supply Chain, Warehousing and Dispensing - Practical Case Study from Sterile Pharmaceutical Industry.

Profile image of Rawidh Alsaidalani

Quality Risk Management (QRM) during medicinal products manufacturing is now becoming an integral part of quality management system (QMS). Most if not all regulatory authorities have revised their current good manufacturing practices (GMP) to incorporate the concept of risk assessment in every single process regardless to the criticality of the process. Different Procedures in pharmaceutical QMS like deviation control, change control, investigation, customer complaints handling, validation & qualification, product release, etc. consider the principles of risk assessment at all steps. Extensive research in this area shows that there is scarcity of research on quality risk management during early stages of medicinal products manufacturing including (1) procurement/supply chain, (2) logistics/warehousing and (3) raw materials dispensing. To cover the gap in the literature, three practical case studies has been studied by selecting one major step from each manufacturing stage and applied risk assessment following the procedure described in ICHQ9 and using Failure Mode Effect Analysis (FMEA) as risk assessment quality tool. As a result of this review, QRM during early stages of medicinal products manufacturing may be useful to avoid unnecessary complaints or delay during subsequent drug processing in the manufacturing site. Being proactive and taking all necessary measures to avoid any possible defects or mishandling is one of the major objectives of QRM and ultimately patient protection. This study shows a model solution for industry professionals and regulators to reduce the possible risks associated with early stages of medicinal products manufacturing thereby paving the way for significant business growth.

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OBJECTIVES: The aims of this study were to compile a model of a Quality Management System (QMS ) for distribution of medicines, identify the risks in distribution to the quality of medicinal products from a Maltese wholesale dealer’s perspective and evaluate these risks using Quality Risk Management (QRM) methodology. The ultimate objective was to indicate whether risks are being well managed and to propose appropriate corrective and preventive actions (CAPA). METHOD: A set of model Standard Operating Procedures (SOPs) which describe the current wholesale dealer’s operations was compiled. These SOPs were written in simple English to facilitate comprehensiveness by the employees. The various steps in the distribution of medicinal products by a wholesaler and the risks in each step were identified and a flowchart was compiled. A QRM assessment was carried out, taking into consideration current risk management activities described in SOPs. No further action was recommended for risks wh...

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According to WHO reports, low quality medicines represent about 10% of the global pharmaceutical market of which about 40% were substandard medicines. Most of the studies of quality of medicines recommend development of additional innovative techniques to control the existence of substandard medicines in the market. Based on recent assessment by WHO, systems applied to detect substandard and/or counterfeit medicines in developing countries were not effective enough. A strong post marketing surveillance system would be a more powerful tool for detecting sub standard medicines. In some countries, it was proven that strengthening the system by applying risk-based model for supporting the decisions is useful and possible approach. This exploration work aimed at exploring possible options to develop a risk-based quality monitoring model for pharmaceutical products. The model proposed based on this review work should help medicines regulatory authorities in resource limited settings to improve surveillance systems. The model was tested for its usefulness and effectiveness and the results obtained showed potential applications of the model in improving the system. This would include its use in the selection technique of products for inclusion in post-marketing quality monitoring. It can also be applied to increase the detection rate of low quality products. INTRODUCTION Partners in areas related to pharmaceutical services are focusing their efforts intensively on assuring the safety, efficacy and quality of pharmaceutical products. By reviewing the global policy directions of most of the initiatives introduced during the last two decades, it is possible to observe a clear focus on safety and efficacy as important dimensions, with safety always put first [1, 2]. Among the three dimensions, quality receives relatively less attention, not because it is less significant but usually due to complicated management systems. Quality management systems rely greatly on national authorities on the one hand and on manufacturers on the other hand. As part of a quality monitoring system, post-marketing surveillance (PMS) for monitoring quality is no exception and has received little consideration in relation to drug monitoring information systems. Unlike the monitoring of adverse drug reactions (ADRs) which has improved markedly during the last 10 years, PMS has not been the subject of major changes aimed at improving the process or outcomes. However, there have been some specific and exceptional initiatives in some countries that have aimed to improve PMS and develop new approaches. The justification for establishing PMS in most countries is that the authorities have only a slight influence on premarketing quality management, which relies rather on the compliance of the pharmaceutical industry with regard to quality assurance schemes. The need for strong PMS was the driving force for all of these initiatives.

Dr. Shivang Chaudhary

B Krishnamoorthy

Background: According to WHO reports, low quality medicines represent about 10% of the global pharmaceutical market of which about 40% were substandard medicines. Most of the studies of quality of medicines recommend development of additional innovative techniques to control the existence of substandard medicines in the market. In Sudan, the system applied to detect substandard and/or counterfeit medicines is not effective enough. A round 9% of pharmaceutical products are reported to be substandard medicines. A strong post marketing surveillance system would be a more powerful tool for detecting substandard and/or counterfeit medicines and showing a true picture of the situation in Sudan. Strengthening the system by applying risk-based model for supporting the decisions is proven to be useful and possible approach. Setting: This study was conducted in Khartoum city, Sudan. Objectives: this research aimed at developing risk-based quality monitoring scheme or model for pharmaceutical products. The model should help medicines regulatory authorities in resource limited settings to improve surveillance systems. The research will provide a practical model for the expanding the existing surveillance system for quality check of pharmaceuticals currently adopted in Sudan. Methods: different methods were used to build this model. These include health professionals’ survey targeting the pharmacists and physicians and chemical analysis of 30 medicines. Based on the outcomes of these substudies, further experiments were conducted that include bioequivalence study of Glibenclamide products, microbiological sensitivity test on Amoxicillin; biological assay of three Ceftriaxone products and modeling process of data generated. Results: A model has been successfully formulated and adopted to improve the surveillance system. This model is unique and it was the first time to develop such tool globally to help in indicating critical information about the quality of medicines and associated hazard factors to its quality. The model was designed as large and complex computerized system using Microsoft excel program. A sample from the outcomes of the model was printed and attached in annex number 9 for reference. The model was tested for its usefulness and effectiveness and the results obtained showed potential applications of the system in improving the system. This would include its use in the selection technique of products for inclusion in post-marketing quality monitoring. It can also be applied to increase the detection rate of low quality products. Using the developed model, the chance to detect substandard and/or counterfeit products will be increased by about 30%. Conclusion: the outcome of this proposed approach will enable the authorities to expand the input measures of its surveillance system beyond quality to consider also the efficacy of medicines.

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IMAGES

  1. (PDF) Using Quality Risk Management in Pharmaceutical Industries: A

    quality risk management case study in pharmaceutical industry

  2. (PDF) Quality Risk Management in Pharmaceutical Manufacturing

    quality risk management case study in pharmaceutical industry

  3. QUALITY BY DESIGN (QbD) IN PHARMACEUTICAL INDUSTRY: TOOLS, PERSPECTIVES

    quality risk management case study in pharmaceutical industry

  4. (PDF) Quality Risk Management in Pharmaceutical Supply Chain

    quality risk management case study in pharmaceutical industry

  5. (PDF) Quality Risk Management in Pharmaceutical Industry: A Review

    quality risk management case study in pharmaceutical industry

  6. (PDF) Best Practices for Quality Risk Management for the Pharmaceutical

    quality risk management case study in pharmaceutical industry

VIDEO

  1. 2023 03 30 Quality Management Systems and Processes

  2. Analysing the risk and issue in Supplier Management 99SPEEDMART (BCM6553)

  3. Process Control

  4. Risk Management

  5. Webinar on Strategic Procurement in Health Supply Chain

  6. Introduction to Quality Risk Management Approach in GMP Inspections Workshop by Shanshan & Bob

COMMENTS

  1. PDF Quality Risk Management Principles and Industry Case Studies

    ABSTRACT: The Pharmaceutical Quality Research Institute Manufacturing Technology Committee (PQRI-MTC) commissioned a Risk Management working group to assemble industry case studies for the purpose of advancing the understanding and application of ICH Q9.

  2. Saxagliptin (Onglyza): A Case Study in Quality Risk Management

    Design: Saxagliptin Film Coated Tablets 2^(5-1) Fractional Factorial with 3 Center Points Design - 19 Runs. Actual Levels used for LOW(-1), CENTER(0), and HIGH(+1) A constant ratio of 1.5:1 was ...

  3. Quality Risk-Management Principles and PQRI Case Studies

    The harmonized Q9 Quality Risk Management guideline from the International Conference on Harmonization (ICH) provides an excellent high-level framework for the use of risk management in pharmaceutical product development and manufacturing quality decision-making applications (1-2).It is a landmark document in acknowledging risk management as a standard and acceptable quality system practice ...

  4. Using Quality Risk Management in Pharmaceutical Industries: A Case Study

    Using Quality Risk Management in Pharmaceutical Industries: A Case Study Authors: Omar Ali Ismael University of Mosul Moyassar Ibraheem Ahmed University of Mosul Patient safety is a matter of...

  5. Quality risk management during pharmaceutical 'good distribution

    A corollary of manufacturing quality risk management has been drawn to the distribution of pharmaceutical products through this study. The quality risk management during pharmaceutical distribution may be useful to avoid market complaints, drug recalls, and regulatory actions.

  6. Quality Risk Management in Pharmaceutical Manufacturing Operations

    Quality Risk Management in Pharmaceutical Manufacturing Operations: Case Study for Sterile Product Filling and Final Product Handling Stage by Rawidh Alsaidalani 1,* and Bassam Elmadhoun 2 1 Pharmacy Program, Pharmaceutical Sciences Department, Batterjee Medical College, Jeddah 21442, Saudi Arabia 2

  7. (PDF) Quality Risk Management in Pharmaceutical Manufacturing

    Quality Risk Management in Pharmaceutical Manufacturing Operations: Case Study for Sterile Product Filling and Final Product Handling Stage CC BY 4.0 Authors: Rawidh Alsaidalani...

  8. Quality risk management: Principles and industry case studies

    The Pharmaceutical Quality Research Institute Manufacturing Technology Committee (PQRI-MTC) commissioned a risk-management working group to assemble industry case studies for the...

  9. PDF An industrial case study: QbD to accelerate time-to-market of a drug

    The use of a Quality by Design (QbD) approach in the development of pharmaceutical products is known to bring many advantages to the table, such as increased product and process knowledge, robust manufacturing processes, and regulatory flexibility regarding changes during the commercial phase.

  10. PDF Quality Risk Management Q9(R1)

    ICH Q9(R1) Guideline 2 33 scenarios, so that appropriate risk control can be decided upon during technology transfer, for 34 use during the commercial manufacturing phase. In this context, knowledge is used to make 35 informed risk-based decisions, trigger re-evaluations and stimulate continual improvements. 36 Effective and proactive quality risk management can facilitate better, more ...

  11. Quality Risk Management (QRM)

    Summary Risk management principles have been established for several decades and are utilised by many business and government sectors to control and mitigate harm to the consumer. This chapter outlines risk management tools typically applied by the pharmaceutical industry.

  12. PDF Technical Report No. 54-3

    This pdf document provides the table of contents for PDA Technical Report No. 54-3, which presents case studies on how to apply quality risk management principles and tools in the manufacturing of pharmaceutical drug products. The report covers topics such as risk assessment, risk control, risk communication, and risk review.

  13. An industrial case study: QbD to accelerate time-to-market of a drug

    This article presents a real-world case study for the development of an industrial pharmaceutical drug product (oral solid dosage form) using the QbD methodology, demonstrating the activities involved and the gains in obtaining systematic process and product knowledge. Introduction

  14. PDF Quality Risk Management in Pharmaceutical Manufacturing Operations

    Quality Risk Management in Pharmaceutical Manufacturing Operations: Case Study for Sterile Product Filling and Final Product Handling Stage Rawidh Alsaidalani1,* and Bassam Elmadhoun2 1Pharmacy Program, Pharmaceutical Sciences Department, Batterjee Medical College, Jeddah 21442, Saudi Arabia

  15. [PDF] Quality Risk Management (QRM) in Pharmaceutical Industry: Tools

    The process of risk management to achieve quality of medicinal products and tools which can be used for risk assessment during manufacturing practices undertaken by small or medium sized WHO approved plants are discussed. Quality risk management (QRM) is one of the most important tasks when it comes to pharmaceutical industry. It is because the industry produces medicines, whose quality is ...

  16. PDF Pharmaceutical Quality and Risk Assessment: A Case study

    INTRODUCTION isk management is process where it helps to find out the harm and severity of a risk in a process. The principle of quality risk management is defined in ICH Q9. According to that quality risk management includes elements such as risk identification, assessment mitigation, elimination and communication.

  17. Quality Risk Management in Pharmaceutical Industry: A Review

    To validate the necessity of this study, an exhaustive search has been attempted through search engines with key phrase 'quality risk management during pharmaceutical distribution' and found that ...

  18. (PDF) Quality Risk Management in Pharmaceutical Supply Chain

    Quality Risk Management in Pharmaceutical Supply Chain, Warehousing and Dispensing - Practical Case Study from Sterile Pharmaceutical Industry. Rawidh Alsaidalani 2021 Quality Risk Management (QRM) during medicinal products manufacturing is now becoming an integral part of quality management system (QMS).

  19. PDF Quality Risk Management in Pharmaceutical Manufacturing Operations

    This study represents a modeled risk mitigation approach for profession-als or regulators in the industry field associated with sterile pharmaceutical production processes such as (a) glass bottle washing and handling, (b) rubber stopper washing and handling, (c) product filling process, (d) final product receiving and handling.

  20. Quality Risk Management in Pharmaceutical Supply ...

    This study shows a model solution for industry professionals and regulators to reduce the possible risks associated with early stages of medicinal products manufacturing thereby paving the way for significant business growth. Quality Risk Management (QRM) during medicinal products manufacturing is now becoming an integral part of quality management system (QMS).

  21. (PDF) Quality Risk Management in Pharmaceutical Supply Chain

    Extensive research in this area shows that there is a scarcity of research on quality risk management during early stages of medicinal products manufacturing including (1) procurement/supply...

  22. Quality risk management during pharmaceutical 'good distribution

    This case study represents a modeled risk mitigation approach for professionals or regulators in the industry field associated with sterile pharmaceutical production processes, providing a proactive means to identify, control, and communicate risks associated with various vital steps, thereby improving decision making and reducing regulatory non-compliant risk.

  23. PDF Using Quality Risk Management A Case Study in Pharmaceutical Industries:

    evidence related to quality management (Botet, and its implementation around the 2012). world for many of Hokamaa In this study, we company, tried for risks to apply reduce the QRM levels...