Why Do Pharmaceutical Industry Requires Quality by Design (QBD)

    Amrendra Roy

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    (This article is a part of PhD thesis of Mr. Abdul Qayum Mohammed, who is my PhD student)

    Authors:

    Abdul Qayum Mohammed, Phani Kiran Sunkari, Amrendra Kumar Roy*

    *Corresponding Author, email: Amrendra@6sigma-concepts.com

    KEYWORDS: QbD, 4A’s, DoE, FMEA, Design space, control strategy

    ABSTRACT

    QbD is of paramount importance for the patient safety but there is another side of the coin. QbD is also required for timely and uninterrupted supply of medicines into the market. This timely uninterrupted supply is required to fulfill the 4A’s requirement of any Regulatory body as it is their main KRA. But the manufacturers are given an impression that the patients are their main customer, which is not true. Due to which QbD implementation by generic API manufacturers has not picked up. This article tries to tell that the real customer is not patients but the Regulatory bodies who on the behalf of patients are dealing with manufacturer. Hence Regulators need to tell the manufacturer that QbD is required not only for the patient safety but also for meeting the 4A’s requirement, which is equally important. This article tries to correlate the effect of inconsistent manufacturing process on the KRA of the Regulatory bodies and makes a business case out of it. It will help in developing a strong customer-supplier relationship between the two parties and can trigger the smooth acceptance of QbD by generic players. This article also presents the detail sequence of steps involved in QbD using by a process flow diagram.

    Introduction:

    Nowadays, Quality by design (QbD) is an essential and interesting topic in the pharmaceutical development, be it for drug substance or drug product. Various guidelines have been published by different Regulatory agencies.[i] There is a plethora of literature available on the QbD approach for the process development[ii],[iii] of drug substance, drug product and analytical method development[iv]. Most of the available literature mainly focus on patient safety (QTPP) but if QbD has to sails through, then the generic manufacturer must know why and for whom it is required (apart from patients) and what is there in for them? They should not be taking regulators as an obstacle to their business but as a part of their business itself. There has to be business perspective behind QbD, as everything in this world is driven by economics. It has to be win-win situation for Regulators and the manufacturers. This means that, there has to be synchronization of each other’s expectation. This synchronization will be most effective if API manufacturer’s (i.e. supplier’s) consider Regulators as their customer and try to understand their requirement. In this context it is very important to understand the Regulator’s expectation and their responsibility towards their fellow countrymen.

    Regulators Expectations:

    Sole responsibility of any Regulator towards its country is to ensure not only acceptable (quality, safety and efficacy) and affordable medicines but also they need to ensure its availability (no shortage) in their country all the time. Even that is not enough for them; those medicines must be easily accessible to patients at their local pharmacies. These may be called as 4A’s and are the KRA of any Regulatory body. If they miss any one of the above ‘4As’, they will be held accountable by their Government for endangering the life of the patients.

    In earlier days when the penetration of health services to large section of the society was not there, the main focus of Regulators was on the quality and price of the medicines. During those days margins were quite high and the effect of reprocessing and reworks on manufacturer’s margins were not much. So Regulators were happy as they were getting good quality at best price for their citizens. Gradually the health services gained penetration in to the large section of the society in developed countries and as a result they needed more and more quantities of medicine at affordable price. The KRA of Regulators changed from “high quality and low price” to “quality medicine at affordable price which is available all the time at the doorstep of patients”. Another event that led to the further cost erosion was the arrival of medical insurance and tender based purchasing system in hospitals. Increased demand made manufacturer to increase their batch size but because of insurance and tender based purchasing system, now they don’t have the advantage of high margins and couldn’t afford batch failures/reprocessing anymore. But now, these wastages led to erratic production and irregular supply of medicine in the market, thereby creating a shortage. This affected the KRA (4A’s) of the regulatory bodies; hence they were forced to interfere with the supplier’s system. They realized that in order to ensure their 4A’s, there has to be a robust process at manufacturer’s site and if it is done the medicines would automatically be available in their country (no shortages) and will be accessible to all patients at affordable price. This process robustness is possible with the use of some proven statistical tools like six sigma and QbD during the manufacturing of an API. This path to robust process was shown by the Regulators in the form of Q8/Q9/Q10/Q11 guidelines1 where QbD was made mandatory for formulators but and it is strongly recommended for API manufacturer and soon it would be made mandatory. While making QbD mandatory, they are emphasizing on how QbD is related to patient safety and how it will make the process robust for the manufacturers which in turn would eliminate the fear of audits. Regulators are right but somewhere they missed to communicate the business perspective, that was behind the QbD implementation i.e. manufacturers were not having much clue about the Regulator’s KRA and as a result a customer-supplier relationship never developed.

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    Figure 1: Regulator’s unsaid expectations

    Manufacturer’s point of view

    As Regulators were insisting on QbD, manufacturers have their own constraints in plant due to inconsistency of the process (Figure 2). As Regulator’s emphasis was on the patient’s safety rather than 4A’s, manufacturer took patients as their customer instead of Regulators and they make sure that there is no compromise with the quality of the medicines to delight the customer ie, patients. It doesn’t matter to manufacturer, if the quality is achieved by reprocessing/rework as far as the material is of acceptable quality to the customers. Due to this misconception about who the real customer is, 4A’s got neglected by the manufacturer.

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    Another problem is the definition of quality perceived by two parties. Quality of an API from the customer’s perspective has always been defined with respect to the patient safety (i.e. QTPPs which is indeed very important) but for the manufacturer quality meant only the purity of the product as he enjoyed handsome margin.

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    Profit = MP – COGS                                                                         Eq-1

    MP                =  market Price

    COGS             = genuine manufacturing cost + waste cost (COPQ)

    COPQ             = Variation/Batch failure/Reprocessing & rework /product    recall = increase in drug product/drug substance cost = loosing customer faith (intangible cost)

    Coming to prevailing market scenario, the manufacturers doesn’t have luxury to define the selling price, now the market is very competitive and the price of goods and services are dictated by the market, hence it is called as market price (MP) instead of selling price (SP). This lead to the change in the perception of quality, now quality was defined as producing goods and services meeting customer’s specification at the right price. The manufacturers are now forced to sell their goods and services at the market rate. As a result the profit is now defined as the difference of market rate and cost of goods sold (COGS). If manufacturing process is not robust enough then COPQ will be high resulting in high COGS and either (patient or manufacturer) of the party has to bear the cost. According to Taguchi, it is a loss to the society as a whole as neither of the party is getting benefitted. If these failures are more frequent it leads to production loss and as a result timely availability of the product in the market is not there and manufacturer is not able to fulfill the 4A’s criteria of the customer. This not only leads to loss of market share but also loss of customer’s confidence and customer in turn would look for other suppliers who can fulfill their requirements. This is an intangible loss to the manufacturer.

    The COPQ has direct relationship with the way in which process has been developed. There are two ways in which a process could be optimized (Figure 3). It is clear from the Figure 3 that if one focus on the process optimization, it will lead to less COPQ and process would be more robust in terms of quality, quantity and timelines thereby reducing the COGS by elimination COPQ. This raises another question, how process optimization is different from product optimization and how it is going to solve all problems related to inconsistency? This can be understood by understanding the relationship between QTPPs/CQAs and CPPs/CMAs. As a manufacturer we must realize that any CQA (y) is a function of CPPs & CMAs (x) i.e. the value of CQA is dictated by the CPPs/CMAs and not vice versa (Figure 4 & 7). It means that by controlling CPPs/CMAs we can control CQAs but in order to do this we need to study and understand the process very well. This will help in quantifying the effect of CPPs/CMAs on CQAs and once it is done, it is possible to control the CQAs at a desired level just by controlling the CPPS/CMAs. This way of process development is called as process optimization and QbD insists on it. Another important concept associated with process optimization is the way in which in-process monitoring of the reaction is done. Traditionally, a desired CQA is monitored for any abnormality during the reaction whereas process optimization methodology it is required to monitor the CPP/CMA (Figure 4) which is responsible for that CQA. Hence it requires a paradigm shift in which the process is developed and control strategy is formulated by a manufacturer if the focus is on the process optimization.

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    Figure 3: Two ways of optimization

    From the above discussion, it is clear that the real customer for a generic manufacturer is not the patients but the Regulators. This is because patients can’t decide and they don’t have capability to test the quality of the medicines, for them all brands are same. Hence Regulators comes into the pictures, who on the behalf of patients are dealing with manufacturers because they have all means and capability of doing so. Going by the Figure 5, patients are the real customer for the Regulators and who in turn are the customer for the manufacturer. In business sense, patients are just the end user of the manufacturer’s product once the product is approved by Regulators for use.

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    Figure 4: Relationship between CQAs and CPPs/CMAs

    As it is clear that the Regulators are the real customers for the manufacturer and with the current inefficient process, manufacturer is not helping his customer in meeting their goal (4A’s). They can now understand the relationship between his inefficient manufacturing process and the customer’s KRA (Figure 6). In addition, they can clearly visualize the advantage of the process optimization over product optimization and how QbD can act as an enabler in developing a robust process thereby fulfilling the requirement of 4A’s . This will encourage manufacturer to adopt QbD because now it makes a strong business case for them for retaining the existing market and also as a strategy for entering the new market. This is a win-win situation for both the parties. Therefore, QbD should be pursued by manufacturer not because of the regulatory fear but as a tool for fulfilling the customer’s KRA which in-turn would benefit manufacturer by minimizing COPQ. In addition, it helps in building customer’s trust which is an intangible asset for any manufacturer. This will enable the manufacturers to accept Regulators as their customer rather than as an obstacle. This would result in better commitment from manufacturers about implementing QbD because the definition of customer as defined by Mahatma Gandhi is very relevant even today.

    “A customer is the most important visitor on our premises. He is not dependent on us. We are dependent on him. He is not an interruption in our work. He is the purpose of it. He is not an outsider in our business. He is part of it. We are not doing him a favor by serving him. He is doing us a favor by giving us an opportunity to do so.”

    ― Mahatma Gandhi

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    Figure 5: Dynamic Customer-Suppliers relationship throughout the supply chain

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    Figure 6: Manufacturer perception after understanding customer-supplier relationship

    Manufacturer in customer’s shoes:

    Another reason provided by the manufacturer for inconsistency is the quality of KSM supplied by their vendors and any quality issue with KSM will affect the quality of the API as shown by Figure 7 and equation 2. Till now manufacturer was acting as a supplier to Regulators but now manufacturer is in the shoes of a customer and can understand the problem faced by him because of the inconsistent quality of KSM from his supplier (Figure 5, Table 1). Now manufacturer can empathize with Regulatory bodies and is in a position to understand the effect of their process on his customer’s KRA(Figure 6). Table 1 is equally applicable to the relationship between manufacturer and the Regulatory bodies.

    Table 1: Effect of process inconsistency from supplier/manufacturer on API quality

    picture10

    Consider Case-1 (Table 1) which represents the ideal condition, where process is robust at both sides. Whereas Case-2 and Case-3 represents an inconsistent process at either of the party and this inconsistency would reflect as an inconsistency in the quality of the API at manufacturer’s site. This would result in an unsatisfied customer (Regulator) and loss of market to someone else. Lastly, an inconsistent process from both the side (Case-4) would result in a disaster situation where it would be difficult for a manufacturer to control the quality of the API because the variance from both the sides would just add up (equation 2). In this case customer can’t even think of getting material from manufacturer as it would pose a threat to the patient’s life and no regulatory body would allow that.

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    Someone can argue that if consistency is an issue from supplier (Case 3) then they would negotiate with them for cherry-picking the good batches, but no supplier would do the cherry-picking without any extra cost, which in turn would increase the cost of the API. Another consequence of this handpicking is the interruption in the timely supply of KSM which will result in delay in the production at manufacturer’s site. This would result in increased idle time of resources thereby increased overheads which ultimately would reflect in increased API cost. Apart from increased cost it would also result in sporadic supply to the customer. Another viable option for circumventing the inconsistency at supplier’s end is to do a reprocessing of KSM at the manufacturer’s site. Obviously this is not the viable solution as it would escalate the COGS. Hence there is no choice but to take your supplier in confidence and make him understand the implication of his product quality on your business and how his business in-turn would get affected by it. Best solution is to discuss with the supplier and ask him for improving his process (if supplier has the capability) or help them in improving his KSM process (if manufacturer has the capability).

    Note: Apart from robust process, Regulators are also auditing the manufacturer’s site for the safety and the ETP facility. It is being done again for the same reason of ensuring the continuous supply of medicines to their country.

    How inconsistency of the process affects the quality? And How QbD will help in getting rid of this inconsistency?

    Realizing that we need to have a consistent quality and uninterrupted production is not enough, as a manufacturer we must understand the various sources of inconsistency and how it can affects the quality of the API.

    Any chemical reaction that is happening in a reactor is a black box (Figure 7) for us and there are three kinds of inputs that go into the reactor. The first input known as MAs are chemical entities that go into the reactor (KSM, reagents and other chemicals). The second input known as PPs are the reaction/process parameters that can be controlled by the manufacturer and third being the environmental/external factors like room temperature, age of the equipment, operators etc. that cannot be controlled. As variance (σ2) has an additive property, hence inconsistency from all the three types of factors amplifies the inconsistency of the product quality. The variation caused by the third type i.e. by external factors is called as inherent variation and we have to live with it. At most the effect of these nuisance factors could be nullified by blocking and randomization during DoE studies. Because of this inherent variation, yield or any other quality parameters are reported as a range instead of a single number. But the variation due to other two types of factors (MAs and PPs) could be controlled by studying its effect on product attributes (QAs) by using a combination of some risk analysis tools and some statistical tools for optimization. The combination of risk based assessment of MAs and PPs and use of statistical tools as DoE/MVA for optimizing the effect of MAs and PPs on QAs is called as QbD. Hence QbD is the tool that manufacturers are looking for, to eliminate the inconsistency in their product thereby fulfilling the customer’s expectations.

    The variance that is being shown by Figure 7 represents the variation only at a single stage. Consider a multi-step synthesis (most common scenario) and in such scenarios the total variance at the API stage would be the culmination of variance from all the stages, resulting in a total out of control process as shown below by equation 3.

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    Figure 7: Cumulative effect of variance from various sources on the variance of API quality

    At what stage of product development QbD required to be applied?

    The traditional approach of process development of any API is focused more on filing the DMF at earliest. As a result of this improper process development there are failures at commercial scale and process comes back to R&D for fine tuning. But if the process is developed with QbD approach at R&D stage itself, certainly it would take more time initially, but its worth investing the time as there will be less failures or no failures at commercial scale and process could be scaled up in very less time. This will reduce the reprocessing and rework at commercial scale thereby minimizing the COPQ, a win-win situation for all as depicted in Figure 8.

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    Figure 8: Risk and reward associated with QbD and traditional approach


    [i].  (a) ICH Q8 Pharmaceutical Development, (R2); U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER): Rockville, MD, Aug 2009. (b) ICH Q9 Quality Risk Management; U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER): Rockville, MD, June 2006. (c) ICH Q10 Pharmaceutical Quality System; U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER): Rockville, MD, April 2009. Understanding Challenges to Quality by Design, Final deliverable for FDA Understanding Challenges to QbD Project, December 18, 2009.

    [ii]. (a) Jacky Musters, Leendert van den Bos, Edwin Kellenbach, Org. Process Res. Dev., 2013, 17, 87. (b) Zadeo Cimarosti, Fernando Bravo, Damiano Castoldi, Francesco Tinazzi, Stefano Provera, Alcide Perboni, Damiano Papini, Pieter Westerduin, Org. Process Res. Dev., 2010, 14, 805. (c) Fernando Bravo, Zadeo Cimarosti, Francesco Tinazzi, Gillian E. Smith, Damiano Castoldi, Stefano Provera, Pieter Westerduin, Org. Process Res. Dev., 2010, 14, 1162.

    [iii]. (a) Sandeep Mohanty, Amrendra Kumar Roy, Vinay K. P. Kumar, Sandeep G. Reddy, Arun Chandra Karmakar, Tetrahedron Letters, 2014, 55, 4585. (b) Sandeep Mohanty, Amrendra Kumar Roy, S. Phani Kiran, G. Eduardo Rafael, K. P. Vinay Kumar, A. Chandra Karmakar, Org. Process Res. Dev., 2014, 18, 875.

    [iv]. Girish R. Deshpande, Amrendra K. Roy, N. Someswara Rao, B. Mallikarjuna Rao, J. Rudraprasad Reddy, Chromatographia, 2011, 73, 639.

     

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