author alistair gillanders dynochem inc date: wednesday, … · successfully adopted and adapted...

Author Alistair Gillanders DynoChem Inc Date: Wednesday, October 1, 08

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©2008 DynoChem Inc. All rights reserved. www.scale‐up.com

Quality By Design: The Role Of Mechanistic Modeling

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Contents 1 INTRODUCTION ........................................................................................................................................................... 3

2 A WH T IS QBD AND WHERE DOES IT FIT? ............................................................................................................ 3

2.1 WHAT IS QBD? .............................................................................................................................................................................. 32.2 A SCIENCE AND RISK BASED APPROACH IN A BROADER REGULATORY LANDSCAPE ........................................................ 4 2.2.1 t ha e .... .... ................................................................................................................................. 42.2.2 Clarifying Terminology ......................................................................................................................................................... 4

2.3 REGULATION CAN BE GOOD FOR BUSINESS ............................................................................................................................. 5

Learning o S r ........ ..... ..............

3 A WH T TECHNIQUES DO WE NEED FOR QBD? .................................................................................................... 6

3.1 A SUMMARY OF QBD REQUIREMENTS ...................................................................................................................................... 63.2 COMMON CLASSES OF TOOL – AND SOME STRENGTHS AND WEAKNESSES ........................................................................ 6 3.2.1 Risk Assessment Tools ............................................................................................................................................................ 7 3.2.2 Statistical Tools ........................................................................................................................................................................ 7 3.2.3 Mechanistic Tools .................................................................................................................................................................... 8 3.2.4 .... ..................................................................................................................................................... 93.2.5 Which Tools for Which Tasks? ........................................................................................................................................... 9

3.3 MECHANISTIC MODELING IN QBD ........................................................................................................................................... 12

Probabilistic Tools ........ ..

3.3.1 For Pharmaceutical API’s ................................................................................................................................................... 12 3.3.2 For Pharmaceutical Drug Products ............................................................................................................................... 13

4 YPIC T AL MODELING APPLICATIONS IN API OPERATIONS.......................................................................... 14

4.1 CHE Y AND LABORATORY APPLICATIONS ..................................................................................................................... 14HARACTERIZATIO

MISTR 4.2 C N AND ASSESSMENT .................................................................................................................................. 16

4.3 REACTION ..................................................................................................................................................................................... 16

RYSTALL ATION ....

4.4 C IZ ................................................................................................................................................................... 174.5 WORKUP OPERATIONS ............................................................................................................................................................... 184.6 PRODUCT STABILITY ................................................................................................................................................................... 19

5 ARI V ABILITY VS. UNCERTAINTY ......................................................................................................................... 19

5.1 PROCESS RI E VARIABLE ....................................................................................................................................... 195.2 MODEL PARAMETERS MAY BE UNCERTAIN ............................................................................................................................ 195.3 UNCERTAIN VARIABLES ............................................................................................................................................................. 20

VA ABLES AR

6 N WHE IS A MODEL GOOD ENOUGH? ................................................................................................................... 21

6.1 C M B ................................................................................................................................ 21O DEN E FOR MODE BASED P C IONS ..

ONFIDENCE FOR ODEL UILDING ....... 6.2 C NFI C L REDI T .................................................................................................................. 22

6.3 CONFIDENCE FOR MODEL VERIFICATION ............................................................................................................................... 246.4 SO WHAT CONFIDENCE DOES THE FDA EXPECT? ................................................................................................................ 25

7 NDE U RSTANDING RISK .......................................................................................................................................... 26

7.1 HOW TO MEASURE RISK? .......................................................................................................................................................... 267.2 WHERE TO FIND VARIABILITY? ................................................................................................................................................ 27 7.2.1 Materials .................................................................................................................................................................................... 27 7.2.2 In ..... 287.2.3 Recipe .......................................................................................................................................................................................... 29

7.3 KNOWLEDGE SPACE, DESIGN SPACE AND CONTROL SPACE ................................................................................................ 30

Equipment and strumentation ...............................................................................................................................

8 MECHANISTIC MODELING WITHIN A QBD STRATEGY ................................................................................. 31

REFERENCES .............................................................................................................................................................. 33


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within the final drug product. We must understand where impurities are first introduced or made during our process and then predict how they change and where they go through subsequent processing and storage. But not all impurities are equal from a risk perspective; it is most important to be able to track back to their source all the real and potential impurities that might actually appear in the final product. Additionally we must understand the physical requirements

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1 Introduction Our objective in this paper is to examine where mechanistic thinking, and specifically mechanistic modeling, fits in the science/risk based approach to quality that the ICH/FDA are championing for drug development and approval. After a brief discussion on QbD (Quality by Design) itself to establish a clear context for what follows, we will consider the ools that are available to support it and then focus on where mechanistic modeling adds tmost value in this landscape. Rather than focus on specific tools, we are more concerned with the different classes of tool; choosing from the various tools available within each class is outside the scope of this paper. Many of these have been around for a long time, often pre‐dating the current QbD initiative. However deciding which to use for what in order to be effective can still be confusing when it comes to defining a QbD strategy for a specific product. Often different approaches will make sense for different steps within a manufacturing process; especially for drug substance vs. drug product steps. These different sets of results must then be re‐combined for a holistic understanding of product quality risks to the patient.

2 What is QbD and Where Does It Fit?

2.1 What is QbD? We are not going to provide an extensive description here, for those readers not already familiar with QbD objectives there are many good resources available including those from the FDA and ICH themselves [1‐5]. I particularly recommend the ICH‐Q8 [4] Annex for some of the more detailed concepts around Design Spaces etc. and ICH‐Q9 [5] for its review f the common types of tools available to support risk identification, assessment and ocontrol. In summary it is about using good science to identify and assess risks to quality; providing a foundation for defining design and control spaces that allow those risks to be eliminated r reduced; and ultimately assuring, as far as possible, that adverse quality occurrences are oavoided or at least detected and corrected reliably. Quality in this regard is about ensuring patient safety so our search for risks will generally start with the product and work back. It is important to realize that for multi‐step manufacturing processes, although impurities are certainly introduced via starting materials, many will be eliminated by subsequent chemical steps. Consequently there is a natural focus on the later stages in drug substance manufacture; the subsequent physical rocessing in formulating the final drug product; and the degradation of components p


and constraints on formulating the drug substance, once made, into a product that will effectively administer a desired dose to a patient. Essentially the QbD initiative is driving us towards building fundamental process understanding to provide a science based framework for making decisions about quality as we establish, and subsequently improve, the processes used to deliver medicines today.

2.2 A Science and Risk Based Approach in a Broader Regulatory Landscape

2.2.1 Learning to Share

We should also be aware that quality is not the only domain where this type of process nderstanding adds value. Nor is QbD, or even quality, the first to use some of the echniques gaining pout

pularity.

For example: HAZOP was ‘invented’ by ICI in the UK in the 1960’s as a systematic approach to identifying process risks from a safety perspective [6]. Similarly tools such as FMECA [7] have been used for quality investigations for decades. QbD has successfully adopted and adapted many of these existing approaches.

This also highlights two other opportunities: we must continue to seek to learn from the techniques developed for exploring these other areas of process knowledge and that can help us in our quest for quality; and we must seek to leverage the process understanding we develop for QbD to offer benefits across all of these regulated areas of running our business. Mechanistic modeling is one example of a tool that has been with us for some time that can be now be applied creatively to supporting a science‐based approach to better understanding of our processes from a risk perspective and for quality objectives.

2.2.2 Clarifying Terminology

Considering this broader landscape all the domains are seeking to identify, assess and then ubsequently manage significant risks. Improved understanding from QbD can and should e levesb

raged across these other areas in the regulatory landscape. Consider:

• FDA and other medicines control agencies seek to protect patients. Currently we s; talk about Critical Quality Attributes being a function of Critical Process Parameter

CQA = fn(CPP). As suggested below this might be clearer as CQA = fn(CQP). • EPA and other environmental agencies seek to protect our local communities and

the environment generally. We could consider this as Critical Environmental Attributes being a function of Critical Environmental Parameters; CEA = fn(CEP).

• OSHA and other safety/occupational health agencies seek to protect the workforce from the hazards inherent in handling materials and in manufacturing processes.

ty We could consider this as Critical Safety Attributes being a function of Critical SafeParameters; CSA = fn(CSP)

• Finally there is the self regulation that we apply to protect our business. We could consider this as Critical Manufacturing Attributes being a function of Critical Manufacturing Parameters; CMA = fn(CMP). This will sound familiar to everyone

©2008 DynoChem Inc. All rights reserved. of 33


who has been involved in understanding “the x’s and y’s” of a process [8] as part of Six‐Sigma and similar lean manufacturing initiatives.

This “alphabet soup” can become a barrier to understanding by itself. The ICH/FDA currently uses the term CPP to mean that subset of all process parameters that are critical to quality; but by constraining the generic term CPP they have accidentally excluded other domains that use the same concepts even if they haven’t laid claim to the acronym (e.g. the EPA and other Environmental Agencies also talk in terms of critical parameters). Because we operate in a world where all these domains must necessarily influence our decisions I suggest a minor terminology change to help clarify domain relevance; CPP in the context of uality would become CQP. The ICH/FDA already uses this naming approach for quality qattributes (CQAs), just not for the critical process parameters. The importance of this will be emphasized when we consider design and control spaces for he real world; increased process understanding should limit these not just for quality but tfor other critical process parameters too. For the rest of this paper we are focusing primarily on quality and so we will mainly use the currently accepted (but generic) term CPPs to mean what are really the CQPs….the subset of all process parameters that are critical to controlling the critical to quality attributes.

2.3 Regulation Can Be Good For Business There is increased scrutiny on a variety of aspects of how we develop and operate our processes to manufacture products. As mentioned above Quality, Safety, Environment and Manufacturing performance are all important here. Sustainability and Green Chemistry is aining attention across the whole of the chemical industries, not just pharmaceuticals, and or good reason. gf

For example: When environmental regulations started to enforce stricter controls on our processes there was widespread concern that this would impose unreasonable cost burdens to implement. However experience has offered a different perspective as embracing these changes has generally produced positive benefits. With process improvements and waste elimination or reduction offering ROI’s that make most sustainability projects very much good business.

Obviously QbD holds out similar promises of dramatic business benefits as demonstrable good science enhances our collaboration with the global regulatory agencies and the FDA in particular. As confidence in our ability, and our practice, of making good decisions rows…so the degree of scrutiny can be reduced and greater flexibility be accepted. This g


will allow the regulators to also focus where risks are greatest…a win‐win. In truth QbD has enabled a major culture shift within our industry with regulation now encouraging innovation and a more rigorous understanding of our processes for quality objectives. However benefits from this change will be evident far beyond just the quality domain, it is starting to offer a unifying language for the science‐based approach to process understanding and the implications of that for managing process risks of all kinds.


3 What Techniques Do We Need For QbD?

3.1 A Summary of QbD Requirements nderstanding what techniques, and therefore tools, we need to effectively implement QbD an be discussed within the context of a few key statements from the FDA [3]. Uc

Effective innovation in development, manufacturing and quality assurance would be expected to better answer questions such as the following: • What are the mechanisms of degradation, drug release and absorption? • What are the effects of product components on quality? ••

What sources of variability are critical? How does the process manage variability?

A process is generally considered well understood when: • all critical sources of variability are identified and explained; • variability is managed by the process; and • product quality attributes can be accurately and reliably predicted over

the design space established for materials used, process parameters, manufacturing, environmental, and other conditions.

The ability to predict reflects a high degree of process understanding. Although retrospective process capability data are indicative of a state of control, these alone may be insufficient to gauge or communicate process understanding.

So QbD is the ability to identify, understand and control over the long term the critical to quality sources of variability in drug products delivered to patients; wherever they may come from. And to do that with confidence we must pro‐actively understand our process…not just react to what has happened in the past. These requirements drive a need for a wide range of techniques to manage this complex task, and a need to integrate the results into a coordinated whole. No one tool is ever going to do all of that which inevitably introduces more complexity of its own.

3.2 Common Classes of Tool – And Some Strengths and Weaknesses There are a wide range of techniques that are applicable to support our QbD efforts and there is no right answer regarding which are the “best” to use in various circumstances. However, considering the typical features of the different techniques, the questions being examined lend themselves to a broad classification that can help: risk assessment tools; statistical analysis and modeling; mechanistic analysis and modeling and probabilistic modeling.



©2008 DynoChem Inc. All rights reserved.

a process to selected input parameters. Some of these techniques can also help simplify the challenges of dealing with complex multivariate problems; particularly popular being the dimensional reduction techniques offered by PCA (principal components analysis) and PLS (partial least squares) methods of processing data. Whilst this can, and does, provide the ability to produce somewhat

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3.2.1 Risk Assessment Tools

These are the tools designed to help identify and prioritize our CQAs. From a starting point of relatively little quantitative knowledge to begin to focus on what the risks are, what the ources of those risks are and which process parameters (CPPs) are most likely to impact squality attributes (CQAs)? These tools are often qualitative or only crudely quantitative in nature; nevertheless they ffer a powerful entry point to understanding where to focus next by prioritizing where the orisks are greatest. Annex 1 to ICH‐Q9 [5] provides a good summary of many of the techniques in this space. Particularly useful are Ishikawa (fishbone) diagrams, FMEA and FTA alongside a range of other facilitation and assessment tools. FMECA [7] is an excellent example of a technique that has been in use for many years and that can be applied directly to QbD. ote: FMEA is Failure Mode and Effects Analysis; FMECA is Failure Mode, Effects and N

Criticality Analysis; and FTA is Fault Tree Analysis. An important aspect here is that these tools can utilize the level of detail commensurate with a continuously developing picture of risk; from simple qualitative metrics to the greater quantitative rigor offered by more detailed statistical and mechanistic modeling. These more detailed analyses require greater time, material and effort but are justified for he areas of greater risk. The techniques are also flexible enough to allow a gradual trefinement of results as new process knowledge becomes available. These risk assessment techniques are often vital in providing an overall framework for pulling together the results of different types of more detailed analysis that are inevitably necessary when considering the overall view of risks to product quality. Indeed formal decision tree techniques allow both qualitative and quantitative results to be combined using flexible weightings and rules agreed by the whole team. This type of approach allows a science based re‐integration of the results of necessarily disparate more detailed analysis of the higher risk items to look at the “big picture”.

3.2.2 Statistical Tools

These are tools that use observed experimental or production data to statistically correlate the CQAs to the CPPs that influence them. They range from simply identifying correlated parameters to deducing a numerical model of the correlation by using the observed data (generally simple empirical or raw multivariate regression models). These tools are often associated with the systematic approach to experimentation defined by formal DOE Design of Experiments) which uses physical experiments to explore the response space of (


predictive models it is not providing fundamental understanding of the process, rather it offers a powerful way to leverage retrospective results for some level of predictability. Also because models developed in this way use a purely deductive approach to process understanding they are only valid within the range of process conditions/parameter values observed. They can however offer very powerful insights into the degree of correlation etween variables within those constraints. And they require no prior scientific basis for bwhy the process might function as it does. The main disadvantages are that to get to adequate levels of confidence a substantial sample size is needed; plus as already mentioned extrapolation outside tested conditions is unreliable as there is absolutely no basis for expecting a purely statistical fit to apply outside of the observed region. Finally it is very difficult to effectively capture the highly non‐linear effects of scale using conventional DOE studies. This will be discussed further in 3.2.5 below when we compare and contrast the tools with the applications within QbD where they might be applied.

3.2.3 Mechanistic Tools

It is worth noting that mechanistic modeling is also statistically based. However, rather than raw deduction from observed data the models are built upon a hypothesis that uses our prior knowledge of the underlying scientific principles that govern the behavior of a process. So, like conventional statistical tools, there is a need for some observed xperimental or production data; but generally far less data is required and, depending on

. ethe model, it is possible to predict outside the range of the original observed values Essentially mechanistic models constrain the relationships between variables and outcomes to known scientific principles but they still call upon statistics to get high confidence in the few crucial unknown parameters that characterize the models behavior to fit observed data. As we will discuss below this offers a range of powerful advantages: rom isolating major process effects and the parameters that impact them; to an ability to fextrapolate in directions that the model parameters support. An additional major benefit is that the degree to which the model fits the data not only provides confidence in the numerical fit itself, but in the validity of the scientific hypothesis used to build the model. This can be a particularly powerful tool for creating true process nowledge about aspects such as chemical mechanism and kinetics, crystallization kinetics kand so on. Similarly it is often possible to apply the law of parsimony (Occam ’s razor) to hypothesize simplified definition of a mechanism that is sufficient to represent the process behavior ithout necessarily fu

aw


lly specifying the true physical/chemical details of the mechanism.

For example: chemistry models where a transient intermediate is known to exist but the details of what the intermediate species is and the mechanism for its formation/reaction is unclear. Simply knowing that an intermediate exists is commonly sufficient for a good model of the rates of consumption of reagents and


production of products; in these circumstances the exact nature of the intermediate mechanism is actually unnecessary detail in creating a very satisfactory mechanistic model for most practical situations.

One disadvantage is that the basic science must exist upon which to build the model that is the underlying framework for this approach. Whilst many unit operations used today are well understood and are therefore amenable to this approach; some operations are more challenging. In these situations the fact that statistical models don’t require any prior nowledge makes them a useful fallback when first principles or heuristic science‐based odels are not realist

km

ic.

For example: in many detailed powder handling operations the basic science is not yet extensive enough to offer high confidence mechanistic models without unrealistic constraints on the properties of the materials to be modeled. This can sometimes be overcome by combining the mechanistic approach to enhance proven engineering heuristics but not always.

3.2.4 Probabilistic Tools

These are tools that start from one of the above methods but then extend them to provide additional insights, especially into the impact of variability on a process. Although other approaches might be classified in this way the most prominent technique is Monte‐Carlo simulation. With Monte‐Carlo some of the input parameters for an underlying base model are provided as a probability distribution rather than a point value and then many runs (often thousands) are simulated with each run specifying input values randomly against the specified distribution. An analysis of the resulting output distributions can give good insights into the impact of variability on the process, where small variations in different parameters may magnify or nullify each other…or not interact at all.

3.2.5 Which Tools for Which Tasks?

The truth is that for any typical drug product a combination of all of the above methods is almost always going to be more optimal than trying to use only one or two of the techniques. Whilst the overall risk assessment tools have fairly universal value a little thought will show that is not true of the statistical vs. mechanistic approaches. The areas of most appropriate application for these techniques are driven by some of the fundamental haracteristics of our processes; in particular between drug substance and drug product cstyles of operations. Consider the following questions:

Do we have an adequate sample size to use raw statistical deduction? • What do we really need to predict? Will that be within the scope of our test data or

must we assume that we have to extrapolate to some degree?

•



In drug substance (API) processing • The “sample” for API processing is generally an operation of interest during a single

batch of a stage. A survey of several of the large, research based, ethical pharmaceutical companies confirmed that often (it was even considered the norm) there can be as few as 20 or less batches of a given manufacturing stage at scales beyond laboratory glassware prior to NDA filing. When considering critical operations within a process this is a statistically very small sample size; especially as the batches that are done are often divided across two or three plants at different

scales and sometimes with the chemistry at different phases in its evolution.

• Once full production is underway retrospective data becomes available and statistical monitoring and control techniques are both viable and valuable. But this cannot help establish the initial process understanding vital to a QbD based filing; only that good tracking of the identified parameters should be possible once processing starts. Strategies involving the traditional “quality by testing” at startup have been suggested to overcome this but that is a weak compromise at best.

• Usually operations in drug substance manufacture will address the needs for scaling up quantities by seeking to use higher concentrations, higher bulk volumes and conditions designed to increase fundamental rates within the process. This is a challenge because the predictability required for these types of process scale‐up are inherently non‐linear and, by definition, outside the scope of many of the early batch conditions. This situation really demands an understanding of the underlying science be involved in any predictions for full scale manufacturing in different types and scales of equip

ment.

For example: trying to run chemistry at higher concentrations in a reactor 10x, or more, larger in volume than pilot plant batches and at temperatures and pressures designed to reduce turnaround time to increase capacity and utilization of capital.

• For the majority of the unit operations common in drug substance manufacture fairly good science is available; at least a proven heuristic understanding that can support a mechanistic approach even if first principles techniques are not available.

In drug product processing • Here the “sample” is, at the base level, an individual dose in whatever form that

takes. As such even relatively small volume “pilot” manufacture can lead to quite large sample sizes running into 000’s or much more within a single “batch” as well as across multiple “batches”. This is the exact opposite of the drug substance situation since we can expect sufficiently large sets of data for statistical techniques


to be immediately applicable.

• Generally, once the final dose and delivery mechanism is established, there is little change in this between test and clinical runs and full scale manufacture; a 100mg tablet is still a 100mg tablet. Scaling up of production quantities here is


predominantly by increasing the count of items produced not the size of items produced. Whilst there are still bulk handling challenges in bringing together the basic ingredients; the end result is something identical to that produced in testing, so the need for extrapolation of parameters outside those tests is dramatically reduced compared to drug substance manufacture.

• Finally, many of the bulk operations that are needed, especially for solids form doses, are those where the basic science for prediction is less developed. Heuristic based methods do exist, and a few of the operations are as well understood as any in API, but often an ability to gain confidence without relying upon a predictive understanding of the core science is helpful. In the main this is because rigorous prediction of real world powder handling operations with full allowance for population balances, particle‐particle interactions, agglomeration, comminution and segregation is either not possible, or not practical within time and resource constraints.

For example: whilst heuristic approaches to modeling powder blending operations are available; especially to help target process equivalence through ncreasing volumes. It is still sensible in this area to look for statistical evidence rom the data itself to support any conclusions. if

In summary Risk assessment and decision support techniques are generally needed througho

• • ut.

Mechanistic tools are better suited to the low sample numbers, scale by volume, world of bulk unit operations especially for APIs.

• Statistical tools are better suited to the high sample numbers, scale by count, world of drug products.



3.3 Mechanistic Modeling in QbD

3.3.1 For Pharmaceutical API’s

As outlined above mechanistic modeling techniques offer most value in developing a science based understanding of many of the common bulk unit operations especially in API processes. Tools of this type have the mathematical solvers to handle integration and often dynamic parameter optimization (for fitting or optimization applications) of multiple simultaneous differential equations. Some of them also provide libraries of pre‐defined templates to model specific operations (see the example here). They range from application specific tools that are designed for a very specific type of operation (commonly reaction and/or distillation), to more generic configurable tools that can be setup for virtually any rate‐based operation. The better the library the quicker and easier it will be for you to apply them to your specific operations; but there will almost always be some further configuration necessary. Having generic configurable tools reduces the number of different tools you need to buy and learn, but then having a usable library becomes ssential to provide a good starting point for the specific eoperation you are looking at. An especially powerful aspect of mechanistic models is the ability to divide and conquer. It is often unnecessary o build a full unit procedure model if the CQAs/CPPs you re interested in do not require that. ta

For example: Consider a reaction operation with exothermic liquid phase chemistry. Temperature has been identified as a CPP impacting a persistent impurity formation (a CQA), atmospheric emissions (a CEA), potential reaction runaway (a CSA) and overall reaction yield (a CMA). In this situation a single mechanistic model addressing the temperature sensitivity of the chemical kinetics may be an adequate starting point. However when scaling the process up to a production reactor there could be new risks associated with the scale‐based limitations on the heat transfer capability of the equipment; the kinetic model can then be readily combined with different vessel models that capture the equipment constraints to allow a very flexible investigation of alternative equipment and operating procedure choices on quality, safety, environment and economics.



High risk issues are addressed first, and then that work can often be leveraged by re‐using it in combination with other models as a more detailed picture emerges.

In this type of scenario the model used to gain an early understanding of the reaction mechanism and temperature sensitive kinetics is generally developed as an equipment independent model. Your production equipment can be characterized separately to provide equipment constraint models. And then, only when it makes sense, are they combined to explore the relationship between the chemistry and the equipment constraints and their potential impact on quality etc. This applies to all the key process and equipment henomena that frequently cause scale‐up risks; in particular for addressing complex non‐plinear and multi‐phase behavior such as mixing, heat transfer and mass transfer. As powerful as this idea of reusable model components is there is a caveat. Any mechanistic model is only as good as the parameters that have been considered during its development. If localized mixing is an issue as you scale up your process, and your model does not include that effect, then obviously you won’t see it in the model results. This means that echanistic modeling needs to be combined with careful thought regarding which CQAs m

and CPPs matter. A mechanistic model is often a very resource effective way of evaluating CQA sensitivity to specific CPPs and some tools can directly produce response surfaces and similar results to allow a more detailed exploration of which CPPs really are critical, and ultimately help contribute to mapping out your design space. One way of looking at this approach is to consider that each predictive run of a model is an experiment “in‐silico”, and surface response generation is analogous to DOE but without the resource burden of physical experimentation. And of course good practice demands that having explored the design space in this way some additional real experiments should be carried out to verify the predictions at the edges of failure that are most likely to be a risk from a QbD perspective.

3.3.2 For Pharmaceutical Drug Products

Within the drug products area the application of mechanistic modeling is less used for several reasons including those outlined above. However there are areas of application where the benefits are just as significant as for the API area and, being very close to final osage forms for patients at this stage, can have even more impact in reducing and dcontrolling major quality risk factors. The main areas where this is true are the bulk processing operations that take place prior to the discrete manufacturing technologies that form the final doses (tablets, vials etc) and the packaging operations that occur after that. As described earlier this is most challenging for solid dosage forms as a number of the operations have limited real science available to use as a basis. Those models that do exist are often idealized academic models that are extremely useful for study but often not flexible enough for real world application; for xample Schlunder’s model for contact drying [9] which assumes equally sized non‐porous pheres for the solid material. es



Work on developing heuristic engineering models of some of the more critical operations, like solids blending, is making advances regularly. And mechanistic tools can harness these new insights to build flexible rate‐based models for examining both design and performance issues.

4 Typical Modeling Applications in API OperationThis section offers a brief overview of current practice based on the types and frequency of projects seen during considerable support, training and consultancy with most of the major pharmaceutical companies. These case histories are frequently confidential but similar examples can often be created to demonstrate an effective approach…in this way the template library becomes a living and growing accumulation of best practices from the field. Although ncluding a lot of case studies is outside the scope of this

s

ipaper…they are available if you want to discuss specifics. Below we first consider some universal applications: early investigations into chemistry; leveraging data from routine laboratory experiments; early scale‐up risk assessment; building equipment constraint models; and modeling key physical properties. Then we look at typical API unit operations where these techniques are applied, and onsider a few of the commonly investigated sources of crisks. The figure here outlines the common operations in a typical API chemical stage and some of the areas of risk that mechanistic models help with today. Along the way we will also mention a few of the non‐typical applications that generic tools have been used for recently.

4.1 Chemistry and Laboratory Applications There are many areas where mechanistic thinking can help very early in a QbD founded work process; long before real equipment choices are made. This is where some of the fundamental thinking that is needed to understand your process happens; in particular developing an understanding of the phenomena that impact creation of both the desired molecule and undesired impurities. Thinking about and modeling the underlying science at this stage helps you develop and challenge your hypotheses, and therefore can often build reater confidence in your results and do so with far less experimentation with scarce gmaterials than traditional techniques. Only from that starting point can you subsequently “build‐in” ways to eliminate, minimize and otherwise control the risks as part of the design for manufacture work process that follows. This basic knowledge can impact many later decisions; and the potential for re‐use



of mechanistic insight and models means this knowledge can more readily be leveraged nt after launch. through development and into continuous improveme

ypical areas of application T in the field today include: Mechanism Investigation: Specifically finding a set of combined chemical and physical rate processes that adequately represent the overall behavior of the system. Careful choice of a set of discriminating experiments can allow different aspects of your hypothesis to be examined. A model that is constrained by a hypothesis, fitting real experimental data with as many variables eliminated as possible, can quickly establish confidence that the hypothesis is reasonable, or start further thinking to understand why it didn’t fit. Is there an intermediate in the reaction step? How about auto‐catalytic behavior? Is it really a simple series/parallel system, or is something much more complex going on? Are there inter‐related physical processes that must be considered like reagent dissolution for example? These questions and more have been asked and answered using this approach. It s focused on understanding what is happening, not how quickly or efficiently…that will ome later. ic Basic Kinetics Assessments: Once you have some confidence in your basic mechanism you next want to understand just how temperature, or pH, or pressure, or concentration, or eagent equivalent sensitive is the process? And you want to be selective, investing time rand effort where the risks are higher. Interestingly the experiments required for this type of analysis are done by safety labs routinely; all businesses recognize that many of the CSAs (critical safety attributes) are established here, but why not use the same work to quantitatively capture intrinsic relationships that will underpin the CxA=fn(CPP) analysis for ALL high risk issues later. he extension of the safety lab role into this area has either already happened, or is under erious consideration, at a numTs ber of pharmaceutical companies today. Lab Reactor Results Analysis: As a very rapid way to maximize the results from a lab reactor run modeling is a powerful tool. It assists even more when some of today’s more sophisticated automated lab reactor systems are considered; an integrated analysis combining basic reactor profiles with results from in‐situ PAT instruments can offer insight from a single run that would have taken many experiments in the past. Library templates tart by already having the equipment described and offering previously tested ways to nclude PAT data in the analsi ysis. ScaleUp “Reality Checks”: Early scale‐up checks can help the risk assessment process greatly. Simple methods for assessing if a process is likely to push the boundaries of mixing or heat and mass transfer when run at typical manufacturing scales can help establish early on if risks associated with these phenomena are likely to have high severity or occurrence rates, especially where problems might be difficult to detect (e.g. solids suspension difficulties, low mass transfer coefficients or unexpected reaction progress due to localized conditions). Examining these and even using the equipment models to aid in “scale‐down” so that laboratory and pilot runs are done in conditions closer approaching those




4.3 Reaction Operability and scaleup: The science based approach that underpins mechanistic models means they can frequently be used to predict the behavior of chemistry when it is operated outside the conditions used for the original experimentation. A reaction mechanism and temperature sensitive kinetics can be established using controlled conditions in the lab and then the performance of the same chemistry predicted when it is run via a different recipe

3

anticipated at full scale can all greatly enhance the value and confidence in early trial results.

4.2 Characterization and Assessment Another of the widely used applications is that of characterization and assessment of both equipment and materials. Both of these applications are really two sides of the same coin. Characterization is the process of using measured data to fit key model coefficients (characteristics). When used with due attention to the assumptions (implicit as well as xplicit) these can then be re‐used to turn the model around and predict the behavior of the esame equipment or material in different operating conditions (assessment). Characterization is about building a high confidence model of equipment or material onstraints; assessment is about using that to answer questions about how they will erform under scenarios of incp terest other than the test conditions. Equipment Characteristics: Interestingly some of the widely accepted models of equipment fall into this category (e.g. Sieder‐Tate for heat transfer); and even though iterature values are now available for many of the parameters they are all based originally n fitting to data from real expelo riments with real assumptions. Material Physical Properties: The models used are often as simple as choosing one of a few accepted equation forms and then finding the correlation parameters (characteristics) for each material. It is so common to use this approach to account for the temperature sensitivity of different properties such as density, viscosity, heat capacity, pure component vapor pressure, thermal conductivity etc that we almost forget that what we are doing is modeling. We constrain the model to one of a limited set of equation forms known to work for a property and then fit the equations coefficients to characterize the property for that material. In some cases the equations used have a foundation in the underlying science mechanistic models), in other cases they are more accepted engineering heuristics empirical models). (( Equilibrium Characteristics: This is a special case of the material physical properties where a multi‐component property is involved; usually binary but others too. These thermodynamic models are so important that large databanks exist for many of the common solvents and reagents; however when NCE’s (new chemical entities) are involved they will obviously not be in the databanks and therefore assumptions or experiments will be required. The most common equilibrium models are vapor‐liquid and liquid‐liquid activity coefficient models (e.g. Wilsons, UNIFAC, NRTL etc), gas‐liquid solubility (Henry’s Law) and solid‐liquid solubility (Van’t Hoff, NRTL etc).


and/or in different equipment at a different scale. The model can be used to map out design and control spaces reducing experimentation requirements to verification of the model predictions towards the edges of failure. The inherently multivariate nature of the models also capture the interactions between the key parameters of the real process well allowing more realistic assessment of design space than simple PARs (proven acceptable ranges) an possibly provide. ac Multiphase reaction systems: Not all mechanistic modeling tools fully deal with multi‐phase systems but those that properly support them bring another powerful dimension to understanding a process. In these circumstances we must add thermodynamic and other equilibrium models alongside mixing, mass transfer, heat transfer and chemical kinetics. These systems have so many interacting non‐linear systems that a conventional statistical model would be difficult to impossible in all but the simplest of scenarios. And it is that non‐linear behavior that is generally the subject of interest in any case. For example understanding and predicting phase change/transfer behavior for a hydrogenation reaction, a rate limiting dissolution or a reactive crystallization can be very successfully tackled with a good mechanistic model. CQAs like particle size and even maintaining the orrect polymorph can be examined to ensure the process achieves quality specifications ven after quite significce ant process and/or scale changes. Continuous Reactors: The ability to extrapolate models that are based on a fundamental understanding of reaction mechanism and kinetics means that very different modes of operation can be assessed. One area that has had quite a lot of activity recently is the potential for continuous reactors for pharmaceutical API manufacture; from PFR’s to CSTR’s in series to the recent growing interest in micro‐reactors. Continuous systems offer a lot of potential to reduce scale, and hence capital investment and hazardous inventory, as well as providing much greater heat transfer per unit volume allowing highly exothermic hemistry to be controlled at concentrations and rates that would be dangerous or cimpossible at the larger volumes required for batch operation. Mechanistic models have a very long pedigree in this type of application in other process industries for both single phase and multi‐phase systems. The kinetics themselves can be derived from small scale batch or continuous laboratory experiments and then alternative reactor designs explored via modeling so that large scale pilot runs are minimized as they are primarily for model verification rather than gathering data for statistical examination. In this type of application mechanistic modeling is generally recognized as a requirement rather than an option.

4.4 Crystallization The potential for crystallization modeling has increased dramatically in recent years as in‐situ PAT techniques have enabled data collection during the operation (cf. relying on start and end point data) allowing the model parameters that characterize crystallization ucleation and growth kinetics to be fit without a very large number of experiments (or atches) being required. nb



All crystallization models necessarily start with ensuring that the supersaturation driving forces are well characterized. This means assessing the solid‐liquid solubility relationship ppropriate to the method of creating supersaturation; against temperature for a cooling acrystallization or anti‐solvent concentration etc. With good solute concentration profiles (e.g. in‐situ FTIR or at‐line HPLC) it is possible to get a lot of insight from solubility based crystallization models. With seeded crystallization data where secondary nucleation and growth dominates this is often enough but to haracterize primary nucleation for unseeded data the model can be greatly improved by cfitting or measuring the MSZW (metastable zone width). Finally it is possible using PAT techniques like FBRM to get good indicators of solid phase particle size distribution and volume allowing predictive models of the particle size distribution to be built. Whilst this is in principle straightforward, in practice it needs high quality data on solubility, solute concentration and size distribution profiles; and it can be challenging to measure sufficient data for a high confidence fit to primary nucleation events for unseeded systems as they tend to happen in a very narrow window during the batch. That said it is now realistic to consider this approach for understanding size distribution issues when necessary, and this was impractical until very recently.

4.5 Workup Operations A range of other operations are also modeled fairly widely and can offer valuable process nderstanding. The most common operations addressed in this way are solid‐liquid useparation and solid drying. It is also notable that these, mostly physical (c.f. chemical), unit operations are frequently challenging to scale‐up and can certainly directly impact product quality so from a QbD erspective should be just as important as the product formation operations where hemical trpc ansformations occur. Filtration: In a similar manner to catalytic reaction modeling, cake filtration is an area that has used engineering heuristic based models extensively in the past. However modern rate based solvers also permit a more rigorous treatment of the problem that can, for example, provide insights into filtration modes as well as resistances. Small scale filtration testing an fit characteristics such as filtration order, cake resistance and compressibility that can cbe used for prediction of conventional cake filtrations and filtering centrifugation. A particular challenge for QbD is filtration washing operations as they are often critical in establishing product quality. The selection of wash solvent and temperature, the choice of re‐slurry vs. displacement washing plus some of the inherent challenges of large scale displacement washing (channeling from cake shrinkage and cracking, cake changes if moothing is used etc.) and the effectiveness of subsequent cake dewatering can all play a art in c s. sp


ontrolling quality and offer candidates for CPPs that can impact impurity CQA Drying: Solids drying is a particularly interesting opportunity since having fit drying kinetics at a small scale it is still challenging to scale‐up because the rate determining steps


can vary through the batch and there can be large periods of the batch that are not kinetically limited. For example in pharmaceuticals it is common to be drying low boiling solvents without a pre‐condenser in the vapor removal system and vapor removal therefore often dominates the rates early in the batch. Later overcoming diffusion or solvation/hydration forces can become dominant. The ability of a mechanistic model to integrate these different rate effects and dynamically re‐balance the kinetics, thermodynamics and equipment constraints through the batch enables greater flexibility than approaches that assume some of these constraints can be eliminated.

4.6 Product Stability Material interactions within a drug product that cause longer term stability problems are a specialist topic but still a potential source of material variability and hence risk to the patient. LTSS (long term stability studies) form an important part of any submission and there is substantial literature that considers this topic in depth [10]. However, given the recognition within existing approaches that the degradation is still just chemical reaction, there is recent interest in the potential for mechanistic modeling to assist in this field too. In principle using kinetics derived from accelerated rate testing to provide science based predictions of longer term stability for product testing and earlier understanding of stability risks.

5 Variability vs. Uncertainty

5.1 Process Variables are Variable As we explore the role of mechanistic modeling within a QbD setting it is important to ifferentiate between input values that are true process variables and those that are model dparameters. A process variable in this context is a physical value that is measurable in some way. It may be an input amount or composition/purity or an environment variable such as temperature or pressure or pH etc. Indeed any value associated with the process itself that might be considered for a CPP. The nature of a variable is that despite our attempts to specify and ontrol it there will be variations in the value from batch to batch and for analogue

through time during a batch. cvariables In short: A process variable represents something that can, and often does, vary during or between batches.


5.2 Model Parameters may be Uncertain A model parameter on the other hand is generally something we expect to have a single defined value like a heat of reaction or the pre‐exponential factor and activation energy associated with a reaction, or the Antoine coefficients for a pure component vapor pressure etc. By definition these are assumed to have some fixed value that is correct; e.g. a heat of reaction is fundamentally dependant on heats of formation and Hess’s Law and so on. Our challenge is that we don’t actually know the real value of many parameters; so we fit values


using experiments to provide data against which we can iterate until we feel we have a mate. good esti

In short: A model parameter represents something that does NOT vary during or between batches, but that may have a level of uncertainty (or confidence) about the actual value to use. This is important because whilst the confidence level of a model parameter may impact the confidence of our predictions from that model, it is NOT a source of variability in the process itself.

5.3 Uncertain Variables There are some situations that may be a little ambiguous and thought is needed to characterize a value as either a variable or a parameter. Indeed it may be that a value treated as a parameter in one model, becomes a variable when a more detailed model is onsidered. In this situation we generally categorize the variable/parameter by the context ithin which we use

cw

it and it becomes an assumption we are choosing to make.

For example: Consider the metastable zone width (MSZW) in crystallization. Whilst solubility is a true thermodynamic property of a pure component and solvent and so is a valid model parameter, theoretically the MSZW is not. Even though we can even measure it experimentally for specific conditions, we know that in reality he MSZW can change from batch to batch and we cannot control it directly. So in treality the value of MSZW is variable as well as uncertain. Most crystallization models make the assumption that there is a definable fixed value for MSZW (when it is considered at all) but attempts could be made to haracterize its variability against process variables such as agitation rates or the crate of change of supersaturation etc. It is of note that this aspect of MSZW should not really cause a major QbD problem because it is inherently unmeasurable during a batch and cannot be directly controlled being a response to the process environment itself. Therefore it is not a candidate for a CPP; other process variables would have to be controlled, as CPPs, to help reduce any MSZW variability if necessary.

In terms of its impact on our process understanding we would generally consider this type of value a model parameter with uncertainty and hence acknowledge an impact on the confidence of our model results. We would only add it as a potential source of real process variability if there is a reasonable way to quantify the variability of the parameter independently of its basic uncertainty.



6 When is a Model Good Enough? The diagram here outlines a view of a typical mechanistic workflow from an earlier paper [11]. This workflow is an excellent basis for a discussion of model confidence, and when to stop driving for greater fidelity in our models.

6.1 Confidence for Model Building To build a mechanistic model we start with a hypothesis about how the process works; or at least those aspects of the process that we are interested in (our identified areas of risk to quality for QbD). We then build a mathematical escription of the rates, equilibrium and other

dphenomena that are implied by our hypothesis. The final information required is experimental results that will allow us to examine the phenomena we are interested in. This often means defining an experimental plan carefully so that we have data that is high quality, has sufficient points nd covers results that will show the impact of hanges in our key mac

odel parameters.

For example: if we are interested in the temperature sensitivity of a reaction we must either do multiple isothermal experiments at different temperatures or at least a non‐isothermal experiment with good temperature and concentration data.

lso, when defining an experimental strategy to support a modeling effort you will often do ifferent experiments tAd

o a classical DOE trial.

For example: For reaction kinetics we might consider a DOE looking at different starting concentrations, reagent equivalents and temperatures with multiple levels and repeats for each. A simple 23 design with one repeat requires 16 experiments. Obviously techniques do exist to reduce this but that should be set against a desire for more temperature levels given the non‐linearity of the temperature vs. rate elationship (going to 3 levels on temperature alone still increases our baseline to r


24 experiments). However with mechanistic modeling a single non isothermal run can often give excellent insight; and 2 or 3 non‐isothermal runs, especially with one of them following a different temperature profile, is likely to significantly improve confidence in the results. And the model can then be run to provide the same response surface you achieve with a classic DOE.


As suggested in 3.2.3 an additional benefit of the mechanistic approach here is that a good fit to the runs with intermediate values or different operating conditions provides confirmation that the scientific hypothesis the model is based on is valid within the circumstances tested. This allows modeling to be used to test alternative hypotheses (e.g. different reaction mechanisms) to gain deeper process

rstanding. unde As such the overall goodness of fit is the real measure of the effectiveness of a model in describing the experimental data. Individual parameter confidence intervals do not ndicate how well the model fits; at this stage they mainly indicate how well the xperimental data prie

ovided reflects the impact of each fitted parameter.

For example: We can get an excellent fit for temperature sensitive kinetics against sothermal data; indeed if the activation energy is zero the pre‐exponential factor ibecomes a true isothermal rate constant. The wide confidence interval on activation energy that would result from an attempt to fit like this in no way reduces the effectiveness of the model for the data provided; it does indicate that the phenomena that depend upon the uncertain parameter (activation energy) were not apparent in the data (obvious as the run was isothermal). When this happens you must decide if the implied assumption is acceptable for the intended use of the model (will you really run at different temperatures?), or do you need additional experiments to evaluate that parameter properly. Note this can still happen for a non‐isothermal run if the range of temperatures is too small; i.e. a run with only a few degrees temperature variation may not be enough to adequately quantify the temperature sensitivity.

Another example: Sieder‐Tate coefficients for heat transfer in vessels are widely used and heating and cooling trials are used to fit these. However if we do not also do appropriate agitation experiments including different types of fluid then we cannot get high confidence in all the parameters. Since most pharmaceutical API operations are, hydrodynamically speaking, fairly similar (low viscosity with a limited range of impeller types that others HAVE done the experimentation needed to characterize) we can use the literature values for those parameters with good confidence based on decades of experience, and just fit the equipment specific parameters that influence the heating and cooling operations themselves. Generally this will offer a good model of the conditions within the scope of the assumptions made. But again it is worth emphasizing that it also means that we must be aware of the constraints our assumptions impose when we wish to use the model to predict changes in scale or equipment.


6.2 Confidence for Model Based Predictions Now we are looking to use our model, and the model parameters fitted during model building, to predict process performance in some new set of circumstances with different equipment and/or process variables. This might be a simple recipe change or a complex scale up to different equipment types and/or geometries.


For minor process tuning there is little risk using the fitted parameters we have found, it is when using a model for prediction outside the range of the original fitting data that we need to pay closer attention to the parameter confidence intervals and both the confidence and prediction bands associated with them [12]. In this situation we are extrapolating using the model so we need to understand why a confidence interval might be wider before e can decide if that is acceptable or not. There are a variety of reasons why this might ccur, cwo

ommon examples include:

• The experimental data sets used to build the model did not capture the process responses that the parameter impacts. In these circumstances, depending how severe the situation is, some solvers may even warn you that a parameter is showing no sensitivity during fitting. When this happens either the parameter is really not a key parameter, or more data is required, or the model itself has not included the equations that allow the parameter to appropriately impact the results. This should be addressed before the model is used for prediction.

• The system may truly be insensitive to that parameter even though you thought it would be more important. If closer examination confirms that to be the case you may be able to simplify the model or be confident to proceed with the parameter values already obtained.

• It may be that the parameters are edge cases, in these situations there are many viable solutions…it is more important to ensure you don’t have a non‐viable one than it is to find a specific correct value.

For example: magine a sequential reaction scheme A → B → C with data available for non‐Iisothermal runs with T, CA and CC against time. Fitting kinetics against this could fit both reactions with tight confidence intervals on all the parameters but it could show a wide confidence interval for one or other reaction. This can happen when one reaction rate is much greater than the other over the whole temperature range tested. In these circumstances the rate determining step, and hence the parameters with most sensitivity to the data will be those of the slower reaction. The other reaction parameters just need to ensure the fast reaction is fast enough and the model will continue to work well; we don’t know the correct values for he fast reaction so a wide confidence interval is reported but as long as it tremains fast enough the model is acceptable. Once this is understood the model can often be simplified by eliminating the insensitive parameters from fitting; for our example setting r1 = 10 x r2 would allow us to fit only the r1 parameters showing good confidence for all the data fit. We should be aware when we extrapolate temperature however



that temperature sensitivity may vary and at a much higher/lower temperature the rate determining step may change.

.3.1. Finally it seems important to emphasize two concepts previously mentioned in 3 Firstly, it may seem obvious but remember that a model can only respond to the arameters provided and can only predict phenomena associated with those parameters if hey have equations ipt

ncluded in the model to respond to them.

For example: if agitation rate is not provided or if the model contains no equations that use the agitation rate to impact the calculated process response then that model will never take agitation rate into account. If agitation is a key parameter then either a complementary agitation model must be used to confirm that the agitation assumptions in the main process model are reasonable, or the main process model must be altered to include this key phenomenon.

Secondly, the concept of using multiple complementary models to divide prediction into more manageable modules is another powerful advantage of mechanistic modeling in a process environment. Often equipment constraint models can be applied independently like the agitation example above) and then a simpler process model can be used with (confidence as long as it is staying within the equipment constraints. In fact for a complex scale‐up project it is rare to finish a project with one single complex model that encapsulates all the behavior; normally several models are used in combination to solve the overall problem.

6.3 Confidence for Model Verification Having used a model for prediction it is important to verify the results before fully trusting them; it is a model after all. When models are supported by powerful tools like dynamic optimization and surface response plotting they can dramatically narrow the search for the best operating conditions for a particular operation in particular equipment using a particular recipe. But good practice still demands that, unless the new conditions are elatively close to the original experimental conditions used to build the model, we seek r


some way of verifying the models prediction capabilities. A common approach is to run a small scale experiment that tests the new conditions as well possible and then compare those results to the model prediction in a similar manner to fitting. Indeed the same statistics used to assess the goodness of fit for model building should be re‐examined here but without varying the model parameters. If the goodness of fit is still reasonable then confidence in the prediction capability of your model can be high (for those parameters included of course). If the new data is from a scaled up run, say a pilot plant system, subsequent fine tuning of the parameters is then justified to shift the basis of the parameters to the data that most closely represents final operating scale and conditions. Fine tuning in these circumstances would involve re‐fitting the parameters and re‐verifying that the reverse prediction is still valid (i.e. verify the prediction against the original fitting data using the updated parameters).


Again, when defining experiments or selecting batches for verification some thought about your ultimate objectives can help guide your strategy. What process variables are you seeking to change and which process parameters will impact/be impacted by those variables? This should allow you to select alternative conditions for verification runs that llow you to confirm the models predictive capabilities for the variables and attributes that ill have most risk as

aw

sociated with the planned use of the model.

For example: If temperature optimization and heat transfer constraints are dominant then you want to verify your activation energies and heat transfer coefficients; so define verification experiments where they are different from the test data (change temperature and/or agitation possibly). However if you expect to operate at previously used temperatures but will change reagent levels then erification runs with alternative concentrations and feed ratios will probably be vmore useful for increasing confidence. Sometimes many effects can be assessed with a single verification trial/batch. But sometimes more thought and special experimental runs are needed to provide the necessary confidence in the predictive capability of the model for a particular objective.

6.4 So What Confidence Does the FDA Expect? This topic has recently been discussed with the FDA and they observed that since every case is going to be different definitive rules or even extensive guidelines would be very difficult; an attempt to provide them could exclude otherwise reasonable cases. Essentially they are seeking justifiable science based decisions with an explanation of any reasoning ncluded in the submission. A “reasonableness test” was suggested…to quote directly from he notes of the discussion [13]: it

• “Use a science and risk based approach”. Put your reasoning in your submission. Give your application to a colleague across the hall from you, a scientist / engineer who you respect and who is not on your project team. They can give the same sort of scrutiny that we [FDA] will. Ask yourself what would help you understand it.

• There seems to be an attitude in industry that we [FDA] do not have people and skills to

review QbD type submissions, e.g. those that contain modeling. We do. If we need experts, we will bring them in. If we have questions about a submission, we will ask experts and/or ask the applicant to explain.”

Stepping back from the understandable desire for a “formula” for justifying a submission we need to decide for ourselves what constitutes reasonable verification. Given the previous discussion on confidence and where and how it might apply we can suggest a asic framework for this for mechanistic modeling. We will explore this further in ubsequent papers but in outline: bs



• For eac h unit operation/procedure investigated using mechanistic modelso Explain the overall strategy, especially if multiple models are used

collaboratively. o For eac le

for acceh individual model explain the work process followed and rationa

pting it as a basis for making decisions:

Initial hypothesis and model building – use overall goodness of fit Describe the key assumptions and limitations of the resulting model

Using the model for specific objectives – review parameter confidence intervals and their importance for that objective

Verification of suitability for purpose against new experimental data – use overall goodness of fit

o When necessary examine the overall impact of process variability and how it might be controlled by changes in the materials handling, equipment and/or recipe. This might include leveraging the base model to do a Monte‐Carlo or other probabilistic assessment of the variability.

o Define your control and design spaces including an assessment of key areas of uncertainty (cf. variability) if necessary to help define safe boundary

conditions. o Outline your procedures for maintaining/re‐verifying models through

process and/or model improvements.

7 Understanding Risk

7.1 How to Measure Risk? There are a number of well documented approaches to measuring risk either qualitatively or quantitatively. The Risk Priority Number (RPN) used in both FMEA and FMECA [5] is a popular and logical choice for QbD balancing the value of a quantitative approach with relatively straightforward analysis, Alternatives would include both qualitative and uantitative criticality analysis (the C in FMECA) [7] which takes a slightly more detailed qapproach to reach a similar objective. Using RPN as our example it offers a deceptively simple formula but a team must assign appropriate values to the main measurements and often these will be judgment based. here a high priority risk is identified the effort of greater analysis, such as modeling, can rovide more a quantitative basis for some of the values. Wp

RPN = Probability of an Occurrence x Probability of Detection x Severity if Undetected The main task is to understand the sources and magnitude of true process variability. If every batch always executed in precisely the same way to produce precisely the same material then failures would not occur. However process variability is a fact of life that should be understood and controlled as far as possible to reduce the likelihood of failure. s discussed at the beginning of this paper that is one of the primary goals of the whole bD initiative.

AQ



It is these sources of variability that give rise to the possibility of a failure and modeling, lthough it is relatively early in its application to pharmaceutical processes, can be used in onjunction with otac

her techniques to provide insights into this area.

For example: You have a process that is sensitive to %excess of a key reagent, in particular if it is too low a persistent impurity is produced at levels that will fail specification. The reagent quantity is, by weight, an order of magnitude smaller than he intermediate/starting compound. What will be the impact of charging errors, tscale calibration and intermediate/reagent purity on the impurity profile? Is it adequate to specify an absolute +/‐ crude weight for charging? Is the added complexity of pure weight charging justified by the additional control over impurity evels? What tolerances on scales are desirable for larger/smaller charge lweights…what if the same scale is used for both?

Basically by using statistical distributions instead of fixed values for the model inputs (potential CPPs) Monte‐Carlo studies can offer direct insight into the associated variability of outputs (CQAs). In effect we use the model to gain a “historical” perspective of variability by running 100’s and even 1000’s of batches very quickly. A useful discussion of the applicable statistics for CPP determination was published recently [14].

7.2 Where to Find Variability?

7.2.1 Variabi d number of sources:

Materials

lity of m te• Differe

aterials in the final product is generally from a limi

o nt amounts of inputs to the process can cause this via Batch to batch variation in actual charge quantities.

o Variation in input lot composition can change amounts and even introduce, or eliminate, chemical species.

o Other unexpected sources of process material entering the batch (e.g. batch to batch or cross product contamination with heels/traces of materials left in the equipment etc).

• Differe nt chemical reactions or conversions of materials through the process

o o As a consequence of variations in the materials and hence concentrations

As a consequence of variations in operating conditions or environment o As a consequence of post‐manufacturing transformations – i.e. product or

impurity degradation

• Different performance of the physical separation operations used for purification and iso m those o

lation can change both the composition and amount of the product froperations

o tc).

©2008 DynoChem Inc. ll rights reserved. of 33 A

Variations in the basic separation efficiency and selectivity because of operating conditions or physical issues (e.g. cake cracking, entrainment e

o Similar variations in associated ancillary operations such as washing etc.


• Unexpected additional inputs Incorrect/accidental addition or withdrawal of materials to/from the p

o Composition changes due to corrosion, abrasion or other failure of the o rocess

materials of construction of the plant equipment Drug product processes tend to have few, if any, deliberate chemical transformations or separations so the variability is predominantly associated with the input materials. However in API processing the situation is usually more complex with all of the above sources of variability being potentially significant. In particular impurities in the final drug substance will often be formed during the process rather than added via an input material; ith a consequent requirement to trace back from the product composition to understand w

where and how all the impurities were created. With modern often highly telescoped and convergent API processes it may be that starting compound impurities persist to the final product; but it is relatively rare. Normally only the final few chemical transformations will be relevant from a strictly QbD perspective since they are the stages that realistically determine the potential impact on the patient. However when we consider the wider perspective of looking at the general CxA = fn(CxP) then clearly we still need to examine every stage fully; and it is good practice to apply the same quality standards to all chemical stages even if the regulatory process ultimately only has to focus on the final few steps. In fact determining which stages impact the ultimate drug substance quality should be a demonstrable outcome of this work.

7.2.2 Equipment and Instrumentation

Variability of process conditions is a potential source of batch to batch and scale‐up differences in many operations; how significant that variability will be depends upon the ensitivity of each individual process. A limited number of general issues tend to dominate he soust

rces of variability in this area also:

• Equipm ent scale (with care these can be minimized by design / equipment testing)o Some process variables change significantly with scale, others do not (e.g.

batch volume vs. temperature) and the tolerances of instruments for larger scales can differ significantly from those for the laboratory. Also important can be the %FSD (full scale deflection) that the process value represents; this can be particularly significant with load cells where the mass of larger scale


equipment means a high dead‐band requirement that does not exist in the lab and impacts feasible tolerances unless specialist sensors are used.

o Homogeneity is generally assumed both by models and by users of sensor results. However the sample point becomes much more significant as scale increases. It may appear that the target property specified from laboratory tests is being achieved but the sample point may not truly represent the average bulk value or may have a very different time delay compared to smaller scale. This is particularly challenging because it appears you have the value you want but the real process value is subtly different from that being reported by the sensor.


• Instrument calibration o Most instruments require calibration on a regular basis to provide continued

performance within tolerances. This is well understood within the pharmaceutical industry where critical instrumentation is generally identified and pro‐actively maintained and re‐calibrated. However the nature of calibration drift is such that there may be a gradual offset introduced into the process over time and different instruments drifting counter to each other can potentially magnify these effects. Given good calibration practices however the magnitude of variability due to this is likely to be much smaller than problems due to poor instrument positioning or either material or recipe sourced variability.

• Contro l system induced variability

o Depending on the sensitivity of the system and how well tuned control loops are a variable response to small differences in offset may be possible. In particular control of temperature ramps and/or large exothermic events may be dynamically limited by the control system. E.g. situations have occurred where slow response by a heavily damped control system has caused significant variations in reaction temperature profiles which can have consequential impact on the purity and yields of the products.

o A different control strategy can often help when large and/or discontinuous offsets are predictable. Over‐riding the control system through these predictable spikes or step changes can reduce variability in the systems response to these extreme situations.

7.2.3 Recipe

The recipe itself may inherently introduce variability, especially material variability. Manufacturing recipes in particular have many different requirements to meet and often eed to trade off technical precision with ease of operation. Two areas where common racticnp

e introduces process variability are:

• The specification of charging quantities for materials. The desire to reduce manual handling and complex dispensing practices and/or improve containment can make crude and variable weight charge amounts compelling. This is sometimes accompanied by “keg selection blending” to smooth out any variation but the fact that this is done recognizes that variation is being introduced. Also variable input weights with fixed reagent weights can significantly impact starting compositions and %excess values with obvious implications on variability.

• Process timings where operator intervention is part of the process either for regulatory reasons or to provide critical judgments on process status by simple observations or IPC (in‐process control) analytical checks. This is a common cause of apparent differences correlated to shifts or even individual operators with different experience levels / motivation.



When considering the potential impact of recipe variability on mechanistic models being used within the context of QbD it is important to remember where the model fits within the overall picture. Most mechanistic models do not attempt to model a complete process stage; rather they are used to focus on high risk operations. Using the ISA‐S88 terminology [15] now widely adopted within the API manufacturing environment a mechanistic process model is generally focused on a specific unit operation or unit procedure. On occasions multiple unit procedures are addressed but only because multiple equipment units need to e involved in something that is still conceptually a single operation (e.g. quenching a breaction into a second vessel or multiple CSTR’s in series etc.). Variability caused by recipe definition or execution upstream of where a model applies may impact the real starting conditions of the operations being modeled. This sort of input variability can be quite subtle since it may arise from differences in process execution rather than specific input variables. In these circumstances considering the variability of actions within the model will allow sources of variability that are within the scope of the model to be explored. However for an overall understanding of the potential design space it is also important to consider the variability of the model initial conditions even though the source of that variability is not directly within the scope of the model.

7.3 Knowledge Space, Design Space and Control Space n principle the Design Space is simply the collection of all possible CPP combinations that

ns. But how to define this? Iwill provide CQAs that meet quality specificatio Several contributors are now recognizing that there are several layers of “space” as indicated in the diagram (borrowed from [16]) but that the true multivariate space is not as simple as the convenient nested ranges the diagram implies. The challenge is that the acceptable ranges are not constant; as we move one variable within the Design Space the true acceptable range of other variables will change, often in a non‐linear manner. So a Design Space defined using a collection of PAR’s will necessarily be more restricted than the true cceptable space. Of course this simplification may be acceptable since it can aid acomprehension, particularly for process operators. We also need to be aware that the FDA definition of “Design Space” is one that takes a focusedConsid

view of quality in isolation of the other aspects of running a process effectively. er:

• Knowledge Space – all that we know about the process and materials. Necessarily edges of


extending beyond all the design space boundaries and ideally exploring thefailure.

• Design Space – the area we can operate in without recourse to a regulating authority. This means there will really be multiple design spaces; the safety, environmental, quality and manufacturing spaces discussed earlier.


• Control Space – the recent ISPE PQLI initiative (Product Quality Lifecycle Implementation) calls this the “normal operating ranges” [17]. Your company may have another name for it. Whatever name you give it the concept is a valuable one that in reality describes the operational region you are targeting in order to safely manufacture within the intersection of the “design spaces” for all the regulated domains; not just within the quality design space as defined for QbD. As such this is mainly a region of interest for effective implementation on plant. Obviously the ICH/FDA focuses on the broader regulatory quality Design Space that forms the overall operating boundaries for product quality.

Recent discussions are recognizing that when mechanistic models have been used to understand an operation they offer a powerful alternative to a collection of PAR’s or other simplified ways of delineating a design space. Since the relationship between CQA=fn(CPP) is captured by the model then the model itself could be the definition of the design space; that way the full multivariate space is captured. This also has the potential to improve on the design space delineation proposals that use statistical dimensional reduction techniques (e.g. PCA/PLS) to simplify the multivariate nature of the problem [16]. As previously discussed when a mechanistic model is not available the statistical approach makes sense, and with complex interacting models it may even make sense to map out the response surfaces using the models and then utilize PCA/PLS or similar to reduce those results to a simplified design space. But verified mechanistic models, maintained as the process is improved, offer perhaps the closest approach to capturing the real Knowledge Space that is available today.

8 Mechanistic Modeling within a QbD Strategy n overall quality strategy needs to address QbD through the complete product lifecycle. nd mechanistic models can add value at all phases of this. Consider: AA

1. Mec ah nistic Modeling for QbD Filing a. A selection of overall risk assessment tools are needed to identify and rank

potential CQAs and CPPs and roll those back up when more detailed risk analysis is done for the higher risk items. This is true whatever more detailed and quantitative tools are used for specific stages and operations in drug product and API synthesis.

b. Mechanistic modeling is well suited to unit operations where there is a good science‐based understanding of the rate processes that drive the operation (the strengths and weaknesses of the different classes of tool were discussed

the in section 3.2). This includes the majority of API operations and several ofbulk operations in drug products

c. A good verified mechanistic model offers a more accurate mapping of the knowledge, design and control spaces and the same model is usable across all the key “regulated” domains (safety, environmental, quality and manufacturing).

d. For operations with large data sets and lower fundamental understanding the traditional statistical techniques offer many of the same benefits but



without the ability to extrapolate findings outside the tested data ranges.

h2. Mec anistic Modeling for QbD Control, Monitoring and Release a. Modeling can help identify key control points and monitoring ranges

including the impact of variability where appropriate (especially in conjunction with probabilistic techniques like Monte‐Carlo simulation).

b. There is good potential, based on benchmarking from other industries, for using model based “soft sensors” that combine several direct process measurements together to predict an added value process result. And whilst soft sensors are often empirically based, mechanistic models could extend this concept to real time model‐based control that adapts a batches control trajectory to maximize the probability of meeting specification. With in‐situ PAT measurements this could even be predictive for impurity profiles and yields etc introducing the potential to truly approach “feed‐forward control” of product quality with dynamic recipes.

c. In a similar manner overall quality performance for a batch could be predicted using actual measurements of CPPs for that batch to predict the CQAs. For example using charge quantities and actual reaction temperature and feed profiles to predict impurity and yield for the batch without the need for expensive PAT on plant. This could provide confidence based recommendations for a real time release vs. hold and test decision for each specific batch.

h3. Mec anistic Modeling for QbD Process Improvement a. Once a process is in manufacturing effective monitoring allows good data

sets to be collected fairly quickly. This can give valuable insights into true process variability and allow models to be fine tuned as experience grows.

b. A good reason for maintaining the models into production is the ability to continue to predict when a process change is proposed. Obviously if a significant change is to be made then the historical data from that point in the operation forwards is no longer valid and you are back to a similar situation to the original filing for subsequent CQAs; at least in that process stage. Mechanistic models can frequently allow the impact of such a change to be explored with at least the same confidence as the original QbD filing…often greater confidence as the updated models should now capture the process even more accurately than was possible for filing.

c. Having such a good representation of the relationships between parameters and attributes is a powerful tool in itself to facilitate continuous improvements. Six Sigma (DMAIC) [8] or other improvement projects on a manufacturing process need to understand those relationships. An up to date verified model allows response surfaces and similar investigations to be done with little or no additional experimentation. Only after optimization are experiments needed to re‐verify a proposed change; and then only if the change is extrapolating significantly outside the existing knowledge space.




9 eR ferences l 1. “Pharmaceutical CGMPs for the 21st Century – A Risk‐Based Approach – FinaReport”, U.S. Food and Drug Administration, September 2004

2. “Guidance for Industry: Quality Systems Approach to Pharmaceutical CGMPRegulations”, U.S. Food and Drug Administration, September 2006

3. “Guidance for Industry: PAT – A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance”, U.S. Food and Drug Administration, September 2004

4. “ICH‐Q8 Pharmaceutical Development”, ICH Harmonised Tripartite Guideline, Step 4 version, 10 Nov 2005

tep 4 5. “ICH‐Q9 Quality Risk Management”, ICH Harmonised Tripartite Guideline, Sversion, 9 Nov 2005

6. “HAZOP & HAZAN Notes on the Identification and Assessment of Hazards”, T. A. Kletz, (1983), IChemE Rugby.

Analysis”, [US] 80

7. “Procedures for Performing a Failure Mode, Effects and CriticalityMilitary Standard MIL‐STD‐1629A, AMSCN3074, 24 November 19

8. “Six Sigma”, M. Harry, Ph.D. and R. Schroeder, Doubleday, 2000, ISBN 0‐385‐49437‐8.

9. “Vacuum Contact Drying of Free Flowing Mechanically Agitated Particulate Material”, Schlunder, E.U., & Mollekopf, N., Chem. Eng. Process 78, (51), 1983, pp1345‐1360 10. “Pharmaceutical Stress Testing : Predicting Drug Degradation”, Baertschi S. W. (editor), 2007, ISBN 978‐0‐8247‐4021‐4

11. “Potential of S88, BatchML and Other Standards in Streamlining Process Modeling”, Alistair G. Gillanders, Proceedings of 2007 AIChE Annual Meeting, ISBN 978‐08169‐1022‐9.

12. “Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building”, George E. P. Box, William G. Hunter, J. Stuart Hunter, William Gordon Hunter, John Wiley & Sons, 1978 13. “Notes of DynoChem presentation to FDA CDER”, J. Hannon and P. Clark, DynoChemInc., 28 February 2008

14. “The Use of Routine Process Capability for the Determination of Process Parameter Criticality in Small‐molecule API Synthesis”, Kevin D. Seibert, Shanthi Sethuraman, Jerry D. Mitchell, Kristi L. Griffiths, Bernard McGarvey,

J Pharm Innov (2008) 3:105–112.

15. ANSI/ISA‐88.01‐1995, Batch Control Part 1: Models and Terminology 16. “A Framework for the Development of Design and Control Spaces”, J. F. MacGregorand M. J. Bruwer, J Pharm Innov, March 2008.

17. “PQLI Design Space”, John Lepore, James Spavins, J Pharm Innov (2008) 3:79–97.

author alistair gillanders dynochem inc date: wednesday, … · successfully adopted and adapted...

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