Analytical techniques with a place in the oral solid dosage formulation toolkit

Developing an identified drug substance into a pharmaceutical product with well-defined and tightly controlled pharmacological performance is an exacting task. Here, Dr Paul Kippax, Product Group Manager, reviews how Malvern Panalytical’ technology directly supports the development of oral solid dosage (OSD) forms by efficiently generating the data needed to drive formulation.

Over the past two decades the empirical processes which once characterized pharmaceutical development have steadily given way to the more systematic, knowledge-led approach enshrined in QbD. A QbD strategy builds quality into a product from the outset through the development of a detailed understanding of the factors that influence clinical efficacy, and of an effective control strategy for manufacture, based on the mitigation of risk.

The starting point for pharmaceutical product formulation is an identified, active drug substance. The goal of formulation development is to incorporate this substance in a product and to develop a manufacturing process that will deliver that product securely and consistently. In QbD terms, this relies on identifying the factors that will define the clinical efficacy of the product, and then learning to control the material and process attributes that will ensure consistent delivery to the resulting quality target performance profile (QTPP).

In this whitepaper we look at the steps involved in moving from a drug substance to a successful oral solid dosage (OSD) form, and at the analytical technologies that can be helpful. In particular, we focus on gathering the required information to progress pharmaceutical development within the context of the regulatory requirements and the application of QbD.

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Developing an identified drug substance into a pharmaceutical product with well-defined and tightly controlled pharmacological performance is an exacting task. Here, Dr Paul Kippax, Product Group Manager, reviews how Malvern Panalytical’s technology directly supports the development of oral solid dosage (OSD) forms by efficiently generating the data needed to drive formulation.

Over the past two decades, the empirical processes which once characterized pharmaceutical development have steadily given way to the more systematic, knowledge-led approach enshrined in QbD. A QbD strategy builds quality into a product from the outset through the development of a detailed understanding of the factors that influence clinical efficacy, and of an effective control strategy for manufacture, based on the mitigation of risk.

The starting point for pharmaceutical product formulation is an identified, active drug substance. The goal of formulation development is to incorporate this substance in a product and to develop a manufacturing process that will deliver that product securely and consistently. In QbD terms, this relies on identifying the factors that will define the clinical efficacy of the product, and then learning to control the material and process attributes that will ensure consistent delivery to the resulting quality target performance profile (QTPP).

In this whitepaper, we look at the steps involved in moving from a drug substance to a successful oral solid dosage (OSD) form, and at the analytical technologies that can be helpful. In particular, we focus on gathering the required information to progress pharmaceutical development within the context of the regulatory requirements and the application of QbD.

The regulatory framework and QbD

ICH Q8(R2) provides detailed guidance about pharmaceutical development and suggests that the following steps represent a minimal requirement for success:

  • Defining the quality target product profile (QTPP).
    This is the definition of what the product must deliver and includes specifications relating to quality, safety, and efficacy. Developing the QTPP requires consideration of how the drug substance will be delivered, the form the pharmaceutical product will take, the strength of the drug, its bioavailability, and how stability will be maintained within the manufactured product.
  • Identifying the critical quality attributes (CQAs) of the product.
    The CQAs of a product are the variables that directly impact clinical efficacy and quality, in other words, those that impact the QTPP. So, for example, a CQA for an OSD might be dose uniformity, since this impacts the amount of drug delivered with each tablet and the efficacy of the product.
  • Determining the CQAs of the drug substance and excipients.
    These are different from the CQAs for the finished product. For example, while the disintegration characteristics of a tablet might impact how quickly drug substance particles are released from the tablet matrix, it could be the size of those particles that influences the rate of dissolution of the drug, and consequently bioavailability. It will also be important to select both the type and amount of excipient required to ensure reproducible tablet disintegration.
  • Selecting an appropriate manufacturing process.
    Once the formulation is defined then attention shifts to identifying how to make the product consistently, as efficiently as possible.
  • Defining a control strategy.
    The final element of pharmaceutical development is to identify which variables must be controlled during manufacture to ensure that the defined quality targets are met. Timely and efficient monitoring and control technologies are required for successful implementation.

QbD wraps around all of these steps and influences the rigor and approach that is taken in tackling them. For example, a QbD approach to investigating the CQAs of the drug product might extend to developing functional relationships between these CQAs and critical material attributes (CMAs) and process parameters (CPPs). In manufacturing, QbD is associated with the development of a design space, an operating window in which success is assured, rather than simply a fixed set of processing conditions. Control within the design space may well then be implemented through the application of relevant process analytical technology (PAT), and be associated with real-time release, as opposed to detailed final product QC.

There is no regulatory requirement to implement QbD as part of an investigational new drug (IND) submission or new drug application (NDA) but there are potential rewards. The application of QbD demands a greater understanding than more empirical approaches and is therefore associated with the more secure demonstration of risk mitigation. Manufacturers that adopt this approach are therefore permitted the freedom of changing operating conditions within the defined design space: a valuable prize. More generally there is the suggestion that by comprehensively satisfying the regulators’ requirement to minimize risk such submissions are subject to a lighter regulatory focus than those where detailed process and product knowledge is less clearly demonstrated.

In summary though, the fundamental steps involved in pharmaceutical development are identical with or without QbD. Focusing on the information required to drive these steps helps to identify analytical strategies that can support the formulation workflow.

Characterizing the drug substance

In the early stages of formulation, the focus lies on the drug substance itself and how to deliver its therapeutic effect to the patient. This leads to the definition of the QTPP. In this whitepaper, the focus is on oral solid dosage forms but this stage would normally include a detailed assessment of the optimal drug delivery technology.

The pharmacological profile of a drug substance can be influenced by both biological and physicochemical properties. Understanding the impact of these properties helps with the development of a detailed specification for the drug substance and other aspects of the QTPP such as the quantity of drug substance in each tablet. ICH Q6A is helpful in identifying which tests are required to securely characterize the drug substance in an IND or NDA situation and suggests that the specifications for the drug substance might relate to:

  • Physical properties such as pH, refractive index, melting point
  • Particle size
  • Polymorphic form/amorphous content
  • Chiral identity
  • Water content
  • Inorganic impurity levels
  • Microbial content

Many of these properties can influence the performance of the product in a number of ways and will therefore go on to be identified as CQAs for the drug. For example, particle size can affect the dissolution, solubility or bioavailability of the drug in a tablet and also, perhaps less obviously, impact the processability of a tablet blend. Finer particles may flow less easily than those that are coarser, for example, and a blend consisting of dissimilarly sized drug and excipient particles may be prone to segregation, leading to poor content uniformity.

For the majority of pharmaceutical applications the need for particle size information, and particle size control, is efficiently met by laser diffraction. Other critical drug properties, such as polymorphic form, are less readily characterized. Many drugs exist in multiple crystal forms. If a certain polymorph is identified as having desirable characteristics then the presence of others may have a detrimental effect on the product. Manual microscopy is one technique for differentiating crystal forms, but it can be both time-consuming and subject to operator variability. The following case study highlights the application of an alternative, relatively new technique, Morphologically Directed Raman Spectroscopy (MDRS), for efficient polymorph characterization.

Case study: Using MDRS to differentiate drug substance polymorphs.

The technique of MDRS, as its name suggests involves using morphological data to guide the efficient application of Raman spectroscopy, which in turn provides chemical identification. The technique is implemented using an automated imaging system with spectroscopy capabilities, such as the Morphologi 4-ID, Malvern Panalytical.

Figure 1 shows images of two different polymorphic forms of a drug substance, measured using a Morphologi-ID. Polymorph A and B have distinctly different morphologies; the former has a square-like crystalline form and the latter a needle-like structure. Automated imaging, which can gather tens of thousands of particle images in just minutes, therefore delivers relatively reliable differentiation between the two polymorphs. By applying size and shape classification it is possible to identify individual particles as polymorph A or B with a high degree of accuracy. However, the sample images below exemplify a small population of particles that may be less securely classified on the basis of size and shape alone.

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Figure 1: Automated imaging is an efficient technique for differentiating polymorphs of a drug substance.

Different crystal structures produce different Raman spectra so bringing spectroscopy to bear, to characterize particles that are not clearly one polymorph, or the other, provides confirmatory data. Figure 2 shows the Raman spectra for polymorph A and B. For the most part, the spectra are closely similar and overlay one another. However, there are clear differences within the 1120 – 1300 cm-1 region. Focusing analysis in this region allows reliable differentiation between particles of closely similar morphology.

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Figure 2: Raman spectra for polymorphs A (orange line) and B (red line). Between 1120 – 1300 cm-1 the spectra are clearly different enabling the reliable classification of particles that are closely similar in morphological form.

This spectroscopic information can be used to validate the data generated with automated imaging and can also provide additional information about the crystal structure of the polymorphs, which can be used to elucidate and control formation mechanisms.

Introducing the excipients

Once a detailed profile of the drug substance has been developed, the next step is to identify the excipients that are required to successfully formulate the drug substance within a tablet. The role of excipients varies considerably depending on the drug substance and the required clinical effect. In some instances, controlled release agents are incorporated to tailor the rate at which the drug substance is released in vivo. In others, excipients enhance the handling characteristics of the tablet blend, by improving bulk properties such as powder flow, bulk density or compressibility.

An excipient may therefore directly influence the QTPP and be associated with a product CQA such as rate of drug substance release. Alternatively, the impact on the QTPP may be less straightforward. By altering the bulk properties of the blend a poorly specified excipient may compromise manufacturing performance and lead to the production of sub-standard products, tablets with poor mechanical properties that are unstable and/or have an inconsistent disintegration profile, for example. Assessing the potential for excipients to impact processability is therefore important.

In summary the choice of each excipient and its grade is guided by the need to:

  • Identify any product CQAs that may be impacted by the excipient, either directly or indirectly, and select a material that will deliver the performance set out in the QTPP.
  • Understand how the CMAs of the excipient, such as molecular weight (MW) or particle size, affect the product CQA.
  • Establish specifications and acceptance criteria for an excipient that will ensure it performs successfully within the formulation.

The following case studies illustrate how gel permeation chromatography/size exclusion chromatography (GPC/SEC) and laser diffraction particle size analysis can be helpful in building the understanding necessary to identify an appropriate excipient and grade.

Case study – Selecting an excipient for controlled drug release [1]

Polymers such as Hypromellose (Hydroxypropyl Methylcellulose, HPMC) and its derivatives are commonly used as controlled drug release agents. The grade of the polymer used directly affects the release profile of the drug substance making it a CQA. The effective use of these materials relies on understanding the polymeric characteristics that define performance which are MW, MW distribution and chain structure. Multi-detector GPC/SEC can be used to measure these properties.

GPC/SEC is a two-step process. The first step involves the separation of a solution of the polymeric sample, on the basis of hydrodynamic volume, using a packed column. The second is the analysis of the resulting size-fractionated eluent stream using one or more detectors. Conventional GPC/SEC systems employ only a single refractive index (RI) to measure concentration and rely on calibration curves to generate relative (to a standard) MW data. In contrast, the use of a detector array that additionally incorporates a light scattering and viscometer detector enables the measurement of absolute MW without any requirement for calibration, and provides structural information about the polymer.

Figure 3 shows MW distribution data for four samples of HPMC and the derivative excipient Hypromellose acetate succinate (HPMCAS) measured with a triple detector GPC/SEC system - Viscotek TDAMax, Malvern Panalytical. In addition, the contrast between the MW data generated with the triple detector array with that produced using just a single RI detector is also shown. These results demonstrate the substantial, and variable, inaccuracies that can be introduced by relying on the calibration of the system with a standard. The incorporation of a light scattering detector eliminates these errors and provides direct and accurate measurements of absolute MW that can be used to reliably differentiate the excipients, and rank their relative performance.

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Figure 3: Triple detector GPC/SEC reliably and directly measures the absolute MW of polymeric excipients for controlled release, providing data that supports the identification of a suitable candidate for any specific formulation

Triple Detector Array (TDA)Conventional Results
SampleMw (Da)Rg(w) (nm)Rh(w) (nm)η(w) (dL/g)Mw (Da)
HPMC88,10018.5414.242.49398,100
HPMCAS-LF162,30013.4910.360.8783,400
HPMCAS-MF167,50013.510.360.88274700
HPMCAS-HF360,90015.9512.250.79272500

Table 1: Comparison of Mw data reported using Triple Detection and conventional GPC/SEC. Data reported by the TDA system include the molecular size (Rg(w) and Rh(w)) and intrinsic viscosity (w).

Triple detection also provides molecular size and intrinsic viscosity data which can be used to quantify important structural features of a polymer such as the degree of cross-linking or chain branching. These features also contribute to the functionality of the polymer excipient within a tablet blend and can be investigated to provide additional information on which to base excipient grade choice.

Case Study: Selecting excipient grade

Lactose is a prime example of an excipient that is most usually added to a formulation simply to provide bulk and enable accurate dosing. Choice of lactose grade is therefore governed not by any direct impact on functionality but rather by the effect of the lactose on processability. The particle size and particle size distribution of the lactose impact powder flowability, compressibility, and the likelihood of segregation and are therefore often identified as CMAs.

Laser diffraction is an ensemble particle sizing technique that measures the particle size of either wet and dry samples containing particles in the size range 0.01 µm to 3500 µm. Figure 4 illustrates the use of laser diffraction to measure the particle size distribution of different lactose blends, some of which contain very small amounts of fines. These data were measured using the Mastersizer 3000, Malvern Panalytical. A key feature here is that the instrument is not only able to comfortably span the particle size range needed to simultaneously measure coarse and fine material, but that it provides the resolution needed to precisely quantify the amount of fines present. As in the preceding case study, this analytical technique is therefore able to securely differentiate different grades of excipient to provide the information that underpins effective choice.

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Figure 4: Particle size distribution data highlights the ability of laser diffraction to precisely determine the amount of fines in lactose grades containing coarse and fine material.

Making the product, bringing the API and excipient together

After formulation has progressed through the characterization of individual ingredients the focus of the workflow shifts to bringing the drug substance and excipients together. At this stage, it is essential to ensure that the individual ingredients within a formulation remain in the required state, after they have been combined.

In most cases, it is the fine drug substance particles that are most susceptible to changes, and indeed of most interest, because of the potential for impacting the QTPP. Changes in particle size, shape or polymorphic form may all occur during mixing and processing. Being able to isolate and characterize the drug substance within the blend is therefore crucial. As in the identification of polymorphic forms, MDRS has a role to play here since it provides the chemical identification required when particles within a blend cannot be securely classified as excipient or drug substances, on the basis of morphology alone. The following case study shows how MDRS can be used to gather information about the drug substance within a blend or matrix.

Case Study: Monitoring process-induced changes in an API [2]

The ability of MDRS to detect changes in individual components within a multi-component blend enables an assessment of any changes to the drug substance induced by processing. In this case study, an investigation was carried out to assess the impact of processing on the morphology of a drug substance within a tablet blend, with the goal being to understand if blending caused particle break-up. Data were obtained for the blending of the drug substance with three different excipients: lactose, microcrystalline cellulose (MCC), and a mixture of lactose and MCC. These excipients are all used routinely as bulking agents and therefore might be expected to have a negligible impact on the functionality of the product. MDRS was used to measure the particle size and shape of the drug substance within the blends, before and after the blending process.

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Figure 5: Measurements of the particle shape of the drug substance reveal that blending results in a reduction in elongation that is suggestive of particle chipping (from reference [2]).

Figure 5 shows how the elongation of the drug substance particles changes as a result of blending. Elongation is a shape parameter defined by the formula (1 – width/length). Needle-like particles will have an elongation close to 1, whereas regular-shaped particles, such as spheres, will have an elongation ratio of close to 0. In this case, blending clearly had a marked impact on elongation. Elongation is lower in all of the blended samples, suggesting that the drug substance particles become less needle-like in shape as a result of this processing step. This shift in elongation is more marked in blends containing MCC than in those containing lactose only, a difference that may relate to the difference in hardness of these excipients.

Detecting this particle breakage mechanism is important since it can increase the amount of drug substance fines in the blend. This may lead to an increase in processing problems, such as the risk of adhesion to equipment surfaces during subsequent stages of the process, and, ultimately, impact dose content uniformity and bioavailability.

Optimizing the manufacturing process

Once there is a secure understanding of how to formulate the excipients and drug substance within the laboratory environment, the manufacturing process becomes a primary focus. In tableting processes, the first step is often granulation. Granulation of a tableting blend helps to reduce the risk of segregation and can improve the processability of the blend. Milling and lubrication of the resulting granules produces an optimized, homogeneous feed for the tablet press. Spray coating of the tablet is usually the final production step.

Measuring and controlling the properties that impact the CQAs of the product, at each stage of the manufacturing process, safeguards the consistent manufacture of products that meet the defined specification. In- or on-line Process Analytical Technology (PAT) can be helpful here in delivering timely monitoring and the necessary control. For example, real-time particle sizing during granulation or milling helps analysts determine that particles meet a size specification that will deliver desirable process performance and ensure that the final tablet meets the QTPP.

Real-time PAT has significant potential to support commercial manufacture but it can also accelerate process development work. Most especially continuous monitoring supports the efficient identification of critical process parameters (CPPs) and definition of the design space, the operation window within which product quality is assured. The following case studies showcase examples of PAT and illustrate their application in the development of wet granulation and spray coating processes.

Case Study – Monitoring the evolution of particle size during wet granulation

High shear wet granulation offers a number of advantages relative to alternative granulation techniques [3] and is often selected for tablet manufacture. In this unit operation, a granulating liquid is used to bind the particles together in a high shear mixer. Particle size measurements can be used to control granulation to a desirable endpoint and also to compare the trajectory of granulation processes at different scales. This can be very helpful during process scale-up which is recognized as being a difficult task

In-line particle sizing probes such as the Parsum IPP70 (Malvern Panalytical) use spatial partial velocimetry [3] to measure particle size in real-time and have proven application as PAT for granulation monitoring. A key feature is the robust nature of the probes which enables continuous, reliable particle size measurement in the moist, fouling environment of the granulator.

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Figure 6: An in-line particle sizing probe allows real-time continuous monitoring of a wet granulation process.

Figure 6 shows the evolution of granule size during a high shear wet granulation process, measured using an in-line Parsum probe. Changes in granule size at each stage of the process are clearly evident and the granulation is efficiently tracked through to a defined endpoint. The real-time information provided enables rapid scoping of the effect of the CPPs associated with granulation control such as moisture content and/or agitator speed and the detailed investigation of process dynamics.

Case Study – Optimizing spray coating

Water-based solutions or suspensions containing an appropriate polymeric excipient are often used to impart a final coating to a tablet. These coatings are typically delivered using a pressurized spraying process that directs a targeted plume onto the formed tablet. The aim is to rapidly and uniformly coat the tablet with a coating of controlled thickness. The coating may impact the disintegration characteristics of the tablet and the dissolution profile, and may be engineered to deliver a closely controlled drug release profile.

The quality of the finished coating is highly dependent on the droplet size of the sprayed coating which impacts transfer efficiency (TE), coating uniformity and finish. Droplet size must therefore be optimized and controlled during the spray coating process. CPPs for this step may include the viscosity of the coating formulation, the distance of the spray nozzle from the tablet and the pressure at which spraying takes place.

Figure 7 shows how the delivered particle size distribution of droplets of a spray coating solution varies as a function of distance from the spray nozzle. These data were measured using a Spraytec from Malvern Panalytical, an instrument designed specifically for the real-time particle sizing of sprays. Spray droplet size measured at a distance of 25, 35 and 46 cm from the nozzle shows that droplets become larger as the distance between the tablet and the nozzle is increased.

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Figure 7: The distance between the tablet and the spray nozzle impacts the delivered droplet size, and consequently the quality of the applied coating.

The data presented in Figure 8 shows how particle size distribution varies as a function of spray pressure, across the range of 30-200 bar. These results quantify the extent to which droplet size decreases with increasing pressure. For this particular coating and nozzle, the median size at 30 bar is 42 µm but reduces to 28 µm at 200 bar.

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Figure 8: Increasing spraying pressures decreases the size of droplets produced during a spray coating process.

Here then, the deployment of laser diffraction allows process developers to rapidly assess how CPPs impact delivered droplet size, enabling the identification of a control strategy that ensures robust, closely controlled performance.

In conclusion

In recent decades formulation has become an increasingly systematic, knowledge-driven process. Successful formulation relies on developing a detailed scientific understanding of the behavior of individual components within a drug product, and of how constituent elements can be brought together to meet pharmacological targets, both in the laboratory and into commercial manufacture. The widespread adoption of QbD reinforces this trend and intensifies the requirement for analytical techniques that can deliver the information needed in a timely and efficient way. This white paper highlights how laser diffraction particle size analysis, automated imaging, MDRS and GPC/SEC can be applied within the context of modern formulation practice to rapidly advance to full-scale manufacture.

References

  1. Chen R.,“ Characterization of Hypromellose Acetate Succinate by Size Exclusion Chromatography (SEC) Using Viscotek Triple Detector”, International Journal of Polymer Analysis and Characterisation,14:7,617-630
  2. Gamble, J et al “Monitoring process-induced attrition of drug substance particles within formulated blends,” International Journal of Pharmaceutics 470 (2014) 77-87
  3. ‘Real-time particle sizing for granulation control’ Malvern Panalytical white paper. Available for download at: http://www.malvern.com/en/support/resource-center/Whitepapers/WP130226RealTimeParticleForGranulat.aspx

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