Enhancing Pharma Processes

X-ray Computed Tomography (CT) is a non-destructive analytical technique used to create three-dimensional images of pharmaceutical samples. It works by rotating a sample between an X-ray source and a detector, capturing numerous 2D projection images at various angles. These images are then computationally reconstructed into a 3D volume, providing an exact digital twin of the sample.

In pharmaceutical quality control (QC), X-ray CT is highly versatile and can be applied at various stages of the drug product life cycle:

• Powder characterization: It can characterize the distribution of active pharmaceutical ingredients (APIs) and other particles in powder mixtures, ensuring even blending.
• Granulation: CT is used to assess granule shape and size, which are crucial for flow properties and tablet pressing.
• Tablet pressing: It provides insight into API distribution within tablets and reveals internal defects that may occur during manufacturing.
• Coating evaluation: CT evaluates the thickness and uniformity of tablet coatings, which are important for taste, ease of swallowing, and controlled drug release.
• Packaging QC: It ensures proper sealing of packaging and identifies any defects.
• Device characterization: For complex devices like eyedropper bottles, CT allows for subvoxel surface determination, enabling detailed analysis of internal mechanisms and comparison with CAD drawings.

A key benefit of X-ray CT is its non-destructive nature, requiring minimal sample preparation, and its ability to provide quantitative data, especially with the aid of AI and computational tools for image analysis and segmentation.

Extracting quantitative information from X-ray CT images involves segmenting, or assigning, different parts of the 3D volume to specific phases or materials. For simpler samples with distinct phases, like an excipient material and voids, a grayscale histogram can be used to set an intensity threshold, quickly differentiating between the phases.

However, for more complex pharmaceutical products, such as omeprazole tablets with multiple components (coating, granules, matrix, cracks, and background), this simple method is insufficient. In these cases, artificial intelligence (AI) and deep learning segmentation software (e.g., Dragonfly) are employed. This involves manually identifying and labeling a few different phases within the image (e.g., background, matrix, coating, granules). The software then uses this input to build a model that automatically characterizes and segments each phase across the entire 3D volume.

• Volume percentage: The percentage of the total tablet volume occupied by each phase (e.g., coating, matrix, granules).
• Thickness measurement: For coatings, a color-coded heat map can be generated to show variations in thickness.
• Granule analysis: Individual granules can be separated to analyze their volume distribution, sphericity, and equivalent spherical diameter, providing insights into manufacturing processes.

While the accuracy of these numbers depends on the quality of the phase assignments, it has been validated against known characteristics in literature, making X-ray CT a powerful quantitative tool.

The X-ray CT analysis of direct-mixture tablets versus Kollitab-mixture tablets (produced using BASF's co-processed excipient) reveals significant differences in their internal structures and resulting physical properties.

Powder mixtures (Pre-tableting):

• Direct mixture: Exhibits greatly different particle sizes (small and very large), a mix of filled and empty particles, and visible API (ibuprofen). The porosity (void space) is higher at 49.4%.
• Kollitab mixture: Shows more uniform particle sizes, most particles are filled, and the size distribution is tighter. The porosity is lower at 46.2%, indicating a denser packing.

Tablets (Post-pressing):

• Direct mixture tablets: Characterized by numerous cracks throughout their structure and sometimes large voids, particularly near the exterior. Their disintegration time is much shorter, and they have lower hardness. These tablets are prone to falling apart during handling and packaging.
• Kollitab mixture tablets: Contain smaller, mostly spherical voids, indicating a more robust and uniform internal structure. Their disintegration time is much longer, and they have higher hardness.

The average porosity for both types of tablets is significantly lower than their powder forms, typically ranging from 3% to 4%. These structural differences directly influence the dissolution properties and overall integrity of the tablets, highlighting how X-ray CT can guide optimization of upstream manufacturing processes to produce more durable and effective drug products.

Raman spectroscopy is a molecular spectroscopy technique that provides a unique "fingerprint" spectrum for each molecule. It was first observed by C.V. Raman in the 1920s. The technique involves shining light (typically from a laser) onto a sample. Most of the light is elastically scattered (Rayleigh scattering) and is filtered out. However, a small fraction (about one in a million photons) undergoes inelastic scattering (Raman scattering), where the energy of the scattered photon is different from the incident photon. This energy difference corresponds to vibrational modes (stretches, bends, waggles) within the molecules, producing a Raman spectrum. Each peak in the spectrum represents a specific molecular vibration at a characteristic frequency.

In pharmaceutical quality control (QC), Raman spectroscopy is highly valuable due to several benefits:

• Non-destructive: Samples are not destroyed or altered during analysis.
• Minimal sample preparation: It can scan through containers (bottles, drums) without needing to open them, reducing contamination risk.
• Versatility: Capable of analyzing solids, liquids, powders, pastes, and gels.
• Compatibility with colored materials/containers: Utilizes a 1064 nm laser, allowing scanning through colored materials and containers.
• Selectivity: Since most APIs and excipients are covalently bonded (containing C, H, O, N), they are Raman active and produce distinct, characteristic spectra, enabling clear identification and differentiation of materials.

For portable or handheld Raman systems, the process is streamlined: a laser scans the sample, the scattered light is collected by a detector, and the resulting spectrum is compared to an internal library of materials (e.g., 13,000 materials). This comparison provides an identification or pass/fail result, typically within 30-40 seconds. Handheld Raman systems are particularly suited for qualitative measurements, screening, and verification.

Handheld Raman spectroscopy dramatically improves the efficiency of raw material identification (RMID) in pharmaceutical manufacturing by transforming a multi-day, multi-step process into one that takes mere minutes.

In a traditional RMID workflow, the process involves:

1. Identifying incoming materials based on their labels.
2. Quarantining the materials.
3. Opening containers and taking samples.
4. Sending samples to a laboratory for analysis on benchtop instruments.
5. Waiting for lab results (which can take days depending on backlog).
6. Releasing materials from quarantine based on lab confirmation.

This traditional approach is time-consuming, labor-intensive, and carries a higher risk of contamination due to repeated handling and opening of containers.

With handheld Raman spectroscopy, the workflow is revolutionized:

1. Materials arrive at the warehouse.
2. An operator directly scans the material through its container (e.g., bottle, drum) using the handheld device.
3. The instrument performs the analysis in 30-40 seconds, comparing the obtained spectrum to an onboard library.
4. A "match" or "pass" result is immediately displayed, allowing for quick release of the material from quarantine into the production process.

This shift from days to minutes results in:

• Faster material release: Significantly reduces lead times for raw materials.
• Reduced handling and contamination: Eliminates the need to open containers and take physical samples for lab analysis.
• Lower cost per analysis: Decreases labor, laboratory, and quarantine-related costs.
• On-site verification: Enables immediate verification at the point of receipt or use.

Beyond raw material identification, handheld Raman spectroscopy has diverse applications throughout the pharmaceutical workflow, enhancing quality assurance (QA) and quality control (QC) at various stages:

1. Verification purposes (QA/QC lab): It ensures consistency and quality of materials going into formulations. This includes verifying chemical solvents, excipients, cell culture media, and finished products. By monitoring materials from raw inspection to final QC, it helps maintain product integrity.
2. Authentication and anti-counterfeiting: Handheld Raman can check for brand security and identify counterfeit materials. For example, it can differentiate between genuine and adulterated products (e.g., methanol-contaminated hand sanitizers vs. pure ethanol-based ones). It can also be used to compare customer-returned products with retained samples to investigate quality concerns.
3. Process monitoring: It allows for in-line or off-line monitoring of reactions. By observing the decrease of starting materials and the increase of product (e.g., ritonavir intermediate), manufacturers can ensure optimal mixing times and reaction completion, preventing over- or under-processing.
4. Polymorph identification: Raman spectroscopy is highly sensitive to changes in crystal packing, making it effective for identifying different polymorph forms of active pharmaceutical ingredients (APIs) and excipients (e.g., anatase vs. rutile titanium oxide, or different forms of ritonavir). This is crucial because polymorphs can significantly impact physical properties like dissolution rate, viability, safety, and ultimately, the efficacy and bioavailability of the final drug product. Ensuring the correct polymorph is present is vital for patient safety and product effectiveness.

These applications collectively contribute to a more efficient, cost-effective, and robust quality control system across the entire pharmaceutical manufacturing process.

Raman spectroscopy is highly effective at identifying and differentiating between different polymorph forms of a drug because it is sensitive to the subtle changes in crystal packing within the material. While the chemical composition of polymorphs is identical, their molecular arrangement in the solid state differs, which affects their vibrational frequencies. These changes in vibrational modes result in distinct shifts and variations in the peaks of the Raman spectrum, creating a unique "fingerprint" for each polymorph form. As long as these spectral differences are within the resolution window of the instrument, Raman can distinguish between them.

This ability to differentiate polymorphs is critical in the pharmaceutical industry for several reasons:

• Impact on physical properties: Different polymorphs exhibit varying physical properties such as dissolution rate, solubility, stability, and bioavailability. A drug's efficacy and how it's absorbed and utilized by the body can be significantly altered by its polymorphic form.
• Manufacturing process implications: The incorrect polymorph can affect downstream processes like tablet compression, flow properties, and overall manufacturability.
• Patient safety and efficacy: Using the wrong polymorph in a drug formulation can lead to sub-optimal therapeutic effects or even adverse outcomes. A notable example is Ritonavir, where an incorrect polymorph form led to drug product withdrawal due to compromised solubility and bioavailability, putting HIV patients at risk.
• Regulatory compliance: Pharmaceutical regulations often require manufacturers to control and specify the polymorphic form of an API due to its impact on drug performance and quality.

By adding certified samples of different polymorphs to an instrument's library, QA/QC personnel can use Raman spectroscopy to quickly verify that the correct polymorphic form is present in raw materials, in-process samples, or finished products, ensuring consistency, safety, and efficacy.

Several key challenges are commonly encountered when implementing new analytical technologies like handheld Raman or X-ray CT in pharmaceutical settings:

• Regulatory compliance and validation: Pharmaceutical manufacturing is highly regulated, and any new technology must undergo rigorous validation processes to ensure it meets Good Manufacturing Practices (GMP) and complies with regulatory standards (e.g., USP, EP, JP pharmacopeias, 21 CFR Part 11 for data integrity). This involves extensive documentation, IQ/OQ/PQ (Installation Qualification, Operational Qualification, Performance Qualification) procedures, and demonstrating that the instrument consistently performs as intended.
• Cost and return on investment: The initial investment in advanced analytical equipment can be substantial. Organizations need to assess the financial viability and demonstrate a clear return on investment, which often involves quantifying the savings in time, labor, reduced waste, and improved product quality that the new technology can provide. Predictive modeling, like correlating porosity to hardness for predicting disintegration, can help justify the cost by potentially reducing the need for costly final QA tests.
• Uncertainty about fit in process: Companies may be unsure exactly where and how a new technology would best integrate into their existing manufacturing or QC workflows. Identifying specific pain points or areas where the technology offers the most significant advantages requires thorough evaluation and understanding of its capabilities.
• Lack of training or expertise: Adopting new technology often necessitates training existing personnel or hiring new experts. Ensuring that staff are proficient in operating the equipment, interpreting data, and performing troubleshooting is crucial for successful implementation and maximizing the technology's benefits.

These challenges highlight that the adoption of these powerful tools goes beyond just the technological capabilities, requiring significant effort in regulatory, financial, and human resource management.