Tackling OSD Manufacturing Challenges With Pharma 4.0 Digital Innovation: Overview

By Ashok Kumar Dasari, Viatris BLR India

Oral solid dosage (OSD) manufacturing has largely relied on batch processing methods developed over 100 years ago. The pharmaceutical industry now faces increasing pressure to improve product quality, shorten time-to-market, and cut costs while continuing to meet strict regulatory requirements. Smart manufacturing, which includes continuous tablet manufacturing, digital formulation modeling, process analytical technology, and AI-powered dissolution prediction, provides an effective way to tackle these challenges.

The change from traditional to smart OSD manufacturing is more than just a technological upgrade. While it has clear benefits in quality assurance, operational efficiency, and regulatory compliance, successful implementation requires a fundamental rethink of how pharmaceutical production is approached, such as capital investment, technical skills, and organizational readiness. This two-part article series offers a thorough technical review of this transition, focusing on both opportunities and challenges with a practical and scientific lens.

Traditional OSD Manufacturing: Persistent Challenges

The Trial-and-Error Paradigm

Traditional formulation development relies heavily on hands-on methods. Formulation scientists usually conduct a series of experiments, adjusting excipient ratios, process parameters, and equipment settings based on results. While this approach has been reliable, it has several limitations:

• Extended Development Timelines: Normal formulation optimization can take 12 to 24 months, with each round needing weeks for stability studies, dissolution testing, and analytical validation. The sequential nature of this experimentation creates delays that push back market entry.
• Limited Design Space Understanding: Traditional methods often explore only a small part of potential formulation and process options. Without thorough design space mapping, manufacturers tend to operate within narrow parameter ranges, missing out on possible optimizations and making the process more sensitive to small changes.
• Knowledge Loss: Much formulation knowledge remains unrecorded and exists only in the experience of individual scientists. When staff leave, valuable insights often go with them.

Batch Variability and Quality Inconsistencies

Batch manufacturing introduces various sources of variability that affect product quality:

• Inter-batch Variability: Even with the same nominal parameters, batch-to-batch differences can arise from equipment variations, raw material lot differences, environmental conditions, and operator technique. The coefficient of variation (CV) for critical quality attributes (CQAs) like content uniformity can range from 3% to 8% in standard processes.
• Scale-up Complications: Laboratory-sized formulations often do not scale well to production size. Differences in mixing dynamics, heat transfer, and powder flow behavior between 2-liter and 200-liter granulators can necessitate extensive reoptimization, sometimes requiring a complete reformulation.
• Material Attribute Variability: Raw material properties — such as particle size distribution, moisture content, crystallinity, and flowability — vary between suppliers and lots. Traditional processes often lack real-time feedback systems to address these variations, resulting in unexpected batch failures.
• Knowledge Loss: Much formulation expertise remains tacit, residing in individual scientists' experience rather than systematized data repositories. When personnel transition, valuable institutional knowledge often departs with them.

Smart Manufacturing Solutions: Technical Evaluation

Continuous Tablet Manufacturing

Continuous manufacturing signifies a major shift from batch processing, allowing for an uninterrupted material flow through integrated unit operations.

Technical Implementation: Modern continuous OSD systems combine feeding systems, continuous mixers/granulators, fluid bed dryers, mills, blenders, and tablet presses into a coordinated production line. Advanced systems use model predictive control (MPC) algorithms to maintain steady operation, despite interruptions.

Residence Time Distribution (RTD): Understanding material flow in continuous systems requires careful RTD characterization. Proper RTD analysis ensures adequate mixing, avoids preferential flow paths, and establishes suitable sampling strategies. Typically, continuous OSD systems reach steady state within two to four residence times (30 to 90 minutes, depending on throughput).

Quality by Design Integration: Continuous manufacturing naturally fits with quality by design (QbD) concepts. The interconnected nature of continuous lines demands a full understanding of design space, the mechanics of unit operations, and strong control strategies — key elements emphasized by QbD.

Advantages:

• Reduced footprint (50%–70% smaller than equivalent batch capacity).
• Improved content uniformity (CV often <2% versus 3%–8% for batch).
• Enhanced process understanding through continuous data generation.
• Flexibility for on-demand manufacturing and rapid product changeover.
• Reduced work-in-progress inventory.

Plan For These Technical Challenges:

• RTD broadening can compromise product quality if not properly managed.
• Equipment failures propagate more rapidly than in batch systems.
• Material tracking and genealogy require sophisticated systems.
• Startup and shutdown generate waste.
• Many organizations have limited experience with continuous processes.

Digital Formulation Modeling

Computational approaches increasingly complement or replace empirical formulation development:

Mechanistic Modeling: Physics-based models simulate powder flow, mixing dynamics, granulation kinetics, and tablet compaction using computational fluid dynamics (CFD), discrete element modeling (DEM), and finite element analysis (FEA). These tools predict formulation behavior before physical experimentation.

Machine Learning Applications: Artificial neural networks, support vector machines, and random forest algorithms trained on historical formulation databases can predict formulation performance. Deep learning models analyzing multivariate data sets identify non-obvious relationships between formulation components and product quality.

Digital Twin Technology: Virtual replicas of physical OSD processes enable scenario testing, optimization, and troubleshooting without production disruption. Digital twins integrate real-time process data with mechanistic models, creating dynamic representations that evolve with the physical system.

Practical Value: A major pharmaceutical company recently reported reducing formulation development time from 18 months to six months using integrated computational modeling. Predictive dissolution models achieved R² > 0.90 correlation with physical testing, enabling virtual screening of formulation candidates.

Limitations:

• Models require extensive validation data.
• Complex formulations (e.g., modified-release, multiparticulates) challenge current modeling capabilities.
• Model accuracy depends on input data quality.
• Regulatory acceptance of purely computational formulation development remains limited.

Process Analytical Technology (PAT)

PAT represents the sensory nervous system of smart manufacturing, providing real-time process understanding:

Near-Infrared (NIR) Spectroscopy: NIR probes monitor blend uniformity, moisture content, API concentration, and polymorphic form non-invasively. Modern instruments collect spectra at millisecond intervals, enabling genuine real-time control. Chemometric models (PLS, PCR) translate spectral data into actionable process information.

Raman Spectroscopy: Complementary to NIR, Raman spectroscopy excels at detecting crystalline forms, monitoring solid-state transformations, and quantifying API in low-dose formulations. Recent advances in spatially offset Raman spectroscopy (SORS) enable through-container analysis.

Acoustic Emission Monitoring: High-frequency acoustic sensors detect granulation endpoint, monitor tablet compression, and identify equipment anomalies. Machine learning algorithms classify acoustic signatures, distinguishing normal operation from deviation.

Tablet Hardness and Thickness Measurement: Inline sensors measure every tablet's mechanical properties rather than periodic sampling. This 100% inspection enables real-time feedback to compression force controllers, maintaining target specifications despite powder property variations.

Implementation Considerations: Successful PAT deployment requires method development equivalent to traditional analytical methods. Calibration models must span the expected operating range, account for raw material variability, and undergo rigorous validation. Regulatory submissions must demonstrate PAT method equivalence to traditional pharmacopeial methods.

Artificial Intelligence For Dissolution Prediction

Dissolution testing remains a critical quality assessment but represents a significant bottleneck due to its time-intensive nature.

AI-Based Approaches: Supervised learning algorithms correlate process parameters, formulation attributes, and inline measurements with dissolution profiles. Successful models incorporate:

• tablet physical properties (hardness, porosity, density)
• particle characteristics (size distribution, surface area)
• formulation composition
• process parameters (compression force, speed)
• environmental conditions (temperature, humidity).

Model Architecture: Ensemble methods combining multiple algorithms (gradient boosting, neural networks, support vector regression) typically outperform individual models. Convolutional neural networks analyzing tablet images show promise for predicting dissolution from visual characteristics.

Validation and Regulatory Considerations: Predictive models require prospective validation demonstrating consistent accuracy across manufacturing campaigns. Regulatory agencies increasingly accept well-validated predictive models for real-time release testing (RTRT), though pharmacopeial dissolution testing typically remains necessary for stability programs.

Performance Metrics: Published studies report prediction accuracies with mean absolute percentage errors (MAPE) of 5%-12% for immediate-release formulations. Modified-release predictions remain more challenging, with MAPE typically 10%-20%.

In my next article, I’ll provide a deep dive on successful implementation of these smart manufacturing solutions, including case studies.

About The Author:

Ashok Kumar Dasari is a life sciences professional at Viatris BLR India. He is a seasoned biopharmaceutical professional with extensive expertise in pharmaceutical practices, aseptic operations, regulatory compliance, and total quality management systems. With a career that spans both local and international landscapes, he has consistently worked to address the evolving challenges of the modern pharmaceutical industry. Dedicated to lifelong learning and knowledge sharing, he remains committed to writing on novel topics in the pharmaceutical domain and beyond, offering narratives that inform, inspire, and innovate. He maintains a blog focused on daily learning insights and advanced technology topics.

Disclaimer: For Professional and Educational Use Only. This article is for informational purposes only and does not constitute regulatory guidance, legal advice, or professional consultation. The author assumes no responsibility for decisions or outcomes based on this content. Readers must conduct thorough independent research, consult relevant regulatory authorities (FDA, EMA, etc.), and engage qualified professionals before implementing any approaches discussed here. Cited performance data are illustrative only; actual results vary significantly. No specific outcomes are guaranteed. Smart manufacturing technologies evolve rapidly; information may become outdated. Implementation decisions and associated risks are solely the reader's responsibility.