Multivariate analysis in the pharmaceutical industry: enabling process understanding and improvement in the PAT and QbD era

Development of novel portable NIR spectroscopy process analytical technology (PAT) tool for monitoring the transition of ibuprofen to ibuprofen sodium during wet granulation process

The aim of this research was to develop a process analytical technology (PAT) tool for monitoring the transformation of the active ingredient ibuprofen into the fast-dissolving salt ibuprofen sodium during the wet granulation process. Two near-infrared (NIR) spectrophotometers, portable and benchtop spectrophotometer, were compared. During the analysis with the built models, both demonstrated comparable accuracy and precision (R2X = 0.995, R2Y = 0.927, Q2 = 0.995, and R2X = 0.990, R2Y = 0.948, Q2 = 0.992, respectively). Considering the applicability, a model based on the portable NIR spectroscopic data was chosen for further development and application as a PAT tool for monitoring different steps during the wet granulation process. The evaluation of the model's predictive capability involved analyzing laboratory trial batches with varying amounts of sodium carbonate, resulting in different concentrations of ibuprofen sodium at the end of the wet granulation process. Subsequently, tablets were manufactured from each trial batch, followed by dissolution analysis. The dissolution rate assays were in good agreement with the NIR-predicted concentrations of ibuprofen sodium at the end of the wet granulation process. Based on the results, the proposed model provides an excellent tool to monitor the ibuprofen acid-salt transformation, to determine the end-point of the reaction, and to efficiently control the wet granulation process.

Analysis of the impact of material properties on tabletability by principal component analysis and partial least squares regression

Principal component analysis (PCA) and partial least squares regression (PLS) were combined in this study to identify key material descriptors determining tabletability in direct compression and roller compaction. An extensive material library including 119 material descriptors and tablet tensile strengths of 44 powders and roller compacted materials with varying drug loads was generated to systematically elucidate the impact of different material descriptors, raw API and filler properties as well as process route on tabletability. A PCA model was created which highlighted correlations between different powder descriptors and respective characterization methods and, thus, can enable reduction of analyses to save resources to a certain extent. Subsequently, PLS models were established to identify key material attributes for tabletability such as density and particle size but also surface energy, work of cohesion and wall friction, which were for the first time demonstrated by PLS as highly relevant for tabletability in roller compaction and direct compression. Further, PLS based on extensive material characterization enabled the prediction of tabletability of materials unknown to the model. Thus, this study highlighted how PCA and PLS are useful tools to elucidate the correlations between powder and tabletability, which will enable more robust prediction of manufacturability in formulation development.

Is it advantageous to use quality by design (QbD) to develop nanoparticle-based dosage forms for parenteral drug administration?

Parenteral administration is one of the most commonly used drug delivery routes for nanoparticle-based dosage forms, such as lipid-based and polymeric nanoparticles. For the treatment of various diseases, parenteral administration include intravenous, subcutaneous, and intramuscular route. In drug development phase, multiparameter strategy with a focus on drug physicochemical properties and the specificity of the administration route is required. Nanoparticle properties in terms of size and targeted delivery, among others, are able to surpass many drawbacks of conventional dosage forms, but these unique properties can be a bottleneck for approval by regulatory authorities. Quality by Design (QbD) approach has been widely utilized in development of parenteral nanoparticle-based dosage forms. It fosters knowledge of product and process quality by involving sound scientific data and risk assessment strategies. A full and comprehensive investigation into the state of implementation and applications of the QbD approach in these complex drug products can highlight the gaps and challenges. In this review, the analysis of critical attributes and Design of Experiment (DoE) approach in different nanoparticulate systems, together with the proper utilization of Process Analytical Technology (PAT) applications are described. The essential of QbD approach for the design and development of nanoparticle-based dosage forms for delivery via parenteral routes is discussed thoroughly.

Application of chemometric tools in the QSAR development of VOC removal in plastic waste recycling

Deodorization and, in a broader sense, the removal of volatile organic compounds (VOCs) from plastic waste have become increasingly important in the field of plastic recycling, and various new decontamination techniques have been developed. Both in research and industrial practice, the selection of VOCs has been random or unsubstantiated, making it difficult to compare studies and assess decontamination processes objectively. Thus, this study proposes the use of Statistical Molecular Design (SMD) and Quantitative Structure - Activity Relationship (QSAR) as chemometric tools for the selection of representative VOCs, based on physicochemical properties. Various algorithms are used for SMD; hence, several frequently used D-Optimal Onion Design (DOOD) and Space-Filling (SF) algorithms were assessed. Hereby, it was validated that DOOD, by dividing the layers based on the equal-distance approach without so-called 'Adjacent Layer Bias', results in the most representative selection of VOCs. QSAR models that describe VOC removal by water-based washing of plastic waste as a function of molecular weight, polarizability, dipole moment and Hansen Solubility Parameters Distance were successfully established. An adjusted-R2 value of 0.77 ± 0.09 and a mean absolute error of 24.5 ± 4 % was obtained. Consequently, by measuring a representative selection of VOCs compiled using SMD, the removal of other unanalyzed VOCs was predicted on the basis of the QSAR. Another advantage of the proposed chemometric selection procedure is its flexibility. SMD allows to extend or modify the considered dataset according to the available analytical techniques, and to adjust the considered physicochemical properties according to the intended process.

Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation

Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

Evaluation of a compact composite sensor array for concentration monitoring of solutions and suspensions via multivariate analysis

Compact composite probes were identified as a priority to alleviate space constraints in miniaturized unit operations and pharmaceutical manufacturing platforms. Therefore, in this proof of principle study, a compact composite sensor array (CCSA) combining ultraviolet and near infrared features at four different wavelengths (280, 340, 600, 860 nm) in a 380 × 30 mm housing (length x diameter, 7 mm diameter at the probe head), was evaluated for its capabilities to monitor in situ concentration of solutions and suspensions via multivariate analysis using partial least squares (PLS) regression models. Four model active pharmaceutical ingredients (APIs): warfarin sodium isopropanol solvate (WS), lidocaine hydrochloride monohydrate (LID), 6-mercaptopurine monohydrate (6-MP), and acetaminophen (ACM) in their aqueous solution and suspension formulation were used for the assessment. The results demonstrate that PLS models can be applied for the CCSA prototype to measure the API concentrations with similar accuracy (validation samples within the United States Pharmacopeia (USP) limits), compared to univariate CCSA models and multivariate models for an established Raman spectrometer. Specifically, the multivariate CCSA models applied to the suspensions of 6-MP and ACM demonstrate improved accuracy of 63% and 31%, respectively, compared to the univariate CCSA models [1]. On the other hand, the PLS models for the solutions WS and LID showed a reduced accuracy compared to the univariate models [1].

Morphological distribution mapping: Utilisation of modelling to integrate particle size and shape distributions

The aim of this work was to develop approaches to utilize whole particle distributions for both particle size and particle shape parameters to map the full range of particle properties in a curated dataset. It is hoped that such an approach may enable a more complete understanding of the particle landscape as a step towards improving the link between particle properties and processing behaviour. A 1-dimensional principal component analysis (PCA) approach was applied to create a 'morphological distribution landscape'. A dataset of imaged APIs, intermediates and excipients encompassing particle size, particle shape (elongation, length and width) and distribution shape was curated between 2008 and 2022. The curated dataset encompassed over 200 different materials, which included over 150 different APIs, and approximately 3500 unique samples. For the purposes of the current work, only API samples were included. The morphological landscape enables differentiation of materials of equivalent size but varying shape and vice versa. It is hoped that this type of approach can be utilised to better understand the influence of particle properties on pharmaceutical processing behaviour and thereby enable scientists to leverage historical knowledge to highlight and mitigate risks associated to materials of similar morphological nature.

Stability assessment of Polatuzumab vedotin and Brentuximab vedotin using different analytical techniques

Antibody-drug conjugates (ADC) are considered to be fast-growing innovative biopharmaceuticals. The science used for conjugating potent cytotoxic payload to the targeted monoclonal antibody through a chemical linker has played a great value in the area of oncology treatment. In this study; Polatuzumab vedotin (POLA) and Brentuximab vedotin (SGN-35) were subjected to various stress conditions enclosing different pH, thermal stress, agitation, and successive cycles of freeze and thaw in order to produce potential degradation by-products and guarantee the appropriateness of the applied testing protocol. Different analytical techniques were established and validated to be used in the quantitation of the degraded products from different perspectives. The formation of ADC aggregates and fragments was monitored using SE-HPLC as well as dynamic light scattering (DLS). The drug antibody ratio (DAR) and ADC conjugation profile were determined using hydrophobic interaction chromatography (HIC-HPLC). In addition to performing a statistical interpretation of HIC-HPLC results by principal component analysis (PCA) to explicate the obtained data. Also, the quantity of the unconjugated toxic drug was quantified using RP-HPLC. Testing the binding activity of ADC to their target receptor ADC was conducted using ELISA. Results presented that used assay protocol had worked as a complementary design for characterization and stability assessment of the used ADC. Variances in the stability profile of both products were observed which could be attributed to the usage of different formulation buffers. This highlighted the importance of using multiple techniques for the assessment of the quality attributes of such sophisticated products. The analytical assay protocol should be used for the evaluation of the quality and stability of several ADC.

Quality by Design in Downstream Process Development of Romiplostim

Background

Background: Downstream processing of therapeutic recombinant proteins expressed as the inclusion bodies (IBs) in E. coli is quite challenging. This study aimed to use the quality by design approach for developing the multi-step downstream process of a structurally complex therapeutic Fc-Peptide fusion protein, romiplostim.

Methods

Methods: For development of a successful downstream process, risk analysis and experimental designs were used to characterize the most critical quality attributes (CQAs) and effects of process parameters on these quality attributes.

Results

Results: The solubilization of IBs was optimized by design of experiment on three parameters with a focus on solubility yield, which resulted in >75% increase of the target protein solubilization. The pH of sample was identified as CQA in anion exchange chromatography that might have an impact on achieving >85% host cell proteins removal and >90% host cell DNA reduction. In the refolding step, process parameters were screened. Cystine/cysteine ratio, pH, and incubation time identified as CPPs were further optimized using Box-Behnken analysis, which >85% of the target protein was refolded. The design space for further purification step by HIC was mapped with a focus on high molecular weight impurities. After polishing by gel filtration, the final product's biological activity showed no statistically significant differences among the groups received romiplostim and Nplate®, as the reference product.

Conclusions

Conclusion: This research presents a precise and exhaustive model for mapping the design space in order to describe and anticipate the link between the yield and quality of romiplostim and its downstream process parameters.

Development of Inline Near-Infrared Spectroscopy Method for Real-Time Monitoring of Blend Uniformity of Direct Compression and Granulation-Based Products at Commercial Scales

Blending is a critical intermediate unit operation for all solid oral formulations. For blend uniformity testing, API content in the blend must be quantified precisely. A detailed study was conducted to demonstrate the suitability of inline NIR (near-infrared) spectroscopy for blend uniformity testing of two solid oral formulations: existing direct compression (DC) product with a multistep blending process and granulation-based product with API granules. Both qualitative and quantitative methods were developed at a laboratory scale using statistical moving block standard deviation (MBSD) and multivariate data analysis such as principal component analysis (PCA) and partial least squares (PLS) regression. The qualitative MBSD method demonstrated that there was no need for multiple steps for the existing DC product. Hence, a simplified single-step process was developed for blending. Quantitative PLS models for blending processes of both the products were developed, validated, and successfully implemented at a commercial scale for the real-time release of blends. Results obtained from the validated model were in good agreement with the current method of sampling and chromatography.

Toward More Universal Prediction of Polymer Solution Viscosity for Solvent-Based Recycling

The viscosity of polymer solutions is important for both polymer synthesis and recycling. Polymerization reactions can become hampered by diffusional limitations once a viscosity threshold is reached, and viscous solutions complicate the cleaning steps during the dissolution–precipitation technique. Available experimental data is limited, which is more severe for green solvents, justifying dedicated viscosity data recording and interpretation. In this work, a systematic study is therefore performed on the viscosity of polystyrene solutions, considering different concentrations, temperatures, and conventional and green solvents. The results show that for the shear rate range of 1–1000 s^–1, the solutions with concentrations between 5 and 39 wt % display mainly Newtonian behavior, which is further confirmed by the applicability of the segment-based Eyring-NRTL and Eyring-mNRF models. Moreover, multivariate data analysis successfully predicts the viscosity of polystyrene solutions under different conditions. This approach will facilitate future data recording for other polymer–solvent combinations while minimizing experimental effort.

Application of Supervised and Unsupervised Learning Approaches for Mapping Storage Conditions of Biopharmaceutical Product-A Case Study of Human Serum Albumin

The stability of biopharmaceutical therapeutics over the storage period/shelf life has been a challenging concern for manufacturers. A noble strategy for mapping best and suitable storage conditions for recombinant human serum albumin (rHSA) in laboratory mixture was optimized using chromatographic data as per principal component analysis (PCA), and similarity was defined using hierarchical cluster analysis. In contrast, separability was defined using linear discriminant analysis (LDA) models. The quantitation was performed for rHSA peak (analyte of interest) and its degraded products, i.e., dimer, trimer, agglomerates and other degradation products. The chromatographic variables were calculated using validated stability-indicating assay method. The chromatographic data mapping was done for the above-mentioned peaks over three months at different temperatures, i.e., 20°C, 5-8°C and at room temperature (25°C). The PCA had figured out the ungrouped variable, whereas supervised mapping was done using LDA. As an outcome result of LDA, about 60% of data were correctly classified with the highest sensitivity for 25°C (Aq), 25°C and 5-8°C (Aq with 5% glucose as a stabilizer), whereas the highest specificity was observed for samples stored at 5-8°C (Aq with 5% glucose as a stabilizer).

Visualising liquid transport through coated pharmaceutical tablets using Terahertz pulsed imaging

Dissolution of pharmaceutical tablets is a complex process, especially for coated tablets where layered structures form an additional barrier for liquid transport into the porous tablet matrix. A better understanding of the role of the coating structure in the mass transport processes that govern drug release, starting with the wetting of the coating layer by the dissolution medium, can benefit the formulation design and optimisation of the production. For this study, terahertz pulsed imaging was used to investigate how dissolution medium can penetrate coated tablets. In order to focus on the fundamental process, the model system for this proof-of-principle study consisted of tablet cores made from pure microcrystalline cellulose compacted to a defined porosity coated with Opadry II, a PVA-based immediate release coating blend. The coating was applied to a single side of flat-faced tablets using vacuum compression moulding. It was possible to resolve the hydration of the coating layer and the subsequent liquid ingress into the dry tablet core. The analysis revealed a discontinuity in density at the interface between coating and core, where coating polymer could enter the pore space at the immediate surface of the tablet cores during the coating process. This structure affected the liquid transport of the dissolution medium into the core. We found evidence for the formation of a gel layer upon hydration of the coating polymer. The porosity of the tablet core impacted the quality of coating and thus affected its dissolution performance (r =  0.6932 for the effective liquid penetration rate RP_eff and the core porosity). This study established a methodology and can facilitate a more in-depth understanding of the role of coating on tablet dissolution.

Quality-by-design in pharmaceutical development: From current perspectives to practical applications

Current pharmaceutical research directions tend to follow a systematic approach in the field of applied research and development. The concept of quality-by-design (QbD) has been the focus of the current progress of pharmaceutical sciences. It is based on, but not limited, to risk assessment, design of experiments and other computational methods and process analytical technology. These tools offer a well-organized methodology, both to identify and analyse the hazards that should be handled as critical, and are therefore applicable in the control strategy. Once implemented, the QbD approach will augment the comprehension of experts concerning the developed analytical technique or manufacturing process. The main activities are oriented towards the identification of the quality target product profiles, along with the critical quality attributes, the risk management of these and their analysis through in silico aided methods. This review aims to offer an overview of the current standpoints and general applications of QbD methods in pharmaceutical development.

Design of experiments and multivariate analysis approach to study dissolution stability of a modified-release drug product to support lean design strategies

OBJECTIVE

The purpose of this paper is to present the use of Design of Experiments and multivariate analysis for evaluation of a modified-release drug product stability to support post-approval lean stability approaches. The focus of the paper was to investigate potential root-causes for acceleration of dissolution upon stability.

METHODS

For statistical evaluation of stability data, multiple linear regression statistics was used. The design space of the stability study was modeled using MODDE 12.1 software. For experimental set-up, parameters such as Temperature, Time, Packaging, Batch, and Active Pharmaceutical Ingredient supplier were selected.

RESULTS

With multiple linear regression modeling of the all generated stability data until six months, we were able to identify or confirm the Stability-related quality attributes and Shelf life limiting attributes. From the multiple linear regression correlation coefficients, we have evaluated that decrease of an antioxidant upon stability could cause potential shift in dissolution. However, main factors for accelerated dissolution can be attributed to other material and process variables. In the last part of the study, we have shown the usefulness of these methodologies for supporting lean stability approaches. With enhanced drug product knowledge, we designed two reduced long-term stability studies and showed that with 'One-half' reduced design, we would still be able to confirm 24-month shelf life.

CONCLUSIONS

Implementing Quality by design approaches on stability studies could reduce the need for excessive analytical testing, help to evaluate meaningfulness of the data and set a risk-based stability testing strategy.

Quality by Design tools reducing the gap from bench to bedside for nanomedicine

Pharmaceutical nanotechnology research is focused on smart nano-vehicles, which can deliver active pharmaceutical ingredients to enhance their efficacy through any route of administration and in the most varied therapeutical application. The design and development of new nanopharmaceuticals can be very laborious. In recent years, the application of mathematics, statistics and computational tools is emerging as a convenient strategy for this purpose. The application of Quality by Design (QbD) tools has been introduced to guarantee quality for pharmaceutical products and improve translational research from the laboratory bench into applicable therapeutics. In this review, a collection of basic-concept, historical overview and application of QbD in nanomedicine are discussed. A specific focus has been put on Response Surface Methodology and Artificial Neural Network approaches in general terms and their application in the development of nanomedicine to monitor the process parameters obtaining optimized system ensuring its quality profile.

Development of a Robust Control Strategy for Fixed-Dose Combination Bilayer Tablets with Integrated Quality by Design, Statistical, and Process Analytical Technology Approach

Control strategy and quality by design (QbD) are widely used to develop pharmaceutical products and improve drug quality; however, studies on fixed-dose combination (FDC) bilayer tablets are limited. In this study, the bilayer tablet consisted of high-dose metformin HCl in a sustained-release layer and low-dose dapagliflozin l-proline in an immediate-release layer. The formulation and process of each layer were optimized using the QbD approach. A d-optimal mixture design and response surface design were applied to optimize critical material attributes and critical process parameters, respectively. The robust design space was developed using Monte Carlo simulations by evaluating the risk of uncertainty in the model predictions. Multivariate analysis showed that there were significant correlations among impeller speed, massing time, granule bulk density, and dissolution in the metformin HCl layer, and among roller pressure, ribbon density, and dissolution in the dapagliflozin l-proline layer. Process analytical technology (PAT) was used with in–line transmittance near-infrared spectroscopy to confirm the bulk and ribbon densities of the optimized bilayer tablet. Moreover, the in vitro drug release and in vivo pharmacokinetic studies showed that the optimized test drug was bioequivalent to the reference drug. This study suggested that integrated QbD, statistical, and PAT approaches can develop a robust control strategy for FDC bilayer tablets by implementing real-time release testing based on the relationships among various variables.

Using a material database and data fusion method to accelerate the process model development of high shear wet granulation

High shear wet granulation (HSWG) has been wildly used in manufacturing of oral solid dosage (OSD) forms, and process modeling is vital to understanding and controlling this complex process. In this paper, data fusion and multivariate modeling technique were applied to develop a formulation-process-quality model for HSWG process. The HSWG experimental data from both literature and the authors' laboratory were fused into a single and formatted representation. A material database and material matching method were used to compensate the incomplete physical characterization of literature formulation materials, and dimensionless parameters were utilized to reconstruct process variables at different granulator scales. The exploratory study on input materials properties by principal component analysis (PCA) revealed that the formulation data collected from different articles generated a formulation library which was full of diversity. In prediction of the median granule size, the partial least squares (PLS) regression models derived from literature data only and a combination of literature data and laboratory data were compared. The results demonstrated that incorporating a small number of laboratory data into the multivariate calibration model could help significantly reduce the prediction error, especially at low level of liquid to solid ratio. The proposed data fusion methodology was beneficial to scientific development of HSWG formulation and process, with potential advantages of saving both experimental time and cost.

Online monitoring and control of upstream cell culture process using 1D and 2D-LC with SegFlow interface

The biopharmaceutical industry is transitioning from currently deployed batch-mode bioprocessing to a highly efficient and agile next-generation bioprocessing with the adaptation of continuous bioprocessing, which reduces capital investment and operational costs. Continuous bioprocessing, aligned with FDA's quality-by-design platform, is designed to develop robust processes to deliver safe and effective drugs. With the deployment of knowledge-based operations, product quality can be built into the process to achieve desired critical quality attributes (CQAs) with reduced variability. To facilitate next-generation continuous bioprocessing, it is essential to embrace a fundamental shift-in-paradigm from "quality-by-testing" to "quality-by-design," which requires the deployment of process analytical technologies (PAT). With the adaptation of PAT, a systematic approach of process and product understanding and timely process control are feasible. Deployment of PAT tools for real-time monitoring of CQAs and feedback control is critical for continuous bioprocessing. Given the current deficiency in PAT tools to support continuous bioprocessing, we have integrated Infinity 2D-LC with a post-flow-splitter in conjunction with the SegFlow autosampler to the bioreactors. With this integrated system, we have established a platform for online measurements of titer and CQAs of monoclonal antibodies as well as amino acid analysis of bioreactor cell culture.

Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets

The tablet manufacturing process is a complex system, especially in continuous manufacturing (CM). It includes multiple unit operations, such as mixing, granulation, and tableting. In tablet manufacturing, critical quality attributes are influenced by multiple factorial relationships between material properties, process variables, and interactions. Moreover, the variation in raw material attributes and manufacturing processes is an inherent characteristic and seriously affects the quality of pharmaceutical products. To deepen our understanding of the tablet manufacturing process, multivariable modeling techniques can replace univariate analysis to investigate tablet manufacturing. In this review, the roles of the most prominent multivariate modeling techniques in the tablet manufacturing process are discussed. The review mainly focuses on applying multivariate modeling techniques to process understanding, optimization, process monitoring, and process control within multiple unit operations. To minimize the errors in the process of modeling, good modeling practice (GMoP) was introduced into the pharmaceutical process. Furthermore, current progress in the continuous manufacturing of tablets and the role of multivariate modeling techniques in continuous manufacturing are introduced. In this review, information is provided to both researchers and manufacturers to improve tablet quality.

Model-based workflow for scale-up of process strategies developed in miniaturized bioreactor systems

Miniaturized bioreactor (MBR) systems are routinely used in the development of mammalian cell culture processes. However, scale-up of process strategies obtained in MBR- to larger scale is challenging due to mainly non-holistic scale-up approaches. In this study, a model-based workflow is introduced to quantify differences in the process dynamics between bioreactor scales and thus enable a more knowledge-driven scale-up. The workflow is applied to two case studies with antibody-producing Chinese hamster ovary cell lines. With the workflow, model parameter distributions are estimated first under consideration of experimental variability for different scales. Second, the obtained individual model parameter distributions are tested for statistical differences. In case of significant differences, model parametric distributions are transferred between the scales. In case study I, a fed-batch process in a microtiter plate (4 ml working volume) and lab-scale bioreactor (3750 ml working volume) was mathematically modeled and evaluated. No significant differences were identified for model parameter distributions reflecting process dynamics. Therefore, the microtiter plate can be applied as scale-down tool for the lab-scale bioreactor. In case study II, a fed-batch process in a 24-Deep-Well-Plate (2 ml working volume) and shake flask (40 ml working volume) with two feed media was investigated. Model parameter distributions showed significant differences. Thus, process strategies were mathematically transferred, and model predictions were simulated for a new shake flask culture setup and confirmed in validation experiments. Overall, the workflow enables a knowledge-driven evaluation of scale-up for a more efficient bioprocess design and optimization.

Spherical agglomerates of lactose as potential carriers for inhalation

We report here on spherical lactose agglomerates as potential carriers for inhalation applications. Micromeritic properties of three spherical lactose agglomerates (SA-A, SA-B, SA-C) and a standard lactose inhalation grade carrier (Lactohale 100; LH100) were evaluated and compared. Ordered mixtures with micronized salbutamol sulfate as the model active pharmaceutical ingredient (API) and lactose carriers at two drug loadings (2 wt%, 5 wt%) were prepared, and in-vitro aerosolization performance was assessed. The spherical crystallization process led to particles with tailored micromeritic properties. These had larger specific surface area and greater fine fraction < 10 µm, compared to LH100, due to their coarse morphology. Their properties were reflected in the flowability parameters, where two types of spherical agglomerates of lactose showed more cohesive behavior compared to the other lactose grades. Blend uniformity showed improved homogeneous distribution of the API at higher drug load. In-vitro aerosolization tests showed that the spherical agglomerates of lactose enhanced the dose of API, compared to LH100. SA-B and SA-C showed significantly higher fine particle fractions at low drug load compared to the others, whereas overall, the largest fine particle fraction was for SA-B at high drug load. The carrier material attributes related to particle size, specific surface area, compressibility, flowability (cohesion, flow function), and air permeability were critical for aerosolization performance.

Multivariate analysis for optimization and validation of the industrial tablet-manufacturing process

OBJECTIVE

This study aimed initially to optimize the industrial tablet-manufacturing process using multivariate analysis, and then to validate the model obtained. The study also provides a comprehensive review of the influence of different factors on relevant biopharmaceutical parameters.

SIGNIFICANCE

This is the first time multivariate analysis has been applied to such a broad set of industrial data to investigate the influence of starting materials and the tablet-manufacturing processes on drug dissolution.

METHODS

Partial least squares regression was retrospectively applied to the data obtained from 2 years production, to study the influence of 90 factors on dissolution of tablets that contained two active pharmaceutical ingredients. The model established was verified using the worst-case approach and process validation.

RESULTS

Croscarmellose sodium had the most significant influence on drug dissolution, with the next significant factors as sodium chloride and sodium glycolate content, settling volume, particle size, suspension pH, loss on drying, and maximum temperature during drying. Loss on drying of microcrystalline cellulose and specific surface area of magnesium stearate were also essential factors. Among the process parameters, auger speed during roller compaction, compression speed, and force feeder speed during tablet compression had significant impacts on the tablet dissolution rate. The multivariate model created satisfied the process validation.

CONCLUSIONS

This multivariate analysis is a useful tool to predict and optimize critical material attributes and process parameters. The variability of the materials can be successfully compensated for using various process parameters, to ensure consistent approved drug quality, to thus provide better patient care.

A Comprehensive Review on Pharmaceutical Film Coating: Past, Present, and Future

Pharmaceutical film coating is considered a key part in the production of solid pharmaceutical dosage forms since it gives superior organoleptic properties products. In addition, it can improve the physical and chemical stability of dosage forms, and modify the release characteristics of the drug. Several troubleshooting problems such as twinning mottling, chipping, etc., may arise during or after or even during the shelf life of the film coated dosage forms. These troubleshooting problems may be due to tablet core faults, coating formulation faults and/or coating process faults. These problems must be overcome to avoid unnecessary product problems. Film coating as well as other parts of the pharmaceutical technology is subjecting to continuous innovation. The innovation may be at different levels including pharmaceutical excipients, processes, software, guidelines and equipment. In fact, of particular note is the growing interest in process analytical technology, quality by design, continuous coating processing and the inclusion of new ready for use coating formulations. In this review, we tried to explore and discuss the status of pharmaceutical film coating, the challenges that face this manufacturing process and the latest technological advances in this important manufacturing process.

Prediction of saxagliptin stability using a new approach based on Partial Least Squares and Design of Experiments

The objective of this study was to assess the possibility of applying Partial Least Squares (PLS) statistics with the use of experimental design approach towards stability evaluation of the Saxagliptin drug product. The influences of temperature, time, dose, packaging, batch, and oxygen protection were analyzed for identification of critical factors responsible for degradation of saxagliptin and prediction of impurity levels at various storage conditions. Predicted levels of the impurity DP-2 were lower for at least 0.2 % when the drug product was protected from oxygen after its manufacture. Additionally, the PLS model revealed that the lower strength is at least twice less stable concerning impurity DP-1. Based on this analysis shelf life for Zone II was proposed at 24 months with high reliability. Comparison of the PLS model estimates with the measured stability data at shelf life revealed good predictive ability of the developed model. Moreover, PLS predictions of DP-1 and Total impurities were more accurate than those obtained with a standard linear least squares regression, while DP-2 predictions were at least as accurate. We can thus propose a more extensive use of this approach for stability evaluation of pharmaceuticals.

A Biopharmetrics Approach for Drug Product Quality Control with Clinical Relevance

To exemplify a systematic and quantitative approach (Biopharmetrics approach) for product quality control with clinical relevance, the study utilized multivariate models to connect raw material attributes of a product to its dissolution obtained in a DOE (design of experiment) and further extended the connection to the in vivo exposures (AUC and Cmax) of the product using an available IVIVC (in vitro/in vivo correlation). Thus, a model for the relationship between in vivo exposure and raw material attributes of the product was established. Based on the model, the exposure ratios between future batches (with various raw material attributes) and the pivotal clinical batches (with confirmed clinical efficacy and safety) were predicted resulting in a set of contour lines in a graphical representation. Among these contour lines, the lines for exposure ratios of 0.8 and 1.2 were defined as lower and upper limits. With the failure edges defined, clinically meaningful limits for the raw material quality control were specified. If the raw material attributes in the future production conform to the specifications, the manufacturing would lead to products bioequivalent to the clinical batches. From these exercises, Biopharmetrics, a subdiscipline of biopharmaceutics, was introduced and its definition, scope, and characteristics were discussed.

Why should the pharmaceutical industry claim for the implementation of second-order chemometric models-A critical review

Today, pharmaceutical products are submitted to a large number of analytical tests, planned to either ensure or construct their quality. The official methods of analysis used to perform these determinations are very different in nature, but almost all demand the intensive use of reagents and manpower as major drawbacks. Thus, analytical development is continuously evolving to find fast and smart approaches. First-order chemometric models are well-known in the pharmaceutical industry, and are extensively used in many fields. Such is the impact of chemometric models that regulatory agencies include them in guidelines and compendia. However, the mention or practical application of higher-order models in the pharmaceutical industry is rather scarce. Herein, we try to bring a brief introduction to chemometric models and useful literature references, focusing on higher-order chemometric models (HOCM) applied to reduce manpower, reagent consumption, and time of analysis, without sacrificing accuracy or precision, while gaining selectivity and sensitivity. The advantages and drawbacks of HOCM are also discussed, and the comparison to first-order chemometric models is also analyzed. Along the work, HOCM are evidenced as a powerful tool for the pharmaceutical industry; moreover, its implementation is shown during several steps of production, such as identification, purity test and assay, and other applications as homogeneity of API distribution, Process Analytical Technology (PAT), Quality by Design (QbD) or natural product fingerprinting. Among these topics, qualitative and quantitative applications were covered. Experimental approaches of chemometrics coupled to several analytical techniques such as UV-vis, fluorescence and vibrational spectroscopies (NIR, MIR and Raman), and other techniques as hyphenated-chromatography and electrochemical techniques applied to production and analysis are discussed throughout this work.

The impact of technical failures during cultivation of an inclusion body process

In biotechnological processes, technical failures in the upstream process often lead to batch loss. It is of great interest to investigate the empirical impact of technical failures to understand and mitigate their impact accurately and reduce economic damage. We investigated the impact in the upstream and downstream of a recombinant antibody fragment inclusion body production process chain to provide integrated empirical data and knowledge. First, we provided a reproducible process chain that yielded high inclusion body content, high specific product titer, and a refolding yield of 30%. The inclusion body downstream proved to be of high reproducibility. Through the intended introduction of technical failures, we were not only able to shed more light on the empirical responses in the upstream and downstream, but also on process-boosting parameters that would have been neglected. Herein, a short increase in temperature during the cultivation clearly increased the refolding yield.

Monitoring microsphere coating processes using PAT tools in a bench scale fluid bed

Among the factors that influence adherence to medication within the pediatric population, taste/irritation has been identified as a critical barrier to patient compliance. With the goal of improving compliance, microspheres (matrix systems within which the drug is dispersed) can be coated with a reverse enteric polymer that will prevent the release of the drug in the oral cavity while maintaining an immediate release once the drug product reaches the stomach, thereby achieving a taste neutral profile. In this work, the in-line performance of three process analytical technology (PAT) tools is evaluated in order to monitor the microsphere coating process. These tools are Raman spectroscopy, near-infrared spectroscopy and focused beam reflectance measurements, together with process data and raw material attributes. The ability of these different sources of information to predict the coating's barrier performance is evaluated by using a combined-data-approach: multiblock partial least squares (MBPLS). Results show that Raman spectroscopy has a superior predictive performance and that it has the potential to monitor the coating process of the microspheres as well as to detect process discrepancies (such as spray rate changes), demonstrating its usefulness for the monitoring of fluid bed coating processes. It was also demonstrated that Raman can be used to clearly differentiate batches with significantly difference in-vitro dissolution performance. This monitoring is considered critical to ensure consistent coating performance for this thin film barrier membrane that is essential to patient compliance.

Data analytics on raw material properties to accelerate pharmaceutical drug development

Manufacturability of active pharmaceutical ingredients (APIs) is often evaluated by an empirical approach during development due to limited material availability. This brings challenges in designing flexible yet robust manufacturing processes under highly accelerated timelines. Hence, good utilisation of a limited material dataset is key to accelerate the delivery of high quality final drug product into the market at minimum cost and maximum process capacity. In this study, we present a data-driven method to investigate a raw materials database where the integration of multivariate analysis and machine learning modelling aids the selection of new incoming materials based on their manufacturability. The procedure was applied to an industrial representative database of thirty-four APIs and seven excipients where eight measurements relevant to flow properties for each of those forty-one materials were collected. The models identified four clusters of materials with different flow properties. These models can serve as a risk assessment tool for new API in early product development phases based on the nearest surrogate material which behave similarly, as well as to identify targeted and material sparring experiments to address key risks during secondary process selection.

Enhanced Understanding of Pharmaceutical Materials Through Advanced Characterisation and Analysis

The impact of pharmaceutical materials properties on drug product quality and manufacturability is well recognised by the industry. An ongoing effort across industry and academia, the Manufacturing Classification System consortium, aims to gather the existing body of knowledge in a common framework to provide guidance on selection of appropriate manufacturing technologies for a given drug and/or guide optimization of the physical properties of the drug to facilitate manufacturing requirements for a given processing route. Simultaneously, material scientists endeavour to develop characterisation methods such as size, shape, surface area, density, flow and compactibility that enable a stronger understanding of materials powder properties. These properties are routinely tested drug product development and advances in instrumentation and computing power have enabled novel characterisation methods which generate larger, more complex data sets leading to a better understanding of the materials. These methods have specific requirements in terms of data management and analysis. An appropriate data management strategy eliminates time-consuming data collation steps and enables access to data collected for multiple methods and materials simultaneously. Methods ideally suited to extract information from large, complex data sets such as multivariate projection methods allow simpler representation of the variability contained within the data and easier interpretation of the key information it contains. In this review, an overview of the current knowledge and challenges introduced by modern pharmaceutical material characterisation methods is provided. Two case studies illustrate how the incorporation of multivariate analysis into the material sciences workflow facilitates a better understanding of materials.

Multivariate analysis as a method to understand variability in a complex excipient, and its contribution to formulation performance

A key part of the Risk Assessment of excipients is to understand how raw material variability could (or does) contribute to differences in performance of the drug product. Here we demonstrate an approach which achieves the necessary understanding for a complex, functional, excipient. Multivariate analysis (MVA) of the certificates of analysis of an ethylcellulose aqueous dispersion (Surelease) formulation revealed low overall variability of the properties of the systems. Review of the scores plot to highlight batches manufactured using the same ethylcellulose raw material in the formulation, indicated that these batches tend to be more closely related than other randomly selected batches. This variability could result in potential differences in the quality of drug product lots made from these batches. Manufacture of a model drug product from Surelease batches coated using different lots of starting material revealed small differences in the release of a model drug, which could be detected by certain model dependent dissolution modeling techniques, but they were not observed when using model-independent techniques. This illustrates that the techniques are suitable for detecting and understanding excipient variability, but that, in this case, the product was still robust.

Comparative analyses of flow and compaction properties of diverse mannitol and lactose grades

Appropriate selection of excipient grade during tablet formulation development depends on thorough knowledge in their compaction and flow properties. Each chemically unique pharmaceutical excipient is usually available in several commercial grades that are widely different in powder properties, which influence their performance for a specific formulation application. In this work, 11 grades of mannitol were systematically characterized, in terms of their particulate, flow and tableting properties, and compared against 5 grades of lactose. Principal component analysis (PCA) identified significant correlations among selected variables, such as particle size, surface area, flowability, wall friction, plasticity parameter, tensile strength, and tablet brittleness. PCA also revealed similar grades of the two excipients, which may be used to select replacement grade, if needed, based on similarity in their overall properties.

Determinants of Antibiotic Consumption - Development of a Model using Partial Least Squares Regression based on Data from India

Antibiotic resistance, a consequence of antibiotic use, is a threat to health, with severe consequences for resource constrained settings. If determinants for human antibiotic use in India, a lower middle income country, with one of the highest antibiotic consumption in the world could be understood, interventions could be developed, having implications for similar settings. Year wise data for India, for potential determinants and antibiotic consumption, was sourced from publicly available databases for the years 2000–2010. Data was analyzed using Partial Least Squares regression and correlation between determinants and antibiotic consumption was evaluated, formulating ‘Predictors’ and ‘Prediction models’. The ‘prediction model’ with the statistically most significant predictors (root mean square errors of prediction for train set-377.0 and test set-297.0) formulated from a combination of Health infrastructure + Surface transport infrastructure (HISTI), predicted antibiotic consumption within 95% confidence interval and estimated an antibiotic consumption of 11.6 standard units/person (14.37 billion standard units totally; standard units = number of doses sold in the country; a dose being a pill, capsule, or ampoule) for India for 2014. The HISTI model may become useful in predicting antibiotic consumption for countries/regions having circumstances and data similar to India, but without resources to measure actual data of antibiotic consumption.