Patient-Centric Quality Standards.

To ensure the quality, safety and efficacy of medicinal products, it is necessary to develop and execute appropriate manufacturing process and product control strategies. Traditionally, product control strategies have focused on testing known quality attributes with limits derived from levels administered in preclinical and clinical studies with an associated statistical analysis to account for variability. However, not all quality attributes have impact to the patient and those with the potential to impact safety and efficacy may not be significant when dosed at patient-centric levels. Therefore, achieving patient-centricity is understanding patient relevance, which is defined as the level of impact that a quality attribute could have on safety and efficacy within the potential exposure range. A patient-centric quality standard (PCQS) is therefore a set of patient relevant attributes and their associated acceptance ranges to which a drug product should conform within the expected patient exposure range. This manuscript describes historical perspectives details the way to create and leverage a PCQS in a variety of pharmaceutical product modalities.

Re-Envisioning Pharmaceutical Manufacturing: Increasing Agility for Global Patient Access.

The traditional paradigm for pharmaceutical manufacturing is focused primarily upon centralized facilities that enable mass production and distribution. While this system reliably maintains high product quality and reproducibility, its rigidity imposes limitations upon new manufacturing innovations that could improve efficiency and support supply chain resiliency. Agile manufacturing methodologies, which leverage flexibility through portability and decentralization, allow manufacturers to respond to patient needs on demand and present a potential solution to enable timely access to critical medicines. Agile approaches are particularly applicable to the production of small-batch, personalized therapies, which must be customized for each individual patient close to the point-of-care. However, despite significant progress in the advancement of agile-enabling technologies across several different industries, there are substantial global regulatory challenges that encumber the adoption of agile manufacturing techniques in the pharmaceutical industry. This review provides an overview of regulatory barriers as well as emerging opportunities to facilitate the use of agile manufacturing for the production of pharmaceutical products. Future-oriented approaches for incorporating agile methodologies within the global regulatory framework are also proposed. Collaboration between regulators and manufacturers to cohesively navigate the regulatory waters is ultimately needed to best serve patients in the rapidly-changing healthcare environment.

Comparing a Statistical Model and Bayesian Approach to Establish the Design Space for the Coating of Ciprofloxacin HCl Beads at Different Scales of Production.

The primary objective of this study was to compare two methods for establishing a design space for critical process parameters that affect ethylcellulose film coating of multiparticulate beads and assess this design space validity across manufacturing scales. While there are many factors that can affect film coating, this study will focus on the effects processing conditions have on the quality and extent of film formation, as evaluated by their impact coating yield and drug release. Ciprofloxacin HCl layered beads were utilized as an active substrate core, ethylcellulose aqueous dispersion as a controlled release polymer, and triethyl citrate as a plasticizer. Thirty experiments were conducted using a central composite design to optimize the coating process and map the response surface to build a design space using either statistical least squares or a Bayesian approach. The response surface was fitted using a linear two-factor interaction model with spraying temperature, curing temperature, and curing time as significant model terms. The design spaces established by the two approaches were in close agreement with the statistical least squares approach being more conservative than the Bayesian approach. The design space established for the critical process parameters using small-scale batches was tested using scale-up batches and found to be scale-independent. The robustness of the design space was confirmed across scales and was successfully utilized to establish process signature for the coating process.

A Systematic Approach of Employing Quality by Design Principles: Risk Assessment and Design of Experiments to Demonstrate Process Understanding and Identify the Critical Process Parameters for Coating of the Ethylcellulose Pseudolatex Dispersion Using Non-Conventional Fluid Bed Process.

The goal of this study was to utilize risk assessment techniques and statistical design of experiments (DoE) to gain process understanding and to identify critical process parameters for the manufacture of controlled release multiparticulate beads using a novel disk-jet fluid bed technology. The material attributes and process parameters were systematically assessed using the Ishikawa fish bone diagram and failure mode and effect analysis (FMEA) risk assessment methods. The high risk attributes identified by the FMEA analysis were further explored using resolution V fractional factorial design. To gain an understanding of the processing parameters, a resolution V fractional factorial study was conducted. Using knowledge gained from the resolution V study, a resolution IV fractional factorial study was conducted; the purpose of this IV study was to identify the critical process parameters (CPP) that impact the critical quality attributes and understand the influence of these parameters on film formation. For both studies, the microclimate, atomization pressure, inlet air volume, product temperature (during spraying and curing), curing time, and percent solids in the coating solutions were studied. The responses evaluated were percent agglomeration, percent fines, percent yield, bead aspect ratio, median particle size diameter (d50), assay, and drug release rate. Pyrobuttons® were used to record real-time temperature and humidity changes in the fluid bed. The risk assessment methods and process analytical tools helped to understand the novel disk-jet technology and to systematically develop models of the coating process parameters like process efficiency and the extent of curing during the coating process.

A new chapter in pharmaceutical manufacturing: 3D-printed drug products.

FDA recently approved a 3D-printed drug product in August 2015, which is indicative of a new chapter for pharmaceutical manufacturing. This review article summarizes progress with 3D printed drug products and discusses process development for solid oral dosage forms. 3D printing is a layer-by-layer process capable of producing 3D drug products from digital designs. Traditional pharmaceutical processes, such as tablet compression, have been used for decades with established regulatory pathways. These processes are well understood, but antiquated in terms of process capability and manufacturing flexibility. 3D printing, as a platform technology, has competitive advantages for complex products, personalized products, and products made on-demand. These advantages create opportunities for improving the safety, efficacy, and accessibility of medicines. Although 3D printing differs from traditional manufacturing processes for solid oral dosage forms, risk-based process development is feasible. This review highlights how product and process understanding can facilitate the development of a control strategy for different 3D printing methods. Overall, the authors believe that the recent approval of a 3D printed drug product will stimulate continual innovation in pharmaceutical manufacturing technology. FDA encourages the development of advanced manufacturing technologies, including 3D-printing, using science- and risk-based approaches.

Advancing Product Quality: a Summary of the Inaugural FDA/PQRI Conference.

On September 16 and 17, 2014, the Food and Drug Administration (FDA) and Product Quality Research Institute (PQRI) inaugurated their Conference on Evolving Product Quality. The Conference is conceived as an annual forum in which scientists from regulatory agencies, industry, and academia may exchange viewpoints and work together to advance pharmaceutical quality. This Conference Summary Report highlights key topics of this conference, including (1) risk-based approaches to pharmaceutical development, manufacturing, regulatory assessment, and post-approval changes; (2) FDA-proposed quality metrics for products, facilities, and quality management systems; (3) performance-based quality assessment and clinically relevant specifications; (4) recent developments and implementation of continuous manufacturing processes, question-based review, and European Medicines Agency (EMA)-FDA pilot for Quality-by-Design (QbD) applications; and (5) breakthrough therapies, biosimilars, and international harmonization, focusing on ICH M7 and Q3D guidelines. The second FDA/PQRI conference on advancing product quality is planned for October 5-7, 2015.

Chemometric analysis for identification of botanical raw materials for pharmaceutical use: a case study using Panax notoginseng.

The overall control of the quality of botanical drugs starts from the botanical raw material, continues through preparation of the botanical drug substance and culminates with the botanical drug product. Chromatographic and spectroscopic fingerprinting has been widely used as a tool for the quality control of herbal/botanical medicines. However, discussions are still on-going on whether a single technique provides adequate information to control the quality of botanical drugs. In this study, high performance liquid chromatography (HPLC), ultra performance liquid chromatography (UPLC), capillary electrophoresis (CE) and near infrared spectroscopy (NIR) were used to generate fingerprints of different plant parts of Panax notoginseng. The power of these chromatographic and spectroscopic techniques to evaluate the identity of botanical raw materials were further compared and investigated in light of the capability to distinguishing different parts of Panax notoginseng. Principal component analysis (PCA) and clustering results showed that samples were classified better when UPLC- and HPLC-based fingerprints were employed, which suggested that UPLC- and HPLC-based fingerprinting are superior to CE- and NIR-based fingerprinting. The UPLC- and HPLC- based fingerprinting with PCA were able to correctly distinguish between samples sourced from rhizomes and main root. Using chemometrics and its ability to distinguish between different plant parts could be a powerful tool to help assure the identity and quality of the botanical raw materials and to support the safety and efficacy of the botanical drug products.

Identification of heparin samples that contain impurities or contaminants by chemometric pattern recognition analysis of proton NMR spectral data.

Chemometric analysis of a set of one-dimensional (1D) (1)H nuclear magnetic resonance (NMR) spectral data for heparin sodium active pharmaceutical ingredient (API) samples was employed to distinguish USP-grade heparin samples from those containing oversulfated chondroitin sulfate (OSCS) contaminant and/or unacceptable levels of dermatan sulfate (DS) impurity. Three chemometric pattern recognition approaches were implemented: classification and regression tree (CART), artificial neural network (ANN), and support vector machine (SVM). Heparin sodium samples from various manufacturers were analyzed in 2008 and 2009 by 1D (1)H NMR, strong anion-exchange high-performance liquid chromatography, and percent galactosamine in total hexosamine tests. Based on these data, the samples were divided into three groups: Heparin, DS ≤ 1.0% and OSCS = 0%; DS, DS > 1.0% and OSCS = 0%; and OSCS, OSCS > 0% with any content of DS. Three data sets corresponding to different chemical shift regions (1.95-2.20, 3.10-5.70, and 1.95-5.70 ppm) were evaluated. While all three chemometric approaches were able to effectively model the data in the 1.95-2.20 ppm region, SVM was found to substantially outperform CART and ANN for data in the 3.10-5.70 ppm region in terms of classification success rate. A 100% prediction rate was frequently achieved for discrimination between heparin and OSCS samples. The majority of classification errors between heparin and DS involved cases where the DS content was close to the 1.0% DS borderline between the two classes. When these borderline samples were removed, nearly perfect classification results were attained. Satisfactory results were achieved when the resulting models were challenged by test samples containing blends of heparin APIs spiked with non-, partially, or fully oversulfated chondroitin sulfate A, heparan sulfate, or DS at the 1.0%, 5.0%, and 10.0% (w/w) levels. This study demonstrated that the combination of 1D (1)H NMR spectroscopy with multivariate chemometric methods is a nonsubjective, statistics-based approach for heparin quality control and purity assessment that, once standardized, minimizes the need for expert analysts.

Class modeling analysis of heparin 1H NMR spectral data using the soft independent modeling of class analogy and unequal class modeling techniques.

To differentiate heparin samples with varying amounts of dermatan sulfate (DS) impurities and oversulfated chondroitin sulfate (OSCS) contaminants, proton NMR spectral data for heparin sodium active pharmaceutical ingredient samples from different manufacturers were analyzed using multivariate chemometric techniques. A total of 168 samples were divided into three groups: (a) Heparin, [DS] ≤ 1.0% and [OSCS] = 0%; (b) DS, [DS] > 1.0% and [OSCS] = 0%; (c) OSCS, [OSCS] > 0% with any content of DS. The chemometric models were constructed and validated using two well-established methods: soft independent modeling of class analogy (SIMCA) and unequal class modeling (UNEQ). While SIMCA modeling was conducted using the entire set of variables extracted from the NMR spectral data, UNEQ modeling was combined with variable reduction using stepwise linear discriminant analysis to comply with the requirement that the number of samples per class exceed the number of variables in the model by at least 3-fold. Comparison of the results from these two modeling approaches revealed that UNEQ had greater sensitivity (fewer false positives) while SIMCA had greater specificity (fewer false negatives). For Heparin, DS, and OSCS, respectively, the sensitivity was 78% (56/72), 74% (37/50), and 85% (39/46) from SIMCA modeling and 88% (63/72), 90% (45/50), and 91% (42/46) from UNEQ modeling. Importantly, the specificity of both the SIMCA and UNEQ models was 100% (46/46) for Heparin with respect to OSCS; no OSCS-containing sample was misclassified as Heparin. The specificity of the SIMCA model (45/50, or 90%) was superior to that of the UNEQ model (27/50, or 54%) for Heparin with respect to DS samples. However, the overall prediction ability of the UNEQ model (85%) was notably better than that of the SIMCA model (76%) for the Heparin vs DS vs OSCS classes. The models were challenged with blends of heparin spiked with nonsulfated, partially sulfated, or fully oversulfated chondroitin sulfate A, dermatan sulfate, or heparan sulfate at the 1.0, 5.0, and 10.0 wt % levels. The results from the present study indicate that the combination of (1)H NMR spectral data and class modeling techniques (viz., SIMCA and UNEQ) represents a promising strategy for assessing the quality of commercial heparin samples with respect to impurities and contaminants. The methodologies show utility for applications beyond heparin to other complex products.

Combining (1)H NMR spectroscopy and chemometrics to identify heparin samples that may possess dermatan sulfate (DS) impurities or oversulfated chondroitin sulfate (OSCS) contaminants.

Heparin is a naturally produced, heterogeneous compound consisting of variably sulfated and acetylated repeating disaccharide units. The structural complexity of heparin complicates efforts to assess the purity of the compound, especially when differentiating between similar glycosaminoglycans. Recently, heparin sodium contaminated with oversulfated chondroitin sulfate A (OSCS) has been associated with a rapid and acute onset of an anaphylactic reaction. In addition, naturally occurring dermatan sulfate (DS) was found to be present in these and other heparin samples as an impurity due to incomplete purification. The present study was undertaken to determine whether chemometric analysis of these NMR spectral data would be useful for discrimination between USP-grade samples of heparin sodium API and those deemed unacceptable based on their levels of DS, OSCS, or both. Several multivariate chemometric methods for clustering and classification were evaluated; specifically, principal components analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and the k-nearest-neighbor (kNN) method. Data dimension reduction and variable selection techniques, implemented to avoid over-fitting the training set data, markedly improved the performance of the classification models. Under optimal conditions, a perfect classification (100% success rate) was attained on external test sets for the Heparin vs OSCS model. The predictive rates for the Heparin vs DS, Heparin vs [DS+OSCS], and Heparin vs DS vs OSCS models were 89%, 93%, and 90%, respectively. In most cases, misclassifications can be ascribed to the similarity in NMR chemical shifts of heparin and DS. Among the chemometric methods evaluated in this study, we found that the LDA models were superior to the PLS-DA and kNN models for classification. Taken together, the present results demonstrate the utility of chemometric methods when applied in combination with (1)H NMR spectral analysis for evaluating the quality of heparin APIs.

Determination of galactosamine impurities in heparin samples by multivariate regression analysis of their (1)H NMR spectra.

Heparin, a widely used anticoagulant primarily extracted from animal sources, contains varying amounts of galactosamine impurities. Currently, the United States Pharmacopeia (USP) monograph for heparin purity specifies that the weight percent of galactosamine (%Gal) may not exceed 1%. In the present study, multivariate regression (MVR) analysis of (1)H NMR spectral data obtained from heparin samples was employed to build quantitative models for the prediction of %Gal. MVR analysis was conducted using four separate methods: multiple linear regression, ridge regression, partial least squares regression, and support vector regression (SVR). Genetic algorithms and stepwise selection methods were applied for variable selection. In each case, two separate prediction models were constructed: a global model based on dataset A which contained the full range (0-10%) of galactosamine in the samples and a local model based on the subset dataset B for which the galactosamine level (0-2%) spanned the 1% USP limit. All four regression methods performed equally well for dataset A with low prediction errors under optimal conditions, whereas SVR was clearly superior among the four methods for dataset B. The results from this study show that (1)H NMR spectroscopy, already a USP requirement for the screening of contaminants in heparin, may offer utility as a rapid method for quantitative determination of %Gal in heparin samples when used in conjunction with MVR approaches.