Advances in Continuous Active Pharmaceutical Ingredient (API) Manufacturing: Real-time Monitoring Using Multivariate Tools

A total productive maintenance & reliability framework for an active pharmaceutical ingredient plant utilising design for Lean Six Sigma

This research investigates the requirement for and relationship and implementation of a total productive maintenance (TPM) and Reliability Centred Maintenance (RCM) strategy within an Active Pharmaceutical Ingredient (API) Plant. This research aimed to study the tools and techniques of TPM and Reliability Engineering and then deploy a designed model to an API plant. A case study involving Design for Lean Six Sigma phases of Define, Measure, Analyse, Design, and Verify was utilised to build an API site TPM pilot program. Data was collected using interviews across Company 'X's local and Global Engineering teams. Process runtime, downtime and plant availability metrics were compiled and a new design for Total Productive Maintenance and Reliability was proposed and verified. A maintenance framework was designed to optimally incorporate Total Productive Maintenance, Reliability and Operational Excellence with an emphasis on Overall Equipment Efficiency (OEE) realizing a 33 % reduction in planned maintenance activities, a 70 % reduction in Corrective Maintenance, Cleaning for Maintenance was reduced by 50 %, the pilot maintenance area of the centrifuge has its OEE increased by 20 % and plant availability increased by two hundred and 6 h. This research highlights the importance of Total Productive Maintenance as a key component of an effective maintenance strategy and its potential to transform maintenance practices. Based on this research and results, TPM is recommended to be applied to any API manufacturing organization. A limitation of the study is that it is a single-site case study. The novelty of this research is based upon the emphasis on Reliability Engineering to remove non-value add Maintenance time from the manufacturing schedule. The Total Productive Maintenance & Reliability model designed and implemented in this research is unique in the literature and can be leveraged by engineering professionals and academics to understand the benefits of TPM.

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].

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

A review on the modernization of pharmaceutical development and manufacturing - Trends, perspectives, and the role of mathematical modeling

Recently, the pharmaceutical industry has been facing several challenges associated to the use of outdated development and manufacturing technologies. The return on investment on research and development has been shrinking, and, at the same time, an alarming number of shortages and recalls for quality concerns has been registered. The pharmaceutical industry has been responding to these issues through a technological modernization of development and manufacturing, under the support of initiatives and activities such as quality-by-design (QbD), process analytical technology, and pharmaceutical emerging technology. In this review, we analyze this modernization trend, with emphasis on the role that mathematical modeling plays within it. We begin by outlining the main socio-economic trends of the pharmaceutical industry, and by highlighting the life-cycle stages of a pharmaceutical product in which technological modernization can help both achieve consistently high product quality and increase return on investment. Then, we review the historical evolution of the pharmaceutical regulatory framework, and we discuss the current state of implementation and future trends of QbD. The pharmaceutical emerging technology is reviewed afterwards, and a discussion on the evolution of QbD into the more effective quality-by-control (QbC) paradigm is presented. Further, we illustrate how mathematical modeling can support the implementation of QbD and QbC across all stages of the pharmaceutical life-cycle. In this respect, we review academic and industrial applications demonstrating the impact of mathematical modeling on three key activities within pharmaceutical development and manufacturing, namely design space description, process monitoring, and active process control. Finally, we discuss some future research opportunities on the use of mathematical modeling in industrial pharmaceutical environments.

Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review

Among the greatest challenges in soft sensor development for bioprocesses are variable process lengths, multiple process phases, and erroneous model inputs due to sensor faults. This review article describes these three challenges and critically discusses the corresponding solution approaches from a data scientist’s perspective. This main part of the article is preceded by an overview of the status quo in the development and application of soft sensors. The scope of this article is mainly the upstream part of bioprocesses, although the solution approaches are in most cases also applicable to the downstream part. Variable process lengths are accounted for by data synchronization techniques such as indicator variables, curve registration, and dynamic time warping. Multiple process phases are partitioned by trajectory or correlation-based phase detection, enabling phase-adaptive modeling. Sensor faults are detected by symptom signals, pattern recognition, or by changing contributions of the corresponding sensor to a process model. According to the current state of the literature, tolerance to sensor faults remains the greatest challenge in soft sensor development, especially in the presence of variable process lengths and multiple process phases.

Integrated twin-screw wet granulation, continuous vibrational fluid drying and milling: A fully continuous powder to granule line

Highly homogeneous low-dose (50 μg) tablets were produced incorporating perfectly free-flowing granules prepared by a fully integrated Continuous Manufacturing (CM) line. The adopted CM equipment consisted of a Twin-Screw Wet Granulator (TSWG), a Continuous Fluid Bed Dryer (CFBD) and a Continuous Sieving (CS) unit. Throughout the experiments a pre-blend of lactose-monohydrate and corn starch was gravimetrically dosed with 1 kg/h into the TSWG, where they were successfully granulated with the drug containing water-based PVPK30 solution. The wet mass was subsequently dried in the CFBD on a vibratory conveyor belt and finally sieved in the milling unit. Granule production efficiency was maximized by determining the minimal Liquid-to-Solid (L/S) ratio (0.11). Design of Experiments (DoE) were carried out in order to evaluate the influence of the drying process parameters of the CFBD on the Loss-on-Drying (LOD) results. The manufactured granules were compressed into tablets by an industrial tablet rotary press with excellent API homogeneity (RSD < 3%). Significant scale-up was realized with the CM line by increasing the throughput rate to 10 kg/h. The manufactured granules yielded very similar results to the previous small-scale granulation runs. API homogeneity was demonstrated (RSD < 2%) with Blend Uniformity Analysis (BUA). The efficiency of TSWG granulation was compared to High-Shear Granulation (HSG) with the same L/S ratio. The final results have demonstrated that both the liquid distribution and more importantly API homogeneity was better in case of the TSWG granulation (RSD 1.3% vs. 4.5%).

Fiberoptic-Coupled Spectrofluorometer with Array Detection as a Process Analytical Chemistry Tool for Continuous Flow Monitoring of Fluoroquinolone Antibiotics

Nowadays, there is an increasing need for sensitive real-time measurements of various analytes and monitoring of industrial products and environmental processes. Herein, we describe a fluorescence spectrometer in continuous flow mode in which the sample is fed to the flow cell using a peristaltic pump. The excitation beam is introduced to the sample chamber by an optical fiber. The fluorescence emitted upon excitation is collected at the right angle using another optical fiber and then transmitted to the fluorescence spectrometer which utilizes an array detector. The array detection, as a key factor in process analytical chemistry, made the fluorescence spectrometer suited for multiwavelength detection of the fluorescence spectrum of the analytes. After optimization of the experimental parameters, the system has been successfully employed for sensitive determination of four fluoroquinolone antibiotics such as ciprofloxacin, ofloxacin, levofloxacin, and moxifloxacin. The linear dynamic ranges of four fluoroquinolones were between 0.25 and 20 μg·mL-1, and the detection limit of the method for ciprofloxacin, ofloxacin, levofloxacin, and moxifloxacin were 81, 36, 35, and 93 ng·mL-1, respectively. Finally, the proposed system is carried out for determination of fluoroquinolones in some pharmaceutical formulations.