Critical Process Parameters Research Articles

Integrating Artificial Intelligence with Quality by Design in the Formulation of Lecithin/Chitosan Nanoparticles of a Poorly Water-Soluble Drug

The aim of the current study is to explore the potential of artificial intelligence (AI) when integrated with Quality by Design (QbD) approach in the formulation of a poorly water-soluble drug, for its potential use in carcinoma. Silymarin is used as a model drug for its potential effectiveness in liver cancer. A detailed QbD approach was applied. The effect of the critical process parameters was studied on each of the particle size, size distribution, and entrapment efficiency. Response surface designs were applied in the screening and optimization of lecithin/chitosan nanoparticles, to obtain an optimized formula. The release rate was tested, where artificial neural network models were used to predict the % release of the drug from the optimized formula at different time intervals. The optimized formula was tested for its cytotoxicity. A design space was established, with an optimized formula having a molar ratio of 18.33:1 lecithin:chitosan and 38.35 mg silymarin. This resulted in nanoparticles with a size of 161 nm, a polydispersity index of 0.2, and an entrapment efficiency of 97%. The optimized formula showed a zeta potential of +38 mV, with well-developed spherical particles. AI successfully showed high prediction ability of the drug’s release rate. The optimized formula showed an enhancement in the cytotoxic effect of silymarin with a decreased IC50 compared to standard silymarin. Lecithin/chitosan nanoparticles were successfully formulated, with deep process and product understanding. Several tools were used as AI which could shift pharmaceutical formulations from experience-dependent studies to data-driven methodologies in the future.Graphical abstract

Satisfying QTPP of Erythropoietin Biosimilar by QbD through DoE-Derived Downstream Process Engineering.

Well-characterized and scalable downstream processes for the purification of biologics are extremely demanding for delivering quality therapeutics to patients at a reasonable price. Erythropoietin (EPO) is a blockbuster biologic with diverse clinical applications, but its application is limited to financially well-off societies due to its high price. The high price of EPO is associated with the technical difficulties related to the purification challenge to obtain qualified products with a cost-effective defined process. Though there are reports for the purification of EPO there is no report of a well-characterized downstream process with critical process parameters (CPPs) that can deliver EPO consistently satisfying the quality target product profile (QTPP), which is a critical regulatory requirement. To advance the field, we applied the quality by design (QbD) principle and design of experiment (DoE) protocol to establish an effective process, which is scalable up to 100× batch size satisfying QTPP. We have successfully transformed the process from static mode to dynamic mode and validated it. Insignificant variation (p > 0.05) within and between 1×, 10×, and 100× batches showed that the process is reproducible and seamlessly scalable. The biochemical analysis along with the biofunctionality data ensures that the products from different scale batches were indifferent and comparable to a reference product. Our study thereby established a robust and scalable downstream process of EPO biosimilar satisfying QTPP. The technological scheme presented here can speed up the production of not only EPO but also many other life-saving biologics and make them available to the mass population at a reduced cost.

Intelligent co-design of material, process, and equipment for manufacturing high-quality traditional Chinese medicine preparations

In the context of Pharma 4.0, the design tools that support the pharmaceutical Quality by Design(QbD) are iterating fast toward intelligent or smart design. The conventional development methods for traditional Chinese medicine(TCM) preparations have the limitations such as over dependence on experience, low dimensions for the designed experiment parameters, poor compatibility between the process and equipment, and high trial-and-error cost during process scale-up. Therefore, this paper innovatively proposed the intelligent co-design involving material, process, and equipment for manufacturing high-quality TCM preparations, and introduced the design philosophy, targets, tools, and applications with TCM oral solid dosage(OSD) as an example. In terms of design philosophy, the pharmaceutical design tetrahedron composed of critical material attributes, critical process parameters, critical equipment attributes, and critical quality attributes was developed. The design targets were put forward based on the product performance classification system. The design tools involve a design platform that contains several modules, such a as the iTCM material database, the processing route classification system, the system modeling and simulation, and reliability-based optimization. The roles of different modules in obtaining essential and universal design knowledge of the key common manufacturing units were introduced. At last, the applications of the co-design methodology involving material, process, and equipment in the high shear wet granulation process development and the improvement of the dissolving or dispersion capability of TCM formula granules are illustrated. The research on advanced pharmaceutical design theory and methodology will help enhance the efficiency and reliability of drug development, improve the product quality, and promote the innovation of high-end TCM products across the industry.

Process validation and a new method for quality control of ultrathin polyelectrolyte multilayer coatings

The properties of polyelectrolyte multilayers are ruled by the process parameters employed during self-assembly. This is the first study in which a design of experiment approach was used to validate and control the production of ultrathin polyelectrolyte multilayer coatings by identifying the ranges of critical process parameters (polyelectrolyte concentration, ionic strength and pH) within which coatings with reproducible properties (thickness, refractive index and hydrophilicity) are created. Mathematical models describing the combined impact of key process parameters on coatings properties were developed demonstrating that only ionic strength and pH affect the coatings thickness, but not polyelectrolyte concentration. While the electrolyte concentration had a linear effect, the pH contribution was described by a quadratic polynomial. A significant contribution of this study is the development of a new approach to estimate the thickness of polyelectrolyte multilayer nanofilms by quantitative rhodamine B staining, which might be useful in all cases when ellipsometry is not feasible due to the shape complexity or small size of the coated substrate. The novel approach proposed here overcomes the limitations of known methods as it offers a low spatial sampling size and the ability to analyse a wide area without restrictions on the chemical composition and shape of the substrate.

Optimizing anaerobic co-digestion of Xyris capensis and duck waste using neuro-fuzzy model and response surface methodology

Optimization of biomethane yield from the anaerobic co-digestion process has taken a new dimension from the application of common statistical techniques to the use of intelligence models in the Artificial Intelligence (AI) field. In this study, the performance of fuzzy c-means (FCM)-clustered adaptive neuro-fuzzy inference system (ANFIS) was compared with the Response Surface Methodology (RSM) in investigating the impact of critical process parameters, namely temperature, retention time, and mixing ratio on the methane yield of anaerobic co-digestion of duck waste and Xyris capensis. Relevant statistical metrics such as root mean square error (RMSE), mean absolute percent error (MAPE), mean absolute error (MAE) and mean absolute deviation (MAD) was used to evaluate the performance of the FCM-ANFIS model developed. The developed RSM and FCM-ANFIS models were compared based on the predicted yields and optimum conditions. It was observed from the results that the process parameters have a significant influence on the methane yield of anaerobic co-digestion of duck waste and Xyris capensis. The cumulative yield of 478.42, 478.43, and 436.20 mL CH4/g VSadded was observed for experimental, RSM, and ANFIS-FCM5clusters models, respectively. The optimum daily methane yield was observed when the mixing ratio was 75% duck waste and 25% Xyris capensis for the experimental and ANFIS-FCM5 clusters model. In contrast, the RSM model observed it at 100% duck waste. The RMSE, MAD, MAE, and MAPE values of the optimal neuro-fuzzy model (ANFIS-FCM5clusters) test indicated that the neuro-fuzzy model developed performs better than the RSM model with minimal error and higher accuracy. RSM and ANFIS models were noticed to be practical models for methane yield optimization and prediction from anaerobic co-digestion of livestock waste and lignocellulose feedstocks in a multiple-input variable without undertaking an experiment within a short time and error. These models can be used to optimize the anaerobic digestion process of lignocellulose materials and livestock waste at the industrial scale.

Chromatography assisted in-vitro refolding and purification of recombinant peptibody: Recombinant Romiplostim a case study

In-vitro protein refolding is one of the key rate-limiting unit operations in manufacturing of fusion proteins such as peptibodies expressed using E. coli. Dilution-assisted refolding is the most commonly used industrial practice to achieve the soluble, native functional form of the recombinant protein from the inclusion bodies. This study is focused on developing a chromatography-assisted in-vitro refolding platform to produce the biologically active, native form of recombinant peptibody. Recombinant Romiplostim was selected as a model protein for the study. A plug flow tubular reactor was connected in series with capture step affinity chromatography to achieve simultaneous in-vitro refolding and capture step purification of recombinant Romiplostim. Effect of various critical process parameters like fold dilution, temperature, residence time, and Cysteine: DTT ratio was studied using a central composite based design of experiment strategy to achieve a maximum refolding yield of selected peptibody. Under optimum refolding conditions, the maximum refolding yield of 57.0 ± 1.5 % and a purity of over 79.73 ± 3.4 % were achieved at 25-fold dilution, 15 °C temperature, 6 h residence time with 6 mM and 10 mM of cysteine and DTT, respectively. The formation of native peptibody structure was examined using various orthogonal analytical tools to study the protein's primary, secondary, and tertiary structure. The amino acid sequence for the disulfide-linked peptide was mapped using collision-induced dissociation (CID) to confirm the formation of interchain disulfide bonds between Cys7-Cys7 and Cys10-Cys10 similarly for intra-chain disulfide bonds between Cys42-Cys102, and Cys148-Cys206. The developed protocol here is a valuable tool to identify high-yield scalable refolding conditions for multi-domain proteins involving inter-domain disulfide bonds.

Biopharmaceutics Risk Assessment-Connecting Critical Bioavailability Attributes with In Vitro, In Vivo Properties and Physiologically Based Biopharmaceutics Modeling to Enable Generic Regulatory Submissions.

Quality risk assessment following ICH Q9 principles is an important activity to ensure optimal clinical efficacy and safety of a drug product. Typically, risk assessment is focused on product performance wherein critical material attributes, formulation variables, and process parameters are evaluated from a manufacturing perspective. Extending ICH Q9 principles to biopharmaceutics risk assessment to identify factors that can impact in vivo performance is an upcoming area. This is evident by recent regulatory trends wherein a new term critical bioavailability attributes (CBA) has been coined to identify such factors. Although significant work has been performed for biopharmaceutics risk assessment for new molecules, there is a need for harmonized biopharmaceutics risk assessment workflow for generic submissions. In this manuscript, we attempted to provide a framework for performing biopharmaceutics risk assessment for generic regulatory submissions. A detailed workflow for performing biopharmaceutics risk assessment includes identification of initial CBA (iCBA), their confirmatory evaluation followed by definition of the control strategy. Tools for biopharmaceutics risk assessment, i.e., bio-discriminatory dissolution method and physiologically based biopharmaceutics modeling (PBBM) were discussed from a practical perspective. Furthermore, a case study for CBA evaluation using PBBM modeling for an extended-release product for regulatory submission has been described using the proposed workflow. Finally, future directions of integrating CBA evaluation, biopharmaceutics risk assessment to the FDA Knowledge Aided Structured Assessment (KASA) initiative, the necessity of risk assessment templates, and knowledge sharing between industry and academia are discussed. Overall, the work described in this manuscript can facilitate and provide guidance for biopharmaceutics risk assessment for generic submissions.

Current challenges and recent advances on the path towards continuous biomanufacturing.

Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever-evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end-to-end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.

Atovaquone smart lipid system: Design, statistical optimization, and in-vitro evaluation

The research was undertaken to design, develop and characterize the smart lipid system of an inadequate bioavailable Atovaquone (ATQ). The poor aqueous solubility and dissolution are the major constrain of inadequate bioavailability. The solubility study reveals that Labrasol-ALF (L-ALF), Tween 80, and Trancutol®P (TP) were screened as oil, surfactant, and co-surfactant, respectively. The pseudo ternary diagram was constructed to locate the appropriate amount of each ingredient, and a 1:3:1 ratio of l-ALF: Tween 80: TP was chosen. The effect of precipitation inhibitor was assessed using the parachute effect. Soluplus® (SP) was chosen as a precipitation inhibitor at 5%. Ishikawa diagram and qualitative risk assessment were performed to screen the critical material attributes (CMAs) and critical process parameters (CPPs). d-optimal mixture design was explored for the optimization of the formulation. The amount of oil, surfactant, and co-surfactant was screened as independent variables, whereas globule size, poly-dispersibility index (PDI), and solubility were designated dependent variables. The design batches were evaluated for the in-vitro dissolution rate, PDI, zeta potential, globule size, etc. The optimal region was located using an overlay plot. The optimized formulation has shown a 97.91% drug release within 1 h. The value of zeta potential (-27.43 mV) and PDI (0.468) indicates the stability of the formulation. The parachute effect had explored for the selection of precipitation inhibitors. SP was able to increase the solubility of ATQ and reduce the precipitation of the drug. The amount of l-ALF, Tween, 80and TP was significant for the formulation of SNEDDS. The formulation was novel, effective, patient-friendly, and industry oriented.

A laboratory-scale compression feed screw for characterizing continuous feeding of dense granular materials

Compression feed screws are commonly used in biorefineries for the continuous feeding of biomass feedstock into reactors. Developing methods to assess the feasibility of feeding granular materials in a compression feed screw is critical for reducing solids handling issues during operation. A laboratory-scale compression feed screw has been built to measure critical process parameters of the feeding operation, namely the driving torque and the mass flow rate. The process parameters of two granular materials, microcrystalline cellulose Avicel PH-200 and milled corn stover are measured using the laboratory-scale feeder. The results show that a larger torque is required to feed corn stover than Avicel PH-200, even though the former consists of more compressible particles. Further, the device can capture real-time fluctuations in the process parameters during operation. The laboratory-scale feeder can be used to compare the feeding of different granular materials and the measured parameters can be used to validate predictive models for a compression screw feeding process.

Application of Quality by Design Principles to the Development of Oral Lyophilizates Containing Olanzapine.

Oral lyophilizates are intended for application to the oral cavity or for dispersing in water. The purposes of this research were: (i) to set up the quality by design approach in the development of oral lyophilizates for drug incorporation; and (ii) to evaluate the established approach by comparing its outcomes with experimentally obtained results. Within the knowledge space, properties about drugs, excipients, and the lyophilization process were acquired, followed by the determination of critical quality attributes via risk identification. Risks were assessed by failure mode and effective analysis, which recognized critical material attributes, i.e., type, concentration, particle size, solubility of drug and excipients, while as main critical process parameters, cooling rate, shelf temperature, and chamber pressure during drying were pointed out. Additionally, design space was established using the Minitab® 17 software and valued with an 88.69% coefficient of determination. A detailed comparison between the model and experimental results revealed that the proposed optimal compositions match in the total concentration of excipients (6%, w/w) in the pre-lyophilized liquid formulation, among which mannitol predominates. On the other hand, a discrepancy regarding the presence of gelatin was detected. The conclusion was that the set model represents a suitable onset toward optimization of drug-based oral lyophilizates development, preventing unnecessary investment of time and resources.

Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges.

Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an "optimal" model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.

Data-Driven Process Analysis for Iron Foundries with Automatic Sand Molding Process

This paper proposes a methodological framework to develop a data-driven process control using pure industrial production data from a cast iron foundry, despite the limitation of complete casting traceability. The aim is to help sand foundries to produce good castings. A reference foundry, which produces mainly automotive and oven parts with automatic sand molding and pouring machines, was selected. Past data, where only good castings were produced, were extracted from the database to determine parameter control limits (upper and lower control limits) with the aid of statistical approach. To identify critical process parameters associated with casting defects, process data from the zero and high scrap production batches were systematically compared. This method clearly identified unstable parameters without exact synchronization between inline and part quality data. Molding sand moisture, temperature and compactability, liquidus temperature of the melt, phosphorus content, carbon equivalent and pouring temperature were found to be the critical parameters to be stabilized. Finally, a regression model for predicting and controlling of molding sand moisture and liquidus temperature of the melt was created. The determined boundaries and the models were helpful for the foundry in assisting ongoing production control and correction of process inputs to achieve target casting quality.

Can Continuous Manufacturing of Topical Semisolids by Hot Melt Extrusion Soon Be a Reality?

For more than five decades, pharmaceutical manufacturers have been relying heavily on batch manufacturing that is a sequential, multistep, laborious, and time-consuming process. However, late advances in manufacturing technologies have prompted manufacturers to consider continuous manufacturing (CM) is a feasible manufacturing process that encompasses fewer steps and is less tedious and quick. Global regulatory agencies are taking a proactive role to facilitate pharmaceutical industries to adopt CM that assures product quality by employing robust manufacturing technologies encountering fewer interruptions, thereby substantially reducing product failures and recalls. However, adopting innovative CM is known to pose technical and regulatory challenges. Hot melt extrusion (HME) is one such state-of-the-art enabling technology that facilitates CM of diverse pharmaceutical dosage forms, including topical semisolids. Efforts have been made to continuously manufacture semisolids by HME integrating the principles of Quality by Design (QbD) and Quality Risk Management (QRM) and deploying Process Analytical Technologies (PAT) tools. Attempts have been made to systematically elucidate the effect of critical material attributes (CMA) and critical process parameters (CPP) on product critical quality attributes (CQA) and Quality Target Product Profiles (QTPP) deploying PAT tools. The article critically reviews the feasibility of one of the enabling technologies such as HME in CM of topical semisolids. The review highlights the benefits of the CM process and challenges ahead to implement the technology to topical semisolids. Once the CM of semisolids adopting melt extrusion integrated with PAT tools becomes a reality, the process can be extended to manufacture sterile semisolids that usually involve more critical processing steps.

Hybrid Deep Modeling of a GS115 (Mut+) Pichia pastoris Culture with State–Space Reduction

Hybrid modeling workflows combining machine learning with mechanistic process descriptions are becoming essential tools for bioprocess digitalization. In this study, a hybrid deep modeling method with state–space reduction was developed and showcased with a P. pastoris GS115 Mut+ strain expressing a single-chain antibody fragment (scFv). Deep feedforward neural networks (FFNN) with varying depths were connected in series with bioreactor macroscopic material balance equations. The hybrid model structure was trained with a deep learning technique based on the adaptive moment estimation method (ADAM), semidirect sensitivity equations and stochastic regularization. A state–space reduction method was investigated based on a principal component analysis (PCA) of the cumulative reacted amount. Data of nine fed-batch P. pastoris 50 L cultivations served to validate the method. Hybrid deep models were developed describing process dynamics as a function of critical process parameters (CPPs). The state–space reduction method succeeded to decrease the hybrid model complexity by 60% and to improve the predictive power by 18.5% in relation to the nonreduced version. An exploratory design space analysis showed that the optimization of the feed of methanol and of inorganic elements has the potential to increase the scFv endpoint titer by 30% and 80%, respectively, in relation to the reference condition.

A machine learning digital twin approach for critical process parameter prediction in a catalyst manufacturing line

Digital twins (DTs) are rapidly changing how manufacturing companies leverage the large volumes of data they generate daily to gain a competitive advantage and optimize their supply chains. When coupled with recent developments in machine learning (ML), DTs have the potential to generate invaluable insights for process manufacturing companies to help them optimize their manufacturing processes. However, this potential has yet to be fully exploited due to the challenges that process manufacturing companies face in developing and implementing DTs in their organizations. Although DTs are receiving increasing attention in both industry and academia, there is limited literature on how to apply them in the process industry. To address this gap, this paper presents a framework for developing ML-based DTs to predict critical process parameters in real time. The proposed framework is tested through a case study at an international process manufacturing company in which it was used to collect and process plant data, build accurate predictive models for two critical process parameters, and develop a DT application to visualize the models’ predictions. The case study demonstrated the usefulness of the proposed DT–ML framework in the sense that it provided the company with more accurate predictions than the models it previously applied. The study provides insights into the value of applying ML-based DT in the process industry and sheds light on some of the challenges associated with the application of this technology.

An ab initio simulation on electron beam physical vapor deposition of Gd2Zr2O7 coating by density functional theory and kinetic Monte Carlo

AbstractWithout any experimental inputs, the electron beam physical vapor deposition (EB‐PVD) of Gd2Zr2O7 with much potential for thermal barrier coatings was investigated by use of the kinetic Monte Carlo, after fitting the potential function using lattice inversion method and “coarse‐grained” mapping on the cohesive energy calculated by density functional theory, to reveal the effect of critical processing parameters of EB‐PVD on the microstructures and porosity of the deposited coating, for example, substrate temperature, deposition rate and incident angle. Based on the lower energy barrier calculated by a nudged elastic band, intra‐layer diffusion is easier than interlayer diffusion, attributed to the fewer bonds in the former. Furthermore, the porosity of the coating decreases with the increase of temperature or decrease of deposition rate, with the gradual disappearing large gaps among columnar crystals and filled pores, because of the increase of transition probability and transition times. With the decreasing incident angle, the area of the shadow zone decreases and less particles can be blocked by previously deposited ones, making the columnar crystals disappear gradually, with more like laminated structures.

Optofluidic force induction as a process analytical technology

Manufacturers of nanoparticle-based products rely on detailed information about critical process parameters, such as particle size and size distributions, concentration, and material composition, which directly reflect the quality of the final product. These process parameters are often obtained using offline characterization techniques that cannot provide the temporal resolution to detect dynamic changes in particle ensembles during a production process. To overcome this deficiency, we have recently introduced Optofluidic Force Induction (of2i) for optical real-time counting with single particle sensitivity and high throughput. In this paper, we apply of2i to highly polydisperse and multi modal particle systems, where we also monitor evolutionary processes over large time scales. For oil-in-water emulsions we detect in real time the transition between high-pressure homogenization states. For silicon carbide nanoparticles, we exploit the dynamic of2i measurement capabilities to introduce a novel process feedback parameter based on the dissociation of particle agglomerates. Our results demonstrate that of2i provides a versatile workbench for process feedback in a wide range of applications.Graphical abstract

Statistically-aided development of protein A affinity chromatography for enhancing recovery and controlling quality of a monoclonal antibody

Protein A chromatography is widely used for isolation of monoclonal antibodies (mAbs) from cell culture components. In this study, the effect of different process parameters of the Protein A purification namely, binding pH, elution pH, flow rate, neutralization pH and tween concentration, on the concentration and quality of the purified mAb were evaluated. Using design of experiments approach, the critical process parameters of protein A chromatography were identified and experimentally optimized. Their impact on quality attributes, such as size variants and charge variants, of the mAb was studied. Multivariate data analysis was subsequently performed using multiple linear regression and partial least squares regression methods. It was observed that the elution pH primarily governed the concentration of the purified mAb and the content of monomers and aggregates, while the tween concentration primarily influenced the main peak of the charge variants. This is the first study that evaluates the impact of tween concentration in buffers on the protein A chromatography purification step. These studies helped in identifying the design space and defining the target robust and optimal setpoints of the responses, which were subsequently verified experimentally. These setpoints not only passed the target criteria but also resulted in the highest recoveries during the investigation. Through this statistically-aided approach, an optimized and robust protein A chromatography process was rationally developed for purification of mAbs, while achieving the desired product quality. This study highlights the influence of multiple parameters of the protein A purification process on critical quality attributes of mAbs, such as the size and charge variants, which has been a very scarcely explored area.

Manufacturing Process Validation of API Drug Moxifloxacin to Evaluate the Critical Process Parameters and Quality Attribute

Product quality is the dependence of medicinal diligence and is deduced from careful attention to a number of factors including selection of quality corridor and accoutrements, acceptable product and manufacturing process design, control of the process variables, in- process and end- product testing. Process confirmation is an integral part of quality assurance program in diligence. By validating each step of product process we can assure that the final product is of stylish quality. This review provides information on objects and benefits of process confirmation, types of process confirmation, major phases in confirmation and nonsupervisory aspects. Guidelines and strategy for process confirmation of solid lozenge form are also bandied. The main thing of this work was to produce a precious Risk Management Approach that enables a Process confirmation over products lifecycle. Quality threat operation (QTO) has been described in nonsupervisory guidance for several aspects of process confirmation, similar as product lifecycle, extent of confirmation, determination of critical quality attributes (CQAs) and critical process parameters (CPPs), process design space( DS), and slice plans and statistical confidence situations. Verification of the process in Moxifloxacin Hcl produced batch, over the product life time is now an anticipation from non-supervisory authorities.