1
|
Srinivasan K, Puliyanda A, Prasad V. Identification of Reaction Network Hypotheses for Complex Feedstocks from Spectroscopic Measurements with Minimal Human Intervention. J Phys Chem A 2024; 128:4714-4729. [PMID: 38836378 DOI: 10.1021/acs.jpca.4c01592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
In this work, we detail an automated reaction network hypothesis generation protocol for processes involving complex feedstocks where information about the species and reactions involved is unknown. Our methodology is process agnostic and can be utilized in any reactive process with spectroscopic measurements that provide information on the evolution of the components in the mixture. We decompose the mixture spectra to obtain spectroscopic signatures of the individual components and use a 1-D convolutional neural network to automatically identify functional groups indicated by them. We employ atom-atom mapping to automatically recover reaction rules that are applied on candidate molecules identified from chemistry databases through fingerprint similarity. The method is tested on synthetic data and on spectroscopic measurements of lab-scale batch hydrothermal liquefaction (HTL) of biomass to determine the accuracy of prediction across datasets of varying complexities. Our methodology is able to identify reaction network hypotheses containing reaction networks close to the ground truth in the case of synthetic data, and we are also able to recover candidate molecules and reaction networks close to the ones reported in the previous literature studies for biomass pyrolysis.
Collapse
Affiliation(s)
- Karthik Srinivasan
- Department of Chemical and Materials Engineering, Donadeo Innovation Centre for Engineering, 9211, 116st NW, Edmonton T6G 1H9, AB, Canada
| | - Anjana Puliyanda
- Department of Chemical and Materials Engineering, Donadeo Innovation Centre for Engineering, 9211, 116st NW, Edmonton T6G 1H9, AB, Canada
| | - Vinay Prasad
- Department of Chemical and Materials Engineering, Donadeo Innovation Centre for Engineering, 9211, 116st NW, Edmonton T6G 1H9, AB, Canada
| |
Collapse
|
2
|
Nie J, Ren H, Sun Y, Li Y, Zhang Y, Bai Z. Application of Multivariate Data Analysis on Historical Recombinant Adenovirus Zoster Vaccine Production Data for Upstream Process Improvements. J Pharm Sci 2024; 113:1168-1176. [PMID: 38447668 DOI: 10.1016/j.xphs.2024.02.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 03/08/2024]
Abstract
In recent years, multivariate data analysis (MVDA) has been widely used for process characterization and fault diagnosis in the biopharmaceutical industry. This study aims to investigate the feasibility of using MVDA for the development and scale-up of a perfusion process for HEK293 cell-based recombinant adenovirus zoster vaccine (Ad-HER) production. The Principal Component Analysis (PCA) results suggested comparable performance among the ATF, PATFP, and BFP perfusion systems in benchtop-scale stirred-tank bioreactor (STR). Then a Batch Evolution Model (BEM) was built using representative data from 10 L STR with a BFP system to assess the Ad-HER perfusion process performance at pilot-scale bioreactor (50 L STR and 50 L wave bioreactor). Furthermore, another BEM model and Batch Level Model (BLM) were built to monitor process parameters over time and predict the final adenovirus titer in 50 L wave bioreactor. The loading plot revealed that lactate dehydrogenase activity, viable cell diameter, and base-added during the virus production phase could be used as preliminary indicators of adenovirus yield. Finally, an adenovirus titer of 2.0±0.3×1010 IFU/mL was achieved in the 50 L wave bioreactor with BFP system, highlighting the robustness of the Ad-HER perfusion process at pilot-scale. Overall, this study emphasizes the effectiveness of MVDA as a tool for advancing the understanding of recombinant adenovirus vaccine perfusion production process development and scale-up.
Collapse
Affiliation(s)
- Jianqi Nie
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi 214122, China
| | - He Ren
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi 214122, China
| | - Yang Sun
- School of Life Sciences, Henan University, Kaifeng 475004, China
| | - Ye Li
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi 214122, China
| | - Yan Zhang
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi 214122, China.
| | - Zhonghu Bai
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, Jiangnan University, Wuxi 214122, China; The Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China; Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi 214122, China.
| |
Collapse
|
3
|
Sattari F, Srinivasan K, Puliyanda A, Prasad V. Data Fusion-Based Approach for the Investigation of Reaction Networks in Hydrous Pyrolysis of Biomass. Ind Eng Chem Res 2023. [DOI: 10.1021/acs.iecr.2c04309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Affiliation(s)
- Fereshteh Sattari
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
| | - Karthik Srinivasan
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
| | - Anjana Puliyanda
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
| | - Vinay Prasad
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
| |
Collapse
|
4
|
Data-driven anomaly detection and diagnostics for changeover processes in biopharmaceutical drug product manufacturing. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2020.12.018] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
5
|
Chaturvedi K, Shah HS, Sardhara R, Nahar K, Dave RH, Morris KR. Protocol development, validation, and troubleshooting of in-situ fiber optic bathless dissolution system (FODS) for a pharmaceutical drug testing. J Pharm Biomed Anal 2020; 195:113833. [PMID: 33358085 DOI: 10.1016/j.jpba.2020.113833] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 12/01/2020] [Accepted: 12/04/2020] [Indexed: 11/25/2022]
Abstract
Currently, there is no systematic approach available for the validation, quantitative assessment, and troubleshooting for the in-situ fiber optic/bathless dissolution system (FODS). In this report, a dissolution protocol was developed and validated for a model product, chlorpheniramine maleate (CPM) 4 mg IR tablets. Dissolution runs were conducted at 37 ± 0.2 °C using a USP apparatus II, at 50 rpm in 500 mL of 0.01 N hydrochloric acid. The dissolution system was validated for linearity, accuracy, precision, specificity, and robustness analogously to an HPLC method validation. The linearity determination method was developed using five concentration levels between 25-125 % of the expected concentration, while for accuracy, 80 %, 100 %, and 120 % levels were used, and precision was determined using six runs at the 100 % level. Probe sampling depth, orientation, analytical wavelength, and paddle speed were varied to evaluate the robustness of the system tested. Method equivalence was established by comparing the dissolution results from FODS and the traditional dissolution method using UV spectrophotometry. Based on the statistics generated using the dissolution tests, the results are linear, accurate, precise, and specific. Robustness testing demonstrates that small changes in operating conditions did not significantly change the result. No significant difference in the amount dissolved at Q-timepoint was observed between FODS and traditional testing. Therefore, the FODS is a suitable alternative to traditional dissolution for CPM immediate-release tablets (many other drug products have been tested in the laboratory, and reports are in preparation). Additionally, the current work discusses problems related to media preparation, probe sensitivity, and excipient effects on data collected using FODS. The instrument-specific artifacts and data analysis problems are addressed and troubleshooting with possible solutions to eliminate or mitigate the errors. Although the FODS method was developed and evaluated using CPM in 500 mL dissolution volume, the dissolution method using a more common pharmacopoeial dissolution volume, i.e., 900 mL, was used to demonstrate the troubleshooting experiments for the drug products requiring 900 mL dissolution media.
Collapse
Affiliation(s)
- Kaushalendra Chaturvedi
- Department of Pharmaceutical Sciences, The Arnold and Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, NY, 11201, United States.
| | - Harsh S Shah
- Department of Pharmaceutical Sciences, The Arnold and Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, NY, 11201, United States
| | - Rusha Sardhara
- Department of Pharmaceutical Sciences, The Arnold and Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, NY, 11201, United States
| | - Kajal Nahar
- Department of Pharmaceutical Sciences, The Arnold and Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, NY, 11201, United States
| | - Rutesh H Dave
- Department of Pharmaceutical Sciences, The Arnold and Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, NY, 11201, United States.
| | - Kenneth R Morris
- Lachman Institute for Pharmaceutical Analysis, The Arnold and Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, NY, 11201, United States.
| |
Collapse
|
6
|
Abstract
In conditional microbial screening, a limited number of candidate strains are tested at different conditions searching for the optimal operation strategy in production (e.g., temperature and pH shifts, media composition as well as feeding and induction strategies). To achieve this, cultivation volumes of >10 mL and advanced control schemes are required to allow appropriate sampling and analyses. Operations become even more complex when the analytical methods are integrated into the robot facility. Among other multivariate data analysis methods, principal component analysis (PCA) techniques have especially gained popularity in high throughput screening. However, an important issue specific to high throughput bioprocess development is the lack of so-called golden batches that could be used as a basis for multivariate analysis. In this study, we establish and present a program to monitor dynamic parallel cultivations in a high throughput facility. PCA was used for process monitoring and automated fault detection of 24 parallel running experiments using recombinant E. coli cells expressing three different fluorescence proteins as the model organism. This approach allowed for capturing events like stirrer failures and blockage of the aeration system and provided a good signal to noise ratio. The developed application can be easily integrated in existing data- and device-infrastructures, allowing automated and remote monitoring of parallel bioreactor systems.
Collapse
|
7
|
Puliyanda A, Sivaramakrishnan K, Li Z, de Klerk A, Prasad V. Data fusion by joint non-negative matrix factorization for hypothesizing pseudo-chemistry using Bayesian networks. REACT CHEM ENG 2020. [DOI: 10.1039/d0re00147c] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We infer reaction networks and chemistry using data fusion of spectroscopic sensors.
Collapse
Affiliation(s)
| | | | - Zukui Li
- Department of Chemical and Materials Engineering
- Edmonton
- Canada
| | - Arno de Klerk
- Department of Chemical and Materials Engineering
- Edmonton
- Canada
| | - Vinay Prasad
- Department of Chemical and Materials Engineering
- Edmonton
- Canada
| |
Collapse
|
8
|
|
9
|
Narayanan H, Luna MF, Stosch M, Cruz Bournazou MN, Polotti G, Morbidelli M, Butté A, Sokolov M. Bioprocessing in the Digital Age: The Role of Process Models. Biotechnol J 2019; 15:e1900172. [DOI: 10.1002/biot.201900172] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 07/15/2019] [Indexed: 12/20/2022]
Affiliation(s)
- Harini Narayanan
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
| | - Martin F. Luna
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
| | | | - Mariano Nicolas Cruz Bournazou
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Gianmarco Polotti
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Massimo Morbidelli
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Alessandro Butté
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Michael Sokolov
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| |
Collapse
|
10
|
Narayanan H, Sokolov M, Butté A, Morbidelli M. Decision Tree-PLS (DT-PLS) algorithm for the development of process: Specific local prediction models. Biotechnol Prog 2019; 35:e2818. [PMID: 30969466 DOI: 10.1002/btpr.2818] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 03/15/2019] [Accepted: 03/25/2019] [Indexed: 12/26/2022]
Abstract
This work presents a novel multivariate statistical algorithm, Decision Tree-PLS (DT-PLS), to improve the prediction and understanding of dynamic processes based on local partial least square regression (PLSR) models for characteristic process groups defined based on Decision Tree (DT) analysis. The DT-PLS algorithm is successfully applied to two different cell culture data sets, one obtained from bioreactors of 3.5 L lab scale and the other obtained from the 15 ml ambr microbioreactor system. Substantial improvement in the predictive capabilities of the model can be achieved based on the localization compared to the classical PLSR approach, which is implemented in the commercially available packages. Additionally, the differences in the model parameters of the local models suggest that the governing process variables vary for the different process regimes indicating the different states of the cell under different process conditions.
Collapse
Affiliation(s)
- Harini Narayanan
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Switzerland
| | - Michael Sokolov
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Switzerland.,DataHow AG, Zurich, Switzerland
| | - Alessandro Butté
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Switzerland.,DataHow AG, Zurich, Switzerland
| | - Massimo Morbidelli
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Switzerland.,DataHow AG, Zurich, Switzerland
| |
Collapse
|
11
|
Sivaramakrishnan K, Puliyanda A, Tefera DT, Ganesh A, Thirumalaivasan S, Prasad V. A Perspective on the Impact of Process Systems Engineering on Reaction Engineering. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b00280] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Kaushik Sivaramakrishnan
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Anjana Puliyanda
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Dereje Tamiru Tefera
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Ajay Ganesh
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Sushmitha Thirumalaivasan
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Vinay Prasad
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| |
Collapse
|
12
|
Nagy B, Farkas A, Borbás E, Vass P, Nagy ZK, Marosi G. Raman Spectroscopy for Process Analytical Technologies of Pharmaceutical Secondary Manufacturing. AAPS PharmSciTech 2018; 20:1. [PMID: 30560395 DOI: 10.1208/s12249-018-1201-2] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Accepted: 10/01/2018] [Indexed: 01/03/2023] Open
Abstract
As the process analytical technology (PAT) mindset is progressively introduced and adopted by the pharmaceutical companies, there is an increasing demand for effective and versatile real-time analyzers to address the quality assurance challenges of drug manufacturing. In the last decades, Raman spectroscopy has emerged as one of the most promising tools for non-destructive and fast characterization of the pharmaceutical processes. This review summarizes the achieved results of the real-time application of Raman spectroscopy in the field of the secondary manufacturing of pharmaceutical solid dosage forms, covering the most common secondary process steps of a tablet production line. In addition, the feasibility of Raman spectroscopy for real-time control is critically reviewed, and challenges and possible approaches to moving from real-time monitoring to process analytically controlled technologies (PACT) are discussed.
Collapse
|
13
|
Montes FCC, Gernaey K, Sin G. Dynamic Plantwide Modeling, Uncertainty, and Sensitivity Analysis of a Pharmaceutical Upstream Synthesis: Ibuprofen Case Study. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b00465] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Frederico C. C. Montes
- PROSYS, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, 2800 Kgs. Lyngby, Denmark
| | - Krist Gernaey
- PROSYS, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, 2800 Kgs. Lyngby, Denmark
| | - Gürkan Sin
- PROSYS, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, 2800 Kgs. Lyngby, Denmark
| |
Collapse
|
14
|
Goldrick S, Holmes W, Bond NJ, Lewis G, Kuiper M, Turner R, Farid SS. Advanced multivariate data analysis to determine the root cause of trisulfide bond formation in a novel antibody-peptide fusion. Biotechnol Bioeng 2017; 114:2222-2234. [PMID: 28500668 PMCID: PMC5600124 DOI: 10.1002/bit.26339] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Revised: 04/12/2017] [Accepted: 05/11/2017] [Indexed: 01/11/2023]
Abstract
Product quality heterogeneities, such as a trisulfide bond (TSB) formation, can be influenced by multiple interacting process parameters. Identifying their root cause is a major challenge in biopharmaceutical production. To address this issue, this paper describes the novel application of advanced multivariate data analysis (MVDA) techniques to identify the process parameters influencing TSB formation in a novel recombinant antibody-peptide fusion expressed in mammalian cell culture. The screening dataset was generated with a high-throughput (HT) micro-bioreactor system (AmbrTM 15) using a design of experiments (DoE) approach. The complex dataset was firstly analyzed through the development of a multiple linear regression model focusing solely on the DoE inputs and identified the temperature, pH and initial nutrient feed day as important process parameters influencing this quality attribute. To further scrutinize the dataset, a partial least squares model was subsequently built incorporating both on-line and off-line process parameters and enabled accurate predictions of the TSB concentration at harvest. Process parameters identified by the models to promote and suppress TSB formation were implemented on five 7 L bioreactors and the resultant TSB concentrations were comparable to the model predictions. This study demonstrates the ability of MVDA to enable predictions of the key performance drivers influencing TSB formation that are valid also upon scale-up. Biotechnol. Bioeng. 2017;114: 2222-2234. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Stephen Goldrick
- Department of Biochemical Engineering, The Advanced Centre of Biochemical EngineeringUniversity College LondonGordon StreetWC1H 0AH LondonUnited Kingdom
- MedImmuneGranta ParkCambridge CB21 6GHUnited Kingdom
| | | | | | - Gareth Lewis
- MedImmuneGranta ParkCambridge CB21 6GHUnited Kingdom
| | - Marcel Kuiper
- MedImmuneGranta ParkCambridge CB21 6GHUnited Kingdom
| | | | - Suzanne S. Farid
- Department of Biochemical Engineering, The Advanced Centre of Biochemical EngineeringUniversity College LondonGordon StreetWC1H 0AH LondonUnited Kingdom
| |
Collapse
|
15
|
Yan X, Bain RM, Li Y, Qiu R, Flick TG, Cooks RG. Online Inductive Electrospray Ionization Mass Spectrometry as a Process Analytical Technology Tool To Monitor the Synthetic Route to Anagliptin. Org Process Res Dev 2016. [DOI: 10.1021/acs.oprd.6b00039] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Xin Yan
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Ryan M. Bain
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Yafeng Li
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Ran Qiu
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Tawnya G. Flick
- Department of Analytical Research & Development, Amgen Inc., 1 Amgen Center Drive, Thousand Oaks, California 91320, United States
| | - R. Graham Cooks
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| |
Collapse
|
16
|
Multivariate modelling to study the effect of the manufacturing process on the complete tablet dissolution profile. Int J Pharm 2015; 486:112-20. [DOI: 10.1016/j.ijpharm.2015.03.040] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 03/16/2015] [Accepted: 03/17/2015] [Indexed: 11/19/2022]
|
17
|
Tescione L, Lambropoulos J, Paranandi MR, Makagiansar H, Ryll T. Application of bioreactor design principles and multivariate analysis for development of cell culture scale down models. Biotechnol Bioeng 2014; 112:84-97. [DOI: 10.1002/bit.25330] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2013] [Revised: 06/01/2014] [Accepted: 06/30/2014] [Indexed: 11/10/2022]
Affiliation(s)
- Lia Tescione
- Biogen Idec, Cell Culture Development; 14 Cambridge Center Cambridge Massachusetts 02142
| | - James Lambropoulos
- Biogen Idec, Cell Culture Development; 14 Cambridge Center Cambridge Massachusetts 02142
| | - Madhava Ram Paranandi
- Biogen Idec, Cell Culture Development; 14 Cambridge Center Cambridge Massachusetts 02142
| | - Helena Makagiansar
- Biogen Idec, Cell Culture Development; 14 Cambridge Center Cambridge Massachusetts 02142
| | - Thomas Ryll
- Biogen Idec, Cell Culture Development; 14 Cambridge Center Cambridge Massachusetts 02142
| |
Collapse
|
18
|
Roy K, Undey C, Mistretta T, Naugle G, Sodhi M. Multivariate statistical monitoring as applied to clean-in-place (CIP) and steam-in-place (SIP) operations in biopharmaceutical manufacturing. Biotechnol Prog 2014; 30:505-15. [PMID: 24532460 DOI: 10.1002/btpr.1880] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2013] [Revised: 02/10/2014] [Indexed: 11/09/2022]
Abstract
Multivariate statistical process monitoring (MSPM) is becoming increasingly utilized to further enhance process monitoring in the biopharmaceutical industry. MSPM can play a critical role when there are many measurements and these measurements are highly correlated, as is typical for many biopharmaceutical operations. Specifically, for processes such as cleaning-in-place (CIP) and steaming-in-place (SIP, also known as sterilization-in-place), control systems typically oversee the execution of the cycles, and verification of the outcome is based on offline assays. These offline assays add to delays and corrective actions may require additional setup times. Moreover, this conventional approach does not take interactive effects of process variables into account and cycle optimization opportunities as well as salient trends in the process may be missed. Therefore, more proactive and holistic online continued verification approaches are desirable. This article demonstrates the application of real-time MSPM to processes such as CIP and SIP with industrial examples. The proposed approach has significant potential for facilitating enhanced continuous verification, improved process understanding, abnormal situation detection, and predictive monitoring, as applied to CIP and SIP operations.
Collapse
Affiliation(s)
- Kevin Roy
- Amgen Inc., Operations Improvement, Industrial Engineering, W. Greenwich, RI, 02817
| | | | | | | | | |
Collapse
|
19
|
Latent variable modeling to assist the implementation of Quality-by-Design paradigms in pharmaceutical development and manufacturing: A review. Int J Pharm 2013; 457:283-97. [DOI: 10.1016/j.ijpharm.2013.08.074] [Citation(s) in RCA: 70] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2013] [Revised: 08/27/2013] [Accepted: 08/28/2013] [Indexed: 11/19/2022]
|
20
|
Xiong H, Yu LX, Qu H. Batch-to-batch quality consistency evaluation of botanical drug products using multivariate statistical analysis of the chromatographic fingerprint. AAPS PharmSciTech 2013; 14:802-10. [PMID: 23636818 DOI: 10.1208/s12249-013-9966-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2012] [Accepted: 04/09/2013] [Indexed: 12/11/2022] Open
Abstract
Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many kinds of industrial products. In this paper, the combined use of multivariate statistical analysis and chromatographic fingerprinting is presented here to evaluate batch-to-batch quality consistency of botanical drug products. A typical botanical drug product in China, Shenmai injection, was selected as the example to demonstrate the feasibility of this approach. The high-performance liquid chromatographic fingerprint data of historical batches were collected from a traditional Chinese medicine manufacturing factory. Characteristic peaks were weighted by their variability among production batches. A principal component analysis model was established after outliers were modified or removed. Multivariate (Hotelling T(2) and DModX) control charts were finally successfully applied to evaluate the quality consistency. The results suggest useful applications for a combination of multivariate statistical analysis with chromatographic fingerprinting in batch-to-batch quality consistency evaluation for the manufacture of botanical drug products.
Collapse
|
21
|
Malwade CR, Qu H, Rong BG, Christensen LP. Conceptual Process Synthesis for Recovery of Natural Products from Plants: A Case Study of Artemisinin from Artemisia annua. Ind Eng Chem Res 2013. [DOI: 10.1021/ie302495w] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Chandrakant R. Malwade
- Department of Chemical Engineering, Biotechnology and Environmental Technology, University
of Southern Denmark, Niels Bohrs Allé 1, DK-5230, Odense M,
Denmark
| | - Haiyan Qu
- Department of Chemical Engineering, Biotechnology and Environmental Technology, University
of Southern Denmark, Niels Bohrs Allé 1, DK-5230, Odense M,
Denmark
| | - Ben-Guang Rong
- Department of Chemical Engineering, Biotechnology and Environmental Technology, University
of Southern Denmark, Niels Bohrs Allé 1, DK-5230, Odense M,
Denmark
| | - Lars P. Christensen
- Department of Chemical Engineering, Biotechnology and Environmental Technology, University
of Southern Denmark, Niels Bohrs Allé 1, DK-5230, Odense M,
Denmark
| |
Collapse
|
22
|
Matero S, van Den Berg F, Poutiainen S, Rantanen J, Pajander J. Towards better process understanding: chemometrics and multivariate measurements in manufacturing of solid dosage forms. J Pharm Sci 2013; 102:1385-403. [PMID: 23423769 DOI: 10.1002/jps.23472] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2013] [Accepted: 01/22/2013] [Indexed: 01/14/2023]
Abstract
The manufacturing of tablets involves many unit operations that possess multivariate and complex characteristics. The interactions between the material characteristics and process related variation are presently not comprehensively analyzed due to univariate detection methods. As a consequence, current best practice to control a typical process is to not allow process-related factors to vary i.e. lock the production parameters. The problem related to the lack of sufficient process understanding is still there: the variation within process and material properties is an intrinsic feature and cannot be compensated for with constant process parameters. Instead, a more comprehensive approach based on the use of multivariate tools for investigating processes should be applied. In the pharmaceutical field these methods are referred to as Process Analytical Technology (PAT) tools that aim to achieve a thorough understanding and control over the production process. PAT includes the frames for measurement as well as data analyzes and controlling for in-depth understanding, leading to more consistent and safer drug products with less batch rejections. In the optimal situation, by applying these techniques, destructive end-product testing could be avoided. In this paper the most prominent multivariate data analysis measuring tools within tablet manufacturing and basic research on operations are reviewed.
Collapse
Affiliation(s)
- Sanni Matero
- Department of Food Science, Quality & Technology, Faculty of Science, University of Copenhagen, DK-1958 Frederiksberg-C, Denmark.
| | | | | | | | | |
Collapse
|
23
|
Xiong H, Gong X, Qu H. Monitoring batch-to-batch reproducibility of liquid–liquid extraction process using in-line near-infrared spectroscopy combined with multivariate analysis. J Pharm Biomed Anal 2012; 70:178-87. [DOI: 10.1016/j.jpba.2012.06.028] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2012] [Revised: 06/18/2012] [Accepted: 06/19/2012] [Indexed: 01/22/2023]
|
24
|
Gernaey KV, Cervera-Padrell AE, Woodley JM. A perspective on PSE in pharmaceutical process development and innovation. Comput Chem Eng 2012. [DOI: 10.1016/j.compchemeng.2012.02.022] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
25
|
A quality by design study applied to an industrial pharmaceutical fluid bed granulation. Eur J Pharm Biopharm 2012; 81:438-47. [DOI: 10.1016/j.ejpb.2012.03.003] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2011] [Revised: 02/29/2012] [Accepted: 03/05/2012] [Indexed: 11/24/2022]
|
26
|
Rosas JG, Blanco M, González JM, Alcalá M. Quality by design approach of a pharmaceutical gel manufacturing process, part 2: Near infrared monitoring of composition and physical parameters. J Pharm Sci 2011; 100:4442-51. [DOI: 10.1002/jps.22607] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2010] [Revised: 03/08/2011] [Accepted: 04/19/2011] [Indexed: 11/05/2022]
|
27
|
Rajalahti T, Kvalheim OM. Multivariate data analysis in pharmaceutics: A tutorial review. Int J Pharm 2011; 417:280-90. [DOI: 10.1016/j.ijpharm.2011.02.019] [Citation(s) in RCA: 245] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2011] [Accepted: 02/10/2011] [Indexed: 10/18/2022]
|
28
|
Lourenço V, Herdling T, Reich G, Menezes JC, Lochmann D. Combining microwave resonance technology to multivariate data analysis as a novel PAT tool to improve process understanding in fluid bed granulation. Eur J Pharm Biopharm 2011; 78:513-21. [PMID: 21333737 DOI: 10.1016/j.ejpb.2011.02.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2010] [Revised: 12/20/2010] [Accepted: 02/12/2011] [Indexed: 11/29/2022]
Abstract
A set of 192 fluid bed granulation batches at industrial scale were in-line monitored using microwave resonance technology (MRT) to determine moisture, temperature and density of the granules. Multivariate data analysis techniques such as multiway partial least squares (PLS), multiway principal component analysis (PCA) and multivariate batch control charts were applied onto collected batch data sets. The combination of all these techniques, along with off-line particle size measurements, led to significantly increased process understanding. A seasonality effect could be put into evidence that impacted further processing through its influence on the final granule size. Moreover, it was demonstrated by means of a PLS that a relation between the particle size and the MRT measurements can be quantitatively defined, highlighting a potential ability of the MRT sensor to predict information about the final granule size. This study has contributed to improve a fluid bed granulation process, and the process knowledge obtained shows that the product quality can be built in process design, following Quality by Design (QbD) and Process Analytical Technology (PAT) principles.
Collapse
Affiliation(s)
- Vera Lourenço
- Institute of Biotechnology and Bioengineering, IST, Technical University of Lisbon, Portugal
| | | | | | | | | |
Collapse
|
29
|
Gernaey KV, Gani R. A model-based systems approach to pharmaceutical product-process design and analysis. Chem Eng Sci 2010. [DOI: 10.1016/j.ces.2010.05.003] [Citation(s) in RCA: 77] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
30
|
Streefland M, Van Herpen P, Van de Waterbeemd B, Van der Pol L, Beuvery E, Tramper J, Martens D, Toft M. A practical approach for exploration and modeling of the design space of a bacterial vaccine cultivation process. Biotechnol Bioeng 2009; 104:492-504. [DOI: 10.1002/bit.22425] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
31
|
Fonseca GE, Dubé MA, Penlidis A. A Critical Overview of Sensors for Monitoring Polymerizations. MACROMOL REACT ENG 2009. [DOI: 10.1002/mren.200900024] [Citation(s) in RCA: 59] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
32
|
Gernaey KV, Woodley JM, Sin G. Introducing mechanistic models in Process Analytical Technology education. Biotechnol J 2009; 4:593-9. [DOI: 10.1002/biot.200800323] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
33
|
Multivariate kinetic hard-modelling of spectroscopic data: A comparison of the esterification of butanol by acetic anhydride on different scales and with different instruments. Chem Eng Sci 2008. [DOI: 10.1016/j.ces.2008.01.020] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
34
|
Matero S, Poutiainen S, Leskinen J, Hakulinen M, Lahtela-Kakkonen M, Ketolainen J, Lappalainen R, Järvinen K, Poso A. Determination of batch-to-batch variation for fluidized bed granulation. Eur J Pharm Sci 2008. [DOI: 10.1016/j.ejps.2008.02.080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
35
|
Affiliation(s)
- Jerome Workman
- Molecular Spectroscopy and Microanalysis, Thermo Fisher Scientific Inc., 5225 Verona Road, Madison, Wisconsin 53711-4495, USA
| | | | | |
Collapse
|