ProSensus Inc.

Ancaster, Canada

ProSensus Inc.

Ancaster, Canada
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Liu Z.,ProSensus Inc. | Bruwer M.-J.,ProSensus Inc. | MacGregor J.F.,ProSensus Inc. | Rathore S.S.S.,Eli Lilly and Company | And 2 more authors.
Journal of Pharmaceutical Innovation | Year: 2011

This paper investigates an approach to modeling and optimizing an industrial tablet manufacturing line for different API and excipient formulations. Multi-block partial lease square (PLS) models are built from historical data on a given class of drug products. The data blocks consisted of data on the mass fractions of API and 11 excipients used in the different formulations, the roller compaction process variables, the tablet press settings and the measured final product quality attributes (tablet weight, hardness, and disintegration time). More than 400 runs are used in the modeling. The multi-block PLS models are first used to show which process blocks and which variables in each of the process blocks are most influential on the product quality variables. An optimization is then performed in the latent variable space of the PLS model to find the optimal combination of settings to use for the critical to quality roller compaction and tablet press variables in order to achieve the desired final tablet properties for a specified drug formulation. This optimization can be used to set up the tableting line prior to running a new formulation or can be used in an on-line mode for making small corrections to the operation of the tablet presses in response to small variations in formulations, raw material properties, and roller compaction operation. © Springer Science+Business Media, LLC 2011.

Rahmani V.,McMaster University | Elshereef R.,ProSensus Inc. | Sheardown H.,McMaster University
European Journal of Pharmaceutics and Biopharmaceutics | Year: 2017

Alginate and cationically modified alginate microparticles were prepared with the goal of developing hydrogel microparticles that offer controlled release of protein drugs mainly by modification of the absolute charge of the hydrogel network. Protein loading and release studies were carried out using model proteins with different net charges (i.e. low, high, and neutral isoelectric points) covering a broad range of molecular weights. The Projection to Latent Structures (PLS) method was used for qualitatively and quantitatively describing the relationships between the properties of proteins such as net charge and molecular weight, polymer properties including degree of substitution and microparticle size, and the release kinetics (ktn). It was found that electrostatic interactions and protein molecular weight had the greatest impact on parameter k while parameter n was mostly affected by polymer and buffer properties. In addition to understanding the current trends, the multivariate statistical method also provided an effective and reliable model as a beneficial tool for predicting and optimizing protein delivery systems. © 2017 Elsevier B.V.

Duchesne C.,Laval University | Liu J.J.,Pukyong National University | MacGregor J.F.,McMaster University | MacGregor J.F.,ProSensus Inc.
Chemometrics and Intelligent Laboratory Systems | Year: 2012

This paper provides an overview of the history, methods and applications of multivariate image analysis methods as developed for use in the process industries. It presents a general framework for the methods and their applications and discusses them under image analysis methods that use only spectral features of the images, such as Multivariate Image Analysis (MIA), and Multivariate Image Regression (MIR), and those that use textural features and combinations of textural and spectral features. The emphasis is on applications such as monitoring, prediction and control, and on those aspects of the methods that make them suitable for these tasks. © 2012 Elsevier B.V.

Golshan M.,McMaster University | MacGregor J.F.,McMaster University | MacGregor J.F.,ProSensus Inc. | Bruwer M.-J.,ProSensus Inc. | Mhaskar P.,McMaster University
Journal of Process Control | Year: 2010

Latent Variable Model Predictive Control (LV-MPC) algorithms are developed for trajectory tracking and disturbance rejection in batch processes. The algorithms are based on multi-phase PCA models developed using batch-wise unfolding of batch data arrays. Two LV-MPC formulations are presented, one based on optimization in the latent variable space and the other on direct optimization over a finite vector of future manipulated variables. In both cases prediction of the future trajectories is accomplished using statistical latent variable missing data imputation methods. The proposed LV-MPCs can handle constraints. Furthermore, due to the batch-wise unfolding approach selected in the modeling section, the nonlinear time-varying behavior of batch processes is captured by the linear LV models thereby yielding very simple and computationally fast nonlinear batch MPC. The methods are tested and compared on a simulated batch reactor case study. © 2010 Elsevier Ltd. All rights reserved.

MacGregor J.F.,ProSensus Inc. | Liu Z.,ProSensus Inc. | Bruwer M.-J.,ProSensus Inc. | Polsky B.,Mondelez International | Visscher G.,Mondelez International
Chemometrics and Intelligent Laboratory Systems | Year: 2016

Establishing meaningful multivariate specification regions on the multiple properties of a single raw material has been presented and illustrated by Duchesne & MacGregor Duchesne and MacGregor (2004). However, the manufacture of most final products usually involves the use of many raw materials each with multiple measured properties and from different suppliers. Setting specifications separately on each of these materials is unreasonable since it is the simultaneous combination of the properties of all the materials that will affect final quality. This paper presents an approach to determining the acceptability of new lots of raw materials from multiple suppliers and of assessing the suitability of combining specific lots of materials currently in inventory that will minimize the risk of manufacturing a poor quality product. Multivariate statistical models based on PLS are used to determine the importance of all the properties of each of the materials and to develop the specification methodology. Use of the models for achieving improved control over the product quality is also discussed. © 2016 Elsevier B.V.

MacGregor J.F.,ProSensus Inc. | Bruwer M.J.,ProSensus Inc. | Miletic I.,ProSensus Inc. | Cardin M.,ProSensus Inc. | Liu Z.,ProSensus Inc.
IFAC-PapersOnLine | Year: 2015

In the process industries Big Data has been around since the introduction of computer control systems, advanced sensors, and databases. Although process data may not really be BIG in comparison to other areas such as communications, they are often complex in structure, and the information that we wish to extract from them is often subtle. Multivariate latent variable regression models offer many unique properties that make them well suited for the analysis of historicaLindustrial data. These properties and use of these models are illustrated with applications to the analysis, monitoring. optimization and control of batch processes, and to the extraction of information from on-line multi-spectraLimages. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Cardin M.,ProSensus Inc. | MacGregor J.F.,ProSensus Inc. | Miletic I.P.,ProSensus Inc. | Bruwer M.-J.,ProSensus Inc.
Iron and Steel Technology | Year: 2011

Accurate on-line prediction of endpoint carbon requires dynamic data taken on key variables during the heat, not just static measurements at charge time. For this paper, a prototype soft-sensor for endpoint carbon prediction was developed through the use of multivariate image analysis methods.

MacGregor J.,ProSensus Inc. | MacGregor J.,McMaster University | Cinar A.,Illinois Institute of Technology
Computers and Chemical Engineering | Year: 2012

Historical data collected from processes are readily available. This paper looks at recent advances in the use of data-driven models built from such historical data for monitoring, fault diagnosis, optimization and control. Latent variable models are used because they provide reduced dimensional models for high dimensional processes. They also provide unique, interpretable and causal models, all of which are necessary for the diagnosis, control and optimization of any process. Multivariate latent variable monitoring and fault diagnosis methods are reviewed and contrasted with classical fault detection and diagnosis approaches. The integration of monitoring and diagnosis techniques by using an adaptive agent-based framework is outlined and its use for fault-tolerant control is compared with alternative fault-tolerant control frameworks. The concept of optimizing and controlling high dimensional systems by performing optimizations in the low dimensional latent variable spaces is presented and illustrated by means of several industrial examples. © 2012 Elsevier Ltd.

Liu Z.,ProSensus Inc. | Bruwer M.-J.,ProSensus Inc. | MacGregor J.F.,ProSensus Inc. | Rathore S.S.S.,Eli Lilly and Company | And 2 more authors.
Industrial and Engineering Chemistry Research | Year: 2011

Garcia-Munoz et al. [Garcia-Munoz, S.; Kourti, T.; MacGregor, J. F. Chemom. Intell. Lab. Syst.2005, 79, 101-114] proposed a new latent variable regression methodology, joint-Y partial least squares (JYPLS), for product transfer between plants. In this paper, this method is used for product scale-up from a type of laboratory-scale roller compactor, a Fitzpatrick IR220, to a type of full-scale roller compactor, a Fitzpatrick IR520, in the pharmaceutical industry. A JYPLS model is first built with the data set collected from historical experiments on these two types of compactors. The JYPLS model relates API mass fraction, excipient mass factions, and roller compaction process measurements to ribbon properties. A constrained optimization is then formulated to invert the JYPLS model to find the key process settings of the Fitzpatrick IR520 to make the same quality of ribbon using the same raw materials formulation as the ribbon that had been produced on the Fitzpatrick IR220. © 2011 American Chemical Society.

Tzoc Torres J.M.G.,McMaster University | Nichols E.,ProSensus Inc. | Macgregor J.F.,ProSensus Inc. | Hoare T.,McMaster University
Polymer (United Kingdom) | Year: 2014

The design of stimulus-responsive materials, particularly those intended to respond to more than one stimulus, is an inherently challenging and typically trial-and-error process involving multiple synthesis/characterization iterations in the laboratory. In this work, latent variable models are applied to existing, "failed" polymer formulations and characterizations to facilitate the rational design of materials with specific, targeted properties and to predict responsive polymer properties before synthesizing the materials in the laboratory. The models are capable of simultaneously predicting three targeted polymer properties (cloud point, molecular weight, and % recovery of polymer mass) for poly(N-isopropylacrylamide)-based materials that can be reversibly photo-crosslinked. Model inversion and optimization are used to identify new polymer formulations that exhibit significantly improved properties relative to the formulations developed by chemical intuition based on available literature. This model-based design approach moves away from the traditional trial-and-error approach to save time, energy, and resources in the production of novel materials while at the same time generating responsive polymers with improved properties.

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