Data mining algorithm for pre-processing biopharmaceutical drug product manufacturing records

Casola, Gioele; Siegmund, Christian; Mattern, M.; Sugiyama, Hirokazu

doi:10.1016/j.compchemeng.2018.12.001

Cited by 14 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Model requirements definitions ''Machine Learning'' AND (Requirements OR Target OR Object OR Performance OR Quality) [49,50] Data collection ''machine learning'' AND ((''data collection'' OR ''data acquisition'' OR ''data gathering'' OR ''data sharing'' OR ''data browsing'' OR ''data augmentation'' OR ''data generation'') OR ''synthetic data'') [10,11,16,26,28,39,[51][52][53][54][55][56] -Cleaning ''data cleaning'' [15,29,38,46,[57][58][59][60] -Labelling ''data labelling'' OR ''data labeling'' OR ''semi-supervised'' OR ''weak supervision'' OR ''weakly supervised'' [8,20,21,30,37,[61][62][63][64][65][66][67] Feature engineering ''Machine Learning'' AND ''Feature Engineering'' OR ''Feature Selection'' [29,[68][69][70][71][72][73][74][75] Model training ''Machine Learning'' AND ''Model Training'' OR ''Modeling'' [5,35,…”

Section: Search Terms Publicationsmentioning

confidence: 99%

“…Besides process data, other sorts of data can also provide useful information for modeling. This includes, for example, operation protocols [66], offline analysis report, discrete data such as events, and visual data, e.g., photos and videos. There are some works with applications of computer vision within the process industry where cameras of different wavelength spectra provide spatial information to the process.…”

Section: Search Terms Publicationsmentioning

confidence: 99%

“…Several effective approaches are described in the literature for one common problem in batch processing, phase recognition for time series. Among them there are an analysis of drilling operations [132,133], a batch fermentation process [134] and a biopharmaceutical batch process [66]. Ko and Kim [65] made use of unlabeled data for fault classification by means of semi-supervised deep generative models for multivariate time series from the Tennessee Eastman benchmark process.…”

Section: Data Labelingmentioning

confidence: 99%

See 2 more Smart Citations

The Machine Learning Life Cycle in Chemical Operations – Status and Open Challenges

Gärtler

Khaydarov

Klöpper

et al. 2021

Chemie Ingenieur Technik

View full text Add to dashboard Cite

Artificial intelligence (AI) has received a lot of attention with many publications in recent years. Interestingly related projects in the industry are mostly still in their early stages. We are convinced that progress will only be possible if the entire machine learning (ML) life cycle is considered. Our study focuses on the practical challenges, uses a recent study as foundation and adopts the life-cycle description, highlights the life-cycle practices in other domains and formulates research directions that can help to improve the utilization of AI and machine learning in the process industry.

show abstract

Section: Search Terms Publicationsmentioning

confidence: 99%

Section: Search Terms Publicationsmentioning

confidence: 99%

Section: Data Labelingmentioning

confidence: 99%

See 1 more Smart Citation

The Machine Learning Life Cycle in Chemical Operations – Status and Open Challenges

Gärtler

Khaydarov

Klöpper

et al. 2021

Chemie Ingenieur Technik

View full text Add to dashboard Cite

show abstract

“…Currently, in addition to backtracking purposes, the recorded process data can be used for process monitoring and fault detection in batch and change-over processes. , A monitoring workflow for the formulation, fill, and finish processes was published . Data-driven batch monitoring in drug product manufacturing for change-over processes has been suggested with a focus on cleaning and sterilization in place (C/SIP) and decontamination . The latter work showed that deteriorations could be observed across multiple batches before a failure occurs.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, in addition to backtracking purposes, the recorded process data can be used for process monitoring and fault detection in batch and change-over processes. 16 , 17 A monitoring workflow for the formulation, fill, and finish processes was published. 18 Data-driven batch monitoring in drug product manufacturing for change-over processes has been suggested with a focus on cleaning and sterilization in place (C/SIP) 17 and decontamination.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Approach toward Long-Term Equipment Condition Assessment in Sterile Drug Product Manufacturing

et al. 2022

Self Cite

View full text Add to dashboard Cite

A two-stage data-driven methodology for long-term equipment condition assessment in drug product manufacturing is presented with a case study for a commercially operating aseptic filling line. The methodology leverages process monitoring data. Sensor measurements are partitioned using process information and maintenance schedules that are available on different databases. Data is processed to tackle heterogeneity in sources and formats. The data is cleaned to remove the effects of short-term variabilities and to enhance underlying long-term trends. Two approaches are presented for data analysis: first, anomaly detection using independent component analysis (ICA), where clusters of outliers are identified. The frequency and timing of such outliers yield important insights regarding maintenance schedules and actions. The second approach enables condition monitoring using principal component analysis (PCA). Long-term operational baselines are identified and shifts therein are linked with different process and equipment faults. This approach highlights the impact of equipment deterioration on shifting operational data baselines and shows the potential for the combined application of ICA and PCA for equipment condition monitoring. It can be applied within predictive maintenance applications where the installation of new specialized sensors is difficult, like in the pharmaceutical industry.

show abstract

Deep learning in drug discovery: an integrative review and future challenges

Askr

Elgeldawi

Ella³

et al. 2022

Artif Intell Rev

130

View full text Add to dashboard Cite

Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug–target interactions (DTIs), drug–drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.

show abstract

Data mining algorithm for pre-processing biopharmaceutical drug product manufacturing records

Cited by 14 publications

References 31 publications

The Machine Learning Life Cycle in Chemical Operations – Status and Open Challenges

The Machine Learning Life Cycle in Chemical Operations – Status and Open Challenges

Data-Driven Approach toward Long-Term Equipment Condition Assessment in Sterile Drug Product Manufacturing

Deep learning in drug discovery: an integrative review and future challenges

Contact Info

Product

Resources

About