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  • 1.
    Alhalaweh, Amjad
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Alzghoul, Ahmad
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Bergström, Christel A. S.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Molecular Drivers of Crystallization Kinetics for Drugs in Supersaturated Aqueous Solutions2019In: Journal of Pharmaceutical Sciences, ISSN 0022-3549, E-ISSN 1520-6017, Vol. 108, no 1, p. 252-259Article in journal (Refereed)
    Abstract [en]

    In this study, we explore molecular properties of importance in solution-mediated crystallization occurring in supersaturated aqueous drug solutions. Furthermore, we contrast the identified molecular properties with those of importance for crystallization occurring in the solid state. A literature data set of 54 structurally diverse compounds, for which crystallization kinetics from supersaturated aqueous solutions and in melt-quenched solids were reported, was used to identify molecular drivers for crystallization kinetics observed in solution and contrast these to those observed for solids. The compounds were divided into fast, moderate, and slow crystallizers, and in silico classification was developed using a molecular K-nearest neighbor model. The topological equivalent of Grav3 (related to molecular size and shape) was identified as the most important molecular descriptor for solution crystallization kinetics; the larger this descriptor, the slower the crystallization. Two electrotopological descriptors (the atom-type E-state index for -Caa groups and the sum of absolute values of pi Fukui(+) indices on C) were found to separate the moderate and slow crystallizers in the solution. The larger these descriptors, the slower the crystallization. With these 3 descriptors, the computational model correctly sorted the crystallization tendencies from solutions with an overall classification accuracy of 77% (test set).

  • 2.
    Alhalaweh, Amjad
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Alzghoul, Ahmad
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Kaialy, Waseem
    Mahlin, Denny
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Bergström, Christel A. S.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Computational predictions of glass-forming ability and crystallization tendency of drug molecules2014In: Molecular Pharmaceutics, ISSN 1543-8384, E-ISSN 1543-8392, Vol. 11, no 9, p. 3123-3132Article in journal (Refereed)
    Abstract [en]

    Amorphization is an attractive formulation technique for drugs suffering from poor aqueous solubility as a result of their high lattice energy. Computational models that can predict the material properties associated with amorphization, such as glass-forming ability (GFA) and crystallization behavior in the dry state, would be a time-saving, cost-effective, and material-sparing approach compared to traditional experimental procedures. This article presents predictive models of these properties developed using support vector machine (SVM) algorithm. The GFA and crystallization tendency were investigated by melt-quenching 131 drug molecules in situ using differential scanning calorimetry. The SVM algorithm was used to develop computational models based on calculated molecular descriptors. The analyses confirmed the previously suggested cutoff molecular weight (MW) of 300 for glass-formers, and also clarified the extent to which MW can be used to predict the GFA of compounds with MW < 300. The topological equivalent of Grav3_3D, which is related to molecular size and shape, was a better descriptor than MW for GFA; it was able to accurately predict 86% of the data set regardless of MW. The potential for crystallization was predicted using molecular descriptors reflecting Hückel pi atomic charges and the number of hydrogen bond acceptors. The models developed could be used in the early drug development stage to indicate whether amorphization would be a suitable formulation strategy for improving the dissolution and/or apparent solubility of poorly soluble compounds.

  • 3.
    Alhalaweh, Amjad
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Alzghoul, Ahmad
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Mahlin, Denny
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Bergström, Christel A. S.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Physical stability of drugs after storage above and below the glass transition temperature: Relationship to glass-forming ability2015In: International Journal of Pharmaceutics, ISSN 0378-5173, E-ISSN 1873-3476, Vol. 495, no 1, p. 312-317Article in journal (Refereed)
    Abstract [en]

    Amorphous materials are inherently unstable and tend to crystallize upon storage. In this study, we investigated the extent to which the physical stability and inherent crystallization tendency of drugs are related to their glass-forming ability (GFA), the glass transition temperature (T-g) and thermodynamic factors. Differential scanning calorimetry was used to produce the amorphous state of 52 drugs [ 18 compounds crystallized upon heating (Class II) and 34 remained in the amorphous state (Class III)] and to perform in situ storage for the amorphous material for 12 h at temperatures 20 degrees C above or below the T-g. A computational model based on the support vector machine (SVM) algorithm was developed to predict the structure-property relationships. All drugs maintained their Class when stored at 20 degrees C below the T-g. Fourteen of the Class II compounds crystallized when stored above the T-g whereas all except one of the Class III compounds remained amorphous. These results were only related to the glass-forming ability and no relationship to e. g. thermodynamic factors was found. The experimental data were used for computational modeling and a classification model was developed that correctly predicted the physical stability above the T-g. The use of a large dataset revealed that molecular features related to aromaticity and pi-pi interactions reduce the inherent physical stability of amorphous drugs.

  • 4.
    Alzghoul, Ahmad
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Alhalaweh, Amjad
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Mahlin, Denny
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Bergström, Christel A. S.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Experimental and Computational Prediction of Glass Transition Temperature of Drugs2014In: JOURNAL OF CHEMICAL INFORMATION AND MODELING, ISSN 1549-9596, Vol. 54, no 12, p. 3396-3403Article in journal (Refereed)
    Abstract [en]

    Glass transition temperature (T-g) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between T-g and melting temperature (T-m) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of T-g were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on T-m predicted T-g with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict T-g of drug-like molecules with high accuracy were developed. If T-m is available, a simple linear regression can be used to predict T-g. However, the results also suggest that support vector regression and calculated molecular descriptors can predict T-g with equal accuracy, already before compound synthesis.

  • 5.
    Alzghoul, Ahmad
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Backe, Björn
    Löfstrand, Magnus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Byström, Arne
    Liljedahl, Bengt
    Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application2014In: Computers in industry (Print), ISSN 0166-3615, E-ISSN 1872-6194, Vol. 65, no 8, p. 1126-1135Article in journal (Refereed)
    Abstract [en]

    The field of fault detection and diagnosis has been the subject of considerable interest in industry. Fault detection may increase the availability of products, thereby improving their quality. Fault detection and diagnosis methods can be classified in three categories: data-driven, analytically based, and knowledge-based methods.

    In this work, we investigated the ability and the performance of applying two fault detection methods to query data streams produced from hydraulic drive systems. A knowledge-based method was compared to a data-driven method. A fault detection system based on a data stream management system (DSMS) was developed in order to test and compare the two methods using data from real hydraulic drive systems.

    The knowledge-based method was based on causal models (fault trees), and principal component analysis (PCA) was used to build the data-driven model. The performance of the methods in terms of accuracy and speed, was examined using normal and physically simulated fault data. The results show that both methods generate queries fast enough to query the data streams online, with a similar level of fault detection accuracy. The industrial applications of both methods include monitoring of individual industrial mechanical systems as well as fleets of such systems. One can conclude that both methods may be used to increase industrial system availability.

  • 6.
    Alzghoul, Ahmad
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Löfstrand, Magnus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Addressing concept drift to improve system availability by updating one-class data-driven models2015In: Evolving Systems, ISSN 1868-6478, E-ISSN 1868-6486, Vol. 6, no 3, p. 187-198Article in journal (Refereed)
    Abstract [en]

    Data-driven models have been used to detect system faults, thereby increasing industrial system availability. The ability to search data streams while dealing with concept drift are challenges for data-driven models. The objective of this work is to demonstrate a general method to manage concept drift when using one-class data-driven models. The method has been used to develop an automatically retrained and updated polygon-based model. In this paper, the available industrial data allowed for use of one-class data-driven models, and the polygon-based model was selected because it has previously been successful. Possible scenarios that allow one-class data-driven models to be retrained or updated were identified. Based on the identified scenarios, a method to automatically update a polygon-based model online is proposed. The method has been tested and verified using data collected from a Bosch Rexroth Mellansel AB hydraulic drive system. Data representing relevant faults was inserted into the data set in close collaboration with engineers from the company. The results show that the developed polygon-based model method was able to address the concept drift issue and was able to significantly improve the classification accuracy compared to the static polygon-based model. Thereby, the model could significantly improve industrial system availability when applied in the relevant production process. This paper shows that the developed polygon-based model requires small memory space while its updating procedure is simple and fast. Finally, the identified scenarios may be helpful as input for supporting other one-class data-driven models to cope with concept drift, thus increasing the generalizability of the results.

  • 7. Lindström, John
    et al.
    Löfstrand, Magnus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Reed, Sean
    Alzghoul, Ahmad
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Use of cloud services in functional products: Availability implications2014In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 16, p. 368-372Article in journal (Refereed)
1 - 7 of 7
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  • nn-NO
  • nn-NB
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