174 results found
Bowskill DH, Sugden IJ, Konstantinopoulos S, et al., 2021, Crystal structure prediction methods for organic molecules: state of the art., Annual Review of Chemical and Biomolecular Engineering, Vol: 14, ISSN: 1947-5438
The prediction of the crystal structures that a given organic molecule is likely to form is an important theoretical problem of significant interest for the pharmaceutical and agrochemical industries, among others. As evidenced by a series of six blind tests organized over the past 2 decades, methodologies for crystal structure prediction (CSP) have witnessed substantial progress and have now reached a stage of development where they can begin to be applied to systems of practical significance. This article reviews the state of the art in general-purpose methodologies for CSP, placing them within a common framework that highlights both their similarities and their differences. The review discusses specific areas that constitute the main focus of current research efforts toward improving the reliability and widening applicability of these methodologies, and offers some perspectives for the evolution of this technology over the next decade. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 12 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Zhang Y, Sugden IJ, Reutzel-Edens SM, et al., 2020, A systematic study of state-of-the-art methods in crystal structure prediction for organic hydrates
Hydrates are co-crystalline materials containing water as one of the molecules in the crystal lattice. The incorporationof water into the crystal lattice produces a unit cell different from that of the anhydrate and, consequently, the physicalproperties of the hydrate can differ significantly from those of the anhydrate. The existence and stability of hydrates isan important consideration in the development of pharmaceutical products: the prevalence of water duringmanufacturing and storage can mean that neat forms of an active pharmaceutical ingredient can undergo a phasetransition to hydrate form, impacting the effectiveness of the drug. Crystal structure prediction (CSP) methods can inprinciple be useful in identifying likely hydrates, by undertaking searches for all polymorphs of water and one or moregiven compounds for a given co-crystal stoichiometry. Minimal information is needed, typically just the chemicalconnectivity diagram , to search for the low lattice energy arrangements of the constituent atoms in space. Applications of CSP to hydrates have resulted in mixed success so far. In the fifth blind test organised by CambridgeCrystallographic Data Centre, one of the targets was a hydrate but none of the 10 groups that attempted to predict itsstructure put forward the correct structure within their shortlist. In the sixth blind test , only 8 groups submittedpredicted structures for the hydrate target, and only one group generated the experimental structure within theirshortlist. In order to gain a better understanding of the challenges that make CSP for hydrates difficult, we present a systematicevaluation of a CSP state-of-the-art method for organic hydrates, in which the lattice energy is partitioned intointramolecular and intermolecular contributions. Intramolecular interactions are modelled via quantum mechanicalcalculations , and intermolecular interactions are divided into electrostatics, modelled using ab initio derived distributedmultipoles , and repu
Pfeiffer BM, Oppelt M, Leingang C, et al., 2020, Nonlinear model predictive control based on existing mechanistic models of polymerisation reactors, Pages: 6076-6081
Model Predictive Control (MPC) is established as the most powerful and most successful method for multivariable control in the process industries. Most industrial applications of MPC rely on linear dynamic process models that are identified from active experiments on the plant. If a rigorous mechanistic model of the respective process unit already exists, it would be attractive to use this model directly inside an MPC algorithm. The PSE software “gPROMS Nonlinear Model Predictive Controller” (gNLMPC) provides precisely this functionality, and this paper describes its application to a polymerization reactor. Properties, features and advantages of linear and nonlinear MPC are compared systematically.
Kusumo KP, Gomoescu L, Paulen R, et al., 2020, Nested Sampling Strategy for Bayesian Design Space Characterization, Editors: Pierucci, Manenti, Bozzano, Manca, Publisher: ELSEVIER SCIENCE BV, Pages: 1957-1962
Konstantinopoulos S, Sugden IJ, Reutzel-Edens SM, et al., 2020, An atomistic lattice dynamics approach for free energy calculations within crystal structure prediction studies
A plethora of organic molecules exhibit polymorphism, which refers to the ability of chemical compounds to pack intodifferent crystalline motifs. This phenomenon is of special importance both to industry and academia since physicaland chemical properties, such as solubility, bioavailability and mechanical strength may vary tremendously betweenpolymorphs. From a thermodynamic standpoint, polymorphs can be identified as minima on the free energy (FE)landscape, with the most stable form corresponding to the global minimum and other forms corresponding to localminima (metastable structures). This thermodynamic understanding has motivated the development of crystalstructure prediction (CSP) tools that are designed to determine all polymorphs for a given compound with the correctorder of stability based on minimal information, such as the chemical connectivity diagram . Recent advances in CSP were highlighted in the last blind test organised by Cambridge Crystallographic Data Centre .It is worth noting that only 7 out of the 25 groups participating in the last test have incorporated FE calculations withintheir workflow, while the remaining groups used only lattice energy in their predictions, thus neglecting temperatureand vibrational effects. Lattice dynamics (LD) theory was deployed successfully for the evaluation of vibrational freeenergies, utilizing either dispersion-corrected periodic density functional theory (DFT-d) or force field methods basedon distributed multipoles expansion (DMA). DFT-d can provide very accurate results at a high computational cost,whereas the DMA-based approach provides a good trade-off between accuracy and efficiency but cannot account forinternal modes arising from intramolecular vibrations. A limitation of both methods is that they rely on the constructionof supercells, which increases computational demands and results in some ambiguity in the generation of dispersioncurves. In this work, we present a recently-developed methodology for pe
Destro F, Salmon AJ, Facco P, et al., 2020, Monitoring a segmented fluid bed dryer by hybrid data-driven/knowledge-driven modeling, 21st IFAC World Congress on Automatic Control - Meeting Societal Challenges, Publisher: ELSEVIER, Pages: 11638-11643, ISSN: 2405-8963
Bowskill DH, Sugden IJ, George N, et al., 2020, Efficient Parameterization of a Surrogate Model of Molecular Interactions in Crystals, Editors: Pierucci, Manenti, Bozzano, Manca, Publisher: ELSEVIER SCIENCE BV, Pages: 493-498
Kusumo KP, Gomoescu L, Paulen R, et al., 2019, Bayesian approach to probabilistic design space characterization: a nested sampling strategy, Industrial & Engineering Chemistry Research, Vol: 59, Pages: 2396-2408, ISSN: 0888-5885
Quality by design in pharmaceutical manufacturing hinges on computational methods and tools that are capable of accurate quantitative prediction of the design space. This paper investigates Bayesian approaches to design space characterization, which determine a feasibility probability that can be used as a measure of reliability and risk by the practitioner. An adaptation of nested sampling—a Monte Carlo technique introduced to compute Bayesian evidence—is presented. The nested sampling algorithm maintains a given set of live points through regions with increasing probability feasibility until reaching a desired reliability level. It furthermore leverages efficient strategies from Bayesian statistics for generating replacement proposals during the search. Features and advantages of this algorithm are demonstrated by means of a simple numerical example and two industrial case studies. It is shown that nested sampling can outperform conventional Monte Carlo sampling and be competitive with flexibility-based optimization techniques in low-dimensional design space problems. Practical aspects of exploiting the sampled design space to reconstruct a feasibility probability map using machine learning techniques are also discussed and illustrated. Finally, the effectiveness of nested sampling is demonstrated on a higher-dimensional problem, in the presence of a complex dynamic model and significant model uncertainty.
de Prada C, Pantelides CC, Luis Pitarch J, 2019, Special Issue on "Process Modelling and Simulation", PROCESSES, Vol: 7
Sugden IJ, Adjiman C, Pantelides C, 2019, Accurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. II. Smoothed intramolecular potentials, Acta Crystallographica Section B: Structural Science, Vol: 75, Pages: 423-433, ISSN: 0108-7681
The application of Crystal Structure Prediction (CSP) to industrially-relevant molecules requires the handling of increasingly large and flexible compounds. We present a revised model for the effect of molecular flexibility on the lattice energy that removes the discontinuities and non-differentiabilities present in earlier models (Sugden et al., 2016), with a view to improving the performance of CSP. The approach is based on the concept of computing a weighted average of local models, and has been implemented within the CrystalPredictor code. Through the comparative investigation of several compounds studied in earlier literature, we show that this new model results in large reductions in computational effort (of up to 65%) and in significant increases in reliability. The approach is further applied to investigate, for the first time, the computational polymorphic landscape of flufenamic acid for Z’=1 structures, resulting in the successful identification of all three experimentally resolved polymorphs within reasonable computational time.
Bernardi A, Gomoescu L, Wang J, et al., 2019, Kinetic Model Discrimination for Methanol and DME Synthesis using Bayesian Estimation, 12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS), Publisher: ELSEVIER SCIENCE BV, Pages: 335-340, ISSN: 2405-8963
Lafitte T, Papaioannou V, Dufal S, et al., 2019, A General framework for solid-liquid equilibria in pharmaceutical systems, Chemical Engineering in the Pharmaceutical Industry, Pages: 439-466, ISBN: 9781119285496
This chapter presents a unified framework for the determination of solid-liquid equilibria in complex pharmaceutical systems. The framework is based on a fundamental thermodynamics formulation of the combined phase and reaction equilibrium problem, supported by the emergence, over the past decade, of new equations of state (EoS) that are capable of accurately predicting liquid-phase behavior of mixtures involving complex intermolecular interactions. An important consideration in the context of any modeling framework is the amount of experimental data required for the characterization of any particular system of interest in terms of the parameters required by the model. This issue is considered in detail, with particular focus on the characterization of solid-phase behavior with minimal experimental solubility data. The chapter presents several examples illustrating key aspects of the proposed framework, and draws some general perspectives from these examples.
Adjiman CSJ, Pantelides C, Gatsiou CA, 2018, Repulsion-dispersion parameters for the modelling of organic molecular crystals containing N, O, S and Cl, Faraday Discussions, Vol: 211, Pages: 297-323, ISSN: 1359-6640
In lattice energy models that combine ab initio and empirical components, it is important to ensureconsistency between these components so that meaningful quantitative results are obtained. Amethod for deriving parameters of atom-atom repulsion dispersion potentials for crystals, tailoredto different ab initio models is presented. It is based on minimization of the sum of squared de-viations between experimental and calculated structures and energies. The solution algorithmis designed to avoid convergence to local minima in the parameter space by combining a deter-ministic low-discrepancy sequence for the generation of multiple initial parameter guesses withan efficient local minimization algorithm. The proposed approach is applied to derive transferableexp-6 potential parameters suitable for use in conjunction with a distributed multipole electrostaticsmodel derived from isolated molecule charge densities calculated at the M06/6-31G(d,p) level oftheory. Data for hydrocarbons, azahydrocarbons, oxohydrocarbons, organosulphur compoundsand chlorohydrocarbons are used for the estimation. A good fit is achieved for the new set ofparameters with a mean absolute error in sublimation enthalpies of 4.1 kJ/mol and an averagermsd15of 0.31 Å. The parameters are found to perform well on a separate cross-validation set of39 compounds.
Pitt JA, Gomoescu L, Pantelides CC, et al., 2018, Critical assessment of parameter estimation methods in models of biological oscillators, IFAC-PapersOnLine, Vol: 51, Pages: 72-75, ISSN: 2405-8963
Many biological systems exhibit oscillations in relation to key physiological or cellular functions, such as circadian rhythms, mitosis and DNA synthesis. Mathematical modelling provides a powerful approach to analysing these biosystems. Applying parameter estimation methods to calibrate these models can prove a very challenging task in practice, due to the presence of local solutions, lack of identifiability, and risk of overfitting. This paper presents a comparison of three state-of-the-art methods: frequentist, Bayesian and set-membership estimation. We use the Fitzhugh-Nagumo model with synthetic data as a case study. The computational performance and robustness of these methods is discussed, with a particular focus on their predictive capability using cross-validation.
Bowskill D, Sugden I, Gatsiou C-A, et al., 2018, New potentials for accurate and efficient ab initio crystal structure prediction methods, Publisher: INT UNION CRYSTALLOGRAPHY, Pages: E362-E362, ISSN: 2053-2733
Lafitte T, Papaioannou V, Dufal S, et al., 2017, gSAFT: Advanced physical property prediction for process modelling, 27th European Symposium on Computer-Aided Process Engineering (ESCAPE), Publisher: ELSEVIER SCIENCE BV, Pages: 1003-1008, ISSN: 1570-7946
Sugden IJ, Adjiman CSA, Pantelides C, 2016, Accurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. I. Adaptive local approximate models, Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials, Vol: 72, Pages: 864-874, ISSN: 2052-5206
The global search stage of Crystal Structure Prediction (CSP) methods requires a fine balance between accuracy and computational cost, particularly for the study of large flexible molecules. A major improvement in the accuracy and cost of the intramolecular energy function used in the CrystalPredictor II (Habgood, M., Sugden, I. J., Kazantsev, A. V., Adjiman, C. S. & Pantelides, C. C. (2015). J Chem Theory Comput 11, 1957-1969) program is presented, where the most efficient use of computational effort is ensured via the use of adaptive Local Approximate Model (LAM) placement. The entire search space of relevant molecule’s conformations is initially evaluated using a coarse, low accuracy grid. Additional LAM points are then placed at appropriate points determined via an automated process, aiming to minimise the computational effort expended in high energy regions whilst maximising the accuracy in low energy regions. As the size, complexity, and flexibility of molecules increase, the reduction in computational cost becomes marked. This improvement is illustrated with energy calculations for benzoic acid and the ROY molecule, and a CSP study of molecule XXVI from the sixth blind test (Reilly et al., (2016). Acta Cryst. B, 72, 439-459), which is challenging due to its size and flexibility. Its known experimental form is successfully predicted as the global minimum. The computational cost of the study is tractable without the need to make unphysical simplifying assumptions.
Doherty MF, Grossmann IE, Pantelides CC, 2016, A tribute to professor Roger Sargent: Intellectual leader of process systems engineering, AIChE Journal, Vol: 62, Pages: 2951-2958, ISSN: 0001-1541
This article and this issue of the AIChE Journal, is a tribute to Professor Roger Sargent who, as pioneer and intellectual leader of process systems engineering, has had a profound impact on the discipline of chemical engineering. Spanning more than five decades, his work has provided a strong mathematical foundation to process systems engineering through the development of sophisticated mathematical and computational tools for the simulation, design, control, operation and optimization of chemical processes. In this article we first give a brief overview of his career that included several leadership positions and the establishment of the Centre for Process Systems Engineering (CPSE) at Imperial College London. We next review his research contributions in the areas of process modeling, differential algebraic systems, process dynamics and control, nonlinear optimization and optimal control, design under uncertainty, and process scheduling. We highlight the tremendous impact that he has had through his students, students' students, and his entire academic family tree, which at present contains over 2000 names, probably one of the largest among the academic leaders of chemical engineering. Finally, we provide a brief overview of him as a modest and charming individual with a wonderful sense of humor. He is without doubt a true intellectual giant who has helped to expand the scope of chemical engineering by providing a strong systems component to it, and by establishing strong multidisciplinary links with other fields. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2951–2958, 2016.
Reilly AM, Cooper RI, Adjiman CS, et al., 2016, Report on the sixth blind test of organic crystal structure prediction methods., Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials, Vol: 72, Pages: 439-459, ISSN: 2052-5206
The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `best practices' for performing CSP calculations. All of the targets, apart from a single potentially disordered Z' = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms.
García Muñoz S, Yu W, Pantelides CC, 2016, SimCU: A new model to assess content uniformity of oral dosages based on particulate mass balances and Monte Carlo simulations, Chemical Engineering Research and Design, Vol: 109, Pages: 532-539, ISSN: 0263-8762
A new model (SimCU) is presented to assess the risk of failure on content uniformity for oral dosage forms. The proposed model is an extension to the algorithm presented by Zhang and Johnson (Int. J. Pharm. 1997;154:179-183), including a revision needed to prevent artificial loss of drug and segregation. Furthermore, the improved approach is extended to enable the user to consider alternate sources of variability such as the weight distribution of the dosage forms and the effect of a drug product intermediate (i.e., a granule) with heterogeneous potency levels across particle sizes. The particle mass balance in the corrected algorithm is consistent and its closed form solution for the prediction of the relative standard deviation of potency is presented herein. Predictions of the relative standard deviation (RSD) from SimCU were extensively verified with data from development, clinical and commercial manufacture. Such results support the usage of the proposed model to assess the risk of failure for content uniformity for oral dosage forms which will ultimately drive the specifications for particle size distribution of the drug.
Habgood M, Sugden IJ, Kazantsev AV, et al., 2015, Efficient handling of molecular flexibility in ab initio generation of crystal structures, Journal of Chemical Theory and Computation, Vol: 11, Pages: 1957-1969, ISSN: 1549-9618
A key step in many approaches to crystal structure prediction (CSP) is the initial generation of large numbers of candidate crystal structures via the exploration of the lattice energy surface. By using a relatively simple lattice energy approximation, this global search step aims to identify, in a computationally tractable manner, a limited number of likely candidate structures for further refinement using more detailed models. This paper presents an effective and efficient approach to modeling the effects of molecular flexibility during this initial global search. Local approximate models (LAMs), constructed via quantum mechanical (QM) calculations, are used to model the conformational energy, molecular geometry, and atomic charge distributions as functions of a subset of the conformational degrees of freedom (e.g., flexible torsion angles). The effectiveness of the new algorithm is demonstrated via its application to the recently studied 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile (ROY) molecule and to two molecules, β-d-glucose and 1-(4-benzoylpiperazin-1-yl)-2-(4,7-dimethoxy-1H-pyrrolo[2,3-c]pyridin-3-yl)ethane-1,2-dione, a Bristol Myers Squibb molecule referenced as BMS-488043. All three molecules present significant challenges due to their high degree of flexibility.
Vasileiadis M, Pantelides CC, Adjiman CS, 2014, Prediction of the crystal structures of axitinib, a polymorphic pharmaceutical molecule, Chemical Engineering Science, Vol: 121, Pages: 60-76, ISSN: 0009-2509
Organic molecules can crystallize in multiple structures or polymorphs, yielding crystals with very different physical and mechanical properties. The prediction of the polymorphs that may appear in nature is a challenge with great potential benefits for the development of new products and processes. A multistage crystal structure prediction (CSP) methodology is applied to axitinib, a pharmaceutical molecule with significant polymorphism arising from molecular flexibility. The CSP study is focused on those polymorphs with one molecule in the asymmetric unit. The approach successfully identifies all four known polymorphs within this class, as well as a large number of other low-energy structures. The important role of conformational flexibility is highlighted. The performance of the approach is discussed in terms of both the quality of the results and various algorithmic and computational aspects, and some key priorities for further work in this area are identified.
Kazantsev AV, Karamertzanis PG, Pantelides CC, et al., 2014, CrystalOptimizer: An Efficient Algorithm for Lattice Energy Minimization of Organic Crystals Using Isolated-Molecule Quantum Mechanical Calculations, Process Systems Engineering, Pages: 1-42, ISBN: 9783527316847
Rodriguez J, Andrade A, Lawal A, et al., 2014, An integrated framework for the dynamic modelling of solvent-based CO2 capture processes, 12th International Conference on Greenhouse Gas Control Technologies (GHGT), Publisher: ELSEVIER SCIENCE BV, Pages: 1206-1217, ISSN: 1876-6102
Pantelides CC, Adjiman CS, Kazantsev AV, 2014, General Computational Algorithms for Ab Initio Crystal Structure Prediction for Organic Molecules, PREDICTION AND CALCULATION OF CRYSTAL STRUCTURES: METHODS AND APPLICATIONS, Vol: 345, Pages: 25-58, ISSN: 0340-1022
Pantelides CC, Renfro JG, 2013, THE ONLINE USE OF FIRST-PRINCIPLES MODELS IN PROCESS OPERATIONS: REVIEW, CURRENT STATUS & FUTURE NEEDS
Vasileiadis M, Adjiman CS, Pantelides CC, 2013, Ab initio prediction of crystal structure and the effects of temperature on the relative stability of enantiotropic polymorphs, Pages: 460-461
Avaullee L, Adjiman CS, Calado F, et al., 2012, Gsaft: Application of the SAFT-γ mie group contribution EoS in the Oil/Gas Industry - From academic research to industrial deployment, AIChE 2012 - 2012 AIChE Annual Meeting, Conference Proceedings
SAFT-γ Mie is a new equation of state recently developed by the Molecular Systems Engineering group at Imperial College London. It is an advanced group-contribution form of the SAFT equation of state making use of the Mie potential for a more accurate and flexible description of the dispersive/repulsive interactions between segments. One of its key characteristics is the accurate description of vapour/liquid phase equilibria, including the region of the critical point, as well as the second-derivative thermodynamic properties such as the thermal expansivity, isothermal compressibility, heat capacity, Joule-Thomson coefficient, and speed of sound. In 2009, Process Systems Enterprise (PSE) acquired the exclusive intellectual property rights associated with SAFT-γ Mie and related work, for the purpose of incorporating these developments within its gSAFT advanced thermodynamics technology for process modelling. In late 2010, TOTAL, PSE and Imperial College embarked on a joint project aimed at exploring in detail the applicability, benefits and limitations of this technology on a wide range of mixtures of interest to the oil & gas industry. The current phase of the project is primarily focused on mixtures of hydrocarbons (alkanes and aromatics), carbon dioxide, water and methanol. The main output is a single, consistent set of group parameters capable of accurately describing the behaviour of these generic mixtures within the SAFT-γ Mie framework. Starting with a brief overview of the SAFT-γ Mie equation of state, this paper primarily focuses on the systematic methodology employed in developing the corresponding like and unlike group parameters. This comprises a sequence of steps including the choice of representative components and mixtures, the definition of an appropriate set of groups required to describe them, the collection of the necessary experimental data, a streamlined set of software tools and workflows employed for the accurate, ef
Vasileiadis M, Kazantsev AV, Karamertzanis PG, et al., 2012, The polymorphs of ROY: application of a systematic crystal structure prediction technique, ACTA CRYSTALLOGRAPHICA SECTION B-STRUCTURAL SCIENCE CRYSTAL ENGINEERING AND MATERIALS, Vol: 68, Pages: 677-685
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