Membrane enhanced peptide synthesis (MEPS) – process development and application

Peptides are polymers of amino acids and are better drug candidates than traditional small-molecule compounds due to high specificity and potency. Conventional synthesis methods include Solid Phase Peptide Synthesis (SPPS) and Liquid Phase Peptide Synthesis (LPPS), which have different technical limitations. Significant improvements can be made by a new technology, Membrane Enhanced Peptide Synthesis (MEPS), which integrates nanofiltration into LPPS for the purification of intermediate products. The research work described in this thesis is an outcome of the MemTide consortium (Imperial College London, Institute for Research in Biomedicine (Barcelona) (IRB) and University of Turku) and three companies (Evonik Membrane Extraction Technology (MET) Ltd, Janssen Pharmaceutica and Lonza AG), whose task was to investigate membrane enhanced synthesis for both peptides and oligonucleotides. This research project demonstrates that MEPS is ready for industrial application in terms of technical feasibility and economic performance relative to SPPS and LPPS. The MEPS of two peptides (Fmoc-Arg-Ala-Asp-Ala-NH2 (fully deprotected Fmoc-RADA-NH2) and Pyr-Ser(Bzl)-Ala-Phe-Asp-Leu-NH2 (partially deprotected Pyr-SAFDL-NH2)) is presented in the form of case studies. In the first case study, MEPS of fully deprotected Fmoc-RADA-NH2 was attempted four times. The first three attempts encountered the problem of incomplete coupling after the post-de-Fmoc diafiltration, which was solved by the extended diafiltration (14 wash volumes). At a scale of 10.01 mmol (those in the proof of concept studies were 0.9 and 1.8 mmol), the fourth attempt at MEPS was successful with a purity of 98.5 % and an overall yield of 78.6 % before cleavage and global deprotection. This shows that the integration of nanofiltration into LPPS was technically feasible for obtaining high purity and decent yield of the anchored peptide that were comparable to those of SPPS (85.3 % and 78.3 % respectively before cleavage and global deprotection). In the second case study, a similar research approach was adopted for the partially deprotected Pyr-SAFDL-NH2 and the same problem of incomplete coupling occurred even with increased wash volumes. The cause was found to be residual piperidine in the system after the post-de-Fmoc diafiltration. The solution was to add a base (diisopropylethylamine (DIEA), which was also a reagent in each coupling) into the system during diafiltration to assist the removal of piperidine. At a scale of 33.65 mmol and an anchor concentration of 10.4 weight % in the starting solution, the third attempt at MEPS was successful with a purity of 88.1 % and an overall yield of 71.2 % before cleavage and global deprotection (98.6 % and 72.6 % respectively for SPPS; 100.0 % and 72.4 % respectively for LPPS (by precipitation)). Furthermore, MEPS outperformed SPPS and LPPS (by precipitation) in terms of material cost (8.7 – 13.0 % lower), process time (33.3 – 91.7 % shorter), volumetric efficiency (15.4 – 15.9 % higher) and E-factor (29.3 – 68.8 % lower). The results proved that this novel process is indeed an attractive alternative to SPPS and LPPS (by precipitation) and is ready for industrial application. Encouraged by the positive results from the two case studies, attempts were made to further improve the performance of MEPS before cleavage and global deprotection by reducing the significant yield loss during diafiltration. Peptide synthesis was performed on two alternative anchors (amine-functionalised silica nanoparticles and a branched compound with poly(ethylene glycol) (PEG) arms (PyPEG)), but each had technical limitations during the coupling of amino acids. On the other hand, promising results were obtained from the modelling of MEPS in a two-stage membrane cascade system, as the second membrane served to recover the anchored peptide that permeated through the first one during diafiltration. As a result, the overall yield would increase from 71.2 to 93.8 %, making the new process even more attractive in terms of material cost (23.6 – 33.5 % lower than the single-stage MEPS and SPPS).Open Acces

Physics-informed machine learning of the correlation functions in bulk fluids

The Ornstein-Zernike (OZ) equation is the fundamental equation for pair correlation function computations in the modern integral equation theory for liquids. In this work, machine learning models, notably physics-informed neural networks and physics-informed neural operator networks, are explored to solve the OZ equation. The physics-informed machine learning models demonstrate great accuracy and high efficiency in solving the forward and inverse OZ problems of various bulk fluids. The results highlight the significant potential of physics-informed machine learning for applications in thermodynamic state theory.Comment: 8 figure

Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model

In this paper, we present a physics-informed neural network (PINN) approach for predicting the performance of an all-vanadium redox flow battery, with its physics constraints enforced by a two-dimensional (2D) mathematical model. The 2D model, which includes 6 governing equations and 24 boundary conditions, provides a detailed representation of the electrochemical reactions, mass transport and hydrodynamics occurring inside the redox flow battery. To solve the 2D model with the PINN approach, a composite neural network is employed to approximate species concentration and potentials; the input and output are normalized according to prior knowledge of the battery system; the governing equations and boundary conditions are first scaled to an order of magnitude around 1, and then further balanced with a self-weighting method. Our numerical results show that the PINN is able to predict cell voltage correctly, but the prediction of potentials shows a constant-like shift. To fix the shift, the PINN is enhanced by further constrains derived from the current collector boundary. Finally, we show that the enhanced PINN can be even further improved if a small number of labeled data is available.Comment: 7 figure

MBE growth and characterization of Ge1-xMnxTe ferromagnetic semiconductors

Ph.DDOCTOR OF PHILOSOPH

KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media

Political perspective detection has become an increasingly important task that can help combat echo chambers and political polarization. Previous approaches generally focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles. In light of these limitations, we propose KCD, a political perspective detection approach to enable multi-hop knowledge reasoning and incorporate textual cues as paragraph-level labels. Specifically, we firstly generate random walks on external knowledge graphs and infuse them with news text representations. We then construct a heterogeneous information network to jointly model news content as well as semantic, syntactic and entity cues in news articles. Finally, we adopt relational graph neural networks for graph-level representation learning and conduct political perspective detection. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on two benchmark datasets. We further examine the effect of knowledge walks and textual cues and how they contribute to our approach's data efficiency.Comment: accepted at NAACL 2022 main conferenc

Development and workflow of a continuous protein crystallization process: A case of lysozyme

In the present work, a workflow on the development of a continuous protein crystallization is introduced, with lysozyme as a model protein, from microliter screening experiments, to small scale batch crystallization experiments in a shaking crystallization platform, and to batch and continuous crystallization experiments in an oscillatory flow platform. The lysozyme crystallizations investigated were for a concentration range from 30 to 100 mg/mL, shaking conditions from 100 to 200 rpm in the batch shaking crystallization platform, and oscillatory conditions with amplitude (x 0 ) from 5 to 30 mm and frequency (f) from 0.1 to 1.0 Hz in the batch oscillatory flow crystallization platform. We propose the use of the Reynold's number (R e ) for scaling up of the process from the shaking batch to the continuous oscillatory flow platform. Additionally, it is shown that the nucleation rate increased with increase in concentration of initial lysozyme solution, or increase in shear rate, inducing smaller size of lysozyme crystals. The properties and qualities of the crystal products indicate that continuous crystallization platforms may offer advantages to the downstream bioprocessing of proteins

Order-to-disorder structural transformation of a coordination polymer and its influence on proton conduction.

Accepted 14 Jul 2014.We observed an ordered-to-disordered structural transformation in a Cu(2+) coordination polymer and investigated its influence on the proton conductivity. The transformation generated highly mobile proton carriers in the structure. The resulting material exhibited a conductivity greater than 10(-2) S cm(-1) at 130 °C. The structural transformation and the conduction mechanism were investigated by EXAFS, TPD-MS and NMR

Surfactant‐directed Pd‐nanoparticle assemblies as efficient nanoreactors for water remediation

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Selective crystallisation of carbamazepine polymorphs on surfaces with differing properties

Surface-induced nucleation of carbamazepine (CBZ) in ethanol was investigated with different surface materials: glass, polytetrafluoroethylene (PTFE) and tin. The introduction of foreign surfaces with different areas and surface chemistries into the solution has an impact on the crystal morphology and polymorphic form (Form II or III). With an increase in supersaturation, a higher possibility of crystallisation of CBZ metastable Form II was observed, as expected. Increasing the number of inserts resulted in a direct increase in the surface area available for heterogeneous nucleation. The increase in surface area resulted in the greater possibility of obtaining the metastable Form II of CBZ. The stable Form III preferred to nucleate on tin rather than on glass and PTFE. The results indicate that the two different polymorphs of CBZ can be selectively crystallised out from solution with the aid of a foreign surface. The kinetic mechanism of heterogeneous nucleation of the different polymorphs induced by foreign surfaces was discussed. The potential applications will be used to control and design the crystallisation process