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Forssén, Patrik
Alternative names
Publications (10 of 47) Show all publications
Fornstedt, T., Forssén, P. & Samuelsson, J. (2023). Chapter 23 - Modeling of preparative liquid chromatography (3ed.). In: Salvatore Fanali; Bezhan Chankvetadze; Paul R. Haddad; Colin F. Poole; Marja-Liisa Riekkola (Ed.), Liquid Chromatography: Fundamentals and Instrumentation (pp. 603-624). Elsevier, 1
Open this publication in new window or tab >>Chapter 23 - Modeling of preparative liquid chromatography
2023 (English)In: Liquid Chromatography: Fundamentals and Instrumentation / [ed] Salvatore Fanali; Bezhan Chankvetadze; Paul R. Haddad; Colin F. Poole; Marja-Liisa Riekkola, Elsevier, 2023, 3, Vol. 1, p. 603-624Chapter in book (Other academic)
Abstract [en]

Preparative chromatography is the best generic method currently available for purifying small drugs and valuable chemical components at the 10-kg level. Progress in computer technology, the development of new non-chiral/chiral stationary phases, and numerous improvements in reliability and economic performance have considerably increased the interest in modeling in academia and industry. This chapter introduces the modeling of preparative liquid chromatography in order to improve the purification process for valuable chemical components such as drugs and chiral components. We review the most important column and adsorption models and the methods for determining the essential thermodynamic adsorption data for both column characterization and process improvement. We also cover important operational modes (e.g., separation in gradient mode), cases involving additives or ion-pair reagents, and operational conditions sometimes neglected in the modeling process, for example, involving the impact of injection profiles. 

Place, publisher, year, edition, pages
Elsevier, 2023 Edition: 3
National Category
Chemical Sciences
Research subject
Chemistry
Identifiers
urn:nbn:se:kau:diva-95429 (URN)10.1016/B978-0-323-99968-7.00003-5 (DOI)2-s2.0-85161168550 (Scopus ID)978-0-323-99968-7 (ISBN)
Available from: 2023-06-19 Created: 2023-06-19 Last updated: 2023-06-19Bibliographically approved
Gutgsell, A. R., Gunnarsson, A., Forssén, P., Gordon, E., Fornstedt, T. & Geschwindner, S. (2022). Biosensor-Enabled Deconvolution of the Avidity-Induced Affinity Enhancement for the SARS-CoV-2 Spike Protein and ACE2 Interaction. Analytical Chemistry, 94(2), 1187-1194
Open this publication in new window or tab >>Biosensor-Enabled Deconvolution of the Avidity-Induced Affinity Enhancement for the SARS-CoV-2 Spike Protein and ACE2 Interaction
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2022 (English)In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 94, no 2, p. 1187-1194Article in journal (Refereed) Published
Abstract [en]

Avidity is an effective and frequent phenomenon employed by nature to achieve extremely high-affinity interactions. As more drug discovery efforts aim to disrupt protein-protein interactions, it is becoming increasingly common to encounter systems that utilize avidity effects and to study these systems using surface-based technologies, such as surface plasmon resonance (SPR) or biolayer interferometry. However, heterogeneity introduced from multivalent binding interactions complicates theanalysis of the resulting sensorgram. A frequently applied practice is to fit the data based on a 1:1 binding model, and if the fit does not describe the data adequately, then the experimental setup is changed to favor a 1:1 binding interaction. This reductionistic approach is informative but not always biologically relevant. Therefore, we aimed to develop an SPR-based assay that would reduce the heterogeneity to enable the determination of the kinetic rate constants for multivalent binding interactions using the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and the human receptor angiotensin-converting enzyme 2 (ACE2) as a model system. We employed a combinatorial approach to generate a sensor surface that could distinguish between monovalent and multivalent interactions. Using advanced data analysis algorithms to analyze the resulting sensorgrams, we found that controlling the surface heterogeneity enabled the deconvolution of theavidity-induced affinity enhancement for the SARS-CoV-2 spike protein and ACE2 interaction.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2022
National Category
Clinical Medicine
Research subject
Chemistry
Identifiers
urn:nbn:se:kau:diva-88057 (URN)10.1021/acs.analchem.1c04372 (DOI)000739329600001 ()2-s2.0-85122750876 (Scopus ID)
Funder
Swedish Research Council, 2015-04627
Available from: 2022-01-13 Created: 2022-01-13 Last updated: 2022-02-03Bibliographically approved
Liangsupree, T., Multia, E., Forssén, P., Fornstedt, T. & Riekkola, M.-L. (2022). Kinetics and interaction studies of anti-tetraspanin antibodies and ICAM-1 with extracellular vesicle subpopulations using continuous flow quartz crystal microbalance biosensor. Biosensors & bioelectronics, 206, Article ID 114151.
Open this publication in new window or tab >>Kinetics and interaction studies of anti-tetraspanin antibodies and ICAM-1 with extracellular vesicle subpopulations using continuous flow quartz crystal microbalance biosensor
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2022 (English)In: Biosensors & bioelectronics, ISSN 0956-5663, E-ISSN 1873-4235, Vol. 206, article id 114151Article in journal (Refereed) Published
Abstract [en]

Continuous flow quartz crystal microbalance (QCM) was utilized to study binding kinetics between EV subpopulations (exomere- and exosome-sized EVs) and four affinity ligands: monoclonal antibodies against tetraspanins (anti-CD9, anti-CD63, and anti-CD81) and recombinant intercellular adhesion molecule-1 (ICAM-1) or CD54 protein). High purity CD9+, CD63+, and CD81+ EV subpopulations of <50 nm exomeres and 50–80 nm exosomes were isolated and fractionated using our recently developed on-line coupled immunoaffinity chromatography – asymmetric flow field-flow fractionation system. Adaptive Interaction Distribution Algorithm (AIDA), specifically designed for the analysis of complex biological interactions, was used with a four-step procedure for reliable estimation of the degree of heterogeneity in rate constant distributions. Interactions between exomere-sized EVs and anti-tetraspanin antibodies demonstrated two interaction sites with comparable binding kinetics and estimated dissociation constants Kd ranging from nM to fM. Exomeres exhibited slightly higher affinity compared to exosomes. The highest affinity with anti-tetraspanin antibodies was achieved with CD63+ EVs. The interaction of EV subpopulations with ICAM-1 involved in cell internalization of EVs was also investigated. EV – ICAM-1 interaction was also of high affinity (nM to pM range) with overall lower affinity compared to the interactions of anti-tetraspanin antibodies and EVs. Our findings proved that QCM is a valuable label-free tool for kinetic studies with limited sample concentration, and that advanced algorithms, such as AIDA, are crucial for proper determination of kinetic heterogeneity. To the best of our knowledge, this is the first kinetic study on the interaction between plasma-derived EV subpopulations and anti-tetraspanin antibodies and ICAM-1

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Adaptive interaction distribution algorithm; Exomere; Exosome; Extracellular vesicle; Kinetics; Quartz crystal microbalance
National Category
Biophysics
Research subject
Chemistry
Identifiers
urn:nbn:se:kau:diva-89651 (URN)10.1016/j.bios.2022.114151 (DOI)000782658200006 ()35259607 (PubMedID)2-s2.0-85125643757 (Scopus ID)
Funder
Academy of Finland, 1311369Swedish Research Council, 20015-04627Knowledge Foundation, 20210021
Available from: 2022-04-28 Created: 2022-04-28 Last updated: 2022-12-01Bibliographically approved
Forssén, P., Samuelsson, J., Lacki, K. & Fornstedt, T. (2020). Advanced Analysis of Biosensor Data for SARS-CoV-2 RBD and ACE2 Interactions [Letter to the editor]. Analytical Chemistry, 92(17), 11520-11524
Open this publication in new window or tab >>Advanced Analysis of Biosensor Data for SARS-CoV-2 RBD and ACE2 Interactions
2020 (English)In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 92, no 17, p. 11520-11524Article in journal, Letter (Refereed) Published
Abstract [en]

The traditional approach for analyzing interaction data from biosensors instruments is based on the simplified assumption that also larger biomolecules interactions are homogeneous. It was recently reported that the human receptor angiotensin-converting enzyme 2 (ACE2) plays a key role for capturing SARS-CoV-2 into the human target body, and binding studies were performed using biosensors techniques based on surface plasmon resonance and bio-layer interferometry. The published affinity constants for the interactions, derived using the traditional approach, described a single interaction between ACE2 and the SARS-CoV-2 receptor binding domain (RBD). We reanalyzed these data sets using our advanced four-step approach based on an adaptive interaction distribution algorithm (AIDA) that accounts for the great complexity of larger biomolecules and gives a two-dimensional distribution of association and dissociation rate constants. Our results showed that in both cases the standard assumption about a single interaction was erroneous, and in one of the cases, the value of the affinity constant K-D differed more than 300% between the reported value and our calculation. This information can prove very useful in providing mechanistic information and insights about the mechanism of interactions between ACE2 and SARS-CoV-2 RBD or similar systems.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2020
Keywords
Biosensor Data; SARS-CoV-2 RBD; ACE2
National Category
Biochemistry and Molecular Biology
Research subject
Chemistry - Analytical Chemistry
Identifiers
urn:nbn:se:kau:diva-80741 (URN)10.1021/acs.analchem.0c02475 (DOI)000568657600006 ()32786452 (PubMedID)2-s2.0-85091901646 (Scopus ID)
Available from: 2020-10-12 Created: 2020-10-12 Last updated: 2021-09-16Bibliographically approved
Glenne, E., Samuelsson, J., Leek, H., Forssén, P., Klarqvist, M. & Fornstedt, T. (2020). Systematic investigations of peak distortions due to additives in supercritical fluid chromatography. Journal of Chromatography & Separation Techniques, 1621, 1-12, Article ID 461048.
Open this publication in new window or tab >>Systematic investigations of peak distortions due to additives in supercritical fluid chromatography
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2020 (English)In: Journal of Chromatography & Separation Techniques, E-ISSN 2157-7064, Vol. 1621, p. 1-12, article id 461048Article in journal (Refereed) Published
Abstract [en]

The impact of eluent components added to improve separation performance in supercritical fluid chromatography was systematically, and fundamentally, investigated. The model system comprised basic pharmaceuticals as solutes and eluents containing three amines (i.e., triethylamine, diethylamine, and isopropylamine) as additives with MeOH as the co-solvent. First, an analytical-scale study was performed, systematically investigating the impact of the additives/co-solvent on solute peak shapes and retentions, using a design of experiments approach; here, the total additive concentration in the eluent ranged between 0.021 and 0.105 % (v/v) and the total MeOH fraction in the eluent between 16 and 26 % (v/v). The co-solvent fraction was found to be the most efficient tool for adjusting retentions, whereas the additive fraction was the prime tool for improving column efficiency and peak analytical performance. Next, the impacts of the amine additives on the shapes of the so-called overloaded solute elution profiles were investigated. Two principal types of preparative peak deformations appeared and were investigated in depth, analyzed using computer simulation with mechanistic modeling. The first type of deformation was due to the solute eluting too close to the additive perturbation peak, resulting in severe peak deformation caused by co-elution. The second type of deformation was also due to additive–solute interactions, but here the amine additives acted as kosmotropic agents, promoting the multilayer adsorption to the stationary phase of solutes with bulkier aryl groups.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Supercritical fluid chromatography, Peak performance Peak distortions, Additives, Basic components, Overloaded peaks
National Category
Analytical Chemistry
Research subject
Chemical Engineering
Identifiers
urn:nbn:se:kau:diva-75764 (URN)10.1016/j.chroma.2020.461048 (DOI)000534277500039 ()
Note

This article was published as manuscript in Emelie Glennes PhD dissertation.

Available from: 2019-11-18 Created: 2019-11-18 Last updated: 2022-11-25Bibliographically approved
Zhang, Y., Yao, Z., Forssén, P. & Fornstedt, T. (2019). Estimating the rate constant from biosensor data via an adaptive variational Bayesian approach. Annals of Applied Statistics, 13(4), 2011-2042
Open this publication in new window or tab >>Estimating the rate constant from biosensor data via an adaptive variational Bayesian approach
2019 (English)In: Annals of Applied Statistics, ISSN 1932-6157, E-ISSN 1941-7330, Vol. 13, no 4, p. 2011-2042Article in journal (Refereed) Published
Abstract [en]

The means to obtain the rate constants of a chemical reaction is a fundamental open problem in both science and the industry. Traditional techniques for finding rate constants require either chemical modifications of the reac-tants or indirect measurements. The rate constant map method is a modern technique to study binding equilibrium and kinetics in chemical reactions. Finding a rate constant map from biosensor data is an ill-posed inverse problem that is usually solved by regularization. In this work, rather than finding a deterministic regularized rate constant map that does not provide uncertainty quantification of the solution, we develop an adaptive variational Bayesian approach to estimate the distribution of the rate constant map, from which some intrinsic properties of a chemical reaction can be explored, including information about rate constants. Our new approach is more realistic than the existing approaches used for biosensors and allows us to estimate the dynamics of the interactions, which are usually hidden in a deterministic approximate solution. We verify the performance of the new proposed method by numerical simulations, and compare it with the Markov chain Monte Carlo algorithm. The results illustrate that the variational method can reliably capture the posterior distribution in a computationally efficient way. Finally, the developed method is also tested on the real biosensor data (parathyroid hor-mone), where we provide two novel analysis tools—the thresholding contour map and the high order moment map—to estimate the number of interactions as well as their rate constants.

Place, publisher, year, edition, pages
Institute of Mathematical Statistics, 2019
Keywords
Adaptive discretization algorithm, Bayesian, Biosensor, Integral equation, Rate constant, Variational method
National Category
Physical Sciences
Research subject
Physics; Physics
Identifiers
urn:nbn:se:kau:diva-76607 (URN)10.1214/19-AOAS1263 (DOI)000509780500001 ()2-s2.0-85076480522 (Scopus ID)
Available from: 2020-01-30 Created: 2020-01-30 Last updated: 2022-05-18Bibliographically approved
Liangsupree, T., Multia, E., Metso, J., Jauhiainen, M., Forssén, P., Fornstedt, T., . . . Riekkola, M.-L. (2019). Rapid affinity chromatographic isolation method for LDL in human plasma by immobilized chondroitin-6-sulfate and anti-apoB-100 antibody monolithic disks in tandem. Scientific Reports, 9, Article ID 11235.
Open this publication in new window or tab >>Rapid affinity chromatographic isolation method for LDL in human plasma by immobilized chondroitin-6-sulfate and anti-apoB-100 antibody monolithic disks in tandem
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2019 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 9, article id 11235Article in journal (Refereed) Published
Abstract [en]

Low-density lipoprotein (LDL) is considered the major risk factor for the development of atherosclerotic cardiovascular diseases (ASCVDs). A novel and rapid method for the isolation of LDL from human plasma was developed utilising affinity chromatography with monolithic stationary supports. The isolation method consisted of two polymeric monolithic disk columns, one immobilized with chondroitin-6-sulfate (C6S) and the other with apolipoprotein B-100 monoclonal antibody (anti-apoB-100 mAb). The first disk with C6S was targeted to remove chylomicrons, very-low-density lipoprotein (VLDL) particles, and their remnants including intermediate-density lipoprotein (IDL) particles, thus allowing the remaining major lipoprotein species, i.e. LDL, lipoprotein(a) (Lp(a)), and high-density lipoprotein (HDL) to flow to the anti-apoB-100 disk. The second disk captured LDL particles via the anti-apoB-100 mAb attached on the disk surface in a highly specific manner, permitting the selective LDL isolation. The success of LDL isolation was confirmed by different techniques including quartz crystal microbalance. In addition, the method developed gave comparable results with ultracentrifugation, conventionally used as a standard method. The reliable results achieved together with a short isolation time (less than 30 min) suggest the method to be suitable for clinically relevant LDL functional assays.

Place, publisher, year, edition, pages
Nature Publishing Group, 2019
Keywords
IMMUNOAFFINITY CHROMATOGRAPHY; FAST SEPARATION; LIPOPROTEINS; INSIGHTS; COLUMNS
National Category
Chemical Engineering
Research subject
Chemistry
Identifiers
urn:nbn:se:kau:diva-74335 (URN)10.1038/s41598-019-47750-z (DOI)000478575000022 ()31375727 (PubMedID)
Available from: 2019-08-15 Created: 2019-08-15 Last updated: 2022-09-15Bibliographically approved
Lin, G., Zhang, Y., Cheng, X., Gulliksson, M., Forssén, P. & Fornstedt, T. (2018). A regularizing Kohn-Vogelius formulation for the model-free adsorption isotherm estimation problem in chromatography. Applicable Analysis, 97(1), 13-40
Open this publication in new window or tab >>A regularizing Kohn-Vogelius formulation for the model-free adsorption isotherm estimation problem in chromatography
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2018 (English)In: Applicable Analysis, ISSN 0003-6811, E-ISSN 1563-504X, Vol. 97, no 1, p. 13-40Article in journal (Refereed) Published
Abstract [en]

Competitive adsorption isotherms must be estimated in order to simulate and optimize modern continuous modes of chromatography in situations where experimental trial-and-error approaches are too complex and expensive. The inverse method is a numeric approach for the fast estimation of adsorption isotherms directly from overloaded elution profiles. However, this identification process is usually ill-posed. Moreover, traditional model-based inverse methods are restricted by the need to choose an appropriate adsorption isotherm model prior to estimate, which might be very hard for complicated adsorption behavior. In this study, we develop a Kohn-Vogelius formulation for the model-free adsorption isotherm estimation problem. The solvability and convergence for the proposed inverse method are studied. In particular, using a problem-adapted adjoint, we obtain a convergence rate under substantially weaker and more realistic conditions than are required by the general theory. Based on the adjoint technique, a numerical algorithm for solving the proposed optimization problem is developed. Numerical tests for both synthetic and real-world problems are given to show the efficiency of the proposed regularization method.

National Category
Chemical Sciences Geophysics Analytical Chemistry
Research subject
Chemistry; Physics
Identifiers
urn:nbn:se:kau:diva-65938 (URN)10.1080/00036811.2017.1284311 (DOI)000417831700003 ()
Available from: 2018-01-25 Created: 2018-01-25 Last updated: 2019-03-28Bibliographically approved
Zhang, Y., Forssén, P., Fornstedt, T., Gulliksson, M. & Dai, X. (2018). An adaptive regularization algorithm for recovering the rate constant distribution from biosensor data. Inverse Problems in Science and Engineering, 26(10), 1464-1489
Open this publication in new window or tab >>An adaptive regularization algorithm for recovering the rate constant distribution from biosensor data
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2018 (English)In: Inverse Problems in Science and Engineering, ISSN 1741-5977, E-ISSN 1741-5985, Vol. 26, no 10, p. 1464-1489Article in journal (Refereed) Published
Abstract [en]

We present here the theoretical results and numerical analysis of a regularization method for the inverse problem of determining the rate constant distribution from biosensor data. The rate constant distribution method is a modern technique to study binding equilibrium and kinetics for chemical reactions. Finding a rate constant distribution from biosensor data can be described as a multidimensional Fredholm integral equation of the first kind, which is a typical ill-posed problem in the sense of J. Hadamard. By combining regularization theory and the goal-oriented adaptive discretization technique, we develop an Adaptive Interaction Distribution Algorithm (AIDA) for the reconstruction of rate constant distributions. The mesh refinement criteria are proposed based on the a posteriori error estimation of the finite element approximation. The stability of the obtained approximate solution with respect to data noise is proven. Finally, numerical tests for both synthetic and real data are given to show the robustness of the AIDA.

Place, publisher, year, edition, pages
Oxon, UK: Taylor & Francis, 2018
Keywords
Rate constant distribution, inverse problem, regularization, adaptive finite element, a posteriori error estimation
National Category
Computational Mathematics Geophysics Chemical Engineering
Research subject
Chemistry
Identifiers
urn:nbn:se:kau:diva-68754 (URN)10.1080/17415977.2017.1411912 (DOI)000438638300005 ()
Note

Zhang, Yue saknar cas! 20181018

Available from: 2018-08-16 Created: 2018-08-16 Last updated: 2019-03-28Bibliographically approved
Forssén, P. & Fornstedt, T. (2018). Impact of column and stationary phase properties on the productivity in chiral preparative LC. Journal of Separation Science, 41(6), 1346-1354
Open this publication in new window or tab >>Impact of column and stationary phase properties on the productivity in chiral preparative LC
2018 (English)In: Journal of Separation Science, ISSN 1615-9306, E-ISSN 1615-9314, Vol. 41, no 6, p. 1346-1354Article in journal (Refereed) Published
Abstract [en]

By generating 1500 random chiral separation systems, assuming two-site Langmuir interactions, we investigated numerically how the maximal productivity (P-R,P-max) was affected by changes in stationary phase adsorption properties. The relative change in P-R,P-max, when one adsorption property changed 10%, was determined for each system and for each studied parameter the corresponding productivity change distribution of the systems was analyzed. We could conclude that there is no reason to have columns with more than 500 theoretical plates and larger selectivity than 3. More specifically, we found that changes in selectivity have a major impact on P-R,P-max if it is below similar to 2 and, interestingly, increasing selectivity when it is above similar to 3 decreases P-R,P-max. Increase in relative saturation capacity will have a major impact on P-R,P-max if it is below similar to 40%, but only modest above this percent. Increasing total monolayer saturation capacity, or decreasing the first eluting enantiomer's retention factor, will have a modest effect on P-R,P-max and increased efficiency will have almost no effect at all on P-R,P-max unless it is below similar to 500 theoretical plates. Finally, we showed that chiral columns with superior analytic performance might have inferior preparative performance, or vice versa. It is, therefore, not possible to assess columns based on their analytical performance alone.

Place, publisher, year, edition, pages
Wiley-VCH Verlagsgesellschaft, 2018
Keywords
enantiomers, preparative chromatography, process optimization, productivity
National Category
Chemical Sciences
Research subject
Chemistry
Identifiers
urn:nbn:se:kau:diva-67070 (URN)10.1002/jssc.201701435 (DOI)000428797000019 ()29359510 (PubMedID)
Available from: 2018-04-19 Created: 2018-04-19 Last updated: 2018-04-26Bibliographically approved
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