Evidence map›Paper›PMID 39582986›Full record

ArticleIndustrial & engineering chemistry research2024

Predicting the Solubility of Amino Acids and Peptides with the SAFT-γ Mie Approach: Neutral and Charged Models.

Ahmed Alyazidi, Shubhani Paliwal, Felipe A Perdomo, Amy Mead, Mingxia Guo, Jerry Y Y Heng, Thomas Bernet, Andrew J Haslam, Claire S Adjiman, George Jackson and 1 more

Abstract read
In one paragraph

Article in Industrial & engineering chemistry research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Ahmed AlyazidiDepartment of Chemical Engineering, Institute for Molecular Science and Engineering, and Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.
Shubhani PaliwalDepartment of Chemical Engineering, Institute for Molecular Science and Engineering, and Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.
Felipe A PerdomoDepartment of Chemical Engineering, Institute for Molecular Science and Engineering, and Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.
Amy MeadDepartment of Chemical Engineering, Institute for Molecular Science and Engineering, and Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.
Mingxia GuoDepartment of Chemical Engineering, Institute for Molecular Science and Engineering, and Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.ORCID https://orcid.org/0000-0001-5957-5915
Jerry Y Y HengDepartment of Chemical Engineering, Institute for Molecular Science and Engineering, and Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.ORCID https://orcid.org/0000-0003-2659-5500
Thomas BernetDepartment of Chemical Engineering, Institute for Molecular Science and Engineering, and Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.ORCID https://orcid.org/0000-0002-4089-0218
Andrew J HaslamDepartment of Chemical Engineering, Institute for Molecular Science and Engineering, and Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.ORCID https://orcid.org/0000-0002-8442-119X
Claire S AdjimanDepartment of Chemical Engineering, Institute for Molecular Science and Engineering, and Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.ORCID https://orcid.org/0000-0002-4573-7722
George JacksonDepartment of Chemical Engineering, Institute for Molecular Science and Engineering, and Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.ORCID https://orcid.org/0000-0002-8029-8868
Amparo GalindoDepartment of Chemical Engineering, Institute for Molecular Science and Engineering, and Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.ORCID https://orcid.org/0000-0002-4902-4156

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modeling approaches that can be used to predict accurately the solubility of amino acids and peptides are of interest for the design of new pharmaceutical processes and in the development of new peptide-based therapeutics. We investigate the capability of the SAFT-γ Mie group-contribution approach to predict the aqueous and alcohol solubility of glycine, alananine, valine, leucine, and serine and of di- and tripeptides containing these amino acids. New SAFT-γ Mie group interactions are characterized using experimental thermodynamic and phase-equilibrium data of compounds and mixtures that contain groups relevant to the amino acids and peptides, but no solubility data (except for the case of glycine). Once all the group interaction parameters are developed, predictive solid-liquid solubility calculations are carried out. Neutral and charged models are considered to account explicitly for the zwitterionic nature of the molecules in aqueous solution, and the solubility of the solution is presented as a function of pH. A detailed discussion of the molecular models and Helmholtz free-energy expressions used to represent the ionic and zwitterionic forms of the amino acids, together with their speciation in solution is also provided. Overall, very good agreement with available data is shown, with an absolute average deviation (AAD) in mole fraction of 0.0038 over 283 solubility data points for the amino acids studied and an AAD in mole fraction of 0.02128 over 141 peptide-solubility points when the systems are studied at their isoelectric point and neutral models are used. The solubility as a function of pH for a range of temperatures is also predicted accurately when charged models are incorporated. These results confirm the predictive accuracy of the SAFT-γ Mie method and pave the way for future studies involving larger peptides.

Identifiers

PMID39582986
PMCPMC11583215

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.