60 TRENDING TECHNOLOGIES
contained in the extract based on their physico- chemical properties (e.g. polarity, size), each molecule is represented by a peak – at this stage, the identity of the molecule is unknown and molecules with similar physico-chemical properties may have their peaks fused – on the UHPLC chromatogram. For each peak, a MS/MS spectrum is acquired (Figure 1).
How GAINS works The monoisotopic masses from the MS spectra are extracted and serve as queries to the phytochemical database. The results will be a list of candidate molecules matching the experimental monoisotopic masses. Then, their theoretical mass spectra are simulated (Figure 2). Candidate molecules with a theoretical spectrum that matches best the experimental data will be best-scored. Therefore, for each peak of the chromatogram, a list of candidate molecules is given with a score ranking them in decreasing probability. The knowledge provided by the
phytochemical profile is very valuable at different levels. We will have insight on molecules that are problematic (e.g. toxicity, instability); as their retention time is known, this will be helpful to guide the development of an extraction method to discard them. On the contrary, molecules of interest can be highlighted by the profiling and also guide the development of extraction methods to concentrate them in a titrated extract. There may be peaks without attribution of candidate because the molecules are not present in the database. These compounds will be of highest interest as they are not yet described (if the database used is rather thorough)! Additionally, classical analytical methods
can be applied to confirm the profiling by introducing an analytical standard (if any) or to help in the characterization and identification of a new product.
Biological profiling After the phytochemical profiling is performed, we obtain a set of molecules that can be in the studied extract. To identify the properties of the extract, we can rely on its molecular composition and use prediction tools to find new applications for the said extract. We can approach this topic by two complementary methods: ■ A ligand-based method: it consists in a database of known active ligands, to identify similar ones to the studied molecules, the hypothesis being that similar molecules (in terms of chemical structures) will have similar properties. In other words, a structure-activity relationship; database, such as ChEMBL, is a good source of active compounds; several similar techniques are implemented to detect chemical similarity such as 3D molecular fingerprints, which reflects the steric and electrostatic properties of a molecule (Figure 3). ■ A protein-based method: it consists in virtual screening on protein 3D structures and simulates the putative interaction of the studied molecules with a protein of interest or a set of proteins (Figure 4).
PERSONAL CARE June 2023
Pre-processed data file
Structural candidates
MS spectra MS/MS spectra
Monoisotopic Masses
Retention time UV spectra
Fragmentation vs in silico fragmentation
Classified candidates
Figure 2: Phytochemical profiling process
Introducing the Selnergy tool We have developed a tool to do both tasks named Selnergy. It relies on several algorithm to derive similar analogues of a studied molecule: similarity based on molecular fingerprints (i.e. a ‘barcode’ corresponding to the absence or presence of some chemical fragments), physico-chemical properties such as steric and electrostatic properties and pharmacophoric groups (i.e. chemical features with a specific 3D pattern that confer particular properties to a molecule). In our case, we used a curated database
of 1.5 million active ligands. As for the protein-based approach, inverse docking is implemented with a database of 21,000 protein 3D structures. Knowing the potential molecules in an
extract, one can also study potential adverse and/or deleterious effects by predicting mutagenicity, genotoxicity, systemic toxicity
and skin sensitization (e.g. NCSTOX project).7 With the biological profiling by in silico tools, we can go beyond what is currently known and published in the scientific literature. We can predict new biological activities and possibly toxicity to anticipate development problems downstream. With both types of profiling, the properties
of an active ingredient is substantiated at the molecular level: how the extract exerts its effects in vivo on the skin, with the whole mechanism of action of the active molecule(s) modulating a particular biological target.
Examples of applications Black tulip ‘Queen of the Night’ as anti-ageing active ingredient We developed this active ingredient based on a customer’s concept: a black flower reflecting luxury and with healthy ageing positioning and complying with the IECIC list. Several
Figure 3: 3D molecular fingerprint. The grid shape represents the molecular volume; the colours correspond to hydrophobic areas in brown, hydrophilic ones in blue and green, intermediate
www.personalcaremagazine.com
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80
