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Application of Quantitative Structure-Activity Relationships in the Prediction of New Compounds with Anti-Leukemic Activity.

ABSTRACT: Leukemia invades the bone marrow progressively and, through unknown mechanisms, outcompetes healthy hematopoiesis. Protein arginine methyltransferases 1 (PRMT1) are found in prokaryotes and eukaryotes cells. They are necessary for a number of biological processes and have been linked to several human diseases, including cancer. Small compounds that target PRMT1 have a significant impact on both functional research and clinical disease treatment. In fact, numerous PRMT1 inhibitors targeting the S-adenosyl-L-methionine binding region have been studied. Through topographical descriptors, quantitative structure-activity relationships (QSAR) were developed in order to identify the most effective PRMT1 inhibitors among 17 compounds. The model built using linear discriminant analysis allows us to accurately classify over 90% of the investigated active substances. Antileukemic activity is predicted using a multilinear regression analysis, and it can account for more than 56% of the variation. Both analyses are validated using an internal "leave some out" test. The developed model could be utilized in future preclinical experiments with novel drugs.

SUBMITTER: Sandoval C

PROVIDER: S-EPMC10418467 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Application of Quantitative Structure-Activity Relationships in the Prediction of New Compounds with Anti-Leukemic Activity.

Sandoval Cristian C Torrens Francisco F Godoy Karina K Reyes Camila C Farías Jorge J

International journal of molecular sciences 20230731 15

Leukemia invades the bone marrow progressively and, through unknown mechanisms, outcompetes healthy hematopoiesis. Protein arginine methyltransferases 1 (PRMT1) are found in prokaryotes and eukaryotes cells. They are necessary for a number of biological processes and have been linked to several human diseases, including cancer. Small compounds that target PRMT1 have a significant impact on both functional research and clinical disease treatment. In fact, numerous PRMT1 inhibitors targeting the S ...[more]

PMID: 37569634

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Project description:BackgroundEmergence of cross-resistance to current anti-malarial drugs has led to an urgent need for identification of potential compounds with novel modes of action and anti-malarial activity against the resistant strains. One of the most promising therapeutic targets of anti-malarial agents related to food vacuole of malaria parasite is haemozoin, a product formed by the parasite through haemoglobin degradation.MethodsWith this in mind, this study developed two-dimensional-quantitative structure-activity relationships (QSAR) models of a series of 21 haemozoin inhibitors to explore the useful physicochemical parameters of the active compounds for estimation of anti-malarial activities. The 2D-QSAR model with good statistical quality using partial least square method was generated after removing the outliers.ResultsFive two-dimensional descriptors of the training set were selected: atom count (a_ICM); adjacency and distance matrix descriptor (GCUT_SLOGP_2: the third GCUT descriptor using atomic contribution to logP); average total charge sum (h_pavgQ) in pKa prediction (pH = 7); a very low negative partial charge, including aromatic carbons which have a heteroatom-substitution in "ortho" position (PEOE_VSA-0) and molecular descriptor (rsynth: estimating the synthesizability of molecules as the fraction of heavy atoms that can be traced back to starting material fragments resulting from retrosynthetic rules), respectively. The model suggests that the anti-malarial activity of haemozoin inhibitors increases with molecules that have higher average total charge sum in pKa prediction (pH = 7). QSAR model also highlights that the descriptor using atomic contribution to logP or the distance matrix descriptor (GCUT_SLOGP_2), and structural component of the molecules, including topological descriptors does make for better anti-malarial activity.ConclusionsThe model is capable of predicting the anti-malarial activities of anti-haemozoin compounds. In addition, the selected molecular descriptors in this QSAR model are helpful in designing more efficient compounds against the P. falciparum 3D7A strain.

Project description:Mitochondria play a central role in metabolic homeostasis, and dysfunction of this organelle underpins the etiology of many heritable and aging-related diseases. Tetrapeptides with alternating cationic and aromatic residues such as SS-31 (elamipretide) show promise as therapeutic compounds for mitochondrial disorders. In this study, we conducted a quantitative structure-activity analysis of three alternative tetrapeptide analogs, benchmarked against SS-31, that differ with respect to aromatic side chain composition and sequence register. We present the first structural models for this class of compounds, obtained with Nuclear Magnetic Resonance (NMR) and molecular dynamics approaches, showing that all analogs except for SS-31 form compact reverse turn conformations in the membrane-bound state. All peptide analogs bound cardiolipin-containing membranes, yet they had significant differences in equilibrium binding behavior and membrane interactions. Notably, analogs had markedly different effects on membrane surface charge, supporting a mechanism in which modulation of membrane electrostatics is a key feature of their mechanism of action. The peptides had no strict requirement for side chain composition or sequence register to permeate cells and target mitochondria in mammalian cell culture assays. All four peptides were pharmacologically active in serum withdrawal cell stress models yet showed significant differences in their abilities to restore mitochondrial membrane potential, preserve ATP content, and promote cell survival. Within our peptide set, the analog containing tryptophan side chains, SPN10, had the strongest impact on most membrane properties and showed greatest efficacy in cell culture studies. Taken together, these results show that side chain composition and register influence the activity of these mitochondria-targeted peptides, helping provide a framework for the rational design of next-generation therapeutics with enhanced potency.

Dataset Information

Application of Quantitative Structure-Activity Relationships in the Prediction of New Compounds with Anti-Leukemic Activity.

Publications

Application of Quantitative Structure-Activity Relationships in the Prediction of New Compounds with Anti-Leukemic Activity.

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