Modeling structure–activity relationships of prodiginines with antimalarial activity using GA/MLR and OPS/PLS

First report on chemometrics-driven multilayered lead prioritization in addressing oxysterol-mediated overexpression of G protein-coupled receptor 183

Contemporary research has convincingly demonstrated that upregulation of G protein-coupled receptor 183 (GPR183), orchestrated by its endogenous agonist, 7α,25-dihydroxyxcholesterol (7α,25-OHC), leads to the development of cancer, diabetes, multiple sclerosis, infectious, and inflammatory diseases. A recent study unveiled the cryo-EM structure of 7α,25-OHC bound GPR183 complex, presenting an untapped opportunity for computational exploration of potential GPR183 inhibitors, which served as our inspiration for the current work. A predictive and validated two-dimensional QSAR model using genetic algorithm (GA) and multiple linear regression (MLR) on experimental GPR183 inhibition data was developed. QSAR study highlighted that structural features like dissimilar electronegative atoms, quaternary carbon atoms, and CH2RX fragment (X: heteroatoms) influence positively, while the existence of oxygen atoms with a topological separation of 3, negatively affects GPR183 inhibitory activity. Post assessment of true external set prediction capability, the MLR model was deployed to screen 12,449 DrugBank compounds, followed by a screening pipeline involving molecular docking, druglikeness, ADMET, protein-ligand stability assessment using deep learning algorithm, molecular dynamics, and molecular mechanics. The current findings strongly evidenced DB05790 as a potential lead for prospective interference of oxysterol-mediated GPR183 overexpression, warranting further in vitro and in vivo validation.

Comparative evaluation and prioritization of key influences on biodegradation of 2,2',4,4'-tetrabrominated diphenyl ether by bacterial isolate B. xenovorans LB400

Polybrominated diphenyl ethers (PBDEs) are a class of persistent organic pollutants being widely distributed and harmful to human health and wildlife, and the development of sustainable rehabilitation strategies including microbial degradation is of great concern. Although the increasing number of bacteria, especially the broad-spectrum and potent aerobes have been isolated for the efficient removal of PBDEs, the external influences and the corresponding influential mechanism on biodegradation are not fully understood yet. Given the wide-spectrum biodegradability of aerobic bacterial isolate, B. xenovorans LB400 for PBDEs, the dual impacts of many pivotal factors including pH, temperature, presence of dissolved organic matter (DOM) and cadmium ion etc. were comprehensively revealed on biodegradation of 2,2',4,4'-tetrabromodiphenyl ether (BDE-47). Due to the structural resemblance and stimulation of specific enzyme activity in bacteria, the biphenyl as substrates showed the greater capacity than non-aromatic compounds in improving biodegradation. The individual adaptation to neutrality and cultivation at about 30 °C was beneficial for biodegradation since the bacterial cellular viability and enzyme activity was mostly preserved. Although it was possibly good for the induction of hormesis and favorable to enhance the permeability or bioavailability of pollutant, the exceeding increase of Cd²⁺ or DOM may not give the profitable increase of biodegradation yet for the detrimental effect. For biodegradation, the mechanistic relationship that took account of the integrative correlation with the influential factors was artfully developed using partial least square (PLS) regression technique. Relative to the most significant influence of culture time and initial concentration of BDE-47, the larger relevance of other factors primarily marked as pH and DOM was consecutively shown after the quantitative prioritization. This may not only help understand the influential mechanism but provide a prioritizing regulation strategy for biodegradation of BDE-47. The PLS-derived relationship was validated with the certain predictability in biodegradation, and could be used as an alternative to accelerate a priori evaluation of suitability or improve the feasibility of such bacteria in remediation of PBDEs in the environment.

Exploring the inhibitory mechanisms of indazole compounds against SAH/MTAN-mediated quorum sensing utilizing QSAR and docking

The world is under the great threat of antimicrobial resistance (AMR) leading to premature deaths. Microorganisms can produce AMR via quorum sensing mechanisms utilizing S-adenosyl homocysteine/methylthioadenosine nucleosidase (SAH/MTAN) biosynthesis. But there is no specific drug developed to date to stop SAH/MTAN, which is a crucial target for the discovery of anti-quorum sensing compound. It has been shown that indazole compounds cause inhibition of SAH/MTAN-mediated quorum sensing, but the biochemical mechanisms have not yet been explored. Therefore, in this original research, an attempt has been made to explore essential structural features of these compounds by quantitative structure-activity relationship (QSAR) and molecular docking of indazole compounds having inhibition of SAH/MTAN-mediated quorum sensing. The validated QSAR predicted five essential descriptors and molecular docking helps to identify the active binding amino acid residues involved in ligand-receptor interactions that are responsible for producing the quorum sensing inhibitory mechanisms of indazole compounds against SAH/MTAN-mediated AMR.

Structure- and ligand-based drug design methods for the modeling of antimalarial agents: a review of updates from 2012 onwards

Malaria still persists as one of the deadliest infectious disease having a huge morbidity and mortality affecting the higher population of the world. Structure and ligand-based drug design methods like molecular docking and MD simulations, pharmacophore modeling, QSAR and virtual screening are widely used to perceive the accordant correlation between the antimalarial activity and property of the compounds to design novel dominant and discriminant molecules. These modeling methods will speed-up antimalarial drug discovery, selection of better drug candidates for synthesis and to achieve potent and safer drugs. In this work, we have extensively reviewed the literature pertaining to the use and applications of various ligand and structure-based computational methods for the design of antimalarial agents. Different classes of molecules are discussed along with their target interactions pattern, which is responsible for antimalarial activity. Communicated by Ramaswamy H. Sarma.

Combined Micellar Liquid Chromatography Technique and QSARs Modeling in Predicting the Blood-Brain Barrier Permeation of Heterocyclic Drug-like Compounds

The quantitative structure-activity relationship (QSAR) methodology was used to predict the blood-brain permeability (log BB) for 65 synthetic heterocyclic compounds tested as promising drug candidates. The compounds were characterized by different descriptors: lipophilicity, parachor, polarizability, molecular weight, number of hydrogen bond acceptors, number of rotatable bonds, and polar surface area. Lipophilic properties of the compounds were evaluated experimentally by micellar liquid chromatography (MLC). In the experiments, sodium dodecyl sulfate (SDS) as the effluent component and the ODS-2 column were used. Using multiple linear regression and leave-one-out cross-validation, we derived the statistically significant and highly predictive quantitative structure-activity relationship models. Thus, this study provides valuable information on the expected properties of the substances that can be used as a support tool in the design of new therapeutic agents.

SMILES-based 2D-QSAR and similarity search for identification of potential new scaffolds for development of SARS-CoV-2 MPRO inhibitors

COVID-19, whose etiological agent is the SARS-CoV-2 virus, has caused over 537.5 million cases and killed over 6.3 million people since its discovery in 2019. Despite the recent development of the first drugs indicated for treating people already infected, the great need to develop new anti-SARS-CoV-2 drugs still exists, mainly due to the possible emergence of new variants of this virus and resistant strains of the current variants. Thus, this work presents the results of QSAR and similarity search studies based only on 2D structures from a set of 32 bicycloproline derivatives, aiming to quickly, reproducibly, and reliably identify potentially useful compounds as scaffolds of new major protease inhibitors (M^pro) of the virus. The obtained QSAR model is based only on topological molecular descriptors. The model has good internal and external statistics, is robust, and does not present a chance correlation. This model was used as one of the tools to support the virtual screening stage carried out in the SwissADME web tool. Five molecules, from an initial set of 2695 molecules, proved to be the most promising, as they were within the model’s applicability domain and linearity range, with low potential to cause carcinogenic, teratogenic, and reproductive toxicity effects and promising pharmacokinetic properties. These five compounds were then selected as the most competent to generate, in future studies, new anti-SARS-CoV-2 agents with drug-likeness properties suitable for use in therapy.

Application of machine learning to a material library for modeling of relationships between material properties and tablet properties

This study investigates the usefulness of machine learning for modeling complex relationships in a material library. We tested 81 types of active pharmaceutical ingredients (APIs) and their tablets to construct the library, which included the following variables: 20 types of API material properties, one type of process parameter (three levels of compression pressure), and two types of tablet properties (tensile strength (TS) and disintegration time (DT)). The machine learning algorithms boosted tree (BT) and random forest (RF) were applied to analysis of our material library to model the relationships between input variables (material properties and compression pressure) and output variables (TS and DT). The calculated BT and RF models achieved higher performance statistics compared with a conventional modeling method (i.e., partial least squares regression), and revealed the material properties that strongly influence TS and DT. For TS, true density, the tenth percentile of the cumulative percentage size distribution, loss on drying, and compression pressure were of high relative importance. For DT, total surface energy, water absorption rate, polar surface energy, and hygroscopicity had significant effects. Thus, we demonstrate that BT and RF can be used to model complex relationships and clarify important material properties in a material library.

Survivin modulation in the antimelanoma activity of prodiginines

Melanoma is a type of skin cancer with an elevated incidence of metastasis and chemoresistance. Such features hamper treatment success of these neoplasms, demanding the search for new therapeutic options. Using a two-step resin-based approach, we recently demonstrated that cytotoxic prodiginines bind to the inhibitor of apoptosis protein, survivin. Herein, we explore the role of survivin in melanoma and whether its modulation is related to the antimelanoma properties of three cytotoxic prodiginines (prodigiosin, cyclononylprodigiosin, and nonylprodigiosin) isolated from marine bacteria. In melanoma patients and cell lines, survivin is overexpressed, and higher levels negatively impact survival. All three prodiginines caused a decrease in cell growth with reduced cytotoxicity after 24 h compared to 72 h treatment, suggesting that low concentrations promote cytostatic effects in SK-Mel-19 (BRAF mutant) and SK-Mel-28 (BRAF mutant), but not in SK-Mel-147 (NRAS mutant). An increase in G1 population was observed after 24 h treatment with prodigiosin and cyclononylprodigiosin in SK-Mel-19. Further studies indicate that prodigiosin induced apoptosis and DNA damage, as detected by increased caspase-3 cleavage and histone H2AX phosphorylation, further arguing for the downregulation of survivin. Computer simulations suggest that prodigiosin and cyclononylprodigiosin bind to the BIR domain of survivin. Moreover, knockdown of survivin increased long-term toxicity of prodigiosin, as observed by reduced clonogenic capacity, but did not alter short-term cytotoxicity. In summary, prodiginine treatment provoked cytostatic rather than cytotoxic effects, cell cycle arrest at G0/G1 phase, induction of apoptosis and DNA damage, downregulation of survivin, and decreased clonogenic capacity in survivin knockdown cells.

Mechanistic studies of congener-specific adsorption and bioaccumulation of polycyclic aromatic hydrocarbons and phthalates in soil by novel QSARs

Polycyclic aromatic hydrocarbons (PAHs) and phthalic acid esters (PAEs) which are structurally featured with one or more aromatic skeletons are often regarded as two important groups of organic pollutants due to the widespread distribution and notorious toxic effects in soils. Relative to the great number of structural analogues or congeners detected in soil, however, the soil adsorption and bioaccumulation of PAHs/PAEs by plant is far less studied for the insufficiency of experimental determinations or lack of insights into the inherent structural requirements. To mechanistically evaluate the congener-specific soil adsorption and bioaccumulation for PAHs/PAEs, the quantitative structure-activity relationships (QSARs) were successfully developed by density functional theory (DFT) computation and partial least squares (PLS) analysis. As verified with the higher cumulative variance coefficients and cross-validated correlation coefficients for strong stability, interpretability and predictability, the QSARs could be used for prediction of unknown adsorption potency or bioavailability within the specified applicability domain, respectively. It was indicated by QSAR that the structural requirements of PAHs/PAEs necessary for strengthening the soil adsorption were mainly attributed to the molecular polarizability and the associated dispersion interaction with soil. As regards the bioaccumulation by carrot, the aggravation of spherical polarity change of molecules and the involved electrostatic interaction with soil entity or electron transfer from the highest occupied molecular orbital (HOMO) of PAHs/PAEs was implied to be inherently decisive for the variance of bioavailability among congeners. Based on the holistic view of negative correlation relationship, the soil adsorption seemed to act as the forceful constraint in decreasing the bioaccumulation of PAHs/PAEs and could also be alternatively gauged as the preliminary evaluation of bioavailability and risks on soil ecosystem. It would thus help better understand the soil adsorption and bioaccumulation with the informative mechanistic insights and provide data support for ecological risk assessment of PAHs/PAEs in soils.

QSAR and molecular docking studies of lethal factor protease inhibitors against Bacillus anthracis

Bacillus anthracis is considered as a biological warfare agent because it is the causative agent of the serious infectious anthrax disease. Delay in treatment leads to lethal factor-mediated toxaemia which is very critical due to lack of therapeutic options. Consequently, attempts have been made to discover potent lethal factor (LF) protease inhibitors such as small-molecule synthetic 2-thio-1,3-thiazolidine-4-one (rhodanine) compounds. But computed descriptor-based quantitative structure-activity relationship (QSAR) and drug design studies on such aspect are poorly represented. Therefore, an attempt was made for developing QSAR models using structural descriptors for 1,3-thiazolidine-4-one compounds. The models were developed on a series of 49 LF protease inhibitors using the combination of constitutional, functional group, atom-centred fragment and molecular property descriptors. The best QSAR model included four variables, namely, C-040, nR05, GVWAI-80 and ALOGP that correlated well with the anti-LF protease activity with a good correlation coefficient (r = 0.870) of good statistical significance (F_{4, 29} = 14.09 (α = 0.001) F_{4, 29} = 6.19). This model was also validated and explained 58.1% of variances of the Bacillus anthracis inhibitory activities of the studied compounds with r²_pred = 0.710 which denotes external predictability. Finally, molecular docking was carried out to predict the mode of binding of some highly active congeneric compounds. It was shown that VAL 1403 is an important residue for phenyl ring. TYR 1456 and HIS 1418 are responsible for interaction with the rhodanine nucleus. Therefore, these residues are considered responsible for the inhibition of LF protease anthrax and can predict significant dimension of essential structural features of these inhibitors to evaluate, screen and help priorities of the synthesis of the candidates against anthrax bioterrorism.

Insights into the anti-infective properties of prodiginines

Prodiginines are a large family of tripyrrole alkaloids that contain natural members produced by various bacteria and non-natural members obtained from chemical synthesis, enzymatic synthesis, and mutasynthesis. These compounds have attracted a great deal of attention due to their wide range of fascinating properties including anti-infective, anticancer, and immunosuppressive activities. In consideration of the great need for novel and effective anti-infective agents, this review is mainly focused on the current status of research on the anti-infective properties of prodiginines, highlighting their antibacterial, antifungal, antiprotozoal, anti-larval, and antiviral activities. Additionally, the multiple mechanisms by which prodiginines exert their anti-infective effects will also be discussed.

(R)-Metacycloprodigiosin-HCl: Chiroptical properties and structure

(R)-Metacycloprodigiosin can exist in three different tautomeric forms, each with hydrogens at C9' and C12 in syn or anti orientation. With the addition of HCl, this structural diversity reduces to syn-(R)-metacycloprodigiosin-HCl (1a) and anti-(R)-metacycloprodigiosin-HCl (1b), each with multiple conformers. Energetics and chiroptical properties, namely, electronic circular dichroism (ECD) and specific optical rotation (SOR), of (R)-metacycloprodigiosin-HCl have been investigated at B3LYP/6-311++G(2d,2p) level. The experimental ECD spectra of (R)-metacycloprodigiosin-HCl have also been measured. Calculations indicated that the lowest energy conformer of 1b is approximately 2.7 kcal/mol lower in energy than that of 1a, and the energy barrier for anti to syn conversion is approximately 13 kcal/mol. The population weighted calculated SORs of 1a and 1b are, respectively, positive and negative. The respective calculated ECD spectra of these pseudoenantiomers show an almost mirror image relationship between them. The experimental SOR and ECD compare well with those predicted for 1b. Thus, 1b is expected to be predominant, a situation confirmed also by nuclear Overhauser effect (NOE) data, with a similar conclusion reached for prodigiosin R1.

Unlike Butylcycloheptylprodigiosin, Isolated Undecylprodigiosin from Streptomyces parvulus Is Not a MDR1 and BCRP Substrate in Multidrug-Resistant Cancers

The search for new chemotherapeutics unaffected by efflux pumps would significantly increase life expectancy in patients with malignant cancers. In this study, butylcycloheptylprodigiosin and undecylprodigiosin were HPLC-purified and verified, using nuclear magnetic resonance spectroscopy. Cell cytotoxicity and transportation kinetics on multiple-drug resistance (MDR) cells were evaluated. Daunorubicin and butylcycloheptylprodigiosin were less toxic in the MDR1 overexpressing line, but undecylprodigiosin revealed potent toxicity toward MDR1 and BCRP expressing malignant cells. There was no noticeable change in MDR1 and BCRP transcripts during 3 days of treatment with prodiginines. While daunorubicin and mitoxantrone uptake from the cell environment significantly decreased with increasing multidrug resistance up to 46% and 62%, respectively, the accumulation of undecylprodigiosin and to a lesser extent butylcycloheptylprodigiosin in the resistance cells occurred cell- and dose-dependently via a passive diffusion process and were almost equally sensitive to the parent lines. The efflux of xenobiotics commenced immediately with different kinetics in various cells. A greater amount of daunorubicin and mitoxantrone were rapidly thrown out of their corresponding MDR cells in the absence of the specific inhibitor (3.01 and 1.81 dF/min, respectively) and represented functional efflux pumps. MDR pumps did not apparently influence undecylprodigiosin efflux patterns; but butylcycloheptylprodigiosin was partially removed from EPG85.257RDB cells at the rate of 2.66 and 1.41 dF/min in the absence and presence of verapamil, respectively.

Combinatorial design and virtual screening of potent anti-tubercular fluoroquinolone and isothiazoloquinolone compounds utilizing QSAR and pharmacophore modelling

The virulence of tuberculosis infections resistant to conventional combination drug regimens cries for the design of potent fluoroquinolone compounds to be used as second line antimycobacterial chemotherapeutics. One of the most effective in silico methods is combinatorial design and high throughput screening by a ligand-based pharmacophore prior to experiment. The combinatorial design of a series of 3850 fluoroquinolone and isothiazoloquinolone compounds was then screened virtually by applying a topological descriptor based quantitative structure activity relationship (QSAR) for predicting highly active congeneric quinolone leads against Mycobacterium fortuitum and Mycobacterium smegmatis. The predicted highly active congeneric hits were then subjected to a comparative study between existing lead sparfloxacin with fluoroquinolone FQ hits as well as ACH-702 with predicted active isothiazoloquinolones, utilizing pharmacophore modelling to focus on the mechanism of drug binding against mycobacterial DNA gyrase. Finally, 68 compounds including 34 FQ and 34 isothiazoloquinolones were screened through high throughput screening comprising QSAR, the Lipinski rule of five and ligand-based pharmacophore modelling.

QSAR modeling in ecotoxicological risk assessment: application to the prediction of acute contact toxicity of pesticides on bees (Apis mellifera L.)

Despite their indisputable importance around the world, the pesticides can be dangerous for a range of species of ecological importance such as honeybees (Apis mellifera L.). Thus, a particular attention should be paid to their protection, not only for their ecological importance by contributing to the maintenance of wild plant diversity, but also for their economic value as honey producers and crop-pollinating agents. For all these reasons, the environmental protection requires the resort of risk assessment of pesticides. The goal of this work was therefore to develop a validated QSAR model to predict contact acute toxicity (LD₅₀) of 111 pesticides to bees because the QSAR models devoted to this species are very scarce. The analysis of the statistical parameters of this model and those published in the literature shows that our model is more efficient. The QSAR model was assessed according to the OECD principles for the validation of QSAR models. The calculated values for the internal and external validation statistic parameters (Q ² and [Formula: see text] are greater than 0.85. In addition to this validation, a mathematical equation derived from the ANN model was used to predict the LD₅₀ of 20 other pesticides. A good correlation between predicted and experimental values was found (R ² = 0.97 and RMSE = 0.14). As a result, this equation could be a means of predicting the toxicity of new pesticides.

QSAR modeling and chemical space analysis of antimalarial compounds

Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones.

In-silico combinatorial design and pharmacophore modeling of potent antimalarial 4-anilinoquinolines utilizing QSAR and computed descriptors

There are very few studies for combinatorial library design and high throughput screening of 4-anilinoquinoline antimalarial compounds having activities against parasitic strain of P. falciparum. Therefore, an attempt has been made in the present paper to design potent lead compounds in this congener utilizing quantitative structure activity relationship utilizing theoretical molecular descriptors. QSAR models for a series of 4-anilinoquinolines considering various theoretical molecular descriptors including topological, constitutional, geometrical, functional group and atom-centered fragments has been carried out by stepwise forward–backward variable selections assimilating multiple linear regression (MLR) methods showing the topological indices contribute maximum impact on parasitic P. falciparum strain. A combinatorial library of 2160 compounds has been generated and finally, 16 compounds were screened through high throughput screening as promising 4-anilinoquinoline antimalarial hits based on their predicted activities utilizing topological descriptor based validated QSAR model. Highly predicted active compounds were then undergone for pharmacophore modeling to predict mode of binding and to optimize leads having greater affinity towards malarial P. falciparum parasitic strain.