Application of ALOGPS to predict 1‐octanol/water distribution coefficients, logP, and logD, of AstraZeneca in‐house database

The Interaction Mechanism of Picolinamide Fungicide Targeting on the Cytochrome bc1 Complex and Its Structural Modification

Picolinamide fungicides, structurally related to UK-2A and antimycin-A, bind into the Qi-site in the bc1 complex. However, the detailed binding mode of picolinamide fungicides remains unknown. In the present study, antimycin-A and UK-2A were selected to study the binding mode of picolinamide inhibitors with four protonation states in the Qi-site by integrating molecular dynamics simulation, molecular docking, and molecular mechanics Generalized Born surface area (MM/GBSA) calculations. Subsequently, a series of new picolinamide derivatives were designed and synthesized to further understand the effects of substituents on the tail phenyl ring. The computational results indicated that the substituted aromatic rings in antimycin-A and UK-2A were the pharmacophore fragments and made the primary contribution when bound to a protein. Compound 9g-hydrolysis formed H-bonds with Hie201 and Ash228 and showed an IC50 value of 6.05 ± 0.24 μM against the porcine bc1 complex. Compound 9c, with a simpler chemical structure, showed higher control effects than florylpicoxamid against cucumber downy mildew and expanded the fungicidal spectrum of picolinamide fungicides. The structural and mechanistic insights obtained from the present study will provide a valuable clue for the future designing of new promising Qi-site inhibitors.

Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state

In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute-solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute-solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ ([Formula: see text]) and mixing the artificially liquid solute into the solvent ([Formula: see text]). In this approach [Formula: see text] is predicted using machine learning models, and the [Formula: see text] is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMOtherm software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMOquick calculations with only marginal reduction in the quality of predicted solubility.

Personalised modelling of clinical heterogeneity between medium-chain acyl-CoA dehydrogenase patients

BACKGROUND

Monogenetic inborn errors of metabolism cause a wide phenotypic heterogeneity that may even differ between family members carrying the same genetic variant. Computational modelling of metabolic networks may identify putative sources of this inter-patient heterogeneity. Here, we mainly focus on medium-chain acyl-CoA dehydrogenase deficiency (MCADD), the most common inborn error of the mitochondrial fatty acid oxidation (mFAO). It is an enigma why some MCADD patients-if untreated-are at risk to develop severe metabolic decompensations, whereas others remain asymptomatic throughout life. We hypothesised that an ability to maintain an increased free mitochondrial CoA (CoASH) and pathway flux might distinguish asymptomatic from symptomatic patients.

RESULTS

We built and experimentally validated, for the first time, a kinetic model of the human liver mFAO. Metabolites were partitioned according to their water solubility between the bulk aqueous matrix and the inner membrane. Enzymes are also either membrane-bound or in the matrix. This metabolite partitioning is a novel model attribute and improved predictions. MCADD substantially reduced pathway flux and CoASH, the latter due to the sequestration of CoA as medium-chain acyl-CoA esters. Analysis of urine from MCADD patients obtained during a metabolic decompensation showed an accumulation of medium- and short-chain acylcarnitines, just like the acyl-CoA pool in the MCADD model. The model suggested some rescues that increased flux and CoASH, notably increasing short-chain acyl-CoA dehydrogenase (SCAD) levels. Proteome analysis of MCADD patient-derived fibroblasts indeed revealed elevated levels of SCAD in a patient with a clinically asymptomatic state. This is a rescue for MCADD that has not been explored before. Personalised models based on these proteomics data confirmed an increased pathway flux and CoASH in the model of an asymptomatic patient compared to those of symptomatic MCADD patients.

CONCLUSIONS

We present a detailed, validated kinetic model of mFAO in human liver, with solubility-dependent metabolite partitioning. Personalised modelling of individual patients provides a novel explanation for phenotypic heterogeneity among MCADD patients. Further development of personalised metabolic models is a promising direction to improve individualised risk assessment, management and monitoring for inborn errors of metabolism.

Development of an embedded molecular structure-based model for prediction of micropollutant treatability in a drinking water treatment plant by machine learning from three years monitoring data

In this study, an autoencoder-based molecular structure embedding model was developed to predict treatability of micropollutant in a drinking water treatment plant (DWTP) by machine learning using 69 micropollutants monitoring data at 18 DWTPs for three years. The molecular structure, which contains physicochemical characteristics, was embedded as a fixed-length vector that is advantageous for data-driven analysis and machine learning. First, the molecular structure of the micropollutants was converted to a sequence of tokens using the simplified molecular-input line-entry system (SMILES) pair encoding tokenizer, a frequency-based tokenization method. It was then compressed into fixed-length vectors using an autoencoder trained on various molecular structures within the Chemical Entities of Biological Interest. To validate the proposed models, a binary classification of micropollutant treatability was performed using the embedded molecular structure of micropollutants with various external features, such as concentration, season, and the presence of specific drinking water treatment processes by machine learning. The accuracy of the developed model for the 69 micropollutants in this study was 0.86, and the molecular structure was determined to be the most important feature. Furthermore, an accuracy of 0.71 was obtained in external validation for pharmaceuticals and personal care products that were not used for training. This shows that the proposed embedding vector can be generalized to unseen molecules during the training process, which means that it reflects the characteristics of the molecular structures.

APPLICATION OF MICELLAR LIQUID CHROMATOGRAPHY TO MODEL ECOTOXICITY OF PESTICIDES. COMPARISON WITH IMMOBILIZED ARTIFICIAL MEMBRANE CHROMATOGRAPHY AND N-OCTANOL-WATER PARTITIONING

The potential of Micellar Liquid Chromatography (MLC) to model ecotoxicological endpoints for a series of pesticides was investigated. To exploit the flexibility in MLC conditions, different surfactants were employed and retention mechanism was tracked and compared to Immobilized Artificial Membrane (IAM) chromatographic retention and n-octanol- water partitioning, logP. Neutral polyoxyethylene (23) lauryl ether (Brij-35), anionic sodium dodecyl sulfate (SDS) and cationic cetyltrimethylammonium bromide (CTAB) were used in presence of PBS at pH=7.40 and acetonitrile as organic modifier when necessary. Similarities/ dissimilarities between MLC retention and IAM or logP were investigated by Principal Component Analysis (PCA) and Liner Solvation Energy Relationships (LSER). LSER revealed that hydrogen bonding acidity is the most important factor for differentiation between MLC and IAM or logP. The impact of hydrogen bonding is exemplified in the relationships of MLC retention factors with IAM or logP, which necessitate the inclusion of a relevant descriptor. PCA further revealed that MLC retention factors are clustered together with IAM indices and logP within a broader ellipse formed by ecotoxicological endpoints, involving LC₅₀/ EC₅₀ values of six aquatic organisms namely Rainbow Trout, Fathead Minnow, Bluegill Sunfish, Sheepshead Minnow, Eastern Oyster and Water Flea as well as LD₅₀ values of Honey Bee, thus justifying their use to construct relevant models. Satisfactory specific models for individual organisms, as well as general fish models, were obtained, in most cases, upon combination of MLC retention factors with Molecular Weight (MW) or/ and hydrogen bond parameters. All models were evaluated and compared to previously reported IAM and logP based models using an external validation data set. Predictions with Brij-35 and SDS based models were comparable, although slightly inferior than those obtained with IAM, while they were in all cases better than those obtained with logP. CTAB led to a satisfactory prediction model for Honey Bee, but it was found less suitable for aquatic organisms.

Design, synthesis and anti-hypoxia activity of HPN derivatives containing lipophilic long chains

OBJECTIVE

To design and synthesize long-chain substituted 2-[(4'-hydroxyethoxy) phenyl]-4,4,5,5-tetramethyl-2-imidazoline-1-oxyl 3-oxide (HPN) derivatives with enhanced anti-hypoxic activity.

METHODS

HPN derivatives 1, 3, 5 containing lipophilic long chains were synthesized via the alkylation of HPN with 6-bromohexan-1-ol, ethyl 6-bromohexanoate or 6-bromohexane, respectively using acetonitrile as the solvent and K ₂CO ₃ as the acid-binding agent at 60 ℃. Derivative 2 was synthesized via hydrolysis reactions of derivative 1 in the NaOH/CH ₃OH/H ₂O system. Using dichloromethane as the solvent and N, N'-diisopropylcarbodiimide as the dehydrating agent, HPN underwent esterification with hexanoic acid to obtain derivative 4. The structures of derivatives 1-5 were characterized by infrared spectroscopy, electron paramagnetic resonance and high resolution mass spectrometry. The purities of derivatives were detected by high performance liquid chromatography, and the lipid solubilities of derivatives were evaluated by calculating the oil-water partition coefficients (log P). Anti-hypoxia activities of HPN and its long-chain lipophilic derivatives 1-5 were evaluated using normobaric hypoxia test and acute decompression hypoxia test.

RESULTS

The structures of the derivatives were confirmed by infrared spectroscopy, electron paramagnetic resonance and high resolution mass spectroscopy. The yields of target derivatives were all above 92%, and the purities were all above 96%. The log P values of derivatives 1-5 were 2.78, 2.00, 2.04, 2.88 and 3.10, which were higher than that of HPN (0.97). Derivatives 1-5 significantly prolonged the survival time of mice at the dose of 0.3 mmol/kg in normobaric hypoxic test and reduced the mortality rate of acute decompression hypoxic mice to 60%, 70%, 60%, 70% and 40%, respectively.

CONCLUSIONS

The synthesis of derivatives 1-5 is convenient, and the yield is high. The synthesized derivatives especially derivative 5 show anti-hypoxic activity similar to or better than HPN at lower doses.

Network analysis for elucidating the mechanisms of Shenfu injection in preventing and treating COVID-19 combined with heart failure

Background

The emergence of the novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to millions of infections and is exerting an unprecedented impact on society and economies worldwide. The evidence showed that heart failure (HF) is a clinical syndrome that could be encountered at different stages during the progression of COVID-19. Shenfu injection (SFI), a traditional Chinese medicine (TCM) formula has been widely used for heart failure therapy in China and was suggested to treat critical COVID-19 cases based on the guideline for diagnosis and treatment of COVID-19 (the 7th version) issued by National Health Commission of the People's Republic of China. However, the active components, potential targets, related pathways, and underlying pharmacology mechanism of SFI against COVID-19 combined with HF remain vague.

Objective

To investigate the effectiveness and possible pharmacological mechanism of SFI for the prevention and treatment of COVID-19 combined with HF.

Methods

In the current study, a network analysis approach integrating active compound screening (drug-likeness, lipophilicity, and aqueous solubility models), target fishing (Traditional Chinese Medicine Systems Pharmacology, fingerprint-based Similarity Ensemble Approach, and PharmMapper databases), compound-target-disease network construction (Cytoscape software), protein-protein interaction network construction (STRING and Cytoscape software), biological process analysis (STRING and Cytoscape plug-in Clue GO) and pathway analysis (Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis) was developed to decipher the active ingredients, potential targets, relevant pathways, and the therapeutic mechanisms of SFI for preventing and treating COVID-19 combined with HF.

Results

Finally, 20 active compounds (DL ≥ 0.18, 1≤Alog P ≤ 5, and −5≤LogS ≤ −1) and 164 relevant targets of SFI were identified related to the development of COVID-19 combined with HF, which were mainly involved in three biological processes including metabolic, hemostasis, and cytokine signaling in immune system. The C-T-D network and reactome pathway analysis indicated that SFI probably regulated the pathological processes of heart failure, respiratory failure, lung injury, and inflammatory response in patients with COVID-19 combined with HF through acting on several targets and pathways. Moreover, the venn diagram was used to identify 54 overlapped targets of SFI, COVID-19, and HF. KEGG pathway enrichment analysis showed that 54 overlapped targets were highly enriched to several COVID-19 and HF related pathways, such as IL-17 signaling pathway, Th17 cell differentiation, and NF-kappa B signaling pathway.

Conclusions

A comprehensive network analysis approach framework was developed to systematically elucidate the potential pharmacological mechanism of SFI for the prevention and treatment of SFI against COVID-19 combined with HF. The current study may not only provide in-depth understanding of the pharmacological mechanisms of SFI, but also a scientific basis for the application of SFI against COVID-19 combined with HF.

A High-Throughput LC-MS/MS Method for the Simultaneous Quantification of Twenty-Seven Drug Molecules in Ocular Tissues

The purpose of this study was to develop a validated LC-MS/MS analytical method for the simultaneous analysis of a large cassette containing a wide range of drug substances with positive, negative, or neutral charge and further apply the method to assess octanol partition coefficient and eye tissue recovery of the drug cassette. A twenty-seven-drug cassette (N-in-one) including beta blockers, NSAIDs, and corticosteroids that range from extremely hydrophilic (sotalol) to very hydrophobic (triamcinolone hexacetanide) was used to develop an LC-MS/MS assay using QTrap 4500. An LC-MS/MS method based on gradient elution, with an eighteen-minute run time including equilibration time, was developed and validated for the rapid and simultaneous quantitation of drugs with a wide range of lipophilicities. Scheduled multiple reaction monitoring was used to maximize the scan time for each peak, ensuring sufficient scans. Method validation included lower limit of quantitation (LLOQ) and intra- and inter-day reproducibility. The LLOQ ranged from 0.5 (sotalol) to 40 fmols (dexamethasone) on column with a %RSD < 20%. The method was tested by measuring octanol:water and octanol:buffer (PBS, pH 7.4) partition coefficients and by quantitation of the drug cassette extracted from rabbit aqueous humor and cornea. Measured partition coefficients correlated positively with predicted values (r²=0.5-0.7). Drug recovery was ≥ 79% from aqueous humor and between 61 and 67% on average from cornea. A rapid, sensitive LC-MS/MS method suitable for N-in-one drug delivery screening was developed for simultaneous quantification of twenty-seven drugs in aqueous solutions and eye tissues.

Improved Parameterization of Phosphatidylinositide Lipid Headgroups for the Martini 3 Coarse-Grain Force Field

Phosphoinositides are a family of membrane phospholipids that play crucial roles in membrane regulatory events. As such, these lipids are often a key part of molecular dynamics simulation studies of biological membranes, in particular of those employing coarse-grain models because of the potential long times and sizes of the involved membrane processes. Version 3 of the widely used Martini coarse-grain force field has been recently published, greatly refining many aspects of biomolecular interactions. In order to properly use it for lipid membrane simulations with phosphoinositides, we put forth the Martini 3-specific parameterization of inositol, phosphatidylinositol, and seven physiologically relevant phosphorylated derivatives of phosphatidylinositol. Compared to parameterizations for earlier Martini versions, focus was put on a more accurate reproduction of the behavior seen in both atomistic simulations and experimental studies, including the signaling-relevant phosphoinositide interaction with divalent cations. The models that we develop improve upon the conformational dynamics of phosphoinositides in the Martini force field and provide stable topologies at typical Martini time steps. They are able to reproduce experimentally known protein-binding poses as well as phosphoinositide aggregation tendencies. The latter was tested both in the presence and absence of calcium and included correct behavior of PI(4,5)P₂ calcium-induced clusters, which can be of relevance for regulation.

Quantitative Electrophysiological Evaluation of the Analgesic Efficacy of Two Lappaconitine Derivatives: A Window into Antinociceptive Drug Mechanisms

Quantitative evaluation of analgesic efficacy improves understanding of the antinociceptive mechanisms of new analgesics and provides important guidance for their development. Lappaconitine (LA), a potent analgesic drug extracted from the root of natural Aconitum species, has been clinically used for years because of its effective analgesic and non-addictive properties. However, being limited to ethological experiments, previous studies have mainly investigated the analgesic effect of LA at the behavioral level, and the associated antinociceptive mechanisms are still unclear. In this study, electrocorticogram (ECoG) technology was used to investigate the analgesic effects of two homologous derivatives of LA, Lappaconitine hydrobromide (LAH) and Lappaconitine trifluoroacetate (LAF), on Sprague-Dawley rats subjected to nociceptive laser stimuli, and to further explore their antinociceptive mechanisms. We found that both LAH and LAF were effective in reducing pain, as manifested in the remarkable reduction of nocifensive behaviors and laser-evoked potentials (LEPs) amplitudes (N2 and P2 waves, and gamma-band oscillations), and significantly prolonged latencies of the LEP-N2/P2. These changes in LEPs reflect the similar antinociceptive mechanism of LAF and LAH, i.e., inhibition of the fast signaling pathways. In addition, there were no changes in the auditory-evoked potential (AEP-N1 component) before and after LAF or LAH treatment, suggesting that neither drug had a central anesthetic effect. Importantly, compared with LAH, LAF was superior in its effects on the magnitudes of gamma-band oscillations and the resting-state spectra, which may be associated with their differences in the octanol/water partition coefficient, degree of dissociation, toxicity, and glycine receptor regulation. Altogether, jointly applying nociceptive laser stimuli and ECoG recordings in rats, we provide solid neural evidence for the analgesic efficacy and antinociceptive mechanisms of derivatives of LA.

Continuous and automated slug flow nanoextraction for rapid partition coefficient measurement

Octanol-water partition coefficients (log Kow) are widely used in pharmaceutical and environmental chemistry to assess the lipophilicity of compounds. Traditionally log Kow is determined using a shake-flask method that uses milliliters of sample and solvent and requires hours for preparation, extraction, and analysis. Here, we report an automated system for rapid log Kow determination for an array of compounds using slug flow nanoextraction (SFNE) enabled by a microfluidic chip. In the method, an autosampler is used to introduce 1 μL of sample into a microfluidic device that segments the injected volume into a series of 4 nL slugs that are each paired to an adjacent octanol slug. Each octanol-water phase pair is compartmentalized by an immiscible fluorous carrier fluid. During flow, rapid extraction occurs at each octanol-water interface. The resulting linear array of slugs flows into an online UV absorbance detector that is used to determine concentrations in the phases, allowing the log Kow to be measured. The microfluidic device allows toggling between two-phase "aqueous plug" generation (aqueous sample separated by fluorous carrier fluid) and three-phase "phase pair" generation. In this way, online calibration for detection in the aqueous phase can be achieved. The method is applied to determining log Kow for a panel of seven pharmaceutical compounds, including complete calibration curves, at three different pHs in under 2 h using 5 μL of extraction standard and 2.9 μL of octanol per extraction standard analyzed.

Exploring the octanol-water partition coefficient dataset using deep learning techniques and data augmentation

Today more and more data are freely available. Based on these big datasets deep neural networks (DNNs) rapidly gain relevance in computational chemistry. Here, we explore the potential of DNNs to predict chemical properties from chemical structures. We have selected the octanol-water partition coefficient (log P) as an example, which plays an essential role in environmental chemistry and toxicology but also in chemical analysis. The predictive performance of the developed DNN is good with an rmse of 0.47 log units in the test dataset and an rmse of 0.33 for an external dataset from the SAMPL6 challenge. To this end, we trained the DNN using data augmentation considering all potential tautomeric forms of the chemicals. We further demonstrate how DNN models can help in the curation of the log P dataset by identifying potential errors, and address limitations of the dataset itself.

Algebraic graph-assisted bidirectional transformers for molecular property prediction

The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although some machine learning models, such as bidirectional encoder from transformer, can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy, it neglects three-dimensional (3D) stereochemical information. Algebraic graph, specifically, element-specific multiscale weighted colored algebraic graph, embeds complementary 3D molecular information into graph invariants. We propose an algebraic graph-assisted bidirectional transformer (AGBT) framework by fusing representations generated by algebraic graph and bidirectional transformer, as well as a variety of machine learning algorithms, including decision trees, multitask learning, and deep neural networks. We validate the proposed AGBT framework on eight molecular datasets, involving quantitative toxicity, physical chemistry, and physiology datasets. Extensive numerical experiments have shown that AGBT is a state-of-the-art framework for molecular property prediction.

Systems Pharmacology Approach and Experiment Evaluation Reveal Multidimensional Treatment Strategy of LiangXueJieDu Formula for Psoriasis

Clinical studies have demonstrated the anti-psoriatic effect of the LiangXueJieDu (LXJD) herbal formula. However, the systemic mechanism and the targets of the LXJD formula have not yet been elucidated. In the present study, a systems pharmacology approach, metabolomics, and experimental evaluation were employed. First, by systematic absorption-distribution-metabolism-excretion (ADME) analysis, 144 active compounds with satisfactory pharmacokinetic properties were identified from 12 herbs of LXJD formula using the TCMSP database. These active compounds could be linked to 125 target proteins involved in the pathological processes underlying psoriasis. Then, the networks constituting the active compounds, targets, and diseases were constructed to decipher the pharmacological actions of this formula, indicating its curative effects in psoriasis treatment and related complications. The psoriasis-related pathway comprising several regulatory modules demonstrated the synergistic mechanisms of LXJD formula. Furthermore, the therapeutic effect of LXJD formula was validated in a psoriasis-like mouse model. Consistent with the systems pharmacology analysis, LXJD formula ameliorated IMQ-induced psoriasis-like lesions in mice, inhibited keratinocyte proliferation, improved keratinocyte differentiation, and suppressed the infiltration of CD3+ T cells. Compared to the model group, LXJD formula treatment remarkably reduced the expression of inflammatory cytokines and factors, such as IL-1β, IL-6, TNF-α, Cox2, and inhibited the phosphorylation of p-P65, p-IқB, p-ERK, p-P38, p-PI3K, p-AKT, indicating that LXJD formula exerts its therapeutic effect by inhibiting the MAPK, PI3K/AKT, and NF-қB signaling pathways. The metabolic changes in the serum of psoriasis patients were evaluated by liquid chromatography coupled with orbitrap mass spectrometry (LC-MS). The LXJD formula improved two perturbed metabolic pathways of glycerophospholipid metabolism and steroid hormone biosynthesis. Overall, this study revealed the complicated anti-psoriatic mechanism of LXJD formula and also offered a reliable strategy to elucidate the complex therapeutic mechanism of this Chinese herbal formula in psoriasis from a holistic perspective.

Quercetin Inhibited the Formation of Lipid Oxidation Products in Thermally Treated Soybean Oil by Trapping Intermediates

In this research, we studied the inhibitory mechanism of quercetin, one popular phenolic compound, against aldehyde formation in thermally treated soybean oil. It was found that quercetin reduced unsaturated aldehyde formation significantly, with the inhibitory effect decreased with the extension of the heating time. Meanwhile, quercetin had minimum effects on the fatty acid profile compared to untreated samples. Some new phenolic derivatives were formed in thermally treated soybean oil with quercetin, further analyzed by liquid chromatography-tandem mass spectrometry, and compared to newly synthesized derivatives (characterized by mass spectrometry and nuclear magnetic resonance spectroscopy). On the basis of their chemical structures, we proposed that quercetin reacted with 13-oxo-octadecadienoic acid, 10-oxo-hexadecenoic acid, and 10-oxo-octadecenoic acid formed from peroxidation of linoleic acid, palmitoleic acid, and oleic acid, respectively, to inhibit aldehyde formation. In addition, newly formed quercetin-3-O-hexanoate, quercetin-3-O-heptanoate, and quercetin-3-O-nonanoate showed weaker 2,2-diphenyl-1-picrylhydrazyl and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt radical cation scavenging activity and weaker antioxidant activity in soybean oil, which explained the decreased inhibitory activity of quercetin against aldehyde formation during heat treatment. More interesting, quercetin-3-O-hexanoate showed improved cellular antioxidant activity compared to the parent quercetin. Overall, quercetin inhibited the formation of lipid oxidation products in thermally treated soybean oil by reacting with early intermediates in the lipid oxidation reaction, and quercetin derivatives formed in the process could be with enhanced cellular antioxidant activity. Our results provide novel insight into the inhibitory mechanism of quercetin against the formation of lipid oxidation products.

Antioxidative Properties and Chemical Changes of Quercetin in Fish Oil: Quercetin Reacts with Free Fatty Acids to Form Its Ester Derivatives

In this research, we studied the antioxidative properties and chemical changes of quercetin in fish oil during accelerated storage at 60 °C for 5 days. Gas chromatography (GC) analysis showed that quercetin inhibited aldehyde formation and unsaturated fatty acid oxidation in fish oil significantly; however, the inhibitory effects decreased gradually with prolonged heating time. Moreover, quercetin was consumed with increasing heating time. Some new phenolic derivatives were discovered in the fish oil with quercetin, with their structures fully elucidated by LC-MS/MS and comparison with newly synthesized ones (characterized by MS and NMR spectroscopy). Based on their chemical structures, we proposed that quercetin reacted with EPA and DHA to form the corresponding quercetin fatty acid esters in fish oil. In addition, the newly formed quercetin-3-O-eicosapentaenoate and quercetin-3-O-docosahexaenoate showed weaker DPPH and ABTS radical cation scavenging activity but much improved lipophilicity, higher cell membrane affinity, and hence enhanced cellular antioxidant activity compared with the parent quercetin. Overall, quercetin could be used as a safe dietary polyphenol to inhibit lipid oxidation. The newly formed quercetin-polyunsaturated fatty acid esters may render improved bioactivity to humans, which needs further investigation.

How Far Are We from the Rapid Prediction of Drug Resistance Arising Due to Kinase Mutations?

In all living organisms, protein kinases regulate various cell signaling events through phosphorylation. The phosphorylation occurs upon transferring an ATP's terminal phosphate to a target residue. Because of the central role of protein kinases in several proliferative pathways, point mutations occurring within the kinase's ATP-binding site can lead to a constitutively active enzyme, and ultimately, to cancer. A select set of these point mutations can also make the enzyme drug resistant toward the available kinase inhibitors. Because of technical and economical limitations, rapid experimental exploration of the impact of these mutations remains to be a challenge. This underscores the importance of kinase-ligand binding affinity prediction tools that are poised to measure the efficacy of inhibitors in the presence of kinase mutations. To this end, here, we compare the performances of six web-based scoring tools (DSX-ONLINE, KDEEP, HADDOCK2.2, PDBePISA, Pose&Rank, and PRODIGY-LIG) in assessing the impact of kinase mutations on their interactions with their inhibitors. This assessment is carried out on a new structure-based BINDKIN benchmark we compiled. BINDKIN contains wild-type and mutant structure pairs of kinase-inhibitor complexes, together with their corresponding experimental binding affinities (in the form of IC₅₀, K _d, and K _i). The performance of various web servers over BINDKIN shows that they cannot predict the binding affinities (ΔGs) of wild-type and mutant cases directly. Still, they could catch whether a mutation improves or worsens the ligand binding (ΔΔGs) where the highest Pearson's R correlation coefficient is reached by DSX-ONLINE over the K _i dataset. When homology models are used instead of K _i-associated crystal structures, DSX-ONLINE loses its predictive capacity. These results highlight that there is room to improve the available scoring functions to estimate the impact of protein kinase point mutations on inhibitor binding. The BINDKIN benchmark with all related results is freely accessible online (https://github.com/CSB-KaracaLab/BINDKIN).

Synthesis, Stability, and Antidiabetic Activity Evaluation of (-)-Epigallocatechin Gallate (EGCG) Palmitate Derived from Natural Tea Polyphenols

This work describes a novel approach for the synthesis of (−)-epigallocatechin gallate (EGCG) palmitate by a chemical-synthesis method, where the elevated stability of the EGCG derivative is achieved. Various parameters affecting the acylation process, such as the base, solvent, as well as the molar ratio of palmitoyl chloride, have been studied to optimize the acylation procedure. The optimized reaction condition was set as follows: EGCG/palmitoyl chloride/sodium acetate was under a molar ratio of 1:2:2, with acetone as the solvent, and the reaction temperature was 40 °C. Under the optimized condition, the yield reached 90.6%. The EGCG palmitate (PEGCG) was isolated and identified as 4′-O-palmitoyl EGCG. Moreover, the stability of PEGCG under different conditions was proved significantly superior to EGCG. Finally, PEGCG showed better inhibition towards α-amylase and α-glucosidase, which was 4.5 and 52 times of EGCG, respectively. Molecular docking simulations confirmed the in vitro assay results. This study set a novel and practical synthetic approach for the derivatization of EGCG, and suggest that PEGCG may act as an antidiabetic agent.

Systematically altering the lipophilicity of rhenium(I) tricarbonyl anticancer agents to tune the rate at which they induce cell death

Rhenium-based anticancer agents have arisen as promising alternatives to conventional platinum-based drugs. Based on previous studies demonstrating how increasing lipophilicity improves drug uptake within the cell, we sought to investigate the effects of lipophilicity on the anticancer activity of a series of six rhenium(i) tricarbonyl complexes. These six rhenium(i) tricarbonyl structures, called Re-Chains, bear pyridyl imine ligands with different alkyl chains ranging in length from two to twelve carbons. The cytotoxicities of these compounds were measured in HeLa cells. At long timepoints (48 h), all compounds are equally cytotoxic. At shorter time points, however, the compounds with longer alkyl chains are significantly more active than those with smaller chains. Cellular uptake studies of these compounds show that they are taken up via both passive and active pathways. Collectively, these studies show how lipophilicity affects the rate at which these Re compounds induce their biological activities.

Metabolic Checkpoint Aldehyde Dehydrogenases Are Important for Diverting β-Oxidation into 1-Butanol Biosynthesis from Kitchen Waste Oil in Pseudomonas aeruginosa

1-Butanol (1-BD) is a promising fuel additive which can be biosynthesized via reversed β-oxidation pathway in bacteria. However, heterologous reversed β-oxidation pathway is a carbon chain prolongation process with several genes overexpressed in most of bacterial hosts, leading to low titer of 1-BD and high cost for production. Here we displayed a forward β-oxidation pathway for 1-BD production in a kitchen waste oil (KWO) degrading Pseudomonas aeruginosa PA-3, and we proved that aldehyde dehydrogenase (ALDH) is a checkpoint for diverting metabolic flux into 1-BD biosynthesis. With nitrogen source supplied, titer of 1-BD was increased accompanied with 12 ALDH coding genes transcriptionally promoted to different degrees. At the same time, binding energies of these ALDHs with different length of acyl-CoAs in β-oxidation were calculated to identify their specificities. Based on the above information, ALDH deletions were conducted. We certified that deletion of ALDH8 and ALDH9 led to significant decreased titers of 1-BD. Finally, these two ALDHs were separately overexpressed in PA-3, and titer of 1-BD was promoted to 1.36 g/L at 72 h in shake flask. Totally in this work, we provided a forward β-oxidation pathway for 1-BD production from KWO, and the roles of ALDHs were confirmed.

A system-level investigation into the pharmacological mechanisms of flavor compounds in liquor

Maotai-flavor liquor, one of the world's most famous natural fermentation food products plays a significant role in traditional Chinese culture and people's daily lives for a long time due to its good taste. However, the pharmacological activities of flavor compounds in Maotai liquor have not been fully elucidated. In answering this question, a system-level analysis was developed by combining in silico ADME evaluation, multi-target prediction, GO enrichment analysis, network pharmacology technology, pathway analysis, as well as experimental verification to elucidate the pharmacological effects of flavor compounds in Maotai liquor. Finally, 55 active compounds and 80 targets were identified to interpret the pharmacological effect of the flavor compounds. Moreover, the key active compounds were verified by in vitro experiments to validate the reliability of our approaches. Our study provides a novel integrated strategy to comprehensively understand the pharmacological activities of complex components in Maotai liquor. PRACTICAL APPLICATIONS: We proposed an integrative strategy by systems pharmacology to investigate the potential active compounds and their related targets, as well as to understand the potential pharmacological mechanism of flavor compounds in Maotai liquor. The present work will not only shed light on the mechanism of active compounds in Maotai liquor at the system level, but also provide a novel approach for discovery of the active compounds that may benefit human health.

Annular oxygenation and rearrangement products of cryptotanshinone by biotransformation with marine-derived fungi Cochliobolus lunatus and Aspergillus terreus

Structural modification of natural products by biotransformation with fungi is an attractive tool to obtain novel bioactive derivatives. In the present study, cryptotanshinone (1), a quinoid abietane diterpene from traditional Chinese medicine Salvia miltiorrhiza (Danshen), was transformed by two marine-derived fungi. By using Cochliobolus lunatus TA26-46, one new oxygenated and rearranged product (2), containing a 5,6-dihydropyrano[4,3-b]chromene moiety, together with one known metabolite (10), were obtained from the converted broth of cryptotanshinone (1) with the isolated yields of 1.0% and 2.1%, respectively. While, under the action of Aspergillus terreus RA2905, seven new transformation products (3-9) as well as 10 with the fragments of 2-methylpropan-1-ol and oxygenated p-benzoquinone were produced and obtained with the isolated yields of 0.1%-1.3%. The structures of the new compounds were elucidated by comprehensive spectroscopic analysis including High Resolution Electrospray Ionization Mass Spectroscopy (HRESIMS), Nuclear Magnetic Resonance (NMR) and Electronic Circular Dichroism (ECD). The metabolic pathways of cryptotanshinone by these two fungi were presumed to be the opening and rearrangement of furan ring, and/or oxygenation of cyclohexane ring. Cryptotanshinone (1) and its metabolites displayed anti-inflammatory activities against NO production in LPS-stimulated BV-2 cells and antibacterial activities towards methicillin-resistant Staphylococcus aureus. These findings revealed the potential of marine fungi to transform the structures of natural products by biotransformation.

Morphological and Semi-empirical Study of the Pluronic F68/Imogolite/Sudan III Intersurfaces Composite for the Controlled Temperature Release of Hydrophobic Drugs

Some PluronicF68 (F68) triblock copolymer properties demonstrate surprising applications in selective drug administration, such as the transportation of hydrophobic anti-inflammatories through epithelial barriers. Nuclear magnetic resonance (1H-NMR) spectroscopy was carried out for micelle precursor dispersions and F68 films modified with a synthetic imogolite (IMO) biocompatible hydrogel. Theoretical calculations and morphological assessment for the process of morphogenesis of dendritic crystallization were performed by molecular docking and atomic force microscopy (AFM) of the Sudan III-IMO-F68 composite, which was more hydrophobic than Sudan III-F68 and carried out the prolonged release of the Sudan III "drug" captured by a water-octanol interface determined by standard absorbance. Surface fusions were measured and compared to the unmodified matrix. However, despite the superior properties of the composite, the critical micelle concentration (CMC) was practically unmodified because solitary IMO strands attached to Sudan III formed Sudan III-IMO. These strands unraveled in a stable manner by expanding like a "spiderweb" in hydrophilic interfaces according to NMR analysis of the hydrogen one H1 polarization of Sudan III and F68 methyl, whose correlation relates hydrophobicity of Sudan III-IMO-F68 with dendrite properties from F68 concentrations. CMC and surface fusions equivalent to F68 surface properties, calculated by differential scanning calorimetry and dynamic Raman spectroscopy, were determined by AFM and high-resolution ellipsometry. Our results show highly specialized pharmacological applications since micelle surfaces expand, triggering maximum deliveries of "Drugs" from its interior to the physiological environment. The implanted sensor prototype determined equilibria reached Sudan III according to temperature (32-50 °C) and time it took to cross the membrane model 1-octanol (48 h). The findings suggest that the targested design of a F68-IMO-"Drug" would function as a microdevice for the prolonged release of hydrophobic drugs. In addition, the said microdevice could regenerate the damaged tissue in the central nervous system or other organs of the body. This is due to the fact that it could perform both tasks simultaneously, given the properties and characteristics acquired by the compatible material depending on the temperature of the physiological environment.

[Conjugation to Branched Glycerol Oligomers, a Novel Strategy for Extremely Hydrophobic Agents]

Ascertaining the absorption, distribution, metabolism, and excretion (ADME) profile of drugs is one of the most crucial factors in the process of drug discovery. Since it is important to combine water solubility and cell permeability within the compound to achieve the desired ADME properties, an appropriate balance between lipophilicity and hydrophilicity is required. It is often necessary to facilitate hydrophilicity of very hydrophobic candidates, because quite lipophobic molecules are rarely hit as positive in molecular-targeted or cell-based screenings. For that purpose, it has been popular to conjugate hydrophobic molecules with polyethylene glycol (PEG). However, PEG is a polymer, and PEG-conjugated molecules are not uniform. Besides, the dosage should be much increased compared with the original molecule due to the increase in molecular weight. Therefore we have been developing alternative ways to endow hydrophobic compounds with extra hydrophilicity by conjugating with symmetrically branched glycerol oligomers. This technology is versatile and easily applicable to various hydrophobic compounds. Water-solubility of fenofibrate, one of the most hydrophobic medicines in clinical use, was facilitated by a factor of more than 2000, and its lipid-lowering effect in vivo improved more than ten-fold, by simply conjugating with branched glycerol trimer, for instance. Here we will briefly introduce the basic concepts and our successful experiences of applying branched glycerol oligomers including antitumor agents in terms of water-solubility, pharmacological effects, and pharmacokinetics, and merits and current issues will be discussed in this review.

Identification of novel potent and non-toxic anticancer, anti-angiogenic and antimetastatic rhenium complexes against colorectal carcinoma

Combination therapy targeting both tumor growth and vascularization is considered to be a cornerstone for colorectal carcinomas (CRC) treatment. However, the major obstacles of most clinical anticancer drugs are their weak selective activity towards cancer cells and inherent inner organs toxicity, accompanied with fast drug resistance development. In our effort to discover novel selective and non-toxic agents effective against CRC, we designed, synthesized and characterized a series of rhenium(I) tricarbonyl-based complexes with increased lipophilicity. Two of these novel compounds were discovered to possess remarkable anticancer, anti-angiogenic and antimetastatic activity in vivo (zebrafish-human HCT-116 xenograft model), being effective at very low doses (1-3 μM). At doses as high as 250 μM the complexes did not provoke toxicity issues encountered in clinical anticancer drugs (cardio-, hepato-, and myelotoxicity). In vivo assays showed that the two compounds exceed the anti-tumor and anti-angiogenic activity of clinical drugs cisplatin and sunitinib malate, and display a large therapeutic window.

Lappaconitine trifluoroacetate contained polyvinyl alcohol nanofibrous membranes: Characterization, biological activities and transdermal application

Lappaconitine (LA), a potent analgesic drug extracted from the root of natural aconitum species, has been clinically used for years because of its effectiveness and non-addictive properties. However, it is mainly limited in oral and intravenous administration in the form of Lappaconitine Hydrobromide (LAH). In this work, Lappaconitine trifluoroacetate (LAF), a new derivative of LA, was successfully obtained by introducing organofluorine group to LA. This new compound had a lower toxicity (LD₅₀ of 21.14 mg·kg^-1), improved analgesic effect and longer half-life (T_1/2 of 2.24 h) when compared with LAH. Moreover, in vitro transdermal permeation (J_ss of 206.82 μg·cm^-2·h^-1) of LAF was 30.54% higher than that of LAH, means that LAF can be conveniently used for transdermal drug delivery (TDD). Therefore, drug membranes with PVA solution (10 wt%) containing LAF in various amounts were fabricated by electrospinning. The in vitro release tests confirmed that up to 81.43% of LAF in the PVA/LAF nanofibrous membranes could be released in 72 h, accompanied by significant analgesic effect when compared with the blank control group. In conclusion, the prepared LAF-loaded membrane is a novel formulation for the treatment of chronic and long-term pain.

Determination of the partition coefficient of isoquinoline alkaloids from Chelidonium majus by reversed phase thin layer chromatography

Determination of the partition coefficient (log P) of alkaloids by the RP-TLC method at different pH values. Distribution coefficient (log D) values were calculated. The investigated chlorides were found to be the most lipophilic of the compounds studied.

A novel ratiometric fluorescent probe based on thienocoumarin and its application for the selective detection of hypochlorite in real water samples and in vivo

A novel ratiometric fluorescent probe based on thieno[3,2-c]coumarin has been prepared for sensing hypochlorite, which could exhibit a large emission shift and a 338-fold emission ratio (I₄₇₀/I₆₄₀).

Preparation of Quercetin Esters and Their Antioxidant Activity

Quercetin, a polyphenolic compound, is widely distributed in plants and has numerous health benefits. However, its hydrophilicity can compromise its use in lipophilic systems. For this reason, quercetin was esterified with 12 different fatty acids as their acyl chlorides with varying chain lengths and degrees of unsaturation. Two monoesters (Q-3'-O-monoester and Q-3-O-monoester) and four diesters (Q-7,3'-O-diester, Q-3',4'-O-diester, Q-3,3'-O-diester, and Q-3,4'-O-diester) were the major products as was shown by HPLC-MS and ¹H-NMR data. The lipophilicity of quercetin derivatives was calculated; this was found to increase with fatty acid chain length. The antioxidant potential of quercetin and its derivatives was evaluated by using DPPH radical and ABTS radical cation scavenging activity; quercetin showed the highest radical scavenging activity among all tested samples. Despite the decrease of antioxidant activity in this study, the derivatives may show better antioxidant activity in lipophilic media and display improved absorption and bioavailability in the body once consumed.

Open-source QSAR models for pKa prediction using multiple machine learning approaches

BACKGROUND

The logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the plasma membrane. Thus, pKa affects chemical absorption, distribution, metabolism, excretion, and toxicity properties. Multiple proprietary software packages exist for the prediction of pKa, but to the best of our knowledge no free and open-source programs exist for this purpose. Using a freely available data set and three machine learning approaches, we developed open-source models for pKa prediction.

METHODS

The experimental strongest acidic and strongest basic pKa values in water for 7912 chemicals were obtained from DataWarrior, a freely available software package. Chemical structures were curated and standardized for quantitative structure-activity relationship (QSAR) modeling using KNIME, and a subset comprising 79% of the initial set was used for modeling. To evaluate different approaches to modeling, several datasets were constructed based on different processing of chemical structures with acidic and/or basic pKas. Continuous molecular descriptors, binary fingerprints, and fragment counts were generated using PaDEL, and pKa prediction models were created using three machine learning methods, (1) support vector machines (SVM) combined with k-nearest neighbors (kNN), (2) extreme gradient boosting (XGB) and (3) deep neural networks (DNN).

RESULTS

The three methods delivered comparable performances on the training and test sets with a root-mean-squared error (RMSE) around 1.5 and a coefficient of determination (R2) around 0.80. Two commercial pKa predictors from ACD/Labs and ChemAxon were used to benchmark the three best models developed in this work, and performance of our models compared favorably to the commercial products.

CONCLUSIONS

This work provides multiple QSAR models to predict the strongest acidic and strongest basic pKas of chemicals, built using publicly available data, and provided as free and open-source software on GitHub.

Design and Synthesis of Benzimidazole-Chalcone Derivatives as Potential Anticancer Agents

Numerous reports have shown that conjugated benzimidazole derivatives possess various kinds of biological activities, including anticancer properties. In this report, we designed and synthesized 24 new molecules comprising a benzimidazole ring, arene, and alkyl chain-bearing cyclic moieties. The results showed that the N-substituted benzimidazole derivatives bearing an alkyl chain and a nitrogen-containing 5- or 6-membered ring enhanced the cytotoxic effects on human breast adenocarcinoma (MCF-7) and human ovarian carcinoma (OVCAR-3) cell lines. Among the 24 synthesized compounds, (2E)-1-(1-(3-morpholinopropyl)-1H-benzimidazol-2 -yl)-3-phenyl-2-propen-1-one) (23a) reduced the proliferation of MCF-7 and OVCAR-3 cell lines demonstrating superior outcomes to those of cisplatin.

Systematic Analysis of the Pharmacological Effects of Alcoholic Components in Maotai

Maotai liquor is one of the most famous traditional Chinese distilled liquor enjoyed by many people all over the world for its unique production method, impressive liquor quality, and soy sauce-like and roasted aroma style. It is known that aroma characteristics of liquor mainly depend on the aroma compounds. Alcohols as one of the most important categories of aroma components have been determined in Maotai liquor. However, the systemic analysis of alcoholic compounds in liquor is limited, especially the active alcoholic components and their pharmacological effects. Therefore, in this study, a systemic analysis method was proposed by combining in silico absorption, distribution, metabolism, and excretion (ADME) evaluation, target fishing, network pharmacology technology, pathway analysis, and experimental verification to interpret the pharmacological mechanism of alcoholic compounds in Maotai liquor. Finally, 15 compounds with favorable pharmacokinetic profiles were screened through in silico ADME models. Thirty-eight related targets of these active compounds were identified by target prediction method. The network pharmacology and pathway analysis were developed to clarify the pharmacological effect of alcoholic compounds in Maotai liquor at the system and pathway level. Moreover, the key active compounds were validated by in vitro experiments that verified the effectiveness of our methods. Our study provides a novel approach to systematically analyze the pharmacological effect of alcoholic compounds in Maotai liquor, which would be beneficial for promoting the in-depth study of various liquors. PRACTICAL APPLICATION: Maotai liquor is popularly enjoyed in the world for a very long time. However, the systemic analysis of compounds in liquor is limited. Our systematic analysis approach was developed to explore the bioactive ingredients and their related target proteins as well as the pharmacological effects of Maotai liquor. This will provide a new method to understand the pharmacological mechanisms of compounds in various liquors at the systems level, so as to promote the development of liquors and to increase the public awareness of science about alcohol consumption.

Immobilized artificial membrane chromatography as a tool for the prediction of ecotoxicity of pesticides

The potential of Immobilized Artificial Membrane (IAM) chromatography to predict ecotoxicological endpoints of pesticides was investigated. For this purpose, retention factors of 39 structurally-diverse pesticides were measured on an IAM stationary phase. A representative test set of 6 pesticides was carefully selected. The training set, involving the remaining pesticides for which experimental data were available, served to establish linear IAM models with LC₅₀/EC₅₀ values in a series of aquatic organisms involving Rainbow Trout, Fathead Minnow, Bluegill Sunfish, Sheepshead Minnow, Eastern Oyster and Water Flea as well as LD₅₀ values in honey bee, compiled from literature sources. For reasons of comparison, corresponding models were derived by replacing IAM retention factors with octanol-water partition coefficients (logP). Considering the similar regression equations obtained for the 4 fish species, general models to predict toxicity in fish were established. Most models were improved upon inclusion of additional physicochemical parameters. The positive contribution of Molecular Weight to ecotoxicity along with the positive sign of hydrogen bond indices in most cases implies that toxic action is manifested mainly by accumulation on the membrane rather than through diffusion across them. IAM models are generally followed by better statistics and superior predictive performance than those based on experimental or computed logP. Predictions based on IAM chromatography were comparable or even superior with those performed by EPI Suite Software. Hence, IAM retention factors are suggested as promising indices in order to screen or rank chemicals with respect to their ecotoxicological risk, especially in the case of new entities.

Combinatorial Synthesis to Identify a Potent, Necrosis-Inducing Rhenium Anticancer Agent

Combinatorial synthesis can be applied for developing a library of compounds that can be rapidly screened for biological activity. Here, we report the application of microwave-assisted combinatorial chemistry for the synthesis of 80 rhenium(I) tricarbonyl complexes bearing diimine ligands. This library was evaluated for anticancer activity in three different cancer cell lines, enabling the identification of three lead compounds with cancer cell growth-inhibitory activities of less than 10 μM. These three lead structures, Re-9B, Re-9C, and Re-9D, were synthesized independently and fully characterized by NMR spectroscopy, mass spectrometry, elemental analysis, and X-ray crystallography. The most potent of these three complexes, Re-9D, was further explored to understand its mechanism of action. Complex Re-9D is equally effective in both wild-type and cisplatin-resistant A2780 ovarian cancer cells, indicating that it circumvents cisplatin resistance. This compound was also shown to possess promising activity against ovarian cancer tumor spheroids. Additionally, flow cytometry showed that Re-9D does not induce cell cycle arrest or flipping of phosphatidylserine to the outer cell membrane. Analysis of the morphological changes of cancer cells treated with Re-9D revealed that this compound gives rise to rapid plasma membrane rupture. Collectively, these data suggest that Re-9D induces necrosis in cancer cells. To assess the in vivo biodistribution and stability of this compound, a radioactive ^99mTc analogue of Re-9D, ^99mTc-9D(H₂O), was synthesized and administered to naı̈ve BALB/c mice. Results of these studies indicate that ^99mTc-9D(H₂O) exhibits high metabolic stability and a distinct biodistribution profile. This research demonstrates that combinatorial synthesis is an effective approach for the development of new rhenium anticancer agents with advantageous biological properties.

Uncovering abnormal changes in logP after fluorination using molecular dynamics simulations

The fluorination-induced changes in the logP (1-octanol/water partition coefficient) of ligands were examined by molecular dynamics simulations. The protocol and force field parameters were first evaluated by calculating the logP values for n-alkanes, and their monofluorinated and monochlorinated analogs. Then, the logP values of several test sets (1-butanol, 3-propyl-1H-indole, and analogs fluorinated at the terminal methyl group) were calculated. The calculated results agree well with experiment, and the root mean square error values are 0.61 and 0.68 log units for the GAFF and GAFF2 force fields, respectively. Finally, the logP estimation was extended to a drug molecule, TAK-438, for which fluorination-induced abnormal logP reduction has been observed experimentally. This abnormal change was qualitatively reproduced by the molecular dynamics simulations. We found that the abnormal logP reduction can be mainly attributed to the effect of fluorination-induced dipole change. Our results suggest that molecular simulation is a useful strategy to predict the fluorination-induced change in logP for drug discovery applications.

In vitro antiplasmodial activity, pharmacokinetic profiles and interference in isoprenoid pathway of 2-aniline-3-hydroxy-1.4-naphthoquinone derivatives

Background

Plasmodium falciparum has shown multidrug resistance, leading to the necessity for the development of new drugs with novel targets, such as the synthesis of isoprenic precursors, which are excellent targets because the pathway is different in several steps when compared with the human host. Naphthoquinone derivatives have been described as potentially promising for the development of anti-malarial leader molecules. In view of that, the focus in this work is twofold: first, evaluate the in vitro naphthoquinone antiplasmodial activity and cytotoxicity; secondly, investigate one possible action mechanism of two derivatives of hydroxy-naphthoquinones.

Results

The two hydroxy-naphthoquinones derivatives have been tested against P. falciparum in vitro, using strains of parasites chloroquine-sensitive (3D7) and chloroquine-resistant (Dd2), causing 50% inhibition of parasite growth with concentrations that varied from 7 to 44.5 μM. The cell viability in vitro against RAW Cell Line displayed IC₅₀ = 483.5 and 714.9 μM, whereas, in primary culture tests using murine macrophages, IC₅₀ were 315.8 and 532.6 μM for the two selected compounds, causing no haemolysis at the doses tested. The in vivo acute toxicity assays exhibited a significant safety margin indicated by a lack of systemic and behavioural toxicity up to 300 mg/kg. It is suggested that this drug seems to inhibit the biosynthesis of isoprenic compounds, particularly the menaquinone and tocopherol.

Conclusions

These derivatives have a high potential for the development of new anti-malarial drugs since they showed low toxicity associated to a satisfactory antiplasmodial activity and possible inhibition of a metabolic pathway distinct from the pathways found in the mammalian host.

Predicting Fraction Unbound in Human Plasma from Chemical Structure: Improved Accuracy in the Low Value Ranges

Predicting the fraction unbound in plasma provides a good understanding of the pharmacokinetic properties of a drug to assist candidate selection in the early stages of drug discovery. It is also an effective tool to mitigate the risk of late-stage attrition and to optimize further screening. In this study, we built in silico prediction models of fraction unbound in human plasma with freely available software, aiming specifically to improve the accuracy in the low value ranges. We employed several machine learning techniques and built prediction models trained on the largest ever data set of 2738 experimental values. The classification model showed a high true positive rate of 0.826 for the low fraction unbound class on the test set. The strongly biased distribution of the fraction unbound in plasma was mitigated by a logarithmic transformation in the regression model, leading to improved accuracy at lower values. Overall, our models showed better performance than those of previously published methods, including commercial software. Our prediction tool can be used on its own or integrated into other pharmacokinetic modeling systems.