Molecules

Although therapeutic advancements have dramatically improved outcomes for cancer patients in recent decades, advanced cancer remains a significant challenge. There is still an urgent need for new and more effective strategies that minimize toxicity and improve survival outcomes in cancer patients. Natural products from microorganisms are a rich and promising source of bioactive compounds with substantial therapeutic potential in cancer treatment. Recently, B. proteolyticus strain OSUB18, isolated from switchgrass, has been demonstrated to promote plant growth and regulate reactive oxygen species (ROS) production. Here, we investigated the anticancer potential of this plant-associated microbe and demonstrated that its crude extracts exhibited broad-spectrum cytostatic activity across diverse solid tumor cell lines. Importantly, OSUB18 extracts displayed limited cytotoxicity toward normal human mammary epithelial cells and mouse fibroblasts. This potentially selective cytostatic activity was retained within a water soluble, heat-labile, and protease-resistant fraction. The extracts primarily suppressed cell proliferation, accompanied by increased lipid peroxidation, with preliminary evidence suggesting the potential involvement of ferroptosis. These findings suggest that the bioactive component may be a selective cytostatic agent with multifaceted mechanisms of action.

Computational safety evaluation of phytochemical libraries commonly relies on models trained on experimentally tested compounds and applied to much larger and chemically diverse collections. An important but often overlooked question is whether predicted compounds are adequately represented within the experimental chemical space underlying these models. Here, we examined this question for the Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) database using experimentally characterized datasets for three anti-target endpoints: hERG blockade, cytochrome P450 (CYP) inhibition, and pregnane X receptor (PXR) induction. After structure standardization, 14,956 unique IMPPAT compounds were compared with 8,877 hERG, 4,905 CYP, and 4,138 PXR reference compounds using consensus nearest-neighbor similarity based on Morgan and atom-pair fingerprints. At a Tanimoto similarity threshold of 0.80, only 0.92%, 0.30%, and 0.46% of IMPPAT compounds had close measured analogs in the hERG, CYP, and PXR datasets, respectively. Relative to the internal nearest-neighbor distributions of the reference datasets, a typical IMPPAT compound lay near the 1st percentile, indicating substantial chemical-space separation. Compounds with close experimental analogs also showed distinct molecular characteristics, with descriptor-based logistic regression yielding cross-validated AUC values of 0.77-0.80. Plant-level enrichment was markedly reduced after excluding widely distributed metabolites and was further restricted when the nearest measured analog was required to have pIC50 or pEC50 [≥] 5. The remaining signals were concentrated in a limited number of taxa, including Rauvolfia, Sinopodophyllum/Dysosma, Cinchona, and Catharanthus roseus. Overall, most IMPPAT phytochemicals lack close structural representation among currently measured anti-target compounds. Computational safety predictions for these molecules should therefore be interpreted as extrapolations and accompanied by explicit measures of structural support. Absence of a close experimental analog should be regarded as a gap in available evidence rather than evidence of safety.

Kaposi's sarcoma (KS) is an angioproliferative tumor associated with infection by human herpesvirus 8 (HHV-8), also known as Kaposi's sarcoma-associated herpesvirus (KSHV). Among the viral genes involved in KS pathogenesis, the viral G protein-coupled receptor (vGPCR) plays a key role in oncogenic transformation and angiogenesis, and its sustained activity contributes to tumor development and maintenance. Quercetin (QUE), a naturally occurring flavonoid with well-recognized anticancer properties, has been shown to reduce cell viability and proliferation and to induce apoptosis in vGPCR-transformed endothelial cells. This study further investigated the mechanisms underlying the antineoplastic effects of QUE in vGPCR cells, an in vitro model of Kaposi's sarcoma. Initially, QUE promoted the activation of MAPK signaling pathways in vGPCR cells, increasing the phosphorylation levels of ERK1/2, JNK1/2, and p38, without affecting AKT phosphorylation. In parallel, QUE increased intracellular oxidant levels, followed by lipid peroxidation and DNA damage, supporting a link between oxidative imbalance and genotoxicity. However, increased total glutathione content, and unaltered Gpx4 mRNA expression, suggest the induction of oxidative stress and compensatory antioxidant responses. Furthermore, increased intracellular oxidant levels were associated with MAPK signaling activation, particularly JNK1/2, whereas ERK1/2 and p38 activation may reflect a compensatory response that is insufficient to overcome QUE-induced cytotoxicity. Taken together, these findings indicate that QUE exerts antiproliferative effects in vGPCR cells through a mechanism involving oxidative stress, MAPK activation, and DNA damage, supporting its potential relevance in the study of therapeutic strategies for virus-associated malignancies.

Hypertriglyceridemia is commonly managed with lipid-lowering drugs such as statins; however, adverse effects, dose intolerance, and financial barriers may limit their use. Therefore, affordable and potentially safer plant-derived alternatives warrant investigation. This study evaluated the lipid-lowering effects of ethanol extracts of Fucus vesiculosus and Phytolacca berry, administered individually or in combination, in mice fed a butter-enriched diet for 12 weeks. Forty-eight mice were randomly assigned to six groups: normal control (Group 1), butter control (Group 2), statin control (Group 3), Fucus vesiculosus extract (Ext-1; Group 4), Phytolacca berry extract (Ext-2; Group 5), and the combined extracts (Group 6). Body weight, organ-to-body-weight ratios, serum lipid parameters, and histopathological changes were assessed. Group 3 showed the lowest final body weight, while Group 6 demonstrated a comparable body-weight trajectory. Organ-to-body-weight ratios varied among groups, with Group 3 generally showing the highest values and Group 6 comparatively lower ratios for several organs. Group 5 had the lowest total cholesterol concentration, followed by Group 3, whereas Group 6 showed cholesterol levels comparable to the normal control and statin groups. Notably, Group 6 exhibited the lowest serum triglyceride concentration (129.9 mg/dL). Histopathological examination revealed relatively mild hepatic alterations and limited vascular plaque formation in Group 6. The combined extracts demonstrated favorable effects on body-weight gain and serum lipid profiles, particularly triglycerides, with comparatively mild histopathological changes. Further mechanistic, dose-response, toxicity, and long-term studies are warranted before clinical application can be considered.

Carbohydrates are essential molecules involved in a wide range of biological processes. While their structural representation in the Protein Data Bank (PDB) has been hindered due to their highly diverse and flexible nature, advancements on X-ray crystallography and CryoEM are accelerating the obtaining of their experimental structures, which in turn supports improved analyses of their structural properties, such as the role of their non-chair conformations on biological processes. In this context, the current work provides a comprehensive and up to date overview of carbohydrate structures within the PDB through classification, quantification, and characterization of monosaccharides and glycan chains, incorporating Cremer-Pople puckering coordinates to evaluate pyranose conformations. Our findings show that, even with a steep increase in CryoEM structures including more complex oligosaccharides, carbohydrate data remains concentrate dinsmallglycanchains, with a about 4% of non-chair conformation sintopresolution structures. Free energy landscapes for the pyranose ring in the presence and absence of nine different binding proteins, including both enzymes and non-enzymes, reinforce the preferred stability of chairs and the rarity of conformational induction upon binding.

BACKGROUND Botanicals are widely consumed as herbal remedies and plant-derived products, but their complex and variable chemical composition presents major challenges for safety assessment. Although hepatotoxicity has been reported for several botanicals, the constituent compounds responsible and the underlying mechanisms often remain poorly characterised. OBJECTIVES We aimed to determine whether Cell Painting could identify candidate constituent drivers of botanical bioactivity and generate hypotheses about potential mechanisms of action in primary human hepatocytes. METHODS We profiled 13 botanical extracts and 85 known constituents across multiple concentrations using Cell Painting alongside assays of metabolic activity and membrane integrity. To interpret morphological phenotypes, we trained machine-learning models using a Cell Painting reference atlas of >1,000 chemicals linked to ToxCast/Tox21 molecular and cellular bioactivity annotations. RESULTS Cell Painting detected bioactivity in 11/13 botanical extracts and 46/85 constituents, compared with 9/13 extracts and 40/85 constituents in the cell-health assays, and generally detected activity at lower concentrations. For 9/13 extracts, at least one constituent was active at a lower concentration than the corresponding botanical extract, supporting the contribution of a subset of potent constituents to mixture-level responses. Aristolochia and goldenseal were the most potent extracts across Cell Painting and cell-health assays and contained highly active constituents. Reference-atlas models generated specific mechanistic hypotheses, including cytotoxicity-, endocrine-, and genotoxicity-associated responses for several botanicals and constituents. CONCLUSION These results support Cell Painting combined with machine learning as a sensitive approach for characterising cellular responses to botanicals, prioritising candidate constituent drivers of hepatotoxicity-associated phenotypes, and generating testable hypotheses about potential mechanisms of action.

Plant-derived flavonoids show measurable anti-dengue activity in cell culture, but their translational value is limited by a narrow therapeutic window: the concentrations that inhibit the virus approach or exceed those that are cytotoxic. Whether nanoparticle formulation can resolve this constraint, rather than simply add potency, remains untested for a chemically defined fraction. Here we show that biogenic silver nanoparticle (AgNP) formation using a flavonoid-enriched fraction of Carica papaya inverts an unusable selectivity profile into a viable one. The unformulated fraction was cytotoxic below the concentrations required for antiviral activity: its 50% cytotoxic concentration (CC50 = 134.9 ug/mL) lay below its 50% effective concentration against dengue virus serotype 2 (DENV-2; EC50 = 254.4 ug/mL), giving a Selectivity Index (SI) of 0.53. Using the same flavonoids as sole reducing and capping agents produced AgNPs (Z-average 128.4 nm; PDI 0.232; zeta potential -28.4 mV) that moved both parameters simultaneously. CC50 rose approximately 8.6-fold to 1,165.74 ug/mL while EC50 fell approximately 8.8-fold to 29.05 ug/mL, raising the SI to 40.12, an approximately 75-fold shift. Time-of-addition analysis localised the effect to the extracellular phase: inhibition was significant under pre-treatment and co-treatment but not after viral adsorption, identifying the AgNPs as entry inhibitors rather than replication inhibitors. Consistent with a serotype-independent physical mechanism, AgNP treatment at 30 ug/mL reduced viral RNA across all four serotypes, using inocula standardised against WHO-traceable NAAT reference reagents. These findings identify capping chemistry, rather than silver content alone, as a determinant of the therapeutic window in phytosynthesised nanoantivirals.

The delivery of small-molecule therapeutics across cellular membranes remains a fundamental challenge in drug development, particularly for compounds whose physicochemical properties limit intracellular bioavailability. Inspired by the low toxicity, biodegradability, and synthetic accessibility of triethanolamine-derived lipids, we investigated a series of C18 aminotriester lipids as excipients for the codelivery of anticancer therapeutics. Four structurally related triester lipids bearing saturated, cis-monounsaturated, trans-monounsaturated, and di-unsaturated C18 tails were synthesized through straightforward esterification reactions with triethanolamine. These lipids were evaluated for their ability to enhance the antiproliferative activity of several anticancer agents, including paclitaxel, doxorubicin, amonafide, homoharringtonine, camptothecin, and SN-38. Across multiple drug classes, the aminotriester lipids exhibited minimal intrinsic cytotoxicity and generally produced limited enhancement of drug potency. However, the fully saturated tris-stearate aminotriester lipid selectively enhanced the antiproliferative activity of amonafide in HCT-116 colorectal cancer cells, particularly at lower concentrations. In contrast, unsaturated lipid analogues displayed negligible enhancement effects, and the activity of the tris-stearate lipid was substantially attenuated in A549 lung adenocarcinoma cells, weakly observed in HT-29 colorectal cancer cells, and absent in CT26 murine colorectal cancer cells. Lactate dehydrogenase release assays indicated that these effects were not associated with membrane lysis or nonspecific cytotoxicity. Further, fluorescence microscopy time course experiments suggest improved membrane trafficking of amonafide when codelivered stoichiometrically with fully saturated C18 aminotriester lipids. Collectively, these findings demonstrate that saturated triethanolamine-based aminotriester lipids may function as selective and biocompatible codelivery excipients for certain small-molecule therapeutics. The observed dependence on both lipid saturation state and cellular context suggests that membrane organization and lipid packing may play important roles in mediating these effects. These results establish a foundation for further mechanistic investigations into aminotriester lipid-assisted small-molecule delivery systems.

This study aims to identify potential inhibitors of the Ag85C enzyme from Mycobacterium tuberculosis through primary docking, pharmacophore modelling, and ADME/pharmacokinetics analysis. Delamanid and Pretomanid emerged as the top hits in primary docking, demonstrating strong binding affinities and interactions with key active site residues. A robust pharmacophore model was developed, identifying essential features for binding and used to screen 4254 antituberculotic compounds, yielding three promising candidates: F0314-0040, F0398-0505, and F0349-2716. These compounds exhibited high binding affinities in major docking studies. ADME and pharmacokinetics analysis revealed favorable Drug likeness and absorption profiles, supporting their potential as effective antituberculotic agents. The integration of computational methods provided a comprehensive approach for rational drug design.

Alkaline ceramidase 2 (ACER2) catalyzes the hydrolysis of ceramides into sphingosine and free fatty acids, playing crucial roles in regulating various biological processes, including but not limited to cell proliferation, survival, and the DNA damage response. However, a pharmacological tool that regulates its catalytic and signaling functions remains to be developed. In this study, N-[(1S,2R)-1-hydroxy-1-phenylpropan-2-yl]dodecane-1-sulfonamide (KPB82) was identified as a potent and selective ACER2 inhibitor. In vitro enzymatic assays showed that KPB82 inhibited ACER2 with an IC50 of 190 nM, while displaying minimal activity against acid and neutral ceramidases (IC50> 100 {micro}M) and no significant inhibition of alkaline ceramidase 1 or alkaline ceramidase 3. Enzymatic kinetic studies revealed that KPB82 is a competitive inhibitor of ACER2. The three-dimensional structure of ACER2 was modeled in the AlphaFold3 server, revealing key Zn2+-coordinating histidine residues and a substrate-binding pocket. Molecular docking studies demonstrated that KPB82 binds ACER2 within this pocket, forming hydrogen bonds with the catalytically important amino residues, Asp93 and His215, and interacting with several hydrophobic residues. Lactate dehydrogenase release assays revealed that KPB82 significantly reduced cytotoxicity in HeLa cells overexpressing ACER2, as well as in HCT116 cells, RT4 cells, and human umbilical vein endothelial cells treated with the DNA-damaging agent doxorubicin. Lipidomic analysis showed that KPB82 inhibited sphingosine production in response to ACER2 overexpression or doxorubicin treatment. Collectively, these findings demonstrate that KPB82 is a potent and selective ACER2 inhibitor capable of suppressing ACER2-dependent biological processes and highlight its potential as a tool for studying ceramide metabolism and ACER2-mediated signaling pathways.

Hit-to-lead optimization is a multi-objective molecular-design problem in which binding-related scores must be considered together with drug-likeness and synthetic accessibility. We developed a closed-loop language-model medicinal-chemistry agent that combines target and starting-hit context, PubMed retrieval, molecular-property tools, docking, and feedback from previously scored analogues. In a matched computational benchmark comprising six kinase targets, six methods, a 40-candidate budget, and six independent campaigns per method, the agent achieved the highest mean aggregate score under a pre-specified composite objective on four targets. Re-analysis of the same candidate sets with drug-likeness-gated docking and docking-drug-likeness hypervolume produced different target-level winners, showing that evaluation rules can change the comparative interpretation of an optimization campaign. Candidate distributions and component ablations indicated that the agent's advantage was associated primarily with retention of scored-candidate feedback and higher-QED regions rather than with docking alone. An EGFR case study illustrates a scaffold-preserving in-silico optimization trajectory from erlotinib. The study is a computational benchmark and evaluation framework; the prioritized molecules are hypotheses for subsequent experimental testing, not experimentally validated leads.

Dehydroascorbic acid (DHA), the oxidized form of vitamin C, exhibits specific toxicity against KRAS-mutant cancer cells; however, the early metabolic reprogramming mechanisms leading to cell death remain incompletely understood. In this study, we reveal the non-linear, dose-dependent response of the intracellular metabolome during the ultra-early phase of 1-hour DHA exposure. Under 1 mM DHA exposure, an early significant accumulation of the primary catabolite erythrulose (p = 0.030) and moderate consumption of glutamate (p = 0.0086) were observed, while core TCA cycle homeostasis and cell viability were preserved. In contrast, under 10 mM DHA exposure, an acute and profound alteration of the metabolic profile was observed. Along with the significant accumulation of glycerate, indicative of a glycolytic blockade, and the terminal peroxidation product oxalate, core TCA cycle intermediates such as -ketoglutarate and malate showed rapid depletion (Log2FC < -2.0, p < 10-7). Furthermore, coupled with this acute energy depletion, a statistically significant accumulation of free methionine (Log2FC = 1.14, p = 0.011), which was below the limit of detection in the control, was identified alongside profound depletions of glutamate (p < 0.001) and cysteine (p = 0.0027). These results strongly suggest that the initial response to high-dose DHA is not merely a linear accumulation of oxidative stress, but rather an "abrupt metabolic switch" characterized by rapid TCA cycle dysfunction followed by the early signature of a methionine trap.

Allosteric modulation of G protein-coupled receptors (GPCR) is a rapidly advancing area of drug discovery with considerable therapeutic potential. In this study, we used various bioluminescence resonance energy transfer (BRET) assay configurations to investigate the differential activity of LY3154207, a dopamine D1 receptor (D1R)- positive allosteric modulator (PAM), in potentiating D1R-mediated signaling pathways. Our data showed that LY3154207 demonstrated clear bias toward {beta}-arrestin recruitment over G-protein engagement. Among the G-protein subtypes, LY3154207 preferentially potentiated Gs signaling induced by DA. Interestingly, when tested for its effect on the Gs-selective D1R agonist DHX and the Golf-selective agonist TAV, LY3154207 further enhanced the selectivity of these compounds, suggesting a probe-dependent effect. We also studied the structural mechanism underlying this subtype selectivity and found that W123 and R130 in the intracellular loop 2 (ICL2) of D1R were crucial for the activity of LY3154207. Furthermore, the N domain of the G-protein appeared to make minor contributions to this G-protein subtype selectivity of LY3154207. Lastly, our data demonstrated that LY3154207 induced cAMP production in the absence of the orthosteric ligand, indicating that this compound is an ago-PAM.

The incidence of infections caused by multidrug-resistant bacterial agents has increased at an alarming rate worldwide. With the aim of identifying novel antimicrobial peptides (AMPs), we screened bacteria isolated from various environmental samples. Our hypothesis was that the sequence space of natural AMPs belonging to known AMP classes is far from saturation. We used a classical pipeline of bacterial culturing, overlay assays, extraction and fractionation to purify AMPs for identification. Using LC/MS analysis, the structure of AMPs isolated from a strain of Paenibacillus was narrowed down and a new subclass of polymyxins was uncovered. These harbor three aliphatic residues within the cyclic C-terminal, and in certain cases Ser replaces Thr at position A2. Four discrete polymyxins fitting the new class, but not identical to the natural polymyxins were synthesized and tested against 29 human pathogenic bacteria. Each displayed an antibacterial spectrum different from that of colistin, with the best candidate surpassing it in potency against ten bacterial strains, but underperforming against three others. These results demonstrate that current screening of natural bacterial isolates can still permit the design of novel antimicrobial peptide variants without the substantial threat of diminishing returns.

Albumin binding can substantially alter the unbound concentration and apparent cellular potency of small-molecule anticancer agents. BAY-1816032 is a selective BUB1 kinase inhibitor with activity in malignant pleural mesothelioma, but its interaction with serum albumin has not been defined. This study combined intrinsic bovine serum albumin (BSA) fluorescence measurements, molecular docking, and cell-viability assays to examine whether albumin association influences BAY-1816032 activity. BAY-1816032 produced pronounced quenching of BSA fluorescence at low drug-to-protein molar ratios, and the quenching response was greater at mildly acidic pH than at mildly basic pH. Docking to BSA (PDB 4F5S) placed the top-ranked pose in Sudlow site I (subdomain IIA), with a Vina score of -7.8 kcal/mol and predicted hydrogen-bond contacts with Tyr149, Arg256, and Asp450. In NCI-H28, NCI-H2052, NCI-H2452, and AB12 mesothelioma cells, supplementation of 10% fetal bovine serum medium with 2% (w/v; 20 mg/mL) BSA shifted BAY-1816032 concentration-response curves to the right. Relative to 10% serum alone, IC50 values increased from 2.4 to 11.1 uM in H28, 3.6 to 22.0 uM in H2052, 2.0 to 4.9 uM in H2452, and 2.4 to 11.1 uM in AB12. These data support an albumin-dependent reduction in the freely available drug fraction and identify site I as the leading computational binding-site hypothesis. Because fluorescence-intensity and docking data do not independently establish a dissociation constant or quenching mechanism, direct unbound-fraction measurements and competitive displacement experiments are required for quantitative confirmation.

Introduction: In both humans and experimental animals, obesity is associated with hypothalamic inflammation and neuronal metabolic dysfunction, which contribute to disrupting body energy homeostasis. Although ciliary neurotrophic factor (CNTF) is a neuroprotective cytokine with recognized anti-obesity effects in peripheral metabolic tissues, its role in obesity-associated hypothalamic dysfunction is still poorly characterized. We investigated whether CNTF exerts any direct effects on mHypoE-N41 (N41) hypothalamic neurons and whether it modulates the effects induced by palmitate (PA), an established model of obesity-associated neuronal metabolic dysfunction. Methods: N41 cells were administered CNTF alone or combined with PA to assess whether CNTF affects PA-induced lipotoxic stress. Gene and protein expression was assessed by qRT-PCR, western blotting, and immunofluorescence analysis. Results: In N41 cells, CNTF activated the STAT3, ERK and AMPK signaling pathways. It also upregulated genes involved in mitochondrial metabolism and fatty acid oxidation, including Ucp2, Cpt1 and Pgc1, as well as the proliferation-associated markers Ki-67 and PCNA. PA treatment induced inflammatory and endoplasmic reticulum stress responses and impaired insulin signaling. In PA-treated N41 cells, CNTF significantly attenuated the expression of pro-inflammatory markers Tnf, Mcp1 and Nfkb, restored insulin-induced AKT phosphorylation, and partially reversed PA-induced changes in the expression of metabolic genes Ucp2 and Cpt1. Conclusions: Collectively, these findings indicate that CNTF exerts direct protective metabolic effects on hypothalamic N41 neurons subjected to lipotoxic stress and supports a role for it in the preservation of neuronal metabolic function under obesogenic conditions.

In 1998 we predicted percent human intestinal absorption (HIA) for 86 drug and drug-like compounds from six molecular descriptors chosen by a genetic algorithm with a small neural network serving as the fitness function. That result rested on one division of the compounds into three sets: training, cross-validation and external prediction. Repeating the division shows it was a rather fortunate one. An extraction is one random draw from a wide distribution, so we repeat every experiment 250 times and report the result as a distribution. Across 250 stratified extractions, 14 match the published cross-validation error and only one matches the published external prediction error. Reconstructed this way, the published six descriptors give a mean extraction error roughly twice the external prediction value we reported in 1998 (Table 1). We then applied the same six descriptors to a larger set of 780 compounds from a public compilation. Under the 1998 network they give 22.50 %HIA units on training compounds against 25.77 on extracted compounds, whereas on the 86 they give 13.43 against 30.39. More compounds close the gap between fitting and prediction. Four datasets are modeled here: 780 small molecules with HIA measured in man, 3,771 small molecules and 1,281 cyclic peptides measured across Caco-2 cell monolayers, and 7,298 cyclic peptides measured across artificial membranes. Each is its own prediction problem, and one procedure is applied to all four. A genetic algorithm chooses which descriptors to use and how many, and a random forest grades each choice on compounds its own trees never saw. The descriptors chosen most often across the 250 extractions form one consensus set for each dataset. For absorption, 25 consensus descriptors under a random forest reach an extraction rms of 20.47 %HIA units at R 0.76. On the same compounds and the same extractions, the six 1998 descriptors under the 1998 network reach 25.77 at R 0.59. The four models are served at predicthia.ai, where a structure can be drawn or pasted, scored against its training set for similarity, and the training data with fitted models downloaded. Each model served is its fixed consensus set fitted on every compound in its dataset; the accuracy quoted beside a prediction comes from evaluating that same set across 250 extractions (Table 3). The fixed sets hold 25 descriptors for absorption, 55 for Caco-2 small-molecule permeability, 50 for Caco-2 peptide permeability and 25 for artificial-membrane permeability. Keywords: human intestinal absorption; QSPR; genetic algorithm; descriptor selection; cyclic peptides; membrane permeability; oral bioavailability; repeated extraction

Cardiovascular diseases (CVD) are the leading cause of morbidity and mortality worldwide. This clinical burden is predominantly attributable to atherothrombotic events resulting from platelet hyperactivation, a process integral to the pathophysiology of atherosclerosis. Cilostazol (CLZ) is a clinically established phosphodiesterase-IIIA inhibitor with antiplatelet and vasodilatory activity. However, its extremely low aqueous solubility results in dissolution-limited and variable oral absorption, while dose-dependent adverse effects and cardiovascular safety restrictions may constrain its clinical use. Lipid-polymer hybrid nanoparticles (LPHNPs) represent a highly promising drug delivery system to address these biopharmaceutical limitations. However, their interactions with blood components remain poorly understood, with surface charge among the most controversial physicochemical parameters implicated in platelet toxicity. Here, we applied an integrated ex vivo and in silico screening strategy to design LPHNPs based on poly(lactic acid) and poly(ethylene glycol)-modified lipids displaying positive (amine), nominally neutral (methoxy), and negative (carboxyl) surface charges to systematically identify safe boundaries at the nano-human platelet interface and evaluate their suitability as CLZ carriers. Positively charged nanoparticles precipitated immediately during self-assembly due to uncontrolled ionic pairing and were therefore structurally unviable. Neutral nanoparticles maintained a stable size (~113 nm hydrodynamic diameter) but unexpectedly induced spontaneous platelet aggregation, limiting their applicability. In contrast, negatively charged nanoparticles exhibited exceptional colloidal stability (hydrodynamic size of ~120 nm and zeta potential of -48 mV), achieved significant inhibition of ADP-induced platelet aggregation, and adhered to the platelet surface without inducing activation or membrane disruption. Steered molecular dynamics simulations, performed under identical non-equilibrium pulling conditions, showed the highest interfacial resistance to CLZ extraction for the negatively charged model, qualitatively consistent with the sustained CLZ release profile observed in vitro. These findings highlight the importance of precise surface charge tuning in the development of safe cardiovascular nanomedicines based on LPHNPs, emphasizing the need for comprehensive platelet compatibility testing before further biological evaluation.

A compound's toxicity can be gauged from the toxicities of the compounds it resembles. We showed that in 2003 for rat oral LD50: the compounds in a reference set of 13,645 that most resemble the query form a consortium and vote on its toxicity. This work returns to that method twenty-three years later, on public data, because the reference set used in 2003 is no longer public. Rebuilt on 11,989 curated rat oral LD50 values, the method answers for 67.0% of the held-out compounds carrying an exact value, and 92.1% of those answers land within one log unit; in 2003 the figures were 94% within one log unit on 57.3% of compounds. We then ran it over 28 further endpoints, each recording whether a compound showed a particular toxicity, from cytochrome inhibition to mutagenicity to clinical toxicity: 149,224 measurements on 34,162 compounds. The method answers only when a compound has close enough neighbors, and on those it correctly identifies a median 72.9% of the compounds that showed the toxicity and 96.5% of those that did not. Separately we asked whether a reverse screen carries information about these endpoints beyond molecular size and lipophilicity. We search a compound against a published index of 27,910 ligands solved inside proteins and count the protein families those sites belong to. A compound whose matches reach no protein family inhibits CYP3A4 2.5% of the time; one whose matches span sixteen or more families inhibits 51.2% of the time. Added to heavy atom count, molecular weight and cLogP, the screen raises the mean area under the ROC curve from 0.72 to 0.76 across the 28 endpoints. The OpenADMET consortium has since measured CYP3A4 inhibition on 2,335 compounds, and the same pattern holds. Compounds that resemble known binders of sixteen or more protein families inhibit CYP3A4 24.6% of the time; compounds whose matches reach no protein family inhibit it 7.3% of the time. The more protein families a compound resembles binders of, the more likely it is to inhibit CYP3A4. This association is strong enough to act on before anything is made. Scoring a library on how far its compounds reach across protein families, together with their size and lipophilicity, and setting aside the riskiest fifth removes two thirds of the androgen receptor binders and about half of the CYP3A4 inhibitors. Scoring needs only a two-dimensional structure, with no docking and no conformer generation at query time. Interactive scoring, the data and the pool are at toxpred.ai. Keywords: toxicity prediction; structural similarity; nearest neighbors; reverse screening; target breadth; promiscuity; LD50; cytochrome P450; virtual library triage

Objective To systematically evaluate the predictability of drug chemical structures against the complete set of MedDRA Preferred Terms (PTs), construct a comprehensive structure adverse reaction landscape, validate the utility of molecular fingerprint representations in pan-screening tasks, and establish quantitative tools for drug safety pre-screening. Methods Drug adverse event association data were compiled from SIDER 4.1 and its derivative compilation Nsider, encompassing 2,005 approved drugs, of which 1,120 small-molecule drugs with successfully retrieved chemical structures were retained for analysis. For each Preferred Term (PT) meeting the minimum sample-size threshold ([&ge;]25 positive and [&ge;]30 negative drugs; n = 768), XGBoost classifiers were trained using MACCS 166-bit fingerprints, with model performance evaluated via 5-fold stratified cross-validation. A PT was designated as high-confidence if it achieved a validation AUC [&ge;] 0.75, a cross-validation AUC [&ge;] 0.70, and passed Benjamini Hochberg false discovery rate (FDR) correction (q < 0.05). The entire analytical pipeline was replicated using Morgan ECFP4 2048-bit fingerprints to assess the robustness of fingerprint selection. The robustness of significance screening was independently validated by permutation testing (500 random label permutations), and model generalizability was evaluated through Bemis-Murcko scaffold-based stratified split validation. SHAP (SHapley Additive exPlanations) analysis was employed to identify critical structural features, and mechanism-based case studies further demonstrated concordance between model-identified features and established pharmacophores. Results Among the 768 modelable PTs, 71 (9.2%) met the high-confidence significance criteria (FDR q < 0.05), predominantly comprising nervous system disorders (10 PTs) and psychiatric disorders (9 PTs). The toxicities exhibiting the strongest structural predictability included hyperprolactinaemia (AUC = 0.968), dermatitis acneiform (AUC = 0.966), tardive dyskinesia (AUC = 0.952), and breast tenderness (AUC = 0.979). Under a unified XGBoost hyperparameter configuration, the low-dimensional MACCS fingerprint (166-bit) demonstrated near-equivalent performance to the high-dimensional Morgan fingerprint (2048-bit) in pan-screening tasks ({Delta}AUC = -0.012, Cohen's d = 0.16), and achieved comparable performance at a substantially lower dimensionality across 18 of 22 organ systems. SHAP analysis identified key structural fragments corresponding to established pharmacophores (six-membered rings, tertiary amine cores, heteroatomic bonds, and bridged heteroatomic chains), and mechanism-based case studies further demonstrated that the model captured structural patterns concordant with known pharmacophores of D2 antagonists, EGFR inhibitors, and related agents. Bemis-Murcko scaffold-based stratified split validation revealed a median AUC decrease of merely 0.042, with 66% of PTs maintaining an AUC [&ge;] 0.70 under scaffold-split conditions. Immune-mediated and multifactorial endpoints remained structurally opaque under the current data conditions. Conclusion This study constructed a comprehensive structure-adverse reaction predictability landscape encompassing 768 MedDRA PTs; seventy-one PTs achieved high-confidence criteria. The MACCS 166-bit fingerprint achieved near-equivalent performance to the Morgan 2048-bit fingerprint at a markedly lower dimensionality, and its chemically explicit features enable direct translation of model outputs into structural alerts, providing a computational tool for prospective screening of potential adverse reaction profiles for any novel compound. A tiered assessment strategy is proposed: machine learning ensemble models for high-confidence screening, substructure alert rules for high-specificity exclusion, and pharmacovigilance signal detection for complementary coverage of structurally unpredictable endpoints. Bemis-Murcko scaffold-based stratified split validation further demonstrated that the median generalization performance decrease was merely 0.042 AUC, with 66% of PTs maintaining an AUC [&ge;] 0.70 under scaffold-split conditions, corroborating the existence of a genuine chemical basis underlying structure-adverse reaction mappings.