ASSAY and Drug Development Technologies

Despite the significant progress made in developing different in silico methodology for structure activity research over the past few decades. The ability to predict correlation structure activity (CSA) from absorption distribution metabolism excretion (ADME) descriptors to select indolizine compounds for human papilloma virus (HPV) anticancer activity continues to pose a challenge. This study employed five machine learning (ML) algorithms for classification, viz., stochastic gradient descent (SGD), random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and logistic regression (LR), to perform the classification based on ADME-related physiochemical descriptors of 8,900 indolizine compounds to predict the CSA. The present study focuses on 26 well-known parameters to optimize the results, which are utilized for ML models SGD, RF, SVM, CNN, and LR for classification. The CNN achieved the best results with the highest overall accuracy and average loss values of 98.33% and 0.16, respectively. On the other hand, the SGD, RF, SVM, and LR recorded the accuracy values of 95.32%, 93.23%, 96.03%, 94.03%, and loss values of 0.046, 0.067, 0.039, and 0.059, respectively. It is stated that from the obtained results, the CNN is performing better compared to other methods. The cross-validation and results are done with the relationship of descriptors, viz., accuracy, correlation, distribution, area under the receiver operating characteristic, area under the precision recall curve, and bootstrap error analysis. This study demonstrated the utility of ML to facilitate early prediction of indolizine compounds for HPV anticancer activity in preclinical development.

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Ischemic stroke (IS) is an important disease leading to high disability and mortality, and the current clinical treatment is limited. Tongnao Decoction (TND) is a traditional Chinese herbal formula for treating IS, but its pharmacological mechanism remains unclear. This study aims to elucidate the molecular mechanism through network pharmacology, molecular docking, and related experimental verification. First, the bioactive components of TND, along with their potential targets and IS-related gene targets, were identified through multiple databases. Subsequently, an “herb-active component-disease gene target” network and a protein–protein interaction (PPI) network were constructed. Combined with enrichment analysis, key biological processes and signaling pathways were identified. Following this, molecular docking experiments were conducted to preliminarily validate drug–target interactions. Finally, the efficacy of the relevant pathway targets was validated in a photochemically induced mouse cerebral ischemia model. A total of 90 active compounds and 615 target genes were screened. PPI network analyses suggested that TP53, EGFR, STAT3, AKT1, and IL-6 were the hub targets. TND significantly modulates inflammatory biological processes and the PI3K-Akt signaling pathway during IS treatment. Molecular docking analysis indicated that the primary components of TND may exhibit favorable binding affinity to multiple hub target proteins, including TP53, EGFR, STAT3, AKT1, and IL-6. Further in vivo experiments showed that TND dramatically improved neurological function, reduced neuronal damage, and decreased IL-6 expression in brain tissue. In addition, TND stimulated the PI3K/Akt/GSK-3β pathway. These findings imply that TND and its key bioactive components, coryincine, dihydrocapsaicin, and 4 (4′-hydroxybenzyloxy)benzyl methylether exert therapeutic effects on IS through the IL-6/PI3K/Akt/GSK-3β pathway.

This study demonstrates through an integrated computational and experimental approach that classical calcium channel blockers (CCBs) from four distinct structural classes possess previously unrecognized Mu-opioid receptor (MOR) activation capabilities. Virtual screening revealed stable binding modes between the investigated compounds (verapamil, cinnarizine, diltiazem, and flunarizine) and the 7SBF protein target, with cinnarizine exhibiting the most favorable interaction profile through strong hydrogen bonding with TYR-236 and GLN-124. Molecular dynamics simulations confirmed binding stability, particularly for cinnarizine, which maintained a root-mean-square deviation below 0.3 nm. Experimental validation via cAMP inhibition assays demonstrated significant MOR activation by all compounds, with cinnarizine showing superior potency (IC₅₀ = 21.4 ± 1.5 nM) and efficacy (Imax = 70% ± 2%). The conserved MOR activation across structurally diverse CCBs supports a polypharmacological mechanism where these drugs concurrently modulate both calcium channels and opioid receptors. These findings not only elucidate a novel aspect of CCB pharmacology but also suggest new avenues for drug repurposing and the development of multi-target cardiovascular therapeutics.

This study aimed to design and develop flurbiprofen-loaded chitosan nanoparticles for ophthalmic drug delivery, with the objectives of enhancing formulation stability, sustaining drug release, and improving patient comfort. This study introduces an optimized chitosan-based nanoparticle system for ocular delivery of flurbiprofen, achieving high encapsulation efficiency, physiological compatibility, and sustained release for 20 h. The findings demonstrate a practical, patient-friendly approach that enhances drug bioavailability and stability compared with conventional eye drops. Flurbiprofen-loaded nanoparticles were prepared using gelation and optimized through a Box–Behnken design. The influence of formulation variables (0.1%–0.3% w/v chitosan concentration, 0.2–0.6 mL/min dropping rate, and 500–900 rpm mixing speed) was assessed. The optimized nanoparticles were evaluated for particle size, polydispersity index (PDI), zeta potential, pH, osmolarity, and entrapment efficiency (%). The optimized formulation was achieved at 0.2% (w/v) chitosan concentration, a dropping rate of 0.4 mL/min, and a mixing speed of 700 rpm. The nanoparticles exhibited a particle size of 110.0 ± 2.20 nm, PDI of 0.347 ± 0.03, and zeta potential of + 15.4 ± 3.8 mV. The entrapment efficiency was 80.89%. The formulation was adjusted to physiological conditions (pH 6.7, osmolarity 300 mOsm/kg). In vitro studies showed a controlled release of flurbiprofen over 20 h. The optimized chitosan-based nanoparticle system demonstrated favorable physicochemical stability, high encapsulation efficiency, and sustained release of flurbiprofen. Compared with conventional ocular formulations, this structured nanoparticle delivery system offers superior drug retention and extended therapeutic action.