The central innovation of the work lies in the fabrication of what the author calls CPT@PLA micronanofibers, a composite mat in which camptothecin is embedded within a matrix of poly(lactic acid), or PLA, a biodegradable and biocompatible polymer that is approved by the US Food and Drug Administration and occurs naturally in the human body. For the first time, a solution containing both camptothecin and PLA was subjected to electrospinning, a technique in which a high voltage of 18 kilovolts is applied to a polymer solution flowing through a stainless-steel needle at a rate of 0.5 milliliters per hour. As the charged jet accelerates toward a grounded collector positioned 20 centimeters away, the solvent evaporates almost instantaneously, leaving behind ultrafine fibers with diameters spanning the nano and micro scales. The resulting mats were vacuum dried for 24 hours at 40 degrees Celsius to remove residual solvent and then stored at 4 degrees Celsius away from direct sunlight until analysis.

A battery of physicochemical characterization techniques confirmed that the drug had been successfully integrated into the fibrous scaffold. Scanning electron microscopy revealed that pristine PLA fibers were smooth, bead-free, and randomly organized, with average diameters of roughly 1 to 1.5 micrometers. Once camptothecin was incorporated, the fibers displayed a broader diameter distribution encompassing both nano and micro dimensions, a shift the author attributes to chemical interactions between the drug and the polymer solution that may have altered the ionic balance of the spinning jet and promoted shrinkage of the fiber diameter. Confocal laser scanning microscopy provided a particularly striking visual confirmation: because camptothecin is intrinsically fluorescent, the fibers glowed brightly under laser excitation at 488 and 543 nanometers, demonstrating that the drug molecules were well dispersed throughout the electrospun matrix rather than clumped into aggregates.

X-ray diffraction analysis added another layer of evidence. Pure camptothecin is crystalline, exhibiting characteristic diffraction peaks at Bragg angles of 16.97 and 24.8 degrees along with several minor reflections, while the PLA polymer is amorphous and shows no prominent peaks. The composite CPT@PLA fibers produced an X-ray diffraction spectrum essentially identical to that of PLA, with only a few low-intensity minor peaks remaining. This disappearance of the drug’s crystalline signature indicates that the rapid solvent evaporation during electrospinning, enabled by the enormous surface area of the forming fibers, did not give the camptothecin molecules enough time to recrystallize, trapping them instead in an amorphous state. Electron probe microanalysis mapping further verified that carbon, oxygen, and nitrogen, the key elemental components of the drug, were uniformly distributed across the fiber surfaces.

Thermogravimetric analysis, performed in a nitrogen atmosphere from 25 to 700 degrees Celsius at a heating rate of 10 degrees per minute, revealed a single-step degradation profile for both materials. Pristine PLA fibers began decomposing at around 210 degrees Celsius and lost approximately 99 percent of their mass, whereas the CPT@PLA composite fibers showed an initial degradation temperature of 280 degrees Celsius and lost about 95 percent of their mass between 200 and 400 degrees Celsius. The thermal decomposition temperature of the composite was therefore elevated by 25 percent relative to pure PLA, a shift the study attributes to the higher breakdown temperature of the encapsulated camptothecin. The first derivative curves of the thermograms showed an additional peak in the composite fibers corresponding to drug decomposition, providing yet more confirmation that camptothecin had been genuinely incorporated into the polymer matrix rather than merely adsorbed onto its surface.

With the material thoroughly characterized, the biological question became paramount: does the fiber-encapsulated drug retain its lethal activity, and how does it kill cells? To answer this, the study turned to C2C12 cells, a mouse myoblast cell line that has a long history as a model for mechanistic toxicology. These cells carry receptors that respond to diverse chemotherapeutic treatments, including agents that interfere with DNA, block RNA synthesis, and inhibit topoisomerase enzymes, and they are known to be sensitive to many apoptotic chemical stimuli. Previous researchers have used C2C12 cells to probe the anticancer properties of copper and gold compounds, making them a well-validated platform for the present investigation. Cells were seeded into 96-well plates at a density of 10,000 cells per well, allowed to adhere overnight until reaching approximately 40 percent confluence, and then exposed to either pristine PLA fibers or CPT@PLA fibers at doses of 5 and 10 micrograms per well for periods of 12, 24, and 36 hours.

The results of the Cell Counting Kit-8 assay, which quantifies cell viability by measuring absorbance of a formazan product at 450 nanometers, were unambiguous. Pristine PLA fibers showed no significant cytotoxicity across all time points, with only an insignificant drop in viability after 36 hours, confirming that the polymer itself is benign. In stark contrast, the CPT@PLA micronanofibers suppressed C2C12 cell survival dramatically, achieving approximately 73 percent inhibition at the 5 microgram dose and 80 percent inhibition at the 10 microgram dose after 36 hours of exposure. The toxicity was both time dependent and concentration dependent, and the author attributes the pronounced effect to the enhanced stability of camptothecin when sequestered within the PLA matrix, which shields the drug’s active lactone ring from hydrolysis. That ring is crucial for the spontaneous diffusion of the drug into unhealthy cells, and its preservation during electrospinning represents one of the study’s most significant technical achievements.

Phase contrast microscopy captured the morphological consequences of this cytotoxic assault. Untreated control cells displayed an organized growth pattern typical of healthy myoblasts, while cells exposed to the CPT@PLA fibers exhibited substantial disfigurement characterized by cell shrinkage and fragmentation, hallmarks of impaired cellular integrity and incipient programmed death. To identify the molecular machinery driving this demise, the study employed reverse transcriptase PCR to measure the messenger RNA expression of two key apoptotic executioners, caspase-3 and caspase-7, with expression levels normalized to the reference gene GAPDH. Exposure to the CPT@PLA micronanofibers substantially amplified the expression of both caspase genes compared with untreated controls, and the increase was dose dependent, with higher fiber concentrations producing greater expression levels.

These molecular findings fit neatly into the established mechanism of camptothecin action. By inhibiting topoisomerase I, the drug induces DNA damage that activates the intrinsic, or mitochondrial, apoptotic pathway. This triggers the release of cytochrome c from mitochondria into the cytosol, where it initiates the caspase cascade. Caspases-3 and 7 serve as the principal executors of apoptosis, dismantling cellular structures, fragmenting DNA, and ultimately dismantling the cell from within. The sensitivity of cells to camptothecin depends in part on their capacity to activate these caspases effectively, and understanding this interplay opens the door to combination therapies that could enhance caspase activation or counteract resistance mechanisms in tumors. The study also notes that camptothecin triggers cell cycle checkpoints that arrest cells in S phase, and if the resulting DNA damage proves irreparable, the cells proceed inexorably to programmed death.

The author is candid about the limitations of the current work and the road ahead. Only caspases-3 and 7 were profiled, and future studies should extend the analysis to caspase-9, the initiator caspase of the mitochondrial pathway, to complete the mechanistic picture. Detailed release kinetics of camptothecin from the fibers remain to be determined, and in vitro toxicity testing against a panel of actual cancer cell lines, including breast, ovarian, colon, lung, and stomach cancers, is strongly advocated. Nevertheless, the study establishes an important proof of principle: electrospinning can encapsulate a fragile plant-derived prodrug in a biocompatible polymer matrix without destroying its active lactone ring, while simultaneously improving its thermal stability, protecting it from hydrolysis, and preserving its capacity to kill cells through the caspase-mediated apoptotic pathway. As electrospun nanofibers continue to find applications as bandages, implants, and drug delivery scaffolds, this work suggests that the humble fiber mat may yet become a formidable weapon in the ongoing war against cancer.

Subject of Research: Camptothecin-loaded PLA micronanofibers inducing caspase-mediated apoptosis in C2C12 cells

Article Title: Caspase mediated apoptosis induced by CPT@PLA micro-nanofibers in C2C12 cells: insights into probable mechanism

Article References: Amna, T. (2025). Caspase mediated apoptosis induced by CPT@PLA micro-nanofibers in C2C12 cells: insights into probable mechanism. Discover Toxicology, 2(1), Article 12. https://doi.org/10.1007/s44339-025-00024-y

Image Credits: AI Generated

DOI: 10.1007/s44339-025-00024-y

Keywords: camptothecin, electrospinning, poly(lactic acid), micronanofibers, apoptosis, caspase-3, caspase-7, C2C12 cells, drug delivery, topoisomerase I, nanomedicine, cytotoxicity

In a study published in Molecular Diversity, researchers led by Huang Zeng and Bo Qiu of Jiaying University describe a systematic computational framework that pairs five types of molecular fingerprints with both classical machine learning and deep learning algorithms. Their goal was twofold: to rigorously compare how different combinations of chemical representations and algorithms perform at predicting TOP1 inhibition, and to convert the best performers into practical tools that any medicinal chemist can use. The work culminated not just in models but in validated hit compounds, two of which showed meaningful inhibition of the enzyme in laboratory tests.

The foundation of the study is data. The team mined the ChEMBL database for compounds with experimentally measured TOP1 inhibitory activity, assembling a dataset suitable for training classification models. Each molecule was then encoded using five distinct fingerprint families: AtomPairs, which describe pairs of atoms and their topological separation; MACCS keys, a set of 166 structurally predefined substructure features; Morgan fingerprints, circular fingerprints that capture local atomic environments much like extended-connectivity fingerprints; PharmacoPFP, which encodes pharmacophore features rather than pure connectivity; and RDKit descriptors, a collection of calculated physicochemical and topological properties. Because each fingerprint represents a different view of what a molecule is, the choice of representation can matter as much as the choice of algorithm.

On top of these representations, the researchers built a total of forty models spanning classical methods such as support vector machines, random forests, and k-nearest neighbors, alongside deep neural networks. The head-to-head comparison produced a clear result: combinations built on Morgan fingerprints dominated the leaderboard. Under random data splitting, the four top performers were SVM paired with Morgan fingerprints, random forest with Morgan, a deep neural network using MACCS keys, and k-nearest neighbors with Morgan. All four achieved area under the receiver operating characteristic curve values, or ROC-AUC, between 0.93 and 0.94, a level of discrimination that places them firmly in the range useful for prioritizing compounds in virtual screening campaigns.

High scores alone can deceive, particularly in cheminformatics where models sometimes memorize dataset quirks rather than learning genuine structure-activity relationships. To guard against this, the team applied Y-scrambling, a validation technique in which the activity labels are randomly shuffled and models are retrained. If the original models had been learning noise, their scrambled counterparts would perform equally well. They did not, supporting the conclusion that the top models had captured real, non-random relationships between molecular structure and TOP1 inhibition. The authors are also candid about a second, more sobering test: when compounds were split by molecular scaffold rather than randomly, AUC values dropped to between 0.67 and 0.82. This scaffold-split performance reveals that the models are less reliable when asked to extrapolate to chemically novel frameworks, a well-known limitation of fingerprint-based approaches that the researchers explicitly quantify rather than hide.

Interpretability was another pillar of the work. Black-box predictions carry little weight in drug design unless chemists can see what drives them. Using SHAP analysis, a game-theoretic method that assigns each molecular feature a contribution to individual predictions, the team identified the key structural elements that the models associated with TOP1 inhibition. This step transforms the models from opaque ranking machines into sources of chemical hypotheses, allowing medicinal chemists to see which substructures and descriptor values push a compound toward or away from the predicted active class, and potentially to use those insights when designing next-generation analogs.

What distinguishes this study from many computational papers is its insistence on usability. The researchers deployed their best models as a freely accessible web application, reachable at drugpred.top:5000, where users can input a molecule as a SMILES string, the standard text notation for chemical structures, or simply draw the structure in a browser and receive a prediction of TOP1 inhibitory activity. For laboratories that cannot or prefer not to rely on an internet connection, standalone desktop executables are available for download on GitHub at github.com/zenghuang8006/TOP1-inhibitor-prediction. The authors note that earlier AI-driven studies of TOP1 inhibitors lacked both systematic fingerprint and algorithm comparisons and accessible predictive tools, and this pipeline is designed to fill both gaps at once.

The real proof, however, came from the laboratory. The team screened a commercial library of 189,554 compounds from SPECS using their computational framework, then took the highest-ranking candidates into in vitro testing. Two compounds, designated AG60 and AI61, emerged as potential TOP1 inhibitor hits. At a concentration of 400 micromolar, AG60 inhibited the enzyme by 64 percent, while AI61 achieved 90 percent inhibition. Converting a computational prediction into experimentally confirmed enzyme inhibition, even at moderate potency, is a meaningful step; the compounds now represent starting points, or hits, that can be chemically optimized into more potent and selective leads through medicinal chemistry.

The biological context underscores why this target continues to attract attention. TOP1 is essential in proliferating cells, and its inhibition selectively harms cancer cells that cannot tolerate the resulting DNA damage. Approved TOP1 poisons remain mainstays of treatment for colorectal, ovarian, and other cancers, and newer agents such as antibody-drug conjugates carrying TOP1 inhibitor payloads have expanded the target’s therapeutic reach. But resistance, toxicity, and the pharmacological limitations of existing scaffolds keep the demand for chemical novelty high. The authors’ own prior work, including the computational identification of FTY720 and COH29 as TOP1 catalytic inhibitors, illustrates a sustained research program aimed at expanding the toolbox of TOP1-directed chemistry.

For the broader field, the study offers a template as much as a set of hits. It demonstrates that fingerprint choice can be systematically benchmarked, that interpretability tools like SHAP can be integrated into the screening workflow, and that honest reporting of scaffold-split performance should accompany headline accuracy figures. It also shows that the distance between a machine learning model and a wet-lab result can be shortened when prediction tools are built for practicing chemists rather than for other data scientists. The forty models, the validated hits AG60 and AI61, and the freely available web and desktop predictors together form a practical framework that other target-focused screening campaigns can adapt. As artificial intelligence continues to compress the early stages of drug discovery, studies that pair rigorous benchmarking with experimental follow-through, and that are transparent about both strengths and limitations, offer the clearest picture of what these methods can actually deliver for anticancer drug development.

Subject of Research: AI-driven screening of topoisomerase I inhibitors using machine learning, deep learning and molecular fingerprints

Article Title: Integrating machine learning and deep learning with multiple molecular fingerprints for topoisomerase I inhibitor screening and lead identification

Article References: Integrating machine learning and deep learning with multiple molecular fingerprints for topoisomerase I inhibitor screening and lead identification. (n.d.). https://doi.org/10.1007/s11030-026-11709-w

Image Credits: AI Generated

DOI: 10.1007/s11030-026-11709-w

Keywords: topoisomerase I, TOP1 inhibitors, machine learning, deep learning, molecular fingerprints, virtual screening, drug discovery, SHAP, ChEMBL, anticancer targets, SPECS compounds, QSAR