An integrated aptamer-discovery and sensing framework that combines capture SELEX, Next-Generation Sequencing (NGS), and artificial intelligence–enhanced data mining, followed by validation on a field-effect transistor (FET) biosensing platform is developed.
Abstract
The advancement of Boron Neutron Capture Therapy (BNCT) requires rapid and accurate monitoring of Boronophenylalanine (BPA), yet existing analytical methods such as ICP-AES and ICP-MS remain too slow and incompatible with real-time clinical needs. To address this limitation, we developed an integrated aptamer-discovery and sensing framework that combines capture SELEX, Next-Generation Sequencing (NGS), and artificial intelligence–enhanced data mining, followed by validation on a field-effect transistor (FET) biosensing platform.
At the molecular selection level, a redesigned capture SELEX protocol was developed to effectively accommodate compact small molecules such as BPA without requiring immobilization on solid substrates. This design minimizes selection bias and preserves native binding configurations. High-resolution NGS analysis, together with AI-assisted motif clustering and structure–function modeling, enabled the identification of aptamer families exhibiting strong and selective affinity toward key chemical domains of BPA, yielding insights beyond the capabilities of classical enrichment-based approaches.
Aptamers identified through this SELEX-NGS-AI pipeline were subsequently integrated onto an extended-gate FET biosensor, which demonstrated clear, reproducible electrical responses with a well-defined detection limit. This confirms that the combined workflow can efficiently generate high-performance small-molecule aptamers and translate them directly into electronic biosensing platforms suitable for real-time BPA detection in BNCT.
Overall, this work establishes a unified and synergistic pipeline in which capture SELEX provides the basis for molecular recognition, NGS offers high-resolution sequence evolution tracking, and AI converts complex sequence patterns into predictive functional insights. The resulting BPA-specific aptamers enabled sensitive, label-free electrical detection on an FET sensor, demonstrating a scalable and clinically relevant route toward high-precision BPA quantification for BNCT monitoring.
Figure 1
Boron Neutron Capture Therapy (BNCT) relies heavily on the precise accumulation of Boronophenylalanine (BPA) in tumor cells to achieve therapeutic efficacy [1]. However, current clinical methods for monitoring BPA concentrations, such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS), are limited by lengthy analysis times and complex sample pretreatment, rendering them unsuitable for real-time pharmacokinetic monitoring. Furthermore, the development of rapid electrical biosensors is often hindered by probe instability and ionic screening effects in physiological environments [2].
To address these challenges, this study presents a label-free Field-Effect Transistor (BioFET) platform [3] designed for the rapid and specific detection of BPA. A robust surface engineering strategy was employed to ensure optimal probe orientation and stability. The gold sensing gate was first coated with a silicon dioxide (SiO
2
) layer to facilitate silanization, followed by functionalization with 3-aminopropyltriethoxysilane (APTES) and the heterobifunctional crosslinker SMCC [4, 5]. This approach enables the site-specific covalent immobilization of thiol-terminated BPA aptamers [6], significantly minimizing non-specific desorption compared to physical adsorption methods. Additionally, fluorescently labeled aptamers (FAM-modified) were utilized to visually verify the uniformity of the sensing monolayer.
Experimental results in buffer solutions demonstrate that the fabricated BioFET sensor achieves a rapid response time of under 15 minutes with high sensitivity. This study successfully establishes the fundamental sensing mechanism and validates the stability of the covalent immobilization protocol. These findings provide a solid foundation for translating the technology to complex biological matrices, such as serum and whole blood, for future clinical applications in BNCT.
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Figure 1.
(A) Schematic illustration of the BioFET interface engineering. The gold gate is modified with SiO
2
/APTES/SMCC to covalently immobilize BPA-aptamers. (B) Real-time electrical response of the sensor to increasing concentrations of BPA in buffer solution, demonstrating rapid detection within 15 minutes.
Figure 1
Early diagnosis of acute diseases is fundamentally constrained by the lack of chemically tractable methods for biomarker discovery directly from a complex plasma. Here, we report PSABD (plasma-SELEX-enriched aptamer-based biomarker discovery and diagnosis), a chemical platform that integrates carboxylate magnetic-bead-mediated plasma protein capture within situ aptamer SELEX, enabling molecular-recognition-driven identification of disease-associated biomarkers. With this strategy, enriched aptamers not only serve as affinity probes but also function as structurally programmable molecular handles to pull down their targets, thereby linking selection chemistry with proteomic discovery. With acute myocardial infarction (AMI) as a model, PSABD identified malate dehydrogenase 2 (MDH2) as an unexpected AMI-associated plasma protein, which was significantly elevated in AMI patient samples and exhibited a high binding affinity to selected aptamers. On the basis of this interaction, we developed an aptamer-based analytical platform capable of distinguishing AMI patients from healthy individuals. Collectively, PSABD establishes a general chemical strategy that bridges molecular recognition, biomarker discovery, and diagnostic development, highlighting aptamers as programmable chemical probes for the acute disease diagnosis and clinical translation.
Small-molecule aptamer discovery combines systematic evolution of ligands by exponential enrichment (SELEX), high-throughput-SELEX, artificial intelligence (AI)-assisted prioritization, structural modeling, experimental binding assays, and biosensor engineering, but these stages generate non-interchangeable evidence. This review examines how target presentation, analog chemistry, sequence analysis, AI, docking, molecular dynamics, and assay architecture support different claims. Representative small-molecule and therapeutic-monitoring studies illustrate routes from candidate generation to direct binding, selectivity, biosensor evaluation, matrix testing, and continuous monitoring. The proposed validation-aware framework aligns claims with the endpoint examined and prevents computational scores, sensor responses, or concentration traces from being interpreted as proof of affinity, mechanism, or clinical utility.
Alexey Podshibiakin, T. Grigoreva· Journal of Advanced Biotechn...· 0 citations
Anthracycline chemotherapeutics such as doxorubicin and daunorubicin remain indispensable in cancer treatment but possess narrow therapeutic windows, motivating the development of reliable molecular recognition elements for therapeutic drug monitoring, biosensing, and toxicity mitigation. Although an anthracycline aptamer has previously been obtained using immobilized daunorubicin, it lacks a well-defined secondary structure and exhibits poor affinity after truncation. Here, we employed capture-SELEX, which uses free target molecules in solution without immobilization, to isolate DNA aptamers that specifically recognize doxorubicin. The selection yielded three distinct sequence families with well-defined secondary structures and low-nanomolar affinities. Using intrinsic fluorescence quenching, isothermal titration calorimetry, and NMR spectroscopy, we rigorously distinguished specific aptamer recognition from nonspecific intercalation and demonstrated rapid, Mg2+-independent binding. The strongest aptamer, DOX-9, bound doxorubicin with a dissociation constant of 6 nM and also recognized daunorubicin with nanomolar affinity. Compared with the previously reported aptamer, the new sequences exhibit higher affinity, clearer structural features, and greater amenability to truncation. Importantly, they significantly inhibited doxorubicin uptake by HeLa cells, demonstrating potential as functional antidotes in addition to sensing elements. Together, these results establish a new generation of anthracycline aptamers and highlight capture-SELEX as an effective strategy for selecting aptamers against DNA-interacting small molecules.
Meheta Datta, Celine Fidella, Lashaun N. Coote et al.· ACS Chemical Biology· 0 citations
Bioanalysis of the immune checkpoint inhibitor nivolumab is crucial for pharmacokinetic studies. However, conventional ligand-binding assays (LBAs) often suffer from the lot-to-lot variability of capture antibodies. Here, we developed a high-affinity anti-nivolumab DNA aptamer as a robust alternative reagent. An integrated strategy combining Fast Protein Liquid Chromatography-based screening, next-generation sequencing, aptamer search algorithm, and computational modeling enabled rapid aptamer identification and optimization. The anti-nivolumab aptamer, mutant 2-3, discovered through in silico modification exhibited a dissociation constant (KD) of 33 nM. Using this aptamer, an enzyme-linked aptamer assay (ELAA) successfully quantified nivolumab in 400-fold diluted plasma samples over a range of 0.02-5.0 µg/mL, demonstrating good precision with a coefficient of variation less than 11.9%. This novel analytical method provides a sufficient quantitative range for the bioanalysis of nivolumab, thus offering a robust, specific, and cost-effective alternative for future pharmacokinetic studies.
Taisei Hojo, Nao Kobayashi, Chiho Hirayama et al.· Chemical and pharmaceutical...· 1 citation
Cholangiocarcinoma (CCA) is an aggressive biliary malignancy with poor prognosis and limited treatment options, underscoring the need for targeted RNA-based therapeutic strategies. Though DNA- aptamers could be one of the promising targeted ligands, conventional enrichment-driven selection often fails to reliably identify high-affinity binders due to PCR bias, stochastic drift, and sequencing errors. In this study, we combined cell-SELEX with a data-driven artificial intelligence framework to identify DNA aptamers targeting CCA cells. Eleven rounds of selection were performed on a library exceeding 6×1014 sequences, with iterative negative selection against normal hepatocytes and cholangiocytes to enhance tumor specificity. Given the limitations of enrichment-based ranking in prioritizing functional binders, we developed and evaluated four deep learning architectures—MLP, CNN, BiLSTM, and a hybrid CNN–BiLSTM—using aptamer pools derived from the final SELEX round. These models were built by integrating one-hot sequential features and DNA structural descriptors of the dataset along with their enrichment patterns across SELEX rounds. Among them, the hybrid CNN–BiLSTM model demonstrated superior performance by capturing both local sequence motifs and long-range dependencies. Model performance was evaluated using AUC-ROC and complementary classification metrics on an independent test set. The hybrid CNN–BiLSTM model achieved the best overall performance with an AUC-ROC of 0.97 and a precision of 68%, indicating strong discriminative power under class imbalance (strong: non-binder=1:68). The model exhibited high specificity (99.5%; 29,855/30,005 true negatives) and moderate sensitivity (71.7%; 312/435 true positives), reflecting its ability to reliably filter non-binding sequences while retaining true functional binders. Model-prioritized candidates were experimentally validated using qPCR-based aptamer–cell binding assays. Notably, two low-abundance/high-prediction aptamers (Read counts: 4) exhibited 2–3-fold higher binding than the reference aptamer (Positive Control) derived from traditional SELEX enrichment. A high-read/high-prediction candidate showed ∼2-fold greater binding than the positive control, highlighting the model’s ability to recover overlooked functional binders. To assess translational relevance, selected aptamers were used to functionalize the lactosome-based nanovesicles for siRNA delivery targeting PD-L1, resulting in measurable gene silencing in HuCCT1 cells after 48 hours. Collectively, this integrative framework enables accurate identification and prioritization of high-affinity, functionally relevant aptamers beyond enrichment alone. This approach enhances SELEX efficiency, supports rational ligand design, and provides a scalable strategy for developing aptamer-guided RNA therapeutics for any target.
Sourav Pal, Julia Driscoll, Brandon Wilbanks, Ayano Kabashima, Rory Smoot, Tushar Patel. Integrating cell-SELEX and deep learning method for discovery of high-affinity aptamers targeting cholangiocarcinoma [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A033.
Sourav Pal, Julia Driscoll, Brandon A. Wilbanks et al.· Clinical Cancer Research· 0 citations