Screening Model Research Articles

Efficient and Rapid Generation of Neural Stem Cells by Direct Conversion Fibroblasts with Single microRNAs.

Neural stem cells (NSCs) have great potentials in the application of neurodegenerative disease therapy, drug screening, and disease modeling. However, current approaches for induced NSCs (iNSCs) generation from somatic cells are still slow and inefficient. Here we establish a rapid and efficient method of iNSCs generation from human and mouse fibroblasts by single microRNAs (miR-302a). These iNSCs exhibited morphological, molecular and functional properties resembling those of adult human and mouse NSCs, and human iNSCs can be expanded for more than 20 passages in vitro. Furthermore, miR-302a alone was demonstrated to be sufficient to reprogram both human and mouse fibroblasts into iNSCs. Our results showed that direct conversion of autologous fibroblasts with miR-302a into iNSCs, which provides a rapid and efficient strategy to generate iNSCs for both basic research and clinical applications.

Finding a needle in a haystack: functional screening for novel targets in cancer immunology and immunotherapies.

Genome-wide functional genetic screening has been widely used in the biomedicine field, which makes it possible to find a needle in a haystack at the genetic level. In cancer research, gene mutations are closely related to tumor development, metastasis, and recurrence, and the use of state-of-the-art powerful screening technologies, such as clustered regularly interspaced short palindromic repeat (CRISPR), to search for the most critical genes or coding products provides us with a new possibility to further refine the cancer mapping and provide new possibilities for the treatment of cancer patients. The use of CRISPR screening for the most critical genes or coding products has further refined the cancer atlas and provided new possibilities for the treatment of cancer patients. Immunotherapy, as a highly promising cancer treatment method, has been widely validated in the clinic, but it could only meet the needs of a small proportion of cancer patients. Finding new immunotherapy targets is the key to the future of tumor immunotherapy. Here, we revisit the application of functional screening in cancer immunology from different perspectives, from the selection of diverse in vitro and in vivo screening models to the screening of potential immune checkpoints and potentiating genes for CAR-T cells. The data will offer fresh therapeutic clues for cancer patients.

Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emerging viral threats requires substantial resources and time, and traditional development platforms for vaccines and antibodies are often too slow to combat continuously evolving immunological escape variants, reducing their efficacy over time. Previously, we designed a next-generation synthetic humanized nanobody (Nb) phage display library and demonstrated that this library could be used to rapidly identify highly specific and potent neutralizing heavy chain-only antibodies (HCAbs) with prophylactic and therapeutic efficacy in vivo against the original SARS-CoV-2. In this study, we used a combination of high throughput screening and machine learning (ML) models to identify HCAbs with potent efficacy against SARS-CoV-2 viral variants of interest (VOIs) and concern (VOCs). To start, we screened our highly diverse Nb phage display library against several pre-Omicron VOI and VOC receptor binding domains (RBDs) to identify panels of cross-reactive HCAbs. Using HCAb affinity for SARS-CoV-2 VOI and VOCs (pre-Omicron variants) and model features from other published data, we were able to develop a ML model that successfully identified HCAbs with efficacy against Omicron variants, independent of our experimental biopanning workflow. This biopanning informed ML approach reduced the experimental screening burden by 78% to 90% for the Omicron BA.5 and Omicron BA.1 variants, respectively. The combined approach can be applied to other emerging viruses with pandemic potential to rapidly identify effective therapeutic antibodies against emerging variants.

Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective Observational Study by Using the Common Data Model.

Background/Objectives: Earlier detection of severe immune-related hematological adverse events (irHAEs) in cancer patients treated with a PD-1 or PD-L1 inhibitor is critical to improving treatment outcomes. The study aimed to develop a simple machine learning (ML) model for predicting irHAEs associated with PD-1/PD-L1 inhibitors. Methods: We utilized the Observational Medical Outcomes Partnership-Common Data Model based on electronic medical records from a tertiary (KHMC) and a secondary (KHNMC) hospital in South Korea. Severe irHAEs were defined as Grades 3-5 by the Common Terminology Criteria for Adverse Events (version 5.0). The predictive model was developed using the KHMC dataset, and then cross-validated against an independent cohort (KHNMC). The full ML models were then simplified by selecting critical features based on the feature importance values (FIVs). Results: Overall, 397 and 255 patients were included in the primary (KHMC) and cross-validation (KHNMC) cohort, respectively. Among the tested ML algorithms, random forest achieved the highest accuracy (area under the receiver operating characteristic curve [AUROC] 0.88 for both cohorts). Parsimonious models reduced to 50% FIVs of the full models showed comparable performance to the full models (AUROC 0.83-0.86, p > 0.05). The KHMC and KHNMC parsimonious models shared common predictive features including furosemide, oxygen gas, piperacillin/tazobactam, and acetylcysteine. Conclusions: Considering the simplicity and adequate predictive performance, our simplified ML models might be easily implemented in clinical practice with broad applicability. Our model might enhance early diagnostic screening of irHAEs induced by PD-1/PD-L1 inhibitors, contributing to minimizing the risk of severe irHAEs and improving the effectiveness of cancer immunotherapy.

Modelling Peroxisomal Disorders in Zebrafish.

Peroxisomes are ubiquitous, dynamic, oxidative organelles with key functions in cellular lipid metabolism and redox homeostasis. They have been linked to healthy ageing, neurodegeneration, cancer, the combat of pathogens and viruses, and infection and immune responses. Their biogenesis relies on several peroxins (encoded by PEX genes), which mediate matrix protein import, membrane assembly, and peroxisome multiplication. Defects in peroxins or peroxisomal enzymes can result in severe disorders, including developmental and neurological abnormalities. The drive to understand the role of peroxisomes in human health and disease, as well as their functions in tissues and organs or during development, has led to the establishment of vertebrate models. The zebrafish (Danio rerio) has become an attractive vertebrate model organism to investigate peroxisomal functions. Here, we provide an overview of the visualisation of peroxisomes in zebrafish, as well as the peroxisomal metabolic functions and peroxisomal protein inventory in comparison to human peroxisomes. We then present zebrafish models which have been established to investigate peroxisomal disorders. These include model zebrafish for peroxisome biogenesis disorders/Zellweger Spectrum disorders, and single enzyme deficiencies, particularly adrenoleukodystrophy and fatty acid beta-oxidation abnormalities. Finally, we highlight zebrafish models for deficiencies of dually targeted peroxisomal/mitochondrial proteins. Advantages for the investigation of peroxisomes during development and approaches to the application of zebrafish models for drug screening are discussed.

Combined first-trimester screening for preterm small-for-gestational-age infants: Australian multicenter clinical feasibility study.

To assess the performance of the Fetal Medicine Foundation (FMF) first-trimester competing-risks screening model for small-for-gestational-age (SGA) fetuses requiring delivery at < 37 weeks' gestation, in a large cohort of women receiving maternity care in Australia. This was a retrospective analysis of prospectively collected data from a cohort of women attending one of two private multicenter fetal medicine practices for first-trimester screening for preterm pre-eclampsia (PE), defined as PE requiring delivery before 37 weeks' gestation. Risk for preterm SGA, defined as SGA requiring delivery before 37 weeks, was calculated but was not disclosed to the patient or referring physician. Screening data were matched to obstetric outcomes. The primary outcome was the efficacy of the FMF screening model in assessing the risk of preterm SGA. The potential effect on identifying other adverse pregnancy outcomes was also assessed. During the study period, 22841 women with a singleton pregnancy underwent combined first-trimester screening for preterm PE. These data were compared with those of 301721 women in the state of Victoria with a singleton pregnancy who did not undergo screening during the study period. Calculation of the risk for preterm SGA identified 3030 (13.3%) pregnancies as high risk. The sensitivity of the model was 48.6% (95% CI, 41.0-56.2%), specificity was 87.0% (95% CI, 86.6-87.5%) and positive and negative predictive values were 2.9% (95% CI, 2.7-3.1%) and 99.5% (95% CI, 99.4-99.6%) respectively. Pregnancies at high risk for preterm SGA were also more likely to have preterm PE (risk ratio (RR), 2.28 (95% CI, 1.72-3.03)) and preterm birth (RR, 1.46 (95% CI, 1.32-1.63)), compared with unscreened pregnancies. Pregnancies at low risk for preterm SGA were less likely to result in a stillbirth (RR, 0.64 (95% CI, 0.47-0.86)) compared with unscreened pregnancies. Combined first-trimester screening for preterm SGA shows moderate screening efficacy and therefore could help to inform pregnancy management and improve antenatal resource allocation. © 2025 International Society of Ultrasound in Obstetrics and Gynecology.

Progress on aspiration assessment methods for patients after esophageal cancer surgery in early: A review.

Esophageal cancer is a relatively common malignant tumor of the digestive tract. Patients with esophageal cancer show a high incidence of aspiration after surgery, which has a serious impact on their prognosis and rehabilitation. Nevertheless, while existing and past endeavors have concentrated on enhancing the diagnostic and therapeutic strategies for esophageal cancer, the necessity of preventing pneumonia caused by postoperative aspiration remains to be adequately addressed. We compiled the presently published literature and offer the latest developments on the causes of postoperative aspiration in patients with esophageal cancer, screening methods, and swallowing assessment tools. Relevant published papers were collected through a search of the China national knowledge infrastructure, Ovid EMBASE, Web of Science, Cochrane, and PubMed databases. There are various methods for assessing swallowing function after surgery for esophageal cancer. Clinically, it is necessary to select appropriate assessment tools for the swallowing function. Research indicates that the application of risk prediction models can better assess aspiration in patients after esophageal cancer surgery, bridge gaps in qualitative analysis, and alter the clinical outcomes of patients. Predictive models for dysphagia screening in patients after esophagectomy have significant clinical advantages and exhibit good clinical applicability.

Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review.

Obstructive sleep apnea (OSA) is a prevalent yet underdiagnosed condition associated with a major healthcare burden. Current diagnostic tools, such as full-night polysomnography (PSG), pose a limited accessibility to diagnosis due to their elevated costs. Recent advances in Artificial Intelligence (AI), including Machine Learning (ML) and deep learning (DL) algorithms, offer novel potential tools for an accurate OSA screening and diagnosis. This systematic review evaluates articles employing AI-powered models for OSA screening and diagnosis in the last decade. A comprehensive electronic search was performed on PubMed/MEDLINE, Google Scholar, and SCOPUS databases. The included studies were original articles written in English, reporting the use of ML algorithms to diagnose and predict OSA in suspected patients. The last search was performed in June 2024. This systematic review is registered in PROSPERO (Registration ID: CRD42024563059). Sixty-five articles, involving data from 109,046 patients, met the inclusion criteria. Due to the heterogeneity of the algorithms, outcomes were analyzed into six sections (anthropometric indexes, imaging, electrocardiographic signals, respiratory signals, and oximetry and miscellaneous signals). AI algorithms demonstrated significant improvements in OSA detection, with accuracy, sensitivity, and specificity often exceeding traditional tools. In particular, anthropometric indexes were most widely used, especially in logistic regression-powered algorithms. The application of AI algorithms to OSA diagnosis and screening has great potential to improve patient outcomes, increase early detection, and lessen the load on healthcare systems. However, rigorous validation and standardization efforts must be made to standardize datasets.

Two- and Three-Dimensional Culture Systems: Respiratory In Vitro Tissue Models for Chemical Screening and Risk-Based Decision Making.

Risk of lung damage from inhaled chemicals or substances has long been assessed using animal models. However, New Approach Methodologies (NAMs) that replace, reduce, and/or refine the use of animals in safety testing such as 2D and 3D cultures are increasingly being used to understand human-relevant toxicity responses and for the assessment of hazard identification. Here we review 2D and 3D lung models in terms of their application for inhalation toxicity assessment. We highlight a key case study for the Organization for Economic Cooperation and Development (OECD), in which a 3D model was used to assess human toxicity and replace the requirement for a 90-day inhalation toxicity study in rats. Finally, we consider the regulatory guidelines for the application of NAMs and potential use of different lung models for aerosol toxicity studies depending on the regulatory requirement/context of use.

Cost Effectiveness of Colorectal Cancer Screening Strategies in Middle- and High-Income Countries: A Systematic Review.

Colorectal cancer (CRC) is a significant global health burden, and screening can greatly reduce CRC incidence and mortality. Previous studies investigated the economic effects of CRC screening. We performed a systematic review to provide the cost-effectiveness of CRC screening strategies across countries with different income levels. We searched relevant scientific databases (PubMed, Embase, Ovid, Web of Science, Scopus) from January 1, 2010, to December 31, 2023. We selected English-language studies related to model-based economic evaluations of CRC screening strategies. Information such as the characters of screening tests, model characteristics, and key cost-effectiveness findings were collected. The net monetary benefit approach was used to compare the outcomes of various strategies. A total of 56 studies were identified, including 46 from high-income countries (HICs), 6 from upper-middle-income countries (UMICs), and 4 from lower-middle-income countries (LMICs). Most annual fecal occult blood tests and fecal immunochemical tests were cost-saving, and colonoscopy every 10 years was cost-saving. Other strategies involving multitarget fecal FIT-DNA detection, computed tomography colonography, and flexible sigmoidoscopy were cost-effective compared with no screening. Newer strategies such as magnetic resonance colonography every 5 years, annual urine metabolomic tests, and fecal bacterial biomarkers were cost-effective compared with no screening. In our updated review, we found that common CRC screening strategies and magnetic resonance colonography continued to be cost-effective compared with no screening. Areas for further development include accurately modeling the natural history of colorectal cancer and obtaining more evidence from UMICs and LMICs.

Establishment of a Yeast Two-Hybrid-Based High-Throughput Screening Model for Selection of SARS-CoV-2 Spike-ACE2 Interaction Inhibitors.

The recent coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has exerted considerable impact on global health. To prepare for rapidly mutating viruses and for the forthcoming pandemic, effective therapies targeting the critical stages of the viral life cycle need to be developed. Viruses are dependent on the interaction between the receptor-binding domain (RBD) of the viral Spike (S) protein (S-RBD) and the angiotensin-converting enzyme 2 (ACE2) receptor to efficiently establish infection and the following replicate. Targeting this interaction provides a promising strategy to inhibit the entry process of the virus, which in turn has both preventive and therapeutic effects. In this study, we developed a robust and straightforward assay based on the Yeast-Two Hybrid system (Y2H) for identifying inhibitors targeting the S-RBD-ACE2 interaction of SARS-CoV-2. Through high-throughput screening, two compounds were identified as potential entry inhibitors. Among them, IMB-1C was superior in terms of pseudovirus entry inhibition and toxicity. It could bind to both ACE2 and S-RBD and induce conformational change in the S-RBD+ACE2 complex. This is the first study to verify the feasibility of utilizing the Y2H system to discover potent SARS-CoV-2 inhibitors targeting the receptor recognition stage. This approach may also be applied in the discovery of other virus receptor recognition inhibitors.

Extending the MST Model to Large Biomolecular Systems: Parametrization of the ddCOSMO-MST Continuum Solvation Model.

Continuum solvation models such as the polarizable continuum model and the conductor-like screening model are widely used in quantum chemistry, but their application to large biosystems is hampered by their computational cost. Here, we report the parametrization of the Miertus-Scrocco-Tomasi (MST) model for the prediction of hydration free energies of neutral and ionic molecules based on the domain decomposition formulation of COSMO (ddCOSMO), which allows a drastic reduction of the computational cost by several orders of magnitude. We also introduce several novelties in MST, like a new definition of atom types based on hybridization and an automatic setup of the cavity for charged regions. The model is parametrized at the B3LYP/6-31+G(d) and PM6 levels of theory and compared to the performance of IEFPCM/MST. Then, we demonstrate the robustness of the parametrization on the SAMPL2, SAMPL4, and C10 datasets. The ddCOSMO/MST models provide errors of ~0.8 and ~3.2 kcal/mol for neutrals and ions, respectively, showing a remarkable balanced and accurate description of cations and anions.

A Novel CNN-Based Framework for Alzheimer's Disease Detection Using EEG Spectrogram Representations.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that poses critical challenges in global healthcare due to its increasing prevalence and severity. Diagnosing AD and other dementias, such as frontotemporal dementia (FTD), is slow and resource-intensive, underscoring the need for automated approaches. Methods: To address this gap, this study proposes a novel deep learning methodology for EEG classification of AD, FTD, and control (CN) signals. The approach incorporates advanced preprocessing techniques and CNN classification of FFT-based spectrograms and is evaluated using the leave-N-subjects-out validation, ensuring robust cross-subject generalizability. Results: The results indicate that the proposed methodology outperforms state-of-the-art machine learning and EEG-specific neural network models, achieving an accuracy of 79.45% for AD/CN classification and 80.69% for AD+FTD/CN classification. Conclusions: These results highlight the potential of EEG-based deep learning models for early dementia screening, enabling more efficient, scalable, and accessible diagnostic tools.

A review of 3D bioprinting for organoids

Abstract Current two-dimensional (2D) cell models for effective drug screening suffer from significant limitations imposed by the lack of realism in the physiological environment. Three-dimensional (3D) organoids models hold immense potential in mimicking the key functions of human organs by overcoming the limitations of traditional 2D cell models. However, current techniques for preparation of 3D organoids models had limitations in reproducibility, scalability, and the ability to closely replicate the complex microenvironment found in vivo. Additionally, traditional 3D cell culture systems often involve lengthy and labor-intensive processes that hinder high-throughput applications necessary for a large-scale drug screening. Advancements in 3D bioprinting technologies offer promising solutions to these challenges by enabling precise spatial control over cell placement and material composition, thereby facilitating the creation of more physiologically relevant organoids than current techniques. This review provides a comprehensive summary of recent advances in 3D bioprinting technologies for creating organoids models, which begins with an introduction to different types of 3D bioprinting techniques (especially focus on volumetric bioprinting (VBP) technique), followed by an overview of bioinks utilized for organoids bioprinting. Moreover, we also introduce the applications of 3D bioprinting organoids in disease models, drug efficiency evaluation and regenerative medicine. Finally, the challenges and possible strategies for the development and clinical translation of 3D bioprinting organoids are concluded.

Elucidation on the performance of various machine learning models for real-time malware detection, malware classification and network packet screening

Elucidation on the performance of various machine learning models for real-time malware detection, malware classification and network packet screening

A macro-transection model of brain trauma for neuromaterial testing with functional electrophysiological readouts.

Functional recovery in penetrating neurological injury is hampered by a lack of clinical regenerative therapies. Biomaterial therapies show promise as medical materials for neural repair through immunomodulation, structural support, and delivery of therapeutic biomolecules. However, a lack of facile and pathology-mimetic models for therapeutic testing is a bottleneck in neural tissue engineering research. We have deployed a two-dimensional, high-density multicellular cortical brain sheet to develop a facile model of injury (macrotransection/scratch wound) in vitro. The model encompasses the major neural cell types involved in pathological responses post-injury. Critically, we observed hallmark pathological responses in injury foci including cell scarring, immune cell infiltration, precursor cell migration, and short-range axonal sprouting. Delivering test magnetic particles to evaluate the potential of the model for biomaterial screening shows a high uptake of introduced magnetic particles by injury-activated immune cells, mimicking in vivo findings. Finally, we proved it is feasible to create reproducible traumatic injuries in the brain sheet (in multielectrode array devices in situ) characterized by focal loss of electrical spiking in injury sites, offering the potential for longer term, electrophysiology plus histology assays. To our knowledge, this is the first in vitro simulation of transecting injury in a two-dimensional multicellular cortical brain cell sheet, that allows for combined histological and electrophysiological readouts of damage/repair. The patho-mimicry and adaptability of this simplified model of brain injury could benefit the testing of biomaterial therapeutics in regenerative neurology, with the option for functional electrophysiological readouts.

Unveiling the Anti-Angiogenic Potential of Small-Molecule (Kinase) Inhibitors for Application in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation leading to joint damage and systemic complications. Angiogenesis promotes inflammation and contributes to RA progression. This study evaluated potential anti-angiogenic effects of several compounds including small-molecule kinase inhibitors, such as sunitinib (pan-kinase inhibitor), tofacitinib (JAK-inhibitor), NIKi (NF-κB-inducing kinase inhibitor), and the integrin-targeting peptide fluciclatide, using a scratch assay and 3D spheroid-based models of angiogenesis. For all drugs tested in the low micromolar range (1-25 μM), sunitinib (as positive anti-angiogenetic control) showed marked inhibition of endothelial cell (EC) migration and sprouting, effectively reducing both scratch closure and sprout formation. Tofacitinib exhibited marginal effectiveness in the scratch assay, but performed better in the 3D models (55% inhibition), whereas NIKi showed around 50% anti-angiogenic effects in both models. Fluciclatide changed EC morphology rather than migration, and only when stimulated with synovial fluid in spheroid model did it show inhibitory effects (at ≥2.5 µM), with none below this dosage. These results highlight the potential of NIKi and tofacitinib for angiogenesis inhibition and of fluciclatide for safe diagnostic targeting of microdose in RA, as well as the need for advanced screening models that mimic RA's complex inflammatory pro-angiogenic environment.

Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function

Integrating deep learning with the search for new electron-phonon superconductors represents a burgeoning field of research, where the primary challenge lies in the computational intensity of calculating the electron-phonon spectral function, α2F(ω), the essential ingredient of Midgal-Eliashberg theory of superconductivity. To overcome this challenge, we adopt a two-step approach. First, we compute α2F(ω) for 818 dynamically stable materials. We then train a deep-learning model to predict α2F(ω), using a training strategy tailored for limited data to temper the model’s overfitting, enhancing predictions. Specifically, we train a Bootstrapped Ensemble of Tempered Equivariant graph neural NETworks (BETE-NET), obtaining an MAE of 0.21, 45 K, and 43 K for the moments derived from α2F(ω): λ, ωlog, and ω2, respectively, yielding an MAE of 2.5 K for the critical temperature, Tc. Further, we incorporate domain knowledge of the site-projected phonon density of states to impose inductive bias into the model’s node attributes and enhance predictions. This methodological innovation decreases the MAE to 0.18, 29 K, and 28 K, respectively, yielding an MAE of 2.1 K for Tc. We illustrate the practical application of our model in high-throughput screening for high-Tc materials. The model demonstrates an average precision nearly five times higher than random screening, highlighting the potential of ML in accelerating superconductor discovery. BETE-NET accelerates the search for high-Tc superconductors while setting a precedent for applying ML in materials discovery, particularly when data is limited.

Modeling and the Use of Surrogate Endpoints: Is This a Valid Approach?

Development of novel colorectal cancer (CRC) screening tests is a dynamic field. Decision analytic modeling based on inputs derived from rigorous prospective studies informs CRC screening guidelines. Exploratory modeling may have a place in early phases of test development. We explored whether (1) surrogate endpoints for long-term, programmatic effectiveness, and cost-effectiveness could be potentially informative in early stages of test development and (2) whether rapid exploratory modeling with a web-based tool would be feasible. First, based on comparisons with published estimates for reductions in CRC mortality with various screening tests in four established decision analytic models of CRC screening, the surrogate endpoint of the number needed to colonoscope to detect one CRC or advanced precancerous lesion (APL) in round 1 of screening appears to hold promise as a measure of clinical effectiveness. Similarly, based on comparisons with published estimates for cost/quality-adjusted life-year gained with screening in the four models, the surrogate endpoint of cost to detect one CRC or APL in round 1 of screening appears to hold promise as a measure of cost-effectiveness. Second, exploration of the impact of lowering the screening initiation age in Israel from age 50 to 45 with the web-based EU-TOPIA tool, compared with the results of a recently published comprehensive modeling study, suggests that exploratory modeling of programmatic screening may be feasible with relatively low time demand vs. that required for typical full-length modeling publications. Further validation in other models and with a broader set of test performance characteristics is prudent before surrogate endpoints or rapid modeling are incorporated into the novel test development process.

Molecular interactions between surface-active ionic liquids based on 2-hydroxyethylammonium laurate with gabapentin: electrical conductivity and surface tension studies.

Surface active ionic liquids (SAILs), offer potential advantages for pharmaceutical applications. Given the low permeability of gabapentin, an antiepileptic drug, in the gastrointestinal tract as classified by the Biopharmaceutics Classification Systems (BCS), understanding the micellization behavior of SAILs is essential for developing effective drug delivery systems to improve gabapentin bioavailability. This study explores the micellization and thermophysical behavior of SAILs (2-hydroxyethyl)ammonium laurate [2-HEA][Lau], bis(2-hydroxyethyl)ammonium laurate [BHEA][Lau], and tris(2-hydroxyethyl)ammonium laurate [THEA][Lau] in the presence of aqueous gabapentin solution at varied temperatures through COSMO analysis, electrical conductivity and surface tension measurements. The electrical conductivity and surface tension measurements were employed to obtain the critical micelle concentration (CMC) and related thermophysical properties of binary (SAILs + water) and ternary (SAILs + water + gabapentin) systems at (298.15 to 318.15) K such as Π (interface surface pressure), σ CMC (CMC point surface tension), A min (minimum surface area occupied per molecule), Γ max (Gibbs maximum excess surface concentration) have been computed. For better understanding the interactions between these components, Conductor like Screening Model (COSMO) was utilized. The study revealed that CMC values increased with temperature but decreased with increasing gabapentin concentration. Finally, interactions between SAILs and gabapentin were investigated through limiting molar conductivity Λ 0, and association constant K A, determination.