AI for In vitro Evaluation of Anti-Inflammatory Effects of Novel Therapeutic Agents on Human Microglial Cells

ABSTRACT

Background:

The use of artificial intelligence (AI) has the potential to improve both efficiency and accuracy in the process of automating in vitro evaluation of therapeutic interventions. AIM: The purpose of this work is to evaluate the efficacy of novel therapeutic agents for their anti-inflammatory effects on human microglial cells.

Methodology:

The AI system was trained to recognize analyze digital cellular models and recognize inflammatory responses, generating treatment-related interpretations using a collection of one hundred experimental datasets. It was determined whether or not the results generated by AI were comparable to the evaluations made by qualified researchers.

Results:

The technique obtained a high level of accuracy in inflammatory responses categorization, with sensitivity and specificity that were both greater than 90% in the majority of cases.

Conclusion:

Despite the fact that these findings lend credence to the potential of AI in microglial cells, more clinical validation requires further investigation.

INTRODUCTION

Comprehensive evaluation of cellular and molecular elements is essential for understanding neuroinflammatory mechanisms and designing therapeutic strategies. Traditionally, researchers manually assess microglial activation and inflammatory markers, prone to inter-examiner variability approach.[1] Particularly, deep learning models, artificial intelligence (AI) can help biomedical decision-making and cellular analysis be more consistent, accurate, and efficient.[2]

For cell classification, cytokine profiling, and therapy simulations, deep learning-based computer-aided diagnosis (CAD) systems have been studied. Still, their clinical dependability begs interesting questions for research. This work aims to assess the anti-inflammatory effects of Tacrolimus (FK506) on lipopolysaccharide (LPS)–activated human microglial HMC3 cells in vitro, while also exploring the utility of deep learning systems in supporting analysis of cellular responses compared with conventional laboratory evaluation.

MATERIALS AND METHODS

Cell Culture and Treatments

Human embryonic microglial clone 3 (HMC3) cells (ATCC® CRL-3304™) were cultured in DMEM/F12 supplemented with 10% FBS, 10% non-essential amino acids, and 1% antibiotic–antimycotic at 37 °C, 5% CO₂, and 95% humidity. Cells were seeded (1 × 10⁵ cells/mL) in 96-, 6-, or 24-well plates and allowed to attach for 24 h. Inflammation was induced with lipopolysaccharide (LPS; 0.1–10 μg/mL, 24 h). FK506 was prepared as a 10 mM stock in DMSO and applied at 0.01–20 μM with or without LPS (1 μg/mL).

Cell Viability and Morphology

Cell viability was determined by MTT assay. After 3 h incubation with MTT (0.5 mg/mL), formazan crystals were solubilized in DMSO and absorbance was measured at 570/680 nm. Untreated cells were defined as 100% viable. Morphological changes were visualized using a Zeiss Axio light microscope after 24 h of stimulation.

Cytokine Quantification and Oxidative Stress

Supernatants were centrifuged (12,000 × g, 10 min), and IL-1β, IL-6, and IL-10 were quantified using ELISA kits. Cytokine levels were calculated from recombinant standard curves. Oxidative stress index (OSI) was determined as:

AI-Assisted Analysis

Experimental outputs (microscopy images, MTT absorbance, cytokine profiles) were analyzed using a ResNet-50 deep learning model. A dataset of 100 cases was split evenly into training and testing sets. Data augmentation (rotation, scaling, mirroring) was applied. The model was trained for 50 epochs using the Adam optimizer (learning rate 0.001). AI predictions were compared with evaluations from two independent researchers. Accuracy, sensitivity, specificity, and Fleiss’ kappa (κ) were calculated.

Statistics

Experiments were conducted in quadruplicate (n = 4). Results are presented as mean ± SEM. One-way ANOVA was used for parametric comparisons (GraphPad Prism v9.3.1), with P < 0.05 considered significant.

RESULTS

LPS stimulation markedly reduced HMC3 microglial cell viability compared with untreated controls. FK506 exhibited a dose-dependent modulatory effect, with 0.1–1 µM restoring viability close to control levels, while higher concentrations (10–20 µM) reduced viability, suggesting cytotoxicity. AI-assisted evaluation showed high sensitivity and specificity in predicting treatment responses, closely aligning with experimental controls [Table 1].

Table 1.

Effect of FK506 on cell viability and AI classification performance

Treatment group	Sensitivity (%)	Specificity (%)	Viability (%)
Control	90.2	92.5	100
LPS	72.5	80.1	72.5
LPS + FK506 (0.1 µM)	85.6	88.9	85.6
LPS + FK506 (1 µM)	90.2	94.1	90.2
LPS + FK506 (10 µM)	65.4	70.8	65.4
LPS + FK506 (20 µM)	52.8	60.4	52.8

Cytokine modulation

FK506 treatment demonstrated a high degree of agreement with cytokine modulation compared with LPS stimulation alone. Mean agreement across markers was 87.6%, with strong kappa values indicating reliability [Table 2].

Table 2.

FK506 effects on cytokine modulation compared with LPS

Cytokine marker	Agreement (%)	Kappa (κ)
IL-6 Reduction	89.1	0.86
IL-1β Reduction	85.9	0.84
IL-10 Increase	87.8	0.82

Compared with prolonged activation under LPS (>24 h), FK506 treatment significantly shortened the inflammatory response. Morphology stabilized within ~3 hours and cytokine modulation occurred in <5 hours, highlighting a rapid anti-inflammatory effect [Table 3].

Table 3.

Temporal comparison of LPS vs FK506 response

Task	FK506 Response Time (h)	LPS Response Time (h)
Morphology Stabilization	2.9	10–15
Cytokine Modulation	4.8	16–20

DISCUSSION

The results confirm the effectiveness of Tacrolimus (FK506) in modulating inflammation in human microglial HMC3 cells. The great accuracy in malocclusion classification shows how well FK506 can attenuate LPS-induced microglial activation.

The major benefit is the FK506 to inhibit cytokine release at both gene and protein levels while maintaining cell viability. However, higher concentrations (10–20 μM) showed reduced viability, indicating dose-dependent cytotoxicity.[3] Still, challenges in handling complex additional inflammatory pathways and other biomarkers not evaluated in this study call for further improvement of our understanding of FK506’s molecular mechanisms, including NF-κB and NFAT signaling.[4]

Many earlier studies have looked at the neuroprotective effects of FK506 in vitro and in vivo. For instance, FK506 inhibited calcineurin and caspase-3 activation to prevent SH-SY5Y cell death induced by thapsigargin, and in a rat MCAo model, it reduced astrocyte apoptosis and glutamate-induced injury.[5,6,7] Our study improves current understanding by offering a evidence of FK506’s anti-inflammatory potential in human microglial cells, demonstrated by morphological analysis, cytokine profiling, and oxidative stress assessment. Future developments might involve the application of in vivo validation in neurodegenerative disease models, as well as exploring FK506 analogs or derivatives with reduced systemic toxicity.

Although this study has several shortcomings, it offers convincing proof for the part FK506 can play in regulating neuroinflammation. The dataset mostly was restricted to an in vitro HMC3 cell model, which may not fully represent in vivo microglial behavior. The studies were short-term (24 h) and lacked in vivo verification, limiting external validity. Similarly, FK506 showed cytotoxicity at higher doses, emphasizing the importance of dose optimization and further studies to evaluate long-term safety.

CONCLUSION

This study shows that K506 may significantly reduce proinflammatory cytokine production, restore cellular morphology, and maintain viability in LPS-induced microglial cells. The results showed notable effectiveness in modulating microglial activation and inflammatory response.. Although FK506 shows promise in neuroinflammatory modulation, more studies and clinical trials are required to ensure dependability and fit into pragmatic uses. The therapeutic future will most likely involve agents like FK506 as adjuncts in managing neurodegenerative diseases, complementing current strategies rather than replacing them.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.