Green RP-HPLC method for estimation of trigonelline: AQbD based development, validation, and application to nanoformulations Pavithra Kothapalli, Manimaran Vasanthan BMC Chemistry, 2026 Trigonelline is an alkaloidal plant derived bioactive compound reported for its various pharmacological activities. Recent advances in the utilization of this compound in drug development have highlighted the importance of establishing a reproducible and efficient analytical method for estimating trigonelline (TRG) in both its pure form and formulated nano systems. Current research aims to develop and validate a green RP-HPLC method for estimating TRG by integrating Analytical Quality by Design (AQbD) with the principles of Green Analytical Chemistry. Optimization of the chromatographic conditions was done by employing a rotatable central composite design with amount of mobile phase and its flow rate selected as critical variables and tailing factor (Tf), retention time (Rt) and theoretical plates as the responses. Optimal separation was achieved using ethanol and water (40:60, v/v) on a Phenomenex C18 (250 × 4.6 mm, 5 μm) column at 264 nm, yielding a sharp, symmetric peak at 5.60 min at a flow rate of 1.5 mL/min. Developed method exhibited excellent linearity over the range of 5–15 µg/mL (r² = 0.9986) with %RSD less than 2% and LOD & LOQ were found to be 0.628 µg/mL and 1.90 µg/mL, respectively. Forced degradation studies showed 12% degradation in acidic media and 9% in alkaline media after exposure of 8 h indicating moderate susceptibility to hydrolysis. Further, the validation was performed for the developed method according to ICH Q2(R1) guidelines. A comprehensive greenness assessment was performed using AES, GAPI, AGREE, AMGS, and AGSA tools, confirming that the newly developed method demonstrates superior greenness and its suitability for sustainable routine analysis compared to existing methods.
Formulation approaches for colon-specific drug delivery: conventional to nanocarrier systems Ashish Sriram Mishra, Bhavna Ghosh, Sivakumar Ponnurengam Malliappan, Gouranga Dutta, Manimaran Vasanthan Rsc Advances, 2026 Colon-targeted drug delivery enables site-specific therapy for colonic disorders with reduced systemic effects. This review highlights conventional and advanced strategies, including stimulus-responsive, polysaccharide-based, and nanocarrier systems.
Heuristic strategy-assisted transfer learning for cervical cancer detection with GAN-based data augmentation Rajesh Arunachalam, Shabana Urooj, Noore Zahra, Suresh Babu Kondaveeti, Gurram Sunitha, Manimaran Vasanthan Intelligent Data Analysis, 2026 Background Cervical cancer mostly occurs in women due to abnormal growth of the cell in cervix region. However, identifying abnormal cells is a significant issue in the computer-based diagnosis model. So, the continuous screening process is required for earlier diagnosis of cervical cancer. Utilizing deep learning techniques helps to differentiate cancer cells from normal cells. Problems The uneven distribution of data in the conventional model is challenging affects the classification results and hence reliable techniques are introduced for detecting cervical cancer. Thus, it slows down the training process and provides delayed treatment. Thus, a novel cervical cancer detection approach is implemented by transfer learning techniques that can be accomplished by various stages. Methods The different stages are (i) data collection, (ii) data augmentation, (iii) patch splitting, and (iv) cancer detection. The standard images are gathered from the publicly available resources. Next, the data augmentation is done through a Cycle Generative Adversarial Network (CycleGAN). Instead of processing the whole image, the CycleGAN model helps to process with smaller patches of images. Then, the splitting of image patches is further undergone in the cancer detection model using Transfer Learning (TL), which is the combined model of ResNet, VGG16, Xception, MobileNet, and DenseNet.Also, the fine-tuning of the weight and thresholds of classifiers is performed using the Improved Density Factor-based Honey Badger Algorithm (IDF-HBA). Finally, the outcomes are attained by taking the average of the obtained scores. Results The developed model achieves improved performance of 96.26% and 96.27% regarding accuracy and sensitivity. Significance This improved performance helps the developed model identifies the cancerous region and allows timely treatment for enhancing the survival rate of the women's. Henceforth, the earlier detection of cervical cancer helps to minimize treatment cost as well as improves the diagnosis performance.
Design and validation of a robust stability-indicating reversed-phase HPLC method for quantification of mesalamine in formulated drug products Ashish Sriram Mishra, Manimaran Vasanthan BMC Chemistry, 2025 A reliable and sensitive RP-HPLC method was developed and validated for the accurate quantification of mesalamine in bulk and formulated pharmaceutical products. The analysis was carried out on a C18 column (150 mm × 4.6 mm, 5 μm) using a mobile phase of methanol: water (60:40 v/v), with a flow rate of 0.8 mL/min, and UV detection at 230 nm. Methanol: water (50:50 v/v) was used as the diluent. The method demonstrated excellent linearity across the concentration range of 10–50 µg/mL (y = 173.53x – 2435.64, R² = 0.9992), high accuracy with recoveries between 99.05% and 99.25% (%RSD < 0.32%), and outstanding precision with intra- and inter-day %RSD values below 1%. Robustness was confirmed under slight method variations (%RSD < 2%), and LOD and LOQ were found to be 0.22 µg/mL and 0.68 µg/mL, respectively. Forced degradation studies under acidic, basic, oxidative, thermal, and photolytic stress confirmed the method’s specificity and stability-indicating capability. Assay of a commercial mesalamine tablet (Mesacol®, 800 mg label claim) showed a recovery of 99.91%, validating the method’s applicability for routine quality control and regulatory compliance.
Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images Ahmad Y. A. Bani Ahmad, Jafar A. Alzubi, Manimaran Vasanthan, Suresh Babu Kondaveeti, J. Shreyas, Thella Preethi Priyanka Scientific Reports, 2025 The most dangerous form of cancer is breast cancer. This disease is life-threatening because of its aggressive nature and high death rates. Therefore, early discovery increases the patient’s survival. Mammography has recently been recommended as diagnosis technique. Mammography, is expensive and exposure the person to radioactivity. Thermography is a less invasive and affordable technique that is becoming increasingly popular. Considering this, a recent deep learning-based breast cancer diagnosis approach is executed by thermography images. Initially, thermography images are chosen from online sources. The collected thermography images are being preprocessed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and contrasting enhancement methods to improve the quality and brightness of the images. Then, the optimal binary thresholding is done to segment the preprocessed images, where optimized the thresholding value using developed Rock Hyraxes Dandelion Algorithm Optimization (RHDAO). A newly implemented deep learning structure StackVRDNet is used for further processing breast cancer diagnosing using thermography images. The segmented images are fed to the StackVRDNet framework, where the Visual Geometry Group (VGG16), Resnet, and DenseNet are employed for constructing this model. The relevant features are extracted usingVGG16, Resnet, and DenseNet, and then obtain stacked weighted feature pool from the extracted features, where the weight optimization is done with the help of RHDAO. The final classification is performed using StackVRDNet, and the diagnosis results are obtained at the final layer of VGG16, Resnet, and DenseNet. A higher scoring method is rated for ensuring final diagnosis results. Here, the parameters present within the VGG16, Resnet, and DenseNet are optimized via the RHDAO to improve the diagnosis results. The simulation outcomes of the developed model achieve 97.05% and 86.86% in terms of accuracy and precision, respectively. The effectiveness of the designed methd is being analyzed via the conventional breast cancer diagnosis models in terms of various performance measures.
Safety assessment of resveratrol surrogate molecule 5 (RSM5): Acute and sub-acute oral toxicity studies in BALB/c mice Arunkumar Subramanian, T. Tamilanban, Amar Daud Iskandar Abdullah, V. Chitra, Mahendran Sekar, Gomathi Swaminathan, Inderjeet Yadav, V. Manimaran, Vinibha Rajakumari, Vetriselvan Subramaniyan Toxicology Reports, 2025 Natural polyphenols have gained greater attention for their potent medicinal properties and potential benefits in addressing various health concerns. Resveratrol, a polyphenolic compound known for its therapeutic properties, has shown limitations in bioavailability, which the novel derivative resveratrol surrogate molecule (RSM5) aims to improve. The present study evaluates the oral toxicity and safety profile of a novel resveratrol derivative through acute and subacute assessments. Acute toxicity was assessed following a single oral administration, while subacute toxicity was evaluated after repeated doses over 28 days in BALB/c mice. Various physiological, biochemical, and histopathological parameters were monitored to determine potential adverse effects. The findings indicate that the RSM5 exhibits no significant toxic effects at the tested doses (15, 30, 60 mg/kg), with both acute and subacute studies showing a favourable safety profile. These results suggest that the novel resveratrol derivative may be safe for further pharmacological development, supporting its potential for therapeutic applications.
Formulation, interaction analysis, and invitro hepatocellular carcinoma studies of Rutin loaded lipid and polymeric nanoparticles Habeeb, Mohammad, Vasanthan, Manimaran International Journal of Nano Dimension, 2025 Hepatocellular carcinoma is a primary liver cancer with a high mortality rate worldwide. The limited efficacy and adverse effects of conventional chemotherapy have driven the exploration of novel therapeutic approaches, including Nano medicine-based anticancer agents. In this study, the best molecule was identified based on molecular-level interaction studies with the three anticancer pathway proteins. The best molecule was encapsulated with polymer and lipid to form nanoparticles and In vitro Hepatocellular carcinoma studies were performed to measure the activity. The interaction analysis revealed that Rutin binds effectively with all three proteins: folate receptor (5IZQ), vascular endothelial growth factor (5ABD), and CD44 (4PZ3), exhibiting binding energies of -44.5015 kcal/mol, -46.8331 kcal/mol, and -42.6949 kcal/mol, respectively. These results indicate that Rutin demonstrates stronger binding affinity compared to other anticancer agents by computation studies. In the formulation, Rutin-formed lipid nanoparticles exhibited higher encapsulation efficacy and drug-loading efficiency compared to polymeric nanoparticles. Dissolution studies of rutin-loaded lipid nanoparticles reveal a gradual release profile, reaching approximately 99% over 6 hours. This sustained release ensures extended drug activity within the tumor environment, improving therapeutic effectiveness. In vitro cytotoxicity studies demonstrated that Rutin-lipid nanoparticles had superior anticancer efficacy, with an IC50 value of 80.45 ± 0.05 µg/mL, compared to 120.45 ± 0.05 µg/mL for Rutin-polymeric nanoparticles against the Hep3B cell lines. Similarly, Fluorescence-based screening studies further confirmed the remarkable anti-cancer potential of Rutin-lipid nanoparticles, primarily by inducing apoptosis in Hep3B cells. These findings suggest that Rutin-lipid nanoparticles hold promise nano-formulations for targeting Hepatocellular carcinoma.
WTO and Its Impact on Pharmaceuticals E. Nirmala, V. Manimaran, Ashish Sriram Mishra, K. Pavithra Pharmaceutical Regulatory Affairs Principles and Practices, 2025
Progressing nanotechnology to improve targeted cancer treatment: overcoming hurdles in its clinical implementation Mohammad Chehelgerdi, Matin Chehelgerdi, Omer Qutaiba B. Allela, Renzon Daniel Cosme Pecho, Narayanan Jayasankar, Devendra Pratap Rao, Tamilanban Thamaraikani, Manimaran Vasanthan, Patrik Viktor, Natrayan Lakshmaiya, Mohamed J. Saadh, Ayesha Amajd, Mabrouk A. Abo-Zaid, Roxana Yolanda Castillo-Acobo, Ahmed H. Ismail, Ali H. Amin, Reza Akhavan-Sigari Molecular Cancer, 2023
Nanogels as novel drug nanocarriers for CNS drug delivery V. Manimaran, R. P. Nivetha, T. Tamilanban, J. Narayanan, Subramaniyan Vetriselvan, Neeraj Kumar Fuloria, Suresh V. Chinni, Mahendran Sekar, Shivkanya Fuloria, Ling Shing Wong, Anupam Biswas, Gobinath Ramachawolran, Siddharthan Selvaraj Frontiers in Molecular Biosciences, 2023
Heat transfer improvement by arrangement of cylindrical phase change material thermal storage International Journal of Mechanical Engineering and Technology, 2018
Formulation and evaluation of naproxen-eudragit® RS 100 nanosuspension using 32 factorial design International Journal of Pharmacy and Pharmaceutical Sciences, 2014
Formulation and In-vitro evaluation of sustained release matrix tablets of losartan potassium Research Journal of Pharmaceutical Biological and Chemical Sciences, 2014
Formulation and evaluation of modafinil fast dissolving tablets by sublimation technique Journal of Chemical and Pharmaceutical Sciences, 2013
Formulation and evaluation of rizatriptan benzoate orally disintegrating tablets International Journal of Drug Development and Research, 2012
Development and characterization of floating tablets of atenolol Der Pharmacia Lettre, 2012
Formulation and evaluation of aceclofenac compression coated tablets for colon drug delivery Research Journal of Pharmaceutical Biological and Chemical Sciences, 2012
Formulation development and evaluation of delayed release doxycycline tablets International Journal of Pharmacy and Pharmaceutical Sciences, 2010
RECENT SCHOLAR PUBLICATIONS
Heuristic strategy-assisted transfer learning for cervical cancer detection with GAN-based data augmentation R Arunachalam, S Urooj, N Zahra, SB Kondaveeti, G Sunitha, ... Intelligent Data Analysis, 1088467X261415695 , 2026 2026
Green RP-HPLC method for estimation of trigonelline: AQbD based development, validation, and application to nanoformulations P Kothapalli, M Vasanthan BMC chemistry , 2026 2026 Citations: 1
Formulation approaches for colon-specific drug delivery: conventional to nanocarrier systems AS Mishra, B Ghosh, SP Malliappan, G Dutta, M Vasanthan RSC advances 16 (11), 10022-10059 , 2026 2026 Citations: 5
Introduction to eye melanoma: Pathogenesis, diagnosis, and current treatment modalities P Kothapalli, M Vasanthan, DU Kapoor Eye Melanoma Unveiled, 1-20 , 2026 2026
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Design and validation of a robust stability-indicating reversed-phase HPLC method for quantification of mesalamine in formulated drug products AS Mishra, M Vasanthan BMC chemistry 19 (1), 303 , 2025 2025
Comparative in silico human and environmental Hazard profiling of ulcerative colitis pharmaceuticals AS Mishra, M Vasanthan, C Pandey Environmental Chemistry and Ecotoxicology , 2025 2025
Ecofriendly analytical quality by design aided RP-HPLC method for the estimation of mesalamine: greenness, blueness and whiteness appraisal AS Mishra, M Vasanthan Microchemical Journal 215, 114258 , 2025 2025 Citations: 2
Safety assessment of resveratrol surrogate molecule 5 (RSM5): Acute and sub-acute oral toxicity studies in BALB/c mice A Subramanian, T Tamilanban, ADI Abdullah, V Chitra, M Sekar, ... Toxicology Reports 14, 101956 , 2025 2025 Citations: 3
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Eudragit Nanofiber for Effective Coating in Colon-Targeted Drug Delivery AS Mishra, K Pavitra, MV Manimaran Pharmakeftiki 37 (1) , 2025 2025
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MOST CITED SCHOLAR PUBLICATIONS
Progressing nanotechnology to improve targeted cancer treatment: overcoming hurdles in its clinical implementation M Chehelgerdi, M Chehelgerdi, OQB Allela, RDC Pecho, N Jayasankar, ... Molecular cancer 22 (1), 169 , 2023 2023 Citations: 1115
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Drug repurposing: A leading strategy for new threats and targets AS Mishra, M Vasanthan, SP Malliappan ACS pharmacology & translational science 7 (4), 915-932 , 2024 2024 Citations: 48
Enhancement of dissolution rate of glibenclamide by solid dispersion technology V Manimaran, N Damodharan, M Mothilal, K Rajkumar, RM Chalackal International journal of current pharmaceutical research 2 (3), 14-17 , 2010 2010 Citations: 32
Formulation development and evaluation of delayed release doxycycline tablets N Damodharan, V Manimaran, B Sravanthi International Journal of Pharmacy and Pharmaceutical Sciences 2 (1), 116-9 , 2010 2010 Citations: 24
Lipid-based nanocarriers for enhanced delivery of plant-derived bioactive molecules: a comprehensive review P Kothapalli, M Vasanthan Therapeutic Delivery 15 (2), 135-155 , 2024 2024 Citations: 23
Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images AYA Bani Ahmad, JA Alzubi, M Vasanthan, SB Kondaveeti, J Shreyas, ... Scientific Reports 15 (1), 13605 , 2025 2025 Citations: 19
A review of safety, quality, regulation, and delivery approaches for phytopharmaceuticals K Pavithra, V Manimaran Jordan Journal of Pharmaceutical Sciences 17 (2), 316-332 , 2024 2024 Citations: 16
Optimisation and Characetrisation of Chitosan microspheres of aceclofenac M Mothilal, M Nagalakshmi, PS Swati, N Damodharan, KS Lakshmi Int. J. Pharma Sci. Res 3, 305-315 , 2012 2012 Citations: 14
DEVELOPMENT OF FAST-DISSOLVING TABLETS OF AMLODIPINE BESYLATE BY SOLID DISPERSION TECHNOLOGY USING POLOXAMER 407 AND POLOXAMER 188 DN Manimaran V Asian Journal of Pharmaceutical and Clinical Research 10 (7), 131-141 , 2017 2017 Citations: 13
The role of quality assurance in clinical trials: safeguarding data integrity and compliance B Prasanna, P Kothapalli, M Vasanthan Cureus 16 (8) , 2024 2024 Citations: 12
Development of Fast Dissolving Tablets of Nisoldipine by Solid Dispersion Technology using Poloxamer 407 and Poloxamer 188. M Vasanthan, D Narayanasamy Journal of Young Pharmacists 8 (4) , 2016 2016 Citations: 12
Formulation and evaluation of modafinil fast dissolving tablets by sublimation technique M Mothilal, AH Kumar, MC Krishna, V Manasa, V Manimaran, ... J Chem Pharm Sci 6 (3), 147-54 , 2013 2013 Citations: 12
Formulation and evaluation of naproxeneudragit® RS 100 nanosuspension using 32 factorial design M Mothilal, MC Krishna, SPS Teja, V Manimaran, N Damodharan Int J Pharm Pharm Sci 6 (7), 449-55 , 2014 2014 Citations: 11
Enhancing pharmaceutical product quality with a comprehensive Corrective and Preventive Actions (CAPA) framework: From reactive to proactive T Arunagiri, KP Kannaiah, M Vasanthan, KP Kannaiah Cureus 16 (9) , 2024 2024 Citations: 10
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Nanoparticle as a powerful tool to penetrate the Blood-brain barrier in the treatment of Neurodegenerative disease: Focus on recent advances S Kalaiselvi, V Manimaran, N Damodharan Research Journal of Pharmacy and Technology 13 (5), 2135-2143 , 2020 2020 Citations: 8
Formulation and evaluation of rizatriptan benzoate orally disintegrating tablets M Mothilal, K Srikanth, BG Sivagirish, K Gnanendra, V Manimaran, ... Int J Drug Dev Res 4, 117-23 , 2012 2012 Citations: 7