INTRODUCTION:

Obesity is a chronic, multifactorial medical condition characterized by excessive body fat, which results from a long-term imbalance between calorie intake and energy expenditure. The surplus energy is stored in the form of adipose tissue, and its prolonged accumulation has detrimental effects on health¹. Increasing obesity levels are closely linked with a rise in diabetes, cardiovascular disease, hypertension, Hypercholesterolemia, osteoarthritis, poor self-esteem, depressive disorders, and sleep apnea, all of which significantly impair quality of life. Additionally, obesity and inactivity during adolescence are risk factors for about one third of all cancers including colon, breast, kidney, and stomach cancers as well as gall bladder disease. In overweight females, reproductive health is also adversely affected, leading to menstrual irregularities such as oligomenorrhea, amenorrhea, dysfunctional uterine bleeding, and infertility^2-3. The body mass index (BMI) is a simple indicator used to assess obesity, and is calculated by dividing weight in kilograms by the square of height in meters. This ratio provides a standardized measure to classify individuals according to their weight status⁴. The WHO defines 'overweight' in adults as a BMI between 25.0 and 29.9, and 'obesity' as a BMI of 30.0 or higher. Obesity is divided into three severity levels: class I (BMI 30.0-34.9), class II (BMI 35.0-39.9), and class III (BMI > 40.0)⁵.

Individual variances in percent body fat for a particular BMI can be ascribed to gender, ethnicity, and age⁵_.Obesity has become one of today’s most critical global health challenges, emerging as the leading preventable cause of death with rapidly rising prevalence among both adults and children worldwide. Prior to the 20th century, obesity was rare; however, the WHO officially declared it as a worldwide epidemic in 1997⁶and by 2008, over 500 million adults, more than 10% of the global population, were classified as obese, with higher prevalence in women⁷. Between 1980 and 2014, global obesity rates more than doubled, reaching over 600 million adults (13%), and rising further to 16% by 2022 (WHO). In the United States, obesity affected approximately 39.6% of adults in 2015-2016, including 37.9% of men and 41.1% of women. The WHO has noted that obesity and overweight have surpassed traditional public health concerns such as undernutrition and infectious diseases. Obesity rates also tend to increase with age, at least until 50-60 years. Notably, severe obesity is growing faster than overall obesity in countries like the U.S., Australia, and Canada. According to OECD, obesity rates are projected to continue rising through 2030, especially in the United States (47%), Mexico (39%), and England (35%)^8-9. According to statistics, the worldwide prevalence of overweight and obesity has tripled since 1975, with over one-third of the world's population now being categorized as overweight or obese¹.

Obesity results from excessive energy storage in adipose tissue, occurring in two main forms: hypertrophy (increased adipocyte size) and hyperplasia (increased number of adipocytes through adipogenesis). White adipose tissue (WAT) primarily regulates lipid storage and energy balance, while brown adipose tissue (BAT) promotes heat production through non shivering thermogenesis. In healthy conditions, WAT safely stores excess energy, but when its storage and functional capacity are exceeded, lipids accumulate in other organs such as liver, muscle, kidney, and pancreas, causing ectopic fat deposition. With obesity progression, WAT becomes inflamed and fibrotic due to macrophage infiltration, leading to loss of metabolic flexibility. This dysfunction disrupts hormone balance, causing the release of inflammatory cytokines and adipokines, which promote insulin resistance, dyslipidemia, and metabolic syndrome. Hypertrophic adipocytes particularly drive chronic inflammation and metabolic abnormalities¹⁰.

Herbal supplements, acupuncture, non-invasive body contouring, bariatric surgery, and medication are all examples of complementary and alternative medicine (CAM) weight loss treatments that do not fall under the purview of traditional western medicine. Despite their popularity and broad commercialization, scientific evidence supporting the long-term efficacy and safety of many complementary and alternative therapies, including herbal supplements and acupuncture, is sparse and inconsistent. Non-invasive body contouring procedures provide minor benefits, particularly for people with moderate body mass, but bariatric surgery remains the most effective choice for severe obesity¹¹, but alters the gastrointestinal structure and physiology, leading in decreased macronutrient and micronutrient absorption¹² and is expensive, and reserved for a restricted group of patients¹¹. Several strategies exist to address overweight and obesity, including lifestyle changes like dietary modification and increased physical activity to promote calorie loss, as well as antiobesity medications that suppress appetite or reduce fat absorption. However, all of these methods tend to be only modestly effective for long term weight reduction. The effectiveness of pharmacological treatments often depends on continual use, as stopping the medication typically leads to weight regain. Among drugs approved for sustained use, orlistat is the sole option sanctioned by the FDA for long-term therapy, yet it can cause significant side effects such as fatty stools, gas, fecal incontinence, oily spotting, constipation, and even potential liver injury. Consequently, there is a pressing need for alternative medications and innovative therapeutic strategies to improve obesity management¹.

Adipogenesis is a complex process that converts preadipocytes into mature fat storing adipocytes. Renewing adipose tissue is important since about 10% of fat cells regenerate each year. This process has two stages pluripotent stem cells commit to become preadipocytes and then these preadipocytes differentiate into mature adipocytes. During commitment phase, stem cells lose the ability to become other cell types and undergo changes driven by signaling pathways, transcription factors, and genes^13-14. Adipocyte formation is most active in childhood and adolescence, determining an individual’s long-term capacity for fat storage. Since the size and number of adipocytes regulate safe fat storage, this mechanism is critical in obesity development. Targeting molecular pathways behind adipogenesis and white adipose tissue (WAT) expansion offers a potential treatment for obesity related disorders¹⁰. In vitro models help study gene regulation during adipogenesis, lipogenesis, and lipolysis and facilitate drug screening. The 3T3-L1 preadipocyte fibroblast cell line is widely used to study fat cell differentiation. Human mesenchymal stem cells (MSCs) from adipose tissue are increasingly popular for overcoming limitations of non-human cells. While 2D cultures are common, they lack physiological relevance, which 3D cultures better mimic. Organ-on-a-chip technology offers even more accurate models of tissue function. In silico methods assist experiments by analyzing complex data, predicting pathways, and aiding drug targeting. Combining in vitro and in silico approaches supports translation to clinical trials while reducing animal use. These advances are crucial for effective anti-obesity drug discovery¹⁵.

Our review aims to synthesize current knowledge on the multifaceted nature of obesity, focusing on the molecular and cellular mechanisms driving adipose tissue expansion and dysfunction. By integrating insights from epidemiological data, pathogenesis, and risk factors, we strive to uncover novel therapeutic avenues, emphasizing the regulation of adipogenesis. Additionally, our review highlights cutting edge in vivo, in vitro, and in silico research methodologies that accelerate the discovery of effective antiobesity interventions, including natural bioactive compounds and alternative treatment strategies. This comprehensive approach aims to inform future research and clinical practices targeting obesity and its metabolic complications.

Types of Obesity:

Obesity is a complex condition with different types based on genetics and fat distribution. Primary (or monogenic) obesity is a rare form caused by mutations in single genes like MC4R, LEPR, and POMC, leading to severe early onset obesity often seen in families. Secondary (or polygenic) obesity is more common and results from multiple genes interacting with environmental factors such as diet, inactivity, and stress. Genome wide studies have linked genes like FTO, MC4R, and TMEM18 to its risk. Obesity is also classified by fat location central (abdominal/visceral) obesity, which is linked to higher metabolic and cardiovascular risks, and peripheral obesity where fat accumulates more around hips and limbs. Another rare type, lipodystrophy, involves selective loss of fat in some areas (like arms and face) with fat accumulating abnormality in others such as liver and muscle, leading to insulin resistance and other metabolic complications¹⁶.

Figure 1 : Types of obesity based on genetics and fat distribution ¹⁶

Pathogenesis of Obesity:

The basic pathophysiology of obesity involves an imbalance between nutrient intake and energy expenditure, resulting in excess fat storage. However, it also encompasses the complex interplay of neuroendocrine and metabolic systems that regulate energy intake, storage, and expenditure¹⁷.The development of obesity is also driven a multifaceted combination of genetic and epigenetic factors, behavioral patterns, and broader environmental and social factors¹⁸, but fundamentally, it is rooted in a chronic positive energy balance where caloric intake consistently exceeds energy expenditure, leading to excessive body fat accumulation. While the widespread availability of calorie dense foods and sedentary living significantly exacerbate this trend, genetics also play a crucial role affecting appetite, energy regulation, and fat storage, which make some individuals inherently more susceptible to weight gain. Neurobiological mechanisms involving the hypothalamus, reward circuitry, and key hormones such as leptin, ghrelin, and insulin further influence appetite and metabolic processes, sustaining or worsening the propensity for obesity. The composition and balance of gut microbiota modulate host metabolism such as disturbances in gut microbial populations (dysbiosis) which can raise fat storage and inflammation, promoting metabolic complications¹⁹. Moreover, epigenetic modifications heritable yet reversible changes in gene expression shaped by early life factors like maternal nutrition can heighten obesity risk independent of genetic sequence changes. Together, these interconnected factors energy surplus, genetic tendency, neuroendocrine and microbial influences, and environmental exposures converge to make obesity a highly complex and multifactorial chronic disease²⁰.

Figure 2. Factors contributing to the pathogenesis of obesity^18-19

Signs and symptoms of obesity:

Obesity is recognized by the American Medical Association as a disease requiring diagnosis and treatment due to its diverse symptoms and complications. The figure 3 below illustrates the broad spectrum of these issues commonly experienced by adults with obesity. Key symptoms include the accumulation of excess body fat and difficulty in breathing, often leading to physical limitations. Individuals may suffer from persistent fatigue and discomfort in joints and the back, impacting daily activities. Emotional distress or isolation is also prevalent among those affected. Other complications depicted are excessive sweating, snoring during sleep, and skin irritation resulting from moisture in skin folds. Collectively, these physical, psychological, and metabolic challenges underscore the serious health impact of obesity²¹.

Figure 3. Signs and symptoms associated with obesity ²¹

Risk factors Associated with Obesity:

Risk factors associated with obesity are multifaceted. Excess adiposity develops gradually over time due to a long-term positive energy balance, leading to the accumulation of triglycerides primarily in adipose tissue but also in skeletal muscle, liver, and other organs. Obese individuals with stable weight generally have increased fat and lean mass, as well as higher resting energy expenditure, cardiac output, blood pressure, and pancreatic β-cell mass compared to non-obese individuals. Insulin production rises linearly with BMI, both in fasting and after glucose intake. Most lipids are stored in subcutaneous adipose tissue, mainly composed of white adipocytes, with smaller populations of thermogenic brown and beige adipocytes present in adults. Obesity triggers immune cell infiltration, especially macrophages, into adipose tissue due to adipocyte apoptosis, promoting proinflammatory cytokines that worsen insulin resistance. Visceral adipose tissue especially omental and mesenteric fat is strongly linked to metabolic abnormalities and negative health outcomes^22-23. Men generally store fat in their abdomens, while premenopausal women store it in their hips and thighs after menopause, about age 50, women's fat distribution shifts to mirror that of men. Health risks increase with waist circumference irrespective of age or gender and central obesity raises the risk of heart attacks and diabetes, and Indians at higher risk due to genetic predisposition to abdominal fat²⁴. Fat surrounding the kidneys may contribute to hypertension, and excess tissue in the pharynx can obstruct airways during sleep, causing obstructive sleep apnea. Increased mechanical load on joints elevates the risk of osteoarthritis, and elevated intraabdominal pressure in obese individuals predisposes them to gastroesophageal reflux disease, Barrett’s esophagus, and esophageal cancer^22-23. Additionally, systemic oxidative stress caused by fatty acid oxidation in adipose tissues can lead to liver failure, inflammation, and damage¹⁹.

Figure 4. Health complications associated with obesity^22-23

Current Treatments for Obesity:

Current treatments for obesity include lifestyle changes, pharmacotherapy, and surgery. Lifestyle interventions emphasize a combination of diet and exercise, with diets like Mediterranean, low carbohydrate, and low fat proving effective for weight loss, though sustained adherence is crucial. Behavioral therapies such as mindfulness and acceptance-based therapies assist in moderating eating behaviors but offer modest weight loss when used alone. Pharmacotherapy is recommended for patients with BMI over 30 or 27 with comorbidities; orlistat reduces fat absorption but often causes gastrointestinal side effects like oily stools and requires vitamin supplements²⁵. Naltrexone-bupropion suppresses appetite centrally but may induce nausea, insomnia, and increased blood pressure. Phentermine-topiramate promotes weight loss but has contraindications including pregnancy due to risk of birth defects²⁶. Setmelanotide is effective for rare genetic obesity forms but carries risks such as depression and injection site reactions. SGLT2 inhibitors, initially for diabetes, yield modest weight loss with risks like urinary infections. GLP-1 receptor agonists, such as liraglutide and semaglutide, achieve significant weight loss but may cause gastrointestinal symptoms and pancreatitis¹⁹.Bariatric surgery offers the most substantial and sustained weight loss, improving comorbidities like diabetes but involves surgical risks and nutritional deficiencies. Emerging treatments including new pharmacologic agents and endoscopic devices are under development to enhance efficacy and safety¹³. Overall, a personalized, multidisciplinary approach is essential for optimal obesity management^25-26.

In vitro Approaches in Obesity Studies:

In vitro models play a crucial role in obesity research by providing controlled environments to evaluate the biological activity and mechanism of potential antiobesity compounds. These assays and cell culture techniques target key enzymes and pathways involved in lipid digestion, metabolism, and oxidative stress, enabling rapid, cost-effective screening. Enzyme inhibition assays such as pancreatic lipase and cholesterol esterase evaluate the reduction of lipid absorption, while cellular models like PPAR agonism and adipogenesis assays provide mechanistic insights into lipid catabolism and adipocyte behavior. Antioxidant capacity is frequently assessed using radical scavenging and ferric reducing power assays. Despite their advantages in throughput and mechanistic clarity, in vitro models have limitations, including incomplete replication of complex in vivo physiology and variability in assay protocols. Together, these approaches constitute foundational tools for preclinical evaluation of therapeutic candidates targeting obesity and metabolic disorders. Table 1 consisting of key in vitro models used in obesity research, summarizing their principles, procedures, measured parameters, advantages, limitations, and references.

Table 1. In vitro methodologies applied in obesity research and their key findings.

In *vitro* model	Principle	Procedure	Measured Parameters	Advantages	Limitations	References
Pancreatic Lipase (PL) Inhibition Assay	Measures the ability of a substance (e.g., peptides, extracts) to inhibit pancreatic lipase, an enzyme crucial for dietary lipid digestion, thereby reducing fat absorption	Peptides or extracts are incubated with pancreatic lipase (human, bovine, or porcine). Inhibition rate is determined. Can also assess thermal stability and activity after simulated gastrointestinal digestion (DGIS).	Lipase activity or inhibitory rate. Direct assessment of an enzyme target for obesity treatment.	Relatively safe, as inhibitors typically do not penetrate human blood vessels or the nervous system. Cost effective, rapid, and enables high throughput screening.	Lack of systemic physiological interactions (e.g., hormonal, neural). Differences between murine and human adipocytes in metabolism and drug response. Simplified model unable to mimic full tissue complexity and microenvironment. Long differentiation protocols and specialized culture conditions required.	27
Cholesterol Esterase (CE) Inhibition Assay	Measures the ability of a substance to inhibit cholesterol esterase, an enzyme that hydrolyzes cholesterol esters, triglycerides, and phospholipids. It is vital for cholesterol transport and linked to obesity and hypercholesterolemia	Peptides or extracts are tested for their CE inhibition potential.	CE activity or inhibition.	Targets a key enzyme involved in lipid metabolism associated with obesity and hypercholesterolemia. Cost-effective, rapid, and enables high-throughput screening	Elucidating specific peptide actions can be challenging when using protein hydrolysates due to their complex mixture of peptides, which may include multiple bioactive and non-bioactive sequences that complicate identification and activity attribution	27
PPARα Agonism Assay (HepG2 cells	Evaluates the ability of compounds to activate Peroxisome Proliferator Activated Receptor alpha (PPARα) in HepG2 cells, which is essential for lipid catabolism and a key target for antiobesity agents.	The cells used are HepG2. Assessments include cell viability, Oil Red O staining for free fatty acid (FFA) and Triglyceride (TG) levels, intracellular PPARα content assay, qRT-PCR for PPARα gene expression, and Western blotting for PPARα protein levels.	Cellular viability, FFA and TG content, PPARα gene and protein expression.	Directly assesses the activation of a crucial regulator of lipid metabolism. Provides insights into the molecular mechanisms of action.	Cell models may not fully replicate the complexity of an in vivo system.	27
DPPH Free Radical Scavenging Activity Assay	Measures the antioxidant potential of a substance by its ability to scavenge 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radicals, with a decrease in absorbance indicating scavenging activity.	Samples (e.g., P. macrocarpa fruit extracts) are prepared at various concentrations in methanol. DPPH solution is added, incubated in the dark, and absorbance is measured at 517 nm. Ascorbic acid is used as a positive control.	Radical scavenging activity (e.g., IC50 value).	A rapid, simple, and widely used method for screening antioxidant activity.	Primarily measures direct radical scavenging, which may not fully reflect all in vivo antioxidant mechanisms	28
α-amylase Inhibition Assay	This test evaluates a substance's capacity for inhibiting α-amylase, an enzyme responsible for the breakdown of complex carbohydrates. Inhibition can help regulate diabetes related postprandial hyperglycemia by slowing carbohydrate breakdown and absorption.	Plant extract (e.g., C. arietinum, H. vulgare) is mixed with α-amylase solution and incubated. Starch solution is added, incubated again. Reaction is terminated with DNS reagent and boiled. Optical Density (OD) is measured at 540 nm. Acarbose is used as a positive control.	α-amylase inhibitory activity (e.g., IC50 value).	Direct assessment of an enzyme target for diabetes management. Relatively simple and cost effective for screening.	Inhibitors may lack selectivity for certain sites and produce side effects such as bloating, flatulence, nausea, and gastroenteritis.	28
In vitro anti-obesity model	Evaluate anti-obesity effects in vitro through measurement of enzyme inhibition (pancreatic lipase, α-glucosidase), antioxidant capacity, and free radical scavenging. Fermentation is used to enhance bioactivity.	Prepare SHLE extract and ferment with Lactobacillus fermentum grx08 for 72 hours. Measure enzyme inhibitory activities using p-nitrophenyl derivatives substrates. Assess antioxidant capacity by FRAP and DPPH assays. Analyze chemical changes by UPLC-MS/MS and GC-MS. Perform sensory evaluation on flavor changes.	Pancreatic lipase inhibition (%). α-glucosidase inhibition (%). Total antioxidant capacity (FRAP), DPPH free radical scavenging activity. Changes in bioactive phytochemical contents (polysaccharides, flavonoids, polyphenols, saponins). Volatile flavor compounds and sensory evaluation	Controlled environment enabling precise biochemical assessment. Facilitates screening of fermentation effects on bioactive compounds. High reproducibility and reduced variability compared to in vivo. Identifies potential mechanisms of anti-obesity activity	Does not reflect systemic physiological complexity and metabolism of a living organism. Cannot assess pharmacokinetics, bioavailability or long-term effects. May not fully predict in vivo efficacy or safety	29
Human Mesenchymal Stem Cells (hMSCs)	Multipotent stem cells from bone marrow can differentiate into adipocytes under specific hormonal and pharmacological stimuli	hMSCs were isolated and cultivated till confluence. Adipogenesis-inducing media (insulin, dexamethasone, indomethacin, IBMX) induces differentiation. Three-day induction and maintenance cycles Optional enhancers include rabbit serum, thiazolidinediones, and MAPK inhibitors. .	Lipid accumulation (Oil Red O staining). Gene expression (PPARγ, C/EBPα, GLUT4, leptin, adipsin) by RT-PCR & Western blot. Protein activity (MAPK, ERK1/2). Optical density of stained lipids	Renewable, multipotent precursors. Low donor variability. Allow study of early commitment and differentiation stages. Respond to pharmacological agents similar to in vivo cells	Differentiation efficiency lower than immortalized murine lines unless optimized (~20-80%). Passage number affects adipogenic potential (declines with extensive subculturing). More technically demanding than pre-adipocyte cultures	30
2D Cell Culture (e.g., 3T3-L1, hMSCs)	Adipogenic differentiation of cells induced by a defined chemical cocktail that activates key transcription factors controlling adipogenesis.	3T3-L1 cells Murine fibroblast like cells converted to adipocytes using an adipogenic cocktail (e.g., Dexamethasone (DEX), Insulin (INS), Isobutyl-1-methylxanthine (IBMX)) typically for 14 days. MSCs/ADSCs: Isolated from human sources, differentiated for 14 days with IBMX, INS, DEX, and Indomethacin (IND).	Lipid accumulation measured by staining techniques (Oil Red O, Nile Red, Bodipy), gene and protein expression profiling of key adipogenic markers (PPARγ, CEBPA, FABP4, ACC1, FASN, GLUT4), glycerol release (indicative of lipolysis), enzymatic activities, mitochondrial oxygen consumption, ROS levels, and glucose uptake assays.	Easy to maintain, cost effective, reproducible, high throughput screening capability. Well characterized protocols and wide availability of commercial reagents and kits. Allows quantitative analysis of differentiation efficiency and drug screening.	Does not fully reflect the physiological situation of tissues. Results from non-human cell lines (like murine 3T3-L1) may not translate well to humans due to interspecies differences. Adipose tissue in vivo expresses a far greater number of genes than can be easily studied in vitro. Sex-related differences in gene expression can introduce bias.	16
3D Adipose Spheroids	3D aggregation of adipocytes in scaffold free culture or hydrogels to mimic tissue environment	Adipose derived stem cells or preadipocytes are cultured in 3D hydrogel or scaffold to form spheroids	Adipokine secretion (leptin, adiponectin), lipid droplet formation, insulin responsiveness	Better recapitulates in vivo physiology compared to 2D; allows cell-cell and cell-matrix interactions	More complex and costly than 2D cultures; longer culture time; technical challenges in handling and analysis	31,32
3D Adipocyte Spheroids (3T3-L1 cells)	3T3-L1 preadipocytes can differentiate into adipocytes, when cultured as spheroids, they better mimic tissue like structure and metabolism than 2D monolayers. TNF-α treatment, induces insulin resistance, modeling obesity-related dysfunction.	3T3-L1 cells grown to ~70% confluence. Cells magnetized with NanoShuttle™ and aggregated into spheroids using magnetic levitation. Differentiation induced with cocktail (insulin, dexamethasone, IBMX, FBS). Maintained for 14 days. Divided into groups: untreated white adipospheres (WA) and TNF-α-treated adipospheres (WA-TNF-α).	Lipid accumulation, Spheroid size & viability (ATP assay, diameter tracking). Glucose uptake (colorimetric assay). Cytokine secretion (ELISA for IFN-γ, others). Proteomics: mitochondrial biogenesis, fatty acid metabolism, adiponectin signaling	Mimics 3D architecture of adipose tissue. Captures key obesity features (insulin resistance, metabolic remodeling) More physiologically relevant than 2D monolayers. Cost effective and reproducible compared to animal models	Uses murine 3T3-L1 cells (not fully human representative). Differentiation efficiency and uniformity can vary. Requires specialized equipment (magnetic levitation, 3D viability assays). Still lacks full complexity of in vivo adipose tissue	33
Organ-on-a-chip (Adipose Tissue)	Microfluidic device integrating living cells with fluid flow to simulate tissue microenvironment	Cells are cultured in microchannels with perfused media, possibly co cultured with immune or endothelial cells	Metabolic function, lipolysis, inflammatory responses, real-time monitoring	High relevance to human physiology; allows dynamic studies; reduced animal use	Technical complexity, low throughput, expensive equipment	34

Table 2. In vivo methodologies applied in obesity research and their key findings.

In *vivo* model	Plant name and part used	Key findings	Parameters	Outcomes	References
HFD induced obesity in rats	Terminalia paniculata (bark, ethanolic extract)	Significant reductions in body weight, fat pad weights, total fat, fat percentage, blood glucose, insulin, insulin resistance, total cholesterol, TG, FFA, LDL, adipocyte size. Improved lipid profile. Gene modulation: Down-regulated SREBP-1c, PPARγ, FAS, leptin; upregulated adiponectin, AMPK-1α.	Body weight, lean mass, fat %/fat-free mass, food intake, blood glucose, insulin/HOMA-IR, lipid profile (TC, HDL, TG, LDL, atherogenic index), liver markers (AST, ALT, ALP), adipogenic gene expression, fat pad weights, adipose histology	TPEE demonstrated potential anti-adipogenic and anti-obesity activities, positioning it as a strong candidate for developing an anti-obesity drug.	35
HFD induced obesity in rats	Gnidia glauca (DCM leaf extract)	Significant reductions in body weights, organ weights, total fat content, adiposity index, atherogenic index, abdominal circumference, Lee index, glucose, TG, TC, LDL, VLDL, total feed intake; increased HDL, rectal temperature	Body weight, organ weights, organo somatic indices, anthropometric indices (abdominal circumference, Lee obesity index), total fat content, adiposity index (BAI), atherogenic index (AIP), lipid profiles (TC, LDL-C, HDL-C, TAG, and VLDL), fasting blood glucose levels, rectal body temperature, and feed intake.	Gnidia glauca exhibited potent anti-obesity effects, validating its traditional use and suggesting it as a potential candidate for new anti-obesity supplements.	36
HFD induced obesity in rats	Achyranthes aspera L. (AEAA) aerial parts	Significant decrease in body weight, fat pad mass, organ weights, BMI, waist circumference, and adipocyte area. Increased glucose, insulin, leptin, lipid profiles (triglycerides, total cholesterol, LDL-C, VLDL-C, atherogenic index), and oxidative stress indicators (LPO); HDL-C levels, enzymatic antioxidants (GPx, SOD, CAT), and non-enzymatic antioxidants (GSH).	Body weight, daily food intake, fat pad weights, adipocyte size (area), organ weights, BMI, waist circumference, blood glucose, serum insulin, leptin, serum lipid profiles, enzymatic antioxidants (GPx, SOD, CAT), (GSH), lipid peroxide (LPO), and liver histopathological observations.	AEAA demonstrated strong anti-obesity potential, which were most likely mediated by delayed intestinal absorption of dietary fat (pancreatic lipase inhibition), increased antioxidant status, and fat metabolism control.	1
HFD induced obesity in rats	Anredera cordifolia leaves (Binahong leaf extract).	Significantly decreased abdominal fat weight and circumference while also lowering ERK levels in abdominal fat, which is linked to adipogenesis inhibition. It also elevated PI3K levels in adipocytes, which improved insulin signaling and glucose uptake. Blood sugar, triglyceride, and cholesterol levels were reduced.	Body weight, length, abdominal circumference, blood glucose, cholesterol levels, triglyceride levels, abdominal fat weight, PI3K levels (in abdominal fat tissue), ERK levels (in abdominal fat tissue).	Anredera cordifolia extract possesses anti-obesity activities by decreasing ERK and increasing PI3K levels, as well as reducing abdominal fat weight, influencing the mechanism of adipogenesis.	37
HFD induced obesity in rats	Ficus hispida	Significantly reduced body weight and liver wet weight, lower blood glucose during oral glucose tolerance tests, and dose-dependently reduced total CHO, TG, and LDL levels. It also lowered oxidative stress markers (NO, MDA, and AOPP), as well as plasma uric acid and creatinine levels. Plasma liver enzymes (AST, ALP, and ALT) improved, whereas antioxidant enzymes (SOD, catalase, GSH, and MPO) remained normal. Downregulated adipogenic genes (leptin, FAS, PPARγ, SREBP-1c)	body weight, liver wet weight, blood glucose levels via oral glucose tolerance test, plasma liver enzymes (AST, ALP, ALT), lipid profile,oxidative stress markers (NO, MDA, AOPP) in liver and plasma, antioxidant enzymes (SOD, GSH, catalase, MPO), renal indicators (uric acid, creatinine), histopathology of liver and adipose tissue including fat deposition and adipocyte size, and expression of adipogenic genes (leptin, FAS, PPARγ, SREBP-1c).	Ficus hispida ethanolic extract reduces oxidative stress and fat accumulation, improves liver and blood markers, lowers body and liver weight, and downregulates adipogenic genes, showing strong anti-obesity potential.	38
HFD induced obesity in rats	Allium cepa L. leaves (powdered leaves).	significantly body weight, fat mass, blood glucose, cholesterol, triglycerides, liver enzymes (AST, ALT), creatinine, and urea levels. Histopathology showed smaller, more numerous liver fat deposits and milder kidney inflammation in treated groups versus obese controls. No significant changes in feed intake or HDL levels.	Body weight, fat mass, liver weight, kidney weight, ALT, AST, blood glucose, lipid profile (total cholesterol, triglycerides, HDL, LDL, VLDL), creatinine, urea, uric acid, liver and kidney histology, and feed intake.	A. cepa leaves demonstrated weight loss potential, suggesting its utility in reducing body weight, glucose level, fat mass, and fat deposits in the liver, while improving lipid profile.	39
HFD induced obesity in rats	Ecklonia cava	Significantly decreased total body and organ weights, including liver (up to 28%), spleen, and kidney, and adipose tissue weight dose-dependently. It lowered liver enzymes (AST, ALP, ALT, GGT), plasma leptin, GIP, insulin, blood glucose (up to 27%), pro-inflammatory cytokines (TNF-α, IL-6), LDL, FFA, triglycerides, total cholesterol, and the atherogenic index, while increasing HDL levels. Histologically, it reduced liver lipid droplets and adipocyte size. Gene expression of PPAR-γ, FAS, LPL, and SREBP-1c was downregulated.	Body weight, organ weights, adipose tissue weight, liver-related biomarkers, leptin, ghrelin, insulin, gastric inhibitory peptides (GIP), blood glucose, plasma TNF-α, IL-6, plasma lipid profile (LDL, HDL, TG, FFA, TC, atherogenic index), histopathological analysis of liver and fat, expression of adipogenic and lipogenic genes (PPAR-γ, FAS, LPL, SREBP-1c).	Ecklonia cava could be a potential candidate for the prevention of obesity induced by a high fat diet due to its anti-obesity effects, strong antioxidant properties, and ability to inhibit adipocyte differentiation.	40
HFD induced obesity in Swiss albino mice	Moringa oleifera Lam. seeds (ethanolic extract, 1% & 2%)	Consumption of HFD containing MOE for nine weeks exhibited significant reduction in lipid parameters, body weight, liver TG, and improved insulin resistance. The increase in fecal content and fecal triglyceride suggests MOE acts by delaying intestinal fat absorption.	Body weight and percentage change in body weight, Average food consumption, Wet weight of feces and TG in feces, Organ weights (heart, kidney, liver) and their ratios, Parametrial adipose tissue (PAT) weight, Liver TG. Serum glucose via Insulin Tolerance Test (ITT) and AUC glucose, Serum lipid parameters (STG, STC, HDL-c, LDL-c, VLDL-c), Markers of dyslipidemia (TC/HDL-c ratio, LDL-c/HDL-c ratio).	Significant reduction in serum TG, TC, VLDL-c, LDL-c, and dyslipidemic markers, and increased serum HDL-c levels (1% and 2% MOE) with Significant reductions in body weight, plasma glucose level and AUC glucose (2% MOE and Orlistat) compared to HFD control, suggesting improved insulin tolerance. increased wet weight of feces and TG in feces compared to HFD control with Significant decrease in liver TG and liver weight compared to HFD control.	41

Table 3. In silico Methodologies Applied in Obesity Research and Their Key Findings.

In *silico* Method	Principle	Procedure	Key Findings	References
Molecular Docking	Predicts binding interactions between small molecules and target proteins to identify potential modulators of adipogenesis.	Protein and ligand structures obtained from databases (PDB, PubChem). Docking simulations performed using software such as AutoDockVina. Binding affinities and interaction sites are analyzed to predict inhibitory or modulatory effects.	Curcuminoids were found to interact differently with PPARγ, CEBPA, and ACC, possibly inhibiting adipogenesis through these targets. Pharmacological agents like arbutin, purpurin, quercetin, and pterostilbene showed significant binding to fatty acid synthase enzyme domains, supporting in vitro results of reduced adipocyte differentiation	27, 42
Gene Enrichment Analysis	Identifies key genes and pathways involved in adipogenesis by analyzing transcriptomic data from public databases	Data collected from GEO, Array Express, SRA; processed through Gene Ontology (GO) and KEGG pathway analysis to identify upregulated genes and key regulatory pathways in adipogenesis	Identified adipogenesisrelated genes (PPARγ, ACSL1, S1PR3, GPC3, CD36, FABP4, DGATs). GO are Upregulated genes in collagen fibril, redox processes. KEGG: AMPK, PPAR, lipolysis regulation pathways. Suggested lipid modifying drugs (gemfibrozil, statins) as potential antiobesity treatments.	42
Drug Designing & Validation	Combines chemical repurposing and cheminformatics to predict drug likeness and ADMET properties of a novel antiobesity compound.	The chemical compound (2-methyl-1-phenylpropan-2-amine) was selected from PubChem and combined with lithium; bis(trimethylsilyl)azanide using Molinspiration software. Physicochemical properties (molecular weight, LogP, hydrogen bond donors/acceptors, TPSA) were analyzed to validate drug likeness based on Lipinski’s Rule of Five. The structure was visualized in 3D using Discovery Studio.	The designed compound had a molecular weight of 149.24 g/mol, LogP of 2.34, and TPSA of 26.02 Å², showing zero violations of Lipinski’s rule, indicating good drug-like properties and oral bioavailability.	43
2D-QSAR Modeling	Uses molecular descriptors to correlate chemical structure with biological activity in a linear regression model.	Dataset of 36 IP6K1 inhibitors was collected and standardized (SMILES to 3D structures). Molecular descriptors were computed via alvaDesc software. Dataset split into training (80%) and test (20%) sets. Multiple Linear Regression (MLR) models were built using sequential forward selection (SFS) and genetic algorithm (GA) approaches. Best models (M09 and M15) were selected based on statistical parameters (Q²LOO, R²Pred).	Identified key descriptors influencing biological activity, such as CMC-50 (lipophilicity related) and F04[C-C] (topological). Hydrophobicity and steric effects are crucial for activity.	44
Molecular docking, Molecular Dynamics (MD) simulations, and ADMET analysis of Withaferin-A against α-amylase	Predict molecular level interactions between phytochemicals and obesity-related enzymes (α-amylase) using computational approaches	Ligand (Withaferin-A) and receptor (α-amylase, PDB: 4W93) prepared. Semi flexible docking via AutoDock 4.0 using Lamarckian Genetic Algorithm (LGA). Visualization with Discovery Studio & Schrodinger Maestro. ADMET properties assessed through Molinspiration& Osiris tools. 100 ns Molecular Dynamics simulations using Desmond (OPLS 2005 force field, TIP3P water model, NPT ensemble)	Withaferin-A showed strong binding affinity: 9.79 kcal/mol with predicted IC50 of 66.61 nM. Stable hydrogen bonding with catalytic residues (TYR59, ASP197, HIS299) of α-amylase. MD simulations confirmed structural stability and conformational changes in α-amylase upon binding. ADMET profile: good oral bioavailability, non-mutagenic, non-tumorigenic, low reproductive effect. Suggests Withaferin-A as a promising α-amylase inhibitor for antiobesity drug design	45
Ligand Based Pharmacophore Mapping	Identifies essential structural features responsible for biological activity by generating pharmacophore models.	Conformers generated using genetic algorithm and Confab. Data split into training (70%) and test (30%) sets. Models built using QPHAR software with random forest (RF) technique. Internal and external predictivity assessed via R², RMSE, ME, SE, and R²Pred.	Hydrophobic and aromatic ring features of the compounds strongly correlated with activity. Compounds with more aromatic rings and hydrophobic features showed higher inhibitory potency.	43
3D-QSAR Modeling	Quantifies 3D structural requirements for potency using steric and electrostatic fields correlated with activity.	Conformers generated and aligned using rigid body alignment. Models built using Open 3DQSAR software and feature selection techniques (FFD-SEL and UVE-PLS). Internal and external predictivity assessed.	Steric and electrostatic contributions account for 60% and 40%, respectively. Potent inhibitors engage both favorable steric and electrostatic regions.	43
Homology Modeling & MD Simulations	Generates a 3D structure of IP6K1 (no X-ray structure available) and performs molecular dynamics to study ligand stability and interactions.	Homology model generated using SWISS-MODEL and refined via MD simulations. CB-Dock2 used to predict binding sites. AutoDockVina used for docking; MD simulations run for 50 ns. Binding free energy calculated using MM-GBSA.	Compound 21 (high activity) exhibited strong polar and hydrophobic interactions, especially with Arg194 and Gln190, leading to high binding stability (ΔGbind-18.35 kcal/mol). Compound 10 (low activity) showed fewer interactions and lower binding energy (ΔGbind-5.67 kcal/mol).	42
Molecular Docking (Glide)	Predicts binding affinity of phytochemicals to obesity target proteins	Phytocompounds were docked with obesity target proteins (FTO, HFAS) using Schrodinger's Glide, visualized with Discovery Studio, and analyzed for energy and interaction sites.	714-Bisabolene-2,3,10,11-tetrol, Moschamine, and N-Feruloyltyramine showed high binding to FTO/HFAS proteins.	46

Future prospects

Future prospects for the field of obesity research are bright and multidisciplinary, building on the molecular, cellular, and epidemiological foundations like continued innovation in three dimensional adipose tissue models and organ-on-a-chip platforms will vastly improve the physiological relevance and predictive value of in vitro assays, allowing for more accurate simulation of human adipose responses and therapeutic testing. In silico computational tools including molecular docking, molecular dynamics simulations, and machine learning are accelerating the discovery and optimization of multi target bioactive compounds for obesity intervention. These in silico predictions, when validated through refined in vivo animal models that closely mimic human metabolic and inflammatory pathways, enhance the translation from laboratory discoveries to clinical candidates. Advances in genomic profiling and biomarker discovery will drive the development of personalized medicine for obesity, enabling tailored intervention strategies and optimized therapeutic outcomes. Simultaneously, standardization of natural product extracts and identification of active phytochemicals are key to improving reproducibility and regulatory approval. Alternative in vitro and in silico models also reduce reliance on animal testing, aligning with ethical considerations. Together, these multidisciplinary directions offer a promising roadmap toward safer, more effective, and individualized obesity treatments that address the complex pathophysiology and global burden of this disease.

Figure 5. Multidisciplinary strategies for advancing obesity research and treatment ¹⁷

CONCLUSION:

Obesity represents a multifactorial disorder driven by the convergence of genetic, metabolic, environmental, and lifestyle influences, culminating in impaired adipose tissue function, disrupted adipogenesis, and systemic metabolic imbalance. The modest benefits and safety concerns associated with conventional therapies emphasize the urgent demand for alternative strategies. Cutting edge experimental platforms, such as three-dimensional adipose spheroids, microfluidic organ-on-a-chip systems, and advanced in silico modelling, are redefining the landscape of obesity research by enabling more precise mechanistic exploration are predictive therapeutic screening. Parallel to these technological strides, natural bioactive compounds are garnering attention as safer, pleiotropic agents with the potential to modulate multiple disease pathways. This review integrates evidence across in vitro, in silico, and in vivo models, alongside pharmacological and natural interventions, to deepen our understanding of obesity pathogenesis and identify promising avenues for novel therapeutic development.

CONFLICT OF INTEREST:

The authors declare no conflicts of interest relevant to this article.

ACKNOWLEDGEMENTS:

Authors are thankful and acknowledge researchers of the original research works whose publications are cited in the present review.

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Received on 21.09.2025 Revised on 24.10.2025

Accepted on 25.11.2025 Published on 12.02.2026

Available online from February 14, 2026

Res.J. Pharmacology and Pharmacodynamics.2026;18(1):46-58.

DOI: 10.52711/2321-5836.2026.00006

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