Diabetic nephropathy (DN), a complication affecting the microvasculature, is reported to be seen in both Type 1 and Type 2 diabetic patients. DN, known to affect 40% of diabetic patients, is also one of the major causes of renal conditions like chronic kidney disease and end stage renal disease (ESRD) specifically in developed countries (Gnudi et al., 2016, Alicic et al., 2017). The intervenable risk factors include high glycemic levels, hypertension and dyslipidemia while the non-modifiable risk factors include geographical location, gender, race and genetic predisposition (Natesan and Kim, 2021). The disease manifests with microalbuminuria which then progresses to macroalbuminuria because of the defects in glomerular filtration and finally to ESRD (Alicic et al., 2017). These functional manifestations correspond to the underlying morphological changes of thickening of glomerular basement membrane, excess proliferation of mesangial cells and decrease in podocyte population (Fioretto and Mauer, 2007).

Pathophysiologically, these changes are reflected by activation of several signalling cascades involving AGE-RAGE (Advanced Glycation Endproduct; Receptor for Advanced Glycation Endproduct) pathway and renin angiotensin system which results in oxidative stress, inflammation and apoptosis (Lim and Tesch, 2012; Lim, 2014). Treating with renin angiotensin converting enzyme inhibitors or angiotensin II receptor blockers is the suggested strategy for DN. However, a combination of good control in glycemic levels and hypertension along with dietary changes helps in disease management as no single approach is found to control or reduce disease progression (Alicic et al., 2017).

Traditional medicines are being employed globally in disease prevention and maintenance of physical and mental health owing to their therapeutic potential, less adverse effects and easy accessibility (Rudra et al., 2017). Diabetes mellitus is referred by several names in the siddha system of medicine, among which one is ‘Madhumegam’, where Madhu refers to sweet and megam to venereal disease (Gaddam et al., 2019). Madhumega kudineer is one of the ancient anti-diabetic, polyherbal siddha formulation given to diabetic patients. This medicinal formulation contains leaf extracts of Aegle marmelos, Syzygium cumini, Mangifera indica, Azadirachta indica, Justicia adhatoda, Gymnema sylvestre and whole plant extracts of Andrographis paniculata. Even though, the therapeutic potential of some of the individual plants mentioned above are previously explored in vitro and in vivo, their combined mechanism of action in treating diabetic nephropathy has not been elucidated (Rahman and Parvin, 2014, Dai et al., 2019).

The classical one drug-one target-one disease approach has rising concerns because of the involvement of multi-gene axes. Therefore, it is also important to intervene at multiple points of the regulatory pathways while treating complicated disorders such as diabetes, cancer, leprosy, tuberculosis, etc. The relatively new approach of integrating network biology and polypharmacology, referred to as network pharmacology utilises the multi-omics data and the concept of multi-target drug towards drug development (Noor et al., 2022). This has resulted in the systematic analysis of the effect of complex and multiple bioactive compounds in the living systems (Chandran et al., 2016). The major challenge encountered by traditional medicine is the lack of scientific validation. The development of network pharmacology overcomes this challenge by providing a holistic understanding of the interaction of bioactive compounds on biological systems.

In this study, the main players of DN targeted by the phytochemicals in MK were investigated using the network pharmacology method. The target proteins of MK phytochemicals and DN were obtained from public databases. Common targets between DN related targets and MK related targets were taken for network (phytochemical- target protein gene) construction and further protein-protein interaction (PPI) analysis. From the PPI network, the main hub genes were selected for further KEGG enrichment analysis and key targets identification. Finally, computational analysis of docking and molecular dynamics simulation was done to understand the interaction of potential therapeutic phytochemicals in MK against DN key targets. The interaction energies of the complexes were also validated by MM-PBSA method. Figure 1 depicts the overall workflow.