The separation of anti-inflammatory effects from metabolic effects of glucocorticoids is a significant goal in drug design due to the wide array of side effects associated with glucocorticoid therapy. The PDF document provided outlines a practical course on computational methods in drug discovery, focusing on the Glucocorticoid Receptor (GR).

Glucocorticoid Receptor (GR)

Glucocorticoids exert their effects through the glucocorticoid receptor (GR), which is a nuclear receptor. Once activated, GR can follow two main pathways:

1. Transactivation: GR dimerizes, binds to glucocorticoid response elements (GREs), and upregulates the transcription of anti-inflammatory proteins.
2. Transrepression: GR remains monomeric, binds to negative glucocorticoid response elements (nGREs), and downregulates the transcription of pro-inflammatory proteins.

Key Points

• Transrepression Pathway: This pathway is associated with the anti-inflammatory effects of glucocorticoids and is believed to have fewer metabolic side effects compared to the transactivation pathway.
• Transactivation Pathway: Although it contributes to the anti-inflammatory effects, it is also associated with a range of side effects including hyperglycemia, weight gain, osteoporosis, and immune suppression.

Course Objectives

The course aims to identify and design non-steroidal ligands that can selectively modulate GR, favoring the transrepression pathway over transactivation. This is achieved through:

1. Data Retrieval: Collecting and analyzing GR structures from databases like UniProt and RCSB PDB.
2. Pharmacophore Modeling: Identifying the key interactions (binding hotspots) between GR and known ligands to build a pharmacophore model.
3. Virtual Screening: Using tools like Pharmit for pharmacophore-based virtual screening to find potential ligands.
4. Docking Studies: Refining the selection through docking studies to assess the binding efficiency and specificity of the ligands.
5. Drug Profiling: Evaluating the drug-likeness and ADMET properties of the best candidates.

Practical Steps

1. Structure Analysis: Analyze and superimpose GR structures to identify key interaction sites.
2. Pharmacophore Generation: Generate pharmacophores based on these interaction sites.
3. Screening: Use the pharmacophores for virtual screening to identify potential non-steroidal modulators.
4. Docking: Dock the identified hits to assess their binding modes and affinities.
5. Selection and Profiling: Select the best candidates based on binding affinity and profile them using ADMET tools like SwissADME and ADMETlab 2.0.

Desired Outcome

The goal is to identify ligands that preferentially induce the transrepression pathway of GR activation, thereby retaining the anti-inflammatory effects while minimizing the metabolic side effects. This approach aims to develop safer anti-inflammatory therapies with fewer side effects compared to current glucocorticoid treatments.

The practical sessions outlined in the document aim to equip participants with the computational tools and techniques necessary to achieve this goal, using a combination of data mining, pharmacophore modeling, virtual screening, docking, and drug profiling.