Effect of lipophilicity modulation on inhibition of human rhinovirus capsid binders

Ensemble Docking Approach to Mitigate Pregnane X Receptor-Mediated CYP3A4 Induction Risk

Three structurally closely related dopamine D1 receptor positive allosteric modulators (D1 PAMs) based on a tetrahydroisoquinoline (THIQ) scaffold were profiled for their CYP3A4 induction potentials. It was found that the length of the linker at the C5 position greatly affected the potentials of these D1 PAMs as CYP3A4 inducers, and the level of induction correlated well with the activation of the pregnane X receptor (PXR). Based on the published PXR X-ray crystal structures, we built a binding model specifically for these THIQ-scaffold-based D1 PAMs in the PXR ligand-binding pocket via an ensemble docking approach and found the model could explain the observed CYP induction disparity. Combined with our previously reported D1 receptor homology model, which identified the C5 position as pointing toward the solvent-exposed space, our PXR-binding model coincidentally suggested that structural modifications at the C5 position could productively modulate the CYP induction potential while maintaining the D1 PAM potency of these THIQ-based PAMs.

A concise review on hPXR ligand-recognizing residues and structure-based strategies to alleviate hPXR transactivation risk

The human pregnane X receptor (hPXR) regulates the expression of major drug metabolizing enzymes. A wide range of drug candidates bind and activate hPXR, and hence are at risk of increasing drug-drug interactions and reducing clinical efficacy. hPXR structural features that function as hot spots for ligand binding are identified and highlighted in this concise review. Based on literature structure-activity relationship data as case studies, structure-based strategies to mitigate hPXR transactivation are summarized for medicinal chemists.

Molecular Quantum Similarity, Chemical Reactivity and Database Screening of 3D Pharmacophores of the Protein Kinases A, B and G from Mycobacterium tuberculosis

Mycobacterium tuberculosis remains one of the world's most devastating pathogens. For this reason, we developed a study involving 3D pharmacophore searching, selectivity analysis and database screening for a series of anti-tuberculosis compounds, associated with the protein kinases A, B, and G. This theoretical study is expected to shed some light onto some molecular aspects that could contribute to the knowledge of the molecular mechanics behind interactions of these compounds, with anti-tuberculosis activity. Using the Molecular Quantum Similarity field and reactivity descriptors supported in the Density Functional Theory, it was possible to measure the quantification of the steric and electrostatic effects through the Overlap and Coulomb quantitative convergence (alpha and beta) scales. In addition, an analysis of reactivity indices using global and local descriptors was developed, identifying the binding sites and selectivity on these anti-tuberculosis compounds in the active sites. Finally, the reported pharmacophores to PKn A, B and G, were used to carry out database screening, using a database with anti-tuberculosis drugs from the Kelly Chibale research group (http://www.kellychibaleresearch.uct.ac.za/), to find the compounds with affinity for the specific protein targets associated with PKn A, B and G. In this regard, this hybrid methodology (Molecular Mechanic/Quantum Chemistry) shows new insights into drug design that may be useful in the tuberculosis treatment today.

Network pharmacology study on the mechanism of traditional Chinese medicine for upper respiratory tract infection

Traditional Chinese medicine (TCM) is a multi-component and multi-target agent and could treat complex diseases in a holistic way, especially infection diseases. However, the underlying pharmacology remains unclear. Fortunately, network pharmacology by integrating system biology and polypharmacology provides a strategy to address this issue. In this work, Reduning Injection (RDN), a well-used TCM treatment in the clinic for upper respiratory tract infections (URTIs), was investigated to interpret the molecular mechanism and predict new clinical directions by integrating molecular docking, network analysis and cell-based assays. 32 active ingredients and 38 potential targets were identified. In vitro experiments confirmed the bioactivities of the compounds against lipopolysaccharide (LPS)-stimulated PGE2 and NO production in RAW264.7 cells. Moreover, network analysis showed that RDN could not only inhibit viral replication but also alleviate the sickness symptoms of URTIs through directly targeting the key proteins in the respiratory viral life cycle and indirectly regulating host immune systems. In addition, other clinical applications of RDN such as neoplasms, cardiovascular diseases and immune system diseases were predicted on the basis of the relationships between targets and diseases.