Tyrosine kinase inhibitors (TKIs) provide more effective targeted treatments for cancer, but are subject to a variety of adverse effects, such as hypothyroidism. TKI-induced hypothyroidism is a highly complicated issue, because of not only the unrealized toxicological mechanisms, but also different incidences of individual TKI drugs. While sunitinib is suspected for causing thyroid dysfunction more often than other TKIs, sorafenib is believed to be less risky. Here we integrated clinical data and in silico drug-protein interactions to examine the pharmacological distinction between sunitinib and sorafenib. Statistical analysis on the FDA Adverse Event Reporting System (FAERS) confirmed that sunitinib is more concurrent with hypothyroidism than sorafenib, which was observed in both female and male patients. Then, we used docking method and identified 3 proteins specifically binding to sunitinib but not sorafenib, i.e., retinoid X receptor alpha, retinoic acid receptors beta and gamma. As potential off-targets of sunitinib, these proteins are well known to assemble with thyroid hormone receptors, which can explain the profound impact of sunitinib on thyroid function. Taken together, we established a strategy of integrated analysis on clinical records and drug off-targets, which can be applied to explore the molecular basis of various adverse drug reactions.

In this paper, a new set of descriptors, Hydrophobic, Electronic, Steric, and Hydrogen (HESH) (principal components scores vectors of the HESH bond contribution properties), were derived from Principal Component Analysis (PCA) on the collected 171 physicochemical properties of 20 coded amino acids. By applying HESH descriptors to Quantitative Structure–Activity Relationship (QSAR) study on three peptides including 58 Angiotensin‐Converting Enzyme (ACE) inhibitors, 48 bitter‐tasting dipeptides, and 20 thromboplastin inhibitors, we get three excellent Partial Least Squares (PLS) models, with the squared multiple correlation coefficients (R$\rm{ _{{\rm{cum}}}^{\rm{2}} }$ ), cross‐validation (R$\rm{ _{{\rm{cum}}}^{\rm{2}} }$ ), and Root Mean Square Error (RMSE) of 0.877, 0.838, and 0.361 for ACE inhibitors, 0.926,0.865, and 0.172 for bitter‐tasting dipeptides and 0.996, 0.865, and 0.115 for thromboplastin inhibitors. These results were superior to many other reported researches. It showed that HESH may be a useful structural expression method for the study on QSAR of peptide.

The mitochondrial cytochrome P450 enzymes inhibitor steroid 11β-hydroxylase (CYP11B1) can decrease the production of cortisol. Therefore, these inhibitors have an effect in the treatment of Cushing’s syndrome. A pharmacophore model generated by Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets (GALAHAD) was used to align the compounds and perform comparative molecular field analysis (CoMFA) with Q² = 0.658, R² = 0.959. The pharmacophore model contained six hydrophobic regions and one acceptor atom, and electropositive and bulky substituents would be tolerated at the A and B sites, respectively. A three-dimensional quantitative structure-activity relationship (3D-QSAR) study based on the alignment with the atom root mean square (RMS) was applied using comparative molecular field analysis (CoMFA) with Q² = 0.666, R² = 0.978, and comparative molecular similarity indices analysis (CoMSIA) with Q² = 0.721, R² = 0.972. These results proved that all the models have good predictability of the bioactivities of inhibitors. Furthermore, the QSAR models indicated that a hydrogen bond acceptor substituent would be disfavored at the A and B groups, while hydrophobic groups would be favored at the B site. The three-dimensional (3D) model of the CYP11B1 was generated based on the crystal structure of the CYP11B2 (PDB code 4DVQ). In order to probe the ligand-binding modes, Surflex-dock was employed to dock CYP11B1 inhibitory compounds into the active site of the receptor. The docking result showed that the imidazolidine ring of CYP11B1 inhibitors form H bonds with the amino group of residue Arg155 and Arg519, which suggested that an electronegative substituent at these positions could enhance the activities of compounds. All the models generated by GALAHAD QSAR and Docking methods provide guidance about how to design novel and potential drugs for Cushing’s syndrome treatment.