Herein, we designed and synthesized certain anilinoquinazoline derivatives bearing bulky arylpyridinyl, arylpropenoyl and arylpyrazolyl moieties at the 4' position of the anilinoquinazoline, as potential dual HER2/EGFR kinase inhibitors. A detailed molecular modeling study was performed by docking the synthesized compounds in the active site of the epidermal growth factor receptor (EGFR). The synthesized compounds were further tested for their inhibitory activity on EGFR and HER2 tyrosine kinases. The aryl 2-imino-1,2-dihydropyridine derivatives 5d and 5e displayed the most potent inhibitory activity on EGFR with IC50 equal to 2.09 and 1.94 μM, respectively, and with IC50 equal to 3.98 and 1.04 μM on HER2, respectively. Furthermore, the anti-proliferative activity of these most active compounds on MDA-MB-231 breast cancer cell lines, known to overexpress EGFR, showed an IC50 range of 2.4 and 2.5 μM, respectively.

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The ability to predict cell-permeable candidate molecules has great potential to assist drug discovery projects. Large molecules that lie beyond the Rule of Five (bRo5) are increasingly important as drug candidates and tool molecules for chemical biology. However, such large molecules usually do not cross cell membranes and cannot access intracellular targets or be developed as orally bioavailable drugs. Here, we describe a random forest (RF) machine learning model for the prediction of passive membrane permeation rates developed using a set of over 1000 bRo5 macrocyclic compounds. The model is based on easily calculated chemical features/descriptors as independent variables. Our random forest (RF) model substantially outperforms a multiple linear regression model based on the same features and achieves better performance metrics than previously reported models using the same underlying data. These features include: (1) polar surface area in water, (2) the octanol-water partitioning coefficient, (3) the number of hydrogen-bond donors, (4) the sum of the topological distances between nitrogen atoms, (5) the sum of the topological distances between nitrogen and oxygen atoms, and (6) the multiple molecular path count of order 2. The last three features represent molecular flexibility, the ability of the molecule to adopt different conformations in the aqueous and membrane interior phases, and the molecular "chameleonicity." Guided by the model, we propose design guidelines for membranepermeating macrocycles. It is anticipated that this model will be useful in guiding the design of large, bioactive molecules for medicinal chemistry and chemical biology applications.