Quantitative structure-activity relationship-guided design and molecular modeling of anaplastic lymphoma kinase L1196M inhibitors: overcoming drug resistance through docking, ADMET (absorption, distribution, metabolism, excretion, and toxicity), and density functional theory studies
Muhammad Tukur Ibrahim, Josiah Joseph Isah, Sani Uba, Adamu Uzairu
Abstract
The ALK L1196M mutation drives resistance to first-generation ALK inhibitors like crizotinib, posing a challenge in targeted therapy. This study employed a computational drug design approach, integrating QSAR modelling, molecular docking, pharmacokinetic analysis, and DFT calculations to identify and optimize novel ALK inhibitors. A dataset of 51 N 2 -(2-methoxyphenyl)pyrimidine derivatives was analysed, and a QSAR model developed using GFA-MLR demonstrated high predictive accuracy (R 2 = 0.929, Q 2 = 0.887), confirming its reliability in estimating inhibitory activity. Molecular docking with AutoDock Vina identified high-affinity compounds for ALK L1196M, with the best scaffold achieving a docking score of -9.2 kcal/mol, outperforming crizotinib (-8.3 kcal/mol). ADMET screening confirmed favourable pharmacokinetic properties, including high intestinal absorption and low toxicity risks, making these compounds suitable for further development. Guided by these results, structurally optimized analogues were designed from the parent scaffold to enhance target affinity and drug-like behaviour. Among them, D1 emerged as the most promising candidate, exhibiting the strongest binding affinity (–9.8 kcal/mol), elevated predicted inhibitory activity (pIC 50 = 8.371), and markedly improved intestinal absorption (95.3%) relative to both compound 2 and crizotinib. D1 also displayed a clean toxicity profile, acceptable clearance, and full compliance with drug-likeness criteria. DFT calculations further supported D1’s suitability, revealing a balanced HOMO–LUMO energy gap and favourable electronic descriptors indicative of stable and effective molecular interactions. These findings highlight the utility of ligand-based scaffold optimization in overcoming resistance mechanisms, positioning D1 as a strong candidate for further experimental validation and therapeutic application.