Prediction model of human ABCC2/MRP2 efflux pump inhibitors: a QSAR study

Computational and artificial intelligence-based approaches for drug metabolism and transport prediction

Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.

Identification of efflux pump inhibitors for Pseudomonas aeruginosa MexAB-OprM via ligand-based pharmacophores, 2D-QSAR, molecular docking, and molecular dynamics approaches

Efflux pumps have been reported as one of the significant mechanisms by which bacteria evade the effects of multiple antibiotics. The tripartite efflux pump MexAB-OprM in Pseudomonas aeruginosa is one of the most significant multidrug efflux systems due to its broad resistance to antibiotics such as chloramphenicol, fluoroquinolones, lipophilic β-lactam antibiotics, nalidixic acid, novobiocin, rifampicin, and tetracycline. A promising strategy to overcome this resistance mechanism is to combine antibiotics with efflux pump inhibitors (EPIs), which can increase their intracellular concentration to enhance their biological activities. Based on 143 EPIs with chemically diverse skeletons, the 3D pharmacophore and 2D-QSAR modelings were developed and used for the virtual screening on 9.2 million compounds including ZINC15, DrugBank, and Traditional Chinese Medicine databases to identify new EPIs. The molecular docking was also performed to evaluate the binding affinity of potential EPIs to the distal-binding pocket of MexB and resulted in 611 potential EPIs. The structure-activity relationship analyses suggested that nitrogen heterocyclic compounds, piperazine and pyridine scaffolds, and amide derivatives are the most favorable chemically features for MexAB inhibitory activities. The results from molecular dynamics analysis in 100 ns indicated that ZINC009296881 and ZINC009200074 were the most potential MexB inhibitors with strong binding affinity to the distal pocket and MM/GBSA ∆Gbind values of - 38.97 and - 30.19 kcal mol-1, respectively. The predicted pharmacokinetic properties and toxicity of these compounds indicated their potential oral drugs. Multistep virtual screening of EPIs for MexAB-OprM, efflux pump multidrug resistant of P. aeruginosa.

Discovery of AcrAB-TolC pump inhibitors: Virtual screening and molecular dynamics simulation approach

AcrAB-TolC tripartite efflux pump, which belongs to the RND superfamily, is a main multi-drug efflux system of Escherichia coli (E. coli) because of the broad resistance on various antibiotics. With the discovering of efflux pump inhibitors (EPIs), a combination between these and antibiotics is one of the most promising therapies. Therefore, building a virtual screening model with prediction capacities for the efflux pump inhibitory activities of candidates from DrugBank and ZINC15 dataset, is one of the key goals of this project. Based on the database of 170 diverse chemical structures collected from 28 research journals, two 2D-QSAR models and a 3D-pharmacophore model have been performed. On the AcrB protein (PDB 4DX7), two binding sites have been discovered that match to the hydrophobic trap in the distal pocket and the switch loop in the proximal pocket. After virtual screening processes, twenty candidate AcrAB-TolC inhibitors have been subjected to molecular dynamics simulations, binding free energy calculations and ADMET predictions. The results indicate that three compounds namely DB09233, DB02581, and DB15224 are potential inhibitors with ΔGbind of -42.30 ± 4.58, -40.76 ± 7.30 and -31.06 ± 7.63 kcal.mol-1, respectively.Communicated by Ramaswamy H. Sarma.

Adenosine triphosphate-binding cassette subfamily C members in liver hepatocellular carcinoma: Bioinformatics-driven prognostic value

Aberrant expression of adenosine triphosphate-binding cassette subfamily C (ABCC), one of the largest superfamilies and transporter gene families of membrane proteins, is associated with various tumors. However, its relationship with liver hepatocellular carcinoma (LIHC) remains unclear.We used the Oncomine, UALCAN, Human Protein Atlas, GeneMANIA, GO, Kyoto Encyclopedia of Genes and Genomes (KEGG), TIMER, and Kaplan-Meier Plotter databases. On May 20, 2021, we searched these databases for the terms ABCC1, ABCC2, ABCC3, ABCC4, ABCC5, ABCC6, ABCC7, ABCC8, ABCC9, ABCC10, ABCC11, ABCC12, ABCC13, and "liver cancer." The exposure group comprised LIHC patients, and the control group comprised normal patients (those with noncancerous liver tissue). All patients shown in the retrieval language search were included. We compared the mRNA expression of these proteins in LIHC and control patients to examine the potential role of ABCC1-13 in LIHC.Relative to the normal liver tissue, mRNA expression of ABCC1/2/3/4/5/6/10 was significantly upregulated (P < .001), and that of ABCC9/11 significantly downregulated (both P < .001), in LIHC. ABCC mRNA expression varied with gender (P < .05), except for ABCC11-13; with tumor grade (P < 0.05), except for ABCC7/12/13; with tumor stage (P < .05), except for ABCC11-13; and with lymph node metastasis status (P < .05), except for ABCC7/8/11/12/13. Based on KEGG enrichment analysis, these genes were associated with the following pathways: ABC transporters, Bile secretion, Antifolate resistance, and Peroxisome (P < .05). Except for ABCC12/13, the ABCCs were significantly associated with B cell, CD8+ T cell, CD4+ T cell, macrophage, neutrophil, and dendritic cell infiltration (P < .05). High mRNA expression of ABCC1/4/5/8 (P < .05) and low expression of ABCC6/7/9/12/13 (P < .05) indicated poor prognosis. Prognostic significance was indicated for ABCC2/13 for both men and women (P < .05); for ABCC1/6/12/13 for tumor grades 1-3 (P < .05); for ABCC5/11/12/13 for all tumor stages (P < .05); for ABCC1/11/12/13 for American Joint Committee on Cancer T stages 1-3 (P < .05); and for ABCC1/5/6/13 for vascular invasion. None showed prognostic significance for microvascular invasion (P < .05).We identified ABCC1/2/3/4/5/6/9/10/11 as potential diagnostic markers, and ABCC1/4/5/6/7/8/9/12/13 as prognostic markers, of LIHC. Our future work will promote the use of ABCCs in the diagnosis and treatment of LIHC.