The absorption, distribution and excretion of drugs are largely determined by their transporters (DTs), the variability of which has thus attracted considerable attention. There are three aspects of variability: epigenetic regulation and genetic polymorphism, species/tissue/disease-specific DT abundances, and exogenous factors modulating DT activity. The variability data of each aspect are essential for clinical study, and a collective consideration among multiple aspects becomes crucial in precision medicine. However, no database is constructed to provide the comprehensive data of all aspects of DT variability. Herein, the Variability of Drug Transporter Database (VARIDT) was introduced to provide such data. First, 177 and 146 DTs were confirmed, for the first time, by the transporting drugs approved and in clinical/preclinical, respectively. Second, for the confirmed DTs, VARIDT comprehensively collected all aspects of their variability (23 947 DNA methylations, 7317 noncoding RNA/histone regulations, 1278 genetic polymorphisms, differential abundance profiles of 257 DTs in 21 781 patients/healthy individuals, expression of 245 DTs in 67 tissues of human/model organism, 1225 exogenous factors altering the activity of 148 DTs), which allowed mutual connection between any aspects. Due to huge amount of accumulated data, VARIDT made it possible to generalize characteristics to reveal disease etiology and optimize clinical treatment, and is freely accessible at: https://db.idrblab.org/varidt/ and http://varidt.idrblab.net/.

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One of the most challenging puzzles in drug discovery is the identification and characterization of candidate drug of well-balanced profile between efficacy and safety. So far, extensive efforts have been made to evaluate this balance by estimating the quantitative structure–therapeutic relationship and exploring target profile of adverse drug reaction. Particularly, the therapeutic index (TI) has emerged as a key indicator illustrating this delicate balance, and a clinically successful agent requires a sufficient TI suitable for it corresponding indication. However, the TI information are largely unknown for most drugs, and the mechanism underlying the drugs with narrow TI (NTI drugs) is still elusive. In this study, the collective effects of human protein–protein interaction (PPI) network and biological system profile on the drugs' efficacy–safety balance were systematically evaluated. First, a comprehensive literature review of the FDA approved drugs confirmed their NTI status. Second, a popular feature selection algorithm based on artificial intelligence (AI) was adopted to identify key factors differencing the target mechanism between NTI and non-NTI drugs. Finally, this work revealed that the targets of NTI drugs were highly centralized and connected in human PPI network, and the number of similarity proteins and affiliated signaling pathways of the corresponding targets was much higher than those of non-NTI drugs. These findings together with the newly discovered features or feature groups clarified the key factors indicating drug's narrow TI, and could thus provide a novel direction for determining the delicate drug efficacy-safety balance.

Lung adenocarcinoma (LUAD) is one of the leading causes of cancer-related death worldwide. There is an urgent need to uncover the pathogenic mechanism to develop new treatments. Agmatinase (AGMAT) expression and its association with clinicopathological characteristics were analyzed via GEO, Oncomine, and TCGA databases, and IHC staining in human LUAD specimens. An EdU cell proliferation kit, propidiumiodide staining, colony formation, cell migration, and invasion assays, and a xenograft tumor model were used to detect the biological function of AGMAT in LUAD. Furthermore, the expression level of nitric oxide (NO) was detected using a DAF-FMDA fluorescent probe or nitrite assay kit, and further validated with Carboxy-PTIO (a NO scavenger). The roles of three isoforms of nitric oxide synthases (nNOS, eNOS, and iNOS) were validated using L-NAME (eNOS inhibitor), SMT (iNOS inhibitor), and spermidine (nNOS inhibitor). AGMAT expression was up-regulated in LUAD tissues. LUAD patients with high AGMAT levels were associated with poorer prognoses. AGMAT promoted LUAD tumorigenesis in NO released by iNOS both in vitro and in vivo. Importantly, NO signaling up-regulated the expression of cyclin D1 via activating the MAPK and PI3K/Akt-dependent c-myc activity, ultimately promoting the malignant proliferation of tumor cells. On the whole, AGMAT promoted NO release via up-regulating the expression of iNOS. High levels of NO drove LUAD tumorigenesis via activating MAPK and PI3K/Akt cascades. AGMAT might be a potential diagnostic and therapeutic target for LUAD patients.