In Silico Prediction Of Drug Metabolism And Renal Transporters-Mediated Elimination, And Discovery Of Inhibitors Targeting The Menin-Mixed Lineage Leukaemia(MLL) Interface

Absorption, distribution, metabolism, excretion and toxicity(ADME/T) characteristics of drugs have significant effect on its efficacy and safety. Earlier evaluation of ADME/T characteristics is helpful to increase the efficiency of drug development, reduce the cost of drug research, avoid drug adverse effects, and provide guidance for rational drug use. In last decade, experimental systems for ADME/T evaluation has been well developed, which help us accumulate large amounts of data for computational models. The properties of high-throughput and low-cost presented by computational prediction demonstrate its advantages in the drug research and development. Growing numbers of research have devoted to the in silico prediction of drug pharmacokinetics properties.In the first section of my dissertation, I give an introduction to the progress of in silico prediction of ADME/T characteristics. We reviewed the recent computational research on the process of absorption, distribution, metabolism, excretion or toxicity. The existing commercial software or free on-line services for ADME/T prediction and their applications in real cases were summarized. At the end of this part, the new trends and challenges faced by computational prediction of ADME/T characteristics were also discussed.Chapter 2 mainly addresses the prediction of drug metabolism properties. Human aldehyde oxidases(AO) is one of the most important phase II metabolism enzyme, which is responsible for the oxidization of aldehydes and azaheterocycles. During the last decades, introduction of a nitrogen atom into the aromatic ring of heterocyclic compounds with the aim of decreasing the P450-mediated aromatic oxidation have led to more azaheterocyclic compounds in drug development, which are all potential substrates of AO. There is a growing awareness and interest in the role of aldehyde oxidase enzyme family in drug metabolism and pharmacokinetics, because many recent clinical studies have been suspended due to the aldehyde oxidase metabolism. We here report a simple rule for identifying sites of metabolism(SOMs) mediated by human AOX1, based on a retrospective analysis of published and in-house AOX1 metabolism data. A decision tree analysis was performed using the parameters relating to the intermediate formation energy and the steric hindrance in AOX1 active site. The derived rule could well separate the experimentally observed SOMs and non-metabolic sites in the test set with the accuracy of 0.875. Besides, these rules could well predict multiple SOMs in one compound and explain the metabolic selectivity of similar structures. Finally, a prospective virtual screening against 14 c-Met inhibitors was conducted using the rule. Confirmed by in vitro cell culture experiments, the rule yields a high predicting accuracy of 92.85%, and two c-Met inhibitors, Capmatinib and NVPBVU972, were correctly identified as new substrates of AOX1. Based on this study, an early assessment model of human aldehyde oxidase 1(AOX1) was proposed, which could lower the risk of fails in late drug development phase.In the third and fourth chapters of this dissertation, kidney transporters involved in renal elimination are discussed. Transporters are membrane proteins which play a significant role in the transmembrane processes, and they selectively transport molecules in the intracellular or extracellular directions. In kidney, transporters have been shown to act concertedly in the excretion of drugs and their metabolites. Organic Cation Transporter 2(OCT2), which is expressed on the basolateral membrane of renal epithelial cells, involves in the first step of renal elimination that uptakes drug molecules from blood, across the membrane, and into the proximal tubule cell. Multidrug and toxin extrusion 1(MATE1) is localized to brush-border membrane, which subsequently pumped out exogenous and endogenous substances from renal cell into urine. A lot of research indicated that the changing activity of these transporters may cause unwanted toxic or side effects. Therefore, to avoid drug-drug interactions(DDIs) in clinical trials, it is urgent to determine what common structures of renal transporters inhibitors are and how these inhibitors affect the transport activity. Compared to the drug targets such as enzymes or receptors, membrane transporters are characterized by their low binding affinity and high structural promiscuity of ligands, which means that multiple mechanisms may be involved in the ligand binding and transporting process. Accordingly, we designed a combinatorial pharmacophore(CP) model to investigate the multiple inhibitory mechanisms.In chapter 3, a CP model for OCT2 inhibitors consisting of four individual pharmacophores, i.e., DHPR18, APR2, PRR5 and HHR4, were developed. Given a query ligand, it is considered as an inhibitor if it matches at least one of the hypotheses, or a non-inhibitor if it fails to match any of four hypotheses. Our CP model shows a remarkably improved recall rate and performs reasonably well to discriminate inhibitors and non-inhibitors. The model emphasizes the role of aromatic ring and positive charge as important structural determinants for inhibiting OCT2 transport. The pharmacophore matching results of known OCT2 inhibitors via different mechanisms suggest that PRR may correspond to the competitive inhibition, while HHR and DHPR are more relevant to noncompetitive inhibition.In chapter 4, the generated CP model of MATE1 inhibitors comprises four individual pharmacophore hypotheses, HHR1, DRR, HHR2 and AAAP. It successfully identified the MATE1 inhibitors with an overall accuracy of 0.73. The model highlights aromatic ring and hydrophobicity as two important structural determinants for MATE1 inhibition. Compared with the pharmacophore model of OCT2, the hypotheses of AAAP and PRR5 are suggested to be responsible for their ligand selectivity, while HHR a common recognition pattern for their dual inhibition. A series of analysis including molecular sizes of inhibitors matching different hypotheses, matching of representative MATE1 inhibitors and molecular docking indicated that the small inhibitors matching HHR1 and DRR involve in competitive inhibition, while the relatively large inhibitors matching AAAP are responsible for the noncompetitive inhibition by locking the conformation changing of MATE1. These results may give insights into understanding of renal transporting mechanisms, and help the identification of transporter inhibitors to avoid unwanted DDIs.The leukemogenic activity of the mixed lineage leukemia(MLL) fusion protein depends on the interaction with menin. The oncogenicity of MLL fusion protein will be lost if the interaction between menin and MLL is interrupted, providing a novel and feasible approach for developing therapeutics against the MLL-mediated leukemia. In chapter 5, we aim to discover novel inhibitors targeting the menin-MLL interface with virtual screening. No-constraint docking model and 3D-QSAR models were developed and selected based on their ability of identifying actives from decoys. A virtual screening strategy combining these two types of models was used to discovery new small-molecule inhibitors for inhibiting the menin-MLL interaction from a commercial chemical database. One hundred and twenty-first compounds were kept after virtual screening. Verified by Fluorescence polarization assays, five menin-MLL inhibitors with novel scaffolds were identified, among which DCZ_M123 exhibited potent inhibitory activity with an IC50 of 4.71±0.12 μM and a Ki of 0.94±0.03 μM.