Chemogenomic Approaches for Revealing Drug Target Interactions in Drug Discovery

The drug discovery process has been a crucial and cost-intensive process. This cost is not only monetary but also involves risks, time, and labour that are incurred while introducing a drug in the market. In order to reduce this cost and the risks associated with the drugs that may result in severe side effects, the in silico methods have gained popularity in recent years. These methods have had a significant impact on not only drug discovery but also the related areas such as drug repositioning, drug-target interaction prediction, drug side effect prediction, personalised medicine, etc. Amongst these research areas predicting interactions between drugs and targets forms the basis for drug discovery. The availability of big data in the form of bioinformatics, genetic databases, along with computational methods, have further supported data-driven decision-making. The results obtained through these methods may be further validated using in vitro or in vivo experiments. This validation step can further justify the predictions resulting from in silico approaches, further increasing the accuracy of the overall result in subsequent stages. A variety of approaches are used in predicting drug-target interactions, including ligand-based, molecular docking based and chemogenomic-based approaches. This paper discusses the chemogenomic methods, considering drug target interaction as a classification problem on whether or not an interaction between a particular drug and target would serve as a basis for understanding drug discovery/drug repositioning. We present the advantages and disadvantages associated with their application.     

Unravelling the complex drug–drug interactions of the cardiovascular drugs,verapamil and digoxin,with P-glycoprotein

Drug–drug interactions (DDIs) and associated toxicity from cardiovascular drugs represents a major problem for effective co-administration of cardiovascular therapeutics. A significant amount of drug toxicity from DDIs occurs because of drug interactions and multiple cardiovascular drug binding to the efflux transporter P-glycoprotein (Pgp), which is particularly problematic for cardiovascular drugs because of their relatively low therapeutic indexes. The calcium channel antagonist, verapamil and the cardiac glycoside, digoxin, exhibit DDIs with Pgp through non-competitive inhibition of digoxin transport, which leads to elevated digoxin plasma concentrations and digoxin toxicity. In the present study, verapamil-induced ATPase activation kinetics were biphasic implying at least two verapamil-binding sites on Pgp, whereas monophasic digoxin activation of Pgp-coupled ATPase kinetics suggested a single digoxin-binding site. Using intrinsic protein fluorescence and the saturation transfer double difference (STDD) NMR techniques to probe drug–Pgp interactions, verapamil was found to have little effect on digoxin–Pgp interactions at low concentrations of verapamil, which is consistent with simultaneous binding of the drugs and non-competitive inhibition. Higher concentrations of verapamil caused significant disruption of digoxin–Pgp interactions that suggested overlapping and competing drug-binding sites. These interactions correlated to drug-induced conformational changes deduced from acrylamide quenching of Pgp tryptophan fluorescence. Also, Pgp-coupled ATPase activity kinetics measured with a range of verapamil and digoxin concentrations fit well to a DDI model encompassing non-competitive and competitive inhibition of digoxin by verapamil. The results and previous transport studies were combined into a comprehensive model of verapamil–digoxin DDIs encompassing drug binding, ATP hydrolysis, transport and conformational changes.     

Mutation Induced Conformational Changes in Genomic DNA from Cancerous K562 Cells Influence Drug-DNA Binding Modes

Normal human genomic DNA (N-DNA) and mutated DNA (M-DNA) from K562 leukemic cells show different thermodynamic properties and binding affinities on interaction with anticancer drugs; adriamycin (ADR) and daunomycin (DNM). Isothermal calorimetric thermograms representing titration of ADR/DNM with N-DNA and M-DNA on analysis best fitted with sequential model of four and three events respectively. From Raman spectroscopy it has been identified that M-DNA is partially transformed to A form owing to mutations and N-DNA on binding of drugs too undergoes transition to A form of DNA. A correlation of thermodynamic contribution and structural data reveal the presence of different binding events in drug and DNA interactions. These events are assumed to be representative of minor groove complexation, reorientation of the drug in the complex, DNA deformation to accommodate the drugs and finally intercalation. Dynamic light scattering and zeta potential data also support differences in structure and mode of binding of N and M DNA. This study highlights that mutations can manifest structural changes in DNA, which may influence the binding efficacy of the drugs. New generation of drugs can be designed which recognize the difference in DNA structure in the cancerous cells instead of their biochemical manifestation.     

肠道微生物群与药物相互作用的研究进展

药物的代谢是机体对药物处置过程的关键步骤,而肠道作为机体中重要的微生态系统,其在药物代谢方面的作用至关重要。肠道微生物群能够对各种药物等外源化合物进行生物转化、积累,并改变这些物质的活性和毒性,从而影响宿主机体对它们的反应。肠道微生物群与药物之间的相互作用相当复杂,亟待更多更加深入、全面的发掘和研究。近年来,随着人们对肠道微生物群代谢及其与药物互作关系,肠道菌-宿主共代谢认知的不断深化,越来越多的研究表明肠道微生物在药代动力学中扮演重要角色。本文通过调研、整理、归纳和总结国内外相关文献资料,对机体肠道微生物的分类、功能,几种常用药物对肠道微生物的影响以及肠道菌群对药物的代谢作用效果与几个主要的机制进行了梳理和综述,并讨论了微生物和药物之间的双向互作。有利于增进对微生物群影响药物疗效及其代谢途径和机制的了解,提高调控肠道微生物改善治疗的可能性,为指导临床合理用药、精准用药、个体化治疗、药物的评价和新药研发等提供科学参考。     

靶向共价键药物的研究进展

药物与靶标的结合是启动药理作用的本源,共价键药物是以共享电子的方式来实现与靶标的结合,其中大多为抗感染、抗肿瘤以及心脑血管、神经系统和代谢类药物。简介共价键药物与非共价键药物的区别以及既往的重磅级共价键药物与靶标的结合特点,分类综述靶向共价键药物的理性设计及与靶标的结合反应。     

π–π Stacking mediated drug–drug interactions in human CYP2E1

Because of having many low molecular mass substrates, CYP2E1 is of particular interests to the pharmaceutical industry. Many evidences showed that this enzyme can adopt multiple substrates to significantly reduce the oxidation rate of the substrates. The detailed mechanism for this observation is still unclear. In the current study, we employed GPU‐accelerated molecular dynamics simulations to study the multiple‐binding mode of human CYP2E1, with an aim of offering a mechanistic explanation for the unexplained multiple‐substrate binding. Our results showed that Thr303 and Phe478 were key factors for the substrate recognition and multiple‐substrate binding. The former can form a significant hydrogen bond to recognize and position the substrate in the productive binding orientation in the active site. The latter acted as a mediator for the substrate communications via π–π stacking interactions. In the multiple‐binding mode, the aforementioned π–π stacking interactions formed by the aromatic rings of both substrates and Phe478 drove the first substrate far away from the catalytic center, orienting in an additional binding position and going against the substrate metabolism. All these findings could give atomic insights into the detailed mechanism for the multiple‐substrate binding in human CYP2E1, providing useful information for the drug metabolism mechanism and personalized use of clinical drugs. Proteins 2013; © 2012 Wiley Periodicals, Inc.     

Differentiation and drug resistance relationships in leukemia cells

It is well established that the effectiveness of anticancer drugs may result from combined cytotoxic and differentiation activities on tumor cells. Also, differentiating agents are able to alter the susceptibility of cancer cells to antineoplastic drug therapy. However, the acquisition and/or development of drug resistance that frequently appears in anticancer treatment can impair these interactions between differentiation agents and cytotoxic drugs. In the present study, we report that the acquisition of resistance to anthracyclines in two humans, promyeolocytic leukemia HL-60 and eythroleukemia K562 cell lines, results in a restricted maturation process induced by differentiating agents with respect to that exhibited by their corresponding drug-sensitive counterparts. Interestingly, differentiating agents are able to decrease the overexpression of drug-efflux pumps as it is the case of MRP1 in the resistant HL-60 cells, thus increasing the sensitivity of cells to drug treatment. In addition, susceptibility of the drug-sensitive cells to certain apoptotic stimuli is significantly reduced after differentiation. The results here reported indicate complex interactions between cytotoxic (drug therapy) and non-cytotoxic (differentiation) cancer treatments, which should be taken into account to improve therapeutic efficiency.     

脂质体递药系统的靶向修饰

作为药物递送载体，脂质体（LPs）由于免疫原性低、稳定性好、毒性低和成本低而被认为是有前途的纳米药物递送系统。然而，LPs的靶向递送效果并不理想，往往会对正常的机体细胞造成伤害，因此，如何优化LPs药物，使其具有靶向性仍然是当前研究的重点。本文结合近年来国内外相关研究进展，重点介绍了多肽、抗体、糖类、配体，以及核酸适配体等靶向修饰物对LPs功能的影响，并归纳总结了各种靶向修饰目前存在的优势与挑战，以期对LPs给药系统的进一步研究提供科学参考及新药研发提供理论依据。     

Mining significant substructure pairs for interpreting polypharmacology in drug-target network

A current key feature in drug-target network is that drugs often bind to multiple targets, known as polypharmacology or drug promiscuity. Recent literature has indicated that relatively small fragments in both drugs and targets are crucial in forming polypharmacology. We hypothesize that principles behind polypharmacology are embedded in paired fragments in molecular graphs and amino acid sequences of drug-target interactions. We developed a fast, scalable algorithm for mining significantly co-occurring subgraph-subsequence pairs from drug-target interactions. A noteworthy feature of our approach is to capture significant paired patterns of subgraph-subsequence, while patterns of either drugs or targets only have been considered in the literature so far. Significant substructure pairs allow the grouping of drug-target interactions into clusters, covering approximately 75% of interactions containing approved drugs. These clusters were highly exclusive to each other, being statistically significant and logically implying that each cluster corresponds to a distinguished type of polypharmacology. These exclusive clusters cannot be easily obtained by using either drug or target information only but are naturally found by highlighting significant substructure pairs in drug-target interactions. These results confirm the effectiveness of our method for interpreting polypharmacology in drug-target network.     

Binding of a Cyano- and Fluoro-containing Drug Bicalutamide to Cytochrome P450 46A1: UNUSUAL FEATURES AND SPECTRAL RESPONSE*

Cytochrome P450 46A1 (CYP46A1) is the cholesterol 24-hydroxylase initiating the major pathways of cholesterol removal from the brain, and bicalutamide (BIC) is a drug of choice for the treatment of progressive androgen-dependent prostate cancer. We evaluated the interactions of BIC with CYP46A1 by x-ray crystallography and by conducting solution and mutagenesis studies. Because BIC is administered to patients as a racemic mixture of the S and R isomers, we studied all three, racemic BIC as well as the S and R isomers. Co-crystallization of CYP46A1 with racemic BIC led to structure determinations at 2.1 Å resolution with the drug complexes exhibiting novel properties. Both BIC isomers bind to the CYP46A1 active site in very similar single orientation and adopt an energetically unfavorable folded conformation. This folded BIC conformation is correlated with drug-induced localized shifts of amino acid side chains in CYP46A1 and unusual interactions in the CYP46A1-BIC complex. One of these interactions involves a water molecule that is coordinated to the P450 heme iron and also hydrogen-bonded to the BIC nitrile. Due to polarization of the water in this environment, the heme elicits previously unreported types of P450 spectral responses. We also observed that access to the P450 active site was affected by differential recognition of S versus R isomers at the CYP46A1 surface arising from BIC conformational polymorphism.     

Pharmacogenetics of cardiovascular drugs.

Pharmacogenetics is a field aimed at understanding the genetic contribution to inter-patient variability in drug efficacy and toxicity. Treatment of cardiovascular disease is, in most cases, guided by evidence from well-controlled clinical trials. Given the solid scientific basis for the treatment of most cardiovascular diseases, it is common for patients with a given disease to be treated in essentially the same manner. Thus, the clinical trials have been very informative about treating large groups of patients with a given disease, but are slightly less informative about the treatment of individual patients. Pharmacogenetics and pharmacogenomics have the potential of taking the information derived from large clinical trials and further refining it to select the drugs with the greatest likelihood for benefit, and least likelihood for harm, in individual patients, based on their genetic make-up. In this paper, the current literature on cardiovascular pharmacogenetics is emphasised, and how the use of pharmacogenetic/pharmacogenomic information may be particularly useful in the future in the treatment of cardiovascular diseases is also highlighted.     

Systematic discovery of drug interaction mechanisms

Drug combinations are increasingly important in disease treatments, for combating drug resistance, and for elucidating fundamental relationships in cell physiology. When drugs are combined, their individual effects on cells may be amplified or weakened. Such drug interactions are crucial for treatment efficacy, but their underlying mechanisms remain largely unknown. To uncover the causes of drug interactions, we developed a systematic approach based on precise quantification of the individual and joint effects of antibiotics on growth of genome‐wide Escherichia coli gene deletion strains. We found that drug interactions between antibiotics representing the main modes of action are highly robust to genetic perturbation. This robustness is encapsulated in a general principle of bacterial growth, which enables the quantitative prediction of mutant growth rates under drug combinations. Rare violations of this principle exposed recurring cellular functions controlling drug interactions. In particular, we found that polysaccharide and ATP synthesis control multiple drug interactions with previously unexplained mechanisms, and small molecule adjuvants targeting these functions synthetically reshape drug interactions in predictable ways. These results provide a new conceptual framework for the design of multidrug combinations and suggest that there are universal mechanisms at the heart of most drug interactions.     

Pharmacomicrobiomics: a novel route towards personalized medicine?

Inter-individual heterogeneity in drug response is a serious problem that affects the patient’s wellbeing and poses enormous clinical and financial burdens on a societal level. Pharmacogenomics has been at the forefront of research into the impact of individual genetic background on drug response variability or drug toxicity, and recently the gut microbiome, which has also been called the second genome, has been recognized as an important player in this respect. Moreover, the microbiome is a very attractive target for improving drug efficacy and safety due to the opportunities to manipulate its composition. Pharmacomicrobiomics is an emerging field that investigates the interplay of microbiome variation and drugs response and disposition (absorption, distribution, metabolism and excretion). In this review, we provide a historical overview and examine current state-of-the-art knowledge on the complex interactions between gut microbiome, host and drugs. We argue that combining pharmacogenomics and pharmacomicrobiomics will provide an important foundation for making major advances in personalized medicine.     

核酸适配体在靶向药物传递中的研究进展

抗癌药物的毒副作用限制了其临床应用,纳米药物载体可实现药物在病灶部位的聚集而不影响正常组织,从而降低药物毒副作用.在药物载体表面修饰靶向配体,以提高药物载体主动靶向进入到细胞的能力,可有效地将药物释放到靶细胞,大大提高药效.核酸适配体(aptamer)作为一种新型的靶向分子,近几年已被运用到靶向药物传递的研究中.本文介绍了几种适配体靶向载药体系,如适配体-药物、适配体-脂质体、适配体-聚合物胶束、适配体-聚合物纳米颗粒、适配体-金属颗粒以及适配体-支化聚合物等载药体系,并对当前研究的热点以及存在的问题和不足进行了评述.     

Metabolic enantiomeric interactions: the inhibition of human (S)-warfarin-7-hydroxylase by (R)-warfarin

Inhibition of the metabolism of (S)-warfarin, the more pharmacologically active enantiomer of the racemic drug, by (R)-warfarin was investigated in microsomes obtained from three human livers. In each case the production of both (S)-6- and (S)-7-hydroxywarfarin was found to be competitively inhibited by (R)-warfarin. The KiS for inhibition of (S)-6- and (S)-7-hydroxylation by (R)-warfarin ranged from 7.0 to 8.4 microM and from 6.0 to 6.9 microM, respectively, while the KmS for the 6- and 7-hydroxylation of (S)-warfarin ranged from 3.6 to 3.8 microM and from 3.3 to 3.9 microM, respectively. In contrast, except for the 4'-hydroxylation pathway (S)-warfarin was found to be a weak inhibitor of the metabolism of (R)-warfarin. Possible implications of these findings include the following: (1) the kinetic parameters defining the interactions of two enantiomers of a racemic drug with the cytochrome P-450s or other macromolecular systems in the living organism can only be properly defined from experiments with the pure enantiomers, (2) an enantiomer of a racemic drug may contribute significantly to biological effect not by its inherent activity but by altering the pharmacokinetics of the eutomer, and (3) enantiomeric interactions are not easily detected unless directly sought and may be relatively common.     

Identifying Novel Drug Indications through Automated Reasoning

Background

With the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process.

Methodology

In this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications.

Conclusion/Significance

To evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.     

Systematic exploration of synergistic drug pairs

Drug synergy allows a therapeutic effect to be achieved with lower doses of component drugs. Drug synergy can result when drugs target the products of genes that act in parallel pathways (‘specific synergy’). Such cases of drug synergy should tend to correspond to synergistic genetic interaction between the corresponding target genes. Alternatively, ‘promiscuous synergy’ can arise when one drug non‐specifically increases the effects of many other drugs, for example, by increased bioavailability. To assess the relative abundance of these drug synergy types, we examined 200 pairs of antifungal drugs in S. cerevisiae. We found 38 antifungal synergies, 37 of which were novel. While 14 cases of drug synergy corresponded to genetic interaction, 92% of the synergies we discovered involved only six frequently synergistic drugs. Although promiscuity of four drugs can be explained under the bioavailability model, the promiscuity of Tacrolimus and Pentamidine was completely unexpected. While many drug synergies correspond to genetic interactions, the majority of drug synergies appear to result from non‐specific promiscuous synergy.     

Drugs,leads, and drug-likeness: an analysis of some recently launched drugs

An analysis of the origins of recently launched drugs reveals that most were derived by modification of known drug structures or from lead structures obtained from the scientific literature. High-throughput screening did not have a significant impact on the derivation of these drugs. The drug structures are very closely related to their leads.     

The effect of diabetes mellitus on pharmacokinetics,pharmacodynamics and adverse drug reactions of anticancer drugs

Diabetes mellitus (DM) and cancer are global problems carrying huge human, social, and economic impact. Type 2 diabetes (T2DM) is associated with an increased risk for a number of cancers, including breast, pancreatic, and liver cancer. Moreover, adverse drug reactions are higher in paitents with cancer with T2DM compared to cancer patients without T2DM. Cellular mechanisms of hyperglycemia and chemotherapy efficacy may be different depending upon the particular cancer type and the condition of the patient. This review evaluates the effect of DM on the pharmacokinetic, pharmacodynamic, and adverse drug reactions of commonly used anticancer drugs such as cisplatin, methotrexate, paclitaxel, doxorubicin, and adriamycin in both clinical and animal models. A literature search was conducted in scientific databases including Web of Science, PubMed, Scopus, and Google Scholar including the relevant keywords. The results of the effectiveness of anticancer therapies in patients with DM are, however, inconsistent because DM can negatively impact multiple diverse entities including nerves and vascular structures, insulin-like growth factor 1, the function of the innate immune system, drug pharmacokinetics, the expression levels of hepatic CYP₄₅₀, Mdr _1b and enzymes that then lead to drug toxicity. However, in a few circumstances, DM led to attenuation of the toxicity of anticancer drugs secondary to attenuation of the energy-dependent renal uptake process. Overall, the impact of DM on patients with cancer is variable because of the diverse types of cancers and the spectrum of anticancer drugs. With respect to the evidence for cancer involvement in DM pathophysiology and the response to anticancer treatment in patients with DM, many questions still remain and further clinical trials are needed.     

发现靶向蛋白质间相互作用的小分子药物研究进展

蛋白质-蛋白质相互作用在多种细胞功能中具有重要的作用。靶向蛋白质-蛋白质相互作用已经成为新药发现的重要策略,但发现能阻断蛋白质-蛋白质相互作用的小分子药物是一个巨大的挑战。尽管如此,近年来人们还是发现了许多能调控蛋白质-蛋白质相互作用的小分子。该文主要总结了在病毒进入、细胞凋亡通路和神经退行性疾病等方面的蛋白质-蛋白质相互作用小分子抑制剂的研究进展。