A substrate pharmacophore for the human organic cation/carnitine transporter identifies compounds associated with rhabdomyolysis

Design, Synthesis, and Bioassay for the Thiadiazole-Bridged Thioacetamide Compound as Cy-FBP/SBPase Inhibitors Based on Catalytic Mechanism Virtual Screening

Cyanobacterial fructose-1,6-/sedoheptulose-1,7-bisphosphatase (Cy-FBP/SBPase) was an important regulatory enzyme in cyanobacterial photosynthesis and was a potential target enzyme for screening to obtain novel inhibitors against cyanobacterial blooms. In this study, we developed a novel pharmacophore screening model based on the catalytic mechanism and substrate structure of Cy-FBP/SBPase and screened 26 S series compounds with different structures and pharmacophore characteristics from the Specs database by computer-assisted drug screening. These compounds exhibited moderate inhibitory activity against Cy-FBP/SBPase, with 9 compounds inhibiting >50% at 100 μM. Among them, compound S5 showed excellent inhibitory activity against both Cy-FBP/SBPase and Synechocystis sp. PCC6803 (IC50 = 6.7 ± 0.7 μM and EC50 = 7.7 ± 1.4 μM). The binding mode of compound S5 to Cy-FBP/SBPase was predicted using the molecular docking theory and validated by sentinel mutation and enzyme activity analysis. Physiochemical, gene transcription level, and metabolomic analyses showed that compound S5 significantly reduced the quantum yield of photosystem II and the maximum electron transfer rate, downregulated transcript levels of related genes encoding the Calvin cycle and photosystem, reduced the photosynthetic efficiency of cyanobacteria, thus inhibited metabolic pathways, such as the Calvin cycle and tricarboxylic acid cycle, and eventually achieved an efficient algicide. In addition, compound S5 had a high safety profile for human-derived cells and zebrafish. In summary, the novel pharmacophore screening model obtained from the current work provides an effective solution to the cyanobacterial bloom problem.

Estrogen receptor-based multi-residue screening of bisphenol compounds in urine

Human exposure to bisphenol compounds (BPs) has been implicated in the development of several chronic diseases. Instead of exploiting the traditional methods for determination of BPs, this work confirms that the human estrogen receptor α ligand binding domain (hERα-LBD) is a powerful recognition element that can be used to monitor multi-residue of BPs in urine samples by fluorescence polarization (FP) assay. Test parameters were optimized for the best performance. Under the optimal conditions, the IC₅₀ values of BPs are in the range of 0.04-1.61 μg mL^-1 . Recovery experiments were then performed to assess the accuracy and precision of the established method. The results detected by FP assay show good agreements with that of liquid chromatography-tandem mass spectrometry method with a fit of R²  = 0.9372 and 0.9640 for BPE and BPAP, respectively. A computational methodology, ligand-based pharmacophore model, was also employed to further explore the broad-specific of tested compounds. It was found that the two hydrogen bond acceptor features and one hydrophobic aliphatic feature were essential for the corresponding cross-reactivity results from the FP assay. All these results suggest that the established method can be successfully applied to monitor the occurrence of BPs in urine.

Recent advances in drug delivery via the organic cation/carnitine transporter 2 (OCTN2/SLC22A5)

INTRODUCTION

Transporters in the plasma membrane have been exploited successfully for the delivery of drugs in the form of prodrugs and nanoparticles. Organic cation/carnitine transporter 2 (OCTN2, SLC22A5) has emerged as a viable target for drug delivery. OCTN2 is a Na⁺-dependent high-affinity transporter for L-carnitine and a Na⁺-independent transporter for organic cations. OCTN2 is expressed in the blood-brain barrier, heart, liver, kidney, intestinal tract and placenta and plays an essential role in L-carnitine homeostasis in the body. Areas covered: In recent years, several studies have been reported in the literature describing the utility of OCTN2 to enhance the delivery of drugs, prodrugs and nanoparticles. Here we summarize the salient features of OCTN2 in terms of its role in the cellular uptake of its physiological substrate L-carnitine in physiological and pathological context; the structural requirements for recognition and the recent advances in OCTN2-targeted drug delivery systems, including prodrugs and nanoparticles, are discussed. Expert opinion: This transporter has great potential to be utilized as a target for drug delivery to improve oral absorption of drugs in the intestinal tract. It also has potential to facilitate the transfer of drugs across the biological barriers such as the blood-brain barrier, blood-retinal barrier, and maternal-fetal barrier.

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.

Molecular properties associated with transporter-mediated drug disposition

Membrane transporters play a key role in the absorption, distribution, clearance, elimination, and transport of drugs. Understanding the drug properties and structure activity relationships (SAR) for affinity to membrane transporters is critical to optimize clearance and pharmacokinetics during drug design. To facilitate the early identification of clearance mechanism, a framework named the extended clearance classification system (ECCS) was recently introduced. Using in vitro and physicochemical properties that are readily available in early drug discovery, ECCS has been successfully applied to identify major clearance mechanism and to implicate the role of membrane transporters in determining pharmacokinetics. While the crystal structures for most of the drug transporters are currently not available, ligand-based modeling approaches that use information obtained from the structure and molecular properties of the ligands have been applied to associate the drug-related properties and transporter-mediated disposition. The approach allows prospective prediction of transporter both substrate and/or inhibitor affinity and build quantitative structure-activity relationship (QSAR) to enable early optimization of pharmacokinetics, tissue distribution and drug-drug interaction risk. Drug design applications can be further improved through uncovering transporter protein crystal structure and generation of quality data to refine and develop viable predictive models.

Making Transporter Models for Drug-Drug Interaction Prediction Mobile

The past decade has seen increased numbers of studies publishing ligand-based computational models for drug transporters. Although they generally use small experimental data sets, these models can provide insights into structure-activity relationships for the transporter. In addition, such models have helped to identify new compounds as substrates or inhibitors of transporters of interest. We recently proposed that many transporters are promiscuous and may require profiling of new chemical entities against multiple substrates for a specific transporter. Furthermore, it should be noted that virtually all of the published ligand-based transporter models are only accessible to those involved in creating them and, consequently, are rarely shared effectively. One way to surmount this is to make models shareable or more accessible. The development of mobile apps that can access such models is highlighted here. These apps can be used to predict ligand interactions with transporters using Bayesian algorithms. We used recently published transporter data sets (MATE1, MATE2K, OCT2, OCTN2, ASBT, and NTCP) to build preliminary models in a commercial tool and in open software that can deliver the model in a mobile app. In addition, several transporter data sets extracted from the ChEMBL database were used to illustrate how such public data and models can be shared. Predicting drug-drug interactions for various transporters using computational models is potentially within reach of anyone with an iPhone or iPad. Such tools could help prioritize which substrates should be used for in vivo drug-drug interaction testing and enable open sharing of models.

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.

A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus

We are currently faced with a global infectious disease crisis which has been anticipated for decades. While many promising biotherapeutics are being tested, the search for a small molecule has yet to deliver an approved drug or therapeutic for the Ebola or similar filoviruses that cause haemorrhagic fever. Two recent high throughput screens published in 2013 did however identify several hits that progressed to animal studies that are FDA approved drugs used for other indications. The current computational analysis uses these molecules from two different structural classes to construct a common features pharmacophore. This ligand-based pharmacophore implicates a possible common target or mechanism that could be further explored. A recent structure based design project yielded nine co-crystal structures of pyrrolidinone inhibitors bound to the viral protein 35 (VP35). When receptor-ligand pharmacophores based on the analogs of these molecules and the protein structures were constructed, the molecular features partially overlapped with the common features of solely ligand-based pharmacophore models based on FDA approved drugs. These previously identified FDA approved drugs with activity against Ebola were therefore docked into this protein. The antimalarials chloroquine and amodiaquine docked favorably in VP35. We propose that these drugs identified to date as inhibitors of the Ebola virus may be targeting VP35. These computational models may provide preliminary insights into the molecular features that are responsible for their activity against Ebola virus in vitro and in vivo and we propose that this hypothesis could be readily tested.

A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus

We are currently faced with a global infectious disease crisis which has been anticipated for decades. While many promising biotherapeutics are being tested, the search for a small molecule has yet to deliver an approved drug or therapeutic for the Ebola or similar filoviruses that cause haemorrhagic fever. Two recent high throughput screens published in 2013 did however identify several hits that progressed to animal studies that are FDA approved drugs used for other indications. The current computational analysis uses these molecules from two different structural classes to construct a common features pharmacophore. This ligand-based pharmacophore implicates a possible common target or mechanism that could be further explored. A recent structure based design project yielded nine co-crystal structures of pyrrolidinone inhibitors bound to the viral protein 35 (VP35). When receptor-ligand pharmacophores based on the analogs of these molecules and the protein structures were constructed, the molecular features partially overlapped with the common features of solely ligand-based pharmacophore models based on FDA approved drugs. These previously identified FDA approved drugs with activity against Ebola were therefore docked into this protein. The antimalarials chloroquine and amodiaquine docked favorably in VP35. We propose that these drugs identified to date as inhibitors of the Ebola virus may be targeting VP35. These computational models may provide preliminary insights into the molecular features that are responsible for their activity against Ebola virus in vitro and in vivo and we propose that this hypothesis could be readily tested.

The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so

A recent paper in this journal sought to counter evidence for the role of transport proteins in effecting drug uptake into cells, and questions that transporters can recognize drug molecules in addition to their endogenous substrates. However, there is abundant evidence that both drugs and proteins are highly promiscuous. Most proteins bind to many drugs and most drugs bind to multiple proteins (on average more than six), including transporters (mutations in these can determine resistance); most drugs are known to recognise at least one transporter. In this response, we alert readers to the relevant evidence that exists or is required. This needs to be acquired in cells that contain the relevant proteins, and we highlight an experimental system for simultaneous genome-wide assessment of carrier-mediated uptake in a eukaryotic cell (yeast).

Pharmacological and pathophysiological roles of carnitine/organic cation transporters (OCTNs: SLC22A4, SLC22A5 and Slc22a21)

The carnitine/organic cation transporter (OCTN) family consists of three transporter isoforms, i.e. OCTN1 (SLC22A4) and OCTN2 (SLC22A5) in humans and animals and Octn3 (Slc22a21) in mice. These transporters are physiologically essential to maintain appropriate systemic and tissue concentrations of carnitine by regulating its membrane transport during intestinal absorption, tissue distribution and renal reabsorption. Among them, OCTN2 is a sodium-dependent, high-affinity transporter of carnitine, and a functional defect of OCTN2 due to genetic mutation causes primary systemic carnitine deficiency (SCD). Since carnitine is essential for beta-oxidation of long-chain fatty acids to produce ATP, OCTN2 gene mutation causes a range of symptoms, including cardiomyopathy, skeletal muscle weakness, fatty liver and male infertility. These functional consequences of Octn2 gene mutation can be seen clearly in an animal model, jvs mouse, which exhibits the SCD phenotype. In addition, although the mechanism is not clear, single nucleotide polymorphisms of OCTN1 and OCTN2 genes are associated with increased incidences of rheumatoid arthritis, Crohn's disease and asthma. OCTN1 and OCTN2 accept cationic drugs as substrates and contribute to intestinal and pulmonary absorption, tissue distribution (including to tumour cells), and renal excretion of these drugs. Modulation of the transport activity of OCTN2 by externally administered drugs may cause drug-induced secondary carnitine deficiency. Rodent Octn3 transports carnitine specifically, particularly in male reproductive tissues. Thus, the OCTNs are physiologically, pathologically and pharmacologically important. Detailed characterization of these transporters will greatly improve our understanding of the pathology associated with common diseases caused by functional deficiency of OCTNs.