Determination of structural factors affecting binding to mu, kappa and delta opioid receptors

Addiction is a complex behavioral phenomenon in which naturally occurring or synthetic chemicals modulate the response of the reward system through their binding to a variety of neuroreceptors, resulting in compulsive substance-seeking and use despite harmful consequences to the individual. Among these, the opioid receptor (OR) family and more specifically, the mu-opioid receptor (MOR) subtype plays a critical role in the addiction to powerful prescription and illicit drugs such as hydrocodone, oxycodone, fentanyl, cocaine, and methamphetamine (Contet et al. in Curr Opin Neurobiol 14(3):370-378, 2004). Conversely, agonists binding to kappa (KOR) and antagonists binding to delta opioid receptors (DOR) have been reported to induce negative reinforcing effects. As more than 700 new psychoactive substances were illegally sold between 2009 and 2016 (DEA-DCT-DIR-032-18), most of them lacking basic toxicological and pharmacological profiles, molecular modeling approaches that could quickly and reliably fill the gaps in our knowledge would be highly desirable tools for determining the effects of these synthetics. Here, we report accurate 3D-spectrometric data-activity relationship classification models for large and diverse datasets of MOR, KOR and DOR binders with areas under the receiver operating characteristic curve for the "blind" prediction sets exceeding 0.88. Structural features associated with (selective) binding to MOR, KOR and/or DOR were identified. These models could assist regulatory agencies in evaluating the health risks associated with the use of unprofiled substances as well as to help the pharmaceutical industry in its search for new drugs to combat addiction.

Comprehensive Identification and Characterization of Human Secretome Based on Integrative Proteomic and Transcriptomic Data

Secreted proteins (SPs) play important roles in diverse important biological processes; however, a comprehensive and high-quality list of human SPs is still lacking. Here we identified 6,943 high-confidence human SPs (3,522 of them are novel) based on 330,427 human proteins derived from databases of UniProt, Ensembl, AceView, and RefSeq. Notably, 6,267 of 6,943 (90.3%) SPs have the supporting evidences from a large amount of mass spectrometry (MS) and RNA-seq data. We found that the SPs were broadly expressed in diverse tissues as well as human body fluid, and a significant portion of them exhibited tissue-specific expression. Moreover, 14 cancer-specific SPs that their expression levels were significantly associated with the patients’ survival of eight different tumors were identified, which could be potential prognostic biomarkers. Strikingly, 89.21% of 6,943 SPs (2,927 novel SPs) contain known protein domains. Those novel SPs we mainly enriched with the known domains regarding immunity, such as Immunoglobulin V-set and C1-set domain. Specifically, we constructed a user-friendly and freely accessible database, SPRomeDB (www.unimd.org/SPRomeDB), to catalog those SPs. Our comprehensive SP identification and characterization gain insights into human secretome and provide valuable resource for future researches.

Dr. Daniel Acosta and In Vitro toxicology at the U.S. Food and Drug Administration's National Center for Toxicological Research

For the past five years, Dr. Daniel Acosta has served as the Deputy Director of Research at the National Center for Toxicological Research (NCTR), a principle research laboratory of the U.S. Food and Drug Administration (FDA). Over his career at NCTR, Dr. Acosta has had a major impact on developing and promoting the use of in vitro assays in regulatory toxicity and product safety assessments. As Dr. Acosta nears his retirement we have dedicated this paper to his many accomplishments at the NCTR. Described within this paper are some of the in vitro studies that have been conducted under Dr. Acosta's leadership. These studies include toxicological assessments involving developmental effects, and the development and application of in vitro reproductive, heart, liver, neurological and airway cell and tissue models.

Cytotoxicity of 34 FDA approved small-molecule kinase inhibitors in primary rat and human hepatocytes

Of the 34 FDA approved oral small-molecule kinase inhibitors (KI), 23 (68%) have warnings for hepatotoxicity in product labeling. To better understand the mechanisms of KI hepatotoxicity and whether such effects can be predicted, we examined 34 KIs for cytotoxicity in primary rat and human hepatocytes. The hepatocytes were treated with KIs at ten concentrations normalized to maximal therapeutic blood levels (Cmax). At 5 and 24 h post treatment, lactate dehydrogenase or alanine aminotransferase leakage, caspase 3/7 activities and cellular adenosine triphosphate levels were measured. At 1 to 100-fold Cmax, while 5 KIs were neither toxic to human nor rat hepatocytes, 3 KIs showed similar cytotoxicity in both species and 26 KIs showed species-biased cytotoxicity, with 16 KIs being more toxic to human hepatocytes and 10 KIs being more toxic to rat hepatocytes. At concentrations of 1-, 2.5-, 5-, 10-, 100-fold Cmax, the number of cytotoxic KIs in human hepatocytes was 4, 8, 11, 14 and 27, respectively, and the corresponding number in rat hepatocytes was 1, 4, 9, 12 and 27, respectively. When hepatocyte cytotoxicity at 100-fold Cmax was used to predict KI clinical hepatotoxicity reflected in product labeling, the accuracy was 0.65 with human hepatocytes and 0.59 with rat cells. When the criterion of daily dose ≥100 mg or Cmax ≥1.1 μM was used to predict KI hepatotoxicity, the accuracy was 0.56 or 0.47, respectively. These results suggest both indirect and direct drug-induced hepatocyte toxicity may contribute to the mechanisms of KI-induced hepatotoxicity seen clinically and use of primary hepatocytes is a useful in vitro model to help predict such toxicity.

Molecular Docking for Identification of Potential Targets for Drug Repurposing

Using existing drugs for new indications (drug repurposing) is an effective method not only to reduce drug development time and costs but also to develop treatments for new disease including those that are rare. In order to discover novel indications, potential target identification is a necessary step. One widely used method to identify potential targets is through molecule docking. It requires no prior information except structure inputs from both the drug and the target, and can identify potential targets for a given drug, or identify potential drugs for a specific target. Though molecular docking is popular for drug development and repurposing, challenges remain for the method. In order to improve the prediction accuracy, optimizing the target conformation, considering the solvents and adding cobinders to the system are possible solutions.

Effects of 31 FDA approved small-molecule kinase inhibitors on isolated rat liver mitochondria

The FDA has approved 31 small-molecule kinase inhibitors (KIs) for human use as of November 2016, with six having black box warnings for hepatotoxicity (BBW-H) in product labeling. The precise mechanisms and risk factors for KI-induced hepatotoxicity are poorly understood. Here, the 31 KIs were tested in isolated rat liver mitochondria, an in vitro system recently proposed to be a useful tool to predict drug-induced hepatotoxicity in humans. The KIs were incubated with mitochondria or submitochondrial particles at concentrations ranging from therapeutic maximal blood concentrations (Cmax) levels to 100-fold Cmax levels. Ten endpoints were measured, including oxygen consumption rate, inner membrane potential, cytochrome c release, swelling, reactive oxygen species, and individual respiratory chain complex (I–V) activities. Of the 31 KIs examined only three including sorafenib, regorafenib and pazopanib, all of which are hepatotoxic, caused significant mitochondrial toxicity at concentrations equal to the Cmax, indicating that mitochondrial toxicity likely contributes to the pathogenesis of hepatotoxicity associated with these KIs. At concentrations equal to 100-fold Cmax, 18 KIs were found to be toxic to mitochondria, and among six KIs with BBW-H, mitochondrial injury was induced by regorafenib, lapatinib, idelalisib, and pazopanib, but not ponatinib, or sunitinib. Mitochondrial liability at 100-fold Cmax had a positive predictive power (PPV) of 72% and negative predictive power (NPV) of 33% in predicting human KI hepatotoxicity as defined by product labeling, with the sensitivity and specificity being 62% and 44%, respectively. Similar predictive power was obtained using the criterion of Cmax ≥1.1 µM or daily dose ≥100 mg. Mitochondrial liability at 1–2.5-fold Cmax showed a 100% PPV and specificity, though the NPV and sensitivity were 32% and 14%, respectively. These data provide novel mechanistic insights into KI hepatotoxicity and indicate that mitochondrial toxicity at therapeutic levels can help identify hepatotoxic KIs.

microRNAs as pharmacogenomic biomarkers for drug efficacy and drug safety assessment

Much evidence has documented that microRNAs (miRNAs) play an important role in the modulation of interindividual variability in the production of drug metabolizing enzymes and transporters (DMETs) and nuclear receptors (NRs) through multidirectional interactions involving environmental stimuli/stressors, the expression of miRNA molecules and genetic polymorphisms. MiRNA expression has been reported to be affected by drugs and miRNAs themselves may affect drug metabolism and toxicity. In cancer research, miRNA biomarkers have been identified to mediate intrinsic and acquired resistance to cancer therapies. In drug safety assessment, miRNAs have been found associated with cardiotoxicity, hepatotoxicity and nephrotoxicity. This review article summarizes published studies to show that miRNAs can serve as early biomarkers for the evaluation of drug efficacy and drug safety.

Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis

BACKGROUND

As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding.

METHODS

Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network.

RESULTS

Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature.

CONCLUSIONS

Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs.

Biomarkers of tobacco smoke exposure

Diseases and death caused by exposure to tobacco smoke have become the single most serious preventable public health concern. Thus, biomarkers that can monitor tobacco exposure and health effects can play a critical role in tobacco product regulation and public health policy. Biomarkers of exposure to tobacco toxicants are well established and have been used in population studies to establish public policy regarding exposure to second-hand smoke, an example being the nicotine metabolite cotinine, which can be measured in urine. Biomarkers of biological response to tobacco smoking range from those indicative of inflammation to mRNA and microRNA patterns related to tobacco use and/or disease state. Biomarkers identifying individuals with an increased risk for a pathological response to tobacco have also been described. The challenge for any novel technology or biomarker is its translation to clinical and/or regulatory application, a process that requires first technical validation of the assay and then careful consideration of the context the biomarker assay may be used in the regulatory setting. Nonetheless, the current efforts to investigate new biomarker of tobacco smoke exposure promise to offer powerful new tools in addressing the health hazards of tobacco product use. This review will examine such biomarkers, albeit with a focus on those related to cigarette smoking.

The Predictive Safety Testing Consortium: A synthesis of the goals, challenges and accomplishments of the Critical Path

The qualification of biomarkers of drug safety requires data on many compounds and nonclinical and clinical studies. The cost and effort associated with these qualifications cannot be easily covered by a single pharmaceutical company. Intellectual property associated with safety biomarkers is also held by many different companies. Consortia between different pharmaceutical companies can overcome cost and intellectual property hurdles to biomarker qualification. The Predictive Safety Testing Consortium (PSTC) is a collaborative effort between 16 different pharmaceutical companies to generate data supporting biomarker qualification. This Consortium is coordinated through the C-Path Institute, and currently has five biomarker qualification working groups engaged in this collaboration: nephrotoxicity, hepatotoxicity, vascular injury, myopathy, and non-genotoxic carcinogenicity. These working groups are aided by a data management team and a translational strategy team. Qualification studies of promising biomarkers are already progressing in several of the working groups, and results in the nephrotoxicity working group warranted a data submission to the FDA and EMEA for regulatory qualification of new nephrotoxicity biomarkers.:

Drug Safety Strategies to De-Risk Compounds--an IBC conference. 4-6 August 2009, Boston, MA, USA

The Drug Safety Strategies to De-Risk Compounds conference, held in Boston, included topics covering new developments in the field of drug safety. This conference report highlights selected presentations on the early prediction of hepatotoxicity, improvements in models of cardiovascular toxicity, the use of biomarkers in drug development, and computational approaches to drug safety predictions. Emerging technologies and drug safety models, as well as case studies of safety assessment advances, are also discussed.

Interlaboratory evaluation of genomic signatures for predicting carcinogenicity in the rat

The Critical Path Institute recently established the Predictive Safety Testing Consortium, a collaboration between several companies and the U.S. Food and Drug Administration, aimed at evaluating and qualifying biomarkers for a variety of toxicological endpoints. The Carcinogenicity Working Group of the Predictive Safety Testing Consortium has concentrated on sharing data to test the predictivity of two published hepatic gene expression signatures, including the signature by Fielden et al. (2007, Toxicol. Sci. 99, 90-100) for predicting nongenotoxic hepatocarcinogens, and the signature by Nie et al. (2006, Mol. Carcinog. 45, 914-933) for predicting nongenotoxic carcinogens. Although not a rigorous prospective validation exercise, the consortium approach created an opportunity to perform a meta-analysis to evaluate microarray data from short-term rat studies on over 150 compounds. Despite significant differences in study designs and microarray platforms between laboratories, the signatures proved to be relatively robust and more accurate than expected by chance. The accuracy of the Fielden et al. signature was between 63 and 69%, whereas the accuracy of the Nie et al. signature was between 55 and 64%. As expected, the predictivity was reduced relative to internal validation estimates reported under identical test conditions. Although the signatures were not deemed suitable for use in regulatory decision making, they were deemed worthwhile in the early assessment of drugs to aid decision making in drug development. These results have prompted additional efforts to rederive and evaluate a QPCR-based signature using these samples. When combined with a standardized test procedure and prospective interlaboratory validation, the accuracy and potential utility in preclinical applications can be ascertained.

Strategic paths for biomarker qualification

Biomarkers may be qualified using different qualification processes. A passive approach for qualification has been to accept the end of discussions in the scientific literature as an indication that a biomarker has been accepted. An active approach to qualification requires development of a comprehensive process by which a consensus may be reached about the qualification of a biomarker. Active strategies for qualification include those associated with context-independent as well as context-dependent qualifications.

Role of DNA intercalation in the inhibition of purified mouse leukemia (L1210) DNA topoisomerase II by 9-aminoacridines

An attempt was made to analyze the mechanism by which 4'-(9-acridinylamino)methanesulfon-m-anisidide (m-AMSA) inhibits mammalian DNA topoisomerase II. The effects of various 9-aminoacridine derivatives differing by their DNA affinities and DNA sequence selectivity of binding were compared in the presence of purified mouse leukemia L1210 DNA topoisomerase II. No correlation was found between DNA unwinding and topoisomerase II inhibition. 9-Aminoacridine was inactive as a topoisomerase II inhibitor and o-AMSA was only weakly active. The location of L1210 topoisomerase II mediated DNA breaks produced in the absence or presence of 9-aminoacridines were studied in [32P]-end-labeled pBR 322 DNA. All 9-aminoacridines, even those differing by their DNA sequence selectivity of binding, produced similar DNA cleavage patterns. Most drug-induced topoisomerase II mediated DNA breaks appeared at sites that were already cleaved by the enzyme in the absence of drug. The present results suggest that 9-aminoacridines inhibit L1210 DNA topoisomerase II by interacting at or near enzyme-DNA complexes by some unknown DNA effect or by direct protein interaction.

[Blunt injury to the neck caused by a seat belt. Stenosis of the internal carotid and vertebral arteries]

A 37-year-old woman sustained blunt trauma to the neck from a seat-belt in a car accident, which resulted in marked long-segment narrowing of the internal carotid artery with secondary occlusion of a main branch of the median cerebral artery as well as circumscribed circular stenosis of the right vertebral artery. Serial angiograms documented the course until there was almost complete recanalization of all affected vessels.

[Cerebral venous and sinus thrombosis (author's transl)]

In a 38-year-old patient who had taken oral contraceptives for several years partial thrombosis of the superior sagittal sinus and cortical veins occurred. Progress could be observed with the computer assisted tomography (CAT). CAT showed multiple localised intracerebral haemorrhages fronto-occipitally and penetrating deep into the white matter. In view of the severity of morphological changes the ability for regression of such haemorrhages is surprising. This equally applies to subsequent restitution without substantial visible defects in cerebral parenchyma following treatment with heparin. Among the presentations of sinus and central venous thromboses in CAT, the empty triangle or delta sign and the cord sign should be mentioned.