The state of the art in secondary pharmacology and its impact on the safety of new medicines.

Secondary pharmacology screening of investigational small-molecule drugs for potentially adverse off-target activities has become standard practice in pharmaceutical research and development, and regulatory agencies are increasingly requesting data on activity against targets with recognized adverse effect relationships. However, the screening strategies and target panels used by pharmaceutical companies may vary substantially. To help identify commonalities and differences, as well as to highlight opportunities for further optimization of secondary pharmacology assessment, we conducted a broad-ranging survey across 18 companies under the auspices of the DruSafe leadership group of the International Consortium for Innovation and Quality in Pharmaceutical Development. Based on our analysis of this survey and discussions and additional research within the group, we present here an overview of the current state of the art in secondary pharmacology screening. We discuss best practices, including additional safety-associated targets not covered by most current screening panels, and present approaches for interpreting and reporting off-target activities. We also provide an assessment of the safety impact of secondary pharmacology screening, and a perspective on opportunities and challenges in this rapidly developing field.

A Predictive Model of Vaccine Reactogenicity Using Data from an In Vitro Human Innate Immunity Assay System.

A primary concern in vaccine development is safety, particularly avoiding an excessive immune reaction in an otherwise healthy individual. An accurate prediction of vaccine reactogenicity using in vitro assays and computational models would facilitate screening and prioritization of novel candidates early in the vaccine development process. Using the modular in vitro immune construct model of human innate immunity, PBMCs from 40 healthy donors were treated with 10 different vaccines of varying reactogenicity profiles and then cell culture supernatants were analyzed via flow cytometry and a multichemokine/cytokine assay. Differential response profiles of innate activity and cell viability were observed in the system. In parallel, an extensive adverse event (AE) dataset for the vaccines was assembled from clinical trial data. A novel reactogenicity scoring framework accounting for the frequency and severity of local and systemic AEs was applied to the clinical data, and a machine learning approach was employed to predict the incidence of clinical AEs from the in vitro assay data. Biomarker analysis suggested that the relative levels of IL-1B, IL-6, IL-10, and CCL4 have higher predictive importance for AE risk. Predictive models were developed for local reactogenicity, systemic reactogenicity, and specific individual AEs. A forward-validation study was performed with a vaccine not used in model development, Trumenba (meningococcal group B vaccine). The clinically observed Trumenba local and systemic reactogenicity fell on the 26th and 93rd percentiles of the ranges predicted by the respective models. Models predicting specific AEs were less accurate. Our study presents a useful framework for the further development of vaccine reactogenicity predictive models.

Target Safety Assessment: Strategies and Resources.

An in-depth evaluation of target safety is an invaluable resource throughout drug discovery and development. The goal of a target safety evaluation is to identify potential unintended adverse consequences of target modulation, and to propose a risk evaluation and mitigation strategy to shepherd compounds through the discovery and development pipeline, to confirm and characterize unavoidable on-target toxicities in a timely manner to assist in early program advancement decisions, and to anticipate, monitor, and manage potential clinical adverse events. The role of an experienced discovery toxicologist in synthesizing the available information into an actionable set of recommendations for a safety evaluation strategy is critical to its successful application in early discovery programs. This chapter presents a summary of some of the information types and sources that should be investigated, and approaches that can be taken to generate an early assessment of potential safety liabilities.

Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study.

BACKGROUND: The ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved. METHODS AND FINDINGS: Over 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola ("cases") were asked if they had exposure to other potential Ebola cases ("potential source contacts") in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO's response during the epidemic, and have been updated for publication. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p < 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = -0.37, p < 0.001) between R and the district proportion of hospitalised cases admitted within ≤4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the case line-list. Linking cases to the contacts who potentially infected them provided information on the transmission network. This revealed a high degree of heterogeneity in inferred transmissions, with only 20% of cases accounting for at least 73% of new infections, a phenomenon often called super-spreading. Multivariable regression models allowed us to identify predictors of being named as a potential source contact. These were similar for funeral and non-funeral contacts: severe symptoms, death, non-hospitalisation, older age, and travelling prior to symptom onset. Non-funeral exposures were strongly peaked around the death of the contact. There was evidence that hospitalisation reduced but did not eliminate onward exposures. We found that Ebola treatment units were better than other health care facilities at preventing exposure from hospitalised and deceased individuals. The principal limitation of our analysis is limited data quality, with cases not being entered into the database, cases not reporting exposures, or data being entered incorrectly (especially dates, and possible misclassifications). CONCLUSIONS: Achieving elimination of Ebola is challenging, partly because of super-spreading. Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. Continued real-time data capture, reporting, and analysis are vital to track transmission patterns, inform resource deployment, and thus hasten and maintain elimination of the virus from the human population.

Nonclinical Evaluations of Small-Molecule Oncology Drugs: Integration into Clinical Dose Optimization and Toxicity Management.

Multidisciplinary approaches that incorporate nonclinical pharmacologic and toxicologic characterization of small-molecule oncology drugs into clinical development programs may facilitate improved benefit-risk profiles and clinical toxicity management in patients. The performance of the current nonclinical safety-testing scheme was discussed, highlighting current strengths and areas for improvement. While current nonclinical testing appears to predict the clinical outcome where the prevalence of specific adverse effects are high, nonclinical testing becomes less reliable for predicting clinical adverse effects that occur infrequently, as with some kinase inhibitors. Although adverse effects associated with kinase inhibitors can often be predicted on the basis of target biology, drugs can be promiscuous and inhibit targets with poorly defined function and associated risks. Improvements in adverse effect databases and better characterization of the biologic activities of drug targets may enable better use of computational modeling approaches in predicting adverse effects with kinase inhibitors. Assessing safety of a lead candidate in parallel with other drug properties enables incorporation of a molecule's best features during chemical design, eliminates the worst molecules early, and permits timely investigation/characterization of toxicity mechanisms for identified liabilities. A safety lead optimization and candidate identification strategy that reduces intrinsic toxicity and metabolic risk and enhances selectivity can deliver selective kinase inhibitors that demonstrate on-target adverse effects identified nonclinically. Integrating clinical and nonclinical data during drug development can facilitate better identification and management of oncology drugs. Follow-up nonclinical studies may be used to better understand the risks in a given patient population and minimize or manage these risks more appropriately. Clin Cancer Res; 22(11); 2618-22. ©2016 AACR SEE ALL ARTICLES IN THIS CCR FOCUS SECTION, "NEW APPROACHES FOR OPTIMIZING DOSING OF ANTICANCER AGENTS".

Leucine-rich repeat kinase 2 (LRRK2)-deficient rats exhibit renal tubule injury and perturbations in metabolic and immunological homeostasis.

Genetic evidence links mutations in the LRRK2 gene with an increased risk of Parkinson's disease, for which no neuroprotective or neurorestorative therapies currently exist. While the role of LRRK2 in normal cellular function has yet to be fully described, evidence suggests involvement with immune and kidney functions. A comparative study of LRRK2-deficient and wild type rats investigated the influence that this gene has on the phenotype of these rats. Significant weight gain in the LRRK2 null rats was observed and was accompanied by significant increases in insulin and insulin-like growth factors. Additionally, LRRK2-deficient rats displayed kidney morphological and histopathological alterations in the renal tubule epithelial cells of all animals assessed. These perturbations in renal morphology were accompanied by significant decreases of lipocalin-2, in both the urine and plasma of knockout animals. Significant alterations in the cellular composition of the spleen between LRRK2 knockout and wild type animals were identified by immunophenotyping and were associated with subtle differences in response to dual infection with rat-adapted influenza virus (RAIV) and Streptococcus pneumoniae. Ontological pathway analysis of LRRK2 across metabolic and kidney processes and pathological categories suggested that the thioredoxin network may play a role in perturbing these organ systems. The phenotype of the LRRK2 null rat is suggestive of a complex biology influencing metabolism, immune function and kidney homeostasis. These data need to be extended to better understand the role of the kinase domain or other biological functions of the gene to better inform the development of pharmacological inhibitors.

Network and pathway analysis of compound-protein interactions.

We describe an integrated system that brings together predictive chemical analyses based on compound structure, knowledge bases of chemogenomics data associating compounds to biological, pharmacological and toxicological properties, and a systems biology functional data analysis and network reconstruction approach, to provide an in silico evaluation of the possible effects of xenobiotics on biological systems. We demonstrate the combination of drug and xenobiotic metabolism prediction, quantitative structure-activity relationship (QSAR) models and structural similarity searching to generate a list of similar compounds to, and possible targets for novel compounds. These lists of compounds and proteins are mapped to functional ontologies such as gene-disease associations, biological processes, and mechanisms of toxicity, and can be used to reconstruct biological networks linking together the component nodes into biologically-meaningful clusters. Thus, an assessment of biological effects can be made early in the discovery and development process that can be used to prioritize the best compounds for additional testing or development, or to direct efforts in medicinal chemistry to improve compound activity profiles.

Update on mortality in the Democratic Republic of Congo: results from a third nationwide survey.

BACKGROUND: The humanitarian crisis in the Democratic Republic of Congo (DRC) has been among the world's deadliest in recent decades. We conducted our third nationwide survey to examine trends in mortality rates during a period of changing political, security, and humanitarian conditions. METHODS: We used a 3-stage, household-based cluster sampling technique to compare east and west DRC. Sixteen east health zones and 15 west zones were selected with a probability proportional to population size. Four east zones were purposely selected to allow historical comparisons. The 20 smallest population units were sampled in each zone, 20 households in each unit. The number and distribution of households determined whether they were selected using systematic random or random walk sampling. Respondents were asked about deaths of household members during the recall period: January 2006-April 2007. FINDINGS: In all, 14,000 households were visited. The national crude mortality rate of 2.2 deaths per 1000 population per month (95% confidence interval [CI] 2.1-2.3) is almost 70% higher than that documented for DRC in the 1984 census (1.3) and is unchanged since 2004. A small but significant decrease in mortality since 2004 in the insecure east (rate ratio: 0.96, P = .026) was offset by increases in the western provinces and a transition area in the center of the country. Nonetheless, the crude mortality rate in the insecure east (2.6) remains significantly higher than in the other regions (2.0 and 2.1, respectively). Deaths from violence have declined since 2004 (rate ratio 0.7, P = .02). CONCLUSIONS: More than 4 years after the official end of war, the crude mortality rate remains elevated across DRC. Slight but significant improvements in mortality in the insecure east coincided temporally with recent progress on security, humanitarian, and political fronts.

Network analysis of human glaucomatous optic nerve head astrocytes.

BACKGROUND: Astrocyte activation is a characteristic response to injury in the central nervous system, and can be either neurotoxic or neuroprotective, while the regulation of both roles remains elusive. METHODS: To decipher the regulatory elements controlling astrocyte-mediated neurotoxicity in glaucoma, we conducted a systems-level functional analysis of gene expression, proteomic and genetic data associated with reactive optic nerve head astrocytes (ONHAs). RESULTS: Our reconstruction of the molecular interactions affected by glaucoma revealed multi-domain biological networks controlling activation of ONHAs at the level of intercellular stimuli, intracellular signaling and core effectors. The analysis revealed that synergistic action of the transcription factors AP-1, vitamin D receptor and Nuclear Factor-kappaB in cross-activation of multiple pathways, including inflammatory cytokines, complement, clusterin, ephrins, and multiple metabolic pathways. We found that the products of over two thirds of genes linked to glaucoma by genetic analysis can be functionally interconnected into one epistatic network via experimentally-validated interactions. Finally, we built and analyzed an integrative disease pathology network from a combined set of genes revealed in genetic studies, genes differentially expressed in glaucoma and closely connected genes/proteins in the interactome. CONCLUSION: Our results suggest several key biological network modules that are involved in regulating neurotoxicity of reactive astrocytes in glaucoma, and comprise potential targets for cell-based therapy.

A comprehensive functional analysis of tissue specificity of human gene expression.

BACKGROUND: In recent years, the maturation of microarray technology has allowed the genome-wide analysis of gene expression patterns to identify tissue-specific and ubiquitously expressed ('housekeeping') genes. We have performed a functional and topological analysis of housekeeping and tissue-specific networks to identify universally necessary biological processes, and those unique to or characteristic of particular tissues. RESULTS: We measured whole genome expression in 31 human tissues, identifying 2374 housekeeping genes expressed in all tissues, and genes uniquely expressed in each tissue. Comprehensive functional analysis showed that the housekeeping set is substantially larger than previously thought, and is enriched with vital processes such as oxidative phosphorylation, ubiquitin-dependent proteolysis, translation and energy metabolism. Network topology of the housekeeping network was characterized by higher connectivity and shorter paths between the proteins than the global network. Ontology enrichment scoring and network topology of tissue-specific genes were consistent with each tissue's function and expression patterns clustered together in accordance with tissue origin. Tissue-specific genes were twice as likely as housekeeping genes to be drug targets, allowing the identification of tissue 'signature networks' that will facilitate the discovery of new therapeutic targets and biomarkers of tissue-targeted diseases. CONCLUSION: A comprehensive functional analysis of housekeeping and tissue-specific genes showed that the biological function of housekeeping and tissue-specific genes was consistent with tissue origin. Network analysis revealed that tissue-specific networks have distinct network properties related to each tissue's function. Tissue 'signature networks' promise to be a rich source of targets and biomarkers for disease treatment and diagnosis.

Integrated pathway analysis of rat urine metabolic profiles and kidney transcriptomic profiles to elucidate the systems toxicology of model nephrotoxicants.

In this study, approximately 40 endogenous metabolites were identified and quantified by (1)H NMR in urine samples from male rats dosed with two proximal tubule toxicants, cisplatin and gentamicin. The excreted amount of a majority of those metabolites in urine was found to be dose-dependent and exhibited a strong correlation with histopathology scores of overall proximal tubule damage. MetaCore pathway analysis software (GeneGo Inc.) was employed to identify nephrotoxicant-associated biochemical changes via an integrated quantitative analysis of both urine metabolomic and kidney transcriptomic profiles. Correlation analysis was applied to establish quantitative linkages between pairs of individual metabolite and gene transcript profiles in both cisplatin and gentamicin studies. This analysis revealed that cisplatin and gentamicin treatments were strongly linked to declines in mRNA transcripts for several luminal membrane transporters that handle each of the respective elevated urinary metabolites, such as glucose, amino acids, and monocarboxylic acids. The integrated pathway analysis performed on these studies indicates that cisplatin- or gentamicin-induced renal Fanconi-like syndromes manifested by glucosuria, hyperaminoaciduria, lactic aciduria, and ketonuria might be better explained by the reduction of functional proximal tubule transporters rather than by the perturbation of metabolic pathways inside kidney cells. Furthermore, this analysis suggests that renal transcription factors HNF1alpha, HNF1beta, and HIF-1 might be the central mediators of drug-induced kidney injury and adaptive response pathways.

The liver pharmacological and xenobiotic gene response repertoire.

We have used a supervised classification approach to systematically mine a large microarray database derived from livers of compound-treated rats. Thirty-four distinct signatures (classifiers) for pharmacological and toxicological end points can be identified. Just 200 genes are sufficient to classify these end points. Signatures were enriched in xenobiotic and immune response genes and contain un-annotated genes, indicating that not all key genes in the liver xenobiotic responses have been characterized. Many signatures with equal classification capabilities but with no gene in common can be derived for the same phenotypic end point. The analysis of the union of all genes present in these signatures can reveal the underlying biology of that end point as illustrated here using liver fibrosis signatures. Our approach using the whole genome and a diverse set of compounds allows a comprehensive view of most pharmacological and toxicological questions and is applicable to other situations such as disease and development.

Characteristics of genomic signatures derived using univariate methods and mechanistically anchored functional descriptors for predicting drug- and xenobiotic-induced nephrotoxicity.

ABSTRACT The ideal toxicity biomarker is composed of the properties of prediction (is detected prior to traditional pathological signs of injury), accuracy (high sensitivity and specificity), and mechanistic relationships to the endpoint measured (biological relevance). Gene expression-based toxicity biomarkers ("signatures") have shown good predictive power and accuracy, but are difficult to interpret biologically. We have compared different statistical methods of feature selection with knowledge-based approaches, using GeneGo's database of canonical pathway maps, to generate gene sets for the classification of renal tubule toxicity. The gene set selection algorithms include four univariate analyses: t-statistics, fold-change, B-statistics, and RankProd, and their combination and overlap for the identification of differentially expressed probes. Enrichment analysis following the results of the four univariate analyses, Hotelling T-square test, and, finally out-of-bag selection, a variant of cross-validation, were used to identify canonical pathway maps-sets of genes coordinately involved in key biological processes-with classification power. Differentially expressed genes identified by the different statistical univariate analyses all generated reasonably performing classifiers of tubule toxicity. Maps identified by enrichment analysis or Hotelling T-square had lower classification power, but highlighted perturbed lipid homeostasis as a common discriminator of nephrotoxic treatments. The out-of-bag method yielded the best functionally integrated classifier. The map "ephrins signaling" performed comparably to a classifier derived using sparse linear programming, a machine learning algorithm, and represents a signaling network specifically involved in renal tubule development and integrity. Such functional descriptors of toxicity promise to better integrate predictive toxicogenomics with mechanistic analysis, facilitating the interpretation and risk assessment of predictive genomic investigations.

Toxicogenomic study of triazole fungicides and perfluoroalkyl acids in rat livers predicts toxicity and categorizes chemicals based on mechanisms of toxicity.

Toxicogenomic analysis of five environmental chemicals was performed to investigate the ability of genomics to predict toxicity, categorize chemicals, and elucidate mechanisms of toxicity. Three triazole antifungals (myclobutanil, propiconazole, and triadimefon) and two perfluorinated chemicals [perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS)] were administered daily via oral gavage for one, three, or five consecutive days to male Sprague-Dawley rats at single doses of 300, 300, 175, 20, or 10 mg/kg/day, respectively. Clinical chemistry, hematology, and histopathology were measured at all time points. Gene expression profiling of livers from three rats per treatment group at all time points was performed on the CodeLink Uniset Rat I Expression array. Data were analyzed in the context of a large reference toxicogenomic database containing gene expression profiles for over 630 chemicals. Genomic signatures predicting hepatomegaly and hepatic injury preceded those results for all five chemicals, and further analysis segregated chemicals into two distinct classes. The triazoles caused similar gene expression changes as other azole antifungals, particularly the induction of pregnane X receptor (PXR)-regulated xenobiotic metabolism and oxidative stress genes. In contrast, PFOA and PFOS exhibited peroxisome proliferator-activated receptor alpha agonist-like effects on genes associated with fatty acid homeostasis. PFOA and PFOS also resulted in downregulation of cholesterol biosynthesis genes, matching an in vivo decrease in serum cholesterol, and perturbation of thyroid hormone metabolism genes matched by serum thyroid hormone depletion in vivo. The concordance of in vivo observations and gene expression findings demonstrated the ability of genomics to accurately categorize chemicals, identify toxic mechanisms of action, and predict subsequent pathological responses.