A further note on Coleophora trigeminella Fuchs and C. coracipennella (Hbn.)

Volume: 89Start Page: 16End Page: 1

Colias alfacariensis Ribbe, 1905 (Insecta, Lepidoptera): proposed availability as a senior synonym of Colias australi Verity, 1911

Volume: 45Start Page: 29End Page: 3

Bayesian modeling for Predicting Promiscuity and Selectivity of Molecules based on Safety Pharmacology Profiling Data.

‘Promiscuity’ is the likelihood property that a compound is found active against many targets which are not necessary the primary target of interest to develop a safe drug. Promiscuous compounds can carry various adverse effects liabilities and thus restrict the use of the drug or prevent its entry into the clinic. Therefore, there is a clear interest to evaluate compound promiscuity or selectivity at the earliest possible phase of drug discovery. In some cases, the design of compounds with multiple activities in a given pathway may be desirable

Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development.

Broad-scale in vitro pharmacology profiling of new chemical entities during early phases of drug discovery has recently become an essential tool to predict clinical adverse effects. Modern, relatively inexpensive assay technologies and rapidly expanding knowledge about G-protein coupled receptors, nuclear receptors, ion channels and enzymes have made it possible to implement a large number of assays addressing possible clinical liabilities. Together with other in vitro assays focusing on toxicology and bioavailability, they provide a powerful tool to aid drug development. In this article, we review the development of this tool for drug discovery, its appropriate use and predictive value

The Powers and Perils of Post-Marketing Data Analysis: Quantification and Mitigation of Biases in FAERS

From a drug safety standpoint, post-marketing data analysis is a valuable tool for gaining insight into adverse drug reactions (ADRs). However, analyzing post-marketing data without the appropriate precautions may result in the omission of crucial drug-ADR associations or the inappropriate ascribing of an ADR based on inadequate statistical treatment. We have analyzed the FDA Adverse Event Reporting System (FAERS) database with improved parsing methods, to expose the caveats that need to be considered when using this and other post-marketing datasets. To decrease noise in drug-ADR signals and reinforce the most important associations, we first minimized drug name redundancy (FAERS reports use > 0.5 million terms used to describe ~3 thousand drug products) by mapping all drugs to the 2,729 molecular ingredients encountered across the database. The unprecedented detail of our results allowed us to observe numerous idiosyncrasies in the data, such as confusion between reported indications and ADRs, and to discover that reporting of adverse events may be highly influenced by reporting trends. For example, the statistical significance of the association between pioglitazone and cardiac failure stemmed from a period of over-reporting that ended in 2004; the precise time at which another hypoglycemic drug, rosiglitazone, was reported to be the cause of numerous myocardial infarctions. The precautions and methods of analysis of FAERS that we discuss in this article will allow for improved interpretation of data in FEARS and utilization of its full potential, thus increasing the overall usefulness and impact of post-marketing data in drug safety

Matched Molecular Pair Analysis: Significance and the Impact of Experimental Uncertainty

Matched molecular pair analysis (MMPA) has become a major tool for analyzing large chemistry data sets for promising chemical transformations. However, the dependence of MMPA predictions on data constraints such as the number of pairs involved, experimental uncertainty, source of the experiments, and variability of the true physical effect has not yet been described. In this contribution the statistical basics for judging MMPA are analyzed. We illustrate the connection between overall MMPA statistics and individual pairs with a detailed comparison of average CHEMBL hERG MMPA results versus pairs with extreme transformation effects. Comparing the CHEMBL results to Novartis data, we find that significant transformation effects agree very well if the experimental uncertainty is considered. This indicates that caution must be exercised for predictions from insignificant MMPAs, yet highlights the robustness of statistically validated MMPA and shows that MMPA on public databases can yield results that are very useful for medicinal chemistry

Reverse translation of adverse event reports paves the way for derisking preclinical off-targets

Despite its important role, the Food and Drug Administration Adverse Event Reporting System (FAERS) – the primary source for postmarketing pharmacovigilance - remains susceptible to reporting biases, including poor standardization, admissible drug synonyms and stimulated reporting. We have designed improved parsing methods to illuminate caveats that must be considered when using this and other post-marketing datasets to provide guidance and tools for medical professionals and researchers to enable bias-free analysis of the FAERS database. To decrease the noise in drug-ADR signals, and to reinforce important associations, we mapped over 500,000 drug identifiers used in FAERS to normalized chemical structures of their ingredients and introduced time-resolved analysis of ADR reporting which reveals similarities between drugs and adverse events across therapeutic classes, enabling unbiased classification of adverse events, indications, and drugs with similar clinical profiles as demonstrated with the case of celecoxib and rofecoxib. We also investigated key idiosyncrasies in the data, such as confusion between reported indications of drugs and their ADRs, multiplications, and the correlation of reported adverse effects with public events, regulatory announcements, and with scientific publications. The comparison of the pharmacological, pharmacokinetic and clinical ADR profiles of methylphenidate, aripiprazole and risperidone demonstrates how underlying molecular mechanisms can be an emergent property of co-analysis of ADRs. The precautions and methods of analysis presented here enable investigators to avoid confounding chemistry-based and reporting biases in FAERS, which is increasingly mined in the community, and to illuminate the possibility to conduct comaparative analysis of ADRs in association with their underlying mechanisms

Reducing safety-related drug attrition: The use of in vitro pharmacological profiling

In vitro pharmacological profiling is increasingly being used earlier in the drug discovery process to identify undesirable off-target activity profiles that could hinder or halt the development of candidate drugs or even lead to market withdrawal if discovered after a drug is approved. Here, for the first time, the rationale, strategies and methodologies for in vitro pharmacological profiling at four major pharmaceutical companies (AstraZeneca, GlaxoSmithKline, Novartis and Pfizer) are presented and illustrated with examples of their impact on the drug discovery process. We hope that this will enable other companies and academic institutions to benefit from this knowledge and consider joining us in our collaborative knowledge sharing

Secondary Pharmacology: Screening and Interpretation of Off-target Activities – Focus on translation

Secondary pharmacology has become an essential tool for the drug discovery process during the past decade and is used extensively in the pharmaceutical industry for off-target mitigation particularly during the lead optimization phase. This was achieved by increasing the translational value of the approach, based on the recognition of biological target – drug molecule – adverse drug reaction (ADR) associations and integration of the secondary pharmacology data with pharmacokinetic parameters. Accumulation of information obtained from reverse translation of clinical ADRs, from recognition of specific phenotypes of target-based animal models and from hereditary diseases provides increasing regulatory confidence in the target-based approach to side effect prediction and mitigation. Here we review the progress of secondary pharmacology during the past decade, highlight and demonstrate its applications and impact in drug discovery