Update of the Cancer Potency Database (CPDB) to enable derivations of Thresholds of Toxicological Concern (TTC) for cancer potency.

The purpose of this study was to update the existing Cancer Potency Database (CPDB) in order to support the development of a dataset of compounds, with associated points of departure (PoDs), to enable a review and update of currently applied values for the Threshold of Toxicological Concern (TTC) for cancer endpoints. This update of the current CPDB, last reviewed in 2012, includes the addition of new data (44 compounds and 158 studies leading to additional 359 dose-response curves). Strict inclusion criteria were established and applied to select compounds and studies with relevant cancer potency data. PoDs were calculated from dose-response modeling, including the benchmark dose (BMD) and the lower 90% confidence limits (BMDL) at a specified benchmark response (BMR) of 10%. The updated full CPDB database resulted in a total of 421 chemicals which had dose-response data that could be used to calculate PoDs. This candidate dataset for cancer TTC is provided in a transparent and adaptable format for further analysis of TTC to derive cancer potency thresholds.

Assessment and Reproducibility of Quantitative Structure-Activity Relationship Models by the Nonexpert.

Model reliability is generally assessed and reported as an intrinsic component of quantitative structure-activity relationship (QSAR) publications; it can be evaluated using defined quality criteria such as the Organisation for Economic Cooperation and Development (OECD) principles for the validation of QSARs. However, less emphasis is afforded to the assessment of model reproducibility, particularly by users who may wish to use model outcomes for decision making, but who are not QSAR experts. In this study we identified a range of QSARs in the area of absorption, distribution, metabolism, and elimination (ADME) prediction and assessed their adherence to the OECD principles, as well as investigating their reproducibility by scientists without expertise in QSAR. Here, 85 papers were reviewed, reporting over 80 models for 31 ADME-related endpoints. Of these, 12 models were identified that fulfilled at least 4 of the 5 OECD principles and 3 of these 12 could be readily reproduced. Published QSAR models should aim to meet a standard level of quality and be clearly communicated, ensuring their reproducibility, to progress the uptake of the models in both research and regulatory landscapes. A pragmatic workflow for implementing published QSAR models and recommendations to modellers, for publishing models with greater usability, are presented herein.

In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects.

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.

How does the quality of phospholipidosis data influence the predictivity of structural alerts?

The ability of drugs to induce phospholipidosis (PLD) is linked directly to their molecular substructures: hydrophobic, cyclic moieties with hydrophilic, peripheral amine groups. These structural properties can be captured and coded into SMILES arbitrary target specification (SMARTS) patterns. Such structural alerts, which are capable of identifying potential PLD inducers, should ideally be developed on a relatively large but reliable data set. We had previously developed a model based on SMARTS patterns consisting of 32 structural fragments using information from 450 chemicals. In the present study, additional PLD structural alerts have been developed based on a newer and larger data set combining two data sets published recently by the United States Food and Drug Administration (US FDA). To assess the predictive performance of the updated SMARTS model, two publicly available data sets were considered. These data sets were constructed using different criteria and hence represent different standards for overall quality. In the first data set high quality was assured as all negative chemicals were confirmed by the gold standard method for the detection of PLD-transmission electron microscopy (EM). The second data set was constructed from seven previously published data sets and then curated by removing compounds where conflicting results were found for PLD activity. Evaluation of the updated SMARTS model showed a strong, positive correlation between predictive performance of the alerts and the quality of the data set used for the assessment. The results of this study confirm the importance of using high quality data for modeling and evaluation, especially in the case of PLD, where species, tissue, and dose dependence of results are additional confounding factors.

In silico models for drug-induced liver injury--current status.

INTRODUCTION: Drug-induced liver injury (DILI) is one of the most important reasons for drug attrition at both pre-approval and post-approval stages. Therefore, it is crucial to develop methods that will detect potential hepatotoxicity among drug candidates as early and quickly as possible. However, the complexity of hepatotoxicity endpoint makes it very difficult to predict. In addition, there is still a lack of sensitive and specific biomarkers for DILI that consequently leads to a scarcity of reliable hepatotoxic data, which are the key to any modelling approach. AREAS COVERED: This review explores the current status of existing in silico models predicting hepatotoxicity. Over the past decade, attempts have been made to compile hepatotoxicity data and develop in silico models, which can be used as a first-line screening of drug candidates for further testing. EXPERT OPINION: Most of the predictive methods discussed in this review are based on the structural properties of chemicals and do not take into account genetic and environmental factors; therefore, their predictions are still uncertain. To improve the predictability of in silico models for DILI, it is essential to better understand its mechanisms as well as to develop sensitive toxicogenomics biomarkers, which show relatively good differentiation between hepatotoxins and non-hepatotoxins.

In Silico Studies of the Relationship Between Chemical Structure and Drug Induced Phospholipidosis.

Drug-induced phospholipidosis (PLD) is a side effect of the administration of cationic amphiphilic drugs (CADs). It is desirable to identify and screen compounds with the potential to induce PLD as early as possible in drug development. Recently, a number of in silico methods have been developed to predict PLD. These models are low-cost and high-throughput strategies; however, they produce a high number of false positive predictions. The aim of this study was to assess the predictive performance of existing in silico approaches and to develop new strategies for the rapid identification of the potential PLD-inducers. Studies on 450 chemicals confirmed the high false positive rate of prediction of models based only on log P and pKa values. Modification of the methods by incorporating structural information gave moderate improvements in the prediction performance. Therefore, a new strategy, based on molecular fragments captured by SMARTS strings was developed. These structural fragments were able to identify potential PLD-inducers and achieved a high sensitivity of 85 %. The results showed that the phospholipidosis is linked directly to the molecular structure of chemical; therefore the SMARTS pattern methodology could be used as a first line of screening of PLD potential during the drug discovery process.