Alexander Amberg

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

Hepatic toxicity is a key concern for novel pharmaceutical drugs since it is difficult to anticipate in preclinical models, and it can originate from pharmacologically unrelated drug effects, such as pathway interference, metabolism, and drug accumulation. Because liver toxicity still ranks among the top reasons for drug attrition, the reliable prediction of adverse hepatic effects is a substantial challenge in drug discovery and development. To this end, more effort needs to be focused on the development of improved predictive in-vitro and in-silico approaches. Current computational models often lack applicability to novel pharmaceutical candidates, typically due to insufficient coverage of the chemical space of interest, which is either imposed by size or diversity of the training data. Hence, there is an urgent need for better computational models to allow for the identification of safe drug candidates and to support experimental design. In this context, a large data set comprising 3712 compounds with liver related toxicity findings in humans and animals was collected from various sources. The complex pathology was clustered into 21 preclinical and human hepatotoxicity endpoints, which were organized into three levels of detail. Support vector machine models were trained for each endpoint, using optimized descriptor sets from chemometrics software. The optimized global human hepatotoxicity model has high sensitivity (68%) and excellent specificity (95%) in an internal validation set of 221 compounds. Models for preclinical endpoints performed similarly. To allow for reliable prediction of "truly external" novel compounds, all predictions are tagged with confidence parameters. These parameters are derived from a statistical analysis of the predictive probability densities. The whole approach was validated for an external validation set of 269 proprietary compounds. The models are fully integrated into our early safety in-silico workflow.

Although early detection of toxicant induced kidney injury during drug development and chemical safety testing is still limited by the lack of sensitive and reliable biomarkers of nephrotoxicity, omics technologies have brought enormous opportunities for improved detection of toxicity and biomarker discovery. Thus, transcription profiling has led to the identification of several candidate kidney biomarkers such as kidney injury molecule (Kim-1), clusterin, lipocalin-2, and tissue inhibitor of metalloproteinase 1 (Timp-1), and metabonomic analysis of urine is increasingly used to indicate biochemical perturbations due to renal toxicity. This study was designed to assess the value of a combined (1)H-NMR and gas chromatography-mass spectrometry (GC-MS) metabonomics approach and a set of novel urinary protein markers for early detection of nephrotoxicity following treatment of male Wistar rats with gentamicin (60 and 120 mg/kg bw, s.c.) for 7 days. Time- and dose-dependent separation of gentamicin-treated animals from controls was observed by principal component analysis of (1)H-NMR and GC-MS data. The major metabolic alterations responsible for group separation were linked to the gut microflora, thus related to the pharmacology of the drug, and increased glucose in urine of gentamicin-treated animals, consistent with damage to the S(1) and S(2) proximal tubules, the primary sites for glucose reabsorption. Altered excretion of urinary protein biomarkers Kim-1 and lipocalin-2, but not Timp-1 and clusterin, was detected before marked changes in clinical chemistry parameters were evident. The early increase in urine, which correlated with enhanced gene and protein expression at the site of injury, provides further support for lipocalin-2 and Kim-1 as sensitive, noninvasive biomarkers of nephrotoxicity.