In Silico Models for Predicting Acute Systemic Toxicity

Toxicity of minoxidil - Comprehensive in silico prediction of main toxicity endpoints: Acute toxicity, irritation of skin and eye, genetic toxicity, health effect, cardiotoxicity and endocrine system disruption

This article focusses on elucidating the toxicological profile of minoxidil, a widely used pharmacological agent for alopecia, through the application of in silico methods (Percepta ACD/Labs software). This research is driven by the need to understand key toxicological endpoints: acute toxicity, skin and eye irritation, genetic toxicity, cardiotoxicity, disruption of the endocrine system, and estimation of various health effects due to the lack of experimental data for this drug. These parameters are critically evaluated to meet the stringent requirements of the pharmaceutical industry's safety assessments. The results obtained for acute toxicity (LD50 for rats and mouse) indicate that minoxidil exhibits a species-dependent acute toxicity profile e.g. 51 mg/kg bw for intravenous administration in mice. The predicted health effects indicate a 93% risk to the gastrointestinal system, 54% for the kidneys, 52% for the liver, 42% for the blood and lungs, and 39% for the cardiovascular system. The prediction of genotoxicity suggests a moderate probability (48%) of inducing a positive Ames test result. Furthermore, moderate inhibition of the hERG channel indicates potential cardiac risks of Minoxidil. Based on the information obtained, we propose subjecting minoxidil to additional toxicological assessments. The successful adoption of these in silico methodologies aligns with the 3 R s principle (replacement, reduction, and refinement) in the field of modern toxicological studies of minoxidil, all without the use of laboratory animals for the novelty of our toxicity assessment.

CORAL Models for Drug-Induced Nephrotoxicity

Drug-induced nephrotoxicity is a major cause of kidney dysfunction with potentially fatal consequences. The poor prediction of clinical responses based on preclinical research hampers the development of new pharmaceuticals. This emphasises the need for new methods for earlier and more accurate diagnosis to avoid drug-induced kidney injuries. Computational predictions of drug-induced nephrotoxicity are an attractive approach to facilitate such an assessment and such models could serve as robust and reliable replacements for animal testing. To provide the chemical information for computational prediction, we used the convenient and common SMILES format. We examined several versions of so-called optimal SMILES-based descriptors. We obtained the highest statistical values, considering the specificity, sensitivity and accuracy of the prediction, by applying recently suggested atoms pairs proportions vectors and the index of ideality of correlation, which is a special statistical measure of the predictive potential. Implementation of this tool in the drug development process might lead to safer drugs in the future.

Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods

Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of GHS classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.