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Paini, A.

Publications and source records attributed to Paini, A..

2 recordsLinked to original sources

Potential Systemic Availability Classification of Chemicals for Safety Assessment

The assessment of chemical safety is essential for protecting human health, yet current approaches have severe limitations. They are unable to address the rapidly growing number of substances requiring evaluation and further rely on animal testing, which the public expects to be phased out for ethical reasons. To address this, the European Partnership for Alternative Approaches to Animal Testing (EPAA) proposed the use of a novel framework for the classification of systemic toxicity based on new approach methodologies. One dimension of this framework is the grouping of substances into different classes of Potential Systemic Availability (PSA) concern (low, medium, high). But so far it remained conceptually and practically unclear how this classification can be achieved. Here, we outline the theoretical considerations for a health-protective definition of PSA concern classes and present a method for the quantitative evaluation of this property. Using high-throughput physiologically based kinetic modelling, we are able to classify the PSA concerns of 139 out of 150 EPAA NAM Designathon compounds. Further, we manually annotate these compounds to evaluate the plausibility of predicted classifications against expert judgement. Our results outline under which circumstances it is appropriate to prioritise or deprioritise chemicals due to their toxicokinetic properties. However, we find that most compounds cannot be assessed on their PSA alone and need to be considered medium PSA concern so that classification into any overall systemic toxicity concern class remains possible. Future integration of bioactivity data will be necessary to fully judge the utility of our method and of the entire framework.

pharmacology and toxicology↗

Systematic Evaluation of High-Throughput PBK Modelling Strategies for the Prediction of Intravenous and Oral Pharmacokinetics in Humans

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for Non-Animal Methods. To address this limitation, High-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000+ publicly available human in vivo concentration-time profiles of 200+ compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic High-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within 10-fold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for High-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of High-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.

pharmacology and toxicology↗