The use of structural alerts to avoid the toxicity of pharmaceuticals

Abstract

In order to identify compounds with potential toxicity problems, particular attention is paid to structural alerts, which are high chemical reactivity molecular fragments or fragments that can be transformed via bioactivation by human enzymes into fragments with high chemical reactivity. The concept has been introduced in order to reduce the likelihood that future candidate substances as pharmaceuticals will have undesirable toxic effects. A significant proportion (∼78–86%) of drugs characterized by residual toxicity contain structural alerts; there is also evidence indicating the formation of active metabolites as a causal factor for the toxicity of 62–69% of these molecules. On the other hand, the pharmacological action of certain drugs depends on the formation of reactive metabolites. Detailed assessment of the potential for the formation of active metabolites is recommended to characterize a biologically active compound. Although many prescribed drugs frequently contain structural alerts and form reactive metabolites, the vast majority of these drugs are administered in low daily doses. Avoiding structural alerts has become almost a norm in new drug design. An in-depth review of the biochemical reactivity of these structural alerts for new drug candidates is critical from a safety point of view and is currently being monitored in the discovery of drugs. The chemical strategies applied to structural alerts in molecules to limit the toxicity are:partial replacement or full replacement of the structural alert;reduction of electronic density;introduction of a structural element of metabolic interest (metabolic switching);multiple approaches.Therefore, chemical intervention strategies to eliminate bioactivation are often interactive processes; their success depends largely on a close working relationship between drug chemists, pharmacologists and researchers in metabolic science.

Algorithm for Extraction of Sub-Structure from Co-Crystallized PDB Ligand for Selective Targeting

“…In another study, structural features were used, which does not assure that relevant fingerprint as required for target selectivity (Alves et al, 2016). Use of molecular fragments does not assure about coverage of inter-fragment connection based substructures (Limban et al, 2018). Similarly there are other aspects which are not covered by ligand based comparative studied with set of compounds.…”

“…In another study, structural features based statistical QSAR models were defined structural alerts to flag potential chemical hazards (Alves et al, 2016). Molecular fragments with high chemical reactivity were also considered as structural alerts and were avoided to reduce toxicity in pharmaceuticals (Limban et al, 2018). Structure-metabolism studies are known to resolve reactive metabolite-related liabilities by "avoidance" strategies for exclusion of structural alerts and possible termination of reactive metabolite-positive compounds (Kalgutkar, 2019).…”

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

“…Derivation of alerts by expert knowledge yields the advantage of an explanation for an alert. This explanation often results in mechanistic insights that subsequently can be used to suggest structural changes, where applicable . Independent of their source, structural alerts can help in the identification of hazards.…”

“…It was also shown that the potency of an alert might be corresponding to the daily dose, therefore alerts might not be relevant for low doses of drugs . Alves and coworkers therefore proposed that structural alerts should not be seen as a ”yes” or ”no” concerning toxicity but rather as a hypothesis about the mode of action and subsequently trigger closer mechanistic studies . Similarly, Limban and coworkers propose the replacement of structural alerts by functional groups that counteract the supposed toxicity mechanism rather than directly rejecting such compounds .…”

Prediction and mechanistic analysis of Drug-Induced Liver Injury (DILI) based on chemical structure

“…From the purely chemical side, we derived interpretable structural alerts related to DILI with the Molecular Substructure miner algorithm (MoSS) implementation of graph-based Molecular Fragment miner algorithm (MoFa) (24) and the fragment-based SARpy package (25), which could guide lead optimization to reduce the risk of DILI, as is currently standard practice for other toxicities (26). We then compared the quality of the derived structural alerts against the recent review of DILI related structural alerts by (27).…”