bioRxiv Science⌕ Search

Biology subjects

Makarov, S.

Publications and source records attributed to Makarov, S..

2 recordsLinked to original sources

Assessing risks and mechanisms of idiosyncratic drug toxicity by fingerprints of cell signaling responses.

Idiosyncratic drug-induced liver injury (DILI) is the leading cause of post-marketing drug withdrawal. Here, we describe a straightforward DILI liability assessment approach based on fingerprinting cell signaling responses. The readout is the activity of transcription factors (TF) that link signaling pathways to genes. Using a multiplex reporter assay for 45 TFs in hepatocytic cells, we assessed TF activity profiles (TFAP) for 13 pharmacological classes. The TFAP signatures were consistent with primary drug activity but transformed into different, off-target signatures at certain concentrations (COFF). We show that the off-target signatures pertained to DILI-relevant mechanisms, including mitochondria malfunction, proteotoxicity, and lipid peroxidation. Based on reported plasma concentrations in humans (CMAX), drugs do not reach the off-target thresholds in vivo, consistent with the lack of overt toxicity in the population. However, DILI liability drugs were dangerously close to the off-target thresholds. We characterized this closeness by the COFF/CMAX ratio termed the safety margin (SM). Most-DILI-concern drugs invariably showed smaller safety margins than their less-concern counterparts in each pharmacological class and across classes (median SM values of 6.4 and 212.7, respectively (P<0.00015)). Therefore, the TFAP approach helps to explain idiosyncratic drug toxicity and provides clear quantitative metrics for its probability and the underlying mechanisms.

pharmacology and toxicology↗

Tackling polypharmacology of kinase inhibitors by transcription factor activity profiling.

Protein kinase inhibitors (PKI) are promising drug candidates for many diseases. However, even selective PKIs interact with multiple kinases and non-kinase targets. Existing technologies detect these interactions but not the resultant biological effects. Here, we describe an orthogonal PKI evaluation approach that entails fingerprinting of cell signaling responses. As the readout, we profiled the activity of 45 transcription factors linking signaling pathways to genes. We found that inhibitors of the same kinase family exhibited a consensus TF activity profile (TFAP) invariant to PKI chemistry and mode of action (allosteric, ATP-competitive, or genetic). Specific PKI consensus signatures were found for multiple kinase families (Akt, CDK, Aurora, RAF, MEK, and ERK) with high-similarity consensus signatures of signaling cascade kinases. Thus, the PKI consensus signatures provide bona fide markers of cell response to on-target PKI activity. However, the consensus signatures appeared only at certain inhibitor concentrations ( on-target windows). Using concentration-response signature analysis, we identified PKI interactions dominating cell response at other concentrations. Finally, we illustrate this approach by selecting putative chemical probes for evaluated kinases. Therefore, the effect-based TFAP approach illuminates PKI biology invisible to target-based technologies and provides clear quantitative metrics to aid the selection of polypharmacological PKIs as chemical probes and drug leads.

pharmacology and toxicology↗