The Drug Toxicity Signature Generation Center is a NIH-funded Systems Pharmacology research center at the Icahn School of Medicine. The proteomics experiments for the center are conducted at the Center for Advanced Proteomics, Rutgers-New Jersey Medical School. DToxS is part of the LINCS consortium of centers. LINCS (Library of Integrated Network-Based Cellular Signatures) is a program supported by the NIH Common Fund. The goals of the LINCS consortium as stated on the LINCS website at the NIH include the “large-scale production of perturbation-induced molecular and cellular signatures and the development of the informatics and analytical tools that enable utilization by all of the research community
The overall goal of the Drug Toxicity Signature Generation Center (DToxS) is to develop robust cellular signatures for drug-induced toxicity and toxicity mitigation. We build these signatures by exposing cells to drugs in culture, and integrating genomic and proteomic high-throughput measurements in multiple cell types with network analyses and simulations using dynamical models. To anchor the signatures in observable human disease and therapeutics, we will leverage the strategy employed in our study, (Zhao et al Science Translational Medicine 2013) in which we searched the FDA-Adverse Event Reporting System Database (FAERS) and found hundreds of combinations, used in humans, where a second drug mitigates serious toxicity associated with the first drug. We hypothesize that we can use these observations to improve our capability to predict drug-induced toxicity and mitigation by drug pairs.
Therapeutically effective drugs often cause serious unintended adverse events that limit or even prohibit their use. A recent example is ponatinib, a new tyrosine kinase inhibitor developed to treat a drug-insensitive form of chronic myeloid leukemia. Although the new drug showed promise, Phase III trials were halted due to the occurrence of multiple toxicities, including cardiotoxicity. Often toxicity arises from the drug working on the intended molecular target (and perhaps other targets) in cells and tissues unassociated with the pathophysiology being treated. Identifying cellular signatures of such unintended toxicity can help accelerate drug development.
Several factors make it challenging to predict toxicity prior to clinical studies. First, although drug targets may be present in multiple tissue types, these targets may be connected to different cellular regulatory networks, leading to differential phenotypic responses. Second, even if a signature such as a static change in gene expression is robust across cell types, the physiological consequences, resulting from altered cellular functional dynamics, may be different. Third, the fact that drugs are frequently given in combination makes toxicity prediction even more complicated. A second drug may either exacerbate or mitigate the toxicity due to the first drug when drugs are given in combination, as we and others have shown.