Drug-induced liver injury (DILI) is a patient-specific, temporal, multifactorial pathophysiological process that cannot yet be recapitulated in a single in vitro model. Current preclinical testing ...regimes for the detection of human DILI thus remain inadequate. A systematic and concerted research effort is required to address the deficiencies in current models and to present a defined approach towards the development of new or adapted model systems for DILI prediction. This Perspective defines the current status of available models and the mechanistic understanding of DILI, and proposes our vision of a roadmap for the development of predictive preclinical models of human DILI.
Drug toxicity evaluation is an essential process of drug development as it is reportedly responsible for the attrition of approximately 30% of drug candidates. The rapid increase in the number and ...types of large toxicology data sets together with the advances in computational methods may be used to improve many steps in drug safety evaluation. The development of in silico models to screen and understand mechanisms of drug toxicity may be particularly beneficial in the early stages of drug development where early toxicity assessment can most reduce expenses and labor time. To facilitate this, machine learning methods have been employed to evaluate drug toxicity but are often limited by small and less diverse data sets. Recent advances in machine learning methods together with the rapid increase in big toxicity data such as molecular descriptors, toxicogenomics, and high-throughput bioactivity data may help alleviate some of the current challenges. In this article, the most common machine learning methods used in toxicity assessment are reviewed together with examples of toxicity studies that have used machine learning methodology. Furthermore, a comprehensive overview of the different types of toxicity tools and data sets available to build in silico toxicity prediction models has been provided to give an overview of the current big toxicity data landscape and highlight opportunities and challenges related to them.
Antibody-drug conjugates (ADCs) are a promising therapeutic modality for oncology indications. The concept of an ADC platform is to increase the therapeutic index (TI) of chemotherapeutics through ...more selective delivery of cytotoxic agents to tumor cells while limiting exposure to healthy normal cells. Despite the use of antibodies targeting antigens abundantly and/or exclusively expressed on cancer cells (i.e., target cells), dose limiting toxicities (DLTs) in normal cells/tissues are frequently reported even at suboptimal therapeutic doses. Although advancement of ADC technology has helped to optimize all three key components (i.e., mAb, linker, and payload), DLTs remain a key challenge for ADC development. Mechanisms of ADC toxicity in normal cells/tissues are not clearly understood, but the majority of DLTs are considered to be target-independent. In addition to linker-drug instability contributing to the premature release of cytotoxic drug (payload) in circulation, uptake/trafficking of intact ADCs by both receptor-dependent (FcγRs, FcRn and C-type lectin receptors), and-independent (non-specific endocytosis) mechanisms may contribute to off-target toxicity in normal cells. In this article, we review potential mechanisms of target-independent ADC uptake and toxicity in normal cells, as well as discuss components of ADCs which may influence these mechanisms. This information will provide a deeper understanding of the underlying mechanisms of ADC off-target toxicity and prove helpful toward improving the overall TI of the next generation of ADCs.
Gene expression profiling is a useful tool to predict and interrogate mechanisms of toxicity. RNA-Seq technology has emerged as an attractive alternative to traditional microarray platforms for ...conducting transcriptional profiling. The objective of this work was to compare both transcriptomic platforms to determine whether RNA-Seq offered significant advantages over microarrays for toxicogenomic studies. RNA samples from the livers of rats treated for 5 days with five tool hepatotoxicants (α-naphthylisothiocyanate/ANIT, carbon tetrachloride/CCl
, methylenedianiline/MDA, acetaminophen/APAP, and diclofenac/DCLF) were analyzed with both gene expression platforms (RNA-Seq and microarray). Data were compared to determine any potential added scientific (i.e., better biological or toxicological insight) value offered by RNA-Seq compared to microarrays. RNA-Seq identified more differentially expressed protein-coding genes and provided a wider quantitative range of expression level changes when compared to microarrays. Both platforms identified a larger number of differentially expressed genes (DEGs) in livers of rats treated with ANIT, MDA, and CCl
compared to APAP and DCLF, in agreement with the severity of histopathological findings. Approximately 78% of DEGs identified with microarrays overlapped with RNA-Seq data, with a Spearman's correlation of 0.7 to 0.83. Consistent with the mechanisms of toxicity of ANIT, APAP, MDA and CCl
, both platforms identified dysregulation of liver relevant pathways such as Nrf2, cholesterol biosynthesis, eiF2, hepatic cholestasis, glutathione and LPS/IL-1 mediated RXR inhibition. RNA-Seq data showed additional DEGs that not only significantly enriched these pathways, but also suggested modulation of additional liver relevant pathways. In addition, RNA-Seq enabled the identification of non-coding DEGs that offer a potential for improved mechanistic clarity. Overall, these results indicate that RNA-Seq is an acceptable alternative platform to microarrays for rat toxicogenomic studies with several advantages. Because of its wider dynamic range as well as its ability to identify a larger number of DEGs, RNA-Seq may generate more insight into mechanisms of toxicity. However, more extensive reference data will be necessary to fully leverage these additional RNA-Seq data, especially for non-coding sequences.
Most small molecule drugs interact with unintended, often unknown, biological targets and these off-target interactions may lead to both preclinical and clinical toxic events. Undesired off-target ...interactions are often not detected using current drug discovery assays, such as experimental polypharmacological screens. Thus, improvement in the early identification of off-target interactions represents an opportunity to reduce safety-related attrition rates during preclinical and clinical development. In order to better identify potential off-target interactions that could be linked to predictable safety issues, a novel computational approach to predict safety-relevant interactions currently not covered was designed and evaluated. These analyses, termed Off-Target Safety Assessment (OTSA), cover more than 7,000 targets (~35% of the proteome) and > 2,46,704 preclinical and clinical alerts (as of January 20, 2019). The approach described herein exploits a highly curated training set of >1 million compounds (tracking >20 million compound-structure activity relationship/SAR data points) with known
activities derived from patents, journals, and publicly available databases. This computational process was used to predict both the primary and secondary pharmacological activities for a selection of 857 diverse small molecule drugs for which extensive secondary pharmacology data are readily available (456 discontinued and 401 FDA approved). The OTSA process predicted a total of 7,990 interactions for these 857 molecules. Of these, 3,923 and 4,067 possible high-scoring interactions were predicted for the discontinued and approved drugs, respectively, translating to an average of 9.3 interactions per drug. The OTSA process correctly identified the known pharmacological targets for >70% of these drugs, but also predicted a significant number of off-targets that may provide additional insight into observed
effects. About 51.5% (2,025) and 22% (900) of these predicted high-scoring interactions have not previously been reported for the discontinued and approved drugs, respectively, and these may have a potential for repurposing efforts. Moreover, for both drug categories, higher promiscuity was observed for compounds with a MW range of 300 to 500, TPSA of ~200, and clogP ≥7. This computation also revealed significantly lower promiscuity (i.e., number of confirmed off-targets) for compounds with MW > 700 and MW<200 for both categories. In addition, 15 internal small molecules with known off-target interactions were evaluated. For these compounds, the OTSA framework not only captured about 56.8% of
confirmed off-target interactions, but also identified the right pharmacological targets for 14 compounds as one of the top scoring targets. In conclusion, the OTSA process demonstrates good predictive performance characteristics and represents an additional tool with utility during the lead optimization stage of the drug discovery process. Additionally, the computed physiochemical properties such as clogP (i.e., lipophilicity), molecular weight, pKa and logS (i.e., solubility) were found to be statistically different between the approved and discontinued drugs, but the internal compounds were close to the approved drugs space in most part.
Abstract
To minimize the occurrence of unexpected toxicities in early phase preclinical studies of new drugs, it is vital to understand fundamental similarities and differences between preclinical ...species and humans. Species differences in sensitivity to acetaminophen (APAP) liver injury have been related to differences in the fraction of the drug that is bioactivated to the reactive metabolite N-acetyl-p-benzoquinoneimine (NAPQI). We have used physiologically based pharmacokinetic modeling to identify oral doses of APAP (300 and 1000 mg/kg in mice and rats, respectively) yielding similar hepatic burdens of NAPQI to enable the comparison of temporal liver tissue responses under conditions of equivalent chemical insult. Despite pharmacokinetic and biochemical verification of the equivalent NAPQI insult, serum biomarker and tissue histopathology analyses revealed that mice still exhibited a greater degree of liver injury than rats. Transcriptomic and proteomic analyses highlighted the stronger activation of stress response pathways (including the Nrf2 oxidative stress response and autophagy) in the livers of rats, indicative of a more robust transcriptional adaptation to the equivalent insult. Components of these pathways were also found to be expressed at a higher basal level in the livers of rats compared with both mice and humans. Our findings exemplify a systems approach to understanding differential species sensitivity to hepatotoxicity. Multiomics analysis indicated that rats possess a greater basal and adaptive capacity for hepatic stress responses than mice and humans, with important implications for species selection and human translation in the safety testing of new drug candidates associated with reactive metabolite formation.
The evaluation of toxicity in preclinical species is important for identifying potential safety liabilities of experimental medicines. Toxicology studies provide translational insight into potential ...adverse clinical findings, but data interpretation may be limited due to our understanding of cross-species biological differences. With the recent technological advances in sequencing and analyzing omics data, gene expression data can be used to predict cross species biological differences and improve experimental design and toxicology data interpretation. However, interpreting the translational significance of toxicogenomics analyses can pose a challenge due to the lack of comprehensive preclinical gene expression datasets. In this work, we performed RNA-sequencing across four preclinical species/strains widely used for safety assessment (CD1 mouse, Sprague Dawley rat, Beagle dog, and Cynomolgus monkey) in ∼50 relevant tissues/organs to establish a comprehensive preclinical gene expression body atlas for both males and females. In addition, we performed a meta-analysis across the large dataset to highlight species and tissue differences that may be relevant for drug safety analyses. Further, we made these databases available to the scientific community. This multi-species, tissue-, and sex-specific transcriptomic database should serve as a valuable resource to enable informed safety decision-making not only during drug development, but also in a variety of disciplines that use these preclinical species.
Idiosyncratic drug toxicity refers to toxic reactions occurring in a small subset of patients and usually cannot be predicted during preclinical or early phases of clinical trials. One hypothesis for ...the pathogenesis of hepatic idiosyncratic drug reactions is that, in certain individuals, underlying inflammation results in sensitization of the liver, such that injury occurs from an agent that typically would not cause hepatotoxicity at a therapeutic dose. We explored this possibility by cotreating rats with nonhepatotoxic doses of bacterial lipopolysaccharide (LPS) and trovafloxacin (TVX), a drug that caused idiosyncratic hepatotoxicity in humans. The combination of LPS and TVX resulted in hepatotoxicity in rats, as determined by increases in serum alanine aminotransferase activity and hepatocellular necrosis, which were not observed with either agent alone. In contrast, treatment with LPS and levofloxacin, a fluoroquinolone without human idiosyncratic liability, did not result in these changes. Liver gene expression analysis identified unique changes induced by the combination of TVX and LPS, including enhanced expression of chemokines, suggestive of liver neutrophil (PMN) accumulation and activation. Consistent with a role for PMN in the hepatotoxicity induced by LPS/TVX, prior depletion of PMN attenuated the liver injury. The results suggest that gene expression profiles predictive of idiosyncratic liability can be generated in rats cotreated with LPS and drug. Furthermore, they identify gene expression changes that could be explored as biomarkers for idiosyncratic toxicity and lead to enhanced understanding of the mechanism(s) underlying hepatotoxicity induced by TVX.
Isolated primary human hepatocytes are a well accepted system for evaluating pharmacological and toxicological effects in humans. However, questions remain regarding how culturing affects the ...liver-specific functions of the hepatocytes. In addition, cryopreservation could also potentially affect the differentiation state of the hepatocytes. The first aim of the present study was to compare gene expression in freshly isolated primary hepatocytes to that of the liver of origin and to evaluate the expression changes occurring after cryopreservation/thawing, both when maintained in suspension and after plating. The second aim of the present study was to evaluate gene expression in hepatocytes after cold storage of suspensions up to 24 h compared with freshly isolated hepatocytes in suspension. Our results show that the gene expression in freshly isolated human hepatocytes in suspension after isolation is similar to that of the liver of origin. Furthermore, gene expression in primary human hepatocytes in suspension is not affected by hepatocyte cold storage and cryopreservation. However, the gene expression is profoundly affected in monolayer cultures after plating. Specifically, gene expression changes were observed in cultured relative to suspensions of human hepatocytes that are involved in cellular processes such as phase I/II metabolism, basolateral and canalicular transport systems, fatty acid and lipid metabolism, apoptosis, and proteasomal protein recycling. An oxidative stress response may be partially involved in these changes in gene expression. Taken together, these results may aid in the interpretation of data collected from human hepatocyte experiments and suggest additional utility for cold storage and cryopreservation of hepatocytes.
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