In-silico approach for drug induced liver injury prediction: Recent advances

Saini, Neha; Bakshi, Shikha; Sharma, Sadhna

doi:10.1016/j.toxlet.2018.06.1216

Cited by 20 publications

(11 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Having a single target simplifies the model: what is the likelihood that the novel structure in question can serve as a ligand for hERG? By contrast, hepatoxicity comprises a heterogenous set of compound classes including protein kinase inhibitors, herbal supplements, and anti-cancer agents (157)(158)(159). Models of drug-induced liver injury predict phenotypic toxicity rather than specific ligand binding and therefore resemble ototoxicity in terms of the complexity needed for predictive power.…”

Section: Computational Modelingmentioning

confidence: 99%

Detecting Novel Ototoxins and Potentiation of Ototoxicity by Disease Settings

Coffin

Boney²,

Hill

et al. 2021

Front. Neurol.

View full text Add to dashboard Cite

Over 100 drugs and chemicals are associated with permanent hearing loss, tinnitus, and vestibular deficits, collectively known as ototoxicity. The ototoxic potential of drugs is rarely assessed in pre-clinical drug development or during clinical trials, so this debilitating side-effect is often discovered as patients begin to report hearing loss. Furthermore, drug-induced ototoxicity in adults, and particularly in elderly patients, may go unrecognized due to hearing loss from a variety of etiologies because of a lack of baseline assessments immediately prior to novel therapeutic treatment. During the current pandemic, there is an intense effort to identify new drugs or repurpose FDA-approved drugs to treat COVID-19. Several potential COVID-19 therapeutics are known ototoxins, including chloroquine (CQ) and lopinavir-ritonavir, demonstrating the necessity to identify ototoxic potential in existing and novel medicines. Furthermore, several factors are emerging as potentiators of ototoxicity, such as inflammation (a hallmark of COVID-19), genetic polymorphisms, and ototoxic synergy with co-therapeutics, increasing the necessity to evaluate a drug's potential to induce ototoxicity under varying conditions. Here, we review the potential of COVID-19 therapies to induce ototoxicity and factors that may compound their ototoxic effects. We then discuss two models for rapidly detecting the potential for ototoxicity: mammalian auditory cell lines and the larval zebrafish lateral line. These models offer considerable value for pre-clinical drug development, including development of COVID-19 therapies. Finally, we show the validity of in silico screening for ototoxic potential using a computational model that compares structural similarity of compounds of interest with a database of known ototoxins and non-ototoxins. Preclinical screening at in silico, in vitro, and in vivo levels can provide an earlier indication of the potential for ototoxicity and identify the subset of candidate therapeutics for treating COVID-19 that need to be monitored for ototoxicity as for other widely-used clinical therapeutics, like aminoglycosides and cisplatin.

show abstract

Section: Computational Modelingmentioning

confidence: 99%

Detecting Novel Ototoxins and Potentiation of Ototoxicity by Disease Settings

Coffin

Boney²,

Hill

et al. 2021

Front. Neurol.

View full text Add to dashboard Cite

show abstract

“…The liver is the most common target of ADRs, because of its crucial role in the metabolism of endogenous and exogenous compounds [3]. Predictive markers of DILI able to identify susceptible patients would give an enormous advantage to accelerate safe drug development and to prevent severe reactions after approval [4,5]. DILI poses particular challenges, as pre-clinical testing for *Correspondence: chierici@fbk.eu † Marco Chierici and Margherita Francescatto contributed equally to this work.…”

Section: Introductionmentioning

confidence: 99%

Predictability of drug-induced liver injury by machine learning

et al. 2020

View full text Add to dashboard Cite

Background: Drug-induced liver injury (DILI) is a major concern in drug development, as hepatotoxicity may not be apparent at early stages but can lead to life threatening consequences. The ability to predict DILI from in vitro data would be a crucial advantage. In 2018, the Critical Assessment Massive Data Analysis group proposed the CMap Drug Safety challenge focusing on DILI prediction. Methods and results: The challenge data included Affymetrix GeneChip expression profiles for the two cancer cell lines MCF7 and PC3 treated with 276 drug compounds and empty vehicles. Binary DILI labeling and a recommended train/test split for the development of predictive classification approaches were also provided. We devised three deep learning architectures for DILI prediction on the challenge data and compared them to random forest and multi-layer perceptron classifiers. On a subset of the data and for some of the models we additionally tested several strategies for balancing the two DILI classes and to identify alternative informative train/test splits. All the models were trained with the MAQC data analysis protocol (DAP), i.e., 10x5 cross-validation over the training set. In all the experiments, the classification performance in both cross-validation and external validation gave Matthews correlation coefficient (MCC) values below 0.2. We observed minimal differences between the two cell lines. Notably, deep learning approaches did not give an advantage on the classification performance. Discussion: We extensively tested multiple machine learning approaches for the DILI classification task obtaining poor to mediocre performance. The results suggest that the CMap expression data on the two cell lines MCF7 and PC3 are not sufficient for accurate DILI label prediction. Reviewers: This article was reviewed by Maciej Kandula and Paweł P. Labaj.

show abstract

“…Compared to the experimental methods, in silico methods are much faster and cheaper. Currently, there have been many in silico models focused on predicting the hepatotoxic risk of drug candidates in the literature [117,118]. However, almost all of these models were developed solely based on the synthetic drugs.…”

Section: Discussionmentioning

confidence: 99%

Herb-Induced Liver Injury: Phylogenetic Relationship, Structure-Toxicity Relationship, and Herb-Ingredient Network Analysis

Zhang

Zhou

et al. 2019

IJMS

View full text Add to dashboard Cite

Currently, hundreds of herbal products with potential hepatotoxicity were available in the literature. A comprehensive summary and analysis focused on these potential hepatotoxic herbal products may assist in understanding herb-induced liver injury (HILI). In this work, we collected 335 hepatotoxic medicinal plants, 296 hepatotoxic ingredients, and 584 hepatoprotective ingredients through a systematic literature retrieval. Then we analyzed these data from the perspectives of phylogenetic relationship and structure-toxicity relationship. Phylogenetic analysis indicated that hepatotoxic medicinal plants tended to have a closer taxonomic relationship. By investigating the structures of the hepatotoxic ingredients, we found that alkaloids and terpenoids were the two major groups of hepatotoxicity. We also identified eight major skeletons of hepatotoxicity and reviewed their hepatotoxic mechanisms. Additionally, 15 structural alerts (SAs) for hepatotoxicity were identified based on SARpy software. These SAs will help to estimate the hepatotoxic risk of ingredients from herbs. Finally, a herb-ingredient network was constructed by integrating multiple datasets, which will assist to identify the hepatotoxic ingredients of herb/herb-formula quickly. In summary, a systemic analysis focused on HILI was conducted which will not only assist to identify the toxic molecular basis of hepatotoxic herbs but also contribute to decipher the mechanisms of HILI.

show abstract

In-silico approach for drug induced liver injury prediction: Recent advances

Cited by 20 publications

References 70 publications

Detecting Novel Ototoxins and Potentiation of Ototoxicity by Disease Settings

Detecting Novel Ototoxins and Potentiation of Ototoxicity by Disease Settings

Predictability of drug-induced liver injury by machine learning

Herb-Induced Liver Injury: Phylogenetic Relationship, Structure-Toxicity Relationship, and Herb-Ingredient Network Analysis

Contact Info

Product

Resources

About