Machine Learning to Predict Drug-Induced Liver Injury and Its Validation on Failed Drug Candidates in Development.

Drug-induced liver injury (DILI) poses a significant challenge for the pharmaceutical industry and regulatory bodies. Despite extensive toxicological research aimed at mitigating DILI risk, the effectiveness of these techniques in predicting DILI in humans remains limited. Consequently, researchers have explored novel approaches and procedures to enhance the accuracy of DILI risk prediction for drug candidates under development. In this study, we leveraged a large human dataset to develop machine learning models for assessing DILI risk. The performance of these prediction models was rigorously evaluated using a 10-fold cross-validation approach and an external test set. Notably, the random forest (RF) and multilayer perceptron (MLP) models emerged as the most effective in predicting DILI. During cross-validation, RF achieved an average prediction accuracy of 0.631, while MLP achieved the highest Matthews Correlation Coefficient (MCC) of 0.245. To validate the models externally, we applied them to a set of drug candidates that had failed in clinical development due to hepatotoxicity. Both RF and MLP accurately predicted the toxic drug candidates in this external validation. Our findings suggest that in silico machine learning approaches hold promise for identifying DILI liabilities associated with drug candidates during development.

Lyme Disease Models of Tick-Mouse Dynamics with Seasonal Variation in Births, Deaths, and Tick Feeding.

Lyme disease is the most common vector-borne disease in the United States impacting the Northeast and Midwest at the highest rates. Recently, it has become established in southeastern and south-central regions of Canada. In these regions, Lyme disease is caused by Borrelia burgdorferi, which is transmitted to humans by an infected Ixodes scapularis tick. Understanding the parasite-host interaction is critical as the white-footed mouse is one of the most competent reservoir for B. burgdorferi. The cycle of infection is driven by tick larvae feeding on infected mice that molt into infected nymphs and then transmit the disease to another susceptible host such as mice or humans. Lyme disease in humans is generally caused by the bite of an infected nymph. The main aim of this investigation is to study how diapause delays and demographic and seasonal variability in tick births, deaths, and feedings impact the infection dynamics of the tick-mouse cycle. We model tick-mouse dynamics with fixed diapause delays and more realistic Erlang distributed delays through delay and ordinary differential equations (ODEs). To account for demographic and seasonal variability, the ODEs are generalized to a continuous-time Markov chain (CTMC). The basic reproduction number and parameter sensitivity analysis are computed for the ODEs. The CTMC is used to investigate the probability of Lyme disease emergence when ticks and mice are introduced, a few of which are infected. The probability of disease emergence is highly dependent on the time and the infected species introduced. Infected mice introduced during the summer season result in the highest probability of disease emergence.

Optic Neuritis After COVID-19 Vaccination: An Analysis of the Vaccine Adverse Event Reporting System.

BACKGROUND: To investigate the association of optic neuritis (ON) after the COVID-19 vaccines. METHODS: Cases of ON from Vaccine Adverse Event Reporting System (VAERS) were collected and divided into the prepandemic, COVID-19 pandemic, and COVID-19 vaccine periods. Reporting rates were calculated based on estimates of vaccines administered. Proportion tests and Pearson χ 2 test were used to determine significant differences in reporting rates of ON after vaccines within the 3 periods. Kruskal-Wallis testing with Bonferroni-corrected post hoc analysis and multivariable binary logistic regression was used to determine significant case factors such as age, sex, concurrent multiple sclerosis (MS) and vaccine manufacturer in predicting a worse outcome defined as permanent disability, emergency room (ER) or doctor visits, and hospitalizations. RESULTS: A significant increase in the reporting rate of ON after COVID-19 vaccination compared with influenza vaccination and all other vaccinations (18.6 vs 0.2 vs 0.4 per 10 million, P < 0.0001) was observed. However, the reporting rate was within the incidence range of ON in the general population. Using self-controlled and case-centered analyses, there was a significant difference in the reporting rate of ON after COVID-19 vaccination between the risk period and control period ( P < 0.0001). Multivariable binary regression with adjustment for confounding variables demonstrated that only male sex was significantly associated with permanent disability. CONCLUSIONS: Some cases of ON may be temporally associated with the COVID-19 vaccines; however, there is no significant increase in the reporting rate compared with the incidence. Limitations of this study include those inherent to any passive surveillance system. Controlled studies are needed to establish a clear causal relationship.

Computational models for predicting liver toxicity in the deep learning era.

Drug-induced liver injury (DILI) is a severe adverse reaction caused by drugs and may result in acute liver failure and even death. Many efforts have centered on mitigating risks associated with potential DILI in humans. Among these, quantitative structure-activity relationship (QSAR) was proven to be a valuable tool for early-stage hepatotoxicity screening. Its advantages include no requirement for physical substances and rapid delivery of results. Deep learning (DL) made rapid advancements recently and has been used for developing QSAR models. This review discusses the use of DL in predicting DILI, focusing on the development of QSAR models employing extensive chemical structure datasets alongside their corresponding DILI outcomes. We undertake a comprehensive evaluation of various DL methods, comparing with those of traditional machine learning (ML) approaches, and explore the strengths and limitations of DL techniques regarding their interpretability, scalability, and generalization. Overall, our review underscores the potential of DL methodologies to enhance DILI prediction and provides insights into future avenues for developing predictive models to mitigate DILI risk in humans.