Drug-Induced Liver Injury in a Patient with Nonsmall Cell Lung Cancer after the Self-Administration of Fenbendazole Based on Social Media Information

Severe Drug-Induced Liver Injury Due to Self-administration of the Veterinary Anthelmintic Medication, Fenbendazole

Fenbendazole is an anthelmintic agent approved for veterinary applications. Even though it is not approved by the US Food and Drug Administration for human use, such use appears to be increasing due to the popularization of fenbendazole's potential anticancer effects by social media. We describe the first case of histologically confirmed severe drug-induced liver injury, hepatocellular pattern, associated with the self-administration of fenbendazole in a 67-year-old woman who presented with 2 weeks of jaundice. Liver function tests normalized in 3 months after the cessation of fenbendazole.

Fenbendazole Exhibits Differential Anticancer Effects In Vitro and In Vivo in Models of Mouse Lymphoma

Fenbendazole (FBZ) has been safely used as an antiparasitic agent in animals for decades, and the anticancer effects of FBZ have been studied through various mechanisms. However, there is a lack of in vivo studies that include lymphoma. Therefore, this study examined the effects of FBZ on EL-4 cells and a mouse T lymphoma model. FBZ induced G2/M phase arrest in EL-4 cells, resulting in cell death and decreased metabolic activity. However, FBZ had no anticancer effects on an EL-4 mouse lymphoma model in vivo, as evident by rapid weight loss and tumor growth comparable to the control. The FBZ-treated EL-4 cells expressed higher levels of PD-L1 and CD86, which are associated with T cell immunity in the tumor microenvironment (TME), than the controls. Furthermore, the hematoxylin and eosin staining of the FBZ-treated tumor tissues showed a starry sky pattern, which is seen in actively proliferating cancer tissues, and an immunohistochemical analysis revealed a high percentage of immunosuppressive M2 macrophages. These changes in the immune activity in the TME contradict the results of the in vitro experiments, and further studies are needed to determine the detailed mechanisms by which FBZ induces these responses.

Preclinical Characterization and Phase I Trial Results of INBRX-109, A Third-Generation, Recombinant, Humanized, Death Receptor 5 Agonist Antibody, in Chondrosarcoma

PURPOSE

Patients with unresectable/metastatic chondrosarcoma have poor prognoses; conventional chondrosarcoma is associated with a median progression-free survival (PFS) of <4 months after first-line chemotherapy. No standard targeted therapies are available. We present the preclinical characterization of INBRX-109, a third-generation death receptor 5 (DR5) agonist, and clinical findings from a phase I trial of INBRX-109 in unresectable/metastatic chondrosarcoma (NCT03715933).

PATIENTS AND METHODS

INBRX-109 was first characterized preclinically as a DR5 agonist, with binding specificity and hepatotoxicity evaluated in vitro and antitumor activity evaluated both in vitro and in vivo. INBRX-109 (3 mg/kg every 3 weeks) was then evaluated in a phase I study of solid tumors, which included a cohort with any subtype of chondrosarcoma and a cohort with IDH1/IDH2-mutant conventional chondrosarcoma. The primary endpoint was safety. Efficacy was an exploratory endpoint, with measures including objective response, disease control rate, and PFS.

RESULTS

In preclinical studies, INBRX-109 led to antitumor activity in vitro and in patient-derived xenograft models, with minimal hepatotoxicity. In the phase I study, INBRX-109 was well tolerated and demonstrated antitumor activity in unresectable/metastatic chondrosarcoma. INBRX-109 led to a disease control rate of 87.1% [27/31; durable clinical benefit, 40.7% (11/27)], including two partial responses, and median PFS of 7.6 months. Most treatment-related adverse events, including liver-related events, were low grade (grade ≥3 events in chondrosarcoma cohorts, 5.7%).

CONCLUSIONS

INBRX-109 demonstrated encouraging antitumor activity with a favorable safety profile in patients with unresectable/metastatic chondrosarcoma. A randomized, placebo-controlled, phase II trial (ChonDRAgon, NCT04950075) will further evaluate INBRX-109 in conventional chondrosarcoma.

Exceptional Repositioning of Dog Dewormer: Fenbendazole Fever

Fenbendazole (FZ) is a benzimidazole carbamate drug with broad-spectrum antiparasitic activity in humans and animals. The mechanism of action of FZ is associated with microtubular polymerization inhibition and glucose uptake blockade resulting in reduced glycogen stores and decreased ATP formation in the adult stages of susceptible parasites. A completely cured case of lung cancer became known globally and greatly influenced the cancer community in South Korea. Desperate Korean patients with cancer began self-administering FZ without their physician’s knowledge, which interfered with the outcome of the cancer treatment planned by their oncologists. On the basis of presented evidence, this review provides valuable information from PubMed, Naver, Google Scholar, and Social Network Services (SNS) on the effects of FZ in a broad range of preclinical studies on cancer. In addition, we suggest investigating the self-administration of products, including supplements, herbs, or bioactive compounds, by patients to circumvent waiting for long and costly FZ clinical trials.

Anti-cancer effects of fenbendazole on 5-fluorouracil-resistant colorectal cancer cells

Benzimidazole anthelmintic agents have been recently repurposed to overcome cancers resistant to conventional therapies. To evaluate the anti-cancer effects of benzimidazole on resistant cells, various cell death pathways were investigated in 5-fluorouracil-resistant colorectal cancer cells. The viability of wild-type and 5-fluorouracil-resistant SNU-C5 colorectal cancer cells was assayed, followed by Western blotting. Flow cytometry assays for cell death and cell cycle was also performed to analyze the anti-cancer effects of benzimidazole. When compared with albendazole, fenbendazole showed higher susceptibility to 5-fluorouracil-resistant SNU-C5 cells and was used in subsequent experiments. Flow cytometry revealed that fenbendazole significantly induces apoptosis as well as cell cycle arrest at G2/M phase on both cells. When compared with wild-type SNU-C5 cells, 5-fluorouracil-resistant SNU-C5 cells showed reduced autophagy, increased ferroptosis and ferroptosis-augmented apoptosis, and less activation of caspase-8 and p53. These results suggest that fenbendazole may be a potential alternative treatment in 5-fluorouracil-resistant cancer cells, and the anticancer activity of fenbendazole does not require p53 in 5-fluorouracil-resistant SNU-C5 cells.

An Update on the Biologic Effects of Fenbendazole

Fenbendazole remains the drug of choice to treat pinworm infection in laboratory rodents. When fenbendazole was last reviewed (15 y ago), the literature supported the drug's lack of toxic effects at therapeutic levels, yet various demonstrated physiologic effects have the potential to alter research outcomes. Although more recent reports continue to reflect an overall discordancy of results, several studies support the premise that fenbendazole affects the bone marrow and the immune system. No effects on reproduction were reported in an extensive study that assessed common treatment protocols in mice, and food intake was unchanged in rats. Behavioral studies are sparse, with only a single report of a subtle change in a rotarod performance in mice. Notably, unexpected results in tumor models during facility treatment with fenbendazole have prompted preclinical and clinical studies of the potential roles of benzimidazoles in cancer.

Pretrained Transformer Language Models Versus Pretrained Word Embeddings for the Detection of Accurate Health Information on Arabic Social Media: Comparative Study

Background

In recent years, social media has become a major channel for health-related information in Saudi Arabia. Prior health informatics studies have suggested that a large proportion of health-related posts on social media are inaccurate. Given the subject matter and the scale of dissemination of such information, it is important to be able to automatically discriminate between accurate and inaccurate health-related posts in Arabic.

Objective

The first aim of this study is to generate a data set of generic health-related tweets in Arabic, labeled as either accurate or inaccurate health information. The second aim is to leverage this data set to train a state-of-the-art deep learning model for detecting the accuracy of health-related tweets in Arabic. In particular, this study aims to train and compare the performance of multiple deep learning models that use pretrained word embeddings and transformer language models.

Methods

We used 900 health-related tweets from a previously published data set extracted between July 15, 2019, and August 31, 2019. Furthermore, we applied a pretrained model to extract an additional 900 health-related tweets from a second data set collected specifically for this study between March 1, 2019, and April 15, 2019. The 1800 tweets were labeled by 2 physicians as accurate, inaccurate, or unsure. The physicians agreed on 43.3% (779/1800) of tweets, which were thus labeled as accurate or inaccurate. A total of 9 variations of the pretrained transformer language models were then trained and validated on 79.9% (623/779 tweets) of the data set and tested on 20% (156/779 tweets) of the data set. For comparison, we also trained a bidirectional long short-term memory model with 7 different pretrained word embeddings as the input layer on the same data set. The models were compared in terms of their accuracy, precision, recall, F₁ score, and macroaverage of the F₁ score.

Results

We constructed a data set of labeled tweets, 38% (296/779) of which were labeled as inaccurate health information, and 62% (483/779) of which were labeled as accurate health information. We suggest that this was highly efficacious as we did not include any tweets in which the physician annotators were unsure or in disagreement. Among the investigated deep learning models, the Transformer-based Model for Arabic Language Understanding version 0.2 (AraBERTv0.2)-large model was the most accurate, with an F₁ score of 87%, followed by AraBERT version 2–large and AraBERTv0.2-base.

Conclusions

Our results indicate that the pretrained language model AraBERTv0.2 is the best model for classifying tweets as carrying either inaccurate or accurate health information. Future studies should consider applying ensemble learning to combine the best models as it may produce better results.