Fibroblast growth factor 1(Venturis Therapeutics)

Non-alcoholic fatty liver disease (NAFLD) has become a prevalent cause of chronic liver disease and ranks third among the causes of transplantation. In the United States alone, annual medical costs are approximately 100 billion dollars. Unfortunately, there is no Federal Drug Administration (FDA)-approved medication for its treatment. However, various clinical trials are investigating several therapeutic classes that could potentially treat NAFLD. It is valuable to have a compilation of the data available on their efficacy.

To assess the efficacy of cyclophilin inhibitors, fibroblast growth factor 21 analogs (FGF21), and dual and pan peroxisome proliferator-activated receptor (PPAR) agonists for treating NAFLD.

A comprehensive literature search using keywords including cyclophilin inhibitor, FGF agonist, pan-PPAR agonists, dual-PPAR agonist, NAFLD, non-alcoholic steatohepatitis, and fatty liver was conducted on October 29, 2022, in PubMed, EMBASE, Cochrane Library, Scopus and Web of Science. Animal and human research, case reports, and published articles in English from all countries with patients aged 18 and above were included. Only articles with a National Institutes of Health (NIH) Quality Assessment score of five or higher out of eight points were included. Articles that were narrative or systematic reviews, abstracts, not in English, focused on patients under 18 years old, did not measure outcomes of interest, were inaccessible, or had a low NIH Quality Assessment score were excluded. Each article was screened by two independent researchers evaluating relevance and quality. Resources were scored based on the NIH Quality Assessment Score; then, pertinent data was extracted in a spreadsheet and descriptively analyzed.

Of the 681 records screened, 29 met the necessary criteria and were included in this review. These records included 12 human studies and 17 animal studies. Specifically, there were four studies on cyclophilin inhibitors, four on FGF agonists/analogs, eleven on pan-PPAR agonists, and ten on dual-PPAR agonists. Different investigational products were assessed: The most common cyclophilin inhibitor was NV556; FGF agonists and analogs was Efruxifermin; pan-PPAR agonists was Lanifibranor; and dual-PPAR agonists was Saroglitazar. All classes were found to be statistically efficacious for the treatment of NAFLD, with animal studies demonstrating improvement in steatosis and/or fibrosis on biopsy and human studies evidencing improvement in different metabolic parameters and/or steatosis and fibrosis on FibroScan (P < 0.05).

The data analyzed in this review showed clinically significant improvement in individual histological features of NAFLD in both animal and human trials for all four classes, as well as good safety profiles (P < 0.05). We believe this compilation of information will have positive clinical implications in obtaining an FDA-approved therapy for NAFLD.

Fibroblast growth factor receptors (FGFRs) are a family of cell surface receptors that bind to fibroblast growth factor (FGF) and mediate various cellular functions (translocating proteins, tissue repair, cell proliferation, development, and differentiation) through complex signaling pathways. The FGFR1 growth receptor is essential in the pathogenesis of numerous malignancies, including but not limited to breast cancer, bladder cancer, hepatocellular carcinoma (HCC), and cholangiocarcinoma. The higher levels of FGFR1 expression on the surface of cancer cells cause overly active signaling, which leads to rapid cell proliferation, resulting in a high spread of cancer cells. The kinases that FGFR1 activates migrate across the cell nucleus, activating genes and kinase proteins necessary for the growth and survival of cancerous cells. Therefore, FGFR1 targeting shows therapeutic promise in some diseases, including cancer. Inhibitors of FGFR1s are being developed and studied for their potential to block aberrant FGFR1 signaling and inhibit cancer growth. Since the discovery of new FGFR1 inhibitors in the laboratory is difficult, expensive, time-consuming, and labor-intensive, only a small number of FGFR1 inhibitors have been approved by the FDA for use in the treatment of cancer. To accelerate drug discovery by efficiently exploring the vast chemical space, and identifying potential candidates with higher accuracy and reduced cost, we developed artificial intelligence (AI)-based prediction models for FGFR1 inhibitors using a dataset of 2356 chemical compounds. Four machine learning (ML) algorithms (SVM, RF, k-NN, and ANN) were used to train different prediction models based on molecular descriptors (1D and 2D, with and without molecular fingerprints). Among all trained models, the random forest (RF)-based prediction model achieved the highest accuracy on the training (98.9%), test (89.8%), and external test (90.3%) datasets. The developed inhibitor prediction model (FGFR1Pred) provides a valuable tool for identifying potential FGFR1 inhibitors, expediting the drug discovery process and ultimately facilitating the development of new therapeutics. The model is made available at https://github.com/PGlab-NIPER/FGFR1Pred.git.