Identification of novel readouts to assess anti-fibrotic efficacy of new compounds in a bleomycin-induced pulmonary fibrosis mouse model

Idiopathic pulmonary fibrosis (IPF) is a fatal interstitial lung disease with a poor prognosis and very limited therapeutic options. To date, Pirfenidone and Nintedanib are the only two therapies approved for IPF worldwide. However, these drugs can slow-down lung function decline without really stopping or reverting the fibrotic process, and in addition their use is associated with a series of side effects. The incomplete understanding of the disease and the limitations of current treatments make IPF a disease with a high medical need requiring novel treatment approaches. For these reasons, the new drugs coming from the research and development pipelines will be crucial to get new treatments for patients. Despite widely used, the current animal models of IPF need to be improved in order to be as much predictive as possible in identifying new promising treatments for pulmonary fibrosis. One of the most challenging aspects of drug discovery for IPF is the identification of new therapies that can be translated effectively to the clinic, implying that very few compounds that have shown efficacy in animal models have been successful in human clinical trials and concluding that most of the preclinical models are poorly predictive and scarcely resembling the human disease. Currently the majority of new drugs investigated in preclinical models of IPF are dosed using a prophylactic dosing regimen, whereas patients are almost always treated after the fibrosis is well established. Moreover, the most popular endpoints examined in preclinical models of IPF are histological scoring and lung collagen content; however, lung function tests are more commonly used as primary endpoints in IPF patients. In this scenario, considering the high unmet medical need and some limits that the preclinical research has to face, the main goal of this PhD project was to generate a robust and reliable preclinical model of pulmonary fibrosis, introducing novel readouts, suitable to select and to identify new pharmacological treatments for IPF with an higher translational potential. The approach pursued by this study could be very impactful to identify new potential treatments for IPF. To achieve the goal of this PhD project, performed in collaboration with Chiesi Farmaceutici, we (1) reproduced the most described preclinical model for IPF, the bleomycin (BLM)-induced pulmonary fibrosis mouse model by intratracheal (IT) administration, and we analyzed its main limitations; (2) looking at the clinic, we optimized the BLM model with the introduction of clinically more relevant parameters (i.e., lung function tests, lung imaging, oximetry (Sp02), and fibrotic biomarkers) through a new BLM oropharyngeal (OA) protocol and finally, (3) we explored the added value of these more relevant readouts by investigating the efficacy of Nintedanib, which was tested under therapeutic regimen. The characterization of the BLM IT model proved to be useful in better understanding the development of BLM-induced lung fibrosis and allowed to define the therapeutic protocol to test the anti-fibrotic efficacy of Nintedanib in the model; however, it highlighted several limitations such as a patchy distribution of fibrotic lesions and poor sensitivity to pharmacological treatment using the two traditional preclinical readouts, histology and hydoxyproline (Hyp) lung content. Those limitations were overcome by the use of the OA administration of BLM which led to a more homogeneous fibrosis throughout the lung lobes and by the introduction of more clinically relevant endpoints such as micro-computer tomography (CT) imaging and lung function measurements that are the same tests used to diagnose and monitor patients with IPF, as well as of emerging biomarkers currently under evaluation in the clinical setting, with the final aim to create a link between the preclinical model and the clinical practice. All these new readouts showed the same profile over time observed with histology in terms of development of fibrotic disease, and Nintedanib was able to significantly modulate them, confirming their relevance for monitoring lung fibrosis as well as the efficacy of new treatments. Among them, the measurement of lung function, in particular the forced vital capacity (FVC), demonstrated to be the most sensitive readout to assess the compounds efficacy and was selected also in our preclinical studies as the primary endpoint as for clinical trials, thus creating an important link between the preclinical model and the clinical setting. In addition, we also worked to refine the histological analysis which still remains an important complementary evaluation to be coupled to the functional readouts. Currently the common histological analysis utilized in preclinical models of lung fibrosis is represented by the Ashcroft scoring system, which revealed some disadvantages such as a time-consuming process, operator-dependent results, limited sensitivity and, most critical, inability to get a direct link to clinics. Therefore, we introduced an automated image analysis by using an artificial intelligence (AI) approach, which improved this analysis recognizing histological features with more accuracy and consistency, reducing significantly the time of the analysis and making the evaluation independent from the operator. In summary, this project demonstrated that in the mouse BLM-induced lung fibrosis model it has been possible to explore the same clinically relevant parameters used in IPF patients; in particular lung function tests such as FVC, that for its high translational value together with the high sensitivity to assess the efficacy of the compounds has been chosen as the primary endpoint to support the selection of novel treatments within our internal drug discovery IPF projects. Furthermore, the introduction of these different readouts, that all go to the same direction, has from one side increased the robustness of the model and from the other side has allowed to bring this preclinical model to a level of complexity that mirrors the one observed in human IPF. Overall, this PhD work has enhanced the translational value of the data obtained with the mouse BLM model increasing the chance of selecting promising compounds to advance to clinical trials and has concretely led to significant benefits to drug discovery process in the IPF research, improving the quality and the reliability of the search of novel anti-fibrotic drugs.