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Objective: Computational drug re-purposing has received a lot of attention in the past decade. However, methods developed to date focused on established compounds for which information on both, successfully treated patients and chemical and genomic impact, were known. Such information does not always exist for first-in-class drugs under development., Methods: To identify indications (diseases) for drugs under development we extended and tested several unsupervised computational methods that utilize Electronic Health Record (EHR) data., Results: We tested the methods on known drugs with multiple indications and show that a variant of matrix factorization leads to the best performance for first-in-line drugs improving upon prior methods that were developed for established drugs. The method also identifies novel predictions for key immunology and oncology drugs. Our results show that the performance of re-purposing methods differ greatly between oncology and inflammation/immunology. We hypothesize that the lower performance in oncology can be explained by the fact that many chemotherapies are not targeted therapies., Conclusion: Finding new indications for drugs is extremely valuable. Our results explore how to best use EHR data for finding new indications for first in class drugs drug using a phenotypical-similarity driven approach. Our methods can be integrated with others methods using multiple data modalities such as chemical, molecular, genetic data., Competing Interests: Declaration of competing interest All authors were full-time employees at Sanofi during the time of writing this article. The method referred to as “closeness to pathway” in the article is pending with the European Patent Office for a patent including the author Ramon Hernandez as an inventor. The authors of this article may also individually have ownership of Sanofi stotcks., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: Disease severity scores, or endpoints, are routinely measured during Randomized Controlled Trials (RCTs) to closely monitor the effect of treatment. In real-world clinical practice, although a larger set of patients is observed, the specific RCT endpoints are often not captured, which makes it hard to utilize real-world data (RWD) to evaluate drug efficacy in larger populations., Methods: To overcome this challenge, we developed an ensemble technique which learns proxy models of disease endpoints in RWD. Using a multi-stage learning framework applied to RCT data, we first identify features considered significant drivers of disease available within RWD. To create endpoint proxy models, we use Explainable Boosting Machines (EBMs) which allow for both end-user interpretability and modeling of non-linear relationships., Results: We demonstrate our approach on two diseases, rheumatoid arthritis (RA) and atopic dermatitis (AD). As we show, our combined feature selection and prediction method achieves good results for both disease areas, improving upon prior methods proposed for predictive disease severity scoring., Conclusion: Having disease severity over time for a patient is important to further disease understanding and management. Our results open the door to more use cases in the space of RA and AD such as treatment effect estimates or prognostic scoring on RWD. Our framework may be extended beyond RA and AD to other diseases where the severity score is not well measured in electronic health records., Competing Interests: Declaration of competing interest All authors are Sanofi employees and may hold shares and/or stock options in the company., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Reperfusion injuries after a period of cardiac ischemia are known to lead to pathological modifications or even death. Among the different therapeutic options proposed, adenosine, a small molecule with platelet anti-aggregate and anti-inflammatory properties, has shown encouraging results in clinical trials. However, its clinical use is severely limited because of its very short half-life in the bloodstream. To overcome this limitation, we have proposed a strategy to encapsulate adenosine in squalene-based nanoparticles (NPs), a biocompatible and biodegradable lipid. Thus, the aim of this study was to assess, whether squalene-based nanoparticles loaded with adenosine (SQAd NPs) were cardioprotective in a preclinical cardiac ischemia/reperfusion model. Obtained SQAd NPs were characterized in depth and further evaluated in vitro. The NPs were formulated with a size of about 90 nm and remained stable up to 14 days at both 4 °C and room temperature. Moreover, these NPs did not show any signs of toxicity, neither on HL-1, H9c2 cardiac cell lines, nor on human PBMC and, further retained their inhibitory platelet aggregation properties. In a mouse model with experimental cardiac ischemia-reperfusion, treatment with SQAd NPs showed a reduction of the area at risk, as well as of the infarct area, although not statistically significant. However, we noted a significant reduction of apoptotic cells on cardiac tissue from animals treated with the NPs. Further studies would be interesting to understand how and through which mechanisms these nanoparticles act on cardiac cells.

Leveraging external control data, especially real-world data (RWD), has drawn particular attention in recent years for facilitating oncology clinical development and regulatory decision-making. Medical regulators have published guidance on accelerating the use of RWD and external controls. However, few systematic discussions have been conducted on external controls in cancer drug submissions and regulatory feedback. This study aimed to identify European oncology drug approvals using external control data to demonstrate clinical efficacy. We included 18 eligible submissions employing 24 external controls and then discussed the use of external control, data sources, analysis methods, and regulators' feedback. The external controls have been actively submitted to the European Medical Agency (EMA) recently. We found that 17 % of the EMA-approved cancer drugs in 2016-2021 used external controls, among which 37 % of the cases leveraged RWD. However, nearly one-third of the external controls were not considered supportive evidence by EMA due to limitations regarding heterogeneous patient populations, missing outcome assessment in RWD, and inappropriate statistical analysis. This study highlighted that proper use of external controls requires a careful assessment of clinical settings, data availability, and statistical methodology. For better use of external controls in oncology clinical trials, we recommend: prospective study designs to avoid selection bias, sufficient baseline data to ensure the comparability of study populations, consistent endpoint measurements to enable outcome comparison, robust statistical methodology for comparative analysis, and collaborative efforts of sponsors and regulators to establish regulatory frameworks., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: XW receives Ph.D. funding from Sanofi and Association Nationale de la Recherche et de la Technologie (ANRT). FD, RH, and CL are employees of Sanofi. The other authors declare no conflict of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Uncontrolled inflammatory processes are at the root of numerous pathologies. Most recently, studies on confirmed COVID-19 cases have suggested that mortality might be due to virally induced hyperinflammation. Uncontrolled pro-inflammatory states are often driven by continuous positive feedback loops between pro-inflammatory signaling and oxidative stress, which cannot be resolved in a targeted manner. Here, we report on the development of multidrug nanoparticles for the mitigation of uncontrolled inflammation. The nanoparticles are made by conjugating squalene, a natural lipid, to adenosine, an endogenous immunomodulator, and then encapsulating α-tocopherol, as antioxidant. This resulted in high drug loading, biocompatible, multidrug nanoparticles. By exploiting the endothelial dysfunction at sites of acute inflammation, these multidrug nanoparticles delivered the therapeutic agents in a targeted manner, conferring survival advantage to treated animals in models of endotoxemia. Selectively delivering adenosine and antioxidants together could serve as a novel therapeutic approach for safe treatment of acute paradoxal inflammation., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Native low-density lipoproteins (LDL) naturally accumulate at atherosclerotic lesions and are thought to be among the main drivers of atherosclerosis progression. Numerous nanoparticular systems making use of recombinant lipoproteins have been developed for targeting atherosclerotic plaque. These innovative formulations often require complicated purification and synthesis procedures which limit their eventual translation to the clinics. Recently, squalenoylation has appeared as a simple and efficient technique for targeting agents to endogenous lipoproteins through a bioconjugation approach. In this study, we have developed a fluorescent squalene bioconjugate to evaluate the biodistribution of squalene-based nanoparticles in an ApoE -/- model of atherosclerosis. By accumulating in LDL endogenous nanoparticles, the squalene bioconjugation could serve as an efficient targeting platform for atherosclerosis. Indeed, in this proof of concept, we show that our squalene-rhodamine (SQRho) nanoparticles, could accumulate in the aortas of atherosclerotic animals. Histological evaluation confirmed the presence of atherosclerotic lesions and the co-localization of SQRho bioconjugates at the lesion sites., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

A large variety of nanoparticle-based delivery systems have become increasingly important for diagnostic and/or therapeutic applications. Yet, the numerous physical and chemical parameters that influence both the biological and colloidal properties of nanoparticles remain poorly understood. This complicates the ability to reliably produce and deliver well-defined nanocarriers which often leads to inconsistencies, conflicts in the published literature and, ultimately, poor translation to the clinics. A critical issue lies in the challenge of scaling-up nanomaterial synthesis and formulation from the lab to industrial scale while maintaining control over their diverse properties. Studying these phenomena early on in the development of a therapeutic agent often requires partnerships between the public and private sectors which are hard to establish. In this study, through the particular case of squalene-adenosine nanoparticles, we reported on the challenges encountered in the process of scaling-up nanomedicines synthesis. Here, squalene (the carrier) was functionalized and conjugated to adenosine (the active drug moiety) at an industrial scale in order to obtain large quantities of biocompatible and biodegradable nanoparticles. After assessing nanoparticle batch-to-batch consistency, we demonstrated that the presence of squalene analogs resulting from industrial scale-up may influence several features such as size, surface charge, protein adsorption, cytotoxicity and crystal structure. These analogs were isolated, characterized by multiple stage mass spectrometry, and their influence on nanoparticle properties further evaluated. We showed that slight variations in the chemical profile of the nanocarrier's constitutive material can have a tremendous impact on the reproducibility of nanoparticle properties. In a context where several generics of approved nanoformulated drugs are set to enter the market in the coming years, characterizing and solving these issues is an important step in the pharmaceutical development of nanomedicines., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

In vitro incubation of nanomaterials with plasma offer insights on biological interactions, but cannot fully explain the in vivo fate of nanomaterials. Here, we use a library of polymer nanoparticles to show how physicochemical characteristics influence blood circulation and early distribution. For particles with different diameters, surface hydrophilicity appears to mediate early clearance. Densities above a critical value of approximately 20 poly(ethylene glycol) chains (MW 5 kDa) per 100 nm 2 prolong circulation times, irrespective of size. In knockout mice, clearance mechanisms are identified for nanoparticles with low and high steric protection. Studies in animals deficient in the C3 protein showed that complement activation could not explain differences in the clearance of nanoparticles. In nanoparticles with low poly(ethylene glycol) coverage, adsorption of apolipoproteins can prolong circulation times. In parallel, the low-density-lipoprotein receptor plays a predominant role in the clearance of nanoparticles, irrespective of poly(ethylene glycol) density. These results further our understanding of nanopharmacology.Understanding the interaction between nanoparticles and biomolecules is crucial for improving current drug-delivery systems. Here, the authors shed light on the essential role of the surface and other physicochemical properties of a library of nanoparticles on their in vivo pharmacokinetics.