Dompé (Italy)

Over the past three decades, the pharmaceutical and agrochemical sectors have embarked on a transformative journey towards greener-by-design processes, firmly rooted in the principles of green chemistry. Building on this foundation, green engineering frameworks have expanded the focus beyond environmental concerns to encompass product quality, economic viability, and the evolving demands of modern healthcare. At the heart of this transformation is continuous and smart manufacturing due to its capacity to reduce raw material use, waste, and energy consumption. While attention has understandably centered on replacing or refining conventional batch operations, the breadth of progress is far wider. Advanced analytics and digitization, as exemplified by AI-driven modeling, are nurturing the rise of "smart factories" that autonomously optimize performance in real time. A prime illustration lies in the purification of fine chemicals, where real-time analytics and advanced process control slash solvent requirements, an acute pollution hotspot, while ensuring consistent product quality. Meanwhile, 3D printing has introduced a genuinely disruptive dimension, challenging traditional notions of scale and location through on-demand, flexible production. In this piece, we explore how these converging technological frontiers lay the groundwork for the patient-centered, eco-conscious pharmaceutical and agrochemical facilities of the future.

In many acute and chronic inflammatory disorders, recruitment of neutrophils plays a critical role in preventing disease severity and ensuring survival. On the other hand, neutrophil accumulation during inflammation can also favor disease progression in conditions such as autoimmune disorders and cancer or during ischemia-reperfusion injury. Therefore, blocking neutrophil influx has been considered an interesting therapeutic concept in diseases with overwhelming neutrophil responses. Using several in vivo and in vitro approaches we investigated the mode of action of ladarixin, a dual non-competitive inhibitor of chemokine receptors CXCR1 and CXCR2, in blocking neutrophil recruitment during inflammation. Examining neutrophil recruitment both in vivo in the mouse cremaster muscle via intravital microscopy and in vitro via flow chamber assays, we investigated the biological significance of the functional selectivity of the allosteric inhibitor, showing that ladarixin blocks neutrophil extravasation selectively at the level of vascular basement membrane penetration, a prerequisite for neutrophils to reach the inflamed tissue, without impairing rolling and adhesion to the inflamed endothelium. Mechanistically, ladarixin abolished neutrophil elastase translocation to the neutrophil surface, indispensable for neutrophil vascular basement membrane penetration and extracellular matrix degradation, in a selective and specific CXCR1/2-dependent fashion leading to reduced neutrophil elastase surface activity in mouse and primary human neutrophils. In brief, the allosteric CXCR1/2 inhibitor ladarixin effectively blocks neutrophil recruitment at the level of neutrophil extravasation without affecting firm adhesion. Clinically, this mode of action has interesting therapeutic potential to prevent neutrophil extravasation in inflammatory diseases including inflammatory bowel disease, psoriasis and other neutrophil-driven disorders.

Abstract Approximately 40% of marketed drugs exhibit suboptimal pharmacokinetic profiles. Co‐crystallization, where pairs of molecules form a multicomponent crystal, constitutes a promising strategy to enhance physicochemical properties without compromising the pharmacological activity. However, finding promising co‐crystal pairs is resource‐intensive due to the large and diverse range of possible molecular combinations. We present DeepCocrystal, a novel deep learning approach designed to predict co‐crystal formation by processing the “chemical language” from a supramolecular vantage point. Rigorous validation of DeepCocrystal showed a balanced accuracy of 78% in realistic scenarios, outperforming existing models. Explainable AI approaches uncovered the decision‐making process of DeepCocrystal, showing its capability to learn chemically relevant aspects of the “supramolecular language” that match experimental co‐crystallization patterns. By leveraging properties of molecular string representations, DeepCocrystal can also estimate the uncertainty of its predictions. We harnessed this capability in a challenging prospective study and successfully discovered two novel co‐crystals of diflunisal, an anti‐inflammatory drug. This study underscores the potential of deep learning—and in particular of chemical language processing—to accelerate co‐crystallization and ultimately drug development, in both academic and industrial contexts. DeepCocrystal is available as an easy‐to‐use web application at https://deepcocrystal.streamlit.app/.

Group Equivariant Non-Expansive Operators (GENEOs) have emerged as mathematical tools for constructing networks for Machine Learning and Artificial Intelligence. Recent findings suggest that such models can be inserted within the domain of eXplainable Artificial Intelligence (XAI) due to their inherent interpretability. In this study, we aim to verify this claim with respect to GENEOnet, a GENEO network developed for an application in computational biochemistry by employing various statistical analyses and experiments. Such experiments first allow us to perform a sensitivity analysis on GENEOnet's parameters to test their significance. Subsequently, we show that GENEOnet exhibits a significantly higher proportion of equivariance compared to other methods. Lastly, we demonstrate that GENEOnet is on average robust to perturbations arising from molecular dynamics. These results collectively serve as proof of the explainability, trustworthiness, and robustness of GENEOnet and confirm the beneficial use of GENEOs in the context of Trustworthy Artificial Intelligence.

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

BackgroundThe C-terminal-binding protein 1/brefeldin A ADP-ribosylation substrate (CtBP1/BARS) acts both as an oncogenic transcriptional co-repressor and as a fission inducing protein required for membrane trafficking and Golgi complex partitioning during mitosis, hence for mitotic entry. CtBP1/BARS overexpression, in multiple cancers, has pro-tumorigenic functions regulating gene networks associated with “cancer hallmarks” and malignant behavior including: increased cell survival, proliferation, migration/invasion, epithelial-mesenchymal transition (EMT).Structurally, CtBP1/BARS belongs to the hydroxyacid-dehydrogenase family and possesses a NAD(H)-binding Rossmann fold, which, depending on ligands bound, controls the oligomerization of CtBP1/BARS and, in turn, its cellular functions.Here, we proposed to target the CtBP1/BARS Rossmann fold with small molecules as selective inhibitors of mitotic entry and pro-tumoral transcriptional activities.MethodsStructured-based screening of drug databases at different development stages was applied to discover novel ligands targeting the Rossmann fold. Among these identified ligands, N-(3,4-dichlorophenyl)-4-{[(4-nitrophenyl)carbamoyl]amino}benzenesulfonamide, called Comp.11, was selected for further analysis. Fluorescence spectroscopy, isothermal calorimetry, computational modelling and site-directed mutagenesis were employed to define the binding of Comp.11 to the Rossmann fold. Effects of Comp.11 on the oligomerization state, protein partners binding and pro-tumoral activities were evaluated by size-exclusion chromatography, pull-down, membrane transport and mitotic entry assays, Flow cytometry, quantitative real-time PCR, motility/invasion, and colony assays in A375MM and B16F10 melanoma cell lines. Effects of Comp.11 on tumor growth in vivo were analyzed in mouse tumor model.ResultsWe identify Comp.11 as a new, potent and selective inhibitor of CtBP1/BARS (but not CtBP2). Comp.11 directly binds to the CtBP1/BARS Rossmann fold affecting the oligomerization state of the protein (unlike other known CtBPs inhibitors), which, in turn, hinders interactions with relevant partners, resulting in the inhibition of both CtBP1/BARS cellular functions: i) membrane fission, with block of mitotic entry and cellular secretion; and ii) transcriptional pro-tumoral effects with significantly hampered proliferation, EMT, migration/invasion, and colony-forming capabilities. The combination of these effects impairs melanoma tumor growth in mouse models. ConclusionsThis study identifies a potent and selective inhibitor of CtBP1/BARS active in cellular and melanoma animal models revealing new opportunities to study the role of CtBP1/BARS in tumor biology and to develop novel melanoma treatments.

Finding a novel drug is a very long and complex procedure. Using computer simulations, it is possible to accelerate the preliminary phases by performing a virtual screening that filters a large set of drug candidates to a manageable number. This paper presents the implementations and comparative analysis of two GPU-optimized implementations of a virtual screening algorithm targeting novel GPU architectures. This work focuses on the analysis of parallel computation patterns and their mapping onto the target architecture. The first method adopts a traditional approach that spreads the computation for a single molecule across the entire GPU. The second uses a novel batched approach that exploits the parallel architecture of the GPU to evaluate more molecules in parallel. Experimental results showed a different behavior depending on the size of the database to be screened, either reaching a performance plateau sooner or having a more extended initial transient period to achieve a higher throughput (up to 5x), which is more suitable for extreme-scale virtual screening campaigns.

In this review, we discuss the progress in the investigation of macromolecular crystals obtained through the use of atomic force microscopy (AFM), a powerful tool for imaging surfaces and specimens at high resolution. AFM enables the visualization of soft samples at the nanoscale and can provide precise visual details over a wide size range, from the molecular level up to hundreds of micrometers. The nonperturbative nature, the ability to scan in a liquid environment, and the lack of need for freezing, fixing, or staining make AFM a well-suited tool for studying fragile samples such as macromolecular crystals. Starting from the first morphological investigations revealing the surface morphology of protein crystals, this review discusses the achievements of AFM in understanding the crystal growth processes, both at the micro- and nanoscale. The capability of AFM to investigate the sample structure at the single molecular level is analyzed considering in-depth the structure of S-layers. Lastly, high-speed atomic force microscopy (HS-AFM) is discussed as the evolution to overcome the limitations of low imaging speed, allowing for the observation of molecular dynamics and weakly adsorbed, diffusing molecules. HS-AFM has provided intuitive views and directly visualized phenomena that were previously described indirectly, answering questions that were challenging to address using other characterization methods.

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of a community effort, the “Billion molecules against Covid-19 challenge”, to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 potentially active molecules, which were subsequently ranked to find ‘consensus compounds’. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (Nsp12 domain), and (alpha) spike protein S. Overall, 27 potential inhibitors were experimentally confirmed by binding-, cleavage-, and/or viral suppression assays and are presented here. All results are freely available and can be taken further downstream without IP restrictions. Overall, we show the effectiveness of computational techniques, community efforts, and communication across research fields (i.e., protein expression and crystallography, in silico modeling, synthesis and biological assays) to accelerate the early phases of drug discovery.