In Silico Design and Selection of New Tetrahydroisoquinoline-Based CD44 Antagonist Candidates

Establishing a Pharmacoinformatics Repository of Approved Medicines: A Database to Support Drug Product Development

Advanced predictive modeling approaches have harnessed data to fuel important innovations at all stages of drug development. However, the need for a machine-readable drug product library which consolidates many aspects of formulation design and performance remains largely unmet. This study presents a scripted, reproducible approach to database curation and explores its potential to streamline oral medicine development. The Product Information files for all centrally authorized drug products containing a small molecule active ingredient were retrieved programmatically from the European Medicines Agency Web site. Text processing isolated relevant information, including the maximum clinical dose, dosage form, route of administration, excipients, and pharmacokinetic performance. Chemical and bioactivity data were integrated through automated linking to external curated databases. The capability of this database to inform oral medicine development was assessed in the context of drug-likeness evaluation, excipient selection, and prediction of oral fraction absorbed. Existing filters of drug-likeness, such as the Rule of Five, were found to poorly capture the chemical space of marketed oral drug products. Association rule learning identified frequent patterns in tablet formulation compositions that can be used to establish excipient combinations that have seen clinical success. Binary prediction models of oral fraction absorbed constructed exclusively from regulatory data achieved acceptable performance (balanced accuracytest = 0.725), demonstrating its modelability and potential for use during early stage molecule prioritization tasks. This study illustrates the impact of highly linked drug product data in accelerating clinical translation and underlines the ongoing need for accuracy and completeness of data reported in the regulatory datasphere.

The IMS Library: from IN-Stock to Virtual

A chemical library is a key element in the early stages of pharmaceutical research. Its design encompasses various factors, such as diversity, size, ease of synthesis, aimed at increasing the likelihood of success in drug discovery. This article explores the collaborative efforts of computational and synthetic chemists in tailoring chemical libraries for cost-effective and resource-efficient use, particularly in the context of academic research projects. It proposes chemoinformatics methodologies that address two pivotal questions: first, crafting a diverse panel of under 1000 compounds from an existing pool through synthetic efforts, leveraging the expertise of organic chemists; and second, expanding pharmacophoric diversity within this panel by creating a highly accessible virtual chemical library. Chemoinformatics tools were developed to analyse initial panel of about 10,000 compounds into two tailored libraries: eIMS and vIMS. The eIMS Library comprises 578 diverse in-stock compounds ready for screening. Its virtual counterpart, vIMS, features novel compounds guided by chemists, ensuring synthetic accessibility. vIMS offers a broader array of binding motifs and improved drug-like characteristics achieved through the addition of diverse functional groups to eIMS scaffolds followed by filtering of reactive or unusual structures. The uniqueness of vIMS is emphasized through a comparison with commercial suppliers' virtual chemical space.

Forskolin improves experimental autoimmune encephalomyelitis in mice probably by inhibiting the calcium and the IL-17-STEAP4 signaling pathway

Multiple sclerosis (MS) is a chronic autoimmune disease in the central nervous system. Forskolin (FSK) is a plant-derived diterpene with excellent immunomodulatory properties and has not been systematically reported for treating MS. This study investigated the therapeutic effects of FSK on cellular and animal MS models and preliminarily explored related mechanisms. The results showed that FSK suppressed the inflammatory response, reduced the expression of STEAP4, and relieved iron deposition in BV-2 cells pretreated by LPS at the cellular level. Meanwhile, at the animal level, FSK treatment halted the progression of experimental autoimmune encephalomyelitis (EAE), alleviated the damage at the lesion sites, reduced the concentration of proinflammatory factors in peripheral blood, and inhibited the immune response of peripheral immune organs in EAE mice. Besides, FSK treatment decreased the expression of STEAP4 in the spinal cord and effectively restored the iron balance in the brain, spinal cord, and serum of EAE mice. Further investigation showed that FSK can reduce IL-17 expression, prevent the differentiation of TH17 cells, and inhibit the calcium signaling pathway. Thus, these results demonstrate that FSK may have the potential to treat MS clinically.

Graph Attention Network based mapping of knowledge relations between chemical spaces of Nuclear factor kappa B and Centella asiatica

The confounding nature of the innate immunity target Nuclear Factor kappa B (NF-κB) and its interaction with Centella asiatica (CA) molecules necessitate the intervention of advanced technologies, such as deep learning methods. The integration of chemical space concepts with deep learning technologies is a new way of knowledge mapping used to explore drug-target interactions, especially in molecular libraries derived from traditional medicine based molecular sources. The current constraint of virtual screening for mechanistic target hunting is the use of a binary classification model that includes active and inactive molecules from in vitro experiments to explore drug-target interaction. This study aims to explore the regulatory nature of the molecules from the inhibition and activation of the NF-κB bioassay data set and map this information for a knowledge-based analysis against the molecules of CA, a low-growing tropical plant. This finding has led to a new direction in the field, transitioning from the conventional active-inactive framework to a more comprehensive active-inactive-regulatory model. This approach can be thoroughly explored by leveraging a graph-based deep learning system. The study presents an innovative approach using a Graph Attention Network (GAT) to rank CA molecules in chemical space based on their similarity with NF-κB bioassay molecules, enabling the efficient analysis of complex relationships between molecules and their regulatory function. Graph Attention Network (GAT) overcomes the limitations of traditional deep learning models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in handling non-Euclidean graph data and allows for a more precise understanding of similarity ranking by utilizing molecular graphs and attention behavior. By measuring similarity and arranging a matrix of similarity ranking based on GAT, deep neural ranking-based algorithms confirmed the regulatory behaviour of an innate immunity target NF-κB with the support of underlying inverse mapping in the surjective chemical spaces of NF-κB bioassays and CA molecular spaces. Overall, the study introduces new techniques for exploring the regulatory behaviour of complex targets like NF-κB. We then used t-SNE for clustering in chemical space and scaffold hunting for scaffold property analysis and identified nine CA molecules that exhibit regulatory behavior of NF-κB target and are recommended for further investigation.

Investigating the Lack of Translation from Cruzain Inhibition to Trypanosoma cruzi Activity with Machine Learning and Chemical Space Analyses

Chagas disease is a neglected tropical disease caused by the protozoa Trypanosoma cruzi. Cruzain, its main cysteine protease, is commonly targeted in drug discovery efforts to find new treatments for this disease. Even though the essentiality of this enzyme for the parasite has been established, many cruzain inhibitors fail as trypanocidal agents. This lack of translation from biochemical to biological assays can involve several factors, including suboptimal physicochemical properties. In this work, we aim to rationalize this phenomenon through chemical space analyses of calculated molecular descriptors. These include statistical tests, visualization of projections, scaffold analysis, and creation of machine learning models coupled with interpretability methods. Our results demonstrate a significant difference between the chemical spaces of cruzain and T. cruzi inhibitors, with compounds with more hydrogen bond donors and rotatable bonds being more likely to be good cruzain inhibitors, but less likely to be active on T. cruzi. In addition, cruzain inhibitors seem to occupy specific regions of the chemical space that cannot be easily correlated with T. cruzi activity, which means that using predictive modeling to determine whether cruzain inhibitors will be trypanocidal is not a straightforward task. We believe that the conclusions from this work might be of interest for future projects that aim to develop novel trypanocidal compounds.

Salinomycin mediated therapeutic targeting of circulating stem like cell population in oral cancer

CD44+ circulating tumor stem cells (CTSCs) have been significantly associated with aggressiveness, resistance and poor prognosis of oral cancer patients. Thus, targeted elimination of these CTSCs could be a new conceptual framework for enhancing the therapeutic outcome of patients. Docking of potential investigational molecules and simulation results identified Salinomycin as a potential lead compound that could effectively inhibit CD44 receptor. To assess the cytotoxic effect, immuno-magnetically sorted circulatory CD44+ cells were subjected to increasing concentrations of 5FU, Cisplatin and Salinomycin. Salinomycin demonstrated significant cytotoxic effect towards the CD44+ subpopulation in a dose and time dependent manner. Further the effect of these compounds was investigated on apoptosis, cell cycle, signaling pathways and gene expression profiles using Muse^TM flow cytometer and Real-Time PCR. It was observed that mRNA expression patterns of CD44v6, Nanog, AKT1, CDKN2A and β-catenin of Salinomycin treated CD44+ cells. Moreover, Salinomycin significantly induced programmed cell death by inducing G2/M cell cycle arrest and inhibiting MAPK/PI3K pathways in this chemo-resistant population. Thus, this study demonstrated the potential of Salinomycin to target the chemo-resistant circulating CD44 population by attenuating its proliferation and survival.Communicated by Ramaswamy H. Sarma.