Drug repurposing in silico screening platforms

Identifying Substructures That Facilitate Compounds to Penetrate the Blood-Brain Barrier via Passive Transport Using Machine Learning Explainer Models

The local interpretable model-agnostic explanation (LIME) method was used to interpret two machine learning models of compounds penetrating the blood-brain barrier. The classification models, Random Forest, ExtraTrees, and Deep Residual Network, were trained and validated using the blood-brain barrier penetration dataset, which shows the penetrability of compounds in the blood-brain barrier. LIME was able to create explanations for such penetrability, highlighting the most important substructures of molecules that affect drug penetration in the barrier. The simple and intuitive outputs prove the applicability of this explainable model to interpreting the permeability of compounds across the blood-brain barrier in terms of molecular features. LIME explanations were filtered with a weight equal to or greater than 0.1 to obtain only the most relevant explanations. The results showed several structures that are important for blood-brain barrier penetration. In general, it was found that some compounds with nitrogenous substructures are more likely to permeate the blood-brain barrier. The application of these structural explanations may help the pharmaceutical industry and potential drug synthesis research groups to synthesize active molecules more rationally.

Structure-Activity Relationships and Therapeutic Applications of Retinoids in View of Potential Benefits from Drug Repurposing Process

Vitamin A, an essential micronutrient, is integral to various biological processes crucial for organismal development and maintenance. Dietary sources of vitamin A encompass preformed retinol, retinyl esters, and provitamin A carotenoids. Retinoic acid (RA), a key component, plays pivotal roles in vision, cell proliferation, apoptosis, immune function, and gene regulation. Drug repurposing, an effective strategy for identifying new therapeutic applications for existing drugs, has gained prominence in recent years. This review seeks to provide a comprehensive overview of the current research landscape surrounding retinoids and drug repurposing. The scope of this review encompasses a comprehensive examination of retinoids and their potential for repurposing in various therapeutic contexts. Despite their efficacy in treating dermatological conditions, concerns about toxicity persist, driving the search for safer and more potent retinoids. The molecular mechanisms underlying retinoid activity involve binding to retinoic acid receptors (RARs) and retinoid X receptors (RXRs), leading to transcriptional regulation of target genes. This review seeks to shed light on the possibilities for repurposing retinoids to cover a wider spectrum of therapeutic uses by exploring recent scientific progress. It also aims to offer a more comprehensive understanding of the therapeutic prospects of retinoids and the broader impact of drug repositioning in contemporary medicine.

Fluoxetine exerts anti-inflammatory effects on human epidermal keratinocytes and suppresses their endothelin release

Fluoxetine is a safe antidepressant with remarkable anti-inflammatory actions; therefore, we aimed to investigate its effects on immortalized (HaCaT) as well as primary human epidermal keratinocytes in a polyinosinic-polycytidylic acid (p(I:C))-induced inflammatory model. We found that a non-cytotoxic concentration (MTT-assay, CyQUANT-assay) of fluoxetine significantly suppressed p(I:C)-induced expression and release of several pro-inflammatory cytokines (Q-PCR, cytokine array, ELISA), and it decreased the release of the itch mediator endothelins (ELISA). These effects were not mediated by the inhibition of the NF-κB or p38 MAPK pathways (western blot), or by the suppression of the p(I:C)-induced elevation of mitochondrial ROS production (MitoSOX Red labeling). Instead, unbiased activity profiling revealed that they were most likely mediated via the inhibition of the phosphoinositide 3-kinase (PI3K) pathway. Importantly, the PI3K-inhibitor GDC0941 fully mimicked the effects of fluoxetine (Q-PCR, ELISA). Although fluoxetine was able to occupy the binding site of GDC0941 (in silico molecular docking), and exerted direct inhibitory effect on PI3K (cell-free PI3K activity assay), it exhibited much lower potency and efficacy as compared to GDC0941. Finally, RNA-Seq analysis revealed that fluoxetine deeply influenced the transcriptional alterations induced by p(I:C)-treatment, and exerted an overall anti-inflammatory activity. Collectively, our findings demonstrate that fluoxetine exerts potent anti-inflammatory effects, and suppresses the release of the endogenous itch mediator endothelins in human keratinocytes, most likely via interfering with the PI3K pathway. Thus, clinical studies are encouraged to explore whether the currently reported beneficial effects translate in vivo following its topical administration in inflammatory and pruritic dermatoses.

Exploring DrugCentral: from molecular structures to clinical effects

DrugCentral, accessible at https://drugcentral.org , is an open-access online drug information repository. It covers over 4950 drugs, incorporating structural, physicochemical, and pharmacological details to support drug discovery, development, and repositioning. With around 20,000 bioactivity data points, manual curation enhances information from several major digital sources. Approximately 724 mechanism-of-action (MoA) targets offer updated drug target insights. The platform captures clinical data: over 14,300 on- and off-label uses, 27,000 contraindications, and around 340,000 adverse drug events from pharmacovigilance reports. DrugCentral encompasses information from molecular structures to marketed formulations, providing a comprehensive pharmaceutical reference. Users can easily navigate basic drug information and key features, making DrugCentral a versatile, unique resource. Furthermore, we present a use-case example where we utilize experimentally determined data from DrugCentral to support drug repurposing. A minimum activity threshold t should be considered against novel targets to repurpose a drug. Analyzing 1156 bioactivities for human MoA targets suggests a general threshold of 1 µM: t = 6 when expressed as - log[Activity(M)]). This applies to 87% of the drugs. Moreover, t can be refined empirically based on water solubility (S): t = 3 - logS, for logS < - 3. Alongside the drug repurposing classification scheme, which considers intellectual property rights, market exclusivity protections, and market accessibility, DrugCentral provides valuable data to prioritize candidates for drug repurposing programs efficiently.

Facilitating the drug repurposing with iC/E strategy: A practice on novel nNOS inhibitor discovery

Over the past decades, many existing drugs and clinical/preclinical compounds have been repositioned as new therapeutic indication from which they were originally intended and to treat off-target diseases by targeting their noncognate protein receptors, such as Sildenafil and Paxlovid, termed drug repurposing (DRP). Despite its significant attraction in the current medicinal community, the DRP is usually considered as a matter of accidents that cannot be fulfilled reliably by traditional drug discovery protocol. In this study, we proposed an integrated computational/experimental (iC/E) strategy to facilitate the DRP within a framework of rational drug design, which was practiced on the identification of new neuronal nitric oxide synthase (nNOS) inhibitors from a structurally diverse, functionally distinct drug pool. We demonstrated that the iC/E strategy is very efficient and readily feasible, which confirmed that the phosphodiesterase inhibitor DB06237 showed a high inhibitory potency against nNOS synthase domain, while other two general drugs, i.e. DB02302 and DB08258, can also inhibit the synthase at nanomolar level. Structural bioinformatics analysis revealed diverse noncovalent interactions such as hydrogen bonds, hydrophobic forces and van der Waals contacts across the complex interface of nNOS active site with these identified drugs, conferring both stability and specificity for the complex recognition and association.

Computer-aided drug design approaches applied to screen natural product's structural analogs targeting arginase in Leishmania spp

Introduction: Leishmaniasis is a disease with high mortality rates and approximately 1.5 million new cases each year. Despite the new approaches and advances to fight the disease, there are no effective therapies. Methods: Hence, this study aims to screen for natural products' structural analogs as new drug candidates against leishmaniasis. We applied Computer-aided drug design (CADD) approaches, such as virtual screening, molecular docking, molecular dynamics simulation, molecular mechanics-generalized Born surface area (MM-GBSA) binding free estimation, and free energy perturbation (FEP) aiming to select structural analogs from natural products that have shown anti-leishmanial and anti-arginase activities and that could bind selectively against the Leishmania arginase enzyme. Results: The compounds 2H-1-benzopyran, 3,4-dihydro-2-(2-methylphenyl)-(9CI), echioidinin, and malvidin showed good results against arginase targets from three parasite species and negative results for potential toxicities. The echioidinin and malvidin ligands generated interactions in the active center at pH 2.0 conditions by MM-GBSA and FEP methods. Conclusions: This work suggests the potential anti-leishmanial activity of the compounds and thus can be further in vitro and in vivo experimentally validated.

Drug Development Strategies for Malaria: With the Hope for New Antimalarial Drug Discovery—An Update

Malaria continued to be a deadly situation for the people of tropical and subtropical countries. Although there has been a marked reduction in new cases as well as mortality and morbidity rates in the last two decades, the reporting of malaria caused 247 million cases and 619000 deaths worldwide in 2021, according to the WHO (2022). The development of drug resistance and declining efficacy against most of the antimalarial drugs/combination in current clinical practice is a big challenge for the scientific community, and in the absence of an effective vaccine, the problem becomes worse. Experts from various research organizations worldwide are continuously working hard to stop this disaster by employing several strategies for the development of new antimalarial drugs/combinations. The current review focuses on the history of antimalarial drug discovery and the advantages, loopholes, and opportunities associated with the common strategies being followed for antimalarial drug development.

Exploring different computational approaches for effective diagnosis of breast cancer

Breast cancer has been identified as one among the top causes of female death worldwide. According to recent research, earlier detection plays an important role toward fortunate medicaments and thus, decreasing the mortality rate due to breast cancer among females. This review provides a fleeting summary involving traditional diagnostic procedures from the past and today, and also modern computational tools that have greatly aided in the identification of breast cancer. Computational techniques involving different algorithms such as Support vector machines, deep learning techniques and robotics are popular among the academicians for detection of breast cancer. They discovered that Convolutional neural network was a common option for categorization among such approaches. Deep learning techniques are evaluated using performance indicators such as accuracy, sensitivity, specificity, or measure. Furthermore, molecular docking, homology modeling and Molecular dynamics Simulation gives a road map for future discussions about developing improved early detection approaches that holds greater potential in increasing the survival rate of cancer patients. The different computational techniques can be a new dominion among researchers and combating the challenges associated with breast cancer.

Nanoscale Technologies in the Fight against COVID-19: From Innovative Nanomaterials to Computer-Aided Discovery of Potential Antiviral Plant-Derived Drugs

The last few years have increasingly emphasized the need to develop new active antiviral products obtained from artificial synthesis processes using nanomaterials, but also derived from natural matrices. At the same time, advanced computational approaches have found themselves fundamental in the repurposing of active therapeutics or for reducing the very long developing phases of new drugs discovery, which represents a real limitation, especially in the case of pandemics. The first part of the review is focused on the most innovative nanomaterials promising both in the field of therapeutic agents, as well as measures to control virus spread (i.e., innovative antiviral textiles). The second part of the review aims to show how computer-aided technologies can allow us to identify, in a rapid and therefore constantly updated way, plant-derived molecules (i.e., those included in terpenoids) potentially able to efficiently interact with SARS-CoV-2 cell penetration pathways.

Going Retro, Going Viral: Experiences and Lessons in Drug Discovery from COVID-19

The severity of the COVID-19 pandemic and the pace of its global spread have motivated researchers to opt for repurposing existing drugs against SARS-CoV-2 rather than discover or develop novel ones. For reasons of speed, throughput, and cost-effectiveness, virtual screening campaigns, relying heavily on in silico docking, have dominated published reports. A particular focus as a drug target has been the principal active site (i.e., RNA synthesis) of RNA-dependent RNA polymerase (RdRp), despite the existence of a second, and also indispensable, active site in the same enzyme. Here we report the results of our experimental interrogation of several small-molecule inhibitors, including natural products proposed to be effective by in silico studies. Notably, we find that two antibiotics in clinical use, fidaxomicin and rifabutin, inhibit RNA synthesis by SARS-CoV-2 RdRp in vitro and inhibit viral replication in cell culture. However, our mutagenesis studies contradict the binding sites predicted computationally. We discuss the implications of these and other findings for computational studies predicting the binding of ligands to large and flexible protein complexes and therefore for drug discovery or repurposing efforts utilizing such studies. Finally, we suggest several improvements on such efforts ongoing against SARS-CoV-2 and future pathogens as they arise.