Conventional Drug Development Research Articles

Abstract PO5-26-11: The Breast Cancer Research Foundation Drug Research Collaborative – A unique and innovative model for industry-funded, academically driven research to advance the field of breast cancer

Abstract The Breast Cancer Research Foundation (BCRF) is committed to preventing and curing breast cancer through groundbreaking research. In pursuit of this mission, the BCRF launched the Drug Research Collaborative (DRC) in 2016—a pioneering program designed to bridge the gap between academic investigators and access to investigational therapies, thereby fostering greater academic-driven research in breast cancer. The DRC's primary objective is to fund independent, innovative, and high-quality investigator-initiated trials (IITs) and pre-clinical/translational research projects. In conventional drug development, industry-sponsored registration trials play a crucial role, leaving limited resources and scope for exploratory and translational IITs that fall outside the industry's drug approval strategy. Additionally, industry-funded IIT research faces challenges such as reputational risks, perceived influence during internal review processes, intellectual property (IP) concerns, and logistics/program management burdens. The BCRF-DRC program addresses these challenges by issuing requests for proposals to generate creative and unbiased ideas, which are then vetted through an academic review process involving leading breast cancer experts. The most promising research projects are selected for further advancement. The BCRF-DRC program is facilitated by the BCRF research staff, providing project management, while the Translational Breast Cancer Research Consortium (TBCRC), founded by the BCRF and also supported by Susan G. Komen, supports the clinical trial aspects of the program. Since its inception, the BCRF-DRC has expanded from one to three industry partners, securing a total funding of $25 million for 15 projects, including seven clinical trials and eight pre-clinical projects. This abstract reports on the ongoing progress and outcomes achieved by these three programs. The BCRF-DRC represents a revolutionary paradigm in research funding and collaboration by harnessing the collective power of industry partners, philanthropic organizations, and academic institutions. Through this collaborative effort, the BCRF-DRC provides a unique platform for researchers to accelerate the translation of scientific discoveries into effective clinical interventions, thus making significant strides in the field of breast cancer in the coming decades. By empowering academic investigators, fostering unbiased research, and addressing the limitations of industry-funded IITs, the BCRF-DRC is revolutionizing breast cancer research and driving the development of novel interventions. This abstract highlights the transformative potential of the BCRF-DRC program and its contribution to advancing breast cancer treatments and ultimately improving patient outcomes. The success of this collaborative model serves as a testament to the power of multi-sector partnerships and the pivotal role they play in shaping the future of breast cancer research. Citation Format: Unnati Jariwala, Dorraya El-Ashry, Judy Garber, Larry Norton. The Breast Cancer Research Foundation Drug Research Collaborative – A unique and innovative model for industry-funded, academically driven research to advance the field of breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-26-11.

Drug Classification and Repurposing Using Decision Tree Algorithm and Data Analysis

Abstract: Fundamentally, drugs are substances that exhibit the capacity to alter biological functions, often serving as interventions to mitigate ailments or enhance physiological processes. However, the landscape of drug development and usage is undergoing transformation, fueled by innovative strategies that hold the promise of revolutionizing healthcare solutions. This research paper explores drug classification and repurposing through the utilization of decision tree algorithms and data analysis. Specifically, the aim is to classify drugs into three distinct types: Drug X which is most used drugs, Drug Y which is less frequently used drugs, and Drug C includes drugs like narcotics. Decision tree algorithms are employed to discern the defining attributes that categorize drugs into these types. By analyzing comprehensive datasets encompassing drug properties, interactions, and clinical outcomes, the study harnesses decision tree models to predict drug classifications accurately. This approach holds the potential to accelerate drug discovery, optimize treatment strategies, and contribute to more efficient healthcare solutions. The proposed algorithm is compared with both Naïve Bayes and K - Nearest Neighbors algorithms to prove it is more accurate. Ultimately, the significance of this paper transcends the boundaries of conventional drug development paradigms and to overcome the problem of shortage of drugs.

A Review on Repurposing of Drug

Background: For discovering novel drugs and to gain market acceptance process of conventional drug discovery is used in which various stages are involved. Aim: To innovate new approaches for minimizing the cost and time of drug discovery. Method: Several attempts were made for the building of plans based on computational tools and on bio-informatics to strengthen the repurposing method off-late. Various approaches used to invent novel signs for FDA accepted drugs are discussed in this review. Results: The repurposing of the drugs has obtained significance in identifying novel therapeutic uses for existing drugs. It is a productive strategy for the discovery of drugs also time and cost-effective.It fills the gap for the absence of efficiency of conventional drug development. Implications: In drug repurposing, selection and decision of suitable repurposing technique depend on previous knowledge and accessible data from particular studies. The best advantage of the drug repurposing technique is that for approved drugs all the required data is available. Conclusion: This technique is currently appearing to overcome the restriction faced during conventional drug discovery in the form of resources, timeline, and financial support . The feasibility of repurposing technique is improved by its systematic application. Some examples of repurposed drugs are also reviewed here. This review also covers the skill of repurposing survival drugs for use against microbes. Keywords: Conventional drug, drug repurposing, repurposing approaches, docking, proteinopathy.

MolToxPred: small molecule toxicity prediction using machine learning approach.

Different types of chemicals and products may exhibit various health risks when administered into the human body. For toxicity reasons, the number of new drugs entering the market through the conventional drug development process has been reduced over the years. However, with the advent of big data and artificial intelligence, machine learning techniques have emerged as a potential solution for predicting toxicity and ensuring efficient drug development and chemical safety. An ML model for toxicity prediction can reduce experimental costs and time while addressing ethical concerns by drastically reducing the need for animals and clinical trials. Herein, MolToxPred, an ML-based tool, has been developed using a stacked model approach to predict the potential toxicity of small molecules and metabolites. The stacked model consists of random forest, multi-layer perceptron, and LightGBM as base classifiers and Logistic Regression as the meta classifier. For training and validation purposes, a comprehensive set of toxic and non-toxic molecules is curated. Different structural and physicochemical-based features in the form of molecular descriptors and fingerprints were employed. MolToxPred utilizes a comprehensive feature selection process and optimizes its hyperparameters through Bayesian optimization with stratified 5-fold cross-validation. In the evaluation phase, MolToxPred achieved an AUROC of 87.76% on the test set and 88.84% on an external validation set. The McNemar test was used as the post-hoc test to determine if the stacked models' performance was significantly different compared to the base learners. The developed stacked model outperformed its base classifiers and an existing tool in the literature, reaffirming its better performance. The hypothesis is that the incorporation of a diverse set of data, the subsequent feature selection, and a stacked ensemble approach give MolToxPred the edge over other methods. In addition to this, an attempt has been made to identify structural alerts responsible for endpoints of the Tox21 data to determine the association of a molecule with a plausible downstream pathway of action. MolToxPred may be helpful for drug discovery and regulatory pipelines in pharmaceutical and other industries for in silico toxicity prediction of small molecule candidates.

Fragment-based drug discovery for disorders of the central nervous system: designing better drugs piece by piece.

Fragment-based drug discovery (FBDD) has emerged as a powerful strategy to confront the challenges faced by conventional drug development approaches, particularly in the context of central nervous system (CNS) disorders. FBDD involves the screening of libraries that comprise thousands of small molecular fragments, each no greater than 300Da in size. Unlike the generally larger molecules from high-throughput screening that limit customisation, fragments offer a more strategic starting point. These fragments are inherently compact, providing a strong foundation with good binding affinity for the development of drug candidates. The minimal elaboration required to transition the hit into a drug-like molecule is not only accelerated, but also it allows for precise modifications to enhance both their activity and pharmacokinetic properties. This shift towards a fragment-centric approach has seen commercial success and holds considerable promise in the continued streamlining of the drug discovery and development process. In this review, we highlight how FBDD can be integrated into the CNS drug discovery process to enhance the exploration of a target. Furthermore, we provide recent examples where FBDD has been an integral component in CNS drug discovery programs, enabling the improvement of pharmacokinetic properties that have previously proven challenging. The FBDD optimisation process provides a systematic approach to explore this vast chemical space, facilitating the discovery and design of compounds piece by piece that are capable of modulating crucial CNS targets.

Advancing pneumonia virus drug discovery with virtual screening: A cutting-edge fast and resource efficient machine learning framework for predictive analysis

Pneumonia, a severe respiratory infection characterized by a significant morbidity and fatality rate, afflicts many individuals globally. The demand for highly effective antiviral medications has experienced a surge because of the emergence of novel pneumonia viruses, such as the COVID-19 coronavirus. Due to their inherent time and cost constraints, conventional drug development strategies sometimes need to be more manageable. Exploring alternative approaches is crucial to identifying and establishing effective therapy choices. This work introduces a computational methodology for analyzing the chemical space of medications targeting the pneumonia virus, employing Python-based data mining tools. Using computer-aided analysis in drug molecules aims to enhance the efficiency of identifying and evaluating potential new therapeutic candidates using Machine Learning (ML). The research successfully discovered two therapeutic compounds by utilizing the Bayesian Ridge approach, which is the most accurate with the least mean squared error, is less computationally expensive in terms of power, memory, and CPU, and is the fastest of the investigated approaches. It discovered the CHEMBL433378 and CHEMBL93653, with promising docking scores of −4.3 and −4.2, respectively. Additionally, both molecules demonstrated significant inhibitory activity against their respective targets, as seen by their IC50 values of 0.0018 and 0.001. Both compounds meet the criteria for the B. Mann Whitney U Test and Lipinski test.

Fragment-based drug nanoaggregation reveals drivers of self-assembly

Drug nanoaggregates are particles that can deleteriously cause false positive results during drug screening efforts, but alternatively, they may be used to improve pharmacokinetics when developed for drug delivery purposes. The structural features of molecules that drive nanoaggregate formation remain elusive, however, and the prediction of intracellular aggregation and rational design of nanoaggregate-based carriers are still challenging. We investigate nanoaggregate self-assembly mechanisms using small molecule fragments to identify the critical molecular forces that contribute to self-assembly. We find that aromatic groups and hydrogen bond acceptors/donors are essential for nanoaggregate formation, suggesting that both π-π stacking and hydrogen bonding are drivers of nanoaggregation. We apply structure-assembly-relationship analysis to the drug sorafenib and discover that nanoaggregate formation can be predicted entirely using drug fragment substructures. We also find that drug nanoaggregates are stabilized in an amorphous core-shell structure. These findings demonstrate that rational design can address intracellular aggregation and pharmacologic/delivery challenges in conventional and fragment-based drug development processes.

Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges.

By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.

Computer Aided Drug Designing Approach for Prospective Human Metastatic Cancer

Abstract: It is well known that finding new drugs is a difficult, expensive, time-consuming, and expensive project. According to a study, the typical time and cost for developing a new medicine through the conventional drug development pipeline is 12 years and 2.7 billion dollars. The pharmaceutical industry is grappling with the difficult and pressing challenge of how to find new drugs faster and at lower research costs.Insilico,The field of computer-aided drug discovery (CADD) has shown significant promise as an advanced technology for secure, cost-effective, and efficient drug design. In recent times, there has been remarkable progress in computational tools for drug discovery, particularly in the development of anticancer therapies. This progress has had a significant impact on the design of anticancer drugs and has provided valuable insights into the field of cancer treatment. To carry out molecular docking, we utilized AutoDock software and prepared the target protein by loading and converting its PDB file format into a macromolecule. Additionally, the ligand structures underwent energy minimization (EM) and were selected alongside the target proteins in AutoDock. To ensure coverage of the binding site residues, a suitable grid box with appropriate dimensions was chosen

Alanine racemase a promising Helicobacter pylori drug target inhibited by propanoic acid

Eradication of Helicobacter pylori, the class 1 carcinogen, faces several obstacles, which demand alternative options to conventional drug development methods. Alanine racemase (Alr) was proposed as H. pylori drug target, inhibited by propanoic acid (PA), in a previous in silico study. We investigated the possible treatment of H. pylori infection through Alr inhibition. A new model of H. pylori Alr was built, validated, and the binding of PA to the active site was modelled via molecular docking with a good docking score. PA minimum inhibitory concentration (MIC) against H. pylori ATCC 43504 and six H. pylori clinical isolates ranged from 312.5 to 416.7 ± 180 μg/ml and remained unchanged after 14 serial passages in increasing PA concentrations. The minimum bactericidal concentration of PA was 625 μg/ml. Selective Alr inhibition was confirmed by a significant PA MIC increase with increasing d-alanine concentrations. Similar PA MIC in other tested pathogens was recorded (312.5–625 μg/ml). PA lacked cytotoxicity in tested cell lines and efficiently eradicated H. pylori in a rat infection model. In conclusion, Alr is a promising broad-spectrum drug target, inhibited by PA without resistance development by repeated exposure for 14 serial passages.

Nanoparticle-mediated targeting of the fusion gene RUNX1/ETO in t(8;21)-positive acute myeloid leukaemia

A hallmark of acute myeloid leukaemias (AMLs) are chromosomal rearrangements that give rise to novel leukaemia-specific fusion genes. Most of these fusion genes are both initiating and driving events in AML and therefore constitute ideal therapeutic targets but are challenging to target by conventional drug development. siRNAs are frequently used for the specific suppression of fusion gene expression but require special formulations for efficient in vivo delivery. Here we describe the use of siRNA-loaded lipid nanoparticles for the specific therapeutic targeting of the leukaemic fusion gene RUNX1/ETO. Transient knockdown of RUNX1/ETO reduces its binding to its target genes and alters the binding of RUNX1 and its co-factor CBFβ. Transcriptomic changes in vivo were associated with substantially increased median survival of a t(8;21)-AML mouse model. Importantly, transient knockdown in vivo causes long-lasting inhibition of leukaemic proliferation and clonogenicity, induction of myeloid differentiation and a markedly impaired re-engraftment potential in vivo. These data strongly suggest that temporary inhibition of RUNX1/ETO results in long-term restriction of leukaemic self-renewal. Our results provide proof for the feasibility of targeting RUNX1/ETO in a pre-clinical setting and support the further development of siRNA-LNPs for the treatment of fusion gene-driven malignancies.

Fragment-based drug nanoaggregation reveals drivers of self-assembly

Self-assembled drug nanoaggregates are of interest due to their potential to form in cells, interfere with protein folding, or facilitate drug delivery approaches to improve the solubility, bioavailability, and pharmacokinetics. However, the physicochemical properties and molecular features of small molecules that mechanistically drive the formation of drug nanoparticles remain elusive. Therefore, the rational design of nanoaggregate-based carriers, and the prediction of intracellular aggregation based on drug structure, is still challenging. Inspired from fragment-based drug discovery, we systematically investigated self-assembly mechanisms using small molecule fragments in indocyanine-based nanoaggregates to identify the critical non-covalent molecular forces that contribute to self-assembly. We found that aromaticity and hydrogen bond acceptors and donors on fragments were essential for nanoparticle formation, suggesting the system is driven by both π-π stacking of the aromatic groups and hydrogen bonding. We applied fragment molecular feature analysis to a kinase inhibitor, a full-size clinical compound, and discovered that nanoparticle formability is intrinsically associated within the drug fragment substructures. We also found that these drug aggregates form core-shell structures but lack ordered internal packings and are amorphous in solution. These findings suggest that incorporation of molecular design features can address intracellular aggregation and pharmacologic or delivery challenges in conventional and fragment-based drug development processes.

Selective Inhibition of Kinase Activity in Mammalian Cells by Bioorthogonal Ligand Tethering.

Enzymes are critical for cellular functions, and malfunction of enzymes is closely related to many human diseases. Inhibition studies can help in deciphering the physiological roles of enzymes and guide conventional drug development programs. In particular, chemogenetic approaches enabling rapid and selective inhibition of enzymes in mammalian cells have unique advantages. Here, we describe the procedure for rapid and selective inhibition of a kinase in mammalian cells by bioorthogonal ligand tethering (iBOLT). Briefly, a non-canonical amino acid bearing a bioorthogonal group is genetically incorporated into the target kinase by genetic code expansion. The sensitized kinase can react with a conjugate containing a complementary biorthogonal group linked with a known inhibitory ligand. As a result, tethering of the conjugate to the target kinase allows selective inhibition of protein function. Here, we demonstrate this method by using cAMP-dependent protein kinase catalytic subunit alpha (PKA-Cα) as the model enzyme. The method should be applicable to other kinases, enabling their rapid and selective inhibition.

Omics Approaches in Drug Development against Leishmaniasis: Current Scenario and Future Prospects.

Leishmaniasis is a zoonotic disease transmitted in humans by the bite of Leishmania-infected phlebotomine sandflies. Each year approximately 58,500 cases of leishmaniasis are diagnosed across the globe, with a mortality rate of nearly seven percent. There are over 20 parasitic strains of Leishmania which are known to cause distinct types of leishmaniasis and pose an endemic threat to humans worldwide. Therefore, it is crucial to develop potential medications and vaccines to combat leishmaniasis. However, the task of developing therapeutic solutions is challenging due to Leishmania's digenetic lifecycle. The challenge is further intensified by cases of resistance against the available drugs. Owing to these challenges, the conventional drug development regimen is further limited by target discovery and ligand suitability for the targets. On the other hand, as an added advantage, the emergence of omics-based tools, such as high-end proteomics, transcriptomics and genomics, has hastened the pace of target discovery and target-based drug development. It is now becoming apparent that multi-omics convergence and an inter-connected systems approach is less time-consuming and more cost-effective for any drug-development process. This comprehensive review is an attempt to summarize the current knowledge on the muti-omics approach in drug development against leishmaniasis. In particular, it elaborates the potential target identification from secreted proteins in various stages of Leishmania infection and also illustrates the convergence of transcriptomic and genomic data towards the collective goal of drug discovery. This review also provides an understanding of the potential parasite's drug targets and drug resistance characteristics of the parasite, which can be used in designing effective and specific therapeutics.

Repurposing Over-the-Counter Drugs and an Iron-Chelator as Antibacterial Agents

The conventional drug discovery and development process takes a long time and is not financially viable at times. Repurposing or repositioning existing drugs for treating new diseases seems to be a feasible alternative to this problem. Over-the-counter (OTC) drugs such as Rantac (antacid), Draminate (antiemetic), Diclofenac (painkiller), Sinarest (for respiratory disorders), and Desifer (iron-chelator) were included in this study against eight laboratory cultures. Objective: Repurposing Desifer and the OTC drugs as antibacterial agents. Methods: Aqueous preparations of the OTC drugs and Desifer were checked for their antibacterial activity by the ditch plate method. The Agar cup diffusion method was used to determine the MIC of the individual drugs against gram-positive and gram-negative organisms. The synergistic activity of supernatants of OTC drugs with Desifer was determined using agar cup diffusion and micro broth dilution methods. MTT assay was performed with cell lines to determine anticancer and cytotoxic activity. Results and Discussion: Supernatants of drugs used showed antibacterial activity against at least one laboratory culture used. MIC of OTC drugs decreased to one-fourth of individual MIC when used in combination with Desifer, indicating that Desifer enhanced their inhibitory action. Desifer and Diclofenac exhibit anticancer activity, and low cytotoxicity, therefore could be good candidates as chemotherapeutic agents. Conclusion: A combination of the drugs such as Diclofenac and Desifer could be an effective alternative therapy to treat bacterial infections. With emerging drug resistance, Desifer with OTC drugs proves to be a good strategy to enhance the effectiveness of antibacterial drugs.

Novel computational pipelines in antiviral structure‑based drug design (Review).

Viral infections constitute a fundamental and continuous challenge for the global scientific and medical community, as highlighted by the ongoing COVID-19 pandemic. In combination with prophylactic vaccines, the development of safe and effective antiviral drugs remains a pressing need for the effective management of rare and common pathogenic viruses. The design of potent antivirals can be informed by the study of the three-dimensional structure of viral protein targets. Structure-based design of antivirals in silico provides a solution to the arduous and costly process of conventional drug development pipelines. Furthermore, rapid advances in high-throughput computing, along with the growth of available biomolecular and biochemical data, enable the development of novel computational pipelines in the hunt of antivirals. The incorporation of modern methods, such as deep-learning and artificial intelligence, has the potential to revolutionize the structure-based design and repurposing of antiviral compounds, with minimal side effects and high efficacy. The present review aims to provide an outline of both traditional computational drug design and emerging, high-level computing strategies.

Repurposing Drugs to Combat Drug Resistance in Leprosy: A Review of Opportunities.

Leprosy is caused by extremely slow-growing and uncultivated mycobacterial pathogens, namely Mycobacterium leprae and M. lepromatosis. Nearly 95% of the new cases of leprosy recorded globally are found in India, Brazil, and 20 other priority countries (WHO, 2019), of which nearly two-third of the cases are reported in India alone. Currently, leprosy is treated with dapsone, rifampicin, and clofazimine, also known as multi-drug therapy (MDT), as per the recommendations of WHO since 1981. Still, the number of new leprosy cases recorded globally has remained constant in last one-decade, and resistance to multiple drugs has been documented in various parts of the world, even though relapses are rare in patients treated with MDT. Antimicrobial resistance testing against M. leprae or the evaluation of the anti-leprosy activity of new drugs remains a challenge as leprosy bacilli cannot grow in vitro. Besides, developing a new drug against leprosy through conventional drug development process is not economically attractive or viable for pharma companies. Therefore, a promising alternative is the repurposing of existing drugs/approved medications or their derivatives for assessing their anti-leprosy potential. It is an efficient method to identify novel medicinal and therapeutic properties of approved drug molecules. Any combinatorial chemotherapy that combines these repurposed drugs with the existing first-line (MDT) and second-line drugs could improve the bactericidal and synergistic effects against these notorious bacteria and can help in achieving the much-cherished goal of "leprosy-free world". This review highlights novel opportunities for drug repurposing to combat resistance to current therapeutic approaches.

An integrated understanding of the evolutionary and structural features of the SARS-CoV-2 spike receptor binding domain (RBD)

Conventional drug development strategies typically use pocket in protein structures as drug-target sites. They overlook the plausible effects of protein evolvability and resistant mutations on protein structure which in turn may impair protein-drug interaction. In this study, we used an integrated evolution and structure guided strategy to develop potential evolutionary-escape resistant therapeutics using receptor binding domain (RBD) of SARS-CoV-2 spike-protein/S-protein as a model. Deploying an ensemble of sequence space exploratory tools including co-evolutionary analysis and deep mutational scans we provide a quantitative insight into the evolutionarily constrained subspace of the RBD sequence-space. Guided by molecular simulation and structure network analysis we highlight regions inside the RBD, which are critical for providing structural integrity and conformational flexibility. Using fuzzy C-means clustering we combined evolutionary and structural features of RBD and identified a critical region. Subsequently, we used computational drug screening using a library of 1615 small molecules and identified one lead molecule, which is expected to target the identified region, critical for evolvability and structural stability of RBD. This integrated evolution-structure guided strategy to develop evolutionary-escape resistant lead molecules have potential general applications beyond SARS-CoV-2.

Nitazoxanide potentiates linezolid against linezolid-resistant Staphylococcus aureus in vitro and in vivo.

Antimicrobial resistance is a growing menace, claiming millions of lives all over the world. In this context, drug repurposing is one approach gaining interest as a suitable alternative to conventional drug discovery and development. Whole-cell assays were used to screen FDA-approved drugs to identify novel antimicrobial agents active against bacterial pathogens. Following identification of nitazoxanide, its various characteristics, such as antimicrobial activity against MDR isolates, time-kill kinetics, ability to synergize with approved drugs, antibiofilm activity and ability to generate resistance in Staphylococcus aureus, were determined, followed by determination of its in vivo potential against MDR S. aureus. Nitazoxanide demonstrated a potent in vitro antistaphylococcal profile, including equipotent activity against clinical drug-resistant S. aureus and Enterococcus spp. Nitazoxanide exhibited concentration-dependent killing, significantly eradicated preformed S. aureus biofilm and S. aureus did not generate resistance to it. Nitazoxanide strongly synergized with linezolid both in vitro and in vivo against linezolid-susceptible and -resistant S. aureus, displaying superior activity to untreated control and drug-alone treatment groups. Nitazoxanide can be utilized in combination with linezolid against infections caused by linezolid-resistant S. aureus as it exhibits strong synergism in vitro and in vivo.

Repurposing Histaminergic Drugs in Multiple Sclerosis.

Multiple sclerosis is an autoimmune disease with a strong neuroinflammatory component that contributes to severe demyelination, neurodegeneration and lesions formation in white and grey matter of the spinal cord and brain. Increasing attention is being paid to the signaling of the biogenic amine histamine in the context of several pathological conditions. In multiple sclerosis, histamine regulates the differentiation of oligodendrocyte precursors, reduces demyelination, and improves the remyelination process. However, the concomitant activation of histamine H1–H4 receptors can sustain either damaging or favorable effects, depending on the specifically activated receptor subtype/s, the timing of receptor engagement, and the central versus peripheral target district. Conventional drug development has failed so far to identify curative drugs for multiple sclerosis, thus causing a severe delay in therapeutic options available to patients. In this perspective, drug repurposing offers an exciting and complementary alternative for rapidly approving some medicines already approved for other indications. In the present work, we have adopted a new network-medicine-based algorithm for drug repurposing called SAveRUNNER, for quantifying the interplay between multiple sclerosis-associated genes and drug targets in the human interactome. We have identified new histamine drug-disease associations and predicted off-label novel use of the histaminergic drugs amodiaquine, rupatadine, and diphenhydramine among others, for multiple sclerosis. Our work suggests that selected histamine-related molecules might get to the root causes of multiple sclerosis and emerge as new potential therapeutic strategies for the disease.