Drug repositioning: re-investigating existing drugs for new therapeutic indications

Harnessing machine learning potential for personalised drug design and overcoming drug resistance

Drug resistance in cancer treatment presents a significant challenge, necessitating innovative approaches to improve therapeutic efficacy. Integrating machine learning (ML) in cancer research is promising as ML algorithms outrival in analysing complex datasets, identifying patterns, and predicting treatment outcomes. Leveraging diverse data sources such as genomic profiles, clinical records, and drug response assays, ML uncovers molecular mechanisms of drug resistance, enabling personalised treatment, maximising efficacy and minimising adverse effects. Various ML algorithms contribute to the drug discovery process - Random Forest and Decision Trees predict drug-target interactions and aid in virtual screening, and SVM classify leads on bioactivity data. Neural Networks model QSAR to optimise lead compounds and K-means clustering group compounds with similar chemical properties aiding compound selection. Gaussian Processes predict drug responses, Bayesian Networks infer causal relationships, Autoencoders generate novel compounds, and Genetic Algorithms optimise molecular structures. These algorithms collectively enhance efficiency and success rates in drug design endeavours, from lead identification to optimisation and are cost-effective, empowering clinicians with real-time treatment monitoring and improving patient outcomes. This review highlights the immense potential of ML in revolutionising cancer care through effective drug design to reduce drug resistance, and we have also discussed various limitations and research gaps to understand better.

Drug repositioning based on residual attention network and free multiscale adversarial training

BACKGROUND

Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently needed.

RESULTS

This paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease associations.

CONCLUSIONS

The comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor for identifying unobserved disease-drug associations.

The Role of AI in Drug Discovery

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

Combination of the Topical Photodynamic Therapy of Chloroaluminum Phthalocyanine Liposomes with Fexinidazole Oral Self-Emulsifying System as a New Strategy for Cutaneous Leishmaniasis Treatment

Cutaneous leishmaniasis (CL) is a neglected tropical disease. The treatment is restricted to drugs, such as meglumine antimoniate and amphotericin B, that exhibit toxic effects, high cost, long-term treatment, and limited efficacy. The development of new alternative therapies, including the identification of effective drugs for the topical and oral treatment of CL, is of great interest. In this sense, a combination of topical photodynamic therapy (PDT) with chloroaluminum phthalocyanine liposomes (Lip-ClAlPc) and the oral administration of a self-emulsifying drug delivery system containing fexinidazole (SEDDS-FEX) emerges as a new strategy. The aim of the present study was to prepare, characterize, and evaluate the efficacy of combined therapy with Lip-ClAlPc and SEDDS-FEX in the experimental treatment of Leishmania (Leishmania) major. Lip-ClAlPc and SEDDS-FEX were prepared, and the antileishmanial efficacy study was conducted with the following groups: 1. Lip-ClAlPc (0.05 mL); 2. SEDDS-FEX (50 mg/kg/day); 3. Lip-ClAlPc (0.05 mL)+SEDDS-FEX (50 mg/kg/day) combination; 4. FEX suspension (50 mg/kg/day); and 5. control (untreated). BALB/c mice received 10 sessions of topical Lip-ClAlPc on alternate days and 20 consecutive days of SEDDS-FEX or FEX oral suspension. Therapeutical efficacy was evaluated via the parasite burden (limiting-dilution assay), lesion size (mm), healing of the lesion, and histological analyses. Lip-ClAlPc and SEDDS-FEX presented physicochemical characteristics that are compatible with the administration routes used in the treatments. Lip-ClAlPc+SEDDS-FEX led to a significant reduction in the parasitic burden in the lesion and spleen when compared to the control group (p < 0.05) and the complete healing of the lesion in 43% of animals. The Lip-ClAlPc+SEDDS-FEX combination may be promising for the treatment of CL caused by L. major.

A yeast-based high-throughput screen identifies inhibitors of trypanosomatid HRG heme transporters with potent leishmanicidal and trypanocidal activity

OBJECTIVES

New drugs are required to treat neglected diseases caused by trypanosomatid parasites such as Leishmania, Trypanosoma brucei and Trypanosoma cruzi. An Achilles' heel of these parasites is their heme auxotrophy; they have an absolute dependence on scavenging this molecule from the host, and trypanosomatid HRG heme transporters (TrypHRG) play an important role in this process. As these proteins are essential for the parasites and have low similarity with their human orthologue, they have been proposed as attractive therapeutic targets. Here, we have developed two yeast-based assays that allow an inexpensive high-throughput screening of TrypHRG inhibitors within a cellular context.

METHODS

We first assessed that Leishmania major, Leishmania donovani and T. brucei HRG proteins were heterologously expressed in the digestive vacuole membrane of a mutant heme auxotrophic yeast strain. Here, TrypHRG imports hemoglobinderived heme into the cytosol, allowing mutant yeast to grow in the presence of low hemoglobin concentrations and promoting the activity of hemeproteins such as catalase, which was used as a reporter of cytosolic heme levels.

RESULTS

In the presence of a TrypHRG inhibitor, both catalase activity (test 1) and yeast growth (test 2) were diminished, being easily monitored. The assays were then tested on a pilot scale for HTS purposes using a collection of repurposing drugs and food antioxidants. Some of the TrypHRG inhibitors identified in yeast presented strong trypanocidal and leishmanicidal activity in the submicromolar range, proving the potential of this approach.

CONCLUSIONS

Cumulatively, it was shown that the inhibition bioassays developed were robust and applicable to large-scale HTS.

Multispecies transcriptomics identifies SIKE as a MAPK repressor that prevents NASH progression

Nonalcoholic fatty liver (NAFL) remains relatively benign, but high-risk to end-stage liver diseases become highly prevalent when it progresses into nonalcoholic steatohepatitis (NASH). Our current understanding of the development of NAFL to NASH remains insufficient. In this study, we revealed MAP kinase (MAPK) activation as the most notable molecular signature associated with NASH progression across multiple species. Furthermore, we identified suppressor of IKKε (SIKE) as a conserved and potent negative controller of MAPK activation. Hepatocyte-specific overexpression of Sike prevented NASH progression in diet- and toxin-induced mouse NASH models. Mechanistically, SIKE directly interacted with TGF-β-activated kinase 1 (TAK1) and TAK1-binding protein 2 (TAB2) to interrupt their binding and subsequent TAK1-MAPK signaling activation. We found that indobufen markedly up-regulated SIKE expression and effectively improved NASH features in mice and macaques. These findings identify SIKE as a MAPK suppressor that prevents NASH progression and provide proof-of-concept evidence for targeting the SIKE-TAK1 axis as a potential NASH therapy.

Drug Repositioning: A Monetary Stratagem to Discover a New Application of Drugs

Drug repurposing, also referred to as drug repositioning or drug reprofiling, is a scientific approach to the detection of any new application for an already approved or investigational drug. It is a useful policy for the invention and development of new pharmacological or therapeutic applications of different drugs. The strategy has been known to offer numerous advantages over developing a completely novel drug for certain problems. Drug repurposing has numerous methodologies that can be categorized as target-oriented, drug-oriented, and problem-oriented. The choice of the methodology of drug repurposing relies on the accessible information about the drug molecule and like pharmacokinetic, pharmacological, physicochemical, and toxicological profile of the drug. In addition, molecular docking studies and other computer-aided methods have been known to show application in drug repurposing. The variation in dosage for original target diseases and novel diseases presents a challenge for researchers of drug repurposing in present times. The present review critically discusses the drugs repurposed for cancer, covid-19, Alzheimer's, and other diseases, strategies, and challenges of drug repurposing. Moreover, regulatory perspectives related to different countries like the United States (US), Europe, and India have been delineated in the present review.

AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding

The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool.

BMC3PM: bioinformatics multidrug combination protocol for personalized precision medicine and its application in cancer treatment

BACKGROUND

In recent years, drug screening has been one of the most significant challenges in the field of personalized medicine, particularly in cancer treatment. However, several new platforms have been introduced to address this issue, providing reliable solutions for personalized drug validation and safety testing. In this study, we developed a personalized drug combination protocol as the primary input to such platforms.

METHODS

To achieve this, we utilized data from whole-genome expression profiles of 6173 breast cancer patients, 312 healthy individuals, and 691 drugs. Our approach involved developing an individual pattern of perturbed gene expression (IPPGE) for each patient, which was used as the basis for drug selection. An algorithm was designed to extract personalized drug combinations by comparing the IPPGE and drug signatures. Additionally, we employed the concept of drug repurposing, searching for new benefits of existing drugs that may regulate the desired genes.

RESULTS

Our study revealed that drug combinations obtained from both specialized and non-specialized cancer medicines were more effective than those extracted from only specialized medicines. Furthermore, we observed that the individual pattern of perturbed gene expression (IPPGE) was unique to each patient, akin to a fingerprint.

CONCLUSIONS

The personalized drug combination protocol developed in this study offers a methodological interface between drug repurposing and combination drug therapy in cancer treatment. This protocol enables personalized drug combinations to be extracted from hundreds of drugs and thousands of drug combinations, potentially offering more effective treatment options for cancer patients.

Repurposing Drugs: An Empowering Approach to Drug Discovery and Development

Drug discovery and development is a time-consuming and costly procedure that necessitates a substantial effort. Drug repurposing has been suggested as a method for developing medicines that takes less time than developing brand new medications and will be less expensive. Also known as drug repositioning or re-profiling, this strategy has been in use from the time of serendipitous drug discoveries to the modern computer aided drug designing and use of computational chemistry. In the light of the COVID-19 pandemic too, drug repurposing emerged as a ray of hope in the dearth of available medicines. Data availability by electronic recording, libraries, and improvements in computational techniques offer a vital substrate for systemic evaluation of repurposing candidates. In the not-too-distant future, it could be possible to create a global research archive for us to access, thus accelerating the process of drug development and repurposing. This review aims to present the evolution, benefits and drawbacks including current approaches, key players and the legal and regulatory hurdles in the field of drug repurposing. The vast quantities of available data secured in multiple drug databases, assisting in drug repurposing is also discussed.

Healthcare Policies to Eliminate Neglected Tropical Diseases (NTDs) in India: A Roadmap

The need for systemic healthcare policies to systematically eliminate NTDs globally and in India has been stressed for more than two decades. Yet, the present policies and the research on them do not meet the need. We present an ontological framework, a research roadmap, and a policy brief to address the gap. The ontology clearly, concisely, and comprehensively represents the combinations of diseases, the objectives regarding the diseases, the entities to address them, the outcomes sought, and the potential policy instruments to invoke. The paper explicates the state of the-policies and state of the research on policies to eliminate NTDs in India. It highlights the significant gaps in the diseases covered, balance in the objectives, comprehensiveness of policies, portfolio of outcomes, and involvement of entities. Last, it presents a set of systemic policies congruent with the ontology to systematically address the gaps. The recommendations are aligned with the present research, policies, practices, and recommendations in India and of the WHO, UN agencies, and other similar bodies. The approach can be generalized to provide roadmaps for other countries facing a similar challenge and for other diseases of similar complexity. The roadmaps, with continuous feedback and learning, can help navigate the challenge efficiently and effectively.

Drug repurposing of mito-atovaquone for cancer treatment

Repurposing of approved drugs in a new strategy to combat cancer that leads to savings in time and investment. Atovaquone is a US FDA-approved drug for treatment of Pneumocystis carinii pneumonia and malaria. Patent US2023017373 describe the use of mito-atovaquone for the treatment of several types of cancer. Mito-atovaquone demonstrated antiproliferative activity in cell lines of pancreatic cancer, lung cancer and brain cancer and inhibited tumor growth in syngeneic mouse models and in animals genetically prone to breast cancer. Mito-atovaquone has the potential to be used successfully in the treatment of various types of tumors.

Drug Repurposing: An Effective Tool in Modern Drug Discovery

Drug repurposing is using an existing drug for a new treatment that was not indicated before. It has received immense attention during the COVID-19 pandemic emergency. Drug repurposing has become the need of time to fasten the drug discovery process and find quicker solutions to the over-exerted healthcare scenario and drug needs. Drug repurposing involves identifying the drug, evaluating its efficiency using preclinical models, and proceeding to phase II clinical trials. Identification of the drug candidate can be made through computational and experimental approaches. This approach usually utilizes public databases for drugs. Data from primary and translational research, clinical trials, anecdotal reports regarding off-label uses, and other published human data information available are included. Using artificial intelligence algorithms and other bioinformatics tools, investigators systematically try to identify the interaction between drugs and protein targets. It can be combined with genetic data, clinical analysis, structure (molecular docking), pathways, signatures, targets, phenotypes, binding assays, and artificial intelligence to get an optimum outcome in repurposing. This article describes the strategies involved in drug repurposing and enlists a series of repurposed drugs and their indications.

The Neural Metric Factorization for Computational Drug Repositioning

Computational drug repositioning aims to discover new therapeutic diseases for marketed drugs and has the advantages of low cost, short development cycle, and high controllability compared to traditional drug development. The matrix factorization model has become the cornerstone technique for computational drug repositioning due to its ease of implementation and excellent scalability. However, the matrix factorization model uses the inner product operation to represent the association between drugs and diseases, which is lacking in expressive ability. Moreover, the degree of similarity of drugs or diseases could not be implied on their respective latent factor vectors, which is not satisfy the common sense of drug discovery. Therefore, a neural metric factorization model for computational drug repositioning (NMFDR) is proposed in this work. We novelly consider the latent factor vector of drugs and diseases as a point in the high-dimensional coordinate system and propose a generalized euclidean distance to represent the association between drugs and diseases to compensate for the shortcomings of the inner product operation. Furthermore, by embedding multiple drug (disease) metrics information into the encoding space of the latent factor vector, the information about the similarity between drugs (diseases) can be reflected in the distance between latent factor vectors. Finally, we conduct wide analysis experiments on three real datasets to demonstrate the effectiveness of the above improvement points and the superiority of the NMFDR model.

Access to medicines for rare diseases: A European regulatory roadmap for academia

Background: Novel or repurposed medicines for rare diseases often emerge from fundamental research or empirical findings in academia. However, researchers may be insufficiently aware of the possibilities and requirements to bring novel medicinal treatment options to the patient. This paper aims to provide an easily applicable, comprehensive roadmap designed for academic researchers to make medicines for rare diseases available for patients by addressing the relevant regulatory frameworks, including marketing authorization and alternative routes. Methods: Key points of the regulatory chapters "Placing on the Market" and "Scope" of Directive 2001/83/EC relating to medicinal products for human use were summarized. Provisions in EU directives regarding blood products, radiopharmaceuticals, and herbal and homeopathic medicinal products were excluded. Cross-referencing to other provisions was included. European case-law was retrieved from the InfoCuria database to exemplify the implications of alternative routes. Results: Medicines may only be placed on the market with a valid marketing authorization. To obtain such authorization in Europe, a "Common Technical Document" comprising reports on quality and non-clinical and clinical studies must be submitted to a "competent authority", a national medicine agency or the European Medicines Agency. Timely interaction of academic researchers with regulators via scientific advice may lead to better regulatory alignment and subsequently a higher chance for approval of academic inventions. Furthermore, reimbursement by national payers could be essential to ensure patient access. Apart from the marketing authorization route, we identified multiple alternative routes to provide (early) access. These include off-label use, named-patient basis, compassionate use, pharmacy compounding, and hospital exemption for Advanced Therapy Medicinal Products. Discussion: Aligning academic (non-)clinical studies on rare diseases with regulatory and reimbursement requirements may facilitate fast and affordable access. Several alternative routes exist to provide (early) pharmaceutical care at a national level, but case-law demonstrates that alternative routes should be interpreted strictly and for exceptional situations only. Academics should be aware of these routes and their requirements to improve access to medicines for rare diseases.

Drug repositioning based on heterogeneous networks and variational graph autoencoders

Predicting new therapeutic effects (drug repositioning) of existing drugs plays an important role in drug development. However, traditional wet experimental prediction methods are usually time-consuming and costly. The emergence of more and more artificial intelligence-based drug repositioning methods in the past 2 years has facilitated drug development. In this study we propose a drug repositioning method, VGAEDR, based on a heterogeneous network of multiple drug attributes and a variational graph autoencoder. First, a drug-disease heterogeneous network is established based on three drug attributes, disease semantic information, and known drug-disease associations. Second, low-dimensional feature representations for heterogeneous networks are learned through a variational graph autoencoder module and a multi-layer convolutional module. Finally, the feature representation is fed to a fully connected layer and a Softmax layer to predict new drug-disease associations. Comparative experiments with other baseline methods on three datasets demonstrate the excellent performance of VGAEDR. In the case study, we predicted the top 10 possible anti-COVID-19 drugs on the existing drug and disease data, and six of them were verified by other literatures.

GCMM: graph convolution network based on multimodal attention mechanism for drug repurposing

Background

The main focus of in silico drug repurposing, which is a promising area for using artificial intelligence in drug discovery, is the prediction of drug–disease relationships. Although many computational models have been proposed recently, it is still difficult to reliably predict drug–disease associations from a variety of sources of data.

Results

In order to identify potential drug–disease associations, this paper introduces a novel end-to-end model called Graph convolution network based on a multimodal attention mechanism (GCMM). In particular, GCMM incorporates known drug–disease relations, drug–drug chemical similarity, drug–drug therapeutic similarity, disease–disease semantic similarity, and disease–disease target-based similarity into a heterogeneous network. A Graph Convolution Network encoder is used to learn how diseases and drugs are embedded in various perspectives. Additionally, GCMM can enhance performance by applying a multimodal attention layer to assign various levels of value to various features and the inputting of multi-source information.

Conclusion

5 fold cross-validation evaluations show that the GCMM outperforms four recently proposed deep-learning models on the majority of the criteria. It shows that GCMM can predict drug–disease relationships reliably and suggests improvement in the desired metrics. Hyper-parameter analysis and exploratory ablation experiments are also provided to demonstrate the necessity of each module of the model and the highest possible level of prediction performance. Additionally, a case study on Alzheimer’s disease (AD). Four of the five medications indicated by GCMM to have the highest potential correlation coefficient with AD have been demonstrated through literature or experimental research, demonstrating the viability of GCMM. All of these results imply that GCMM can provide a strong and effective tool for drug development and repositioning.

Probenecid induces the recovery of renal ischemia/reperfusion injury via the blockade of Pannexin 1/P2X7 receptor axis

Renal ischemia/reperfusion injury (RI/RI) is one of the main driving causes of acute kidney injury. However, effective treatment to limit injury and promote recovery and/or survival is still unavailable. Probenecid (PBN), a drug indicated for refractory gout, exhibits protective activities against several preclinical diseases including cerebral and myocardial I/RI via Pannexin 1 (Panx1) and P2X7 receptors' (P2X7R) inhibition. However, its protective role against RI/RI has not been previously addressed. Accordingly, we subjected rats to bilateral RI/RI with/or without PBN treatment. Twenty-four hours post-reperfusion, PBN showed mild tubular injury and reduced serum nephrotoxicity indices, gene and protein expression levels of Panx 1 and P2X7R, and ATP and pro-inflammatory cytokines' levels. The nucleotide-binding domain-like receptor protein 3 (NLRP3) inflammasome signaling was also downregulated, as demonstrated by reduced gene and protein expression of NLRP3 and caspase-1, along with suppressed IL-1β maturation. Furthermore, PBN enhanced Tregs activity as indicated by elevated FoxP3 gene expression, IL-10, and TGF-β renal levels. On day 5 post-reperfusion, PBN noticeably enhanced renal recovery, as demonstrated by intact tubular epithelium and restored nephrotoxicity indices, Panx 1 and P2X7R gene and protein expression levels, ATP and pro-inflammatory cytokine levels, and NLRP3 inflammasome signaling. Besides, renal Tregs activity was also significantly increased. Our study elaborates for the first time the effectiveness of PBN in recovering post-ischemic renal injury through synergistic inhibition in Panx1/P2X7R axis leading to inactivation of NLRP3 inflammasome signaling and activation of Tregs in ischemic renal tissues. Therefore, PBN can be considered a promising drug for RI/RI treatment.

Pretreatment with Zonisamide Mitigates Oxaliplatin-Induced Toxicity in Rat DRG Neurons and DRG Neuron–Schwann Cell Co-Cultures

Oxaliplatin (OHP) is a platinum-based agent that can cause peripheral neuropathy, an adverse effect in which the dorsal root ganglion (DRG) neurons are targeted. Zonisamide has exhibited neuroprotective activities toward adult rat DRG neurons in vitro and therefore, we aimed to assess its potential efficacy against OHP-induced neurotoxicity. Pretreatment with zonisamide (100 μM) alleviated the DRG neuronal death caused by OHP (75 μM) and the protective effects were attenuated by a co-incubation with 25 μM of the mitogen-activated protein kinase (MAPK; MEK/ERK) inhibitor, U0126, or the phosphatidyl inositol-3′-phosphate-kinase (PI3K) inhibitor, LY294002. Pretreatment with zonisamide also suppressed the OHP-induced p38 MAPK phosphorylation in lined DRG neurons, ND7/23, while the OHP-induced DRG neuronal death was alleviated by pretreatment with the p38 MAPK inhibitor, SB239063 (25 μM). Although zonisamide failed to protect the immortalized rat Schwann cells IFRS1 from OHP-induced cell death, it prevented neurite degeneration and demyelination-like changes, as well as the reduction of the serine/threonine-specific protein kinase (AKT) phosphorylation in DRG neuron–IFRS1 co-cultures exposed to OHP. Zonisamide’s neuroprotection against the OHP-induced peripheral sensory neuropathy is possibly mediated by a stimulation of the MEK/ERK and PI3K/AKT signaling pathways and suppression of the p38 MAPK pathway in DRG neurons. Future studies will allow us to solidify zonisamide as a promising remedy against the neurotoxic adverse effects of OHP.

Prediction of Drug-Related Diseases Through Integrating Pairwise Attributes and Neighbor Topological Structures

Identifying new disease indications for the approved drugs can help reduce the cost and time of drug development. Most of the recent methods focus on exploiting the various information related to drugs and diseases for predicting the candidate drug-disease associations. However, the previous methods failed to deeply integrate the neighborhood topological structure and the node attributes of an interested drug-disease node pair. We propose a new prediction method, ANPred, to learn and integrate pairwise attribute information and neighbor topology information from the similarities and associations related to drugs and diseases. First, a bi-layer heterogeneous network with intra-layer and inter-layer connections is established to combine the drug similarities, the disease similarities, and the drug-disease associations. Second, the embedding of a pair of drug and disease is constructed based on integrating multiple biological premises about drugs and diseases. The learning framework based on multi-layer convolutional neural networks is designed to learn the attribute representation of the pair of drug and disease nodes from its embedding. The sequences composed of neighbor nodes are formed based on random walk on the heterogeneous network. A framework based on fully-connected autoencoder and skip-gram module is constructed to learn the neighbor topological representations of nodes. The cross-validation results indicate the performance of ANPred is superior to several state-of-the-art methods. The case studies on 5 drugs further confirm the ability of ANPred in discovering the potential drug-disease association candidates.

RP-HPLC Method Development, Validation, and Drug Repurposing of Sofosbuvir Pharmaceutical Dosage Form: A Multidimensional Study

A smooth, exceptionally sensitive, correct, and extra reproducible RP-HPLC technique was developed and demonstrated to estimate Sofosbuvir (SOF) in pharmaceutical dosage formulations. This process was carried out by Agilent High-Pressure Liquid Chromatograph 1260 with GI311C Quat. Pump, Phenomenex Luna C-18 (150 mm × 4.6 mm × 5 μm) (USA), and Photodiode Array Detector (PDA) G1315D. The cell section, including acetonitrile and methanol with 80:20 v/v and solution (B) 0.1% phosphoric acid (40:60), was used for the study. However, 10 μL of the sample was injected with a drift flow of 1 mL/min. The separation occurred at a column temperature of 30 °C, and the eluents used PDA set at 260 nm. The retention time of SOF was 5 min. The calibration curve was modified linearly within the range of 0.05-0.15 mg/mL with a correlation coefficient of 0.99 and genuine linear dating among top vicinity and consciousness in the calibration curve. The detection and quantification restrictions were 0.001 and 0.003 mg/mL, respectively. SOF recovery from pharmaceutical components ranged from 98% to 99%. The percentage assay of SOF was 99%. Analytical validation parameters, such as specificity, linearity, precision, accuracy, and selectivity, were studied, and the percentage relative standard deviation (%RSD) was less than 2%. All other key parameters were observed within the desired thresholds. Hence, the proposed RP-HPLC technique was proven effective for developing SOF in bulk and pharmaceutical pill dosage forms. SOF was found to interact with SARS-COV-2 nsp12, and molecular docking results revealed its high affinity and firm binding within the active site groove of nsp12. The key interacting residues include; LYS-72, GLN-75, MET-80 ALA-99, ASN-99, TRP-100, TYR-101 with ASN-99 and TRP-100 forming hydrogen bonds. Molecular Dynamics simulation of SOF and nsp12 complex elucidated that the system was stable throughout 20ns. Therefore, this drug repurposing strategy for SOF can be used for treating COVID-19 infections by performing animal experiments and accurate clinical trials in the future.

Locally Applied Repositioned Hormones for Oral Bone and Periodontal Tissue Engineering: A Narrative Review

Bone and periodontium are tissues that have a unique capacity to repair from harm. However, replacing or regrowing missing tissues is not always effective, and it becomes more difficult as the defect grows larger. Because of aging and the increased prevalence of debilitating disorders such as diabetes, there is a considerable increase in demand for orthopedic and periodontal surgical operations, and successful techniques for tissue regeneration are still required. Even with significant limitations, such as quantity and the need for a donor area, autogenous bone grafts remain the best solution. Topical administration methods integrate osteoconductive biomaterial and osteoinductive chemicals as hormones as alternative options. This is a promising method for removing the need for autogenous bone transplantation. Furthermore, despite enormous investigation, there is currently no single approach that can reproduce all the physiologic activities of autogenous bone transplants. The localized bioengineering technique uses biomaterials to administer different hormones to capitalize on the host’s regeneration capacity and capability, as well as resemble intrinsic therapy. The current study adds to the comprehension of the principle of hormone redirection and its local administration in both bone and periodontal tissue engineering.

Simultaneous Delivery of Doxorubicin and Protease Inhibitor Derivative to Solid Tumors via Star-Shaped Polymer Nanomedicines Overcomes P-gp- and STAT3-Mediated Chemoresistance

The derivative of protease inhibitor ritonavir (5-methyl-4-oxohexanoic acid ritonavir ester; RD) was recently recognized as a potent P-gp inhibitor and cancerostatic drug inhibiting the proteasome and STAT3 signaling. Therefore, we designed high-molecular-weight HPMA copolymer conjugates with a PAMAM dendrimer core bearing both doxorubicin (Dox) and RD (Star-RD + Dox) to increase the circulation half-life to maximize simultaneous delivery of Dox and RD into the tumor. Star-RD inhibited P-gp activity, potently sensitizing both low- and high-P-gp-expressing cancer cells to the cytostatic and proapoptotic activity of Dox in vitro. Star-RD + Dox possessed higher cytostatic and proapoptotic activities compared to Star-Dox and the equivalent mixture of Star-Dox and Star-RD in vitro. Star-RD + Dox efficiently inhibited STAT3 signaling and induced caspase-3 activation and DNA fragmentation in cancer cells in vivo. Importantly, Star-RD + Dox was found to have superior antitumor activity in terms of tumor growth inhibition and increased survival of mice bearing P-gp-expressing tumors.

Heterogeneous multi-scale neighbor topologies enhanced drug-disease association prediction

MOTIVATION

Identifying new uses of approved drugs is an effective way to reduce the time and cost of drug development. Recent computational approaches for predicting drug-disease associations have integrated multi-sourced data on drugs and diseases. However, neighboring topologies of various scales in multiple heterogeneous drug-disease networks have yet to be exploited and fully integrated.

RESULTS

We propose a novel method for drug-disease association prediction, called MGPred, used to encode and learn multi-scale neighboring topologies of drug and disease nodes and pairwise attributes from heterogeneous networks. First, we constructed three heterogeneous networks based on multiple kinds of drug similarities. Each network comprises drug and disease nodes and edges created based on node-wise similarities and associations that reflect specific topological structures. We also propose an embedding mechanism to formulate topologies that cover different ranges of neighbors. To encode the embeddings and derive multi-scale neighboring topology representations of drug and disease nodes, we propose a module based on graph convolutional autoencoders with shared parameters for each heterogeneous network. We also propose scale-level attention to obtain an adaptive fusion of informative topological representations at different scales. Finally, a learning module based on a convolutional neural network with various receptive fields is proposed to learn multi-view attribute representations of a pair of drug and disease nodes. Comprehensive experiment results demonstrate that MGPred outperforms other state-of-the-art methods in comparison to drug-related disease prediction, and the recall rates for the top-ranked candidates and case studies on five drugs further demonstrate the ability of MGPred to retrieve potential drug-disease associations.

Review of bioinformatics in Azheimer's Disease Research

Alzheimer's disease (AD) is a severe neurodegenerative disease with slow course of onset and deterioration with time. With the speedup of global aging, AD has become a disease that seriously threatens the physical health of the elderly; therefore, the effective prevention and treatments of AD is an extremely important area of study for researchers and clinicians. Rapid technological developments have promoted the analysis of various kinds of complex data sets using machine learning methods. The common machine learning algorithms, such as Lasso, SVM and Random Forest, are very important in AD research. To help accelerate AD-related research, we review some recent research progress on Alzheimer's disease, including database, image analysis, gene expression, etc., which can provide AD researchers with more comprehensive research methods.

Integration of Neighbor Topologies Based on Meta-Paths and Node Attributes for Predicting Drug-Related Diseases

Identifying new disease indications for existing drugs can help facilitate drug development and reduce development cost. The previous drug–disease association prediction methods focused on data about drugs and diseases from multiple sources. However, they did not deeply integrate the neighbor topological information of drug and disease nodes from various meta-path perspectives. We propose a prediction method called NAPred to encode and integrate meta-path-level neighbor topologies, multiple kinds of drug attributes, and drug-related and disease-related similarities and associations. The multiple kinds of similarities between drugs reflect the degrees of similarity between two drugs from different perspectives. Therefore, we constructed three drug–disease heterogeneous networks according to these drug similarities, respectively. A learning framework based on fully connected neural networks and a convolutional neural network with an attention mechanism is proposed to learn information of the neighbor nodes of a pair of drug and disease nodes. The multiple neighbor sets composed of different kinds of nodes were formed respectively based on meta-paths with different semantics and different scales. We established the attention mechanisms at the neighbor-scale level and at the neighbor topology level to learn enhanced neighbor feature representations and enhanced neighbor topological representations. A convolutional-autoencoder-based module is proposed to encode the attributes of the drug–disease pair in three heterogeneous networks. Extensive experimental results indicated that NAPred outperformed several state-of-the-art methods for drug–disease association prediction, and the improved recall rates demonstrated that NAPred was able to retrieve more actual drug–disease associations from the top-ranked candidates. Case studies on five drugs further demonstrated the ability of NAPred to identify potential drug-related disease candidates.

Learning multi-scale heterogenous network topologies and various pairwise attributes for drug-disease association prediction

MOTIVATION

Identifying new therapeutic effects for the approved drugs is beneficial for effectively reducing the drug development cost and time. Most of the recent computational methods concentrate on exploiting multiple kinds of information about drugs and disease to predict the candidate associations between drugs and diseases. However, the drug and disease nodes have neighboring topologies with multiple scales, and the previous methods did not fully exploit and deeply integrate these topologies.

RESULTS

We present a prediction method, multi-scale topology learning for drug-disease (MTRD), to integrate and learn multi-scale neighboring topologies and the attributes of a pair of drug and disease nodes. First, for multiple kinds of drug similarities, multiple drug-disease heterogenous networks are constructed respectively to integrate the similarities and associations related to drugs and diseases. Moreover, each heterogenous network has its specific topology structure, which is helpful for learning the corresponding specific topology representation. We formulate the topology embeddings for each drug node and disease node by random walking on each heterogeneous network, and the embeddings cover the neighboring topologies with different scopes. Because the multi-scale topology embeddings have context relationships, we construct Bi-directional long short-term memory-based module to encode these embeddings and their relationships and learn the neighboring topology representation. We also design the attention mechanisms at feature level and at scale level to obtain the more informative pairwise features and topology embeddings. A module based on multi-layer convolutional networks is constructed to learn the representative attributes of the drug-disease node pair according to their related similarity and association information. Comprehensive experimental results indicate that MTRD achieves the superior performance than several state-of-the-art methods for predicting drug-disease associations. MTRD also retrieves more actual drug-disease associations in the top-ranked candidates of the prediction result. Case studies on five drugs further demonstrate MTRD's ability in discovering the potential candidate diseases for the interested drugs.

Exploration of Protein Aggregations in Parkinson's Disease Through Computational Approaches and Big Data Analytics

Protein aggregation has been implicated in numerous neurodegenerative disorders whose etiologies are poorly understood, and for which there are no effective treatments. Here we show that the computational approaches may help us to better understand the basics of Parkinson's disease (PD). The high-resolution structural, dynamical, and mechanistic insights delivered by computational studies of protein aggregation have a unique potential to enable the rational manipulation of oligomer formation. Additionally, big data and machine learning methods may provide valuable insights to better understand the nature of proteins involved in PD and their aggregative behavior for the betterment of PD treatment.

Repurposing old molecules for new indications: Defining pillars of success from lessons in the past

Drug repurposing or studying existing drugs for potential therapeutic utility in newer indications has been identified as an attractive option for treating a number of diseases. Various strategies of drug repurposing include serendipitous observation of drug's unexpected effects, directing the failed investigational drugs to new indications and currently adopted systematic approach to identify, screen and develop existing drug molecules for new off-label indications. Drug repurposing is able to constructively overcome the bottleneck restraints encountered during traditional de novo drug development process in grounds of timelines, cost and resources. However, success rates of drug repurposing programs are not very impressive. Through a meticulous examination of some failed repurposing attempts we aimed to identify key factors leading to high attrition rate in such studies. Based on the fundamental elements of knowledge and evaluation, we have defined four pillars toward improving success rate in drug repurposing programs viz. sound knowledge of the repurposed drug's pharmacological characteristics (pillar 1: drug pharmacology); drug formulation considerations in new indication (pillar 2: drug formulation); evaluation in representative biological assays with translational potential (pillar 3: evaluation in biological assays); and robust clinical trial methodologies including biomarker driven approach to provide conclusive evidence of repurposed drug's efficacy in new indication (pillar 4: clinical evaluation). In addition to the pharmacological challenges, certain regulatory concerns, including lack of clear guidelines for evaluation and market exclusivity pose hurdles in the application of drug repurposing, which may however be overcome to a great extent by adopting some strategies as discussed in this review.

Prediction of drug-disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets

MOTIVATION

The development process of a new drug is time-consuming and costly. Thus, identifying new uses for approved drugs, named drug repositioning, is helpful for speeding up the drug development process and reducing development costs. Existing drug-related disease prediction methods mainly focus on single or multiple drug-disease heterogeneous networks. However, heterogeneous networks, and drug subnets and disease subnet contained in heterogeneous networks cover the common topology information between drug and disease nodes, the specific information between drug nodes and the specific information between disease nodes, respectively.

RESULTS

We design a novel model, CTST, to extract and integrate common and specific topologies in multiple heterogeneous networks and subnets. Multiple heterogeneous networks composed of drug and disease nodes are established to integrate multiple kinds of similarities and associations among drug and disease nodes. These heterogeneous networks contain multiple drug subnets and a disease subnet. For multiple heterogeneous networks and subnets, we then define the common and specific representations of drug and disease nodes. The common representations of drug and disease nodes are encoded by a graph convolutional autoencoder with sharing parameters and they integrate the topological relationships of all nodes in heterogeneous networks. The specific representations of nodes are learned by specific graph convolutional autoencoders, respectively, and they fuse the topology and attributes of the nodes in each subnet. We then propose attention mechanisms at common representation level and specific representation level to learn more informative common and specific representations, respectively. Finally, an integration module with representation feature level attention is built to adaptively integrate these two representations for final association prediction. Extensive experimental results confirm the effectiveness of CTST. Comparison with six latest methods and case studies on five drugs further verify CTST has the ability to discover potential candidate diseases.

Natural Products Are a Promising Source for Anthelmintic Drug Discovery

Parasitic nematodes infect almost all forms of life. In the human context, parasites are one of the major causative factors for physical and intellectual growth retardation in the developing world. In the agricultural setting, parasites have a great economic impact through a reduction in livestock performance or control cost. The main method of controlling these devastating conditions is the use of anthelmintic drugs. Unfortunately, there are only a few anthelmintic drug classes available in the market and significant resistance has developed in most of the parasitic species of livestock. Therefore, development of new anthelmintics with different modes of action is critical for sustainable parasitic control in the future. The drug development pipeline is broadly limited to two types of molecules, namely synthetic compounds and natural plant products. Compared to synthetic compounds, natural products are highly diverse, and many have historically proven valuable in folk medicine to treat various gastrointestinal ailments. This review focus on the use of traditional knowledge-based plant extracts in the development of new therapeutic leads, the approaches used as screening techniques, and common bottlenecks and opportunities in plant-based anthelmintic drug discovery.

Daptomycin suppresses tumor migration and angiogenesis via binding to ribosomal protein S19 in humans

We have previously reported that daptomycin (DAP), a last resort antibiotic, binds to ribosomal protein S19 (RPS19) in humans and exhibits selective anti-cancer activity against MCF7 breast cancer cells. Here, we investigated the role of RPS19 in the anti-cancer effects of DAP and have found that DAP does not induce autophagy, apoptosis or cell viability but does reduce cell proliferation. Our results suggest that an extraribosomal function of RPS19 involves the regulation of vascular endothelial growth factor (VEGF) but not EGF, PDGF or FGF. Engagement of RPS19 by DAP was shown by CETSA and ITDRF_CETSA assays, and knocking down of RPS19 with siRNA increased the potency of DAP in MCF7 cells. In addition, DAP suppressed the secretion of VEGF in cancer cells and thereby inhibited cell migration. Collectively, these data provide an outline of the underlying mechanism of how DAP exhibits anti-cancer activity and suggests that RPS19 could be a promising target for the development of new anticancer drugs.

Repurposing Small Molecules to Target PPAR-γ as New Therapies for Peripheral Nerve Injuries

The slow rate of neuronal regeneration that follows peripheral nerve repair results in poor recovery, particularly where reinnervation of muscles is delayed, leading to atrophy and permanent loss of function. There is a clear clinical need to develop drug treatments that can accelerate nerve regeneration safely, restoring connections before the target tissues deteriorate irreversibly. The identification that the Rho/Rho-associated kinase (ROCK) pathway acts to limit neuronal growth rate is a promising advancement towards the development of drugs. Targeting Rho or ROCK directly can act to suppress the activity of this pathway; however, the pathway can also be modulated through the activation of upstream receptors; one of particular interest being peroxisome proliferator-activated receptor gamma (PPAR-γ). The connection between the PPAR-γ receptor and the Rho/ROCK pathway is the suppression of the conversion of inactive guanosine diphosphate (GDP)-Rho to active guanosine triphosphate GTP-Rho, resulting in the suppression of Rho/ROCK activity. PPAR-γ is known for its role in cellular metabolism that leads to cell growth and differentiation. However, more recently there has been a growing interest in targeting PPAR-γ in peripheral nerve injury (PNI). The localisation and expression of PPAR-γ in neural cells following a PNI has been reported and further in vitro and in vivo studies have shown that delivering PPAR-γ agonists following injury promotes nerve regeneration, leading to improvements in functional recovery. This review explores the potential of repurposing PPAR-γ agonists to treat PNI and their prospective translation to the clinic.

Drug repurposing of dextromethorphan as a cellular target for the management of influenza

BACKGROUND

Influenza viruses are responsible for seasonal epidemics and sporadic pandemics of varying severity in humans, and additional treatment options are needed. High-throughput siRNA screens and a pre-clinical research model demonstrated that dextromethorphan (DM) has anti-viral activity as a cellular target for treatment of influenza. This study examined DM usage and hospitalization rates among patients with laboratory-confirmed influenza in a national cohort of United States veterans. We aimed to evaluate the potential drug repurposing of DM as a cellular target for the management of influenza utilizing a large, national claims and electronic health record database.

METHODS

This retrospective drug-disease association cohort study was conducted using data from the Veterans Affairs Informatics and Computing Infrastructure (VINCI). We used a cohort with laboratory-confirmed diagnosis of influenza and international classification of disease (ICD)-9/10 diagnosis codes of fever, cough, influenza, or acute upper respiratory infection in an outpatient setting. The study outcome is inpatient hospitalization (all-cause and respiratory) within 30 days of influenza diagnosis. We estimated the relative risk for all-cause and respiratory hospitalizations using Poisson generalized linear model (GLM) and a greedy nearest neighbor propensity score 1:1 matched sub-analysis for both hospitalization models.

FINDINGS

A total of 18,677 patients met the inclusion and exclusion criteria and were evaluated in our study. The cohorts consisted of 2801 patients dispensed DM and 15,876 untreated patients (no DM). The Poisson GLM adjusted for covariates demonstrated a relative risk reduction of 34% for all-cause hospitalizations (Relative Risk (RR) 0.66, 95% Confidence Interval (CI) 0.525-0.832) and 40% for respiratory hospitalizations (RR 0.597, 95% CI 0.423-0.843) in patients with influenza treated with DM.

CONCLUSION

Influenza viruses continue to emerge and cause infection (including pandemics) in humans, so there remains a critical need to advance the understanding of influenza treatment. Our results demonstrated reduced hospitalization rates for influenza patients treated with DM. Further research on cellular targets and/or DM is warranted for the treatment of influenza.

Antidepressants and Antipsychotic Agents as Repurposable Oncological Drug Candidates

Drug repurposing, also known as drug repositioning/reprofiling, is a relatively new strategy for the identification of alternative uses of well-known therapeutics that are outside the scope of their original medical indications. Such an approach might entail a number of advantages compared to standard de novo drug development, including less time needed to introduce the drug to the market, and lower costs. The group of compounds that could be considered as promising candidates for repurposing in oncology include the central nervous system drugs, especially selected antidepressant and antipsychotic agents. In this article, we provide an overview of some antidepressants (citalopram, fluoxetine, paroxetine, sertraline) and antipsychotics (chlorpromazine, pimozide, thioridazine, trifluoperazine) that have the potential to be repurposed as novel chemotherapeutics in cancer treatment, as they have been found to exhibit preventive and/or therapeutic action in cancer patients. Nevertheless, although drug repurposing seems to be an attractive strategy to search for oncological drugs, we would like to clearly indicate that it should not replace the search for new lead structures, but only complement de novo drug development.

Brief Overview of Approaches and Challenges in New Antibiotic Development: A Focus On Drug Repurposing

Drug repurposing, or identifying new uses for existing drugs, has emerged as an alternative to traditional drug discovery processes involving de novo synthesis. Drugs that are currently approved or under development for non-antibiotic indications may possess antibiotic properties, and therefore may have repurposing potential, either alone or in combination with an antibiotic. They might also serve as "antibiotic adjuvants" to enhance the activity of certain antibiotics.

Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations

Predicting novel uses for approved drugs helps in reducing the costs of drug development and facilitates the development process. Most of previous methods focused on the multi-source data related to drugs and diseases to predict the candidate associations between drugs and diseases. There are multiple kinds of similarities between drugs, and these similarities reflect how similar two drugs are from the different views, whereas most of the previous methods failed to deeply integrate these similarities. In addition, the topology structures of the multiple drug-disease heterogeneous networks constructed by using the different kinds of drug similarities are not fully exploited. We therefore propose GFPred, a method based on a graph convolutional autoencoder and a fully-connected autoencoder with an attention mechanism, to predict drug-related diseases. GFPred integrates drug-disease associations, disease similarities, three kinds of drug similarities and attributes of the drug nodes. Three drug-disease heterogeneous networks are constructed based on the different kinds of drug similarities. We construct a graph convolutional autoencoder module, and integrate the attributes of the drug and disease nodes in each network to learn the topology representations of each drug node and disease node. As the different kinds of drug attributes contribute differently to the prediction of drug-disease associations, we construct an attribute-level attention mechanism. A fully-connected autoencoder module is established to learn the attribute representations of the drug and disease nodes. Finally, the original features of the drug-disease node pairs are also important auxiliary information for their association prediction. A combined strategy based on a convolutional neural network is proposed to fully integrate the topology representations, the attribute representations, and the original features of the drug-disease pairs. The ablation studies showed the contributions of data related to three types of drug attributes. Comparison with other methods confirmed that GFPred achieved better performance than several state-of-the-art prediction methods. In particular, case studies confirmed that GFPred is able to retrieve more actual drug-disease associations in the top k part of the prediction results. It is helpful for biologists to discover real associations by wet-lab experiments.

Developing Novel Anticancer Drugs for Targeted Populations: An Update

BACKGROUND

Due to higher failure rates, lengthy time and high cost of the traditional de novo drug discovery and development process, the rate of opportunity to get new, safe and efficacious drugs for the targeted population, including pediatric patients with cancer, becomes sluggish.

OBJECTIVES

This paper discusses the development of novel anticancer drugs focusing on the identification and selection of targeted anticancer drug development for the targeted population.

METHODS

Information presented in this review was obtained from different databases, including PUBMED, SCOPUS, Web of Science, and EMBASE. Various keywords were used as search terms.

RESULTS

The pharmaceutical companies currently are executing drug repurposing as an alternative means to accelerate the drug development process that reduces the risk of failure, time and cost, which take 3-12 years with almost 25% overall probability of success as compared to de novo drug discovery and development process (10- 17 years) which has less than 10% probability of success. An alternative strategy to the traditional de novo drug discovery and development process, called drug repurposing, is also presented.

CONCLUSION

Therefore, to continue with the progress of developing novel anticancer drugs for the targeted population, identification and selection of target to specific disease type is important. Considering the aspects of the age of the patient and the disease stages such as each cancer types are different when we study the disease at a molecular level. Drug repurposing technique becomes an influential alternative strategy to discover and develop novel anticancer drug candidates.

The Contribution of Clinical Pharmacologists in Precision Medicine: An Opportunity for Health Care Improvement

Background

Clinical pharmacologists play an important role and have professional value in the field, especially regarding their role within precision medicine (PM) and personalized therapies.

Objective

In this work, we sought to stimulate debate on the role of clinical pharmacologists.

Methods

A literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement, through electronic consultation of 2 databases, PubMed/Medline and Embase, and Google Scholar with manual research taking into account the peer-reviewed literature such as observational studies, reviews, original research articles, comments, mini-reviews, and opinion papers published in English between 2010 and February 2020. Titles and abstracts were screened by 1 author, and studies identified for full-text analysis and selected according to inclusion criteria were agreed on by 2 reviewers.

Results

We identified a total of 535 peer-reviewed articles and the number of full texts eligible for the project was 43. Several publications highlight the clinical value of pharmacologists in highly complex hospitals, where the strategies of PM are implemented. Although there are still no studies measuring the clinical efficiency and the efficacy of clinical pharmacology services, and the applicability of PM protocols, this review shows the considerable debate around the future mission of clinical pharmacology services as a bridging discipline capable of combining the complex knowledge and different professional skills needed to fully implement PM.

Conclusions

Various strategies have been conceived and planned to facilitate the transition from mainstream medicine to PM, which will enable patients to be treated more accurately, with significant advantages in terms of safety and effectiveness of treatments. Therefore, in the future, to ensure that the evolutionary process of medicine can involve as many patients and caregivers as possible, infrastructures capable of bringing together different multidisciplinary skills among health professionals will have to be implemented. Clinical pharmacologists could be the main drivers of this strategy because they already, with their multidisciplinary training, operate in a series of services in high-level hospitals, facilitating the clinical governance of the most challenging patients. The implementation of these strategies will lastly allow national health organizations to adequately address the management and therapeutic challenges related to the advent of new drugs and cell and gene therapies by facilitating the removal of economic and organizational barriers to ensure equitable access to PM. (Curr Ther Res Clin Exp. 2021; 82:XXX–XXX)

The Current Challenges for Drug Discovery in CNS Remyelination

The myelin sheath wraps around axons, allowing saltatory currents to be transmitted along neurons. Several genetic, viral, or environmental factors can damage the central nervous system (CNS) myelin sheath during life. Unless the myelin sheath is repaired, these insults will lead to neurodegeneration. Remyelination occurs spontaneously upon myelin injury in healthy individuals but can fail in several demyelination pathologies or as a consequence of aging. Thus, pharmacological intervention that promotes CNS remyelination could have a major impact on patient’s lives by delaying or even preventing neurodegeneration. Drugs promoting CNS remyelination in animal models have been identified recently, mostly as a result of repurposing phenotypical screening campaigns that used novel oligodendrocyte cellular models. Although none of these have as yet arrived in the clinic, promising candidates are on the way. Many questions remain. Among the most relevant is the question if there is a time window when remyelination drugs should be administrated and why adult remyelination fails in many neurodegenerative pathologies. Moreover, a significant challenge in the field is how to reconstitute the oligodendrocyte/axon interaction environment representative of healthy as well as disease microenvironments in drug screening campaigns, so that drugs can be screened in the most appropriate disease-relevant conditions. Here we will provide an overview of how the field of in vitro models developed over recent years and recent biological findings about how oligodendrocytes mature after reactivation of their staminal niche. These data have posed novel questions and opened new views about how the adult brain is repaired after myelin injury and we will discuss how these new findings might change future drug screening campaigns for CNS regenerative drugs.

Recycling the Purpose of Old Drugs to Treat Ovarian Cancer

The main challenge in ovarian cancer treatment is the management of recurrences. Facing this scenario, therapy selection is based on multiple factors to define the best treatment sequence. Target therapies, such as bevacizumab and polymerase (PARP) inhibitors, improved patient survival. However, despite their achievements, ovarian cancer survival remains poor; these therapeutic options are highly costly and can be associated with potential side effects. Recently, it has been shown that the combination of repurposed, conventional, chemotherapeutic drugs could be an alternative, presenting good patient outcomes with few side effects and low costs for healthcare institutions. The main aim of this review is to strengthen the importance of repurposed drugs as therapeutic alternatives, and to propose an in vitro model to assess the therapeutic value. Herein, we compiled the current knowledge on the most promising non-oncological drugs for ovarian cancer treatment, focusing on statins, metformin, bisphosphonates, ivermectin, itraconazole, and ritonavir. We discuss the primary drug use, anticancer mechanisms, and applicability in ovarian cancer. Finally, we propose the use of these therapies to perform drug efficacy tests in ovarian cancer ex vivo cultures. This personalized testing approach could be crucial to validate the existing evidences supporting the use of repurposed drugs for ovarian cancer treatment.

Physicochemical characterization and in vitro biological evaluation of solid compounds from furazolidone-based cyclodextrins for use as leishmanicidal agents

The discovery of new drugs and dosage forms for the treatment of neglected tropical diseases, such as human and animal leishmaniasis, is gaining interest in the chemical, biological, pharmaceutical, and medical fields. Many pharmaceutical companies are exploring the use of old drugs to establishing new drug dosage forms and drug delivery systems, in particular for use in neglected diseases. The formation of complexes with cyclodextrins is widely used to improve the stability, solubility, and bioavailability of pharmaceutical drugs, as well as reduce both the toxicity and side effects of many of these drugs. The aim of this study was to characterize solid compounds obtained from the association between furazolidone (FZD) and β-cyclodextrin (β-CD) or hydroxypropyl-β-cyclodextrin (HP-β-CD). The solid compounds were prepared in molar ratios of 1:1 and 1:2 (drug:CD) by kneading and lyophilization. Molecular docking was used to predict the preferred relative orientation of FZD when bound in both studied cyclodextrins. The resulting solid compounds were qualitatively characterized by scanning electron microscopy (SEM), thermal analysis (DSC and TG/DTG), X-ray diffraction (XRD), Raman spectroscopy with image mapping (Raman mapping), and ¹³C nuclear magnetic resonance spectroscopy (¹³C NMR) in the solid state. The cytotoxicity of the compounds against THP-1 macrophages and the 50% growth inhibition (IC₅₀) against Leishmania amazonensis promastigote forms were subsequently investigated using in vitro techniques. For all of the solid compounds obtained, the existence of an association between FZD and CD were confirmed by one or more characterization techniques (TG/DTG, DSC, SEM, XRD, RAMAN, and ¹³C NMR), particularly by a significant decrease in the crystallinity of these materials and a reduction in the melting enthalpy associated with furazolidone thermal events. The formation of more effective interactions occurred in the compounds prepared by lyophilization, in a 1:2 molar ratio of the two CDs studied. However, the formation of an inclusion complex was confirmed only for the solid compound obtained from HP-β-CD prepared by lyophilization (LHFZD1:2). The absence of cytotoxicity on the THP-1 macrophage lineages and the leishmanicidal activity were confirmed for all compounds. MHFZD1:2 and LHFZD1:2 were found to be very active against promastigote forms of L. amazonensis, while all others were considered only active. These results are in line with the literature, demonstrating the existence of biological activity for associations between drugs and CDs in the form of complexes and non-complexes. All solid compounds obtained were found to be promising for use as leishmanicidal agents against promastigote forms of L. amazonensis.

The potential of drug repositioning as a short-term strategy for the control and treatment of COVID-19 (SARS-CoV-2): a systematic review

The novel human coronavirus (SARS-CoV-2), the causative agent of COVID-19, has quickly become a threat to the public health and economy worldwide. Despite the severity of some cases, there are no current pathogen-specific antivirals available to treat the disease. Therefore, many studies have focused on the evaluation of the anti-SARS-CoV-2 activity of clinically available drugs. Here, we conducted a systematic review to describe the drug repositioning strategy against SARS-CoV-2 and to discuss the clinical impact of this approach in the current pandemic context. The systematic review was performed on March 23, 2020, using PubMed/MEDLINE, Scopus, Cochrane Library, and Biblioteca Virtual de Saúde (BVS). The data were summarized in tables and critically analyzed. After the database search, 12 relevant studies were identified as eligible for the review. Among the drugs reported in these studies, 57 showed some evidence of antiviral activity. Antivirals, especially antiretrovirals, are the main class of therapeutic agents evaluated against COVID-19. Moreover, studies have reported the anti-SARS-CoV-2 activity of antitumor (16%; 9/57), antimalarial (7%, 4/57), and antibacterial (5%; 3/57) agents. Additionally, seven pharmacological agents (chloroquine, tetrandrine, umifenovir (arbidol), carrimycin, damageprevir, lopinavir/ritonavir) are in phase IV of clinical trials. Due to the evidence of the anti-SARS-CoV-2 activity of various clinically available agents, drug repositioning stands out as a promising strategy for a short-term response in the fight against the novel coronavirus.

Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs

Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.

Drug repurposing against Parkinson's disease by text mining the scientific literature

Purpose

Drug repurposing involves the identification of new applications for existing drugs. Owing to the enormous rise in the costs of pharmaceutical R&D, several pharmaceutical companies are leveraging repurposing strategies. Parkinson's disease is the second most common neurodegenerative disorder worldwide, affecting approximately 1–2 percent of the human population older than 65 years. This study proposes a literature-based drug repurposing strategy in Parkinson's disease.

Design/methodology/approach

The literature-based drug repurposing strategy proposed herein combined natural language processing, network science and machine learning methods for analyzing unstructured text data and producing actional knowledge for drug repurposing. The approach comprised multiple computational components, including the extraction of biomedical entities and their relationships, knowledge graph construction, knowledge representation learning and machine learning-based prediction.

Findings

The proposed strategy was used to mine information pertaining to the mechanisms of disease treatment from known treatment relationships and predict drugs for repurposing against Parkinson's disease. The F1 score of the best-performing method was 0.97, indicating the effectiveness of the proposed approach. The study also presents experimental results obtained by combining the different components of the strategy.

Originality/value

The drug repurposing strategy proposed herein for Parkinson's disease is distinct from those existing in the literature in that the drug repurposing pipeline includes components of natural language processing, knowledge representation and machine learning for analyzing the scientific literature. The results of the study provide important and valuable information to researchers studying different aspects of Parkinson's disease.

NF-κB Inhibitors Attenuate MCAO Induced Neurodegeneration and Oxidative Stress-A Reprofiling Approach

Stroke is the leading cause of morbidity and mortality worldwide. About 87% of stroke cases are ischemic, which disrupt the physiological activity of the brain, thus leading to a series of complex pathophysiological events. Despite decades of research on neuroprotectants to probe for suitable therapies against ischemic stroke, no successful results have been obtained, and new alternative approaches are urgently required in order to combat this pathological torment. To address these problems, drug repositioning/reprofiling is explored extensively. Drug repurposing aims to identify new uses for already established drugs, and this makes it an attractive commercial strategy. Nuclear factor-kappa beta (NF-κB) is reported to be involved in many physiological and pathological conditions, such as neurodegeneration, neuroinflammation, and ischemia/reperfusion (I/R) injury. In this study, we examined the neuroprotective effects of atorvastatin, cephalexin, and mycophenolate against the NF-κB in ischemic stroke, as compared to the standard NF-κB inhibitor caeffic acid phenethyl ester (CAPE). An in-silico docking analysis was performed and their potential neuroprotective activities in the in vivo transient middle cerebral artery occlusion (t-MCAO) rat model was examined. The percent (%) infarct area and 28-point composite neuro score were examined, and an immunohistochemical analysis (IHC) and enzyme-linked immunosorbent assay (ELISA) were further performed to validate the neuroprotective role of these compounds in stroke as well as their potential as antioxidants. Our results demonstrated that these novels NF-κB inhibitors could attenuate ischemic stroke-induced neuronal toxicity by targeting NF-κB, a potential therapeutic approach in ischemic stroke.

Current and Novel Therapies Against Helminthic Infections: The Potential of Antioxidants Combined with Drugs

Infections caused by Schistosoma haematobium and Opisthorchisviverrini are classified as Group 1 biological carcinogen and it has been postulated that parasites produce oxysterol and estrogen-like metabolites that might be considered as initiators of infection-associated carcinogenesis. Chemotherapy for these helminthic infections relies on a single drug, praziquantel, (PZQ) that mainly targets the parasite. Additionally, PZQ has some major drawbacks as inefficacy against juvenile form and alone it is not capable to counteract pathologies associated to infections or prevent carcinogenesis. There is an urgent need to develop novel therapeutic approaches that not only target the parasite but also improve the pathologies associated to infection, and ultimately, counteract or/and prevent the carcinogenesis processes. Repurposing the drug in combination of compounds with different modes of action is a promising strategy to find novel therapeutics approaches against these helminthic infections and its pathologies. Here, we emphasized that using antioxidants either alone or combined with anthelmintic drugs could ameliorate tissue damage, infection-associated complications, moreover, could prevent the development of cancer associated to infections. Hence, antioxidants represent a potential adjuvant approach during treatment to reduce morbidity and mortality. Despite the success of some strategies, there is a long way to go to implement novel therapies for schistosomiasis.

Biapenem as a Novel Insight into Drug Repositioning against Particulate Matter-Induced Lung Injury

The screening of biologically active chemical compound libraries can be an efficient way to reposition Food and Drug Adminstration (FDA)-approved drugs or to discover new therapies for human diseases. Particulate matter with an aerodynamic diameter equal to or less than 2.5 μm (PM_2.5) is a form of air pollutant that causes significant lung damage when inhaled. This study illustrates drug repositioning with biapenem (BIPM) for the modulation of PM-induced lung injury. Biapenem was used for the treatment of severe infections. Mice were treated with BIPM via tail-vein injection after the intratracheal instillation of PM_2.5. Alterations in the lung wet/dry weight, total protein/total cell count and lymphocyte count, inflammatory cytokines in the bronchoalveolar lavage fluid (BALF), vascular permeability, and histology were monitored in the PM_2.5-treated mice. BIPM effectively reduced the pathological lung injury, lung wet/dry weight ratio, and hyperpermeability caused by PM_2.5. Enhanced myeloperoxidase (MPO) activity by PM_2.5 in the pulmonary tissue was inhibited by BIPM. Moreover, increased levels of inflammatory cytokines and total protein by PM_2.5 in the BALF were also decreased by BIPM treatment. In addition, BIPM markedly suppressed PM_2.5-induced increases in the number of lymphocytes in the BALF. Additionally, the activity of mammalian target of rapamycin (mTOR) was increased by BIPM. Administration of PM_2.5 increased the expression levels of toll-like receptor 4 (TLR4), MyD88, and the autophagy-related proteins LC3 II and Beclin 1, which were suppressed by BIPM. In conclusion, these findings indicate that BIPM has a critical anti-inflammatory effect due to its ability to regulate both the TLR4-MyD88 and mTOR-autophagy pathways, and may thus be a potential therapeutic agent against diesel PM_2.5-induced pulmonary injury.