scholarly journals Considerations and Challenges for Sex-Aware Drug Repurposing

2021 ◽  
Author(s):  
Jennifer Fisher ◽  
Emma Jones ◽  
Victoria Flanary ◽  
Avery Williams ◽  
Elizabeth Ramsey ◽  
...  
Keyword(s):  
Sex Differences ◽  
Drug Discovery ◽  
Study Design ◽  
Drug Response ◽  
Drug Repurposing ◽  
Multiple Hypothesis Testing ◽  
Sex Bias ◽  
Therapeutic Effects ◽  
Drug Candidates ◽  
Traditional Drug

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration (1). The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health’s (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER)) policies to motivate researchers to consider sex differences (2). However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses (1,3–5). Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information (3,6,7). They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex (8). Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods (3). However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods (9,10). Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

ReSimNet: drug response similarity prediction using Siamese neural networks

2019 ◽  
Vol 35 (24) ◽  
pp. 5249-5256 ◽  
Author(s):  
Minji Jeon ◽  
Donghyeon Park ◽  
Jinhyuk Lee ◽  
Hwisang Jeon ◽  
Miyoung Ko ◽  
...  
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Drug Response ◽  
Pearson Correlation ◽  
Transcriptional Response ◽  
Chemical Compounds ◽  
Supplementary Information ◽  
Drug Candidates ◽  
Traditional Drug ◽  
The Difference

Abstract Motivation Traditional drug discovery approaches identify a target for a disease and find a compound that binds to the target. In this approach, structures of compounds are considered as the most important features because it is assumed that similar structures will bind to the same target. Therefore, structural analogs of the drugs that bind to the target are selected as drug candidates. However, even though compounds are not structural analogs, they may achieve the desired response. A new drug discovery method based on drug response, which can complement the structure-based methods, is needed. Results We implemented Siamese neural networks called ReSimNet that take as input two chemical compounds and predicts the CMap score of the two compounds, which we use to measure the transcriptional response similarity of the two compounds. ReSimNet learns the embedding vector of a chemical compound in a transcriptional response space. ReSimNet is trained to minimize the difference between the cosine similarity of the embedding vectors of the two compounds and the CMap score of the two compounds. ReSimNet can find pairs of compounds that are similar in response even though they may have dissimilar structures. In our quantitative evaluation, ReSimNet outperformed the baseline machine learning models. The ReSimNet ensemble model achieves a Pearson correlation of 0.518 and a precision@1% of 0.989. In addition, in the qualitative analysis, we tested ReSimNet on the ZINC15 database and showed that ReSimNet successfully identifies chemical compounds that are relevant to a prototype drug whose mechanism of action is known. Availability and implementation The source code and the pre-trained weights of ReSimNet are available at https://github.com/dmis-lab/ReSimNet. Supplementary information Supplementary data are available at Bioinformatics online.

Development and Advances of Drugs for Cancer Theranostics – PART-IV

2021 ◽  
Vol 21 (18) ◽  
pp. 1644-1644
Author(s):  
Lian-Shun Feng
Keyword(s):  
Drug Discovery ◽  
Side Effects ◽  
Anticancer Activity ◽  
Anticancer Drug ◽  
Anticancer Agents ◽  
Drug Repurposing ◽  
Biological Properties ◽  
Drug Candidates ◽  
Pharmacokinetic Profiles ◽  
Hybrid Molecules

Cancer, a highly heterogeneous disease at intra/inter patient levels, is one of the most serious threats to human health across the world [1, 2]. Notwithstanding the noteworthy advances in its treat-ment, the morbidity and mortality of cancer are projected to grow for a long period, and the global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020 [3]. Accordingly, there is a constant need to explore novel anticancer agents. <p> There are several strategies to discover novel anticancer candidates: (1) new lead hits or candidates from natural resources [4] whichexhibit various biological properties and are a rich source of com-pounds in drug discovery due to the structural and mechanistic diversity, and more than 60% anti-cancer agents can be traced to a natural product; (2) Molecular hybridization is one of the most prom-ising strategies for the discovery of novel anticancer drug candidates since hybrid molecules have the potential to bind multiple targets or to enhance the effect through acting with another bio-target or to counterbalance the side effects caused by the other part of the hybrid [5]; (3) Dimerization is a useful tool to develop novel anticancer drug candidates with enhanced biological activity, reduced side effects and improved pharmacokinetic profiles [6]; (4) Drug repurposing strategy is is an attractive strategy and has been approved, along with non-anticancer macrolide drugs for the treatment of cancer, for anticancer drug discovery since toxicity and pharmacokinetic profiles have already been estab-lished [7]. <p> Heterocycles coumarin, β-lactone, macrolide and triazole are useful anticancer pharmacophores since their derivatives could exert the anticancer activity through diverse mechanisms, inclusive of inhibition of aromatase, carbonic anhydrase, ki-nase, P-glycoprotein, sulfatase, telomerase, vascular endothelial growth factor receptor 2 and tubulin [8-11]. In particular, nat-ural-derived coumarin, β-lactone and macrolide derivatives are important sources of new anticancer lead hits/candidates; mac-rolide repurposed drugs can circumvent high cost and long-time associated with traditional drug discovery strategies; couma-rin, β-lactone and macrolide hybrids as well as bis-triazole compounds have the potential to enhance the anticancer activity, overcome drug resistance, reduce the side effects and improve pharmacokinetic profiles.

Drug Discovery by Drug Repurposing: Combating COVID-19 in the 21st Century

2020 ◽  
Vol 20 ◽  
Author(s):  
Nitesh Sanghai ◽  
Kashfia Shafiq ◽  
Geoffrey K. Tranmer
Keyword(s):  
Drug Discovery ◽  
Drug Repurposing ◽  
Compassionate Use ◽  
Drug Candidates ◽  
Working Day ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Day By Day ◽  
Potential Use

: Due to the rapidly developing nature of the current COVID-19 outbreak and its almost immediate humanitarian and economic toll, coronavirus drug discovery efforts have largely focused on generating potential COVID-19 drug candidates as quickly as possible. Globally, scientists are working day and night to find the best possible solution to treat the deadly virus. During the first few months of 2020, the SARS-CoV-2 outbreak quickly developed into a pandemic, with a mortality rate that was increasing at an exponential rate day by day. As a result, scientists have turned to a drug repurposing approach, to rediscover the potential use and benefits of existing approved drugs. Currently, there is no single drug approved by the U.S. Food and Drug Administration (FDA), for the treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously known as 2019-nCoV) that causes COVID-19. Based on only in-vitro studies, several active drugs are already in the clinical pipeline, made possible by following the compassionate use of medicine protocols. This method of repurposing and the use of existing molecules like Remdesivir (GS-5734), Chloroquine, Hydroxychloroquine, etc. has proven to be a landmark in the field of drug rediscovery. In this review article we will discuss the repurposing of medicines for treating the deadly novel coronavirus (SARS-CoV-2).

scholarly journals Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Sepehr Golriz Khatami ◽  
Sarah Mubeen ◽  
Vinay Srinivas Bharadhwaj ◽  
Alpha Tom Kodamullil ◽  
Martin Hofmann-Apitius ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Response ◽  
Clinical Decision Making ◽  
Biological Process ◽  
Drug Repurposing ◽  
Clinical Decision ◽  
Patient Specific ◽  
Drug Candidates ◽  
Scoring Algorithm ◽  
Specific Pathway

AbstractThe utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of applications including precision medicine, drug repurposing, and drug discovery. In this work, we leverage highly predictive ML models for drug response simulation in individual patients by calibrating the pathway activity scores of disease samples. Using these ML models and an intuitive scoring algorithm to modify the signatures of patients, we evaluate whether a given sample that was formerly classified as diseased, could be predicted as normal following drug treatment simulation. We then use this technique as a proxy for the identification of potential drug candidates. Furthermore, we demonstrate the ability of our methodology to successfully identify approved and clinically investigated drugs for four different cancers, outperforming six comparable state-of-the-art methods. We also show how this approach can deconvolute a drugs’ mechanism of action and propose combination therapies. Taken together, our methodology could be promising to support clinical decision-making in personalized medicine by simulating a drugs’ effect on a given patient.

scholarly journals Recent Advances in In Silico Target Fishing

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5124 ◽  
Author(s):  
Salvatore Galati ◽  
Miriana Di Stefano ◽  
Elisa Martinelli ◽  
Giulio Poli ◽  
Tiziano Tuccinardi
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Drug Targets ◽  
Biological Activities ◽  
Protein Structures ◽  
Drug Repurposing ◽  
Specific Protein ◽  
Protein Targets ◽  
Drug Candidates ◽  
Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

scholarly journals Drug Repurposing: A Paradigm Shift in Drug Discovery

2020 ◽  
Vol 5 (04) ◽  
pp. 60-68
Author(s):  
Saravanan Jayaram ◽  
Emdormi Rymbai ◽  
Deepa Sugumar ◽  
Divakar Selvaraj
Keyword(s):  
Drug Discovery ◽  
Success Rate ◽  
Chemical Structure ◽  
Target Identification ◽  
Drug Repurposing ◽  
New Drugs ◽  
Attractive Option ◽  
Traditional Drug ◽  
Repurposed Drugs

The traditional methods of drug discovery and drug development are a tedious, complex, and costly process. Target identification, target validation; lead identification; and lead optimization are a lengthy and unreliable process that further complicates the discovery of new drugs. A study of more than 15 years reports that the success rate in the discovery of new drugs in the fields of ophthalmology, cardiovascular, infectious disease, and oncology to be 32.6%, 25.5%, 25.2% and 3.4%, respectively. A tedious and costly process coupled with a very low success rate makes the traditional drug discovery a less attractive option. Therefore, an alternative to traditional drug discovery is drug repurposing, a process in which already existing drugs are repurposed for conditions other than which were originally intended. Typical examples of repurposed drugs are thalidomide, sildenafil, memantine, mirtazapine, mifepristone, etc. In recent times, several databases have been developed to hasten drug repurposing based on the side effect profile, the similarity of chemical structure, and target site. This work reviews the pivotal role of drug repurposing in drug discovery and the databases currently available for drug repurposing.

Harnessing Drug Repurposing for Exploration of New Diseases: An Insight to Strategies and Case Studies

2020 ◽  
Vol 20 ◽  
Author(s):  
Priti Jain ◽  
Shreyans K Jain ◽  
Munendra Jain
Keyword(s):  
Drug Discovery ◽  
Case Studies ◽  
Drug Repositioning ◽  
Pediatric Population ◽  
Clinical Status ◽  
Drug Repurposing ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Traditional Drug ◽  
Approved Drugs

Background: Traditional drug discovery is time consuming, costly, and risky process. Owing to the large investment, excessive attrition, and declined output; drug repurposing has become a blooming approach for the identification and development of new therapeutics. The method has gained momentum in the past few years and has resulted in many excellent discoveries. Industries are resurrecting the failed and shelved drugs to save time and cost. The process accounts for approximately 30% of the new US Food and Drug Administration approved drugs and vaccines in recent years. Methods: A systematic literature search using appropriate keywords were made to identify articles discussing the different strategies being adopted for repurposing and various drugs that have been/are being repurposed. Results: This review aims to describe the comprehensive data about the various strategies (Blinded search, computational approaches, and experimental approaches) used for the repurposing along with success case studies (treatment for orphan diseases, neglected tropical disease, neurodegenerative diseases, and drugs for pediatric population). It also inculcates an elaborated list of more than 100 drugs that have been repositioned, approaches adopted, and their present clinical status. We have also attempted to incorporate the different databases used for computational repurposing. Conclusion: The data presented is proof that drug repurposing is a prolific approach circumventing the issues poised by conventional drug discovery approaches. It is a highly promising approach and when combined with sophisticated computational tools it also carries high precision. The review would help researches in prioritizing the drug-repositioning method much needed to flourish the drug discovery research.

scholarly journals Drug Repurposing (DR): An Emerging Approach in Drug Discovery

2020 ◽  
Author(s):  
Mithun Rudrapal ◽  
Shubham J. Khairnar ◽  
Anil G. Jadhav
Keyword(s):  
Drug Discovery ◽  
De Novo ◽  
Drug Repositioning ◽  
Development Program ◽  
Drug Repurposing ◽  
New Drugs ◽  
Time Saving ◽  
Biological Targets ◽  
Drug Molecules ◽  
Traditional Drug

Drug repurposing (DR) (also known as drug repositioning) is a process of identifying new therapeutic use(s) for old/existing/available drugs. It is an effective strategy in discovering or developing drug molecules with new pharmacological/therapeutic indications. In recent years, many pharmaceutical companies are developing new drugs with the discovery of novel biological targets by applying the drug repositioning strategy in drug discovery and development program. This strategy is highly efficient, time saving, low-cost and minimum risk of failure. It maximizes the therapeutic value of a drug and consequently increases the success rate. Thus, drug repositioning is an effective alternative approach to traditional drug discovery process. Finding new molecular entities (NME) by traditional or de novo approach of drug discovery is a lengthy, time consuming and expensive venture. Drug repositioning utilizes the combined efforts of activity-based or experimental and in silico-based or computational approaches to develop/identify the new uses of drug molecules on a rational basis. It is, therefore, believed to be an emerging strategy where existing medicines, having already been tested safe in humans, are redirected based on a valid target molecule to combat particularly, rare, difficult-to-treat diseases and neglected diseases.

scholarly journals Drug Repurposing in Neurological Diseases: Opportunities and Challenges

2020 ◽  
Author(s):  
Xiao-Yuan Mao
Keyword(s):  
Neurological Diseases ◽  
Drug Repurposing ◽  
New Drugs ◽  
Drug Candidates ◽  
Therapeutic Benefits ◽  
Therapeutic Value ◽  
Traditional Drug ◽  
Approved Drugs ◽  
Structure Complexity ◽  
Refractory Diseases

Drug repurposing or repositioning refers to “studying of clinically approved drugs in one disease to see if they have therapeutic value and do not trigger side effects in other diseases.” Nowadays, it is a vital drug discovery approach to explore new therapeutic benefits of existing drugs or drug candidates in various human diseases including neurological disorders. This approach overcomes the shortage faced during traditional drug development in grounds of financial support and timeline. It is especially hopeful in some refractory diseases including neurological diseases. The feature that structure complexity of the nervous system and influence of blood–brain barrier permeability often becomes more difficult to develop new drugs in neuropathological conditions than diseases in other organs; therefore, drug repurposing is particularly of utmost importance. In this chapter, we discuss the role of drug repurposing in neurological diseases and make a summarization of repurposing candidates currently in clinical trials for neurological diseases and potential mechanisms as well as preliminary results. Subsequently we also outline drug repurposing approaches and limitations and challenges in the future investigations.

scholarly journals Mapping the SARS-CoV-2–Host Protein–Protein Interactome by Affinity Purification Mass Spectrometry and Proximity-Dependent Biotin Labeling: A Rational and Straightforward Route to Discover Host-Directed Anti-SARS-CoV-2 Therapeutics

2021 ◽  
Vol 22 (2) ◽  
pp. 532
Author(s):  
Rosa Terracciano ◽  
Mariaimmacolata Preianò ◽  
Annalisa Fregola ◽  
Corrado Pelaia ◽  
Tiziana Montalcini ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Targets ◽  
Affinity Purification ◽  
Drug Repurposing ◽  
Host Protein ◽  
Host Cells ◽  
Traditional Drug ◽  
Approved Drugs

Protein–protein interactions (PPIs) are the vital engine of cellular machinery. After virus entry in host cells the global organization of the viral life cycle is strongly regulated by the formation of virus-host protein interactions. With the advent of high-throughput -omics platforms, the mirage to obtain a “high resolution” view of virus–host interactions has come true. In fact, the rapidly expanding approaches of mass spectrometry (MS)-based proteomics in the study of PPIs provide efficient tools to identify a significant number of potential drug targets. Generation of PPIs maps by affinity purification-MS and by the more recent proximity labeling-MS may help to uncover cellular processes hijacked and/or altered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), providing promising therapeutic targets. The possibility to further validate putative key targets from high-confidence interactions between viral bait and host protein through follow-up MS-based multi-omics experiments offers an unprecedented opportunity in the drug discovery pipeline. In particular, drug repurposing, making use of already existing approved drugs directly targeting these identified and validated host interactors, might shorten the time and reduce the costs in comparison to the traditional drug discovery process. This route might be promising for finding effective antiviral therapeutic options providing a turning point in the fight against the coronavirus disease-2019 (COVID-19) outbreak.

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