scholarly journals Drug REpurposing using AI/ML tools - for Rare Diseases (DREAM-RD): A case study with Fragile X Syndrome (FXS)

2020 ◽  
Author(s):  
Kavitha Agastheeswaramoorthy ◽  
Aarti Sevilimedu
Keyword(s):  
Fragile X Syndrome ◽  
Drug Repositioning ◽  
Fragile X ◽  
Drug Repurposing ◽  
Machine Learning Algorithms ◽  
Specific Gene ◽  
Drug Induced ◽  
Approved Drugs ◽  
Disease Specific

AbstractDrug repositioning is emerging as an increasingly relevant option for rare disease therapy and management. Various methods for identifying suitable drug candidates have been tried and range from clinical symptomatic repurposing to data driven strategies which are based on the disease-specific gene or protein expression, modification, signalling and physiological perturbation profiles. The use of Artificial Intelligence (AI) and machine learning algorithms (ML) allows one to combine diverse data sets, and extract disease-specific data profiles which may not be intuitive or apparent from a subset of data. In this case study with Fragile X syndrome and autism, we have used multiple computational methodologies to extract profiles, which are then combined to arrive at a comprehensive signature (disease DEG). This DEG was then used to interrogate the large collection of drug-induced perturbation profiles present in public databases, to find appropriate small molecules to reverse or mimic the disease-profiles. We have labelled this pipeline Drug Repurposing using AI/ML tools - for Rare Diseases (DREAM-RD). We have shortlisted over 100 FDA approved drugs using the aforementioned pipeline, which may potentially be useful to ameliorate autistic phenotypes associated with FXS.

scholarly journals NOGEA: Network-Oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning

2020 ◽  
Author(s):  
Zihu Guo ◽  
Yingxue Fu ◽  
Chao Huang ◽  
Chunli Zheng ◽  
Ziyin Wu ◽  
...  
Keyword(s):  
Gene Networks ◽  
Drug Repositioning ◽  
High Reliability ◽  
Rapid Development ◽  
Small Neighborhood ◽  
Specific Gene ◽  
Master Genes ◽  
Disease Initiation ◽  
Disease Specific

AbstractRapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes on the interactome network, which provides a new way for predicting new drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

scholarly journals RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Ghazale Fahimian ◽  
Javad Zahiri ◽  
Seyed Shahriar Arab ◽  
Reza H. Sajedi
Keyword(s):  
Breast Cancer ◽  
Biological Networks ◽  
Drug Repositioning ◽  
Machine Learning Algorithms ◽  
New Drugs ◽  
Stage Ii ◽  
Cancer Stage ◽  
New Drug ◽  
Approved Drugs ◽  
Fda Approved Drugs

Abstract Background It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time. Methods In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. Results The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. Conclusion Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.

scholarly journals Computational guided approach for drug repurposing against SARS-CoV-2

2021 ◽  
Author(s):  
Jigisha Anand ◽  
Tanmay Ghildiyal ◽  
Aakanksha Madhwal ◽  
Rishabh Bhatt ◽  
Devvret Verma ◽  
...  
Keyword(s):  
Drug Targets ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Computational Docking ◽  
Drug Candidates ◽  
Docking Tool ◽  
Inhibitory Potential ◽  
Approved Drugs ◽  
Tract Infections

Background: In the current SARS-CoV-2 outbreak, drug repositioning emerges as a promising approach to develop efficient therapeutics in comparison to de novo drug development. The present investigation screened 130 US FDA-approved drugs including hypertension, cardiovascular diseases, respiratory tract infections (RTI), antibiotics and antiviral drugs for their inhibitory potential against SARS-CoV-2. Materials & methods: The molecular drug targets against SARS-CoV-2 proteins were determined by the iGEMDOCK computational docking tool. The protein homology models were generated through SWISS Model workspace. The pharmacokinetics of all the ligands was determined by ADMET analysis. Results: The study identified 15 potent drugs exhibiting significant inhibitory potential against SARS-CoV-2. Conclusion: Our investigation has identified possible repurposed drug candidates to improve the current modus operandi of the treatment given to COVID-19 patients.

scholarly journals Drug Repurposing: A Review

2021 ◽  
pp. 161-169
Author(s):  
Rani Teksinh Bhagat ◽  
Santosh Ramarao Butle
Keyword(s):  
Drug Development ◽  
Research And Development ◽  
Development Process ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
New Drugs ◽  
Preclinical Development ◽  
Therapeutic Uses ◽  
Original Application ◽  
Approved Drugs

The drug development is a very time consuming and complex process. Drug development Process is Expensive. Success rate for the new drug development is very small. In recent years, decreases the new drugs development. The powerful tools are developed to support the research and development (R&D) process is essential. The Drug repurposing are helpful for research and development process. The drug re-purposing as an approach finds new therapeutic uses for current candidates or existing candidates or approved drugs, different from its original application. The main aimed of Drug repurposing is to reduce costs and research time investments in Research & Development. It is used for the diagnosis and treatment of various diseases. Repositioning is important over traditional approaches and need for effective therapies. Drug re-purposing identifies new application for already banned or existing drugs from market. In drug design, drug repurposing plays important role, because it helps to preclinical development. It reducing time efforts, expenses and failures in drug discovery process. It is also called as drug repositioning, drug redirecting, drug reprofiling.

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 High-Throughput Screening Using iPSC-Derived Neuronal Progenitors to Identify Compounds Counteracting Epigenetic Gene Silencing in Fragile X Syndrome

2015 ◽  
Vol 20 (9) ◽  
pp. 1101-1111 ◽  
Author(s):  
Markus Kaufmann ◽  
Ansgar Schuffenhauer ◽  
Isabelle Fruh ◽  
Jessica Klein ◽  
Anke Thiemeyer ◽  
...  
Keyword(s):  
Mental Retardation ◽  
Drug Discovery ◽  
Fragile X Syndrome ◽  
Large Scale ◽  
Fragile X ◽  
Scale Up ◽  
Machine Learning Algorithms ◽  
Fmr1 Gene ◽  
High Content Imaging ◽  
Neuronal Progenitors

Fragile X syndrome (FXS) is the most common form of inherited mental retardation, and it is caused in most of cases by epigenetic silencing of the Fmr1 gene. Today, no specific therapy exists for FXS, and current treatments are only directed to improve behavioral symptoms. Neuronal progenitors derived from FXS patient induced pluripotent stem cells (iPSCs) represent a unique model to study the disease and develop assays for large-scale drug discovery screens since they conserve the Fmr1 gene silenced within the disease context. We have established a high-content imaging assay to run a large-scale phenotypic screen aimed to identify compounds that reactivate the silenced Fmr1 gene. A set of 50,000 compounds was tested, including modulators of several epigenetic targets. We describe an integrated drug discovery model comprising iPSC generation, culture scale-up, and quality control and screening with a very sensitive high-content imaging assay assisted by single-cell image analysis and multiparametric data analysis based on machine learning algorithms. The screening identified several compounds that induced a weak expression of fragile X mental retardation protein (FMRP) and thus sets the basis for further large-scale screens to find candidate drugs or targets tackling the underlying mechanism of FXS with potential for therapeutic intervention.

scholarly journals Gene Signature-Based Drug Repositioning

2021 ◽  
Author(s):  
Zhilong Jia ◽  
Xinyu Song ◽  
Jinlong Shi ◽  
Weidong Wang ◽  
Kunlun He
Keyword(s):  
Drug Repositioning ◽  
Drug Repurposing ◽  
Gene Signature ◽  
Learning Technology ◽  
Omics Data ◽  
Future Trends ◽  
Omics Technology ◽  
Global Projects ◽  
Approved Drugs

With the advent of dynamical omics technology, especially the transcriptome and proteome, a huge amount of data related to various diseases and approved drugs are available under multi global projects or researches with their interests. These omics data and new machine learning technology largely promote the translation of drug research into clinical trials. We will cover the following topics in this chapter. 1) An introduction to the basic discipline of gene signature-based drug repurposing; 2) databases of genes, drugs and diseases; 3) gene signature databases of the approved drugs; 4) gene signature databases of various diseases; 5) gene signature-based methods and tools for drug repositioning; 6) new omics technology for drug repositioning; 7) drug repositioning examples with reproducible code. And finally, discuss the future trends and conclude.

scholarly journals Vorinostat corrects cognitive and non-cognitive symptoms in a mouse model of fragile X syndrome

2020 ◽  
Author(s):  
Qi Ding ◽  
Xueting Wu ◽  
Xuan Li ◽  
Hongbing Wang
Keyword(s):  
Mouse Model ◽  
Fragile X Syndrome ◽  
Fragile X ◽  
Repetitive Behavior ◽  
Clinical Intervention ◽  
Behavioral Symptoms ◽  
Therapeutic Effects ◽  
Object Location Memory ◽  
Approved Drugs ◽  
Cellular Pathology

ABSTRACTFragile X syndrome (FXS) is caused by mutations in the FMR1 (fragile X mental retardation 1) gene. It is a significant form of heritable intellectual disability with comorbidity of other symptoms such as autism. Due to the lack of efficacious medication, repurposing the existing FDA-approved drugs may offer an opportunity to advance clinical intervention for FXS. Analysis of the whole-genome transcription signatures predicts new therapeutic action of vorinostat to correct pathological alterations associated with FXS. We further find that the administration of vorinostat restores object location memory and passive avoidance memory in the Fmr1 knockout (KO) mice. For the non-cognitive behavioral symptoms, vorinostat corrects the autism-associated alterations, including repetitive behavior and social interaction deficits. In the open field test, vorinostat dampens hyperactivity in the center area of the arena. Surprisingly, vorinostat does not affect the abnormally elevated protein synthesis in Fmr1 KO neurons, suggesting different outcomes from correcting behavioral symptoms and specific aspects of cellular pathology. Our data reveal the therapeutic effects of the FDA-approved drug vorinostat in a mouse model of FXS and advocate efficacy testing with human patients.

scholarly journals RepCOOL: Computational Drug Repositioning Via Integrating Heterogeneous Biological Networks

2019 ◽  
Author(s):  
Ghazale Fahimian ◽  
Javad Zahiri ◽  
Seyed Sh. Arab ◽  
Reza H. Sajedi
Keyword(s):  
Breast Cancer ◽  
Biological Networks ◽  
Drug Repositioning ◽  
Machine Learning Algorithms ◽  
New Drugs ◽  
Stage Ii ◽  
Cancer Stage ◽  
New Drug ◽  
Benchmark Datasets ◽  
Approved Drugs

AbstractBackgroundIt often takes more than 10 years and costs more than one billion dollars to develop a new drug for a disease and bring it to the market. Drug repositioning can significantly reduce costs and times in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed.MethodsIn this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease.ResultsThe proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. Final drug repositioning model has been built based on random forest classifier, after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II.ConclusionResults show the strength of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab and Tamoxifen.

Oral drug repositioning candidates and synergistic remdesivir combinations for the prophylaxis and treatment of COVID-19

2020 ◽  
Author(s):  
Malina A. Bakowski ◽  
Nathan Beutler ◽  
Emily Chen ◽  
Tu-Trinh H. Nguyen ◽  
Melanie G. Kirkpatrick ◽  
...  
Keyword(s):  
Drug Repositioning ◽  
Drug Repurposing ◽  
Oral Drug ◽  
Therapeutic Drug ◽  
The Novel ◽  
High Content Imaging ◽  
Drug Candidates ◽  
Polymerase Inhibitor ◽  
Approved Drugs

AbstractThe ongoing pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), necessitates strategies to identify prophylactic and therapeutic drug candidates for rapid clinical deployment. A high-throughput, high-content imaging assay of human HeLa cells expressing the SARS-CoV-2 receptor ACE2 was used to screen ReFRAME, a best-in-class drug repurposing library. From nearly 12,000 compounds, we identified 66 compounds capable of selectively inhibiting SARS-CoV-2 replication in human cells. Twenty-four of these drugs show additive activity in combination with the RNA-dependent RNA polymerase inhibitor remdesivir and may afford increased in vivo efficacy. We also identified synergistic interaction of the nucleoside analog riboprine and a folate antagonist 10-deazaaminopterin with remdesivir. Overall, seven clinically approved drugs (halofantrine, amiodarone, nelfinavir, simeprevir, manidipine, ozanimod, osimertinib) and 19 compounds in other stages of development may have the potential to be repurposed as SARS-CoV-2 oral therapeutics based on their potency, pharmacokinetic and human safety profiles.

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