Drug repurposing and adverse event prediction using high-throughput literature analysis

Drug repurposing for neurodegenerative diseases

Neurodegenerative diseases (NDDs) are neuronal problems that include the brain and spinal cord and result in loss of sensory and motor dysfunction. Common NDDs include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS) etc. The occurrence of these diseases increases with age and is one of the challenging problems among elderly people. Though, several scientific research has demonstrated the key pathologies associated with NDDs still the underlying mechanisms and molecular details are not well understood and need to be explored and this poses a lack of effective treatments for NDDs. Several lines of evidence have shown that NDDs have a high prevalence and affect more than a billion individuals globally but still, researchers need to work forward in identifying the best therapeutic target for NDDs. Thus, several researchers are working in the directions to find potential therapeutic targets to alter the disease pathology and treat the diseases. Several steps have been taken to identify the early detection of the disease and drug repurposing for effective treatment of NDDs. Moreover, it is logical that current medications are being evaluated for their efficacy in treating such disorders; therefore, drug repurposing would be an efficient, safe, and cost-effective way in finding out better medication. In the current manuscript we discussed the utilization of drugs that have been repurposed for the treatment of AD, PD, HD, MS, and ALS.

Integrated Computational Approaches for Drug Design Targeting Cruzipain

Cruzipain inhibitors are required after medications to treat Chagas disease because of the need for safer, more effective treatments. Trypanosoma cruzi is the source of cruzipain, a crucial cysteine protease that has driven interest in using computational methods to create more effective inhibitors. We employed a 3D-QSAR model, using a dataset of 36 known inhibitors, and a pharmacophore model to identify potential inhibitors for cruzipain. We also built a deep learning model using the Deep purpose library, trained on 204 active compounds, and validated it with a specific test set. During a comprehensive screening of the Drug Bank database of 8533 molecules, pharmacophore and deep learning models identified 1012 and 340 drug-like molecules, respectively. These molecules were further evaluated through molecular docking, followed by induced-fit docking. Ultimately, molecular dynamics simulation was performed for the final potent inhibitors that exhibited strong binding interactions. These results present four novel cruzipain inhibitors that can inhibit the cruzipain protein of T. cruzi.

MultiGML: Multimodal graph machine learning for prediction of adverse drug events

Adverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources. Thus, many current computational approaches neglect a wealth of information which results from the integration of different data sources, such as biological protein function, gene expression, chemical compound structure, cell-based imaging and others. In this work we propose an integrative and explainable multi-modal Graph Machine Learning approach (MultiGML), which fuses knowledge graphs with multiple further data modalities to predict drug related adverse events and general drug target-phenotype associations. MultiGML demonstrates excellent prediction performance compared to alternative algorithms, including various traditional knowledge graph embedding techniques. MultiGML distinguishes itself from alternative techniques by providing in-depth explanations of model predictions, which point towards biological mechanisms associated with predictions of an adverse drug event. Hence, MultiGML could be a versatile tool to support decision making in preclinical drug development.

A Re-Purposing Strategy: Sub-Lethal Concentrations of an Eicosanoid Derived from the Omega-3-Polyunsaturated Fatty Acid Resolvin D1 Affect Dual Species Biofilms

The fungal species Candida parapsilosis and the bacterial species Staphylococcus aureus may be responsible for hospital-acquired infections in patients undergoing invasive medical interventions or surgical procedures and often coinfect critically ill patients in complicating polymicrobial biofilms. The efficacy of the re-purposing therapy has recently been reported as an alternative to be used. PUFAs (polyunsaturated fatty acids) may be used alone or in combination with currently available traditional antimicrobials to prevent and manage various infections overcoming antimicrobial resistance. The objectives of the study were to evaluate the effects of Resolvin D1 (RvD1) as an antimicrobial on S. aureus and C. parapsilosis, as well as the activity against the mixed biofilm of the same two species. Microdilution assays and time-kill growth curves revealed bacterial and fungal inhibition at minimum concentration values between 5 and 10 μg mL-1. In single-species structures, an inhibition of 55% and 42% was reported for S. aureus and C. parapsilosis, respectively. Moreover, RvD1 demonstrated an eradication capacity of 60% and 80% for single- and mixed-species biofilms, respectively. In association with the inhibition activity, a downregulation of genes involved in biofilm formation as well as ROS accumulation was observed. Eradication capability was confirmed also on mature mixed biofilm grown on silicone platelets as shown by scanning electron microscopy (SEM). In conclusion, RvD1 was efficient against mono and polymicrobial biofilms in vitro, being a promising alternative for the treatment of mixed bacterial/fungal infections.

In Silico study for acyclovir, ganciclovir and its derivatives to fight the COVID-19: Molecular docking, DFT calculations, ADME and td-Molecular dynamics simulations

In the present work, we have designed three molecules, acyclovir (A), ganciclovir (G) and derivative of hydroxymethyl derivative of ganciclovir (CH₂OH of G, that is D) and investigated their biological potential against the Mpro of nCoV via in silico studies. Further, density functional theory (DFT) calculations of A, G and D were performed using Gaussian 16 on applying B3LYP under default condition to collect the information for the delocalization of electron density in their optimized geometry. Authors have also calculated various energies including free energy of A, G and D in Hartree per particle. It can be seen that D has the least free energy. As mentioned, the molecular docking of the A, G and D against the Mpro of nCoV was performed using iGemdock, an acceptable computational tool and the interaction has been studied in the form of physical data, that is, binding energy for A, G and D were calculated in kcal/mol. It can be seen the D showed effective binding, that is, maximum inhibition that A and G. For a better understanding for the inhibition of the Mpro of nCoV by A, G and D, temperature dependent molecular dynamics simulations were performed. Different trajectories like RMSD, RMSF, Rg and hydrogen bond were extracted and analyzed. The results of molecular docking of A, G and D corroborate with the td-MD simulations and hypothesized that D could be a promising candidate to inhibit the activity of Mpro of nCoV.

Evaluation of Colocasia esculenta Schott in anti-cancerous properties with proximity extension assays

Background

Colocasia esculenta Schott (called as Xiangshayu in Chinese) is an excellent local cultivar of the genus polymorpha in Jiangsu Province, China.

Objective

In the present study, we have performed a comparative study before and after dietary consumption with Colocasia esculenta Schott to evaluate its anti-cancerous properties.

Design

Forty-two healthy volunteers were recruited, and dietary consumption with 200 g of tap water cooked Colocasia esculenta Schott daily was conducted for 1 month. Plasma samples from the subjects before and after dietary consumption with Colocasia esculenta Schott were analyzed with proximity extension assays for the alteration of 92 proteins in relation with cancers, while blood samples were examined for physiological parameters with an automatic biochemical analyzer. Bioinformatic analyses were conducted using MalaCards and GEPIA.

Results

After taking dietary consumption with Colocasia esculenta Schott, circulating CYR61, ANXA1, and VIM protein levels in the subjects was found to be most significantly downregulated, while for ITGB5, EPHA2, and CEACAM1, it was upregulated. Alternation of these proteins was predicted to be associated with the development of tumors such as pancreatic adenocarcinoma and breast and prostate cancers.

Conclusion

The present study provides evidence that Colocasia esculenta Schott, as a healthy food, has anti-cancerous properties. Further investigation of phytochemistry in Colocasia esculenta Schott has been taken into our consideration.

Prospective adverse event risk evaluation in clinical trials

Proactive and objective regulatory risk management of ongoing clinical trials is limited, especially when it involves the safety of the trial. We seek to prospectively evaluate the risk of facing adverse outcomes from standardized and routinely collected protocol data. We conducted a retrospective cohort study of 2860 Phase 2 and Phase 3 trials that were started and completed between 1993 and 2017 and documented in ClinicalTrials.gov. Adverse outcomes considered in our work include Serious or Non-Serious as per the ClinicalTrials.gov definition. Random-forest-based prediction models were created to determine a trial's risk of adverse outcomes based on protocol data that is available before the start of a trial enrollment. A trial's risk is defined by dichotomic (classification) and continuous (log-odds) risk scores. The classification-based prediction models had an area under the curve (AUC) ranging from 0.865 to 0.971 and the continuous-score based models indicate a rank correlation of 0.6-0.66 (with p-values < 0.001), thereby demonstrating improved identification of risk of adverse outcomes. Whereas related frameworks highlight the prediction benefits of incorporating data that is highly context-specific, our results indicate that Adverse Event (AE) risks can be reliably predicted through a framework of mild data requirements. We propose three potential applications in leading regulatory remits, highlighting opportunities to support regulatory oversight and informed consent decisions.

Drug repurposing: Misconceptions, challenges, and opportunities for academic researchers

[Figure: see text].

Luteolin: a blocker of SARS-CoV-2 cell entry based on relaxed complex scheme, molecular dynamics simulation, and metadynamics

Natural products have served human life as medications for centuries. During the outbreak of COVID-19, a number of naturally derived compounds and extracts have been tested or used as potential remedies against COVID-19. Tetradenia riparia extract is one of the plant extracts that have been deployed and claimed to manage and control COVID-19 by some communities in Tanzania and other African countries. The active compounds isolated from T. riparia are known to possess various biological properties including antimalarial and antiviral. However, the underlying mechanism of the active compounds against SARS-CoV-2 remains unknown. Results in the present work have been interpreted from the view point of computational methods including molecular dynamics, free energy methods, and metadynamics to establish the related mechanism of action. Among the constituents of T. riparia studied, luteolin inhibited viral cell entry and was thermodynamically stable. The title compound exhibit residence time and unbinding kinetics of 68.86 ms and 0.014 /ms, respectively. The findings suggest that luteolin could be potent blocker of SARS-CoV-2 cell entry. The study shades lights towards identification of bioactive constituents from T. riparia against COVID-19, and thus bioassay can be carried out to further validate such observations.

Using Literature Based Discovery to Gain Insights Into the Metabolomic Processes of Cardiac Arrest

In this paper, we describe how we applied LBD techniques to discover lecithin cholesterol acyltransferase (LCAT) as a druggable target for cardiac arrest. We fully describe our process which includes the use of high-throughput metabolomic analysis to identify metabolites significantly related to cardiac arrest, and how we used LBD to gain insights into how these metabolites relate to cardiac arrest. These insights lead to our proposal (for the first time) of LCAT as a druggable target; the effects of which are supported by in vivo studies which were brought forth by this work. Metabolites are the end product of many biochemical pathways within the human body. Observed changes in metabolite levels are indicative of changes in these pathways, and provide valuable insights toward the cause, progression, and treatment of diseases. Following cardiac arrest, we observed changes in metabolite levels pre- and post-resuscitation. We used LBD to help discover diseases implicitly linked via these metabolites of interest. Results of LBD indicated a strong link between Fish Eye disease and cardiac arrest. Since fish eye disease is characterized by an LCAT deficiency, it began an investigation into the effects of LCAT and cardiac arrest survival. In the investigation, we found that decreased LCAT activity may increase cardiac arrest survival rates by increasing ω-3 polyunsaturated fatty acid availability in circulation. We verified the effects of ω-3 polyunsaturated fatty acids on increasing survival rate following cardiac arrest via in vivo with rat models.

Anti-cancer potential of some commonly used drugs

Cancer is a global concern leading to millions of deaths every year. A declining trend in new drug discovery and development is becoming one of the major issues among the pharmaceutical, biotechnology industries, and regulatory agencies. New drug development is proven to be a very lengthy and costly process. The launch of a new drug takes 8-12 years and huge investments. The success rate in oncology therapeutics is also low due to toxicities at the pre-clinical and clinical trial levels. Many oncological drugs get rejected at a very promising stage, showing adverse reactions on healthy cells. Thus, exploring new therapeutic benefits of the existing, shelved drugs for their anti-cancerous action could result in a therapeutic approach preventing the toxicities which occur during clinical trials. Drug repurposing has the potential to overcome the challenges faced via conventional way of drug discovery and is becoming an area of interest for researchers and scientists. However, very few in vivo studies are conducted to prove the anti-cancerous activity of the drugs. Insufficient in vivo animal studies and a lack of human clinical trials are the lacunae in the field of drug repurposing. This review focuses on an aspect of drug repurposing for cancer therapeutics. Various studies that show that drugs approved for clinical indications other than cancer have shown promising anti-cancer activities. Some of the commonly used drugs like Benzodiazepines (Diazepam, Midzolam), Antidepressants (Imipramine, Clomipramine, and Citalopram), Antiepileptic (Valporic acid, Phenytoin), Antidiabetics (metformin), etc. have been reported to show potential activity against the cancerous cells.

Recent advances in biomedical literature mining

The recent years have witnessed a rapid increase in the number of scientific articles in biomedical domain. These literature are mostly available and readily accessible in electronic format. The domain knowledge hidden in them is critical for biomedical research and applications, which makes biomedical literature mining (BLM) techniques highly demanding. Numerous efforts have been made on this topic from both biomedical informatics (BMI) and computer science (CS) communities. The BMI community focuses more on the concrete application problems and thus prefer more interpretable and descriptive methods, while the CS community chases more on superior performance and generalization ability, thus more sophisticated and universal models are developed. The goal of this paper is to provide a review of the recent advances in BLM from both communities and inspire new research directions.

PubMed-Scale Chemical Concept Embeddings Reconstruct Physical Protein Interaction Networks

PubMed is the largest resource of curated biomedical knowledge to date, entailing more than 25 million documents. Large quantities of novel literature prevent a single expert from keeping track of all potentially relevant papers, resulting in knowledge gaps. In this article, we present CHEMMESHNET, a newly developed PubMed-based network comprising more than 10,000,000 associations, constructed from expert-curated MeSH annotations of chemicals based on all currently available PubMed articles. By learning latent representations of concepts in the obtained network, we demonstrate in a proof of concept study that purely literature-based representations are sufficient for the reconstruction of a large part of the currently known network of physical, empirically determined protein-protein interactions. We demonstrate that simple linear embeddings of node pairs, when coupled with a neural network-based classifier, reliably reconstruct the existing collection of empirically confirmed protein-protein interactions. Furthermore, we demonstrate how pairs of learned representations can be used to prioritize potentially interesting novel interactions based on the common chemical context. Highly ranked interactions are qualitatively inspected in terms of potential complex formation at the structural level and represent potentially interesting new knowledge. We demonstrate that two protein-protein interactions, prioritized by structure-based approaches, also emerge as probable with regard to the trained machine-learning model.

An Analytical Review of Computational Drug Repurposing

Drug repurposing is a vital function in pharmaceutical fields and has gained popularity in recent years in both the pharmaceutical industry and research community. It refers to the process of discovering new uses and indications for existing or failed drugs. It is cost-effective and reliable in contrast to experimental drug discovery, which is a costly, time-consuming, and risky process and limited to a relatively small number of targets. Accordingly, a plethora of computational methodologies have been propounded to repurpose drugs on a large scale by utilizing available high throughput data. The available literature, however, lacks a contemporary and comprehensive analysis of the current computational drug repurposing methodologies. In this paper, we presented a systematic analysis of computational drug repurposing which consists of three main sections: Initially, we categorize the computational drug repurposing methods based on their technical approach and artificial intelligence perspective and discuss the strengths and weaknesses of various methods. Secondly, some general criteria are recommended to analyze our proposed categorization. In the third and final section, a qualitative comparison is made between each approach which is a guide to understanding their preference to one another. Further, this systematic analysis can help in the efficient selection and improvement of drug repurposing techniques based on the nature of computational methods implemented on biological resources.

Drug Repurposing: An Emerging Tool for Drug Reuse, Recycling and Discovery

Drug repositioning or repurposing is a revolutionary breakthrough in drug development that focuses on rediscovering new uses for old therapeutic agents. Drug repositioning can be defined more precisely as the process of exploring new indications for an already approved drug while drug repurposing includes overall re-development approaches grounded in the identical chemical structure of the active drug moiety as in the original product. The repositioning approach accelerates the drug development process, curtails the cost and risk inherent to drug development. The strategy focuses on the polypharmacology of drugs to unlocks novel opportunities for logically designing more efficient therapeutic agents for unmet medical disorders. Drug repositioning also expresses certain regulatory challenges that hamper its further utilization. The review outlines the eminent role of drug repositioning in new drug discovery, methods to predict the molecular targets of a drug molecule, advantages that the strategy offers to the pharmaceutical industries, explaining how the industrial collaborations with academics can assist in the discovering more repositioning opportunities. The focus of the review is to highlight the latest applications of drug repositioning in various disorders. The review also includes a comparison of old and new therapeutic uses of repurposed drugs, assessing their novel mechanisms of action and pharmacological effects in the management of various disorders. Various restrictions and challenges that repurposed drugs come across during their development and regulatory phases are also highlighted.

Repurposing Individualized Nutritional Intervention as a Therapeutic Component to Prevent the Adverse Effects of Radiotherapy in Patients With Cervical Cancer

Worldwide, cervical cancer was the fourth leading cause of cancer death among women, while in Mexico was the second cause (5.28%). Cancer patients receiving chemotherapy and radiotherapy have a high risk of malnutrition secondary to the disease and treatment, affects the patient's overall, with adverse effects on gastrointestinal symptoms. These use affects the medical therapy. The aim of the present study was to evaluate the benefits on individualized nutritional therapy on decrease weight loss and gastrointestinal adverse effects and to consider these outcomes in pharmacology research, especially in repurposing drugs. We conducted a longitudinal design with two comparation groups with medical diagnosis of cervical cancer and received radiotherapy weekly, 1) the intervention group (nutritional intervention and counseling -INC-) with 20 participants and 2) control group (retrospective cohort -CG-) with 9 participants. Weekly body composition, dietary intake, adverse effects (gastrointestinal symptoms), glucose, hemoglobin, and blood pressure were analyzed during 4 to 5 weeks. Both groups had weight loss weekly (p = 0.013 and p = 0.043 respectively) but the CG vs INC presented loss fat-free mass ≥500g in 67 and of 37% respectively. By the end of the intervention a 25% of the INC group had <10 g/dL of hemoglobin vs 60% for the CG. To compare the dietary intake of vitamins (A and folic acid), fiber (p = 0.006), iron (p = 0.03) and energy (mainly carbohydrates) (p = 0.04) were according to the recommendations in INC group (p>0.05). The number needed to treat was 4 (95% CI, 2 to 13). The nutritional intervention and counseling weekly during radiotherapy in cervical cancer to maintain/improve muscle mass, hemoglobin, and dietary intake above 70% of the recommendations for INC group compared to the evidence. Adequate nutritional status was maintained and decrease the rate of complications, mainly gastrointestinal symptoms, in INC group. The efficacy of drug repurposing can improve through individualized nutritional therapy for preventing adverse effects of radiotherapy in patients with cervical cancer.

Adverse drug event detection using reason assignments in FDA drug labels

Adverse drug events (ADEs) are unintended incidents that involve the taking of a medication. ADEs pose significant health and financial problems worldwide. Information about ADEs can inform health care and improve patient safety. However, much of this information is buried in narrative texts and needs to be extracted with Natural Language Processing techniques, in order to be useful to computerized methods. ADEs can be found on drug labels, contained in the different sections such as descriptions of the drug's active components or more prominently in descriptions of studied side-effects. Extracting these automatically could be useful in triaging and processing drug reports. In this paper, we present three base methods consisting of a Conditional Random Field (CRF), a bi-directional Long Short Term Memory unit with a CRF layer (biLSTM+CRF), and a pre-trained Bi-directional Encoder Representations from Transformers (BERT) model. We also present several ensembles of the CRF and biLSTM+CRF methods for extracting ADEs and their Reason from FDA drug labels. We show that all three methods perform well on our task, and that combining the models through different ensemble methods can improve results, providing increases in recall for the majority class and improving precision for all other classes. We also show the potential of framing ADE extraction from drug labels as a multi-class classification task on the Reason, or type, of ADE.

The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation

BACKGROUND

Driven by the COVID-19 pandemic and the dire need to discover an antiviral drug, we explored the landscape of the SARS-CoV-2 biomedical publications to identify potential treatments.

OBJECTIVE

The aims of this study are to identify off-label drugs that may have benefits for the coronavirus disease pandemic, present a novel ranking algorithm called CovidX to recommend existing drugs for potential repurposing, and validate the literature-based outcome with drug knowledge available in clinical trials.

METHODS

To achieve such objectives, we applied natural language processing techniques to identify drugs and linked entities (eg, disease, gene, protein, chemical compounds). When such entities are linked, they form a map that can be further explored using network science tools. The CovidX algorithm was based upon a notion that we called "diversity." A diversity score for a given drug was calculated by measuring how "diverse" a drug is calculated using various biological entities (regardless of the cardinality of actual instances in each category). The algorithm validates the ranking and awards those drugs that are currently being investigated in open clinical trials. The rationale behind the open clinical trial is to provide a validating mechanism of the PubMed results. This ensures providing up to date evidence of the fast development of this disease.

RESULTS

From the analyzed biomedical literature, the algorithm identified 30 possible drug candidates for repurposing, ranked them accordingly, and validated the ranking outcomes against evidence from clinical trials. The top 10 candidates according to our algorithm are hydroxychloroquine, azithromycin, chloroquine, ritonavir, losartan, remdesivir, favipiravir, methylprednisolone, rapamycin, and tilorone dihydrochloride.

CONCLUSIONS

The ranking shows both consistency and promise in identifying drugs that can be repurposed. We believe, however, the full treatment to be a multifaceted, adjuvant approach where multiple drugs may need to be taken at the same time.

Repurposing of Drugs Is a Viable Approach to Develop Therapeutic Strategies against Central Nervous System Related Pathogenic Amoebae

Brain-eating amoebae including Acanthamoeba spp., Naegleria fowleri, and Balamuthia mandrillaris cause rare infections of the central nervous system that almost always result in death. The high mortality rate, lack of interest for drug development from pharmaceutical industries, and no available effective drugs present an alarming challenge. The current drugs employed in the management and therapy of these devastating diseases are amphotericin B, miltefosine, chlorhexidine, pentamidine, and voriconazole which are generally used in combination. However, clinical evidence shows that these drugs have limited efficacy and high host cell cytotoxicity. Repurposing of drugs is a practical approach to utilize commercially available, U.S. Food and Drug Administration approved drugs for one disease against rare diseases caused by brain-eating amoebae. In this Perspective, we highlight some of the success stories of drugs repositioned against neglected parasitic diseases and identify future potential for effective and sustainable drug development against brain-eating amoebae infections.

Understanding Drug Repurposing From the Perspective of Biomedical Entities and Their Evolution: Bibliographic Research Using Aspirin

BACKGROUND

Drug development is still a costly and time-consuming process with a low rate of success. Drug repurposing (DR) has attracted significant attention because of its significant advantages over traditional approaches in terms of development time, cost, and safety. Entitymetrics, defined as bibliometric indicators based on biomedical entities (eg, diseases, drugs, and genes) studied in the biomedical literature, make it possible for researchers to measure knowledge evolution and the transfer of drug research.

OBJECTIVE

The purpose of this study was to understand DR from the perspective of biomedical entities (diseases, drugs, and genes) and their evolution.

METHODS

In the work reported in this paper, we extended the bibliometric indicators of biomedical entities mentioned in PubMed to detect potential patterns of biomedical entities in various phases of drug research and investigate the factors driving DR. We used aspirin (acetylsalicylic acid) as the subject of the study since it can be repurposed for many applications. We propose 4 easy, transparent measures based on entitymetrics to investigate DR for aspirin: Popularity Index (P₁), Promising Index (P₂), Prestige Index (P₃), and Collaboration Index (CI).

RESULTS

We found that the maxima of P₁, P₃, and CI are closely associated with the different repurposing phases of aspirin. These metrics enabled us to observe the way in which biomedical entities interacted with the drug during the various phases of DR and to analyze the potential driving factors for DR at the entity level. P₁ and CI were indicative of the dynamic trends of a specific biomedical entity over a long time period, while P₂ was more sensitive to immediate changes. P₃ reflected the early signs of the practical value of biomedical entities and could be valuable for tracking the research frontiers of a drug.

CONCLUSIONS

In-depth studies of side effects and mechanisms, fierce market competition, and advanced life science technologies are driving factors for DR. This study showcases the way in which researchers can examine the evolution of DR using entitymetrics, an approach that can be valuable for enhancing decision making in the field of drug discovery and development.

Molecular basis of mood and cognitive adverse events elucidated via a combination of pharmacovigilance data mining and functional enrichment analysis

Drug-induced Mood- and Cognition-related adverse events (MCAEs) are often only detected during the clinical trial phases of drug development, or even after marketing, thus posing a major safety concern and a challenge for both pharmaceutical companies and clinicians. To fill some gaps in the understanding and elucidate potential biological mechanisms of action frequently associated with MCAEs, we present a unique workflow linking observational population data with the available knowledge at molecular, cellular, and psychopharmacology levels. It is based on statistical analysis of pharmacovigilance reports and subsequent signaling pathway analyses, followed by evidence-based expert manual curation of the outcomes. Our analysis: (a) ranked pharmaceuticals with high occurrence of such adverse events (AEs), based on disproportionality analysis of the FDA Adverse Event Reporting System (FAERS) database, and (b) identified 120 associated genes and common pathway nodes possibly underlying MCAEs. Nearly two-thirds of the identified genes were related to immune modulation, which supports the critical involvement of immune cells and their responses in the regulation of the central nervous system function. This finding also means that pharmaceuticals with a negligible central nervous system exposure may induce MCAEs through dysregulation of the peripheral immune system. Knowledge gained through this workflow unravels putative hallmark biological targets and mediators of drug-induced mood and cognitive disorders that need to be further assessed and validated in experimental models. Thereafter, they can be used to substantially improve in silico/in vitro/in vivo tools for predicting these adversities at a preclinical stage.

Turning liabilities into opportunities: Off-target based drug repurposing in cancer

Targeted drugs and precision medicine have transformed the landscape of cancer therapy and significantly improved patient outcomes in many cases. However, as therapies are becoming more and more tailored to smaller patient populations and acquired resistance is limiting the duration of clinical responses, there is an ever increasing demand for new drugs, which is not easily met considering steadily rising drug attrition rates and development costs. Considering these challenges drug repurposing is an attractive complementary approach to traditional drug discovery that can satisfy some of these needs. This is facilitated by the fact that most targeted drugs, despite their implicit connotation, are not singularly specific, but rather display a wide spectrum of target selectivity. Importantly, some of the unintended drug "off-targets" are known anticancer targets in their own right. Others are becoming recognized as such in the process of elucidating off-target mechanisms that in fact are responsible for a drug's anticancer activity, thereby revealing potentially new cancer vulnerabilities. Harnessing such beneficial off-target effects can therefore lead to novel and promising precision medicine approaches. Here, we will discuss experimental and computational methods that are employed to specifically develop single target and network-based off-target repurposing strategies, for instance with drug combinations or polypharmacology drugs. By illustrating concrete examples that have led to clinical translation we will furthermore examine the various scientific and non-scientific factors that cumulatively determine the success of these efforts and thus can inform the future development of new and potentially lifesaving off-target based drug repurposing strategies for cancers that constitute important unmet medical needs.

Recent Advances in Drug Repurposing for Parkinson's Disease

As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson's disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson's disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson's disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson's disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson's disease will be discussed.

Fingerprinting CANDO: Increased Accuracy with Structure- and Ligand-Based Shotgun Drug Repurposing

We have upgraded our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug repurposing by including ligand-based, data fusion, and decision tree pipelines. The goal of shotgun drug repurposing is to screen and rank every existing human use drug or compound for every disease/indication. The first version of CANDO implemented a structure-based pipeline that modeled interactions between compounds and proteins on a large scale, generating compound-proteome interaction signatures used to infer the similarity of drug behavior; the new pipelines accomplish this by incorporating molecular fingerprints and the Tanimoto coefficient. We obtain improved benchmarking performance with the new pipelines across all three evaluation metrics used: average indication accuracy, pairwise accuracy, and coverage. The best performing pipeline achieves an average indication accuracy of 19.0% at the top10 cutoff, compared to 11.7% for v1, and 2.2% for a random control. Our results demonstrate that the CANDO drug recovery accuracy is substantially improved by integrating multiple pipelines, thereby enhancing our ability to generate putative therapeutic repurposing candidates, and increasing drug discovery efficiency.

SemNet: Using Local Features to Navigate the Biomedical Concept Graph

Literature-Based Discovery (LBD) aims to connect scientists across silos by assembling models of the literature to reveal previously hidden connections. Unfortunately, LBD systems have been unable to achieve user adoption on a large scale. This work develops opens source software in Python to convert a database of semantic predications of all of PubMed's 27.9 million indexed abstracts into a semantic inference network and biomedical concept graph in Neo4j. The developed software, called SemNet, queries a modified version of the publicly available SemMedDB and computes feature vectors on source-target pairs. Each unique United Medical Language System (UMLS) concept is represented as a node and each predication as an edge. Each node is assigned one of 132 node labels (e.g., Amino Acid, Peptide, or Protein (AAPP); Gene or Genome (GG); etc.) and each edge is labeled with one of 58 predications (e.g. treats, causes, inhibits, etc.). SemNet computes a single feature value for each metapath, or sequence of node types, between a source node and user-specified target node(s). Several different types of metapath-based features (count, degree weighted path count, and HeteSim metric) are computed and vectorized. SemNet employs an unsupervised learning algorithm for rank aggregation (ULARA) to rank identified source nodes that are most relevant to the user-specified target nodes(s). Statistical analysis of correlation among identified source nodes or resultant literature network features are used to identify patterns that can guide future research. Analysis of high residual nodes is used to compare and contrast SemNet rankings between different targets of interest. An example SemNet use case is presented to assess “the differential impact of smoking on cognition in males and females” using the following target nodes: nicotine, learning, memory, tetrahydrocannabinol (THC), cigarette smoke, X chromosome, and Y chromosome. Detailed rankings are discussed. Overall results suggest a hypothesis where smoking negatively impacts cognition to a greater extent in females, but smoking has stronger cardiovascular impacts in males. In summary, SemNet provides an adoptable method for efficient LBD of PubMed that extends beyond omics-only relationships to true multi-scalar connections that can provide actionable insight for predictive medicine, research prioritization, and clinical care.

Statins attenuate outgrowth of breast cancer metastases

Background

Metastasis in breast cancer foreshadows mortality, as clinically evident disease is aggressive and generally chemoresistant. Disseminated breast cancer cells often enter a period of dormancy for years to decades before they emerge as detectable cancers. Harboring of these dormant cells is not individually predictable, and available information suggests that these micrometastatic foci cannot be effectively targeted by existing therapies. As such, long-term, relatively non-toxic interventions that prevent metastatic outgrowth would be an advance in treatment. Epidemiological studies have found that statins reduce breast cancer specific mortality but not the incidence of primary cancer. However, the means by which statins reduce mortality without affecting primary tumor development remains unclear.

Methods

We examine statin efficacy against two breast cancer cell lines in models of breast cancer metastasis: a 2D in vitro co-culture model of breast cancer cell interaction with the liver, a 3D ex vivo microphysiological system model of breast cancer metastasis, and two independent mouse models of spontaneous breast cancer metastasis to the lung and liver, respectively.

Results

We demonstrate that statins can directly affect the proliferation of breast cancer cells, specifically at the metastatic site. In a 2D co-culture model of breast cancer cell interaction with the liver, we demonstrate that atorvastatin can directly suppress proliferation of mesenchymal but not epithelial breast cancer cells. Further, in an ex vivo 3D liver microphysiological system of breast cancer metastasis, we found that atorvastatin can block stimulated emergence of dormant breast cancer cells. In two independent models of spontaneous breast cancer metastasis to the liver and to the lung, we find that statins significantly reduce proliferation of the metastatic but not primary tumor cells.

Conclusions

As statins can block metastatic tumor outgrowth, they should be considered for use as long-term adjuvant drugs to delay clinical emergence and decrease mortality in breast cancer patients.

Inventing new therapies without reinventing the wheel: the power of drug repurposing

LINKED ARTICLES

This article is part of a themed section on Inventing New Therapies Without Reinventing the Wheel: The Power of Drug Repurposing. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.2/issuetoc.

DeCoST: A New Approach in Drug Repurposing From Control System Theory

In this paper, we propose DeCoST (Drug Repurposing from Control System Theory) framework to apply control system paradigm for drug repurposing purpose. Drug repurposing has become one of the most active areas in pharmacology since the last decade. Compared to traditional drug development, drug repurposing may provide more systematic and significantly less expensive approaches in discovering new treatments for complex diseases. Although drug repurposing techniques rapidly evolve from "one: disease-gene-drug" to "multi: gene, dru" and from "lazy guilt-by-association" to "systematic model-based pattern matching," mathematical system and control paradigm has not been widely applied to model the system biology connectivity among drugs, genes, and diseases. In this paradigm, our DeCoST framework, which is among the earliest approaches in drug repurposing with control theory paradigm, applies biological and pharmaceutical knowledge to quantify rich connective data sources among drugs, genes, and diseases to construct disease-specific mathematical model. We use linear-quadratic regulator control technique to assess the therapeutic effect of a drug in disease-specific treatment. DeCoST framework could classify between FDA-approved drugs and rejected/withdrawn drug, which is the foundation to apply DeCoST in recommending potentially new treatment. Applying DeCoST in Breast Cancer and Bladder Cancer, we reprofiled 8 promising candidate drugs for Breast Cancer ER+ (Erbitux, Flutamide, etc.), 2 drugs for Breast Cancer ER- (Daunorubicin and Donepezil) and 10 drugs for Bladder Cancer repurposing (Zafirlukast, Tenofovir, etc.).

Lipophilic statins limit cancer cell growth and survival, via involvement of Akt signaling

The HMG-CoA reductase inhibitors, statins, have been used as lipid lowering drugs for decades and several epidemiological studies suggest statin usage correlates with a decreased incidence of cancer specific mortality in patients. However, the mechanism of this mortality benefit remains unclear. Here, we demonstrate that statin drug lipophilicity and affinity for its target enzyme, HMGCR, determine their growth suppressive potency against various tumor cell lines. The lipophilic atorvastatin decreases cancer cell proliferation and survival in vitro. Statin sensitivity coincided with Ras localization to the cytosol instead of the membrane, consistent with a decrement in prenylation. To investigate signaling pathways that may be involved with sensitivity to statin therapy, we employed inhibitors of the PI3K-Akt and Mek-Erk signaling cascades. We found that inhibition of PI3K signaling through Akt potentiated statin sensitivity of breast cancer cells in vitro and in co-culture with primary human hepatocytes. The same effect was not observed with inhibition of Mek signaling through Erk. Moreover, the sensitivity of breast cancer cells to atorvastatin-mediated growth suppression correlated with a decrease in EGF-mediated phosphorylation of Akt. As an increase in Akt activity has been shown to be involved in the metastasis and metastatic outgrowth of many cancer types (including breast), these data suggest a mechanism by which statins may reduce cancer specific mortality in patients.

Biomarker identification for statin sensitivity of cancer cell lines

Statins are potent cholesterol reducing drugs that have been shown to reduce tumor cell proliferation in vitro and tumor growth in animal models. Moreover, retrospective human cohort studies demonstrated decreased cancer-specific mortality in patients taking statins. We previously implicated membrane E-cadherin expression as both a marker and mechanism for resistance to atorvastatin-mediated growth suppression of cancer cells; however, a transcriptome-profile-based biomarker signature for statin sensitivity has not yet been reported. Here, we utilized transcriptome data from fourteen NCI-60 cancer cell lines and their statin dose-response data to produce gene expression signatures that identify statin sensitive and resistant cell lines. We experimentally confirmed the validity of the identified biomarker signature in an independent set of cell lines and extended this signature to generate a proposed statin-sensitive subset of tumors listed in the TCGA database. Finally, we predicted drugs that would synergize with statins and found several predicted combination therapies to be experimentally confirmed. The combined bioinformatics-experimental approach described here can be used to generate an initial biomarker signature for anticancer drug therapy.

Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews

Adverse drug reactions (ADRs) are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions. Traditional channels for identifying ADRs are reliable but very slow and only produce a small amount of data. Text reviews, either on specialized web sites or in general-purpose social networks, may lead to a data source of unprecedented size, but identifying ADRs in free-form text is a challenging natural language processing problem. In this work, we propose a novel model for this problem, uniting recurrent neural architectures and conditional random fields. We evaluate our model with a comprehensive experimental study, showing improvements over state-of-the-art methods of ADR extraction.

A structure- and chemical genomics-based approach for repositioning of drugs against VCP/p97 ATPase

Valosin-containing protein (VCP/p97) ATPase (a.k.a. Cdc48) is a key member of the ER-associated protein degradation (ERAD) pathway. ERAD and VCP/p97 have been implicated in a multitude of human diseases, such as neurodegenerative diseases and cancer. Inhibition of VCP/p97 induces proteotoxic ER stress and cell death in cancer cells, making it an attractive target for cancer treatment. However, no drugs exist against this protein in the market. Repositioning of drugs towards new indications is an attractive alternative to the de novo drug development due to the potential for significantly shorter time to clinical translation. Here, we employed an integrative strategy for the repositioning of drugs as novel inhibitors of the VCP/p97 ATPase. We integrated structure-based virtual screening with the chemical genomics analysis of drug molecular signatures, and identified several candidate inhibitors of VCP/p97 ATPase. Importantly, experimental validation with cell-based and in vitro ATPase assays confirmed three (ebastine, astemizole and clotrimazole) out of seven tested candidates (~40% true hit rate) as direct inhibitors of VCP/p97 and ERAD. This study introduces an effective integrative strategy for drug repositioning, and identified new drugs against the VCP/p97/ERAD pathway in human diseases.

Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study

Background

Drug repurposing (defined as discovering new indications for existing drugs) could play a significant role in drug development, especially considering the declining success rates of developing novel drugs. Typically, new indications for existing medications are identified by accident. However, new technologies and a large number of available resources enable the development of systematic approaches to identify and validate drug-repurposing candidates. Patients today report their experiences with medications on social media and reveal side effects as well as beneficial effects of those medications.

Objective

Our aim was to assess the feasibility of using patient reviews from social media to identify potential candidates for drug repurposing.

Methods

We retrieved patient reviews of 180 medications from an online forum, WebMD. Using dictionary-based and machine learning approaches, we identified disease names in the reviews. Several publicly available resources were used to exclude comments containing known indications and adverse drug effects. After manually reviewing some of the remaining comments, we implemented a rule-based system to identify beneficial effects.

Results

The dictionary-based system and machine learning system identified 2178 and 6171 disease names respectively in 64,616 patient comments. We provided a list of 10 common patterns that patients used to report any beneficial effects or uses of medication. After manually reviewing the comments tagged by our rule-based system, we identified five potential drug repurposing candidates.

Conclusions

To our knowledge, this is the first study to consider using social media data to identify drug-repurposing candidates. We found that even a rule-based system, with a limited number of rules, could identify beneficial effect mentions in patient comments. Our preliminary study shows that social media has the potential to be used in drug repurposing.

Discovery of New Potential Anti-Infective Compounds Based on Carbonic Anhydrase Inhibitors by Rational Target-Focused Repurposing Approaches

In academia, compound recycling represents an alternative drug discovery strategy to identify new pharmaceutical targets from a library of chemical compounds available in house. Herein we report the application of a rational target-based drug-repurposing approach to find diverse applications for our in-house collection of compounds. The carbonic anhydrase (CA, EC 4.2.1.1) metalloenzyme superfamily was identified as a potential target of our compounds. The combination of a thoroughly validated docking screening protocol, together with in vitro assays against various CA families and isoforms, allowed us to identify two unprecedented chemotypes as CA inhibitors. The identified compounds have the capacity to preferentially bind pathogenic (bacterial/protozoan) CAs over human isoforms and represent excellent hits for further optimization in hit-to-lead campaigns.

'RE:fine drugs': an interactive dashboard to access drug repurposing opportunities

The process of discovering new drugs has been extremely costly and slow in the last decades despite enormous investment in pharmaceutical research. Drug repurposing enables researchers to speed up the process of discovering other conditions that existing drugs can effectively treat, with low cost and fast FDA approval. Here, we introduce ‘RE:fine Drugs’, a freely available interactive website for integrated search and discovery of drug repurposing candidates from GWAS and PheWAS repurposing datasets constructed using previously reported methods in Nature Biotechnology. ‘RE:fine Drugs’ demonstrates the possibilities to identify and prioritize novelty of candidates for drug repurposing based on the theory of transitive Drug–Gene–Disease triads. This public website provides a starting point for research, industry, clinical and regulatory communities to accelerate the investigation and validation of new therapeutic use of old drugs.

Database URL:http://drug-repurposing.nationwidechildrens.org

Repositioning Clofazimine as a Macrophage-Targeting Photoacoustic Contrast Agent

Photoacoustic Tomography (PAT) is a deep-tissue imaging modality, with potential clinical applications in the diagnosis of arthritis, cancer and other disease conditions. Here, we identified Clofazimine (CFZ), a red-pigmented dye and anti-inflammatory FDA-approved drug, as a macrophage-targeting photoacoustic (PA) imaging agent. Spectroscopic experiments revealed that CFZ and its various protonated forms yielded optimal PAT signals at wavelengths −450 to 540 nm. CFZ’s macrophage-targeting chemical and structural forms were detected with PA microscopy at a high contrast-to-noise ratio (CNR > 22 dB) as well as with macroscopic imaging using synthetic gelatin phantoms. In vivo, natural and synthetic CFZ formulations also demonstrated significant anti-inflammatory activity. Finally, the injection of CFZ was monitored via a real-time ultrasound-photoacoustic (US-PA) dual imaging system in a live animal and clinically relevant human hand model. These results demonstrate an anti-inflammatory drug repurposing strategy, while identifying a new PA contrast agent with potential applications in the diagnosis and treatment of arthritis.

Repositioning "old" drugs for new causes: identifying new inhibitors of prostate cancer cell migration and invasion

The majority of prostate cancer (PCa) deaths occur due to the metastatic spread of tumor cells to distant organs. Currently, there is a lack of effective therapies once tumor cells have spread outside the prostate. It is therefore imperative to rapidly develop therapeutics to inhibit the metastatic spread of tumor cells. Gain of cell motility and invasive properties is the first step of metastasis and by inhibiting motility one can potentially inhibit metastasis. Using the drug repositioning strategy, we developed a cell-based multi-parameter primary screening assay to identify drugs that inhibit the migratory and invasive properties of metastatic PC-3 PCa cells. Following the completion of the primary screening assay, 33 drugs were identified from an FDA approved drug library that either inhibited migration or were cytotoxic to the PC-3 cells. Based on the data obtained from the subsequent validation studies, mitoxantrone hydrochloride, simvastatin, fluvastatin and vandetanib were identified as strong candidates that can inhibit both the migration and invasion of PC-3 cells without significantly affecting cell viability. By employing the drug repositioning strategy instead of a de novo drug discovery and development strategy, the identified drug candidates have the potential to be rapidly translated into the clinic for the management of men with aggressive forms of PCa.

A Practical Guide for Exploring Opportunities of Repurposing Drugs for CNS Diseases in Systems Biology

Systems biology has shown its potential in facilitating pathway-focused therapy development for central nervous system (CNS) diseases. An integrated network can be utilized to explore the multiple disease mechanisms and to discover repositioning opportunities. This review covers current therapeutic gaps for CNS diseases and the role of systems biology in pharmaceutical industry. We conclude with a Multiple Level Network Modeling (MLNM) example to illustrate the great potential of systems biology for CNS diseases. The system focuses on the benefit and practical applications in pathway centric therapy and drug repositioning.

Hypothyroidism is a Risk Factor for New-Onset Diabetes: A Cohort Study

OBJECTIVE

To identify risk factors for the development of statin-associated diabetes mellitus (DM).

RESEARCH DESIGN AND METHODS

The study was conducted in two phases. Phase one involved high-throughput in silico processing of a large amount of biomedical data to identify risk factors for the development of statin-associated DM. In phase two, the most prominent risk factor identified was confirmed in an observational cohort study at Clalit, the largest health care organization in Israel. Time-dependent Poisson regression multivariable models were performed to assess rate ratios (RRs) with 95% CIs for DM occurrence.

RESULTS

A total of 39,263 statin nonusers were matched by propensity score to 20,334 highly compliant statin initiators in 2004-2005 and followed until the end of 2010. Within 59,597 statin users and nonusers in a multivariable model, hypothyroidism and subclinical hypothyroidism carried an increased risk for DM (RR 1.53 [95% CI 1.31-1.79] and 1.75 [1.40-2.18], respectively). Hypothyroidism increased DM risk irrespective of statin treatment (RR 2.06 [1.42-2.99] and 1.66 [1.05-2.64] in statin users and nonusers, respectively). Subclinical hypothyroidism risk for DM was prominent only upon statin use (RR 1.94 [1.13-3.34] and 1.20 [0.52-2.75] in statin users and nonusers, respectively). Patients with hypothyroidism treated with thyroid hormone replacement therapy were not at increased risk for DM.

CONCLUSIONS

Hypothyroidism is a risk factor for DM. Subclinical hypothyroidism-associated risk for DM is prominent only upon statin use. Identifying and treating hypothyroidism and subclinical hypothyroidism might reduce DM risk. Future clinical studies are needed to confirm the findings.

Optimising chemical named entity recognition with pre-processing analytics, knowledge-rich features and heuristics

Background

The development of robust methods for chemical named entity recognition, a challenging natural language processing task, was previously hindered by the lack of publicly available, large-scale, gold standard corpora. The recent public release of a large chemical entity-annotated corpus as a resource for the CHEMDNER track of the Fourth BioCreative Challenge Evaluation (BioCreative IV) workshop greatly alleviated this problem and allowed us to develop a conditional random fields-based chemical entity recogniser. In order to optimise its performance, we introduced customisations in various aspects of our solution. These include the selection of specialised pre-processing analytics, the incorporation of chemistry knowledge-rich features in the training and application of the statistical model, and the addition of post-processing rules.

Results

Our evaluation shows that optimal performance is obtained when our customisations are integrated into the chemical entity recogniser. When its performance is compared with that of state-of-the-art methods, under comparable experimental settings, our solution achieves competitive advantage. We also show that our recogniser that uses a model trained on the CHEMDNER corpus is suitable for recognising names in a wide range of corpora, consistently outperforming two popular chemical NER tools.

Conclusion

The contributions resulting from this work are two-fold. Firstly, we present the details of a chemical entity recognition methodology that has demonstrated performance at a competitive, if not superior, level as that of state-of-the-art methods. Secondly, the developed suite of solutions has been made publicly available as a configurable workflow in the interoperable text mining workbench Argo. This allows interested users to conveniently apply and evaluate our solutions in the context of other chemical text mining tasks.

Predicting high-throughput screening results with scalable literature-based discovery methods

The identification of new therapeutic uses for existing agents has been proposed as a means to mitigate the escalating cost of drug development. A common approach to such repurposing involves screening libraries of agents for activities against cell lines. In silico methods using knowledge from the biomedical literature have been proposed to constrain the costs of screening by identifying agents that are likely to be effective a priori. However, results obtained with these methods are seldom evaluated empirically. Conversely, screening experiments have been criticized for their inability to reveal the biological basis of their results. In this paper, we evaluate the ability of a scalable literature-based approach, discovery-by-analogy, to identify a small number of active agents within a large library screened for activity against prostate cancer cells. The methods used permit retrieval of the knowledge used to infer their predictions, providing a plausible biological basis for predicted activity.

Identifying plausible adverse drug reactions using knowledge extracted from the literature

Pharmacovigilance involves continually monitoring drug safety after drugs are put to market. To aid this process; algorithms for the identification of strongly correlated drug/adverse drug reaction (ADR) pairs from data sources such as adverse event reporting systems or Electronic Health Records have been developed. These methods are generally statistical in nature, and do not draw upon the large volumes of knowledge embedded in the biomedical literature. In this paper, we investigate the ability of scalable Literature Based Discovery (LBD) methods to identify side effects of pharmaceutical agents. The advantage of LBD methods is that they can provide evidence from the literature to support the plausibility of a drug/ADR association, thereby assisting human review to validate the signal, which is an essential component of pharmacovigilance. To do so, we draw upon vast repositories of knowledge that has been extracted from the biomedical literature by two Natural Language Processing tools, MetaMap and SemRep. We evaluate two LBD methods that scale comfortably to the volume of knowledge available in these repositories. Specifically, we evaluate Reflective Random Indexing (RRI), a model based on concept-level co-occurrence, and Predication-based Semantic Indexing (PSI), a model that encodes the nature of the relationship between concepts to support reasoning analogically about drug-effect relationships. An evaluation set was constructed from the Side Effect Resource 2 (SIDER2), which contains known drug/ADR relations, and models were evaluated for their ability to "rediscover" these relations. In this paper, we demonstrate that both RRI and PSI can recover known drug-adverse event associations. However, PSI performed better overall, and has the additional advantage of being able to recover the literature underlying the reasoning pathways it used to make its predictions.

Identification of levothyroxine antichagasic activity through computer-aided drug repurposing

Cruzipain (Cz) is the major cysteine protease of the protozoan Trypanosoma cruzi, etiological agent of Chagas disease. A conformation-independent classifier capable of identifying Cz inhibitors was derived from a 163-compound dataset and later applied in a virtual screening campaign on the DrugBank database, which compiles FDA-approved and investigational drugs. 54 approved drugs were selected as candidates, 3 of which were acquired and tested on Cz and T. cruzi epimastigotes proliferation. Among them, levothyroxine, traditionally used in hormone replacement therapy in patients with hypothyroidism, showed dose-dependent inhibition of Cz and antiproliferative activity on the parasite.

Systematic drug repurposing through text mining

Drug development remains a time-consuming and highly expensive process with high attrition rates at each stage. Given the safety hurdles drugs must pass due to increased regulatory scrutiny, it is essential for pharmaceutical companies to maximize their return on investment by effectively extending drug life cycles. There have been many effective techniques, such as phenotypic screening and compound profiling, which identify new indications for existing drugs, often referred to as drug repurposing or drug repositioning. This chapter explores the use of text mining leveraging several publicly available knowledge resources and mechanism of action representations to link existing drugs to new diseases from biomedical abstracts in an attempt to generate biologically meaningful alternative drug indications.