The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions

Accepted for publication in a future issue of Future Medicinal Chemistry.The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative platforms. The evolution of computational approaches is driven by the essential criteria that a drug molecule has to fulfill, from the affinity to targets to minimal side effects while having adequate absorption, distribution, metabolism, and excretion (ADME) properties. A combination of ligand- and structure-based drug development approaches is already used to obtain consensus predictions of small molecule activities and their off-target interactions. Further integration of these methods into easy-to-use workflows informed by systems biology could realize the full potential of available data in the drug discovery and reduce the attrition of drug candidates.Peer reviewe

Knowledge graphs for covid-19: An exploratory review of the current landscape

Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was "covid-19 knowledge graph". In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research

Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning

Simple Summary Drug repurposing is an accelerated route for drug development and a promising approach for finding medications for orphan and common diseases. Here, we compiled databases that comprise both computationally- or experimentally-derived data, and categorized them based on quiddity and origin of data, further focusing on those that present high throughput omic data or drug screens. These databases were then contextualized with genome-wide screening methods such as CRISPR/Cas9 and RNA interference, as well as state of art systems biology approaches that enable systematic characterizations of multi-omic data to find new indications for approved drugs or those that reached the latest phases of clinical trials. Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches

Artificial intelligence, machine learning, and drug repurposing in cancer

Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means. Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication. Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.Peer reviewe

Use of Text Data in Identifying and Prioritizing Potential Drug Repositioning Candidates

New drug development costs between 500 million and 2 billion dollars and takes 10-15 years, with a success rate of less than 10%. 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. In the period 2007-2009, drug repurposing led to the launching of 30-40% of new 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 with significantly lower cost. A variety of resources have been utilized to identify novel drug repurposing candidates such as biomedical literature, clinical notes, and genetic data. In this dissertation, we focused on using text data in identifying and prioritizing drug repositioning candidates and conducted five studies. In the first study, we aimed to assess the feasibility of using patient reviews from social media to identify potential candidates for drug repurposing. 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. 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. To our knowledge, this was 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 the comments of patients. Our preliminary study shows that social media has the potential to be used in drug repurposing. In the second study, we investigated the significance of extracting information from multiple sentences specifically in the context of drug-disease relation discovery. We used multiple resources such as Semantic Medline, a literature-based resource, and Medline search (for filtering spurious results) and inferred 8,772 potential drug-disease pairs. Our analysis revealed that 6,450 (73.5%) of the 8,772 potential drug-disease relations did not occur in a single sentence. Moreover, only 537 of the drug-disease pairs matched the curated gold standard in the Comparative Toxicogenomics Database (CTD), a trusted resource for drug-disease relations. Among the 537, nearly 75% (407) of the drug-disease pairs occur in multiple sentences. Our analysis revealed that the drug-disease pairs inferred from Semantic Medline or retrieved from CTD could be extracted from multiple sentences in the literature. This highlights the significance of the need for discourse-level analysis in extracting the relations from biomedical literature. In the third and fourth study, we focused on prioritizing drug repositioning candidates extracted from biomedical literature which we refer to as Literature-Based Discovery (LBD). In the third study, we used drug-gene and gene-disease semantic predications extracted from Medline abstracts to generate a list of potential drug-disease pairs. We further ranked the generated pairs, by assigning scores based on the predicates that qualify drug-gene and gene-disease relationships. On comparing the top-ranked drug-disease pairs against the Comparative Toxicogenomics Database, we found that a significant percentage of top-ranked pairs appeared in CTD. Co-occurrence of these high-ranked pairs in Medline abstracts is then used to improve the rankings of the inferred drug-disease relations. Finally, manual evaluation of the top-ten pairs ranked by our approach revealed that nine of them have good potential for biological significance based on expert judgment. In the fourth study, we proposed a method, utilizing information surrounding causal findings, to prioritize discoveries generated by LBD systems. We focused on discovering drug-disease relations, which have the potential to identify drug repositioning candidates or adverse drug reactions. Our LBD system used drug-gene and gene-disease semantic predication in SemMedDB as causal findings and Swanson’s ABC model to generate potential drug-disease relations. Using sentences, as a source of causal findings, our ranking method trained a binary classifier to classify generated drug-disease relations into desired classes. We trained and tested our classifier for three different purposes: a) drug repositioning b) adverse drug-event detection and c) drug-disease relation detection. The classifier obtained 0.78, 0.86, and 0.83 F-measures respectively for these tasks. The number of causal findings of each hypothesis, which were classified as positive by the classifier, is the main metric for ranking hypotheses in the proposed method. To evaluate the ranking method, we counted and compared the number of true relations in the top 100 pairs, ranked by our method and one of the previous methods. Out of 181 true relations in the test dataset, the proposed method ranked 20 of them in the top 100 relations while this number was 13 for the other method. In the last study, we used biomedical literature and clinical trials in ranking potential drug repositioning candidates identified by Phenome-Wide Association Studies (PheWAS). Unlike previous approaches, in this study, we did not limit our method to LBD. First, we generated a list of potential drug repositioning candidates using PheWAS. We retrieved 212,851 gene-disease associations from PheWAS catalog and 14,169 gene-drug relationships from DrugBank. Following Swanson’s model, we generated 52,966 potential drug repositioning candidates. Then, we developed an information retrieval system to retrieve any evidence of those candidates co-occurring in the biomedical literature and clinical trials. We identified nearly 14,800 drug-disease pairs with some evidence of support. In addition, we identified more than 38,000 novel candidates for re-purposing, encompassing hundreds of different disease states and over 1,000 individual medications. We anticipate that these results will be highly useful for hypothesis generation in the field of drug repurposing

DEVELOPING A WORKFLOW TO EVALUATE MEDICATIONS FOR REPURPOSING USING HEALTH CLAIMS DATA: APPLICATION TO SUBSTANCE USE DISORDERS

Healthcare big data are a growing source of real-world data with which to identify and validate medications with repurposing potential. Previously, we developed a claims-based workflow to evaluate medications with potential to treat stimulant use disorders. In order to test the workflow, the framework was applied in the context of opioid use disorders (OUDs), for which there are medications with known efficacy. Using the Truven Marketscan Commercial Claims Database, a nested case-control analysis was conducted to determine the association between OUD medications (buprenorphine, naltrexone) and remission. Cases were defined as enrollees with a remission diagnosis and matched (1:4) to controls (individuals without remission) using incidence density sampling, with age group, sex, region, and index year as additional matching variables. After adjusting for behavioral health visits, polysubstance use disorders, and psychiatric disorders using conditional logistic regression, the odds of OUD medication exposure were 3.8 (99% confidence interval: 3.0 – 4.9) times higher in cases than controls. Evaluation of angiotensin converting enzyme inhibitors (e.g. lisinopril) as a negative control revealed no significant association between the medication and remission. This work demonstrates the feasibility of using administrative health claims data to evaluate the effectiveness of medications to treat substance use disorders

Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

Artificial Intelligence as an Enabler of Quick and Effective Production Repurposing Manufactur-ing: An Exploratory Review and Future Research Propositions

The outbreak of Covid-19 created disruptions in manufacturing operations. One of the most serious negative impacts is the shortage of critical medical supplies. Manufacturing firms faced pressure from governments to use their manufacturing capacity to repurpose their production for meeting the critical demand for necessary products. For this purpose, recent advancements in technology and artificial intelligence (AI) could act as response solutions to conquer the threats linked with repurposing manufacturing (RM). The study’s purpose is to investigate the significance of AI in RM through a systematic literature review (SLR). This study gathered around 453 articles from the SCOPUS database in the selected research field. Structural Topic Modeling (STM) was utilized to generate emerging research themes from the selected documents on AI in RM. In addition, to study the research trends in the field of AI in RM, a bibliometric analysis was undertaken using the R-package. The findings of the study showed that there is a vast scope for research in this area as the yearly global production of articles in this field is limited. However, it is an evolving field and many research collaborations were identified. The study proposes a comprehensive research framework and propositions for future research development

Systematic, comprehensive, evidence-based approach to identify neuroprotective interventions for motor neuron disease: using systematic reviews to inform expert consensus

Objectives: Motor neuron disease (MND) is an incurable progressive neurodegenerative disease with limited treatment options. There is a pressing need for innovation in identifying therapies to take to clinical trial. Here, we detail a systematic and structured evidence-based approach to inform consensus decision making to select the first two drugs for evaluation in Motor Neuron Disease-Systematic Multi-arm Adaptive Randomised Trial (MND-SMART: NCT04302870), an adaptive platform trial. We aim to identify and prioritise candidate drugs which have the best available evidence for efficacy, acceptable safety profiles and are feasible for evaluation within the trial protocol. Methods: We conducted a two-stage systematic review to identify potential neuroprotective interventions. First, we reviewed clinical studies in MND, Alzheimer’s disease, Huntington’s disease, Parkinson’s disease and multiple sclerosis, identifying drugs described in at least one MND publication or publications in two or more other diseases. We scored and ranked drugs using a metric evaluating safety, efficacy, study size and study quality. In stage two, we reviewed efficacy of drugs in MND animal models, multicellular eukaryotic models and human induced pluripotent stem cell (iPSC) studies. An expert panel reviewed candidate drugs over two shortlisting rounds and a final selection round, considering the systematic review findings, late breaking evidence, mechanistic plausibility, safety, tolerability and feasibility of evaluation in MND-SMART. Results: From the clinical review, we identified 595 interventions. 66 drugs met our drug/disease logic. Of these, 22 drugs with supportive clinical and preclinical evidence were shortlisted at round 1. Seven drugs proceeded to round 2. The panel reached a consensus to evaluate memantine and trazodone as the first two arms of MND-SMART. Discussion: For future drug selection, we will incorporate automation tools, text-mining and machine learning techniques to the systematic reviews and consider data generated from other domains, including high-throughput phenotypic screening of human iPSCs