Many drugs have been withdrawn from the market worldwide, at a cost of billions of dollars, because of patient fatalities due to them unexpectedly disturbing heart rhythm. Even drugs for ailments as mild as hay fever have been withdrawn due to an unacceptable increase in risk of these heart rhythm disturbances. Consequently, the whole pharmaceutical industry expends a huge effort in checking all new drugs for any unwanted side effects on the heart. The predominant root cause has been identified as drug molecules blocking ionic current flows in the heart. Block of individual types of ionic currents can now be measured experimentally at an early stage of drug development, and this is the standard screening approach for a number of ion currents in many large pharmaceutical companies. However, clinical risk is a complex function of the degree of block of many different types of cardiac ion currents, and this is difficult to understand by looking at results of these screens independently. By using ordinary differential equation models for the electrical activity of heart cells (electrophysiology models) we can integrate information from different types of currents, to predict the effect
Allosteric drugs are increasingly used because they produce fewer side effects. Allosteric signal propagation does not stop at the 'end' of a protein, but may be dynamically transmitted across the cell. Here, we propose that the concept of allosteric drugs can be broadened to allo-network drugs, whose effects can propagate either within a protein, or across several proteins, to enhance or inhibit specific interactions along a pathway. We posit that current allosteric drugs are a special case of allo-network drugs, and suggest that allo-network drugs can achieve specific, limited changes at the systems level, and in this way can achieve fewer side effects and lower toxicity. Finally, we propose steps and methods to identify allo-network drug targets and sites outlining a new paradigm in systems-based drug design.
Background: Psychedelic drugs facilitate profound changes in consciousness and have potential to provide insights into the nature of human mental processes and their relation to brain physiology. Yet published scientific literature reflects a very limited understanding of the effects of these drugs, especially for newer synthetic compounds. The number of clinical trials and range of drugs formally studied is dwarfed by the number of written descriptions of the many drugs taken by people. Analysis of these descriptions using machine-learning techniques can provide a framework for learning about these drug use experiences. Methods: We collected 1000 reports of 10 drugs from the drug information website Erowid.org and formed a term-document frequency matrix. Using variable selection and a random-forest classifier, we identified a subset of words that differentiated between drugs. Results: A random forest using a subset of 110 predictor variables classified with accuracy comparable to a random forest using the full set of 3934 predictors. Our estimated accuracy was 51.1%, which compares favorably to the 10% expected from chance. Reports of MDMA had the highest accuracy at 86.9%; those
Based on previous work done in this field, we build a dynamical system that describes changes in drug addiction in an isolated population when two addictive substances are available simultaneously. We then use our model to investigate whether the system captures the process of users switching drug habits. One of the motivations for this project is to mathematically check the conjecture that being addicted to a less-addictive substance will effectively lead individuals to become dependent on more addictive and potentially more dangerous drugs. We introduce additional assumptions, under which our model is reduced to a competitive Lotka-Volterra system. This dynamical system has three or four fixed points, stability of which then gives an implication about the outcomes of the competition between addictive substances and, therefore, the fate of individuals in the population. From the analysis of the reduced model, we determine that there actually exist parameter regimes that capture the following dynamics: depending on the initial distribution of the drug preference; either the use of both drugs will die out, the usage of one of the drugs will become prevalent, or addictions to both dr
Three drugs, Ibuprofen, Aspirin and Erythromycin, are encapsulated in spherical Pluronic F127 micelles. The shapes and the size distributions of the micelles in dilute, aqueous solutions, with and without drugs, are ascertained using cryo- Scanning Electron Microscopy and Dynamic Light Scattering (DLS) experiments, respectively. Uptake of drugs above a threshold concentration is seen to reduce the critical micellization temperature of the solution. The mean hydrodynamic radii and polydispersities of the micelles are found to increase with decrease in temperature and in the presence of drug molecules. The hydration of the micellar core at lower temperatures is verified using fluorescence measurements. Increasing solution pH leads to the ionization of the drugs incorporated in the micellar cores. This causes rupture of the micelles and release of the drugs into the solution at the highest solution pH value of 11.36 investigated here and is studied using DLS and fluorescence spectrocopy.
In the absence of sufficient medication for COVID patients due to the increased demand, disused drugs have been employed or the doses of those available were modified by hospital pharmacists. Some evidences for the use of alternative drugs can be found in the existing scientific literature that could assist in such decisions. However, exploiting large corpus of documents in an efficient manner is not easy, since drugs may not appear explicitly related in the texts and could be mentioned under different brand names. Drugs4Covid combines word embedding techniques and semantic web technologies to enable a drug-oriented exploration of large medical literature. Drugs and diseases are identified according to the ATC classification and MeSH categories respectively. More than 60K articles and 2M paragraphs have been processed from the CORD-19 corpus with information of COVID-19, SARS, and other related coronaviruses. An open catalogue of drugs has been created and results are publicly available through a drug browser, a keyword-guided text explorer, and a knowledge graph.
Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential
Drug repurposing provides an opportunity to redeploy drugs, which ideally are already approved for use in humans, for the treatment of other diseases. For example, the repurposing of dexamethasone and baricitinib has played a crucial role in saving patient lives during the ongoing SARS-CoV-2 pandemic. There remains a need to expand therapeutic approaches to prevent life-threatening complications in patients with COVID-19. Using an in silico approach based on structural similarity to drugs already in clinical trials for COVID-19, potential drugs were predicted for repurposing. For a subset of identified drugs with different targets to their corresponding COVID-19 clinical trial drug, a mechanism of action analysis was applied to establish whether they might have a role in inhibiting the replication of SARS-CoV-2. Of sixty drugs predicted in this study, two with the potential to inhibit SARS-CoV-2 replication were identified using mechanism of action analysis. Triamcinolone is a corticosteroid that is structurally similar to dexamethasone; gallopamil is a calcium channel blocker that is structurally similar to verapamil. In silico approaches indicate possible mechanisms of action for
Alzheimer's disease (AD) is a devastating neurodegenerative disorder with no cure and limited treatment solutions that are unable to target any of the suspected causes. Increasing evidence suggests that one of the causes of neurodegeneration is the overproduction of amyloid beta (A\b{eta}) and the inability of A\b{eta} peptides to be cleared from the brain, resulting in self-aggregation to form toxic oligomers, fibrils and plaques. One of the potential treatment options is to target A\b{eta} and prevent self-aggregation to allow for a natural clearing of the brain. In this paper, we review the drugs and drug delivery systems that target A\b{eta} in relation to Alzheimer's disease. Many attempts have been made to use anti-A\b{eta} targeting molecules capable of targeting A\b{eta} (with much success in vitro and in vivo animal models), but the major obstacle to this technique is the challenge posed by the blood brain barrier (BBB). This highly selective barrier protects the brain from toxic molecules and pathogens and prevents the delivery of most drugs. Therefore novel A\b{eta} aggregation inhibitor drugs will require well thought-out drug delivery systems to deliver sufficient conc
Drug-drug interaction (DDI) prediction is central to drug discovery and clinical development, particularly in the context of increasingly prevalent polypharmacy. Although existing computational methods achieve strong performance on standard benchmarks, they often fail to generalize to realistic deployment scenarios, where most candidate drug pairs involve previously unseen drugs and validated interactions are scarce. We demonstrate that proximity in the embedding spaces of prevailing molecule-centric DDI models does not reliably correspond to interaction labels, and that simply scaling up model capacity therefore fails to improve generalization. To address these limitations, we propose GenRel-DDI, a generalizable relation learning framework that reformulates DDI prediction as a relation-centric learning problem, in which interaction representations are learned independently of drug identities. This relation-level abstraction enables the capture of transferable interaction patterns that generalize to unseen drugs and novel drug pairs. Extensive experiments across multiple benchmark demonstrate that GenRel-DDI consistently and significantly outperforms state-of-the-art methods, with
Drug repositioning aims to identify potential new indications for existing drugs to reduce the time and financial costs associated with developing new drugs. Most existing deep learning-based drug repositioning methods predominantly utilize graph-based representations. However, graph-based drug repositioning methods struggle to perform effective inference in cold-start scenarios involving novel drugs because of the lack of association information with the diseases. Unlike traditional graph-based approaches, we propose a bidirectional behavior learning strategy for drug repositioning, known as BiBLDR. This innovative framework redefines drug repositioning as a behavior sequential learning task to capture drug-disease interaction patterns. First, we construct bidirectional behavioral sequences based on drug and disease sides. The consideration of bidirectional information ensures a more meticulous and rigorous characterization of the behavioral sequences. Subsequently, we propose a two-stage strategy for drug repositioning. In the first stage, we construct prototype spaces to characterize the representational attributes of drugs and diseases. In the second stage, these refined protot
The extraction of biomedical data has significant academic and practical value in contemporary biomedical sciences. In recent years, drug repositioning, a cost-effective strategy for drug development by discovering new indications for approved drugs, has gained increasing attention. However, many existing drug repositioning methods focus on mining information from adjacent nodes in biomedical networks without considering the potential inter-relationships between the feature spaces of drugs and diseases. This can lead to inaccurate encoding, resulting in biased mined drug-disease association information. To address this limitation, we propose a new model called Dual-Feature Drug Repurposing Neural Network (DFDRNN). DFDRNN allows the mining of two features (similarity and association) from the drug-disease biomedical networks to encode drugs and diseases. A self-attention mechanism is utilized to extract neighbor feature information. It incorporates two dual-feature extraction modules: the single-domain dual-feature extraction (SDDFE) module for extracting features within a single domain (drugs or diseases) and the cross-domain dual-feature extraction (CDDFE) module for extracting fe
Adverse drug-drug interactions~(DDIs) can compromise the effectiveness of concurrent drug administration, posing a significant challenge in healthcare. As the development of new drugs continues, the potential for unknown adverse effects resulting from DDIs becomes a growing concern. Traditional computational methods for DDI prediction may fail to capture interactions for new drugs due to the lack of knowledge. In this paper, we introduce a new problem setup as zero-shot DDI prediction that deals with the case of new drugs. Leveraging textual information from online databases like DrugBank and PubChem, we propose an innovative approach TextDDI with a language model-based DDI predictor and a reinforcement learning~(RL)-based information selector, enabling the selection of concise and pertinent text for accurate DDI prediction on new drugs. Empirical results show the benefits of the proposed approach on several settings including zero-shot and few-shot DDI prediction, and the selected texts are semantically relevant. Our code and data are available at \url{https://github.com/zhufq00/DDIs-Prediction}.
Understanding the interaction between different drugs (drug-drug interaction or DDI) is critical for ensuring patient safety and optimizing therapeutic outcomes. Existing DDI datasets primarily focus on textual information, overlooking multimodal data that reflect complex drug mechanisms. In this paper, we (1) introduce MUDI, a large-scale Multimodal biomedical dataset for Understanding pharmacodynamic Drug-drug Interactions, and (2) benchmark learning methods to study it. In brief, MUDI provides a comprehensive multimodal representation of drugs by combining pharmacological text, chemical formulas, molecular structure graphs, and images across 310,532 annotated drug pairs labeled as Synergism, Antagonism, or New Effect. Crucially, to effectively evaluate machine-learning based generalization, MUDI consists of unseen drug pairs in the test set. We evaluate benchmark models using both late fusion voting and intermediate fusion strategies. All data, annotations, evaluation scripts, and baselines are released under an open research license.
Drug-side effect research is vital for understanding adverse reactions arising in complex multi-drug therapies. However, the scarcity of higher-order datasets that capture the combinatorial effects of multiple drugs severely limits progress in this field. Existing resources such as TWOSIDES primarily focus on pairwise interactions. To fill this critical gap, we introduce HODDI, the first Higher-Order Drug-Drug Interaction Dataset, constructed from U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) records spanning the past decade, to advance computational pharmacovigilance. HODDI contains 109,744 records involving 2,506 unique drugs and 4,569 unique side effects, specifically curated to capture multi-drug interactions and their collective impact on adverse effects. Comprehensive statistical analyses demonstrate HODDI's extensive coverage and robust analytical metrics, making it a valuable resource for studying higher-order drug relationships. Evaluating HODDI with multiple models, we found that simple Multi-Layer Perceptron (MLP) can outperform graph models, while hypergraph models demonstrate superior performance in capturing complex multi-drug interact
Drug repurposing is an emerging approach for drug discovery involving the reassignment of existing drugs for novel purposes. An alternative to the traditional de novo process of drug development, repurposed drugs are faster, cheaper, and less failure prone than drugs developed from traditional methods. Recently, drug repurposing has been performed in silico, in which databases of drugs and chemical information are used to determine interactions between target proteins and drug molecules to identify potential drug candidates. A proposed algorithm is NeuroCADR, a novel system for drug repurposing via a multi-pronged approach consisting of k-nearest neighbor algorithms (KNN), random forest classification, and decision trees. Data was sourced from several databases consisting of interactions between diseases, symptoms, genes, and affiliated drug molecules, which were then compiled into datasets expressed in binary. The proposed method displayed a high level of accuracy, outperforming nearly all in silico approaches. NeuroCADR was performed on epilepsy, a condition characterized by seizures, periods of time with bursts of uncontrolled electrical activity in brain cells. Existing drugs f
It is a common practice in modern medicine to prescribe multiple medications simultaneously to treat diseases. However, these medications could have adverse reactions between them, known as Drug-Drug Interactions (DDI), which have the potential to cause significant bodily injury and could even be fatal. Hence, it is essential to identify all the DDI events before prescribing multiple drugs to a patient. Most contemporary research for predicting DDI events relies on either information from Biomedical Knowledge graphs (KG) or drug SMILES, with very few managing to merge data from both to make predictions. While others use heuristic algorithms to extract features from SMILES and KGs, which are then fed into a Deep Learning framework to generate output. In this study, we propose a KG-integrated Transformer architecture to generate an end-to-end fully automated Machine Learning pipeline for predicting DDI events with high accuracy. The algorithm takes full-scale molecular SMILES sequences of a pair of drugs and a biomedical KG as input and predicts the interaction between the two drugs with high precision. The results show superior performance in two different benchmark datasets compare
Drug recommendation assists doctors in prescribing personalized medications to patients based on their health conditions. Existing drug recommendation solutions adopt the supervised multi-label classification setup and only work with existing drugs with sufficient prescription data from many patients. However, newly approved drugs do not have much historical prescription data and cannot leverage existing drug recommendation methods. To address this, we formulate the new drug recommendation as a few-shot learning problem. Yet, directly applying existing few-shot learning algorithms faces two challenges: (1) complex relations among diseases and drugs and (2) numerous false-negative patients who were eligible but did not yet use the new drugs. To tackle these challenges, we propose EDGE, which can quickly adapt to the recommendation for a new drug with limited prescription data from a few support patients. EDGE maintains a drug-dependent multi-phenotype few-shot learner to bridge the gap between existing and new drugs. Specifically, EDGE leverages the drug ontology to link new drugs to existing drugs with similar treatment effects and learns ontology-based drug representations. Such d
Twitter is a popular platform for e-commerce in the Arab region including the sale of illegal goods and services. Social media platforms present multiple opportunities to mine information about behaviors pertaining to both illicit and pharmaceutical drugs and likewise to legal prescription drugs sold without a prescription, i.e., illegally. Recognized as a public health risk, the sale and use of illegal drugs, counterfeit versions of legal drugs, and legal drugs sold without a prescription constitute a widespread problem that is reflected in and facilitated by social media. Twitter provides a crucial resource for monitoring legal and illegal drug sales in order to support the larger goal of finding ways to protect patient safety. We collected our dataset using Arabic keywords. We then categorized the data using four machine learning classifiers. Based on a comparison of the respective results, we assessed the accuracy of each classifier in predicting two important considerations in analysing the extent to which drugs are available on social media: references to drugs for sale and the legality/illegality of the drugs thus advertised. For predicting tweets selling drugs, Support Vect
Relation-aware graph structure embedding is promising for predicting multi-relational drug-drug interactions (DDIs). Typically, most existing methods begin by constructing a multi-relational DDI graph and then learning relation-aware graph structure embeddings (RaGSEs) of drugs from the DDI graph. Nevertheless, most existing approaches are usually limited in learning RaGSEs of new drugs, leading to serious over-fitting when the test DDIs involve such drugs. To alleviate this issue, we propose a novel DDI prediction method based on relation-aware graph structure embedding with co-contrastive learning, RaGSECo. The proposed RaGSECo constructs two heterogeneous drug graphs: a multi-relational DDI graph and a multi-attribute drug-drug similarity (DDS) graph. The two graphs are used respectively for learning and propagating the RaGSEs of drugs, aiming to ensure all drugs, including new ones, can possess effective RaGSEs. Additionally, we present a novel co-contrastive learning module to learn drug-pairs (DPs) representations. This mechanism learns DP representations from two distinct views (interaction and similarity views) and encourages these views to supervise each other collaborativ