Identification Of Drug Combinations Research Articles

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery.

Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.

Machine learning approaches for drug combination therapies.

Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, toxicity and cell line heterogeneity. Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized drug combination therapies into three categories and discuss each method in each category. We also present a short description of relevant databases used as a benchmark in drug combination therapies and provide a list of well-known, publicly available interactive data analysis portals. We highlight the importance of data integration on the identification of drug combinations. Finally, we address the advantages of combining multiple data sources on drug combination analysis by showing an experimental comparison.

Identification of drug combinations on the basis of machine learning to maximize anti-aging effects.

Aging is a multifactorial process that involves numerous genetic changes, so identifying anti-aging agents is quite challenging. Age-associated genetic factors must be better understood to search appropriately for anti-aging agents. We utilized an aging-related gene expression pattern-trained machine learning system that can implement reversible changes in aging by linking combinatory drugs. In silico gene expression pattern-based drug repositioning strategies, such as connectivity map, have been developed as a method for unique drug discovery. However, these strategies have limitations such as lists that differ for input and drug-inducing genes or constraints to compare experimental cell lines to target diseases. To address this issue and improve the prediction success rate, we modified the original version of expression profiles with a stepwise-filtered method. We utilized a machine learning system called deep-neural network (DNN). Here we report that combinational drug pairs using differential expressed genes (DEG) had a more enhanced anti-aging effect compared with single independent treatments on leukemia cells. This study shows potential drug combinations to retard the effects of aging with higher efficacy using innovative machine learning techniques.

A Knowledge Graph of Combined Drug Therapies Using Semantic Predications From Biomedical Literature: Algorithm Development.

BackgroundCombination therapy plays an important role in the effective treatment of malignant neoplasms and precision medicine. Numerous clinical studies have been carried out to investigate combination drug therapies. Automated knowledge discovery of these combinations and their graphic representation in knowledge graphs will enable pattern recognition and identification of drug combinations used to treat a specific type of cancer, improve drug efficacy and treatment of human disorders.ObjectiveThis paper aims to develop an automated, visual approach to discover knowledge about combination therapies from biomedical literature, especially from those studies with high-level evidence such as clinical trial reports and clinical practice guidelines.MethodsBased on semantic predications, which consist of a triple structure of subject-predicate-object (SPO), we proposed an automated algorithm to discover knowledge of combination drug therapies using the following rules: 1) two or more semantic predications (S1-P-O and Si-P-O, i = 2, 3…) can be extracted from one conclusive claim (sentence) in the abstract of a given publication, and 2) these predications have an identical predicate (that closely relates to human disease treatment, eg, “treat”) and object (eg, disease name) but different subjects (eg, drug names). A customized knowledge graph organizes and visualizes these combinations, improving the traditional semantic triples. After automatic filtering of broad concepts such as “pharmacologic actions” and generic disease names, a set of combination drug therapies were identified and characterized through manual interpretation.ResultsWe retrieved 22,263 clinical trial reports and 31 clinical practice guidelines from PubMed abstracts by searching “antineoplastic agents” for drug restriction (published between Jan 2009 and Oct 2019). There were 15,603 conclusive claims locally parsed using the search terms “conclusion*” and “conclude*” ready for semantic predications extraction by SemRep, and 325 candidate groups of semantic predications about combined medications were automatically discovered within 316 conclusive claims. Based on manual analysis, we determined that 255/316 claims (78.46%) were accurately identified as describing combination therapies and adopted these to construct the customized knowledge graph. We also identified two categories (and 4 subcategories) to characterize the inaccurate results: limitations of SemRep and limitations of proposal. We further learned the predominant patterns of drug combinations based on mechanism of action for new combined medication studies and discovered 4 obvious markers (“combin*,” “coadministration,” “co-administered,” and “regimen”) to identify potential combination therapies to enable development of a machine learning algorithm.ConclusionsSemantic predications from conclusive claims in the biomedical literature can be used to support automated knowledge discovery and knowledge graph construction for combination therapies. A machine learning approach is warranted to take full advantage of the identified markers and other contextual features.

Abstract C006: DrugCell: A visible neural network to guide precision medicine

Abstract Many potential cancer therapies fail during clinical trials, in part due to difficulty in predicting how a particular population of cancer cells will respond to a given drug. Here we develop DrugCell, a “visible” neural network that predicts anti-cancer drug responses by modeling the hierarchical organization of a human cancer cell. We devised a branched visible neural network combining two heterogeneous neural networks. The structure of the first neural network mirrors the hierarchical organization of cellular subsystems (e.g., complexes, pathways and organelles). During training, this neural network learns which cellular subsystems contribute to therapeutic responses based on the mutational profile of the genome. The second neural network is fully connected and models the chemical structure of each drug. These two neural networks connect to a series of small, fully connected layers that simulate a cellular drug treatment conditioned on a genetic mutation landscape. By this design, DrugCell can be used to predict how any cancer cell would respond to any small molecule inhibitor. DrugCell outperformed Elastic Net (rho = 0.80 versus 0.74) while maintaining performance similar to that of a fully connected (black box) neural network (rho = 0.82). When making predictions using DrugCell, genotypes and chemical compound structures induce differential patterns of subsystem activity, enabling in silico investigations of the molecular mechanisms underlying cancer drug response. We therefore applied DrugCell to highlight pathways mediating sensitivity to multiple FDA-approved therapies. Top pathways regulating paclitaxel sensitivity included G2 DNA damage checkpoint, consistent with previous studies showing BRCA1 mediates G2/M arrest in response to paclitaxel, and regulation of TOR signaling, which has previously been described to mediate paclitaxel resistance in multiple cancers. Accordingly, combination of paclitaxel with the mTOR inhibitor, MK8669, was highly synergistic across multiple cell lines. Similarly, pathways mediating sensitivity to topoisomerase II inhibitors included cholinergic receptor signaling and regulation of histone deacetylation. In support of these mechanisms, combination of etoposide, a topoisomerase II inhibitor, with AKT or MEK inhibitors (downstream of the cholinergic receptor) or the HDAC inhibitor vorinostat were highly synergistic across multiple cell lines. Similar results were observed upon CRISPR/Cas9 mediated knockdown of key genes in these pathways. Finally, we used DrugCell to predict effective therapeutic combinations for acute myeloid leukemia patients, leading to identification of drug combinations currently in clinical trials including lenalidomide + DNMT inhibitors and tandutinib + PI3K/MTOR inhibitors. Armed with interpretability and generalizability, DrugCell serves as an important step towards a next generation of intelligent systems in drug discovery and precision medicine. Citation Format: Brent M Kuenzi, Jisoo Park, Samson Fong, Jason Kreisberg, Trey Ideker. DrugCell: A visible neural network to guide precision medicine [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr C006. doi:10.1158/1535-7163.TARG-19-C006

Exploring the Correlation between the Cognitive Benefits of Drug Combinations in a Clinical Database and the Efficacies of the Same Drug Combinations Predicted from a Computational Model.

Identification of drug combinations that could be effective in Alzheimer’s disease treatment is made difficult by the sheer number of possible combinations. This analysis identifies as potentially therapeutic those drug combinations that rank highest when their efficacy is determined jointly from two independent data sources. Estimates of the efficacy of the same drug combinations were derived from a clinical dataset on cognitively impaired elderly participants and from pre-clinical data, in the form of a computational model of neuroinflammation. Linear regression was used to show that the two sets of estimates were correlated, and to rule out confounds. The ten highest ranking, jointly determined drug combinations most frequently consisted of COX2 inhibitors and aspirin, along with various antihypertensive medications. Ten combinations of from five to nine drugs, and the three-drug combination of a COX2 inhibitor, aspirin, and a calcium-channel blocker, are discussed as candidates for consideration in future pre-clinical and clinical studies.

High-Throughput Screening for Drug Combinations.

The identification of drug combinations as alternatives to single-agent therapeutics has traditionally been a slow, largely manual process. In the last 10years, high-throughput screening platforms have been developed that enable routine screening of thousands of drug pairs in an in vitro setting. In this chapter, we describe the workflow involved in screening a single agent versus a library of mechanistically annotated, investigation, and approved drugs using a full dose-response matrix scheme using viability as the readout. We provide details of the automation required to run the screen and the informatics required to process data from screening robot and subsequent analysis and visualization of the datasets.

Predicting combinative drug pairs via multiple classifier system with positive samples only

Background and ObjectiveDue to the synergistic effects of drugs, drug combination is one of the effective approaches for treating complex diseases. However, the identification of drug combinations by dose-response methods is still costly. It is promising to develop supervised learning-based approaches to predict potential drug combinations on a large scale. Nevertheless, these approaches have the inadequate utilization of heterogeneous features, which causes the loss of information useful to classification. Moreover, they have an intrinsic bias, because they assume unknown drug pairs as non-combinations, of which some could be real drug combinations in practice. MethodsTo address above issues, this work first designs a two-layer multiple classifier system (TLMCS) to effectively integrate heterogeneous features involving anatomical therapeutic chemical codes of drugs, drug-drug interactions, drug-target interactions, gene ontology of drug targets, and side effects. To avoid the bias caused by labelling unknown samples as negative, it then utilizes the one-class support vector machines, (which requires no negative instance and only labels approved drug combinations as positive instances), as the member classifiers in TLMCS. Last, both a 10-fold cross validation (10-CV) and a novel prediction are performed to validate the performance of TLMCS. ResultsThe comparison with three state-of-the-art approaches under 10-CV exhibits the superiority of TLMCS, which achieves the area under the receiver operating characteristic curve = 0.824 and the area under the precision-recall curve = 0.372. Moreover, the experiment under the novel prediction demonstrates its ability, where 9 out of the top-20 predicted combinative drug pairs are validated by checking the published literature. Furthermore, for each of the newly-validated drug combinations, this work analyses the combining mode of the member drugs and investigates their relationship in terms of drug targeting pathways. ConclusionsThe proposed TLMCS provides an effective framework to integrate those heterogeneous features and is trained by only positive samples such that the bias of taking unknown drug pairs as negative samples can be avoided. Furthermore, its results in the novel prediction reveal five types of drug combinations and three types of drug relationships in terms of pathways.

Identification of drug combinations administered by continuous subcutaneous infusion that require analysis for compatibility and stability

BackgroundA continuous subcutaneous infusion (CSCI) delivered via syringe pump is a method of drug administration used to maintain symptom control when a patient is no longer able to tolerate oral medication. Several classes of drugs, such as opioids, antiemetics, anticholinergics, antipsychotics and benzodiazepines are routinely administered by CSCI alone or in combinations. Previous studies attempting to identify the most-common CSCI combinations are now several years old and no longer reflect current clinical practice. The aim of this work was to review current clinical practice and identify CSCI drug combinations requiring analysis for chemical compatibility and stability.MethodsUK pharmacy professionals involved in the delivery of care to palliative patients in hospitals and hospices were invited to enter CSCI combinations comprised of two or more drugs onto an electronic database over a 12-month period. In addition, a separate Delphi study with a panel of 15 expert healthcare professionals was completed to identify a maximum of five combinations of drugs used to treat more complex, but less commonly encountered symptoms unlikely to be identified by the national survey.ResultsA total of 57 individuals representing 33 separate palliative care services entered 1,945 drug combinations suitable for analysis, with 278 discrete combinations identified. The top 40 drug combinations represented nearly two-thirds of combinations recorded. A total of 23 different drugs were administered in combination and the median number of drugs in a combination was three. The Delphi study identified five combinations for the relief of complex or refractory symptoms.ConclusionThis study represents the first step towards developing authoritative national guidance on the administration of drugs by CSCI. Further work will ensure healthcare practitioners have the knowledge and confidence that a prescribed combination will be both safe and efficacious.

Zoledronic acid increases docetaxel cytotoxicity through pMEK and Mcl-1 inhibition in a hormone-sensitive prostate carcinoma cell line

BackgroundIn prostate cancer, the identification of drug combinations that could reduce the tumor cell population and rapidly eradicate hormone-resistant cells potentially present would be a remarkable breakthrough in the treatment of this disease.MethodsThe study was performed on a hormone-sensitive prostate cancer cell line (LNCaP) grown in normal or hormone-deprived charcoal-stripped (c.s.) medium. Cell viability and apoptosis were assessed by SRB assay and Annexin-V/TUNEL assays, respectively. Activated caspase-3, p21, pMEK and MCL-1 expression levels were detected by western blotting.ResultsThe simultaneous exposure of zoledronic acid [100 μM] and docetaxel [0.01 μM] for 1 h followed by treatment with zoledronic acid for 72, 96 or 120 h produced a high synergistic interaction (R index = 5.1) with a strong decrease in cell viability. This cytotoxic effect was associated with a high induction of apoptosis in both LNCaP and in c.s. LNCaP cells. The induction of apoptosis was paralleled by a decrease in pMEK and Mcl-1 expression.ConclusionThe zoledronic acid-docetaxel combination produced a highly significant synergistic effect on the LNCaP cell line grown in normal or hormone-deprived medium, the principal molecular mechanisms involved being apoptosis and decreased pMEK and Mcl-1 expression. This experimentally derived schedule would seem to prevent the selection and amplification of hormone-resistant cell clones and could thus be potentially used alongside standard androgen deprivation therapy in the management of hormone-sensitive prostate carcinoma.