Drug-drug Combinations Research Articles

Assessing novel partner antimicrobials to protect ceftriaxone against gonococcal resistance: An in vitro evaluation.

The emergence of ceftriaxone-resistant Neisseria gonorrhoeae poses a significant threat to existing treatment regimens. Our study aimed to assess the efficacy of antimicrobials that could be combined with ceftriaxone to reduce the probability of ceftriaxone resistance emerging and spreading in N. gonorrhoeae. Broth microdilution was used to determine the minimal inhibitory concentrations (MICs) for a panel of ceftriaxone-resistant (WHO X, Y, Z) and ceftriaxone-susceptible (WHO L, N, P) N. gonorrhoeae WHO reference strains for the following antimicrobials: ceftriaxone, doxycycline, azithromycin, zoliflodacin, fosfomycin, pristinamycin, ramoplanin, gentamicin and NAI-107. The MICs for zoliflodacin and pristinamycin for all strains were lower than or equal to the available breakpoints. A checkerboard assay was used to determine the drug-drug combination effect, which showed either an indifferent or an additive effect for all the combinations tested with ceftriaxone. The low MICs of zoliflodacin and pristinamycin for the three ceftriaxone-resistant strains suggest that these antimicrobials could be used as partner drugs with ceftriaxone to reduce the probability of ceftriaxone resistance spreading in areas with a high prevalence of ceftriaxone resistance.

Concomitant Administration of Psychotropic and Prostate Cancer Drugs: A Pharmacoepidemiologic Study Using Drug-Drug Interaction Databases.

Prostate cancer (PC) represents the second most common diagnosed cancer in men. The burden of diagnosis and long-term treatment may frequently cause psychiatric disorders in patients, particularly depression. The most common PC treatment option is androgen deprivation therapy (ADT), which may be associated with taxane chemotherapy. In patients with both PC and psychiatric disorders, polypharmacy is frequently present, which increases the risk of drug-drug interactions (DDIs) and drug-related adverse effects. Therefore, this study aimed to conduct a pharmacoepidemiologic study of the concomitant administration of PC drugs and psychotropics using three drug interaction databases (Lexicomp®, drugs.com®, and Medscape®). This study assayed 4320 drug-drug combinations (DDCs) and identified 814 DDIs, out of which 405 (49.63%) were pharmacokinetic (PK) interactions and 411 (50.37%) were pharmacodynamic (PD) interactions. The most common PK interactions were based on CYP3A4 induction (n = 275, 67.90%), while the most common PD interactions were based on additive torsadogenicity (n = 391, 95.13%). Proposed measures for managing the identified DDIs included dose adjustments, drug substitutions, supplementary agents, parameters monitoring, or simply the avoidance of a given DDC. A significant heterogenicity was observed between the selected drug interaction databases, which can be mitigated by cross-referencing multiple databases in clinical practice.

Prevalence of contraindicated combinations amid behavioral and mental health medications filled in a pediatric population

BackgroundBehavioral or mental health disorders are common in children, adolescents, and young adults. Medication use is increasingly common, with few data describing drug-drug combinations in ambulatory settings. The objectives of this study were to describe the pharmaco-epidemiology of behavioral and mental health (BMH) medications among children, adolescents, and young adults in New York Medicaid and assess the prevalence of contraindicated drug pairs within this population.MethodsThis observational cross-sectional study evaluated New York State Medicaid managed care and fee-for-service enrollees under 21 years of age dispensed BMH medications in 2014. Main outcomes included number of members with prescriptions filled; number filling > 1 medication prescription concurrently for ≥ 30 days (polypharmacy), and number and nature of potentially contraindicated drug pairs.ResultsOf 2,430,434 children, adolescents, and young adults, 422,486 (17.4%) had a visit associated with a BMH diagnosis and 141,363 (5.8%) received one or more BMH medications. With 84 distinct medications evaluated, polypharmacy was common, experienced by 53,388 individuals (37.8% of those with a prescription filled), generating 11,115 distinct drug combinations. 392 individuals filled prescriptions for a contraindicated pair of ≥ 2 BMH medications for 30 days or longer. With ≥ 1 day overlap, 651 were exposed to contraindicated medications. The most common contraindicated pairs increased potential risk for prolonged QT interval and serotonin syndrome (n = 378 and n = 250 patients, respectively). Most combinations involved ziprasidone (3247.1 per 10,000 ziprasidone prescriptions filled).ConclusionsWith nearly 6% of members dispensed a BMH medication, contraindicated drug pairs were uncommon. However, any of those combinations represent a potential risk. Clinicians should attend to the balance of potential risks and benefits before contraindicated pairs are dispensed. The methodology described could serve as a basis for monitoring such rare instances and might reduce harm.

Abstract 3900: A high-throughput platform identifies novel drug combinations towards acute myeloid leukemia therapy

Abstract Molecularly targeted therapies have reshaped oncology in recent decades, though not all patients are eligible for such treatments and resistance mechanisms limit clinical benefit. In response, combination therapies, which can induce potent synthetic lethalities, bypass drug resistance, and expand treatment options, are increasingly being developed. A major hurdle limiting novel combination therapy discovery is the sheer combinatorics associated with broadly testing two or more agents. Thus, there is a practical need for methodologies that prioritize test compounds and technologies for rapid and efficient screening. Towards these ends, we have developed a novel combination drug screening workflow that integrates machine learning-based drug synergy predictions with a high throughput droplet microfluidics-based screening platform. Our physical system, or FlowMatrix, consists of 9,216 nano wells arrayed in a 96 × 96 grid in which cells are incubated and drug compounds delivered via emulsified droplets along both its rows and columns to facilitate combination screening. Following multi-day incubation, live cells are imaged with fluorescence microscopy and drug combination synergies are calculated from cellular viabilities. In our current setup, which includes four dose drug treatments with replicates and controls, we capture complete data (control, single agent, and 4 × 4 drug-drug combination viability measurements) for up to 100 unique drug combinations within the 9,216 dual droplet loaded wells of one FlowMatrix. Our to-date screening efforts have focused on acute myeloid leukemia (AML) specifically, where we have profiled 12,700 AML cell line-drug combinations in our system. This encompasses >3,700 unique drug combinations profiled in 7 distinct AML cell lines with 183 total matrix runs. Among the strongest hits observed in our screens and confirmed with validation studies (~65% confirmation rate) are combinations currently being tested in clinical trials for other cancers and established therapies paired with non-cancer indicated drugs. In particular, we are interrogating combinations involving CDK12 inhibitors, which affect the transcription of DNA repair proteins, splicing, and cancer cell progression, including unexpected partner drugs that potentiate the effects of CDK12 blockade in AML cell lines. We have uncovered additional unexpected synergistic combinations with KIT inhibitors in RUNX1-RUNX1T1 altered AML lines, ATR inhibitors (including an established combination with Gemcitabine), and the BCL-2 inhibitor Venetoclax, including known partnerships with chemotherapies (e.g. Decitabine and Daunorubicin) and other targeted inhibitors. Deeper studies are underway to further validate and characterize these candidates towards expanding treatment options for AML. Ultimately, our platform may radically enhance drug combination screening potential. Citation Format: Anthony R. Soltis, Boryana Zhelyazkova, Pascal Drane, Efstathios Eleftheriadis, Andrew Ventresco, David A. Weitz, Arlinda Lee, Anthony J. Iafrate. A high-throughput platform identifies novel drug combinations towards acute myeloid leukemia therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3900.

Exploring the Synergistic Effect of Sildenafil and Green Tea Polyphenols on Breast Cancer Stem Cell-like Cells and their Parental Cells: A Potential Novel Therapeutic Approach.

Many cancer studies have intensely focused on the role of diet, among other factors involved in cancer establishment. The positive effect of green tea polyphenols (GTP) on controlling breast cancer cells has been reported in several studies. Cancer stem cell-like cells (CSC-LCs) possessing self-renewal, metastatic, and drug-resistant capacities are considered prominent therapeutic targets. In many tumors, inducible nitric oxide synthase (iNOS) expression levels are high; however, they have a dual effect on breast cancer pathogenesis. This study aimed to investigate the cytotoxicity of the iNOS agonist (Sildenafil) and antagonist (LNAME), both alone and in combination with GTP, on MDA-MB-231, CD44+/CD24- CSC-LCs, and their parental cells (MCF-7). The cell viability assay has been studied using the MTT assay. To analyze drug-drug combinations, CompuSyn and Combenefit software were used. The cytotoxicity mechanism was determined using flow cytometric analysis. L-NAME and GTP showed a synergistic effect on MDA-MB-231 and CSC-LCs. Such an effect was not observed on MCF-7. Sildenafil and GTP, on the other hand, showed synergistic cytotoxicity in all the cells mentioned above. Flow cytometric tests resulted in more than 70% apoptosis in MDA-MB-231 and MCF-7. Also, sub-G1 arrest among MCF-7 cells and a considerable decrease in ROS production by MDA-MB-231 cells following treatment with Sildenafil and GTP were observed. Sildenafil, in combination with flavonoids, may be considered a novel strategy for cancer treatment.

A Review of Dipeptidyl Peptidase-4 (DPP-4) and its potential synthetic derivatives in the management of Diabetes Mellitus

Dipeptidyl peptidase-4 (DPP-4) inhibitors are a type of oral medication used to treat type 2 diabetes mellitus (T2DM). They have become increasingly popular due to their effectiveness and safety in managing the condition. DPP-4 inhibitors function by inhibiting the enzyme that breaks down the incretin hormones, namely glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP). These medicines efficiently raise the levels of active GLP-1 and GIP by blocking DPP-4 activity. As a result, there is an increase in the production of insulin, a decrease in the release of glucagon, and a lowering of glucose levels after a meal. Multiple clinical trials have conclusively shown that DPP-4 inhibitors effectively lower glycated hemoglobin (HbA1c) levels. Certain studies have even proved their equivalent efficacy to other anti-diabetic medications such as metformin or sulfonylureas. Furthermore, DPP-4 inhibitors possess the benefit of being weight-neutral and exhibiting a little risk of hypoglycemia. These qualities render them a compelling option for patients with type 2 diabetes mellitus who are overweight or susceptible to hypoglycemia episodes. In general, DPP-4 inhibitors are a promising therapeutic choice for the management of type 2 diabetes mellitus (T2DM), offering effective regulation of blood sugar levels with a minimal likelihood of adverse effects. Nevertheless, it is important to acknowledge some restrictions and factors to take into account, including the possibility of heightened susceptibility to pancreatitis, nasopharyngitis, and certain drug-drug combinations. Additional investigation is necessary to completely clarify the long-term safety and potential supplementary advantages of DPP-4 inhibitors.

Abstract A093: Deep Cyclic Inhibition of the MAPK pathway with IMM-6-415, alone and in combination with encorafenib, demonstrates anti-tumor activity and tolerability in RAF mutant tumors in vivo

Abstract Introduction: A significant proportion of cancer patients have tumors addicted to uncontrolled MAPK signaling. Activating mutations in RAS or RAF are often directly responsible and have been observed in over 20% of human tumors1. Because MEK is downstream of RAS and RAF, it is an appealing drug target. However, MEK inhibitors have historically suffered from poor clinical durability, high toxicity and a susceptibility to pathway reactivation that has limited monotherapy activity in the RAS mutant setting. Unlike other MEK inhibitors, IMM-1-104 [NCT05585320] and IMM-6-415 are designed with distinctive features including both (1.) a unique target engagement mechanism that helps resist MAPK pathway reactivation and (2.) a pharmacokinetic (PK) profile that enables fast cadence deep cyclic inhibition (DCI). DCI drives pulsatile targeted inhibition that deprives tumor cells of a critical oncogenic pathway while limiting drug-related toxicities by affording normal cells an adequate PK recovery window between drug doses. To our knowledge, IMM-6-415’s preclinical activity is driven by the shortest drug plasma half-life (0.3-hours in mice) of any MEK inhibitor developed to date. Experimental Procedures: IMM-6-415 has already demonstrated promising activity in RAS-mutant xenograft tumor models (SITC 2022). Here, the antitumor activity of IMM-6-415 was evaluated in over 60 humanized 3D tumor growth assays (3D-TGA), which included 30 BRAF class I-mutant tumor models. Additionally, multiple drug-drug combinations have been explored, including vertical drug combinations with BRAF inhibitors. IMM-6-415, binimetinib and encorafenib were tested head-to-head as single agents and in combination with encorafenib in BRAFV600E melanoma and colorectal subcutaneous tumor xenograft models in female BALB/c nude mice. Summary of New Data: As monotherapy, IMM-6-415 demonstrated antitumor activity in over 50% (34 of 66) of the 3D-TGA models tested, including 30 BRAF-mutant preclinical models in which 19 (63%) showed activity. Therefore, we further explored both monotherapy and combination activity of IMM-6-415 in the A-375 (melanoma) and HT-29 (colorectal) BRAF V600E tumor xenograft models. Monotherapy treatment with encorafenib or IMM-6-415 displayed superior tumor growth inhibition (TGI) when compared to binimetinib. Furthermore, the combination of IMM-6-415 plus encorafenib prompted greater TGI with superior durability of response when tested head-to-head against the combination of binimetinib plus encorafenib in vivo at human equivalent doses for registered drugs. Conclusions: IMM-6-415 demonstrated promising activity and tolerability in preclinical models alone and in combination with encorafenib. In combination with encorafenib, IMM-6-415 achieved a greater TGI in vivo than the combination of encorafenib plus binimetinib in BRAFV600E colorectal cancer and melanoma tumor models, suggesting an opportunity for IMM-6-415 as monotherapy or in combination in BRAF mutant tumors. Citation Format: Anna Travesa, Mai Johnson, Peter King, Praveen Nair, Jason Funt, Sarah Kolitz, Kevin D Fowler, John Brothers II, Amy Axel, Scott Barrett, Benjamin J Zeskind, Brett Hall. Deep Cyclic Inhibition of the MAPK pathway with IMM-6-415, alone and in combination with encorafenib, demonstrates anti-tumor activity and tolerability in RAF mutant tumors in vivo [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A093.

Nano Strategies for Artemisinin Derivatives to Enhance Reverse Efficiency of Multidrug Resistance in Breast Cancer.

Artemisinin (ART) has been found to exert anti-tumor activity by regulating the cell cycle, inducing apoptosis, inhibiting angiogenesis and tumor invasion and metastasis. Its derivatives (ARTs) can regulate the expression of drug-resistant proteins and reverse the multidrug resistance (MDR) of tumor cells by inhibiting intracellular drug efflux, inducing apoptosis and autophagy of tumor cells, thus enhancing the sensitivity of tumor cells to chemotherapy and radiotherapy. Recent studies have shown that nanodrugs play an important role in the diagnosis and treatment of cancer, which can effectively solve the shortcomings of poor hydrophilicity and low bioavailability of ARTs in the human body, prolong the in vivo circulation time, improve the targeting of drugs (including tumor tissues or specific organelles), and control the release of drugs in target tissues, thereby reducing the side effect. This review systematically summarized the latest research progress of nano-strategies of ARTs to enhance the efficiency of MDR reversal in breast cancer (BC) from the following two aspects: (1) Chemicals encapsulated in nanomaterials based on innovative anti-proliferation mechanism: non-ABC transporter receptor candidate related to ferroptosis (dihydroartemisinin/DHA analogs). (2) Combination therapy strategy of nanomedicine (drug-drug combination therapy, drug-gene combination, and chemical-physical therapy). Self-assembled nano-delivery systems enhance therapeutic efficacy through increased drug loading, rapid reactive release, optimized delivery sequence, and realization of cascade-increasing effects. New nanotechnology methods must be designed for specific delivery routines to achieve targeting administration and overcome MDR without affecting normal cells. The significance of this review is to expect that ART and ARTs can be widely used in clinical practice. In the future, nanotechnology can help people to treat multidrug resistance of breast cancer more accurately and efficiently.

Abstract C176: Cancer-specific AI identifies multi-modal biomarkers of therapeutic response for 1,951 drugs including TNG348, a highly selective USP1 inhibitor

Abstract Understanding drug mechanism-of-action and improving clinical response rates are critical challenges to cancer therapeutic development. We designed a deep learning framework that learns molecular mechanisms of drug sensitivity and resistance from preclinical and/or clinical drug response data. The trained model can then generate biomarker strategies, suggest companion diagnostics, and propose rational drug-drug combinations that increase or restore drug sensitivity. The key innovation is the model’s architecture; the framework learns an embedding that reflects an interpretable relationship between a cellular process and drug response by emulating a tumor cell’s proteomic architecture. We applied the framework to learn genetic determinants of response for 1,951 drugs and 17,386 CRISPR gene knockouts screened in human cancer cell lines. The learned embeddings reflected known and novel mechanisms. RAF inhibitors, MEK inhibitors, and BRAF CRISPR knockout colocalized (P=1.7×10-11) due to their shared dependency on hyperactivation of RAF signaling. CDK4/6 inhibitor efficacy was strongly associated with a set of physically interacting proteins that control G1-to-S transition (P=5.6×10-12). Validating the clinical applicability of the finding, the trained model stratified patient response in a retrospective analysis of 70 ER+ metastatic breast cancer patients treated with the CDK4/6 inhibitor palbociclib (P=0.05, log-rank predicted responsive vs. non-responsive). As a case study, we examined mechanisms of sensitivity and resistance to TNG348 – a potent and highly selective USP1 inhibitor – in non-small cell lung cancer (NSCLC). By integrating genetics, gene expression, and CRISPR screens, the model identified seven genes whose expression explained >50% of the variation in response to TNG348 across 32 held-out NSCLC cell lines (Spearman R=0.74; P=8.7×10-7). The findings extend TNG348’s known relationship with homologous repair to additional protein complexes involved in cell cycle and DNA repair. The model leveraged the new associations to generate a six-biomarker inclusion-exclusion logic that captures both a large population (31.4% of TCGA lung adenocarcinoma patients meet inclusion criteria) and substantially enriches for strong response amongst preclinical models (OR=14.8, P=3.0×10-3). Finally, we asked the model to design rational combinations to overcome acquired resistance mechanisms. Proposed combinations included clinically successful combinations such as RAF+MEK inhibitors and were enriched for synergistic matches in a cell line screen of 986 drug pairings (Spearman rho P=6.6×10-4). These results highlight the promise of explainable AI to learn complex cellular mechanisms-of-action and generate non-obvious biomarker hypothesis and rational drug combination strategies for clinical development. Citation Format: Adam Yaari, Lee McDaniel, Antoine Simoneau, Samuel Meier, Oliver Priebe, Eduardo Farias, Alan Huang, Jannik Andersen, Yi Yu, Maxwell Sherman. Cancer-specific AI identifies multi-modal biomarkers of therapeutic response for 1,951 drugs including TNG348, a highly selective USP1 inhibitor [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C176.

EXTH-71. AUTOMATED AND MULTIPLEXED COMBINATORIAL DRUG SCREENING OF FDA-APPROVED DRUGS IDENTIFIES POTENTIAL SYNERGISTIC DRUG PARTNERS FOR THE TREATMENT OF AGGRESSIVE MENINGIOMAS

Abstract The goal of combinatorial therapy is to improve the effectiveness of treatment by targeting multiple aspects of cancer cells or by overcoming resistance to single drugs. In search of effective synergistic drug combinations for the treatment of aggressive meningiomas, we performed an automated and multiplexed combinatorial drug screening of 166 FDA-approved anticancer drugs in three grade 3 meningioma cell lines. Three anaplastic meningioma cell lines (NCH93, IOMM-Lee, and KT21-MG1) were stably transduced with blue, green, and red fluorescence proteins. The cell lines were multiplexed into 384-well plates and treated in 5x5 dose-response matrices (1 to 1000 nmol/L) for 48 hours by the automated liquid handler Hamilton MicroLAB STAR®. The drug library consisted of 166 FDA-approved anticancer drugs. To demultiplex cell viability from a single well, fluorescence as a surrogate marker for viability was measured before adding an ATP-based end-point viability assay, which served as an independent combined result of all cell lines (combined group). Synergism was calculated by the ZIP Synergy model and was defined as a ZIP score over 10. This drug screening effort generated 13695 unique drug-drug combinations per cell line. Most synergistic combinations were observed in the combined group (n = 188, 1.37%), followed by IOMM-Lee (n = 157, 1.14%), and KT21-MG1 (n = 122, 0.88%). The lowest number of synergistic combinations was observed in NCH93 (n = 75, 0.54%). Interestingly, 86 combinations were uniquely found in IOMM-Lee, 54 in KT21-MG1, and 20 in NCH93, indicating specific tumor vulnerabilities. 19 drug combinations were observed in all cell lines. Of those, the drugs Temsirolimus, Gemcitabine, and Romidepsin were highly enriched. For example, mTORi Temsirolimus concordantly showed strong synergism with the androgen receptor antagonist Darolutamide in all cell lines (ZIP score: 27.75, P< .0001). This comprehensive combinatorial drug screening identified synergistic drug-drug partners for the treatment of aggressive meningiomas. Validation studies in patient-derived meningioma organoids are ongoing.

Integrated High-Throughput Screening and Large-Scale Isobolographic Analysis to Accelerate the Discovery of Radiosensitizers With Greater Selectivity for Cancer Cells

High-throughput screening (HTS) platforms have been widely used to identify candidate anticancer drugs and drug-drug combinations; however, HTS-based identification of new drug-ionizing radiation (IR) combinations has rarely been reported. Herein, we developed an integrated approach including cell-based HTS and computational large-scale isobolographic analysis to accelerate the identification of radiosensitizing compounds acting strongly and more specifically on cancer cells. In a 384-well plate format, 160 compounds likely to interfere with the cell response to radiation were screened on human glioblastoma (U251-MG) and cervix carcinoma (ME-180) cell lines, as well as on normal fibroblasts (CCD-19Lu). After drug exposure, cells were irradiated or not and short-term cell survival was assessed by high-throughput cell microscopy. Computational large-scale dose-response and isobolographic approach were used to identify promising synergistic drugs radiosensitizing cancer cells rather than normal cells. Synergy of a promising compound was confirmed on ME-180 cells by an independent 96-well assay protocol, and finally, by the gold-standard colony forming assay. We retained 4 compounds synergistic at 2 isoeffects in U251-MG and ME-180 cell lines and 11 compounds synergistically effective in only one cancer cell line. Among these 15 promising radiosensitizers, 5 compounds showed limited toxicity combined or not with IR on normal fibroblasts. Overall, this study demonstrated that HTS chemoradiation screening together with large-scale computational analysis is an efficient tool to identify synergistic drug-IR combinations, with concomitant assessment of unwanted toxicity on normal fibroblasts. It sparks expectations to accelerate the discovery of highly desired agents improving the therapeutic index of radiation therapy.

Effect of Cyclodextrin Complex Formation on Solubility Changes of Each Drug Due to Intermolecular Interactions between Acidic NSAIDs and Basic H2 Blockers.

One of the solubilization of poorly water-soluble drugs is the use of cyclodextrin (CD)-based inclusion complexes. On the other hand, few studies have investigated how CD functions on the solubility of drugs in the presence of multiple drugs that interact with each other. In this study, we used indomethacin (IND) and diclofenac (DIC) as acidic drugs, famotidine (FAM) and cimetidine (CIM) as basic drugs, and imidazole (IMZ), histidine (HIS), and arginine (ARG) as compounds structurally similar to basic drugs. We attempted to clarify the effect of β-CD on the solubility change of each drug in the presence of multiple drugs. IND and DIC formed a eutectic mixture in the presence of CIM, IMZ, and ARG, which greatly increased the intrinsic solubility of the drugs as well as their affinity for β-CD. Furthermore, the addition of high concentrations of β-CD to the DIC-FAM combination, which causes a decrease in solubility due to the interaction, improved the solubility of FAM, which was decreased in the presence of DIC. These results indicate that β-CD synergistically improves the solubility of drugs in drug-drug combinations, where the solubility is improved, whereas it effectively improves the dissolution rate of drugs in situations where the solubility is reduced by drug-drug interactions, such as FAM-DIC. This indicates that β-CD can be used to improve the physicochemical properties of drugs, even when they are administered in combination with drugs that interact with each other.

BioREx: Improving biomedical relation extraction by leveraging heterogeneous datasets

Biomedical relation extraction (RE) is the task of automatically identifying and characterizing relations between biomedical concepts from free text. RE is a central task in biomedical natural language processing (NLP) research and plays a critical role in many downstream applications, such as literature-based discovery and knowledge graph construction. State-of-the-art methods were used primarily to train machine learning models on individual RE datasets, such as protein–protein interaction and chemical-induced disease relation. Manual dataset annotation, however, is highly expensive and time-consuming, as it requires domain knowledge. Existing RE datasets are usually domain-specific or small, which limits the development of generalized and high-performing RE models. In this work, we present a novel framework for systematically addressing the data heterogeneity of individual datasets and combining them into a large dataset. Based on the framework and dataset, we report on BioREx, a data-centric approach for extracting relations. Our evaluation shows that BioREx achieves significantly higher performance than the benchmark system trained on the individual dataset, setting a new SOTA from 74.4% to 79.6% in F-1 measure on the recently released BioRED corpus. We further demonstrate that the combined dataset can improve performance for five different RE tasks. In addition, we show that on average BioREx compares favorably to current best-performing methods such as transfer learning and multi-task learning. Finally, we demonstrate BioREx’s robustness and generalizability in two independent RE tasks not previously seen in training data: drug-drug N-ary combination and document-level gene-disease RE. The integrated dataset and optimized method have been packaged as a stand-alone tool available at https://github.com/ncbi/BioREx.

A Novel Drug-Drug Salt of Naproxen and Vortioxetine: Synthesis, Characterization, and Solubility Study

Naproxen (NAP) is an aromatic propionic acid nonsteroidal anti-inflammatory drug, and vortioxetine (VOT) is a novel antidepressant drug. In this study, a new 1 : 1 drug-drug salt of NAP and VOT (namely, NAP-VOT) was designed and synthesized by liquid-assisted grinding and slow evaporation. The obtained salt was characterized by single-crystal X-ray diffraction, powder X-ray diffraction (PXRD), and differential scanning calorimetry (DSC). Single-crystal structure showed that NAP-VOT is a molecular salt. The NAP and VOT molecules in the salt were connected by N-H+⋯O hydrogen bonds between the carbonyl oxygen of NAP and the piperazine group of VOT. In addition, solubility and dissolution rate experiments were performed in water and pH 6.86 phosphate buffers, and the result suggested that salt formation could increase the solubility and dissolution rate of NAP and VOT in water. Furthermore, this study provides a new research idea to solve the problem of drug-drug combination by achieving drug-drug association at the molecular level.

Cross-sectional study of potential incompatibility of intravenous medications in a neonatal intensive care unit in Indonesia

ObjectiveThe purpose of this study is to determine the most common incompatible and unknown compatibility drug combinations and determine the compatibility of each pair of drugs used in hospitals based on reference books and journals. MethodsThis is a prospective cross sectional study. All babies who were admitted to the Neonatal Intensive Care Units from May 1 to July 31 2021 were sample of the study. Patients who did not receive at least two drug coadministrated concurrently and who stayed less than 24 hours were excluded. Only drug-drug combinations were considered and the other non-drug administrations (electrolyte solutions, parenteral nutritions, and blood products) were excluded. Compatibility data were obtained from literature and online search engines [micromedex NeoFax Essentials 2020, UCL Hospitals Injectable Medicines Administration Guide: Pharmacy Department, 3rd Edition, Trissel Handbook on injectable drugs 15th edition, and published journals]. ResultsThe most commonly prescribed drug combinations were ampicillin-gentamicin (31.72%), amikacin-ampicillin sulbactam (9.05%), amikacin-ampicillin sulbactam-aminophylline (3.08%). The most common drug incompatible combination was ampicillin - gentamicin (31.71%), for the most drug combinations whose compatibility unknown were amikacin-ampicillin sulbactam (9.05%). ConclusionThe high prevalence of incompatible drugs and unknown compatibility was identified, so checking its compatibility can be carried out through a two-dimensional chart to minimize the incidence of incompatibilities.

Artículo traducido] Estudio transversal de posibles incompatibilidades entre los medicamentos intravenosos en una unidad de cuidados intensivos neonatales de Indonesia

ObjectiveThe purpose of this study is to determine the most common incompatible and unknown compatibility drug combinations and determine the compatibility of each pair of drugs used in hospitals based on reference books and journals. MethodsThis is a prospective cross-sectional study. All babies who were admitted to the Neonatal Intensive Care Units from May First to July 31st 2021 were sample of the study. Patients who did not receive at least two drugs coadministrated concurrently and who stayed less than 24 hours were excluded. Only drug-drug combinations were considered and the other non-drug administrations (electrolyte solutions, parenteral nutritions, and blood products) were excluded. Compatibility data were obtained from literature and online search engines (micromedex NeoFax Essentials 2020, UCL Hospitals Injectable Medicines Administration Guide: Pharmacy Department, 3rd Edition, Trissel Handbook on injectable drugs 15th edition, and published journals). ResultsThe most commonly prescribed drug combinations were ampicillin-gentamicin (31.72%), amikacin-ampicillin sulbactam (9.05%), amikacin-ampicillin sulbactam-aminophylline (3.08%). The most common drug incompatible combination was ampicillin-gentamicin (31.71%), for the most drug combinations whose compatibility unknown were amikacin-ampicillin sulbactam (9.05%). ConclusionsThe high prevalence of incompatible drugs and unknown compatibility was identified, so checking its compatibility can be carried out through a two-dimensional chart to minimize the incidence of incompatibilities.

Pro-drug Nanoparticle Loaded Supramolecular Hydrogels for Drug Delivery to IDH1 Wild-Type Glioblastoma

Abstract AIMS A hyaluronic acid-based hydrogel with mechanical properties comparable to human brain tissue is being investigated as an implantable drug-delivery vehicle for post-surgical treatment of IDH1 wild-type glioblastoma (GBM). Hydrogels are to be loaded with polymeric pro-drug nanoparticles to enable controlled drug release and increased drug penetration through brain parenchyma for enhanced treatment of residual disease within and beyond the invasive margin. METHOD A panel of patient-derived glioblastoma cell lines resected from tumour core and invasive margin, have been used to generate tumour spheroids. Chemotherapeutic drug screening has been performed to select lead compounds for formulation and gel loading. Synergies of drug combinations have been studied for potential combination loading. Upcoming work will investigate nanoparticle internalisation, spheroid penetration and prodrug nanoparticle potency. Nanoparticle loaded hydrogels are then to be studied for validation of effective in vitro application. RESULTS Current results demonstrate that patient-derived cell lines can generate robust, reproducible tumour spheroids with diameters approx. 350-400 µm. Patient-to-patient differences in drug potencies were observed, with differences also noted between counterpart core and marginal cells from the same patients, illustrating the importance of using patient-derived cells and distinct regional cells to study heterogenous disease such as GBM. Drug combination experiments indicated synergy of topoisomerase I inhibitor (Irinotecan) with PARP inhibitor (Ola-parib). Interestingly, combination of PARP inhibition with topoisomerase II inhibitors (Doxorubicin, Etoposide) was found not synergistic. CONCLUSION Together, data gathered on single drug and drug-drug combinations will inform upcoming formulation and in vitro application of hydrogels planned, with eventual translation to pre-clinical in vivo models.

Over the counter drugs and self-medication: A worldwide paranoia and a troublesome situation in India during the COVID-19 pandemic

Self-medication is the use of drugs to treat self-diagnosed ailments without the use of a formal prescription. Self-medication is defined by the World Health Organization as the use of medications to address self-diagnosed diseases or symptoms. Over-the-counter (OTC) pharmaceuticals are medications that can be sold without a prescription directly to the client in accordance with the laws of each nation. During the COVID-19 pandemic, an increasing tendency in the use of OTC and self-medication was seen, with the situation in India particularly deteriorating due to lax regulatory restrictions. This has resulted in a slew of problems, ranging from a lack of drugs to severe responses due to overdosage and drug-drug combinations. There is an urgent need for more tangible regulatory control over self-medication and OTC medications to safeguard the uninformed populace from harm.

Compatibility of Drotaverine Hydrochloride with Ibuprofen and Ketoprofen Nonsteroidal Anti-Inflammatory Drugs Mixtures.

Formulations with two or more active pharmaceutical ingredients (APIs) are a researched trend due to their convenient use compared with multiple medications. Moreover, drug-drug combinations may have a synergistic effect. Drotaverine hydrochloride (D-HCl) is commonly used for its antispasmodic action. The combination of a spasmolytic and an analgesic drug such as ibuprofen (Ibu) or ketoprofen (Ket) could become the answer for the treatment of localized pain. D-HCl:Ibu and D-HCl:Ket drug-drug interactions leading to the formation of eutectic compositions with increased bioavailability, obtained by mechanosynthesis, a green, solvent-free method was explored for the first time. The compatibility of Ibuprofen, Ketoprofen, and Drotaverine Hydrochloride was investigated using differential scanning calorimetry (DSC), X-ray diffraction (XRD), and Fourier-Transform Infrared spectroscopy (FTIR). Solid-liquid equilibrium (SLE) phase diagrams for the binary systems of active pharmaceutical ingredients were developed and the Tammann diagrams were designed to determine the eutectic compositions. The excess thermodynamic functions GE for the pre-, post-, and eutectic compositions were obtained using the computed activity coefficients data. Results show that drotaverine-based pharmaceutical forms for pain treatment may be obtained at 0.9 respectively 0.8 molar fractions of ibuprofen and ketoprofen which is advantageous because the maximum allowed daily dose of Ibu is about 6 times higher than those of D-HCl and Ket. The obtained eutectics may be a viable option for the treatment of pain associated with cancer therapy.

A Random Walk with Restart Model Based on Common Neighbors for Predicting the Clinical Drug Combinations on Coronary Heart Disease.

As the approaching of the clinical big data era, the prediction of whether drugs can be used in combination in clinical practice is a fundamental problem in the analysis of medical data. Compared with high-throughput screening, it is more cost-effective to treat this problem as a link prediction problem and predict by algorithms. Inspired by the rule of combined clinical medication, a new computational model is proposed. The drug-drug combination was predicted by combining the number of adjacent complete subgraphs shared by the two points with the restart random walk algorithm. The model is based on the semisupervised random walk algorithm, and the same neighborhood is used to improve the random walk with restart (CN-RWR). The algorithm can effectively improve the prediction performance and assign a score to any combination of drugs. To fairly compare the predictive performance of the improved model with that of the random walk with restart model (RWR), a cross-validation of the two models on the same drug data was performed. The AUROC of CN-RWR and RWR under the LOOCV validation framework is 0.9741 and 0.9586, respectively, and the improved model results are more reliable. In addition, the top 3 predictive drug combinations have been approved by the public. The new model is expected that this model can be extended to predict the use of combination drugs for other diseases to find combinations of drugs with potential clinical benefits.