Multi-targeted Drug Combinations Research Articles

Synergistic Combinatorial effect of L-asparaginase and Trastuzumab against HER2+ breast cancer cells

Multi-targeted drug combinations which selectively inhibit the proliferation of cancer cells are required for effective anticancer treatment. The current anti-HER2 positive breast cancer therapy involves Trastuzumab and doxorubicin drug combination which produces toxic side effects in clinical settings including high cardiotoxicity. In this study, anticancer activity of single drug as well as the drug combination effect of L-asparaginase (Celginase) and Trastuzumab (Herceptin) was studied on HER2 positive breast cancer (SKBR3) cells. Inhibition of cell proliferation assay based on fluorescence readout was studied to estimate the anticancer effect of the drugs. HUVEC cells were used as negative control cells. Individually as a single drug, Trastuzumab (Herceptin) and L-asparaginase (Celginase) showed anticancer activity against SKBR3 cells with IC50 value of 0.031ng/ml and 1.168µg/ml respectively. The drug combination interaction of Trastuzumab and L-asparaginase resulted in combination index of less than 1(CI < 1) showing their synergistic effect against SKBR3 cells. No cytotoxic effect was observed in control HUVEC cells. The results suggested that the antitumor activity of Trastuzumab and L-asparaginase against HER2 positive breast cancer was found specific to HER2 positive cancer (SKBR3) cells. This synergistic drug interaction of L-asparaginase and Trastuzumab could be further explored to as an alternative to current drug combination therapy against the HER2 positive breast cancer.

Rapid Tumor Debulking of Relapsed/Refractory CLL Patients By PI3Kδ Inhibition and Anti-CD20 Monoclonal Antibody Treatment

Introduction Effective multi-targeted drug combinations could rapidly achieve deep remissions in CLL allowing for defined duration treatment. The combination of a novel highly-specific phosphoinositide 3-kinase delta (PI3Kδ) inhibitor and casein kinase 1 epsilon (CSK1ε) inhibitor, umbralisib (UMB), and a glycoengineered chimeric monoclonal antibody (mAb) targeting a unique epitope on CD20, ublituximab (UBL), with the BCL2 inhibitor venetoclax (VEN) may decrease drug resistance, reduce risk of tumor lysis syndrome (TLS) and achieve higher levels of undetectable minimal residual disease (MRD). This triple agent therapy is being evaluated in relapsed or refractory CLL patients (U2-VEN, ClinicalTrials.gov NCT03379051). Patients received oral 800 mg UMB once-daily. For Cycle 1, UBL was given via IV on Day 1 (150 mg), Day 2 (750 mg), Day 8 (900 mg), Day 15 (900 mg), and then on Day 1 in Cycles 2 and 3 (900 mg); for 3 cycles of 28 days, followed by consolidation with UMB and VEN for cycles 4 to 24. Patient (n = 25) blood samples were collected Pre- and Post-treatment on days of UBL administration during cycles 1-3. Our studies focused on UMB and UBL treatment effects on: 1) The decrease in the circulating CLL tumor burden and thus the subsequent risk of TLS from the addition of VEN to the regimen; and 2) CD20 antigen loss on CLL cells, a major mechanism of anti-CD20 mAb resistance. Methods Blood was analyzed by clinical laboratory automated cell counter and then stained with antibodies against CD3, CD5, CD19, and CD20 for flow cytometry analysis. CLL cell counts were calculated from flow cytometry and clinical laboratory results. To quantitate CD20 surface molecules, peripheral blood mononuclear cells were isolated and stained for flow cytometry analysis with the addition of a Molecules of Equivalent Soluble Fluorochrome standard curve. All human specimen collection and usage was conducted with written informed consent after approval of our Institutional Research Subjects Review Board according to the ethical guidelines of the Declaration of Helsinki. Results Average CLL cell counts (47 x 109/L) decreased rapidly after administration of the first dose of UMB and UBL (C1D1 Post, 26 x 109/L), which rebounded the next day (C1D2 Pre, 43 x 109/L) and then decreased after the second treatment (C1D2 Post, 24 x 109/L). By Day 15, the average CLL cell count had dropped to 9 x 109/L, which decreased further to 5 x 109/L by the start of cycle 3 (Fig. 1). Notably, the variation in CLL cell counts (error bars) decreases over treatment time, suggesting that most CLL patients' tumor burdens are decreasing to a similar low level independent of starting CLL cell count. The change in average CLL cell count between Pre and Post measurements is initially very high (45% decrease C1D1) with decreasing effectiveness over treatment time (8% decrease C3D1). This change could be due to the loss of CLL cell membrane CD20 levels leading to mAb resistance. Pre-treatment (C1D1 Pre) average CLL cell membrane CD20 levels (n=18) were 29,000 CD20 molecules/cell. This decreased to 9,000 molecules/cell after the first treatment (C1D1 Post), stabilized at 9,100 molecules/cell on the next day (C1D2 Pre) and then decreased further after the second treatment (C1D2 Post) to 1600 molecules/cell. After day 8 treatment, CD20 levels remained at 1,000 molecules/cell or less until the end of this study (C3D1). Discussion UMB and UBL rapidly decrease circulating CLL counts over three cycles of treatment, reducing most CLL patient tumor burden to similar low levels. This clinical response could facilitate addition of VEN in the 4thcycle of therapy by decreasing the risk of TLS. The rapid loss of surface CD20 from CLL cells to 1,000 CD20 molecules/cell or less by C1D8 Post has the potential to cause acquired resistance to anti-CD20 mAb therapy. This rapid and sustained loss of CD20 levels is consistent with that reported at standard doses for other anti-CD20 mAbs. However, an effect of concomitant UMB on CD20 levels cannot be ruled out. Previous work suggests that lower anti-CD20 mAb doses can prevent these large decreases in CD20 levels and increase UBL efficacy. In conclusion, our initial data show that treatment with a selective PI3Kδ and CSK1ε inhibitor (UMB) combined with an anti-CD20 mAb (UBL) is effective in decreasing CLL cell counts in all patients assessed in the U2-VEN clinical trial. However, treatment efficacy could be limited by large decreases in the CLL surface CD20 levels. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Preclinical multi-target strategies for myocardial ischemia-reperfusion injury.

Despite promising breakthroughs in diagnosing and treating acute coronary syndromes, cardiovascular disease’s high global mortality rate remains indisputable. Nearly half of these patients died of ischemic heart disease. Primary percutaneous coronary intervention (PCI) and coronary artery bypass grafting can rapidly restore interrupted blood flow and become the most effective method for salvaging viable myocardium. However, restoring blood flow could increase the risk of other complications and myocardial cell death attributed to myocardial ischemia-reperfusion injury (IRI). How to reduce the damage of blood reperfusion to ischemic myocardium has become an urgent problem to be solved. In preclinical experiments, many treatments have substantial cardioprotective effects against myocardial IRI. However, the transition from these cardioprotective therapies to clinically beneficial therapies for patients with acute myocardial infarction remains elusive. The reasons for the failure of the clinical translation may be multi-faceted, and three points are summarized here: (1) Our understanding of the complex pathophysiological mechanisms of myocardial IRI is far from enough, and the classification of specific therapeutic targets is not rigorous, and not clear enough; (2) Most of the clinical patients have comorbidities, and single cardioprotective strategies including ischemia regulation strategies cannot exert their due cardioprotective effects under conditions of hyperglycemia, hypertension, hyperlipidemia, and aging; (3) Most preclinical experimental results are based on adult, healthy animal models. However, most clinical patients had comorbidities and received multiple drug treatments before reperfusion therapy. In 2019, COST Action proposed a multi-target drug combination initiative for prospective myocardial IRI; the optimal cardioprotective strategy may be a combination of additive or synergistic multi-target therapy, which we support. By establishing more reasonable preclinical models, screening multi-target drug combinations more in line with clinical practice will benefit the translation of clinical treatment strategies.

Bipartite network models to design combination therapies in acute myeloid leukaemia

Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.

Adiponectin predicts poor response to antidepressant drugs in major depressive disorder.

Produced by adipocytes, adiponectin crosses the blood-brain barrier to bind with specific receptors in the hypothalamus, brainstem, hippocampus, and cortex. In patients with major depressive disorder (MDD), circulating levels of adiponectin inversely related with antidepressant response to ketamine, and predicted a better response to multi-target drug combinations than to escitalopram. We investigated the effect of adiponectin on response to antidepressants in a naturalistic setting. We assessed baseline plasma levels of adiponectin in 121 MDD inpatients, treated with antidepressant drug monotherapy based on clinical need (selective serotonin reuptake inhibitors, venlafaxine, duloxetine) in a specialized hospital setting. Severity of depression was weekly assessed with Hamilton scale ratings. Adiponectin plasma levels were higher in patients with MDD compared with healthy controls, and negatively influenced the pattern of antidepressant response (higher baseline levels, worse response) independent of the drug class and of the baseline severity of depression, and of age, sex, and body mass index. The identification of adiponectin as a predictor of antidepressant response to drugs of different mechanism of action, such as ketamine, SSRIs, and SNRIs, and both in experimental and in clinical settings, warrants interest for further study of its pathways to search for novel biomarkers and therapeutic targets.

Systematic evaluation of rationally chosen multitargeted drug combinations: a combination of low doses of levetiracetam, atorvastatin and ceftriaxone exerts antiepileptogenic effects in a mouse model of acquired epilepsy

Epileptogenesis, the gradual process that leads to epilepsy after brain injury or genetic mutations, is a complex network phenomenon, involving a variety of morphological, biochemical and functional brain alterations. Although risk factors for developing epilepsy are known, there is currently no treatment available to prevent epilepsy. We recently proposed a multitargeted, network-based approach to prevent epileptogenesis by rationally combining clinically available drugs and provided first proof-of-concept that this strategy is effective. Here we evaluated eight novel rationally chosen combinations of 14 drugs with mechanisms that target different epileptogenic processes. The combinations consisted of 2-4 different drugs per combination and were administered systemically over 5 days during the latent epileptogenic period in the intrahippocampal kainate mouse model of acquired temporal lobe epilepsy, starting 6 h after kainate. Doses and dosing intervals were based on previous pharmacokinetic and tolerability studies in mice. The incidence and frequency of spontaneous electrographic and electroclinical seizures were recorded by continuous (24/7) video linked EEG monitoring done for seven days at 4 and 12 weeks post-kainate, i.e., long after termination of drug treatment. Compared to vehicle controls, the most effective drug combination consisted of low doses of levetiracetam, atorvastatin and ceftriaxone, which markedly reduced the incidence of electrographic seizures (by 60%; p<0.05) and electroclinical seizures (by 100%; p<0.05) recorded at 12 weeks after kainate. This effect was lost when higher doses of the three drugs were administered, indicating a synergistic drug-drug interaction at the low doses. The potential mechanisms underlying this interaction are discussed. We have discovered a promising novel multitargeted combination treatment for modifying the development of acquired epilepsy.

Combination therapy with cilostazol, aripiprazole, and donepezil protects neuronal cells from β-amyloid neurotoxicity through synergistically enhanced SIRT1 expression

Alzheimer’s disease (AD) is a multi-faceted neurodegenerative disease. Thus, current therapeutic strategies require multitarget-drug combinations to treat or prevent the disease. At the present time, single drugs have proven to be inadequate in terms of addressing the multifactorial pathology of AD, and multitarget-directed drug design has not been successful. Based on these points of views, it is judged that combinatorial drug therapies that target several pathogenic factors may offer more attractive therapeutic options. Thus, we explored that the combination therapy with lower doses of cilostazol and aripiprazole with add-on donepezil (CAD) might have potential in the pathogenesis of AD. In the present study, we found the superior efficacies of donepezil add-on with combinatorial mixture of cilostazol plus aripiprazole in modulation of expression of AD-relevant genes: Aβ accumulation, GSK-3β, P300, acetylated tau, phosphorylated-tau levels, and activation of α-secretase/ADAM 10 through SIRT1 activation in the N2a Swe cells expressing human APP Swedish mutation (N2a Swe cells). We also assessed that CAD synergistically raised acetylcholine release and choline acetyltransferase (CHAT) expression that were declined by increased β-amyloid level in the activated N2a Swe cells. Consequently, CAD treatment synergistically increased neurite elongation and improved cell viability through activations of PI3K, BDNF, β-catenin and a7-nicotinic cholinergic receptors in neuronal cells in the presence of Aβ1-42. This work endorses the possibility for efficient treatment of AD by supporting the synergistic therapeutic potential of donepezil add-on therapy in combination with lower doses of cilostazol and aripiprazole.

Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations.

Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed.

Abstract B1-39: Multi-target drug combinations from single drug responses measured at the level of single cells using Mixture Nested Effects Models (MNEMs) applied to cancer

Abstract Purpose: To identify an optimal drug combination for addressing intratumor heterogeneity by measuring single drug responses at the level of single cells. Background: Drug combination strategies have been derived from drug-drug and drug-target interaction inferences made based on genomic network analysis, such as synthetic lethality network analysis and protein network analysis. A key limitation with these approaches is that they do not account for intratumor heterogeneity. A next generation high throughput single cell technology such as Cytometry Time-of-Flight (CyTOF) has the potential to circumvent this limitation by providing the key information for optimally targeting intratumor heterogeneity within the context of personalized medicine. Methods: We propose a systems level modeling framework for analyzing heterogeneous drug response data across a population of single cells. Our approach, called Mixture Nested Effects Models (MNEM), identifies multi-target drug combinations from single drug effects measured at the level of single cells. MNEM analyzes drug responses among subpopulations (aka clusters) of similar cells, where the subpopulations are defined by cellular surface markers. MNEM infers the nested drug target hierarchy (directed network) from a heterogeneous population of drug effects by combining subpopulation identification, subpopulation matching to infer the drug response pre- and post-treatment, and nested effects modeling for each subpopulation individually or through a mixture of subpopulations. An MNEM infers that Drug A targets are a superset of Drug B targets, if the heterogeneous downstream target effects resulting from Drug B are a noisy subset of those resulting from Drug A. This relationship is represented as a directed edge from Drug A to Drug B. From the graphical model of nested drug effects, a scoring function is superimposed to identify the optimal drug combinations. Results: We apply MNEM to CyTOF drug screening data derived from treating three well-established cancer cell lines: HeLa (ovarian), NCIH460 (lung) and HCT116 (colon) with Mek, mTOR, pP38MAPK, JnK I and PI3K inhibitors at different optimal dose levels in addition to the base line treatment TRAIL, an apoptotic stimulator. MNEM indicates that pP38MAPK and Mek inhibitors are important candidates for targeting the ovarian and lung cancer cell lines. The MNEM analysis from the colon cancer cell line data indicates that several optimal drug regimens may have equivalent efficacy. Survival data from drug combination invitro experiments using clonogenic assays support the results from MNEM. Overall, these analyses suggest that MNEM could be used to optimize drug combinations based on responses from single drugs measured at the single cell level. Conclusion: Intratumor heterogeneity in tumor response, measured at the level of single cells, upon exposure to single drugs may be combined through Mixture Nested Effects Modeling (MNEM) to infer optimal drug combinations. Citation Format: Benedict Anchang, Harris Fienberg, Sean Bendall, Robert Tibshirani, Sylvia K. Plevritis. Multi-target drug combinations from single drug responses measured at the level of single cells using Mixture Nested Effects Models (MNEMs) applied to cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-39.

TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples.

Summary: Network pharmacology-based prediction of multi-targeted drug combinations is becoming a promising strategy to improve anticancer efficacy and safety. We developed a logic-based network algorithm, called Target Inhibition Interaction using Maximization and Minimization Averaging (TIMMA), which predicts the effects of drug combinations based on their binary drug-target interactions and single-drug sensitivity profiles in a given cancer sample. Here, we report the R implementation of the algorithm (TIMMA-R), which is much faster than the original MATLAB code. The major extensions include modeling of multiclass drug-target profiles and network visualization. We also show that the TIMMA-R predictions are robust to the intrinsic noise in the experimental data, thus making it a promising high-throughput tool to prioritize drug combinations in various cancer types for follow-up experimentation or clinical applications.Availability and implementation: TIMMA-R source code is freely available at http://cran.r-project.org/web/packages/timma/.Contact: jing.tang@helsinki.fiSupplementary information: Supplementary data are available at Bioinformatics online.

Searching for Drug Synergy in Complex Dose–Response Landscapes Using an Interaction Potency Model

Rational design of multi-targeted drug combinations is a promising strategy to tackle the drug resistance problem for many complex disorders. A drug combination is usually classified as synergistic or antagonistic, depending on the deviation of the observed combination response from the expected effect calculated based on a reference model of non-interaction. The existing reference models were proposed originally for low-throughput drug combination experiments, which make the model assumptions often incompatible with the complex drug interaction patterns across various dose pairs that are typically observed in large-scale dose–response matrix experiments. To address these limitations, we proposed a novel reference model, named zero interaction potency (ZIP), which captures the drug interaction relationships by comparing the change in the potency of the dose–response curves between individual drugs and their combinations. We utilized a delta score to quantify the deviation from the expectation of zero interaction, and proved that a delta score value of zero implies both probabilistic independence and dose additivity. Using data from a large-scale anticancer drug combination experiment, we demonstrated empirically how the ZIP scoring approach captures the experimentally confirmed drug synergy while keeping the false positive rate at a low level. Further, rather than relying on a single parameter to assess drug interaction, we proposed the use of an interaction landscape over the full dose–response matrix to identify and quantify synergistic and antagonistic dose regions. The interaction landscape offers an increased power to differentiate between various classes of drug combinations, and may therefore provide an improved means for understanding their mechanisms of action toward clinical translation.